A Multilevel Analysis of Individual, Health System, and Neighborhood Factors Associated with Depression within a Large Metropolitan Area.

Depression prevalence is known to vary by individual factors (gender, age, race, medical comorbidities) and by neighborhood factors (neighborhood deprivation). However, the combination of individual- and neighborhood-level data is rarely available to assess their relative contribution to variation in depression across neighborhoods. We geocoded depression diagnosis and demographic data from electronic health records for 165,600 patients seen in two large health systems serving the Denver population (Kaiser Permanente and Denver Health) to Denver's 144 census tracts, and combined these data with indices of neighborhood deprivation obtained from the American Community Survey. Non-linear mixed models examined the relationships between depression rates and individual and census tract variables, stratified by health system. We found higher depression rates associated with greater age, female gender, white race, medical comorbidities, and with lower rates of home owner occupancy, residential stability, and higher educational attainment, but not with economic disadvantage. Among the Denver Health cohort, higher depression rates were associated with higher crime rates and a lower percent of foreign born residents and single mother households. Our findings suggest that individual factors had the strongest associations with depression. Neighborhood risk factors associated with depression point to low community cohesion, while the role of education is more complex. Among the Denver Health cohort, language and cultural barriers and competing priorities may attenuate the recognition and treatment of depression.