title: Influence of transportation cost on long-term retention in clinic for HIV patients in rural Haiti.
abstract: BACKGROUND
  With improved access to antiretroviral therapy in resource-constrained settings, long-term retention in HIV clinics has become an important means of reducing costs and improving outcomes. Published data on retention in HIV clinics beyond 24 months are, however, limited. In our clinic in rural Haiti, we hypothesized that individuals residing in locations with higher transportation costs to clinic would have poorer retention than those who had lower costs.
  
  
  METHODS
  We used a retrospective cohort design to evaluate potential predictors of HIV clinic retention. Patient information was abstracted from the electronic medical records. Cox proportional hazards regression was used to identify independent predictors of 4-year clinic retention.
  
  
  RESULTS
  There were 410 patients in our cohort, 266 (64.9%) females and 144 (35.1%) males. Forty-five (11%) patients lived in locations with transportation costs >$2. Males were 1.5 times more likely to live in municipalities with transportation costs to clinic of >$2. Multivariate analysis suggested that age <30 years, male gender, and transportation cost were independent predictors of loss to follow-up (LTFU): risk ratio of 2.98, 95% confidence interval (CI): 1.73 to 4.96, P < 0.001; 1.71, CI: 1.08 to 2.70, P = 0.02; and 1.91, CI: 1.08 to 3.36, P = 0.02, respectively.
  
  
  CONCLUSIONS
  Patients with transportation costs greater than $2 were 1.9 times more likely to be lost to care compared with those who paid less for transportation. HIV treatment programs in resource-constrained settings may need to pay closer attention to issues related to transportation cost to improve patient retention.
authors.affiliation: *Institute of Human Virology, University of Maryland School of Medicine, Baltimore, MD; †Université Notre-Dame d'Haiti; ‡College of Medicine, University of Florida, Gainesville, FL; and §Hôpital Sacré Coeur du, Milot, Haiti.
authors.forename: Leonard A
authors.initials: LA
authors.lastname: Sowah
authors.forename: Franck V
authors.initials: FV
authors.lastname: Turenne
authors.forename: Ulrike K
authors.initials: UK
authors.lastname: Buchwald
authors.forename: Guesly
authors.initials: G
authors.lastname: Delva
authors.forename: Romaine N
authors.initials: RN
authors.lastname: Mesidor
authors.forename: Camille G
authors.initials: CG
authors.lastname: Dessaigne
authors.forename: Harold
authors.initials: H
authors.lastname: Previl
authors.forename: Devang
authors.initials: D
authors.lastname: Patel
authors.forename: Anthony
authors.initials: A
authors.lastname: Edozien
authors.forename: Robert R
authors.initials: RR
authors.lastname: Redfield
authors.forename: Anthony
authors.initials: A
authors.lastname: Amoroso
mesh_terms: D000293:Adolescent; D000328:Adult; D003365:Costs and Cost Analysis / Q000706:statistics & numerical data*; D005260:Female; D015658:HIV Infections / Q000188:drug therapy*; D006205:Haiti / Q000453:epidemiology; D006801:Humans; D008297:Male; D008875:Middle Aged; D012189:Retrospective Studies; D012424:Rural Population / Q000706:statistics & numerical data; D014186:Transportation / Q000191:economics* / Q000706:statistics & numerical data; D055815:Young Adult; D016903:Drug Monitoring