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Additional file 1: Table S1. Demonstration Instructions. Figure S1. Laboratory reference testing algorithm for STAR clinical performance study. Table S2. Video Checklist. Table S3. Sensitivity analysis excluding known HIV+. Table S4. Sensitivity of RDT Vs Laboratory Reference Standard. Table S5. Testing details of participants with discordant RDT and laboratory algorithms. Table S6. Characteristics of respondents in CPS with video and CPS-only sub-samples. Table S7. Determinants of completing all steps correctly among video sub-sample. Table S8. Errors observed during video review. Table S9. User perceptions of HIVST (N=85). Table S10. User-reported errors in CPS with video and CPS-only sub-samples.

Additional file 1. Supplementary materials including: Sources of data; Model details; Inference on delay and duration, posterior distribution by country sensitivity analysis

Additional file 1: Table S1. Detailed summary of participant quotes from focus group discussions and in-depth interviews across the life course.

Additional File 3: Table S3. Univariable and multivariable associations between sociodemographic factors and willingness to self-test among men in Zimbabwe, by testing history, 2015–16. Supplementary data with univariable and multivariable associations between sociodemographic factors and willingness to self-test among men in Zimbabwe

Additional File 2: Table S2. Univariable and multivariable associations between sociodemographic factors and ever-self-testing for HIV in Malawi and Zimbabwe, 2015–16. Supplementary data with univariable and multivariable associations between sociodemographic factors and ever-self-testing for HIV in Malawi and Zimbabwe

Introduction: Although HIV infection and its associated co-morbidities remain the commonest reason for hospitalisation in Africa, their impact on economic costs and health-related quality of life (HRQoL) are not well understood. This information is essential for decision-makers to make informed choices about how to best scale-up anti-retroviral treatment (ART) programmes. This study aimed to quantify the impact of HIV infection and ART on economic outcomes in a prospective cohort of hospitalised patients with high HIV prevalence.\ud \ud Methods: Sequential medical admissions to Queen Elizabeth Central Hospital, Malawi, between June-December 2014 were followed until discharge, with standardised classification of medical diagnosis and estimation of healthcare resources used. Primary costing studies estimated total health provider cost by medical diagnosis. Participants were interviewed to establish direct non-medical and indirect costs. Costs were adjusted to 2014 US$ and INT$. HRQoL was measured using the EuroQol EQ-5D. Multivariable analyses estimated predictors of economic outcomes.\ud \ud Results: Of 892 eligible participants, 80.4% (647/892) were recruited and medical notes found. In total, 447/647 (69.1%) participants were HIV-positive, 339/447 (75.8%) were on ART prior to admission, and 134/647 (20.7%) died in hospital. Mean duration of admission for HIV-positive participants not on ART and HIV-positive participants on ART was 15.0 days (95%CI: 12.0-18.0) and 12.2 days (95%CI: 10.8-13.7) respectively, compared to 10.8 days (95%CI: 8.8-12.8) for HIV-negative participants. Mean total provider cost per hospital admission was US$74.78 (bootstrap 95%CI: US$25.41-US$124.15) higher for HIV-positive than HIV-negative participants. Amongst HIV-positive participants, the mean total provider cost was US$106.87 (bootstrap 95%CI: US$25.09-US$106.87) lower for those on ART than for those not on ART. The mean total direct non-medical and indirect cost per hospital admission was US$87.84. EQ-5D utility scores were lower amongst HIV-positive participants, but not significantly different between those on and not on ART. \ud \ud Conclusions: HIV-related hospital care poses substantial financial burdens on health systems and patients; however, per-admission costs are substantially lower for those already initiated onto ART prior to admission. These potential cost savings could offset some of the additional resources needed to provide universal access to ART.

Additional file 1 Table S1. Characteristics of adult acute clinic attendances by exit interview participation.

Additional file 1 Table S1. Characteristics of adult acute clinic attendances by exit interview participation.

Additional File 1: Table S1. Baseline characteristics of men in Zimbabwe reporting on willingness to self-test, 2015–16. Supplementary data with baseline characteristics of men in Zimbabwe reporting on willingness to self-test

Additional File 1: Table S1. Baseline characteristics of men in Zimbabwe reporting on willingness to self-test, 2015–16. Supplementary data with baseline characteristics of men in Zimbabwe reporting on willingness to self-test