R01DA054267
Project Grant
Overview
Grant Description
Predict to Prevent: Dynamic Spatiotemporal Analyses of Opioid Overdose to Guide Pre-emptive Public Health Responses - Predict to Prevent: Dynamic Spatiotemporal Analyses of Opioid Overdose to Guide Pre-emptive Public Health Responses
Project Summary/Abstract
Opioid overdose (OD) fatalities have reached crisis levels in all socioeconomic and geographic communities in the US. By utilizing a first-of-its-kind statewide public health data warehouse (PHD) with multiple linked administrative datasets and state-of-the-art Bayesian spatiotemporal models, we are in a unique position to fill in the fundamental gaps in the field's ability to rapidly identify current OD patterns, predict future OD epidemics, and evaluate the effectiveness of public health and clinical interventions.
In Massachusetts (MA), the state legislature enacted policy in 2015 that provided authorization to the MA Department of Public Health (MDPH) to develop a massively linked administrative dataset to allow public health officials and policymakers to better understand the extent of and contributors to the opioid OD epidemic. The PHD warehouse, representing 98% of the MA population, currently links data from 25+ distinct sources (e.g., death records, all-payer claims, post-mortem toxicology, hospital discharges, and the prescription monitoring program).
Supported by strong preliminary studies demonstrating the power of the PHD and our strong partnership with MDPH, we aim to develop a new population health analytic framework to support opioid OD control in MA that can be generalizable to other parts of the country. Our specific aims are to:
1) Develop a Bayesian multilevel spatiotemporal model to identify individual, interpersonal, community, and societal factors that contribute to opioid OD;
2) Develop an efficient Bayesian spatiotemporal model to identify time-space OD clusters, and extend the model to construct a dynamic predictive model; and,
3) Evaluate and predict policy and intervention effects through model-based simulation studies to provide practical guidance and decision-making support to public health officials.
Aims 1, 2, and 3 can be easily adopted and reproduced by users in other public health jurisdictions and sectors to foster cross-sector, cross-agency opioid OD control. Our approach is innovative due to the use of PHD and sophisticated Bayesian spatiotemporal modeling approaches.
The proposed study is highly significant because it is conceptualized to improve current and future public health practice, facilitating data-driven and evidence-based implementation science interventions in the locations at greatest risk and at the time when they are most needed. Our results can immediately and significantly influence opioid OD prevention policies and practices, guiding pre-emptive public health and clinical responses.
We will develop our visualization tools, analytical approaches, and related code in collaboration with MDPH and our Community Advisory Board (CAB) to enhance PHD capabilities and improve dissemination of findings. Our tools, approaches, and code will also be made available for national dissemination, providing paradigm-shifting approaches to address the opioid crisis.
Our research directly addresses NIDA's goal to "develop new and improved strategies to prevent drug use and its consequences."
Project Summary/Abstract
Opioid overdose (OD) fatalities have reached crisis levels in all socioeconomic and geographic communities in the US. By utilizing a first-of-its-kind statewide public health data warehouse (PHD) with multiple linked administrative datasets and state-of-the-art Bayesian spatiotemporal models, we are in a unique position to fill in the fundamental gaps in the field's ability to rapidly identify current OD patterns, predict future OD epidemics, and evaluate the effectiveness of public health and clinical interventions.
In Massachusetts (MA), the state legislature enacted policy in 2015 that provided authorization to the MA Department of Public Health (MDPH) to develop a massively linked administrative dataset to allow public health officials and policymakers to better understand the extent of and contributors to the opioid OD epidemic. The PHD warehouse, representing 98% of the MA population, currently links data from 25+ distinct sources (e.g., death records, all-payer claims, post-mortem toxicology, hospital discharges, and the prescription monitoring program).
Supported by strong preliminary studies demonstrating the power of the PHD and our strong partnership with MDPH, we aim to develop a new population health analytic framework to support opioid OD control in MA that can be generalizable to other parts of the country. Our specific aims are to:
1) Develop a Bayesian multilevel spatiotemporal model to identify individual, interpersonal, community, and societal factors that contribute to opioid OD;
2) Develop an efficient Bayesian spatiotemporal model to identify time-space OD clusters, and extend the model to construct a dynamic predictive model; and,
3) Evaluate and predict policy and intervention effects through model-based simulation studies to provide practical guidance and decision-making support to public health officials.
Aims 1, 2, and 3 can be easily adopted and reproduced by users in other public health jurisdictions and sectors to foster cross-sector, cross-agency opioid OD control. Our approach is innovative due to the use of PHD and sophisticated Bayesian spatiotemporal modeling approaches.
The proposed study is highly significant because it is conceptualized to improve current and future public health practice, facilitating data-driven and evidence-based implementation science interventions in the locations at greatest risk and at the time when they are most needed. Our results can immediately and significantly influence opioid OD prevention policies and practices, guiding pre-emptive public health and clinical responses.
We will develop our visualization tools, analytical approaches, and related code in collaboration with MDPH and our Community Advisory Board (CAB) to enhance PHD capabilities and improve dissemination of findings. Our tools, approaches, and code will also be made available for national dissemination, providing paradigm-shifting approaches to address the opioid crisis.
Our research directly addresses NIDA's goal to "develop new and improved strategies to prevent drug use and its consequences."
Awardee
Funding Goals
TO SUPPORT BASIC, CLINICAL, TRANSLATIONAL, AND IMPLEMENTATION RESEARCH IN THE FIELD OF SUBSTANCE USE. TO DEVELOP NEW KNOWLEDGE AND APPROACHES FOR THE PREVENTION, DIAGNOSIS, AND TREATMENT OF DRUG USE, MISUSE, AND ADDICTION, DRUG OVERDOSE, AND RELATED HEALTH OUTCOME, INCLUDING HIV/AIDS. TO SUPPORT RESEARCH TRAINING AND RESEARCH SCIENTIST DEVELOPMENT. TO SUPPORT DISSEMINATION OF RESEARCH FINDINGS. SMALL BUSINESS INNOVATION RESEARCH (SBIR) LEGISLATION IS INTENDED TO EXPAND AND IMPROVE THE SBIR PROGRAMS TO EMPHASIZE AND INCREASE PRIVATE SECTOR COMMERCIALIZATION OF TECHNOLOGY DEVELOPED THROUGH FEDERAL SBIR RESEARCH AND DEVELOPMENT; INCREASE SMALL BUSINESS PARTICIPATION IN FEDERAL RESEARCH AND DEVELOPMENT; AND FOSTER AND ENCOURAGE PARTICIPATION OF SOCIALLY AND ECONOMICALLY DISADVANTAGED SMALL BUSINESS CONCERNS AND WOMEN-OWNED SMALL BUSINESS CONCERNS IN THE SBIR PROGRAM. THE SMALL BUSINESS TECHNOLOGY TRANSFER (STTR) LEGISTLATION IS INTENDED TO STIMULATE AND FOSTER SCIENTIFIC AND TECHNOLOGICAL INNOVATION THROUGH COOPERATIVE RESEARCH AND DEVELOPMENT CARRIED OUT BETWEEN SMALL BUSINESS CONCERNS AND RESEARCH INSTITUTIONS; FOSTER TECHNOLOGY TRANSFER BETWEEN SMALL BUSINESS CONCERNS AND RESEARCH INSTITUTIONS; INCREASE PRIVATE SECTOR COMMERCIALIZATION OF INNOVATIONS DERIVED FROM FEDERAL RESEARCH AND DEVELOPMENT; AND FOSTER AND ENCOURAGE PARTICIPATION OF SOCIALLY AND ECONOMICALLY DISADVANTAGED SMALL BUSINESS CONCERNS AND WOMEN-OWNED SMALL BUSINESS CONCERNS IN TECHNOLOGICAL INNOVATION.
Grant Program (CFDA)
Awarding / Funding Agency
Place of Performance
Boston,
Massachusetts
021111817
United States
Geographic Scope
Single Zip Code
Related Opportunity
Analysis Notes
Amendment Since initial award the total obligations have increased 353% from $760,041 to $3,446,178.
Trustees Of Tufts College was awarded
Predictive Opioid Overdose Analysis for Public Health Response
Project Grant R01DA054267
worth $3,446,178
from National Institute on Drug Abuse in May 2022 with work to be completed primarily in Boston Massachusetts United States.
The grant
has a duration of 4 years 10 months and
was awarded through assistance program 93.279 Drug Abuse and Addiction Research Programs.
The Project Grant was awarded through grant opportunity Accelerating the Pace of Drug Abuse Research Using Existing Data (R01 Clinical Trial Optional).
Status
(Ongoing)
Last Modified 4/6/26
Period of Performance
5/15/22
Start Date
3/31/27
End Date
Funding Split
$3.4M
Federal Obligation
$0.0
Non-Federal Obligation
$3.4M
Total Obligated
Activity Timeline
Subgrant Awards
Disclosed subgrants for R01DA054267
Transaction History
Modifications to R01DA054267
Additional Detail
Award ID FAIN
R01DA054267
SAI Number
R01DA054267-2735124008
Award ID URI
SAI UNAVAILABLE
Awardee Classifications
Private Institution Of Higher Education
Awarding Office
75N600 NIH National Insitute on Drug Abuse
Funding Office
75N600 NIH National Insitute on Drug Abuse
Awardee UEI
C1F5LNUF7W86
Awardee CAGE
3G627
Performance District
MA-07
Senators
Edward Markey
Elizabeth Warren
Elizabeth Warren
Budget Funding
| Federal Account | Budget Subfunction | Object Class | Total | Percentage |
|---|---|---|---|---|
| National Institute on Drug Abuse, National Institutes of Health, Health and Human Services (075-0893) | Health research and training | Grants, subsidies, and contributions (41.0) | $1,446,558 | 100% |
Modified: 4/6/26