Geo-spatial risk factor analysis for drug overdose death in South Florida from 2014 to 2019, and the independent contribution of social determinants of health

PURPOSE

The physical environment and social determinants of health have been shown to influence health behaviors including drug use and fatal drug overdose. The current research examines the effects of the built environment, social determinants of health measures and aggregated risk from the built environment at neighborhood-level on drug overdose death locations in Miami-Dade County, Florida.

METHODS

Risk Terrain Modeling (RTM) was used to assess the place features risk factors that significantly increase the risk of drug overdose death spatially in Miami-Dade County ZIP Code Tabulation Areas, Florida from 2014 to 2019. An aggregated neighborhood risk of fatal drug overdose measure was developed by averaging the risk per grid cell from the RTM within census block groups each year. Six logistic and zero-inflated regression models were built to examine the effects of three indices of incident-specific social determinants of health (IS-SDH) measures and aggregated risk measures separately, and simultaneously on drug overdose death locations each year.

RESULTS

Seven place features including parks, bus stops, restaurants and grocery stores were significantly related to the occurrence of fatal drug overdoses. When examined separately, one or more indices of the IS-SDH were significant covariates of drug overdose locations in some years. When examined simultaneously, the three indices of the IS-SDH and aggregated risk of fatal drug overdose measure could be all significant in certain years.

CONCLUSIONS

The patterns of high-risk areas and place features identified from the RTM related to drug overdose deaths may be used to inform the placement of treatment and prevention resources. A multi-factor approach that combines an aggregated neighborhood risk measure reflecting the risk from the built environment and the incident-specific social determinants of health measures can be used to identify the drug overdose death locations in certain years.

Does routine activity theory still matter during COVID-19 restrictions? The geography of sexual assaults before, during, and after COVID-19 restrictions

On March 10, 2020, the Governor of the State of Michigan, USA, declared a state of emergency in response to COVID-19. Within days, schools were closed; in-person dining was restricted; and lockdowns and precautionary stay-at-home orders were issued. These restrictions dramatically impacted the mobility of offenders and victims through space and time. As routine activities were forced to change and crime generators were closed, did hot spots and risky locations for victimization change as well? The purpose of this research is to analyze potential shifts in high-risk areas for sexual assaults before, during, and after COVID-19 restrictions. Using data from the City of Detroit, Michigan, USA, optimized hot spot analysis and Risk Terrain Modeling (RTM) were used to identify critical spatial factors for the occurrence of sexual assaults before, during, and after COVID-19 restrictions. The results suggested that hot spot areas for sexual assaults were more concentrated during the COVID timeframe, compared to the Pre-COVID timeframe. While blight complaints, public transit stops, points of sale for liquor, and the locations of drug arrests were consistent risk factors for sexual assaults before and after COVID restrictions, other factors, such as casinos and demolitions, were only influential in the COVID period.

Pharmacies and features of the built environment associated with opioid overdose: A geospatial comparison of rural and urban regions in Alabama, USA

BACKGROUND

Elements of the physical environment have been shown to influence health behaviors including drug use and overdose mortality. Throughout the opioid epidemic in the United States, rural regions have been disproportionately affected by opioid overdose. Although the relationship between the urban built environment and opioid overdose has been established, little is known as to how trends may differ in rural areas.

METHODS

Risk terrain modeling was used as a spatial analytical approach to assess environmental features that significantly increase the risk of opioid overdose in Jefferson County, Alabama. Spatial risk assessments were conducted for urban and rural regions as well as for the county as a whole. Criminogenic, opioid-related, and community variables were included and compared across spatial risk models.

RESULTS

The geographic context, rural or urban, influenced the relationship between environmental features and opioid overdose. In rural areas, community features such as bus stops and public schools were related to the occurrence of opioid overdose. In urban areas, inpatient treatment centers, transitional living facilities, express loan establishments, and liquor vendors were significantly related to the locations of opioid overdose.

CONCLUSION

Risk terrain modeling can be used to locate high-risk areas for opioid overdose while identifying factors that are contributing to the risk of events occurring in communities. The patterns of overdose risk differ in rural and urban contexts and may be used to inform the placement of treatment and prevention resources.

Exploring the geography of suicide threats and suicide attempts: An application of Risk Terrain Modeling

The purpose of this research is twofold: first, the spatial distribution of 911 emergency calls for service to the police for suicides in progress and threats of suicide in the City of Detroit, Michigan will be explored to determine whether these events exhibit different patterns of spatial clustering. Second, this research will explore the utility of Risk Terrain Modeling (RTM) to assist in our understanding of the locations of calls for service to the police related to suicide threats and suicides in progress. The results suggest that these events are different social phenomenon, both with respect to the spatial clustering of these events as well as qualitative differences in the environmental factors that may contribute to their occurrence.

Forecasting homicide in the red stick: Risk terrain modeling and the spatial influence of urban blight on lethal violence in Baton Rouge, Louisiana

Incorporating features of the built environment, risk terrain modeling (RTM), is used to predict future criminal events in micro-units (i.e., city blocks). The current study examines the application of RTM to forecast homicide in the capital city of Baton Rouge, Louisiana while including a novel environmental risk factor, blighted properties. Based upon the extant literature and knowledge of the city, eighteen environmental risk factors are expected to spatially influence homicide. Results indicate that places most at risk of experiencing a homicide are located in areas where blighted properties are concentrated and in close proximity to convenience stores. RTM successfully identities and evaluates environmental risk factors that spatially influence lethal violence. Additionally, RTM is able to accurately forecast future acts of homicide. The results underscore how crime prevention through environmental design (CPTED) and blight remediation could be utilized as straightforward and prudent strategies to reduce lethal violence.

Risk terrain modeling predicts child maltreatment

As indicated by research on the long-term effects of adverse childhood experiences (ACEs), maltreatment has far-reaching consequences for affected children. Effective prevention measures have been elusive, partly due to difficulty in identifying vulnerable children before they are harmed. This study employs Risk Terrain Modeling (RTM), an analysis of the cumulative effect of environmental factors thought to be conducive for child maltreatment, to create a highly accurate prediction model for future substantiated child maltreatment cases in the City of Fort Worth, Texas. The model is superior to commonly used hotspot predictions and more beneficial in aiding prevention efforts in a number of ways: 1) it identifies the highest risk areas for future instances of child maltreatment with improved precision and accuracy; 2) it aids the prioritization of risk-mitigating efforts by informing about the relative importance of the most significant contributing risk factors; 3) since predictions are modeled as a function of easily obtainable data, practitioners do not have to undergo the difficult process of obtaining official child maltreatment data to apply it; 4) the inclusion of a multitude of environmental risk factors creates a more robust model with higher predictive validity; and, 5) the model does not rely on a retrospective examination of past instances of child maltreatment, but adapts predictions to changing environmental conditions. The present study introduces and examines the predictive power of this new tool to aid prevention efforts seeking to improve the safety, health, and wellbeing of vulnerable children.