Joseph Larmarange

Scaling Up HIV Self-Testing in Africa: Insights From National Programmatic Data in Eight Countries

Joseph LARMARANGE — 2026-04-29T18:06:11Z

Scaling Up HIV Self‐Testing in Africa: Insights From National Programmatic Data in Eight Countries

Adrien Allorant , Anne Bekelynck , Aliza Monroe-Wise , Carlota Baptista da Silva , Thato Chidarikire , Olanrewaju Edun , Leonid Joaquim , Christine Kisia , Joseph Larmarange , Johnson John Lyimo , Juma Mcollogi James , Christine Musanhu , Getrude Ncube , Isabel Sathane , Arlette Simo-Fotso , Geoffrey Taasi , Cheryl Johnson

Journal of the International AIDS Society, 2026, 29 (5), ⟨10.1002/jia2.70120⟩

Article dans une revue

hal-05607277

Authors

Adrien Allorant, Anne Bekelynck, Aliza Monroe-Wise, Carlota Baptista da Silva, Thato Chidarikire, Olanrewaju Edun, Leonid Joaquim, Christine Kisia, Joseph Larmarange, Johnson John Lyimo, Juma McOllogi James, Christine Musanhu, Getrude Ncube, Isabel Sathane, Arlette Simo-Fotso, Geoffrey Taasi, Cheryl Case Johnson

Abstract

Introduction

Evidence from routine, national programme data on HIV self-testing (HIVST) scale-up is limited. This study examines HIVST scale-up in eight African countries, describing how HIVST has been integrated into testing strategies and how testing coverage, test positivity, and linkage to antiretroviral therapy (ART) have evolved.

Methods

We conducted a retrospective descriptive analysis of national programme data from January 2019 to December 2023 across Kenya, Lesotho, Malawi, Mozambique, South Africa, Tanzania, Uganda and Zimbabwe. Data were disaggregated by quarter and subnational district. Indicators included HIVST kits distributed, conventional testing volumes, new HIV diagnoses and new ART initiations. We derived testing rates, testing positivity, ART linkage, and stability of HIVST distribution by district and over time.

Results

HIVST scale-up varied across countries. By the most recent quarter, HIVST accounted for 63% of total testing in Lesotho, 19%–25% in Malawi and Zimbabwe, but <15% in Kenya, Tanzania, Uganda and South Africa. In Malawi, Lesotho and Zimbabwe, large volumes of HIVST partially offset declines in conventional testing during the COVID-19 pandemic. HIVST remained modest (<15% of total tests) in Kenya and Tanzania. In Mozambique, both conventional testing and HIVST expanded. In South Africa, conventional testing remained high after COVID-19, while HIVST expanded slowly. Despite divergent trajectories, new HIV diagnoses and ART initiations remained stable in most settings, indicating programmes adapted to maintain case-finding even as testing volumes shifted.

Conclusions

This descriptive analysis shows HIVST has been scaled to different degrees, with its contribution to overall testing shaped by national contexts, and distribution models. Interpretation is constrained by incomplete reporting, the inability to identify kits used out of kits distributed and distinguishing first-time from repeat testers. These findings can guide optimizing HIV testing services, an essential step towards meeting global HIV targets and ending AIDS by 2030.

Estimating effect of non response on HIV prevalence estimates from Demographic and Health Surveys

Joseph LARMARANGE — 2009-06-20T13:41:00Z

Larmarange Joseph, Vallo Roselyne, Yaro Seydou, Msellati Philippe, Méda Nicolas, Ferry Benoît (2009). Estimating effect of non response on HIV prevalence estimates from Demographic and Health Surveys, CEPED's Working Papers, numéro 3, CEPED, Paris (http://ceped.org/wp), RIS, BibTeX.

Abstract

In most countries in Sub‐Saharan Africa, Demographic and Health Surveys (DHS) with HIV testing became the only measure of HIV prevalence in general population. Significant non response rates were often cited to explain differences between DHS results and estimations from sentinel surveillance in antenatal clinics. The objective of this paper consists to predict with multivariate models the prevalence of non tested persons in order to estimate the effect of non response on national HIV prevalence measure.

We used data from 9 DHS, conducted in sub‐Saharan Africa, where HIV results could be linked with data from household and individual questionnaires. Logistic regressions were performed for each country, separately for men and women 15‐49 years old, with a common set of predictor variables. For each group, adjusted prevalence was calculated by using observed prevalence for tested people and estimated probability to be HIV positive for non tested persons.

The non response rates in these 9 studies vary from 7.9% to 39.3%. Estimated prevalence of non tested persons is usually higher than observed prevalence of tested persons (13 groups on 18) Nevertheless, ratios of adjusted prevalence to observed prevalence remain relatively close to 1 (from 0.970 to 1.109). Differences between adjusted and observed prevalence is less than 0.32. A significant negative correlation was found between non‐response rates and ratios of non‐tested to tested, but there is no correlation between ratios of adjusted to observed prevalence and proportions of non‐tested.

The overall effect of non response biases on national HIV estimates tends to be small and remains inferior to sample variations. If adjustments need to be interpreted with caution due to the limited information available to predict the prevalence of non tested people, we can conclude that national population‐based surveys can provide quality and representative national HIV prevalence estimates.

Keywords

HIV Infections, Population surveillance, Health Surveys, Developing Countries, Prevalence Estimates.

Estimating effect of non response on HIV prevalence estimates with DHS data

Joseph LARMARANGE — 2007-05-03T09:28:22Z

8th AIDS Impact conference, Marseille, 1-4 juillet 2007

Authors

Joseph LARMARANGE, Roselyne VALLO, Seydou YARO, Philippe MSELLATI, Nicolas MÉDA, Benoît FERRY.

Abstract

Aim

In most countries in Sub-Saharan Africa, Demographic and Health Surveys (DHS) with HIV testing became the only measure of HIV prevalence in general population. Significant non response rate were often cited to explain differences between DHS results and estimations from sentinel surveillance in antenatal clinics. The objective of this presentation consists to predict with multivariate models the prevalence of non tested persons in order to estimate the effect of non response on national estimates.

Method / Issue

We used data from 9 DHS surveys (Burkina Faso 2003, Cameroon 2004, Ethiopia 2005, Ghana 2003, Kenya 2003, Lesotho 2004, Malawi 2004, Senegal 2005 and Tanzanie 2003) where HIV results could be linked with data from household and individual questionnaires. Logistic regression were calculated for each country, separately for men and women 15-49 years old, with common predictor variables : region, place of residence, age group, education, wealth index, marital status, work status, having radio or television, age at first sexual intercourse, recent sexual activities, using condom at last sexual intercourse, number of partners in last 12 months, smoking, STI in last 12 months, female and male circumcision and willing to care for relative with AIDS. For each group, adjusted prevalence was calculated by using observed prevalence for tested people and estimated prevalence for non tested people.

Results / Comments

The non response rates in these 9 studies vary from 7.9% to 34.2%. Estimated prevalence of non tested persons is usually higher than observed prevalence of tested persons : in 15 groups on 18, the ratio exceeds 1 (it vary from 0.820 to 2.424). Nevertheless, ratios of adjusted prevalence to observed prevalence remain relatively small (from 0.956 to 1.251). Except for men in Lesotho and women in Malawi, differences between adjusted and observed prevalence is less than 0.5 points. In both cases, number of tested persons was small (less than 3'000). No relation was found between non response rate and ratio of non tested to tested or ratio of adjusted prevalence to observed prevalence. Nevertheless, highest ratio of adjusted prevalence to observed prevalence were found for groups with smallest prevalence (<3%). But this effect is probably a consequence of a small statistical power.

Discussion

If differences between adjusted and observed prevalence are more important than in a precedent survey conducted by Mishra et al. in 2006 on 5 DHS, the overall effect of non response bias on national HIV estimates tend to be small. Adjustments need to be interpreted with caution due to the limited information available to predict the prevalence of non tested people, in particular for people who did not answer the individual questionnaire and for whom only household questionnaire data were used.