Identifying observable medication use time in administrative databases: a tutorial using nursing home residents

Nursing home (NH) residents are an important population for pharmacoepidemiologic research due to their prevalence of multimorbidity and polypharmacy. Medicare claims are commonly used to study medication use in this population, but medications dispensed during hospitalizations or post-acute care are unobservable due to bundled payment structures. We developed algorithms to identify NH days when medication dispensings can be observed in claims. Using a cohort of NH residents in the United States from 2013 to 2020, we linked Medicare fee-for-service (FFS) claims with Minimum Data Set clinical assessments. NH days were classified as "observable medication use time" if residents were enrolled in Medicare parts A, B, and D were not receiving post-acute care and were not hospitalized. Among 12.3 million NH residents and 2.7 billion NH days, 1.1 billion days (72.4% of Medicare-enrolled days and 39.6% of all NH days) were identified as observable medication use time. Within the first 100 days of NH admission, 27.3% of days were medication-observable, increasing to 89.4% after 100 days. On average, we identified 68% more person-time, and 51% more residents, compared to standard 100-day definitions for "long-stay" NH residents. Our algorithms enhance researchers' ability to measure medication exposure time, improving the validity of pharmacoepidemiologic studies.

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Publication Details

Journal
American Journal of Epidemiology
Published
2025-10-09
DOI
https://doi.org/10.1093/aje/kwaf227
Citations
2
Primary Topic
Pharmaceutical Practices and Patient Outcomes
Type
article
Field-Weighted Citation Impact
2.45
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article

Identifying observable medication use time in administrative databases: a tutorial using nursing home residents

Hemalkumar B. Mehta, Lori A. Daiello, Douglas P. Kiel, Laura Reich et al.
2 citations
American Journal of Epidemiology
Pharmaceutical Practices and Patient Outcomes
2.45
article

Identifying observable medication use time in administrative databases: a tutorial using nursing home residents

Hemalkumar B. Mehta, Lori A. Daiello, Douglas P. Kiel, Laura Reich, Andrew R. Zullo, Jimmie Roberts, Charles E. Leonard, Kaleen N. Hayes, Daniel A. Harris, Yu‐Chia Hsu, Melissa R. Riester, Peyton Free, Adam M. D’Amico, Sarah Berry
article en
2 citations

Abstract

Nursing home (NH) residents are an important population for pharmacoepidemiologic research due to their prevalence of multimorbidity and polypharmacy. Medicare claims are commonly used to study medication use in this population, but medications dispensed during hospitalizations or post-acute care are unobservable due to bundled payment structures. We developed algorithms to identify NH days when medication dispensings can be observed in claims. Using a cohort of NH residents in the United States from 2013 to 2020, we linked Medicare fee-for-service (FFS) claims with Minimum Data Set clinical assessments. NH days were classified as "observable medication use time" if residents were enrolled in Medicare parts A, B, and D were not receiving post-acute care and were not hospitalized. Among 12.3 million NH residents and 2.7 billion NH days, 1.1 billion days (72.4% of Medicare-enrolled days and 39.6% of all NH days) were identified as observable medication use time. Within the first 100 days of NH admission, 27.3% of days were medication-observable, increasing to 89.4% after 100 days. On average, we identified 68% more person-time, and 51% more residents, compared to standard 100-day definitions for "long-stay" NH residents. Our algorithms enhance researchers' ability to measure medication exposure time, improving the validity of pharmacoepidemiologic studies.

American Journal of EpidemiologyVol. 195(2)
Beth Israel Deaconess Medical Center (US), Johns Hopkins University (US), John Brown University (US), Brown University (US), Department of Behavioral Health (US), Hebrew SeniorLife (US), Department of Health Services (NP), Department of Health Services (US), University of Pennsylvania (US), University of Delaware (US)
Openalex Percentile: Top 10%
Pharmaceutical Practices and Patient Outcomes
2.45
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