A community-led intervention to build neighbourhood identification predicts better wellbeing following prolonged COVID-19 lockdowns

Connecting to Community: A Social Identity Approach to Neighborhood Mental Health

ACADEMIC ABSTRACT

Integrative theorizing is needed to advance our understanding of the relationship between where a person lives and their mental health. To this end, we introduce a social identity model that provides an integrated explanation of the ways in which social-psychological processes mediate and moderate the links between neighborhood and mental health. In developing this model, we first review existing models that are derived primarily from a resource-availability perspective informed by research in social epidemiology, health geography, and urban sociology. Building on these, the social identity model implicates neighborhood identification in four key pathways between residents' local environment and their mental health. We review a wealth of recent research that supports this model and which speaks to its capacity to integrate and extend insights from established models. We also explore the implications of the social identity approach for policy and intervention.

PUBLIC ABSTRACT

We need to understand the connection between where people live and their mental health better than we do. This article helps us do this by presenting an integrated model of the way that social and psychological factors affect the relationship between someone's neighborhood and their mental health. This model builds on insights from social epidemiology, health geography, and urban sociology. Its distinct and novel contribution is to point to the importance of four pathways through which neighborhood identification shapes residents' mental health. A large body of recent research supports this model and highlights its potential to integrate and expand upon existing theories. We also discuss how our model can inform policies and interventions that seek to improve mental health outcomes in communities.

Impact of Neighborhood Social Cohesion and Rodent Sightings on Mental Health among Residents of New York City During the COVID-19 Pandemic

Common mental disorders such as depression and anxiety are prevalent globally, and rates are especially high in New York City (NYC) since the COVID-19 pandemic. Neighborhood social and physical environments have been found to influence mental health. We investigated the impact of neighborhood social cohesion and neighborhood rodent sightings (as an indicator of neighborhood cleanliness) on nonspecific serious psychological distress (NSPD) status using 2020 NYC Community Health Survey data from 8781 NYC residents. Multivariable logistic regression was used to evaluate the relationships among social cohesion, rodent sightings, and NSPD adjusted for confounders and complex sampling and weighted to the NYC population. Effect measure modification of rodent sightings on the effect of social cohesion on NSPD was evaluated on the multiplicative scale by adding the interaction term to the multivariable model and, if significant, stratifying on the effect modifier, and on the additive scale using the relative excess risk due to interaction (RERI). Social cohesion was found to decrease the odds of NSPD, and rodent sightings were found to increase the odds of NSPD. We found significant evidence of effect measure modification on the multiplicative scale. In the stratified models, there was a protective effect of social cohesion against NSPD among those not reporting rodent sightings, but no effect among those reporting rodent sightings. Our findings suggest that both neighborhood social cohesion and rodent sightings impact the mental health of New Yorkers and that rodent infestations may diminish the benefit of neighborhood social cohesion.

Loneliness and sense of community are not two sides of the same coin: Identifying different determinants using the 2019 Nova Scotia Quality of Life data

The purpose of this study was to explore the relative importance of lifestyle factors and living conditions when predicting loneliness and sense of community (SOC) in a representative sample of 12,871 participants from Nova Scotia collected in 2019. Using multiple regression and measures of relative importance based on the Lindeman, Merenda and Gold (lmg) method, we identified which variables are most important to predicting measures of loneliness and SOC. Twenty-two predictors accounted for 46% of the variance in SOC and the top 10 predictors accounted for 36% of the variance: satisfaction with quality of the natural environment in the neighborhood (ri = 0.09), life satisfaction (ri = 0.05), number of neighbors one can rely on (ri = 0.05), confidence in institutions (ri = 0.05), feeling better off due to government policy or programming (ri = 0.04), feeling safe walking in neighborhood after dark (ri = 0.03), mental health (ri = 0.02), number of friends one can rely on (ri = 0.02), volunteering (ri = 0.02), and perceptions of time adequacy (ri = 0.02). Only six of these variables were also the top predictors of loneliness. These results show that both community- and individual-level variables are substantial predictors of social well-being. The effect sizes differ between models, which suggests that there may be important predictors of loneliness that we have not accounted for. This study may inform community-level programming and policy that seeks to promote social well-being for individuals and their communities.