Do HIV prevalence trends in antenatal clinic surveillance represent trends in the general population in the antiretroviral therapy era? The case of Manicaland, East Zimbabwe. (10th September 2015) - Gregson, Simon; Dharmayat, Kanika; Pereboom, Monique; Takaruza, Albert; Mugurungi, Owen; Schur, Nadine; Nyamukapa, Constance A.
<x xml:space="preserve">Abstract</x> Objective: National estimates of HIV trends in generalized epidemics rely on HIV prevalence data from antenatal clinic (ANC) surveillance. We investigate whether HIV prevalence trends in ANC data reflect trends in men and women in the general population during the scale-up of antiretroviral therapy (ART) in Manicaland, Zimbabwe. Methods: Trends in HIV prevalence in local ANC attendees and adults aged 15–49 years in towns, agricultural estates, and villages were compared using five rounds of parallel ANC (N = 1200) and general-population surveys (N = 10 000) and multivariable log-linear regression. Changes in the age pattern of HIV prevalence and the age distribution of ANC attendees were compared with those in the general population. Age-specific pregnancy prevalence rates were compared by HIV infection and ART status. Results: Cumulatively, from 1998–2000 to 2009–2011, HIV prevalence fell by 60.0% (95% confidence interval, 51.1–67.3%) in ANC surveillance data and by 34.3% (30.8–37.7%) in the general population. Most of the difference arose following the introduction of ART (2006–2011). The estates and villages reflected this overall pattern but HIV prevalence in the towns was lower at local ANCs than in the general population, largely because of attendance by pregnant women from outlying (lower prevalence) areas. The ageing of people living with HIV in the general population (52.4% aged >35 years, 2009–2011) was under-represented in the ANC data (12.6%) because of lower fertility in older and HIV-infected women. Conclusion: After the introduction of ART in Manicaland, HIV prevalence declined more steeply in ANC surveillance data than in the general population. Models used for HIV estimates must reflect this change in bias.