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INTRODUCTION: Population-based biomarker surveys are the gold standard for estimating HIV prevalence but are susceptible to substantial non-participation (up to 30%). Analytical missing data methods, including inverse-probability weighting (IPW) and multiple imputation (MI), are biased when data are missing-not-at-random, for example when people living with HIV more frequently decline participation. Heckman-type selection models can, under certain assumptions, recover unbiased prevalence estimates in such scenarios. METHODS: We pooled data from 142,706 participants aged 15-49 years from nationally representative cross-sectional Population-based HIV Impact Assessments in seven countries in sub-Saharan Africa, conducted between 2015 and 2018 in Tanzania, Uganda, Malawi, Zambia, Zimbabwe, Lesotho and Eswatini. We compared sex-stratified HIV prevalence estimates from unadjusted, IPW, MI and selection models, controlling for household and individual-level predictors of non-participation, and assessed the sensitivity of selection models to the copula function specifying the correlation between study participation and HIV status. RESULTS: In total, 84.1% of participants provided a blood sample to determine HIV serostatus (range: 76% in Malawi to 95% in Uganda). HIV prevalence estimates from selection models diverged from IPW and MI models by up to 5% in Lesotho, without substantial precision loss. In Tanzania, the IPW model yielded lower HIV prevalence estimates among males than the best-fitting copula selection model (3.8% vs. 7.9%). CONCLUSIONS: We demonstrate how HIV prevalence estimates from selection models can differ from those obtained under missing-at-random assumptions. Further benefits include exploration of plausible relationships between participation and outcome. While selection models require additional assumptions and careful specification, they are an important tool for triangulating prevalence estimates in surveys with substantial missing data due to non-participation.

Supplemental material, sj-docx-1-isp-10.1177_00207640231175778 for Prevalence and correlates of adolescent self-injurious thoughts and behaviors: A population-based study in Burkina Faso by Tracie I Ebalu, Jaclyn C Kearns, Lucienne Ouermi, Mamadou Bountogo, Ali Sié, Till Bärnighausen and Guy Harling in International Journal of Social Psychiatry

Supplemental material, sj-docx-1-isp-10.1177_00207640231175778 for Prevalence and correlates of adolescent self-injurious thoughts and behaviors: A population-based study in Burkina Faso by Tracie I Ebalu, Jaclyn C Kearns, Lucienne Ouermi, Mamadou Bountogo, Ali Sié, Till Bärnighausen and Guy Harling in International Journal of Social Psychiatry

We use an individual-level transmission and contact simulation model to explore the effectiveness and resource requirements of various test-trace-isolate (TTI) strategies for reducing the spread of SARS-CoV-2 in the UK, in the context of different scenarios with varying levels of stringency of non-pharmaceutical interventions. Based on modelling results, we show that self-isolation of symptomatic individuals and quarantine of their household contacts has a substantial impact on the number of new infections generated by each primary case. We further show that adding contact tracing of non-household contacts of confirmed cases to this broader package of interventions reduces the number of new infections otherwise generated by 5-15%. We also explore impact of key factors, such as tracing application adoption and testing delay, on overall effectiveness of TTI.

Authors’ original file for figure 1

Additional file 2 Thetha Nami Participatory Mapping Workshop Tools.

Additional file 5 Thetha Nami Peer Mentorship Tool.

Additional file 1 Thetha Nami Peer Navigator Training Programme.

Additional file 1: Supplementary Table 1. Description of types of violence experience by Common Mental Disorder status.

Additional file 3 Thetha Nami Peer Navigator Monitoring Tool.

Additional file 2. Supplementary content 2: Assessment of risk of bias for the quantitative studies/ study part. The assessment follows the methods commentary: Risk of Bias in cross-sectional surveys of attitudes and practices (Agarwal et al. 2017).

Additional file 1. Supplementary Content 1: Search terms and hits by database.

Supplemental material, HAALSI_unmet_needs_-_R1_-_18Oct2018_-_SM_clean for Impairment in Activities of Daily Living, Care Receipt, and Unmet Needs in a Middle-Aged and Older Rural South African Population: Findings From the HAALSI Study by Guy Harling, Collin F. Payne, Justine I. Davies, F. Xavier Gomez-Olive, Kathleen Kahn, Lenore Manderson, Farrah J. Mateen, Stephen M. Tollman and Miles D. Witham in Journal of Aging and Health

Table S1. Regression forms for variables considered. Table S2. Impact of interviewer identity on associations between baseline covariates and HIV testing history in an HIV self-test trial amongst female sex workers in Zambia, full regression results. Table S3. Comparison of bivariate associations from hierarchical models containing random intercepts only for interviewers (two-level) or interviewers and peer educators (three-level). (PDF 120 kb)

Appendix tables and figures. PDF containing all appendix tables and figures. (PDF 1020Â kb)

Background: Network-based interventions are most powerful against epidemics when the full network structure is known. However, resource constraints often require decisions based on partial network data. We investigated how the effectiveness of network-based vaccination schemes varied based on the accuracy of network data available. Methods: We simulated propagating an SIR process on static empirical social networks from 75 rural Indian villages. First, we used regression analysis to predict the percentage of individuals ever infected (cumulative incidence) based on village-level networks. Second, we simulated vaccinating 10% of each village at baseline, selecting vaccinees using 1 of 5 network-based approaches: at random (Random); random contacts of random individuals (Nomination); random high-degree individuals (High Degree); highest degree (Highest Degree); or most central (Central). The first three approaches require only sample data; the latter two require full network data. We also simulated imposing a limit on how many contacts an individual can nominate (Fixed Choice Design, FCD), which reduces data collection burden but generates only partially observed networks. Results: We found the mean and standard deviation of the degree distribution to strongly predict cumulative incidence. In simulations, Nomination reduced cumulative incidence by one-sixth compared to Random vaccination; full network methods reduced infection by two-thirds. High Degree had intermediate effectiveness. Somewhat surprisingly, FCD truncating individuals' degrees at three was as effective as using complete networks. Conclusions: Using even partial network information to prioritize vaccines at either the village or individual level substantially improved epidemic outcomes. Such approaches may be feasible and effective in outbreak settings, and full ascertainment of network structure may not be required., Comment: 38 pages

Supplemental Material, harling_FM_8-25-16_supplemental for Interviewer-driven Variability in Social Network Reporting: Results from Health and Aging in Africa: A Longitudinal Study of an INDEPTH Community (HAALSI) in South Africa by Guy Harling, Jessica M. Perkins, Francesc Xavier Gómez-Olivé, Katherine Morris, Ryan G. Wagner, Livia Montana, Chodziwadziwa W. Kabudula, Till Bärnighausen, Kathleen Kahn, and Lisa Berkman in Field Methods

Additional analyses and materials. (PDF 692 kb)

Hierarchical multivariable logistic regression analysis of: being recommended DOT (Model 1); abandoning DOT conditional on having been recommended DOT (Model 2). (DOCX 42Â kb)

Choropleth maps of selected covariates. (DOCX 483Â kb)

Hierarchical multivariable logistic regression analysis of: being recommended DOT (Model 1); abandoning DOT conditional on having been recommended DOT (Model 2). (DOCX 42Â kb)

Descriptive statistics for individual-level variables. (DOCX 30Â kb)

Additional file 4. Peer Navigator indepth interview topic guide.

Additional file 4. Peer Navigator indepth interview topic guide.