Elsevier

Social Science & Medicine

Volume 65, Issue 9, November 2007, Pages 1867-1881
Social Science & Medicine

The importance of social context: Neighborhood stressors, stress-buffering mechanisms, and alcohol, drug, and mental health disorders

https://doi.org/10.1016/j.socscimed.2007.05.045Get rights and content

Abstract

This study examines the relationship among neighborhood stressors, stress-buffering mechanisms, and likelihood of alcohol, drug, and mental health (ADM) disorders in adults from 60 US communities (n=12,716). Research shows that larger support structures may interact with individual support factors to affect mental health, but few studies have explored buffering effects of these neighborhood characteristics. We test a conceptual model that explores effects of neighborhood stressors and stress-buffering mechanisms on ADM disorders. Using Health Care for Communities with census and other data, we found a lower likelihood of disorders in neighborhoods with a greater presence of stress-buffering mechanisms. Higher neighborhood average household occupancy and churches per capita were associated with a lower likelihood of disorders. Cross-level interactions revealed that violence-exposed individuals in high crime neighborhoods are vulnerable to depressive/anxiety disorders. Likewise, individuals with low social support in neighborhoods with high social isolation (i.e., low-average household occupancy) had a higher likelihood of disorders. If replicated by future studies using longitudinal data, our results have implications for policies and programs targeting neighborhoods to reduce ADM disorders.

Introduction

Although a growing body of evidence indicates that neighborhood characteristics are associated with prevalence of alcohol, drug, and mental health (ADM) conditions, research identifying mechanisms through which contextual effects operate is necessary to plan and develop health care systems that integrate aspects of place with service. “Neighborhood effects” research has focused on demonstrating how neighborhood disadvantage contributes to risk for ADM problems (Aneshensel & Sucoff, 1996; Boardman, Finch, Ellison, Williams, & Jackson, 2001; Park & Nelson, 1998; Reijneveld & Schene, 1998; Ross, 2000; Weich, Twigg, Holt, Lewis, & Jones, 2003; Wheaton & Clarke, 2003). However, neighborhoods can also provide resources to support routine activity and facilitate social support, lowering risk for ADM conditions (Heaney & Israel, 2002; Robert, 1999).

Some research has shown that larger support structures may interact with individual supports to affect mental health (Lin, Ye, & Ensel, 1999); few have explored effects of neighborhood characteristics and their stress-buffering effects for ADM disorders. In this paper, we test a conceptual model that explores effects of neighborhood stressors and stress-buffering mechanisms on ADM disorders. We use neighborhood-level data from US Census and other sources to develop neighborhood measures that parallel stress-buffering functions of individual social support and stress. We have two research questions. First, net of neighborhood economic context, what role do neighborhood stressors and stress-buffering mechanisms play in prevalence of ADM disorders? Second, does the impact of individual-level stress and social support on likelihood of ADM disorders differ depending on neighborhood stressors and stress-buffering mechanisms?

Section snippets

Neighborhood context, stress, and ADM conditions

Durkheim's studies of suicide (Durkheim, 1951) began a long tradition of exploring the association between social environment and individual well-being. Faris and Dunham (1939) were the first to link “social disorganization” with schizophrenia and substance abuse at the neighborhood level, arguing that disorganized neighborhoods increased social isolation, as positive relationships were more difficult to develop and maintain. Within the life stress paradigm, the effects of social support and

Conceptual framework

Fig. 1 illustrates pathways through which neighborhood economic context may impact mental health, demonstrating hypothesized relationships among neighborhood economic context, stressors, and stress-buffering mechanisms. It acknowledges that individual demographics, stress, and social support affect likelihood of ADM disorders. Below we discuss relevant literature supporting our conceptual model. To summarize, neighborhood disadvantage operates through its effects on neighborhood stressors and

Data

We used cross-sectional data from Healthcare for Communities (HCC) wave 2 telephone survey, supplemented with cases from wave 1. HCC was designed to track effects of the changing health care system on risk for ADM disorders. Both HCC waves were drawn from participants in the Community Tracking Study (CTS), a sequence of telephone surveys that focused on health care and health insurance coverage (Kemper et al., 1996). The first HCC wave (1997–1998) sampled 14,985 individuals from the 30,375

Any alcohol, drug, or mental health disorders

Model 1 (Table 3) shows that with all individual-level variables in the model, neighborhood economic context was not significantly associated with likelihood of any ADM disorder. Non-whites, married, and individuals with more family savings had lower likelihoods of ADM disorders, while having less than high school education increased likelihood. Likelihood of ADM disorders decreased with age and higher social support. The significant interaction terms for social support by age and social

Conclusions

Building on previous research that points to physical and structural characteristics of disadvantaged neighborhoods as sources of stress (Boardman et al., 2001; Robert, 1999), we found evidence that one neighborhood stressor (high violent crime) is associated with worse mental health outcomes, above and beyond effects of neighborhood economic context. In contrast to studies suggesting that signs of public drinking and alcohol-related problems are sources of stress, alcohol availability was not

Acknowledgments

The authors would like to acknowledge and thank the following people for their contributions to the conceptualization of the analysis variables, technical assistance, and helpful comments: Ruth Klap, Roland Sturm, and Diana Liao. This research was supported by grants from the Robert Wood Johnson Foundation (038273), the National Institute of Mental Health (P30MH068639:01), and the National Institute of Environmental Health Sciences (P50ES012383).

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