OBJECTIVE: This study evaluates an employer-based diabetes/prediabetes screening intervention that invited at-risk employees via letters, secure e-mails, and automated voice messages to complete blood glucose testing at a health plan facility. METHODS: Quasi-experimental cohort study among health plan members insured by two employers that received the intervention and three employers that were selected as control sites. RESULTS: The proportion of at-risk members that completed a screening was higher in the intervention group than in the control group (36% vs 13%, P < 0.001, adjusted for patient characteristics). Among those screened in the intervention group, the presence of obesity, hypertension, hyperlipidemia, and tobacco use were significant predictors of having a result that indicated diabetes or prediabetes (P < 0.05, all comparisons). CONCLUSIONS: A low-intensity, employer-based intervention conducted in collaboration with a health care delivery system effectively increased screening for diabetes/prediabetes.
OBJECTIVE: This study evaluates an employer-based diabetes/prediabetes screening intervention that invited at-risk employees via letters, secure e-mails, and automated voice messages to complete blood glucose testing at a health plan facility. METHODS: Quasi-experimental cohort study among health plan members insured by two employers that received the intervention and three employers that were selected as control sites. RESULTS: The proportion of at-risk members that completed a screening was higher in the intervention group than in the control group (36% vs 13%, P < 0.001, adjusted for patient characteristics). Among those screened in the intervention group, the presence of obesity, hypertension, hyperlipidemia, and tobacco use were significant predictors of having a result that indicated diabetes or prediabetes (P < 0.05, all comparisons). CONCLUSIONS: A low-intensity, employer-based intervention conducted in collaboration with a health care delivery system effectively increased screening for diabetes/prediabetes.
Authors: X R Pan; G W Li; Y H Hu; J X Wang; W Y Yang; Z X An; Z X Hu; J Lin; J Z Xiao; H B Cao; P A Liu; X G Jiang; Y Y Jiang; J P Wang; H Zheng; H Zhang; P H Bennett; B V Howard Journal: Diabetes Care Date: 1997-04 Impact factor: 19.112
Authors: Oliver Bembom; Maya L Petersen; Soo-Yon Rhee; W Jeffrey Fessel; Sandra E Sinisi; Robert W Shafer; Mark J van der Laan Journal: Stat Med Date: 2009-01-15 Impact factor: 2.373
Authors: Courtney E Gamston; Anna N Kirby; Richard A Hansen; David T Redden; Heather P Whitley; Courtney Hanson; Kimberly B Lloyd Journal: J Am Pharm Assoc (2003) Date: 2019-07-13
Authors: Mohammed K Ali; Frank Wharam; O Kenrik Duru; Julie Schmittdiel; Ronald T Ackermann; Jeanine Albu; Dennis Ross-Degnan; Christine M Hunter; Carol Mangione; Edward W Gregg Journal: Curr Diab Rep Date: 2018-11-20 Impact factor: 4.810
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