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Proposal for a New Approach (502 KB)
Abstract: This article
describes the development of a theory-based,
data-driven replacement for the Health Professional
Shortage Area (HPSA) and Medically Underserved
Area (MUA) designation systems. Data describing
utilization of primary medical care and the
distribution of practitioners were used to develop
estimates of the effects of demographic and
community characteristics on use of primary
medical care. A scoring system was developed
that estimates each community's effective access
to primary care. This approach was reviewed
and contributed to by stakeholder groups. The
proposed formula would designate over 90% of
current geographic and low-income population
HPSA designations. The scalability of the method
allows for adjustment for local variations in
need and was considered acceptable by stakeholder
groups. A data-driven, theory-based metric to
calculate relative need for geographic areas
and geographically-bounded special populations
can be developed and used. Its use, however,
requires careful explanation to and support
from affected groups.
Key words: Access, primary care, underservice,
Health Professional Shortage Area, Medically
Underserved Area, resource allocation.
Thomas Ricketts is a Professor
affiliated with the Cecil G. Sheps Center for
Health Services Research at The University of
North Carolina at Chapel Hill, 725 Martin Luther
King, Jr. Blvd., CB# 7590, Chapel Hill, NC 27599-7590;
(919) 966-5541; ricketts@schsr.unc.edu.
Laurie Goldsmith is an
Assistant Professor in the Faculty of Health
Sciences at Simon Fraser University in British
Columbia. George Holmes is
a Senior Research Fellow and Randy
Randolph is a Research Fellow at
the Sheps Center. Richard Lee is
a Public Health Analyst at the Bureau of Primary
Health Care, Division of Clinical Quality in
Bethesda, Maryland. DonaLd Tayloris
an Assistant Professor of Public Policy at the
Terry Sanford Institute of Public Policy and
Jan Osterman is an Assistant
Research Professor at the Center for Health
Policy, both at Duke University.
Journal of Health Care for the Poor and Underserved
18 (2007): 567-589.
Background
The search for an optimal method to prioritize
the allocation of health care resources among
areas and populations has been a long and often
frustrating process. This paper briefly reviews
that history in the United States and describes
an alternative to current methods for designating
and prioritizing areas and populations eligible
for health care assistance from the U.S. federal
government. This alternative measure of medical
underservice and provider shortage was designed
using guiding principles agreed upon by various
stakeholders, theory from the academic literature,
and methods drawn from econometrics and general
statistical analysis. The development of this
replacement measure was supported in part by
the Health Resources and Services Administration;
a proposed regulation incorporating its use
is under consideration by the U.S. Department
of Health and Human Services.
Attempts to identify medically underserved
places date back to the 1930s1
and the discussion of indicators of need was
a part of the broader discussion of standards
for medical care planning.2
In 1970, the Emergency Health Personnel Act
established the National Health Service Corps
to serve "Critical Health Manpower Shortage
Areas" (HMSAs). The regulations implementing
the law set a criterion of one full-time-equivalent
(FTE) primary care physician per 4,000 people
as the threshold for designation of such areas.
This ratio was applied to rational service
areas, which were meant to approximate the
catchment areas for primary care practices.
Initially, these were primarily whole counties,
but part-county and multi-county areas were
later considered and designated. The Health
Professions Education Act of 1976 then created
Section 332 of the Public Health Service Act,
which defined a review process for the designation
of HMSAs and required that criteria be developed
for designation of areas, population groups,
and facilities with such shortages. These criteria
were issued in 1978 and, for primary care physician
shortages, involved lower ratios of 1:3,500
for geographic areas and 1:3,000 for population
groups. (Criteria were also defined for Dental,
Mental Health and other types of shortage areas.)
The 1973 Health Maintenance Organization Act,
P.L. 93-222, took an even broader view of community
need and called for the identification of Medically
Underserved Areas (MUAs). An Index of Medical
Underservice (IMU) was developed using a nominal
process where a group of experts reviewed the
statistical characteristics of a large number
of areas considered well- or under-served and
proposed a summary measure. The IMU included
four factors: the primary care physician-to-population
ratio; the infant mortality rate; percentage
of people age 65 and over; and percentage of
population with incomes below the federal poverty
level.
The two systems were criticized early in their
implementation. The IMU was described as lacking
a conceptual core and unable to differentiate
underservice from access or health status 3,
4 and as being unable
to identify truly needy areas adequately.5
An evaluation of the health manpower shortage
criteria concluded that the "HMSA criteria cannot
successfully delineate areas in a way that meets
multiple and inconsistent objectives. The inconsistent
objectives are the requirement that areas be
capable of developing the support needed for
a viable practice, and the requirement that
need for care be addressed without regard to
manpower availability" (p. 304).6
They recommended that ". . . greater consideration
be given to indicators of effective demand"
(p. 305).6
Beginning in 1975, the MUA designation was
required to qualify areas as eligible for Community
Health Center (CHC) grants. The adaptation of
the MUA to the CHC program started a general
trend of using these designation systems to
qualify applicants for programs that formed
what came to be called the federal health care
safety net. By the mid-1990s, the MUA and the
HMSA (by this time renamed Health Professional
Shortage Areas (HPSAs)) were being used to determine
eligibility for over 30 different federal assistance
programs.
In the early 1990s, the Bureau of Primary Health
Care started work on revisions to the HPSA and
MUA systems (now expanded to include a Medically
Underserved Population (MUP) definition, often
combined with MUAs as MUA/Ps). In 1995, the
U.S. General Accounting Office (GAO) published
a report titled, Health Care Shortage Areas:
Designations Not a Useful Tool for Directing
Resources to the Underserved.7
The report found a number of flaws in the HPSA
and MUA designation systems for identifying
shortage areas and their use for targeting funding
to the underserved. The report also found that
the designation systems were neither timely
nor consistently accurate and suggested they
did not necessarily merit renewal or updating.
The report recommended that the HPSA and MUA/P
designation systems be replaced with more specific
designation criteria created for each of the
different federal assistance programs that were
using them. The GAO observations were echoed
in the field as stakeholders expressed the view
that the system had become unwieldy and arbitrary.8
During the same period, there were apparent
shifts in how policymakers viewed the distribution
of primary care resources in the nation. A previously
recognized national shortage of primary care
professionals had been replaced by a perceived
potential surplus of physicians coexisting with
continuing inequity in geographic distribution.9
At the same time, more federal programs were
linked to the HPSA and MUA/P designations.10
These factors contributed to a growing perception,
beyond the Congress and including the implementing
agency and stakeholders, that the existing HPSA
and MUA/P designations were not adequate for
the identification of underserved communities.
In response to the GAO Report and other stakeholder
concerns, the Bureau of Primary Health Care
(BPHC) developed an alternative designation
process, making use of an enlarged set of variables
and a series of weights to qualify areas and
populations for assistance. The Bureau issued
a Notice of Proposed Rulemaking (NPRM) in the
September 1998 Federal Register11
(referred to in this paper as NPRM-1) which
proposed combining the two designation processes
into one new method. The BPHC invited comments
on the proposed rule and received an unusually
large number (800), most of them from stakeholder
groups objecting to some specific element of
the proposed formula that would create more
"losers" (undesignated places) than "winners"
among their constituents. External analysts
modeled the effects of the proposed system of
designation and found that up to half of all
previously designated areas would lose their
designation if the new formula were applied
to current data for the communities and populations.12
As a result, HRSA withdrew its proposal, but
committed to developing a new one based on analysis
of the public comments received and with input
from analysts who had modeled the impact of
the previous proposal. Ultimately, the agency
entered into a cooperative agreement with the
University of North Carolina to create a revised
method. This article describes the results of
that work, which forms the basis for a revision
of the designation rules. The proposed rule
changes were approved by the Secretary of Health
and Human Services on March 26, 2007 and a "notice
of proposed rulemaking" will appear in the Federal
Register sometime in 2007. After a 6-month comment
and review period, which may result in modifications
to the proposal, the final rule is scheduled
for publication in early 2008.
Guidelines Constraining
the New Scheme
Among the criticisms of the revision proposed
by HRSA in 1998 (NPRM-1) was that its development
did not make use of the most current data, and
that it did not develop out of a general theory
of access and underservice. The 1998 proposal
was based on the extant literature, but the
working group did not conduct original data
analysis to develop weights or link the process
to a formal generalized theory of access. The
HRSA team did conduct an impact analysis of
the effects of the proposal but the analysis
used 1994 or earlier data, resulting in an underestimate
of the number of places that would lose designation.
To assist the study group in defining the scope
of the problem, five key elements were specified
as highly desirable in a future method for designation.
These were developed with contributions from
key stakeholders, including federal agency staff,
state organizations that supported safety net
providers, and the safety net organizations
themselves. Those elements were:
- Simplicity: The new underservice
measure must be understandable and usable
by those who seek designation. The use of
reference tables to convert raw data to scores
(similar to those currently used in the calculation
of the MUA/P) was particularly desirable.
Furthermore, the number of factors included
in the calculation should be limited. The
process should be simple enough that, given
the data, the score could be computed in about
5-10 minutes.
- 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.
There is no empirical justification for the
percentage of the population below the poverty
line having a weight equal to the infant mortality
rate. The contribution of each variable to
an overall measure should be based on some
verifiable statistical relationship.
- 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. Stakeholders in the process
should contribute to the selection of indicators.
- Retention of designations for places
with safety net providers: The new underservice
measure should not dramatically affect the
overall number of designations for places
with safety net providers. Most places that
currently have safety net resources and that
are serving a substantial number of uninsured,
low-income people, or people who would otherwise
not have ready access to primary care, should
retain their associated designations. Secondly,
the new measure should designate approximately
the same overall total population included
in currently designated areas and populations,
but better focus the designations to more
needy areas and populations.
- Acceptable performance: The new
system must perform better than alternative
proposals and better than the current designation
criteria using updated data. Better is
vaguely defined, since multiple criteria will
likely be used to judge whether the new system
is an improvement over current rules. The
new rule should be seen as an improvement
by the multiple key stakeholder groups.
The guiding principles received roughly equal
weight in the construction of the new method
and its application. When two principles were
in conflict and the advantages from choosing
one over the other were roughly equal, the principle
listed first on the list was given priority.
Thus, if the use of a more complex set of tables
and calculations on the part of applicants would
bring only minimal improvement of the accuracy
of the estimate of underservice, then the priority
would be given to the simpler option.
The Population Denominator
To integrate the HPSA and MUA/P designation
processes logically and scientifically required
some common theoretical basis for the two. This
was drawn from frameworks and theories that
defined or described the concept of access
to health care. This is consistent with
the goals of the programs that make use of the
HPSA and MUA systems, which are to improve access
to care for underserved populations. 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.
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 revealed a preference
for using that metric as the basis for a revised
method. 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.
However, there was no consensus on the right
threshold for a ratio that would trigger designation
and there was pressure to create an abstract,
multifactorial index, or score, that did not
refer statistically or lexically to the population-practitioner-ratios.
Following the guiding principles agreed upon
at the outset of the project, the team elected
to attempt to create an index that was related
in scale and form to a ratio but was derived
from a weighted, multifactorial process. The
index was conceived to reflect the logic 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
the age-gender structure as well as characteristics
that would affect use of services.
The assumption was made that, for groups without
significant barriers to care, primary care utilization
rates would cluster around the most appropriate
level. Office-based primary care visits were
considered the most appropriate metric of use
since they corresponded to the central "product"
of safety net programs. The initial analysis
examined survey data on the use of services
drawn from the 1996 Medical Expenditure Panel
Survey (MEPS). In the MEPS, use rates vary by
age and sex but also by characteristics that
can be related to community level rates (e.g.,
unemployment, income, race, and geographic location).
These variables, when aggregated, have been
commonly used to describe restrictions on realized
access for populations and have been used to
estimate need for services and underservice.
Recent work by Krieger and colleagues has supported
the utility of linking areal socioeconomic data
with individual measures of health status.13
The project goal was then to estimate the degree
of shortage or underservice faced by a population
based on the aggregate characteristics of the
population and the relationship between those
characteristics and the available supply of
primary care services.
Use of services is considered an outcome of
a health care system. The lower use rates of
minority, unemployed, low-income, and certain
rural and inner city populations who do not
have an established or acute illness are reflected
in lower primary care office visits reported
in the MEPS. The association of a characteristic
of an individual, such as being unemployed,
on access can be expressed for populations in
the relationship between a related aggregated
factor (% unemployed) and population access.
These aggregated factors that create barriers
to care are often also associated with lower
numbers of primary care practitioners in communities.
These correlations raise the question of whether
the use rates are depressed due to lack of practitioner
supply or to the restricting effects of individual
and aggregate characteristics on demand for
practitioners. Some researchers have observed
a relationship between the supply of primary
care practitioners and health outcomes measured
as preventable hospitalizations.14,
15 This potentially
creates a paradox since low access results in
subsequent illness that may require hospitalization
which, due to the entry of the patient into
a structured care system, may actually induce
subsequently higher rates of use of primary
care services incident to the hospitalization
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 reduced
and 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 barriers + Absolute number of increased
visits caused by delayed care or greater morbidity
= Total visits to be provided by accessible
providers
The Numerator in an Underservice
Index: Practitioners
The programs that rely on a shortage designation
are structured to provide solutions that do
not allow for small incremental additions to
capacity. Clinics and professionals, when placed
into communities require sufficient demand to
justify the expense of their support. Thus,
a measure that is used to trigger assignment
of a practitioner or the decision to fund a
clinic should reflect a threshold level of need
for, at least, an additional, potentially autonomous,
practitioner. This measure has been expressed
as a population-to-primary care physician ratio;
the identification of the optimal ratio has
been the subject of contention for decades.
Goodman and colleagues suggested benchmark ratios
to compare relative supply; their preferred
ratios bracketed 1,500:1.16
That ratio as a gold standard for reasonable
access is supported by data from the National
Ambulatory Medical Care Survey (NAMCS). The
NAMCS annually estimates the number of physician
office visits per person per year.17,
18 The visit rate to
primary care physicians in 1998 was 1.94 per
person. This translates into a ratio of 2,132
persons per full-timeequivalent (FTE) primary
care if all primary care visits and only primary
care visits are allocated to primary care practitioners.
However, it is reasonable to assume that a portion
of visits to specialists are for primary care
reasons and, in creating an optimal rate for
programs that place or support only primary
care services, the potential need or demand
for those visits should be included in the calculation
of a community's level of underservice. The
NAMCS data indicate that 20% of visits to non-surgical
specialists were primary care visits; this produces
a ratio of 1:1,909. However in a community made
up of a mix of generalist practitioners (family
medicine, pediatrics, obstetrics/gynecology,
internal medicine), it is reasonable to expect
practitioners to be able to see 90% of the total
office visit demand (effectively, 2.763 visits
per person); the national mean ratio would then
be 1:1,498. Based on this overall mean ratio,
we posited a preferred ratio of 1,500 people
per full-time primary care physician as a central-tendency
standard of adequate access. Setting a ratio
of 1:3,000 as a trigger for designation would
then be a conservative approach to identifying
a threshold since it reflects the productivity
of an additional FTE physician. We chose to
accept that level as guidance for a score or
index of underservice both because it reflected
the logic of adequate demand for services as
well as because it was in agreement with prior
policies that used similar ratios in federal
designation systems.6,
19
Combining Numerator and
Denominator to Calculate an Index of Underservice
The project team sought to create a measure
of underservice that was based on recognizable
concepts of supply of services and population-based
need. Need for services would be expressed as
a population adjusted to reflect community and
individual barriers to access as well as induced
need. That adjusted population was included
in a ratio to FTE primary care practitioners.
The population to FTE ratio was then further
adjusted to account for community or service
area factors that are thought to increase need
further (above the population adjustments already
made) to create what we have called an Underservice
Index.
Underservice Index = Adjusted population-to-practitioner
ratio + Total score from demographic, economic,
and health status factors
This new measure is intended to resemble the
current MUA/P method in that it creates a score
or index of underservice. The implementation
is also similar to the current MUA/P and HPSA
methods in its use of a population-to-primary
care provider ratio and the accommodation of
other high need variables; these two components
are key pieces of the new underservice measure.
The following section describes the process
used to calculate the Underservice Index, starting
first with the development of an adjusted population
component, which is then modified to consider
service area variables.
The Population-to-Povider
Ratio
The ratio numerator. The ratio includes
a denominator, which is termed the "barrierfree,
use adjusted population." Unlike previous underservice
measures' use of an actual population in a ratio,
the proposed system's ratio is based on an adjusted
population that is meant to represent an effective
or apparent population and its primary
health care needs. Pursuant to the theory presented
earlier, the population used for the ratio is
adjusted to reflect age and sex-specific primary
care rates in an access barrier-free (or minimal
barrier) population. That is, 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 poverty, race, or ethnicity,
what would the use rate be for each age-sex
group and for the entire population? The reason
for the restriction to a barrier-free population
is that income or racial barriers may have effects
that vary by age and sex, distorting age and
sex-related differences in primary care use
rates.
The standard for utilization is based on the
estimated primary care office visit rate for
the national population segment considered to
have the fewest or no access barriers. The Medical
Expenditure Panel Survey (MEPS) sponsored by
the U.S. Agency for Healthcare Research and
Quality (AHRQ) periodically fields a national
survey of the population to estimate overall
utilization of health care services. We operationalized
this desired visit rate as the overall primary
care office visit rate for the population that
is (1) White, (2) non-Hispanic, and (3) non-poor,
estimated using the 1996 MEPS. Employment status,
although included in the MEPS survey and a significant
correlate of use of service, was also intercorrelated
with the other variables and was not included
in the final visit calculation. These rates
were estimated for six age groups each for males
and females. Table 1 shows the utilization rates
for the White, non-Hispanic, non-poor, by age
and sex.
This target visit rate can be calculated for
any area for which we have population data broken
down into these 12 age-sex classifications;
population data at this level are available
for all counties and all sub-county census areas.
Using a community's age and sex distribution,
these rates were used to calculate a visit requirement
for each community {i.e., 4.046 * (# Females
0-4) 1 2.256 * (# Females 5-17) 1 ... 1 8.056
* (# Males 75 and over)}. Dividing this visit
requirement by the average number of visits
reported in MEPS in a barrier-free population,
3.741 visits per person per year, gave an area's
barrier-free use adjusted population. For example,
a county with a total population of 12,000 people
with 1,000 in each of the 12 cells would have
an optimal use rate of 61,067 visits, the sum
of each of the visit rates times 1,000. The
effects of the adjustment effectively increase
county populations by a mean of 16.3% (range
5 6.7% to 40.3%).
|
0-4
years |
5-17
years |
18-44
years |
45-64
years |
65-74
years |
75 years and over |
Female |
4.406 |
2.256 |
5.007 |
5.480 |
6.710 |
8.160 |
Male |
5.164 |
2.499 |
2.867 |
4.410 |
6.052 |
8.056 |
MEPS = Medical Expenditure Panel Survey |
The ratio denominator. Following current
federal practice, the providers included in
the ratio include primary care doctors of medicine
(MDs), including interns and residents, and
primary care doctors of osteopathy (DOs), including
interns and residents; nurse practitioners (NPs)
and physicians assistants (PAs) who are associated
with a primary care physician; and all certified
nurse-midwives (CNMs). Eligible providers must
be non-federal providers of direct patient care.
Primary care physicians (MDs and DOs) are practicing
principally in general practice, family practice,
general internal medicine, pediatrics, or obstetrics
and gynecology. Primary care NPs, PAs, and CNMs
are similarly defined.
All practitioners are measured in full-time
equivalency (FTE) units weighted for relative
productivity and scope of practice. The proposal
matches current practice in allowing applicants
to adjust the FTE numbers to agree with the
actual availability of practitioners to the
general population; this is done via local or
statewide surveys. The relative weights for
the practitioners were determined externally
to the process by consensus among the stakeholders
and the federal agency. That weighting process
is under further review at the federal level
and may be modified prior to inclusion in a
final rule. At the time this article was written,
the productivity/scope of practice weights were
1.0 for physicians (MDs and DOs, not including
interns and residents), 0.5 for NPs, PAs, and
CNMs, and 0.1 for MD and DO interns and residents.
The assignment of relative weights to primary
care practitioners has been controversial and
was subject to much debate in the development
of the process. However, there was no consensus
among the stakeholders on how to provide more
accuracy or specificity to the weighting so
the criteria were set at levels that had been
suggested in the past.
Need variables. The goal of the programs
that are linked to designation is to improve
access, thereby improving health. This consideration
drove the design of the analysis to develop
weights for need for services in areas
and for populations. We followed the conceptualization
of access proposed by Andersen and colleagues,
who posit that there are predisposing and enabling
characteristics that can represent need.20-22
There is no consensus set of community-level
indicators that reflect need within their framework.
Given the emphasis on the placement of primary
care practitioners and their staffing of the
clinics and primary care centers that were linked
to designation, the project chose to use primary
care population-to-practitioner ratios as a
proxy indicator of relevant need and to examine
how those ratios varied with socio-demographic
indicators at an appropriate geographic level,
in this case the rational service area as defined
by the agency. Geographic adjustments to the
supply of practitioners were not used in the
analysis because it was felt by the funding
agency that these methods had not gained wide
acceptance in the field. There are several methods
available to account for cross-boundary use
of primary care services using GIS systems including
floating catchment areas,23
smoothing algorithms,24
raster-assisted weighting,25
and geographically-weighted regression
techniques.26 These
methods are gaining wider acceptance and will
likely be used in future revisions of regulatory
mechanisms intended to identify populations
in need.
Candidate indicators were drawn from earlier
analytical work 27 and
from contributions 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. The staff and leadership of the DSD also
contributed extensively to the design. More
than 60 discrete variables were suggested during
the process and the stakeholder group proposed
a listing of 18 general variables with multiple
specific indicators, ranging from specific health
status or use indicators, such as ambulatory
care sensitive condition admission rates, to
census-derived linguistic isolation, to general
morbidity rates for common diseases such as
diabetes and more rare diseases such as cancer.
Behavior-linked variables were also suggested,
including obesity and smoking rates along with
utilization of existing safety net providers.
Some promising candidate variables could not
be used, despite being highly correlated with
primary care practitioner-to-population ratios
and despite representing health outcomes that
safety net programs were to address (e.g., the
number of uninsured persons). This was mostly
due to their lack of consistent availability
at the small area level appropriate for designation.
The final choice of variables and the priority
for inclusion in the analysis were based on
the degree to which the variables reflected
underlying components of access as qualitatively
assessed by the UNC-CH team, the PCA/PCO group,
and staff of the Bureau of Primary Health Care
(BPHC) as well as their stability and regular
availability at the county level or the level
of smaller areas. The final measures included
the demographic, economic, and health status
indicators summarized in Box 1.
Demographic characteristics. Population
characteristics, especially racial and ethnic
characteristics, have been consistently shown
to affect access to primary care.28-30
Measures of the proportion of the population
that is non-White, non-Hispanic and proportion
of the population that is Hispanic were used
to adjust the ratio further. The proportion
of the population older than 65 years was also
included because communities with higher proportions
of elderly residents have unique community characteristics
not captured in the initial population adjustment.
This could be 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
worse access.
Economic characteristics. Income and
employment are very strong indicators of ability
to access primary health care and to afford
health insurance.31-33
The unemployment rate and the proportion
of the population below 200% of the federal
poverty level were used to further adjust the
ratio.
Demographic
Population density |
Economic |
Health status
|
- Percent population >
65 years
|
- Percent population < 200% FPL
|
- Actual/expected death
rate (adjusted)
|
Health status characteristics. 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 (SMR),34
and the infant mortality rate or the low
birthweight rate.35,
36 These special epidemiological
conditions that increase need are not fully
represented in the age-sex adjustment.
Unit of Analysis to
Derive Weights
The goal of this step was to weight the relative
effects of local population characteristics
on practitioner supply appropriately and to
include that in the calculation of need. The
assumption was that a place or population might
have attracted more or fewer practitioners than
would be expected based on a summary regression
model. The general approach was to take population-level
variables characteristic of beneficiaries of
the federal programs that used the HPSA/MUA
methods 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 the overall index.
To approximate normal market geography, a sample
of counties and county equivalents that serve
as proxies for a health care market were selected
to derive the area characteristic weights. This
step was carried out in order to identify places
that functioned as primary care service areas
and that reported stable, reliable, usable data.
Many U.S. counties meet these general qualifications,
and the process selected a range of counties
that met certain further criteria: populations
less than 125,000; area less than 900 square
miles; and unadjusted population-to-practitioner
ratio less than 4,250 to one. This yielded 1,643
counties of the total of 3,040. Variations in
the criteria were used and tested, altering
population between 80,000 and 150,000; area
between 700 and 1200 sq. miles; and the ratio
between 3,000 and 4,250. The estimates derived
from the models were not substantially different
among the different samples. In effect, the
criteria eliminated very small and very large
counties and counties with unusual distributions
of health practitioners.
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 subcounty-designated and subcounty-undesignated
areas 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 most available health resource
and health status data are calculated and reported
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.
The Dependent Variable:
Adjusted Population-to-Private Supply Provider
Ratio
The dependent variable in the regression model
is the age-sex adjusted population-toprimary
care provider ratio. While the practitioner
count follows the general guidelines described
earlier (non-federal, direct patient care MDs,
DOs, NPs, PAs, and CNMs), an additional restriction
is imposed. The analysis included only those
practitioners practicing in the community without
federal support or without incentives to practice
in state- or federally-operated facilities.
Practitioners in the National Health Services
Corps (NHSC) and State Loan Repayment Programs
(SLRP) and J-1 visa physicians are not included
in the ratio for the regression model.
Independent Variables
as Percentile Scores
The value for each need variable was assigned
a percentile rank based on the distribution
of actual values of all U.S. counties. This
was done to allow for future changes in the
scaling of the scores when there are changes
in the distribution of values. The use of percentiles
will allow policymakers a choice of how often
(or whether) to update the values without having
to change the overall approach to developing
component scores.
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). Population density was the only
need variable lacking a natural theoretically
worse value. Both very low density and very
high density areas would be expected to have
greater health service needs and problems with
primary care access than moderately dense areas.
Since we found that other indicators of need
increased consistently with higher density,
we set the lowest population density at the
99th percentile.
Due to a skewed distribution across the areas,
we modified the definition for the percentage
of non-White population so that only the top
(most non-White) 60% of areas could be included
in the weighting for the non-White variable.
Areas with non-White populations lower than
the 40th percentile were assigned to the zero
percentile; the actual value at the 40th percentile
is 2.6% non-White. Following existing agency
practice, the analysis also combined low birth
weight and infant mortality into one measure,
taking the higher of the two as the percentile
value for adverse birth outcomes for a given
area.
The associated percentile values for all need
variables were subsequently 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 was defined such that
the fastest acceleration in the poverty component
score occurred at high levels of poverty rather
than at low levels. The model thus allowed a
greater relative weight difference between the
95th and the 96th percentile than between the
5th and 6th percentile.
Controls for Multicollinearity
Because many of the need measures were moderately
inter-correlated, we performed a principal components
factor analysis to create uncorrelated factor
scores for the selected variables to use in
the regression modeling. To further ensure unbiased
estimators, the regression model was structured
as a weighted least squares regression using
county total populations as weights. Parameter
estimates from the regression were further adjusted
for their statistical significance by weighting
the parameter contributions to the need component
scores using transformed standard errors.*
A set of scores that could be added to the adjusted
population portion of the ratio were derived
for every combination of assigned percentile
values for all the variables. However, the scores,
at this stage, did not represent the full range
of association between the variables and the
ratios. The scores were derived using county-level
data, where the maximum ratio was restricted
to 4,250:1. If the scores were to estimate ratios
larger than this maximum accurately, the dimension
of the scores would have to be changed to allow
for those higher values. In reality, 10% of
all U.S. counties have ratios greater than 4,250.
A second consideration was that the ratios themselves
were constructed with the assumption that the
numbers of primary care practitioners reported
in national data sets overstate the actual numbers
providing care in the counties and areas designated
as HPSAs.37 Applicants
for HPSA designation are currently encouraged
to adjust for this by surveying locally to estimate
the actual FTE supply in their rational service
areas; this is done by most applicants and the
actual FTEs are reported by the agency in its
summary of HPSAs. This adjustment yields a reduction
of FTEs of approximately 20%. To compensate
for the overcount of practitioners and the exclusion
of the high ratio counties, the scores were
adjusted to levels that would predict the full
range of actual ratios, were they translated
back into parameter weights in a regression.
This adjustment to the scores is in a sense
arbitrary but necessary to make use of the intuitive
appeal of the 3,000 cut-off point. This decision
was supported by the impact analysis described
below. The distribution of the final scores
is depicted in Figure 1.
*The process involved four
steps: (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 = Sb* as above.
An alternative treatment would be to discard
any statistically insignificant estimates. We
have strong conceptual biases against employing
such stepwise procedures.
Application of the Proposed
Method
The goal of the regression process was to derive
weights that could be used to adjust the population
to practitioner ratio to reflect the relative
effect of aggregate population and area-level
characteristics on demand and use of services.
The weights are in the same metric and can be
interpreted as population-equivalent additions
that are added to the demand facing each FTE.
The scores were then added to the adjusted population
total to create a "total score" that resembled
a further adjusted population. Figure 2 provides
a summary of the steps involved in combining
the adjusted population ratio with the scores
for demographic, economic, and health status
factors derived from the regressions. Table
2 presents the calculations for data from a
random set of U.S counties ranging from very
urban to very rural. The designation status
as of 1999 is also indicated. Whole means
the entire county was designated; part means
that part of the county was designated; and
lowinc means that the low-income population
in the county was designated.
[D]
Data Gathering |
Applying the Formula |
Identify "Rational" Service Area
Adjust for Age and Sex
Calculate Weights for Barrier Factors
Adjust FTE
Practitioners |
Practitioner: Population Ratio
Plus
Need/Barrier Scores
Minus
Federal Practitioners
=
Final Adjusted "Score"
|
Table 2 also shows the application of the scores
to the ratios of population to practitioners;
this is presented in two ways, before and after
accounting for federal practitioners who may
be placed in the area by some program that depends
on a designation. The scores from the weights
change the ratios into a designation score and,
without the removal of the practitioners placed
in areas by federal programs, three of the counties
have scores above 3,000, the designation threshold
(Score1 in bold). The initial total score, Score1,
includes all primary care providers regardless
of the reason for practicing in the community.
The federal government recognized, however,
that including safety net providers in a designation
measure could result in a yo-yo cycle whereby
the safety net providers provide enough capacity
for an area to lose its designation status.
Thus, the final total score, Score2 in Table
3, takes out those practitioners; in the example,
an additional county reaches the threshold ratio
as a result. The practical application of the
system would make use of Score1 for an initial
determination and, if the applicant falls below
the threshold, the FTE adjustment to create
Score2 would be carried out. This step would
make use of national data sets that identified
practitioners placed by federal programs or,
where possible, local surveys to count the FTEs
of primary care practitioners accurately to
adjust supply. Although the proposed scoring
system is expressed in terms that appear to
be population counts, it is a far more complex
metric and actually represents the integration
of a number of ecological and individual characteristics
of any group or place and not a population per
se.
Effects of the Proposed
Underservice Index
The agency and stakeholder groups were very
interested in the effects of any revised designation
formula and part of the contracted work included
impact testing on all designated areas. The
revised scoring method was designed for so-called
geographic designations, or designations
that include the entire population in a rational
service area, or fixed geographic area.
Other designation types are provided for under
current rules, including population and
facility designations. Population designations
single out a specific population in a geographic
area and include low-income, Medicaid, homeless,
and migrant farm worker categories (e.g., the
low-income population of Madison County or the
Medicaid-eligible population of Jones and Smith
Counties). Low-income population designations
are the most common current population designation.
In the data set used for the impact analysis,
there were 1,710 geographic and 809 population
primary care HPSAs; of the population HPSAs,
592 were low-income population group designations.
There also were 3,504 total MUA/Ps, and 46 of
these were low-income population designations.
After accounting for overlap between HPSAs and
MUA/P, there were 3,960 whole or part geographic
HPSAs or MUAs and 487 low-income HPSAs or MUPs.
County Name |
HPSA designation 1999 |
MUA/P designation 1999 |
Total population 1999 |
Age-gender adjusted population |
Total FTE primary |
Adjusted population FTE ratio |
Score from weights* |
Score1 |
Ratio w/o Fed. FTE |
Score2 |
Coconino, AZ |
part |
part |
116,977 |
127,492 |
91.7 |
1,389.6 |
1,161.4 |
2,550.9 |
1,444.7 |
2606.1 |
St. Lucie, FL |
low-inc. |
whole |
180,937 |
222,417 |
105.1 |
2,116.5 |
918.3 |
3,034.8 |
2,314.7 |
3233.0 |
E. Baton Rouge, LA |
part |
part |
395,635 |
447,680 |
379.5 |
1,179.7 |
640.2 |
1,819.8 |
1,185.9 |
1826.1 |
Dunklin, MO |
none |
whole |
33,006 |
40,146 |
22.8 |
1,764.6 |
1,469.4 |
3,234.1 |
1,764.6 |
3234.1 |
Bronx, NY |
low inc. & part |
part |
1,185,970 |
1,366,382 |
1,210.6 |
1,128.7 |
1,655.3 |
2,793.9 |
1,199.6 |
2864.8 |
Burlington, NJ |
none |
none |
416,853 |
482,594 |
411.2 |
1,173.6 |
251.6 |
1,425.3 |
1,179.4 |
1431.0 |
Guernsey, OH |
part |
part |
40,854 |
48,273 |
20.2 |
2,389.8 |
751.7 |
3,141.5 |
2,389.8 |
3141.5 |
Rusk, WI |
low-inc. |
whole |
15,449 |
18,501 |
10.8 |
1,713.0 |
1,070.5 |
2,783.6 |
8,043.7 |
9114.2 |
*This is the score that is calculated by multiplying
the regression parameters by the percentiles
rank for each area or population for the 9 variables
in Table 2.
Figure 1 depicts the values for the score components
by percentile rank.
Boldface scores reach threshold.
HPSA = Health Professional Shortage Areas
MUA/P - Medically Underserved Area or Medically
Underserved Population
Fed. = Federal
FTE = full-time equivalent
low inc. = low income
Low-Income Population
Designation Modification
The intention was to create a system that could
be applied to all of the potentially designatable
populations and groups. Adjusting for the higher
needs and lower demand for primary care among
low-income populations is difficult because
existing data sets based on county boundaries,
even census tracts and ZIP code areas, do not
always reflect the distribution of people by
income or health care need. However, it was
possible to create a base ratio for areas that
used the percentage of an area's total population
that are in low-income categories (e.g., below
200% of the federal poverty level) along with
an estimate of the numbers of primary care practitioners
who serve those people. In this variation in
the application of the scoring formula, termed
the low-income adjustment, the population
and the primary care provider FTEs are adjusted.
The low-income population is used for the population
portion of the population-to-primary care ratio
rather than the total population of the area
(the low-income population is assumed to have
the same age and sex distribution as the total
population for the population adjustment). The
number of primary care provider FTEs used in
the population-to-primary care ratio is multiplied
by 0.21* to adjust for
the estimate of the providers available for
the low-income population. This revised base
ratio becomes the starting ratio for an alternative
application that was impact-tested using national
data.
Effects on Designated
and Undesignated Areas and Populations
The proposed scoring formula was tested using
data from all U.S. counties, existing geographic
HPSAs and MUAs, and low-income population HPSAs
and MUPs designated in 1999. That sensitivity
analysis used data relevant to that year. Of
the 4,447 unduplicated existing geographic and
low-income HPSAs and MUPs, 2,962 (66.6%) met
the designation threshold under the original
(geographic) proposed formula (Table 3). Fifty-one
(51) previously undesignated areas reached the
threshold and 177 areas that were designated
under low-income population rules reached the
threshold as geographic areas. The total population
meeting the threshold using the proposed formula
was 52.9 million people, or 55.5% of the currently
designated population. The low-income adjustment
to the proposed scoring system qualified an
additional 24.5% of existing areas and covered
an additional 31.7% of the baseline population.
In comparison, applying the current rules resulted
in fewer designations (2,188, 49.2% of those
designated by the federal government in 1999)
and less population coverage (32.7 million people,
34.3% of baseline) than using the proposed formula.
State-specific analyses showed that the number
and proportions of areas and populations that
would be de- or re-designated would vary by
state; the majority of states experienced net
losses of baseline designations.
*This number is an average
of the FTE adjustment from all low-income designations
updated in 1998 and 1999. There were 288 areas
that were updated during this time period. The
Bureau of Primary Health Care conducted the
review and provided these data in November 2001.
HPSA
or MUA/P status |
Number
of areas designated |
Baseline
designations |
Current
regulation, new data |
Proposed
scoring system |
Geographic |
Additionally
designated using low-income adjustment |
Geographic, 1999 |
3,960 |
2,085 (53%) |
2,734 (69%) |
805 (20%) |
Low-income |
487 |
85 (17%) |
177 (36%) |
166 (34%) |
Not designated* 1999 |
-- |
18 |
51 (1%) |
117 (2.6%) |
Total |
4,447 |
2,188 |
2,962 |
1,088 |
* Not Designated in this dataset
means not designated as either a geographic
HPSA or MUA or a low-income population HPSA
or MUP. The area may have another type of designation
or be undesignated entirely.
HPSA = Health Professional Shortage Areas
MUA/P = Medically Underserved Area or Medically
Underserved Population
We also examined the effects of the proposed
formula on areas that included safety net institutions
and providers that use the HPSA and MUA/P designation
process with the same restrictions on the analysis
of population and low-income adjustments; the
results are summarized in Table 4. Applying
the proposed method to geographically designated
areas alone results in a 34.9% decrease in the
places that include a federal CHC clinic, a
30.8% decrease in the number of areas with Rural
Health Clinics (RHC), and a 44.7% decrease in
the number of geographically-designated areas
with NHSC placements. The addition of the low-income
adjustment to the analysis increases the inclusion
of safety net programs by more than 20% but
would still result in a 11.2% decrease in the
number of areas with CHCs, a 2.5% decrease in
the number of RHC areas, and a 13.4% decrease
in the number of geographically-designated areas
with NHSC placements.
|
Sites |
Curent criteria
using updated data |
Geographic
method |
Geographic
and low-income method |
Safety net program |
N |
N |
% |
N |
% |
N |
% |
CHC 1999 |
1,481 |
639 |
43.1 |
964 |
65 |
1,315 |
88.8 |
RHC 1999 |
2,842 |
1,317 |
46.3 |
1,967 |
69 |
2,771 |
97.5 |
NHSC 1999 |
932 |
314 |
33.7 |
515 |
55 |
807 |
86.6 |
CHC = Community Health Center
RHC = Rural Health Center
NHSC = National Health Service Corps
Discussion
This designation system has been developed in the context of real world policy.
It is an attempt to work from prior theory and
research to improve the application of federal
safety net policies by better targeting places
that are underserved as well as accommodating
the on-the-ground realities of existing safety
net institutions. The method will be judged
against a standard of political and practical
acceptability more so than by its theoretical
purity. The four years of work that went into
its development included substantial discussion
of options and alternatives as well as modeling
to estimate its effects, and this was open to
inspection by all stakeholders.
The proposed method is conceptually and computationally complex, violating one
of the original guiding principles for the exercise.
However, the system has 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.
The extrapolation of the relationships between individual characteristics and
use of services to aggregate relationships for
communities introduces potential weaknesses.
For example, Robert and House, in their review
of the relative contribution of individual-,
community-, and societal-level research on the
relationship between socioeconomic factors and
health, found that, "Although multilevel studies
indicate an independent role of community socioeconomic
conditions . . . most of the community level
effects have been small in size."38,
p. 122 There, however, remain substantial support
and evidence for the contributory role of community
characteristics to health status and need for
services.39
The combination of the scoring formula proposed
here with the low-income adjustment addresses
many of the concerns of stakeholder groups expressed
in comments on the original proposed rules (NPRM-1)
of September 1998. It is not anticipated that
the methods proposed here will be the only avenue
for determining eligibility in the final rules,
however. For example, these methods are not
intended to identify fully low-access populations
embedded in larger population groups, special
access barriers that are masked by aggregate
data, or the civil and postal boundary lines
used to derive data that divide or arbitrarily
delineate communities. The proposed measure
is intended to be used only as an approach for
determining eligibility for designation where
applicant areas and populations that initially
score above the threshold would receive designation
but other applicants might also qualify under
more subjective criteria if need is sufficiently
documented in their application. The proposed
data-driven formula is able to predict current
designations remarkably well given that the
application of the current rules makes extensive
use of negotiation and local refinements of
secondary data.
The data reported here were those used in the
original development of the proposed modification;
the impact analysis was completed soon after
that work was done. The lengthy review process
for the proposal has made those estimates somewhat
dated but the system can be quickly revised
to reflect more recent data. For example, the
most recent MEPS visit rates (currently 2004)
can be applied to the population weighting process
and the area and population characteristics
can be updated to the most recent U.S. Census
enumeration data or estimates. Some of that
work is progressing at the request of the Bureau
of Health Professions but, based on preliminary
analyses using these strategies, a full-scale
re-estimation of modified impacts would not
reveal a pattern of de- or re-designation substantially
different from what is described here.
Safety net providers and advocates have expressed
the greatest concern with the effects of any
revision to the designation process. While safety
net facilities and providers could be associated
with particular geographic areas in the analysis,
it was not possible to know whether these safety
net facilities and providers were exclusively
serving the low-income populations of those
areas or whether a substantial amount of boundary-crossing
took place. A potential loss of a geographic
designation for an area with a safety net facility
or provider may be replaced with a designation
based more closely on a service population,
provided those data are available. Our analysis
of safety net facilities and providers therefore
presents a worst-case scenario.
The key theoretical innovation of the process
is the simultaneous estimation of parameters
for factors that deter use of services with
those that create need for care. In real communities
and for real people, both things are happening.
In places that have safety net programs such
as a clinic, an access program is promoting
appropriate utilization by overcoming access
barriers. Where a program is absent, clinicians
who might not see patients for preventive care
are often called on to care for them in emergency
conditions when complications have arisen because
the patient did not seek care earlier. The amount
of the increase in use brought about by delayed
care must be added into the reduction in use
to produce an accurate estimate of the entire
access problem in a community.
Acknowledgments
This work was commissioned by the Bureau of
Primary Health Care, Division of Shortage Designations,
Health Resources and Services Administration,
U.S. DHHS, under Cooperative Agreements through
the Office of Rural Health Policy (HRSA) (1
UIC RH 0027-01). Constructive comments and suggestions
were provided by Trudy Pedergraft, Ann Howard,
Andy Jordan, Jerilyn Thornburg, and anonymous
reviewers.
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Thomas C. Ricketts, III, PhD
Laurie J. Goldsmith, PhD, MSc
George M. Holmes, PhD
Randy Randolph, MRP
Richard Lee, MS
Donald H. Taylor, Jr., PhD, MPA
Jan Ostermann, PhD
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