Comorbidities in Privately Insured South Africans With Systemic Lupus Erythematosus.

Introduction Comorbidities in systemic lupus erythematosus (SLE) impact negatively on health-related quality of life (HRQoL) and life expectancy. We investigated the frequency and spectrum of comorbidities in privately insured South Africans with SLE. Methods The data of SLE patients based on International Classification of Diseases, Tenth Revision (ICD-10) codes and insured with Discovery Health Medical Scheme (DHMS), South Africa, aged ≥16 years at diagnosis and with ≥6 months of follow-up were reviewed. Demographics, comorbidities listed in the Charlson comorbidity index (CCI), other common comorbidities, intercurrent illnesses, and drug therapy were documented. Results Of the 520 patients coded as SLE, 207 met the inclusion criteria. Most were females (90.8%), with a median (interquartile range {IQR}) age and follow-up duration of 39 (30.3-53.0) and 6.1 (3.7-8.1) years, respectively. All patients had at least one comorbidity; the most frequent CCI comorbidities were pulmonary disease (30.9%), congestive heart failure (CHF, 15%), and renal disease (14.0%). Other common comorbidities were hypertension (53.1%) and mood and anxiety disorders (46.9%). Urinary tract infections (UTIs, 37.7%) and pneumonia (33.8%) were common intercurrent illnesses. The independent predictors of CHF were renal disease (odds ratio {OR}=855), dyslipidemia (OR=15.3), and male gender (OR=43.0); the independent predictors of hypertension were age at diagnosis (OR=1.03), type 2 diabetes (OR=4.45), and renal disease (OR=4.34); and the independent predictors of mood and anxiety disorders were female gender (OR=3.98), stroke (OR=3.18), UTI (OR=2.39), and chloroquine use (OR=1.94). Conclusion In this study of privately insured South Africans with SLE, comorbidities were common, and all patients had at least one comorbidity. Hypertension, infections, and mood and anxiety disorders were the leading comorbidities overall, and pulmonary disease was the most common CCI comorbidity. There is an obvious need to formally study the burden of mental health disorders in South African SLE patients.

Spatial heterogeneity in relationship between district patterns of HIV incidence and covariates in Zimbabwe: a multi-scale geographically weighted regression analysis.

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.

Estimating HIV incidence over a decade in Zimbabwe: A comparison of the catalytic and Farrington models.

Over the years, numerous modelling studies have been proposed to estimate HIV incidence. As a result, this study aimed to evaluate two alternative methods for predicting HIV incidence in Zimbabwe between 2005 and 2015. We estimated HIV incidence from seroprevalence data using the catalytic and Farrington-2-parameter models. Data were obtained from 2005-06, 2010-11, and 2015 Zimbabwe Demographic Health Survey (ZDHS). These models were validated at the micro and macro-level using community-based cohort incidence and empirical estimates from UNAIDS EPP/SPECTRUM, respectively. The HIV incidence for the catalytic model was 0.32% (CI: 0.28%, 0.36%), 0.36% (CI: 0.33%, 0.39%), and 0.28% (CI: 0.26%, 0.30%), for the years 2005-06, 2010-11, and 2015, respectively. The HIV incidence for the Farrington model was 0.21% (CI: 0.16%, 0.26%), 0.22% (CI: 0.20%, 0.25%), and 0.19% (CI: 0.16%, 0.22%), for the years 2005-06, 2010-11, and 2015, respectively. According to these findings, the catalytic model estimated a higher HIV incidence rate than the Farrington model. Compared to cohort estimates, the estimates were within the observed 95% confidence interval, with 88% and 75% agreement for the catalytic and Farrington models, respectively. The limits of agreement observed in the Bland-Altman plot were narrow for all plots, indicating that our model estimates were comparable to cohort estimates. Compared to UNAIDS estimates, the catalytic model predicted a progressive increase in HIV incidence for males throughout all survey years. Without a doubt, HIV incidence declined with each subsequent survey year for all models. To improve programmatic and policy decisions in the national HIV response, we recommend the triangulation of multiple methods for incidence estimation and interpretation of results. Multiple estimating approaches should be considered to reduce uncertainty in the estimations from various models.

Predicting the drop out from the maternal, newborn and child healthcare continuum in three East African Community countries: application of machine learning models.

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.

Predicting HIV infection in the decade (2005-2015) pre-COVID-19 in Zimbabwe: A supervised classification-based machine learning approach.

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.

Small area analysis of HIV viral load suppression patterns in a high priority district (2012-2016), South Africa.

Globally, high viral load (VL) suppression rates are indicators of successful HIV treatment programs. Evaluation of these programmes at lower levels is likely to highlight variations that are masked at the provincial or national levels. This ecological study used routinely collected clinical and surveillance data on the HIV programme from 88 sampled Ekurhuleni wards. Between January 2012 and December 2016, 26 222 HIV VL tests for 2817 patients were conducted. We conducted a secondary analysis to determine the predictors of high VL suppression accounting for space and time random effects and estimate the impact of the national universal test-and-treat roll-out in 2016 and forecast VL suppression rates for five years post-2016.The proportion of VL suppression increased over the years: 2012 (47.8%: 95% confidence interval (CI): 36.7%-67.4%); 2013 (58.2%: 95%CI: 41.4%-79.6%); 2014 (62.7%: 95%CI: 45.2%-84.7%); 2015 (67.2%: 95%CI: 49.0%-89.9%) and 2016 (61.2%: 95%CI: 43.9%-83.0%). For every percentage increase in ART initiation, high VL suppression rates increased by 35% (RR: 1.345; 95% credible interval (Crl) 1.221-1.492) and for every percentage increase in women in the ward, high VL suppression increased by 44% (RR: 1.442; 95%CrI: 1.056-1.962). There was evidence of high and low clusters of viral load suppression observed at ward-level. The VL suppression rates in Ekurhuleni were lower than the 90% UNAIDS target. There was heterogeneity of high VL suppression across wards and study period. Targeted interventions strengthening ART initiation and retention in care are critical to achieving optimal VL suppression in Ekurhuleni and districts with similar profiles.

Population-based temporal trends and ethnic disparity in breast cancer mortality in South Africa (1999-2018): Joinpoint and age-period-cohort regression analyses.

Globally, breast cancer is the leading cause of cancer deaths, accounting for 15.5% of female cancer deaths in 2020. Breast cancer is also the leading cause of female cancers in South Africa. The rapid epidemiological transition in South Africa may have an impact on the trends in breast cancer mortality in the country. We therefore evaluated the trends in the breast cancer mortality in SA over 20 years (1999-2020). Methods: Joinpoint regression analyses of the trends in crude and age-standardized mortality rates (ASMR) of breast cancer among South African women were conducted from 1999 to 2018 using mortality data from Statistics South Africa. Age-period-cohort regression analysis was then conducted to evaluate the independent effect of age, period, and cohort on breast cancer mortality, and analysis was stratified by ethnicity. Results: The mortality rate of breast cancer (from 9.82 to 13.27 per 100,000 women) increased at around 1.4% per annum (Average Annual Percent Change (AAPC): 1.4%, 95% CI:0.8-2.0, P-value< 0.001). Young women aged 30-49 years (1.1%-1.8%, P-value< 0.001) had increased breast cancer mortality. The risk of breast cancer mortality increased among successive birth cohorts from 1924 to 1928 but decreased among recent cohorts born from 1989 to 1993. In 2018, the breast cancer mortality rate among Blacks (9.49/100,000 women) was around half of the rates among the non-Blacks. (Coloreds: 18.11 per 100,000 women; Whites: 17.77/100,000 women; Indian/Asian: 13.24 per 100,000 women). Conclusions: Contrary to the trends in high- and middle-income countries, breast cancer mortality increased in South Africa especially among young women. Breast cancer prevention programs should be intensified and should also target young women. The marked disparity in ethnic burden of breast cancer should be considered during planning and implementation of interventions.

Cancer mortality distribution in South Africa, 1997-2016.

Introduction: The mortality data in South Africa (SA) have not been widely used to estimate the patterns of deaths attributed to cancer over a spectrum of relevant subgroups. There is no research in SA providing patterns and atlases of cancer deaths in age and sex groups per district per year. This study presents age-sex-specific geographical patterns of cancer mortality at the district level in SA and their temporal evolutions from 1997 to 2016. Methods: Individual mortality level data provided by Statistics South Africa were grouped by three age groups (0-14, 15-64, and 65+), sex (male and female), and aggregated at each of the 52 districts. The proportionate mortality ratios (PMRs) for cancer were calculated per 100 residents. The atlases showing the distribution of cancer mortality were plotted using ArcGIS. Spatial analyses were conducted through Moran's I test. Results: There was an increase in PMRs for cancer in the age groups 15-64 and 65+ years from 2006 to 2016. Ranges were 2.83 (95% CI: 2.77-2.89) -4.16 (95% CI: 4.08-4.24) among men aged 15-64 years and 2.99 (95% CI: 2.93-3.06) -5.19 (95% CI: 5.09-5.28) among women in this age group. The PMRs in men and women aged 65+ years were 2.47 (95% CI: 2.42-2.53) -4.06 (95% CI: 3.98-4.14), and 2.33 (95% CI: 2.27-2.38) -4.19 (95% CI: 4.11-4.28). There were considerable geographical variations and similarities in the patterns of cancer mortality. For the age group 15-64 years, the ranges were 1.18 (95% CI: 0.78-1.71) -8.71 (95% CI: 7.18-10.47), p < 0.0001 in men and 1.35 (95% CI: 0.92-1.92) -10.83 (95% CI: 8.84-13.14), p < 0.0001 in women in 2016. There were higher PMRs among women in the Western Cape, Northern Cape, North West, and Gauteng compared to other areas. Similar patterns were also observed among men in these provinces, except in North West and Gauteng. Conclusion: The identification of geographical and temporal distributions of cancer mortality provided evidence of periods and districts with similar and divergent patterns. This will contribute to understanding the past, present, future trends and formulating interventions at a local level.

Digital delivery of behavioural activation therapy to overcome depression and facilitate social and economic transitions of adolescents in South Africa (the DoBAt study): protocol for a pilot randomised controlled trial.

INTRODUCTION: Scalable psychological treatments to address depression among adolescents are urgently needed. This is particularly relevant to low-income and middle-income countries where 90% of the world's adolescents live. While digital delivery of behavioural activation (BA) presents a promising solution, its feasibility, acceptability and effectiveness among adolescents in an African context remain to be shown. METHODS AND ANALYSIS: This study is a two-arm single-blind individual-level randomised controlled pilot trial to assess the feasibility, acceptability and initial efficacy of digitally delivered BA therapy among adolescents with depression. The intervention has been coproduced with adolescents at the study site. The study is based in the rural northeast of South Africa in the Bushbuckridge subdistrict of Mpumalanga province. A total of 200 adolescents with symptoms of mild to moderately severe depression on the Patient Health Questionnaire Adolescent Version will be recruited (1:1 allocation ratio). The treatment group will receive BA therapy via a smartphone application (the Kuamsha app) supported by trained peer mentors. The control group will receive an enhanced standard of care. The feasibility and acceptability of the intervention will be evaluated using a mixed methods design, and signals of the initial efficacy of the intervention in reducing symptoms of depression will be determined on an intention-to-treat basis. Secondary objectives are to pilot a range of cognitive, mental health, risky behaviour and socioeconomic measures; and to collect descriptive data on the feasibility of trial procedures to inform the development of a further larger trial. ETHICS AND DISSEMINATION: This study has been approved by the University of the Witwatersrand Human Research Ethics Committee (MED20-05-011) and the Oxford Tropical Research Ethics Committee (OxTREC 34-20). Study findings will be published in scientific open access peer-reviewed journals, presented at scientific conferences and communicated to participants, their caregivers, public sector officials and other relevant stakeholders. TRIAL REGISTRATION NUMBERS: This trial was registered on 19 November 2020 with the South African National Clinical Trials Registry (DOH-27-112020-5741) and the Pan African Clinical Trials Registry (PACTR202206574814636).

Population-Based Temporal Trends and Ethnic Disparity in Cervical Cancer Mortality in South Africa (1999-2018): A Join Point and Age-Period-Cohort Regression Analyses.

Cervical cancer is one of the leading causes of cancer deaths among women in low- and middle-income countries such as South Africa. The current impact of national cervical cancer control and sexual and reproductive health interventions in South Africa reduce its burden. The aim of this study was to assess the trends in cervical cancer mortality and its relation to breast and gynaecological cancers in South Africa from 1999 to 2018. We conducted joinpoint regression analyses of the trends in crude and age-standardised mortality rates (ASMR) for cervical cancer mortality in South Africa from 1999 to 2018. An age−period−cohort regression analysis was also conducted to determine the impact of age, period, and cohort on cervical cancer mortality trends. Analyses were stratified by ethnicity. Cervical cancer (n = 59,190, 43.92%, 95% CI: 43.65−44.18%) was responsible for about 43.9% of breast and gynecological cancer deaths. The mortality rate of cervical cancer (from 11.7 to 14.08 per 100,000) increased at about 0.9% per annum (Average Annual Percent Change (AAPC): 0.9% (AAPC: 0.9%, p-value < 0.001)), and young women aged 25 to 49 years (AAPC: 1.2−3.5%, p-value < 0.001) had increased rates. The risk of cervical cancer mortality increased among successive birth cohorts. In 2018, cervical cancer mortality rate among Blacks (16.74 per 100,000 women) was about twice the rates among Coloureds (8.53 deaths per 100,000 women) and approximately four-fold among Indians/Asians (4.16 deaths per 100,000 women), and Whites (3.06 deaths per 100,000 women). Cervical cancer control efforts should be enhanced in South Africa and targeted at ethnic difference, age, period, and cohort effects.

Malaria attributable fractions with changing transmission intensity: Bayesian latent class vs logistic models.

BACKGROUND: Asymptomatic carriage of malaria parasites is common in high transmission intensity areas and confounds clinical case definitions for research studies. This is important for investigations that aim to identify immune correlates of protection from clinical malaria. The proportion of fevers attributable to malaria parasites is widely used to define different thresholds of parasite density associated with febrile episodes. The varying intensity of malaria transmission was investigated to check whether it had a significant impact on the parasite density thresholds. The same dataset was used to explore an alternative statistical approach, using the probability of developing fevers as a choice over threshold cut-offs. The former has been reported to increase predictive power. METHODS: Data from children monitored longitudinally between 2005 and 2017 from Junju and Chonyi in Kilifi, Kenya were used. Performance comparison of Bayesian-latent class and logistic power models in estimating malaria attributable fractions and probabilities of having fever given a parasite density with changing malaria transmission intensity was done using Junju cohort. Zero-inflated beta regressions were used to assess the impact of using probabilities to evaluate anti-merozoite antibodies as correlates of protection, compared with multilevel binary regression using data from Chonyi and Junju. RESULTS: Malaria transmission intensity declined from over 49% to 5% between 2006 and 2017, respectively. During this period, malaria attributable fraction varied between 27-59% using logistic regression compared to 10-36% with the Bayesian latent class approach. Both models estimated similar patterns of fevers attributable to malaria with changing transmission intensities. The Bayesian latent class model performed well in estimating the probabilities of having fever, while the latter was efficient in determining the parasite density threshold. However, compared to the logistic power model, the Bayesian algorithm yielded lower estimates for both attributable fractions and probabilities of fever. In modelling the association of merozoite antibodies and clinical malaria, both approaches resulted in comparable estimates, but the utilization of probabilities had a better statistical fit. CONCLUSIONS: Malaria attributable fractions, varied with an overall decline in the malaria transmission intensity in this setting but did not significantly impact the outcomes of analyses aimed at identifying immune correlates of protection. These data confirm the statistical advantage of using probabilities over binary data.

Performance evaluation of machine learning and Computer Coded Verbal Autopsy (CCVA) algorithms for cause of death determination: A comparative analysis of data from rural South Africa.

Computer Coded Verbal Autopsy (CCVA) algorithms are commonly used to determine the cause of death (CoD) from questionnaire responses extracted from verbal autopsies (VAs). However, they can only operate on structured data and cannot effectively harness information from unstructured VA narratives. Machine Learning (ML) algorithms have also been applied successfully in determining the CoD from VA narratives, allowing the use of auxiliary information that CCVA algorithms cannot directly utilize. However, most ML-based studies only use responses from the structured questionnaire, and the results lack generalisability and comparability across studies. We present a comparative performance evaluation of ML methods and CCVA algorithms on South African VA narratives data, using data from Agincourt Health and Demographic Surveillance Site (HDSS) with physicians' classifications as the gold standard. The data were collected from 1993 to 2015 and have 16,338 cases. The random forest and extreme gradient boosting classifiers outperformed the other classifiers on the combined dataset, attaining accuracy of 96% respectively, with significant statistical differences in algorithmic performance (p < 0.0001). All our models attained Area Under Receiver Operating Characteristics (AUROC) of greater than 0.884. The InterVA CCVA attained 83% Cause Specific Mortality Fraction accuracy and an Overall Chance-Corrected Concordance of 0.36. We demonstrate that ML models could accurately determine the cause of death from VA narratives. Additionally, through mortality trends and pattern analysis, we discovered that in the first decade of the civil registration system in South Africa, the average life expectancy was approximately 50 years. However, in the second decade, life expectancy significantly dropped, and the population was dying at a much younger average age of 40 years, mostly from the leading HIV related causes. Interestingly, in the third decade, we see a gradual improvement in life expectancy, possibly attributed to effective health intervention programmes. Through a structure and semantic analysis of narratives where experts disagree, we also demonstrate the most frequent terms of traditional healer consultations and visits. The comparative approach also makes this study a baseline that can be used for future research enforcing generalization and comparability. Future study will entail exploring deep learning models for CoD classification.

Assessing spatial patterns of HIV prevalence and interventions in semi-urban settings in South Africa. Implications for spatially targeted interventions.

Equitable allocation of resources targeting the human immunodeficiency virus (HIV) at the local level requires focusing interventions in areas of the greatest need. Understanding the geographical variation in the HIV epidemic and uptake of selected HIV prevention and treatment programmes are necessary to identify such areas. Individual-level HIV data were obtained from a 2012 national HIV survey in South Africa. Spatial regression models on each outcome measure (HIV infection, sub-optimal condom use or non-anti-retroviral treatment (ART) adjusted for spatial random effects at the ward level were fitted using WINBUGS software. In addition, ward-level data was utilized to estimate condom use coverage and ART initiation rates which were obtained from routinely collected data in 2012. Ordinary Kriging was used to produce smoothed maps of HIV infection, condom use coverage and ART initiation rates. HIV infection was associated with individuals undertaking tertiary education [posterior odds ratio (POR): 19.53; 95% credible intervals (CrI): 3.22- 84.93]. Sub-optimal condom use increased with age (POR: 1.09; 95%CrI: 1.06-1.11) and was associated with being married (POR: 4.14; 95%CrI: 1.23-4.28). Non-ART use was associated with being married (POR: 6.79; 95%CrI: 1.43-22.43). There were clusters with high HIV infection, sub-optimal condom use, and non- ART use in Ekurhuleni, an urban and semi-urban district in Gauteng province, South Africa. Findings show the need for expanding condom programmes and/or strengthening other HIV prevention programmes such as pre-exposure prophylaxis and encouraging sustained engagement in HIV care and treatment in the identified areas with the greatest need in Ekurhuleni Metropolitan Municipality.

Loss to Follow-Up Risk among HIV Patients on ART in Zimbabwe, 2009-2016: Hierarchical Bayesian Spatio-Temporal Modeling.

Loss to follow-up (LTFU) is a risk factor for poor outcomes in HIV patients. The spatio-temporal risk of LTFU is useful to identify hotspots and guide policy. Secondary data on adult HIV patients attending a clinic in provinces of Zimbabwe between 2009 and 2016 were used to estimate the LTFU risk in each of the 10 provinces. A hierarchical Bayesian spatio-temporal Poisson regression model was fitted using the Integrated Nested Laplace Approximation (INLA) package with LTFU as counts adjusting for age, gender, WHO clinical stage, tuberculosis coinfection and duration on ART. The structured random effects were modelled using the conditional autoregression technique and the temporal random effects were modelled using first-order random walk Gaussian priors. The overall rate of LTFU was 22.7% (95%CI: 22.6/22.8) with Harare (50.28%) and Bulawayo (31.11%) having the highest rates. A one-year increase in the average number of years on ART reduced the risk of LTFU by 35% (relative risk (RR) = 0.651; 95%CI: 0.592-0.712). In general, the provinces with the highest exceedance LTFU risk were Matabeleland South and Matabeleland North. LTFU is one of the drawbacks of HIV prevention. Interventions targeting high-risk regions in the southern and northern regions of Zimbabwe are a priority. Community-based interventions and programmes which mitigate LTFU risk remain essential in the global HIV prevention campaign.

Measurement of kidney function in Malawi, South Africa, and Uganda: a multicentre cohort study.

BACKGROUND: The burden of kidney disease in many African countries is unknown. Equations used to estimate kidney function from serum creatinine have limited regional validation. We sought to determine the most accurate way to measure kidney function and thus estimate the prevalence of impaired kidney function in African populations. METHODS: We measured serum creatinine, cystatin C, and glomerular filtration rate (GFR) using the slope-intercept method for iohexol plasma clearance (mGFR) in population cohorts from Malawi, Uganda, and South Africa. We compared performance of creatinine and cystatin C-based estimating equations to mGFR, modelled and validated a new creatinine-based equation, and developed a multiple imputation model trained on the mGFR sample using age, sex, and creatinine as the variables to predict the population prevalence of impaired kidney function in west, east, and southern Africa. FINDINGS: Of 3025 people who underwent measured GFR testing (Malawi n=1020, South Africa n=986, and Uganda n=1019), we analysed data for 2578 participants who had complete data and adequate quality measurements. Among 2578 included participants, creatinine-based equations overestimated kidney function compared with mGFR, worsened by use of ethnicity coefficients. The greatest bias occurred at low kidney function, such that the proportion with GFR of less than 60 mL/min per 1·73 m2 either directly measured or estimated by cystatin C was more than double that estimated from creatinine. A new creatinine-based equation did not outperform existing equations, and no equation, including the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) 2021 race-neutral equation, estimated GFR within plus or minus 30% of mGFR for 75% or more of the participants. Using a model to impute kidney function based on mGFR, the estimated prevalence of impaired kidney function was more than two-times higher than creatinine-based estimates in populations across six countries in Africa. INTERPRETATION: Estimating GFR using serum creatinine substantially underestimates the individual and population-level burden of impaired kidney function in Africa with implications for understanding disease progression and complications, clinical care, and service provision. Scalable and affordable ways to accurately identify impaired kidney function in Africa are urgently needed. FUNDING: The GSK Africa Non-Communicable Disease Open Lab. TRANSLATIONS: For the Luganda, Chichewa and Xitsonga translations of the abstract see Supplementary Materials section.

Comorbidities in Black South Africans with established rheumatoid arthritis.

OBJECTIVE: Comorbidities contribute both to morbidity and mortality in rheumatoid arthritis (RA). The aim of the current study was to investigate the prevalence and spectrum of comorbidities in South Africans with established RA. METHODS: A retrospective, consecutive case record review of 500 Black South African patients with established disease of ≥5 years attending a tertiary rheumatology service was performed. Common comorbidities including those listed in the Charlson Comorbidity Score (CCS) were documented. RESULTS: Most patients, 463 known alive (AG) and 37 known deceased (DG), were female (87%). Mean (SD) age and disease duration were 60 (11.1) and 10.7 (5.0) years respectively, and 98% had ≥1 comorbidities. Median CCS was 2, significantly higher in DG than AG (4 vs 2, P < .0001). Despite hypertension (70%) and hypercholesterolemia (47%) being the commonest comorbidities overall and type 2 diabetes (T2D) occurring in 15.4%, clinical cardiovascular events were rare (0.6%). Peptic ulcer disease (odds ratio [OR] = 8.67), congestive cardiac failure (OR = 7.09), serious infections (OR = 7.02) and tuberculosis (OR = 2.56) were significantly more common in DG than AG. Multivariate analysis showed that American College of Rheumatology functional class 3/4 was associated with increased risk for serious infections (OR = 3.84) and tuberculosis (OR = 2.10). CONCLUSION: Despite the high burden of cardiometabolic comorbidities in South Africans with established RA, cardiovascular events were rare. Serious infections and tuberculosis, both associated with severe functional disability, are a major cause of morbidity and mortality.

Rule-Based Information Extraction from Free-Text Pathology Reports Reveals Trends in South African Female Breast Cancer Molecular Subtypes and Ki67 Expression.

Clinical information on molecular subtypes and the Ki67 index is critical for breast cancer (BC) prognosis and personalised treatment plan. Extracting such information into structured data is essential for research, auditing, and cancer incidence reporting and underpins the potential for automated decision support. Herewith, we developed a rule-based natural language processing algorithm that retrieved and extracted important BC parameters from free-text pathology reports towards exploring molecular subtypes and Ki67-proliferation trends. We considered malignant BC pathology reports with different free-text narrative attributes from the South African National Health Laboratory Service. The reports were preprocessed and parsed through the algorithm. Parameters extracted by the algorithm were validated against manually extracted parameters. For all parameters extracted, we obtained accurate annotations of 83-100%, 93-100%, 91-100%, and 92-100% precision, recall, F 1-score, and kappa, respectively. There was a significant trend in the proportion of each molecular subtype by patient age, histologic type, grade, Ki67, and race. The findings also showed significant association in the Ki67 trend with hormone receptors, human epidermal growth factors, age, grade, and race. Our approach bridges the gap between data availability and actionable knowledge and provides a framework that could be adapted and reused in other cancers and beyond cancer studies. Information extracted from these reports showed interesting trends that may be exploited for BC screening and treatment resources in South Africa. Finally, this study strongly encourages the implementation of a synoptic style pathology report in South Africa.

Understanding factors influencing utilization of HIV prevention and treatment services among patients and providers in a heterogeneous setting: A qualitative study from South Africa.

Despite advances made in HIV prevention and treatment interventions in South Africa, barriers to their utilization continue to exist. Understanding perspectives from patients and providers of healthcare can shed light on the necessary strategies to enhance uptake of HIV services. A cross-sectional qualitative study was conducted in July 2020 in Ekurhuleni District. Based on HIV prevalence estimates from a national survey, male condom use coverage and antiretroviral treatment (ART) initiation rates from routinely collected clinical data for 2012, we selected facilities from geographical areas with varying HIV prevalence and uptake of HIV services. In-depth interviews were conducted with adult (≥18 years) patients and healthcare workers in selected primary healthcare facilities. Thematic analysis was performed following a framework built around the social cognitive theory to describe behavioural, personal, and social/environmental factors influencing utilization of HIV services. Behavioural factors facilitating uptake of HIV services included awareness of the protective value of condoms, and the benefits of ART in suppressing viral load and preventing mother-to-child HIV transmission which was evident across geographical areas. Barriers in high prevalence areas included suboptimal condom use, fears of a positive HIV result, and anticipated HIV-related stigma while seeking healthcare services. Across the geographical areas, personal factors included ability to correctly use available services enhanced by knowledge acquired during counselling sessions and community-based health promotion activities. Further, social support from family reinforced engagement in care. Compared to low uptake areas, clinics in high uptake areas used care-facilitators, outreach teams and decanting programs to address the environmental barriers including staff shortages and long queues. Barriers at multiple levels prevent optimal utilization of HIV services, calling for strategies that target and address the different levels and tailored to needs of specific settings. Overall, improved delivery of HIV prevention or treatment interventions can be achieved through strengthening training of healthcare providers in facilities and communities and addressing negative sequelae from utilising services in low uptake areas.

Trends and determinants of late antenatal care initiation in three East African countries, 2007-2016: A population based cross-sectional analysis.

Early antenatal care is critical for the mother and newborn's health. Antenatal care is often delayed in Sub-Saharan Africa. The study aims to examine the trends and determinants of late antenatal care initiation in the Democratic Republic of Congo, Kenya, and Tanzania from 2007-2016. The study employed Demographic Health Surveys data of reproductive-age women seeking antenatal care in the Democratic Republic of Congo (2007-2013/14), Kenya (2008-2014), and Tanzania (2010-2015/16). Bivariate and multivariate analysis was conducted per survey, taking sampling weights into account. The determinants of late antenatal care initiation were measured using multivariate logistic regression models and the trends were assessed using prediction scores. Late antenatal care initiation declined in Tanzania (60.9%-49.8%) and Kenya (67.8%-60.5%) but increased in the Democratic Republic of Congo (56.8%-61.0%) between surveys. In the Democratic Republic of Congo, higher birth order was associated with antenatal care initiation delays from 2007-2014, whilst rural residency (AOR:1.28;95%CI:1.09-1.52), lower maternal education (AOR:1.29;95%CI:1.13-1.47) and lower-income households (AOR:1.30;95%CI:1.08-1.55) were linked to antenatal care initiation delays in 2014. In Kenya, lower maternal education and lower-income households were associated with antenatal care initiation delays from 2008-2014, whilst rural residency (AOR:1.24;95%CI:1.11-1.38) and increased birth order (AOR:1.12; 95%CI:1.01-1.28) were linked to antenatal care initiation delays in 2014. In Tanzania, higher birth order and larger households were linked to antenatal care initiation delays from 2010-2016, whilst antenatal care initiation delays were associated with lower maternal education (OR:1.51;95%CI:1.16-1.97) in 2010 and lower-income households (OR:1.45;95%CI:1.20-1.72) in 2016. Except for the Democratic Republic of Congo, the sub-region is making progress in reducing antenatal care delays. Women from various geographic, educational, parity, and economic groups exhibited varying levels of delayed antenatal care uptake. Increasing women's access to information platforms and strengthening initiatives that enhance female education, household incomes, and localise services may enhance early antenatal care utilisation.

Estimating age specific prevalence and force of infection in Zimbabwe using combined cross-sectional surveys from 2005 to 2015.

Objective: Age structured sexual mixing patterns have been noted to be associated with HIV prevalence and force of infection. Therefore, this study aimed to estimate the age dependent HIV force of infection using survey cross-sectional data from Zimbabwe. Methods: We fit generalized additive models namely; linear, semi-parametric, non-parametric and non-proportional hazards models. Using the 2005-06, 2010-11 and 2015 Zimbabwe Demographic Health Surveys data. The Akaike Information Criteria was used to select the best model. The best model was then used to estimate the age dependent HIV prevalence and force-of-infection. Results: Based on birth year cohort-specific prevalence, the female HIV prevalence reaches the highest peak at around 29 years of age, then declines thereafter. Males have a lower cohort specific prevalence between 15 and 30 years than females. Male cohort-specific prevalence slightly decreases between the ages of 33 and 39, then peaks around the age of 40. The cohort-specific FOI is greater in females than in males throughout all age categories. In addition, the cohort-specific HIV FOI peaked at ages 22 and 40 for females and males, respectively. The observed 18-year age difference between the HIV FOI peaks of males and females. Conclusion: Our model was appealing because we did not assume that the FOI is stationary over time; however, we used serological survey data to distinguish the FOI's age-and-time effect. The cohort-specific FOI peaked 18 years earlier in females than males, indicative of age-mixing patterns. We recommend interventions that target younger females so as to reduce HIV transmission rates.