Background In Nigeria, results from the pilot of the Test and Treat strategy showed higher loss to follow up (LTFU) among people living with HIV compared to before its implementation. The aim of this evaluation was to assess the effects of antiretroviral therapy (ART) initiation within 14 days on LTFU at 12 months and viral suppression. Methods We conducted a retrospective cohort study using routinely collected de-identified patient-level data hosted on the Nigeria National Data Repository from 1,007 facilities. The study population included people living with HIV age ≥15. We used multivariable Cox proportional frailty hazard models to assess time to LTFU comparing ART initiation strategy and multivariable log-binomial regression for viral suppression. Results Overall, 26,937 (38.13%) were LTFU at 12 months. Among individuals initiated within 14 days, 38.4% were LTFU by 12 months compared to 35.4% for individuals initiated >14 days (p<0.001). In the adjusted analysis, individuals who were initiated ≤14 days after HIV diagnosis had a higher hazard of being LTFU (aHR 1.15, 95% CI 1.10–1.20) than individuals initiated after 14 days of HIV diagnosis. Among individuals with viral load results, 86.2% were virally suppressed. The adjusted risk ratio for viral suppression among individuals who were initiated ≤14 days compared to >14 days was not statistically significant. Conclusion LTFU was higher among individuals who were initiated within 14 days compared to greater than 14 days after HIV diagnosis. There was no difference for viral suppression. The provision of early tailored interventions to support newly diagnosed people living may contribute to reducing LTFU.

Abstract Introduction The potential disruption in antiretroviral therapy (ART) services in Africa at the start of the COVID‐19 pandemic raised concern for increased morbidity and mortality among people living with HIV (PLHIV). We describe HIV treatment trends before and during the pandemic and interventions implemented to mitigate COVID‐19 impact among countries supported by the US Centers for Disease Control and Prevention (CDC) through the President's Emergency Plan for AIDS Relief (PEPFAR). Methods We analysed quantitative and qualitative data reported by 10,387 PEPFAR‐CDC‐supported ART sites in 19 African countries between October 2019 and March 2021. Trends in PLHIV on ART, new ART initiations and treatment interruptions were assessed. Viral load coverage (testing of eligible PLHIV) and viral suppression were calculated at select time points. Qualitative data were analysed to summarize facility‐ and community‐based interventions implemented to mitigate COVID‐19. Results The total number of PLHIV on ART increased quarterly from October 2019 (n = 7,540,592) to March 2021 (n = 8,513,572). The adult population (≥15 years) on ART increased by 14.0% (7,005,959–7,983,793), while the paediatric population (<15 years) on ART declined by 2.6% (333,178–324,441). However, the number of new ART initiations dropped between March 2020 and June 2020 by 23.4% for adults and 26.1% for children, with more rapid recovery in adults than children from September 2020 onwards. Viral load coverage increased slightly from April 2020 to March 2021 (75–78%) and viral load suppression increased from October 2019 to March 2021 (91–94%) among adults and children combined. The most reported interventions included multi‐month dispensing (MMD) of ART, community service delivery expansion, and technology and virtual platforms use for client engagement and site‐level monitoring. MMD of ≥3 months increased from 52% in October 2019 to 78% of PLHIV ≥ age 15 on ART in March 2021. Conclusions With an overall increase in the number of people on ART, HIV programmes proved to be resilient, mitigating the impact of COVID‐19. However, the decline in the number of children on ART warrants urgent investigation and interventions to prevent further losses experienced during the COVID‐19 pandemic and future public health emergencies.

(1) Background: Examine global data from 48 African countries to estimate the SARS-CoV-2 infection fatality rate; (2) Methods: We analyzed time series data on the 135,126 confirmed cases and 3922 deaths from COVID-19 disease outbreak in Africa through 30 May 2020. In a Bayesian prediction model based on the Monte Carlo approach, we adjusted for demographic, economic, biological, and societal variables to account for the untested people; (3) Results: We calculated a total of 1,686,879 COVID-19 infections after correcting for possible risk variables in the Bayesian model, equal to 13 infections per confirmed case. In Africa, the IFR is projected to be 0.23% (95% CI: 0.14–0.33%). The percentages varied by country, ranging from 0.004% in Botswana and the Central African Republic to 1.53% in Nigeria. The projected IFR is twelvefold greater than the WHO’s 2009 H1N1 influenza pandemic estimate (0.02%). In four countries: Morocco, Nigeria, Cameroon, and South Africa, the inverse distance weighted interpolation map shows high IFR variability; (4) Conclusions: COVID-19 infection mortality rates can vary significantly between regions, and this might be due to changes in demography, underlying health conditions in the community, healthcare system capacity, positive health seeking behavior, and other variables.

Background Lifelong antiretroviral therapy (ART) improves optimal health outcomes for HIV‐positive individuals, but is threatened by fluctuations in sustained care, self efficacy and hence poor adherence. Retention of patients in the HIV care continuum is crucial for epidemic control. This study aimed to aggregate loss to follow-up (LTFU) behaviour in People Living with HIV (PLHIV) into clusters in order to examine and describe PLHIV clusters having similar characteristics and patterns according to their risk profile. Methods This was a retrospective, cross-sectional study that randomly reviewed 11,589 records of LTFU adult patients initiated on first-line ART from 313 USAID/PEPFAR-supported HIV clinics spread across 5 of Nigeria’s 6 geographical regions between July 1, 2008 and June 30, 2020. LTFU, was defined for PLHIV on ART as > 28 days without an encounter since the last scheduled ART refill appointment. Using the Minkowski method and ward.D2 clustering technique for unsupervised machine learning algorithm "agglomerative hierarchical clustering" in R, we identified 6 clusters associated with patients LTFU behaviour. Results Within the review period, 497,620 patients were ever enrolled on ART. 324,225 (65.2%) remained on treatment, 101,716 (20.4%) had an LTFU event captured, 36,021 (7.2%) were transferred out to other facilities, 25,633 (5.2%) died and 10,025 (2.0%) self-terminated treatment. Approximately 11% (11,589) of LTFU patients were included. Majority (66.7%) of the clusters consist of female LTFUs. LTFU doubled steadily by age among adolescents (15-19 years) and young people (15-29 years), but as age increased above 40-years the rate of LTFU decreased. High rate of LTFU was reflective of shorter-time on ART. Patients classified in clusters with shorter-time on ART [8-months (female) vs. 72.6-months (male)] indicated the highest rates of LTFU [31.0% (female) vs. 14.9% (male)]. LTFU rate varied by region, was highest among clusters confined in the North West (50%) followed by the South South (33%) and lowest in the North East (17%). Viral load test was low, with only half (50.0%) of the clusters having a documented viral load test result. Conclusion LTFU rates in HIV-positive patients receiving ART in our clinical sites have varied by the duration of ART, with rates declining in recent years. Our study demonstrates that aggregating LTFU behaviour among patients on ART offers great benefit for LTFU surveillance in the HIV care continuum. Our findings would inform targeted HIV program interventions for patient-centered care, reduce LTFU and promote optimal retention.

Introduction: Coronavirus disease 2019 (COVID-19) is an emerging infectious disease that was first reported in Wuhan, China, and has subsequently spread worldwide. Knowledge of coronavirus-related risk factors can help countries build more systematic and successful responses to COVID-19 disease outbreak. Here we used Supervised Machine Learning and Empirical Bayesian Kriging (EBK) techniques to reveal correlates and patterns of COVID-19 Disease outbreak in sub-Saharan Africa (SSA).Methods: We analyzed time series aggregate data compiled by Johns Hopkins University on the outbreak of COVID-19 disease across SSA. COVID-19 data was merged with additional data on socio-demographic and health indicator survey data for 39 of SSA’s 48 countries that reported confirmed cases and deaths from coronavirus between February 28, 2020 through March 26, 2020. We used supervised machine learning algorithm, Lasso for variable selection and statistical inference. EBK was used to also create a raster estimating the spatial distribution of COVID-19 disease outbreak.Results: The lasso Cross-fit partialing out predictive model ascertained seven variables significantly associated with the risk of coronavirus infection (i.e. new HIV infections among pediatric, adolescent, and middle-aged adult PLHIV, time (days), pneumococcal conjugate-based vaccine, incidence of malaria and diarrhea treatment). Our study indicates, the doubling time in new coronavirus cases was 3 days. The steady three-day decrease in coronavirus outbreak rate of change (ROC) from 37% on March 23, 2020 to 23% on March 26, 2020 indicates the positive impact of countries' steps to stymie the outbreak. The interpolated maps show that coronavirus is rising every day and appears to be severely confined in South Africa. In the West African region (i.e. Burkina Faso, Ghana, Senegal, Cote d'Iviore, Cameroon, and Nigeria), we predict that new cases and deaths from the virus are most likely to increase.Interpretation: Integrated and efficiently delivered interventions to reduce HIV, pneumonia, malaria and diarrhea, are essential to accelerating global health efforts. Scaling up screening and increasing COVID-19 testing capacity across SSA countries can help provide better understanding on how the pandemic is progressing and possibly ensure a sustained decline in the ROC of coronavirus outbreak.

Significant gaps persist in providing HIV treatment to all who are in need. Restricting care delivery to healthcare facilities will continue to perpetuate this gap in limited resource settings. We assessed a large‐scale community‐based programme for effectiveness in identifying people living with HIV and linking them to antiretroviral treatment.