External validation and application of the Diabetes Population Risk Tool (DPoRT) for prediction of type 2 diabetes onset in the US population
Authors' conclusion
DPoRT has transportability for predicting population-level diabetes risk in the USA using routinely collected survey data. We demonstrate the model's applicability for population health assessment and diabetes prevention planning. Our modeling predicted that the combination of community-wide and targeted prevention approaches for those at highest risk are needed to reduce diabetes burden in the USA.
Study context
- Population
- US adults from the 2009 National Health Interview Survey (NHIS) (validation cohort); 2018 NHIS cohort for 10-year risk predictionvalidation cohort · 2018 NHIS cohort 21,187
- Participants
- 23,477 adults
- Comparison
- DPoRT-predicted vs observed incident diabetes; three diabetes prevention scenarios
- Outcome
- Predictive performance of DPoRT for 10-year incident T2D: discrimination (C-statistic) and calibration vs observed incident diabetes cases
- Follow-up
- 10-year; NHIS 2009-2018 cycles
Methodological detailspopulation survey data, simulation of prevention scenarios
- Population survey data
- Simulation of prevention scenarios
Performance
As reported in the abstract
C-statistic, males
0.778
C-statistic, females
0.787
Further findings
Diabetes risk reduction by prevention scenario · 10 years
Diabetes cases averted in 10 years Combined approach 843 000
Predicted 10-year diabetes risk in the US, 2018–2028
- Baseline risk
- 10.2%
- New cases of diabetes
- 21 076 000
Original publication
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