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Principal Investigator: John Richard Pamplin
Organization: COLUMBIA UNIVERSITY HEALTH SCIENCES
Fiscal Year: 2024
Award: $177,962
Funding agency: National Institute on Drug Abuse
PROJECT SUMMARY
Deaths due to opioid overdose are a pressing public health crisis and the number of deaths per year is
increasing. Rates of fatal overdose are rising faster for Black people than for any other racial/ethnic group,
largely driven by the increased prominence of synthetic opioids. Black people have the highest percentage of
overdose mortality attributable to synthetic opioids and have experienced the greatest increase in related
mortality rates. Interventions are critically needed that can effectively reduce overdose mortality and do so
equitably for Black people. Overdose prevention policies (OPPs) (i.e., Good Samaritan laws and naloxone
access laws), a class of state-level policy interventions intended to reduce overdose mortality, may be able to
address rising racial inequities in fatal overdose. However, there are critical gaps in the literature regarding the
effectiveness of OPPs, including a lack of prior research into which provisions may better reduce overdose
deaths for Black people, and a lack of prior research into the extent to which local contextual factors modify the
effects of state-level OPP provisions. Additionally, the common practice of enacting OPP provisions in
packages creates a significant methodological challenge for standard causal inference approaches of
assessing the effectiveness of individual OPP provisions. The scientific objective of this research plan is to
assess the effectiveness of state-level OPP provisions to equitably reduce overdose mortality and identify
local-level factors that may produce racialized effectiveness of provisions. This project uses novel causal
machine learning methods in conjunction with a combination of restricted mortality data from the National Vital
Statistics System and multiple publicly available data sources to address the methodological challenge and fill
the critical gaps outlined above. This innovative data-driven approach will be complemented by taxonomies of
hypothesized OPP provision effectiveness produced by a panel of opioid policy experts using the Delphi
method. By doing so, this project will empirically evaluate which sets of OPP provisions are most effective at
reducing overdose mortality overall, and specifically among Black people, and estimate the role of local
contextual factors (e.g., access to harm reduction services or local law enforcement practices) in producing
varied effects of OPP provisions. This research plan is complemented by a career development plan that
builds upon the applicant’s background in epidemiology and biostatistics and includes new training in (1)
development and implementation of state-level drug policies; (2) measurement and evaluation of policy
intervention effectiveness; and (3) machine learning methods to identify salient causal measures from high-
dimensional data. The combined research and training plan will prepare the applicant to successfully transition
to an independent research career aimed at using novel statistical and computational methods to identify and
evaluate interventions to reduce racial inequities in substance use related harms.
Terms: <Address><Application Context><Behavior><Biometrics><Biometry><Biostatistics><Black><Black Populations><Black group><Black individual><Black people><Black race><Blacks><COVID crisis><COVID epidemic><COVID pandemic><COVID-19 crisis><COVID-19 epidemic><COVID-19 era><COVID-19 global health crisis><COVID-19 global pandemic><COVID-19 health crisis><COVID-19 pandemic><COVID-19 period><COVID-19 public health crisis><COVID-19 years><Cessation of life><Computing Methodologies><County><Data><Data Set><Data Sources><Death><Death Rate><Development><Development Plans><Disparities><Disparity><Drug user><Drugs><Effectiveness><Effectiveness of Interventions><Epidemiology><Equity><Ethnic Group><Ethnic People><Ethnic Population><Ethnic individual><Ethnicity People><Ethnicity Population><Fatality rate><Foundations><General Taxonomy><Harm Minimization><Harm Reduction><Individual><Intervention><Intervention Strategies><Investigators><Knowledge><Law Enforcement><Laws><Literature><Machine Learning><Measurement><Measures><Medication><Mentorship><Methodology><Methods><NCHS><Naloxone><Narcan><Narcanti><National Center for Health Statistics><Neighborhoods><Opiate user><Opiates><Opioid><Opioid drug user><Overdose><Overdose reduction><Overdose reversal><PWUD><PWUO><Pattern><Persons><Pharmaceutical Preparations><Policies><Policy Analyses><Policy Analysis><Psychiatric epidemiology><Public Health><Questionnaires><Race><Races><Racial Group><Recovery><Reporting><Research><Research Personnel><Research Resources><Researchers><Resources><Role><SARS-CoV-2 epidemic><SARS-CoV-2 global health crisis><SARS-CoV-2 global pandemic><SARS-CoV-2 pandemic><SARS-coronavirus-2 epidemic><SARS-coronavirus-2 pandemic><Services><Severe Acute Respiratory Syndrome CoV 2 epidemic><Severe Acute Respiratory Syndrome CoV 2 pandemic><Severe acute respiratory syndrome coronavirus 2 epidemic><Severe acute respiratory syndrome coronavirus 2 pandemic><Site><Statistical Methods><System><Taxonomy><Training><United States><United States National Center for Health Statistics><Vital Statistics><assess effectiveness><attributable death><attributable mortality><career><career development><computational methodology><computational methods><computer based method><computer methods><computing method><contextual factors><coronavirus disease 2019 crisis><coronavirus disease 2019 epidemic><coronavirus disease 2019 global health crisis><coronavirus disease 2019 global pandemic><coronavirus disease 2019 health crisis><coronavirus disease 2019 pandemic><coronavirus disease 2019 public health crisis><coronavirus disease crisis><coronavirus disease epidemic><coronavirus disease pandemic><coronavirus disease-19 global pandemic><coronavirus disease-19 pandemic><data framework><determine effectiveness><developmental><disparities in race><disparity due to race><drug/agent><effective intervention><effectiveness assessment><effectiveness evaluation><epidemiologic><epidemiological><ethnic subgroup><ethnicity group><evaluate effectiveness><examine effectiveness><experience><help seeking><help-seeking behavior><high dimensional data><inequality due to race><inequity due to race><innovate><innovation><innovative><interventional strategy><machine based learning><machine learning based method><machine learning method><machine learning methodologies><mortality><mortality rate><mortality ratio><multidimensional data><multidimensional datasets><multidisciplinary><novel><opiate crisis><opiate deaths><opiate mortality><opiate overdose><opiate related overdose><opioid crisis><opioid deaths><opioid drug overdose><opioid epidemic><opioid guideline><opioid induced overdose><opioid intoxication><opioid law><opioid legislation><opioid medication overdose><opioid mortality><opioid overdose><opioid overdose death><opioid poisoning><opioid policy><opioid related death><opioid related overdose><opioid toxicity><opioid user><overdose death><overdose fatalities><overdose prevention><people who use drugs><people who use illicit drugs><people who use opioids><persons who use drugs><persons who use opioids><policy evaluation><prescription drug abuse><prescription medication abuse><race based disparity><race based inequality><race based inequity><race disparity><race related disparity><race related inequality><race related inequity><racial><racial background><racial disparity><racial diversity><racial inequality><racial inequity><racial origin><racial population><racial subgroup><racially diverse><racially unequal><recruit><reduce overdose><reduction in overdose><response><reverse overdose><severe acute respiratory syndrome coronavirus 2 global health crisis><severe acute respiratory syndrome coronavirus 2 global pandemic><skills><social role><statistic methods><substance use><substance using><synthetic opiate><synthetic opioid><treatment effect>