CASTNet: Community-Attentive Spatio-Temporal Networks for Opioid Overdose Forecasting

Opioid overdose is a growing public health crisis in the United States. This crisis, recognized as "opioid epidemic," has widespread societal consequences including the degradation of health, and the increase in crime rates and family problems. To improve the overdose surveillance and to identify the areas in need of prevention effort, in this work, we focus on forecasting opioid overdose using real-time crime dynamics. Previous work identified various types of links between opioid use and criminal activities, such as financial motives and common causes. Motivated by these observations, we propose a novel spatio-temporal predictive model for opioid overdose forecasting by leveraging the spatio-temporal patterns of crime incidents. Our proposed model incorporates multi-head attentional networks to learn different representation subspaces of features. Such deep learning architecture, called "community-attentive" networks, allows the prediction of a given location to be optimized by a mixture of groups (i.e., communities) of regions. In addition, our proposed model allows for interpreting what features, from what communities, have more contributions to predicting local incidents as well as how these communities are captured through forecasting. Our results on two real-world overdose datasets indicate that our model achieves superior forecasting performance and provides meaningful interpretations in terms of spatio-temporal relationships between the dynamics of crime and that of opioid overdose.Comment: Accepted as conference paper at ECML-PKDD 201

Reaching the Hard-to-Reach: A Probability Sampling Method for Assessing Prevalence of Driving under the Influence after Drinking in Alcohol Outlets

Drinking alcoholic beverages in places such as bars and clubs may be associated with harmful consequences such as violence and impaired driving. However, methods for obtaining probabilistic samples of drivers who drink at these places remain a challenge – since there is no a priori information on this mobile population – and must be continually improved. This paper describes the procedures adopted in the selection of a population-based sample of drivers who drank at alcohol selling outlets in Porto Alegre, Brazil, which we used to estimate the prevalence of intention to drive under the influence of alcohol. The sampling strategy comprises a stratified three-stage cluster sampling: 1) census enumeration areas (CEA) were stratified by alcohol outlets (AO) density and sampled with probability proportional to the number of AOs in each CEA; 2) combinations of outlets and shifts (COS) were stratified by prevalence of alcohol-related traffic crashes and sampled with probability proportional to their squared duration in hours; and, 3) drivers who drank at the selected COS were stratified by their intention to drive and sampled using inverse sampling. Sample weights were calibrated using a post-stratification estimator. 3,118 individuals were approached and 683 drivers interviewed, leading to an estimate that 56.3% (SE = 3,5%) of the drivers intended to drive after drinking in less than one hour after the interview. Prevalence was also estimated by sex and broad age groups. The combined use of stratification and inverse sampling enabled a good trade-off between resource and time allocation, while preserving the ability to generalize the findings. The current strategy can be viewed as a step forward in the efforts to improve surveys and estimation for hard-to-reach, mobile populations

Minimum pricing of alcohol versus volumetric taxation:which policy will reduce heavy consumption without adversely affecting light and moderate consumers?

Background We estimate the effect on light, moderate and heavy consumers of alcohol from implementing a minimum unit price for alcohol (MUP) compared with a uniform volumetric tax. Methods We analyse scanner data from a panel survey of demographically representative households (n = 885) collected over a one-year period (24 Jan 2010–22 Jan 2011) in the state of Victoria, Australia, which includes detailed records of each household's off-trade alcohol purchasing. Findings The heaviest consumers (3% of the sample) currently purchase 20% of the total litres of alcohol (LALs), are more likely to purchase cask wine and full strength beer, and pay significantly less on average per standard drink compared to the lightest consumers (A$1.31 [95$2.21 [95% CI 2.10–2.31]). Applying a MUP of A$1 per standard drink has a greater effect on reducing the mean annual volume of alcohol purchased by the heaviest consumers of wine (15.78 LALs [95$393.60 [95% CI 374.19–413.00]) and beer ($108.26 [95$552.46 [95% CI 530.55–574.36] and $163.92 [95% CI 152.79–175.03], respectively). Both a MUP and uniform volumetric tax have little effect on changing the annual cost of wine and beer for light and moderate consumers, and likewise little effect upon their purchasing. Conclusions While both a MUP and a uniform volumetric tax have potential to reduce heavy consumption of wine and beer without adversely affecting light and moderate consumers, a MUP offers the potential to achieve greater reductions in heavy consumption at a lower overall annual cost to consumers

Using Routinely Collected Administrative Data in Public Health Research: Geocoding Alcohol Outlet Data

We describe our process of geocoding alcohol outlets to create a national longitudinal exposure dataset for Wales, United Kingdom from 2006 to 2011. We investigated variation in the availability of data items and the quality of alcohol outlet addresses held within unitary authorities. We used a standard geocoding method augmented with a manual matching procedure to achieve a fully spatially referenced dataset. We found higher quality addresses are held for outlets based in urban areas, resulting in the automatic geocoding of 68 % of urban outlets, compared to 48 % in rural areas. Missing postcodes and a lack of address structure contributed to a lower geocoding proportion. An urban rural bias was removed with the development of a manual matching procedure. Only one-half of the unitary authorities provided data on on/off sales and opening times, which are important availability factors. The resulting outlet dataset is suitable for contributing to the evidence-base of alcohol availability and alcohol-related harm. Local government should be encouraged to use standardised data fields, including addresses, to enable accurate geocoding of alcohol outlets and facilitate research that aims to prevent alcohol-related harm. Standardising data collection would enable efficient secondary data reuse using record linkage techniques, allowing the retrospective creation and evaluation of population-based natural experiments to provide evidence for policy and practice

An Information Theory Approach to Hypothesis Testing in Criminological Research

Background: This research demonstrates how the Akaike information criterion (AIC) can be an alternative to null hypothesis significance testing in selecting best fitting models. It presents an example to illustrate how AIC can be used in this way. Methods: Using data from Milwaukee, Wisconsin, we test models of place-based predictor variables on street robbery and commercial robbery. We build models to balance explanatory power and parsimony. Measures include the presence of different kinds of businesses, together with selected age groups and social disadvantage. Results: Models including place-based measures of land use emerged as the best models among the set of tested models. These were superior to models that included measures of age and socioeconomic status. The best models for commercial and street robbery include three measures of ordinary businesses, liquor stores, and spatial lag. Conclusions: Models based on information theory offer a useful alternative to significance testing when a strong theoretical framework guides the selection of model sets. Theoretically relevant ‘ordinary businesses’ have a greater influence on robbery than socioeconomic variables and most measures of discretionary businesses

Does Proximity to Retailers Influence Alcohol and Tobacco Use Among Latino Adolescents?

Despite decades of research surrounding determinants of alcohol and tobacco (A&T) use among adolescents, built environment influences have only recently been explored. This study used ordinal regression on 205 Latino adolescents to explore the influence of the built environment (proximity to A&T retailers) on A&T use, while controlling for recognized social predictors. The sample was 45% foreign-born. A&T use was associated with distance from respondents’ home to the nearest A&T retailer (−), acculturation (+), parents’ consistent use of contingency management (−), peer use of A&T (+), skipping school (+), attending school in immediate proximity to the US/Mexico border (+), and the interaction between the distance to the nearest retailer and parents’ consistent use of contingency management (+). The association between decreasing distance to the nearest A&T retailer and increased A&T use in Latino adolescents reveals an additional risk behavior determinant in the US–Mexico border region