title: Assessing medication exposures and outcomes in the frail elderly: assessing research challenges in nursing home pharmacotherapy.
abstract: 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.
authors.affiliation: Center for Health Services Research on Pharmacotherapy, Chronic Disease Management and Outcomes, Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ 08901, USA.
authors.forename: Stephen
authors.initials: S
authors.lastname: Crystal
authors.forename: Dorothy
authors.initials: D
authors.lastname: Gaboda
authors.forename: Judith
authors.initials: J
authors.lastname: Lucas
authors.forename: Tobias
authors.initials: T
authors.lastname: Gerhard
authors.forename: Sujoy
authors.initials: S
authors.lastname: Chakravarty
mesh_terms: D000367:Age Factors; D000368:Aged; D057186:Comparative Effectiveness Research / Q000379:methods*; D015986:Confounding Factors, Epidemiologic; D003625:Data Collection / Q000379:methods*; D004363:Drug Utilization; D006707:Homes for the Aged / Q000706:statistics & numerical data; D006801:Humans; D007345:Insurance Claim Review / Q000706:statistics & numerical data*; D008484:Medicaid / Q000706:statistics & numerical data; D054524:Medicare Part D / Q000706:statistics & numerical data; D009735:Nursing Homes / Q000706:statistics & numerical data; D055553:Prescription Drugs / Q000627:therapeutic use*; D012107:Research Design; D012723:Sex; D012959:Socioeconomic Factors; D014481:United States
chemical_list: D055553:Prescription Drugs