Identifying High-Risk Neighborhoods Using Electronic Medical Records: A Population-Based Approach for Targeting Diabetes Prevention and Treatment Interventions

<div><p>Background</p><p>Increasing attention is being paid to the marked disparities in diabetes prevalence and health outcomes in the United States. There is a need to identify the small-area geographic variation in diabetes risk and related outcomes, a task that current health surveillance methods, which often rely on a self-reported diagnosis of diabetes, are not detailed enough to achieve. Broad adoption of electronic health records (EHR) and routine centralized reporting of patient-level data offers a new way to examine diabetes risk and highlight hotspots for intervention.</p><p>Methods and Findings</p><p>We examined small-area geographic variation in hemoglobin A1c (HgbA1C) levels in three counties though a retrospective observational analysis of the complete population of diabetic patients receiving at least two ambulatory care visits for diabetes in three counties (two urban, one rural) in Minnesota in 2013, with clinical performance measures re-aggregated to patient home zip code area. Patient level performance measures included HgbA1c, blood pressure, low-density lipoprotein cholesterol and smoking. Diabetes care was provided to 63,053 patients out of a total population of 1.48 million people aged 18–74. Within each zip code area, on average 4.1% of the population received care for diabetes. There was significant and largely consistent geographic variation in the proportion of patients within their zip code area of residence attaining HgbA1C <8.0%, ranging from 59–90% of patients within each zip code area (interquartile range (IQR) 72.0%-78.1%). Attainment of performance measures for a zip code area were correlated with household income, educational attainment and insurance coverage for the same zip code area (all p < .001).</p><p>Conclusions</p><p>We identified small geographic areas with the least effective control of diabetes. Centrally-aggregated EHR provides a new means of identifying and targeting at-risk neighborhoods for community-based interventions.</p></div

Distribution of Zip Code Area Performance with Top and Bottom 10 Zip Code Areas by Summary Score.

<p>Distribution of Zip Code Area Performance with Top and Bottom 10 Zip Code Areas by Summary Score.</p

Descriptive Statistics of the Patient Population and Zip Code Areas of Residence.

<p>Descriptive Statistics of the Patient Population and Zip Code Areas of Residence.</p

Percent of diabetic patients achieving HbA1c <8.0%.

<p>Percent of diabetic patients achieving HbA1c <8.0%.</p

Reported management of heart failure, by region.

<p>Reported management of heart failure, by region.</p

Meta-regression of ischaemic heart disease against study period.

<p>Meta-regression of ischaemic heart disease against study period.</p

Meta-regression of diuretic use against study period.

<p>Meta-regression of diuretic use against study period.</p

Aetiology of heart failure: valvular heart disease by region.

<p>Percentage of heart failure cases with a documented cause of valvular heart disease.</p

Reported causes of heart failure, by region.

<p>Reported causes of heart failure, by region.</p

Beta-blocker use by region.

<p>∧Rahimzadeh S, Farzadfar F, Ghaziani M (2013) Iranian hospital data project (unpublished data).</p