Hasheem Mannan

Malaria remains a critical global health crisis, placing a disproportionate burden on children under five in Uganda. To transition from broad surveillance to targeted intervention, this study applies interpretable machine learning to identify key socioeconomic predictors of malaria using the 2018–2019 Uganda Malaria Indicator Survey. By employing Random Forests for feature selection and Decision Trees for classification, we addressed the inherent class imbalance using robust metrics such as the F2-score, Matthews Correlation Coefficient, and Precision-Recall Curve. Specifically, the Random Under-Sampling technique enabled the model to achieve a Recall of 77%, prioritizing the reliable detection of true positives over simple accuracy. The analysis highlights the hierarchical importance of determinants such as household size, mosquito net ownership, and maternal education. The study’s defining contribution is the extraction of explicit “if-then” rules that visualize how these factors combine to create risk profiles, particularly revealing distinct disparities across regions such as Busoga and West Nile. These interpretable findings empower policymakers with actionable, evidence-based insights, moving beyond simple prediction to facilitate the design of structural and region-specific public health strategies.

Interest in child health and well-being is growing, and both are multidimensional. Composite indicators (CIs) are useful tools for analyzing their relationships. This study examines the correlation between the CI of child well-being and that of public health system performance and proposes a weighting scheme for constructing CIs. Among schemes, entropy weighting yielded the strongest correlation. Uncertainty analysis revealed that country rankings with entropy weighting are the most stable, demonstrating robust CI. Strong explanatory power indicates that the CIs correlate with GDP, validating their compatibility with a key reference variable. High discriminant power confirms that CIs provide informational diversity, helping decision-makers distinguish between countries. The analysis shows that better health system performance is associated with higher child well-being, reinforcing the role of public health systems in promoting sustainable development and child well-being.

Although mental health is critical at all stages of life, childhood and adolescence are especially sensitive periods, as psychological difficulties during these phases are shaped by multifaceted social, economic, and environmental factors and may lead to long-term biopsychosocial consequences. Understanding these determinants is complex because they involve interrelated sub-indicators. To address this challenge, this study developed composite indicators representing the susceptibility to anxiety and depression among children and adolescents in LMICs, using a subjective–objective optimization approach informed by expert opinion. By integrating specialist knowledge with data-driven weighting, the proposed method strikes a balance between conceptual relevance and empirical consistency. Results indicate that functional difficulties in self-care, mobility, concentration, memory, and parental involvement affect both anxiety and depression. Depression is more associated with child labor, discipline, adaptability, and GDP, whereas anxiety relates more to communication, social interaction, and neighborhood safety. The proposed composite indicators provide a scalable, interpretable tool to identify priority regions, guide targeted interventions, and support evidence-based mental health policies for children and adolescents in LMICs.

Background: Malawi, like other developing countries, is not prepared to adequately address the health needs of older persons. Objectives: This article explores challenges older persons experience in accessing health care. Method: This article is based on data from two large. Conducted in Blantyre, Phalombe, Dowa and Rumphi Districts, the EquitAble study examined self-reported barriers. The GeoHealthAccess study examined geographical barriers, conducted in Blantyre and Phalombe. A sub-sample of 338 (17%) older persons of the 1991 respondents in the EquitAble study is used in this article. Respondents were asked to identify factors that made it difficult for them to access health care. Sixty-six in-depth interviews and two focus group discussions with older persons were conducted to obtain in-depth information about barriers to accessing health care. Results: Barriers experienced by older persons in accessing health care included being unable to pay to for services at fee paying health facilities; failure to pay for transportation; long distances to health centres; mountainous terrains; having severe rheumatism; living alone; poor attitudes of health workers; and shortage of staff, medicines and equipment. Conclusion: To address these challenges, there is need to have adequate health workers with knowledge of diseases and conditions experienced by older persons and the introduction of outreach health services targeting them. Contribution: Poverty at individual level and health system factors are major barriers to accessing health care by older persons. There is a need for the development and implementation of strategies to better improve access for older persons.

This study proposes a novel approach to constructing composite indicators, utilizing discriminant analysis to identify the determining factors for the diagnosis of multidimensional depression and to provide an index that represents the multidimensionality of this construct. By focusing solely on factors that significantly correlate with the diagnosis of multidimensional depression, this method provides a more precise and objective representation of the problem. The application of the method to the 2019 Brazilian Health Survey data demonstrated its effectiveness, resulting in a composite indicator that separates individuals who self-declare as having depression from individuals who self-declare as not having depression. The results highlight individuals who have a limiting chronic condition, high levels of education, less support from friends and family, perform unhealthy work, and are male. In contrast, individuals with the opposite characteristics are associated with a negative multidimensional depression diagnosis. The proposed composite indicator not only improves the measurement accuracy but also offers a new means of visualizing and understanding the multidimensional nature of depression diagnosis, providing valuable information for the formulation of targeted public health policies aimed at reducing the time for which people show symptoms of depression.

Children and adolescents are increasingly susceptible to issues related to anxiety and depression symptoms. The literature does not present a consensus on the composition of indicators that make predictions, prognostic algorithms, or management strategies in mental health promotion and prevention. Most studies primarily focus on the consequences observed in adulthood. This study develops a multidimensional representation of the propensity of children and adolescents to have difficulties in the field of anxiety and depression. The Ordered Weighted Averaging (OWA) operator was used to create a composite indicator, and three quality tests validated the results. For this, it uses information about different dimensions associated with adversity in childhood and adolescence from 54 countries sourced from UNICEF’s Multiple Indicator Cluster Surveys to compare the values of proposed dimensions across continents. The generated composite indicator reveals that, on average, countries in Africa show a higher propensity for children and adolescents to present difficulties in the anxiety and depression fields. In the opposite position, the Americas have the lowest average propensity for these mental health conditions. The validation of the results through quality tests reinforces confidence in the direction indicated by the findings, enhancing the decision-making process when dealing with multidimensional phenomena.

Machine learning has increasingly gained prominence in the healthcare sector due to its ability to address various challenges. However, a significant issue remains unresolved in this field: the handling of imbalanced data. This process is crucial for ensuring the efficiency of algorithms that utilize classification techniques, which are commonly applied in risk management, monitoring, diagnosis, and prognosis of patient health. This study conducts a comparative analysis of techniques for handling imbalanced data and evaluates their effectiveness in combination with a set of classification algorithms, specifically focusing on stroke prediction. Additionally, a new approach based on Particle Swarm Optimization (PSO) and Naive Bayes was proposed. This approach was applied to the real problem of Chagas disease. The application of these techniques aims to improve the quality of life for individuals, reduce healthcare costs, and allocate available resources more efficiently, making it a preventive action.

More than half of the older population of the European Union-28 reported difficulties with at least one personal care or household activity. Within the context of the United Nations Convention on the Rights of Persons with Disabilities, robotics needs to be better understood, as it has great potential for realizing human rights. The aim of using care robots is to provide physical, cognitive, or emotional support to older persons, their families, and care professionals. This chapter focuses on care robots as a form of assistive technology, referring to robotic assistance used in older people’s care in Ireland. The study has a qualitative descriptive design with an inductive approach using semi-structured interviews. The informants included technology developers, researchers, policymakers, advocacy group representatives, care providers, and care professionals. An in-depth understanding of ethical concerns, facilitators, and barriers to attaining the full potential of robotics would go a long way in enabling healthy aging and aging in place.