Dian Kusuma

BACKGROUND: Analysing trends and levels of the burden of disease at the national level can mask inequalities in health-related progress in lower administrative units such as provinces and districts. We used results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to analyse health patterns in Indonesia at the provincial level between 1990 and 2019. Long-term analyses of disease burden provide insights on Indonesia's advance to universal health coverage and its ability to meet the United Nations Sustainable Development Goals by 2030. METHODS: We analysed GBD 2019 estimated cause-specific mortality, years of life lost (YLLs), years lived with disability (YLDs), disability-adjusted life-years (DALYs), life expectancy at birth, healthy life expectancy, and risk factors for 286 causes of death, 369 causes of non-fatal health loss, and 87 risk factors by year, age, and sex for Indonesia and its 34 provinces from 1990 to 2019. To generate estimates for Indonesia at the national level, we used 138 location-years of data to estimate Indonesia-specific demographic indicators, 317 location-years of data for Indonesia-specific causes of death, 689 location-years of data for Indonesia-specific non-fatal outcomes, 250 location-years of data for Indonesia-specific risk factors, and 1641 location-years of data for Indonesia-specific covariates. For subnational estimates, we used the following source counts: 138 location-years of data to estimate Indonesia-specific demographic indicators; 5848 location-years of data for Indonesia-specific causes of death; 1534 location-years of data for Indonesia-specific non-fatal outcomes; 650 location-years of data for Indonesia-specific risk factors; and 16 016 location-years of data for Indonesia-specific covariates. We generated our GBD 2019 estimates for Indonesia by including 1 915 207 total source metadata rows, and we used 821 total citations. FINDINGS: Life expectancy for males across Indonesia increased from 62·5 years (95% uncertainty interval 61·3-63·7) to 69·4 years (67·2-71·6) between 1990 and 2019, a positive change of 6·9 years. For females during the same period, life expectancy increased from 65·7 years (64·5-66·8) to 73·5 years (71·6-75·6), an increase of 7·8 years. There were large disparities in health outcomes among provinces. In 2019, Bali had the highest life expectancy at birth for males (74·4 years, 70·90-77·9) and North Kalimantan had the highest life expectancy at birth for females (77·7 years, 74·7-81·2), whereas Papua had the lowest life expectancy at birth for males (64·5 years, 60·9-68·2) and North Maluku had the lowest life expectancy at birth for females (64·0 years, 60·7-67·3). The difference in life expectancy for males between the highest-ranked and lowest-ranked provinces was 9·9 years and the difference in life expectacy for females between the highest-ranked and lowest-ranked provinces was 13·7 years. Age-standardised death, YLL, and YLD rates also varied widely among the provinces in 2019. High systolic blood pressure, tobacco, dietary risks, high fasting plasma glucose, and high BMI were the five leading risks contributing to health loss measured as DALYs in 2019. INTERPRETATION: Our findings highlight that Indonesia faces a double burden of communicable and non-communicable diseases that varies across provinces. From 1990 to 2019, Indonesia witnessed a decline in the infectious disease burden, although communicable diseases such as tuberculosis, diarrhoeal diseases, and lower respiratory infections have remained a main source of DALYs in Indonesia. During that same period, however, all-ages death and disability rates from non-communicable diseases and exposure to their risk factors accounted for larger shares of health loss. The differences in health outcomes between the highest-performing and lowest-performing provinces have also widened since 1990. Our findings support a comprehensive process to revisit current health policies, examine the root causes of variation in the burden of disease among provinces, and strengthen programmes and policies aimed at reducing disparities across the country. FUNDING: The Bill & Melinda Gates Foundation and the Government of Indonesia. TRANSLATION: For the Bahasa Indonesia translation of the abstract see Supplementary Materials section.

BACKGROUND: The food environment has been found to impact population dietary behaviour. Our study aimed to systematically review the impact of different elements of the food environment on dietary intake and obesity. METHODS: We searched MEDLINE, Embase, PsychInfo, EconLit databases to identify literature that assessed the relationship between the built food environments (intervention) and dietary intake and obesity (outcomes), published between database inception to March 26, 2020. All human studies were eligible except for those on clinical sub-groups. Only studies with causal inference methods were assessed. Studies focusing on the food environment inside homes, workplaces and schools were excluded. A risk of bias assessment was conducted using the CASP appraisal checklist. Findings were summarized using a narrative synthesis approach. FINDINGS: 58 papers were included, 55 of which were conducted in high-income countries. 70% of papers focused on the consumer food environments and found that in-kind/financial incentives, healthy food saliency, and health primes, but not calorie menu labelling significantly improved dietary quality of children and adults, while BMI results were null. 30% of the papers focused on the neighbourhood food environments and found that the number of and distance to unhealthy food outlets increased the likelihood of fast-food consumption and higher BMI for children of any SES; among adults only selected groups were impacted - females, black, and Hispanics living in low and medium density areas. The availability and distance to healthy food outlets significantly improved children's dietary intake and BMI but null results were found for adults. INTERPRETATION: Evidence suggests certain elements of the consumer and neighbourhood food environments could improve populations dietary intake, while effect on BMI was observed among children and selected adult populations. Underprivileged groups are most likely to experience and impact on BMI. Future research should investigate whether findings translate in other countries.

BACKGROUND: Onchocerciasis is a disease caused by infection with Onchocerca volvulus, which is transmitted to humans via the bite of several species of black fly, and is responsible for permanent blindness or vision loss, as well as severe skin disease. Predominantly endemic in parts of Africa and Yemen, preventive chemotherapy with mass drug administration of ivermectin is the primary intervention recommended for the elimination of its transmission. METHODS: A dataset of 18,116 geo-referenced prevalence survey datapoints was used to model annual 2000-2018 infection prevalence in Africa and Yemen. Using Bayesian model-based geostatistics, we generated spatially continuous estimates of all-age 2000-2018 onchocerciasis infection prevalence at the 5 × 5-km resolution as well as aggregations to the national level, along with corresponding estimates of the uncertainty in these predictions. RESULTS: As of 2018, the prevalence of onchocerciasis infection continues to be concentrated across central and western Africa, with the highest mean estimates at the national level in Ghana (12.2%, 95% uncertainty interval [UI] 5.0-22.7). Mean estimates exceed 5% infection prevalence at the national level for Cameroon, Central African Republic, Democratic Republic of the Congo (DRC), Guinea-Bissau, Sierra Leone, and South Sudan. CONCLUSIONS: Our analysis suggests that onchocerciasis infection has declined over the last two decades throughout western and central Africa. Focal areas of Angola, Cameroon, the Democratic Republic of the Congo, Ethiopia, Ghana, Guinea, Mali, Nigeria, South Sudan, and Uganda continue to have mean microfiladermia prevalence estimates exceeding 25%. At and above this level, the continuation or initiation of mass drug administration with ivermectin is supported. If national programs aim to eliminate onchocerciasis infection, additional surveillance or supervision of areas of predicted high prevalence would be warranted to ensure sufficiently high coverage of program interventions.

Diabetes Mellitus is one of the biggest health problems in Indonesia but the research on the disease's projection is still limited. This study aimed to make a projection model of prevalence and mortality of diabetes in Indonesia based on risk factors and NCD programs. The study was a quantitative non-experimental study through multiple linear regression models and system dynamics. The baseline projection was created by 2018 data and projections until 2045 involved the dynamization of risk factors and programs, population, and case fatality rate. The model was created from 205 districts data. This study used secondary data from Basic Health Research, BPJS Kesehatan, NCD programs, and Ministry of Health. The prevalence of diabetes in Indonesia is estimated to increase from 9.19% in 2020 (18.69 million cases) to 16.09% in 2045 (40.7 million cases). The prevalence will be lower to 15.68% (39.6 million) if interventions of programs were carried out, and to 9.22% (23.2 million) if the programs were added with prevention of risk factors. The projected number of deaths due to diabetes increases from 433,752 in 2020 to 944,468 in 2045. Deaths due to stroke among diabetes increases from 52,397 to 114,092 in the same period. Deaths from IHD among diabetes increase from 35,351 to 76,974, and deaths from chronic kidney disease among diabetes increase from 29,061 to 63,279. Diabetes prevalence and mortality in Indonesia rise significantly in Indonesia and can be reduced by intervention of several programs and risk factors. This study findings could be source of planning and evaluation of Diabetes prevention and control program at national and provincial level in the future related to risk factors control and program development.