Assessing Medication Exposures and Outcomes in the Frail Elderly

BACKGROUND: Large administrative datasets such as Medicare and Medicaid claims have much potential utility in clinical and comparative effectiveness (CE) studies. Among their advantages are the inclusion of clinically heterogeneous populations, without exclusions typical in clinical trials; the ability to study extremely large study populations with power to examine differential outcomes across individual drugs, treatment effect modification, and the risk of uncommon outcomes. However, claims data by themselves are subject to many limitations, notably, in their lack of information on such clinical characteristics as functional status, behaviors, and symptoms, which are important both as outcomes and as covariates. METHODS: We describe data from multiple sources including standardized, electronically recorded clinical and functional data from the Nursing Home (NH) Minimum Data Set; prescription drug data from Medicaid and Medicare claims; and facility data. We present the strengths and challenges of using merged data about the NH population to study prescription drug exposures and outcomes in the frail elderly, and suggest strategies to address methodological difficulties. RESULTS: Merged data from NH sources can support unique study designs in CE research and provide great power. However, given the differing longitudinal structure, timing of observations, and other complex features of the underlying data sources, such studies pose many challenging design and analytic issues. CONCLUSIONS: Integrated data on the NH population have great potential for CE research among frail elderly persons, if methodological and measurement challenges can be adequately addressed

Clinical study design · Clinical trial · Covariate · Data science · Environmental health · Health care · Machine learning · Medicaid · Medical prescription · MEDLINE · Minimum Data Set · Nursing homes · Pharmacoepidemiology · Population · Predictive power · Prescription drug · Research design · Set (abstract data type) · Statistics · Advanced Causal Inference Techniques · Computer Science · Health Systems, Economic Evaluations, Quality of Life · Medication Adherence and Compliance · Medicine · Nursing · Gerontology

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