Prevalence and predictors of undiagnosed diabetes mellitus in Indonesia (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
Interventional Medicine and Applied Science, 2017
Objective: To develop and validate a risk score model for recognizing prediabetes among Indonesian adults in primary care. Methods: This was a cross-sectional diagnostic study. After excluding subjects with diabetes from Indonesian National Basic Health Survey (INBHS) data set, 21,720 subjects who have completed fasting plasma glucose test and aged >18 years were selected for development stage. About 6,933 subjects were selected randomly from INBHS for validation stage in different diagnostic criteria of prediabetes-based random plasma glucose. Logistic regression was used to determine significant diagnostic variable and the receiver operating characteristic analysis was used to calculate area under the curve (AUC), cutoff point, sensitivity, specificity, and predictive values. Results: Age, sex, education level, family history of diabetes, smoking habit, physical activity, body mass index, and hypertension were significant variables for Indonesian Prediabetes Risk Score (INA-PRISC). The scoring range from 0 to 24, the AUC was 0.623 (95% CI 0.616-0.631) and cutoff point of 12 yielded sensitivity/specificity (50.03%/67.19%, respectively). The validation study showed the AUC was 0.646 (95% CI 0.623-0.669) and cutoff point of 12 yielded sensitivity/specificity (55.11%/65.81%, respectively). Conclusion: INA-PRISC, which consists of eight demographical and clinical variables, is a valid and a simple prediabetes risk score in primary care.
Journal of the ASEAN Federation of Endocrine Societies, 2022
Objective. To develop a simple, non-invasive tool for predicting the onset of type 2 diabetes mellitus (T2DM). Methodology. A total of 4418 nondiabetic respondents living in Bogor were included in this cohort study. Their ages ranged from 25 to 60 years old and were followed for 6 years with interviews, physical examinations and laboratory tests. The investigators used logistic regression to create a tool for diabetes risk determination. Results. The cumulative incidence of T2DM was 17.9%. Risk factors significantly associated with T2DM included age, obesity, central obesity, hypertension and lack of physical activity. The Bogor Diabetes Risk Prediction (BDRP) chart had a cutoff of 0.128, with sensitivity of 76.6% and specificity of 50.3%. The Positive Predictive Value (PPV) was 21.6% and Negative Predictive Value (NPV) was 92.3%. The Area under the Curve (AUC) was 0.70 with a 95% confidence interval ranging from 0.675-0.721. Conclusion. The BDRP chart is a simple and non-invasive tool to predict T2DM. In addition, the BDRP chart is reliable and can be easily used in primary health care.
Prevalence and clinical profile of diabetes mellitus in productive aged urban Indonesians
Journal of Diabetes Investigation, 2013
Aims/Introduction: To estimate the prevalence and clinical profile of diabetes mellitus in productive aged urban Indonesians based on the National Basic Health Research 2007. Materials and Methods: The statistical analyses of a cross-sectional survey included the data of 15,332 adults, aged 18-55 years, living in an urban area. Blood glucose was measured by an automatic clinical chemistry analyzer by 2-h, 75-g post glucose load after an overnight fast. Weight, height, waist circumference and blood pressure data were measured and recorded, whereas the sociodemographic and prior illness data were collected by interviewing the participants. Results: The prevalence of diabetes mellitus in productive age urban Indonesians was 4.6%, consisting of 1.1% previously diagnosed diabetes mellitus and 3.5% undiagnosed diabetes mellitus. Diabetes mellitus affected more women than men, which increased with age, was higher among the high socioeconomic group and increased with increasing body mass index. The prevalence of diabetes mellitus was higher in centrally obese people. Hypertension was highly related with diabetes mellitus occurrence. The prevalence of previously diagnosed diabetes mellitus with overweight or obese was 68.4%, with central obesity 41.7%, with hypertension 41.4% and with dyslipidemia more than 50%. The prevalence of undiagnosed diabetes respondents with overweight or obese was 68,7%, with central obesity 43.8%, with hypertension 49.4% and with dyslipidemia more than 50%. Conclusions: These results show that comprehensive strategies for the prevention and control of the problem of diabetes are urgently required. (J Diabetes Invest,
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...
Characteristics of Patients with Type 2 Diabetes Mellitus in Al-Ihsan Regional General Hospital
Global Medical and Health Communication (GMHC), 2021
The prevalence of type 2 diabetes mellitus (T2DM) in Indonesia is high, contributing to the fourth mortality rate for non-communicable diseases in Indonesia. The population of T2DM patients spread across all provinces, including West Java, which is the most populous province in Indonesia. One of the referral hospitals in West Java is Al-Ihsan Regional General Hospital in Bandung regency. The purpose of this study was to describe the characteristics of T2DM patients who came to Al-Ihsan Regional General Hospital according to age, gender, and comorbidities parameters. It was a descriptive cross-sectional study using secondary data from medical records of T2DM patients between January 2017 and November 2020. The results were the highest prevalence and incidence of T2DM were in 2017 with as many as 5,051 and 653 respectively; the highest gender each year was female, range between 584–3,333, with the highest male: female ratio of 1:2 in 2017; the age group with the highest prevalence was...
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...