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Perm J. 2018 Oct 5;22. doi: 10.7812/TPP/18-096. [Epub ahead of print]

Using Neighborhood-Level Census Data to Predict Diabetes Progression in Patients with Laboratory-Defined Prediabetes.

Author information

1
Research Scientist at the Kaiser Permanente Northern California Division of Research in Oakland (julie.a.schmittdiel@kp.org).
2
Senior Data Consultant at the Kaiser Permanente Northern California Division of Research in Oakland (wendy.dyer@kp.org).
3
Assistant Professor at the University of California, Berkeley (cassiejm@berkeley.edu).
4
Professor in the Department of History at the University of Warwick in Coventry, UK (r.bivins@warwick.ac.uk).

Abstract

CONTEXT:

Research on predictors of clinical outcomes usually focuses on the impact of individual patient factors, despite known relationships between neighborhood environment and health.

OBJECTIVE:

To determine whether US census information on where a patient resides is associated with diabetes development among patients with prediabetes.

DESIGN:

Retrospective cohort study of all 157,752 patients aged 18 years or older from Kaiser Permanente Northern California with laboratory-defined prediabetes (fasting plasma glucose, 100 mg/dL-125 mg/dL, and/or glycated hemoglobin, 5.7%-6.4%). We assessed whether census data on education, income, and percentage of households receiving benefits through the US Department of Agriculture's Supplemental Nutrition Assistance Program (SNAP) was associated with diabetes development using logistic regression controlling for age, sex, race/ethnicity, blood glucose levels, and body mass index.

MAIN OUTCOME MEASURE:

Progression to diabetes within 36 months.

RESULTS:

Patients were more likely to progress to diabetes if they lived in an area where less than 16% of adults had obtained a bachelor's degree or higher (odds ratio [OR] =1.22, 95% confidence interval [CI] = 1.09-1.36), where median annual income was below $79,999 (OR = 1.16 95% CI = 1.03-1.31), or where SNAP benefits were received by 10% or more of households (OR = 1.24, 95% CI = 1.1-1.4).

CONCLUSION:

Area-level socioeconomic and food assistance data predict the development of diabetes, even after adjusting for traditional individual demographic and clinical factors. Clinical interventions should take these factors into account, and health care systems should consider addressing social needs and community resources as a path to improving health outcomes.

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