Multimorbidity in African ancestry populations: a scoping review.

OBJECTIVES: Multimorbidity (MM) is a growing concern linked to poor outcomes and higher healthcare costs. While most MM research targets European ancestry populations, the prevalence and patterns in African ancestry groups remain underexplored. This study aimed to identify and summarise the available literature on MM in populations with African ancestry, on the continent, and in the diaspora. DESIGN: A scoping review was conducted in five databases (PubMed, Web of Science, Scopus, Science Direct and JSTOR) in July 2022. Studies were selected based on predefined criteria, with data extraction focusing on methodology and findings. Descriptive statistics summarised the data, and a narrative synthesis highlighted key themes. RESULTS: Of the 232 publications on MM in African-ancestry groups from 2010 to June 2022-113 examined continental African populations, 100 the diaspora and 19 both. Findings revealed diverse MM patterns within and beyond continental Africa. Cardiovascular and metabolic diseases are predominant in both groups (80% continental and 70% diaspora). Infectious diseases featured more in continental studies (58% continental and 16% diaspora). Although many papers did not specifically address these features, as in previous studies, older age, being women and having a lower socioeconomic status were associated with a higher prevalence of MM, with important exceptions. Research gaps identified included limited data on African-ancestry individuals, inadequate representation, under-represented disease groups, non-standardised methodologies, the need for innovative data strategies, and insufficient translational research. CONCLUSION: The growing global MM prevalence is mirrored in African-ancestry populations. Recognising the unique contexts of African-ancestry populations is essential when addressing the burden of MM. This review emphasises the need for additional research to guide and enhance healthcare approaches for African-ancestry populations, regardless of their geographic location.

Skin Tone Analysis for Representation in Educational Materials (STAR-ED) using machine learning.

Images depicting dark skin tones are significantly underrepresented in the educational materials used to teach primary care physicians and dermatologists to recognize skin diseases. This could contribute to disparities in skin disease diagnosis across different racial groups. Previously, domain experts have manually assessed textbooks to estimate the diversity in skin images. Manual assessment does not scale to many educational materials and introduces human errors. To automate this process, we present the Skin Tone Analysis for Representation in EDucational materials (STAR-ED) framework, which assesses skin tone representation in medical education materials using machine learning. Given a document (e.g., a textbook in .pdf), STAR-ED applies content parsing to extract text, images, and table entities in a structured format. Next, it identifies images containing skin, segments the skin-containing portions of those images, and estimates the skin tone using machine learning. STAR-ED was developed using the Fitzpatrick17k dataset. We then externally tested STAR-ED on four commonly used medical textbooks. Results show strong performance in detecting skin images (0.96 ± 0.02 AUROC and 0.90 ± 0.06 F1 score) and classifying skin tones (0.87 ± 0.01 AUROC and 0.91 ± 0.00 F1 score). STAR-ED quantifies the imbalanced representation of skin tones in four medical textbooks: brown and black skin tones (Fitzpatrick V-VI) images constitute only 10.5% of all skin images. We envision this technology as a tool for medical educators, publishers, and practitioners to assess skin tone diversity in their educational materials.

Principled Subpopulation Analysis of the BetterBirth Study and the Impact of WHO's Safe Childbirth Checklist Intervention.

The World Health Organization (WHO) developed the Safe Childbirth Checklist as an intervention to improve care and outcomes in maternal and newborn health. The original study reported that the intervention did not significantly improve the outcomes. In this work, we employ a principled data-driven analysis to identify subpopulations with divergent characteristics: 1) vulnerable subgroups with the highest risk of neonatal deaths and 2) subgroups in the intervention arm that benefited from the Checklist intervention with significantly reduced risks of deaths and complications. Results demonstrate that low birth weight represented the most vulnerable group, whereas mother-baby dyads described by normal gestational age at birth, known parity, and unknown number of abortions was found to benefit from the Checklist intervention (OR : 0.70, 95%CI : 0.62-0.79, p < 0.001). Generally, the flexibility of our approach helps to answer subgroup-based queries in the broader global health domain, which also provides further insights to domain experts.

Data-level Linkage of Multiple Surveys for Improved Understanding of Global Health Challenges.

Data-driven approaches can provide more enhanced insights for domain experts in addressing critical global health challenges, such as newborn and child health, using surveys (e.g., Demographic Health Survey). Though there are multiple surveys on the topic, data-driven insight extraction and analysis are often applied on these surveys separately, with limited efforts to exploit them jointly, and hence results in poor prediction performance of critical events, such as neonatal death. Existing machine learning approaches to utilise multiple data sources are not directly applicable to surveys that are disjoint on collection time and locations. In this paper, we propose, to the best of our knowledge, the first detailed work that automatically links multiple surveys for the improved predictive performance of newborn and child mortality and achieves cross-study impact analysis of covariates.

Successes and Misses of Global Health Development: Detecting Temporal Concept Drift of Under-5 Mortality Prediction Models with Bias Scan.

Under-5 Mortality rates have been decreasing across Africa for the past two decades. Contributing factors include policy changes, technology, and health investments. This study identifies sub-populations that have experienced more-than-expected change in mortality rates (either increasing or decreasing) during this time period. We train under-5 mortality predictive models on Demographic and Health Survey (DHS) datasets from the early 2000s and apply those models to data collected in more recent versions of the survey. This provides an estimate of the risk current families would have faced in the past. We then apply techniques from anomalous pattern detection to identify sub-populations that have the most divergence between their predicted and observed mortality rates; higher and lower. These detected groups are examples of successes and possible misses of the health progress observed in Africa over the course of decades. Identifying these groups through data-driven discovery may lead to a better understanding of health policies in developing countries.

Detection of Anomalous Patterns Associated with the Impact of Medications on 30-Day Hospital Readmission Rates in Diabetes Care.

Improving quality of care in diabetes requires a good understanding of variations in diabetes outcomes and related interventions. However, little is known about the impact of diabetes interventions on outcome measures at the subpopulation-level. In this study, we developed methods that combine causal inference techniques with subset scanning techniques to study the heterogeneous effects of treatments on binary health outcomes. We analyzed a diabetes dataset consisting of 70,000 initial inpatient encounters to investigate the anomalous patterns associated with the impact of 4 anti-diabetic medication classes on 30-day readmission in diabetes. We discovered anomalous subpopulations where the likelihood of readmission was up to 1.8 times higher than that of the overall population suggesting subpopulation-level heterogeneity. Identifying such subpopulations may lead to a better understanding of the heterogeneous effects of treatments and improve targeted intervention planning.

Identifying Factors Associated with Neonatal Mortality in Sub-Saharan Africa using Machine Learning.

This study aimed at identifying the factors associated with neonatal mortality. We analyzed the Demographic and Health Survey (DHS) datasets from 10 Sub-Saharan countries. For each survey, we trained machine learning models to identify women who had experienced a neonatal death within the 5 years prior to the survey being administered. We then inspected the models by visualizing the features that were important for each model, and how, on average, changing the values of the features affected the risk of neonatal mortality. We confirmed the known positive correlation between birth frequency and neonatal mortality and identified an unexpected negative correlation between household size and neonatal mortality. We further established that mothers living in smaller households have a higher risk of neonatal mortality compared to mothers living in larger households; and that factors such as the age and gender of the head of the household may influence the association between household size and neonatal mortality.