Comparison of self-reported HIV testing data with medical records data in Houston, TX 2012–2013

An, Qian; Chronister, Karen J.; Song, Ruiguang; Pearson, Megan; Pan, Y.; Yang, Biru; Khuwaja, Salma; Hernández, Ángela; Hall, H. Irene

doi:10.1016/j.annepidem.2016.02.013

Cited by 14 publications

(8 citation statements)

References 15 publications

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“…Quantifying the sensitivity and specificity of those self-reports is difficult and their accuracy could differ by HIV status [14] . However, limited evidence suggest that testing histories are probably better reported than other potential indicators [50] but incorrect reports of HIV testing history could result in underestimation of the "first 90" [51] . As evidence accrues on the sensitivity and specificity of those self-reports, adjustments for potential misclassification, if warranted, could be incorporated into the model.…”

Section: Model Limitationsmentioning

confidence: 99%

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

Maheu‐Giroux

Marsh

Doyle

et al. 2019

Preprint

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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.

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Section: Model Limitationsmentioning

confidence: 99%

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

Maheu‐Giroux

Marsh

Doyle

et al. 2019

Preprint

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“…In total, there were 1503 participants included in the data analysis. The data analysis was completed by a team of scientists from CDC and HHD and reported in a separate study [22].…”

Section: Resultsmentioning

confidence: 99%

Assessing the accuracy of self-reporting HIV testing behaviour in Houston/Harris County, Texas

Rehman¹,

Khuwaja²,

Siddiqui³

et al. 2019

HIV & AIDS Review

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Introduction: In 2006, the Centers for Disease Control and Prevention (CDC) began directly estimating human immunodeficiency virus (HIV) incidence based upon the Serologic Testing Algorithm for Recent HIV Seroconversion (STARHS) and individuals' testing and treatment history, collected as part of the HIV Incidence Surveillance (HIS) system. The algorithm relies largely upon individuals' self-reported testing history. The primary objective of this study is to describe the methodology and procedures used to assess the similarities between self-reported and medical record data on HIV status and the dates of first positive and last negative tests. Material and methods: The testing history from the individuals is used in combination with the latest laboratory assay tests to obtain a direct population-based estimate of HIV incidence. Understanding how accurate the self-report testing information is will help in estimating HIV incidence. Partnerships were made with the medical clinics and Counselling and Testing facilities, and the participants were recruited from the patients attending these facilities. Participants were interviewed to ascertain the self-report of HIV test, HIV status, the date and location of the most recent HIV-negative test and the first HIV-positive test. Medical record abstraction was done after participants' authorisation to compare the accuracy of self-report testing data. Results: The participants' response rate was 83.7%, and the most common reason of not participating was lack of interest in the study. Data analysis is reported by CDC and Houston Health Department in a separate article. Conclusions: The methodology and procedures adopted in this study can be adopted, replicated, and improved in future studies exploring self-report accuracy.

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“…Assessing the validity of self-reports of HIV testing is challenging, notably because accuracy may differ depending on HIV status. 28,29 As inequalities measurements rely on the quantification of an association, a differential accuracy in self-reporting between socio-economic groups could have conducted to biased results. To our knowledge, there is currently limited evidence regarding sensitivity and specificity of self-reported HIV testing depending on socio-economic conditions.…”

Section: Discussionmentioning

confidence: 99%

Time trends in socio-economic inequalities in HIV testing: insights from population-based surveys in 16 sub-Saharan African countries

Ante-Testard

Benmarhnia

Bekelynck

et al. 2019

Preprint

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Background Overall increase in the uptake of HIV testing in the past decades may hide discrepancies across socio-economic groups. We used population-based surveys conducted in sub-Saharan Africa to quantify socio-economic inequalities in recent HIV testing uptake, together with their trends over the two past decades. Methods We analyzed the data from Demographic and Health Surveys in sub-Saharan African countries where at least one survey was conducted before and after 2008. Country- and gender-specific proportions of recent (<12 month) HIV testing were assessed across wealth and education groups, and inequalities were quantified using the relative and slope indices of inequalities. Time trends in inequalities were assessed and results were pooled across countries using random-effect meta-analyses. Findings We analyzed data from 32 surveys conducted between 2003 and 2016 in 16 countries among 537,784 participants. In pre-2008 surveys, women reported higher HIV testing uptake than men in 8 out of 16 countries, and in 15 out of 16 countries in post-2008 surveys. After 2008, the wealthiest women were on average 2.77 (95% CI 1.42-5.40) times more likely to report recent testing than the poorest; and 3.55 (1.85-6.81) times in men. The averaged absolute difference in recent testing between the richest and poorest was 11.1 (4.6-17.5) percentage points in women and 15.1 (9.6-20.6) in men. Over time, relative inequalities in recent HIV testing decreased in both genders, while absolute inequalities plateaued in women and increased in men. Interpretations The overall increase in HIV testing uptake that was stimulated by the impetus to scale up HIV treatment in sub-Saharan Africa led to a decrease in relative inequalities, while absolute inequalities persisted. Within most countries, large inequalities still remained, both in absolute and relative scales, especially in West and Central Africa. A greater focus should be put on equity in monitoring HIV testing programs. A French version of this article is available in the Appendices [Une version francaise de cet article est disponible en appendice].

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Comparison of self-reported HIV testing data with medical records data in Houston, TX 2012–2013

Cited by 14 publications

References 15 publications

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

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

Assessing the accuracy of self-reporting HIV testing behaviour in Houston/Harris County, Texas

Time trends in socio-economic inequalities in HIV testing: insights from population-based surveys in 16 sub-Saharan African countries

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