title: Can population differences explain the contrasting results of the Mwanza, Rakai, and Masaka HIV/sexually transmitted disease intervention trials?: A modeling study.
abstract: OBJECTIVE
  To determine whether population differences can explain the contrasting impacts on HIV observed in the Mwanza trial of sexually transmitted disease (STD) syndromic treatment (ST), the Rakai trial of STD mass treatment (MT), and the Masaka trial of information, education, and communication (IEC) with and without ST as well as to predict the effectiveness of each intervention strategy in each population.
  
  
  METHODS
  Stochastic modeling of the transmission of HIV and 6 STDs was used with parameters fitted to demographic, sexual behavior, and epidemiological data from the trials and general review of STD/HIV biology.
  
  
  RESULTS
  The baseline trial populations could be simulated by assuming higher risk behavior in Uganda compared with Mwanza in the 1980s, followed by reductions in risk behavior in Uganda preceding the trials. In line with trial observations, the projected HIV impacts were larger for the ST intervention in Mwanza than for the MT intervention in Rakai or the IEC and IEC + ST interventions in Masaka. All 4 simulated intervention strategies were more effective in reducing incidence of HIV infection in Mwanza than in either Rakai or Masaka.
  
  
  CONCLUSIONS
  Population differences in sexual behavior, curable STD rates, and HIV epidemic stage can explain most of the contrast in HIV impact observed between the 3 trials. This study supports the hypothesis that STD management is an effective HIV prevention strategy in populations with a high prevalence of curable STDs, particularly in an early HIV epidemic.
authors.affiliation: London School of Hygiene and Tropical Medicine, London, UK. richard.white@lshtm.ac.uk
authors.forename: Richard G
authors.initials: RG
authors.lastname: White
authors.forename: Kate K
authors.initials: KK
authors.lastname: Orroth
authors.forename: Eline L
authors.initials: EL
authors.lastname: Korenromp
authors.forename: Roel
authors.initials: R
authors.lastname: Bakker
authors.forename: Mwita
authors.initials: M
authors.lastname: Wambura
authors.forename: Nelson K
authors.initials: NK
authors.lastname: Sewankambo
authors.forename: Ron H
authors.initials: RH
authors.lastname: Gray
authors.forename: Anatoli
authors.initials: A
authors.lastname: Kamali
authors.forename: James A G
authors.initials: JA
authors.lastname: Whitworth
authors.forename: Heiner
authors.initials: H
authors.lastname: Grosskurth
authors.forename: J Dik F
authors.initials: JD
authors.lastname: Habbema
authors.forename: Richard J
authors.initials: RJ
authors.lastname: Hayes
mesh_terms: D000293:Adolescent; D000328:Adult; D002986:Clinical Trials as Topic; D003198:Computer Simulation; D005069:Evaluation Studies as Topic; D005260:Female; D015658:HIV Infections / Q000453:epidemiology* / Q000517:prevention & control; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D010353:Patient Education as Topic; D015995:Prevalence; D012309:Risk-Taking; D012680:Sensitivity and Specificity; D012725:Sexual Behavior / Q000523:psychology*; D012749:Sexually Transmitted Diseases / Q000188:drug therapy / Q000453:epidemiology; D013636:Tanzania; D014454:Uganda