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TB Notes 4, 2006
Director's Letter
Highlights from State and Local Programs
  Los Angeles Presents "The Opera and Perspectives on TB"
  Arizona's and Sonora's Meet and Greet Program for Deportees with TB
  The Flex Power of Memoranda of Agreements (MOAs)
  HIV Status Not Routinely Determined for TB Cases: an Evaluation of Four California Local TB Programs
CDC/ATSDR Group Award for Minority Health Mentor/Champion of Excellence
Laboratory Update
  New Technologies Unveiled at the 2006 National TB Controllers' Workshop
Nursing Updates
  The Red Snappers of National Tuberculosis Nurses Coalition (NTNC)
  Pacific Island TB Controllers Association (PITCA) -  Workshop for Nurses
TB Education and Training Network Updates
  Member Highlight
  Sixth Annual Conference Highlights
  Cultural Competency Subcommittee Update
Communication, Education, and Behavioral Studies Branch Update
  New Communication Efforts to Stop TB in the African-American Community
Clinical and Health Systems Research Branch Update
  Using a Private Claims Database for TB Health Services Research, Evaluation, and Analysis
Surveillance, Epidemiology, and Outbreak Investigations Branch Updates
  9th Semiannual Meeting of the Tuberculosis Epidemiologic Studies Consortium
  TBESC Task Order #10: Monitoring Performance and Measuring Cost of TB Public Health Practice at County and State Health Departments: Are We Making a Health Impact?
New CDC Publications
Personnel Notes
Calendar of Events
 
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TB Notes Newsletter

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:

  1. Who (in terms of age, sex, region, employment status, and industry) receives care for active TB disease from private providers?
  2. What types of private providers perform TB diagnosis and treatment?
  3. What are the duration of care for TB disease, types of services provided, procedures performed, drugs prescribed, and insurance payments to the provider?
  4. What is the cost of TB care?
  5. 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

 


Released October 2008
Centers for Disease Control and Prevention
National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention
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