title: Looking upstream to prevent HIV transmission: can interventions with sex workers alter the course of HIV epidemics in Africa as they did in Asia?
abstract: BACKGROUND
  High rates of partner change in 'upstream' sex work networks have long been recognized to drive 'downstream' transmission of sexually transmitted infections (STIs). We used a stochastic microsimulation model (STDSIM) to explore such transmission dynamics in a generalized African HIV epidemic.
  
  
  METHODS
  We refined the quantification of sex work in Kisumu, Kenya, from the 4-cities study. Interventions with sex workers were introduced in 2000 and epidemics projected to 2020. We estimated the contribution of sex work to transmission, and modelled standard condom and STI interventions for three groups of sex workers at feasible rates of use and coverage.
  
  
  RESULTS
  Removing transmission from sex work altogether would have resulted in 66% lower HIV incidence (range 54-75%) and 56% lower prevalence (range 44-63%) after 20 years. More feasible interventions reduced HIV prevalence from one-fifth to one-half. High rates of condom use in sex work had the greatest effect, whereas STI treatment contributed to HIV declines at lower levels of condom use. Interventions reaching the 40% of sex workers with most clients reduced HIV transmission nearly as much as less targeted approaches attempting to reach all sex workers. Declines were independent of antiretroviral therapy rollout and robust to realistic changes in parameter values.
  
  
  CONCLUSIONS
  'Upstream' transmission in sex work remains important in advanced African HIV epidemics even in the context of antiretroviral therapy. As in concentrated Asian epidemics, feasible condom and STI interventions that reach the most active sex workers can markedly reduce the size of HIV epidemics. Interventions targeting 'transactional' sex with fewer clients have less impact.
authors.affiliation: aDepartment of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands bCentre for Health Policy, Faculty of Health Sciences, School of Public Health, University of Witwatersrand, South Africa cCreating 010, Rotterdam University of Applied Sciences, Rotterdam dNijmegen International Center for Health System Analysis and Education, Department of Primary and Community Care, Radboud University Nijmegen Medical Centre, The Netherlands eLondon School of Hygiene and Tropical Medicine, London, United Kingdom.
authors.forename: Richard
authors.initials: R
authors.lastname: Steen
authors.forename: Jan A C
authors.initials: JA
authors.lastname: Hontelez
authors.forename: Andra
authors.initials: A
authors.lastname: Veraart
authors.forename: Richard G
authors.initials: RG
authors.lastname: White
authors.forename: Sake J
authors.initials: SJ
authors.lastname: de Vlas
mesh_terms: D000293:Adolescent; D000328:Adult; D003140:Communicable Disease Control / Q000379:methods*; D003198:Computer Simulation; D018562:Disease Transmission, Infectious / Q000517:prevention & control*; D005260:Female; D015658:HIV Infections / Q000453:epidemiology / Q000517:prevention & control* / Q000635:transmission*; D006801:Humans; D015994:Incidence; D007630:Kenya / Q000453:epidemiology; D008297:Male; D008875:Middle Aged; D060386:Sex Workers; D012725:Sexual Behavior; D055815:Young Adult