TY - JOUR
T1 - Informative censoring by health plan disenrollment among commercially insured adults
AU - Butler, Anne M.
AU - Todd, Jonathan V.
AU - Sahrmann, John M.
AU - Lesko, Catherine R.
AU - Brookhart, M. Alan
N1 - Funding Information:
Data programming for this study was conducted by the Center for Administrative Data Research, which is supported in part by the Washington University Institute of Clinical and Translational Sciences grant UL1 TR002345 from the National Center for Advancing Translational Sciences (NCATS) of the National Institutes of Health (NIH), Grant Number R24 HS19455 through the Agency for Healthcare Research and Quality (AHRQ).
PY - 2019/5
Y1 - 2019/5
N2 - 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.
AB - 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.
KW - administrative claims
KW - epidemiologic methods
KW - informative censoring
KW - pharmacoepidemiology
KW - selection bias
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U2 - 10.1002/pds.4750
DO - 10.1002/pds.4750
M3 - Article
C2 - 30788887
AN - SCOPUS:85061780875
VL - 28
SP - 640
EP - 648
JO - Pharmacoepidemiology and Drug Safety
JF - Pharmacoepidemiology and Drug Safety
SN - 1053-8569
IS - 5
ER -