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How Should We Identify Home- and Community-Based Services (HCBS) in Medicaid Claims Data? An Evaluation and Synthesis
Vimbainashe Dihwa1, Eric T Roberts1, Eliza Macneal1
1Perelman School of Medicine and Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Objective:
To assess existing algorithms for measuring Home- and Community-Based Services (HCBS) in Medicaid claims, and to develop and evaluate a unified, modified algorithm.
Study Setting And Design:
Using Transformed Medicaid Statistical Information System Analytic Files (TAF), we compared two existing algorithms for identifying and classifying 1915(c) waiver and state plan HCBS. Informed by that analysis, we developed a third, unified and modified algorithm. Using each algorithm, we constructed annual state-level estimates of HCBS use per 100,000 residents, which we compared from 2021 to 2023.
Data Sources And Analytic Sample:
National 20% sample of TAF data. The sample included continuously enrolled, full-benefit Medicaid beneficiaries age 19 or older in 2021 for primary analyses, and 2022-2023 to compare counts over time.
Principal Findings:
The existing algorithms showed a high level of concordance (observed agreement, Po = 93% and kappa = 0.77). Disagreement resulted from missing or inconclusive program type codes, differences in service-related codes used to classify HCBS, and differences in classifying claims for capitated payments. Our algorithm addressed some of this disagreement with the novel use of waiver identification numbers and modification of some TAF data elements and codes. HCBS user counts were mostly consistent across algorithms, with our algorithm identifying the broadest population of users. However, some states exhibited inconsistent counts depending on the algorithm, implausibly high counts (Pennsylvania and Illinois), or implausibly low counts (Mississippi). In several states, HCBS user counts varied substantially from 2021 to 2023 (> 50% in absolute value), suggesting changes in data quality or reporting in those states across years.
Conclusions:
Our algorithm resolves some discordances between two existing algorithms for measuring HCBS use in TAF. However, in certain states and years, inconsistent or implausible HCBS user counts suggest variations in data reliability and coding patterns that could limit accurate measurement of HCBS, regardless of the algorithm used.
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