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Lopez DS, Parent J, Stegnicki T, Kenyon Z, Arcoleo K, Malloy LC, Mello MJ. Overdosing in a Motor Vehicle: Examination of Human, Geographic, and Environmental Factors. Nurs Res 2024; 73:195-202. [PMID: 38329965 PMCID: PMC11039364 DOI: 10.1097/nnr.0000000000000716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2024]
Abstract
BACKGROUND Fentanyl, a type of opioid, in impaired driving cases increased across cities in the United States. OBJECTIVES No empirical studies have examined motor vehicle overdoses with fentanyl use. We investigated the magnitude of the motor vehicle overdose problem in Providence, RI, and the environmental, socioeconomic, and geographic conditions associated with motor vehicle overdose occurrence. METHODS This was a retrospective observational study of emergency medical services data on all suspected opioid overdoses between January 1, 2017, and October 31, 2020. The data contain forced-choice fields, such as age and biological sex, and an open-ended narrative in which the paramedic documented clinical and situational information. The overdoses were geocoded, allowing for the extraction of sociodemographic data from the U.S. Census Bureau's American Community Survey. Seven other data sources were included in a logistic regression to understand key risk factors and spatial patterns of motor vehicle overdoses. RESULTS Of the 1,357 opioid overdose cases in this analysis, 15.2% were defined as motor vehicle overdoses. In adjusted models, we found a 61% increase in the odds of a motor vehicle overdose involvement for men versus women, a 16.8% decrease in the odds of a motor vehicle overdose for a one-unit increase in distance to the nearest gas station, and a 10.7% decrease in the odds of a motor vehicle overdose for a one-unit increase in distance to a buprenorphine clinic. CONCLUSION There is a need to understand the interaction between drug use in vehicles to design interventions for decreasing driving after illicit drug use.
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Slade E, Mangino AA, Daniels L, Liford M, Quesinberry D. Modelling overdose case fatality rates over time: The collaborative process. Stat (Int Stat Inst) 2023. [DOI: 10.1002/sta4.510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Affiliation(s)
- Emily Slade
- Department of Biostatistics University of Kentucky Lexington Kentucky 40536 USA
| | - Anthony A. Mangino
- Department of Biostatistics University of Kentucky Lexington Kentucky 40536 USA
| | - Lara Daniels
- Kentucky Injury Prevention and Research Center University of Kentucky Lexington Kentucky 40536 USA
| | - Madison Liford
- Kentucky Injury Prevention and Research Center University of Kentucky Lexington Kentucky 40536 USA
| | - Dana Quesinberry
- Kentucky Injury Prevention and Research Center University of Kentucky Lexington Kentucky 40536 USA
- Department of Health Management and Policy University of Kentucky Lexington Kentucky 40536 USA
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Li Y, Miller HJ, Hyder A, Jia P. Understanding the spatiotemporal evolution of opioid overdose events using a regionalized sequence alignment analysis. Soc Sci Med 2023; 334:116188. [PMID: 37651825 DOI: 10.1016/j.socscimed.2023.116188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 06/26/2023] [Accepted: 08/22/2023] [Indexed: 09/02/2023]
Abstract
BACKGROUND Opioid overdose events and deaths have become a serious public health crisis in the United States, and understanding the spatiotemporal evolution of the disease occurrences is crucial for developing effective prevention strategies, informing health systems policy and planning, and guiding local responses. However, current research lacks the capability to observe the dynamics of the opioid crisis at a fine spatial-temporal resolution over a long period, leading to ineffective policies and interventions at the local level. METHODS This paper proposes a novel regionalized sequential alignment analysis using opioid overdose events data to assess the spatiotemporal similarity of opioid overdose evolutionary trajectories within regions that share similar socioeconomic status. The model synthesizes the shape and correlation of space-time trajectories to assist space-time pattern mining in different neighborhoods, identifying trajectories that exhibit similar spatiotemporal characteristics for further analysis. RESULTS By adopting this methodology, we can better understand the spatiotemporal evolution of opioid overdose events and identify regions with similar patterns of evolution. This enables policymakers and health researchers to develop effective interventions and policies to address the opioid crisis at the local level. CONCLUSIONS The proposed methodology provides a new framework for understanding the spatiotemporal evolution of opioid overdose events, enabling policymakers and health researchers to develop effective interventions and policies to address this growing public health crisis.
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Affiliation(s)
- Yuchen Li
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, UK.
| | - Harvey J Miller
- Department of Geography, The Ohio State University, Columbus, USA; Center for Urban and Regional Analysis, The Ohio State University, Columbus, USA
| | - Ayaz Hyder
- College of Public Health, The Ohio State University, Columbus, USA
| | - Peng Jia
- School of Resource and Environmental Sciences, Wuhan University, Wuhan, China; Hubei Luojia Laboratory, Wuhan, China; School of Public Health, Wuhan University, Wuhan, China; International Institute of Spatial Lifecourse Health (ISLE), Wuhan University, Wuhan, China.
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4
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Lin B, Zheng Y, Roussos-Ross D, Gurka KK, Gurka MJ, Hu H. An external exposome-wide association study of opioid use disorder diagnosed during pregnancy in Florida. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 870:161842. [PMID: 36716893 PMCID: PMC9998369 DOI: 10.1016/j.scitotenv.2023.161842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 01/21/2023] [Accepted: 01/22/2023] [Indexed: 06/18/2023]
Abstract
The prevalence of opioid use disorder (OUD) during pregnancy has quadrupled in recent years and widely varies geographically in the US. However, few studies have examined which environmental factors are associated with OUD during pregnancy. We conducted an external exposome-wide association study (ExWAS) to investigate the associations between external environmental factors and OUD diagnosed during pregnancy. Data were obtained from a unique, statewide database in Florida comprising linked individual-level birth and electronic health records. A total of 255,228 pregnancies with conception dates between 2012 and 2016 were included. We examined 82 exposome measures characterizing seven aspects of the built and social environment and spatiotemporally linked them to each individual record. A two-phase procedure was utilized for the external ExWAS. In Phase 1, we randomly divided the data into a discovery set (50 %) and a replication set (50 %). Associations between exposome measures (normalized and standardized) and OUD initially diagnosed during pregnancy were examined using logistic regression. A total of 15 variables were significant in both the discovery and replication sets. In Phase 2, multivariable logistic regression was used to fit all variables selected from Phase 1. Measures of walkability (the national walkability index, OR: 1.23, 95 % CI: 1.17, 1.29), vacant land (the percent vacant land for 36 months or longer, OR: 1.06, 95 % CI: 1.00, 1.12) and food access (the percentage of low food access population that are seniors at 1/2 mile, OR: 1.47, 95 % CI: 1.38, 1.57) were each associated with diagnosis of OUD during pregnancy. This is the first external ExWAS of OUD during pregnancy, and the results suggest that low food access, high walkability, and high vacant land in under-resourced neighborhoods are associated with diagnosis of OUD during pregnancy. These findings could help develop complementary tools for universal screening for substance use and provide direction for future studies.
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Affiliation(s)
- Boya Lin
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA
| | - Yi Zheng
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Dikea Roussos-Ross
- Department of Obstetrics and Gynecology, University of Florida, Gainesville, FL, USA
| | - Kelly K Gurka
- Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, FL, USA
| | - Matthew J Gurka
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA; Department of Obstetrics and Gynecology, University of Florida, Gainesville, FL, USA; Department of Pediatrics, University of Florida, Gainesville, FL, USA
| | - Hui Hu
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
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5
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Mavragani A, Bradley H, Li W, Bernson D, Dammann O, LaRochelle MR, Stopka TJ. Small Area Forecasting of Opioid-Related Mortality: Bayesian Spatiotemporal Dynamic Modeling Approach. JMIR Public Health Surveill 2023; 9:e41450. [PMID: 36763450 PMCID: PMC9960038 DOI: 10.2196/41450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 12/14/2022] [Accepted: 12/26/2022] [Indexed: 02/11/2023] Open
Abstract
BACKGROUND Opioid-related overdose mortality has remained at crisis levels across the United States, increasing 5-fold and worsened during the COVID-19 pandemic. The ability to provide forecasts of opioid-related mortality at granular geographical and temporal scales may help guide preemptive public health responses. Current forecasting models focus on prediction on a large geographical scale, such as states or counties, lacking the spatial granularity that local public health officials desire to guide policy decisions and resource allocation. OBJECTIVE The overarching objective of our study was to develop Bayesian spatiotemporal dynamic models to predict opioid-related mortality counts and rates at temporally and geographically granular scales (ie, ZIP Code Tabulation Areas [ZCTAs]) for Massachusetts. METHODS We obtained decedent data from the Massachusetts Registry of Vital Records and Statistics for 2005 through 2019. We developed Bayesian spatiotemporal dynamic models to predict opioid-related mortality across Massachusetts' 537 ZCTAs. We evaluated the prediction performance of our models using the one-year ahead approach. We investigated the potential improvement of prediction accuracy by incorporating ZCTA-level demographic and socioeconomic determinants. We identified ZCTAs with the highest predicted opioid-related mortality in terms of rates and counts and stratified them by rural and urban areas. RESULTS Bayesian dynamic models with the full spatial and temporal dependency performed best. Inclusion of the ZCTA-level demographic and socioeconomic variables as predictors improved the prediction accuracy, but only in the model that did not account for the neighborhood-level spatial dependency of the ZCTAs. Predictions were better for urban areas than for rural areas, which were more sparsely populated. Using the best performing model and the Massachusetts opioid-related mortality data from 2005 through 2019, our models suggested a stabilizing pattern in opioid-related overdose mortality in 2020 and 2021 if there were no disruptive changes to the trends observed for 2005-2019. CONCLUSIONS Our Bayesian spatiotemporal models focused on opioid-related overdose mortality data facilitated prediction approaches that can inform preemptive public health decision-making and resource allocation. While sparse data from rural and less populated locales typically pose special challenges in small area predictions, our dynamic Bayesian models, which maximized information borrowing across geographic areas and time points, were used to provide more accurate predictions for small areas. Such approaches can be replicated in other jurisdictions and at varying temporal and geographical levels. We encourage the formation of a modeling consortium for fatal opioid-related overdose predictions, where different modeling techniques could be ensembled to inform public health policy.
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Affiliation(s)
| | | | - Wenjun Li
- Department of Public Health, University of Massachusetts Lowell, Lowell, MA, United States
| | - Dana Bernson
- Office of Population Health, Department of Public Health, The Commonwealth of Massachusetts, Boston, MA, United States
| | - Olaf Dammann
- Department of Public Health and Community Medicine, Tufts University School of Medicine, Boston, MA, United States.,Department of Gynecology and Obstetrics, Hannover Medical School, Hannover, Germany
| | - Marc R LaRochelle
- Clinical Addiction Research and Education Unit, Section of General Internal Medicine, Department of Medicine, Boston University School of Medicine, Boston, MA, United States.,Grayken Center for Addiction, Boston Medical Center, Boston, MA, United States
| | - Thomas J Stopka
- Department of Public Health and Community Medicine, Tufts University School of Medicine, Boston, MA, United States.,Department of Urban and Environmental Policy and Planning, Tufts University, Medford, MA, United States.,Department of Community Health, Tufts University, Medford, MA, United States
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Helderop E, Nelson JR, Grubesic TH. 'Unmasking' masked address data: A medoid geocoding solution. MethodsX 2023; 10:102090. [PMID: 36915860 PMCID: PMC10006849 DOI: 10.1016/j.mex.2023.102090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 02/20/2023] [Indexed: 02/24/2023] Open
Abstract
In recent years, there has been a consistent push for more open data initiatives, particularly for datasets collected by public agencies or groups that receive public funding. However, there is a tension between the release of open data and the preservation of individual and household privacy, whose balance shifts due to increased data availability, the sophistication of analysis techniques, and the computational power available to users. As a result, data masking is a standard tool used to preserve privacy. This is a process in which the data publishers obfuscate some identifying features in the dataset while attempting to maintain as much accuracy and precision as possible. For spatial datasets, the geocoding of administratively-masked data has been a consistent problem. Here, we present a medoid-based technique that geocodes masked data while minimizing the spatial uncertainty associated with the masking approach. Unfortunately, many commercial geocoding software packages either fail to geocode administratively-masked data or provide false positives by assigning points to city or street centroids. We demonstrate the results of our medoid-based geocoding approach by comparing it to commercial geocoding software. The results suggest that a medoid geocoding approach is mechanically simple to deploy and maximizes the spatial accuracy of the resulting geocodes.•Administratively-masked data are difficult to geocode•A medoid geocoding method maximizes geocoding accuracy•This method outperforms commercial geocoding software.
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Affiliation(s)
- Edward Helderop
- Center for Geospatial Sciences, School of Public Policy, University of California Riverside
| | | | - Tony H Grubesic
- Center for Geospatial Sciences, School of Public Policy, University of California Riverside
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7
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Choi JI, Lee J, Yeh AB, Lan Q, Kang H. Spatial clustering of heroin-related overdose incidents: a case study in Cincinnati, Ohio. BMC Public Health 2022; 22:1253. [PMID: 35752791 PMCID: PMC9233379 DOI: 10.1186/s12889-022-13557-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Accepted: 05/24/2022] [Indexed: 12/03/2022] Open
Abstract
Background Drug overdose is one of the top leading causes of accidental death in the U.S., largely due to the opioid epidemic. Although the opioid epidemic is a nationwide issue, it has not affected the nation uniformly. Methods We combined multiple data sources, including emergency medical service response, American Community Survey data, and health facilities datasets to analyze distributions of heroin-related overdose incidents in Cincinnati, Ohio at the census block group level. The Ripley’s K function and the local Moran’s I statistics were performed to examine geographic variation patterns in heroin-related overdose incidents within the study area. Then, conditional cluster maps were plotted to examine a relationship between heroin-related incident rates and sociodemographic characteristics of areas as well as the resources for opioid use disorder treatment. Results The global spatial analysis indicated that there was a clustered pattern of heroin-related overdose incident rates at every distance across the study area. The univariate local spatial analysis identified 7 hot spot clusters, 27 cold spot clusters, and 1 outlier cluster. Conditional cluster maps showed characteristics of neighborhoods with high heroin overdose rates, such as a higher crime rate, a high percentage of the male, a high poverty level, a lower education level, and a lower income level. The hot spots in the Southwest areas of Cincinnati had longer distances to opioid treatment programs and buprenorphine prescribing physicians than the median, while the hot spots in the South-Central areas of the city had shorter distances to those health resources. Conclusions Our study showed that the opioid epidemic disproportionately affected Cincinnati. Multi-phased spatial clustering models based on various data sources can be useful to identify areas that require more policy attention and targeted interventions to alleviate high heroin-related overdose rates. Supplementary Information The online version contains supplementary material available at 10.1186/s12889-022-13557-3.
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Affiliation(s)
- Jung Im Choi
- Data Science, Bowling Green State University, 221 Hayes Hall, Bowling Green, OH, 43403, USA
| | - Jinha Lee
- Faculty of Public and Allied Health, Bowling Green State University, 111 Health and Human Services Building, Bowling Green, OH, 43403, USA.
| | - Arthur B Yeh
- Faculty of Applied Statistics and Operations Research, Bowling Green State University, 1001 E Wooster Street, Maurer Center 241J, Bowling Green, OH, 43403, USA
| | - Qizhen Lan
- Data Science, Bowling Green State University, 221 Hayes Hall, Bowling Green, OH, 43403, USA
| | - Hyojung Kang
- Faculty of Kinesiology and Community Health, University of Illinois at Urbana-Champaign, 1206 Fourth Street, IL, 61820, Champaign, USA.
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Li Y, Miller HJ, Root ED, Hyder A, Liu D. Understanding the role of urban social and physical environment in opioid overdose events using found geospatial data. Health Place 2022; 75:102792. [PMID: 35366619 DOI: 10.1016/j.healthplace.2022.102792] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 03/10/2022] [Accepted: 03/11/2022] [Indexed: 01/05/2023]
Abstract
Opioid use disorder is a serious public health crisis in the United States. Manifestations such as opioid overdose events (OOEs) vary within and across communities and there is growing evidence that this variation is partially rooted in community-level social and economic conditions. The lack of high spatial resolution, timely data has hampered research into the associations between OOEs and social and physical environments. We explore the use of non-traditional, "found" geospatial data collected for other purposes as indicators of urban social-environmental conditions and their relationships with OOEs at the neighborhood level. We evaluate the use of Google Street View images and non-emergency "311" service requests, along with US Census data as indicators of social and physical conditions in community neighborhoods. We estimate negative binomial regression models with OOE data from first responders in Columbus, Ohio, USA between January 1, 2016, and December 31, 2017. Higher numbers of OOEs were positively associated with service request indicators of neighborhood physical and social disorder and street view imagery rated as boring or depressing based on a pre-trained random forest regression model. Perceived safety, wealth, and liveliness measures from the street view imagery were negatively associated with risk of an OOE. Age group 50-64 was positively associated with risk of an OOE but age 35-49 was negative. White population, percentage of individuals living in poverty, and percentage of vacant housing units were also found significantly positive however, median income and percentage of people with a bachelor's degree or higher were found negative. Our result shows neighborhood social and physical environment characteristics are associated with likelihood of OOEs. Our study adds to the scientific evidence that the opioid epidemic crisis is partially rooted in social inequality, distress and underinvestment. It also shows the previously underutilized data sources hold promise for providing insights into this complex problem to help inform the development of population-level interventions and harm reduction policies.
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Affiliation(s)
- Yuchen Li
- Department of Geography, The Ohio State University, United States.
| | - Harvey J Miller
- Department of Geography, The Ohio State University, United States; Center for Urban and Regional Analysis, The Ohio State University, United States
| | - Elisabeth D Root
- Department of Geography, The Ohio State University, United States; College of Public Health, The Ohio State University, United States
| | - Ayaz Hyder
- College of Public Health, The Ohio State University, United States
| | - Desheng Liu
- Department of Geography, The Ohio State University, United States
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Marshall BDL, Alexander-Scott N, Yedinak JL, Hallowell BD, Goedel WC, Allen B, Schell RC, Li Y, Krieger MS, Pratty C, Ahern J, Neill DB, Cerdá M. Preventing Overdose Using Information and Data from the Environment (PROVIDENT): protocol for a randomized, population-based, community intervention trial. Addiction 2022; 117:1152-1162. [PMID: 34729851 PMCID: PMC8904285 DOI: 10.1111/add.15731] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 10/08/2021] [Indexed: 12/28/2022]
Abstract
BACKGROUND AND AIMS In light of the accelerating drug overdose epidemic in North America, new strategies are needed to identify communities most at risk to prioritize geographically the existing public health resources (e.g. street outreach, naloxone distribution efforts). We aimed to develop PROVIDENT (Preventing Overdose using Information and Data from the Environment), a machine learning-based forecasting tool to predict future overdose deaths at the census block group (i.e. neighbourhood) level. DESIGN Randomized, population-based, community intervention trial. SETTING Rhode Island, USA. PARTICIPANTS All people who reside in Rhode Island during the study period may contribute data to either the model or the trial outcomes. INTERVENTION Each of the state's 39 municipalities will be randomized to the intervention (PROVIDENT) or comparator condition. An interactive, web-based tool will be developed to visualize the PROVIDENT model predictions. Municipalities assigned to the treatment arm will receive neighbourhood risk predictions from the PROVIDENT model, and state agencies and community-based organizations will direct resources to neighbourhoods identified as high risk. Municipalities assigned to the control arm will continue to receive surveillance information and overdose prevention resources, but they will not receive neighbourhood risk predictions. MEASUREMENTS The primary outcome is the municipal-level rate of fatal and non-fatal drug overdoses. Fatal overdoses will be defined as unintentional drug-related death; non-fatal overdoses will be defined as an emergency department visit for a suspected overdose reported through the state's syndromic surveillance system. Intervention efficacy will be assessed using Poisson or negative binomial regression to estimate incidence rate ratios comparing fatal and non-fatal overdose rates in treatment vs. control municipalities. COMMENTS The findings will inform the utility of predictive modelling as a tool to improve public health decision-making and inform resource allocation to communities that should be prioritized for prevention, treatment, recovery and overdose rescue services.
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Affiliation(s)
- Brandon D. L. Marshall
- Department of Epidemiology, Brown University School of Public Health, Providence, RI, USA
| | | | - Jesse L. Yedinak
- Department of Epidemiology, Brown University School of Public Health, Providence, RI, USA
| | | | - William C. Goedel
- Department of Epidemiology, Brown University School of Public Health, Providence, RI, USA
| | - Bennett Allen
- Center for Opioid Epidemiology and Policy, Department of Population Health, Grossman School of Medicine, New York University, New York, NY, USA
| | - Robert C. Schell
- Division of Health Policy and Management, School of Public Health, University of California, Berkeley, Berkeley, CA, USA
| | - Yu Li
- Department of Epidemiology, Brown University School of Public Health, Providence, RI, USA
| | - Maxwell S. Krieger
- Department of Epidemiology, Brown University School of Public Health, Providence, RI, USA
| | - Claire Pratty
- Department of Epidemiology, Brown University School of Public Health, Providence, RI, USA
| | - Jennifer Ahern
- Division of Epidemiology, School of Public Health, University of California, Berkeley, Berkeley, CA, USA
| | - Daniel B. Neill
- Center for Urban Science and Progress, New York University, New York, NY, USA
- Courant Institute of Mathematical Sciences, Department of Computer Science, New York University, New York, NY, USA
- Robert F. Wagner Graduate School of Public Service, New York University, New York, NY, USA
| | - Magdalena Cerdá
- Center for Opioid Epidemiology and Policy, Department of Population Health, Grossman School of Medicine, New York University, New York, NY, USA
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10
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Yedinak JL, Li Y, Krieger MS, Howe K, Ndoye CD, Lee H, Civitarese AM, Marak T, Nelson E, Samuels EA, Chan PA, Bertrand T, Marshall BDL. Machine learning takes a village: Assessing neighbourhood-level vulnerability for an overdose and infectious disease outbreak. THE INTERNATIONAL JOURNAL OF DRUG POLICY 2021; 96:103395. [PMID: 34344539 PMCID: PMC8568646 DOI: 10.1016/j.drugpo.2021.103395] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 07/08/2021] [Accepted: 07/16/2021] [Indexed: 12/13/2022]
Abstract
BACKGROUND Multiple areas in the United States of America (USA) are experiencing high rates of overdose and outbreaks of bloodborne infections, including HIV and hepatitis C virus (HCV), due to non-sterile injection drug use. We aimed to identify neighbourhoods at increased vulnerability for overdose and infectious disease outbreaks in Rhode Island, USA. The primary aim was to pilot machine learning methods to identify which neighbourhood-level factors were important for creating "vulnerability assessment scores" across the state. The secondary aim was to engage stakeholders to pilot an interactive mapping tool and visualize the results. METHODS From September 2018 to November 2019, we conducted a neighbourhood-level vulnerability assessment and stakeholder engagement process named The VILLAGE Project (Vulnerability Investigation of underlying Local risk And Geographic Events). We developed a predictive analytics model using machine learning methods (LASSO, Elastic Net, and RIDGE) to identify areas with increased vulnerability to an outbreak of overdose, HIV and HCV, using census tract-level counts of overdose deaths as a proxy for injection drug use patterns and related health outcomes. Stakeholders reviewed mapping tools for face validity and community distribution. RESULTS Machine learning prediction models were suitable for estimating relative neighbourhood-level vulnerability to an outbreak. Variables of importance in the model included housing cost burden, prior overdose deaths, housing density, and education level. Eighty-nine census tracts (37%) with no prior overdose fatalities were identified as being vulnerable to such an outbreak, and nine of those were identified as having a vulnerability assessment score in the top 25%. Results were disseminated as a vulnerability stratification map and an online interactive mapping tool. CONCLUSION Machine learning methods are well suited to predict neighborhoods at higher vulnerability to an outbreak. These methods show promise as a tool to assess structural vulnerabilities and work to prevent outbreaks at the local level.
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Affiliation(s)
- Jesse L Yedinak
- Department of Epidemiology, Brown University School of Public Health, Providence, RI, USA
| | - Yu Li
- Department of Epidemiology, Brown University School of Public Health, Providence, RI, USA
| | - Maxwell S Krieger
- Department of Epidemiology, Brown University School of Public Health, Providence, RI, USA
| | - Katharine Howe
- Center for HIV, Hepatitis, STD, and TB Epidemiology, Rhode Island Department of Health, Providence, RI, USA
| | - Colleen Daley Ndoye
- Project Weber/Renew: Harm Reduction & Recovery Services Provider, Providence, RI, USA
| | - Hyunjoon Lee
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Anna M Civitarese
- Center for HIV, Hepatitis, STD, and TB Epidemiology, Rhode Island Department of Health, Providence, RI, USA
| | - Theodore Marak
- Center for HIV, Hepatitis, STD, and TB Epidemiology, Rhode Island Department of Health, Providence, RI, USA
| | - Elana Nelson
- Department of Epidemiology, Brown University School of Public Health, Providence, RI, USA
| | - Elizabeth A Samuels
- Department of Emergency Medicine, Alpert Medical School of Brown University, Providence, RI, USA; Overdose Prevention Program, Rhode Island Department of Health, Providence, RI, USA
| | - Philip A Chan
- Department of Behavioral and Social Sciences, Brown University School of Public Health, Providence, RI, USA; Department of Medicine, Alpert Medical School of Brown University, Providence, RI, USA; Center for HIV, Hepatitis, STD, and TB Epidemiology, Rhode Island Department of Health, Providence, RI, USA
| | - Thomas Bertrand
- Center for HIV, Hepatitis, STD, and TB Epidemiology, Rhode Island Department of Health, Providence, RI, USA
| | - Brandon D L Marshall
- Department of Epidemiology, Brown University School of Public Health, Providence, RI, USA.
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11
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Bauer C, Champagne-Langabeer T, Bakos-Block C, Zhang K, Persse D, Langabeer JR. Patterns and risk factors of opioid-suspected EMS overdose in Houston metropolitan area, 2015-2019: A Bayesian spatiotemporal analysis. PLoS One 2021; 16:e0247050. [PMID: 33705402 PMCID: PMC7951926 DOI: 10.1371/journal.pone.0247050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Accepted: 01/29/2021] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Opioid-related overdose deaths are the top accidental cause of death in the United States, and development of regional strategies to address this epidemic should begin with a better understanding of where and when overdoses are occurring. METHODS AND FINDINGS In this study, we relied on emergency medical services data to investigate the geographical and temporal patterns in opioid-suspected overdose incidents in one of the largest and most ethnically diverse metropolitan areas (Houston Texas). Using a cross sectional design and Bayesian spatiotemporal models, we identified zip code areas with excessive opioid-suspected incidents, and assessed how the incidence risks were associated with zip code level socioeconomic characteristics. Our analysis suggested that opioid-suspected overdose incidents were particularly high in multiple zip codes, primarily south and central within the city. Zip codes with high percentage of renters had higher overdose relative risk (RR = 1.03; 95% CI: [1.01, 1.04]), while crowded housing and larger proportion of white citizens had lower relative risks (RR = 0.9; 95% CI: [0.84, 0.96], RR = 0.97, 95% CI: [0.95, 0.99], respectively). CONCLUSIONS Our analysis illustrated the utility of Bayesian spatiotemporal models in assisting the development of targeted community strategies for local prevention and harm reduction efforts.
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Affiliation(s)
- Cici Bauer
- Department of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
- * E-mail:
| | - Tiffany Champagne-Langabeer
- ACE Research Lab, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Christine Bakos-Block
- ACE Research Lab, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Kehe Zhang
- Department of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - David Persse
- Office of Emergency Medical Services, City of Houston Fire Department, Houston, Texas, United States of America
| | - James R. Langabeer
- ACE Research Lab, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
- Department of Emergency Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
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Li Y, Hyder A, Southerland LT, Hammond G, Porr A, Miller HJ. 311 service requests as indicators of neighborhood distress and opioid use disorder. Sci Rep 2020; 10:19579. [PMID: 33177583 PMCID: PMC7658248 DOI: 10.1038/s41598-020-76685-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Accepted: 10/30/2020] [Indexed: 01/19/2023] Open
Abstract
Opioid use disorder and overdose deaths is a public health crisis in the United States, and there is increasing recognition that its etiology is rooted in part by social determinants such as poverty, isolation and social upheaval. Limiting research and policy interventions is the low temporal and spatial resolution of publicly available administrative data such as census data. We explore the use of municipal service requests (also known as "311" requests) as high resolution spatial and temporal indicators of neighborhood social distress and opioid misuse. We analyze the spatial associations between georeferenced opioid overdose event (OOE) data from emergency medical service responders and 311 service request data from the City of Columbus, OH, USA for the time period 2008-2017. We find 10 out of 21 types of 311 requests spatially associate with OOEs and also characterize neighborhoods with lower socio-economic status in the city, both consistently over time. We also demonstrate that the 311 indicators are capable of predicting OOE hotspots at the neighborhood-level: our results show code violation, public health, and street lighting were the top three accurate predictors with predictive accuracy as 0.92, 0.89 and 0.83, respectively. Since 311 requests are publicly available with high spatial and temporal resolution, they can be effective as opioid overdose surveillance indicators for basic research and applied policy.
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Affiliation(s)
- Yuchen Li
- Department of Geography, The Ohio State University, Columbus, OH, USA
- Center for Urban and Regional Analysis, The Ohio State University, Columbus, OH, USA
| | - Ayaz Hyder
- College of Public Health, The Ohio State University, Columbus, OH, USA
| | | | | | - Adam Porr
- Center for Urban and Regional Analysis, The Ohio State University, Columbus, OH, USA
| | - Harvey J Miller
- Department of Geography, The Ohio State University, Columbus, OH, USA.
- Center for Urban and Regional Analysis, The Ohio State University, Columbus, OH, USA.
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Exploring the Influence of Drug Trafficking Gangs on Overdose Deaths in the Largest Narcotics Market in the Eastern United States. SOCIAL SCIENCES-BASEL 2020. [DOI: 10.3390/socsci9110202] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
Research has found that drug markets tend to cluster in space, potentially because of the profit that can be made when customers are drawn to areas with multiple suppliers. But few studies have examined how these clusters of drug markets—which have been termed “agglomeration economies”—may be related to accidental overdose deaths, and in particular, the spatial distribution of mortality from overdose. Focusing on a large neighborhood in Philadelphia, Pennsylvania, known for its open-air drug markets, this study examines whether deaths from accidental drug overdose are clustered around street corners controlled by drug trafficking gangs. This study incorporates theoretically-informed social and physical environmental characteristics of street corner units into the models predicting overdose deaths. Given a number of environmental changes relevant to drug use locations was taking place in the focal neighborhood during the analysis period, the authors first employ a novel concentration metric—the Rare Event Concentration Coefficient—to assess clustering of overdose deaths annually between 2015 and 2019. The results of these models reveal that overdose deaths became less clustered over time and that the density was considerably lower after 2017. Hence, the predictive models in this study are focused on the two-year period between 2018 and 2019. Results from spatial econometric regression models find strong support for the association between corner drug markets and accidental overdose deaths. In addition, a number of sociostructural factors, such as concentrated disadvantage, and physical environmental factors, particularly blighted housing, are associated with a higher rate of overdose deaths. Implications from this study highlight the need for efforts that strategically coordinate law enforcement, social service provision and reductions in housing blight targeted to particular geographies.
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Tsai AC, Alegría M, Strathdee SA. Addressing the context and consequences of substance use, misuse, and dependence: A global imperative. PLoS Med 2019; 16:e1003000. [PMID: 31770369 PMCID: PMC6879121 DOI: 10.1371/journal.pmed.1003000] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
In an Editorial, Guest Editors Alexander Tsai, Margarita Alegria and Steffanie Strathdee discuss the accompanying Special Issue on Substance Use, Misuse and Dependence.
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Affiliation(s)
- Alexander C. Tsai
- Center for Global Health, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
- Mbarara University of Science and Technology, Mbarara, Uganda
| | - Margarita Alegría
- Harvard Medical School, Boston, Massachusetts, United States of America
- Disparities Research Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Steffanie A. Strathdee
- Division of Infectious Diseases and Global Public Health, University of California at San Diego School of Medicine, San Diego, California, United States of America
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