Monitoring the HIV treatment and care cascade is central to the Joint United Nations Programme on HIV/AIDS’ (UNAIDS) objective of ending the AIDS epidemic as a public health threat by 2030 . Routine tracking of population-level progress towards the UNAIDS’ 2030 95–95–95 diagnostic, treatment, and viral load suppression targets can guide public health initiatives and improve programmatic efficiencies . However, estimating progress towards the first pillar of the targets – the percentage of people living with HIV (PLHIV) who know their HIV status – is challenging. In sub-Saharan Africa (SSA), where 67% of the 38 million PLHIV were estimated to reside in 2019 , measures of awareness are typically constructed from model synthesized data using self-reported HIV testing history (i.e. ever tested for HIV) [4,5] or reported directly from nationally representative household surveys (i.e. reporting a positive HIV test) [6–9].
Consideration of the potential for measurement bias is needed when interpreting self-reported survey data. Studies have shown that self-reports concerning sensitive information, for instance, an individual's HIV-testing history or status, could be affected by nondisclosure [8,10,11]. For example, inconsistencies have been documented in Kenya and Malawi between self-reported data and biomarkers for metabolites of antiretrovirals (ARVs) and viral load suppression [7,11]. Although previous studies have sought to validate the accuracy of self-reported HIV status [11–13], analyzing recent data on both nondisclosure of self-reported HIV-testing history and HIV status among PLHIV is key to improving the validity of these estimates.
Surveys that collect both self-reported information and ARV metabolite biomarkers can be used to assess the accuracy of those self-reporters. In this study, Bayesian latent class models are used to estimate the sensitivity of (i) self-reported HIV-testing history and (ii) awareness of HIV status among PLHIV based on the presence of detectable ARVs .
The Population-based HIV Impact Assessment (PHIA) surveys are nationally representative multistage household-based surveys designed to provide population-level information on the burden of HIV disease and to document the progress of HIV programs [15–18]. All four PHIA surveys with available microdata on PLHIV aged 15+ years were included in our analysis: Swaziland (Eswatini) HIV Incidence Measurement Survey 2 (2015–2016), Malawi PHIA (2015–2016), Tanzania HIV Impact Survey (2016–2017), and Zambia PHIA (2016).
Self-reports and antiretroviral status
Participants who reported having received the results of any HIV test were classified as ever tested and received results (hereafter referred to as ‘ever tested’; see Table S1, https://links.lww.com/QAD/B928). Participants who reported having ever received a positive test result after any HIV test, were classified as aware of HIV-positive status. A detailed description of the PHIA and ARV laboratory algorithms can be found Text S2, https://links.lww.com/QAD/B928.
Bayesian latent class models
Bayesian latent class models are useful when there are no gold standards  and can efficiently account for parameter uncertainty . We used this type of model to quantify the sensitivity of two self-reported outcomes among PLHIV: HIV-testing history and HIV-positive status awareness. Cross-tabulations of self-reports with ARV biomarkers provide empirical information on their sensitivity among those with detectable antiretrovirals (ARVs) (Fig. 1).
For participants with detectable ARVs, it assumed that: they had been tested for HIV, received their results, and were aware of their status (as they were receiving care); and there were no false positives in the detection of ARV metabolites. In the four PHIA surveys, an overall 99.9% of HIV-negative participants report having never received a positive HIV result, which aligns with other findings , we assumed no false positive for self-reported HIV-testing history, or awareness of HIV-positive status.
As ARV biomarker data only provide information about the sensitivity of self-reports among participants receiving treatment, the ratio of nondisclosure for PLHIV without versus with detectable ARVs was given a log-normal prior distribution with a mean of log(1.48) (standard error: 4) to estimate the sensitivity among people without detectable ARVs. This prior was elicited by reviewing available studies. The pooling of two studies conducted in rural Mozambique and Malawi [20,21] suggests that people not receiving ARVs are 1.48 more likely to not disclose their diagnosis. Additional analyses were conducted to investigate the influence of this prior on our results. Equations and prior distributions are presented in Table S2, https://links.lww.com/QAD/B928 and Text S1, https://links.lww.com/QAD/B928.
Given known biases in self-reported estimates of HIV status awareness, analysts often reclassify individuals not aware of their status but with detectable ARVs as being aware of their HIV-positive status, as in published PHIA reports. To examine the impact of this partial adjustment, we compared the unadjusted, ARV-reclassified (as in PHIA reports), and Bayesian-adjusted (ARV status and nondisclosure) estimates of PLHIV aware of their status.
Models were calibrated separately for each country and for subgroup analyses (i.e. age, sex, urban/rural, and socio-economic status). Bayesian hierarchical models using Markov Chain Monte Carlo, implemented through the JAGS software  and the rjags packages, were used to approximate the posterior densities [23,24].
Ethics approval for secondary data analyses was obtained from McGill University's Faculty of Medicine's Institutional Review Board (A10-E72-17B).
Overall, 3003 PLHIV from Eswatini, 2227 from Malawi, 1831 from Tanzania, and 2467 from Zambia were included in the analyses, with 76.0%, 68.1%, 61.5%, and 53.9% of them with detectable ARVs. In all countries, a high fraction of PLHIV reported having ever been tested and the proportion of PLHIV reporting being aware of their status ranged from 58.6% in Tanzania to 86.5% in Eswatini (Table S3, https://links.lww.com/QAD/B928).
Sensitivity of self-reports
Self-reported testing history
Among participants with detectable ARVs, the estimated sensitivity was highest in Eswatini (99.5%; 95% credible interval [95% Crl]: 99.2–99.8%), followed by Malawi (98.2%; 97.5–98.8%), Zambia (97.4%; 96.5–98.1%), and Tanzania (96.6%; 95.3–97.6%) (Fig. 2 ). For people without detectable ARVs, the estimated sensitivity was 2.4% points (0.1–11.4%) lower than those with detectable ARVs in Tanzania. The differences were smaller elsewhere (Table S4, https://links.lww.com/QAD/B928).
Self-reported awareness of HIV status
The sensitivity of self-reported awareness of HIV-positive status among participants with ARV metabolites was 97.4% (96.7–98.0%) in Eswatini, 94.2% (93.0–95.4%) in Malawi, 92.3% (90.5–93.8%) in Tanzania, and 91.6% (90.1–92.9%) in Zambia (Fig. 2 a). The estimated differences in sensitivity between PLHIV with and without detectable ARVs ranged from 1.8% points (0.1–8.5%) in Eswatini to 6.2% points (0.3–29.0%) in Zambia.
Differences by sex, age, rural/urban, and socioeconomic status
Among participants with detectable ARVs, women had higher sensitivities of self-reported HIV-testing history (0.9–2.4% points) and HIV status awareness (0.8–4.5% points) than men (Fig. 2 b). The estimated sensitivities were the lowest at age 15–24 years (94.7–97.2% for HIV-testing history and 83.9–91.9% for HIV status awareness) in all countries (Fig. 2 c). Participants residing in urban and rural areas had similar sensitivities and variations by socio-economic status were also small (Figure S1, https://links.lww.com/QAD/B928). Similar results for PLHIV without detectable ARVs were observed (Figure S2, https://links.lww.com/QAD/B928).
Adjusted proportion of people living with HIV ever tested and people living with HIV aware of their status
Adjusting for ARV status and nondisclosure influenced the estimates of the proportion ever tested for HIV less (largest difference between the adjusted and the self-reports was 3.9% points in Tanzania) than the estimated proportions of PLHIV aware of their status (largest difference was 7.2% points in Zambia) (Fig. 2 d). Results were robust to the assumed nondisclosure ratio for PLHIV without detectable ARVs when antiretroviral therapy (ART) coverage is high (Figure S3, https://links.lww.com/QAD/B928).
Self-reported information on HIV-testing and diagnosis are primary data sources used to monitor trends in the HIV treatment and care continuum [2,10]. Such data have also been proposed to estimate cross-sectional HIV incidence . In this study, we leveraged ARV biomarkers from representative surveys in Eswatini, Malawi, Tanzania, and Zambia to estimate the sensitivity of self-reported HIV-testing history and awareness of HIV-positive status among PLHIV. We found that, among PLHIV with detectable ARVs, self-reports of HIV-testing history have a high sensitivity (>96%) and self-reported awareness of HIV status had a marginally lower sensitivity (>91%) across these settings. These findings support the use of self-reported testing history to estimate testing trends and to model diagnosis coverage .
Social desirability bias may partly explain the nonnegligible lower sensitivities among male PLHIV and those aged 15–24 years . However, differences in survey instruments could also result in higher sensitivities for specific groups. For example, in the PHIA survey, women were asked about HIV-testing up to four times (before pregnancy, during pregnancy, during labor, and at their last HIV test), whereas men were asked only once. The classification of women based on the positive response to any of the four questions could increase the probability of women disclosing their true status.
The availability of ARV biomarkers enables the reclassification of PLHIV who do not disclose their status but for which ARV metabolites are detected as ‘aware’. We have found that this partial adjustment may be insufficient, especially if ART coverage is low in the surveyed population and the ratio of nondisclosure among those not on ART is high [27–30]. To accurately estimate awareness of status, results must also be adjusted for nondisclosure among PLHIV not receiving ART.
Our results need to be interpreted considering certain limitations. First, none of the PHIA surveys with publicly available microdata, are located in the West and Central African regions, where nondisclosure could be higher . Indeed, a recent meta-analysis suggest that, globally, one in five PLHIV may not report their status . The PHIA included here had some of the lowest levels of nondisclosure of the reviewed studies, suggesting that other settings could have lower sensitivities. Second, our study design limited our assessment of the sensitivity of testing history to PLHIV, and findings should not be extrapolated to people not living with HIV. Finally, it is not possible to empirically validate the sensitivity of self-reports among PLHIV without ARV metabolites. As such, we used information from two previous studies that used medical records to inform the nondisclosure ratio. Results could be sensitive to this nondisclosure ratio but the high ART coverage in the four countries mitigates this influence (Figure S3, https://links.lww.com/QAD/B928).
Strengths of this study include the use of standardized survey and laboratory data (i.e. detection of ARV metabolites). Second, the Bayesian latent class models propagate uncertainty to our results by assuming prior distributions and generating posterior credible intervals. Finally, we examined differences in the sensitivity of self-reports within different subgroups.
In conclusion, self-reported HIV-testing histories have high sensitivities in the countries examined but self-reported awareness of HIV status is lower. Whenever available, ARV biomarker data can be used to adjust self-reports but such adjustments may still underestimate diagnosis coverage, especially if ART coverage is low in that population. Our results can be used to produce more accurate estimates of the UNAIDS targets. Future research should extend this work to other regions and populations.
We acknowledge funding from the Steinberg Fund for Interdisciplinary Global Health Research (McGill University). MMG's research program is funded through a Canada Research Chair (Tier 2) in Population Health Modeling. J.W.E. acknowledges funding from the Bill and Melinda Gates Foundation and UNAIDS. M.C.B. acknowledge funding from MRC Centre for Global Infectious Disease Analysis (MRC GIDA, MR/R015600/1). This award is jointly funded by the UK Medical Research Council (MRC) and the UK Department for International Development (DFID) under the MRC/DFID Concordat agreement and is also part of the EDCTP2 programme supported by the European Union. L.F.J. acknowledges funding from UNAIDS.
Authors’ statement: Y.X., A.G., R.M.M., and M.M.G. were responsible for study design. Y.X. was responsible for literature search, drafting the article, and statistical analysis. R.M.M., A.G., M.C.B., L.F.J., K.M., J.W.E., and M.M.G. were responsible for reviewing and editing the article.
Conflicts of interest
There are no conflicts of interest.
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