UPSpace Repository

Faculty Research Collections

UP Research Output Collections

Featured

Recent Submissions

Beyond prescriptions : chronic medication adherence predicts mortality risk in a large-scale cohort study
Blanco, Jessica Hamuy; Janse van Rensburg, Dina Christina; Jansen van Rensburg, Audrey; Uys, Corrie; Schellack, Natalie (Frontiers Media, 2025-11-25)
OBJECTIVES : The Medication Adherence Risk Score (MARS) is a calculated score using pharmacy transactional data spanning 50% of the South African private pharmacy market. This study aims to demonstrate that the existing MARS model enhances risk stratification by identifying individuals at increased risk of mortality related to non-adherence to chronic medication. METHODS : This was a retrospective cohort study in which an analysis of the relative mortality experience was compared to a standard fully underwritten base was performed for each of the MARS categories (low, medium, high and very high). The actual-to-expected ratio (AER) and relative risk (RR) for each category were compared across age groups and gender. The least absolute shrinkage and selection operator (LASSO) regression analysis method was applied to determine the most important variables within the dataset, providing insight into whether MARS offered more benefit than traditional risk rating factors. A time-to-event analysis by MARS categories was performed using the Cox proportional hazards model. RESULTS : The mortality experience of the study population was higher than the expected fully underwritten base (AER = 175%). For the overall sample, increasing AER and RR did not correlate with increasing MARS categories. However, use of the MARS in addition to age band allowed for differentiation of risk within the 25 to 55 age bands, with a higher MARS score indicating a higher AER and RR. The time-to-event analysis showed a statistically significant difference in the mean number of months before death occurred between the different MARS categories (low = 26.53; medium = 8.93; high = 7.02; very high = 6.92; p < 0.001). CONCLUSION : The MARS is not generalisable across all groups, as evidenced by the absence of a monotonic trend in the overall sample. However, when combined with age, it effectively differentiated mortality risk for individuals aged 25–55. The standard fully underwritten model underestimated the number of deaths within this pharmacy population. The time-to-event analysis showed a significant inverse relationship between MARS category and survival time.
Exploring machine learning classification for community based health insurance enrollment in Ethiopia
Yilema, Seyifemickael Amare; Shiferaw , Yegnanew A.; Moyehodie, Yikeber Abebaw; Fenta , Setegn Muche; Belay, Denekew Bitew; Fenta, Haile Mekonnen; Nigussie, Teshager Zerihun; Chen, Ding-Geng (Din) (Frontiers Media, 2025-07-18)
BACKGROUND : Community-based health insurance (CBHI) is a vital tool for achieving universal health coverage (UHC), a key global health priority outlined in the sustainable development goals (SDGs). Sub-Saharan Africa continues to face challenges in achieving UHC and protecting individuals from the financial burden of disease. As a result, CBHI has become popular in low- and middle-income countries, including Ethiopia. Therefore, this study aimed to identify the ML algorithm with the best predictive accuracy for CBHI enrollment and to determine the most influential predictors among the dataset. METHODS : The 2019 Ethiopian Mini Demographic and Health Survey (EMDHS) data were used. The CBHI were predicted using seven machine learning models: linear discriminant analysis (LDA), support vector machine with radial basis function (SVM), k-nearest neighbors (KNN), classification and regression tree (CART), and random forest (RF). Receiver operating characteristic curves and other metrics were used to evaluate each model’s accuracy. RESULTS : The RF algorithm was determined to be the best machine learning model based on different performance assessments. The result indicates that age, wealth index, household members, and land usage all significantly affect CBHI in Ethiopia. CONCLUSION : This study found that RF machine learning models could improve the ability to classify CBHI in Ethiopia with high accuracy. Age, wealth index, household members, and land utilization are some of the most significant variables associated with CBHI that were determined by feature importance. The results of the study can help health professionals and policymakers create focused strategies to improve CBHI enrollment in Ethiopia.
Hypertension pharmacogenetics and limitations in Africa - a focus on the ACE, AGTR1 and CYP2C9 genes
Gomera, Rejoice T.; Van Hougenhouck-Tulleken, Wesley; Brand, Sarel J.; Van Niekerk, Chantal; Outhoff, Kim (Springer, 2026)
Hypertension affects approximately a billion people worldwide and is a major risk for adverse cardiovascular and renal outcomes, particularly in the sub-Saharan African population. Only a small number of treated hypertensive patients achieve blood pressure control. Apart from factors such as poor medication adherence, the limited efficacy of some therapies could be attributed to inter-individual genetic variability. Thus, identifying genetic markers linked to antihypertensive drug response could assist in individualizing hypertension treatment and optimizing antihypertensive regimens to provide the greatest efficacy with the lowest risk for adverse effects. The Angiotensin-converting enzyme (ACE), Angiotensin II type I receptor (AGTR1) and Cytochrome P450 family 2 subtype C member 9 (CYP2C9) genes play pivotal roles in hypertension, and several key single-nucleotide variations (SNV) in these genes are known to have substantial effects on drug response in non-African populations. Numerous research findings corroborate that genotype-targeted antihypertension treatment regimens are more successful and can reduce costs by mitigating the likelihood of serious side effects. However, these findings may not be directly applicable to African populations due to the limited number of studies conducted and increased genomic variability within African populations. Two interconnected but distinct challenges impede translation of these benefits to African populations, namely limited implementation of pharmacogenetic testing for actionable drug-gene pairs across African healthcare systems, and the underrepresentation of African genetic ancestry in global genomic datasets, which hinders the identification and validation of population-specific variants. This review explores these dual challenges by examining the pharmacogenetic landscape of hypertension, with a focus on three clinically relevant genes: ACE, AGTR1, and CYP2C9. We highlight known gene-drug interactions, population-specific data gaps, and the need for research and infrastructure development to advance precision medicine in Africa.
Dignity of the nurse : a hermeneutic literature review
Combrinck, Yvonne; Van Wyk, Neltjie C. (Sage, 2026)
The dignity of the nurse is a value that remains to be fully embedded in the nursing profession. It is a hidden concept that nurses tend to overlook. Regard for the dignity of the nurse is crucial because it enables nurses to fulfil their duties to the best of their potential. This literature review aimed to explore and better understand the meaning of the dignity of the nurse as it appears in education and nursing practice. A hermeneutic approach underpinned the methodology in the search, acquisition, and analysis of 16 studies. Dignity of the nurse as conceptualised in this review resulted in answering the following questions: in what ways is it defined; in what context does it appear; how is it sensed, and why does it matter? The review results confirmed the impact of dignity and indignity encounters on nurses. It is crucial to prioritise the dignity of the nurse as a value of equal importance in nursing.
South African Grade R teachers’ perspectives on integrating coding and robotics in early education
Willemse, Kayla; Callaghan, Ronel (Education Association of South Africa, 2026-02)
The integration of coding and robotics has emerged as a groundbreaking trend in early education. With this study we examined the perspectives of 10 South African Grade R teachers over 4 months using a participatory action research design. Through semi-structured interviews, guided classroom observations and collaborative discussion groups, we explored how teachers conceptualised and implemented coding and robotics (C&R) in their classrooms. Thematic analysis, informed by the technological pedagogical content knowledge framework, revealed a range of perspectives, both affirming and critical, with positive views being more prevalent. Teachers highlighted the benefits of C&R for fostering collaboration, creativity, critical thinking, innovation and learner enjoyment, particularly when integrated with pedagogical intent. However, concerns were raised about the possible displacement of foundational developmental practices, the risk of learner dependency on technology, and the constrained functionality of tools like the Bee-Bot. The findings underscore the importance of balanced, contextually responsive implementation. By incorporating teacher insight into practice, policy and professional development, early education practitioners may harness the transformative potential of C&R while safeguarding foundational developmental needs.