Premium
Informative censoring by health plan disenrollment among commercially insured adults
Author(s) -
Butler Anne M.,
Todd Jonathan V.,
Sahrmann John M.,
Lesko Catherine R.,
Brookhart M. Alan
Publication year - 2019
Publication title -
pharmacoepidemiology and drug safety
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.023
H-Index - 96
eISSN - 1099-1557
pISSN - 1053-8569
DOI - 10.1002/pds.4750
Subject(s) - medicine , biostatistics , confidence interval , descriptive statistics , health care , censoring (clinical trials) , demography , epidemiology , statistics , mathematics , pathology , sociology , economics , economic growth
Purpose Health plan disenrollment occurs frequently in commercial insurance claims databases. If individuals who disenroll are different from those who remain enrolled, informative censoring may bias descriptive statistics as well as estimates of causal effect. We explored whether patterns of disenrollment varied by patient or health plan characteristics. Methods In a large cohort of commercially insured adults (2007‐2013), we examined two primary outcomes: (a) within‐year disenrollment between January 1 and December 30, which was considered to occur due to patient disenrollment from the health plan, and (b) end‐of‐year disenrollment on December 31, which was considered to occur due to either patient disenrollment from the health plan or withdrawal of the entire health plan from the commercial insurance database. In yearly cohorts, we identified factors independently associated with disenrollment by using log‐binomial regression models to estimate risk ratios (RR) and 95% confidence intervals (CI). Results Among 2 053 100 unique patient years, the annual proportion of within‐year disenrollment remained steady across years (range, 13% to 14%) whereas the annual proportion of end‐of‐year disenrollment varied widely (range, 8% to 26%). Independent predictors of within‐year disenrollment were related to health status, including age, comorbidities, frailty, hospitalization, emergency room visits, use of durable medical equipment, use of preventive care, and use of prescription medications. In contrast, independent predictors of end‐of‐year disenrollment were related to health plan characteristics including insurance plan type and geographic characteristics. Conclusions Differential risk of disenrollment suggests that analytic approaches to address selection bias should be considered in studies using commercial insurance databases.