Obesity is strongly associated with multiple risk factors. It is significantly contributing to an increased risk of chronic disease morbidity and mortality worldwide. There are various challenges to better understand the association between risk factors and the occurrence of obesity. The traditional regression approach limits analysis to a small number of predictors and imposes assumptions of independence and linearity. Machine Learning (ML) methods are an alternative that provide information with a unique approach to the application stage of data analysis on obesity. This study aims to assess the ability of ML methods, namely Logistic Regression, Classification and Regression Trees (CART), and Naïve Bayes to identify the presence of obesity using publicly available health data, using a novel approach with sophisticated ML methods to predict obesity as an attempt to go beyond traditional prediction models, and to compare the performance of three different methods. Meanwhile, the main objective of this study is to establish a set of risk factors for obesity in adults among the available study variables. Furthermore, we address data imbalance using Synthetic Minority Oversampling Technique (SMOTE) to predict obesity status based on risk factors available in the dataset. This study indicates that the Logistic Regression method shows the highest performance. Nevertheless, kappa coefficients show only moderate concordance between predicted and measured obesity. Location, marital status, age groups, education, sweet drinks, fatty/oily foods, grilled foods, preserved foods, seasoning powders, soft/carbonated drinks, alcoholic drinks, mental emotional disorders, diagnosed hypertension, physical activity, smoking, and fruit and vegetables consumptions are significant in predicting obesity status in adults. Identifying these risk factors could inform health authorities in designing or modifying existing policies for better controlling chronic diseases especially in relation to risk factors associated with obesity. Moreover, applying ML methods on publicly available health data, such as Indonesian Basic Health Research (RISKESDAS) is a promising strategy to fill the gap for a more robust understanding of the associations of multiple risk factors in predicting health outcomes.

A cluster-randomized, double-blinded, placebo-controlled trial was conducted to estimate the protective efficacy (PE) of a spatial repellent (SR) against malaria infection in Sumba, Indonesia. Following radical cure in 1,341 children aged ≥ 6 months to ≤ 5 years in 24 clusters, households were given transfluthrin or placebo passive emanators (devices designed to release vaporized chemical). Monthly blood screening and biweekly human-landing mosquito catches were performed during a 10-month baseline (June 2015–March 2016) and a 24-month intervention period (April 2016–April 2018). Screening detected 164 first-time infections and an accumulative total of 459 infections in 667 subjects in placebo-control households, and 134 first-time and 253 accumulative total infections among 665 subjects in active intervention households. The 24-cluster protective effect of 27.7% and 31.3%, for time to first-event and overall (total new) infections, respectively, was not statistically significant. Purportedly, this was due in part to zero to low incidence in some clusters, undermining the ability to detect a protective effect. Subgroup analysis of 19 clusters where at least one infection occurred during baseline showed 33.3% (P-value = 0.083) and 40.9% (P-value = 0.0236, statistically significant at the one-sided 5% significance level) protective effect to first infection and overall infections, respectively. Among 12 moderate- to high-risk clusters, a statistically significant decrease in infection by intervention was detected (60% PE). Primary entomological analysis of impact was inconclusive. Although this study suggests SRs prevent malaria, additional evidence is required to demonstrate the product class provides an operationally feasible and effective means of reducing malaria transmission.

Backgrounds: The increasing burden of cardiovascular disease (CVD) has become a major challenge globally, including in Indonesia. Understanding the readiness of primary health care facilities is necessary to confront the challenge of providing access to quality CVD health care services. Our study aimed to provide information regarding readiness to deliver CVD health services in public primary health care namely Puskesmas.Methods: The study questionnaire was adapted from the World Health Organization (WHO) Service Availability and Readiness Assessment (SARA), modified based on the package of essentials for non-communicable disease (PEN) and the Indonesian Ministry of health regulation. Data were collected from all Puskesmas facilities (N = 47) located in Makassar city. We analysed relevant data following the WHO-SARA manual to assess the readiness of Puskesmas to deliver CVD services. Human resources, diagnostic capacity, supporting equipment, essential medication, infrastructure and guidelines, and ambulatory services domain were assessed based on the availability of each tracer item in a particular domain. The mean domain score was calculated based on the availability of tracer items within each domain. Furthermore, the means of all domains’ scores are expressed as an overall readiness index. Higher scores indicate greater readiness of Puskesmas to deliver CVD-related health care.Results: Puskesmas delivers health promotion, disease prevention, and prompt diagnosis for cardiovascular-related diseases, including hypertension, diabetes, coronary heart disease (CHD), and stroke. Meanwhile, basic treatments were observed in the majority of the Puskesmas. Long-term care for hypertension and diabetes patients and rehabilitation for CHD and stroke were only observed in a few Puskesmas. The readiness score of Puskesmas to deliver CVD health care ranged from 60 to 86 for. Furthermore, there were 11 Puskesmas (23.4%) with a score below 75, indicating a sub-optimal readiness for delivering CVD health services. A shortage of essential medicines and a low capacity for diagnostic testing were the most noticeable shortcomings leading to suboptimal readiness for high-quality CVD health services.Conclusion: Close cooperation with the government and other related stakeholders is required to tackle the identified shortcomings, especially the continuous monitoring of adequate supplies of medicines and diagnostic tools to achieve better CVD care for patients in Indonesia.

Backgrounds: The majority of risk factors for cardiovascular diseases (CVDs) are modifiable. Continuous monitoring and control of these factors could significantly reduce the risk of CVDs-related morbidity and mortality. This study estimated the prevalence of modifiable risk factors in Indonesia and its co-occurence of multiple risk factors stratified by prior CVDs diagnosis status and sex. Methods: Adult participants (> 15 years, N = 36,329, 57% women) with median age of 40 years were selected from a nationwide Indonesian cross-sectional study called Basic Health Research or Riset Kesehatan Dasar (Riskesdas) conducted in 2018. Thirteen risk factors were identified from the study, including smoking, a high-risk diet, inadequate fruit and vegetable consumption, a low physical activity level, the presence of mental-emotional disorders, obesity, a high waist circumference (WC), a high waist-to-height ratio (WtHR), hypertension, diabetes, a high total cholesterol level, a high low-density lipoprotein (LDL) cholesterol level, and a low high-density lipoprotein (HDL) cholesterol level. Age-adjusted prevalence ratios stratified by CVDs status and sex were calculated using Poisson regression with the robust covariance estimator. Results: CVDs were found in 3% of the study population. Risk factor prevalence in the overall population ranged from 5.7 to 96.5% for diabetes and inadequate fruit and vegetable consumption respectively. Smoking, a high-risk food diet, and a low HDL cholesterol level were more prevalent in men, whereas a low physical activity level, the presence of mental-emotional disorders, obesity, a high WC, a high WtHR, hypertension, diabetes, a high total cholesterol level, and a high LDL cholesterol level were more prevalent in women. Approximately 22% of men and 18% of women had at least 4 risk factors, and these proportions were higher in participants with prior CVDs diagnosis. Conclusions: There is a high prevalence of modifiable risk factors in the Indonesian adult population. Sex, age, and the presence of CVD are major determinants of the variations in risk factors. The presence of multiple risk factors, which are often inter-related, requires a comprehensive approach through health promotion, lifestyle modification and patient education.

The decline in intensity of malaria transmission in many areas now emphasizes greater importance of understanding the epidemiology of low to moderate transmission settings. Marked heterogeneity in infection risk within these populations creates opportunities to understand transmission and guide resource allocation to greater impact. In this study, we examined spatial patterns of malaria transmission in a hypo- to meso-endemic area of eastern Indonesia using malaria prevalence data collected from a cross-sectional socio-demographic and parasitological survey conducted from August to November 2010. An entomological survey performed in parallel, identified, mapped, and monitored local anopheline larval habitats. A single spatial cluster of higher malaria prevalence was detected during the study period (relative risk=2.13; log likelihood ratio=20.7; P<0.001). In hierarchical multivariate regression models, risk of parasitemia was inversely correlated with distance to five Anopheles sundaicus known larval habitats [odds ratio (OR)=0.21; 95% confidence interval (CI)=0.14-0.32; P<0.001], which were located in a geographically restricted band adjacent to the coastline. Increasing distance from these sites predicted increased hemoglobin level across age strata after adjusting for confounders (OR=1.6; 95% CI=1.30-1.98; P<0.001). Significant clustering of malaria parasitemia in close proximity to very specific and relatively few An. sundaicus larval habitats has direct implications for local control strategy, policy, and practice. These findings suggest that larval source management could achieve profound if not complete impact in this region.

Indriasari R, et al. Nutr Res Pract. 2021 Oct;15(5):639-654. https://doi.org/10.4162/nrp.2021.15.5.639