BACKGROUND: Many health programs struggle with low enrollment rates. OBJECTIVES: To compare the characteristics of populations enrolled in a new health plan when employer groups implement voluntary versus automatic enrollment approaches. STUDY DESIGN: We analyzed enrollment rates resulting from 2 different strategies: voluntary and automatic enrollment. We used regression modeling to estimate the associations of patient characteristics with the probability of enrolling within each strategy. The subjects were 5014 eligible employees from 11 self-insured employers who had purchased the Diabetes Health Plan (DHP), which offers free or discounted copayments for diabetes related medications, testing supplies, and physician visits. Six employers used voluntary enrollment while 5 used automatic enrollment. The main outcome of interest was enrollment into the DHP. Predictors were gender, age, race/ethnicity, dependent status, household income, education level, number of comorbidities, and employer group. RESULTS: Overall, the proportion of eligible members who were enrolled within the automatic enrollment strategy was 91%, compared with 35% for voluntary enrollment. Income was a significant predictor for voluntary enrollment but not for automatic enrollment. Within automatic enrollment, covered dependents, Hispanics, and persons with 1 nondiabetes comorbidity were more likely to enroll than other subgroups. Employer group was also a significant correlate of enrollment. Notably, all demographic groups had higher DHP enrollment rates under automatic enrollment than under voluntary enrollment. CONCLUSIONS: For employer-based programs that struggle with low enrollment rates, especially among certain employee subgroups, an automatic enrollment strategy may not only increase the total number of enrollees but may also decrease some enrollment disparities.
BACKGROUND: Many health programs struggle with low enrollment rates. OBJECTIVES: To compare the characteristics of populations enrolled in a new health plan when employer groups implement voluntary versus automatic enrollment approaches. STUDY DESIGN: We analyzed enrollment rates resulting from 2 different strategies: voluntary and automatic enrollment. We used regression modeling to estimate the associations of patient characteristics with the probability of enrolling within each strategy. The subjects were 5014 eligible employees from 11 self-insured employers who had purchased the Diabetes Health Plan (DHP), which offers free or discounted copayments for diabetes related medications, testing supplies, and physician visits. Six employers used voluntary enrollment while 5 used automatic enrollment. The main outcome of interest was enrollment into the DHP. Predictors were gender, age, race/ethnicity, dependent status, household income, education level, number of comorbidities, and employer group. RESULTS: Overall, the proportion of eligible members who were enrolled within the automatic enrollment strategy was 91%, compared with 35% for voluntary enrollment. Income was a significant predictor for voluntary enrollment but not for automatic enrollment. Within automatic enrollment, covered dependents, Hispanics, and persons with 1 nondiabetes comorbidity were more likely to enroll than other subgroups. Employer group was also a significant correlate of enrollment. Notably, all demographic groups had higher DHP enrollment rates under automatic enrollment than under voluntary enrollment. CONCLUSIONS: For employer-based programs that struggle with low enrollment rates, especially among certain employee subgroups, an automatic enrollment strategy may not only increase the total number of enrollees but may also decrease some enrollment disparities.
Authors: Chien-Wen Tseng; Edward F Tierney; Robert B Gerzoff; R Adams Dudley; Beth Waitzfelder; Ronald T Ackermann; Andrew J Karter; John Piette; Jesse C Crosson; Quyen Ngo-Metzger; Richard Chung; Carol M Mangione Journal: Diabetes Care Date: 2007-11-13 Impact factor: 19.112
Authors: O Kenrik Duru; Norman Turk; Susan L Ettner; Romain Neugebauer; Tannaz Moin; Jinnan Li; Lindsay Kimbro; Charles Chan; Robert H Luchs; Abigail M Keckhafer; Anya Kirvan; Sam Ho; Carol M Mangione Journal: J Gen Intern Med Date: 2015-05-06 Impact factor: 5.128
Authors: O Kenrik Duru; Carol M Mangione; Hector P Rodriguez; Dennis Ross-Degnan; J Frank Wharam; Bernard Black; Abel Kho; Nathalie Huguet; Heather Angier; Victoria Mayer; David Siscovick; Jennifer L Kraschnewski; Lizheng Shi; Elizabeth Nauman; Edward W Gregg; Mohammed K Ali; Pamela Thornton; Steven Clauser Journal: Curr Diab Rep Date: 2018-02-05 Impact factor: 4.810