| Introduction Diabetes, a recognized and increasing public health problem, disproportionately affects low poverty neighborhoods. The mechanisms connecting place and health disparities have not been well-described at the community-level due to a lack of robust data representative of neighborhoods. Identifying disease prevention and management gaps in neighborhoods has importance for public health practitioners, policymakers, and population health.;Methods Our cross-sectional study included adults who either met American Diabetes Association diabetes screening criteria or reported a non-gestational prediabetes or diabetes mellitus diagnosis, and participated in the Monitoring Disparities in Chronic Conditions (MDCC) Study, a pilot chronic disease surveillance system in King County (KC), Washington (2011-2012). Data were collected by questionnaire (self-reported) and pharmacy record abstraction. Neighborhood socioeconomic status (SES) was defined using the percentage of residents living at ≤200% federal poverty level from the United States Census Bureau American Community Survey. Weighted logistic regression modeled the association between neighborhood SES and health indicators for quality care, healthy behaviors, disease self-management, and clinical biomarkers. Sensitivity, specificity, and Cohen's kappa assessed concordance between self-reported medication use and pharmacy dispensing records.;Results Among KC adults screened to participate in the MDCC Study, 52.4% who were eligible and agreed to participate completed the core survey; 89.9% of these participants met this dissertation's inclusion criteria. Given the same age, education, and regular source of healthcare status, adults with diabetes in low SES neighborhoods, compared to high SES neighborhoods, are significantly less likely to achieve glycemic control (HbA1c, <8%) (OR: 0.18, 95% CI: 0.06-0.62). Significant neighborhood differences in the prevalence of risk factors, lifestyle, and unmet medical need may contribute to observed health disparities.;Conclusion Using community-level data, we found that neighborhood SES was associated significantly with multiple health indicators, with adults living in lower poverty neighborhoods often faring worse. Interventions to prevent disease should target adults with multiple diabetes risk factors, prior to prediabetes diagnosis, to prevent the development of clinical disease and comorbidities. High unmet medical need, and the absence of continuous, coordinated care contribute to health disparities, but we found evidence that neighborhood context is associated with health inequalities and disproportionate disease burden. |
