Analysis of the Determinants of Diabetes Mellitus in Indonesia: A Case Study of the 2014 Indonesian Family Life Survey (original) (raw)
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Acta medica Indonesiana, 2017
BACKGROUND diabetes mellitus is a silent-killer. Its prevalence and impact on health expenses increase from year to year. This study aims to investigate the characteristics and the risk factors that affect diabetes mellitus in Indonesia. METHODS this is a cross sectional study. Data were obtained from the Basic Health Research (RISKESDAS) in 2013. The samples were individuals aged ≥15 years, whose fasting blood glucose and 2 hours blood glucose after the imposition have been measured. 38.052 individuals were selected for this study. The variables of age, sex, marital status, level of education, employment status, living area, regional status, hypertension, obesity, smoking habit, and dyslipidemia are analyzed as risk factors for diabetes mellitus. Bivariate analysis was using chi-square test with significance level of p<0.05 and confidence interval (CI) of 95%, and multivariate analysis using multiple logistic regression test. RESULTS our study showed that 13% have diabetes mell...
Determinants of Diabetes Mellitus Prevalence in Indonesia
KEMAS: Jurnal Kesehatan Masyarakat, 2022
The number of people with diabetes mellitus (DM) worldwide continues to increase. In 2019, Indonesia was the seventh country with the largest number of people with DM worldwide. The people with DM in Indonesia were dominated by the productive age population. This study aims to determine the variables affecting the prevalence of DM in Indonesia in 2018. The analysis unit used is 34 provinces in Indonesia, where the data comes from the Health Ministry of the Republic of Indonesia and Statistics Indonesia. Graph analysis and multiple linear regression are the methods used in this study. DKI Jakarta has the highest DM prevalence in Indonesia, reaching 3.4 percent. The prevalences of obesity and hypertension have a positive effect on the prevalence of DM. The result shows that every one percent increase in the prevalence of obesity will increase the prevalence of DM by 0.049 percent. While, every one percent increase will increase the prevalence of DM by 0.168 percent. The percentage of the population smoking, not exercising, the unemployment rate, and the average length of schooling does not affect the prevalence of DM in Indonesia.
Analysis of Diabetes Mellitus Determinants in Indonesia
Background: diabetes mellitus is a silent-killer. Its prevalence and impact on health expenses increase from year to year. This study aims to investigate the characteristics and the risk factors that affect diabetes mellitus in Indonesia. Methods: this is a cross sectional study. Data were obtained from the Basic Health Research (RISKESDAS) in 2013. The samples were individuals aged ≥15 years, whose fasting blood glucose and 2 hours blood glucose after the imposition have been measured. 38.052 individuals were selected for this study. The variables of age, sex, marital status, level of education, employment status, living area, regional status, hypertension, obesity, smoking habit, and dyslipidemia are analyzed as risk factors for diabetes mellitus. Bivariate analysis was using chi-square test with significance level of p<0.05 and confidence interval (CI) of 95%, and multivariate analysis using multiple logistic regression test. Results: our study showed that 13% have diabetes mellitus in 2013. Factors affecting diabetes mellitus were age>55 years (OR=5.10; 95%CI 4.42 to 5.89; p<0.001), female (OR=1.37; 95%CI 1.26 to 1.49; p<0.001), rural (OR=1.16; 95%CI 1.08 to 1.26; p<0.001), married (OR=1.31; 95%CI 1.07 to 1.58; p<0.05), unemployed (OR=1.14; 96%CI 1.05 to 1.23; p<0.05), obesity (OR=1.46; 95%CI 1.35 to 1.58; p<0.001), hypertension (OR=1.68; 95%CI 1.55 to 1.81; p<0.001) and dyslipidemia (OR=1.53; 95%CI 1.39- 1.68; P<0.001). Conclusion: as many as 13% of individuals have diabetes mellitus in 2013. Age, gender, living area, employment status, obesity, hypertension, and dyslipidemia are the contributing factors to diabetes mellitus. Keywords: diabetes mellitus, determinant factors, blood glucose
Jurnal Gizi Klinik Indonesia, 2021
Background: The prevalence of diabetes mellitus is increasing globally and remains debated. Objective: This study examines the association of socioeconomic status with the prevalence of diabetes mellitus in Indonesia. Methods: This study used a cross-sectional design. Data obtained from the 2014 Indonesia Family Life Survey (IFLS), a nationally representative population survey data, which polled 30,497 individuals age 16 years and over in 13 provinces in Indonesia. Logistic regression models were used to estimate odds ratios (OR) and 95% confidence intervals (CI) for the prevalence of diabetes mellitus with socioeconomic status. Results: Education level, employment status, age, and hypertension are related to the prevalence of diabetes mellitus. According to educational level, individuals with lower education level were more likely to have diabetes mellitus than those who had a higher level of education (OR=1.42; 95% CI: 1.21-1.67), higher risk was also found in those who were unemp...
Effect of socio-economic status on the prevalence of diabetes mellitus in Indonesia
International Journal of Public Health Science (IJPHS), 2022
The case of diabetes mellitus in Indonesia shows an increasing trend, especially in the upper socioeconomic groups. This study aimed to determine the effect of socioeconomic status on the prevalence of diabetes mellitus in Indonesia using data from the Indonesia family life survey (IFLS). This study was intentionally targeted at individuals aged 15 years and over who are respondents to IFLS-5. Diabetes mellitus becomes the dependent variable, while the independent variables comprise age, gender, education, income, body mass index, and smoking behavior. Logistic regression was performed to determine the potential socioeconomic factors associated with diabetes mellitus. The results reveal that socioeconomic status influences the prevalence of diabetes mellitus in Indonesia. The higher the income level, the probability of diabetes mellitus increases by 0.466 percent. Meanwhile, individuals who attended university had a 2.86 percent higher probability than individuals with a primary level of education.
2021
Background Type 2 diabetes mellitus (DM) is a non-communicable disease that constitutes a huge health burden, with the presence of comorbidities of DM adding to it. This study aimed to obtain the main determinants of the combined incidence of DM and its main comorbidities in adults. Methods This was a further analysis of the Non-Communicable Disease Risk Factor Cohort Study 2011 – 2018 involving 3730 subjects. Data of diabetes-free respondents at baseline were followed up every 2 years for 6 years. Data collection was carried out through interviews and health examinations. All subjects were assayed for blood glucose and lipid parameters. Chi-square test and Cox regression were implemented for data analysis. Results During 6 years of follow-up, DM incidence occurred in 567 (15.2%) subjects. The most common comorbidities were increased low density lipoprotein (LDL), central obesity, increased total cholesterol, obesity and hypertension. Most of the comorbidities occurred before the di...
BMC Public Health
Background Based on Basic Health Research (RISKESDAS) conducted by Ministry of Health, Indonesia, prediabetes prevalence tends to increase from 2007 until 2018. The numbers are relatively higher in rural than those in urban area despite of small discrepancies between the two (~ 2–4%). The purpose of this study was to identify urban-rural differences in potential determinants for prediabetes in Indonesia. Methods This analysis used secondary data collected from nationwide Health Survey in 2018. Respondents were aged ≥15 years who met inclusion criteria of analysis with no history of diabetes mellitus. Prediabetes criteria followed American Diabetes Association 2019. Multiple logistic regression was also employed to assess the transition probability of potential determinants for prediabetes in urban and rural Indonesia. Results Up to 44.8% of rural respondents were prediabetics versus their urban counterparts at 34.9%, yet non-response bias was observed in the two. Young adults aged 3...
2020
Type 2 diabetes mellitus (T2DM) is the sixth highest cause of death in Indonesia. Thus, it must be prevented and overcome with appropriate management. This study aimed to determine the contribution of physiological factors and physical activities to the incidence of T2DM. The study used a cohort retrospective design using secondary data from the Indonesian Family Life Survey (IFLS) from 2007 to 2014, which included a sample of 14,517 people involved for more than 20 years. Data analysis was performed using multiple logistic regressions. The results revealed a 3.8% incidence of T2DM in Indonesia. Record of hypertension risk increased the likelihood of T2DM by 1.7 times compared to without hypertension. A normal body mass index (BMI) increased the risk by 2.2 times, a higher BMI at 5.5 times, and BMI whose risk obesity was 7 times had greater likelihood of having T2DM compared with respondents with a thin BMI. After controlling for sex, age, marital status, record of parents’ DM, resi...
Prevalence and determinants of obesity among individuals with diabetes in Indonesia
F1000Research
Background: Obesity and diabetes mellitus (DM), both individually or simultaneously, increase the risk of morbidity and mortality. The present study aimed to determine the prevalence and determinants of obesity among diabetic individuals in Indonesia. Methods: Data were extracted based on 2018 Indonesian Basic Health Survey (Riset Kesehatan Dasar=RISKESDAS). This study involved all individuals with DM and categorized obesity based on body mass index. After data clearing, this study analyzed 3911 DM subjects of the 33.905 subjects acquired from the 2018 RISKESDAS. The study also observed demographic data, diabetes control parameters, history of hypertension, lipid profiles, and food consumption patterns. These variables were involved in a Chi-square test, and related variables were then involved in the Binary logistic regression to define the independent determinants of obesity among DM subjects. Results: Of the 3911DM subjects included, the study found an obesity prevalence of 32.9%...