title: Statistical models for incorporating data from routine HIV testing of pregnant women at antenatal clinics into HIV/AIDS epidemic estimates.
abstract: HIV prevalence data collected from routine HIV testing of pregnant women at antenatal clinics (ANC-RT) are potentially available from all facilities that offer testing services to pregnant women and can be used to improve estimates of national and subnational HIV prevalence trends. We develop methods to incorporate these new data source into the Joint United Nations Programme on AIDS Estimation and Projection Package in Spectrum 2017.
  
  
  
  We develop a new statistical model for incorporating ANC-RT HIV prevalence data, aggregated either to the health facility level (site-level) or regionally (census-level), to estimate HIV prevalence alongside existing sources of HIV prevalence data from ANC unlinked anonymous testing (ANC-UAT) and household-based national population surveys. Synthetic data are generated to understand how the availability of ANC-RT data affects the accuracy of various parameter estimates.
  
  
  
  We estimate HIV prevalence and additional parameters using both ANC-RT and other existing data. Fitting HIV prevalence using synthetic data generally gives precise estimates of the underlying trend and other parameters. More years of ANC-RT data should improve prevalence estimates. More ANC-RT sites and continuation with existing ANC-UAT sites may improve the estimate of calibration between ANC-UAT and ANC-RT sites.
  
  
  
  We have proposed methods to incorporate ANC-RT data into Spectrum to obtain more precise estimates of prevalence and other measures of the epidemic. Many assumptions about the accuracy, consistency, and representativeness of ANC-RT prevalence underlie the use of these data for monitoring HIV epidemic trends and should be tested as more data become available from national ANC-RT programs.
authors.affiliation: aDepartment of Statistics, Pennsylvania State University, University Park, Pennsylvania, USA bStrategic Information and Monitoring Division, UNAIDS, Geneva, Switzerland cDepartment of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom.
authors.forename: Ben
authors.initials: B
authors.lastname: Sheng
authors.forename: Kimberly
authors.initials: K
authors.lastname: Marsh
authors.forename: Aleksandra B
authors.initials: AB
authors.lastname: Slavkovic
authors.forename: Simon
authors.initials: S
authors.lastname: Gregson
authors.forename: Jeffrey W
authors.initials: JW
authors.lastname: Eaton
authors.forename: Le
authors.initials: L
authors.lastname: Bao
mesh_terms: D000293:Adolescent; D000328:Adult; D058872:Epidemics; D005260:Female; D015658:HIV Infections / Q000175:diagnosis / Q000453:epidemiology*; D006801:Humans; D008875:Middle Aged; D015233:Models, Statistical; D011247:Pregnancy; D011251:Pregnancy Complications, Infectious / Q000175:diagnosis / Q000453:epidemiology*; D011295:Prenatal Care; D015995:Prevalence; D055815:Young Adult