Background: Cardiovascular diseases (CVDs) accounted for over 17 million deaths and 353 million disabilityadjusted 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.

In the midst of tackling the persistent burden of maternal mortality and infectious diseases, Indonesia is facing an increasing burden of non-communicable diseases (NCDs). Although there are increasing national efforts in NCD prevention and control, the worsening trend of NCD risk factors and morbidity is alarming. We provided assessment and discussion on the policies and actions needed in the country including comprehensive efforts to reduce tobacco use and unhealthy diet, the need to reorient the health systems for better NCD prevention and control, and the promotion of NCD-related research that are still lacking.

BACKGROUND: Indonesia has the second highest smoking prevalence among adult males in the world, and smoking prevalence is increasing among youths.OBJECTIVE: To evaluate the smoke-free policy (SFP), a flagship national tobacco control programme, by providing evidence on geographic distribution, socio-economic disparities and policy determinants of SFP adoption by district in Indonesia.METHODS: We employed spatial and quantitative methods to obtain data respectively on geographic distribution of SFP adoption, and on disparities and associations between national and provincial SFP regulations and SFP adoption by the districts.RESULTS: Twenty-one of 34 provinces, and 345 of 514 districts adopted SFP. We found significant geographic disparities: all districts outside of Papua were up to 6.3 times more likely to adopt the policy and to implement it for a period of up to 3 years longer in duration. We also found significant socio-economic disparities: urban districts, those that were wealthiest and those most educated were respectively 3.9, 9.1 and 2.8 times more likely to adopt the policy. Moreover, districts in provinces that had SFP regulation were 3.2 times more likely to adopt. Finally, the adoption rate in the period after the 2012 national regulation was up to 7.8 times higher than that before.CONCLUSION: In addition to geographic and socio-economic disparities, national and provincial regulations and policies were determinants of SFP adoption.

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: With 61.4 million smokers in 2018, Indonesia makes a significant contribution to the global number of smokers. The latest data show that smoking is increasingly common in youth.OBJECTIVE: To examine the density of cigarette retailers around formal and informal educational facilities in Indonesia.METHODS: We employed geospatial and quantitative analyses using data on cigarette retailers (from surveys during July–August 2019) and educational facilities in Depok City. Data analyses, in ArcMap 10.6 and Stata v15, compared the density within 100 m and 100–200 m from the facility.RESULTS: We found a 40% higher density of cigarette retailers in areas close to educational facilities. This high density is similar between formal (i.e., primary, junior high, and senior high schools) and informal educational facilities (i.e., early years education centers and mosques). Moreover, compared to the average, the density is higher near primary schools and senior high schools.CONCLUSION: There is a higher density of cigarette retailers around formal and informal educational facilities in Indonesia. This evidence confirms there is a need to regulate cigarette retailers near educational facilities, to enforce the ban on sale to minors and to ban product displays at retailers.