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      Performance of self-reported HIV status in determining true HIV status among older adults in rural South Africa: a validation study

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          Abstract

          Introduction: In South Africa, older adults make up a growing proportion of people living with HIV. HIV programmes are likely to reach older South Africans in home-based interventions where testing is not always feasible. We evaluate the accuracy of self-reported HIV status, which may provide useful information for targeting interventions or offer an alternative to biomarker testing.

          Methods: Data were taken from the Health and Aging in Africa: A Longitudinal Study of an INDEPTH Community in South Africa (HAALSI) baseline survey, which was conducted in rural Mpumalanga province, South Africa. A total of 5059 participants aged ≥40 years were interviewed from 2014 to 2015. Self-reported HIV status and dried bloodspots for HIV biomarker testing were obtained during at-home interviews. We calculated sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for self-reported status compared to “gold standard” biomarker results. Log-binomial regression explored associations between demographic characteristics, antiretroviral therapy (ART) status and sensitivity of self-report.

          Results: Most participants (93%) consented to biomarker testing. Of those with biomarker results, 50.9% reported knowing their HIV status and accurately reported it. PPV of self-report was 94.1% (95% confidence interval (CI): 92.0–96.0), NPV was 87.2% (95% CI: 86.2–88.2), sensitivity was 51.2% (95% CI: 48.2–54.3) and specificity was 99.0% (95% CI: 98.7–99.4). Participants on ART were more likely to report their HIV-positive status, and participants reporting false-negatives were more likely to have older HIV tests.

          Conclusions: The majority of participants were willing to share their HIV status. False-negative reports were largely explained by lack of testing, suggesting HIV stigma is retreating in this setting, and that expansion of HIV testing and retesting is still needed in this population. In HIV interventions where testing is not possible, self-reported status should be considered as a routine first step to establish HIV status.

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          Estimating wealth effects without expenditure data--or tears: an application to educational enrollments in states of India.

          Using data from India, we estimate the relationship between household wealth and children's school enrollment. We proxy wealth by constructing a linear index from asset ownership indicators, using principal-components analysis to derive weights. In Indian data this index is robust to the assets included, and produces internally coherent results. State-level results correspond well to independent data on per capita output and poverty. To validate the method and to show that the asset index predicts enrollments as accurately as expenditures, or more so, we use data sets from Indonesia, Pakistan, and Nepal that contain information on both expenditures and assets. The results show large, variable wealth gaps in children's enrollment across Indian states. On average a "rich" child is 31 percentage points more likely to be enrolled than a "poor" child, but this gap varies from only 4.6 percentage points in Kerala to 38.2 in Uttar Pradesh and 42.6 in Bihar.
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            Diagnostic tests. 1: Sensitivity and specificity.

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              A comparison of two methods for estimating prevalence ratios

              Background It is usually preferable to model and estimate prevalence ratios instead of odds ratios in cross-sectional studies when diseases or injuries are not rare. Problems with existing methods of modeling prevalence ratios include lack of convergence, overestimated standard errors, and extrapolation of simple univariate formulas to multivariable models. We compare two of the newer methods using simulated data and real data from SAS online examples. Methods The Robust Poisson method, which uses the Poisson distribution and a sandwich variance estimator, is compared to the log-binomial method, which uses the binomial distribution to obtain maximum likelihood estimates, using computer simulations and real data. Results For very high prevalences and moderate sample size, the Robust Poisson method yields less biased estimates of the prevalence ratios than the log-binomial method. However, for moderate prevalences and moderate sample size, the log-binomial method yields slightly less biased estimates than the Robust Poisson method. In nearly all cases, the log-binomial method yielded slightly higher power and smaller standard errors than the Robust Poisson method. Conclusion Although the Robust Poisson often gives reasonable estimates of the prevalence ratio and is very easy to use, the log-binomial method results in less bias in most common situations, and because it fits the correct model and obtains maximum likelihood estimates, it generally results in slightly higher power, smaller standard errors, and, unlike the Robust Poisson, it always yields estimated prevalences between zero and one.
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                Author and article information

                Journal
                J Int AIDS Soc
                J Int AIDS Soc
                ZIAS
                zias20
                Journal of the International AIDS Society
                Taylor & Francis
                1758-2652
                2017
                18 July 2017
                : 20
                : 1
                : 21691
                Affiliations
                [ a ] Center for Population and Development Studies, Harvard University , Cambridge, MA, USA
                [ b ] MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of the Witwatersrand , Johannesburg, South Africa
                [ c ] INDEPTH Network , Accra, Ghana
                [ d ] Department of Epidemiology and Biostatistics, Indiana University School of Public Health-Bloomington , Bloomington, IN, USA
                [ e ] Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Harvard University , Boston, MA, USA
                [ f ] Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School , Boston, MA, USA
                [ g ] School of Demography, Australian National University , Canberra, Australia
                [ h ] CU Population Center, Institute of Behavioral Science, University of Colorado at Boulder , Boulder, CO, USA
                [ i ] Umeå Centre for Global Health Research, Division of Epidemiology and Global Health, Department of Public Health and Clinical Medicine, Umeå University , Umeå, Sweden
                [ j ] Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Harvard University , Boston, MA, USA
                [ k ] Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University , Boston, MA, USA
                [ l ] Africa Health Research Institute (AHRI) , Mtubatuba, South Africa, KwaZulu-Natal
                [ m ] Institute of Public Health, University of Heidelberg , Heidelberg, Germany
                Author notes
                [ § ]Corresponding author: Julia Rohr, Center for Population and Development Studies, Harvard University , 9 Bow Street, Cambridge, MA 02138, USA. Tel: (617) 384-7681. ( jkrohr@ 123456hsph.harvard.edu )
                Article
                1351215
                10.7448/IAS.20.1.21691
                5577734
                28782333
                8c5cb81c-4afb-4221-9494-e45931e133d5
                © 2017 Rohr J et al; licensee International AIDS Society

                This is an Open Access article distributed under the terms of the Creative Commons Attribution 3.0 Unported (CC BY 3.0) License ( http://creativecommons.org/licenses/by/3.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 23 November 2016
                : 3 July 2017
                Page count
                Figures: 2, Tables: 2, References: 50, Pages: 8
                Funding
                Funded by: National Institutes of Health 10.13039/100000002
                Award ID: R01-AI124389
                Funded by: National Institutes of Health 10.13039/100000002
                Award ID: P01-AG041710
                Funded by: National Institutes of Health 10.13039/100000002
                Award ID: R01-HD084233
                Funded by: Wellcome Trust 10.13039/100004440
                Award ID: 085477/B/08/Z
                Funded by: Wellcome Trust 10.13039/100004440
                Award ID: 069683/Z/02/Z
                Funded by: Wellcome Trust 10.13039/100004440
                Award ID: 058893/Z/99/A
                Funded by: Wellcome Trust 10.13039/100004440
                Award ID: 085477/Z/08/Z
                This study was funded by the National Institute on Aging (NIA) of the National Institutes of Health (NIH) [P01-AG041710] and is nested within the MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt) and funded by Wellcome Trust [058893/Z/99/A; 069683/Z/02/Z; 085477/Z/08/Z; 085477/B/08/Z] with important contributions from the University of the Witwatersrand and the South African Medical Research Council. Till Bärnighausen was supported by the Alexander von Humboldt Foundation through the Alexander von Humboldt Professor award, funded by the German Federal Ministry of Education and Research. He is also supported by the Wellcome Trust, the European Commission, the Clinton Health Access Initiative and NICHD of NIH [R01-HD084233], NIAID of NIH [R01-AI124389 and R01-AI112339] and FIC of NIH [D43-TW009775].
                Categories
                Article
                Research Article

                Infectious disease & Microbiology
                validation study,south africa,hiv status,self-report,older adults,public health

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