Background: Globally, in 2020, 45 million children were estimated to be wasted, and 149 million children under five years of age were estimated to be stunted. Undernutrition makes children in particular much more vulnerable to disease and death. Our study aims to examine geographic and socioeconomic disparities in child undernutrition across 514 districts in Indonesia. Methods: Employing both geospatial and quantitative analyses (descriptive statistics and Ordinary Least Squares regressions), we analyzed the disparities in the prevalence of underweight, severe underweight, wasting, severe wasting, stunting, and severe stunting among districts. Child undernutrition data were from Indonesia Basic Health Survey (Riskesdas) 2018, which included a sample of 93,620 children under five years. Socioeconomic data were from the World Bank. Results: We found a relatively large geographic and socioeconomic disparity in child undernutrition in Indonesia. By region, districts in the Papua region (including Maluku and Nusa Tenggara) had a significantly higher prevalence of underweight and wasting than those in the Java region (including Bali). Districts in Papua had 44%, 121%, 38%, and 57% higher prevalence of underweight, severe underweight, wasting, and severe wasting, respectively. Similarly, the poorest districts had a significantly higher prevalence of underweight, wasting, and stunting than the wealthiest districts. The poorest districts had 30%, 83%, 16%, 21%, and 74% higher prevalence of underweight, severe underweight, wasting, stunting, and severe stunting, respectively. These results were similar among rural districts. Conclusion: There is a significant disparity in child undernutrition across districts in Indonesia. The government needs to prioritize the reduction of child undernutrition, especially in rural areas, districts outside of Java and Bali, and the poorest and least educated areas.

Background: Cardiovascular diseases (CVDs) accounted for over 17 million deaths and 353 million disability-adjusted life years lost in 2016. The risk factors are also high and increasing with high blood pressure, smoking, and high body mass index contributed to up to 212 million disability-adjusted life years in 2016. To help reduce the burden, it is crucial to understand the geographic and socioeconomic disparities in CVD risk factors. Methods: Employing both geospatial and quantitative analyses, we analyzed the disparities in the prevalence of smoking, physical inactivity, obesity, hypertension, and diabetes in Indonesia. CVD data was from Riskesdas 2018, and socioeconomic data was from the World Bank. Results: Our findings show a very high prevalence of CVD risk factors with the prevalence of smoking, physical activity, obesity, hypertension ranged from 28 to 33%. Results also show the geographic disparity in CVD risk factors in all five Indonesian regions. Moreover, results show socioeconomic disparity with the prevalence of obesity, hypertension, and diabetes are higher among urban and the richest and most educated districts while that physical inactivity and smoking is higher among rural and the least educated districts. Conclusion: The CVD burden is high and increasing in particularly among urban areas and districts with higher income and education levels. While the government needs to continue tackling the persistent burden from maternal mortality and infectious diseases, they need to put more effort into the prevention and control of CVDs and their risk factors.

This review aims to map the available evidence on biopsychosocial predictors of elderly mental health. The articles were independently screened in three selected databases, namely Pubmed, Proquest and Google Scholar. The stages consist of identifying the research questions, seeking and selecting relevant evidence, mapping data, and concluding and reporting results. The PRISMA flowchart was used to show the PEOS evidence search flow. A total of 23,722 articles were obtained from all databases during the initial search, where 458 titles fulfilled the eligibility criteria at the title screening stage. Furthermore, 383 articles passed through abstract screening, where 75 met the inclusion criteria and were included for full-text screening. Based on the full-text screening stage, 28 articles were excluded and the remaining 47 articles that matched the search process were included for data extraction. This review creates biopsychosocial variables related to the mental health of the elderly. The biological factors consist of age, biomarkers, female, health conditions, chronic diseases, and physical function. Variables related to psychological factors are affect, personality traits, and subjective well-being. Meanwhile, social factors include smoking, sleep quality, physical activity, daily living, social support, marital status, loneliness, religiosity, spirituality, and early life conditions.

Type 2 diabetes mellitus (T2DM) and cardiovascular disease (CVD) are leading and closely interlinked global health challenges. ¹ The burdens of T2DM and CVD are especially high in South Asia, one of the most populous and the most densely populated regions of the world. ^{2, 3} The prevalence of diabetes in South Asia has risen more rapidly than in other large geographical regions, ⁴ and it is projected that South Asia will account for 40% of the global CVD burden by 2020. ⁵ In addition, T2DM and CVD develop at an earlier age in South Asians than in their European counterparts ^{6, 7}.