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Am J Prev Med. Author manuscript; available in PMC 2018 January 01. Published in final edited form as: Am J Prev Med. 2017 January ; 52(1): 74–84. doi:10.1016/j.amepre.2016.08.022.
Built Environment and Depression in Low-Income African Americans and Whites Peter James, ScD1,2,3, Jaime E. Hart, ScD2,3, Rachel F. Banay, MPH2, Francine Laden, ScD1,2,3, and Lisa B. Signorello, ScD1,4
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1Department
of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 2Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 3Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts 4Cancer Prevention Fellowship Program, National Cancer Institute, Rockville, Maryland
Abstract Introduction—Urban environments are associated with a higher risk of adverse mental health outcomes; however, it is unclear which specific components of the urban environment drive these associations.
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Methods—Using data collected in 2002–2009 from 73,225 low-income, racially diverse individuals across the Southeastern U.S., analyses evaluated the cross-sectional relationship between a walkability index and depression. Walkability was calculated from population density, street connectivity, and destination count in the 1,200-meter area around participants’ homes, and depression was measured using the Center for Epidemiologic Studies Depression Scale for depression symptomatology and questionnaire responses regarding doctor-diagnosed depression and antidepressant use. Data were analyzed in 2015.
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Results—Participants living in neighborhoods with the highest walkability index had 6% higher odds of moderate or greater depression symptoms (score ≥15, 95% CI=0.99, 1.14), 28% higher odds of doctor-diagnosed depression (95% CI=1.20, 1.36), and 16% higher odds of current antidepressant use (95% CI=1.08, 1.25) compared with those in the lowest walkability index. Higher walkability was associated with higher odds of depression symptoms only in the most deprived neighborhoods, whereas walkability was associated with lower odds of depression symptoms in the least deprived neighborhoods. Conclusions—Living in a more walkable neighborhood was associated with modestly higher levels of doctor-diagnosed depression and antidepressant use, and walkability was associated with
Address correspondence to: Peter James, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Department of Environmental Health, Harvard T.H. Chan School of Public Health, 401 Park Dr., 3rd Floor West, Boston MA 02215.,
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greater depression symptoms in neighborhoods with higher deprivation. Although dense urban environments may provide opportunities for physical activity, they may also increase exposure to noise, air pollution, and social stressors that could increase levels of depression.
Introduction
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Depression is a disabling psychiatric disorder that is the fourth leading cause of disability worldwide and the leading cause of nonfatal disease burden.1 Approximately 9% of U.S. adults meet the criteria for current depression,2 although depression prevalence varies by race and SES. African Americans are at lower lifetime risk of depression, but are overrepresented in underserved populations, have reduced access to mental health services, and often receive poorer-quality care than whites.3 Meta-analyses demonstrate that low-SES individuals have 80% increased odds of being depressed compared with higher-SES individuals.4 However, little is known about the environmental drivers of depression in these populations. The social-ecologic model of health5 explains that individual behavior shapes, and is shaped by, the context in which an individual lives. Research indicates that health may be related to the contextual built environment, defined as land use patterns, transportation systems, and urban design.6 Although this conceptual definition has not been standardized, the measures of population density, land use mix, and street connectivity have commonly been linked to health outcomes.7,8
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A growing literature demonstrates associations between the built environment and mental health9; however, there is sparse research on the built environment and mental health in minority and low-SES populations. Qualitative studies report that the built environment is perceived as a critical determinant of mental health in African American populations,10 and built environment quality (e.g., deteriorating buildings) has been linked to depression, psychiatric symptoms, and psychosocial distress.11–15
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The built environment could influence mental health through multiple mechanisms. Research demonstrates that population density, land use mix, and street connectivity are associated with utilitarian walking,16–18 and a clear inverse relationship between physical activity and depression risk exists.19 Higher population density may increase social interactions that may decrease depression risk.20 Conversely, indicators of walkabilty may correlate with adverse mental health: Higher population density, land use mix, and street connectivity can increase air pollution and noise by concentrating traffic,21 and studies have reported associations between these exposures and depression and stress.22–25 Urbanicity has been correlated with adverse mental health, potentially due to social disorganization, selective migration, increased infection, and overcrowding.26,27 The objective of this analysis is to contribute to the growing literature on the the built environment and depression. The authors hypothesized that more-walkable environments would be associated with lower prevalence of depression for reasons outlined above. The built environment may be particularly relevant for mental health in minority and lower-SES
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populations, where exposures to poor quality built environments are more prevalent28 and chronic stress levels are higher.29
Methods Study Population The Southern Community Cohort Study (SCCS) is a study of >85,000 adults aged 40–79 years designed to investigate health disparities in low-income African Americans and whites.30,31 Enrollment largely occurred at community health centers in 12 states in the Southeastern U.S.
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Participants provided residential addresses on the baseline questionnaire (2002–2009). Addresses were geocoded using a protocol developed to maximize assignment to an addresslevel geographic coordinate.32 Briefly, addresses were parsed, cleaned, and standardized before applying a combination of automated (e.g., TeleAtlas, Lebanon, NH) and interactive (e.g., Google Earth) geocoding tools. Overall, 99.96% of participant addresses were geocoded.
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For this analysis, participants were excluded if they had no geocoded address at the street level (n=4,802); lived on rural routes with inaccurate geocodes (n=207); or were missing information on the following: depression (n=3,905), race, income, smoking, marital status, or employment (n=2,207), or built environment measures (n=373). Excluded participants were more likely to report doctor-diagnosed depression and were more likely to be married, but had similar prevalence of antidepressant use. There were 73,225 participants eligible for this analysis (66.9% African American, 59.6% female). The SCCS was approved by the IRB at Vanderbilt University and Meharry Medical College, and this project was approved by the Human Subjects Committee at the Harvard T.H. Chan School of Public Health. All participants provided written informed consent. Measures
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A series of built environment measures were developed as indicators of neighborhood walkability using ArcGIS, version 10.4. Based on previous work evaluating the stability of associations between built environment measures and physical activity across different buffer sizes, 1,200-meter line-based network buffers were created around each participant’s geocoded address.33 Although there is uncertainty over the geographic area most relevant to health,34 previous analyses have identified this buffer size as holding the strongest energy balance associations.35 The buffering process involved identifying streets accessible to pedestrians and evaluating the portion of the street network within 1,200 meters from each subject’s home, adding a 50-meter buffer on either side of the road. The elements of walkability were detailed as follows. Street information came from ArcGIS, version 10.1 StreetMap® USA data, which is based on the TIGER 2007 road network. The number of three-way or greater intersections were calculated within each participant’s network buffer as a measure of street connectivity.
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Business data were obtained from the commercially available InfoUSA 2009 database. The database included points of interest such as grocery stores, restaurants, banks, and hospitals. Geographic coordinates were assigned to destinations based on addresses in this database. Recent studies assessed the validity of similar commercial facility databases using field audits and found good to moderate agreement and sensitivity for correctly locating facilities.36–38 Population density was defined as the number of individuals per square mile based on the 2000 U.S. Census for the Census tract of each participant’s geocoded address.
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Because measures of the built environment were correlated (Spearman correlation coefficients, 0.7–0.8), a standardized walkability index was created from Z-scores of each measure with a mean of 0 and a SD of 1. Scores were summed to create a walkability index with a mean of 0 (range, −2.7 to 18.1), with higher values indicating higher levels of neighborhood walkability. Similar indices have been used previously.39–41 To measure depression symptoms, the Center for Epidemiologic Studies ten-item Depression Scale (CES-D) was included on the baseline SCCS questionnaire. The CES-D is a validated instrument for depression symptoms42–44 and has demonstrated measurement equivalency across a broad range of ages and ethnicities.45,46 This analysis used a CES-D score (range, 0–30) cut off of ≥15, which indicates moderate or greater depression symptoms.47,48 Two self-reported, dichotomous measures of depression were used. Doctor-diagnosed depression was recorded based on positive responses to: Has a doctor ever told you that you have had depression” Antidepressant use was coded based on positive responses to: Are you
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currently taking any antidepressant or antianxiety prescription medication, such as Prozac, Zoloft, Paxil, or Buspar? Statistical Analysis
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The cross-sectional association between each built environment measure and moderate or greater depression symptomatology (CES-D ≥15), self-reported depression (ever), or current antidepressant use was determined using logistic regression. Although the walkability index was the main exposure of interest, each component of the index was examined separately to see which component had the greatest influence on depression outcomes. Each exposure metric was divided into quintiles and ORs and 95% CIs were calculated for each quintile compared to the lowest quintile. Covariates were selected a priori based on being related to both the built environment and depression. Models were adjusted for age, sex, race, household income, marital status, smoking, and employment status from questionnaires. Models were additionally adjusted for a deprivation index used previously within the SCCS,49,50 which combines Census 2000 tract-level education, employment, housing, occupation, poverty, race, and residential stability variables. Further analyses adjusted for self-reported walking MET hours per day,51 based on responses to questions about time per day walking fast or slow. Because patients using antidepressants may have lower depression symptoms or not report doctor-diagnosed depression, additional analyses were conducted for these outcomes adjusting for antidepressant use. To account for the temporal mismatch
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between walkability based on the current neighborhood and doctor-diagnosed depression that could have occurred years earlier, sensitivity analyses were conducted focusing on doctor-diagnosed depression within the past year. Effect modification by sex, race, age, marital status, years in current home, household income, Census tract deprivation index, and whether the participant lived in a metropolitan (urban area ≥50,000 people)/micropolitan (urban cluster of 10,000–49,999)/small town or rural (urban cluster of
