A Population Based Risk Algorithm for the Development of Type 2 Diabetes: in the United States

Transcription

1 A Population Based Risk Algorithm for the Development of Type 2 Diabetes: Validation of the Diabetes Population Risk Tool (DPoRT) in the United States Christopher Tait PhD Student Canadian Society for Epidemiology and Biostatistics June 4 th, 2015

2 Outline Background Objectives Methods Results Significance + Impact 2

3 Age-Adjusted Prevalence of Obesity and Diagnosed Diabetes Among US Adults 1994 Obesity (BMI 30 kg/m 2 ) Diabetes Missing Data <14.0% 14.0% 17.9% 18.0% 21.9% 22.0% 25.9% 26.0% Missing data <4.5% 4.5% 5.9% 6.0% 7.4% 7.5% 8.9% 9.0% CDC s Division of Diabetes Translation. National Diabetes Surveillance System available at

23 Background Type 2 Diabetes in the United States Type 2 diabetes is one of the most common chronic diseases 2014 prevalence: 9.3% (29.1 million Americans) 27.8% of people with diabetes are undiagnosed 7 th leading cause of mortality $245 billion in costs in 2012 Direct medical costs Indirect costs (disability, lost productivity, premature death) (CDC, National Diabetes Report 2014) 23

24 Background Risk Prediction Tools Clinical Setting Ex. Framingham Heart Score Important contributions to individual patient treatment and disease prevention Usually require clinical data that are rarely available at the population level Some apply only to specific subgroups of the population (i.e. specific age ranges) Population Health Setting Provide insight into the influence of risk factors on the future burden of disease Estimate the value of interventions at the population level Input variables should be representative of the entire population Meaningful for health policy makers Feasible for Type 2 Diabetes because the risk factors are well known and routinely measured through population health surveys 24

25 Population Risk Tools Diabetes Population Risk Tool (DPoRT) Risk prediction algorithm in the Canadian population Accurately predicts diabetes risk up to 10 years using self reported measures available in population health surveys Validated against observed rates in two external validation cohorts 30% Validation Diabetes Risk (%) 25% 20% 15% 10% 5% Predicted Observed 0% Quantiles of Risk (15) (Rosella et al., 2010) 25

26 DPoRT Application Canadian Community Health Survey (CCHS) Restrict sample (E.g. Manitoba residents who are 20 years without diabetes) Re code CCHS variables for DPoRT Calculate summary statistics for overall population Use DPoRT risk equation to estimate individual 10 year risk and the number of diabetes cases they represent Identify the effects of prevention activities E.g. Approximately 74,900 new diabetes cases expected between E.g. Approximately 3,955 cases can be prevented in Manitoba if average weight loss among entire population was 5% Identify high risk Individuals Identify risk across population strata Identify future health care needs E.g. 12.9% of Manitoba has a 10 year risk 20% E.g. 10 year risk is 10.7% and 8.4% in lowest and highest income quintiles respectively E.g. Approximately $292 million in new care costs over next 10 years within Peel, Ontario region

27 Validation of DPoRT in the US Objectives 1 To establish the accuracy of DPoRT in a nationally representative sample of the United States population for the first time. 27

28 Validation of DPoRT in the US Objectives 1 To establish the accuracy of DPoRT in a nationally representative sample of the United States population for the first time. 2 To calibrate a new DPoRT USA model adding important predictors for the United States population. 28

29 Data Sources National Health Interview Survey (NHIS) Continuous annual cross sectional household interview survey aimed at monitoring the health of the US population (similar to CCHS in Canada) Multi staged area probability sampling representative of the noninstitutionalized population of the United States (Centers for Disease Control and Prevention) 29

30 Statistical Analysis 1. Estimating Observed Number of Incident Cases Adults asked if a health professional had ever told them they had diabetes Pregnant women asked whether they were told they had diabetes other than during pregnancy, to exclude gestational diabetes Adults who reported being diagnosed with diabetes were asked at what age they were diagnosed Those with a value equal to their current age were identified as incident cases To calculate total new cases per survey cycle, the number of incident cases in the past year was the weighted estimate of the entire population 30

31 Statistical Analysis 2. Accounting for the Underestimation of Self Reported Diabetes Self reported diabetes is known to be an underestimate of true diabetes rates when compared to physician diagnosed methods A 25% correction factor was applied to the crude observed estimate to account for this underestimation Taken from validation studies on CCHS self reported vs. Ontario Diabetes Database (Shah, 2008) 31

32 Statistical Analysis 3. Assessing Predictive Accuracy with DPoRT Crude The crude DPoRT model (validated for Canadian population) was applied to the risk factor data from 2003 NHIS Calculated crude estimate of the number of incident cases expected from NHIS Survey Cycle DPoRT Crude applied (including standard predictors) 2013 Predicted number of incident cases over 10 years 32

33 Statistical Analysis 4. Assessing Predictive Accuracy with New DPoRT USA A newly calibrated DPoRT USA model was applied to the same risk factor data from 2003 NHIS Ethnicity and insurance coverage were added to the model as predictors Logistic model with log DPoRT crude risk + new covariates tested for predictive accuracy against the naïve model 2003 NHIS Survey Cycle DPoRT USA applied (adding new predictors) 2013 Predicted number of incident cases over 10 years 33

34 Comparison of Estimates Results Table 1. Observed vs. Predicted Number of Incident Cases ( ) NHIS Observed NHIS Observed + Correction Factor DPoRT Crude Predicted DPoRT USA Predicted Total 17,231,000 21,539,000 24,917,000 22,758,000 Men 9,477,050 11,846,313 14,451,860 12,061,740 Women 7,763,950 9,705,938 10,465,140 10,696,260 34

35 Comparison of Estimates Results Figure 1. Observed vs. Predicted Number of Incident Cases by Decile of Risk ( ) Number of Incident Cases 7,000,000 6,000,000 5,000,000 4,000,000 3,000,000 2,000,000 1,000,000 NHIS Observed + Correction Factor DPoRT Crude Predicted DPoRT USA Predicted 0 C Statistics Decile of Risk DPoRT Crude: 0.76 ( ) DPoRT USA: 0.83 ( ) 35

36 Comparison of Estimates Results Figure 2. Observed vs. Predicted Number of Incident Cases by Ethnicity ( ) 14,000,000 Number of Incident Cases 12,000,000 10,000,000 8,000,000 6,000,000 4,000,000 NHIS Observed + Correction Factor DPoRT Crude Predicted DPoRT USA Predicted 2,000,000 0 Non Hispanic White Non Hispanic Black Hispanic Asian American Indian/Alaskan Native Ethnicity 36

37 Results Newly calibrated DPoRT USA model performed better than the DPoRT crude model when estimating expected number of incident cases DPoRT crude underestimated risk particularly among certain ethnic groups Findings indicate that ethnicity and insurance coverage are important calibration variables when adapting DPoRT for the US population 37

38 Significance + Impact This study represents the first validation of DPoRT outside of Canada using nationally representative data from the United States Paradigms to Pragmatism This establishes a methodology that can be used to adapt population risk tools across populations and nations Results highlight the importance of considering population specific drivers of risk when applying risk tools in new populations Next steps: Calibrated DPoRT USA model to evaluate effectiveness of prevention strategies 38

39 ACKNOWLEDGEMENTS Dr. Laura Rosella Assistant Professor, Dalla Lana School of Public Health, University of Toronto Scientist, Public Health Ontario Adjunct Scientist, Institute for Clinical Evaluative Sciences 39

40 THANK YOU! QUESTIONS? 40