Assessing the validity of and factors that influence accurate self-reporting of HIV status after testing: a population-based study

Abstract

Objectives: To assess the validity of self-reported HIV status, and investigate factors that influence accurate reporting of HIV-positive status, in a population tested and informed of their HIV test result. Design: Prospective cohort study. Methods: We compared self-reported HIV status with biomarker-confirmed HIV test status among participants of Karonga Health and Demographic Surveillance Site in rural northern Malawi. We linked information on HIV test results to subsequent self-reported HIV status, and calculated sensitivity, specificity, positive predictive value and negative predictive value for self-reported HIV status (considered as a diagnostic test). We used Poisson regression with robust variance estimators to examine predictors of accurate self-reporting of HIV-positive status. Results: Among 17 445 adults who tested for HIV, were recorded as having received their HIV test results, and had a subsequent self-reported HIV status between 2007 and 2018: positive predictive value of self-reported HIV status was 98.0% (95% confidence interval: 97.3–98.7); negative predictive value was 98.3 (98.1–98.5); sensitivity was 86.1% (84.5–87.7); and specificity was 99.8% (99.7–99.9). Among true HIV-positive people, those who were younger, interviewed in community settings, and had tested for HIV longer ago were more likely to misreport their HIV-positive status. Conclusion: In this setting, self-report provides good estimates of test-detected HIV prevalence, suggesting that it can be used when HIV test results are not available. Despite frequent HIV testing, younger people and those interviewed in community settings were less likely to accurately report their HIV-positive status. More research on barriers to self-reporting of HIV status is needed in these subgroups.

Individual and household factors associated with non-disclosure of positive HIV status in a population-based HIV serosurvey

“…Young people with HIV were significantly more likely to misreport their HIV diagnosis to survey interviewers than older adults, findings similar to other population-based surveys across sub-Saharan Africa [4,10,17]. Young adults living with HIV may be reluctant to disclose their positive status in household surveys due to fear of sensitive information being released, since HIV stigma and discrimination are key obstacles for youth [18–20].…”

“…However, it is unlikely that deeply rooted structural factors such as HIV stigma will be overcome by improving training and questionnaires alone. In Malawi, HIV disclosure was shown to be lower in community settings compared to clinical settings, suggesting a mistrust of fieldworkers [10]. We surmise that strengthening community engagement and mobilization in populationbased HIV surveys may be a more holistic route to improving trust, participation, and accuracy from respondents.…”

“…Therefore, ARV biomarkers are powerful tools for validating selfreported HIV status and adjusting population-based estimates of HIV diagnosis [3,6]. Several studies have identified concerning levels of non-disclosure in household surveys throughout sub-Saharan Africa, the region with the majority of the global HIV burden [4,[7][8][9][10][11][12].…”

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

“…This prior was elicited by reviewing available studies an meta-analyzing the evidence. The pooling of two studies conducted in rural Mozambique and Malawi (19,20) 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.…”

Guidance for triangulating data and estimates of HIV prevalence among pregnant women and coverage of PMTCT using the Spectrum AIDS Impact Module

“…Countries should assess the reliability of data used to estimate age-specific HIV prevalence among adults, considering documented biases in HIV surveillance data. [32][33][34][35] Where death registration data are used, countries should consider how frequent misclassification of cause of death is. 35 2.…”