The HIV prevalence and the SDCs in TB affected households (prevalence modeling scenario).

The HIV prevalence and the SDCs in TB affected households (prevalence modeling scenario).</p

HIV acquisitions averted through pre-exposure prophylaxis (PrEP) (incidence modeling scenario).

HIV acquisitions averted through pre-exposure prophylaxis (PrEP) (incidence modeling scenario).</p

Data sources for prevalence and incidence modeling scenarios.

Data sources for prevalence and incidence modeling scenarios.</p

Flow diagrams of mathematical models used in this study.

(A) Flow-chart depicting the process of estimating the number of individuals living in households affected by tuberculosis (TB) and the frequency of individuals living with human immunodeficiency virus (HIV) in these households. (B) Flow-chart depicting the process of generating the proportion of HIV-serodifferent couples (SDCs) using outputs from (A). Global Burden of Disease, Injuries, and Risk Factors Study (GBD); Demographic and Health Surveys (DHS); Households (HHs); Household contacts (HHC); Population-based HIV Impact Assessment (PHIA); the proportion of serodifferent couples among people living with HIV who are in stable partnerships (PSDC); Relative risk (RR); Serodifferent couples (SDCs).</p

Fig 2 -

Forest plot of the relationship between HIV prevalence among adult household TB contacts (HHCs) vs. HIV prevalence in the general adult population in studies conducted in Kenya (A), Uganda (B), and South Africa (C).</p

The HIV prevalence and the SDCs in TB affected households (incidence modeling scenario).

The HIV prevalence and the SDCs in TB affected households (incidence modeling scenario).</p

Simulated relationship between the proportion of SDC among all people living with HIV who are in stable partnerships (<i>P</i>_<i>SDC</i>) and HIV prevalence using α between 0.7 and 0.9.

Simulated relationship between the proportion of SDC among all people living with HIV who are in stable partnerships (PSDC) and HIV prevalence using α between 0.7 and 0.9.</p

Supplementary methods and results.

Household-based tuberculosis (TB) contact evaluation may be an efficient strategy to reach people who could benefit from oral pre-exposure prophylaxis (PrEP) because of the epidemiological links between HIV and TB. This study estimated the number of HIV serodifferent couples in TB-affected households and potential HIV acquisitions averted through their PrEP use in 4 TB-HIV high-burden countries. We conducted a model-based analysis set in Ethiopia, Kenya, South Africa, and Uganda using parameters from population-based household surveys, systematic literature review and meta-analyses, and estimates from the Global Burden of Diseases, Injuries, and Risk Factors Study 2019. We parameterized the nonlinear relationship between the proportion of serodifferent couples among people living with HIV and population-level HIV prevalence using Markov chain Monte Carlo methods. We integrated all parameters in a mathematical model and propagated uncertainty using a Monte Carlo approach. We estimated the HIV prevalence among adults aged 15–49 living in TB-affected households to be higher than in the general population in all 4 countries. The proportion of serodifferent couples among all couples in TB-affected households was also higher than in the general population (South Africa: 20.7% vs. 15.7%, Kenya: 15.7% vs. 5.7%, Uganda: 14.5% vs. 6.0%, Ethiopia: 4.1% vs. 0.8%). We estimated that up to 1,799 (95% UI: 1,256–2,341) HIV acquisitions in South Africa could be prevented annually by PrEP use in serodifferent couples in TB-affected households, 918 (95% UI: 409–1,450) in Kenya, 686 (95% UI: 505–871) in Uganda, and 408 (95% UI: 298–522) in Ethiopia. As couples in TB-affected households are more likely to be serodifferent than couples in the general population, offering PrEP during household TB contact evaluation may prevent a substantial number of HIV acquisitions.</div

HIV acquisitions averted through PrEP (prevalence modeling scenario).

HIV acquisitions averted through PrEP (prevalence modeling scenario).</p

Additional file 1 of Understanding the barriers and facilitators of COVID-19 risk mitigation strategy adoption and COVID-19 vaccination in refugee settlements in Uganda: a qualitative study

Supplementary Material