National HIV testing and diagnosis coverage in sub-Saharan Africa: a new modeling tool for estimating the “first 90” from program and survey data

Abstract

Objective: HIV testing services (HTS) are a crucial component of national HIV responses. Learning one's HIV diagnosis is the entry point to accessing life-saving antiretroviral treatment and care. Recognizing the critical role of HTS, the Joint United Nations Programme on HIV/AIDS (UNAIDS) launched the 90-90-90 targets stipulating that by 2020, 90% of people living with HIV know their status, 90% of those who know their status receive antiretroviral therapy, and 90% of those on treatment have a suppressed viral load. Countries will need to regularly monitor progress on these three indicators. Estimating the proportion of people living with HIV who know their status (i.e., the "first 90"), however, is difficult. Methods:We developed a mathematical model (henceforth referred to as "F90") that formally synthesizes population-based survey and HTS program data to estimate HIV status awareness over time. The proposed model uses country-specific HIV epidemic parameters from the standard UNAIDS Spectrum model to produce outputs that are consistent with other national HIV estimates. The F90 model provides estimates of HIV testing history, diagnosis rates, and knowledge of HIV status by age and sex. We validate the F90 model using both in-sample comparisons and out-of-sample predictions using data from three countries: Côte d'Ivoire, Malawi, and Mozambique. Results: In-sample comparisons suggest that the F90 model can accurately reproduce longitudinal sex-specific trends in HIV testing. Out-of-sample predictions of the fraction of PLHIV ever tested over a 4-to-6-year time horizon are also in good agreement with empirical survey estimates. Importantly, out-of-sample predictions of HIV knowledge are consistent (i.e., within 4% points) with those of the fully calibrated model in the three countries, when HTS program data are included. The F90 model's predictions of knowledge of status are higher than available selfreported HIV awareness estimates, however, suggesting -in line with previous studies-that these self-reports could be affected by non-disclosure of HIV status awareness. Conclusion: Knowledge of HIV status is a key indicator to monitor progress, identify bottlenecks, and target HIV responses. The F90 model can help countries track progress towards their "first 90" by leveraging surveys of HIV testing behaviors and annual HTS program data.

Global, regional and country-level 90–90–90 estimates for 2018

“…AIDS 2019, Vol 33 (Suppl 3) People living with HIV who know their HIV status Countries used one of three different methods to report on the number of PLHIV who know their status at endyear. Most countries in sub-Saharan Africa used a UNAIDS supported 'Shiny90' model to estimate this outcome for the current year and for historical trends from 2010 [20]. The model uses HIV incidence, prevalence and ART coverage from the national Spectrum file, data about the proportion ever tested for HIV by HIV status from national population surveys, and where available, HIV testing and positivity programme data to estimate the proportion of PLHIV aware of their status over time.…”

“…The model uses HIV incidence, prevalence and ART coverage from the national Spectrum file, data about the proportion ever tested for HIV by HIV status from national population surveys, and where available, HIV testing and positivity programme data to estimate the proportion of PLHIV aware of their status over time. More details on this approach are provided elsewhere in this supplement [20].…”

Trends in knowledge of HIV status and efficiency of HIV testing services in Sub-Saharan Africa (2000-2020): a modelling study of survey and HIV testing program data

“…This model has been described in detail elsewhere. 13 Estimating knowledge of HIV status, positivity, and yield…”

“…12 Aggregate HTS data including the number of HIV tests conducted and number of HIV diagnoses, are routinely collected, but reports are often not deduplicated and rates of retesting and re-diagnosis can be high. 13,14 Household surveys provide cross-sectional data about testing history by HIV status at intervals roughly every five years in most countries, but only a few surveys directly ask respondents if they are aware of their HIV status, a sensitive question that has high potential for non-disclosure. [15][16][17] These challenges are compounded by imprecise estimates for the number of new infections by age, sex and geographical area, and by incomplete ascertainment of mortality among the previously diagnosed and undiagnosed population.…”

Accuracy of self-reported HIV-testing history and awareness of HIV-positive status in four sub-Saharan African countries

“…In sub-Saharan Africa (SSA), where 67% of the 38 million PLHIV were estimated to reside in 2019 (3), measures of awareness are typically constructed from data about self-reported HIV testing behaviour or reported directly from nationally representative household surveys (4)(5)(6)(7)(8).…”