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Introduction Childhood malnutrition is a complex issue with a range of contributing factors. The consequences of malnutrition are severe, particularly for children. This study aims to identify the factors contributing to inequality gaps in childhood malnutrition. Our study provides insights into modifiable elements to inform interventions targeted at distinct contexts and populations to improve child nutrition. Methods This study utilized data from the Demographic and Health Surveys (DHS) of 27 countries. First, the risk differences (RDs) between the prevalence of childhood malnutrition among the determinant variables, household income, and maternal education categories were calculated. The Blinder‒Oaxaca decomposition was subsequently used to determine the extent to which the difference in childhood malnutrition prevalence between low-income and high-income groups and maternal education levels results from the contributory effects of the explanatory variables: child and maternal individual-level compositional factors. Results We examined data from 138,782 children in 27 countries from 2015 to 2020. The prevalence of childhood malnutrition (10.5%) varied across countries, ranging from 6.5% in Burundi to 29.5% in Timor Leste. On average, the prevalence of childhood malnutrition was 11.0% in low-income households and 10.7% among mothers without education. Some nations had pro-low-income (i.e., malnutrition concentrated among children from poor households) or pro-no-maternal education (i.e., malnutrition concentrated among children from mothers with no formal education) inequality in childhood malnutrition, but most did not. We found a complex interplay of compositional effects, such as the child’s age, maternal education, maternal health behavior, and place of residence, that influence the inequality in childhood malnutrition rates across 10 pro-low-income countries. In addition, we also found that a complex mix of compositional effects, such as the household wealth index, maternal health behavior, and maternal age, contribute to childhood malnutrition inequality between educated and uneducated mothers across the 7 pro-no maternal education countries. Conclusion The prevalence of childhood malnutrition varies among low-income, high-income, and no maternal education-maternal education groups. This study highlights the need for a country-specific approach to addressing childhood malnutrition, with policies and interventions tailored to each country’s specific context.

Background Potentially inappropriate medications (PIM) are associated with adverse health outcomes. The use of PIM among persons living with HIV (PLHIV) could be more problematic due to peculiarities such as antiretroviral toxicity and drug-disease interactions. However, PIM among PLHIV continues to receive little attention. Therefore, we aimed to systematically review and estimate the prevalence of PIM among PLHIV. Methods We searched the PubMed, Scopus, and Web of Science databases to identify relevant studies from 1 January 2014 to 30 June 2024. Studies included were peer-reviewed articles that determined PIM prevalence using validated explicit or implicit tools and provided statistical information on the measured outcomes. Systematic, narrative, and literature reviews as well as case studies, commentaries, and editorials were excluded. Risk of bias was assessed using the Joanna Briggs Institute Prevalence Critical Appraisal Tool. Subgroup analysis was performed for the different geographic regions, PIM criteria, age, study period, and median numbers of medicines. Statistical heterogeneity was assessed using the I2 statistic. Sensitivity analyses, publication bias, and cumulative meta-analysis were conducted. Results A total of 13 articles, including 3200 PLHIV, were included for the systematic review, while meta-analysis was conducted using 8 studies comprising 2546 PLHIV. Most of the studies (46%) employed a cross-sectional study design. 11 out of the 13 studies were conducted in Europe. Most studies reported benzodiazepines as the most common PIM among PLHIV, while hypertension and dyslipidaemia were identified as the most frequent comorbidities. Overall, the pooled prevalence of PIM was 44.8% (95% CI, 32%-58%) using a random-effect model. PIM prevalence among PLHIV was higher among those exposed to polypharmacy (59.3%; CI: 48.9% – 68.9%), those aged > 70 years (44.6%; CI: 24.1–67.1), and post-COVID (48.9%; CI: 39%-59%). Conclusion This study suggests that PIM exposure among PLHIV is a budding issue. Findings from this review advocate for mitigating measures against PIM use. Also, this review contributes data on the use of PIM across different cohorts, creating awareness for policymakers and health managers. Review registration PROSPERO – CRD42024529411.

Introduction Neighbourhood effect on health outcomes is well established, but little is known about its effect on access to essential health services (EHS). Therefore, this study aimed to assess the contributing factors to access to EHS in slum versus non-slum settings. Methodology The most recent data from 58 Demographic and Health Surveys (DHS) conducted between 2011 and 2018 were used, including a total of 157,000 pairs of currently married women aged 15–49 and their children aged 12–23 months. We used meta-analysis techniques to examine the inequality gaps in suboptimal access to EHS between mother-children pairs living in slums and non-slums. Blinder-Oaxaca decomposition technique was used to identify the factors contributing to the inequality gaps in each low- and middle-income country (LMIC) included. Result The percentage of mother–child pairs living in slums ranged from 0.5% in Egypt to 63.7% in Congo. Meta-analysis of proportions for the pooled sample revealed that 31.2% [27.1, 35.5] of slum residents and 20.0% [15.3, 25.2] among non-slum residents had suboptimal access to EHS. We observed significant pro-slum inequalities in suboptimal access to EHS in 28 of the 52 LMICs with sufficient data. Of the 34 African countries included, 16 showed statistically significant pro-slum inequality in suboptimal access to EHS, with the highest in Egypt and Mali (2.64 [0.84–4.44] and 1.76 [1.65, 1.87] respectively). Findings from the decomposition analysis showed that, on average, household wealth, neighbourhood education level, access to media, and neighbourhood-level illiteracy contributed mostly to slum & non-slum inequality gaps in suboptimal access to EHS. Conclusion The study showed evidence of inequality in access to EHS due to neighbourhood effects in 26 LMICs. This evidence suggests that increased focus on the urban poor might be a important for increasing access to EHS and achieving the universal health coverage (UHC) goals.