Features of neighborhoods or residential environments may affect health and contribute to social and race/ethnic inequalities in health. The study of neighborhood health effects has grown exponentially over the past 15 years. This chapter summarizes key work in this area with a particular focus on chronic disease outcomes (specifically obesity and related risk factors) and mental health (specifically depression and depressive symptoms). Empirical work is classified into two main eras: studies that use census proxies and studies that directly measure neighborhood attributes using a variety of approaches. Key conceptual and methodological challenges in studying neighborhood health effects are reviewed. Existing gaps in knowledge and promising new directions in the field are highlighted.

A review of published observational studies of neighbourhoods and depression/depressive symptoms was conducted to inform future directions for the field. Forty-five English-language cross-sectional and longitudinal studies that analysed the effect of at least one neighbourhood-level variable on either depression or depressive symptoms were analysed. Of the 45 studies, 37 reported associations of at least one neighbourhood characteristic with depression/depressive symptoms. Seven of the 10 longitudinal studies reported associations of at least one neighbourhood characteristic with incident depression. Socioeconomic composition was the most common neighbourhood characteristic investigated. The associations of depressive symptoms/depression with structural features (socioeconomic and racial composition, stability and built environment) were less consistent than with social processes (disorder, social interactions, violence). Among the structural features, measures of the built environment were the most consistently associated with depression but the number of studies was small. The extent to which these associations reflect causal processes remains to be determined. The large variability in studies across neighbourhood definitions and measures, adjustment variables and study populations makes it difficult to draw more than a few general qualitative conclusions. Improving the quality of observational work through improved measurement of neighbourhood attributes, more sophisticated consideration of spatial scale, longitudinal designs and evaluation of natural experiments will strengthen inferences regarding causal effects of area attributes on depression.

A critical step in developing sexual assault prevention and treatment is identifying groups at high risk for sexual assault. We explored the independent and interaction effects of sexual identity, gender identity, and race/ethnicity on past-year sexual assault among college students. From 2011–2013, 71,421 undergraduate students from 120 U.S. post-secondary education institutions completed cross-sectional surveys. We fit multilevel logistic regression models to examine differences in past-year sexual assault. Compared to cisgender (i.e., non-transgender) men, cisgender women (adjusted odds ratios [AOR]=2.47; 95% confidence interval [CI]: 2.29, 2.68) and transgender people (AOR=3.93; 95% CI: 2.68, 5.76) had higher odds of sexual assault. Among cisgender people, gays/lesbians had higher odds of sexual assault than heterosexuals for men (AOR=3.50; 95% CI: 2.81, 4.35) but not for women (AOR=1.13; 95% CI: 0.87, 1.46). People unsure of their sexual identity had higher odds of sexual assault than heterosexuals, but effects were larger among cisgender men (AOR=2.92; 95% CI: 2.10, 4.08) than cisgender women (AOR=1.68; 95% CI: 1.40, 2.02). Bisexuals had higher odds of sexual assault than heterosexuals with similar magnitude among cisgender men (AOR=3.19; 95% CI: 2.37, 4.27) and women (AOR=2.31; 95% CI: 2.05, 2.60). Among transgender people, Blacks had higher odds of sexual assault than Whites (AOR=8.26; 95% CI: 1.09, 62.82). Predicted probabilities of sexual assault ranged from 2.6% (API cisgender men) to 57.7% (Black transgender people). Epidemiologic research and interventions should consider intersections of gender identity, sexual identity, and race/ethnicity to better tailor sexual assault prevention and treatment for college students.

Understanding the impact of place on health is a key element of epidemiologic investigation, and numerous tools are being employed for analysis of spatial health-related data. This review documents the huge growth in spatial epidemiology, summarizes the tools that have been employed, and provides in-depth discussion of several methods. Relevant research articles for 2000–2010 from seven epidemiology journals were included if the study utilized a spatial analysis method in primary analysis (n = 207). Results summarized frequency of spatial methods and substantive focus; graphs explored trends over time. The most common spatial methods were distance calculations, spatial aggregation, clustering, spatial smoothing and interpolation, and spatial regression. Proximity measures were predominant and were applied primarily to air quality and climate science and resource access studies. The review concludes by noting emerging areas that are likely to be important to future spatial analysis in public health.

There is a growing interest in understanding the effects of specific neighborhood conditions on psychological wellbeing. We examined cross-sectional associations of neighborhood stressors (perceived violence and disorder, physical decay and disorder) and social support (residential stability, family structure, social cohesion, reciprocal exchange, social ties) with depressive symptoms in 3105 adults in Chicago. Subjects lived in 343 neighborhood clusters, areas of about two census tracts. Depressive symptoms were assessed with an 11-item version of the CES-D scale. Neighborhood variables were measured using rater assessments, surveys, and the US Census. We used two-level gender-stratified models to estimate associations of neighborhood conditions with depressive symptoms after adjusting for individual-level covariates. Most social support variables were associated with lower levels of depressive symptoms in women but not men, while stressors were moderately associated with higher levels in all subjects. Adjusting concurrently for stressors and social support did not change results. This suggests both neighborhood stressors and social support are associated with depressive symptoms.