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Samuels EA, Goedel WC, Jent V, Conkey L, Hallowell BD, Karim S, Koziol J, Becker S, Yorlets RR, Merchant R, Keeler LA, Reddy N, McDonald J, Alexander-Scott N, Cerda M, Marshall BDL. Characterizing opioid overdose hotspots for place-based overdose prevention and treatment interventions: A geo-spatial analysis of Rhode Island, USA. THE INTERNATIONAL JOURNAL OF DRUG POLICY 2024; 125:104322. [PMID: 38245914 DOI: 10.1016/j.drugpo.2024.104322] [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: 10/15/2023] [Revised: 12/10/2023] [Accepted: 01/02/2024] [Indexed: 01/23/2024]
Abstract
OBJECTIVE Examine differences in neighborhood characteristics and services between overdose hotspot and non-hotspot neighborhoods and identify neighborhood-level population factors associated with increased overdose incidence. METHODS We conducted a population-based retrospective analysis of Rhode Island, USA residents who had a fatal or non-fatal overdose from 2016 to 2020 using an environmental scan and data from Rhode Island emergency medical services, State Unintentional Drug Overdose Reporting System, and the American Community Survey. We conducted a spatial scan via SaTScan to identify non-fatal and fatal overdose hotspots and compared the characteristics of hotspot and non-hotspot neighborhoods. We identified associations between census block group-level characteristics using a Besag-York-Mollié model specification with a conditional autoregressive spatial random effect. RESULTS We identified 7 non-fatal and 3 fatal overdose hotspots in Rhode Island during the study period. Hotspot neighborhoods had higher proportions of Black and Latino/a residents, renter-occupied housing, vacant housing, unemployment, and cost-burdened households. A higher proportion of hotspot neighborhoods had a religious organization, a health center, or a police station. Non-fatal overdose risk increased in a dose responsive manner with increasing proportions of residents living in poverty. There was increased relative risk of non-fatal and fatal overdoses in neighborhoods with crowded housing above the mean (RR 1.19 [95 % CI 1.05, 1.34]; RR 1.21 [95 % CI 1.18, 1.38], respectively). CONCLUSION Neighborhoods with increased prevalence of housing instability and poverty are at highest risk of overdose. The high availability of social services in overdose hotspots presents an opportunity to work with established organizations to prevent overdose deaths.
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Affiliation(s)
- Elizabeth A Samuels
- Department of Emergency Medicine, UCLA David Geffen School of Medicine, Los Angeles, CA, USA; Department of Emergency Medicine, Alpert Medical School of Brown University, Providence, RI, USA; Department of Epidemiology, Brown University School of Public Health, Providence, RI, USA; Drug Overdose Prevention Program, Rhode Island Department of Health, Providence, RI, USA.
| | - William C Goedel
- Department of Epidemiology, Brown University School of Public Health, Providence, RI, USA
| | - Victoria Jent
- Center for Opioid Epidemiology and Policy, Department of Population Health, NYU Grossman School of Medicine, New York University, New York City, NY, USA
| | - Lauren Conkey
- Drug Overdose Prevention Program, Rhode Island Department of Health, Providence, RI, USA
| | - Benjamin D Hallowell
- Drug Overdose Prevention Program, Rhode Island Department of Health, Providence, RI, USA
| | - Sarah Karim
- Drug Overdose Prevention Program, Rhode Island Department of Health, Providence, RI, USA
| | - Jennifer Koziol
- Drug Overdose Prevention Program, Rhode Island Department of Health, Providence, RI, USA
| | - Sara Becker
- Center for Dissemination and Implementation Science, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Rachel R Yorlets
- Department of Epidemiology, Brown University School of Public Health, Providence, RI, USA; Population Studies and Training Center, Brown University, Providence, RI, USA
| | - Roland Merchant
- Department of Emergency Medicine, Mount Sinai, New York City, NY, USA
| | - Lee Ann Keeler
- Department of Emergency Medicine, Alpert Medical School of Brown University, Providence, RI, USA
| | - Neha Reddy
- Department of Obstetrics and Gynecology, UChicago Medicine, Chicago, IL, USA
| | - James McDonald
- Drug Overdose Prevention Program, Rhode Island Department of Health, Providence, RI, USA
| | - Nicole Alexander-Scott
- Drug Overdose Prevention Program, Rhode Island Department of Health, Providence, RI, USA
| | - Magdalena Cerda
- Center for Opioid Epidemiology and Policy, Department of Population Health, NYU Grossman School of Medicine, New York University, New York City, NY, USA
| | - Brandon D L Marshall
- Department of Epidemiology, Brown University School of Public Health, Providence, RI, USA
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Allen B, Schell RC, Jent VA, Krieger M, Pratty C, Hallowell BD, Goedel WC, Basta M, Yedinak JL, Li Y, Cartus AR, Marshall BDL, Cerdá M, Ahern J, Neill DB. PROVIDENT: Development and Validation of a Machine Learning Model to Predict Neighborhood-level Overdose Risk in Rhode Island. Epidemiology 2024; 35:232-240. [PMID: 38180881 PMCID: PMC10842082 DOI: 10.1097/ede.0000000000001695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2024]
Abstract
BACKGROUND Drug overdose persists as a leading cause of death in the United States, but resources to address it remain limited. As a result, health authorities must consider where to allocate scarce resources within their jurisdictions. Machine learning offers a strategy to identify areas with increased future overdose risk to proactively allocate overdose prevention resources. This modeling study is embedded in a randomized trial to measure the effect of proactive resource allocation on statewide overdose rates in Rhode Island (RI). METHODS We used statewide data from RI from 2016 to 2020 to develop an ensemble machine learning model predicting neighborhood-level fatal overdose risk. Our ensemble model integrated gradient boosting machine and super learner base models in a moving window framework to make predictions in 6-month intervals. Our performance target, developed a priori with the RI Department of Health, was to identify the 20% of RI neighborhoods containing at least 40% of statewide overdose deaths, including at least one neighborhood per municipality. The model was validated after trial launch. RESULTS Our model selected priority neighborhoods capturing 40.2% of statewide overdose deaths during the test periods and 44.1% of statewide overdose deaths during validation periods. Our ensemble outperformed the base models during the test periods and performed comparably to the best-performing base model during the validation periods. CONCLUSIONS We demonstrated the capacity for machine learning models to predict neighborhood-level fatal overdose risk to a degree of accuracy suitable for practitioners. Jurisdictions may consider predictive modeling as a tool to guide allocation of scarce resources.
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Affiliation(s)
- Bennett Allen
- From the 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
| | - Victoria A Jent
- From the Center for Opioid Epidemiology and Policy, Department of Population Health, Grossman School of Medicine, New York University, New York, NY, USA
| | - Maxwell Krieger
- Department of Epidemiology, School of Public Health, Brown University, Providence, RI, USA
| | - Claire Pratty
- Department of Epidemiology, School of Public Health, Brown University, Providence, RI, USA
| | - Benjamin D Hallowell
- Center for Health Data and Analysis, Rhode Island Department of Health, Providence, RI, USA
| | - William C Goedel
- Department of Epidemiology, School of Public Health, Brown University, Providence, RI, USA
| | - Melissa Basta
- Center for Health Data and Analysis, Rhode Island Department of Health, Providence, RI, USA
| | - Jesse L Yedinak
- Department of Epidemiology, School of Public Health, Brown University, Providence, RI, USA
| | - Yu Li
- Department of Epidemiology, School of Public Health, Brown University, Providence, RI, USA
| | - Abigail R Cartus
- Department of Epidemiology, School of Public Health, Brown University, Providence, RI, USA
| | - Brandon D L Marshall
- Department of Epidemiology, School of Public Health, Brown University, Providence, RI, USA
| | - Magdalena Cerdá
- From the Center for Opioid Epidemiology and Policy, Department of Population Health, Grossman School of Medicine, New York University, New York, NY, USA
| | - Jennifer Ahern
- Division of Epidemiology, School of Public Health, University of California, Berkeley, CA, USA
| | - Daniel B Neill
- Center for Urban Science and Progress, New York University, New York, NY, USA
- Department of Computer Science, Courant Institute for Mathematical Sciences, New York University, New York, NY, USA
- Robert F. Wagner Graduate School of Public Service, New York University, New York, NY, USA
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Yedinak J, Krieger MS, Joseph R, Levin S, Edwards S, Bailer DA, Goyer J, Daley Ndoye C, Schultz C, Koziol J, Elmaleh R, Hallowell BD, Hampson T, Duong E, Shihipar A, Goedel WC, Marshall BD. Public Health Dashboards in Overdose Prevention: The Rhode Island Approach to Public Health Data Literacy, Partnerships, and Action. J Med Internet Res 2024; 26:e51671. [PMID: 38345849 PMCID: PMC10897802 DOI: 10.2196/51671] [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: 08/08/2023] [Revised: 12/12/2023] [Accepted: 01/09/2024] [Indexed: 02/15/2024] Open
Abstract
As the field of public health rises to the demands of real-time surveillance and rapid data-sharing needs in a postpandemic world, it is time to examine our approaches to the dissemination and accessibility of such data. Distinct challenges exist when working to develop a shared public health language and narratives based on data. It requires that we assess our understanding of public health data literacy, revisit our approach to communication and engagement, and continuously evaluate our impact and relevance. Key stakeholders and cocreators are critical to this process and include people with lived experience, community organizations, governmental partners, and research institutions. In this viewpoint paper, we offer an instructive approach to the tools we used, assessed, and adapted across 3 unique overdose data dashboard projects in Rhode Island, United States. We are calling this model the "Rhode Island Approach to Public Health Data Literacy, Partnerships, and Action." This approach reflects the iterative lessons learned about the improvement of data dashboards through collaboration and strong partnerships across community members, state agencies, and an academic research team. We will highlight key tools and approaches that are accessible and engaging and allow developers and stakeholders to self-assess their goals for their data dashboards and evaluate engagement with these tools by their desired audiences and users.
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Affiliation(s)
- Jesse Yedinak
- Department of Epidemiology, Brown University School of Public Health, Providence, RI, United States
| | - Maxwell S Krieger
- Department of Epidemiology, Brown University School of Public Health, Providence, RI, United States
| | | | - Stacey Levin
- Parent Support Network, Warwick, RI, United States
| | - Sarah Edwards
- Rhode Island Department of Health, Providence, RI, United States
| | | | | | | | - Cathy Schultz
- State of Rhode Island Executive Office of Health and Human Services, Cranston, RI, United States
| | - Jennifer Koziol
- Rhode Island Department of Health, Providence, RI, United States
| | - Rachael Elmaleh
- Rhode Island Department of Health, Providence, RI, United States
| | | | - Todd Hampson
- Department of Epidemiology, Brown University School of Public Health, Providence, RI, United States
| | - Ellen Duong
- Center for Computation and Visualization, Brown University, Providence, RI, United States
| | - Abdullah Shihipar
- Department of Epidemiology, Brown University School of Public Health, Providence, RI, United States
| | - William C Goedel
- Department of Epidemiology, Brown University School of Public Health, Providence, RI, United States
| | - Brandon Dl Marshall
- Department of Epidemiology, Brown University School of Public Health, Providence, RI, United States
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Allen B, Neill DB, Schell RC, Ahern J, Hallowell BD, Krieger M, Jent VA, Goedel WC, Cartus AR, Yedinak JL, Pratty C, Marshall BDL, Cerdá M. Translating Predictive Analytics for Public Health Practice: A Case Study of Overdose Prevention in Rhode Island. Am J Epidemiol 2023; 192:1659-1668. [PMID: 37204178 PMCID: PMC10558193 DOI: 10.1093/aje/kwad119] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 03/09/2023] [Accepted: 05/15/2023] [Indexed: 05/20/2023] Open
Abstract
Prior applications of machine learning to population health have relied on conventional model assessment criteria, limiting the utility of models as decision support tools for public health practitioners. To facilitate practitioners' use of machine learning as a decision support tool for area-level intervention, we developed and applied 4 practice-based predictive model evaluation criteria (implementation capacity, preventive potential, health equity, and jurisdictional practicalities). We used a case study of overdose prevention in Rhode Island to illustrate how these criteria could inform public health practice and health equity promotion. We used Rhode Island overdose mortality records from January 2016-June 2020 (n = 1,408) and neighborhood-level US Census data. We employed 2 disparate machine learning models, Gaussian process and random forest, to illustrate the comparative utility of our criteria to guide interventions. Our models predicted 7.5%-36.4% of overdose deaths during the test period, illustrating the preventive potential of overdose interventions assuming 5%-20% statewide implementation capacities for neighborhood-level resource deployment. We describe the health equity implications of use of predictive modeling to guide interventions along the lines of urbanicity, racial/ethnic composition, and poverty. We then discuss considerations to complement predictive model evaluation criteria and inform the prevention and mitigation of spatially dynamic public health problems across the breadth of practice. This article is part of a Special Collection on Mental Health.
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Affiliation(s)
- Bennett Allen
- Correspondence to Dr. Bennett Allen, Center for Opioid Epidemiology and Policy, Grossman School of Medicine, New York University, 180 Madison Avenue, 4th Floor, Room 4-15, New York, NY 10016 (e-mail: )
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Cartus AR, Goedel WC, Jent VA, Macmadu A, Pratty C, Hallowell BD, Allen B, Li Y, Cerdá M, Marshall BDL. Neighborhood-level association between release from incarceration and fatal overdose, Rhode Island, 2016-2020. Drug Alcohol Depend 2023; 247:109867. [PMID: 37084507 PMCID: PMC10198932 DOI: 10.1016/j.drugalcdep.2023.109867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 03/06/2023] [Accepted: 04/02/2023] [Indexed: 04/23/2023]
Abstract
The association between recent release from incarceration and dramatically increased risk of fatal overdose is well-established at the individual level. Fatal overdose and. arrest/release are spatially clustered, suggesting that this association may persist at the neighborhood level. We analyzed multicomponent data from Rhode Island, 2016-2020, and observed a modest association at the census tract level between rates of release per 1000 population and fatal overdose per 100,000 person-years, adjusting for spatial autocorrelation in both the exposure and outcome. Our results suggest that for each additional person released to a given census tract per 1000 population, there is a corresponding increase in the rate of fatal overdose by 2 per 100,000 person years. This association is more pronounced in suburban tracts, where each additional release awaiting trial is associated with an increase in the rate of fatal overdose of 4 per 100,000 person-years and 6 per 100,000 person-years for each additional release following sentence expiration. This association is not modified by the presence or absence of a licensed medication for opioid use disorder (MOUD) treatment provider in the same or surrounding tracts. Our results suggest that neighborhood-level release rates are moderately informative as to tract-level rates of fatal overdose and underscore the importance of expanding pre-release MOUD access in correctional settings. Future research should explore risk and resource environments particularly in suburban and rural areas and their impacts on overdose risk among individuals returning to the community.
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Affiliation(s)
- Abigail R Cartus
- Department of Epidemiology, Brown University School of Public Health, United States
| | - William C Goedel
- Department of Epidemiology, Brown University School of Public Health, United States
| | - Victoria A Jent
- Department of Population Health, New York University Grossman School of Medicine, United States; Center for Opioid Epidemiology and Policy, New York University Grossman School of Medicine, United States
| | - Alexandria Macmadu
- Department of Epidemiology, Brown University School of Public Health, United States
| | - Claire Pratty
- Department of Epidemiology, Brown University School of Public Health, United States
| | | | - Bennett Allen
- Department of Population Health, New York University Grossman School of Medicine, United States; Center for Opioid Epidemiology and Policy, New York University Grossman School of Medicine, United States
| | - Yu Li
- Department of Epidemiology, Brown University School of Public Health, United States
| | - Magdalena Cerdá
- Department of Population Health, New York University Grossman School of Medicine, United States; Center for Opioid Epidemiology and Policy, New York University Grossman School of Medicine, United States
| | - Brandon D L Marshall
- Department of Epidemiology, Brown University School of Public Health, United States.
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Rhodes T, Lancaster K. Early warnings and slow deaths: A sociology of outbreak and overdose. THE INTERNATIONAL JOURNAL OF DRUG POLICY 2023; 117:104065. [PMID: 37229960 DOI: 10.1016/j.drugpo.2023.104065] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 05/04/2023] [Accepted: 05/06/2023] [Indexed: 05/27/2023]
Abstract
In this paper, we offer a sociological analysis of early warning and outbreak in the field of drug policy, focusing on opioid overdose. We trace how 'outbreak' is enacted as a rupturing event which enables rapid reflex responses of precautionary control, based largely on short-term and proximal early warning indicators. We make the case for an alternative view of early warning and outbreak. We argue that practices of detection and projection that help to materialise drug-related outbreaks are too focused on the proximal and short-term. Engaging with epidemiological and sociological work investigating epidemics of opioid overdose, we show how the short-termism and rapid reflex response of outbreak fails to appreciate the slow violent pasts of epidemics indicative of an ongoing need and care for structural and societal change. Accordingly, we gather together ideas of 'slow emergency' (Ben Anderson), 'slow death' (Lauren Berlant) and 'slow violence' (Rob Nixon), to re-assemble outbreaks in 'long view'. This locates opioid overdose in long-term attritional processes of deindustrialisation, pharmaceuticalisation, and other forms of structural violence, including the criminalisation and problematisation of people who use drugs. Outbreaks evolve in relation to their slow violent pasts. To ignore this can perpetuate harm. Attending to the social conditions that create the possibilities for outbreak invites early warning that goes 'beyond outbreak' and 'beyond epidemic' as generally configured.
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Affiliation(s)
- Tim Rhodes
- London School of Hygiene and Tropical Medicine, London, UK; University of New South Wales, Sydney, Australia.
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Tay Wee Teck J, Oteo A, Baldacchino A. Rapid opioid overdose response system technologies. Curr Opin Psychiatry 2023:00001504-990000000-00063. [PMID: 37185583 DOI: 10.1097/yco.0000000000000870] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
PURPOSE OF REVIEW Opioid overdose events are a time sensitive medical emergency, which is often reversible with naloxone administration if detected in time. Many countries are facing rising opioid overdose deaths and have been implementing rapid opioid overdose response Systems (ROORS). We describe how technology is increasingly being used in ROORS design, implementation and delivery. RECENT FINDINGS Technology can contribute in significant ways to ROORS design, implementation, and delivery. Artificial intelligence-based modelling and simulations alongside wastewater-based epidemiology can be used to inform policy decisions around naloxone access laws and effective naloxone distribution strategies. Data linkage and machine learning projects can support service delivery organizations to mobilize and distribute community resources in support of ROORS. Digital phenotyping is an advancement in data linkage and machine learning projects, potentially leading to precision overdose responses. At the coalface, opioid overdose detection devices through fixed location or wearable sensors, improved connectivity, smartphone applications and drone-based emergency naloxone delivery all have a role in improving outcomes from opioid overdose. Data driven technologies also have an important role in empowering community responses to opioid overdose. SUMMARY This review highlights the importance of technology applied to every aspect of ROORS. Key areas of development include the need to protect marginalized groups from algorithmic bias, a better understanding of individual overdose trajectories and new reversal agents and improved drug delivery methods.
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Affiliation(s)
- Joseph Tay Wee Teck
- DigitAS Project, Population and Behavioural Science Division, School of Medicine, University of St Andrews, St Andrews
- Forward Leeds and Humankind Charity, Durham, UK
| | - Alberto Oteo
- DigitAS Project, Population and Behavioural Science Division, School of Medicine, University of St Andrews, St Andrews
| | - Alexander Baldacchino
- DigitAS Project, Population and Behavioural Science Division, School of Medicine, University of St Andrews, St Andrews
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Allen B, Cerdá M. Opportunities for opioid overdose prediction: building a population health approach. Lancet Digit Health 2022; 4:e403-e404. [PMID: 35623796 PMCID: PMC9897051 DOI: 10.1016/s2589-7500(22)00097-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 05/04/2022] [Indexed: 02/06/2023]
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