Environmental characteristics of hotspots of alcohol-related health incidents outside of the entertainment districts of Amsterdam

Abstract

While hotspots of alcohol-related incidents have been associated with alcohol outlet density, few studies have considered other environmental factors. We aimed to characterise the environment of hotspots outside of outlet density areas in Amsterdam. Using geographical information systems, we identified hotspots of alcohol-related health incidents outside of outlet density areas based on the registry of alcohol related ambulance attendances. While quantitative data and methods were used to analyse victim profiles (including age, sex and home address) and temporal profiles (time of incident), we combined quantitative and qualitative information to develop environmental profiles of these hotspots. We identified 11 hotspots with 242 incidents outside outlet density areas. Results indicate substantial differences between incidents in these hotspots and incidents in high outlet density areas. Victims in these hotspots are older, more often male and live closer to the incident location compared to victims in high outlet density hotspots. Incidents are less likely to take place during the night and at weekends. In-depth analysis of four hotspots showed different combinations of contributing environmental factors, including (a) public parks bordering deprived neighbourhoods, (b) dance event facilities close to a traffic black spot, and (c) residential services for disadvantaged groups near 'hang-outs'. The study shows that hotspots of alcohol-related incidents can arise outside outlet density areas at locations where several other environmental factors cluster, particularly in more deprived areas. Such hotspots can be addressed in urban planning and urban design.

Investigating Health Risk Environments in Housing Programs for Young Adults: Protocol for a Geographically Explicit Ecological Momentary Assessment Study

“…At a minimum, information gained from this project can inform providers of the typical risks experienced by their clients and potentially inform structural interventions in housing programs. Geospatial data could point to hotspots (see Veldhuizen et al [89]) or specific risky environments in which young adults often interact. Furthermore, ecologically valid methods allow researchers to approximate what might be occurring in real time and bring us one step closer to identifying leverage points where intervention might be particularly fruitful.…”

A Predictive Model of Regional Dementia Prevalence Using Geographic Weighted Regression Analysis

“…In this study, using the spatial autocorrelation analysis method validated in previous studies [36][37][38][39][40][41], the inter-regional association of dementia prevalence was identified, and risk factors for dementia prevalence at the regional level were identified as well. It can be said that these findings are methodologically meaningful in that the risk factors for the prevalence of preventable dementia at the regional level reflecting the spatial characteristics of the region were identified using geographic regression analysis.…”