Evaluation of a geriatrics primary care model using prospective matching to guide enrollment.
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BackgroundFew definitive guidelines exist for rigorous large-scale prospective evaluation of nonrandomized programs and policies that require longitudinal primary data collection. In Veterans Affairs (VA) we identified a need to understand the impact of a geriatrics primary care model (referred to as GeriPACT); however, randomization of patients to GeriPACT vs. a traditional PACT was not feasible because GeriPACT has been rolled out nationally, and the decision to transition from PACT to GeriPACT is made jointly by a patient and provider. We describe our study design used to evaluate the comparative effectiveness of GeriPACT compared to a traditional primary care model (referred to as PACT) on patient experience and quality of care metrics.
MethodsWe used prospective matching to guide enrollment of GeriPACT-PACT patient dyads across 57 VA Medical Centers. First, we identified matches based an array of administratively derived characteristics using a combination of coarsened exact and distance function matching on 11 identified key variables that may function as confounders. Once a GeriPACT patient was enrolled, matched PACT patients were then contacted for recruitment using pre-assigned priority categories based on the distance function; if eligible and consented, patients were enrolled and followed with telephone surveys for 18 months.
ResultsWe successfully enrolled 275 matched dyads in near real-time, with a median time of 7 days between enrolling a GeriPACT patient and a closely matched PACT patient. Standardized mean differences of < 0.2 among nearly all baseline variables indicates excellent baseline covariate balance. Exceptional balance on survey-collected baseline covariates not available at the time of matching suggests our procedure successfully controlled many known, but administratively unobserved, drivers of entrance to GeriPACT.
ConclusionsWe present an important process to prospectively evaluate the effects of different treatments when randomization is infeasible and provide guidance to researchers who may be interested in implementing a similar approach. Rich matching variables from the pre-treatment period that reflect treatment assignment mechanisms create a high quality comparison group from which to recruit. This design harnesses the power of national administrative data coupled with collection of patient reported outcomes, enabling rigorous evaluation of non-randomized programs or policies.
Published Version (Please cite this version)
Smith, Valerie A, Courtney Harold Van Houtven, Jennifer H Lindquist and Susan N Hastings (2021). Evaluation of a geriatrics primary care model using prospective matching to guide enrollment. BMC medical research methodology, 21(1). p. 167. 10.1186/s12874-021-01360-4 Retrieved from https://hdl.handle.net/10161/26131.
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Valerie A. Smith, DrPH, is an Associate Professor in the Duke University Department of Population Health Sciences and Senior Research Director of the Biostatistics Core at the Durham Veterans Affairs Medical Center's Center of Innovation. Her methodological research interests include: methods for semicontinuous and zero-inflated data, economic modeling methods, causal inference methods, observational study design, and longitudinal data analysis. Her current methodological research has focused on the development of marginalized models for semicontinuous data.
Dr. Smith works largely in collaboration with a multidisciplinary team of researchers, with a focus on health policy interventions, health care utilization and expenditure patterns, program and policy evaluation, obesity and weight loss, bariatric surgery evaluation, and family caregiver supportive services.
Areas of expertise: Biostatistics, Health Services Research, Health Economics, and Health Policy
Dr. Courtney Van Houtven is a Professor in The Department of Population Health Science, Duke University School of Medicine and Duke-Margolis Center for Health Policy. She is also a Research Career Scientist in The Center of Innovation to Accelerate Discovery and Practice Transformation (ADAPT), Durham Veterans Affairs Health Care System. Dr. Van Houtven’s aging and economics research interests encompass long-term care financing, intra-household decision-making, unpaid family and friend care, and home- and community-based services. She examines how family caregiving affects health care utilization, expenditures, health and work outcomes of care recipients and caregivers. She is also interested in understanding how best to support family caregivers to optimize caregiver and care recipient outcomes.
Dr. Van Houtven is co-PI on the QUERI Program Project, “Optimizing Function and Independence”, in which her caregiver skills training program developed as an RCT in VA, now called Caregivers FIRST, has been implemented at 125 VA sites nationally. The team will evaluate how intensification of an implementation strategy changes adoption. She directs the VA-CARES Evaluation Center, which evaluates the VA’s Caregiver Support Program. She leads a mixed methods R01 study as PI from the National Institute on Aging that will assess the value of "home time" for persons living with dementia and their caregivers (RF1 AG072364).
Areas of expertise: Health Services Research and Health Economics
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