Sociodemographic characteristics of the neighborhood and depressive symptoms in older adults: using multilevel modeling in geriatric psychiatry.

Objective: Neighborhood sociodemographic characteristics may be important to the mental health of older adults who have decreased mobility and fewer resources. Our objective was to examine the association between neighborhood context and level of depressive symptomatology in older adults in a diverse geographic region of central North Carolina.

Methods: The sample included 2,998 adults 65 or older residing in 91 census tracts. Depressive symptoms were measured using the Center for Epidemiologic Studies-Depression scale (CES-D). Neighborhoods were characterized by five census-based characteristics: socioeconomic disadvantage, socioeconomic advantage, racial/ethnic heterogeneity, residential stability, and age structure.

Results: In ecologic level analyses, level of census tract socioeconomic disadvantage was associated with increased depressive symptoms. To determine whether neighborhood context was associated with depressive symptoms independently of individual characteristics, the authors used multilevel modeling. The authors examined the ability of each of five neighborhood (level 2) characteristics to predict a level 1 outcome (CES-D symptoms) controlling for the effects of individual (level 1) characteristics. Younger age, being widowed, lower income, and having some functional limitations were associated with increased depression symptoms conditional on census tract random effects. However, none of the neighborhood characteristics was significantly associated with depression symptoms, conditional on census tract random effects, either unadjusted or adjusted for individual characteristics.

Conclusion: Any observed association between neighborhood sociodemographic characteristics and individual depressive symptoms in our sample may reflect the characteristics of the individuals who reside in the neighborhood rather than the neighborhood characteristics themselves. The use of multilevel modeling is important to separate these effects.