Faculty Bibliography
Searched for:
person:cerdam01 or freids01 or hamill07 or krawcn01
Total Results:
1305
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
PrEP Care Continuum Engagement Among Persons Who Inject Drugs: Rural and Urban Differences in Stigma and Social Infrastructure
Pre-exposure prophylaxis (PrEP) is a medication that prevents HIV acquisition, yet PrEP uptake has been low among people who inject drugs. Stigma has been identified as a fundamental driver of population health and may be a significant barrier to PrEP care engagement among PWID. However, there has been limited research on how stigma operates in rural and urban settings in relation to PrEP. Using in-depth semi-structured qualitative interviews (n = 57) we explore PrEP continuum engagement among people actively injecting drugs in rural and urban settings. Urban participants had more awareness and knowledge. Willingness to use PrEP was similar in both settings. However, no participant was currently using PrEP. Stigmas against drug use, HIV, and sexualities were identified as barriers to PrEP uptake, particularly in the rural setting. Syringe service programs in the urban setting were highlighted as a welcoming space where PWID could socialize and therefore mitigate stigma and foster information sharing.
PMCID:8501360
PMID: 34626265
ISSN: 1573-3254
CID: 5067872
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
The stigma system: How sociopolitical domination, scapegoating, and stigma shape public health
Stigma is a fundamental driver of adverse health outcomes. Although stigma is often studied at the individual level to focus on how stigma influences the mental and physical health of the stigmatized, considerable research has shown that stigma is multilevel and structural. This paper proposes a theoretical approach that synthesizes the literature on stigma with the literature on scapegoating and divide-and-rule as strategies that the wealthy and powerful use to maintain their power and wealth; the literatures on racial, gender, and other subordination; the literature on ideology and organization in sociopolitical systems; and the literature on resistance and rebellion against stigma, oppression and other forms of subordination. we develop a model of the "stigma system" as a dialectic of interacting and conflicting structures and processes. Understanding this system can help public health reorient stigma interventions to address the sources of stigma as well as the individual problems that stigma creates. On a broader level, this model can help those opposing stigma and its effects to develop alliances and strategies with which to oppose stigma and the processes that create it.
PMID: 34115390
ISSN: 1520-6629
CID: 4900342
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
A Telemedicine Buprenorphine Clinic to Serve New York City: Initial Evaluation of the NYC Public Hospital System's Initiative to Expand Treatment Access during the COVID-19 Pandemic
OBJECTIVES/OBJECTIVE:The purpose of this study was to assess the feasibility and clinical impact of telemedicine-based opioid treatment with buprenorphine-naloxone following the Coronavirus disease 2019 pandemic. METHODS:Participants included in this retrospective analysis consisted of adult New York City residents with opioid use disorder eligible for enrollment in the NYC Health+Hospitals Virtual Buprenorphine Clinic between March and May 2020 (n = 78). Follow-up data were comprised of rates of retention in treatment at 2 months, referrals to community treatment, and induction-related events. RESULTS:During the initial 9 weeks of clinic operations, the clinic inducted 78 patients on to buprenorphine-naloxone and completed 252 visits. Patient referrals included non-NYC Health + Hospitals (n = 22, 28.2%) and NYC Health + Hospitals healthcare providers (n = 17, 21.8%), homeless shelter staff (n = 13, 16.7%), and the NYC Health + Hospitals jail reentry program in Rikers Island (n = 11, 14.1%). At 8 weeks, 42 patients remained in care (53.8%), 21 were referred to a community treatment program (26.9%), and 15 were lost to follow-up (19.2%). No patients were terminated from care due to disruptive behavior or suspicions of diversion or misuse of Buprenorphine. Adverse clinical outcomes were uncommon and included persistent withdrawal symptoms (n = 8, 4.3%) and one nonfatal opioid overdose (0.5%). CONCLUSIONS:Telemedicine-based opioid treatment and unobserved home induction on buprenorphine-naloxone offers a safe and feasible approach to expand the reach of opioid use disorder treatment, primary care, and behavioral health for a highly vulnerable urban population during an unprecedented natural disaster.
PMID: 33560696
ISSN: 1935-3227
CID: 4779622
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
