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.

We conducted an interdisciplinary One Health study of potential links between agricultural, health and associated livelihood factors on the livelihoods of smallholder cocoa-growing families in West Sulawesi. Our 2017 survey of 509 cocoa smallholder family members in 120 households in Polewali-Mandar District, West Sulawesi, Indonesia showed that farmers face many challenges to improving their livelihoods, including land management, agricultural practices, nutrition and human health, animal health, aging and demographic changes. Price fluctuations, limited access to capital and poor health deterred farmers from applying agricultural inputs and resulted in levels of low cocoa production (275 kg/annum per household). While market demand for live goats in the region is substantial and expected to increase, uptake of mixed farming with goats by smallholders was low. However, most households kept chickens. Bank accounts were held by 31% of households. Inadequate sanitation and unsafe water were reported in >50% households. Anthropometric measures showed that 42% of children under five years were significantly stunted and 32% of women were overweight. Joint, back pain and blurry vision were reported by 30% of adult respondents. High blood pressure contributed to complications in 20% of pregnancies. Primary health care provided by district health services mainly focuses on maternal and child health, leaving chronic health problems such as Type 2 diabetes, cataracts, arthritis and mental illness under-diagnosed, and if diagnosed, with inadequate treatment. Availability of food was a source of worry for 58% of households with 63% reporting limited food variety. Dietary diversity was low with an average of four out of ten food categories consumed in each household. Positive correlations were recorded for household cocoa productivity, land size, dietary diversity and perceptions that food availability and variety was sufficient. The results showed that an integrated One Health approach provides deep understanding of priority areas for improving livelihoods.

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.

BackgroundThe gamma-aminobutyric acid (GABA) receptor-chloride channel complex is known to be the target site of dieldrin, a cyclodiene insecticide. GABA-receptors, with a naturally occurring amino acid substitution, A302S/G in the putative ion-channel lining region, confer resistance to cyclodiene insecticides that includes aldrin, chlordane, dieldrin, heptachlor, endrin and endosulphan.MethodsA total of 154 mosquito samples from 10 provinces of malaria-endemic areas across Indonesia (Aceh, North Sumatra, Bangka Belitung, Lampung, Central Java, East Nusa Tenggara, West Nusa Tenggara, West Sulawesi, Molucca and North Molucca) were obtained and identified by species, using morphological characteristic. The DNA was individually extracted using chelex-ion exchanger and the DNA obtained was used for analyses using sequencing method.ResultsMolecular analysis indicated 11% of the total 154 Anopheles samples examined, carried Rdl mutant alleles. All of the alleles were found in homozygous form. Rdl 302S allele was observed in Anopheles vagus (from Central Java, Lampung, and West Nusa Tenggara), Anopheles aconitus (from Central Java), Anopheles barbirostris (from Central Java and Lampung), Anopheles sundaicus (from North Sumatra and Lampung), Anopheles nigerrimus (from North Sumatra), whereas the 302 G allele was only found in Anopheles farauti from Molucca.ConclusionThe existence of the Rdl mutant allele indicates that, either insecticide pressure on the Anopheles population in these areas might still be ongoing (though not directly associated with the malaria control programme) or that the mutant form of the Rdl allele is relatively stable in the absence of insecticide. Nonetheless, the finding suggests that integrated pest management is warranted in malaria-endemic areas where insecticides are widely used for other purposes.

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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 existence of the Rdl mutant allele indicates that, either insecticide pressure on the Anopheles population in these areas might still be ongoing (though not directly associated with the malaria control programme) or that the mutant form of the Rdl allele is relatively stable in the absence of insecticide. Nonetheless, the finding suggests that integrated pest management is warranted in malaria-endemic areas where insecticides are widely used for other purposes.

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