Introduction: Although neighborhood-level factors influence diabetes and diabetes complications, these determinants are often ignored in clinical practice. The widespread use of electronic medical records (EMR) represents a unique opportunity to incorporate these factors into clinical decision making through geocoding of a patient’s residential address. However, despite increasing interest, few examples exist of how to leverage geospatial data within a clinical care system to inform patient care. The objective of this study is to examine the association between neighborhood deprivation and diabetes complications using geocoded address and clinical data extracted from patients’ EMR. Methods: Using retrospective EMR data from UCSF Health (01/01/2015-06/30/2018), we identified a cohort of patients with diabetes using a combination of labs, diagnosis codes and medications (n=9,876). Patients’ residential addresses were extracted from EMR, geocoded, and assigned to census block/track. Area deprivation index (ADI) was calculated for each census block/track, categorized as high neighborhood-level deprivation (ADI ≥80 th percentile) or not (ADI <80 th percentile), and linked to patient-level data. ADI includes 17 measures of education, employment, housing-quality, and poverty and incorporates information from the Census and American Community Survey. We used logistic regression models to examine the association between ADI and the following diabetes-related outcomes: poor glycemic control (HbA1c ≥9%), severe hypoglycemia (ICD-9/10 codes for hypoglycemia-related ED visit or hospitalization), and retinopathy (ICD-9/10 codes). Models were adjusted for age, sex, race/ethnicity, and insurance coverage. Hypoglycemia and retinopathy models were additionally adjusted for last available HbA1c measure. Results: In fully-adjusted models, high neighborhood-level deprivation was associated with increased risk of poor glycemic control (OR: 1.18, 95% CI: 1.01, 1.40) and severe hypoglycemia (OR: 1.35, 95% CI: 1.04, 1.76). Conversely, high neighborhood-level deprivation was associated with decreased risk of retinopathy (OR: 0.78, 95% CI: 0.63, 0.96). Conclusions: Using EMR data, after controlling for several patient-level factors, neighborhood-level deprivation was associated with increased risk of poor glycemic control and severe hypoglycemia and decreased risk retinopathy. Understanding the patient’s broader social context could help inform risk stratification and tailoring of diabetes management. Future analyses will more fully characterize patients’ clinical and utilization-related characteristics and will include additional diabetes complications such as nephropathy, neuropathy and chronic kidney disease. This study represents an example of how neighborhood-level data obtained through patient’s EMR could be used to inform clinical practice.
Read full abstract