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OBJECTIVE To describe the prevalence of HIV viral suppression and assess the factors associated with HIV viral suppression among persons receiving ART in Malawi in 2021. METHODS Implementation study using routinely collected patient-level HIV RNA-PCR test result data extracted from the national Laboratory Management Information System (LIMS) database managed by the Department of HIV/AIDS in 2021. We calculated frequencies, proportions and odds ratios (OR) of HIV viral suppression with their associated 95% confidence intervals (95%CI). We performed a random-effects logistic regression to determine the risk factors associated with HIV viral suppression amongst ART patients, controlling for the spatial autocorrelation between districts and adjusting for other variables. RESULTS We evaluated 515,797 adults and children receiving ART and having a viral load test in 2021. Of these, 92.8% had HIV viral suppression. ART patients living in urban areas had lower likelihood of HIV viral suppression than those living in rural areas (adjusted OR [aOR]=0.95, 95%CI: 0.92-0.99, P=0.01). There was an increasing trend in HIV viral suppression with increasing ART duration. Routine VL monitoring samples were 39% more likely to have suppressed VL values than confirmatory HIV VL monitoring samples (aOR=1.39; 95%CI:1.34-1.43, P

BackgroundDespite the high availability of individual-level data of infants accessing HIV DNA polymerase chain reaction (DNA-PCR) testing service, there has been little in-depth analysis of such data. Therefore, we describe spatial and temporal trends in risk of HIV infection among Malawi’s HIV-exposed infants (HEI) with DNA-PCR HIV test result from 2013 to 2020.MethodsThis is an implementation study using routinely collected patient-level HIV DNA-PCR test result data extracted from the national Laboratory Management Information System database managed by the Department of HIV/AIDS between 1 January 2013 and 30 June 2020. We calculated frequencies, proportions and odds ratios (OR) with their associated 95% confidence intervals (95%CI). We performed a random-effects logistic regression to determine the risk factors associated with HIV infection in infants, controlling for the spatial autocorrelation between districts and adjusting for other variables.ResultsWe evaluated 255,229 HEI across 750 facilities in 28 districts. The overall risk of HIV infection among all tested HEI between 2013 and 2020 was 7.2% (95%CI: 7.1-7.3). We observed a decreasing trend in the proportion of HEI that tested HIV positive from 7.0% (95%CI: 6.6-7.4) in 2013 to 5.7% (95%CI: 5.4-5.9) in 2015 followed by an increase to 9.9% (95%CI: 9.6-10.2) in 2017 and then a decreasing trend to 4.2% (95%CI: 3.7-4.6) in 2020. The risk of HIV infection increased by age of the HEI. There was spatial heterogeneity of HIV prevalence between districts of Malawi.ConclusionWe summarised spatial and temporal trends of risk of HIV infection amongst HEI in Malawi between 2013 and 2020. There is need for further strengthening of EID program to ensure that all the HEI are enrolled in care by eight weeks of age in order to further reduce mother-to-child transmission of HIV.Key messageThere is need for further strengthening of the Malawi early infant diagnosis program to ensure that all the HIV-exposed infants are enrolled in care by eight weeks of age in order to eliminate mother-to-child transmission of HIV by 2030.What is known about the study?Malawi has implemented the Early Infant Diagnosis (EID) since 2009. Most of the studies on EID in Malawi have focused on just one or two health facilities.What the study adds?This is to our knowledge the first in-depth analysis of national routine data on HIV DNA-PCR tests among HIV-exposed infants with in Malawi. Our study has shown that there is spatial and temporal heterogeneity in risk of HIV infection amongst the HEI in Malawi between 2013 and 2020.

ObjectiveTo describe the trends in the incidence rate of HIV VL rebound and assess the factors associated with the incidence rate of HIV VL rebound among the persons receiving ART in Malawi between 2011 and 2020.MethodsWe analyzed routinely collected patient-level HIV VL data extracted from the Malawi Laboratory Management Information System (LIMS). We fitted a multivariable Cox regression model of having HIV VL rebound (>1000 copies/mL) using a forward step-wise selection method, with age and sex entered as a priori variables. We presented both crude and adjusted hazard ratios (aHR) of HIV VL rebound for the various independent variables. Statistical significance was set at PResultsWe evaluated 230,465 adults and children receiving ART and having at least one viral load test between 2011 and 2020. The median time between ART initiation and having a VL sample was 55 months (interquartile range: 25-92). The aHR for having HIV VL rebound were: 0.79 for females (95%CI:0.77-0.80, PConclusionThis is the first national analysis of Malawi HIV VL data from LIMS. Our findings show the need to take into account the geographic and demographic characteristics of the patients in order to persistently suppress HIV VL and consequently achieve 95% HIV VL suppression by 2030.

BackgroundAlthough the COVID-19 pandemic has resulted in lower reported number of cases and deaths within the paediatric population, indirect impacts on the health of children in Sub-Saharan Africa such as malnutrition are evident. Data on the socioeconomic factors affecting malnutrition in the under-age population of Sub-Saharan Africa brought by the COVID-19 pandemic remain limited. This paper assesses socioeconomic factors of malnutrition in relation with COVID-19 and potential mitigating measures.MethodsA scoping review of PubMed, Embase, and Web of Science from March 11, 2020, to May 1, 2021, was conducted. The included studies focused on COVID-19, children malnutrition, and Sub-Saharan Africa and adhered to the PRISMA guideline.ResultsAmong 73 total screened articles, 15 studies filled the inclusion criteria. The identified socioeconomic factors leading to malnutrition in children were reduction in average income or increase in unemployment rate, access to healthcare and food supplements, disrupted food supply chains and increased prices of food products, pauses in humanitarian responses, and reduced access to school-based meals. Potential mitigation measures were food subsidies, food price control measures, the identification of new vulnerable groups and the implementation of financial interventions.ConclusionMalnutrition amongst Sub-Saharan African children due to COVID-19 is a result of a combination of multiple socioeconomic factors. To stabilize household purchasing power and eventually malnutrition in children in SSA, a combined strategy of initial detection of newly developing vulnerable groups and efficient, rapid financial assistance through mobile phone transfers was suggested. These strategies were proposed in combination with other economical models.

Background We aimed to estimate the disease burden of Tuberculosis (TB) and return on investment of TB care in selected high-burden countries of the Western Pacific Region (WPR) until 2030. Methods We projected the TB epidemic in Viet Nam and Lao People's Democratic Republic (PDR) 2020–2030 using a mathematical model under various scenarios: counterfactual (no TB care); baseline (TB care continues at current levels); and 12 different diagnosis and treatment interventions. We retrieved previous modeling results for China and the Philippines. We pooled the new and existing information on incidence and deaths in the four countries, covering >80% of the TB burden in WPR. We estimated the return on investment of TB care and interventions in Viet Nam and Lao PDR using a Solow model. Findings In the baseline scenario, TB incidence in the four countries decreased from 97•0/100,000/year (2019) to 90•1/100,000/year (2030), and TB deaths from 83,300/year (2019) to 71,100/year (2030). Active case finding (ACF) strategies (screening people not seeking care for respiratory symptoms) were the most effective single interventions. Return on investment (2020–2030) for TB care in Viet Nam and Lao PDR ranged US$4-US$49/dollar spent; additional interventions brought up to US$2•7/dollar spent. Interpretation In the modeled countries, TB incidence will only modestly decrease without additional interventions. Interventions that include ACF can reduce TB burden but achieving the End TB incidence and mortality targets will be difficult without new transformational tools (e.g. vaccine, new diagnostic tools, shorter treatment). However, TB care, even at its current level, can bring a multiple-fold return on investment. Funding World Health Organization Western Pacific Regional Office; Swiss National Science Foundation Grant 163878.

The extent of SARS-CoV-2 circulation in many African countries remains unclear, underlining the need for antibody sero-surveys to assess the cumulative attack rate. Here, we present the results of a cross-sectional sero-survey of a random sample of residents of a health district in Yaoundé, Cameroon, conducted from October 14 to November 26, 2020. Among the 971 participants, the test-adjusted seroprevalence of anti-SARS-CoV-2 IgG antibodies was 29·2% (95%CI 24·3–34·1). This is about 323 times greater than the 0.09% nationwide attack rate implied by COVID-19 case counts at the time. Men, obese individuals and those living in large households were significantly more likely to be seropositive, and the majority (64·2% [58·7–69·4]) of seropositive individuals reported no symptoms. Despite the high seroprevalence, most of the population had not been infected with SARS-CoV-2, highlighting the importance of continued measures to control viral spread and quick vaccine deployment to protect the vulnerable.

The extent of SARS-CoV-2 circulation in many African countries remains unclear, underlining the need for antibody sero-surveys to assess the cumulative attack rate. Here, we present the results of a cross-sectional sero-survey of a random sample of residents of a health district in Yaounde, Cameroon, conducted from October 14 to November 26, 2020. Among the 971 participants, the test-adjusted seroprevalence of anti-SARS-CoV-2 IgG antibodies was 29·2% (95% CI 24·3–34·1). This is about 322 times greater than the 0.09% nationwide attack rate implied by COVID-19 case counts at the time. Men, obese individuals and those living in large households were significantly more likely to be seropositive, and the majority (64·2% [58·7–69·4]) of seropositive individuals reported no symptoms. Despite the high seroprevalence, most of the population had not been infected with SARS-CoV-2, highlighting the importance of continued measures to control viral spread and quick vaccine deployment to protect the vulnerable., Many African countries have reported relatively low numbers of COVID-19 cases but the true scale of the epidemic is unclear. Here, the authors conduct a population-based survey in a province of Cameroon and estimate 29% seroprevlance, >300 fold higher than the nationwide attack rate implied by case counts.

High yield HIV testing strategies are critical to reach epidemic control in high prevalence and low-resource settings such as East and Southern Africa. In this study, we aimed to predict the HIV status of individuals living in Angola, Burundi, Ethiopia, Lesotho, Malawi, Mozambique, Namibia, Rwanda, Zambia and Zimbabwe with the highest precision and sensitivity for different policy targets and constraints based on a minimal set of socio-behavioural characteristics.We analysed the most recent Demographic and Health Survey from these 10 countries to predict individual's HIV status using four different algorithms (a penalized logistic regression, a generalized additive model, a support vector machine, and a gradient boosting trees). The algorithms were trained and validated on 80% of the data, and tested on the remaining 20%. We compared the predictions based on the F1 score, the harmonic mean of sensitivity and positive predictive value (PPV), and we assessed the generalization of our models by testing them against an independent left-out country. The best performing algorithm was trained on a minimal subset of variables which were identified as the most predictive, and used to 1) identify 95% of people living with HIV (PLHIV) while maximising precision and 2) identify groups of individuals by adjusting the probability threshold of being HIV positive (90% in our scenario) for achieving specific testing strategies.Overall 55,151 males and 69,626 females were included in the analysis. The gradient boosting trees algorithm performed best in predicting HIV status with a mean F1 score of 76.8% [95% confidence interval (CI) 76.0%-77.6%] for males (vs [CI 67.8%-70.6%] for SVM) and 78.8% [CI 78.2%-79.4%] for females (vs [CI 73.4%-75.8%] for SVM). Among the ten most predictive variables for each sex, nine were identical: longitude, latitude and, altitude of place of residence, current age, age of most recent partner, total lifetime number of sexual partners, years lived in current place of residence, condom use during last intercourse and, wealth index. Only age at first sex for male (ranked 10th) and Rohrer's index for female (ranked 6th) were not similar for both sexes. Our large-scale scenario, which consisted in identifying 95% of all PLHIV, would have required testing 49.4% of males and 48.1% of females while achieving a precision of 15.4% for males and 22.7% for females. For the second scenario, only 4.6% of males and 6.0% of females would have had to be tested to find 55.7% of all males and 50.5% of all females living with HIV.We trained a gradient boosting trees algorithm to find 95% of PLHIV with a precision twice higher than with general population testing by using only a limited number of socio-behavioural characteristics. We also successfully identified people at high risk of infection who may be offered pre-exposure prophylaxis or voluntary medical male circumcision. These findings can inform the implementation of new high-yield HIV tests and help develop very precise strategies based on low-resource settings constraints.

The recent lifting of COVID-19 related restrictions in Switzerland causes uncertainty about the future of the epidemic. We developed a compartmental model for SARS-CoV-2 transmission in Switzerland and projected the course of the epidemic until the end of year 2020 under various scenarios. The model was age-structured with three categories: children (0-17), adults (18-64) and seniors (65- years). Lifting all restrictions according to the plans disclosed by the Swiss federal authorities by mid-May resulted in a rapid rebound in the epidemic, with the peak expected in July. Measures equivalent to at least 76% reduction in all contacts were able to eradicate the epidemic; a 54% reduction in contacts could keep the intensive care unit occupancy under the critical level and delay the next wave until October. In scenarios where strong contact reductions were only applied in selected age groups, the epidemic could not be suppressed, resulting in an increased risk of a rebound in July, and another stronger wave in September. Future interventions need to cover all age groups to keep the SARS-CoV-2 epidemic under control.

This study analysed the reported incidence of COVID-19 and associated epidemiological and socio-economic factors in the WHO African region. Data from COVID-19 confirmed cases and SARS-CoV-2 tests reported to the WHO by Member States between 25 February and 31 December 2020 and publicly available health and socio-economic data were analysed using univariate and multivariate binomial regression models. The overall cumulative incidence was 1846 cases per million population. Cape Verde (21 350 per million), South Africa (18 060 per million), Namibia (9840 per million), Eswatini (8151 per million) and Botswana (6044 per million) recorded the highest cumulative incidence, while Benin (260 per million), Democratic Republic of Congo (203 per million), Niger (141 cases per million), Chad (133 per million) and Burundi (62 per million) recorded the lowest. Increasing percentage of urban population (β= −0.011,P= 0.04) was associated with low cumulative incidence, while increasing number of cumulative SARS-CoV-2 tests performed per 10 000 population (β= 0.0006,P= 0.006) and the proportion of population aged 15–64 years (adjustedβ= 0.174,P< 0.0001) were associated with high COVID-19 cumulative incidence. With limited testing capacities and overwhelmed health systems, these findings highlight the need for countries to increase and decentralise testing capacities and adjust testing strategies to target most at-risk populations.

BACKGROUND Demographic and sociobehavioral factors are strong drivers of HIV infection rates in sub-Saharan Africa. These factors are often studied in qualitative research but ignored in quantitative analyses. However, they provide in-depth insight into the local behavior and may help to improve HIV prevention. OBJECTIVE To obtain a comprehensive overview of the sociobehavioral factors influencing HIV prevalence and incidence in Malawi, we systematically reviewed the literature using a newly programmed tool for automatizing part of the systematic review process. METHODS Due to the choice of broad search terms (“HIV AND Malawi”), our preliminary search revealed many thousands of articles. We, therefore, developed a Python tool to automatically extract, process, and categorize open-access articles published from January 1, 1987 to October 1, 2019 in the PubMed, PubMed Central, JSTOR, Paperity, and arXiV databases. We then used a topic modelling algorithm to classify and identify publications of interest. RESULTS Our tool extracted 22,709 unique articles; 16,942 could be further processed. After topic modelling, 519 of these were clustered into relevant topics, of which 20 were kept after manual screening. We retrieved 7 more publications after examining the references so that 27 publications were finally included in the review. Reducing the 16,942 articles to 519 potentially relevant articles using the software took 5 days. Several factors contributing to the risk of HIV infection were identified, including religion, gender and relationship dynamics, beliefs, and sociobehavioral attitudes. CONCLUSIONS Our software does not replace traditional systematic reviews, but it returns useful results to broad queries of open-access literature in under a week, without a priori knowledge. This produces a “seed dataset” of relevance that could be further developed. It identified known factors and factors that may be specific to Malawi. In the future, we aim to expand the tool by adding more social science databases and applying it to other sub-Saharan African countries.

Background Demographic and sociobehavioral factors are strong drivers of HIV infection rates in sub-Saharan Africa. These factors are often studied in qualitative research but ignored in quantitative analyses. However, they provide in-depth insight into the local behavior and may help to improve HIV prevention. Objective To obtain a comprehensive overview of the sociobehavioral factors influencing HIV prevalence and incidence in Malawi, we systematically reviewed the literature using a newly programmed tool for automatizing part of the systematic review process. Methods Due to the choice of broad search terms (“HIV AND Malawi”), our preliminary search revealed many thousands of articles. We, therefore, developed a Python tool to automatically extract, process, and categorize open-access articles published from January 1, 1987 to October 1, 2019 in the PubMed, PubMed Central, JSTOR, Paperity, and arXiV databases. We then used a topic modelling algorithm to classify and identify publications of interest. Results Our tool extracted 22,709 unique articles; 16,942 could be further processed. After topic modelling, 519 of these were clustered into relevant topics, of which 20 were kept after manual screening. We retrieved 7 more publications after examining the references so that 27 publications were finally included in the review. Reducing the 16,942 articles to 519 potentially relevant articles using the software took 5 days. Several factors contributing to the risk of HIV infection were identified, including religion, gender and relationship dynamics, beliefs, and sociobehavioral attitudes. Conclusions Our software does not replace traditional systematic reviews, but it returns useful results to broad queries of open-access literature in under a week, without a priori knowledge. This produces a “seed dataset” of relevance that could be further developed. It identified known factors and factors that may be specific to Malawi. In the future, we aim to expand the tool by adding more social science databases and applying it to other sub-Saharan African countries.

BackgroundDemographic and socio-behavioural factors are strong drivers of HIV infection rates in sub-Saharan Africa. These factors are often studied in qualitative research but ignored in quantitative analyses. However, they provide an in-depth insight into the local behaviour, and may help to improve HIV prevention.MethodsTo obtain a comprehensive overview of the socio-behavioural factors influencing HIV prevalence and incidence in Malawi, we systematically reviewed the literature. Due to the choice of broad search terms (“HIV AND Malawi”), our preliminary search revealed many thousands of articles. We, therefore, developed a Python tool to automatically extract, process, and categorise open-access articles published from January 1st, 1987 until October 1st, 2019 in Pubmed, Pubmed Central, JSTOR, Paperity, and arXiV databases. We then used a topic modelling algorithm to classify and identify publications of interest.ResultsOur tool extracted 22’709 unique articles; 16’942 could be further processed. After topic modelling, 519 of these were clustered into relevant topics; 20 of which were kept after hand-screening. We retrieved 7 more publications after examining references so that 27 publications were finally included in the review. Reducing the 16’942 articles to 519 potentially relevant ones by using the software took 5 days. Several factors were identified to contribute to the risk of HIV infection, including religion, gender and relationship dynamics, beliefs, and socio-behavioural attitudes.ConclusionsOur software does not replace traditional systematic reviews, but it returns useful results to broad queries of open-access literature in under a week, without a priori knowledge. This produces a “seed data-set” of relevance which could be further developed. It identified known factors and rare factors that may be specific to Malawi. In the future, we aim to expand the tool by adding more social science databases and applying it to other sub-Saharan African countries.

BackgroundThere is a need for high yield HIV testing strategies to reach epidemic control. We aimed to predict the HIV status of individuals based on socio-behavioural characteristics.MethodsWe analysed over 3,200 variables from the most recent Demographic Health Survey from 10 countries in East and Southern Africa. We trained four machine-learning algorithms and selected the best based on the f1 score. Training and validation were done on 80% of the data. The model was tested on the remaining 20% and on a left-out country which was rotated around. The best algorithm was retrained on the variables which were most predictive. We studied two scenarios: one aiming to identify 95% of people living with HIV (PLHIV) and one aiming to identify individuals with 95% or higher probability of being HIV positive.FindingsOverall 55,151 males and 69,626 females were included. XGBoost performed best in predicting HIV with a mean f1 of 76·8% [95% confidence interval 76·0%-77·6%] for males and 78·8% [78·2%-79·4%] for females. Among the ten most predictive variables, nine were identical for both sexes: longitude, latitude and, altitude of place of residence, current age, age of most recent partner, total lifetime number of sexual partners, years lived in current place of residence, condom use during last intercourse and, wealth index. Model performance based on these variables decreased minimally. For the first scenario, 7 males and 5 females would need to be tested to identify one HIV positive person. For the second scenario, 4·2% of males and 6·2% of females would have been identified as high-risk population.InterpretationWe were able to identify PLHIV and those at high risk of infection who may be offered pre-exposure prophylaxis and/or voluntary medical male circumcision. These findings can inform the implementation of HIV prevention and testing strategies.FundingSwiss National Science Foundation.

IntroductionHIV incidence varies widely between sub-Saharan African (SSA) countries. This variation coincides with a substantial sociobehavioural heterogeneity, which complicates the design of effective interventions. In this study, we investigated how sociobehavioural heterogeneity in sub-Saharan Africa could account for the variance of HIV incidence between countries.MethodsWe analysed aggregated data, at the national-level, from the most recent Demographic and Health Surveys of 29 SSA countries [2010-2017], which included 594’644 persons (183’310 men and 411’334 women). We preselected 48 demographic, socio-economic, behavioural and HIV-related attributes to describe each country. We used Principal Component Analysis to visualize sociobehavioural similarity between countries, and to identify the variables that accounted for most sociobehavioural variance in SSA. We used hierarchical clustering to identify groups of countries with similar sociobehavioural profiles, and we compared the distribution of HIV incidence (estimates from UNAIDS) and sociobehavioural variables within each cluster.ResultsThe most important characteristics, which explained 69% of sociobehavioural variance across SSA among the variables we assessed were: religion; male circumcision; number of sexual partners; literacy; uptake of HIV testing; women’s empowerment; accepting attitude toward people living with HIV/AIDS; rurality; ART coverage; and, knowledge about AIDS. Our model revealed three groups of countries, each with characteristic sociobehavioural profiles. HIV incidence was mostly similar within each cluster and different between clusters (median(IQR); 0.5/1000(0.6/1000), 1.8/1000(1.3/1000) and 5.0/1000(4.2/1000)).