TB Notes Newsletter
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No. 4, 2006
Clinical and Health Systems
Research Branch Update
Using a Private Claims Database for TB Health
Services Research, Evaluation, and Analysis
The Institute of Medicine (IOM) report on TB
elimination in the United States highlighted several
challenges and opportunities for TB prevention and
control related to the use of managed care systems and
privatization of health services. A DTBE study revealed
that of the 733 patients hospitalized for TB in 1996,
incurring total costs of $11,263,853 (1999 dollars),
approximately 12% were covered by private insurance
(which paid 9% [$1,038,759] of the total costs), 21% by
Medicaid, 10% by Medicare, and 6% by both Medicaid and
Medicare. However, the insurance status of all TB
patients (including outpatients) and the cost of their
care was and remains unknown. The IOM report anticipated
that the percentage of TB patients cared for in the
private sector was likely to grow, because increasingly
Medicare recipients are insured by private for-profit
organizations and Medicaid recipients are cared for by
Medicaid managed-care organizations. In addition, some
public health departments contract TB care to private
organizations or companies. Understanding the role that
private insurance plays in TB care can provide valuable
guidance for possible interventions.
When an administrative claims and encounters database
of private employers (MarketScan) became available for
use by DTBE, the Health Systems Research Team accessed
it to attempt to answer several research questions
related to TB care provision in the private sector. The
methods and results of this exploratory project are
presented below. In summary, though we were unable to
answer the research questions using the Marketscan data,
we describe the challenges we encountered and
suggestions for addressing them.
The MarketScan database is a large, multisource,
longitudinal database of inpatient and outpatient
insurance claims and encounter information of
individuals covered by employers’ benefit plans. The
database was created and maintained by Medstat, a
for-profit health care information company (www.medstat.com).
While MarketScan does not include Medicaid managed care
providers, it does include Medicare recipients and
others covered by private insurance. Previously, the
MarketScan database has been used by CDC researchers in
the National Immunization Program and the Division of
STD Prevention, among others, to examine costs and
service utilization patterns. Approximately 65 employers
and 200 payers, including commercial insurance
companies, contribute data. MarketScan links medical
procedures and prescription data with provider
descriptions, patient enrollment, and benefit plan
information. The database retrospectively captures 10
years, which includes approximately 3.6 million persons,
75 million services, and prescription drug information
for 2.8 million covered lives.
The primary objective of this project was to
demonstrate the utility of the MarketScan database in
describing TB service provision in private settings.
Five research questions were initially posed:
- Who (in terms of age, sex, region, employment
status, and industry) receives care for active TB
disease from private providers?
- What types of private providers perform TB
diagnosis and treatment?
- What are the duration of care for TB disease,
types of services provided, procedures performed,
drugs prescribed, and insurance payments to the
provider?
- What is the cost of TB care?
- What are the rates of TB screening and LTBI
treatment among persons at risk for TB disease?
The initial steps were to identify which years
(1997–2002) and TB-related “procedural” and “diagnostic”
codes (Current Procedural Terminology (CPT) and
International Classification of Diseases, 9th Revision
(ICD-9)) codes to include. We obtained data through the
specialized Medstat software, Dataprobe, and created SAS
datasets. The following are some of the challenges we
faced finding and describing true TB cases from the
MarketScan data:
- Enrollment data, the sole source of stable
demographic data and enrollment dates, were
available for only a portion of total patients
(50%-60% in the years selected).
- Diagnostic test results were not included in the
database.
- Medication data were available for only a
portion of patients (50%-60%) for the specific years
included in the analysis.
- The inpatient admissions record listed one
principal ICD-9 diagnosis code and 15 secondary
diagnosis codes chronologically (i.e., not in order
of “clinical importance”).
- Any TB diagnostic code may have been applied
during patient evaluation when TB is suspected, and
tests are ordered to confirm or rule out TB.
- We were unable to determine if a TB patient was
referred to the public sector for diagnosis or
treatment.
- We were unable to differentiate suspected TB
cases from true TB cases since we could not match to
a confirmed TB case registry. There were no shared
identifiers that could be linked to an existing case
registry.
We used various strategies to identify TB cases and
isolate claims related to TB treatment:
- We completed an extensive process of determining
best “criteria” for identifying TB cases, with
expert DTBE physician consultation; used a claims
algorithm with CPT codes, ICD-9 codes, and number of
intervening days (specifically, presence of TB
procedure code, a TB diagnostic code within the 60
days after, and another TB diagnostic code within
180 days after the first).
- We identified 190 possible TB patients (35
inpatients and 155 outpatients) who received some
amount of care (as indicated by our algorithm) and
described them by age, sex, geographic region,
industry, and employment status.
- We randomly selected 10 of the 190 potential TB
patients, then asked DTBE physicians to perform a
detailed review of each patient’s claims history
within 3 months of initial and last TB ICD-9 code to
assess the validity of our algorithm, and evaluated
the reviewer’s agreement.
- In a separate query of the MarketScan database,
we identified 100 inpatients having TB as the
principal diagnosis on the discharge record and
conducted another in-depth claims review of records
linked with hospital stays.
These strategies were unsuccessful in identifying
true TB patients and TB claims. New and significant
limitations were identified at this stage. First, our
algorithms for identifying TB patients could not be
validated. For example, the 35 inpatients identified
using our claims algorithm and the 100 inpatients
identified using hospital discharge records were not the
same; there were only 15 overlapping patient ID numbers.
This suggested a lack of validity in our case criteria,
assuming that TB indicated on the hospital discharge
record was the “gold standard” (i.e., that a hospital
discharge record with TB as the principal ICD-9 code
should be that of a TB true patient, even if some true
TB patients do not have a TB ICD-9 code on their
hospital discharge record). The detailed claims review
(independent of hospital discharge data) yielded
concerns about the validity of the case criteria among
outpatients as well; the DTBE physicians who examined
the claims records concluded that 9 out of 10 were
unlikely to be true TB patients.
The second challenge was the relationship between the
CPT and ICD-9 codes. We originally aimed to extract
specific claims associated with TB care to conduct
episode-of-care analysis for both inpatients and
outpatients. It was expected that a claim with a
TB-related CPT code (e.g., 87116, “Culture, tubercle or
other acid-fast bacilli”) would usually be linked to a
TB diagnostic code, but this was not observed in the
data. Conversely, the TB ICD-9 codes were not
consistently associated with procedures that are
relevant or specific to TB diagnosis or care. Upon
examination, we found no clear and consistent linkage
between possible TB CPT and ICD-9 codes; thus, we did
not identify a mechanism to isolate TB claims of true TB
cases.
Because there was no link to a TB registry, we could
not determine when TB treatment started or ended to
estimate a relevant time period for TB-related services
or costs. Also, we could not calculate the TB screening
rates among persons at risk for TB disease because it
was not possible to identify an accurate denominator of
patients with diseases that place them at risk for TB
(e.g., HIV, silicosis, diabetes). Rather, we could only
ascertain whether someone was receiving care for that
condition while insured. For example, a diabetic patient
may not have had a tuberculin skin test (TST) CPT code
because he or she was tested before or after the claim
reporting period (and enrollment dates were available
for only half of the population). No TST results were
available, so we were unable to use these data to
determine TST positivity rates.
We reflected upon the study research questions and
concluded that we were unable to answer them with the
MarketScan data. This was mainly because we could not
identify individual TB patients or patients at risk for
TB solely through the MarketScan database.
However, we offer a few suggestions for meeting these
challenges. It might be possible for states or local
areas, through negotiation and confidentiality
agreements with large private employers, to link the
MarketScan data to TB patients or TB suspects in their
area. Alternatively, local TB programs might be able to
access the MarketScan data for their geographic area and
could attempt to match TB patients, without linking the
databases, by patient employment status, county, gender,
and year of birth, though the combination may not yield
an exact match. After identifying a cohort of TB
patients or TB suspects, TB programs would need to
determine if any TB-related diagnostic codes or CPT
codes are documented in the claims histories for matched
patients. If analysis is limited to known TB patients,
programs could describe the characteristics of who is
served by private insurers, types of private insurers
that TB patients use, and duration of and type of care
received (i.e., research questions 1, 2, and 3). Some of
the limitations mentioned above would still pose
challenges to analysis. However, this strategy would
facilitate analysis of the costs of TB care in the
private sector, which is lacking in the literature, and
might permit us to assess whether the movement towards
provision of TB care in the private sector is cost
efficient for society.
References
1. Institute of Medicine. Ending Neglect: The
Elimination of Tuberculosis in the United States.
Washington D.C.: National Academy Press, 2000:73-74
2. Marks SM, Taylor Z, Miller BI. Tuberculosis
prevention versus hospitalization: taxpayers save with
prevention. The Journal of Health Care for the Poor
and Underserved. 2002;13(3):392-401.
3. Draper DA, Hurley RE, and Short AC. Medicaid
managed care: The last bastion of the HMO? Health
Affairs. 2004;23(2):155-167.
—Reported by Heather Joseph, MPH
Div of HIV/AIDS Prevention, and
Suzanne Marks, MPH, MA
Div of TB Elimination |
