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The COVID-19 pandemic has overwhelmed health systems, especially in sub-Saharan African countries. Vaccination is one of the easily accessible interventions that can help reduce the burden on the health system. However, vaccination coverage remains low in sub-Saharan African countries. The determinants of vaccine acceptance and hesitancy among adolescents and youths remain unknown. Therefore, this study explored the pooled prevalence and determinants of COVID-19 vaccine acceptance and hesitancy among adolescents and youths in sub-Saharan African Countries. A systematic literature search of Scopus, PubMed Central, PubMed, Embase, African Journal Online, Research 4 Life, Embase, and Google Scholar was performed from 6 th May to 31 st December 2023, using developed keywords with a focus on sub-Saharan African countries. Twenty-three (N = 23) studies were finally selected for analysis. The pooled prevalence of vaccine acceptance among adolescents and youths was 38.7% (n = 23). The subgroup analysis of the pooled prevalence of acceptance among adolescents was 36.1% (n = 36.1) while youths were 42% (n = 10). At the region level, West Africa had 42.2% (n = 13), East Africa had 39.8% (n = 6), Central Africa had 33% (n = 1), and Southern Africa had 24.2% (n = 3). The determinants of vaccine acceptance were the desire for self-immunity (AOR = 1.97, 95%, CI, 1.083.47, I 2 = 94.15%, p < 0.05), receiving Health Officers’ information (AOR = 4.36, 95%, CI, 2.28-8.32, I 2 = 97.74, p < 0.001), the effectiveness of COVID-19 vaccine (AOR = 2.14, 95%, CI, 1.14-4.05, I 2 = 97.4%, p < 0.05). The odds of having an unconfirmed source of information (AOR = 0.22, 95% CI, 0.10-0.45, I 2 = 94.09%, p< 0.001) was responsible for vaccine hesitancy. The findings indicate the low pooled prevalence of COVID-19 vaccine acceptance and high levels of hesitancy among adolescents and youths in sub-Saharan African countries. Therefore, there is a need to ensure that extensive research is undertaken into age-appropriate health promotion messages and strategies to encourage the uptake of vaccines. PROSPERO ID number CRD42023403071.

Background The HIV/AIDS pandemic has had a very devastating impact at a global level, with the Eastern and Southern African region being the hardest hit. The considerable geographical variation in the pandemic means varying impact of the disease in different settings, requiring differentiated interventions. While information on the prevalence of HIV at regional and national levels is readily available, the burden of the disease at smaller area levels, where health services are organized and delivered, is not well documented. This affects the targeting of HIV resources. There is need, therefore, for studies to estimate HIV prevalence at appropriate levels to improve HIV-related planning and resource allocation. Methods We estimated the district-level prevalence of HIV using Small-Area Estimation (SAE) technique by utilizing the 2016 Zambia Population-Based HIV Impact Assessment Survey (ZAMPHIA) data and auxiliary data from the 2010 Zambian Census of Population and Housing and the HIV sentinel surveillance data from selected antenatal care clinics (ANC). SAE models were fitted in R Programming to ascertain the best HIV predicting model. We then used the Fay–Herriot (FH) model to obtain weighted, more precise and reliable HIV prevalence for all the districts. Results The results revealed variations in the district HIV prevalence in Zambia, with the prevalence ranging from as low as 4.2% to as high as 23.5%. Approximately 32% of the districts ( n  = 24) had HIV prevalence above the national average, with one district having almost twice as much prevalence as the national level. Some rural districts have very high HIV prevalence rates. Conclusions HIV prevalence in Zambian is highest in districts located near international borders, along the main transit routes and adjacent to other districts with very high prevalence. The variations in the burden of HIV across districts in Zambia point to the need for a differentiated approach in HIV programming within the country. HIV resources need to be prioritized toward districts with high population mobility.

Obstetric fistulas are abnormal open connection(s) between the vagina and the urinary tract or the rectum resulting from tragic injuries sustained by mothers during childbirth that lead to urine and/or faecal incontinence. Due to the rapidly growing middle class in sub-Saharan Africa (SSA) and the corresponding quest for hospital delivery and caesarean section, surgery-related (iatrogenic) obstetric fistulas are on the rise. Worryingly, there is scanty data on surgery-related fistulas. This review aims to collate empirical evidence on the magnitude of iatrogenic obstetric fistulas in SSA, generate country-specific data and explore factors that influence obstetric surgery-related fistulas. All relevant databases, PubMed, LILACS, CINAHL, SCOPUS and Google Scholar will be searched from 1st January 2000 to 31st March 2024 using search terms developed from the major concepts in the title without restrictions by language. The Cochrane Library, African Journals Online, Data Base of African Thesis and Dissertations Including Research (DATAD-R D Space) and preprint repositories will also be searched. Reference lists of relevant studies will be searched and experts in the field will be contacted for additional (unpublished) studies. The search output will be exported to Endnote where duplicate studies will be removed. The deduplicated studies will be exported to Rayyan where study screening and selection will be conducted. At least two authors will independently select studies, extract data and assess quality in the included studies using pretested tools. Disagreements between reviewers will be resolved through discussion. Data analysis will be performed with RevMan 5.4. Comparative binary outcomes will be reported as odds ratio (OR) or risk ratio (RR) and for continuous outcomes, mean difference and standard deviations (SDs) will be used. Non-comparative studies will be analysed as weighted proportions. Heterogeneity between studies will be assessed graphically and statistically, and where a significant level is detected, the random-effects model meta-analysis will be performed. All estimates will be reported with their 95% confidence intervals (CIs). Where data permit, we will conduct subgroup and sensitivity analyses to test the robustness of the estimates on key quality domains. The overall quality of the evidence will be assessed using GRADE (Grading of Recommendations Assessment, Development and Evaluation). This systematic review and meta-analysis uses rigorous methods and best practices to attempt to collate all empirical evidence and estimate country-specific proportions of iatrogenic (surgery-related) fistulas among obstetric fistula patients across countries in SSA. This review will explore context-specific variables, provide insights into their impact and relate them to the type and experience of personnel performing the obstetric procedures that lead to obstetric fistulas. The findings of the full review are expected to inform the development of national and regional Training Programs for Medical Officers, support the development of a consensus \"minimum acceptable standard of care\" and inform quality assurance standards for clinicians involved in the provision of surgical obstetric care.

Background Adolescents face significant barriers to access and utilization of sexual and reproductive health services in many low-income settings, which in turn may be associated with adverse consequences such as early pregnancy, sexually transmitted infections, unsafe abortion and mortality. There is evidence suggesting that limited access to sexual and reproductive health information and services among adolescents contributes to these outcomes. We aimed to find out the factors that affect the fertility of adolescents aged 15 to 19 years in Zambia and to identify possible drivers of adolescents’ fertility. Methods Secondary analysis of the ZDHS 2013/14 data was carried out to find out the factors that affect the fertility rate of adolescents aged 15 to 19 years using multivariate logistic regression (n = 3666). Results Overall, 23.1% of adolescents had given birth at least once in the 5 years leading to the survey (n = 3666, 99.4% response), and 49.8% were rural-based while 50.2% were urban-based. The median number of schooling was 8 years (IQR 6–10). About 52% of the adolescents were in the poorer, poor and medium wealth quintiles while the other 48% were in the rich and richer quintiles. Factors found to affect fertility include residence, wealth status, educational attainment, marriage and abortion. An urban-based adolescent with a lower socioeconomic status was 2.4 times more likely to give birth compared to rural-based poorer adolescents (aOR = 2.4, 95% CI: 1.5, 3.7, p < 0.001). Although odds of giving birth were much higher among rural-based married adolescents (aOR = 8.0, 95% CI: 5.4, 11.9, p < 0.001) compared to urban married adolescents (aOR = 5.5, 95% CI: 8.3, 16.0, p < 0.001), and these relationships both statistically significant, higher educational attainment (aOR = 0.7, 95% CI: 0.6, 0.8 p < 0.001) and abortion (aOR = 0.3, 95% CI: 0.1, 0.8, p = 0.020) reduced these odds, particularly for rural-based adolescents. Conclusion Despite response aimed at reducing adolescent fertility, low wealth status, low educational attainment and early marriage remain significant drivers of adolescent fertility in Zambia. There is a need to address sexual and reproductive health needs of urban-based adolescents with a lower socioeconomic status. Plain Language Summary Adolescents go through serious challenges related to accessing and using sexual and reproductive health services in many low-income settings, and may also be related to negative consequences such as early pregnancy, sexually transmitted infections and unsafe abortion and death. Research has revealed that limited access to sexual and reproductive health information and services among young people contributes to these negative consequences. This analysis aimed to find out the factors that affect the fertility of 3666 adolescents aged 15 to 19 years in Zambia and to identify possible drivers of adolescents’ fertility, using the Zambia Demographic and Health Survey conducted in 2013/2014. A little over a quarter of the adolescents had given birth at least once in the five years leading to the survey. About half of the young people were rural-basedwhile the other half were urban-based, with an average of about 8 years in school. The rural-based adolescents had slightly lower average years in school compared to the urban-based; 7 years and 9 years respectively. Living in a rural area, residing in a home with a low wealth status and being married were all linked to higher chances of giving birth, while terminating a pregnancy and having more years of education were linked to lower chances of giving birth among the adolescents. Urban-based adolescents with lower wealth status were also linked to higher chances of giving birth compared to urban-based adolescents with higher wealth status. These results suggest that while residing in rural areas and being married increase the chances of higher fertility, the adolescents in urban areas but with lower wealth status also need interventions aimed at reducing their fertility.

Background Large social networks have been associated with better overall survival after a breast cancer diagnosis in some but not all study populations. This study evaluated associations of social support and religiosity/spirituality with survival among Black and White women with breast cancer of largely low socioeconomic status in the United States (US). Methods The study used data from the prospective Southern Community Cohort Study, which enrolled approximately 86,000 adults in the southeastern US during 2002–2009. A total of 1,347 Black and White women with incident breast cancer were identified in the cohort and followed through December 2020 for mortality via linkage with the National Death Index. Exposures of interest were social support and religiosity obtained via baseline questionnaire, including number of close friends/relatives who can provide instrumental and emotional support, and frequency of attendance at religious services. Multivariable Cox regression models were used to estimate hazard ratios (HR) and 95% confidence intervals (CI) for all-cause mortality in association with social support and religiosity. The models were tested for proportional hazards assumption using Schoenfeld residuals. Results Among the 1,347 women with breast cancer, 365 (27.1%) died during follow up. The participants were followed up for 17 years with a median follow-up time of 5 years. In all-cause mortality analyses, women who reported having 2 + relatives/friends for emotional support had a 20% reduced hazard of death compared to women with ≤  1 relative/friend (HR = 0.80, 95% CI: 0.67–0.96) after adjusting for age at breast cancer diagnosis, race, time from cohort enrollment to diagnosis, income, education, marital status, insurance, and tumor hormone receptor status. Similarly, women reporting having 2 + people able to provide instrumental support (render assistance in an emergency or lend money) had a 25% (HR = 0.75, 95% CI: 0.59–0.95) reduced hazard of death compared to those who had  ≤  1. Frequent attendance at religious services/meetings was associated with reduced hazard of death compared to those who did not attend (HR = 0.60, CI: 0.41–0.89); addition of cancer stage in the models attenuated this association. Conclusions A large social support network and regular attendance at faith-based services were associated with better survival among women with breast cancer. This calls for incorporating appropriate interventions to cancer care such as social support groups to improve survival.

South Africa was the most affected country in Africa by the coronavirus disease 2019 (COVID-19) pandemic, where over 4 million confirmed cases of COVID-19 and over 102,000 deaths have been recorded since 2019. Aside from clinical methods, artificial intelligence (AI)-based solutions such as machine learning (ML) models have been employed in treating COVID-19 cases. However, limited application of AI for COVID-19 in Africa has been reported in the literature. This study aimed to investigate the performance and interpretability of several ML algorithms, including deep multilayer perceptron (Deep MLP), support vector machine (SVM) and Extreme gradient boosting trees (XGBoost) for predicting COVID-19 mortality risk with an emphasis on the effect of cross-validation (CV) and principal component analysis (PCA) on the results. For this purpose, a dataset with 154 features from 490 COVID-19 patients admitted into the intensive care unit (ICU) of Tygerberg Hospital in Cape Town, South Africa, during the first wave of COVID-19 in 2020 was retrospectively analysed. Our results show that Deep MLP had the best overall performance (F1 = 0.92; area under the curve (AUC) = 0.94) when CV and the synthetic minority oversampling technique (SMOTE) were applied without PCA. By using the Shapley Additive exPlanations (SHAP) model to interpret the mortality risk predictions, we identified the Length of stay (LOS) in the hospital, LOS in the ICU, Time to ICU from admission, days discharged alive or death, D-dimer (blood clotting factor), and blood pH as the six most critical variables for mortality risk prediction. Also, Age at admission, Pf ratio (PaO2/FiO2 ratio), troponin T (TropT), ferritin, ventilation, C-reactive protein (CRP), and symptoms of acute respiratory distress syndrome (ARDS) were associated with the severity and fatality of COVID-19 cases. The study reveals how ML could assist medical practitioners in making informed decisions on handling critically ill COVID-19 patients with comorbidities. It also offers insight into the combined effect of CV, PCA, and SMOTE on the performance of ML models for COVID-19 mortality risk prediction, which has been little explored.

Lesotho remains one of the world's 30 high-tuberculosis (TB) burden countries. In Butha-Buthe district, unfavourable TB treatment outcomes were higher than those set forth by the WHO. This study's objective was to evaluate TB treatment adherence and treatment resistance among patients enrolled in the 12 health facilities in Butha-Buthe. data were collected from the medical records of patients with sputum smear-positive TB and extra-pulmonary forms of TB between January 2015 and December 2020. Results were presented in frequencies and percentages. Univariate and multivariable logistic regression analyses were conducted to identify factors associated with treatment adherence. among 1,792 patients who were enrolled, 1,320 were included in the study. The overall mean TB treatment adherence rate was estimated at 37.20%. Factors found to be associated with treatment adherence in multivariate analysis were age ≥60 years (aOR: 0.59, 95%CI: 0.54- 0.66; P<0.001), being a mine worker (aOR 1.09, 95%CI: 1.03-1.14; P<0.001), having pulmonary TB (aOR: 1.23, 95%CI: 1.17-1.29, P<0.001), being in the continuation phase of the treatment (aOR 1.38, 95%CI: 1.33, 1.45; P<0.001) and category 2 (aOR 0.93, 95%CI: 0.88-0.99; P = 0.016). Regarding TB contact support, family members (aOR: 1.08, 95%CI: 1.03-1.14; P<0.001), friends (aOR 1.30, 95%CI: 1.19-1.41; P<0.001), spouses (aOR: 1.24, 95%CI 1.16-1.34; P<0.001), and unreported contacts (aOR 1.18, 95%CI: 1.09-1.27; P = 0.015) all showed increased TB adherence. the overall adherence to TB therapy was poor in Butha-Buthe district. Lesotho urgently needs district-level strategies to improve TB treatment adherence and reduce treatment resistance.

Background The COVID-19 pandemic had a devastating impact on childhood routine immunization programs, resulting in increased measles mortalities and complications. In Zambia, the likelihood of measles-related deaths and complications in children was possibly increased because of high rates of unvaccinated children, late diagnosis, and poor case management, which could have been a consequence of exclusive focus on COVID-19 interventions. This study aimed at examining the effect of the COVID-19 pandemic on measles mortality and its predictors among patients seen at health facilities in Zambia. Methods We used longitudinal data (January 2020 to August 2023) from outbreak investigations and time series data from 2017 to 2023 to understand the impact of COVID-19 on measles immunization and know the predictors of measles mortalities. The period running from January 2017 to February 2020, just before the first reported COVID-19 case, was defined as pre-COVID-19, and March 2020 to December 2023 as post-COVID-19. Multivariable logistic regression analysis was used to determine predictors of mortality. A segmented Poisson regression model was used to determine the correlation between the underlying patterns of measles mortality and the commencement of the COVID-19 pandemic. Results A total of 3429 measles cases were reported during the study period. Of these, 1261 had complete metadata and were included in the analysis. The median age was 3 years (IQR, 1–7). Out of the 1261 enrolled, 54 (4.3%) were reported died. A total of 205 (21.0%) were IgM positive, and 207 (16.9%) were vaccinated. Monthly measles mortality increased by 220%, from 0.06 per 100,000 before COVID-19 to 0.23 during the pandemic. Predictors of mortality were younger age category (0–4) (AOR = 2.78; 95% CI 1.16–7.14), testing positive for measles IgM (AOR = 2.17; 95% CI 1.07–4.39), rush (AOR = 3.66; 95% CI 1.31, 6.21), and female sex (AOR = 1.90; 95% CI 1.04–3.50), which increased the odds of dying. However, being vaccinated (AOR = 0.06; 95% CI 0.01–0.42) reduced the odds of dying. Evidence for the COVID-19 effect was strongly associated with increased measles mortality (RR, 1.02; 95% CI 1.00, 1.04; 0.017) with a trend step change of 81% (RR, 1.81; 95% CI 1.14–2.87). There was also an increased trend of measles cases (RR, 1.04; 95% CI 1.01–1.06) during the pandemic. Measles dose 2 vaccination trends increased by about 0.3% during the COVID-19 pandemic due to the Supplementary Immunization Activity (SIA) (RR, 1.003; 95% CI 1.000–1.010). However, there was a dramatic drop of about 42% (RR = 0.58, 95% CI 0.46–0.72). Conclusions Measles caused a significant increase in child mortality during the pandemic period. A mix of systemic, clinical, and individual factors affected measles mortality. Prioritizing vaccine coverage, especially for younger children and marginalized populations; enhancing diagnostic and treatment capacities; and addressing gender and healthcare access disparities are all essential components of interventions aimed at lowering mortality. The findings suggest that public health interventions focusing on measles vaccination, rapid detection, and appropriate case management are crucial to reducing mortality and preventing further transmission. To achieve population immunity, sustained efforts are required to maintain high coverage rates.

Background COVID-19 vaccines offer different levels of immune protection but do not provide 100% protection. Vaccinated persons with pre-existing comorbidities may be at an increased risk of SARS-CoV-2 breakthrough infection or reinfection. The aim of this study is to identify the critical variables associated with a higher probability of SARS-CoV-2 breakthrough infection using machine learning. Methods A dataset comprising symptoms and feedback from 257 persons, of whom 203 were vaccinated and 54 unvaccinated, was used for the investigation. Three machine learning algorithms – Deep Multilayer Perceptron (Deep MLP), XGBoost, and Logistic Regression – were trained with the original (imbalanced) dataset and the balanced dataset created by using the Random Oversampling Technique (ROT), and the Synthetic Minority Oversampling Technique (SMOTE). We compared the performance of the classification algorithms when the features highly correlated with breakthrough infection were used and when all features in the dataset were used. Result The results show that when highly correlated features were considered as predictors, with Random Oversampling to address data imbalance, the XGBoost classifier has the best performance (F1 = 0.96; accuracy = 0.96; AUC = 0.98; G-Mean = 0.98; MCC = 0.88). The Deep MLP had the second best performance (F1 = 0.94; accuracy = 0.94; AUC = 0.92; G-Mean = 0.70; MCC = 0.42), while Logistic Regression had less accurate performance (F1 = 0.89; accuracy = 0.88; AUC = 0.89; G-Mean = 0.89; MCC = 0.68). We also used Shapley Additive Explanations (SHAP) to investigate the interpretability of the models. We found that body temperature, total cholesterol, glucose level, blood pressure, waist circumference, body weight, body mass index (BMI), haemoglobin level, and physical activity per week are the most critical variables indicating a higher risk of breakthrough infection. Conclusion These results, evident from our unique data source derived from apparently healthy volunteers with cardiovascular risk factors, follow the expected pattern of positive or negative correlations previously reported in the literature. This information strengthens the body of knowledge currently applied in public health guidelines and may also be used by medical practitioners in the future to reduce the risk of SARS-CoV-2 breakthrough infection.