ArticleAmerican journal of preventive medicine2017
Individual- and Area-Level SES in Diabetes Risk Prediction: The Multi-Ethnic Study of Atherosclerosis.
Article in American journal of preventive medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Adding social determinants of health to the equation: Development of a cardiometabolic disease staging model using clinical and social determinants of health to predict type 2 diabetes.Diabetes, obesity & metabolism · 2025Article
- Structural and social determinants of health: The multi-ethnic study of atherosclerosis.PloS one · 2024Article
- Neighborhood Environment and Epigenetic Age: A Scoping Review.Western journal of nursing research · 2023Article
- Age and sex differences in the association between neighborhood socioeconomic environment and incident diabetes: Results from the diabetes location, environmental attributes and disparities (LEAD) network.SSM - population health · 2023Article
- Effects of social determinants of health on mortality and incident liver-related events and cardiovascular disease in steatotic liver disease.Alimentary pharmacology & therapeutics · 2023Article
- One Quarter of Medicare Hospitalizations in Patients with Systemic Lupus Erythematosus Readmitted within Thirty Days.Seminars in arthritis and rheumatism · 2021Article
- Neighborhood disadvantage and chronic disease management.Health services research · 2019Article
- Neighborhood Environments and Diabetes Risk and Control.Current diabetes reports · 2018Review
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8 authors.
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Abstract
introductionThe purpose of this study was to evaluate if adding SES to risk prediction models based upon traditional risk factors improves the prediction of diabetes.
methodsRisk prediction models without and with individual- and area-level SES predictors were compared using the prospective Multi-Ethnic Study of Atherosclerosis. Cox proportional hazards models were utilized to estimate hazard ratios for SES predictors and to generate 10-year predicted risks for 5,021 individuals without diabetes at baseline followed from 2000 to 2012. C-statistics were used to compare model discrimination, and the proportion of individuals reclassified into higher or lower risk categories with the addition of SES predictors was calculated. The accuracy of risk prediction by SES was assessed by comparing observed and predicted risks across tertiles of the SES variables. Statistical analyses were performed in 2015-2016.
resultsOver a median of 9.2 years of follow-up, 615 individuals developed diabetes. Individual- and area-level SES variables did not significantly improve model discrimination or reclassify substantial numbers of individuals across risk categories. Models without SES predictors generally underestimated risk for low-SES individuals or individuals residing in low-SES areas (underestimates ranging from 0.31% to 1.07%) and overestimated risk for high-SES individuals or individuals residing in high-SES areas (overestimates ranging from 0.70% to 1.30%), and the addition of SES variables largely mitigated these differences.
conclusionsStandard diabetes risk models may underestimate risk for low-SES individuals and overestimate risk for those of high SES. Adding SES predictors helps correct this systematic misestimation, but may not improve model discrimination.
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