B. Impact on Number
of MUA/P Designations
C. Impact on number
of unduplicated HPSA/MUP designations
D. Impact on
Population of all Designated HPSAs and/or
MUPs
E. Impact on Number
of CHCs Covered by Designations
F. Impact on Number
of NHSC Sites Covered by Designations
G. Impact on Number
of RHCs Covered by Designations
H. Impact
on Distribution of Designations by Metropolitan/Non-Metropolitan
and Frontier Status
I. Impact on Distribution
of Population of Underserved Area and Underserved
Populations by Metropolitan/Non-Metropolitan
and Frontier Status
J. Impact of Practitioner
``Back-outs'' on Number of Designations
and Safety-Net Providers
VII. Economic
Impact
VIII. Information
Collection Requirements under Paperwork
Reduction Act of 1995
IX. Appendix A: References
X. Appendix
B: A Proposal for a Method to Designate
Communities as Underserved: Technical Report
on the Derivation of Weights
I.
Background
An earlier version of proposed rules for
a consolidated, revised MUP/HPSA designation
methodology and implementation process
was published on September 1, 1998 [63
FR 46538-55]. Those proposed rules generated
nearly 800 public comments, principally
concerning the perceived high impact in
terms the safety-net programs which would
have lost their existing designations
if the rule were finalized. Comments were
also received on several other important
issues related to the methodology, types
of primary care clinicians included, and
data collection burden. On June 3, 1999,
a Federal Register document was published
[64 FR 29831] which extended the comment
period based on the large volume of comments
received and the level of concern expressed.
In light of the volume of comments, it
was determined that the impact of the
proposal as published would be more carefully
tested, possible revisions and alternative
approaches developed as necessary, and
a new notice of proposed rulemaking (NPRM)
would be published.
A.
Explanation of Provisions
This
proposed rule describes a revised methodology
which combines indicators of diminished
access to health care services, shortages
of health professionals, and reduced health
status. Developed by a research team at
the University of North Carolina's Cecil
G. Sheps Center in consultation with staff
from the Health Resources and Services
Administration (HRSA) and a group of State
partners in the designation process, this
approach was also tested with a comprehensive
impact analysis (see section VI).
This proposed rule will replace the existing
Part 5 with regulations governing both
MUP and HPSA designations, and will make
conforming changes to Part 51c. Together,
these changes meet the legislative requirements
for both MUP designation and HPSA designation,
while consolidating the two processes
to the greatest extent possible given
the differences in the two authorities.
This combined metric, which we propose
to call ``the Index of Primary Care Underservice,''
will replace the existing MUP and HPSA
criteria and procedures, while maintaining
the two separate designations in order
to meet the legislative requirements of
the relevant statutes. Note that the abbreviation
MUP used here includes not only population
group designations but also the populations
of designated geographic areas, also known
as medically underserved areas or MUAs.
Similarly, the abbreviation HPSA includes
not only geographic area designations,
but also population group and facility
designations. Pursuant to Section 302(b)
of the Health Care Safety Net Amendments
of 2002, a copy of this NPRM will be submitted
to the Committee on Energy and Commerce
of the House of Representatives and to
the Committee on Health, Education, Labor
and Pensions of the Senate upon or before
the date of its publication, in fulfillment
of the statutory requirement for a report
to those committees describing any regulation
that revises the definition of a health
professional shortage area. HRSA has also
asked a panel of outside experts to review
the proposed methodology and provide an
assessment of its appropriateness, validity,
and general approach.
These regulations will not be finalized
until the public comment period referenced
above is over, and any comments received
during that time from the public, the
panel of outside experts, and from the
referenced House and Senate Committees
have been taken into consideration. Moreover,
this rule will not be finalized until
180 days after delivery of the report
to the Congressional committees identified
above, in accordance with statute.
B.
Current Uses of Designations
The
MUP and HPSA designations are currently
used in a number of Departmental programs.
The major use of MUP designations is as
a basis for eligibility for grant funding
of health centers under sections 330(c)
and (e) of the Act, which require that
these health centers serve medically underserved
populations. The major use of HPSA designations
is by the National Health Service Corps
(NHSC); health professionals placed through
the NHSC can be assigned only to designated
HPSAs.
Other health centers not funded by section
330 grants but otherwise meeting the definition
of a health center in section 330(a)--including
those which provide services to a MUP--may
be certified by the Centers for Medicare
and Medicaid Services (CMS) upon recommendation
by HRSA as federally qualified health
center (FQHC) look-alikes. FQHC look-alikes,
like all health centers funded under Section
330, are eligible for special Medicare
and Medicaid reimbursement methods. Clinics
in rural areas designated either as an
MUA or as a geographic or population group
HPSA, and whose staff include nurse practitioners
and/or physician assistants, may be certified
by CMS as Rural Health Clinics (RHCs).
These RHCs are also eligible for special
methods for determining Medicaid and Medicare
reimbursement. Physicians delivering services
in an area designated as a geographic
HPSA are eligible for the Medicare Incentive
Payments (MIP) of an additional 10 percent
above the Medicare reimbursement they
would otherwise receive. The Medicare
Modernization Act of 2003 included beneficial
changes to this incentive program. Payments
to providers are now automated based on
the zip codes of the providers, and the
information on eligibility is now available
on the CMS Web site. The MIP, also known
as the HPSA Bonus Payment, is distinct
from the Physician Scarcity Area Program,
which does not use HRSA designations in
determining eligibility.
Interested Federal Government Agencies
and State Health Departments can also
recommend waiver of the return-home requirements
for an International Medical Graduate
physician who came to the United States
on a J-1 visa, in return for three years
of service by that physician in a particular
HPSA or MUA.
In addition, a number of health professions
programs funded under Title VII of the
Public Health Service Act give preference
to applicants with a high rate of training
health professionals in medically underserved
communities and/or for placing graduates
in medically underserved communities,
defined (in Section 799B of the Act) to
include both HPSAs and MUPs.
For most of the programs that use these
designations, designation of the area
or population to be served is a necessary
but not sufficient condition for allocation
of program resources, in that other eligibility
requirements must also be met and/or there
is competition among eligible applicants
for available resources.
II.
Revising the Methodology and Designation
Mechanisms
A.
Relevant Statutes
Authorizing
Statutes
The current HPSA criteria date back to
1978, when they were issued under Section
332 of the Public Heath Service (PHS)
Act, as amended in 1976; their predecessor,
the ``Critical Health Manpower Shortage
Area'' or CHMSA criteria, dates back to
the 1971 legislation creating the NHSC.
Section 332(b) of the Public Health Service
Act states that the Secretary shall take
into consideration the following when
establishing criteria for the designation
of areas, groups, or facilities as HPSAs:
(1) The ratio of available health manpower
to the number of individuals in an area
or population group, and (2) Indicators
of a need for health services, notwithstanding
the supply of health manpower.
The current MUA/P criteria date back to
1975, when they were issued to implement
legislation enacted in 1973 and 1974 creating
grants for Health Maintenance Organizations
(HMOs) and Community Health Centers (CHCs),
respectively. Section 330(b)(3) of the
Public Health Service Act defines ``medically
underserved population'' as the population
of an urban or rural area designated by
the Secretary of Health and Human Services
as an area with a shortage of personal
health services, or a population group
designated by the Secretary as having
a shortage of such services. No specific
criteria were included in the statute.
Health Care Safety Net Amendments of 2002.
The Health Care Safety Net Amendments
of 2002, Public Law 107-251, as amended
by Public Law 108-163, included modification
of Section 332 to require the ``automatic''
designation as HPSAs of all FQHCs and
RHCs meeting the requirements of Section
334 (concerning the provision of services
without regard to ability-to-pay) for
at least six years. After six years, such
entities must demonstrate that they meet
the designation criteria for HPSAs, as
then in force. This legislative provision
appears to have had two major goals:
1.
To avoid requiring FQHCs or RHCs from
going through two separate designation
processes. Given that most FQHCs must
demonstrate service to an MUP in order
to be funded (or to be certified as an
FQHC look- alike), it was deemed unnecessary
to also require these entities to obtain
a HPSA designation in order to apply for
placement of NHSC clinicians. Similarly,
every RHC must obtain one of several types
of designation in order to achieve RHC
status (either a HPSA, MUA, or Governor
Designated and Secretary Certified Shortage
Area designation); arguably, those for
whom this was not a HPSA designation should
not be required to obtain a second type
of designation to apply for NHSC. (It
is worth noting that this goal will be
met once the regulations herein are in
force, since areas and population groups
designated or updated under the criteria
herein would be both HPSAs and MUPs, eligible
for the FQHC, RHC and NHSC programs).
2. To allow a long transition period for
phasing in the new designation criteria
as they might affect existing projects.
Existing FQHCs and RHCs will have plenty
of time to show that the areas where they
are located, the populations they serve,
or the facilities involved in fact meet
the new criteria, so that their services
will not be disrupted due to the criteria
change. Although an extensive impact analysis
of the proposed new criteria has been
conducted to demonstrate that such disruption
is unlikely in all but a few cases, this
legislatively required smooth transition
should ease concerns about the changes
and allow plenty of time to adapt to the
new designation criteria.
B.
Purpose of Revising the Methodology and
Designation Process
As
previously stated, the current HPSA and
MUA/P criteria date back to the 1970s.
The original CHMSA criteria required that
a simple population-to-primary care physician
ratio threshold be exceeded to demonstrate
shortage. The HPSA criteria went further
and allowed a lower threshold ratio for
areas with high needs as indicated by
high poverty, infant mortality or fertility
rates, and for population groups with
access barriers. The original MUA/P criteria,
still in effect, employ a four-variable
Index of Medical Underservice, including
percent of the population with incomes
below poverty, population-to-primary care
physician ratio, infant mortality rate
and percent elderly. Since the time these
designation criteria were first developed,
there has been an evolution both in the
types of requests for designation received
and the application of the HPSA criteria.
Instead of relatively simple geographic
area requests, such as whole counties
and rural subcounty areas, more requests
have been made for urban neighborhood
and population group designations. The
availability of census data on poverty,
race, and ethnicity at the census tract
level has enabled the delineation of urban
service areas based on their economic
and race/ethnicity characteristics. Areas
with concentrations of poor, minority
and/or linguistically isolated populations
have achieved area or population group
HPSA designations based on their limited
access to physicians serving other parts
of their metropolitan areas. As a result,
the differences between HPSA and MUA/P
designations have become less distinct.
The methodology for identifying underserved
areas, as well as the process by which
interested State and community parties
can obtain designation as underserved
areas, are being revised to accomplish
several goals and alleviate problems associated
with the existing methods of designation.
In revising the underlying methodology
for identifying underserved areas, our
goals were to create a new system that:
(a) Is simple to understand for those
who seek designation;
(b) is intuitive and has face validity;
(c) incorporates better measures or correlates
of health status and access;
(d) is based on scientifically recognized
methods and is replicable;
(e) minimize unnecessary disruption; and
(f) constitutes an improvement over current
methods in fairly and consistently identifying
places and people who are in need of primary
health care and who encounter barriers
to meeting those needs.
In revising the designation process, our
goals were to:
(a) Consolidate the two existing procedures,
sets of criteria, and lists of designations;
(b) make the system more proactive and
better able to identify new, currently
undesignated areas of need and areas no
longer in need;
(c) automate the scoring process as much
as possible, making maximum use of national
data and reducing the effort at State
and
community levels associated with information
gathering for designation and updating;
(d) expand the State role in the designation
process, with special attention to the
State role in definition of rational service
areas;
(e) reduce the need for time-consuming
population group designations, by specifically
including indicators representing access
barriers experienced by these groups in
the criteria applied to area data.
These goals are explained more fully below.
We believe the proposed methodology and
designation process address all of these
goals and therefore offers a significant
improvement in the identification of communities
experiencing limited access to primary
care services. In turn, we believe these
revisions will assist the Department in
targeting key resources more effectively
to areas of greater relative need for
assistance.
1. Methodological Goals
Simplicity
The new underservice measure must be understandable
and usable by those who seek designation.
In this vein, we decided the new methodology
should continue to use the population-to-provider
ratio as the fundamental metric of underservice
because such ratios are well- recognized
and understood by the program participants
and would provide some continuity between
a new proposal and the older methods that
included the ratios very prominently in
the calculations. Discussions with the
federal agencies and stakeholder groups
during the development of the revised
approach also revealed a preference for
using that metric as the basis for a revised
method.
Face Validity
The new underservice measure must be intuitive
and have face validity. For example, factors
that reflect progressively worse access
should result in proportionately increasing
scores. Incorporate Better Measures or
Correlates of Health Status and Access
While both designation statutes speak
of the inclusion of health status indicators,
the only specific measure of health status
historically mentioned in either statute
or included in the existing designation
criteria is infant mortality rate. Low
birthweight rate is a more robust indicator
of health status because there are more
events per unit population. Because both
infant mortality and low birthweight rate
are nationally available for all counties
and for a limited number of sub-county
areas (generally, for places of population
10,000 or more), these measures were incorporated
in the proposed methodology. In addition,
a new measure of actual/ expected death
rate (standardized mortality ratio) is
incorporated. As described in more detail
in section IV, this methodology further
incorporates other correlates of health
status and access, such as ethnic minority
status and unemployment, based on ready
national availability of data and the
health inequalities literature.
Science-Based
The new underservice measure must be based
on scientifically recognized methods and
be replicable. For example, the current
Index of Medical Underservice comprises
four variables, each of which contributes
approximately a quarter to the maximum
score. In other words, each of the four
variables are weighted equally. However,
there is no empirical justification for
why the income variable should have a
weight equal to the infant mortality rate
variable. Rather, in designing the new
methodology, we believed the contribution
of each variable to an overall measure
should be based on some verifiable statistical
relationship. As discussed further in
section IV, the new methodology used an
overall conceptual framework to describe
access and used analytical techniques
such as regression and factor analysis
to arrive at the weighting/scoring system
proposed herein.
Minimize Unnecessary Disruption
Partly due to the Health Care Safety net
Amendments of 2002, as described earlier,
we have attempted to achieve a reasonable
transition to this new methodology for
underserved areas. Though the revised
designation method will not (and should
not) generate the exact same designations
as the previous method, we have attempted
to minimize unnecessary disruption where
applicable. The new measure will allow
us to better focus the designations to
more needy areas and populations.
Acceptable Performance
The new system must perform better than
the current designation criteria using
updated data, and it should be seen as
an improvement by the multiple key stakeholder
groups who rely on these designations.
We used many different evaluating criteria
for this guiding principle, but the fundamental
criterion we used is whether the method
fairly and consistently identifies places
and people who were in need of primary
health care and who had barriers to meeting
those needs.
2. Designation Process Goals
Consolidation and Simplification
The separate statutes authorizing MUP
and HPSA designations address the same
fundamental policy concern: That is, the
identification of those areas and populations
with unmet health care needs for the purpose
of determining eligibility for certain
Federal health care resources. The existence
of two similar but quite distinct procedures
and sets of criteria has been confusing
to many and has often led to contradictory
or inconsistent results.
The legislative requirements for the two
designations are similar in many respects,
but the designation processes have, until
now, been largely separate. A major reason
for the disparity in the designation process
is that regular updating of HPSAs is required
by statute, though such updating is not
statutorily required for the MUA/Ps and
has not regularly been done.
The rules proposed below attempt to establish
uniform procedures and criteria, not only
to simplify the designation process for
the agencies, communities, entities, and
individuals involved, but also to increase
the efficient and effective use of Departmental
resources. To do so, all the legislatively
mandated elements of both statutes are
included in the proposed procedures. The
revised criteria for geographic HPSAs
and MUAs are identical, as are those for
most types of MUPs and corresponding population
group HPSAs, wherever permitted by statutory
requirements. Since facility designations
are only authorized for HPSAs, this is
one domain for which the two could not
be the same.
Proactivity
The proposed methodology can be applied
using national data obtained by HRSA and
made available to State partners in the
designation process, thereby enabling
more universal application of the designation
criteria. Applicant familiarity with the
designation process should also become
less of a factor in obtaining designation,
and the need for independent data collection
by applicants will be less of a barrier
and burden.
The national databases include updated
versions of the data used in the development
of this methodology: Provider data from
appropriate professional associations,
such as the American Medical Association
(AMA) physician data; socio-demographic
data from the U.S. Census Bureau or a
vendor which produces intercensal estimates;
unemployment data from the Department
of Labor; and health status data from
the National Center for Health Statistics.
At the same time, States and communities
will continue to have the opportunity
to substitute State and local data for
the national data if the State and local
data are more reliable and/or more current.
Data from recognized sources such as State
Data Centers, economic forecasting agencies
such as J.D. Powers, and similar entities,
and that are used for other state purposes
may be submitted. Provider data may be
secured from a variety of sources: State
licensing boards, state or local professional
societies, professional directories, etc.
Data sources, methodologies, and dates
must be specified.
Automation
The proposed methodology will enable a
more automated process for designation,
through the use of a tabular method for
scoring areas and updating these scores.
The new method makes considerable use
of census variables for which data are
available not only at the county level
but also at subcounty levels (e.g., for
census tracts and census divisions), so
that a wide variety of State- and community-defined
service areas can be evaluated for possible
designation. Also, an interactive system
for processing designation requests and
updates will permit State partners in
the designation process to work together
with the federal designation staff using
the same databases. The intent is to minimize
the effort required by States, communities,
and other entities to designate an area
or update its designation.
Increased State Role
The proposed approach seeks to foster
an increased partnership between the various
levels of government involved in designation,
including a significantly larger State
and local role in defining service areas,
underserved population groups and unusual
local conditions. The new criteria are
less prescriptive in terms of travel time
and mileage standards for defining service
areas. Each State will be encouraged to
define, with community input and in collaboration
with the Secretary, a complete set of
rational service areas (RSA) covering
its territory. Once developed, these service
areas will be used in underservice/shortage
area designations unless and until new
census data or health system changes require
further area boundary changes. Currently
the agency allows States to provide their
own provider data through a new interactive
system. States with more reliable data
can substitute them for national data,
which will reduce the time required for
case-by-case review.
Reduce the Need for Population Group Designations
Designation of population groups is typically
more resource- intensive than designation
of geographic areas, both from the standpoint
of data collection (since obtaining data
for a particular population is often more
difficult than for the area as a whole)
and in terms of review. As discussed below,
specific indicators included in the proposed
approach represent the access barriers
of poverty/low income, unemployment, racial
minority or Hispanic ethnicity, population
density and population over 65 years.
This approach specifically adjusts an
area's base population-to-primary care
clinician ratio for the effects of these
variables. Therefore, it is hoped that
this method will reduce the need for specific
population group designations by increasing
the probability of designation of geographic
areas with concentrations of these groups.
III.
Development of Methodology To Achieve
Goals
A. 1998 NPRM and Summary
of Comments Received
Following consultation with two panels of
experts and in-house impact testing, an
NPRM to revise the designation methodology
was published on September 1, 1998. Those
proposed rules (referred to hereinafter
as ``NPRM1'') would have created one process
for simultaneous designation of MUPs and
HPSAs; set forth revised criteria for designation
of MUPs using a new Index of Primary Care
Services (IPCS); and defined HPSAs as a
subset of the MUPs, consisting of those
MUPs with a population-to-practitioner ratio
exceeding a certain level. The use of RSAs
would have been required for application
of both the MUP and HPSA criteria.
The IPCS score would have been calculated
based on a weighted combination of seven
variables: Population-to-primary care clinician
ratio, percent population below 200% poverty,
percent population racial minorities, percent
population Hispanic, percent population
linguistically isolated, infant mortality
rate or percent low birthweight births,
and low population density. The maximum
possible IPCS score would have been 100,
and RSAs whose IPCS score equaled or exceeded
35 would qualify for MUP designation. In
counts of primary care clinicians, nurse
practitioners (NP), physician assistants
(PA), and certified nurse midwives (CNM)
would have been included with a weight of
0.5 full time equivalents (FTE) relative
to primary care physicians. There would
have been two tiers of designations, with
the first tier consisting of those areas
which meet the criteria when all primary
care clinicians practicing in the area are
counted, and the second tier consisting
of those additional areas which meet the
criteria when certain categories of practitioners
(NHSC assignees and those practicing in
CHCs) are excluded from clinician counts.
HPSA designation would have required a minimum
population-to- primary care physician ratio
of 3,000:1, but this threshold could only
be applied to those RSAs found to have an
IPCS score which exceeded the MUP designation
threshold of 35.
The period for public comment on the 1998
proposed rule was extended to January 4,
1999. Over 800 comments were received, analyzed,
and categorized. Major issues raised are
summarized briefly below:
1. Impact in Terms of Designations Lost--Many
commenters estimated
that unacceptably high numbers of HPSA designations
would be lost in their State if the proposed
methodology were adopted, particularly in
rural and frontier areas, as well as significant
numbers of MUPs. They believed that the
impact stated in NPRM1's preamble, in terms
of percentages of designations lost, was
substantially underestimated.
2. Inclusion of nonphysician primary care
providers--A number of commenters objected
to the inclusion of NPs/PAs/CNMs in primary
care clinician counts, based on the additional
burden on applicants of counting them, and
cited the lack of adequate State or national
databases for these clinicians. Others questioned
the reasonableness of weighting them at
0.5 FTE relative to a primary care physician.
Typically, responding NPs, PAs, CNMs, professional
organizations representing them, and certain
other health care advocates felt the 0.5
should be adjusted upward; others felt it
should be adjusted downward, particularly
in States where the scope of practice of
these clinicians is limited. There were
also concerns that NPs, PAs and CNMs who
were not in clinical, primary care practice
would be inadvertently counted if available
data were used, and that truly underserved
areas would lose designation as a result.
3. Threshold for HPSA Designation--The proposed
3,000:1 population- to-primary care clinician
threshold ratio for HPSA designation was
considered too high by many commenters,
especially if NPs/PAs/CNMs were to be counted
as well as primary care physicians.
4. Urban/Rural Balance--Many of the indicators
selected for inclusion in the new IPCS (such
as race, Hispanic ethnicity, linguistic
isolation, and low birthweight births),
were viewed as tending to bias the new index
toward designation of urban areas (as compared
with indicators like percent elderly, which
had been included in the previously-used
Index of Medical Underservice and was seen
as favoring rural areas).
5. HPSAs required to be a subset of MUPs--the
proposed requirement that an area could
receive HPSA designation only if it first
qualified as an MUP (by having an IPCS score
which exceeded the 35 threshold) was seen
as threatening many legitimate currently-designated
HPSAs (i.e., HPSAs with population-to-practitioner
ratios higher than 3000:1 but whose poverty
rates and scores on other IPCS variables
were not high enough to achieve the IPCS
threshold).
6. Two-tiered Designations--The idea of
two-tiered designations was generally supported,
but an issue arose as to which federally-supported
primary care clinicians should be excluded
from counts in tier 2. Most agreed that
NHSC assignees and physicians in CHCs should
be excluded (as the proposed rule did).
Many felt that those physicians on J-1 waivers
should also be excluded from tier 2 counts,
and some suggested that primary clinicians
in other safety-net settings (such as RHCs
or State-funded health centers) should also
be excluded. On June 3, 1999, notice was
given in the Federal Register that further
analysis would be conducted, to include
a thorough, updated analysis of the impact
of the proposed approach as published, as
well as the testing of alternatives based
on analysis of the comments received. The
Notice indicated that these impact analyses
would be applied to the most current obtainable
national data for all counties and currently-defined
subcounty MUPs and HPSAs, and that one or
more outside organizations would verify
the impact testing. A new NPRM would then
be published for public comment.
B. Development of Method
Proposed in This NPRM
During the remainder of 1999, HRSA acquired
components of the national databases necessary
for impact testing, such as practice addresses
for primary care physicians, PAs, NPs, and
CNMs. An extensive data cleaning and provider
site geocoding process ensued. Simultaneously,
HRSA began working with researchers at HRSA-funded
Rural Health Research Centers and Health
Professions Workforce Centers to develop
specifics of the plan for further analysis
and testing. Ultimately, the Cecil G. Sheps
Center of the University of North Carolina
(UNC) was funded to undertake national testing
of the previously-proposed methodology in
NPRM1 and alternative methodologies, and
to coordinate efforts by other research
groups who would do State or regional testing.
In January 2000, a group of sixteen State
Primary Care Office (PCO) representatives
volunteered to assist by providing recommendations
for a revised approach to designation from
their standpoint, as the ones primarily
responsible for providing data to HRSA in
support of designation requests and updates
for their States. This led to a series of
conference calls, a two-day meeting, and
eventual preparation of draft recommendations
for consideration by the appropriate federal
officials. Meanwhile, researchers at the
Sheps Center were considering alternative
methodologies for simultaneous consideration
of various indicators of shortage and underservice.
The two groups met on several occasions
to coordinate efforts; the methodology finally
developed by Sheps researchers and used
as the basis for these proposed rules was
consistent with the recommendations of the
group of PCOs. Over time, the following
specific steps took place:
(a) A comprehensive database for impact
testing was established. This entailed:
``cleaning'' and geocoding the various physician
databases acquired (from professional associations
and from federal and State agencies approving
J-1 visa waivers), and matching them with
each other and with HRSA's NHSC database;
similar activity for data acquired on non-physician
primary care clinicians (NP/PA/CNM); adding
geocoded location data for HHS-sponsored
safety-net provider sites, including CHCs,
NHSC sites and RHCs; and the inclusion of
appropriate Census data (or vendor-supplied
intercensal estimates for Census variables)
as well as data on other health status and
access-related variables.
(b) The group of sixteen PCOs developed
their recommended approach to a new designation
methodology and provided their recommendations
to HRSA staff. Their original recommendation
was essentially to expand the number of
high need indicators which could be used
to adjust the population-to-practitioner
ratio threshold for designation, to allow
several different threshold levels depending
on the number of high need indicators present,
and then to compare the area's actual ratio
with the adjusted threshold appropriate
for that area.
(c) HRSA staff worked with the UNC-Sheps
Center team to develop a conceptual framework
and a methodology responsive to concerns
raised in public comments and in the PCO
recommendations. In response to the criticism
of the earlier 1998 proposal as using appropriate
indicators but an arbitrary weighting scheme,
this methodology was developed based on
a general conceptual framework of access
and underservice and statistical methods.
The overall goal was to identify areas and
communities in need of services to increase
access, relative to other communities across
the country. The conceptual framework and
methodology will be described further in
sections IV.A and IV.B. A more technical
description is also provided in Appendix
B. The way the method is applied to determine
designation status is described in Sections
IV.C and V. below. Finally, further details
are available on HRSA's
Web site and in a journal article recently
published in the Journal of Health Care
for the Poor and Underserved entitled ``Designating
Places and Populations as Medically Underserved:
A Proposal for a New Approach'' (Ricketts
et al., 2007).
(d) The impact of the proposed method on
the number and population of geographic
and low income designations at national
and state levels was explored and compared
with alternatives using updated national
data allied to: (a) The criteria currently
in place; (b) the criteria proposed in the
September 1, 1998 rule, and (c) the new
methodology proposed in this rule. In addition,
impact analyses with State data were performed
by Regional Centers for Health Workforce
Studies and/or PCOs in four States. This
analysis, discussed in detail in Section
VI below, indicated that this proposed method
would not have severe adverse effects on
most safety net providers, and would--at
the transition from the old method to the
new--maintain a similar total underserved
population.
(e) However, there remained concerns that
some safety net facilities--despite serving
populations clearly underserved, such as
the uninsured--might be located in areas
that did not meet geographic or population
group criteria. Consequently, with the help
of the group of 16 PCOs, a separate method
was developed (hereafter referred to as
the ``facility designation method'') for
facility designation of those safety-net
facilities which could demonstrate high
levels of service to the uninsured and/or
Medicaid-eligibles. This was tested using
the Uniform Data System for community health
centers and found to support designation
of most Section 330-funded health centers.
(f) The new methodology's concepts and impact
analysis approaches have been discussed
in a preliminary fashion at various meetings
of national and State organizations whose
members are affected by shortage/underservice
designations.
IV. Description of Conceptual Framework
and Methodology and Alternatives Considered
A. Conceptual Framework
In our model, as in health services research
more widely, we consider utilization of
services an outcome of the demand and supply
forces within the healthcare system. The
conceptual framework for the model is based
on the idea that barriers to care reduce
appropriate use, which is reflected in delayed
and therefore higher subsequent use rates.
We call this concept ``thwarted demand.''
For example, individuals with diabetes living
in remote, rural areas may put off seeing
their doctors regularly-not because they
do not recognize the need for regular treatment-but
because of the distances involved or other
potential barriers. These barriers initially
reduce utilization. When these individuals
eventually do seek treatment, it is often
because their condition worsened to the
point where they could no longer defer treatment.
As the severity of their condition worsens
and their need for care increases, so too
does their utilization of services, in terms
of treatment volume and/or intensity. They
may require hospitalization, for instance,
or present at an emergency room. To estimate
the dimensions of both the (a) delayed--and
thus initially reduced utilization rate--as
well as the (b) subsequent higher use rates,
we created a methodology that centers around
the level of care experienced by a ``well-served
population'' in order to establish an initial
standard against which an ``under-served
population'' can be defined. In a ``well-served
population,'' where there are no barriers
to care, healthcare utilization will be
an expression of healthcare demand (i.e.,
demand is not thwarted). The assumption
was made that, for groups without significant
barriers to care, primary care utilization
rates would cluster around the most appropriate
level of care and, in turn, that their demand
for care will also reflect their need for
care. In an ``under-served population,''
by contrast, demand will be initially thwarted
and healthcare utilization will therefore
understate true demand.
Moreover, healthcare needs tend to be greater
in areas with disadvantaged populations.
The health inequalities literature has shown,
for example, that conditions like diabetes
and cancer are more prevalent among minorities.
In turn, we can expect that areas with a
high proportion of minorities will--on average--have
greater healthcare needs than areas with
a lower proportion of minorities. To the
extent that healthcare needs tend to be
greater in underserved populations, the
level of healthcare utilization observed
in underserved populations would understate
true demand even further. Thus, the model
adjusts for this increased need and thwarted
demand.
As stated earlier, however, thwarted demand
potentially creates a paradox since low
access often results in subsequent illness
that may require a higher level of health
care use, in terms of either treatment volume
or intensity. The entry of the patient into
a structured care system may also induce
subsequently higher rates of use of primary
care services incident to hospitalizations
or due to raised familiarity with the system.
This paradox is likely to affect overall
use rates in low- access areas in such a
way as to increase use rates. We accepted
that these positive and negative factors
would be simultaneously operating and sought
ways to estimate their individual effects
in terms of both initially reduced and subsequently
increased visits. The net, overall need
for services can be reflected in a combination
of visits precluded with visits induced.
+ |
Absolute
number of reduced visits caused by
access barrierrs
Absolute number of increased visits
caused by delayed care or greater
morbidity |
|
Total
visits that would be demandeed if
population were barrier free |
By
adjusting for these bi-directional effects
of thwarted demand, this methodology effectively
allows us to ask, ``What level of care
would these individuals utilize if they
were well-served and barrier free?'' This
adjusted utilization rate becomes the
proxy in our revised model for the ``effective
need'' in an underserved population. For
example, an underserved area that contains
100 people may nevertheless ``effectively
need'' the same level of services an area
of 1,000 people needs. In this underserved
area, the ``actual'' population may be
100 but the ``effective'' population can
be thought of as 1,000.
We then compare this ``effective need''
in an underserved population to the available
supply of primary care providers in that
area to create a population-to-provider
ratio. The underlying logic is that meeting
community needs could be expressed in
ratios of appropriate use to optimal service
productivity. The use rate would be expressed
in population counts and the service productivity
in practitioner counts. The goal was to
reflect the level of a population's need
for office-based primary care visits in
terms of an adjusted population count
that took into consideration characteristics
that would affect use of services.
We considered various other proxies for
need besides the population-to-provider
ratio. We ultimately decided to use an
adjusted population-to-provider ratio
for several reasons. First, the prominence
of population-to-practitioner ratios in
the two existing measurements of underservice
was recognized. Discussions with the federal
agencies and stakeholder groups during
the development of the revised approach
also revealed a preference for using that
metric as the basis for a revised method.
Furthermore, practical reasons for the
use of this ratio as a starting point
for the construction of an index included
the fact that such ratios are well-recognized
and understood by the program participants
and would provide some continuity between
a new proposal and the older methods that
included the ratios in the calculations.
Such a metric is also sensitive to the
two different sources of unmet need--provider
shortages and barriers to care--that programs
which rely on the HPSA and MUA/P designations
attempt to address. In HPSAs, by definition,
access is restricted because there are
few or no primary care health professionals
who will take care of certain patients.
The remedy for this is to supplement the
professional supply with practitioners
who will see all patients, in order to
bring the numbers of professionals more
into line with a level of supply generally
considered adequate. For MUA/Ps, the primary
reasons for designation relate to barriers
to accessing existing primary care services
(e.g., financial) or the combination of
higher needs and lower availability. The
central task in combining these two systems
was to find a common metric that was sensitive
to both of these characteristics of underservice,
which the adjusted population-to- provider
ratio is.
B. Methodology
The model can be thought of as compromising
six basic steps. Step 1: Calculate the
numerator for the population-to-provider
ratio: The ``effective barrier free population.''
The first step is to estimate the effects
that differences in the structure of the
population would have on service utilization
based on age and gender by assigning weights
according to the national use rates for
people without barriers to care. Accordingly,
we call this the ``effective barrier free
population'' because it allows us to estimate
what the utilization rate would be, after
adjusting for age and gender, if the population
of a community were able to use primary
care services at the same rate as a population
with no constraints due to factors like
poverty, race, or ethnicity. This step
is necessary because research shows that
age and gender affect utilization rates
independent of barriers to care. The elderly,
for example, use services at higher rates
than the non-elderly even when barriers
to care are controlled for.
To calculate the ``effective barrier free
population,'' we adjust the area's base
population to reflect differential requirements
by age and gender for primary care services,
using utilization rates for populations
who are effectively ``barrier-free.''
This adjustment uses the latest available
Medical Expenditure Panel Survey (MEPS)
utilization data to determine what the
expected number of primary care office
visits for the area's population would
be (based on its age/ gender make-up)
if usage were at the national average
for persons who are non-minority, not
poor, and employed. This total expected
number of primary care visits is then
divided by the corresponding current national
mean number of primary care visits per
person to obtain the ``effective barrier
free population.'' The effect of this
adjustment is that a community with more
older people or more women of child-bearing
age than the average national age-gender
distribution will appear to be a larger
population than if the age-gender mix
were like the nation's as a whole.
The utilization rates used in developing
and testing the methodology proposed herein
are shown in Table IV-1. These will be
updated when this regulation is finalized
and periodically thereafter by notice
in the Federal Register that updated data
will be posted on the HRSA Web site.
Table
IV-1.--Barrier Free Population Use Rate,
Adjusted for Age and Gender, Expressed
as Primary Care Visits Per Person Per
Year
Age |
Average
primary care visits ( per year) by
age group category |
0–4 |
5–17 |
18–44 |
45–64 |
65–74 |
75+ |
Male |
5.164 |
2.499 |
2.867 |
4.410 |
6.052 |
8.056 |
Standard
Error |
.488 |
.401 |
.372 |
.386 |
.469 |
.533 |
Female |
4.046 |
2.256 |
5.007 |
5.480 |
6.710 |
8.160 |
Standard
Error |
.491 |
.403 |
.373 |
.389 |
.456 |
.533* |
The above table is from MEPS, 1996. These
data are applied to the actual area age-gender
total to derive the barrier free total utilization
for a population with these age and gender
characteristics. The corresponding national
mean utilization rate is 3.471. *Imputed.
The
calculations for Wichita County, Kansas
are shown as an illustration of how this
step of the model works. The chart below
provides the population breakout by age
and gender, the visit rates for each category,
and the adjusted population that results
from dividing by the average visit rate.
The steps are detailed below the chart.
The basic formula is: Barrier-free use
rate = 4.046 * ( # of females
aged 0-4) + 2.256 * ( # of females
aged 5-17) +5.007* ( # of females
aged 18-44) + 5.480 * ( # of
females aged 45-64) + 6.710 * (
# of females aged 65-74) + 8.160 * (
# of females aged 75+) + 5.164 * (
# of males aged 0-4) + 2.499 * (
of males aged 5-17) + 2.867 * (
# of males aged 18-44) + 4.410 * (
# of males aged 45-64) + 6.052 * (
# of males aged 65- 74) + 8.056 * (
# of males aged 75+)
Table
IV-1A.--Applying Table IV-1 Using Wichita,
Kansas as an Example
Females: |
Ages
0–4 |
5–17
|
18–44
|
45–64 |
65–74
|
75
and over |
Population |
65 |
207 |
363 |
281 |
106 |
113 |
Multiplier
(from Table IV-1) |
4.046 |
2.256 |
5.007 |
5.48 |
6.71 |
8.16 |
Visits |
262.99 |
466.992 |
1817.541 |
1539.88 |
711.26 |
922.08 |
Males: |
Ages
0–4 |
5–17
|
18–44
|
45–64 |
65–74
|
75
and over |
Population |
93 |
234 |
386 |
108 |
321 |
94 |
Multiplier
(from Table IV-1) |
5.164 |
2.499 |
2.867 |
4.41 |
6.052 |
8.056 |
Visits |
480.252 |
584.766 |
1106.662 |
476.28 |
1942.692 |
757.264 |
Female
visits |
5720.743 |
|
|
|
|
|
Male
visits |
5347.916 |
|
|
|
|
|
Total
visits |
11068.659 |
|
|
|
|
|
For
Wichita, the calculations are: Barrier-free
use rate = 4.046 * (65) + 2.256 * (207)
+ 5.007 * (363) + 5.480 * (281) + 6.710
* (1060) + 8.160 * (113) + 5.164 * (93)
+ 2.499 * (234) + 2.867 * (386) + 4.410
* (108) + 6.052 * (321) + 8.056 * (94) =
262.99 + 466.992 + 1817.541 + 1539.88 +
711.26 + 922.08 + 480.252 + 584.766 + 1106.662
+ 476.28 +1942.692 + 757.264 = 11068.659
visits. Using
1996 MEPS data, individuals who were barrier
free had, on average, 3.741 visits to
their primary care providers. If we then
divide the barrier-free use rate by this
average number of visits, we can obtain
the ``effective barrier-free population''
estimate. In Wichita, the calculation
would be: Effective barrier-free population
= 11068.659 / 3.741 = 2958.74338. This
``effective barrier-free population''
becomes the numerator-- the ``population''
value--in the population-to-provider ratio.
For example, the actual population of
Wichita, Kansas was 2,436. By going through
these calculations, however, we see in
Table IV-2 that the effective barrier-free
population is 2,959.
Table
IV-2
|
A |
B |
County
name |
Total
pop 1999 |
Effective
barrier-free population |
Wichita,
KS |
2,436 |
2959 |
Step
2: Calculate the denominator in the population-to-provider
ratio: The supply of primary care providers.
The second step is to calculate the actual
number of FTE primary care clinicians
in the target area, including primary
care physicians (allopathic and osteopathic),
NPs, PAs, and CNMs in primary care settings.
Each active physician in the primary care
specialties (i.e., General Practice, Family
Practice, General Internal Medicine, General
Pediatrics, Ob/Gyn) is included as 1.0
FTE unless there is evidence of less than
full-time practice, in which case their
actual FTE in the area is used based on
guidance set by the Secretary on the calculation
of FTEs. As before, physicians in residency
training in these specialties are counted
as 0.1 FTE. In this proposed rule, NP/PA/CNMs
are also included, but they are counted
either as 0.5 FTE or, at the applicant's
option, 0.8 times a State-specific practice
scope factor running from 0.5 to 1.0 (in
recognition that not all NP/PA/CNM practices
operate at the same level due to state
policies). We discuss this issue in further
detail in section V.G below. Data sources
are: American Medical Association Masterfile-Dec.
1998, American Osteopathic Association-May
1999, American College of Nurse Midwives-1999,
American Association of Nurse Practitioners-1999,
and American Association of Physician
Assistants-July 1999. For example, there
are 2.5 FTE primary care providers in
Wichita, Kansas, according to our national
data. Step 3: Calculate the base population-to-provider
ratio. The population-to-provider ratio
is then calculated using the ``effective
barrier-free population'' (from step 1)
as the numerator and the number of FTE
primary care clinicians (from step 2)
as the denominator. Using Wichita, Kansas
as an example, the base population- to-provider
ratio is 1,183 (table IV-3, column E).
Table
IV-3
|
A |
B |
C |
D |
E |
County
name |
Total
pop |
Effective
barrier-free population |
Tot
FTE primary care |
Actual
population to FTE ratio (A÷C) |
Effective
barrier-free pop/FTE ratio (B÷C) |
Wichita,
KS |
2436 |
2959 |
2.5 |
974 |
1183 |
Step
4: Adjust for increases in need for primary
care services based on community characteristics.
Because the programs that rely on HPSA
and MUA/P designations aim to improve
access and thereby improve health, this
consideration drove the design of the
analysis to develop weights for need for
services in areas and for populations.
The fourth step of this methodology thus
computes the effects of community factors
that have been demonstrated to indicate
an even greater need for services but
also a lower utilization of services than
the average well-insured and healthy population
due to barriers to care.
The
general approach was to take population-level
variables that correlate with barriers
to care and then determine the relationship
of those variables to the adjusted population-to-practitioner
ratio described above, using regression
analysis. From this analysis, the relative
influence of those variables on the ratio
would be derived and, from those parameters,
scores could be estimated to adjust or
``weight'' the overall index.
Because
step 4 can be quite technical, we present
only an overview here. For a more detailed
discussion of step 4 and its place in
the overall methodology, please refer
to Appendix B (please note that what we
refer to in this rule as ``step 4'' is
referred to as ``steps 4-5'' and ``step
7'' in Appendix B). The methodology is
also described in a journal article recently
published in the Journal of Health Care
for the Poor and Underserved entitled
``Designating Places and Populations as
Medically Underserved: A Proposal for
a New Approach'' (Ricketts et al., 2007).
In
developing step 4, we followed the conceptual
framework of access proposed by Andersen
and colleagues, who posit that there are
predisposing and enabling characteristics
that can represent need (Andersen et al.,
1973; Andersen 1995; Aday and Andersen
1975). There is no consensus set of community-level
indicators that reflect need within their
framework. Because the programs that rely
on HPSA and MUA/ P designations largely
address unmet need by placing primary
care practitioners in areas designated
as underserved, we chose to use the effective
barrier-free population-to-practitioner
ratio (calculated in steps 1, 2, and 3)
as a proxy indicator of relevant need
for this step in the methodology.
We
then ran regression analyses to examine
how the ratio varied with socio-demographic
indicators that research has shown to
correlate with low access and/or poor
health status (Mansfield et al., 1999;
CDC, 2000; Krieger et al., 2003; Andersen
and Newman 1973; Aday and Andersen 1975;
Robert 1999; Robert and House, 2000; Kawachi
and Berkman, 2003). We also included factors
in the regression model that closely parallel
the statutory elements of the current
HPSA and MUA designation processes (health
status, ability to pay for services and
their accessibility), and also directly
relate to the programs they initially
were designed to support: the NHSC and
the CHC Programs.
Three
categories of high need indicators were
ultimately used, for a total of nine indicators,
as described in Table IV-4. These factors
were used because they were shown by the
regression to have independent effects
on access to care as measured by the population-provider
ratio.
Table
IV-4.--Variables Used in Creating Proposed
Method
Demographic |
Economic |
Health
status |
Percent
Non-white ‘‘NONWHITE’’,
(src: 1998 Claritas estimates). |
Percent
population <200% FPL ‘‘POVERTY’’,
(src: 1998 Claritas estimates). |
Actual/expected
death rate (adj) ‘‘SMR’’,
(src: National Center for Health Statistics,
1998: for previous 5 year period).
|
Percent
Hispanic ‘‘HISPANIC’’,
(src: 1998 Claritas estimates). |
Unemployment
rate ‘‘UNEMPLOYMENT’’,
(src: Bureau of Labor Statistics,
1998). |
Low
birth weight rate ‘‘LBW’’,
(src: National Center for Health Statistics,
1998: for pre-vious 5 year period).
|
Percent
population >65 years ‘‘ELDERLY’’,
(src: 1998 Claritas estimates). |
|
Infant
mortality rate ‘‘IMR’’,
(src: National Cen-ter for Health
Statistics, 1998: for previous 5 year
period). |
Population
density ‘‘DENSITY’’*
(src: 1998 Claritas estimates) |
|
* Population density is a measure of the
market potential for an area as well as
an indicator of the rural or urban character
of a place. As places become more densely
populated, they tend to attract employment
and services. Density is also associated
with rural and urban settings and the
behavioral characteristics of populations
vary along that continuum (Amato and Zuo,
1992). A number of other need indicators
were considered in the development of
the methodology. Table IV-5 provides a
brief listing and an explanation why they
were not chosen. In many cases, these
elements are highly correlated with the
ones listed above, so their impact on
access is already captured by the variables
that are included.
Table
IV-5.--Variables Considered for Inclusion
But Not Chosen
Suggested
variables |
Reason
for rejection |
Percent
low income elderly |
Used
elderly and low income. |
Percent
children <6 |
Used
component in adjusted pop. |
Percent
children low income |
Used
overall low income. |
Percent
children <4 |
Used
component in adjusted pop. |
pop.
Dependency ratio (%>65+%<18/total
population) |
Used
combination of factors that capture
this. |
Racial
disparity in low birth weight rates |
Not
available for small areas. |
Disparity
in IMR rates |
Small
numbers.1
|
Birth
rate |
Highly
correlated with chosen measures. |
Teen
birth rate |
Not
available in sub-county areas. |
Prenatal
care (Kessner) |
Unstable
in small areas.1 |
Prenatal
care index (Kotelchuck) |
Unstable
in small areas.1 |
Ambulatory
care sensitive admissions (ACS rates) |
Not
available in all states. |
Ambulatory
care sensitive admissions for children |
Not
available in all states. |
ACS
rates restricted to common disease
(diabetes, hypertension, cellulitis
|
Not
available in all states. |
ACS
rates for Medicare population |
Not
available in all states. |
ACS
Rates for common disease for Medicare
population |
Not
available in all states. |
Ratio
of 100–200% poverty to 100%
poverty |
High
correlation with chosen variables.
|
Uninsured
population |
Not
available in small areas. |
Uninsured
<18 years |
Not
available in small areas. |
Population
density threshold (LT 6 p sq mile,
7 p sq mile) |
Density
used as a continuous variable instead.
|
Linguistic
isolation |
Not
calculated on a regular basis. Imputed
data.2
|
Migrant
impact |
Not
available. |
Farmworker
impact |
Not
available. |
Seasonal
worker impact |
Not
available. |
Percent
refugees, immigrant |
Not
calculated on a regular basis. Imputed
data.2 |
Medicaid
eligible population |
Not
readily available in small areas.
|
Tuberculosis
incidence |
Not
available in small areas. |
HIV
incidence |
Not
available in small areas. |
STD
incidence |
Not
available in small areas. |
Cancer
incidence |
Not
available in small areas. |
Cervical
cancer incidence |
Not
available in small areas. |
Breast
cancer incidence |
Not
available in small areas. |
Hypertension
rate |
Not
available in small areas. |
COPD
rates |
Not
available in small areas. |
Diabetes
rates |
Not
available in small areas. |
Diabetes
rates for children |
Not
available in small areas. |
Asthma
rates |
Not
available in small areas. |
Asthma
rates for children |
Not
available in small areas. |
Smoking
rates |
Not
available in small areas. |
Smoking
rates for children/adolescents |
Not
available in small areas. |
Obesity |
Not
available in small areas. |
Obesity
among children |
Not
available in small areas. |
Alcohol
use rates |
Not
available in small areas. |
Alcohol
use rates for adolescents |
Not
available in small areas. |
Binge
drinking rates |
Not
available in small areas. |
Disparity
measures (ratio of rates for whites
and minorities for disease incidence
various combinations). |
Not
available in small areas. |
Raw
mortality rate |
Prefer
adjusted mortality rate.3 |
Disparity
in mortality rate |
Small
numbers. |
Cancer
mortality |
Small
numbers. |
Cardiovascular
disease mortality |
Small
numbers. |
Infectious
disease mortality |
Small
numbers. |
Suicide
rate |
Small
numbers. |
Teen
suicide rate |
Small
numbers. |
Percent
rural population |
Density
captures. |
Percent
urban population |
Density
captures. |
Perceptual
measures (other designations) |
Varied
from state to state. |
1
Infant mortality remains a relatively
rare phenomenon and published rates are
often compiled from multi-year data. Comparing
rates for small areas would compound the
instability of those rates. The same problems
are encountered with data that describe
the character of prenatal care in small
and rural areas, although these Indices
are based on assessments of all births,
the degree to which prenatal care meets
standards of adequacy in smaller and less
populated areas may vary from year to
year due to isolated events or poor care
for a limited number of newborns due to
factors that do not reflect the character
of the health care in the area (e.g. weather,
relocation).
2
These data are reported by the Census
Bureau and are ``imputed'' from other
variables (reported ethnicity and the
likelihood of being a refugee or immigrant).
The data are not collected directly.
3
The mortality rate varies widely according
to the age structure of a place. A much
higher proportion of elderly is often
associated with a much higher mortality
rate. Adjusting for the age structure
allows for a better comparison of the
mortality burden of the community relative
to its risk. To calculate the adjustment
factors or ``weights,'' the actual value
of each high need indicator was converted
to a percentile relative to the national
county distribution, using a conversion
table (see Table IV-6). For all variables
except population density, the theoretically
worst actual value corresponded to the
99th percentile (e.g., the higher the
unemployment rate in an area, the higher
the percentile.) In Wichita, Kansas for
example, 3.59% of the population were
unemployed.
Table
IV-6 is used to translate this percentage
into a percentile: In this case, Wichita
falls in the 24th percentile.
Table
IV-6.--High Need Indicators--Breakpoints
for Conversion From Community Values to
National Percentiles *
Percentile |
Poverty |
Unemp |
Elderly |
Density |
Hispanic |
Non
white |
Death
rate |
LBW |
IMR |
1 |
13.31 |
1.70 |
6.32 |
0.66 |
0.13 |
0.23 |
0.674 |
3.23 |
0.00 |
2 |
16.15 |
1.90 |
7.55 |
1.01 |
0.19 |
0.30 |
0.729 |
3.66 |
0.00 |
3 |
18.29 |
2.10 |
8.18 |
1.49 |
0.23 |
0.36 |
0.766 |
3.94 |
0.00 |
4 |
19.74 |
2.20 |
8.79 |
1.79 |
0.26 |
0.40 |
0.788 |
4.13 |
0.00 |
5 |
21.15 |
2.30 |
9.34 |
2.16 |
0.29 |
0.45 |
0.805 |
4.32 |
3.09 |
6 |
22.27 |
2.40 |
9.70 |
2.54 |
0.30 |
0.48 |
0.816 |
4.44 |
3.49 |
7 |
23.25 |
2.40 |
9.97 |
3.01 |
0.33 |
0.53 |
0.826 |
4.60 |
3.89 |
8 |
24.24 |
2.50 |
10.23 |
3.38 |
0.34 |
0.58 |
0.837 |
4.69 |
4.13 |
9 |
25.01 |
2.60 |
10.50 |
3.80 |
0.36 |
0.61 |
0.846 |
4.80 |
4.43 |
10 |
25.68 |
2.70 |
10.71 |
4.24 |
0.38 |
0.64 |
0.853 |
4.88 |
4.63 |
11 |
26.25 |
2.70 |
10.90 |
4.73 |
0.40 |
0.67 |
0.861 |
4.95 |
4.76 |
12 |
26.83 |
2.80 |
11.11 |
5.32 |
0.41 |
0.71 |
0.867 |
5.02 |
4.90 |
13 |
27.36 |
2.90 |
11.26 |
6.23 |
0.42 |
0.76 |
0.873 |
5.10 |
4.99 |
14 |
27.83 |
2.90 |
11.43 |
6.82 |
0.44 |
0.79 |
0.878 |
5.16 |
5.09 |
15 |
28.42 |
3.00 |
11.61 |
7.82 |
0.46 |
0.83 |
0.883 |
5.22 |
5.22 |
16 |
28.93 |
3.10 |
11.75 |
8.41 |
0.47 |
0.88 |
0.889 |
5.28 |
5.33 |
17 |
29.39 |
3.10 |
11.92 |
9.36 |
0.49 |
0.93 |
0.894 |
5.34 |
5.43 |
18 |
29.91 |
3.20 |
12.06 |
9.97 |
0.50 |
0.97 |
0.899 |
5.38 |
5.55 |
19 |
30.29 |
3.20 |
12.17 |
10.98 |
0.51 |
1.01 |
0.903 |
5.42 |
5.63 |
20 |
30.66 |
3.30 |
12.30 |
11.96 |
0.53 |
1.06 |
0.908 |
5.47 |
5.74 |
21 |
31.12 |
3.30 |
12.46 |
13.02 |
0.55 |
1.11 |
0.913 |
5.52 |
5.86 |
22 |
31.57 |
3.40 |
12.57 |
13.90 |
0.56 |
1.16 |
0.917 |
5.57 |
5.91 |
23 |
31.90 |
3.40 |
12.72 |
14.60 |
0.58 |
1.20 |
0.920 |
5.60 |
6.00 |
24 |
32.24 |
3.50 |
12.82 |
15.78 |
0.59 |
1.27 |
0.925 |
5.65 |
6.08 |
25 |
32.62 |
3.60 |
12.94 |
16.66 |
0.60 |
1.33 |
0.928 |
5.71 |
6.17 |
26 |
32.98 |
3.60 |
13.04 |
17.63 |
0.62 |
1.40 |
0.932 |
5.76 |
6.27 |
27 |
33.43 |
3.70 |
13.14 |
18.40 |
0.64 |
1.49 |
0.937 |
5.80 |
6.32 |
28 |
33.71 |
3.70 |
13.24 |
19.03 |
0.65 |
1.54 |
0.938 |
5.84 |
6.39 |
29 |
34.07 |
3.80 |
13.33 |
19.94 |
0.67 |
1.63 |
0.941 |
5.88 |
6.45 |
30. |
34.45 |
3.80 |
13.41 |
20.92 |
0.68 |
1.73 |
0.945 |
5.92 |
6.53 |
31 |
34.83 |
3.90 |
13.51 |
22.15 |
0.70 |
1.79 |
0.948 |
5.96 |
6.62 |
32 |
35.15 |
3.90 |
13.63 |
22.85 |
0.72 |
1.89 |
0.952 |
6.00 |
6.68 |
33 |
35.57 |
4.00 |
13.73 |
23.76 |
0.74 |
1.99 |
0.956 |
6.03 |
6.74 |
34 |
35.85 |
4.00 |
13.83 |
24.61 |
0.76 |
2.06 |
0.958 |
6.08 |
6.82 |
35 |
36.22 |
4.10 |
13.90 |
25.83 |
0.78 |
2.12 |
0.961 |
6.12 |
6.88 |
36 |
36.53 |
4.10 |
14.02 |
26.76 |
0.81 |
2.20 |
0.965 |
6.15 |
6.95 |
37 |
36.82 |
4.20 |
14.12 |
27.67 |
0.83 |
2.29 |
0.968 |
6.20 |
7.05 |
38 |
37.07 |
4.30 |
14.18 |
28.48 |
0.85 |
2.44 |
0.971 |
6.24 |
7.11 |
39 |
37.34 |
4.30 |
14.26 |
29.56 |
0.87 |
2.57 |
0.974 |
6.28 |
7.18 |
40 |
37.62 |
4.40 |
14.31 |
30.35 |
0.90 |
2.69 |
0.978 |
6.33 |
7.26 |
41 |
37.83 |
4.40 |
14.39 |
31.51 |
0.93 |
2.82 |
0.981 |
6.36 |
7.35 |
42 |
38.16 |
4.50 |
14.49 |
32.46 |
0.95 |
3.04 |
0.985 |
6.41 |
7.42 |
43 |
38.35 |
4.50 |
14.57 |
33.33 |
0.98 |
3.18 |
0.989 |
6.45 |
7.48 |
44 |
38.63 |
4.60 |
14.67 |
34.49 |
1.01 |
3.35 |
0.992 |
6.49 |
7.55 |
45 |
38.85 |
4.60 |
14.76 |
35.63 |
1.04 |
3.49 |
0.996 |
6.54 |
7.61 |
46 |
39.14 |
4.70 |
14.84 |
36.72 |
1.07 |
3.67 |
0.999 |
6.60 |
7.67 |
47 |
39.44 |
4.80 |
14.94 |
37.69 |
1.11 |
3.87 |
1.002 |
6.63 |
7.74 |
48 |
39.74 |
4.80 |
15.00 |
38.72 |
1.15 |
4.04 |
1.005 |
6.67 |
7.81 |
49 |
40.06 |
4.90 |
15.12 |
39.88 |
1.20 |
4.22 |
1.009 |
6.70 |
7.86 |
50 |
40.31 |
4.90 |
15.20 |
41.38 |
1.24 |
4.44 |
1.013 |
6.76 |
7.91 |
51 |
40.61 |
5.00 |
15.31 |
42.64 |
1.27 |
4.65 |
1.018 |
6.78 |
7.98 |
52 |
40.93 |
5.00 |
15.43 |
44.24 |
1.30 |
4.90 |
1.021 |
6.82 |
8.08 |
53 |
41.21 |
5.10 |
15.52 |
45.78 |
1.35 |
5.17 |
1.024 |
6.86 |
8.14 |
54 |
41.49 |
5.20 |
15.63 |
47.24 |
1.39 |
5.50 |
1.027 |
6.91 |
8.19 |
55 |
41.72 |
5.20 |
15.71 |
48.65 |
1.44 |
5.81 |
1.030 |
6.96 |
8.27 |
56 |
42.04 |
5.30 |
15.78 |
49.94 |
1.49 |
6.12 |
1.034 |
7.00 |
8.32 |
57 |
42.35 |
5.30 |
15.91 |
51.61 |
1.54 |
6.37 |
1.039 |
7.06 |
8.43 |
58 |
42.62 |
5.40 |
15.99 |
53.18 |
1.60 |
6.72 |
1.042 |
7.10 |
8.50 |
59 |
42.98 |
5.50 |
16.09 |
54.53 |
1.65 |
7.03 |
1.045 |
7.14 |
8.58 |
60 |
43.38 |
5.50 |
16.21 |
56.26 |
1.72 |
7.31 |
1.049 |
7.20 |
8.66 |
61 |
43.67 |
5.60 |
16.30 |
58.03 |
1.80 |
7.74 |
1.052 |
7.25 |
8.76 |
62 |
44.01 |
5.70 |
16.39 |
61.20 |
1.88 |
8.23 |
1.055 |
7.29 |
8.81 |
63 |
44.25 |
5.80 |
16.52 |
63.54 |
1.98 |
8.69 |
1.060 |
7.33 |
8.87 |
64 |
44.65 |
5.90 |
16.67 |
66.32 |
2.08 |
9.24 |
1.064 |
7.38 |
8.92 |
65 |
44.90 |
5.90 |
16.76 |
68.59 |
2.16 |
9.60 |
1.067 |
7.44 |
9.02 |
66 |
45.15 |
6.00 |
16.86 |
70.91 |
2.26 |
9.97 |
1.071 |
7.50 |
9.11 |
67 |
45.38 |
6.10 |
16.96 |
73.19 |
2.37 |
10.40 |
1.074 |
7.55 |
9.18 |
68 |
45.77 |
6.30 |
17.11 |
74.78 |
2.48 |
10.96 |
1.079 |
7.61 |
9.24 |
69 |
46.13 |
6.40 |
17.24 |
79.13 |
2.60 |
11.54 |
1.083 |
7.65 |
9.35 |
70 |
46.52 |
6.50 |
17.38 |
82.37 |
2.74 |
12.36 |
1.087 |
7.73 |
9.41 |
71 |
46.90 |
6.60 |
17.49 |
85.72 |
2.89 |
13.18 |
1.093 |
7.78 |
9.54 |
72 |
47.19 |
6.70 |
17.64 |
88.76 |
3.05 |
14.08 |
1.097 |
7.83 |
9.64 |
73 |
47.48 |
6.80 |
17.76 |
92.97 |
3.17 |
14.81 |
1.102 |
7.90 |
9.76 |
74 |
47.85 |
6.90 |
17.90 |
97.05 |
3.35 |
15.80 |
1.108 |
7.95 |
9.89 |
75 |
48.14 |
7.00 |
17.99 |
101.55 |
3.58 |
16.60 |
1.112 |
8.01 |
10.00 |
76 |
48.49 |
7.10 |
18.17 |
107.04 |
3.78 |
17.38 |
1.117 |
8.07 |
10.16 |
77 |
48.83 |
7.30 |
18.33 |
113.07 |
4.03 |
18.18 |
1.122 |
8.14 |
10.27 |
78 |
49.15 |
7.30 |
18.48 |
120.40 |
4.35 |
19.40 |
1.127 |
8.23 |
10.34 |
79 |
49.66 |
7.50 |
18.64 |
129.38 |
4.61 |
20.67 |
1.132 |
8.30 |
10.50 |
80 |
50.03 |
7.70 |
18.88 |
137.50 |
5.04 |
22.01 |
1.137 |
8.42 |
10.63 |
81 |
50.39 |
7.80 |
19.10 |
147.51 |
5.62 |
23.26 |
1.143 |
8.48 |
10.75 |
82 |
50.88 |
7.90 |
19.29 |
157.66 |
5.99 |
24.48 |
1.146 |
8.56 |
10.94 |
83 |
51.22 |
8.00 |
19.53 |
168.72 |
6.64 |
25.73 |
1.153 |
8.69 |
11.11 |
84 |
51.70 |
8.10 |
19.79 |
184.45 |
7.43 |
26.83 |
1.160 |
8.81 |
11.28 |
85 |
52.21 |
8.20 |
20.09 |
198.45 |
8.05 |
28.24 |
1.167 |
8.93 |
11.53 |
86 |
52.63 |
8.40 |
20.31 |
215.14 |
8.88 |
30.57 |
1.173 |
9.04 |
11.76 |
87 |
53.05 |
8.60 |
20.62 |
236.02 |
9.74 |
31.78 |
1.181 |
9.16 |
11.98 |
88 |
53.51 |
8.80 |
20.89 |
264.75 |
10.66 |
33.74 |
1.190 |
9.24 |
12.25 |
89 |
54.01 |
9.00 |
21.25 |
291.58 |
12.34 |
35.30 |
1.200 |
9.36 |
12.50 |
90 |
54.75 |
9.30 |
21.54 |
321.29 |
13.82 |
37.43 |
1.210 |
9.58 |
12.81 |
91 |
55.46 |
9.50 |
21.92 |
357.86 |
15.88 |
39.16 |
1.218 |
9.77 |
13.15 |
92 |
56.23 |
9.80 |
22.33 |
413.68 |
17.90 |
41.17 |
1.230 |
9.92 |
13.58 |
93 |
57.26 |
10.10 |
22.67 |
488.71 |
21.81 |
43.77 |
1.238 |
10.17 |
13.87 |
94 |
58.23 |
10.50 |
23.16 |
595.16 |
25.73 |
46.18 |
1.252 |
10.35 |
14.21 |
95 |
59.13 |
10.80 |
23.53 |
755.53 |
28.66 |
48.01 |
1.268 |
10.55 |
14.79 |
96 |
61.07 |
11.50 |
24.53 |
995.22 |
34.72 |
52.62 |
1.289 |
10.87 |
15.63 |
97 |
62.59 |
12.20 |
25.06 |
1356.41 |
42.03 |
57.51 |
1.310 |
11.31 |
16.56 |
98 |
65.07 |
13.20 |
26.22 |
1759.93 |
48.46 |
62.78 |
1.341 |
11.72 |
17.54 |
99 |
68.05 |
15.20 |
27.75 |
3090.35 |
65.75 |
69.42 |
1.407 |
12.47 |
19.70 |
Data Sources: Census Estimates from Claritas
1998; Bureau of Labor Statistics 1998,
National Center for Health Statistics
1998. The resulting percentile rankings
for each of the high need indicators in
the area are then converted to a score,
using a second table (see Table IV-7),
which expresses the results of the regression
analysis in terms of partial scores or
weights for each indicator. Using Table
IV-7 and using Wichita as an example,
we see that a percentile ranking of 24
for unemployment translates into a score
of 32.21.
Table
IV-7.--Scores for High Need Indicators,
Given Their National Percentiles
Percentile |
Poverty |
Unemp |
Elderly |
Density |
Hispanic |
Non
white |
Death
rate |
LBW/IMR |
0 |
0.00 |
0.00 |
0.00 |
995.20 |
0.00 |
0.00 |
0.00 |
0.00 |
1 |
3.01 |
1.18 |
0.54 |
831.13 |
0.81 |
0.00 |
0.82 |
0.72 |
2 |
6.04 |
2.37 |
1.09 |
735.15 |
1.64 |
0.00 |
1.65 |
1.44 |
3 |
9.11 |
3.58 |
1.65 |
667.05 |
2.47 |
0.00 |
2.49 |
2.17 |
4 |
12.21 |
4.79 |
2.21 |
614.23 |
3.31 |
0.00 |
3.33 |
2.91 |
5 |
15.34 |
6.02 |
2.77 |
571.07 |
4.15 |
0.00 |
4.19 |
3.65 |
6 |
18.50 |
7.26 |
3.34 |
534.58 |
5.01 |
0.00 |
5.05 |
4.40 |
7 |
21.70 |
8.52 |
3.92 |
502.98 |
5.88 |
0.00 |
5.93 |
5.17 |
8 |
24.93 |
9.79 |
4.51 |
475.10 |
6.75 |
0.00 |
6.81 |
5.93 |
9 |
28.20 |
11.07 |
5.10 |
450.16 |
7.64 |
0.00 |
7.70 |
6.71 |
10 |
31.50 |
12.37 |
5.69 |
427.59 |
8.53 |
0.00 |
8.60 |
7.50 |
11 |
34.84 |
13.68 |
6.30 |
407.00 |
9.44 |
0.00 |
9.52 |
8.29 |
12 |
38.22 |
15.00 |
6.91 |
388.05 |
10.35 |
0.00 |
10.44 |
9.10 |
13 |
41.64 |
16.35 |
7.53 |
370.51 |
11.28 |
0.00 |
11.37 |
9.91 |
14 |
45.10 |
17.70 |
8.15 |
354.18 |
12.21 |
0.00 |
12.32 |
10.73 |
15 |
48.59 |
19.08 |
8.78 |
338.90 |
13.16 |
0.00 |
13.27 |
11.57 |
16 |
52.13 |
20.46 |
9.42 |
324.55 |
14.12 |
0.00 |
14.24 |
12.41 |
17 |
55.71 |
21.87 |
10.07 |
311.02 |
15.09 |
0.00 |
15.22 |
13.26 |
18 |
59.34 |
23.29 |
10.72 |
298.22 |
16.07 |
0.00 |
16.21 |
14.12 |
19 |
63.00 |
24.73 |
11.39 |
286.08 |
17.07 |
0.00 |
17.21 |
15.00 |
20 |
66.72 |
26.19 |
12.06 |
274.53 |
18.07 |
0.00 |
18.22 |
15.88 |
21 |
70.48 |
27.67 |
12.74 |
263.52 |
19.09 |
0.00 |
19.25 |
16.78 |
22 |
74.29 |
29.16 |
13.43 |
253.00 |
20.12 |
0.00 |
20.29 |
17.68 |
23 |
78.15 |
30.68 |
14.12 |
242.92 |
21.17 |
0.00 |
21.34 |
18.60 |
24 |
82.06 |
32.21 |
14.83 |
233.26 |
22.23 |
0.00 |
22.41 |
19.53 |
25 |
86.02 |
33.77 |
15.55 |
223.98 |
23.30 |
0.00 |
23.49 |
20.48 |
26 |
90.03 |
35.34 |
16.27 |
215.04 |
24.39 |
0.00 |
24.59 |
21.43 |
27 |
94.10 |
36.94 |
17.01 |
206.43 |
25.49 |
0.00 |
25.70 |
22.40 |
28 |
98.22 |
38.56 |
17.75 |
198.13 |
26.61 |
0.00 |
26.83 |
23.38 |
29 |
102.40 |
40.20 |
18.51 |
190.10 |
27.74 |
0.00 |
27.97 |
24.38 |
30 |
106.64 |
41.86 |
19.28 |
182.34 |
28.89 |
0.00 |
29.13 |
25.39 |
31 |
110.95 |
43.55 |
20.05 |
174.83 |
30.05 |
0.00 |
30.30 |
26.41 |
32 |
115.31 |
45.27 |
20.84 |
167.54 |
31.23 |
0.00 |
31.49 |
27.45 |
33 |
119.74 |
47.01 |
21.64 |
160.47 |
32.43 |
0.00 |
32.70 |
28.50 |
34 |
124.24 |
48.77 |
22.45 |
153.61 |
33.65 |
0.00 |
33.93 |
29.57 |
35 |
128.80 |
50.56 |
23.28 |
146.94 |
34.89 |
0.00 |
35.18 |
30.66 |
36 |
133.44 |
52.38 |
24.12 |
140.46 |
36.14 |
0.00 |
36.45 |
31.76 |
37 |
138.15 |
54.23 |
24.97 |
134.15 |
37.42 |
0.00 |
37.73 |
32.88 |
38 |
142.93 |
56.11 |
25.83 |
128.00 |
38.72 |
0.00 |
39.04 |
34.02 |
39 |
147.79 |
58.02 |
26.71 |
122.00 |
40.03 |
0.00 |
40.37 |
35.18 |
40 |
152.74 |
59.96 |
27.61 |
116.16 |
41.37 |
0.00 |
41.72 |
36.36 |
41 |
157.76 |
61.93 |
28.51 |
110.46 |
42.73 |
1.39 |
43.09 |
37.55 |
42 |
162.87 |
63.94 |
29.44 |
104.89 |
44.12 |
2.81 |
44.48 |
38.77 |
43 |
168.07 |
65.98 |
30.38 |
99.44 |
45.53 |
4.25 |
45.90 |
40.01 |
44 |
173.36 |
68.06 |
31.33 |
94.12 |
46.96 |
5.71 |
47.35 |
41.27 |
45 |
178.75 |
70.17 |
32.31 |
88.92 |
48.42 |
7.20 |
48.82 |
42.55 |
46 |
184.24 |
72.33 |
33.30 |
83.83 |
49.90 |
8.72 |
50.32 |
43.86 |
47 |
189.83 |
74.52 |
34.31 |
78.85 |
51.42 |
10.27 |
51.85 |
45.19 |
48 |
195.52 |
76.75 |
35.34 |
73.97 |
52.96 |
11.85 |
53.40 |
46.54 |
49 |
201.33 |
79.03 |
36.39 |
69.18 |
54.53 |
13.46 |
54.99 |
47.92 |
50 |
207.25 |
81.36 |
37.46 |
64.50 |
56.14 |
15.10 |
56.60 |
49.33 |
51 |
213.29 |
83.73 |
38.55 |
59.90 |
57.77 |
16.77 |
58.25 |
50.77 |
52 |
219.45 |
86.15 |
39.66 |
55.39 |
59.44 |
18.48 |
59.94 |
52.24 |
53 |
225.75 |
88.62 |
40.80 |
50.97 |
61.15 |
20.22 |
61.66 |
53.74 |
54 |
232.18 |
91.15 |
41.96 |
46.62 |
62.89 |
22.00 |
63.41 |
55.27 |
55 |
238.75 |
93.73 |
43.15 |
42.36 |
64.67 |
23.82 |
65.21 |
56.83 |
56 |
245.47 |
96.36 |
44.37 |
38.17 |
66.49 |
25.68 |
67.04 |
58.43 |
57 |
252.34 |
99.06 |
45.61 |
34.05 |
68.35 |
27.58 |
68.92 |
60.07 |
58 |
259.38 |
101.82 |
46.88 |
30.01 |
70.26 |
29.53 |
70.84 |
61.74 |
59 |
266.59 |
104.65 |
48.18 |
26.03 |
72.21 |
31.53 |
72.81 |
63.46 |
60 |
273.97 |
107.55 |
49.52 |
22.11 |
74.21 |
33.57 |
74.83 |
65.21 |
61 |
281.54 |
110.52 |
50.89 |
18.27 |
76.26 |
35.67 |
76.89 |
67.02 |
62 |
289.30 |
113.57 |
52.29 |
14.48 |
78.36 |
37.82 |
79.02 |
68.87 |
63 |
297.28 |
116.70 |
53.73 |
10.75 |
80.52 |
40.03 |
81.19 |
70.76 |
64 |
305.47 |
119.92 |
55.21 |
7.08 |
82.74 |
42.30 |
83.43 |
72.71 |
65 |
313.89 |
123.22 |
56.73 |
3.47 |
85.02 |
44.63 |
85.73 |
74.72 |
66 |
322.56 |
126.63 |
58.30 |
-0.09 |
87.37 |
47.03 |
88.10 |
76.78 |
67 |
331.49 |
130.13 |
59.91 |
-3.60 |
89.79 |
49.50 |
90.54 |
78.91 |
68 |
340.69 |
133.74 |
61.58 |
-7.06 |
92.28 |
52.05 |
93.05 |
81.10 |
69 |
350.18 |
137.47 |
63.29 |
-10.46 |
94.85 |
54.68 |
95.64 |
83.36 |
70 |
359.98 |
141.32 |
65.06 |
-13.82 |
97.51 |
57.39 |
98.32 |
85.69 |
71 |
370.12 |
145.30 |
66.90 |
-17.13 |
100.25 |
60.20 |
101.09 |
88.10 |
72 |
380.61 |
149.41 |
68.79 |
-20.40 |
103.10 |
63.11 |
103.95 |
90.60 |
73 |
391.49 |
153.68 |
70.76 |
-23.62 |
106.04 |
66.12 |
106.92 |
93.19 |
74 |
402.77 |
158.11 |
72.80 |
-26.79 |
109.10 |
69.24 |
110.01 |
95.87 |
75 |
414.50 |
162.72 |
74.92 |
-29.93 |
112.27 |
72.49 |
113.21 |
98.67 |
76 |
426.70 |
167.51 |
77.12 |
-33.02 |
115.58 |
75.87 |
116.54 |
101.57 |
77 |
439.43 |
172.50 |
79.42 |
-36.08 |
119.03 |
79.39 |
120.02 |
104.60 |
78 |
452.72 |
177.72 |
81.83 |
-39.09 |
122.63 |
83.07 |
123.65 |
107.76 |
79 |
466.63 |
183.18 |
84.34 |
-42.07 |
126.39 |
86.93 |
127.45 |
111.08 |
80 |
481.22 |
188.91 |
86.98 |
-45.01 |
130.35 |
90.97 |
131.43 |
114.55 |
81 |
496.55 |
194.93 |
89.75 |
-47.92 |
134.50 |
95.21 |
135.62 |
118.20 |
82 |
512.72 |
201.28 |
92.67 |
-50.78 |
138.88 |
99.69 |
140.04 |
122.05 |
83 |
529.81 |
207.98 |
95.76 |
-53.62 |
143.51 |
104.42 |
144.70 |
126.11 |
84 |
547.94 |
215.10 |
99.03 |
-56.42 |
148.42 |
109.44 |
149.65 |
130.43 |
85 |
567.23 |
222.68 |
102.52 |
-59.19 |
153.65 |
114.79 |
154.92 |
135.02 |
86 |
587.86 |
230.77 |
106.25 |
-61.93 |
159.23 |
120.50 |
160.56 |
139.93 |
87 |
610.02 |
239.47 |
110.26 |
-64.63 |
165.23 |
126.64 |
166.61 |
145.21 |
88 |
633.95 |
248.87 |
114.58 |
-67.31 |
171.72 |
133.26 |
173.15 |
150.90 |
89 |
659.97 |
259.08 |
119.28 |
-69.95 |
178.76 |
140.47 |
180.25 |
157.10 |
90 |
688.47 |
270.27 |
124.43 |
-72.57 |
186.48 |
148.36 |
188.04 |
163.88 |
91 |
719.97 |
282.63 |
130.13 |
-75.15 |
195.02 |
157.08 |
196.64 |
171.38 |
92 |
755.19 |
296.46 |
136.49 |
-77.71 |
204.56 |
166.84 |
206.26 |
179.76 |
93 |
795.11 |
312.13 |
143.71 |
-80.24 |
215.37 |
177.89 |
217.16 |
189.27 |
94 |
841.20 |
330.23 |
152.04 |
-82.75 |
227.85 |
190.66 |
229.75 |
200.24 |
95 |
895.72 |
351.63 |
161.89 |
-85.23 |
242.62 |
205.75 |
244.64 |
213.21 |
96 |
962.43 |
377.82 |
173.95 |
-87.68 |
260.69 |
224.23 |
262.86 |
229.10 |
97 |
1048.45 |
411.58 |
189.50 |
-90.11 |
283.99 |
248.05 |
286.36 |
249.57 |
98 |
1169.68 |
459.18 |
211.41 |
-92.51 |
316.83 |
281.62 |
319.47 |
278.43 |
99 |
1376.93 |
540.53 |
248.87 |
-94.89 |
372.97 |
339.02 |
376.07 |
327.76 |
This same conversion of percentages to
percentiles to scores is then done for
each of the nine high need indicators.
An example is included in Table IV-8 to
illustrate this step, again using Wichita
as an example.
Table
IV-8
High
need indicators |
Wichita
County, KS |
%
< 200% Poverty |
49
.8% |
Percentile
|
79 |
Score |
467 |
Unemployment
Rate |
3
.59% |
Percentile |
24 |
Score
|
32 |
%
65+ |
15
.6% |
Percentile |
53 |
Score |
41 |
Population/Sq
Mile |
3.70% |
Percentile |
8 |
Score |
475 |
%
Hispanic |
16.40% |
Percentile |
91 |
Score |
195 |
Death
Rate |
0.67% |
Percentile |
0 |
Score |
0 |
LBW
(Low Birth Weight) |
7.78% |
Percentile |
71 |
Score |
88 |
IMR
(Infant Mortality Rate) |
N/A* |
Percentile |
|
Score |
|
Total
Score To Be Added |
1298 |
* The infant mortality rate was not used
for Wichita County since it was unstable
(too few events-births and death in low
population county). The alternative low
birth weight rate was used.
Because
the same metric (i.e. population-to-provider
ratio) was used to calculate both the
effective barrier-free population and
the scores, the scores can simply be added
to the effective barrier-free population-to-
primary care provider ratio to derive
the final adjusted population-to- primary
care provider ratio. This adjusted ratio
reflects the combination of the ``effective
barrier free population'' (age-adjusted)
and the effect of community needs and
use factors. These ratios can then be
used to reflect the relative need of the
areas, with the highest ratios indicating
the areas of greatest need. An example
is included in Table IV-9, again using
Wichita as an example and Burlington,
New Jersey for comparison. Column G reflects
the new measure of underservice proposed
in these rules and is intended to resemble
the current MUA/P method in that it creates
a score or index of underservice.
Table
IV-9
County
name |
Total
pop 1999 |
Effective barrier-free population
|
Total
FTE primary care |
Actual
population to FTE ratio (A÷C) |
Effective
barrier- free pop/ FTE ratio (B÷C)
|
Score
from weights |
Final
adjusted effective barrier- free pop/
FTE ratio (E+F) |
A |
B |
C |
D |
E |
F |
G |
Wichita,
KS |
2,436 |
2,959 |
2.5
|
974
|
1184
|
1298
|
2482 |
Burlington,
NJ |
416,853
|
482,594
|
411.2
|
1014
|
1173
.6 |
251
.6 |
1425
.3 |
Even though there are far fewer people
in Wichita than in Burlington and the
actual population-to-provider ratios are
roughly equivalent (column D), this methodology
shows that the true need in Wichita (i.e.,
the level of care the Wichita population
would demand if they did not have any
barriers to care) is actually much greater
than in Burlington (column G).
Though this underlying methodology is
conceptually and computationally complex,
one advantage of this new method is that
the actual calculations involved have
been automated through the use of the
conversion tables. The new method is,
therefore, relatively simple to implement
by State and local applicants. The system
has also been developed in a way that
allows an applicant to enter their area-
specific or population-specific data into
an Internet-based query system and have
their score returned in real time. This
would allow applicants to compare their
level of underservice with those of other
designated and undesignated areas and
populations in an accessible system. Moreover,
the use of a tabular method for scoring
allows for future changes in the scaling
of the scores when there are changes in
the distribution of values. It also allows
HRSA to update these values without having
to change the overall approach to developing
scores.
Step 5: Comparing the final adjusted effective
barrier-free population-to-provider ratio
against a threshold of underservice.
The fifth step in this method involves
comparing the final adjusted ratios for
various areas against a threshold of underservice.
A county or other RSA will be designated
as undeserved if its final adjusted ratio
equals or exceeds this threshold. The
threshold level proposed is 3,000 persons
for every FTE primary care clinician.
A population of 3,000, distributed according
to the national average age-sex distribution,
is about twice the normal load for a busy
primary care physician, which is approximately
1500:1. Accordingly, when the threshold
level of 3000:1 is reached, an area is
already one primary care clinician short
for each primary care clinician it has.
The impact analysis in Section VI below
deals with the effect of this choice on
the number and population of designated
areas. While there is no one figure that
is a universally accepted standard, the
3000:1 threshold is based on an adequacy
ratio of 1500:1 as noted above and is
similar to the target ratio used in a
number of organizations and identified
in a variety of studies:
-
A study of the Canadian system
and its process for measuring medical
underservice, for example, identified
1500:1 or greater as a level of underservice
appropriate for a recruitment incentive
program (Goldsmith 2000).
- A
Veterans Administration study recommended
a target for a primary care panel between
1,000-1,400 patients (Perlin and Miller,
2003).
- According
to the Bureau of Primary Health Care
(unpublished data), Community Health
Centers averaged 1,439 medical users
per medical FTE in 1999, and this number
is very consistent with the 1997 and
1998 figures. In addition, the NHSC
reports an average of 1,527 patients
per provider.
- A
George Washington University (GWU) report
on Standards for Managed Care related
to the Balanced Budget Act of 1997 found
that State Medicaid programs most frequently
required that Medicaid HMOs have a panel
size of 1500:1
- An
article published in the Journal of
the American Medical Association suggested
benchmark ratios to compare relative
supply that were slightly above and
below 1500:1 (Goodman et al, 1996).
- Using
data from the National Ambulatory Medical
Care Survey (NAMCS), which estimates
visits per person per year to physicians,
the national mean ratio of primary care
physicians per population of 1498:1,
very close to 1500:1.
The 3000:1 threshold is a very conservative
estimate of the level of need and identifies
the worst quartile of the areas analyzed,
which is a similar standard to that
used when the original thresholds were
set in the existing designation methods.
Moreover, this threshold is consistent
with the level used for HPSA designation
of high-need areas and population groups
in the past.
Step 6: Determining tiers of shortage.
An important issue in the preparation
of these regulations is whether federally-sponsored
primary care providers who are present
in currently-designated areas should
be included in computations when updating
the designations. On the one hand, including
these providers in the provider count
could result in ``yo-yo'' effects, in
which an area is designated as underserved;
a CHC or NHSC intervention occurs as
a result of the designation; those practitioners
are then counted, resulting in a loss
of the designation; the intervention
is removed; the area again becomes eligible
for designation; and the cycle repeats
itself. On the other hand, there are
concerns about areas remaining on the
list of designations whose needs have
already been met through a federally
supported program or provider. This
has led to situations in which additional
resources are allocated to an area where
providers or clinics have previously
been placed to help meet the needs of
the area.
To deal with both sides of this issue,
we propose to publish a two- tiered
list of designations. Each designated
area or population group will be identified
as having either a first or second tier
of shortage. Tier 1 designations will
be those areas which continue to exceed
the threshold even when all federal
resources placed in the area are counted.
Tier 2 designations will be those areas
exceed the threshold only when certain
federal resources placed in those areas
are excluded.
Thus, one final set of calculations
is undertaken to identify those ``Tier
2'' areas which fall below the threshold
when certain federally- sponsored clinicians
are counted but would exceed the threshold
if they were withdrawn. The federally-sponsored
clinicians considered here are NHSC
affiliated clinicians, clinicians obligated
under the State Loan Repayment Program
(SLRP) (a loan repayment program involving
joint Federal and State funding), physicians
with J-1 visa return-home waivers, and
other clinicians providing services
at health centers funded under Section
330.
When determining Tier 2 designations,
these federally-sponsored clinicians
are not counted in the denominator of
the area's ratio. Finally, steps 3 and
4 are repeated to recalculate the final
adjusted ratio using this lower clinician
count and to compare it with the designation
threshold. The areas exceeding the threshold
when this procedure is followed are
identified as ``Tier 2'' designations.
Both types of designations would be
eligible for federal programs authorized
to place resources in MUPs or HPSAs.
However, Tier 2 areas would typically
be eligible only to maintain the approximate
levels of federal resources already
deployed, while Tier 1 areas could apply
for additional resources.
C.
Example Calculations
Table
IV-10 shows calculations for actual population-to-provider
ratios, the effective barrier-free population-to-provider
ratios, the scores based on high need
indicator percentiles for the area, and
the resulting population to primary care
clinician ratios.
Table
IV-10.--Example of calculation of Adjusted
Population-to-Primary Care Clinician Ratio
County
name |
Total
pop 1999 |
Effective
barrier-free
population |
Total
FTE
primary care |
Effective
barrier-
free pop/ FTE ratio
(B÷C) |
Score
from
weights |
‘‘Tier
1’’
Final
adjusted effective barrier-
free pop/FTE
ratio (D+E) |
Ratio
w/o fed FTE
(C-Federally sponsored clinicians) |
‘‘Tier
2’’ Final
adjusted
effective barrier- free pop/FTE ratio
(G+E) |
|
A |
B |
C |
D |
E |
F |
G |
H |
Wichita,
KS |
2,436
|
2,959 |
2.5 |
1184 |
1298 |
2482 |
*
5918 |
7216
|
Burlington,
NJ |
416,853 |
482,594 |
411.2 |
1173.6 |
251.6 |
1425.3 |
1179.4 |
1431.0 |
Coconino
AZ |
116,977 |
127,492 |
91.7 |
1389.6 |
1161.4 |
2551 |
1444.7 |
2606.1 |
St.
Lucie, FL |
180,937 |
222,417 |
105.1 |
2116.5 |
918.3 |
3034.8 |
2314.7 |
3233.0 |
E.
Baton Rouge, LA. |
395,635 |
447,680 |
379.5 |
1179.7 |
640.2 |
1819.8 |
1185.9 |
1826.1 |
Dunklin,
MO |
33,006 |
40,146 |
22.8 |
1764.6 |
1469.4 |
3234.1 |
1764.6 |
3234.1
|
Bronx,
NY |
1,185,970 |
1,366,382 |
1210.6 |
1128.7 |
1665.3 |
2793.9 |
1199.6 |
2864.8 |
Guernsey,
OH |
40,854 |
48,273 |
20.2 |
2389.8 |
751.7 |
3141.5 |
2389.8 |
3141.5
|
Rusk,
WI |
15,449
|
18,501 |
10.8 |
1713.0 |
1070.5 |
2783.6 |
8043.7 |
9114.2 |
* Non-federally sponsored FTE = 0.5; 2959/0.5
= 5917/1. According to these calculations,
Wichita would not qualify for designation
as a Tier 1 underserved area. However,
Wichita would qualify for designation
as a Tier 2 underserved area when federally
sponsored FTEs are deleted and high need
weights are added.
D.
Alternative Approaches Considered A variety
of other alternative measures and options
were considered during the development
of the method. The research team at the
University of North Carolina conducted
a comprehensive review of current and
alternative measures of underservice,
as noted in a 1995 report (Ricketts et
al., 1995. As part of this effort, two
workshops were convened in 1999 and 2000
on modeling health professions supply
and healthcare needs and on measurement
of underservice. Several of the options
considered and the reasons for not pursuing
them are described below:
--There was consideration of using the
simple population to provider ratio as
the index, but there was no consensus
on the ``right'' ratio, and there was
strong interest in a more multi-factorial
approach to take other high need factors
into account. The PCO Work Group's initial
recommendations were based primarily on
the ratio, with adjustments to the ratio
for high needs, similar to the current
process for HPSAs. After continued discussion
with HRSA staff and the contractors, the
Work Group acknowledged that the proposed
methodology accomplished much the same
by incorporating the need variables into
the analysis rather than adjusting the
target ratio, although final agreement
was held pending review of the impact
data. The approach used in the 1998 proposal,
which was an Index of Primary Care Services
from 1-100 based on a variety of ``need''
factors, was not chosen partly due to
the history and partly due to the fact
that such a scale had no intrinsic meaning
as a measure of access, while a score
related to a ratio of population to the
providers is more easily understood across
the board.
--We considered using hospitalization
rates for Ambulatory Care Sensitive Conditions
(ACSC) as proxies for underservice as
they could reflect failures in the primary
care system to meet the needs of the population.
However, comprehensive data are not universally
available, particularly at the sub-county
level, where primary care analysis is
based. In addition, the analysis indicates
that these rates are more indicative of
problems with access to care related to
income, employment, and race, rather than
to lack of providers or services.
--Alternative methodologies used in Canada
and the United Kingdom (UK) were reviewed
for possible use. In Canada, however,
each province had a different methodology,
which did not meet the comprehensive national
approach. In the UK, the focus was specifically
on the location of General Practitioners
(GPs), whose practice locations are partially
controlled by the government. In addition,
they were partially based on interviews
with GPs to identify areas of underservice,
which is not an approach that can be replicated
on a national scale and has no scientific
basis. Both countries did, however, have
models that incorporated many of the same
concepts used in this proposal, including
distance to care (which has a functional
similarity to population density in our
model), census variables such as ``class,''
unemployment, age, and the availability
of providers. This reinforces the validity
of taking into account such variables
when measuring access to care and underservice.
--Extensive research on the state of the
art in health care access led to a paper
by Dr. Donald Taylor (Taylor et al., 2000)
which examined the relationship between
theoretical need for care and resources
to provide the care. His conclusion was
that there is no one simple construct
of underservice and no unitary measure,
but that there are several interlocking
components that need to be considered.
These conceptual components were not actually
alternative measures of underservice but
five components of a comprehensive model.
His hypothetical model, at the county
level, included the following components:
-
Momentum: the economic and population
dynamics of an area and changes over
time
-
Demand: based on the age and gender
of the population Infrastructure:
presence of hospitals and other providers,
insurance coverage, etc.
-
Need: based on proxies for health
status
-
FIT: describes the degree of ``fit''
of the various factors, which represents
the level of service or underservice
The
conceptual model, the Taylor Indices of
Underservice, was tested using simultaneous
multiple correlations and was found to
be robust for the prediction of demand,
infrastructure and needs but not for FIT
and momentum. A latent variables testing
method was applied and the concept of
FIT was supported via this analysis. A
second order confirmatory analysis (CFA)
supported this result, which suggested
that a combination of variables that reflect
demand and infrastructure with appropriate
proxies for need--especially the age structure
of the community--could generate a useful
index, FIT, that summarized community
underservice. The current proposal builds
on this notion of FIT as a latent indicator
of overall need, as reflected in the score
that is calculated in the process.
For several reasons, Dr. Taylor's approach
could not have been used without modification
for purposes of this rulemaking. For example,
this approach did not appear to correlate
well with indicators of utilization, which
is considered a reliable indicator of
access. Moreover, counties are not considered
an appropriate level of analysis in many
areas served by HRSA's programs.
However, the principles and detailed analytical
methods used in Dr. Taylor's model were
incorporated to a large extent in the
current proposed methodology, which includes
age/gender utilization projections for
expressed need or demand, need (as captured
by socio-demographic and health status
indicators), and infrastructure (as reflected
in unemployment, poverty, and availability
of providers).
--Years of Potential Life Lost (YPLL)
was also considered as a potential measure.
However, similar to the ACSC analysis,
there was a much stronger correlation
between socio-economic factors (race,
education, etc.) than with the presence
or absence of primary care providers and
services.
V.
Description of the Proposed Regulations
A. Procedures
(Subpart A)
The
proposed approach to processing MUA, MUP
and HPSA designation requests, set forth
in Subpart A below, is an adaptation of
the HPSA designation procedures currently
in effect, as codified at 42 CFR Part
5. The previous procedures have been modified
to include the particular comment and
consultation requirements of the MUP legislation,
but otherwise closely follow the present
HPSA designation procedures, including
those specifically required by statute.
As before, the proposed procedures involve
an interactive process between the Secretary,
the States, and individual applicants
[see Sec. 5.3(a)-(h)]. Any individual,
community group, State or other agency
may apply for designation of a geographic
area or population group MUP and/ or HPSA,
or for a facility HPSA; the Secretary
may also propose such designations. Such
requests are reviewed both at State and
federal levels, including a 30-day comment
period for Governors, State health agency
contacts, State Offices of Rural Health,
county or city health officials, State
primary care associations (non-profit
membership organizations representing
federally qualified health centers and
other community-based providers of primary
care), appropriate medical, dental or
other health professional societies, and
heads of any facilities proposed for HPSA
designation. Efforts are made to complete
action on new designation requests within
60 days of receipt.
Annually, the Secretary will review all
designations utilizing the proposed methodology,
with emphasis on those for which updated
data have not been submitted during the
previous three years; this extends to
MUA/Ps the review process previously used
for HPSAs [see Sec. 5.3(d)]. As part of
such reviews, the latest relevant data
from national sources described earlier
(for those previously-designated areas
which the Secretary requires be updated)
will be made available by the Secretary
to the appropriate State entities and
others for review and comment. If no corrections
are provided, the national data will be
used as the Secretary's basis for decisions.
(The national data for census-collected
variables are not typically corrected
during the designation process with data
from State and local sources. On the other
hand, State and local data regarding provider
locations and FTEs are often more up-to-date
and accurate; use of such data in designation
will continue to be encouraged where readily
available.)
An expedited review process is also proposed
for urgent cases [see Sec. 5.3(i)], allowing
designations to be obtained within 30
days of the date of request when a practitioner
dies, retires, or leaves an area, thereby
causing a sudden and dramatic increase
in the area's population-to-clinician
ratio. The number of requests that will
be processed per year on this expedited
basis is limited.
Results of designation reviews will be
provided in writing or electronically
to applicants, State partners, and other
interested parties [see Sec. 5.4]. No
less than annually, complete lists of
designated HPSAs/MUPs will be published
by notice in the Federal Register
that an updated list will be posted on
the HRSA Web site; more frequent updates
will be posted online continuously, reflecting
designation decisions as they occur. Two
tiers will be identified in published
or posted listings of designated shortage
areas. As discussed previously, the first
tier will include only those areas that
meet the designation criteria when all
relevant (i.e., active primary care) clinicians
in the area are counted, while the second
tier will include those additional areas
that meet the criteria when certain Federally-sponsored
clinicians are subtracted.
The regulation also includes a section
[Sec. 5.5] describing procedures for the
transition from the current designation
system to the new system. These include
a process for resolution of any overlapping
boundaries that may exist between currently-designated
primary care HPSAs and currently-designated
MUA/Ps at the time the new regulations
go into effect. The new criteria for designation
of MUA/Ps and/or primary care HPSAs will
be phased in over a period of three years
from the date of publication of the final
rule in the Federal Register,
with State input on the review schedule
but with the oldest MUA/P and primary
care HPSA designations being reviewed
first. This will relieve States, communities
and others from having to provide updated
data on all designations that are more
than three years old during the first
year the new regulations go into effect.
In addition, the regulation includes a
section [Sec. 5.6] describing how the
``automatic designation'' provisions of
the Health Care Safety Net Amendments
of 2002, as amended by Public Law 108-163,
will be implemented. Briefly, all FQHC
and RHC delivery sites that are automatically
designated will be listed separately as
``automatic'' HPSAs until the area or
population group they serve or the facility
achieves designation under the proposed
criteria or until 6 years from the date
of their automatic designation, whichever
comes first. Any FQHC or RHC sites still
being carried on the list of ``automatically''
designated sites six years from their
date of automatic designation will then
be required to demonstrate that they meet
the criteria in order to remain on the
list, through the review process outlined
in section Sec. 5.6.
B.
General Criteria for Designation of Geographic
Areas as MUAs/Primary Care HPSAs
The
criteria and methodology for designation
of geographic areas as MUAs and primary
care HPSAs are set out in Subpart B (Sec.
5.102). In brief, areas to be designated
must first be RSAs for the delivery of
primary care services. As described earlier,
an adjusted population-to- primary care
clinician ratio is then computed for each
such area, by combining the area's ``effective
barrier-free'' population (based on age
and gender utilization patterns) to its
supply of primary care clinicians, with
adjustments for access barriers through
additive scores for a defined group of
demographic, economic, and health status
variables. When this adjusted ratio exceeds
the designation threshold of 3000:1, the
area is eligible for designation. Under
certain limited conditions, resources
in contiguous areas must also be taken
into consideration.
C.
Rational Service Areas
The
proposed rules would continue to require
that each area proposed for geographic
designation be a rational area for the
delivery of primary care services. A general
(or default) definition of the term ``rational
service area'' is included [see Sec. 5.103],
in terms of geographic size and cohesiveness,
which relates its size to the accessibility
of primary medical services in the area
within 30 minutes travel time, and its
cohesiveness to topography, demographic
distinctness from contiguous communities,
and/or established market patterns. Contiguous
RSAs would normally be defined so as to
have a separation of at least 30 minutes
travel time from the population center(s)
of one RSA to the population center(s)
of each contiguous RSA, with exceptions
for RSAs within high-density portions
of metropolitan areas that demonstrate
cohesiveness in other ways.
RSAs may be defined in terms of U.S. Census
Bureau geographic units, including counties,
census tracts, census divisions, and Zip
Code Tabulation Areas (ZCTAs), as long
as data can be obtained at that level.
However, States are allowed the flexibility
to define their RSAs in terms of travel
time parameters between 20 and 40 minutes,
where the final RSA approach to be used
is approved by the Secretary.
States are encouraged to develop a State-wide
system that subdivides the territory of
the State into RSAs, either incrementally
or all at once, using the general RSA
criteria specified in the proposed rule
or State-specific criteria developed through
the partnership process just mentioned.
Where a State has developed such a statewide
system of areas, the designation status
of a particular RSA will be determined
through application of the proposed geographic
HPSA/ MUA criteria to current data for
the RSA, without regard to contiguous
area resources. Elsewhere, the contiguous
area considerations set forth in proposed
Sec. 5.105 are to be used.
The proposal allows for State and local
input, but is expected to greatly reduce
the level of effort required at the local
and State level. At present, no designation
takes place without a specific request
being submitted with the required information,
including the defined service area, the
data on population, physicians, and other
appropriate information. Upon publication
of a final regulation, HRSA will first
score all existing MUAs and HPSAs using
the national databases. Areas that qualify
using those calculations will be designated
as underserved with no need for input
from the State or local level. The submission
of additional information will only be
required for those areas that do not qualify
based on national data.
HRSA expects that a significant number
of areas will qualify based on national
data alone. For example, there were 877
whole county and 803 geographic service
area HPSAs as of March 31, 2007. If the
majority of these areas meet the criteria
using the national calculations, 55 percent
of the current designations (excluding
the facility designations) would require
no action on behalf of the State or local
agency. In addition, many areas could
be qualified with the submission of revised
data on providers alone, which is a much
simpler approach than currently required.
Areas where special population groups
would need to be defined would continue
to require State or local involvement,
though we anticipate the number of these
would decrease as a result of the inclusion
of some of the need factors directly in
the formula itself.
D.
Applying the Designation Methodology
As
mentioned above in section IV.B, the proposed
rules provide that the Secretary of HHS
will determine an adjusted effective barrier-free
population-to-primary care clinician ratio
for each RSA considered for a primary
care underservice designation. The specific
methodology for this calculation is set
forth in proposed Sec. 5.104. Tables IV-1
and IV-6 will be updated periodically
by notice in the Federal Register
that updated data will be posted on the
HRSA web site as the national utilization
data and national distributions of the
variables used in the method change. (Updating
these tables will not require proposed
rulemaking, since the regulations themselves
will not be changed.) The timeframe for
updates will be determined by the availability
of updated data for the nine high need
indicators. Table IV-7, which appears
in the regulation itself as Appendix A
to Part 5, may also be recalibrated periodically,
but not necessarily on the same timetable,
since revising it requires repeating the
regression analysis.
E.
Data Definitions
The proposed rules identify the data elements
needed to determine the effective barrier
free population, the high need indicator
score, the final adjusted population-to-primary
care clinician ratio, and the manner of
calculation of these variables. See proposed
Sec. 5.104(a) to 5.104(c).
F.
Population and Clinician Counts
Although
the clinician count requirements are similar
to those for physicians in the current
Part 5, some important changes have been
made. Foreign (International) medical
graduates who are not citizens or permanent
residents, but entered the U.S. on J-1
visas and have had their return-home requirements
waived in return for obligated service,
and/or are here on H visas, are to be
counted in ``first tier'' designation
calculations unless they have restricted
licenses; they are to be excluded from
``second tier'' designation counts.
Similarly, clinicians providing medical
services for the NHSC, as SLRP obligors,
or at health facilities funded under section
330 of the Act are counted for the first
tier and excluded from the second tier.
It should be noted that, although the
proposed rules would allow NHSC and section
330 health center practitioners to be
excluded from the practitioner count for
second tier designations, the numbers
of these practitioners already allocated
or funded are included by the Department
in making decisions as to how to allocate
additional NHSC and health center grant
resources.
Also, the current HPSA provision allowing
the discounting of physicians with restricted
practices on a case-by-case basis is proposed
to be eliminated because our experience
has been that this provision is neither
useful nor practical.
G.
Non-Physician Primary Care Clinicians
The significant expansion over the past
decade in the numbers of NPs, PAs, and
CNMs practicing in primary care settings
has made their inclusion in counts of
primary care clinicians essential to the
validity of any revised designation process,
particularly in those States and areas
where they practice, in effect, as independent
providers of care and particularly given
their role in the RHC program. However,
there has been controversy as to whether
available data permit them to be counted
accurately and how they should be weighted
relative to primary care physicians.
There are several related issues involved.
First, significant differences exist among
the States as to the scope of practice
allowed for these clinicians, including
the extent to which they are allowed to
work independently, and what medical tasks
they are legally allowed to perform. Second,
the national databases currently available
for them have some limitations, particularly
where practice addresses are concerned.
While some States have accurate data on
the number, location and practice characteristics
of these clinicians, others do not. Finally,
for those States in which non-physician
clinicians can legally provide many of
the same services as primary care physicians,
exactly how they complement physicians
and, therefore, how they should be weighted
relative to physicians has not been well-defined.
This proposed rule includes these non-physician
clinicians by requiring that all of them
be counted with a weight of 0.5 relative
to primary care physicians, unless the
applicant opts for weighting based on
the scope of practice in the State involved.
(See State option for weighting described
below.) Please note that the 0.5 relative
weighting is proposed here only for purposes
of estimating primary care clinician counts
for shortage area designation purposes;
it should not be construed as representing
the relative cost or value of these providers'
services compared to physician services.
For non-physician clinicians, there has
been a long-standing acceptance of counting
them as less that a full FTE, for a variety
of reasons. In the Bureau of Primary Health
Care, and its predecessors, which oversees
the FQHC Program, productivity standards
and calculations have used the .5 FTE
figure. In part, this is a way to encourage
these programs to hire non-physician providers
in areas where recruitment is difficult
but there may be some resistance otherwise
to having a mixed practice model. Its
use is also consistent with productivity
standards currently used by CMS for RHCs
and FQHCs, which are 2100 visits per year
for NPs and PAs as compared with 4200
visits per year for physicians.
While there is no absolute standard for
estimating the FTE contribution of a non-physician
provider, there are also a number of studies
in the literature that support an estimate
of 0.5:
-
An Integrated Requirements
Model (Sekscenski et al., 1999) in 1999
used a 0.5 FTE calculation.
- An
article in Health Affairs in 1997 (Hart
et al., 1997) of staffing ratios indicated
patient volume levels for NPs from 875-
1,000 per NP.
Given the lack of data regarding the impact
of adding these providers to the designation
process and the continued need to encourage
the use of the range of providers who
can help meet the needs of the underserved,
we believe the 0.5 FTE approach is a reasonable
choice for the proposed method.
Data on NPs, PAs and CNMs are available
from national sources (``A Comparison
of Changes in the Professional Practice
of Nurse Practitioners, Physician Assistants,
and Certified Nurse Midwives: 1992 and
2000'' The Center for Health Workforce
Studies at the University of Albany, available
online at http://frwebgate.access.gpo.gov/cgi-bin/leaving.cgi?from=leavingFR.html&log=linklog&to=http://bhpr.hrsa.gov/healthworkforce/reports/scope/scope1-2.htm.)
These data will be made available for
use as a first approximation, but States
will be encouraged to provide more accurate
State data, where available.
Some have suggested that different equivalencies
be used in different States, depending
on the degree of independence allowed
by the different State laws. This option
is offered in the proposed rule. At the
applicant's option, a maximum weighting
factor of 0.8 can be used together with
a State scope of practice factor between
0.5 and 1.0, using tables from ``Scope
of Practice of PAs, NPs, and CNMs in the
Fifty States,'' (Wing et al., 2003). This
document is available at http://frwebgate.access.gpo.gov/cgi-bin/leaving.cgi?from=leavingFR.html&log=linklog&to=http://bhpr.hrsa.gov/healthworkforce/reports/scope/scope1-2.htm
Those Federally-sponsored NPs, PAs, and
CNMs in the NHSC, SLRP, or at health facilities
funded under Section 330 would be counted
for Tier 1 designations but excluded for
Tier 2 designations, just as done for
physicians.
H. Contiguous
Area Considerations
The
previous HPSA criteria required that,
when considering any area for designation,
resources located in all contiguous areas
must be shown to be excessively distant,
overutilized, or otherwise inaccessible
to the population of the area requested
for designation. The approach proposed
herein would eliminate this requirement
wherever a set of RSAs has been developed,
requiring consideration of contiguous
area resources only in States where a
system of RSAs does not exist, or in those
portions of a State where RSAs have not
yet been defined. See Sec. 5.105.
I.
Population Group Designations
The
inclusion in the proposed methodology
of a number of variables representing
the access barriers and/or negative health
status experienced by certain at-risk
populations is likely to decrease the
need for specific population group designations,
which tend to be more difficult procedurally
for both applicants and reviewers. However,
the proposed rules continue to provide
for certain types of population group
designations within geographic areas which,
taken as a whole, do not meet the criteria
for designation. (See Subpart C.) These
generally build on the criteria for designating
geographic areas, with several key differences.
First, the proposed rules recognize two
specific additional types of areas as
rational areas for the delivery of primary
care services for specific population
groups (i.e. agricultural areas for migratory
and seasonal agricultural workers; reservations
for Native American population groups).
Second, each variable is to be calculated
based on data for the population group
for which designation is sought, as nearly
as possible, rather than on the population
of the area as a whole.
The eligible population groups specifically
identified for designation are: Low income
populations (defined to include all those
with incomes below 200% of the poverty
level); Medicaid-eligible populations;
linguistically isolated populations; migrant
and seasonal farmworkers and their families;
homeless populations; residents of public
housing; and Native Americans. A new category
of MUP is recognized, consisting of those
uninsured and Medicaid-eligible patients
who are served by safety net facilities
designated as primary care HPSAs under
Subpart D. Finally, the category ``other
population groups recommended by state
and local officials'' is retained, consistent
with the MUP statutory authority.
The proposed provisions also allow for
HPSA designation of the ``special medically
underserved'' populations as defined by
section 330 of the PHS Act (as amended
by Pub. L. 104-299), which are considered
already designated as MUPs. These provisions
include a ``simplified'' designation procedure
for migrant, homeless and Native American
population groups, for use in cases where
the area in which the requested population
group is located has been defined, data
on the number of individuals in the population
group is provided and the total is found
to exceed 1000, but specific information
on the number of FTE clinicians accessible
to the population group is not available.
In these cases, a population-to-clinician
ratio of 3000:1 may be assumed. Requirements
for the statutory ``permissible'' designation
of ``other population groups recommended
by state and local officials'' are included.
``Local officials'' for this purpose are
defined. Such requests must document the
``unusual local conditions'' which are
the basis for the request; these must
involve factors not already considered
by the general criteria for designation
of areas and population groups as set
forth in Subparts A and B.
J.
``Facility Designation Method'':
Designation
of Facility Primary Care HPSAs The criteria
and procedures for designating facility
primary care HPSAs are set out in proposed
Subpart D. The current criteria for designation
of ``public or non-profit private medical
facilities'' as HPSAs are eliminated and
replaced by new criteria for the designation
of ``safety-net facility'' primary care
HPSAs (see proposed Sec. 5.301). These
criteria would allow for HPSA designation
of facilities not in geographic HPSAs
designated under Subpart B, if and when
these facilities qualify as ``safety-net
facilities'' by virtue of their service
to specified minimum percentages of patients
that are Medicaid- eligible and/or low
income uninsured, as measured by the number
of patients treated under a sliding fee
scale. Eligibility for this type of designation
is limited to FQHCs, RHCs, or other public
or non-profit private clinical sites providing
primary medical care services on an ambulatory
or outpatient basis. The minimum levels
of service to indigent uninsured and/or
Medicaid-eligibles are described in proposed
Sec. 5.301(b) and shown in Table V-1 below.
Table
V-1.--Minimum Levels of Service to Indigent
Uninsured and/or Medicaid-Eligibles
Metropolitan
areas |
Non-Metropolitan
areas (except frontier areas) |
Frontier
areas |
At
least 10% of all patients are served
under a posted, sliding fee schedule,
or for no charge.
At least 40% of all patients are served
either under Medicaid, under a posted
sliding fee schedule, or for no charge. |
At
least 10% of all patients are served
under a posted, sliding fee schedule,
or for no charge.
At least 30% of all patients are served
either under Medicaid, under a posted,
sliding fee schedule, or for no charge. |
At
least 10% of all patients are served
under a posted, sliding fee schedule,
or for no charge.
At least 20% of all patients are served
either under Medicaid, under a posted
sliding fee schedule, or for no charge.
|
Payment source documentation to establish
initial and ongoing designation as a facility
primary care HPSA will be as required
by the Secretary. This Safety Net Facility
designation would not be recognized by
CMS for RHC certification.
The criteria and methodology for designating
federal and state correctional institutions
and youth detention facilities as primary
care HPSAs in Sec. 5.302 are essentially
unchanged from those in the current Part
5.
K.
Dental and Mental Health HPSAs
The
proposed procedures in Subpart A would
apply to the designation of dental and
mental health HPSAs as well. The criteria
currently in use for these types of HPSA
designations are contained in Appendices
B and C of the current part 5. No changes
to these appendices are proposed at this
time, but efforts are under way to revise
the criteria for dental shortage areas
(pursuant to Section 302(d)(1) of the
Health Care Safety Net Amendments of 2002)
and those for mental health professional
shortage areas. When these efforts are
complete, Appendices B and C will be revised.
L.
Podiatry, Vision Care, Pharmacy And Veterinary
Care HPSAs
The
existing HPSA regulations at part 5 also
contain, in appendices D, E, F, and G,
criteria for the designation of vision
care, podiatric, pharmacy, and veterinary
care HPSAs. These criteria were originally
developed for use in connection with student
loan repayment programs for individuals
in those health professions; however,
these programs are no longer authorized
or funded. Consequently, the proposed
rule would abolish these types of designation
by revoking these appendices.
M.
Technical and Conforming Amendments
Minor
technical and conforming amendments to
the CHC regulations at 42 CFR Part 51c
are proposed. These amendments refer to
Part 5 for definition of designated medically
underserved populations, and for factors
to be considered in assessing the needs
of populations to be served by grantee
projects. In addition, they amend the
definitions section of the CHC regulations
to include a definition of ``special medically
underserved populations,'' which refers
to language in the statute as amended
by Public Law 104-299. This definition
states that such populations are not required
to be designated pursuant to part 5; this
is consistent with their treatment under
prior legislation. Finally, the amendments
add a provision explicitly stating that
a grantee which was serving a designated
MUA/P at the beginning of a project period
will be assumed to be serving an MUP for
the duration of the project period, even
if that particular designation is withdrawn
during the project period.
VI.
Impact Analysis
The agency has conducted an extensive
analysis of the national impact of the
proposed new designation methodology on
the designation status of whole counties,
previously-defined part-county geographic
HPSAs and MUAs, and low-income population
groups, as well as its impact on grant-funded
CHCs, NHSC sites, and CMS-certified RHCs.
This national analysis was conducted under
a HRSA cooperative agreement with UNC's
Cecil G. Sheps Center for Health Services
Research, using data from national sources
for all variables. In order to validate
this national analysis, impact analyses
using State data sources were performed
by Regional Health Workforce Centers and/or
PCOs in four states.
In the actual designation review process,
evaluation of areas' potential designation
status based on application of the criteria
to national data would represent only
the first step in an exchange with State
and local partners. However, we believe
that the aggregate results of this impact
analysis (in terms of total numbers of
areas designated or de-designated nationally)
represent a reasonable approximation to
the likely results of the real designation
process. (If anything, these impact estimates
may err on the side of overstating negative
impacts, since local data in support of
designation are more likely to be received
from areas which the national data would
tend to de-designate than from areas which
they would newly designate or continue
in designation.)
The impact is shown below in a series
of tables describing different types of
impact, each of which enables comparison
of several different scenarios. In general,
the first column of each table shows baseline
numbers corresponding to actual HPSA and
MUA designations on September 30, 1999;
the second column shows the revised numbers
that would result if these designations
were updated by applying the criteria
now in force to the national database
used in this analysis; the third column
shows the revised numbers that would result
if the methods proposed in the 1998 NPRM
(``NPRM1'') were applied; the fourth column
shows the results of applying the criteria
proposed herein (``NPRM2'' criteria) to
geographic areas only; the fifth column
shows the estimated results of applying
NPRM2 low-income population group criteria
to areas not meeting the geographic criteria;
and the final column shows the estimated
combined results of applying the ``NPRM2''
criteria first to geographic areas and
then to low-income population groups in
areas not meeting the geographic criteria.
The first three rows of Tables VI:1-9
provides the breakout of the various types
of HPSA and/or MUA/P designations, whole
county geographic, partial county geographic,
and low income populations. This breakout
allows an analysis of the impact of the
new method on the different types of designations
if desired. Row 4 then is total of these
three rows and includes the aggregate
numbers that were used in the impact analysis.
Row 5 calculates the percentage of the
original HPSAs/MUA-Ps that was designated
under the various methodologies using
updated data. For example, in Table VI:1,
949 of the original 2282 HPSAs tested
would still be designated using the current
method and updated data, which is a retention
rate of 41.6% (Column 3/Column 2). Row
6 is the number of new designations that
resulted from the various designation
methodologies, i.e. areas that had not
previously been designated that would
become designated. Row 7 is the total
of Rows 5 and 6, capturing the total number
of areas, old and new, that would be designated
under the various options. Row 8 calculates
the percentage of designated areas as
a percentage of the original baseline
number, in order to measure the impact
of the various methods in terms of degree
of change in the number of areas that
would be designated. For example, under
the updated current method with new data,
1055 areas would be designated, which
is 46.2% of the baseline number of 2282
(Column 3/ Column 2). The same general
process is followed for each of the columns
in the Tables VI:1-V:7. Table VI:8 and
VI:9 follow the same process for the combined
HPSA/MUA-P designations to assess the
impact of metropolitan/non-metropolitan/frontier
areas and populations, with the percentages
and the actual numbers now in the same
row rather than separate rows. For example,
in Table VI:8, 49% of the total designations
were retained using the updated current
method; Row 2, Column 2 divided by Row
2 Column 1 (2188/4447).
A.
Impact on Number of HPSA Designations
As
column 1 of table VI-1 shows, in the baseline
year of 1999 there were 832 whole counties,
858 part-county geographic areas, and
592 low- income population groups designated
as HPSAs in the United States, for a total
of 2282 designations.
Since approximately one quarter of the
HPSAs are updated each year, the 2282
designations considered valid in 1999
represent the results of case-by-case
review of requests received over the 1996-99
period from State and local sources, and
were based on a combination of national,
State and local data as of 1998 or earlier.
Column 2 shows the impact of simultaneously
updating all these designations using
the current HPSA criteria applied to the
Impact Test Data Base assembled by HRSA
and the UNC Sheps Center. [This data base
included 1998 data for population, income
and other census variables (using Claritas
intercensus estimates); 1998 national
primary care clinician data; and county-level
vital statistics data for the five-year
period 1994-98.] The results indicate
that only 949 or 42% of the 2282 baseline
areas would retain their designations
if updated under the current criteria.
However, 106 additional counties would
be newly designated, so that the new total
number of HPSAs would be 46% of the original
total.
Column 3 of Table VI-1 shows the impact
of applying the HPSA criteria proposed
in ``NPRM1'', as published in 1998, to
the 2282 baseline areas, using the same
Impact Test Data Base of 1998 national
data. The results indicate that only 652
or 29% of the baseline areas would retain
their HPSA designation; 71 counties would
be added, for a new total of 723 HPSAs,
32% of the baseline total. It is therefore
quite understandable that the public comments
received on NPRM1 expressed concern about
potential loss of many HPSA designations.
At the same time, it is useful to realize
(from comparing column 3 with column 2)
that 80% of the HPSA designations that
would be lost if the NPRM1 criteria were
adopted would also be lost by simply simultaneously
updating all areas using the HPSA criteria
already in force.
By contrast, Column 4 of Table VI-1 shows
that, when the NPRM2 Tier 1 geographic
area criteria are applied, 1660 or 73%
of the baseline HPSAs retain their HPSA
designations. An additional 325 counties
are newly designated, for a new total
of 1985 HPSAs, 87% of the baseline total.
While this result does not in itself demonstrate
the superiority of the proposed NPRM2
method, it does indicate that application
of the proposed method would not result
in the loss of many existing HPSA designations,
a major concern of commenters on the NPRM1
proposal.
Table
VI-1.--Impact of NPRM-1 and NPRM-2 Methods
on Number of HPSA Designations
Baseline
HPSA status |
Number
of areas designated as of 1999 (baseline)
|
Number
of areas designated by current criteria/
updated data |
Number
of areas designated by NPRM1 (meets
IPCS & HPSA) (*) |
Number
of areas designated by NPRM2- geographic
method |
Number
of population groups additionally
designated using NPRM2 low income
pop group method |
Total
number of areas and pop groups designated
using NPRM2- geographic and low in-come
pop group method |
Whole
County Geographic HPSA |
832 |
372 |
243 |
694 |
114 |
808 |
Part
County Geographic HPSA |
858 |
473 |
332 |
681 |
139 |
820 |
Low
Income Population HPSA |
592 |
104 |
77 |
285 |
190 |
475 |
Subtotal:
Number of Baseline HPSA Designations
Retained |
2,282 |
949 |
652 |
1,660 |
443 |
2,103 |
Percent
of Baseline Designations Re-tained |
|
41.6% |
28.6% |
72.7% |
19.4% |
92.2% |
New
Designations (1,197 Counties had no
Baseline HPSA Designation) |
|
106 |
71 |
325 |
452 |
777 |
Total
Number of HPSA Designations |
2,282 |
1,055 |
723 |
1,985 |
895 |
2,880 |
Total
HPSAs as a Percent of Base-line |
|
46.2% |
31.7% |
87.0% |
39.2% |
126.2% |
*For NPRM1, 4 areas are not included because
of missing data.
We
also estimated the results of applying
the NPRM2 Tier 1 low- income population
group designation criteria to those baseline
HPSA areas and counties that do not meet
the NPRM2 geographic criteria. Column
5 shows the number of low-income population
group HPSAs that would result; they include
253 in areas previously designated as
geographic HPSAs, 190 previous HPSA population
groups retained, and 452 potential new
low-income population group HPSAs in counties
not previously HPSA-designated.
Column 6 shows the combined result of
applying NPRM2 Tier 1 geographic and low-income
population group criteria: 2103 or 92%
of areas with baseline HPSA designations
would keep either a geographic or a low-income
population group designation if the NPRM2
criteria were applied, while 777 additional
geographical areas or low-income population
groups could potentially be designated.
While this last number may seem large,
this may be related to the fact that all
areas designated with the NPRM2 approach
are both HPSAs and MUAs. Under the previous
criteria there were considerably more
MUAs than HPSAs. Therefore, in a new system
with combined criteria, even if the total
number of areas designated (as either
MUAs or HPSAs) were to remain approximately
the same as before, one could expect the
number of HPSAs to increase.
B.
Impact on Number of MUA/P Designations
As
column 1 of table VI-2 shows, in the baseline
year of 1999 there were 1411 whole counties,
1909 part-county geographic areas, and
138 low-income population groups designated
as MUA/Ps in the United States, for a
total of 3458 designations.
Unlike the case with HPSAs, regular reviews
and updates to the list of MUA/Ps are
not legislatively required, and no major
review/update has occurred since 1982;
rather, additions and deletions have been
made upon request (requested deletions
have been infrequent). Therefore, the
3458 MUA/P designations considered valid
in 1999 include many not updated since
1982, plus the results of case-by-case
review of requests received over the 1982-99
period from State and local sources. Column
2 shows the impact of simultaneously updating
all these designations using the current
MUA criteria applied to the Impact Test
Data Base discussed above (assembled by
HRSA and the UNC Sheps Center from 1998
data). The results are that only 1312
or 38% of these areas would retain their
MUA designations. At the same time, 28
additional counties would be newly designated,
so that the new total number of MUAs would
be 39% of the baseline total. Thus, using
the current methodology to update the
MUA list would result in more change for
MUAs than for HPSAs.
Column 3 of Table VI-2 shows the results
of applying the MUA criteria proposed
in ``NPRM1'', as published in 1998, to
the same 3458 areas, using the same Impact
Test Data Base of 1998 national data.
Here 2405, or 70% of the baseline areas,
would retain their MUA designation; 143
counties would be added, for a new total
of 2548 MUAs, 74% of the baseline total.
So the method proposed in NPRM1 would
not have decreased existing MUA designations,
in contrast to the effect it would have
had on HPSAs. And it would have performed
significantly better than the option of
updating using current criteria in terms
of retention of MUA designations.
Column 4 of Table VI-2 shows that, when
the NPRM2 Tier 1 geographic area criteria
are applied, 2319 or 67% of the baseline
MUAs retain their MUA designations. An
additional 168 counties are newly designated,
for a new total of 2487 MUAs, 72% of the
original total.
Table
VI-2.--Impact of NPRM-1 and NPRM-2 Methods
on Number of MUA/P Designations
Baseline
MUA/P status |
Number
of areas des-ignated as of 1999 (base-line) |
Number
of areas des-ignated by current cri-teria/updated
data (*) |
Number
of areas des-ignated by NPRM1 (meets
IPCS) (**) |
Number
of areas designated by NPRM2–
geographic method |
Estimated
number of pop groups designated using
NPRM2–low income pop group meth-od
|
Total
number of areas and pop groups designated
using NPRM2–geo-graphic and
low income pop group method |
Whole
County Geographic MUA
Part County Geographic MUA
Low Income Population MUP |
1,411
1,909
138 |
499
795
18 |
1,067
1,286
52 |
1,031
1,233
55 |
319
347
33 |
1,350
1,580
88 |
Subtotal:
Number of Baseline MUA/P Designations
Retained. |
3,458 |
1,312 |
2,405 |
2,319 |
699 |
3,018 |
Percent
of Baseline Designations Retained
New Designations (674 Counties had
no Baseline MUA/P Designation). |
|
37.9%
28 |
69.5%
143 |
67.1%
168 |
20.2%
219 |
87.3%
387 |
Total
Number of MUA/P Designations |
3,458 |
1,340 |
2,548 |
2,487 |
918 |
3,405 |
Total MUA/Ps as a Percent of Baseline
|
|
38.8% |
73.7% |
71.9% |
26.5% |
98.5% |
* For Current Criteria, Updated Data, 327
areas are not included because of missing
data. We
also estimated the results of applying
the NPRM2 Tier 1 low- income population
group designation criteria to those baseline
MUAs and other counties that do not meet
the NPRM2 geographic criteria. Column
5 of Table VI-2 shows the number of low-income
MUPs that would result; they include 666
in areas previously designated as geographic
MUAs, 33 previous low-income MUPs retained,
and 219 potential new low-income MUPs
in counties not previously MUA/P-designated.
Column 6 shows the combined result of
applying NPRM2 Tier 1 geographic and low-income
population group criteria: 3018 or 87%
of areas with baseline MUA/P designations
would keep either a geographic or a low-income
population group designation if the NPRM2
criteria were applied, while 387 additional
geographical areas or low-income population
groups could potentially be designated,
for a total of 3405 MUA/P designations,
98% of the baseline number.
C. Impact on
Number of Unduplicated HPSA/MUP Designations
Areas and population groups designated
under the criteria proposed herein would
be considered both MUA/Ps and HPSAs. Therefore,
it is important to examine not only the
impact on HPSA and MUA/P designations
separately, but also the combined impact
on unduplicated HPSA and MUA/P designations.
This is shown in Table VI-3. As column
1 shows, 1610 whole counties were designated
either as MUAs or HPSAs or both in 1999;
2350 additional part-county areas were
geographically designated as MUAs and/or
as HPSAs; and 487 low-income population
groups in other areas were designated
as MUPs and/or population group HPSAs,
for a total of 4447 unduplicated baseline
designations (as compared with the baseline
HPSA total of 2282 and the baseline MUA/P
total of 3458). We have characterized
this combined group of basis areas as
the ``any designation'' layer of areas.
Column 2 of Table VI-3 shows the impact
on unduplicated number of designations
of updating using the current HPSA/MUA/P
criteria (against the 1998 database described
above). 2170 or 48.8% of the baseline
areas would retain designation; 18 additional
counties would achieve designation, so
that the new total of 2188 areas would
be 49.2% of the baseline total.
Column 3 shows the impact of applying
the previously-published NPRM1 criteria
to the unduplicated baseline areas. Here
2994 or 67% of the baseline areas would
retain their designation; with 42 new
designations, a total of 3036 unduplicated
designations would result, or 68% of the
baseline number. This is compared to the
50% loss associated with updating under
current criteria, but application of the
NPRM1 criteria would still have decreased
(nearly \1/3\) of unduplicated designations.
Column 4 shows the impact of applying
the proposed NPRM2 geographic criteria
to the unduplicated baseline areas. Here
a total of 2962 areas are geographically
designated, or 67% of the baseline areas,
roughly the same as the NPRM1 impact.
However, when the estimated NPRM2 low-
income population group adjustment is
applied and added, we get the considerably
more favorable combined result shown in
Column 5: A total of 3882 designations
(or 87% of the unduplicated baseline)
are retained by the NPRM2 method, while
168 new designations are added, for a
total of 4050 designations or 91% of the
baseline.
Table
VI-3.--Impact on Number of Combined HPSA/MUA
Designations
Baseline
HPSA and MUA/P status |
Number
of areas designated |
As
of 1999 (baseline) |
By
curent cri-teria/updated data |
By
NPRM1 (meets IPCS threshold) |
By
NPRM2 geographic method |
Total
using NPRM2 geo-graphic and low income
adjustment (2 step) method |
Designated
as Geog or Low Income Population HPSA
or MUA/P as of 1999 (Old): Whole County
Geog HPSA or MUA. |
1,610 |
734 |
1,177 |
1,163 |
1,536 |
Part
County Geog HPSA or MUA. |
2,350 |
1,351 |
1,607 |
1,571 |
2,003 |
Low
Income Population HPSA or MUP |
487 |
85 |
210 |
177 |
343 |
Subtotal:
Areas Designated (of 1999 Designated
Areas) |
4,447 |
2,170
48.8% |
2,994
67.3% |
2,911
65.5% |
3,882
87.3% |
New
Designations (not Designated 1999) |
|
18 |
42 |
51 |
168 |
Total:
Areas Designated (of 1999 Designated
and Undesignated Areas) |
4,447 |
2,188
49.2% |
3,036
68.3% |
2,962
66.6% |
4,050
91.1% |
(Note: Tables VI-1 and VI-2 show that
777 new HPSA designations and 387 new
MUA/P designations result when the proposed
NPRM2 criteria are applied separately
to baseline HPSAs plus other counties
and to baseline MUAs plus other counties.
By contrast, when the unduplicated set
of baseline areas are used in Table VI-3,
we find only 168 new designations that
were not either HPSAs or MUAs previously.
Also, while Tables 1 and 2 show the total
numbers of Tier 1 HPSAs and MUA/Ps under
NPRM2 to be 126% and 98% of their baselines,
respectively, Table 3 shows that the total
unduplicated designations under NPRM2
Tier 1 are only 91% of the unduplicated
baseline. From here on, impact analysis
results are displayed in terms of the
unduplicated baseline areas.)
D. Impact
on Population of all Designated HPSAs
and/or MUPs
While the number and percent of designations
retained and the new total number of designations
under alternative methods are important
measures of the impact of a change in
criteria, these measures can also be misleading,
since all areas are not equal; different
areas have different populations, different
levels of need, and different numbers
of safety net providers. Using 1998 Claritas
population estimates, the total population
of all 1999-designated (baseline) HPSAs
was 59.1 million, while the total population
of baseline MUA/Ps was 72.1 million; the
unduplicated total population of baseline
areas designated as HPSAs and/or MUA/Ps
was 95.3 million.
Table VI-4 shows the impact of the various
alternatives on this unduplicated total
designated population. Updating using
the current criteria against the 1998
Impact Test Database would lower the total
designated population to 32.7 million,
or 34% of the baseline. Use of the NPRM2
geographic criteria would result in a
total designated population of 53.0 million,
or 56% of the baseline. Finally, use of
the NPRM2 method would result in a total
designated population of 83.1 million,
or 87% of the baseline. (This is actually
quite close to the percentage expressed
in number of designations, which was 91%.)
Table
VI-4.--Impact on Unduplicated Population
of HPSAs and MUA/Ps
Baseline
HPSA and MUA/P Status |
Population
in areas |
As
of 1999 (Baseline) |
By
current cri-teria/updated data |
By
NPRM2 geographic method [A] |
By
NPRM2 low income adjustment (2 step)
method [B](*) |
Total
using NPRM2 geo-graphic and low income
adjustment (2 step) method [A+B] |
Designated
as Geog or Low Income Population HPSA
or MUA/P as of 1999 (Old): |
|
|
|
|
|
Whole
County Geog HPSA or MUA. |
38,400,153 |
12,044,723 |
23,080,444 |
11,501,134 |
34,581,578 |
Part
County Geog HPSA or MUA |
37,747,979 |
17,986,210 |
24,044,227 |
8,308,592 |
32,352,819 |
Low
Income Population HPSA or MUP (*) |
19,132,742 |
2,199,545 |
4,692,078 |
6,352,471 |
11,044,549 |
Subtotal:
Population in Areas Designated (of
1999 Designated Areas) |
95,280,874 |
32,230,478 |
51,816,749 |
26,162,197 |
77,978,946 |
Subtotal:
Share of Population in Areas Designated
in 1999 |
|
33.8% |
54.4% |
27.5% |
81.8% |
Not
Designated as Geog or Low Income Population
HPSA or MUA/P as of 1999 (New): |
|
|
|
|
|
New
Designations [28,490,624] Population
in Areas without Baseline Designation) |
|
481,198
|
1,111,149
|
4,057,976
|
5,169,125 |
Total:
Population Areas Designated (of 1999
Designated and Undesignated Areas) |
95,280,874 |
32,711,676 |
52,927,898
|
30,220,173
|
83,148,071
|
Total:
Share of Population in Areas Designated
in 1999. |
|
34.3%
|
55.5% |
31.7% |
87.3% |
*
Though these designations are associated
with Low Income Population, the population
counts provided here are for all residents
of the area [Total Population].
The
results in Table VI-4 suggest that use
of the NPRM2 method will better target
designations--both the number and population
of all designated areas will decrease
by about 10%. At the same time, the NPRM2
method should result in a much smoother
transition from current designation levels
than would either updating using current
criteria (which would significantly decrease
MUAs) or updating using NPRM1 (which would
significantly decrease HPSAs).
E.
Impact on Number of CHCs Covered by Designations
Table
VI-5 shows, for those CHC sites identified
as located in areas which were designated
in the baseline year, the percentage that
retain their designations under the various
scenarios. Under the proposed method,
86% would be in areas that retain designation
(either as a geographic area or as a low
income population group-see fourth line
of table, last column). By contrast, the
NPRM1 method would have retained only
76%, while updating the designations under
current criteria would have retained only
43%.
Table
VI-5.--Impact on Number of CHCs Covered
by Designations
Baseline
HPSA and MUA/P Status |
Number
of CHCs in areas |
As
of 1999 (Baseline) |
By
current cri-teria/updated data |
By
NPRM1 (meets IPCS threshold) |
By
NPRM2 geographic method |
Total
using NPRM2 geo-graphic and low income
adjustment (2 step) method |
Designated
as Geog or Low Income Population HPSA
or MUA/P as of 1999 (Old): |
|
|
|
|
|
Whole
County Geog HPSA or MUA |
618 |
252 |
474 |
456 |
583 |
Part
County Geog HPSA or MUA. |
741 |
354 |
583 |
453 |
629 |
Low
Income Population HPSA or MUP (*) |
122 |
31 |
61 |
51 |
93 |
Subtotal:
CHCs in Designated Areas (% of 1999
CHCs) |
1,481 |
637 |
1,118 |
960 |
1,305 |
|
|
43% |
75.5% |
64.8% |
88.1% |
Not
Designated as Geog or Low Income Population
HPSA or MUA/P as of 1999 (New) New
Designations (43 CHCs without Baseline
Designation). |
|
2 |
7 |
4 |
10 |
Total:
CHCs in Designated Areas (% of 1999
CHCs) |
1,481 |
639 |
1,125 |
964 |
1,315 |
|
|
43.1 |
75.9% |
62.1 |
88.8 |
*
The number of CHCs is based on the number
of FQHC, Community Health Center sites
which offer a full range of primary care
services and where the designation is
based on area characteristics or low income.
Most part- time, special population and
satellite clinics are excluded.
F.
Impact on Number of NHSC Sites Covered
by Designations
Table
VI-6 shows, for those NHSC sites identified
as located in areas which were designated
in the baseline year, the percentage that
retain their designations under the various
scenarios. Under the proposed method,
86% would be in areas that retain designation
(either as a geographic area or as a low
income population group--see fifth line
of table, last column). By contrast, updating
the designations using current criteria
would have retained only 34%.
Table
VI-6.--Impact on Number of NHSC Sites
Covered by Designations
Baseline
HPSA and MUA/P status |
Number
of areas with NHSCs designation |
As
of 1999 (Baseline) |
By
current cri-teria/updated data |
By
NPRM2 geographic method [A] |
By
NPRM2 low income adjustment (2 step)
method [B] |
Total
using NPRM2 geo-graphic and low income
adjustment (2 step) method [A+B] |
Designated
as Geog or Low Income:
Whole County Geog HPSA or MUA. |
340 |
123 |
218 |
97 |
315 |
Population
HPSA or MUA/P as of 1999 (Old): Part
County Geog HPSA or MUA |
414 |
172 |
245 |
119 |
364 |
Low
Income Population HPSA or MUA/P |
178
|
19 |
52 |
72 |
124 |
Subtotal:
NHSC Areas Designated (of 1999 Designated
Areas) |
932 |
314 |
515 |
288 |
803 |
Subtotal:
Share of NHSC Areas Designated in
1999 |
|
33.7% |
55.3% |
30.9% |
86.2% |
Designated
as Geog or Low Income Population HPSA
or MUA/P as of 1999 (New): New Designations
(15 Areas with NHSCs without Baseline
Designation) |
|
0 |
0 |
4 |
4 |
Total:
NHSC Areas Designated (of 1999 Des-ignated
and Undesignated Areas) |
932 |
314 |
515 |
292 |
807 |
Total:
Share of NHSC in Areas Designated
in 1999. |
|
33.7% |
55.3% |
31.3% |
86.6% |
G.
Impact on Number of RHCs Covered by Designations
Table VI-7 shows, for those RHC sites
identified as located in areas which were
designated in the baseline year, the percentage
that retain their designations under the
various scenarios. Under the proposed
method, 94% of RHCS in currently designated
areas would be in areas that retain designation
(either as a geographic area or as a low
income population group--see fifth line
of table, last column). An additional
94 RHCs that were not in designated areas
at the time of testing would be in areas
designated under the new methodology,
resulting in 97.5% of RHCs being located
in designated areas. By contrast, updating
under current criteria would have retained
46%.
Table
VI-7.--Impact on Number of RHCs Covered
by Designations
Baseline
HPSA and MUA/P status |
Number
of RHCs in areas designated |
As
of 1999 (Baseline) |
By
current cri-teria/updated data |
By
NPRM2 geographic method [A] |
By
NPRM2 low income adjustment (2 step)
method [B] |
Total
Using NPRM2 geo-graphic and low income
adjustment (2 step) method [A+B] |
Designated
as Geog or Low Income Population HPSA
or MUA/P as of 1999 (Old): |
|
|
|
|
|
Whole
County Geog HPSA or MUA |
2,173 |
946 |
1,503 |
569 |
2,072 |
Part
County Geog HPSA or MUA. |
544
|
336 |
393 |
127 |
520 |
Low
Income Population HPSA or MUA/P |
125 |
24 |
43 |
42 |
85 |
Subtotal:
RHCs Designated (of 1999 Designated
Areas) |
2,842 |
1,306 |
1,939 |
738 |
2,677 |
Subtotal:
Share of RHCs Designated in 1999 |
|
46.0% |
68.2% |
26.0% |
94.2% |
Designated
as Geog or Low Income Population HPSA
or MUA/P as of 1999 (New): |
|
|
|
|
|
New
Designations (120 RHCs in Areas without
Base-line Designation) |
|
11 |
28 |
66 |
94 |
Total:
RHCs Designated (of 1999 Designated
and Undesignated Areas) |
2,842 |
1,317 |
1,967 |
804 |
2,771 |
Total:
Share of RHCs Designated in 1999 |
|
46.3% |
69.2% |
28.3% |
97.5% |
H.
Impact on Distribution of Designations
by Metropolitan/Non- Metropolitan and
Frontier Status
Table
VI-8 enables comparison of the impact
on number of designated areas in metropolitan,
non-metropolitan, and frontier areas.
(Here metropolitan areas are those so
designated by the Office of Management
and Budget; non-metropolitan areas are
all other areas. Frontier areas are generally
defined as the subset of non-metropolitan
areas with population densities less than
7 persons per square mile, but for the
purpose of these impact tests a file of
frontier areas was used that was provided
by the Frontier Education Center and involved
a more expansive definition of frontier
areas that included a formula based on
population density and isolation [time
and distance from a market area as well
as other factors]). Table VI-8 (last column)
shows that, while 91% of all baseline
designations are retained under the proposed
method, 82% of those in metropolitan areas,
98% of those in non- metropolitan areas,
and 99% of those in frontier areas are
retained. Therefore, non-metropolitan
and frontier areas are not more negatively
impacted than metropolitan areas (contrary
to the impression many commentors seemed
to have of the NPRM1 method).
Table
VI-8.--Impact on Distribution of Designations
by Met/Non-Met/Frontier
|
Baseline
|
Current
criteria updated |
NPRM1 |
NPRM2
|
NPRM2
Geog + Low-income pop |
Total
No. of Designations |
4,447 |
2,188
(49%) |
3,036
(68%) |
2,962
(67%) |
4,050
(91%) |
Metropolitan |
1,880 |
861
(46%) |
1,223
(65%) |
1,112
(59%) |
1,532
(82%) |
Non-Metro |
2,567 |
1,327
(52%) |
1,813
(71%) |
1,850
(72%) |
2,518
(98%) |
Frontier |
1,026 |
544
(53%) |
800
(78%) |
751
(73%) |
1,014
(99%) |
I.
Impact on Distribution of Population of
Underserved Area and Underserved Populations
by Metropolitan/Non-Metropolitan and Frontier
Status
Table VI-9 enables comparison of the impact
on the population of underserved areas
and underserved populations in metropolitan,
non- metropolitan, and frontier areas.
Table VI-9 (last column) shows that, while
the total designated population under
the proposed method would be 87% of the
baseline designated population, the metropolitan
component of this NPRM2 designated population
is 81% of the baseline metropolitan underserved,
the non-metropolitan component is 99%
of the baseline non-metropolitan underserved,
and the frontier component is 102% of
the baseline frontier underserved. Therefore,
the designated population of non-metropolitan
and frontier areas would not decrease.
The metropolitan population identified
as underserved would appear to decrease,
however. We expect this represents better
targeting of the metropolitan underserved
under the proposed method: It may also
represent the fact that use of a national
physician database together with gross
estimates of the percent of urban practices
devoted to low- income and uninsured populations
leads to overestimates of the number of
FTE clinicians and underestimates of the
number of designations and the underserved
population in metropolitan areas. This
suggests that case-by-case activity will
continue to be necessary in reviewing
some urban designations, while many non-metropolitan
designations will be able to be processed
using national data together with the
new method.
Table
VI-9.--Impact on Population of Underserved
Areas by Met/Non-Met/Frontier
|
Baseline
|
Current
criteria updated |
NPRM2
Geog |
NPRM2
Geog + Low-income pop |
Total
Underserved |
95,280,874 |
32,711,676
(34%) |
52,927,898
(56%) |
83,148,071
(87%) |
Metropolitan
Underserved |
63,791,345 |
21,044,647
(33%) |
31,951,255
(50%) |
51,804,251
(81%) |
Non-Metro
Underserved |
31,489,529 |
11,667,029
(37%) |
20,976,643
(67%) |
31,343,820
(99%) |
Frontier
Underserved |
8,328,049 |
3,396,268
(41%) |
5,784,509
(70%) |
8,528,643
(102%) |
J.
Impact of Practitioner ``Back-outs'' on
Number of Designations and Safety-Net
Providers
The
tables above represent the impacts when
all clinicians are counted, i.e. the ``Tier
1'' designations. The tables below describe
the impact of subtracting federally placed,
obligated or funded clinicians from the
practitioner counts, i.e. the changes
that occur when ``Tier 2'' designations
are included. For example, Table VI-10
shows the effect on number of designations.
Column 1 shows the number of baseline
designations; column 2 shows the number
of Tier 1 designations under the proposed
method. Column 3 shows the new total of
designations if NHSC and SLRP clinicians
are subtracted. Column 4 shows the revised
total if physicians with J-1 visa return-home
waivers who are performing obligated service
are also subtracted. Finally, column 5
shows the total number of designations
when any other CHC-Based clinicians are
also subtracted.
Table
VI-10.--Impact of Practitioner ``Back-outs''
on Total Number of HPSA or MUA/P Areas
Designated
Baseline
HPSA and MUA/P status |
Number
of areas designated |
As
of 1999 (baseline) |
By
NPRM2 geographic and 2 step low income
meth-od Tier 1 (all primary care providers) |
By
NPRM2 geographic and 2 step low income
meth-od Tier 2–1 (Tier 1 less
NHSC and SLRP pro-viders) |
By
NPRM2 geographic and 2 step low income
meth-od Tier 2–2 (Tier 1 less
NHSC, SLRP, and J–1 pro-viders)
|
By
NPRM2 geographic and 2 step low income
meth-od Tier 2–3 (Tier 1 less
NHSC, SLRP, J–1, and any designation)
|
Designated
as Geog or Low Income Population HPSA
or MUA/P as of 1999 (Old): Whole County
Geog HPSA or MUA |
1,610 |
1,536 |
1,546 |
1,551 |
1,553 |
Part
County Geog HPSA or MUA |
2,350 |
2,003 |
2,010 |
2,015 |
2,038 |
Low
Income Population HPSA or MUP |
487 |
343 |
346 |
350 |
356 |
Subtotal:
Areas Designated (of 1999 Designated
Areas) |
4,447 |
3,882 |
3,902 |
3,916 |
3,947 |
Subtotal:
Share of Areas Designated in 1999 |
|
87.3% |
87.7% |
88.1% |
88.8% |
Designated
as Geog or Low Income Population HPSA
or MUA/P as of 1999 (New): New Designations
(376 Areas Designated as HPSA or MUA
without Baseline Designation) |
|
168 |
168 |
168 |
172 |
Total:
Areas Designated (of 1999 Designated
and Undesignated Areas) |
4,447 |
4,050 |
4,070 |
4,084 |
4,119 |
Total:
Share of Areas Designated in 1999 |
|
91.1% |
91.5% |
91.8% |
92.6% |
As
can be seen, the number of additional
designations resulting from these practitioner
back-outs is quite small. However, HRSA
considered that there could be a significant
impact on some particular safety-net projects,
i.e. certain CHCs, NHSC sites, and RHCs.
Table VI-11 summarizes the impact on CHCs,
NHSC sites, and RHCs. It indicates that
49 additional CHCs, 32 additional NHSC
sites, and 43 additional RHCs are in areas
which would receive Tier 2 designation
(change from Column 2 to Column 5). While
this is not a large number, it clearly
would be important for the affected sites.
HRSA therefore concluded that the Tier
2 designations (with all three types of
backouts) should be implemented.
Table
VI-11.--Impact of Practitioner Back-Outs
on Numbers of CHCs, NHSC Sites, and RHCs
Covered by Designations
Type
of safety-net provider |
Number
in baseline designated areas |
Number
in NPRM2- designated tier 1 areas
(All primary care clinicians counted)
|
Number
in NPRM2- designated tier 1/tier 2–1
areas (NHSC and SLRP clini-cians sub-tracted)
|
Number
in NPRM2- designated tier 1/tier 2–2
areas (NHSC, SLRP and J–1 clini-cians
sub-tracted) |
Number
in NPRM2- designated tier 1/tier 2–3
areas (NHSC, SLRP, J–1, and
other section 330 funded clini-cians
sub-tracted) |
CHCs |
1,481 |
1,315 |
1,322 |
1,328 |
1,364 |
(%
of baseline CHCs) |
|
(88.8%)
|
(89.3%) |
(89.7%)
|
(92.1%)
|
NHSC
sites |
932 |
807 |
825 |
828 |
839 |
(%
of baseline NHSC sites) |
|
(86.6%)
|
(88.5%)
|
(88.8%)
|
(90.0%)
|
RHCs |
2,842 |
2,771 |
2,790 |
2,794 |
2,814 |
(%
of baseline RHCs) |
|
(97.5%) |
(98.2%)
|
(98.3%) |
(99.0%) |
In
conclusion, it should be stated that it
is impossible to predict the exact final
impact on specific communities and States
because of the iterative process built
into the system. As described above, State
and local officials will have the opportunity
to examine the data used to develop these
first approximations during the actual
designation process, and to correct inaccurate
provider and other data. In addition,
they will have the opportunity to reconfigure
service areas so as to more closely identify
the boundaries of areas where shortages
now exist, which may have changed since
some of these service areas were constructed
(particularly the MUAs). We believe this
is a major strength of the proposal, since
States and communities know best their
service areas and practitioner supplies.
At the same time, it makes it difficult
to predict precisely the impact of the
new method at the local level, since the
data used will be altered by State and
local input.
VII.
Economic Impact
Executive Order 12866 requires that all
regulations reflect consideration of alternatives,
costs, benefits, incentives, equity, and
available information. Regulations must
meet certain standards, such as avoiding
unnecessary burden. Regulations which
are found to be ``significant'' because
of their cost, adverse effects on the
economy, inconsistency with other agency
actions, budgetary impact, or raising
of novel legal or policy issues require
special analysis. The Department has determined
that this rule will not have an annual
effect on the economy of $100 million
or more. However, because this rule raises
novel policy issues, it does meet the
definition of a ``significant'' rule under
Executive Order 12866.
The Regulatory Flexibility Act requires
that agencies analyze regulatory proposals
to determine whether they create a significant
economic impact on a substantial number
of small entities. ``Small entity'' is
defined in the Regulatory Flexibility
Act as ``having the same meaning as the
terms `small business,' `small organization,'
and `small governmental jurisdiction'
``; ``Small organizations'' are defined
in the Regulatory Flexibility Act as not-for-profit
enterprises which are independently owned
and operated and not dominant in their
field.
The small organizations most relevant
to this regulation would be Health Center
grantees. The impact analyses discussed
above suggest that very few health center
service areas would lose MUA/P designation
under the proposed criteria. In addition,
because of the proposed new safety net
facility type of designation, any negatively
affected health center will be able to
submit a request for this alternate type
of designation. Moreover, the ``automatic''
designation of all FQHCs as HPSAs for
six years under the Safety Net Amendments
of 2002 will allow additional time for
any transition to unfunded status that
may prove to be necessary for some health
centers.
With regard to small businesses, while
the designation process may negatively
affect some small profit-making health
care-related businesses, it is unlikely
that it could have a significant economic
impact, defined as five percent or more
of total revenues on three percent or
more of all such small businesses. Physician
practices can obtain a 10 percent Medicare
Incentive Payment bonus for those services
delivered in geographic HPSAs; however,
this would be unlikely to amount to five
percent of the total revenues of a practice
operated as a small business.
Private
RHCs could be considered small businesses;
non-profit RHCs could be considered small
organizations. RHCs already certified
based in part on an MUA or HPSA designation
have not been adversely affected by loss
of such designations in the past, since
the legislative authority for them had
a ``grandfather'' clause; once certified,
the RHC certification could not be withdrawn
based only on loss of designation. However,
the Balanced Budget Act of 1997 provided
that, effective January 1, 1999, an RHC
in an area that has lost designation or
was designated over 3 years ago is subject
to loss of its RHC certification, unless
the Secretary determines that the RHC
is essential to the delivery of primary
care services in its area. The impact
analysis shows only 2% of the non-metro
designations will be lost under the proposed
new method, so the likely impact is minimal.
Therefore, implementation of these regulations
will not automatically decertify any RHCs.
"Small
governmental jurisdictions" are defined
by the Regulatory Flexibility Act to include
governments of those cities, counties,
towns, townships, villages, or districts
with a population of less than 50,000.
Typically, one can expect that such jurisdictions
will be found in non-metropolitan areas.
Our impact analysis indicated that only
2 percent of all designations in non-metropolitan
areas are likely to lose a designation
(see Table VI-8 above). This suggests
that a substantial number of small government
jurisdictions will not be affected. Furthermore,
it is unlikely that the economic impact
on any such affected jurisdictions would
be significant, i.e. that they would lose
more than 5 percent of their federal funding,
as discussed in more detail below.
The
impact on particular jurisdictions of
loss of designation can take one or more
of three forms: Loss of grant funding
for primary care services, loss of a source
of clinicians to provide primary care
services, or loss of a more favorable
level of Medicaid and/or Medicare reimbursement.
The first of these types of impact would
occur only in the case of a Health Center
which has lost its area and/or population
designation, and does not qualify for
designation as a safety net site. Typically,
grant funding forms approximately 25-30
percent of the income to a CHC; it is
possible that such a health center would
be able to continue in operation without
this revenue. Moreover, dedesignation
could indicate that not only provider
availability but also the income of the
area's population had increased. As a
result, the percentage impact on the economy
of the area involved would likely be relatively
low.
The second of these types of impact corresponds
to an area which, due to loss of its HPSA
designation, is no longer eligible for
NHSC clinicians, once the tour of duty
of any NHSC personnel already placed there
is completed. If such an area has recently
been dedesignated, logically there must
have been an increase in the number of
primary care providers in the area and/or
a decreased population and/or improved
demographics, so that loss of NHSC clinicians
will be unlikely to have a major economic
effect on the area. (Furthermore, the
``automatic'' HPSA designation of FQHCs
and RHCs should mitigate any adverse effects
here during the next several years.)
The third type of impact applies in the
case of FQHCs and/or RHCs which lose eligibility
for special reimbursement methods, and
private physicians in former geographic
HPSAs which lose the 10 percent Medicare
bonus. None of these entities would actually
cease receiving Medicare or Medicaid reimbursement;
they simply would receive a lower level
of reimbursement. In the latter case,
it is a loss of 10 percent, but it is
unlikely that it would amount to 5 percent
of the physician's total revenue. In the
FQHC/RHC case, there could be a 20-30
percent decrease in reimbursement to the
provider in question, but again this would
not necessarily be a major economic loss
to the county or other jurisdiction as
a whole.
It should also be noted that, to the extent
that the proposed regulation ultimately
results in some areas losing designation
while others gain designation, and some
areas therefore losing program benefits
which go to designated areas while others
gain such benefits, the total benefits
available in a particular fiscal year
will not decrease but will have been better
targeted to the neediest areas, because
the criteria will have been improved and
will have been applied to more current
data.
The Department nevertheless requests comments
on whether there are any aspects of this
proposed rule which can be improved to
make the designation process proposed
more effective, more equitable, or less
costly.
VII.
Information Collection Requirements Under
Paperwork Reduction Act of 1995
Sections 5.3 and 5.5 of the proposed rule
contain information collection requirements
as defined under the Paperwork Reduction
Act of 1995 and implementing regulations.
As required, the Department of Health
and Human Services is submitting a request
for approval of these information collection
provisions to OMB for review. These collection
provisions are summarized below, together
with a brief description of the need for
the information and its proposed use,
and an estimate of the burden that will
result.
Title: Information for use in designation
of MUA/Ps and HPSAs.
Summary of Collection: These regulations
revise existing criteria and processes
used for designation of Medically Underserved
Areas/ Populations (MUA/P) and Health
Professional Shortage Areas (HPSA). As
discussed above, service to an area or
population group with such a designation
is one requirement for entities to obtain
Federal assistance from one or more of
a number of programs, including the National
Health Service Corps and the Community
and Migrant Health Center Program.
In order to initially obtain such a designation,
a community, individual or State agency
or organization must request the designation
in writing. Requests must include data
showing that the area, population group
or facility meets the criteria for designation,
although these data need not necessarily
be collected by the applicant, but may
be based on data obtained from a State
entity or data available from the Secretary.
If the request is made by a community
or individual, the State entities identified
in the regulation are given an opportunity
to review it, which implies maintenance
by these State entities of some record
keeping on designation requests previously
made or commented upon by the State. These
requirements apply under both current
rules and the proposed rule.
Once a designation based on the proposed
criteria has been made, it must be updated
periodically (at least once every three
years) or it will be removed from the
list of designations. Although in the
past this requirement applied only to
HPSA designations, the proposed rule would
extend the regular periodic update requirement
to MUA/P designations (in response to
concerns raised by the GAO and Congressional
committees, among others). The update
process involves the Secretary each year
informing State (and/or community) entities
as to which of their designations require
updates, and providing these entities
with the most current data available to
the Secretary for the areas, population
groups and facilities involved, with respect
to the data elements used in designation.
The State entities are then asked to verify
whether the designations are still valid,
using the data furnished by the Secretary
from national sources together with any
additional, more current or otherwise
more accurate data available to the State
entity (in consultation with the communities
involved, as necessary). In the past,
this has generally meant that the State
(or community) entities have needed to
verify primary care physician counts in
most of the areas involved, especially
subcounty areas, since only county-level
physician data have typically been available
from national sources. National population
data have been largely limited to decennial
census data and official Census Bureau
intercensus county- level updates, so
that State population estimates were sometimes
necessary; other relevant data have generally
been available from national sources.
Under the proposed new process, the data
furnished by the Secretary will include
provider data and population estimates
for subcounty areas as well as counties,
in an easily accessible database, and
these data from national sources (including
intercensus demographic and population
projections) may be used without further
collection and analysis, if acceptable
to the State and community involved. This
should minimize the burden on States and
communities, except where the Secretary's
data suggest withdrawal of a designation,
in which cases the State or community
will need to obtain local data to support
continued designation. In such cases,
the inclusion of non-physician providers
under the proposed new rules will have
a higher burden on those States or communities
which wish to challenge provider data
furnished by the Secretary.
Need for the information. The information
involved is needed in order to determine
whether the areas, populations and facilities
involved satisfy the criteria for designation
and, therefore, are eligible for programs
for which these designations are a prerequisite.
While furnishing such information is purely
voluntary, failure to provide it can prevent
some needy communities from becoming eligible
for certain programs. The Secretary will
make a proactive effort to identify such
communities using national data, but feedback
from State entities and others with appropriate
data is vital to ensuring that the designation/need
determination process is accurate and
current.
Likely respondents. The entities that
generally submit this designation-related
information to DHHS are the State Primary
Care Offices (normally within State Health
Departments) or the State Primary Care
Associations (non-profit associations
of health centers and other organizations
rendering primary care). The total burden
placed on these entities will be determined
by the number of applications they submit,
review or update each year, and, therefore,
will vary from State to State. Updates
of all designated areas will not be required
immediately when the new method is initiated;
State entities will be given the opportunity
to spread out updates of previously designated
areas over a 3-year period following implementation
of the proposed regulation.
Burden estimate. The overall public reporting
and record keeping burden for this collection
of information is estimated to be minimal
under the new method. This is primarily
because, while the new method will require
some data collection from the same sources
utilized in the previous MUA/P and HPSA
designation procedures, there is no need
to submit separate requests for the two
types of designation and allows the use
of national data where acceptable to the
State and community. We also plan to allow
electronic submission of data.
The burden for compiling a request for
new designation (including supporting
data) or for update of an existing designation,
under the existing system, was estimated
by consulting with State entities who
prepare such requests/updates about the
amount of time required for the various
aspects of request preparation, varying
these estimates for requests with several
different levels of difficulty, and then
factoring in the approximate frequency
of that type of request. Similar estimates
for the new system were then made, revising
the contributing factors to account for
those aspects that would require more
or less effort under the new approach.
These estimates also assume that some
applications are State-prepared, while
others involve both an applicant and a
State consultation or review; the estimates
include both parties' time where two parties
are involved. Under the new method, States
and communities may use data provided
by the Secretary; as mentioned above;
however, some may wish to provide their
own data for primary care physicians,
while others may wish to provide data
for both primary care physicians and for
the nonphysician primary medical care
providers which are included in the new
designation criteria and system (Nurse
Practitioners, Physician Assistants, and
Certified Nurse Midwives). Use of State
and/or community data will be more likely
in those cases where the national data
suggest dedesignation. The estimates below
include consideration of the extent to
which such local data collection will
likely be necessary.
Designation
type |
Number
of respondents |
Number
of expected responses |
Hours
per response |
Total
hours |
MUA/P/HPSA
Metro Area |
54 |
391 |
27.4 |
10,713 |
MUP/HPSA
Non-Metro Area |
*
54 |
909 |
10.9 |
9,908 |
Facility
Designations |
25 |
70 |
2.6 |
182 |
Total |
79 |
1,370 |
|
20,803 |
Mean |
|
|
15.2 |
|
*
The Non-Metro applications are completed
by the same respondents who complete Metro
Area designation requests. To prevent
double-counting of respondents, these
54 are added only once; therefore, 79
is shown as the total.
Public
comments on information collection requirements:
Comments by the public on this proposed
collection of information are solicited
and will be considered in (1) evaluating
whether the proposed collection of information
is necessary for the proper performance
of the functions of the Department, including
whether the information will have a practical
use; (2) evaluating the accuracy of the
Department's estimate of the burden of
the proposed collection of information,
including the validity of the methodology
and assumptions used; (3) enhancing the
quality, usefulness, and clarity of the
information to be collected; and (4) minimizing
the burden of collection of information
on those who are to respond, including
through the use of appropriate automated
electronic, mechanical, or other technological
collection techniques or other forms of
information technology; e.g., permitting
electronic submission of responses.
Address for comments on information collection
requirements: Any public comments specifically
regarding these information collection
requirements should be submitted to: Fax
Number--202-395-6974, or OIRA_submission@omb.eop.gov,
Attn: Desk Officer for HRSA. Comments
on the information collection requirements
will be accepted by OMB throughout the
60-day public comment period allowed for
the proposed rules, but will be most useful
to OMB if received during the first 30
days, since OMB must either approve the
collection requirement or file public
comments on it by the end of the 60-day
period.
Appendix
A.--References
Works
Cited
A description of this revised methodology
can be found in:
Ricketts TC, Goldsmith LJ, Holmes GM,
Randolph R, Lee R, Taylor DH, Osterman
J. Designating Places and Populations
as Medically Underserved: A Proposal for
a New Approach. Journal of Health Care
for the Poor and Underserved. 2007; 18:
567-589.
These
following articles are a sampling of the
many source documents that provide historical
background on measurements of underservice
and relate to two key factors in the methodology:
need indicators and benchmarking provider
productivity.
Indicators
of Need:
Aday
L, Andersen R. Development of Indices
of Access to Medical care. Ann Arbor,
MI: Health Administration Press; 1975.
Amato,
Paul R. and Jiping Zuo. 1992. Rural Poverty,
Urban Poverty and Psychological Well Being.
The Sociological Quarterly. Vol 33, No.
2 pp 229-40, June 1992. Andersen RM, Newman
JF. Societal and individual determinants
of medical care utilization in the United
States. Milbank Memorial Fund Quarterly.
1973; 51(1):95-124.
Andersen RM. Revisiting the behavioral
model and access to medical care: does
it matter? Journal of Health and Social
Behavior. 1995; 36(1):1-10.
CDC.
Community Indicators and Health Related
Quality of Life. MMWR Weekly April 7,
2000.
Kawachi
I, Berkman LF. Neighborhoods and Health.
New York: Oxford University Press; 2003.
Krieger
N, Chen JT, Waterman P, Rehkopf D, Subramanian
SV. Race/ Ethnicity, gender, and monitoring
socioeconomic gradients in health: A comparison
of area-based socioeconomic measures--the
public health disparities project. American
Journal of Public Health. 2003; 93(10):1655-1671.
Mansfield
CJ, Wilson JL, Kobrinski EJ, Mitchell
J. ``Premature mortality in the United
States: the roles of geographic area,
socioeconomic status, household type,
and availability of medical care''; American
Journal of Public Health. 1999; 89(6):893-898.
Robert
SA. ``Neighborhood socioeconomic context
and adult health.'' The mediating role
of individual health behaviors and psychosocial
factors''; Annals of the New York Academy
of Sciences. 1999; 896:465-468.
Robert
SA, House JS. ``Socioeconomic inequalities
in health: integrating individual-, community-,
and societal-level theory and research.''
In: Albrecht GL, Fitzpatrick R, Scrimshaw
S, eds. Handbook of Social Studies in
Health and Medicine. London: Sage Publications;
2000:115-135.
Ratio
of Provider/Population and Measures of
Underservice:
Bureau
of Health Manpower. Report on Development
of Criteria for Designation of Health
Manpower Shortage Areas. Rockville, MD:
Health Resources Administration; November,
1977. 78-03. Bureau of Primary Health
Care; Uniformed Data System Annual Report
data; CY 2000-2003; unpublished data
Coyte, P.C., Catz, M., et al. (1997) ``Distribution
of physicians in Ontario.'' Where are
there too few or too many family physicians
and general practitioners'' Canadian Family
Physician 43: 677-83, 733.
Dial, T.H., Palsbo, S.E., et al. (1995)
``Clinical staffing in staff- and group-model
HMOs.'' Health Affairs, Summer 1995; 14
(2): 168-80.
Goldsmith,
L. J. (2000, March 3). Invitational Workshop
of Measurement of the Measurement of Medical
Underservice. Presented at Cecil G. Sheps
Center for Health Services Research at
The University of North Carolina.
Goodman
DC, Fisher ES, Bubolz TA, Mohr JE, Poage
JF, Wennberg JE. ``Benchmarking the U.S.
physician workforce.'' An alternative
to needs-based or demand-based planning''
[published erratum appears in JAMA 1997
Mar 26; 277(12):966]. JAMA. 1996; 276(22):1811-1817.
Hart, L.G., et al, ``Physician Staffing
Ratios in staff-model HMOs: a cautionary
tale''; Health Affairs, January/February
1997; 16(10; 55-70
Kehrer
B, Wooldridge J. ``An evaluation of criteria
to designate urban health manpower shortage
areas''; Inquiry. 1983; 20:264-275.
Larson,
E.H. et al, ``The Contribution of Nurse
Practitioners and Physician Assistants
to Generalist Care in Underserved Areas
of Washington State''; June 2001; WWAMI
Center for Health Workforce Studies
Perlin
J., MD, Miller, L * * * Report of the
Primary Care Subcommittee; VHA Physician
Productivity and Staffing Advisory Group;
Veterans Administration; June 30, 2003.
Ricketts
TC, Taylor DH. Examining Alternative Measures
of Underservice. Proceedings of the 1995
Public Health Conference on Records and
Statistics. Washington, DC: National Center
for Health Statistics, 1995 pp. 207-9
Sekscenski,
T, Moses, E, (1999) HRSA's Bureau of Health
Professions, Division of Nursing and Office
of Research & Planning Health Resources
& Services Administration, Integrated
Requirements Model
Taylor,
DH, Goldsmith, LJ (2000, March 3). Invitational
Workshop of Measurement of the Measurement
of Medical Underservice. Presented at
Cecil G. Sheps Center for Health Services
Research at The University of North Carolina.
Woodwell
DA. National Ambulatory Medical Care Survey:
1997 Summary. Hyattsville, MD: National
Center for Health Statistics; May 20,
1999.
Woodwell
DA. National Ambulatory Medical Care Survey:
1998 Summary. Advance Data from Vital
and Health Statistics of the Centers for
Disease Control and Prevention. Hyattsville,
MD: National Center for Health Statistics;
July 15, 2000. 315.
Appendix
B.--A Proposal for a Method To Designate
Communities as Underserved
Technical
Report on the Derivation of Weights
This
Appendix is intended to provide more technical
details about the proposed methodology
and how it was developed. The principal
authors of this document are, alphabetically:
Laurie Goldsmith, Mark Holmes, Jan Ostermann,
and Tom Ricketts.
The General Approach
The
overall approach for deriving an empirical,
data driven system to identify underserved
areas and populations is to estimate the
effect of demographic factors on the population-to-practitioner
ratio, using a sample of counties as proxies
for a health care market. These effects
are then translated to a score which is
added to an adjusted ratio for a total
``need'' measure. Thus, the implementation
is similar to the current IPCS or MUA
method in that it creates a ``score''
or ``index'' of underservice, however,
the proposed system's score is based on
an adjusted ratio that is meant to represent
an ``effective'' or ``apparent'' population
and its primary health care needs.
There
are eight steps to the project, which
we divide for expository purposes into
two distinct ``Tasks''. Please note that
the specific steps described earlier in
the preamble to this rule may not match
up to the steps described below (for example,
``step 4'' in the preamble matches up
with ``steps 4-5'' and ``step 7'' in this
appendix).
Task
One: Calculate the Weights That Will Be
Used To Adjust Ratios (``Analysis'')
This
is the analytical portion of the project
in which we explore the degree to which
observable demographic characteristics
tend to be associated with population
to provider ratios. The specific steps
in this task include:
-
Create an age-sex adjusted population.
-
Calculate the base population-provider
ratio for regression to determine weights
for need variables.
-
Select study sample primary care service
area proxies.
-
Create factor scores to control for
interactions of variables.
-
Run regression models to create weights
for community variables.
Task
Two: Calculate the Scores Based on These
Factors (``Computation'')
This
is the portion of the process in which
scores are assigned to geographic areas
based on the weights calculated in Task
One.
-
6. Calculate the base population-practitioner
ratio for designation determination.
-
Calculate the scores for each area based
on the values for each variable for
each area and add to the ratio.
-
Step 8: Compare the ratio to a designation
threshold ratio.
We
describe each of these steps in detail
in the following sections.
Task 1: Analysis Steps
Step
1: Create an Age-Sex Adjusted Population
Using
estimated visit rates from individual-level
surveys, we weight the population to create
a ``base population.'' In this manner,
populations can be compared across areas.
The use of these data for this adjustment
are discussed in detail in reports and
background papers for the proposal including
the report that estimates the national
impact of the NPRM-2 proposal, ``National
Impact Analysis of a Proposed Method to
Designate Communities as Underserved''
dated September 7, 2001; the background
paper, ``Designating Underserved Populations.
A Proposal For An Integrated System Of
Identifying Communities With Multiple
Access Challenges,'' which is in draft
form; and the ``Executive Summary'' of
the ``Designating * * *'' paper, which
has been circulated in draft form to the
Bureau of Primary Health Care. The weights
are summarized in Table 1.
Table
1.--Visit Weights for Age-Sex Adjustment
|
0-4 |
5-17 |
18-44 |
45-64 |
65-74 |
75
and over |
Female |
4.046
|
2.256 |
5.007 |
5.480 |
6.710 |
8.160 |
Male |
5.164 |
2.499 |
2.867 |
4.410 |
6.052 |
8.056 |
These are the original weights using 1996
data. The weighted sum of these populations
is calculated as 4.046 * (# Females 0-4)
+ 2.256 * (# Females 5-17) +. . .+ 8.056
*( #Males 75 and over) and equals an age-sex
adjusted number of visits for a particular
population. Dividing this number of visits
by the mean visit rate (3.741) creates
a ``base population''. Areas with equal
base populations (and equal demographics)
have an equal need for primary care visits
per year. This adjustment allows us to
compare, say, the population-based visit
differentials between an area with a high
concentration of elderly (with a higher
need for visits) and an area with a high
population of middle aged individuals
(with a lower need for visits). The visit
rates were obtained from the Medical Expenditure
Panel Survey (1996) and were calculated
for non-poor, white, non- Hispanic individuals.
Employment status, which was included
in the MEPS survey and was a significant
correlate of use of service, was also
intercorrelated with the other variables
and was not included in the final visit
calculation.
Step
2: Calculate the Base Population-Provider
Ratio for Regression To Determine Weights
for Need Variables
With
the base population in hand, we calculate
the population- provider ratio to use
in the regression to determine factor
weights. When applying the formula for
the initial estimation of weights, the
number of practitioners is calculated
as:
Providers = physicians-(J1--physicians
+ MHSC--physicians + SLRP-- physicians)
+ .5* [midlevels-(NHSC--midlevels + SLRP--midlevels)]
+ .1* [residents-(NHSC--residents + SLRP--residents)]
where
all practitioners are measured in FTE
units and the practitioner total includes
NPs, PAs and CNMs weighted according to
agency guidelines. The number of practitioners
used in the regression to determine weights
for the need variables represents only
those practitioners that are considered
to be the ``private'' supply. That is,
the practitioners who would choose to
practice in the community without federal
support or incentives to practice in state-
or federally-operated facilities. As such,
government practitioners (whether federal
or state) are not counted here. Community
Health Center practitioners who are not
federal employees, however, are counted
since many of these are not ``placed''
into communities but are practitioners
already located in the area that are ``reclassified''
as CHC practitioners for later subtraction
from the practitioner supply at a later
step. For the estimation of the formula,
an area with no practitioners is dropped
from use in the regression analysis to
determine weights for the need variables
as a ratio is undefined (not calculable).
Step
3: Select Study Sample
A sample of counties and county equivalents
that serve as proxies for a health care
market are then selected for analysis
to derive formula weights. This step was
done to identify places which functioned
as primary care service areas and which
reported stable, reliable, usable data.
According to 2000 Census data, the median
county land area is 616 square miles,
corresponding to an approximate radius
of 14 miles. The tenth and ninetieth percentiles
are 288 and 1847 square miles, corresponding
to approximate radii of 10 and 24 miles
respectively. The approximate radius of
a county that is between the tenth and
ninetieth percentiles in land area reflects
a consensus of the extent of distances
traveled for primary care services. The
report describing PCSAs developed by Dartmouth
and VCU did not identify a median or mean
size rather they indicated that ``A land
area of 1,256 square miles or a radius
of 20 miles (assuming a circular shape)
was used as a crude indicator of geographically
large PCSAs.'' (Good,man, et al., 2003
p. 297). The population threshold we proposed
of 125,000 was chosen based on a perception
that cities and counties with populations
greater than this level were likely to
have many more specialists and tertiary
care services structure that would substitute
for primary care alone, thus skewing the
relationship between primary care practitioners
and population. No specific studies were
done to further support this assumption.
The PCSA project reported a median population
of 17,276 with multiple PCSAs exceeding
that threshold. Many U.S. counties meet
these general qualifications and the process
selected a range of counties that met
three criteria, including:
-
Populations below 125,000 (410 eliminated\*\)
-
Area below 900 square miles (856 eliminated)
-
Base population to provider ratio below
4250 (336 eliminated)
\*\Some counties had combinations of
both values.
The
third criterion effectively eliminated
very small counties and counties with
unusual distributions of health practitioners.
The goal was to determine the relationship
of area characteristics to practitioner
supply under ``normal'' conditions in
order to create stable estimates of those
relationships in order to apply them to
all appropriate populations and areas.
These
sample selection criteria were varied;
we tested over 2000 combinations in the
estimation process described in the next
step to test for robustness and sensitivity.
The variations included testing within
the following ranges: Population 80,000-150,000;
area 700- 1200 sq. miles; ratio 3000-4250.
Overall, the estimations derived from
the models were not substantially different
among the different samples. The study
sample contained 1643 counties. Counties
were chosen because they are well-defined
and are not endogenous to the current
system.
Using
currently designated areas would lead
to biased conclusions due to the fact
the subcounty areas are carefully and
deliberately constructed for purposes
of designation. Furthermore, dividing
a county into a subcounty-designated and
subcounty- undesignated would generate
an extremely large number of possible
observations in the analysis since the
county could be divided in many different
ways and into many subsets of county parts.
Finally, since some data are calculated
and available primarily on a county level,
measurement error is minimized by using
counties. Using other units of analysis
requires interpolating values for subcounty
and multicounty areas based on the constituent
geographic units.
Step 4: Create Factors
The
proposed designation process, in keeping
with the original MUA/MUP and HPSA approaches,
identified commonly available statistics
that correlated with a small number of
primary care practitioners-to-population
ratio. The selection of the measures was
based on reviews of the scientific literature
on access to care and preliminary work
on the development of an alternative measures
of underservice conducted by Donald H.
Taylor, Jr. (Taylor & Ricketts, 1994).
Candidate statistics were also suggested
by a working group of State Primary Care
Associations (PCAs) and Primary Care Offices
(PCOs) convened by the Division of Shortage
Designation (DSD) to gather state-level
input into the process of revising the
method. The staff and leadership of the
DSD also provided extensive input into
the design. More than 20 specific variables
were suggested during this process. Some
candidate variables could not be used,
despite being highly correlated with low
access and poor health outcomes, due to
lack of availability of data for small
areas (e.g. lack of health insurance).
Ultimately, the high intercorrelations
among candidate variables restricted the
calculation to 7-9 individual indicators
(the actual number to be tested depended
upon the specific combination of variables).
The final choice of variables and the
priority for inclusion in the analysis
was based on the degree to which the variables
best reflected underlying components of
access as qualitatively assessed by the
UNC-CH team, the PCA/PCO group, and staff
of Bureau of Primary Health Care (BPHC).
The final measures consist of demographic,
economic and health status indicators
(presented in Table 2).
Demographic:
Population characteristics, especially
racial and ethnic characteristics, have
been consistently shown to affect access
to primary care (Berk, Bernstein, &
Taylor, 1983; Berk, Schur, & Cantor,
1995; Schur & Franco, 1999). Measures
of the percent of population that is non-White
and percent of population that is Hispanic
were used to further adjust the ratio.
The inclusion of the percentage of population
older than 65 years was also included
because communities with higher percentages
of elderly have different community characteristics
not captured in the initial population
adjustment. This is likely due to the
relative lack of younger people to provide
supportive care and the fact that communities
with declining economies, especially rural
communities, have older age profiles that
combine with other factors to create overall
lower access.
Economic:
Income and employment are very strong
indicators of ability to access primary
health care and to afford health insurance
(Mansfield, Wilson, Kobrinski, & Mitchell,
1999; Prevention, 2000; Robert, 1999).
The unemployment rate and the percent
of population below 200 percent of the
poverty level were used to further adjust
the ratio.
Health
Status: Certain populations and communities
have higher than average need for health
care services based primarily on their
health status independent of other factors.
Therefore, health status measures used
to adjust the ratio include the standardized
mortality ratio (General Accounting Office,
1996) and either the infant mortality
rate or the low birthweight rate (Matteson,
Burr, & Marshall, 1998; O'Campo, Xue,
Wang, & Caughy, 1997). These special
epidemiological conditions that increase
need are not fully represented in the
age-gender adjustment.
Table
2.--Variables Used in Creating Proposed
Method
Demographic |
Economic |
Health
status |
Percent
Non-white "NONWHITE" |
Percent
population<200% FPL "POVERTY" |
Actual/expecteddeath
rate (adj) "SMR" |
Percent
Hispanic "HISPANIC" |
Unemployment
rate "UNEMPLOYMENT" |
Low
birth weight rate "LBW" |
Percent
population >65 years "ELDERLY" |
|
Infant
mortality rate "IMR" |
Population
density "DENSITY" |
|
These measures are highly intercorrelated.
Table 3 below shows the Pearson-product
moment correlations. The first column
shows that poverty and unemployment are
positively correlated (+0.64), meaning,
in counties with high proportions of the
population living in poverty there is
usually a higher unemployment rate. Poverty
and density are negatively correlated
(-0.55), meaning that where there is higher
density there are lower percentages of
the population living in poverty. The
correlation matrix is population-weighted.
Table
3.--Percentile Correlation Matrix
|
Poverty |
Unemp |
Density |
Elderly |
Hispanic |
NonWhite |
SMR |
IMR |
LBW |
Poverty |
1.00 |
|
|
|
|
|
|
|
|
Unemp |
0.64 |
1.00 |
|
|
|
|
|
|
|
Density |
-0.55 |
-0.21 |
1.00 |
|
|
|
|
|
|
Elderly |
0.36 |
0.28 |
-0.47 |
1.00 |
|
|
|
|
|
Hispanic |
-0.32 |
-0.23 |
0.22 |
-0.25 |
1.00 |
|
|
|
|
NonWhite |
0.10 |
0.12 |
0.22 |
-0.29 |
0.25 |
1.00 |
|
|
|
SMR |
0.57 |
0.55 |
-0.04 |
0.04 |
-0.26 |
0.42 |
1.00 |
|
|
IMR |
0.33 |
0.25 |
-0.10 |
0.08 |
-0.08 |
0.41 |
0.43 |
1.00 |
|
LBW |
0.40 |
0.37 |
0.05 |
-0.05 |
-0.14 |
0.63 |
0.69 |
0.54 |
1.00 |
Variable Definitions Variables were assigned
a percentile based on the distribution
of values of all U.S. counties to all
U.S. counties. This allows for continuity
in the use of the proposed scores if variables
are defined differently in the future
(e.g. the poverty measure is changed to
100 percent below poverty instead of 200
percent). It also allows policymakers
a choice of how often (or whether) to
update the percentile values without having
to change the weights. If poverty conditions
improve markedly across the nation, scores
will tend to fall unless the percentile
tables are updated. For all variables
except DENSITY the theoretically worst
value corresponded to the 99th percentile.
At first glance, it might appear that
places with very low population density
would be worse off with regard to primary
care access and health service needs.
Places with extremely high density may
also have problems caused by overcrowding
and the population density may reflect
problems that are commonly encountered
in inner-cities. For this variable there
is no apparent ``right'' direction for
the weights. We arbitrarily specified
the functional form such that lower population
density corresponds to a worse off (higher
percentile score) community. Accounting
for the negative effects of very high
density is described below. We combined
low birth weight and infant mortality
into one measure (called HEALTH), defined
as the maximum percentile of low birth
weight and the infant mortality rate for
a given area. This is due to a medium
level of correlation between the two and
the fact that not all areas report both
measures. Finally, the use of the infant
mortality rate in measures of underservice
is required by existing law and there
is precedent for using these measures
as rough substitutes. The original Index
of Primary Care Shortage described in
NPRM-1 of September 1, 1998 used them
interchangeably. We defined nonwhite as
the maximum of zero or the percentile
minus 40, so that only the top (most nonwhite)
60 percent of areas get ``points'' for
the nonwhite variable. In other words,
all areas less than the 40th percentile
are treated equally. There were two main
reasons for this. The first is that many
of the areas have low nonwhite percentages
(the 40th percentile is about 2.6 percent
nonwhite). By not making this adjustment,
we are differentiating areas that have
little difference in the underlying measure.
The second reason is that without this
adjustment, the scores were not stable;
small differences in the definition of
this variable resulted in wide swings
in the magnitude of the nonwhite variable
when testing multiple randomly chosen
samples. We experimented with a multitude
of cutoff points (0-50 in 10 unit increments).
In the final specification, small changes
in the definition of NONWHITE had little
substantive effect. With the corresponding
percentiles in hand, the associated scores
were transformed to a logarithmic scale
so that the highest derivative corresponded
to the theoretically worst end of the
scale. For example, the independent variable
corresponding to poverty (lnpcpov) was
defined as Inpcpov = In(100 - pcpov) so
that the fastest acceleration in the poverty
score occurs at high levels of poverty
rather than at low levels. In other words,
we specified the model to allow a greater
score to accrue to areas ``moving'' from
the 95th percentile to the 96th percentile
than to areas ``moving'' from the 5th
percentile to the 6th percentile. All
variables were assumed to have this shape
(so that the theoretically worst values
have the largest derivative). A more detailed
description of the regression approach
is included at the end of this appendix
(Notes to Appendix B). Basing the Scores
on the Population-Practitioner Ratio Although
this approach specifies the shape of the
function as logarithmic and this constrains
the rate of change in the scoring as variables
differ from one percentile to another,
it does not constrain the sign nor the
absolute magnitude of the parameters that
create the weights. That is, the regression
models are indifferent to whether a parameter
comes out positive or negative or how
large or small it is when the statistical
model is run to create the weights. The
magnitude is the most important parameter
of the three and will be used for estimating
the scores but the potential effects of
the size and sign of the weights must
fit into our logic of additivity of factors.
The magnitude of the weights are expressed
as a synthetic unit which cannot be compared
to any other unit--the weight for UNEMPLOYMENT,
for example, when transformed to the log-
normal form and constrained to a positive
value in the course of the estimation,
is not a ``percent of workforce not working
but seeking work'' but an abstract number
that describes the relative contribution
of that factor to a total access score
at that percentile of unemployment given
all the value of all the other variables
and the population structure. The final
model creates an estimate for the weight
for each set of variables using this abstract
number but that number has to be brought
back into a logical relationship with
the key unit of access we are using--the
population portion of a practitioner-to-population
ratio. The final combined sum of these
abstract values has to be adjusted back
to an interpretable relationship with
the practitioner-population ratio. This
requires that some form of restraint on
the parameter (weight) values be imposed
or the solution set may produce a ``best
result'' that causes one or two variables
to dominate the weighting and others to
vary from positive indicators of barriers
to access to negative in various combinations.
In the application of the process this
means that the parameter is used along
with the intercept of the regression models
to generate the specific weight for each
variable. This was done to normalize the
scores so that the minimum score was zero.
This is done by adding a fixed number
to the log result. In an unconstrained
solution of the regression models this
is, indeed, the case. There are possible
solution sets that include mixes of positive
and negative values; in statistical parlance
the functions are ``two-sided.'' The logic
of the scoring system anticipated this
when we stipulated that factors which
restrain use of services by creating barriers
to access, also create subsequent higher
levels of need likely to be met by higher
levels of use, use of services that was
preventable but now necessary. In the
real community, both things are happening,
an access program is promoting appropriate
utilization by overcoming access barriers
and all practitioners are involved in
caring for people who are using the system
because emergent conditions were not treated
appropriately. The amount of the increase
in use brought about by delayed care must
be added into the reduction in use to
produce a sum of the access ``problem''
in a community. To account for the ``mirror''
effects of these variables, the final
value, the sum of the weights are doubled,
to produce a population estimate that
is scaled to represent the overall effect
on the population need. Factor Analysis
Because many of these measures are highly
correlated, we perform factor analysis
in order to compute factors for the independent
variables defined above. Essentially,
factor analysis provides a method to translate
highly correlated variables into orthogonal
measures to obtain more precise estimates
and minimize the impact of multicollinearity
in the variables of interest. Often used
as an end product statistical tool, we
use it here to improve the precision of
the estimates.1
Our procedure here was to decompose the
independent variables into factors and
then create scores based on these factors.
The factor scores follow in Table 4. The
largest weight in the row is the one on
which factor the variable weighs most
heavily (except for SMR, which has two
maximum weights of almost equal magnitude).
Four factors might be interpreted as structuring
the data: I. High health risk, nonwhite
II. Geo-demographics III. Economic conditions
IV. Hispanic
Table
4.--Factor Scores
|
Factor |
Variable |
1 |
2 |
3 |
4 |
Poverty |
-0.005 |
0.208 |
-0.423 |
0.044 |
Unemp |
-0.044 |
-0.074 |
-0.338 |
0.009 |
Elderly |
-0.039 |
0.355 |
0.021 |
-0.226 |
Density |
0.042 |
0.440 |
0.051 |
0.189 |
Hispanic |
0.018 |
-0.002 |
0.046 |
0.291 |
NonWhite |
0.408 |
-0.012 |
0.136 |
0.099 |
SMR |
0.206 |
-0.107 |
-0.226 |
-0.124 |
Health |
0.353 |
0.066 |
0.100 |
-0.046 |
Step 5: Run Regressions
We
regress the base population-to-private
supply practitioner ratio on the scores
obtained from the factor analysis (Ratio
= Factor I + Factor II . . . + error).
By combining the scores from the factor
analysis with the estimated coefficients
from the regression, we obtain the effect
of our underlying variables on the ratio.
As an example, the factor analysis might
yield a result such as:
Variable |
factor1 |
factor2 |
Poverty |
.2 |
.4 |
Unemployment |
.3 |
-.1 |
Which we could translate into a
matrix
Suppose regressing the ratio onto these
two scores yields estimates of
Variable  beta
factor1 Â Â Â Â 1
factor2Â Â Â -.4
which would translate to a vector
By
multiplying these two matrices, we can
obtain the total effect of one variable
on the ratio:
(1)
Thus,
(in this simple example) the overall effect
of Poverty on the ratio is calculated
as .04 and the overall effect of Unemployment
is .34. We use the rightmost matrix for
computing the scores (see the next section)
except for one correction (see below).
Weights/Heteroskedasticity Because the
dependent variable is a ratio with population
in the denominator, we are concerned about
possible heteroskedasticity in the dependent
variable. This is the property that the
sampling variability in the dependent
variable is not constant across the sample.
Specifically, we expect the ratio to be
estimated more precisely as the population
grows. See Figure 1 below for support
of this hypothesis--the ratio tends to
become less variable as the population
increases (population category 1 is the
lowest population category and population
category 10 is the highest population
category). (The upper and lower bands
are the values for the 25th and 75th percentiles).
The consequence of this violation is that
the standard errors from the regression
are biased and a more efficient estimator
may exist. As such, we weight the regressions
by the total population of the county.
Figure
1: Heteroskedasticity in Ratio
There
is a question of whether we are even dealing
with a ``sample'' in the conventional
statistical sense. If our analysis is
composed of the population of interest,
then classical statistical inference is
a bit artificial; there is no uncertainty
if we have data on all the units of interest.
We argue that this is a sample in the
conventional sense, for reasons including
but not limited to the following:
a.
Measurement error occurs more often than
we expect. County population values are
estimated in 1997 and the accuracy of
provider supply is not 100 percent. As
the nation observed in the presidential
vote count in Florida, even simple computations
are not immune from error. Thus, because
the data used here are affected by measurement
error, we have a sample drawn from the
possible data for the population of counties.
b. The units used here are a sample of
a much bigger population of interest.
Not only are we interested in counties
other than those included in the analysis
due to sample criteria, ultimately we
are using counties as approximations for
``health care markets'' or rational primary
care service areas, whether they follow
the boundaries of a county or not. These
methods are designed to be applied to
data for future years and the construction
of the areas may vary from one based on
geography to ZIP code boundaries. Other
considerations, such as errors in model
specification or the discrete ``lumpiness''
associated with using a dependent variable
like this one provide support for the
use of factor scores.
Sampling
Error in the Regression
We
wish to reduce the error in predicting
the designation of communities. As such,
we seek to incorporate the precision with
which the regression parameters are estimated
into the scoring procedure. As an example,
it is entirely possible, given two factors,
to have one coefficient be estimated as
100 with a standard error of 1 and the
other coefficient to be estimated as 400
with a standard error of 1000. If asked
which factor is more important, most people
would probably admit that although the
400 is a larger point estimate, the 100
is probably more important given its statistical
significance. As such, the regression
estimates are adjusted for the statistical
significance by the algorithm defined
below.2
2
An alternative treatment would be to discard
any statistically insignificant estimates.
We have strong conceptual biases against
employing such stepwise procedures.
1. Obtain the variance-covariance matrix
V of the parameter estimates from the
regression.
2. Compute the weighting matrix W defined
as the inverse of the Cholesky transformation
of a zero matrix except for the diagonal,
which consists of the diagonal of V. (This
is identical to a zero matrix with diagonal
elements equal to the reciprocal of the
standard errors of the parameter estimates).
3. Transform the vector of parameter estimates
(omitting the constant) b by b* = b *W*
number of factors/trace(W). The trace()
portion of the expression ensures the
weights sum to the number of factors.
4. Compute F = S b* as above.
As an example, return to the hypothetical
results for poverty and unemployment above.
Suppose the (estimated) variance-covariance
matrix from the regression was
The
estimated scores in equation (2) differ
from those obtained in equation (1) (page
17) due to the weight. Because the regression
estimate for the first factor is estimated
with roughly three times the precision
as the estimate for the second factor
(5/1.42 [ap] 3), the estimate for the
first factor (1) is weighted more heavily
than the estimate for the second factor
(-.4). In this case, this has the end
result of increasing the scores from .04
to .24 for poverty and .34 to .4844 for
unemployment. Vector F is the scoring
vector used in the next step.
Although
the process for obtaining matrix F is
complex and multi-stage the process was
completed for all possible values of the
variables. Having done this, data describing
a service area can be translated readily
into percentile scores using a look-up
table, a simple spreadsheet, or a web-based
application. This parallels the existing
MUA scoring process. Applicants do not
need to perform Cholesky transformations
or any other mathematical calculations.
Fundamentally, the ``weighting'' step
rescales the regression parameters, placing
more weight on more precisely estimated
parameters. We are not aware of other
published research performing this reweighting,
but there are at least two reasons this
approach has intuitive appeal. The reweighted
models performed better empirically in
the sense of minimizing disruption to
current designation status. We considered
dropping statistically insignificant principal
components from the regression and not
weighting. Although this would be a more
traditional use of principal, components
regression (with both its advantages and
disadvantages), in addition to subpar
performance, the omission of insignificant
components drops factors that theory suggests
should contribute to access barriers.
At its core, this unconventional approach
represented the best tradeoff we could
devise between health care access theory,
statistical theory, and empirical performance.
Task
2: Computation
Step
6: Calculate the Base Population-Provider
Ratio for Designation Determination Using
the same age-sex adjusted population from
Step 1, we calculate the population-practitioner
ratio. All primary care practitioner FTEs
in the area are counted to initially determine
designation, this is termed the ``Tier
1 designation ratio'' and follows the
FTE allocation of Providers = active non-federal,
primary care physicians + 0.5 * primary
care NPs, PAs, and CNMs + 0.1 * medical
residents in training For applicants not
meeting the threshold criterion, the FTEs
for practitioners who are supported by
safety net programs ( e.g., NHSC providers,
J-1 visa practitioners, CHC providers)
are subtracted from the supply total and
the applicant ratio is compared to the
threshold. That step is termed ``Tier
2 designations.'' The formula for that
calculation follows the same logic as
in Step 2, above: Providers = physicians--(J1--physicians
+ NHSC--physicians + SLRP-- physicians)
+ .5* [midlevels--(NHSC--midlevels + SLRP--midlevels)]
+ .1* [residents--(NHSC--residents + SLRP--residents)]
Step 7: Calculate Scores With row vector
F in hand, we then turn to computing scores
for geographical units. We compute the
ratio of population to providers using
the algorithm outlined above. We use the
percentile scores as computed above for
the counties. See the document ``Completing
the NPRM2 Application'' for these percentiles.
We then calculate the score for the communities
and add this score, upweighted by 2 to
account for the 2-sided properties of
the regression estimates so the total
score for the community equals ADJUSTED
RATIO (or ``INDEX'') = RATIO + 2 * SCORE
This is the total score for the community
and determines its designation status.
The applicants never see the regression
multiplier; it is embedded in the tables.
Because the use of the multiplier for
the score is applied at this stage of
the process, it may be seen as an ad-hoc
adjustment. The statistical logic for
this has been described above, the policy
logic for applying this adjustment is
supported by these points:
1.
The multiplier is used to account for
the fact that the existing measures and
processes including: the HPSA formula,
the IPCS/MUA formulae, and the practical
application of the CHC/RHC clinic placement
process--all recognize the importance
of the basic population-to-practitioner
ratio in determining need. Indeed, some
simple models run on the study sample
provide evidence that the multiplier should
be closer to 10 rather than 2 if the goal
were to include every area containing
a CHC under the proposed designation process
(this assumes that the presence of a CHC
is an indicator of need in and of itself
as opposed to the result of the calculation
of pre-existing unmet need). The IPCS
mechanism provided for a maximum score
from the population-practitioner ratio
of 35 points. The maximum score available
from other factors (poverty 35 points,
IMR/ LBW 5 points, minority 5 points,
Hispanic 5 points, LI 5 points, density
10 points = 65 points) are, collectively,
almost twice that in terms of potential
contribution. Thus, the weighted contribution
of the factors besides the ratio is roughly
twice that of the ratio itself. Multiplying
the ratio denominator by two intensifies
the relative effect of the underlying,
basic population to practitioner ratio
in the designation process providing continuity
with prior policy.
2. The multiplier functions as a scale/weighting
factor. The score has a much smaller variance
than the ratio. This is not just an annoyance--it
is used to generate a prediction, and
thus will have smaller variance than the
dependent variable. The dependent variable
and the score used here have some sort
of meaning, a person per provider, although
the various adjustments make this unit
of measurement not as meaningful as we
might think. One alternative we considered
is rescaling the ratio and the score into
z-scores and using these standardized
measures rather than the unscaled measures.
This rescaling would involve multiplying
the score by a larger factor than the
ratio.
3.
The multiplier helps control for the (observed)
low ratios in, (e.g., metro) areas with
high scores. The following example illustrates
this:
Table
2.--Example Score and Ratios
County
of HPSA |
State |
Ratio |
Score |
IPCS
|
IMR
|
LBW
|
Poverty
|
Bronx |
NY |
1357.2 |
1043.5 |
54 |
10.1 |
10.1 |
77.8 |
Coconino |
AZ |
1266.8 |
1005.6 |
56 |
8.1 |
7.2 |
65.1 |
Kings |
NY |
1634.7 |
897.8 |
52 |
10.3 |
9.2 |
59.2 |
East
Baton Rouge |
LA |
1660.5 |
874 |
46 |
11.3 |
10.2 |
69 |
St.
Lucie |
FL |
1138.5 |
873 |
44 |
10 |
7.3 |
67 |
Philadelphia |
PA |
1055.9 |
861.2 |
47 |
13.3 |
11.4 |
61.1 |
Mahoning |
OH |
1505.3 |
839.3 |
44 |
10.7 |
8.9 |
67.5 |
The (unmultiplied) maximum score is about
1300. The areas listed above are all in
the worst 10 percent of scores. Note that
these areas would not qualify without
the ``score x 2'' multiplier rule (see
below). Perhaps the ratio is a misleading
measure in some circumstances.
4. The multiplier fills a statistical
role. The score is (likely) more stable
across years; e.g., if one physician moves
out of a rural area, the ratio varies
dramatically. The score is not going to
change drastically across years. Thus,
it should be given more weight.
5. The multiplier creates a standard which
designates roughly the same number of
people as the IPCS and the current HPSA
designations.
6.
It performs better than without the doubling.
Although this particular argument has
little theoretical basis, it is still
compelling.
Why is a portion of the density score
function negative? The astute reader will
note that the constant from the regression
was dropped and never used. The reason
for this is that the constant has no clear
meaning in this context. We decided to
norm the scores so that the minimum score--that
is, the best area in the country--was
zero. Thus, although in theory an area
could receive a negative score if it had
very favorable demographics and had a
high population density, in practice no
area had a negative score (by definition).
Step
8: Compare to Threshold
Areas
are designated if and only if the ``adjusted
ratio'' (or ratio+score) is greater than
3000. This threshold was adopted for its
reflection of the clear need for a single
full-time equivalent primary care physicians,
its consistency with prior threshold values,
and its familiarity to stakeholders.
Areas
With No Practitioners
The problem of how to treat areas with
zero providers emerged early in the process
of ranking areas as medically underserved.Â
There is an informative treatment of the
phenomenon in Black and Chui (1981).*Â
For areas with zero providers, we have
not made any firm recommendations and
have treated them in one of three ways
for various parts of the analysis
- Every area with zero providers automatically
gets an adjusted ratio of 3000 (which
guarantees them designation), to which
a score for community need indicators
are added. This results in all areas
having a NPRM2 score, including areas
with zero providers. This method was
used in early tabulations and compilations.
- Automatically designate areas with
zero providers without assigning an
adjusted ratio or a score for community
need indicators. Therefore, areas with
zero providers will not have a NPRM2
total score. This has occurred when
calculations and tabulations of the
database using the NPRM2 scoring system
was applied. The places with no score
were dropped. This method was used
in the final impact analysis.
- Assigning an arbitrarily small FTE
to the area, such as 0.1 to create a
score that is primarily dependent upon
the denominator population. This was
used only in selected tests of the scoring
system as an alternative.
Notes
to Appendix B: Regression approach for
assignment of weights to correlates of
``shortage''
The
basic method for assigning weights to
individual variables involved the estimation
of a county-level linear regression model
with the adjusted population-to-physician
ratio as the left-hand side variable,
and the variables described in step 4
as right-hand side variables. Coefficients
on the right-hand side variables can be
interpreted directly as average differences
in the population-to- physician ratio
for counties with specified characteristics
relative to counties without those characteristics.
To
reduce the effects of extreme outliers
(e.g., population density in New York
City, or per capita income in Silicon
Valley), all variables were converted
into percentages. To allow for non- linear
relationships between each variable and
the ratio, the variables were further
converted from a linear variable, ranging
from 1 to 100, into twenty five-percentile
categorical variables, i.e., one each
for 1-5th percentile, 6-10th percentile,
* * * 96th- 100th percentile. When all
but one of these variables are entered
on the right-hand side of a regression
with the population-to-physician ratio
as the dependent variable, the coefficients
on each variable represent the average
difference in the adjusted population-to-
physician ratio relative to the omitted
reference category. In most cases, the
omitted reference category is the 1-5th
percentile, i.e., the five percent of
counties with the lowest values for a
particular variable.
Entering
highly collinear variables, such as income
and poverty, into a single regression
model usually results in one coefficient
being positive, and the other being negative.
In order to develop a ``user-friendly''
scoring system in which all weights are
positive, variables were added sequentially
to the regression model, with the effects
of previously entered variables constrained
to their estimated effects. As a result,
coefficients on all variable other than
the first represent the ``marginal differences''
in the ratio, after controlling for all
previously included variables.
A
decision was made to use a population-to-physician
ratio of 3000:1 as a cutoff criterion
for designation. The following analysis
was restricted to counties with adjusted
population-to-physician ratios between
500:1 and 3000:1, for which the dependent
variables was not missing (N=2,493).
Income was the single most important correlate
of the ratio. It was entered first, and
estimates were obtained for each of 19
categories; counties in the 95-100th percentile
were the excluded category. Each of the
estimated coefficients represents the
average difference in the ratio for counties
in the respective percentile range relative
to the omitted group of counties with
the highest income. Coefficients were
graphed and examined visually, and differences
between the coefficients for ``neighboring''
categories were evaluated for statistical
significance. Categories with no statistically
significant differences were combined
into single variables. As a result of
this process, three categories (plus reference
category) remained, one each for the 1-75th,
76-85th, and 86-95th percentiles. The
regression was run again, suggesting that
counties in these categories had higher
ratios by 628, 344, and 216 ``units'',
respectively. (These units are the average
differences in the population-to-physician
ratio).
Constraining
the coefficients on these variables to
these values, 19 percentile ranges for
the next-highest correlate of the ratio,
population density, were added to the
analysis. Visual inspection pointed to
clear non-linearities in the relationship.
There appeared to be a statistically significant
difference between counties in the 95-100th
percentile relative to all other counties.
Furthermore, the effect was increasing
up to the 35th percentile of counties,
and then decreased between the 36th and
95th percentiles. Note that these relationships
describe the relationship between population
density and the population-to-physician
ratio after controlling for the effects
of income. Consistent with the observed
relationship, three variables were defined,
a categorical variable for the 1-95th
percentile range, and two splines for
the 1-35th and 36-95th percentiles, respectively.
These
three variables describing population
density were entered into the model together
with the income variables, and the estimated
coefficients were used to analyze the
marginal effect of unemployment according
to the same method. Relative to the omitted
reference group of counties in the 1-5th
percentile, counties in the 6-20th and
21-100th percentile ranges had significantly
higher population-to-physician ratios,
after controlling for income and population
density. Consequently, two dummy variables
for counties in these categories were
entered into the model. The process was
repeated for percent of the population
under 200% FPL, which suggested that--after
controlling for income, population density,
and unemployment--the ratio was lowest
for counties with a percentage of the
population below 200% poverty around the
20th percentile of all counties. Below
this threshold, the average ratio was
higher by about 110 ``units'', above that,
the ratio gradually increased by about
2.5 ``units'' per percentile increment.
Table
2 shows the results of the final regression
model containing the four variables described
above. After controlling for these variables,
none of the remaining variables was significantly
associated with shortage. This finding
is consistent with other studies of the
effects of community characteristics on
access to health care, in that the economic/barrier
variables have been shown to have much
greater impact than other characteristics.
However, legislation requires the use
of selected morbidity and mortality measures
such as infant mortality and, even if
marginal in their net effect, these measures
are tied closely to the logic of need
for primary care and access to primary
care.
To comply with this requirement, the analysis
was repeated for actual/expected deaths,
the maximum of low birth weight/infant
mortality rate, and the percentage of
the population over the age of 65. Table
3 shows the results of the final regression
model and the specification of each variable.
The coefficient estimates in Tables 2
and 3 were used to create a single table
containing the weights associated with
each variable, for each percentile increment,
usually rounding to the nearest increment
of 5.
Table
2.-Coefficient Estimates for Economic/Barrier
Correlates of Shortage
Correlate
of shortage |
Cutoffs
(percentiles) |
Specification |
Coefficient |
SE |
t |
Income |
0–74 |
Dummy
Variable |
355.9 |
59.3 |
5.997 |
|
75–84 |
Dummy
Variable |
186.0 |
59.6 |
3.121 |
|
85–84 |
Dummy
Variable |
69.7 |
53.6 |
1.301 |
Population
Density |
0–95 |
Dummy
Variable |
318.6 |
51.4 |
6.197 |
|
0–35 |
Spline |
4.23 |
0.95 |
4.432
|
|
35–95 |
Spline |
-3.73 |
0.84 |
-4.467
|
Unemployment |
5–19 |
Dummy
Variable |
167.8 |
52.0 |
3.228
|
|
20–99 |
Dummy
Variable |
245.4 |
48.0 |
5.110 |
Below
200% FPL |
0–14 |
Dummy
Variable |
109.0 |
38.8 |
2.807
|
|
15–99 |
Spline |
2.36 |
0.54 |
4.406
|
Constant |
|
|
732.0 |
78.7 |
9.297 |
Table
3.--Coefficient Estimates for Health/Demographic
Correlates of Shortage
Correlate
of shortage |
Cutoffs
(percentiles) |
Specification |
Coefficient |
SE |
t |
Actual/Expected
Deaths |
6–15 |
Dummy
Variable |
66.4 |
64.0 |
1.038 |
|
16–55 |
Dummy
Variable |
121.6 |
57.2 |
2
.124 |
|
56–75 |
Dummy Variable |
211.2 |
59.4 |
3.554 |
|
76–100 |
Dummy
Variable |
278.5 |
60.2 |
4.625 |
Infant
Morality |
81–100 |
Dummy
Variable |
65.73 |
27.41 |
2.398 |
Percent
65+ |
1–100 |
Continuous |
1.93 |
0.37 |
5.161
|
Constant |
|
|
1364.4 |
57.2 |
23.872 |
List of Subjects
42
CFR Part 5
Health
care, Health facilities, Health professions,
Health statistics, Health status indicators,
Medical care, Medical facility, Dental
health, Mental health programs, Physicians,
Population census, Poverty, Primary care,
Shortages, Underserved, Uninsured.
42
CFR Part 51c Grant programs--Health, Health
care, Health facilities, Reporting and
recordkeeping requirements.
For the reasons set out in the preamble
the Department of Health and Human Services
proposes to amend parts 5 and 51c of title
42 of the Code of Federal Register
as follows:
PART 5--DESIGNATION OF MEDICALLY UNDERSERVED
POPULATIONS AND HEALTH PROFESSIONAL SHORTAGE
AREAS
1. The heading for part 5 is revised as
set forth above.
2.
The authority citation for part 5 is revised
to read as follows: Authority: 42 U.S.C.
254b, 254e.
3.
The existing text consisting of Sec. 5.1
through 5.4 is designated as subpart A
and revised to read as follows:
Subpart
A--General Procedures for Designation
of Medically Underserved Populations (MUPs)
and Health Professional Shortage Areas
(HPSAs)
Sec.
5.1 Purpose.
5.2 Definitions.
5.3 Procedures for designation and withdrawal
of designation.
5.4 Notice and publication of designation
and withdrawals.
5.5 Transition provisions.
5.6 Provisions related to Automatic HPSA
designation of certain Federally Qualified
Health Centers (FQHC) and Rural Health
Clinics (RHC)
Subpart A--General Procedures for Designation
of Medically Underserved Populations (MUPs)
and Health Professional Shortage Areas
(HPSAs)
Sec.
5.1 Purpose.
This
part establishes criteria and procedures
for the designation and withdrawal of
designations of medically underserved
populations pursuant to section 330(b)(3)
of the Public Health Service Act and of
health professional shortage areas pursuant
to section 332 of the Act.
Sec.
5.2 Definitions.
As used in this part:
(a)
Act means the Public Health Service Act,
as amended (42 U.S.C. 201 et seq.).
(b)
Department means the Department of Health
and Human Services.
(c)
Frontier Area means those areas identified
by the Secretary (through the Frontier
Work Group of the Office for the Advancement
of Telehealth) as frontier areas, or,
until an official list of frontier areas
is issued, those U.S. counties or county-equivalent
units with a population density less than
or equal to 6 persons per square mile.
(d)
FTE means full-time equivalent, and shall
be computed using such guidance as the
Secretary may provide.
(e)
Governor means the Governor or other chief
executive officer of a State.
(f)
Health professional shortage area (or
HPSA) means any of the following which
the Secretary determines in accordance
with this part has a shortage of health
professionals:
(1)
A rational, geographic service area;
(2)
A population group; or
(3)
A public or nonprofit private medical
facility or other public facility that
provides primary medical, dental or mental
health services.
(g)
Inner portions of urban areas means core
areas of urbanized central places areas
as defined by HRSA, based on data from
the Bureau of the Census.
(h)
Population Center means the census area
(tract, division, town, etc.) with the
largest population within a proposed rational
service area.
(i)
Medical facility (or other public facility
that provides primary medical, dental
or mental health services) includes:
(1)
A health center, as defined in Section
330(a) of the Public Health Service Act,
means an entity that serves a population
that is medically underserved, or a special
medically underserved population comprised
of migratory and seasonal agricultural
workers, the homeless, and residents of
public housing, by providing, either through
the staff and supporting resources of
the center or through contracts or cooperative
arrangements, required primary health
services and, as may be appropriate for
particular centers, additional health
services necessary for the adequate support
of the primary health services required
for all residents of the area served by
the center (including a community health
center, migrant health center, health
center for the homeless, or health center
for residents of public housing);
(2)
Any Federally qualified health center
(FQHC), as defined in Section 1861(aa)(4)
of the Social Security Act term ``Federally
qualified health center'' means an entity
which is receiving a grant under section
330 (other than subsection (h)) of the
Public Health Service Act, or is receiving
funding from such a grant under a contract
with the recipient of such a grant, and
meets the requirements to receive a grant
under section 330 (other than subsection
(h)) of such Act; based on the recommendation
of the Health Resources and Services Administration
within the Public Health Service, is determined
by the Secretary to meet the requirements
for receiving such a grant; was treated
by the Secretary, for purposes of part
B, as a comprehensive Federally funded
health center as of January 1, 1990; or
is an outpatient health program or facility
operated by a tribe or tribal organization
under the Indian Self-Determination Act
or by an urban Indian organization receiving
funds under Title V of the Indian Health
Care Improvement Act.
(3)
A rural health clinic [RHC] as defined
in Section 1861(aa)(2) of the Social Security
Act is primarily engaged in furnishing
to outpatients services which is located
in an area that is not an urbanized area
(as defined by the Bureau of the Census)
and in which there are insufficient numbers
of needed health care practitioners which
is located in an area that is not an urbanized
area (as defined by the Bureau of the
Census) and in which there are insufficient
numbers of needed health care practitioners;
a public health center or other medical,
dental or mental health facility operated
by a city or county or State health department;
or a community mental health center (see
Section 520 of the Act);
(4)
An ambulatory or outpatient clinic of
a hospital;
(5)
An Indian Health Service facility, or
a health program or facility operated
under the Indian Self-Determination Act
by a tribe or tribal organization; or
an Urban Indian Health Program; or
(6) A facility for delivery of health
services to inmates in a U.S. penal or
correctional institution (under section
323 of the Act), or a State correctional
institution; or
(7)
A State mental hospital.
(j)
Medically underserved population (or ``MUP'')
means:
(1)
The population of a geographic area designated
by the Secretary in accordance with this
part as having a shortage of personal
health services (also called a medically
underserved area or MUA); or
(2)
A population group designated by the Secretary
in accordance with this part as having
a shortage of such services.
(k) Metropolitan statistical area means
an area that has been designated by the
Office of Management and Budget as a metropolitan
statistical area. All other areas are
``micropolitan'' or ``non- metropolitan''
areas.
(l)
Poverty level means the current poverty
threshold as defined by the Bureau of
the Census, which uses a set of money
income thresholds that vary by family
size and composition to determine who
is in poverty. If a family's total income
is less than the family's threshold, then
that family and every individual in it
is considered in poverty. The thresholds
are updated annually.
(m) Primary care clinician means a physician,
nurse practitioner, physician assistant,
or certified nurse midwife who practices
in a primary care specialty as defined
in Sec. 5.104(e)(2) of this part, provides
direct patient care, and practices in
a primary care setting, as defined in
paragraph (n) of this section.
(n)
Primary care setting means a setting where
integrated, accessible health care services
are provided by clinicians who are accountable
for addressing a large majority of personal
health care needs, developing a sustained
partnership with patients, practicing
in the context of family and community,
and providing continuity and integration
of health care. It includes but is not
limited to health centers as defined in
Sec. 5.2(i)(2) of this part, health maintenance
organizations, generalist physicians'
offices, and ambulatory care facilities
operated by hospitals including outpatient
facilities that are separate but a part
of inpatient facilities; it excludes inpatient
facilities, non-primary care physician
specialist's offices, and facilities for
long term care.
(o)
Secretary means the Secretary of Health
and Human Services, or any officer or
employee of the Department to whom the
Secretarial authority involved has been
delegated.
(p)
State includes the 50 States, the District
of Columbia, the Commonwealth of Puerto
Rico, American Samoa, Guam, the Northern
Mariana Islands, the U.S. Virgin Islands,
the Federated States of Micronesia, the
Marshall Islands, and the Republic of
Palau.
Sec.
5.3 Procedures for designation and withdrawal
of designation.
(a) Any agency or individual may request
the Secretary to designate (or withdraw
the designation of) an area, population
group, or facility as an MUP and/or as
a HPSA. Requests by State agencies participating
in the Department's electronic shortage
designation system should be made electronically.
(b)
The Applicant will forward a copy of (or
relevant electronic information on) each
such designation request to the officials
and entities listed below in each State
affected by the request, asking that they
review the request and offer their recommendations,
if any, to the Secretary within 30 days:
(1)
The Governor;
(2)
The head of the State health department
or State health agency designated by the
Governor, or other health official to
whom this reviewing authority has been
delegated (such as the Director of the
Primary Care Office), and the Director
of the State Office of Rural Health;
(3)
Appropriate local officials within the
State, such as health officers of counties
or cities affected;
(4)
The State primary care association or
other State organization, if any, that
represents federally qualified health
centers and other community-based primary
care organizations in the State;
(5)
Affected State medical, dental, and other
health professional societies; and
(6)
Where a public facility (including a Federal
medical facility) is proposed for designation
or withdrawal of designation, the chief
administrative officer of such facility.
(c) The Secretary may propose the designation,
or withdrawal of the designation, of an
area, population group, or facility under
this part. Where such a designation or
withdrawal is proposed, the Secretary
will notify the agencies, officials, and
entities described in paragraph (b) of
this section and request comment as therein
provided.
(d) Using data available to the Secretary
from national and State sources, and based
upon the applicable criteria in the remaining
subparts and appendices to this part,
the Secretary will annually prepare listings
(by State) of currently designated MUPs
and HPSAs, together with relevant data
available to the Secretary, and will identify
those MUPs and HPSAs within the State
whose designations, because of age or
other factors, are required to be updated.
The Secretary will provide the listing
for each State and a description of any
required information to the entities in
that State identified in paragraph (b)(2)
and (4) of this section, either electronically
or in hard copy, and will request review
and comment within 90 days.
(e)
The Secretary will furnish, upon request,
an information copy of a request made
pursuant to paragraph (a) of this section
or applicable portions of the materials
provided pursuant to paragraph (c) of
this section to other interested persons
and groups for their review and comment.
Resulting comments or recommendations
may be provided to the Secretary, the
Governor, and/or the State health official
identified in paragraph (b)(2) of this
section.
(f)
In the case of a proposed withdrawal of
a designation, the Secretary shall afford
other interested persons and groups in
the affected area an opportunity to submit
data and information concerning the proposed
action, including entities directly dependent
on the designation and primary care associations
and State health professional associations,
to the extent practicable.
(g)
The Secretary may request such further
data and information as he/she deems necessary
to evaluate particular proposals or requests
for designation or withdrawal of designation
under paragraph (a) of this section. Any
data so requested must be submitted within
30 days of the request, unless a longer
period is approved by the Secretary. If
the information requested under paragraph
(c) of this section or under this section
is not provided, the Secretary will evaluate
the proposed designation (including continuation
of designation) or withdrawal of designation
of the areas, population groups, and/or
facilities for which the information was
requested on the basis of the information
available to the Secretary.
(h)
After review and consideration of the
available information and the comments
and recommendations submitted, the Secretary
will designate those areas, population
groups, and facilities as MUPs and/or
HPSAs, as applicable, which have been
determined to meet the applicable criteria
under this part, and will withdraw the
designations of those which have been
determined no longer to meet the applicable
criteria under this part.
(i)
Urgent Review. If a clinician dies, retires,
or leaves an area that is not already
designated as an MUP or HPSA with no or
limited notice, causing a sudden and dramatic
change in primary medical care, dental
or mental health services available to
that area's population, the State health
agency or official identified in paragraph
(b)(2) of this section may submit an urgent
request to the Secretary on behalf of
the affected community that the area be
immediately designated as an MUP and/or
HPSA. Such urgent requests will be reviewed
on an expedited basis, within 30 days
of receipt. If
(j) The Secretary fails to complete review
of the request within 30 days after receipt,
the area as defined by the State agency
will be considered designated as an MUP
and/or HPSA, as applicable, until and
unless subsequent review by the Secretary
indicates that inaccurate data were provided
or that the situation has changed. Each
year, each State may invoke this urgent
procedure for processing no more than
five percent of the total number of designations
the State had at the end of the preceding
calendar year.
Sec. 5.4 Notice and publication of designations
and withdrawals.
(a) In the case of a request under Sec.
5.3(a) of this part, the Secretary will
give written or electronic notice of the
determination made to the individual or
agency that made the request. The date
of this notice will reflect the actual
date of determination.
(b)
The Secretary will also give written or
electronic notice of a designation (or
withdrawal of designation) under this
part on or not later than 60 days after
the effective date, as noted in paragraph
(a) of this section , of the designation
(or withdrawal), to:
(1)
The Governor of each State in which the
designated or withdrawn MUP or HPSA is
located in whole or in part;
(2)
The State health department or other agency
or official identified under paragraph
Sec. 5.3(b)(2) of this part of the affected
State or States, and any other State agency
deemed appropriate by the Secretary; and
(3) Other appropriate public or nonprofit
private entities which are located in
or which the Secretary determines have
a demonstrated interest in the area designated
or withdrawn, including entities directly
dependent on the designation and primary
care associations and State health professional
associations.
(c)
The Secretary will publish updated lists
of designated MUPs and HPSAs in the Federal
Register after the end of each
fiscal year, reflecting designations current
at the end of each fiscal year, and make
the complete list available on-line, by
type of designation and by State, and
will maintain a regularly updated Web
site of current designations between Federal
Register list publications. Such
listings will distinguish between first
and second tier designations as determined
pursuant to Sec. 5.103 of this part.
(d)
The effective date of the designation
of an MUP or HPSA shall be the date of
the notification letter or electronic
notice provided pursuant to paragraph
(a) or (b) of this section, or the date
of publication in the Federal
Register, whichever occurs first.
(e)
The effective date of the withdrawal of
the designation of an MUP or HPSA shall
be the date of the notification letter
or electronic notice provided pursuant
to paragraph (a) or (b) of this section,
or the date on which notification of the
withdrawal is published in the Federal
Register, or the date of publication
in the Federal Register of
an updated list of designations of the
type concerned which does not include
the designation, whichever occurs first.
Sec.
5.5 Transition provisions.
(a)
Continuation of currently designated MUPs
and primary care HPSAs. Except as otherwise
provided in this section and Sec. 5.6
of this part, these new criteria for the
designation of a MUP or a primary care
HPSA will be phased in over a period of
three years from the date of publication
of the final rule in the Federal
Register, with the oldest MUP
and HPSA designations being reviewed first.
Existing designations will remain in effect
until reviewed under the new criteria
on the schedule set by the Secretary after
consultation with State entities as described
below.
(b)
Revision of MUPs and primary care HPSAs.
(1)
The Secretary will, within 90 days after
publication of this final rule in the
Federal Register, submit
to the entities in each State identified
pursuant to Sec. 5.3(b)(2) and (4) of
this part a listing of the adjusted population-to-primary
care clinician ratio computed under Sec.
5.104 of this part for each currently
designated MUP and primary care HPSA within
its boundaries, based on the data and
information available to the Secretary.
(2)
The State health agency or other designee
of the Governor shall have 90 days from
receipt of such listing, or such longer
time period as the Secretary may approve,
to provide comments to the Secretary.
Such comments should take into account
the effects on local communities and any
comments by affected entities and should
include recommendations on the following
topics:
(i)
Where the boundaries of a currently designated
MUP and primary care HPSA overlap but
do not coincide--
(A)
Which service area boundaries the State
recommends be continued in effect;
(B)
Whether the State proposes to have any
remaining area separately designated,
either on its own or as part of another
service area; or
(C)
If the State wishes to identify and consider
for designation a new service area instead
of either area currently designated, identification
of the boundaries recommended.
(ii)
Any other service area boundaries (of
existing designated areas) that the State
recommends be revised;
(iii)
The State's suggestions as to which areas
should be updated in the first transition
year, which in the second, and which in
the third;
(iv) The State's recommendations concerning
those areas it suggests be updated during
the first transition year; and
(v)
The accuracy of the FTE primary care clinician
data and other data used in scoring.
(3)
Where a current MUP and a primary care
HPSA designation overlap, and the State
makes an election under paragraph (b)(2)(i)(A)
of this section, the MUP or primary care
HPSA that is not selected will be deemed
to be automatically withdrawn.
(4) If part of the area of a currently
designated MUP or primary care HPSA is
revised under this part and the State
does not request designation of the remaining
area, the current designation covering
the remaining area will be deemed to be
automatically withdrawn.
(5)
If a State does not provide recommendations
to resolve overlapping area situations
under paragraph (a) of this section, the
Secretary may revise the areas involved,
based on the applicable criteria and data
and information available.
Sec.
5.6 Provisions related to Automatic HPSA
designation of certain Federally Qualified
Health Centers (FQHC) and Rural Health
Centers (RHC).
(a)
The Health Care Safety Net Amendments
of 2002, as amended by Public Law 108-163,
provide automatic HPSA designation for
at least six years, for all entities that:
(1)
Were deemed or certified as an FQHC or
RHC, Sec. 5.2(h) of this part, on or after
October 26, 2002;
(2)
Meet the requirements of section 334 of
the Act (concerning the provision of services
regardless of ability to pay); and
(3)
Do not lose their FQHC or RHC status and/or
cease to meet the requirements of section
334 of the Act during that time period.
(b) After the date these regulations take
effect, some of the FQHC and RHC entities
with automatic HPSA designation as described
under paragraph (a) of this section, [or
some of the clinical sites of these entities],
may also be found to:
(1) Be located in a geographic area that
has been designated under the criteria
for geographic primary care designations
in Subpart B of this part;
(2) Be located in an area containing a
population group that has been designated
under the population group criteria in
Subpart C of this part and serving the
designated population group, as determined
by the Secretary (e.g., a migrant health
center serving a designated migrant population;
a homeless health center serving a designated
homeless population; a public housing
or community health center serving a designated
low-income population group); or
(3)
Have met the criteria for designation
as a safety-net facility in Subpart D
of this part.
(c) A list of FQHC and RHC clinical sites
that are automatically designated pursuant
to paragraph (a) of this section, excluding
any clinical sites that have also been
found to be covered by another HPSA designation
as set forth in paragraph (b) of this
section, shall be maintained. This list
of automatically designated clinical sites,
with their addresses, shall be appended
to each list of designated HPSAs published
in the Federal Register or
posted on the web in accordance with Sec.
5.4 (c) of this part.
(d)
To maintain HPSA designation after six
years of automatic designation, FQHC or
RHC clinical sites remaining on the appended
list of ``automatic'' HPSAs (or the most
recent previous date that the HPSA list
was published in the Federal Register
or posted on the web) will be
required to demonstrate that their area
meets the criteria in subpart B of this
part, that they are serving a population
group which meets the criteria in subpart
C of this part, or that they meet the
facility criteria in subpart D of this
part. At or near the end of the six-year
period of automatic designation, the FQHCs
and RHCs involved will be informed of
this requirement by mail, and shall then
have 90 days to provide evidence that
the criteria are met for the sites in
question.
(e)
If an FQHC or RHC is notified as described
in paragraph (d) of this section that
it needs to demonstrate that one or more
of its clinical sites meet the designation
criteria herein, and fails to submit materials
in support of such a finding within 90
days, the sites involved shall then be
removed from the HPSA list, unless additional
time to provide further information is
granted by the Secretary on a case-by-case
basis. Sites so removed can reapply for
HPSA/MUP designation under the criteria
herein at a later date if their situation
changes so that they are able to provide
such evidence.
(f)
If evidence in support of designation
of an FQHC or RHC site under the criteria
herein is provided within the 90 day timeframe
specified in paragraph (d) of this section,
or during such additional time as the
Secretary may allow in paragraph (e) of
this section, the Secretary will review
the evidence submitted and make a determination,
within 60 days of receipt. Such sites
will remain on the HPSA list until this
determination is made.
(g)
After review of any information provided
as described in paragraph (f) of this
section, any FQHC or RHC clinical site
which the Secretary determines does not
meet the criteria herein shall be removed
from the HPSA list. The FQHC or RHC involved
will be so notified, and subsequent published
or posted HPSA lists will not include
such sites.
4. Subpart B is added to read as follows:
Subpart B--Criteria and Methodology
for Designation of Geographic Areas as
Medically Underserved Areas (MUAs) and
Primary Care HPSAs
Sec. 5.101 Applicability.
5.102
Criteria for designation of geographic
areas as MUAs and Primary Care HPSAs.
5.103
Identification of rational service areas
for the delivery of primary medical care.
5.104 Determination of adjusted population-to-primary
care clinician ratio.
5.105 Contiguous area considerations.
Subpart
B--Criteria and Methodology for Designation
of Geographic Areas as Medically Underserved
Areas (MUAs) and Primary Care HPSAs
Sec.
5.101 Applicability.
The
following criteria and methodology shall
be used to designate geographic areas
as medically underserved (under section
330(b) of the Public Health Service Act)
and as primary care HPSAs (under section
332 of the Act). Sec. 5.102 Criteria for
designation of geographic areas as MUAs
and Primary Care HPSAs. A geographic area
will be designated both as a medically
underserved area (pursuant to section
330(b) of the Act) and as a primary care
HPSA (under Section 332 of the Act) if
it is demonstrated, by such data and information
as the Secretary may require, that the
area meets the following criteria:
(a)
The area meets the requirements for a
rational service area for the delivery
of primary medical care services under
Sec. 5.103 of this part; and
(b) The area's adjusted population-to-primary
care clinician ratio/ score, computed
under Sec. 5.104 of this part, equals
or exceeds 3,000:1; and
(c) In the case of specific types of areas
identified in Sec. 5.105 of this part,
resources in contiguous areas are shown
to be overutilized or otherwise inaccessible,
as defined in Sec. 5.105 of this part.
Sec.
5.103 Identification of rational service
areas for the delivery of primary medical
care.
(a)
General definition: A rational service
area (RSA) is a geographically delimited,
continuous and cohesive area around one
or more population centers within which
a preponderance of the population normally
seeks and can reasonably expect to receive
primary medical care services.
(b)
Each rational service area should be large
enough to sustain services and small enough
to ensure that primary medical care resources
within the RSA are accessible to the population
of the RSA within a reasonable travel
time, assumed to be 40 minutes for a frontier
area and 30 minutes for all other areas
unless the provisions of paragraph (g)
of this section are invoked by a State.
(1) Travel times in most areas shall be
measured by the estimated time required
to get from point A to point B by principal
roads in an automobile traveling at the
speed limit, in typical traffic for the
area, taking into consideration the area's
terrain.
(2) Travel times within inner portions
of urban areas may be computed in terms
of travel by public transportation, in
areas with at least 20% of the population
under 100% of the poverty level and/or
a significant reliance on public transportation
(e.g. at least over 30% dependent according
to the U.S. Census.)
(c)
Individual RSAs shall be defined in terms
of one or more contiguous U.S. Census
Bureau geographic units for which census
data are available (e.g. counties, census
tracts, census divisions (MCDs/ CCDs),
or zip code tabulation areas (ZCTAs),
the boundaries of which do not overlap
with the boundaries of another rational
service area.
(d) Cohesiveness for paragraph (a) of
this section can be established by demonstrating
that the area:
(1)
Is isolated from contiguous areas due
to topography, market or transportation
patterns or other physical barriers, or
(2)
Has a homogeneous socioeconomic composition
different from those in contiguous areas,
and is isolated from or has limited interaction
with contiguous communities and/or access
barriers to resources in those areas,
or
(3) Has a tradition of primarily internal
interaction or independence as defined
by transportation or market patterns,
or
(4) Is a single whole county.
(e)
Size of an RSA shall be limited, where
an RSA has more than one population center
(towns of equivalent size), by a maximum
of 30 minutes travel time between population
centers within a single RSA.
(f)
Geographic separation of RSAs
(1) Geographic separation of RSAs shall
be measured by the travel times between
the population center(s) of one RSA and
those of contiguous RSAs, normally involving
a minimum of 30 minutes travel time between
population centers of different RSAs.
(2)
Travel time from the population center
of an RSA to the population center of
a contiguous RSA may be less than 30 minutes
within metropolitan statistical areas
where established neighborhoods and communities
display a strong self-identity (as indicated
by a homogeneous socioeconomic or demographic
structure and/or a tradition of interaction
or interdependence), have limited interaction
with contiguous areas, and, in general,
have a population density equal to or
greater than 100 persons per square mile.
(g)
RSA parameters determined by State--
(1)
RSA parameters different from those defined
in paragraph (f) of this section, but
within the ranges defined in paragraph
(g)(2) of this section, may be used for
RSA delineation within a State if:
(i)
Such parameters and the method for defining
RSAs to be used with those parameters
are adopted by the State through a partnership
approach with affected State and community
officials/stakeholders and in consultation
with the Secretary,
(ii)
The RSA parameters and method selected
have the approval of the State health
department or other designee of the Governor
identified in Sec. 5.3(b)(2) of this part,
and
(iii)
The final RSA approach to be used has
been reviewed by the Secretary in advance
of the State submitting particular RSA
definitions using its approach.
(2)
Permissible Ranges for RSA parameters
adopted by States:
(i) The maximum travel time to assure
access to care within the RSA is set at
30 minutes in paragraph (b) of this section
and the maximum travel time between population
centers within the RSA, set generally
at 30 minutes in paragraph (e) of this
section, may be set at any value greater
than or equal to 20 minutes but less than
or equal to 40 minutes, for non-frontier
RSAs.
(ii) Maximum travel time to assure access
to care within a frontier or other sparsely-populated
RSA, set generally at 40 minutes in paragraph
(e) of this section, may be set at any
value greater than 30 minutes but less
than or equal to 60 minutes, where topography,
market, transportation, or other conditions
and patterns lead to utilization of providers
at greater distances.
(iii)
Separation between RSAs--Minimum travel
time from the population center(s) of
the RSA to the population center of a
contiguous RSA may be set at any value
greater than or equal to 20 minutes and
less than or equal to 40 minutes.
(h)
State-wide system. Each State is encouraged
to develop a State- wide system which
divides the territory of the State into
rational service areas (RSAs) for the
delivery of primary care services within
the State.
(1)
This may be done all at once or incrementally,
by developing State RSA criteria using
the parameter ranges defined above and
a process for defining the State's RSAs
according to those criteria over a period
of time. A full statewide plan is encouraged
to maximize its effectiveness in improving
the designation process.
(2)
Each State-wide system of rational service
areas or process for developing State
RSAs shall be developed in consultation
with the Secretary and be approved by
the State health department or other designee
of the Governor.
Sec.
5.104 Determination of adjusted population-to-primary
care clinician ratio.
The adjusted population-to-primary care
clinician ratio is computed as the sum
of the ``barrier-free'' population-to-primary
care clinician ratio of an area, calculated
as in paragraph (a) of this section, and
the area's High Need Indicator score,
calculated as paragraph (b) of this section:
(a) Effective Barrier-Free Population-to
Clinician Ratio for an area is computed
as follows:
(1) Estimate the primary care utilization
of the area's population if no barriers
to accessing health care existed, in total
expected visits per year. This shall be
done by applying current national utilization
rates for populations without access barriers,
to current data on the population composition
of each area by age and gender. The national
utilization rates to be used for this
purpose (in visits per year, by age group
and gender) will be published in tabular
form by the Secretary from time to time.
The utilization rate table applicable
at the time of publication of this regulation
will be included in the preamble; later
updates will be made available periodically
but no more often than annually.
(2) Divide the resulting total estimated
number of annual barrier- free visits
for the area by the national mean utilization
rate (consistent with the tabular utilization
data used and published along with it)
to obtain the area's effective (barrier-free)
population.
(3)
Where an area has a significant number
of migratory workers, homeless persons,
or seasonal residents, the effective population
calculated in paragraph (a)(2) of this
section may be adjusted further by multiplying
by the factor [Resident Civilian Pop.
+ Migratory workers & families + Homeless
+ Seasonal Residents] / Resident Civilian
Pop., where these quantities are defined
as in paragraph (c)(1) of this section.
The resident-civilian population does
include some components of the homeless
population, so any additions should avoid
duplication.
(4)
Calculate the ratio of the final effective
population to the area's number of FTE
primary care clinicians, calculated as
discussed in paragraph (c)(2) of this
section, to determine the area's barrier-
free population-to-primary care clinician
ratio.
(b)
High Need Indicator Score. (1) The High
Need Indicator score for an area is computed
as the sum of the area's partial scores
for each of the nine variables listed
in this paragraph (b)(1):
(i)
Percentage of population below 200% of
the federal poverty level;
(ii)
Unemployment rate;
(iii) Percentage of population that is
non-White;
(iv) Percentage of population that is
Hispanic;
(v)
Percentage of population that is over
age 65;
(vi)
Population density;
(vii) Actual/expected death rate
(viii)
Low birth weight birth rate
(ix)
Infant mortality rate
(2) A current national Percentiles Table
IV-6 (relating raw scores for each indicator
to the national percentile distribution
of that indicator at the county level)
shall be used to determine an area's percentile
rank for each high need indicator at the
time of proposed designation or update.
HRSA will publish revised percentile tables
as a Notice in the Federal Register if
there are significant changes in the indicators
in paragraph (b)(1) in this section.
(3)
The percentile rank for each indicator
shall then be converted to a partial score,
using the Scores Table IV-7.
(4) The total High Need Indicator score
is computed as the sum of the nine partial
scores computed in paragraph (b)(3) of
this section for each indicator.
(c)
The barrier-free population-to-primary
care clinician ratio/ score, as computed
in paragraph (a) of this section, is added
to the High Need Indicator Score, as computed
in paragraph (b) of this section, to obtain
the final adjusted population-to-primary
care clinician ratio.
(d)
The threshold for designation is an adjusted
population-to- primary care clinician
ratio/score that exceeds 3,000:1.
(e)
Calculation of specific variables
(1) Population counts. The population
of an area is the total resident civilian
population, excluding inmates and residents
of institutions, based on the most recent
U.S. Census data, adjusted for increases/decreases
to the current year using the best available
intercensus projections, and making the
following adjustments, as appropriate:
(i)
Migratory workers and their families may
be added to the adjusted resident civilian
population, if significant numbers of
migratory workers are present in the area,
using the latest Migrant Health Atlas
or best available Federal or State estimates.
Estimates used must be adjusted to reflect
the percentage of the year that migratory
workers are present in the area.
(ii)
If an area includes significant numbers
of homeless individuals not reflected
in the census figures, and reasonable
estimates of their numbers are available,
these data may be submitted for consideration
as an adjustment to the population of
the area.
(iii)
Where seasonal residents significantly
affect the effective total population
of an area, seasonal residents (not including
tourists) may be added to the adjusted
resident civilian population, if supported
by acceptable State or local estimates.
Estimates used must be adjusted to reflect
the percentage of the year that seasonal
residents are present in the area.
(iv) Significant numbers of these populations
are indicated when the numbers are large
enough to reflect an additional burden
on the health care system that the census
data do not capture effectively.
(2)
Counting of primary care clinicians.
(i)
In determining an area's adjusted population-to-primary
care clinician ratio for designation as
a tier 1 shortage area, clinicians shall
be counted as follows:
(A)
All non-Federal doctors of medicine (M.D.)
and doctors of osteopathy (D.O.) who provide
direct patient care and practice principally
in one of the four primary care specialties
(general or family practice, general internal
medicine, pediatrics, and obstetrics and
gynecology), shall be included in clinician
counts.
(B) All non-Federal nurse practitioners,
physician's assistants, and certified
nurse midwives practicing in primary care
settings shall be included in clinician
counts, but with a multiplier of:
(1) 0.5, or, at the applicant's option,
(2)
0.8 times an additional factor whose value
is between 0.5 and 1.0, depending on the
scope of practice allowed for each type
of non- physician clinician in the State
involved. A table of these factors for
each State and for each type of non-physician
clinician will be provided in the final
regulation. HRSA will publish an updated
table of these factors as a Notice in
the Federal Register if such updates become
available.
(C) Where clinicians are practicing less
than full-time, or have more than one
practice address, their contribution to
the total count may be reduced based on
their estimated full-time-equivalency
(FTE) practicing within the area being
considered, using available data.
(D) Each intern or resident physician
shall be 0.1 FTE physician
(E)
Hospital staff physicians practicing in
organized outpatient departments and primary
care clinics shall be counted only on
an FTE basis, based on their time in outpatient/ambulatory
settings, not in inpatient care.
(F) The following shall be excluded from
primary care clinician counts:
(1) Practitioners who are engaged solely
in administration, research, or teaching;
(2)
Hospital staff physicians involved exclusively
in inpatient and/or in emergency room
care; and
(3) Clinicians who are suspended under
provisions of the Medicare- Medicaid Anti-Fraud
and Abuse Act, during the period of suspension.
(ii) In determining an area's adjusted
population-to-primary care clinician ratio
for designation as a tier 2 shortage area,
clinicians shall be counted as provided
for above, except that the following clinicians
shall also be excluded:
(A) Primary care clinicians who are members
of the National Health Service Corps (NHSC),
established by section 331(a) of the Act,
are fulfilling a service obligation incurred
under the NHSC Scholarship or Loan Repayment
Program (sections 338A and 338B of the
Act) or are fulfilling a service obligation
incurred under the State Loan Repayment
program (section 338I of the Act);
(B)
Physicians who are practicing in the United
States under a waiver of their J-1 Visa
requirements; and
(C)
Primary care clinicians who are providing
services at a health center receiving
a grant under section 330 of the Act and
who are not otherwise excluded under paragraphs
(e)(2)(ii)(A) or (B) of this section.
(iii)
Counting of FTEs.
(A) Clinician count data in the Department's
electronic designation database (from
national data, augmented by State data
where approved by the Secretary) may be
used by applicants without adjustments
for designation purposes.
(B)
If applicants prefer, they may conduct
surveys of the clinicians in area(s) requested
for designation. When this is done, FTEs
shall be computed using such guidance
as the Secretary may provide.
(3)
Data Sources for High Need Indicators
(i) The Unemployment Rate, High Need Indicator
at paragraph (b)(1)(i)(B) of this section,
shall be calculated based on the latest
Bureau of Labor Statistics unemployment
data available for the lowest- level area
(county, city, place, or other labor statistics
area) that comprises or includes the area.
(ii)
Data for the percent below poverty and
demographic High Need Indicators at paragraphs
(b)(1)(i)(A) and (ii) of this section,
for an area shall be aggregated from the
latest available U.S. Census data for
the counties, census tracts, census divisions
or ZCTAs which comprise the area, or from
more recent updates thereof if available
and approved by the Secretary.
(iii)
The health status High Need Indicators
at paragraph (b)(1)(iii) of this section
shall be calculated based on the latest
available five-year average data available,
from DHHS or the State involved, for the
county of which the service area is a
part, unless the area is a subcounty area
and statistically significant five-year
average subcounty data on these variables
are available for that subcounty area.
For service areas which cross county lines,
a population-weighted combination of the
rates for the counties involved shall
be used.
Sec.
5.105 Contiguous area considerations
.
(a) An analysis of resources in areas
contiguous to the area being considered
for designation shall be required only
if the State involved has not developed
a system of RSAs, or has a partially-developed
system which does not include all areas
contiguous to the requested area, and
the population center of the area for
which designation (or update of designation)
is sought is less than 30 minutes from
the nearest providers.
(b)
Where contiguous area analysis is required
under paragraph (a) of this section, resources
in a particular contiguous area will be
deemed to be overutilized or otherwise
inaccessible if any of the following conditions
exists:
(1) All primary care clinicians in the
contiguous area are located more than
30 minutes travel time from the population
center(s) of the requested area;
(2)
The adjusted (or unadjusted) population-to-FTE
primary care clinician ratio within the
contiguous area is in excess of 2000:1;
or
(3)
Primary care clinician(s) located in the
contiguous area appear to be inaccessible
to the population of the requested area
because of specific access barriers, such
as:
(i) A lack of economic access to contiguous
area resources, particularly where a very
high proportion of the requested area's
population is poor, and Medicaid-covered
or public (sliding-fee- schedule or free)
primary care services are not available
in the contiguous area; or
(ii)
Significant differences exist between
the demographic characteristics of the
requested area and those of the contiguous
area (and its clinicians), indicative
of isolation of the requested area's population
from the contiguous area, such as language
or cultural difference.
5.
Subpart C is added to read as follows:
Subpart
C--Criteria and Methodology for Designation
of Population Groups as MUPs and/or Primary
Care HPSAs
Sec.
5.201 Applicability.
5.202 General criteria for designation
of specific population groups as MUPs
and/or primary care HPSAs.
5.203 Criteria for designation of migratory
and seasonal agricultural workers as primary
care HPSAs.
5.204 Criteria for designation of homeless
populations as primary care HPSAs.
5.205 Criteria for designation of Native
American populations as primary care HPSAs
and MUPs.
5.206 Requirements for ``permissible''
designation of other population groups
as MUPs.
Subpart
C--Criteria and Methodology for Designation
of Population Groups as MUPs and/or Primary
Care HPSAs
Sec. 5.201 Applicability.
(a)
Certain specific population groups will
be designated as both MUPs and primary
care HPSAs if it is demonstrated that
the criteria in Sec. 5.202 of this part
are met when applied to data on these
population groups. These specific population
groups are:
(1)
The low income population, defined as
that portion of an area's population whose
incomes are below 200% of the poverty
level.
(2)
The Medicaid-eligible population of the
area.
(3)
Linguistically-isolated populations, defined
as the Secretary may with reference to
census definitions of linguistically-isolated
households and/or populations for whom
English is not spoken at all or is a second
language not spoken well.
(b)
Migratory and seasonal agricultural workers
and their families within specific service
areas are defined in law as ``special
medically underserved populations''. They
will also be designated as primary care
HPSAs if it is demonstrated that the criteria
in Sec. 5.203 of this part are met.
(c)
Homeless populations are defined in law
as ``special medically underserved populations''.
They will also be designated as primary
care HPSAs if it is demonstrated that
the criteria in Sec. 5.204 of this part
are met.
(d) Residents of Public Housing are defined
in law as ``special medically underserved
populations''. They will also be designated
as primary care HPSAs if it is demonstrated
that the criteria in Sec. 5.202 of this
part are met when computed for the low
income population group residing in a
particular Public Housing community.
(e)
Native American population groups (including
American Indian tribes or Alaska Native
entities) will be designated as both MUPs
and primary care HPSAs if it is demonstrated
that the criteria in Sec. 5.205 of this
part are met.
(f) If an FQHC, RHC, or other public or
nonprofit private clinical site has been
designated as a safety-net facility primary
care HPSA under Subpart D, Sec. 5.301
of this part (based on service to significant
numbers of uninsured and Medicaid-eligible
patients), the population group of uninsured
and Medicaid-eligible patients served
by the clinical site shall be considered
designated as an MUP.
(g) Other population groups recommended
by State and local officials may be designated
as MUPs under unusual local conditions
which are a barrier to access to or availability
of health services, under procedures described
in Sec. 5.206.
Sec.
5.202 General criteria for designation
of specific population groups as MUPs
and/or primary care HPSAs.
(a)
Any of the specific population groups
identified in Sec. 5.201(a) of this part
may be designated if it is demonstrated,
using such documentation as the Secretary
may require, that the following criteria
are met when applied to data for the population
group:
(1) The area in which the population group
resides meets the requirements for a rational
service area under Sec. 5.103 of this
part;
(2) The rational service area in which
the population group resides does not
meet the criteria for designation as a
geographic area under Sec. 5.102 of this
part;
(3) There are access barriers that prevent
the population group from accessing primary
medical care services available to the
general population of the area, as demonstrated
by an adjusted population-to- primary
care clinician ratio computed for the
population group that equals or exceeds
the 3000:1 designation threshold in Sec.
5.104 of this part.
(b)
In calculating the adjusted population-to-primary
care clinician ratio for a population
group, the methodology described in Sec.
5.104 of this part shall be used, except
that:
(1) The group's population shall be used
instead of the area's population,
(2)
The FTE clinicians available to the population
group shall be used rather than those
available to the area in general (i.e.
Medicaid FTE/claims and sliding fee scale
FTE for a low income population), and
(3)
High Need Indicators shall be calculated
based as nearly as possible on their values
for the applicable population group within
the service area, using such approximations
as the Secretary may allow.
Sec.
5.203 Criteria for designation of migratory
and seasonal agricultural workers as primary
care HPSAs.
(a) Where data availability permits, the
method described in Sec. 5.202 of this
part may be used to calculate an adjusted
population-to- primary care clinician
ratio for a population group composed
of migratory and seasonal agricultural
workers, and to compare this ratio with
the 3000:1 designation threshold, with
these additional conditions:
(1)
For a migratory and seasonal agricultural
worker population group, an agricultural
area (as defined by the Secretary) may
be used as a rational service area.
(2) The population of the migratory and
seasonal population group identified must
be adjusted by a factor representing the
fraction of the year that this population
is present in the area.
(b)
Alternatively, a simplified designation
procedure may be used, as follows:
(1) Define the boundaries of the agricultural
area or other service area within which
the migratory and seasonal agricultural
worker population reside or temporarily
reside for a portion of the year.
(2) Provide data on the number of individuals
in the population group (including workers
and their families) and the number of
months they are present in the area during
a typical year.
(3) If the number of individuals times
the number of months divided by 12 exceeds
1000, this special medically underserved
population group will also be considered
a primary care HPSA, with its population-to-
primary care clinician ratio assumed equal
to 3000:1.
Sec.
5.204 Criteria for designation of homeless
populations as primary care HPSAs.
(a)
Where data availability permits, the method
described in Sec. 5.202 of this part may
be used to calculate an adjusted population-to-
primary care clinician ratio for a homeless
population group (or for a combined homeless
and other low-income population group),
and compare this ratio with the 3000:1
designation threshold. For such population
groups, the area in which homeless populations
congregate and/or are sheltered may be
used as a rational service area. (b) Alternatively,
a simplified designation procedure may
be used, as follows: (1) Define the boundaries
of the area in which homeless populations
congregate and/or are sheltered. (2) Provide
data on the average number of homeless
individuals in the defined area during
a typical year, and the average number
of months they are homeless. (3) If the
average number of homeless individuals
during a typical year exceeds 1000, this
special medically underserved population
group will also be considered a primary
care HPSA, with its population-to- primary
care clinician ratio assumed equal to
3000:1.
Sec.
5.205 Criteria for designation of Native
American population groups as primary
care HPSAs and MUPs.
(a)
Those American Indian tribes or Alaska
Native entities identified by the Department
of the Interior as federally recognized
are automatically designated as population
group primary care HPSAs and MUPs and
will be given a baseline ratio of 3000:1.
(b) Where data availability permits, the
method described in Sec. 5.202(b) of this
part may be used to calculate a higher
population-to- primary care clinician
ratio for a Native American population
group and/or to facilitate scoring such
a designation for purposes of allocating
program resources. For such designations,
a reservation may be used as a rational
service area.
Sec. 5.206 Requirements for ``permissible''
designation of other population groups
as MUPs.
The population of a service area that
does not meet the criteria at Sec. 5.102
of this part, or a population group that
does not meet the criteria in Sec. Sec.
5.202 through 5.205 of this part, may
nevertheless be designated as an MUP if
the following requirements are met: (a)
The area or population group is recommended
for designation by the Governor of the
State in which the area is located and
by at least one local official of the
area. A local official for this purpose
may be-- (1) The chief executive of the
local governmental entity which includes
all or a substantial portion of the requested
area or population group (such as the
county executive of a county, mayor of
a town, mayor or city manager of a city);
or (2) A city or county health official
(such as the head of a city or county
health department) of the local governmental
entity which includes all or a substantial
portion of the requested area or population
group. (b) The request for designation
is based on the presence of unusual local
conditions, not covered by the criteria
at Sec. 5.102 and/or Sec. Sec. 5.202 through
5.205 of this part, which are a barrier
to access to or the availability of personal
health services in the area or for the
population group for which designation
is sought. (c) The request contains such
documentation as the Secretary may require.
6.
Subpart
D is added to read as follows:
Subpart
D--Criteria and Methodology for Designation
of Facilities as Primary Care Health Professional
Shortage Areas
Sec.
5.301 Criteria for designation of public
and nonprofit private medical facilities
as safety-net facility primary care HPSAs.
5.302 Criteria for designation of Federal
and State correctional institutions as
primary care HPSAs.
Subpart D--Criteria and Methodology for
Designation of Facilities as Primary Care
Health Professional Shortage Areas
Sec. 5.301 Criteria for designation of
public and nonprofit private medical facilities
as safety-net-facility primary care HPSAs.
(a) A public or nonprofit private medical
facility, or a remote clinical site of
such a facility, which is located in a
geographic area that is not designated
as a geographic primary care HPSA under
Subpart B of this part, shall be designated
as a ``safety-net-facility'' primary care
HPSA if the following criteria are met:
(1) The facility or site is or is part
of an FQHC, RHC or other public or nonprofit
private medical facility which provides
primary medical care services on an ambulatory
or outpatient basis, and (2) The facility
or clinical site is identifiable as a
safety-net facility based on service to
significant numbers of uninsured and Medicaid-eligible
patients, as determined using payment
source data and the minimum requirements
by type of area described in paragraph
(b) of this section. (b) Methodology.
In determining whether public or nonprofit
private facilities or clinical sites are
safety-net facilities for purposes of
this designation, the following methodology
will be used: (1) The facility or particular
site for which designation is sought must
meet all of the following requirements:
(i) Currently provides full-time ambulatory
or outpatient primary medical care; (ii)
Provides services regardless of an individual's
ability to pay for such services; and
(iii) Has a posted, discounted sliding-fee-scale
which is available to all uninsured patients
with incomes below 200% of the poverty
line. (2) Payment source criteria. Using
such documentation as may be required
by the Secretary, it must be demonstrated
that: (i) At least 10% of all patients
served at each facility or clinical site
(or group of such sites, where payment
source data are available only for the
group) are indigent uninsured, receiving
services free or on a discounted sliding
fee scale. (ii) The number of patients
served that are paid under Medicaid, plus
the number who receive services free or
on a discounted sliding fee scale, as
a percentage of all patients served at
each facility or clinical site (or group
of such sites, where payment source data
are available only for the group) must
equal or exceed the following: (A) 40%
in metropolitan areas (B) 30% in non-metropolitan,
non-frontier areas (C) 20% of all patients
in frontier, non-metropolitan areas
Sec.
5.302 Criteria for designation of Federal
and State correctional institutions as
primary care HPSAs. (a) Medium to maximum
security Federal and State correctional
institutions and youth detention facilities
will be designated as primary care HPSAs,
if both of the following criteria are
met: (1) The institution has at least
250 inmates; and (2) The institution has
no primary medical care clinicians, or
the ratio of the number of inmates per
year to the number of FTE primary care
clinicians, determined in accordance with
Sec. 5.104(e)(2) of this part, serving
the institution is at least 1,000:1. (b)
For purposes of this paragraph, the number
of inmates shall be determined as follows:
(1) If the number of new inmates per year
and the average length- of-stay are not
specified, or if the information provided
does not indicate that intake medical
examinations are routinely performed upon
entry, then the number of inmates is used.
(2) If the average length-of-stay is specified
as one year or more, and intake medical
examinations are routinely performed upon
entry, then the number of inmates equals
the average number of inmates plus 0.3
multiplied by the number of new inmates
per year; or (3) If the average length-of-stay
is specified as less than one year, and
intake examinations are routinely performed
upon entry, then the number of inmates
equals the average number of inmates plus
0.2 multiplied by (1 + ALOS/2) multiplied
by the number of new inmates per year,
where ALOS is the average length of stay,
in fraction of a year. (c) Clinicians
permanently employed by the Federal Bureau
of Prisons or by States to provide services
to Federal or State prisoners shall be
counted based on the FTE services they
provide, calculated as provided for in
Sec. 5.104(c)(2).
7. Subpart E is added to read as follows:
Subpart
E--Identification of Primary Care Health
Professional Shortage Areas of Greatest
Need
Sec. 5.401 Use of methodology for identification
of HPSAs of greatest need.
The
adjusted population to clinician ratios
that are the result of the calculations
in the methodology will be used as the
relative scores to identify those HPSAs
of Greatest Need. Areas will be ranked
according to the ratios calculated to
determine an area's eligibility for designation.
8. Appendix A to part 5 is revised to
read as follows:
Appendix A to Part 5--Scoring Table for
High Need Indicators Used in MUP and Primary
Care HPSA Designation
Table A-1.--Scores for High Need
Indicators, Given Their National Percentiles
Percentile |
Poverty |
Unemp |
Elderly |
Density |
Hispanic |
Non
white |
Death
rate |
LBW/IMR |
0 |
0.00 |
0.00 |
0.00 |
995.20 |
0.00 |
0.00 |
0.00 |
0.00 |
1 |
3.01 |
1.18 |
0.54 |
831.13 |
0.81 |
0.00 |
0.82 |
0.72 |
2 |
6.04 |
2.37 |
1.09 |
735.15 |
1.64 |
0.00 |
1.65 |
1.44 |
3 |
9.11 |
3.58 |
1.65 |
667.05 |
2.47 |
0.00 |
2.49 |
2.17 |
4 |
12.21 |
4.79 |
2.21 |
614.23 |
3.31 |
0.00 |
3.33 |
2.91 |
5 |
15.34 |
6.02 |
2.77 |
571.07 |
4.15 |
0.00 |
4.19 |
3.65 |
6 |
18.50 |
7.26 |
3.34 |
534.58 |
5.01 |
0.00 |
5.05 |
4.40 |
7 |
21.70 |
8.52 |
3.92 |
502.98 |
5.88 |
0.00 |
5.93 |
5.17 |
8 |
24.93 |
9.79 |
4.51 |
475.10 |
6.75 |
0.00 |
6.81 |
5.93 |
9 |
28.20 |
11.07 |
5.10 |
450.16 |
7.64 |
0.00 |
7.70 |
6.71 |
10 |
31.50 |
12.37 |
5.69 |
427.59 |
8.53 |
0.00 |
8.60 |
7.50 |
11 |
34.84 |
13.68 |
6.30 |
407.00 |
9.44 |
0.00 |
9.52 |
8.29 |
12 |
38.22 |
15.00 |
6.91 |
388.05 |
10.35 |
0.00 |
10.44 |
9.10 |
13 |
41.64 |
16.35 |
7.53 |
370.51 |
11.28 |
0.00 |
11.37 |
9.91 |
14 |
45.10 |
17.70 |
8.15 |
354.18 |
12.21 |
0.00 |
12.32 |
10.73 |
15 |
48.59 |
19.08 |
8.78 |
338.90 |
13.16 |
0.00 |
13.27 |
11.57 |
16 |
52.13 |
20.46 |
9.42 |
324.55 |
14.12 |
0.00 |
14.24 |
12.41 |
17 |
55.71 |
21.87 |
10.07 |
311.02 |
15.09 |
0.00 |
15.22 |
13.26 |
18 |
59.34 |
23.29 |
10.72 |
298.22 |
16.07 |
0.00 |
16.21 |
14.12 |
19 |
63.00 |
24.73 |
11.39 |
286.08 |
17.07 |
0.00 |
17.21 |
15.00 |
20 |
66.72 |
26.19 |
12.06 |
274.53 |
18.07 |
0.00 |
18.22 |
15.88 |
21 |
70.48 |
27.67 |
12.74 |
263.52 |
19.09 |
0.00 |
19.25 |
16.78 |
22 |
74.29 |
29.16 |
13.43 |
253.00 |
20.12 |
0.00 |
20.29 |
17.68 |
23 |
78.15 |
30.68 |
14.12 |
242.92 |
21.17 |
0.00 |
21.34 |
18.60 |
24 |
82.06 |
32.21 |
14.83 |
233.26 |
22.23 |
0.00 |
22.41 |
19.53 |
25 |
86.02 |
33.77 |
15.55 |
223.98 |
23.30 |
0.00 |
23.49 |
20.48 |
26 |
90.03 |
35.34 |
16.27 |
215.04 |
24.39 |
0.00 |
24.59 |
21.43 |
27 |
94.10 |
36.94 |
17.01 |
206.43 |
25.49 |
0.00 |
25.70 |
22.40 |
28 |
98.22 |
38.56 |
17.75 |
198.13 |
26.61 |
0.00 |
26.83 |
23.38 |
29 |
102.40 |
40.20 |
18.51 |
190.10 |
27.74 |
0.00 |
27.97 |
24.38 |
30 |
106.64 |
41.86 |
19.28 |
182.34 |
28.89 |
0.00 |
29.13 |
25.39 |
31 |
110.95 |
43.55 |
20.05 |
174.83 |
30.05 |
0.00 |
30.30 |
26.41 |
32 |
115.31 |
45.27 |
20.84 |
167.54 |
31.23 |
0.00 |
31.49 |
27.45 |
33 |
119.74 |
47.01 |
21.64 |
160.47 |
32.43 |
0.00 |
32.70 |
28.50 |
34 |
124.24 |
48.77 |
22.45 |
153.61 |
33.65 |
0.00 |
33.93 |
29.57 |
35 |
128.80 |
50.56 |
23.28 |
146.94 |
34.89 |
0.00 |
35.18 |
30.66 |
36 |
133.44 |
52.38 |
24.12 |
140.46 |
36.14 |
0.00 |
36.45 |
31.76 |
37 |
138.15 |
54.23 |
24.97 |
134.15 |
37.42 |
0.00 |
37.73 |
32.88 |
38 |
142.93 |
56.11 |
25.83 |
128.00 |
38.72 |
0.00 |
39.04 |
34.02 |
39 |
147.79 |
58.02 |
26.71 |
122.00 |
40.03 |
0.00 |
40.37 |
35.18 |
40 |
152.74 |
59.96 |
27.61 |
116.16 |
41.37 |
0.00 |
41.72 |
36.36 |
41 |
157.76 |
61.93 |
28.51 |
110.46 |
42.73 |
1.39 |
43.09 |
37.55 |
42 |
162.87 |
63.94 |
29.44 |
104.89 |
44.12 |
2.81 |
44.48 |
38.77 |
43 |
168.07 |
65.98 |
30.38 |
99.44 |
45.53 |
4.25 |
45.90 |
40.01 |
44 |
173.36 |
68.06 |
31.33 |
94.12 |
46.96 |
5.71 |
47.35 |
41.27 |
45 |
178.75 |
70.17 |
32.31 |
88.92 |
48.42 |
7.20 |
48.82 |
42.55 |
46 |
184.24 |
72.33 |
33.30 |
83.83 |
49.90 |
8.72 |
50.32 |
43.86 |
47 |
189.83 |
74.52 |
34.31 |
78.85 |
51.42 |
10.27 |
51.85 |
45.19 |
48 |
195.52 |
76.75 |
35.34 |
73.97 |
52.96 |
11.85 |
53.40 |
46.54 |
49 |
201.33 |
79.03 |
36.39 |
69.18 |
54.53 |
13.46 |
54.99 |
47.92 |
50 |
207.25 |
81.36 |
37.46 |
64.50 |
56.14 |
15.10 |
56.60 |
49.33 |
51 |
213.29 |
83.73 |
38.55 |
59.90 |
57.77 |
16.77 |
58.25 |
50.77 |
52 |
219.45 |
86.15 |
39.66 |
55.39 |
59.44 |
18.48 |
59.94 |
52.24 |
53 |
225.75 |
88.62 |
40.80 |
50.97 |
61.15 |
20.22 |
61.66 |
53.74 |
54 |
232.18 |
91.15 |
41.96 |
46.62 |
62.89 |
22.00 |
63.41 |
55.27 |
55 |
238.75 |
93.73 |
43.15 |
42.36 |
64.67 |
23.82 |
65.21 |
56.83 |
56 |
245.47 |
96.36 |
44.37 |
38.17 |
66.49 |
25.68 |
67.04 |
58.43 |
57 |
252.34 |
99.06 |
45.61 |
34.05 |
68.35 |
27.58 |
68.92 |
60.07 |
58 |
259.38 |
101.82 |
46.88 |
30.01 |
70.26 |
29.53 |
70.84 |
61.74 |
59 |
266.59 |
104.65 |
48.18 |
26.03 |
72.21 |
31.53 |
72.81 |
63.46 |
60 |
273.97 |
107.55 |
49.52 |
22.11 |
74.21 |
33.57 |
74.83 |
65.21 |
61 |
281.54 |
110.52 |
50.89 |
18.27 |
76.26 |
35.67 |
76.89 |
67.02 |
62 |
289.30 |
113.57 |
52.29 |
14.48 |
78.36 |
37.82 |
79.02 |
68.87 |
63 |
297.28 |
116.70 |
53.73 |
10.75 |
80.52 |
40.03 |
81.19 |
70.76 |
64 |
305.47 |
119.92 |
55.21 |
7.08 |
82.74 |
42.30 |
83.43 |
72.71 |
65 |
313.89 |
123.22 |
56.73 |
3.47 |
85.02 |
44.63 |
85.73 |
74.72 |
66 |
322.56 |
126.63 |
58.30 |
-0.09 |
87.37 |
47.03 |
88.10 |
76.78 |
67 |
331.49 |
130.13 |
59.91 |
-3.60 |
89.79 |
49.50 |
90.54 |
78.91 |
68 |
340.69 |
133.74 |
61.58 |
-7.06 |
92.28 |
52.05 |
93.05 |
81.10 |
69 |
350.18 |
137.47 |
63.29 |
-10.46 |
94.85 |
54.68 |
95.64 |
83.36 |
70 |
359.98 |
141.32 |
65.06 |
-13.82 |
97.51 |
57.39 |
98.32 |
85.69 |
71 |
370.12 |
145.30 |
66.90 |
-17.13 |
100.25 |
60.20 |
101.09 |
88.10 |
72 |
380.61 |
149.41 |
68.79 |
-20.40 |
103.10 |
63.11 |
103.95 |
90.60 |
73 |
391.49 |
153.68 |
70.76 |
-23.62 |
106.04 |
66.12 |
106.92 |
93.19 |
74 |
402.77 |
158.11 |
72.80 |
-26.79 |
109.10 |
69.24 |
110.01 |
95.87 |
75 |
414.50 |
162.72 |
74.92 |
-29.93 |
112.27 |
72.49 |
113.21 |
98.67 |
76 |
426.70 |
167.51 |
77.12 |
-33.02 |
115.58 |
75.87 |
116.54 |
101.57 |
77 |
439.43 |
172.50 |
79.42 |
-36.08 |
119.03 |
79.39 |
120.02 |
104.60 |
78 |
452.72 |
177.72 |
81.83 |
-39.09 |
122.63 |
83.07 |
123.65 |
107.76 |
79 |
466.63 |
183.18 |
84.34 |
-42.07 |
126.39 |
86.93 |
127.45 |
111.08 |
80 |
481.22 |
188.91 |
86.98 |
-45.01 |
130.35 |
90.97 |
131.43 |
114.55 |
81 |
496.55 |
194.93 |
89.75 |
-47.92 |
134.50 |
95.21 |
135.62 |
118.20 |
82 |
512.72 |
201.28 |
92.67 |
-50.78 |
138.88 |
99.69 |
140.04 |
122.05 |
83 |
529.81 |
207.98 |
95.76 |
-53.62 |
143.51 |
104.42 |
144.70 |
126.11 |
84 |
547.94 |
215.10 |
99.03 |
-56.42 |
148.42 |
109.44 |
149.65 |
130.43 |
85 |
567.23 |
222.68 |
102.52 |
-59.19 |
153.65 |
114.79 |
154.92 |
135.02 |
86 |
587.86 |
230.77 |
106.25 |
-61.93 |
159.23 |
120.50 |
160.56 |
139.93 |
87 |
610.02 |
239.47 |
110.26 |
-64.63 |
165.23 |
126.64 |
166.61 |
145.21 |
88 |
633.95 |
248.87 |
114.58 |
-67.31 |
171.72 |
133.26 |
173.15 |
150.90 |
89 |
659.97 |
259.08 |
119.28 |
-69.95 |
178.76 |
140.47 |
180.25 |
157.10 |
90 |
688.47 |
270.27 |
124.43 |
-72.57 |
186.48 |
148.36 |
188.04 |
163.88 |
91 |
719.97 |
282.63 |
130.13 |
-75.15 |
195.02 |
157.08 |
196.64 |
171.38 |
92 |
755.19 |
296.46 |
136.49 |
-77.71 |
204.56 |
166.84 |
206.26 |
179.76 |
93 |
795.11 |
312.13 |
143.71 |
-80.24 |
215.37 |
177.89 |
217.16 |
189.27 |
94 |
841.20 |
330.23 |
152.04 |
-82.75 |
227.85 |
190.66 |
229.75 |
200.24 |
95 |
895.72 |
351.63 |
161.89 |
-85.23 |
242.62 |
205.75 |
244.64 |
213.21 |
96 |
962.43 |
377.82 |
173.95 |
-87.68 |
260.69 |
224.23 |
262.86 |
229.10 |
97 |
1048.45 |
411.58 |
189.50 |
-90.11 |
283.99 |
248.05 |
286.36 |
249.57 |
98 |
1169.68 |
459.18 |
211.41 |
-92.51 |
316.83 |
281.62 |
319.47 |
278.43 |
99 |
1376.93 |
540.53 |
248.87 |
-94.89 |
372.97 |
339.02 |
376.07 |
327.76 |
9. The heading for Appendix B to part
5 is revised to read as follows:
Appendix B to Part 5--Criteria
for Designation of Areas Having Shortages
of Dental Professionals
* * * * *
Appendices
D, E, F, G [Removed]
10.
Appendices D, E, F, and G of part 5 are
removed.
PART
51c--GRANTS FOR COMMUNITY HEALTH SERVICES
11.
The authority citation for part 51c is
revised to read as follows: Authority:
42 U.S.C. 216, 254c.
12. Section 51c.102 is amended by revising
paragraph (e) and adding paragraph (k)
to read as follows: Sec. 51c.102 Definitions.
* * * * *
(e) Medically underserved population means
the population of an urban or rural area
which is designated as a medically underserved
population by the Secretary under part
5 of this chapter.
* * * * *
(k)
Special medically underserved population
means a population defined in section
330(g), 330(h), or 330(i) of the Act.
These include migratory and seasonal agricultural
workers, homeless populations, and residents
of public housing, A special medically
underserved population is not required
to be designated in accordance with part
5 of this chapter.
13. Section 51c.104 is amended by revising
paragraph (b)(3) and adding paragraph
(d) to read as follows: Sec. 51c.104 Applications.
* * * * *
(b) * * *
(3) The results of an assessment of the
need that the population served or proposed
to be served has for the services to be
provided by the project (or in the case
of applications for planning and development
projects, the methods to be used in assessing
such need), utilizing, but not limited
to, the factors set forth in Sec. 5.104
of this chapter.
* * * * *
(d)
If an application funded under this part
demonstrates that the grantee would serve
a designated medically underserved population
at the time of application, then the grantee
will be assumed to be serving a medically
underserved population for the duration
of the project period, even if the designation
is withdrawn during the project period.
14. Section 51c.203 is amended by revising
paragraph (a) to read as follows: Sec.
51c.203 Project elements. * * * * * (a)
Prepare an assessment of the need of the
population proposed to be served by the
community health center for the services
set forth in Sec. 51c.102(c)(1), with
special attention to the need of the medically
underserved population for such services.
Such assessment of need shall, at a minimum,
consider the factors listed in Sec. 5.103(b)
of this chapter. * * * * * Dated: May
23, 2005. Betty Duke, Administrator, Health
Resources and Services Administration.
Approved: March 26, 2007. Michael O. Leavitt,
Secretary, Department of Health and Human
Services. Editorial Note: This document
was received at the Office of the Federal
Register on February 21, 2008. [FR Doc.
E8-3643 Filed 2-28-08; 8:45 am] BILLING
CODE 4165-15-P
1
Greene (2003) (Greene W. Econometric Analysis,
5th Ed. Prentice Hall, New Jersey) acknowledges
that the use of principal components regression
is sometimes used in the presence of multicollinearity.
One of his criticisms is the inability to
interpret the underlying regression parameters
(p. 59), although this criticism is not
very applicable here (the underlying parameters
are never considered by the applicants.)
More importantly, Greene lays out the tradeoffs:
``If the data suggest that a variable is
unimportant in the model, the theory notwithstanding,
the researcher ultimately has to decide
how strong the commitment is to that theory.''
One of the guiding principles was face validity,
which essentially says conventionally accepted
wisdom on important determinants of access
should suggest included variables.