title: Sociodemographic and clinical characteristics are not clinically useful predictors of refill adherence in patients with hypertension.
abstract: BACKGROUND
  Although many studies have identified patient characteristics or chronic diseases associated with medication adherence, the clinical utility of such predictors has rarely been assessed. We attempted to develop clinical prediction rules for adherence with antihypertensive medications in 2 healthcare delivery systems.
  
  
  RESULTS
  We performed retrospective cohort studies of hypertension registries in an inner-city healthcare delivery system (n=17 176) and a health maintenance organization (n=94 297) in Denver, Colo. Adherence was defined by acquisition of 80% or more of antihypertensive medications. A multivariable model in the inner-city system found that adherent patients (36.3% of the total) were more likely than nonadherent patients to be older, white, married, and acculturated in US society, to have diabetes or cerebrovascular disease, not to abuse alcohol or controlled substances, and to be prescribed fewer than 3 antihypertensive medications. Although statistically significant, all multivariate odds ratios were 1.7 or less, and the model did not accurately discriminate adherent from nonadherent patients (C statistic=0.606). In the health maintenance organization, where 72.1% of patients were adherent, significant but weak associations existed between adherence and older age, white race, the lack of alcohol abuse, and fewer antihypertensive medications. The multivariate model again failed to accurately discriminate adherent from nonadherent individuals (C statistic=0.576).
  
  
  CONCLUSIONS
  Although certain sociodemographic characteristics or clinical diagnoses are statistically associated with adherence to refills of antihypertensive medications, a combination of these characteristics is not sufficiently accurate to allow clinicians to predict whether their patients will be adherent with treatment.
authors.affiliation: Colorado Health Outcomes Program, the Department of Family Medicine, University of Colorado, Denver, CO, USA. john.f.steiner@kp.org
authors.forename: John F
authors.initials: JF
authors.lastname: Steiner
authors.forename: P Michael
authors.initials: PM
authors.lastname: Ho
authors.forename: Brenda L
authors.initials: BL
authors.lastname: Beaty
authors.forename: L Miriam
authors.initials: LM
authors.lastname: Dickinson
authors.forename: Rebecca
authors.initials: R
authors.lastname: Hanratty
authors.forename: Chan
authors.initials: C
authors.lastname: Zeng
authors.forename: Heather M
authors.initials: HM
authors.lastname: Tavel
authors.forename: Edward P
authors.initials: EP
authors.lastname: Havranek
authors.forename: Arthur J
authors.initials: AJ
authors.lastname: Davidson
authors.forename: David J
authors.initials: DJ
authors.lastname: Magid
authors.forename: Raymond O
authors.initials: RO
authors.lastname: Estacio
mesh_terms: D000328:Adult; D000368:Aged; D000959:Antihypertensive Agents / Q000627:therapeutic use*; D015331:Cohort Studies; D003120:Colorado / Q000453:epidemiology; D011307:Drug Prescriptions / Q000706:statistics & numerical data*; D005260:Female; D006279:Health Maintenance Organizations / Q000706:statistics & numerical data; D006801:Humans; D006973:Hypertension / Q000188:drug therapy* / Q000453:epidemiology* / Q000517:prevention & control; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D011237:Predictive Value of Tests; D012042:Registries; D012189:Retrospective Studies; D012959:Socioeconomic Factors; D019037:Urban Health Services / Q000706:statistics & numerical data; D016903:Drug Monitoring
chemical_list: D000959:Antihypertensive Agents