Continuity of Care in Elderly Diabetics with Universally-Insured Health Care (original) (raw)
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Risk Prediction for Diabetes Mellitus - A Population Based Approach
International Journal of Current Research and Review, 2020
Introduction: Etiological models use identical estimation procedures as most predictive modelling (i.e., regression) to quantify the relative risk related to a selected exposure on an outcome. Though regression is usually used for both purposes, the way within which the model is built will differ thanks to the goals of the model. The goal of a prediction model differs in several important ways. Mathods: Using a cohort design that links baseline risk factors to a validated population-based diabetes registry, a model (Diabetes Population Risk Tool or DPoRT) to predict risk factors for diabetes using commonly-collected national survey data was developed and validated. The event cohort was the National Population Health Survey (NPHS) linked to the validated Diabetes Database, a provincial component of the National Diabetes closed-circuit television (NDSS). Variables were restricted to factors routinely measured within the population. The probability of developing diabetes was modelled using sex-specific survival functions for those > 20 years, without diabetes and not pregnant at baseline (N = 19,000). Results: The age-standardized 5-year incidence rates in the development cohorts were 6.52 % for males and 5.42 % for females. The 3-year age-standardized incidence rates in the development cohort were 3.42 % for males and 2.41% for females. The age-standardized 5-year incidence rates in the development cohorts were 6.42 % for males and 4.20 % for females. The age-standardized 3-year incidence rates for validation cohort was 3.45 % for males and 3.22 % for females. Conclusion: Determinants of weight and weight change are essential when developing strategies to prevent or reduce the future diabetes burden. In monitoring trends over time researchers are often faced with the dilemma of separating trends between individuals and trends within individuals. Multilevel growth models allow us to model both these aspects which strengthen the ability to model trends that vary between and within individuals.
Predicting the 20-year diabetes incidence rate
Diabetes/Metabolism Research and Reviews, 2007
Background The long-range prediction from clinical variables of the onset of diabetes is important to patients and clinicians. Our objective was to evaluate the efficacy of various glucose-related clinical measurements in predicting the 20-year risk of developing type 2 diabetes (T2DM) in an elderly population.
The Journal of Clinical Endocrinology & Metabolism, 2019
ContextWe previously developed and validated an inexpensive and parsimonious prediction model of 2-year all-cause mortality in real-life patients with type 2 diabetes.ObjectiveThis model, now named ENFORCE (EstimatioN oF mORtality risk in type 2 diabetiC patiEnts), was investigated in terms of (i) prediction performance at 6 years, a more clinically useful time-horizon; (ii) further validation in an independent sample; and (iii) performance comparison in a real-life vs a clinical trial setting.DesignObservational prospective randomized clinical trial.SettingWhite patients with type 2 diabetes.PatientsGargano Mortality Study (GMS; n = 1019), Foggia Mortality Study (FMS; n = 1045), and Pisa Mortality Study (PMS; n = 972) as real-life samples and the standard glycemic arm of the ACCORD (Action to Control Cardiovascular Risk in Diabetes) clinical trial (n = 3150).Main Outcome MeasureThe endpoint was all-cause mortality. Prediction accuracy and calibration were estimated to assess the mo...
Diabetes Care, 2009
OBJECTIVE -To test the validity of the Framingham, Systematic Coronary Risk Evaluation (SCORE), and UK Prospective Diabetes Study (UKPDS) risk function in the prediction of risk of coronary heart disease (CHD) in populations with normal glucose tolerance (NGT), intermediate hyperglycemia, and type 2 diabetes. RESEARCH DESIGN AND METHODS -Calibration and discrimination of the three prediction models were tested using prospective data for 1,482 Caucasian men and women, 50 -75 years of age, who participated in the Hoorn Study. All analyses were stratified by glucose status. RESULTS -During 10 years of follow-up, a total of 197 CHD events, of which 43 were fatal, were observed in this population, with the highest percentage of first CHD events in the diabetic group. The Framingham and UKPDS prediction models overestimated the risk of first CHD event in all glucose tolerance groups. Overall, the prediction models had a low to moderate discriminatory capacity. The SCORE risk function was the best predictor of fatal CHD events in the group with NGT (area under the receiver operating characteristic curve 0.79 [95% CI 0.70 -0.87]), whereas the UKPDS performed better in the intermediate hyperglycemia group (0.84 [0.74 -0.94]) in the estimation of fatal CHD risk. After exclusion of known diabetic patients, all prediction models had a higher discriminatory ability in the group with diabetes. CONCLUSIONS -The use of the Framingham function for prediction of the first CHD event is likely to overestimate an individual's absolute CHD risk. In CHD prevention, application of the SCORE and UKPDS functions might be useful in the absence of a more valid tool.
2021
Background We aimed to develop a risk model, monitoring the FDRs of patients with type 2 diabetes, who have normal glucose tolerance, to predict the onset of developing diabetes and prediabetes. In this study, 1765 FDRs of patients with type 2 diabetes mellitus, who had normal glucose tolerance, were subjected to statistical analysis. Diabetes risk factors including anthropometric indices, physical activity, fast plasma glucose, plasma glucose concentrations two-hour after oral glucose administration, glycosylated hemoglobin, blood pressure, and lipid profile at the baseline were considered as independent variables. Kaplan-Meier, log Rank test, univariate, and multivariable proportional hazard Cox regression were conducted. The optimal cut point for risk score was created according to receiver operating characteristic curve (ROC) analysis. Results The best diabetes predictability was achieved by a model in which waist to hip ratio (WHR), HbA1c, OGTT and the lipid profile were includ...
Are Population-Based Diabetes Models Useful for Individual Risk Estimation?
The Journal of the American Board of Family Medicine, 2011
Background: Predictive models are increasingly used in guidelines and informed decision-making interventions. We compared predictions from 2 prominent models for diabetes: the United Kingdom Prospective Diabetes Study (UKPDS) outcomes model and the Archimedes-based Diabetes Personal Health Decisions (PHD) model. Methods: Ours was a simulation study comparing 10-year and 20-year model predictions for risks of myocardial infarction (MI), stroke, amputation, blindness, and renal failure for representative test cases. Results: The Diabetes PHD model predicted substantially higher risks of MI and stroke in most cases, particularly for stroke and for 20-year outcomes. In contrast, the UKPDS model predicted risks of amputation and blindness ranging from 2-fold to infinitely higher than the Diabetes PHD model. Predictions for renal failure all differed by more than 2-fold but in a complicated pattern varying by time frame and specific risk factors. Relative to their predictions for white men, the UKPDS model predicted much lower MI and stroke risks for women and Afro-Caribbean men than the Diabetes PHD model did for women and black men. A substantial majority of the Diabetes PHD point estimates fell outside of the UKPDS outcomes model's 95% CIs. Conclusions: These models produced markedly different predictions. Patients and providers considering risk estimates from such models need to understand their substantial uncertainty and risk of misclassification.(J Am Board Fam Med 2011;24:399-406.
Population-Level Prediction of Type 2 Diabetes From Claims Data and Analysis of Risk Factors
We present a new approach to population health, in which data-driven predictive models are learned for outcomes such as type 2 diabetes. Our approach enables risk assessment from readily available electronic claims data on large populations, without additional screening cost. Proposed model uncovers early and late-stage risk factors. Using administrative claims, pharmacy records, healthcare utilization, and laboratory results of 4.1 million individuals between 2005 and 2009, an initial set of 42,000 variables were derived that together describe the full health status and history of every individual. Machine learning was then used to methodically enhance predictive variable set and fit models predicting onset of type 2 diabetes in. We compared the enhanced model with a parsimonious model consisting of known diabetes risk factors in a real-world environment, where missing values are common and prevalent. Furthermore, we analyzed novel and known risk factors emerging from the model at different age groups at different stages before the onset. Parsimonious model using 21 classic diabetes risk factors resulted in area under ROC curve (AUC) of 0.75 for diabetes prediction within a 2-year window following the baseline. The enhanced model increased the AUC to 0.80, with about 900 variables selected as predictive (p < 0.0001 for differences between AUCs). Similar improvements were observed for models predicting diabetes onset 1-3 years and 2-4 years after base-line. The enhanced model improved positive predictive value by at least 50% and identified novel surrogate risk factors for type 2 diabetes, such as chronic liver disease (odds ratio [OR] 3.71), high alanine aminotransferase (OR 2.26), esophageal reflux (OR 1.85), and history of acute bronchitis (OR 1.45). Liver risk factors emerge later in the process of diabetes development compared with obesity-related factors such as hypertension and high hemoglobin A1c. In conclusion , population-level risk prediction for type 2 diabetes using readily available administrative data is feasible and has better prediction performance than classical diabetes risk prediction algorithms on very large populations with missing data. The new model enables intervention allocation at national scale quickly and accurately and recovers potentially novel risk factors at different stages before the disease onset.