Obesity Risk-Factor Variation Based on Island Clusters: A Secondary Analysis of Indonesian Basic Health Research 2018.

Obesity has become a rising global health problem affecting quality of life for adults. The objective of this study is to describe the prevalence of obesity in Indonesian adults based on the cluster of islands. The study also aims to identify the risk factors of obesity in each island cluster. This study analyzes the secondary data of Indonesian Basic Health Research 2018. Data for this analysis comprised 618,910 adults (≥18 years) randomly selected, proportionate to the population size throughout Indonesia. We included 20 variables for the socio-demographic and obesity-related risk factors for analysis. The obesity status was defined using Body Mass Index (BMI) ≥ 25 kg/m2. Our current study defines 7 major island clusters as the unit analysis consisting of 34 provinces in Indonesia. Descriptive analysis was conducted to determine the characteristics of the population and to calculate the prevalence of obesity within the provinces in each of the island clusters. Multivariate logistic regression analyses to calculate the odds ratios (ORs) was performed using SPSS version 27. The study results show that all the island clusters have at least one province with an obesity prevalence above the national prevalence (35.4%). Six out of twenty variables, comprising four dietary factors (the consumption of sweet food, high-salt food, meat, and carbonated drinks) and one psychological factor (mental health disorders), varied across the island clusters. In conclusion, there was a variation of obesity prevalence of the provinces within and between island clusters. The variation of risk factors found in each island cluster suggests that a government rethink of the current intervention strategies to address obesity is recommended.

One-year stability of the mandibular advancement and counterclockwise rotation for correction of the skeletal class II malocclusion and high mandibular plane angle: Dental and skeletal aspect.

BACKGROUND: The purpose of this study was to evaluate the stability on dental and skeletal aspect after surgical advancement and counterclockwise rotation for the correction of the mandibular deficiency in patients with high mandibular plane angle (MPA). METHODS: We analyzed the records of patients who had undergone surgical treatment for dentofacial deformities with mandibular deficiency and high MPA. Clinical and radiological data were taken 1 month before surgery (T0), 6 weeks after surgery (T1) and 1 year after surgery (T2). Cephalometric values of the MPA were recorded and compared. The cephalometric changes in the different time periods were defined as follows: A: postsurgical changes (T0-T1), B: one-year changes (T1-T2), and C: short term changes (T0-T2). RESULTS: Twenty-seven patients had prominent mandibular deficiency with an MPA of over 35° (high angle). The mean age of patients at surgery was 29.7 years. Seven patients had a single jaw procedure, 20 patients had bilateral sagittal split osteotomy (BSSO) combined with a Le Fort I osteotomy, and 14 patients had additional genioplasty. MPA values differed significantly between the time periods (p < 0.05) with an observed relapse of the angle. However, satisfactory clinical improvement was achieved in the dental and skeletal presentation. The overjet improvement was evident from 8.815 ± 2.085 mm (T0) to 3.426 ± 1.253 mm (T2). CONCLUSION: Counterclockwise surgical advancement of the mandible to correct mandibular deficiency in patients with a high mandibular plane angle showed an overall acceptable stability during one-year follow-up.

Predicting Obesity in Adults Using Machine Learning Techniques: An Analysis of Indonesian Basic Health Research 2018.

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.

Bayesian spatial survival modelling for dengue fever in Makassar, Indonesia.

OBJECTIVE: To understand the spatial pattern of dengue fever (DF) patients' survival and investigated factors influencing DF patients' survival. METHOD: A Bayesian spatial survival method via a conditional autoregressive approach was used to analyze the factors that influence DF patients' survival in 14 sub-districts from January 2015 to May 2017 in Makassar city, Indonesia. Bayesian spatial and a non-spatial model were compared by using deviance information criterion. RESULTS: The spatial model was more suitable than a non-spatial model. Under the Bayesian spatial model, there was a substantive relationship between age, grade and DF patients' survival time. CONCLUSIONS: The relative risk map and related factors of DF patients' survival can indicate the health policy makers to give special attention to the high risk areas in order to faster and more targeted treatment.

Bayesian spatial survival modelling for dengue fever in Makassar, Indonesia

Objective: To understand the spatial pattern of dengue fever (DF) patients’ survival and investigated factors influencing DF patients’ survival. Method: A Bayesian spatial survival method via a conditional autoregressive approach was used to analyze the factors that influence DF patients’ survival in 14 sub-districts from January 2015 to May 2017 in Makassar city, Indonesia. Bayesian spatial and a non-spatial model were compared by using deviance information criterion. Results: The spatial model was more suitable than a non-spatial model. Under the Bayesian spatial model, there was a substantive relationship between age, grade and DF patients’ survival time. Conclusions: The relative risk map and related factors of DF patients’ survival can indicate the health policy makers to give special attention to the high risk areas in order to faster and more targeted treatment. (AU)

Establishing cleft services in developing countries: Complications of cleft lip and palate surgery in rural areas of Indonesia.

BACKGROUND: Cleft treatment is frequently performed in Indonesia, mostly in charity missions, but without a postoperative protocol it is difficult to establish the risks and complications of cleft treatment. The present study was designed to give an overview of current cleft lip and palate treatment strategies in Indonesia and to assess the complication rates during and after surgery. METHODS: This prospective study evaluated anesthetic, intraoperative surgical, and short-term postoperative complications in patients undergoing primary, secondary, or corrective surgery for cleft lip and palate deformities. The population consisted of 98 non-syndromic cleft patients. The main anesthetic complication that occurred during general anesthesia was high blood pressure, whereas the main intraoperative surgical complication was excessive bleeding and the main early postoperative complication was extremely poor wound hygiene. RESULTS: In this study, there were no cases of perioperative or postoperative mortality. However, in 23 (23.4%) of the 98 operations performed, at least one perioperative complication related to anesthesia occurred. The intraoperative and early postoperative complications following cleft lip and/or palate were assessed. There was a significant difference in the complication rate between procedure types (χ2=0.02; P<0.05). However, no relationship was found between perioperative complications related to anesthesia and the occurrence of postoperative complications (χ2=1.00; P>0.05). Nonetheless, a significant difference was found between procedure types regarding perioperative complications and the occurrence of postoperative complications (χ2=0.031; P<0.05). CONCLUSIONS: Further evaluation of these outcomes would help direct patient management toward decreasing the complication rate.

Modelling survival data to account for model uncertainty: a single model or model averaging?

ABSTRACT: This study considered the problem of predicting survival, based on three alternative models: a single Weibull, a mixture of Weibulls and a cure model. Instead of the common procedure of choosing a single "best" model, where "best" is defined in terms of goodness of fit to the data, a Bayesian model averaging (BMA) approach was adopted to account for model uncertainty. This was illustrated using a case study in which the aim was the description of lymphoma cancer survival with covariates given by phenotypes and gene expression. The results of this study indicate that if the sample size is sufficiently large, one of the three models emerge as having highest probability given the data, as indicated by the goodness of fit measure; the Bayesian information criterion (BIC). However, when the sample size was reduced, no single model was revealed as "best", suggesting that a BMA approach would be appropriate. Although a BMA approach can compromise on goodness of fit to the data (when compared to the true model), it can provide robust predictions and facilitate more detailed investigation of the relationships between gene expression and patient survival.