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Prevalence diabetes is increasing rapidly especially in low and middle- income countries, one of which is Indonesia. Based on Riskesdas in 2018, the prevalence of diabetes mellitus based on the diagnosis of doctors in the population of all ages by province reaches 1,5%. The incidence rate of kidney disease in the diabetic population does not decrease. Some large cross-sectional studies in the world reveal that the prevalence of chronic kidney disease in people with type 2 diabetes even reaches 50%. Duration suffering diabetes is a risk factor for chronic kidney disease that needs to be considered. This study aims to determine the relationship duration suffering from diabetes with chronic kidney disease in patients with type 2 diabetes mellitus in Indonesia. This type of research is quantitative, with cross-sectional study design. This study uses secondary data from the 2018 Riskesdas survey. The number of samples was 639 people, who met the inclusion and exclusion criteria in this study. The analysis used cox regression The prevalence of chronic kidney disease in patients with type 2 diabetes mellitus in Indonesia is 17.68%. There was a relationship duration suffering diabetes with chronic kidney disease in patient type 2 diabetes mellitus in Indonesia which is statistically significant with p = 0.0000. So, important to screening mass type 2 diabetes mellitus as early as possible and routine screening kidney function since type 2 diabetes mellitus diagnose by a doctor.
Previous studies have reported that there are long-term effects of quitting smoking on weight gain which also exacerbate prehypertension, but the effects are unclear. This study aims to determine the association of smoking and obesity on the incidence of prehypertension in young adults in Indonesia. This quantitative research is a cross-sectional analytic study using secondary data from Riskesdas 2018 with samples of 17,698 and Cox regression analysis. The results of this study explain that the prevalence of prehypertension in young adults in Indonesia is 52.61%. Multivariate analysis showed non-smokers and obese young adults had the greatest risk of developing prehypertension, which was 1.33 times. However, a decreasing effect was found in young adults who smoked and were obese on the incidence of prehypertension, which was 1.17 times, and a protective effect was found in those who smoked and were not obese (PR=0.88) due to the antagonistic interaction of smoking and obesity on prehypertension by 3.42%. Check blood pressure using applications on smartwatches and smartphones in young adults who smoke and focus on those who are obese plus increasing the implementation of Posbindu PTM in public places and promoting health through social media.
Prediabetes is a global public health issue. Prevalence of prediabetes isincreasing worldwide. Generally, it is high among adults and as a high risk statefor DM. Obesity has essential role in pathophysiology of prediabetes. This studyaimed to explore whether both of general obesity and abdominal obesity related toprediabetes on age group 20-65 years in Bogor tengah sub-district by familyhistory of DM, sex, age, smoking, hypertension, physical activity and stress. Thisstudy used the cross sectional design study with Cox Regression to multivariableanalysis. Data for this analysis were collected during the baseline stage of cohortstudy of risk factors of non-communicable disease in 2011-2012. There were3244 respondents from Bogor tengah were taken by random sample technique..The result indicated that obesity to prediabetes adjusted by age; general obesityalone PR 1,58 (95% CI: 1,17-2,15), abdominal obesity alone PR 1,45 (95% CI;1,19-1,87), general obesity and abdominal obesity jointly PR 1,92 (95% CI;1,62-2,28). Therefore, general obesity and abdominal obesity jointly contributedmost to the increase prevalence of prediabetes. Awareness raising and screeningof prediabetes of those at high risk group by assessing obesity by BMI and waistcircumference joinlty are essential to be considered as part of efforts for haltingthe epidemic of prediabetes in community.Keyword : general obesity, abdominal obesity, prediabetes.
Diabetes melitus is a disease with high complication rates, thus requirestreatment, which is known as the four pillars of DM management. Prolanisparticipant data at Puskesmas Pulo Gadung in November 2015-January 2016,respectively by 87%, 84%, and 88% of diabetic have uncontrolled PostprandialGlucose (PPG) without a process of evaluation. This study aims to determine theinhibiting factors in controlling the PPG. This is a cross sectional study withquantitative and qualitative approaches. The place and time of the study isconducted at Puskesmas Pulo Gadung, in April 2016. The quantitative data wereobtained from the questionnaires, assessment of body mass index, and the resultsof the examination PPG 84 of selected diabetic. The samples are diabetic in ninePuskesmas that fulfill the inclusion and exclusion criterias. Sampling was done bynon-probability sampling. While the qualitative data is intended to get moreinformation about the four pillars of diabetes management. Quantitative data wereanalyzed by descriptive and qualitative data were analyzed by thematic analysis.Research shows that only 4.8% diabetic who have controlled PPG. Factorscausing uncontrolled PPG are non-compliance of diabetic in implementing mealplanning and physical exercise, lack of family and management support. Requiredincrease in educational activities, monitoring and evaluation, and build crosssector cooperation between Puskesmas, Sudin Kesehatan, and BPJS.Keywords: Diabetic, DM management, postprandial glucose
The prevalence of type 2 diabetes mellitus tends to increase and will increase in several years in Indonesia. Meanwhile, the prevalence of obesity closely related to the incidence of diabetes mellitus type 2 has also increased and is expected to increase in few years later. The study as a retrospective cohort aims to find out the relationship between the combination of general obesity (body mass index/BMI) and central obesity (waist-toheight ratio/WtHR) with the incidence of type 2 diabetes mellitus in the adult population of Central Bogor Subdistrict, Bogor City year 2011-2018, using secondary data of Studi Kohor Faktor Risiko PTM. The results showed the cumulative incidence of type 2 diabetes mellitus was 18.3% and more than half (51.2%) of respondents were obese. The proportion of incidence of type 2 diabetes mellitus in each category was 24.7% for the combination of general obesity and central obesity; 12.5% for central obesity only; and 50.0% for general obesity only. The results of multivariate analysis showed that the combination of general obesity and central obesity (RR = 1.914; 95% CI 1.514-2.418; p = 0.000) and general obesity only (RR = 5.013; 95% CI 1.58215.889; p = 0.006) were significantly associated with type 2 diabetes mellitus after controlled by age and triglyceride levels. Meanwhile, the central obesity only was not significantly associated with type 2 diabetes mellitus (RR = 1.024; 95% CI: 0.7611.377). The results of this study are still reliable and influenced by several things, including the AUC value for the cut-off point of LP-TB ratio is not ideal; the minimum sample size for each category (both exposed and unexposed); lower power of study in certain categories; remaining chance effect; the possibility of misclassification; and selection bias because of loss to follow up
Objective: This studi aims to find the risk factors and develop risk scorefor undiagnosed diabetes and prediabetes. Method: Risk score madebased on available data from Basic Health Research 2013 in Indonesia,with criteria 18-55 years old, newly diagnosed diabetes, and not affectedby chronic /infectious diseases before.β coeff value from multinomiallogistic regression analysis results of predictive models are used todevelop risk score. The accuracy of risk score assessed with ROC(Receiver Operating Characteristic). Result: 2 prediction models are useto develop risk score. The accuracy form 7 predictors for undiagnoseddiabetes in model 1 are AUC 73.5%, sen 62.2%, spes 70.8%, PPV 12.8%,NPV 96.5%, cut off ≥22. The accuracy form 5 predictors for undiagnoseddiabetes in model 2 are AUC 72.4%, sen 68.3%, spes 64.7%, PPV 11.8%,NPV 96.7%, cut off ≥20 . Score predikction for diabetes not developed,because of poor accuray, but the result of analysis can showed prediabetesdominant risk factors. Conclusion: Indonesia may have a risk scorecalculation for predicting undiagnosed diabetes based on data from HealthResearch provided. The risk score can be used by health workers toindentified individuals with high-risk and the general public are able touse these scores.Keyword : prediabetes, undiagnosed diabetes, risk factor, score
