On the road to universal health care in Indonesia, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016.

BACKGROUND: As Indonesia moves to provide health coverage for all citizens, understanding patterns of morbidity and mortality is important to allocate resources and address inequality. The Global Burden of Disease 2016 study (GBD 2016) estimates sources of early death and disability, which can inform policies to improve health care. METHODS: We used GBD 2016 results for cause-specific deaths, years of life lost, years lived with disability, disability-adjusted life-years (DALYs), life expectancy at birth, healthy life expectancy, and risk factors for 333 causes in Indonesia and in seven comparator countries. Estimates were produced by location, year, age, and sex using methods outlined in GBD 2016. Using the Socio-demographic Index, we generated expected values for each metric and compared these against observed results. FINDINGS: In Indonesia between 1990 and 2016, life expectancy increased by 8·0 years (95% uncertainty interval [UI] 7·3-8·8) to 71·7 years (71·0-72·3): the increase was 7·4 years (6·4-8·6) for males and 8·7 years (7·8-9·5) for females. Total DALYs due to communicable, maternal, neonatal, and nutritional causes decreased by 58·6% (95% UI 55·6-61·6), from 43·8 million (95% UI 41·4-46·5) to 18·1 million (16·8-19·6), whereas total DALYs from non-communicable diseases rose. DALYs due to injuries decreased, both in crude rates and in age-standardised rates. The three leading causes of DALYs in 2016 were ischaemic heart disease, cerebrovascular disease, and diabetes. Dietary risks were a leading contributor to the DALY burden, accounting for 13·6% (11·8-15·4) of DALYs in 2016. INTERPRETATION: Over the past 27 years, health across many indicators has improved in Indonesia. Improvements are partly offset by rising deaths and a growing burden of non-communicable diseases. To maintain and increase health gains, further work is needed to identify successful interventions and improve health equity. FUNDING: The Bill & Melinda Gates Foundation.

Spatial analysis of stunting determinants in 514 Indonesian districts/cities: Implications for intervention and setting of priority.

While the national prevalence of stunting in Indonesia has decreased, the level remains high in many districts/cities and there is significant variation. This ecological study employed aggregated data from the Basic Health Research Report and the District/City Poverty Data from 2018. We investigated the determinants of stunting prevalence at the district/city level, including autocorrelation applying the spatial autoregressive (SAR) model. The analyses revealed stunting prevalence above the national average in 282 districts/cities (54.9%), i.e. ≥30% in 297 districts/cities (57.8%) and ≥40% in 91 districts/cities (17.7%). Autocorrelation was found between Sumatra, Java, Sulawesi as well as Bali, East Nusa Tenggara and West Nusa Tenggara (Bali NTT NTB). The SAR modelling revealed the following variables with significant impact on the stunting prevalence in various parts of the country: closet defecation, hand washing, at least four antenatal care visits during pregnancy, poverty, immunisation and supplementary food for children under 5 years.

Mobile application development for improving medication safety in tuberculosis patients: A quasi-experimental study protocol.

The COVID-19 pandemic, the growth of smartphones, and the internet have driven the use of technology for monitoring TB patients. Innovation in management of TB patients is needed to improve treatment outcomes. The study was conducted to obtain a predictive model of medication safety and solution model for at-risk patients, and to improve medication safety through mobile applications. The research was conducted in 4 stages, namely qualitative, quantitative (cross-sectional), qualitative, and quantitative (quasi-experimental, post-test group control design). Data were taken at the Public Health Center in Jakarta, Indonesia. Samples were taken by cluster random sampling. For quantitative research, 2nd phase (n = 114) and 4th phase (n = 96) were analyzed using logistic regression. This study analyzed predictors of medication safety to assist in monitoring patients undergoing treatment. At-risk patients were educated using an algorithm programmed in the application.