Self-reported Testing History Research Articles

A new method for estimating HIV incidence from a single cross-sectional survey.

Estimating incidence from cross-sectional data sources is both important to the understanding of the HIV epidemic and challenging from a methodological standpoint. We develop a new incidence estimator that measures the size of the undiagnosed population and the amount of time spent undiagnosed in order to infer incidence and transmission rates. The estimator is calculated using commonly collected information on testing history and HIV status and, thus, can be deployed in many HIV surveys without additional cost. If ART biomarker status and/or viral load information is available, the estimator can be adjusted for biases in self-reported testing history. The performance of the estimator is explored in two large surveys in Kenya, where we find our point estimates to be consistent with assay-derived estimates, with much smaller standard errors.

Non-disclosure of HIV testing history in population-based surveys: implications for estimating a UNAIDS 90-90-90 target

ABSTRACTBackground: HIV/AIDS programmes and organisations around the world use routinely updated estimates of the UNAIDS 90-90-90 targets to track progress and prioritise further programme implementation. Any bias in these estimates has the potential to mislead organisations on where gaps exist in HIV testing and treatment programmes.Objective: To measure the extent of undisclosed HIV testing history and its impact on estimating the proportion of people living with HIV (PLHIV) who know their HIV status (the ‘first 90’ of the UNAIDS 90-90-90 targets).Methods: We conducted a retrospective cohort study using population-based HIV serological surveillance conducted between 2010 and 2016 and linked, directly observed HIV testing records in Kisesa, Tanzania. Generalised estimating equations logistic regression models were used to detect associations with non-disclosure of HIV testing history adjusting for demographic, behavioural, and clinical characteristics. We compared estimates of the ‘first 90’ using self-reported survey data only and augmented estimates using information from linked records to quantify the absolute and relative impact of undisclosed HIV testing history.Results: Numbers of participants in each of the survey rounds ranged from 7171 to 7981 with an average HIV prevalence of 6.9%. Up to 33% of those who tested HIV-positive and 34% of those who tested HIV-negative did not disclose their HIV testing history. The proportion of PLHIV who reported knowing their status increased from 34% in 2010 to 65% in 2016. Augmented estimates including information from directly observed testing history resulted in an absolute impact of 6.7 percentage points and relative impact of 12.4%.Conclusions: In this population, self-reported testing history in population-based HIV serological surveys under-estimated the percentage of HIV positives that are diagnosed by a relative factor of 12%. Research should be employed in other surveillance systems that benefit from linked data to investigate how bias may vary across settings.