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Endometrial cancer is the sixth leading cause of cancer among females and about 97,000 global deaths of endometrial cancer. The changes in the trends of obesity, fertility rates and other risk factors in South Africa (SA) may impact the endometrial cancer trends. The aim of this study was to utilise the age period cohort and join point regression modelling to evaluate the national and ethnic trends in endometrial cancer mortality in South Africa over a 20year period (1999-2018). Data from Statistics South Africa was obtained to calculate the annual number of deaths, and annual crude and age standardised mortality rates (ASMR) of endometrial cancer from 1999-2018. The overall and ethnic trends of endometrial cancer mortality was assessed using the Join point regression model, while Age-period-cohort (APC) regression modelling was conducted to estimate the effect of age, calendar period and birth cohort. During the period 1999-2018, 4,877 deaths were due to endometrial cancer which constituted about 3.6% of breast and gynecological cancer deaths (3.62%, 95% CI: 3.52%-3.72%) in South Africa. The ASMR of endometrial cancer doubled from 0.76 deaths per 100,000 women in 1999 to 1.5 deaths per 100,000 women in 2018, with an average annual rise of 3.6% per annum. (Average Annual Percentage change (AAPC): 3.6%, 95%CI:2.7-4.4, P-value < 0.001). In 2018, the overall mean age at death for endometrial cancer was was 67.40 ± 11.04 years and, the ASMR of endometrial cancer among Indian/Asians (1.69 per 100,000 women), Blacks (1.63 per 100,000 women) and Coloreds (1.39 per 100,000 women) was more than doubled the rates among Whites (0.66 deaths per 100,000 women). Indian/Asians had stable rates while other ethnic groups had increased rates. The Cohort mortality risk ratio (RR) of endometrial cancer increased with successive birth cohort from 1924 to 1963 (RR increased from 0.2 to 1.00), and subsequently declined among successive cohorts from 1963 to 1998 (1.00 to 0.09). There was strong age and cohort but not period effect among the South African women. Ethnic disparity showed that there was age effect among all the ethnic groups; Cohort effect among Blacks and Coloureds only, while Period effect occurred only among Blacks. The mortality rates of endometrial cancer doubled over a twenty-year period in South Africa from 1999-2018. There was strong ethnic disparity, with age and cohort effect on endometrial cancer trends. Thus, targeted efforts geared towards prevention and prompt treatment of endometrial cancer among the high-risk groups should be pursued by stake holders.

Unsafe abortion is still a leading cause of maternal death in most Sub-Saharan African countries. Post-abortion care (PAC) aims to minimize morbidity and mortality following unsafe abortion, addressing incomplete abortion by treating complications, and reducing possible future unwanted pregnancies by providing contraceptive advice. In this article, we draw on data from PAC service providers and patients in Kenya to illustrate how the quality of PAC in healthcare facilities is impacted by law and government policy. A cross-sectional design was used for this study, with in-depth interviews conducted to collect qualitative data from PAC service providers and seekers in healthcare facilities. Data were analyzed both deductively and inductively, with diverse sub-themes related to specific components of PAC quality. The provision of quality PAC in healthcare facilities in Kenya is still low, with access hindered by restrictions on abortion. Negative attitudes towards abortion result in the continued undirected self-administration of abortifacients. Intermittent service interruptions through industrial strikes and inequitable access to care also drive unsafe terminations. Poor PAC service availability and lack of capacity to manage complications in primary care facilities result in multiple referrals and delays in care following abortion, leading to further complications. Inefficient infection control exposes patients and caregivers to unrelated infections within facilities, and the adequate provision of contraception is a continued challenge. Legal, policy and cultural restrictions to access PAC increase the level of complications. In Kenya, there is limited policy focus on PAC, especially at primary care level, and no guidelines for health providers to provide legal, safe abortion. Discrimination at the point of care discourages women from presenting for care, and discourages providers from freely offering post-abortion contraceptive guidance and services. Poor communication between facilities and communities continues to result in delayed care and access-related discrimination. Greater emphasis should be placed on the prevention of unsafe abortion and improved access to post-abortion care services in healthcare facilities. There is a definite need for service guidelines for this to occur.

Background For optimal health, the maternal, newborn, and child healthcare (MNCH) continuum necessitates that the mother/child receive the full package of antenatal, intrapartum, and postnatal care. In sub-Saharan Africa, dropping out from the MNCH continuum remains a challenge. Using machine learning, the study sought to forecast the MNCH continuum drop out and determine important predictors in three East African Community (EAC) countries. Methods The study utilised Demographic Health Surveys data from the Democratic Republic of Congo (DRC) (2013/14), Kenya (2014) and Tanzania (2015/16). STATA 17 was used to perform the multivariate logistic regression. Python 3.0 was used to build five machine learning classification models namely the Logistic Regression, Random Forest, Decision Tree, Support Vector Machine and Artificial Neural Network. Performance of the models was assessed using Accuracy, Precision, Recall, Specificity, F1 score and area under the Receiver Operating Characteristics (AUROC). Results The prevalence of the drop out from the MNCH continuum was 91.0% in the DRC, 72.4% in Kenya and 93.6% in Tanzania. Living in the rural areas significantly increased the odds of dropping out from the MNCH continuum in the DRC (AOR:1.76;95%CI:1.30–2.38), Kenya (AOR:1.23;95%CI:1.03–1.47) and Tanzania (AOR:1.41;95%CI:1.01–1.97). Lower maternal education also conferred a significant increase in the DRC (AOR:2.16;95%CI:1.67–2.79), Kenya (AOR:1.56;95%CI:1.30–1.84) and Tanzania (AOR:1.70;95%CI:1.24–2.34). Non exposure to mass media also conferred a significant positive influence in the DRC (AOR:1.49;95%CI:1.15–1.95), Kenya (AOR:1.46;95%CI:1.19–1.80) and Tanzania (AOR:1.65;95%CI:1.13–2.40). The Random Forest exhibited superior predictive accuracy (Accuracy = 75.7%, Precision = 79.1%, Recall = 92.1%, Specificity = 51.6%, F1 score = 85.1%, AUROC = 70%). The top four predictors with the greatest influence were household wealth, place of residence, maternal education and exposure to mass media. Conclusions The MNCH continuum dropout rate is very high in the EAC countries. Maternal education, place of residence, and mass media exposure were common contributing factors to the drop out from MNCH continuum. The Random Forest had the highest predictive accuracy. Household wealth, place of residence, maternal education and exposure to mass media were ranked among the top four features with significant influence. The findings of this study can be used to support evidence-based decisions in MNCH interventions and to develop web-based services to improve continuity of care retention.

Ovarian cancer is the most lethal and third leading cause of gynaecological cancers globally and in South Africa (SA). However, its current mortality trends have not been evaluated in most sub-Saharan African Countries including South Africa that is currently undergoing epidemiological and health transitions. We evaluate the trends in the ovarian cancer mortality rates in SA over 20 years (1999–2018). Methods Crude (CMR) and age standardised mortality rates (ASMR) of ovarian cancer was calculated based on national mortality data of South Africa. The overall and ethnic trends of ovarian cancer mortality among women aged 15 years and older from 1999 to 2018 was assessed using the Join point regression model, while Age-period-cohort regression analysis was conducted to evaluate the underlying impact of age, period and cohort on ovarian cancer mortality. Results In all, 12,721 ovarian cancer deaths were reported in South Africa from 1999 to 2018 and the mortality rates increased from 2.34 to 3.21 per 100,00 women at 1.8% per annum. In 2018, the overall mean age at ovarian cancer death in South Africa was 62.30 ± 14.96 years while the mean age at death among Black women (58.07 ± 15.56 years), was about 11 years earlier than among White women (69.48 ± 11.71 years). In 2018, the White ethnic group (4.93 deaths per 100,000 women) had about doubled the ovarian cancer ASMR for the non-Whites (Indian/Asians, 2.92/100,000 women, mixed race, 2.49/100,000 women and Black women (2.36/ 100,000 women). All the ethnic groups had increased ASMR with Black women (Average annual percent change, [AAPC]: 4.7%, P-value < 0.001) and Indian/Asian women (AAPC: 2.5%, P-value < 0.001) having the highest rise. Cohort mortality risk ratio of ovarian cancer increased with successive birth cohort from 0.35 among 1924–1928 birth cohorts to 3.04 among 1999–2003 cohort and the period mortality risk increased by about 13% and 7.5% from 1999 to 2003 to 2004–2008 (RR: 0.87, 95% CI: 0.80–0.94), and from 2004 to 2008 to 2009–2013 (RR: 1.075, 95% CI:1.004–1.152) respectively. The longitudinal age analysis revealed that ovarian cancer increased with age, but there was an exponential increase from 55 years. Conclusions Our study showed that there was increasing trends in ovarian cancer mortality among all the South African ethnic groups, driven partly by increasing cohort and period mortality risks. We therefore highlight the huge burden of ovarian cancer in SA and the need for targeted intervention. Public health interventions geared towards reducing ovarian cancer mortality should be instituted and ethnic disparity should be incorporated in the cancer control policy.

IntroductionPostnatal care (PNC) is recommended for the optimal health of mothers and newborns; however, PNC uptake remains poor in sub-Saharan Africa. Traditional statistical approaches have been used to predict healthcare service utilisation more often but are limited in examining complex relationships compared with decision tree (DT) models.ObjectivesThis study aims to predict the main risk factors of PNC non-utilisation in three neighbouring East African countries using the DT models.MethodsPNC non-utilisation meant that both the mother and neonate did not receive at least one postnatal check within 6 weeks after delivery. Demographic and Health Surveys data from the Democratic Republic of Congo (DRC) (2013/2014), Kenya (2014) and Tanzania (2015/2016) were used. The DT model’s predictive performance was compared with the standard logistic regression (LR) using accuracy, sensitivity, specificity and area under the receiver operating characteristic curve.ResultsDT models exhibited higher accuracy and sensitivity than LR models. In the DRC, women with low-quality antenatal care (ANC), home deliveries and unemployment had the highest probability of PNC non-utilisation (92.0%). In Kenya, women who had home deliveries, unemployment and limited access to mass media showed the highest likelihood of PNC non-utilisation (87.0%). In Tanzania, women with home deliveries, low-quality ANC and unwanted pregnancies exhibited the highest likelihood of PNC non-utilisation (100.0%).ConclusionWomen with low-quality ANC, home deliveries, unemployment, unwanted pregnancies and limited access to mass media were classified as high-risk groups of PNC non-utilisation. These findings can help prioritise interventions and enhance PNC uptake in East Africa. Additionally, DT models can be applied as valuable tools for predicting maternal and child healthcare services utilisation in other sub-Saharan African countries.

This study used data from the second Nigeria Malaria Indicator Survey (NMIS) conducted in 2015 to investigate the spatial distribution of malaria prevalence in the country and identify its associated factors. Nigeria is divided into 36 states with 109 senatorial districts, most of which are affected by malaria, a major cause of morbidity and mortality in children under five years of age. We carried out an ecological study with analysis at the senatorial district level. A malaria prevalence map was produced combining geographic information systems data from the Nigeria Malaria Indicator Survey (NMIS) of 2015 with shape files from an open data-sharing platform. Spatial autoregressive models were fitted using a set of key covariates. Malaria prevalence in children under-five was highest in Kebbi South senatorial district (70.6%). It was found that poorest wealth index (β = 0.10 (95% CI: 0.01, 0.20), p = 0.04), mothers having only secondary level of education (β = 0.78 (95% CI: 0.05, 1.51), p = 0.04) and households without mosquito bed nets (β = 0.21 (95% CI: 0.02, 0.39), p = 0.03) were all significantly associated with higher malaria prevalence. Moran’s I (54.81, p<0.001) showed spatial dependence of malaria prevalence across contiguous districts and spatial autoregressive modelling demonstrated significant spill-over effect of malaria prevalence. Maps produced in this study provide a useful graphical representation of the spatial distribution of malaria prevalence based on NMIS-2015 data. Clustering of malaria prevalence in certain areas further highlights the need for sustained malaria elimination interventions across affected regions in order to break the chain of transmission.

Evidence from high-income countries suggests that childhood trauma is associated with schizophrenia. Studies of childhood trauma and schizophrenia in low and middle income (LMIC) countries are limited. This study examined the prevalence of childhood traumatic experiences among cases and controls and the relationship between specific and cumulative childhood traumatic experiences and schizophrenia in a sample in South Africa. Data were from the Genomics of Schizophrenia in the South African Xhosa people study. Cases with schizophrenia and matched controls were recruited from provincial hospitals and clinics in the Western and Eastern Cape regions in South Africa. Childhood traumatic experiences were measured using the Childhood Trauma Questionnaire (CTQ). Adjusted logistic regression models estimated associations between individual and cumulative childhood traumatic experiences and schizophrenia. Traumatic experiences were more prevalent among cases than controls. The odds of schizophrenia were 2.44 times higher among those who experienced any trauma than those who reported no traumatic experiences (95% CI 1.77-3.37). The odds of schizophrenia were elevated among those who experienced physical/emotional abuse (OR 1.59, CI 1.28-1.97), neglect (OR 1.39, CI 1.16-1.68), and sexual abuse (OR 1.22, CI 1.03-1.45) compared to those who did not. Cumulative physical/emotional abuse and neglect experiences increased the odds of schizophrenia as a dose-response relationship. Childhood trauma is common in this population. Among many other benefits, interventions to prevent childhood trauma may contribute to a decreasing occurrence of schizophrenia.

A study was conducted to investigate the district-level patterns of incidence of the human immunodeficiency virus (HIV) in Zimbabwe in the period 2005-2015 and explore variations in the relationship between covariates and HIV incidence across different districts. Demographic health survey data were analysed using hotspot analysis, spatial autocorrelation, and multi-scale geographically weighted regression (MGWR) techniques. The analysis revealed hotspots of the HIV epidemic in the southern and western regions of Zimbabwe in contrast to the eastern and northern regions. Specific districts in Matabeleland South and Matabeleland North provinces showed clusters of HIV incidence in 2005-2006, 2010-2011 and 2015. Variables studied were multiple sex partners and sexually transmitted infections (STI) condom use and being married. Recommendations include implementing targeted HIV prevention programmes in identified hotspots, prioritising interventions addressing the factors mentioned above as well as enhancing access to HIV testing and treatment services in high-risk areas, strengthening surveillance systems, and conducting further research to tailor interventions based on contextual factors. The study also emphasizes the need for regular monitoring and evaluation at the district level to inform effective responses to the HIV epidemic over time. By addressing the unique challenges and risk factors in different districts, significant progress can be made in reducing HIV transmission and improving health outcomes in Zimbabwe. These findings should be valuable for policymakers in resource allocation and designing evidence-based interventions.

The burden of HIV and related diseases have been areas of great concern pre and post the emergence of COVID-19 in Zimbabwe. Machine learning models have been used to predict the risk of diseases, including HIV accurately. Therefore, this paper aimed to determine common risk factors of HIV positivity in Zimbabwe between the decade 2005 to 2015. The data were from three two staged population five-yearly surveys conducted between 2005 and 2015. The outcome variable was HIV status. The prediction model was fit by adopting 80% of the data for learning/training and 20% for testing/prediction. Resampling was done using the stratified 5-fold cross-validation procedure repeatedly. Feature selection was done using Lasso regression, and the best combination of selected features was determined using Sequential Forward Floating Selection. We compared six algorithms in both sexes based on the F1 score, which is the harmonic mean of precision and recall. The overall HIV prevalence for the combined dataset was 22.5% and 15.3% for females and males, respectively. The best-performing algorithm to identify individuals with a higher likelihood of HIV infection was XGBoost, with a high F1 score of 91.4% for males and 90.1% for females based on the combined surveys. The results from the prediction model identified six common features associated with HIV, with total number of lifetime sexual partners and cohabitation duration being the most influential variables for females and males, respectively. In addition to other risk reduction techniques, machine learning may aid in identifying those who might require Pre-exposure prophylaxis, particularly women who experience intimate partner violence. Furthermore, compared to traditional statistical approaches, machine learning uncovered patterns in predicting HIV infection with comparatively reduced uncertainty and, therefore, crucial for effective decision-making.

ObjectiveTo determine the loss to follow-up (LTFU) rates at different healthcare levels after antiretroviral therapy (ART) services decentralisation among ART patients who initiated ART between 2004 and 2017 using the competing risk model in addition to the Kaplan-Meier and Cox regressions analysis.DesignA retrospective cohort study.SettingThe study was done in Zimbabwe using a nationwide routinely collected HIV patient-level data from various health levels of care facilities compiled through the electronic patient management system (ePMS).ParticipantsWe analysed 390 771 participants aged 15 years and above from 538 health facilities.OutcomesThe primary endpoint was LTFU defined as a failure of a patient to report for drug refill for at least 90 days from last appointment date or if the patient missed the next scheduled visit date and never showed up again. Mortality was considered a secondary outcome if a patient was reported to have died.ResultsThe total exposure time contributed was 1 544 468 person-years. LTFU rate was 5.75 (95% CI 5.71 to 5.78) per 100 person-years. Adjustment for the competing event independently increased LTFU rate ratio in provincial and referral (adjusted sub-HRs (AsHR) 1.22; 95% CI 1.18 to 1.26) and district and mission (AsHR 1.47; 95% CI 1.45 to 1.50) hospitals (reference: primary healthcare); in urban sites (AsHR 1.61; 95% CI 1.59 to 1.63) (reference: rural); and among adolescence and young adults (15–24 years) group (AsHR 1.19; 95% CI 1.16 to 1.21) (reference: 35–44 years). We also detected overwhelming association between LTFU and tuberculosis-infected patients (AsHR 1.53; 95% CI 1.45 to 1.62) (reference: no tuberculosis).ConclusionsWe have observed considerable findings that ‘leakages’ (LTFU) within the ART treatment cascade persist even after the decentralisation of health services. Risk factors for LTFU reflect those found in sub-Saharan African studies. Interventions that retain patients in care by minimising any ‘leakages’ along the treatment cascade are essential in attaining the 90–90–90 UNAIDS fast-track targets.