title: Characteristics of insured patients with persistent gaps in diabetes care services: the Translating Research into Action for Diabetes (TRIAD) study.
abstract: BACKGROUND
  Although preventing diabetes complications requires long-term management, little is known about which patients persistently fail to get recommended care.
  
  
  OBJECTIVE
  To determine the frequency and correlates of persistent, long-term gaps in diabetes care.
  
  
  METHODS
  : The study population included 8392 patients with diabetes. Patient surveys and medical records from 10 health plans over 3 years provided data on socioeconomic characteristics, access to care, social support, and mental and physical health, and diabetes preventive care services. We defined a "persistent gap" as a participant's missing a preventive care service for the entire 3 years. Services considered included hemoglobin A1c, cholesterol, and albuminuria tests, and foot and dilated eye examinations.
  
  
  RESULTS
  Thirty percent of participants had at least 1 persistent gap. The most common gaps were lipid testing (11.6%), microalbuminuria testing (9.7%), and eye examinations (9.0%). Persistent gaps were 18% to 42% higher for young patients, lean persons, those with low income, employed persons, smokers, those with diabetes less than 5 years, and patients with none or 1 comorbid conditions. Sex, education, marital status, family demands, transportation, trust in physicians, and mental health were not associated with gaps in care.
  
  
  CONCLUSIONS
  Persistent gaps in diabetes care are common even among insured patients. Patients with lower income, younger age, fewer years of diabetes, having fewer comorbidities, taking fewer medications, and poor health behaviors are vulnerable to persistent gaps in care and a group who warrant targeted interventions to improve preventive diabetes care.
authors.affiliation: Division of Diabetes Translation, Centers for Disease Control and Prevention, Atlanta, GA 30341, USA. edg7@cdc.gov
authors.forename: Edward W
authors.initials: EW
authors.lastname: Gregg
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Karter
authors.forename: Robert B
authors.initials: RB
authors.lastname: Gerzoff
authors.forename: Monika
authors.initials: M
authors.lastname: Safford
authors.forename: Arleen F
authors.initials: AF
authors.lastname: Brown
authors.forename: Chien-Wen
authors.initials: CW
authors.lastname: Tseng
authors.forename: Beth
authors.initials: B
authors.lastname: Waitzfielder
authors.forename: William H
authors.initials: WH
authors.lastname: Herman
authors.forename: Carol M
authors.initials: CM
authors.lastname: Mangione
authors.forename: Joseph V
authors.initials: JV
authors.lastname: Selby
authors.forename: Theodore J
authors.initials: TJ
authors.lastname: Thompson
authors.forename: R Adams
authors.initials: RA
authors.lastname: Dudley
mesh_terms: D000367:Age Factors; D000368:Aged; D015992:Body Mass Index; D015897:Comorbidity; D048909:Diabetes Complications / Q000517:prevention & control*; D003920:Diabetes Mellitus; D005260:Female; D015438:Health Behavior; D006801:Humans; D007345:Insurance Claim Review / Q000706:statistics & numerical data; D019458:Insurance Coverage / Q000706:statistics & numerical data*; D007348:Insurance, Health / Q000706:statistics & numerical data*; D008297:Male; D008875:Middle Aged; D011322:Primary Prevention / Q000706:statistics & numerical data*; D012737:Sex Factors; D012907:Smoking; D012959:Socioeconomic Factors; D013997:Time Factors