Exposure to suicide in residential neighborhood and mental distress symptoms in Hong Kong: A spatiotemporal analysis

Neighborhood collective efficacy in stressful events: The stress-buffering effect

Although research on neighborhood effects has shown positive outcomes of collective efficacy in mental health, few studies have examined whether its protective role is universally applicable to all residents or the vulnerable population. Building on a stress-buffering model, this study examines whether or not neighborhood collective efficacy serves as a stress buffer to ameliorate the deleterious effects of exposure to stressful events across different population groups. Analyses are conducted based on a city-wide representative sample in Hong Kong linked to suicide events through spatial and temporal information. Neighborhood-level collective efficacy is constructed by the aggregated mean score of individual perceived collective efficacy within the same residential neighborhoods. Results from the logistic regression models show that individuals exposed to suicide in the residential surroundings have a higher risk of mental distress symptoms. Moreover, neighborhood-level collective efficacy tends to alleviate the mental distress upon exposure, but such a stress-buffering effect is only observed in older adults. Our findings provide a new perspective informed by the variation of stress-buffering effect across population groups. Thus, this study contributes to the understandings of neighborhood collective by demonstrating the stress-buffering effects among the vulnerable population.

Identifying spatiotemporal patterns of COVID-19 transmissions and the drivers of the patterns in Toronto: a Bayesian hierarchical spatiotemporal modelling

Spatiotemporal patterns and trends of COVID-19 at a local spatial scale using Bayesian approaches are hardly observed in literature. Also, studies rarely use satellite-derived long time-series data on the environment to predict COVID-19 risk at a spatial scale. In this study, we modelled the COVID-19 pandemic risk using a Bayesian hierarchical spatiotemporal model that incorporates satellite-derived remote sensing data on land surface temperature (LST) from January 2020 to October 2021 (89 weeks) and several socioeconomic covariates of the 140 neighbourhoods in Toronto. The spatial patterns of risk were heterogeneous in space with multiple high-risk neighbourhoods in Western and Southern Toronto. Higher risk was observed during Spring 2021. The spatiotemporal risk patterns identified 60% of neighbourhoods had a stable, 37% had an increasing, and 2% had a decreasing trend over the study period. LST was positively, and higher education was negatively associated with the COVID-19 incidence. We believe the use of Bayesian spatial modelling and the remote sensing technologies in this study provided a strong versatility and strengthened our analysis in identifying the spatial risk of COVID-19. The findings would help in prevention planning, and the framework of this study may be replicated in other highly transmissible infectious diseases.