Faculty Bibliography
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292
Preventing Overdose Using Information and Data from the Environment (PROVIDENT): protocol for a randomized, population-based, community intervention trial
BACKGROUND AND AIMS/OBJECTIVE: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/METHODS:Randomized, population-based, community intervention trial. SETTING/METHODS:Rhode Island, USA. PARTICIPANTS/METHODS:All people who reside in Rhode Island during the study period may contribute data to either the model or the trial outcomes. INTERVENTION/METHODS: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/METHODS: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/CONCLUSIONS: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.
PMID: 34729851
ISSN: 1360-0443
CID: 5090872
G-computation and agent-based modeling for social epidemiology: Can population interventions prevent post-traumatic stress disorder?
Agent-based modeling and G-computation can both be used to estimate impacts of intervening on complex systems. We explored each modeling approach within an applied example: interventions to reduce posttraumatic stress disorder (PTSD). We used data from a cohort of 2,282 adults representative of the adult population of the New York City metropolitan area from 2002-2006, of whom 16.3% developed PTSD over their lifetimes. We built four models: G-computation, an agent-based model with no between-agent interactions, an agent-based model with violent interaction dynamics, and an agent-based model with neighborhood dynamics. Three interventions were tested: reducing violent victimization by 1) 37.2% (real-world reduction), 2) 100%, and 3) supplementing the income of 20% of lower-income participants. The G-computation model estimated population-level PTSD risk reductions of 0.12% (95% CI: -0.16, 0.29), 0.28% (95% CI: -0.30, 0.70), and 1.55% (95% CI: 0.40, 2.12), respectively. The agent-based model with no interactions replicated the findings from G-computation. Introduction of interaction dynamics modestly decreased estimated intervention effects (income supplement risk reduction dropped to 1.47%), whereas introduction of neighborhood dynamics modestly increased effectiveness (income supplement risk reduction increased to 1.58%). As compared with G-computation, agent-based modeling permitted deeper exploration of complex systems dynamics at the cost of further assumptions.
PMID: 34409437
ISSN: 1476-6256
CID: 5090842
A Systematic Review of Simulation Models to Track and Address the Opioid Crisis
The opioid overdose crisis is driven by an intersecting set of social, structural, and economic forces. Simulation models offer a tool to help us understand and address this complex, dynamic, and nonlinear social phenomenon. We conducted a systematic review of the literature on simulation models of opioid use and overdose up to September 2019. We extracted modeling types, target populations, interventions, and findings. Further, we created a database of model parameters used for model calibration, and evaluated study transparency and reproducibility. Of the 1,398 articles screened, we identified 88 eligible articles. The most frequent types of models were compartmental (36%), Markov (20%), system dynamics (16%), and Agent-Based models (16%). Over a third evaluated intervention cost-effectiveness (40%), and another third (39%) focused on treatment and harm reduction services for people with opioid use disorder (OUD). More than half (61%) discussed calibrating their models to empirical data, and 31% discussed validation approaches used in their modeling process. From the 63 studies that provided model parameters, we extracted the data sources on opioid use, OUD, OUD treatment, cessation/relapse, emergency medical services, and mortality parameters. This database offers a tool that future modelers can use to identify potential model inputs and evaluate comparability of their models to prior work. Future applications of simulation models to this field should actively tackle key methodological challenges, including the potential for bias in the choice of parameter inputs, investment in model calibration and validation, and transparency in the assumptions and mechanics of simulation models to facilitate reproducibility.
PMID: 34791110
ISSN: 1478-6729
CID: 5049332
Explaining US Adolescent Depressive Symptom Trends Through Declines in Religious Beliefs and Service Attendance
Over the past decade, US adolescents' depressive symptoms have increased, and changing religious beliefs and service attendance may be contributing factors. We examined the contribution of religious factors to depressive symptoms among 417,540 US adolescents (grades: 8, 10, 12), years:1991-2019, in survey-weighted logistic regressions. Among adolescents who felt religion was personally important, those who never attended services had 2.23 times higher odds of reporting depressive symptoms compared to peers attending weekly. Among adolescents who did not feel that religion was important, the pattern was reversed. Among adolescents, concordance between importance of religion and religious service attendance may lower risk of depressive symptoms. Overall, we estimate that depressive symptom trends would be 28.2% lower if religious factors had remained at 1991 levels.
PMID: 34417680
ISSN: 1573-6571
CID: 4998372
Pain, cannabis use, and physical and mental health indicators among veterans and non-veterans: results from National Epidemiologic Survey on Alcohol and Related Conditions-III
ABSTRACT/UNASSIGNED:Chronic pain is associated with mental and physical health difficulties and is prevalent among veterans. Cannabis has been put forth as a treatment for chronic pain, and changes in laws, attitudes, and use patterns have occurred over the last two decades. Differences in prevalence of non-medical cannabis use and cannabis use disorder (CUD) were examined across two groups: veterans/non-veterans and those reporting/not reporting recent pain. Data from the National Epidemiologic Survey on Alcohol and Related Conditions-III (2012-2013; n=36,309) were analyzed using logistic regression. Prevalence Differences (PD) for three cannabis outcomes: (1) past-year non-medical cannabis use, (2) frequent (≥3 times a week) non-medical use, and (3) DSM-5 CUD were estimated for those reporting recent moderate-severe pain (veterans/non-veterans), and veterans reporting/not reporting recent pain. Difference in differences were calculated to investigate prevalence differences on outcomes associated with residence in a state with medical cannabis laws (MCLs). Associations between physical and mental health and cannabis variables were tested. Results indicated that the prevalence of recent pain was greater among veterans (PD=7.25%, 95% CI [4.90, 9.60]). Among veterans, the prevalence of frequent cannabis use was greater among those with pain (PD=1.92%, 98% CI [0.21, 3.63]), and, among veterans residing in a state with MCLs, the prevalence of CUD was greater among those reporting recent pain (PD=3.88%, 98% CI [0.36, 7.39]). Findings failed to support the hypothesis that cannabis use improves mental or physical health for veterans with pain. Providers treating veterans with pain in MCL states should monitor such patients closely for CUD.
PMID: 34108436
ISSN: 1872-6623
CID: 4900072
Big Events theory and measures may help explain emerging long-term effects of current crises
ISI:000639089700001
ISSN: 1744-1692
CID: 5915122
Measurement of the Fluctuations in the Number of Muons in Extensive Air Showers with the Pierre Auger Observatory
We present the first measurement of the fluctuations in the number of muons in extensive air showers produced by ultrahigh energy cosmic rays. We find that the measured fluctuations are in good agreement with predictions from air shower simulations. This observation provides new insights into the origin of the previously reported deficit of muons in air shower simulations and constrains models of hadronic interactions at ultrahigh energies. Our measurement is compatible with the muon deficit originating from small deviations in the predictions from hadronic interaction models of particle production that accumulate as the showers develop.
PMID: 33929235
ISSN: 1079-7114
CID: 5911002
When effects cannot be estimated: redefining estimands to understand the effects of naloxone access laws [PrePrint]
ORIGINAL:0015879
ISSN: 2331-8422
CID: 5305112
Substance, use in relation to COVID-19: A scoping review
BACKGROUND:We conducted a scoping review focused on various forms of substance use amid the pandemic, looking at both the impact of substance use on COVID-19 infection, severity, and vaccine uptake, as well as the impact that COVID-19 has had on substance use treatment and rates. METHODS:A scoping review, compiling both peer-reviewed and grey literature, focusing on substance use and COVID-19 was conducted on September 15, 2020 and again in April 15, 2021 to capture any new studies. Three bibliographic databases (Web of Science Core Collection, Embase, PubMed) and several preprint servers (EuropePMC, bioRxiv, medRxiv, F1000, PeerJ Preprints, PsyArXiv, Research Square) were searched. We included English language original studies only. RESULTS:Of 1564 articles screened in the abstract and title screening phase, we included 111 research studies (peer-reviewed: 98, grey literature: 13) that met inclusion criteria. There was limited research on substance use other than those involving tobacco or alcohol. We noted that individuals engaging in substance use had increased risk for COVID-19 severity, and Black Americans with COVID-19 and who engaged in substance use had worse outcomes than white Americans. There were issues with treatment provision earlier in the pandemic, but increased use of telehealth as the pandemic progressed. COVID-19 anxiety was associated with increased substance use. CONCLUSIONS:Our scoping review of studies to date during COVID-19 uncovered notable research gaps namely the need for research efforts on vaccines, COVID-19 concerns such as anxiety and worry, and low- to middle-income countries (LMICs) and under-researched topics within substance use, and to explore the use of qualitative techniques and interventions where appropriate. We also noted that clinicians can screen and treat individuals exhibiting substance use to mitigate effects of the pandemic. FUNDING/BACKGROUND:Study was funded by the Institution for Social and Policy Studies, Yale University and The Horowitz Foundation for Social Policy. DH was funded by a NIDA grant (R01DA048860). The funding body had no role in the design, analysis, or interpretation of the data in the study.
PMID: 34959077
ISSN: 1873-6327
CID: 5090882
Association of medical cannabis licensure with prescription opioid receipt: A population-based, individual-level retrospective cohort study
BACKGROUND:The endocannabinoid system has been implicated in physiological processes fundamental to pain, giving plausibility to the hypothesis that cannabis may be used as a substitute or complement to prescription opioids in the management of chronic pain. We examined the association of medical cannabis licensure with likelihood of prescription opioid receipt using administrative records. METHODS:This study linked registry information for medical cannabis licensure with records from the prescription drug monitoring program from April 1, 2016 to March 31, 2019 to create a population-based, retrospective cohort in Rhode Island. We examined within-person changes in receipt of any opioid prescription and receipt of an opioid prescription with a morphine equivalent dose of 50 mg or more, and of 90 mg or more. RESULTS:The sample included 5,296 participants with medical cannabis license. Medical cannabis licensure was not associated with the odds of filling any opioid prescription (OR: 0.99; 95% CI: 0.94-0.1.05) or the odds of filling a prescription with a morphine equivalent dose of 50 mg or more (OR: 0.93; 95% CI: 0.84-1.04) and 90 mg or more (OR: 0.99; 95% CI: 0.86-1.15). CONCLUSION/CONCLUSIONS:Medical cannabis licensure was not associated with subsequent cessation and reduction in prescription opioid use. Re-scheduling of cannabis will allow for the conduct of randomized controlled trials to determine the efficacy of medical cannabis as an alternative to prescription opioid use or a complement to the use of lower doses of prescription opioids in patients with chronic pain.
PMID: 34695720
ISSN: 1873-4758
CID: 5090862
