Introduction

Increasingly, policy makers and public health officials are calling for patient-centered health interventions that are based in communities rather than clinics in order to prevent and treat cardiometabolic risk factors. [1, 2] Unfortunately, data on disease and risk factors is lacking for small geographic areas; sample-sizes of large health exam surveys do not allow for identifying the specific neighborhoods where at-risk individuals live and biomarkers like HgbA1C remain difficult to obtain as part of routine surveillance. [3,4] Without high-quality data on where people at highest risk from diabetes actually live, interventions are tested across broader areas with lower average risk, which in turn leads to negative results or minimal measured effect sizes for these trials. In turn, the evidence for high-quality interventions to prevent and treat diabetes remains extremely limited. [5]

Minnesota Community Measurement (MNCM) is a non-profit organization that receives patient-level data from electronic health records (EHRs) for almost all ambulatory clinics in Minnesota, as mandated by the Minnesota Legislature. [6] Using this data, we sought to better understand geographic variation in achieved levels of HgbA1c among diabetics at the level of zip code areas. This analysis served as the needs assessment to inform selection of interventions that will be implemented as part of the HealthRise program. HealthRise is a 5-year initiative to evaluate community-based interventions to improve cardiometabolic health in the United States, India, Brazil and South Africa. [7]

Methods

Results

Out of a total population of 1.48 million people aged 18–74 living in 120 zip code areas, 63,053 (4.3%) were identified in our data as having received regular primary care for diabetes in 2013. The median prevalence for a diabetic receiving care within each zip code area was 4.1%, (interquartile range (IQR) 3.1–4.8%) (Table 1). Slightly more than half of diabetic patients were male, 68.2% were identified as white race and two-thirds of diabetic patients were aged 55–75. Only three-quarters of patients achieved an HgbA1C <8.0% with slightly higher rates for achieving blood pressure targets. Current tobacco smoking was reported for 15% of diabetic patients. An LDL-cholesterol < 100 mg/dl was achieved by only a third.

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Table 1. Descriptive Statistics of the Patient Population and Zip Code Areas of Residence.

https://doi.org/10.1371/journal.pone.0159227.t001

There was significant geographic variation in the proportion of patients within a zip code area attaining a HgbA1C <8.0%, ranging from 59–90% across zip code areas (IQR 72.0%-78.1%) (Fig 1). Areas with the smallest proportion of patients meeting their HgbA1c target were clustered in the urban areas of Hennepin and Ramsey (The Minneapolis-St. Paul corridor) and more rural areas of Rice County. Similar patterns were seen for the other performance measures (Figs A–C in S1 Appendix).

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Fig 1. Percent of diabetic patients achieving HbA1c <8.0%.

https://doi.org/10.1371/journal.pone.0159227.g001

The same zip code areas tended to perform poorly across all four indicators, with 8 of the 10 zip code areas ranked lowest overall also in the lowest quintile for all 4 clinical quality indicators individually. Similarly, nine of the 10 highest-ranking zip code areas fell within the top quintile for at least 3 of the measures and above the mean for all of them. The distribution of zip code area-level performance was broadest for LDL-cholesterol and narrowest for blood pressure. The distribution of zip code area performance by measure is shown in Fig 2, with the highest- and lowest-ranking zip code areas identified according to their average rank across all measures.

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Fig 2. Distribution of Zip Code Area Performance with Top and Bottom 10 Zip Code Areas by Summary Score.

https://doi.org/10.1371/journal.pone.0159227.g002

Using census data, we examined the correlation between the percentage of diabetic residents achieving performance measures within a zip code area and measures of socioeconomic status (SES) (Fig B in S1 Appendix). The blood glucose performance measure was significantly positively correlated with all three zip code area-level indicators of SES (median household income, percent completing high school, and percent with insurance coverage). The Spearman rank correlation coefficient for income, education and insurance coverage were 0.66 [p < .001], 0.70 [p < .001], and 0.63 [p < .001], respectively. These SES indicators were even more strongly associated with the proportion of patients abstaining from smoking, with Spearman correlation coefficients of 0.79 [p < .001], 0.72 [p < .001], and 0.70 [p < .001], respectively. The correlation with blood pressure and LDL-cholesterol were weaker but still significant (0.42 [p < .001], 0.42 [p < .001], and 0.40 [p < .001] and 0.55 [p < .001], 0.45 [p < .001], and 0.39 [p < .001], respectively).

Discussion

Using electronic health record data, we identified the small number of neighborhoods where targets for HgbA1c, blood pressure, LDL-cholesterol and tobacco cessation were least commonly achieved among diabetic patients. When examined by a patient’s zip code area of residence, these clinical performance measures were strongly correlated with the average level of income, education, and insurance coverage in that zip code area. Our approach offers a new way to identify the areas most at risk due to diabetes and can guide the design of studies in patient-centered community-based cardiometabolic risk factor modification.

Our analysis adds to the existing evidence showing disparities in the prevalence of risk factors, disease, and poor clinical outcomes by showing the effect of a patient’s home neighborhood on diabetes outcomes. In line with previous publications, we’ve demonstrated that disease clustering is found not only among sociodemographic groups but also neighborhood geographies. [11–14] As the pressure for community-based interventions intensifies, it becomes more important than ever that we are able to identify those communities at highest risk of disease and complications and design appropriate interventions based on their unique disease and risk factor profile.

Studies of regional variation in diabetes care have often relied on Medicare data or reports of Healthcare Effectiveness Data and Information Set (HEDIS) quality measures and focused on the influence of race and geography in general. Most studies have relied on aggregate data representing large geographic regions, such as hospital referral regions, counties, states or the entire country. [1–4, 11–14] Larger-scale population-wide survey studies capture overall trends, but are limited in their ability to narrow-in on smaller geographies: biomarkers in the NHANES survey are representative only at the multi-state or national level, the Behavioral Risk Factor Surveillance Survey is limited to self-report; in Minnesota itself, county health surveys are usually only self-report and consist of aggregated “health areas” smaller than counties but still too large for the identification of community-level interventions. [15–18] These data sources, though meant to inform public health action, obscure important disparities in health at the most local level. In contrast, our analysis includes almost the entire patient population and shows small-area variation in achievement of quality indicators according to where patients live, not where they seek care. Our results suggest an opportunity to apply more advanced geostatistics, such as Kriging and measurement of spatial autocorrelation, to enhance the usefulness of electronic health record data.

Based on these analyses, we were able to identify 7 heavily burdened zip code areas burdened by a disproportionately low level of control for cardiometabolic risk factors. This data was compiled and shared with local stakeholders including the Minnesota Department of Health, County and City Departments of Health, local accountable care organizations and other health care providers, non-governmental organizations, and other public health experts. As part of the HealthRise project, the analysis was used by local stakeholders to design community-based interventions that will be implemented and evaluated across high risk zip code areas over the next three years. MNCM data could also be used to evaluate the impact of these interventions.

Our analysis was limited by important patient anonymity safeguards: Minnesota Community Measurement provided tabulated data rather than patient-level microdata, and all cells with fewer than 15 people at any level of aggregation were excluded. To avoid bias, we restricted our analysis to zip code areas where the subpopulations of interest were non-missing for groups of interest (for instance, White and non-White). The observed correlations between HgbA1C and zip code area characteristics are ecological and hypothesis-generating in nature rather than proof of a causal relationship. However, the use of biomarker outcomes including HgbA1c, blood pressure level and LDL-cholesterol level rather than self-reported diagnosis or diagnosis billing codes should provide some reassurance regarding the measurement of prevalent risk factors and disease.

There is a growing need for community-based interventions that can prevent diabetes and address persistent disparities in health outcomes. To show even a moderate-sized effect, studies of these interventions will need to be tested in small geographic areas where blood glucose and other risk factors are highest and adverse health outcomes are common. We have shown that aggregated electronic health records offer a novel approach for identifying these small, high risk neighborhoods that are missed by the larger regions identified in health examination surveys. Our results suggest that regularly-collected EHR data may be a useful, low-cost approach for identifying the hotspots where diabetes prevention programs can have the largest impact.

Supporting Information