Faculty Bibliography

Predictors of opioid overdose death in neighborhoods are important to identify, both to understand characteristics of high-risk areas and to prioritize limited prevention and intervention resources. Machine learning methods could serve as a valuable tool for identifying neighborhood-level predictors. We examined statewide data on opioid overdose death from Rhode Island (log-transformed rates for 2016-2019) and 203 covariates from the American Community Survey for 742 US Census block groups. The analysis included a least absolute shrinkage and selection operator (LASSO) algorithm followed by variable importance rankings from a random forest algorithm. We employed double cross-validation, with 10 folds in the inner loop to train the model and 4 outer folds to assess predictive performance. The ranked variables included a range of dimensions of socioeconomic status, including education, income and wealth, residential stability, race/ethnicity, social isolation, and occupational status. The R2 value of the model on testing data was 0.17. While many predictors of overdose death were in established domains (education, income, occupation), we also identified novel domains (residential stability, racial/ethnic distribution, and social isolation). Predictive modeling with machine learning can identify new neighborhood-level predictors of overdose in the continually evolving opioid epidemic and anticipate the neighborhoods at high risk of overdose mortality.

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.

This article discusses the ways in which healthcare professionals can use emotion as part of developing an ethical response to the COVID-19 pandemic. Affect theory, a growing approach to inquiry in the social sciences and humanities that appraises the historical and cultural contexts of emotions as expressed through art and politics, offers a frame for clinicians and researchers to consider ethical questions that surround the reopening of the United States economy in the wake of COVID-19. This article uses affect theory to describe how healthcare workers' emotions are useful for formulating a reopening plan grounded in collective action and a duty to do no harm.

BACKGROUND:Mental disorders might be a risk factor for severe COVID-19. We aimed to assess the specific risks of COVID-19-related mortality, hospitalisation, and intensive care unit (ICU) admission associated with any pre-existing mental disorder, and specific diagnostic categories of mental disorders, and exposure to psychopharmacological drug classes. METHODS:statistic, and publication bias was tested with Egger regression and visual inspection of funnel plots. We used the GRADE approach to assess the overall strength of the evidence and the Newcastle Ottawa Scale to assess study quality. We also did subgroup analyses and meta-regressions to assess the effects of baseline COVID-19 treatment setting, patient age, country, pandemic phase, quality assessment score, sample sizes, and adjustment for confounders. This study is registered with PROSPERO, CRD42021233984. FINDINGS:=88·80%). No significant associations with mortality were identified for ICU admission. Subgroup analyses and meta-regressions showed significant associations of baseline COVID-19 treatment setting (p=0·013) and country (p<0·0001) with mortality. No significant associations with mortality were identified for other covariates. No evidence of publication bias was found. GRADE assessment indicated high certainty for crude mortality and hospitalisation, and moderate certainty for crude ICU admission. INTERPRETATION:Pre-existing mental disorders, in particular psychotic and mood disorders, and exposure to antipsychotics and anxiolytics were associated with COVID-19 mortality in both crude and adjusted models. Although further research is required to determine the underlying mechanisms, our findings highlight the need for targeted approaches to manage and prevent COVID-19 in at-risk patient groups identified in this study. FUNDING:None. TRANSLATIONS:For the Italian, French and Portuguese translations of the abstract see Supplementary Materials section.

BACKGROUND:Evidence suggests that individuals with history of substance use disorder (SUD) are at increased risk of COVID-19, but little is known about relationships between SUDs, overdose and COVID-19 severity and mortality. This study investigated risks of severe COVID-19 among patients with SUDs. METHODS:We conducted a retrospective review of data from a hospital system in New York City. Patient records from 1 January to 26 October 2020 were included. We assessed positive COVID-19 tests, hospitalizations, intensive care unit (ICU) admissions and death. Descriptive statistics and bivariable analyses compared the prevalence of COVID-19 by baseline characteristics. Logistic regression estimated unadjusted and sex-, age-, race- and comorbidity-adjusted odds ratios (AORs) for associations between SUD history, overdose history and outcomes. RESULTS:Of patients tested for COVID-19 (n = 188 653), 2.7% (n = 5107) had any history of SUD. Associations with hospitalization [AORs (95% confidence interval)] ranged from 1.78 (0.85-3.74) for cocaine use disorder (COUD) to 6.68 (4.33-10.33) for alcohol use disorder. Associations with ICU admission ranged from 0.57 (0.17-1.93) for COUD to 5.00 (3.02-8.30) for overdose. Associations with death ranged from 0.64 (0.14-2.84) for COUD to 3.03 (1.70-5.43) for overdose. DISCUSSION/CONCLUSIONS:Patients with histories of SUD and drug overdose may be at elevated risk of adverse COVID-19 outcomes.

Prescription drug monitoring programs (PDMPs) are databases that track controlled substances at the provider, patient, and pharmacy levels. While these databases are widely available at the state level throughout the United States, several jurisdictions in recent years have mandated the use of these systems by health care providers. This study explores the implementation of mandatory PDMP technology in primary care practice and the effects on treatment of people with possible substance use disorders. Findings are based on 53 in-depth interviews with primary care providers in New York City, collected shortly following the passage of legislation mandating use of a PDMP by health care providers in New York State. Findings suggest that use of the PDMP highlighted tensions between provider stigma toward substance use disorders and the clinical care of people who use drugs, challenging their stereotypes and biases. The parallel clinical and law enforcement purposes of PDMP technology placed providers in dual roles as clinicians and enforcers and encouraged the punitive treatment of patients. Finally, PDMP technology standardized the clinical assessment process toward a "diagnosis first" approach, consistent with prior scholarship on the implementation of emerging medical technologies.

Drug seizure data indicate the presence of fentanyl in the cocaine supplies nationally and in New York City (NYC). In NYC, 39% of cocaine-only involved overdose deaths in 2017 also involved fentanyl, suggesting that fentanyl in the cocaine supply is associated with overdose deaths. To raise awareness of fentanyl overdose risk among people who use cocaine, the NYC Department of Health and Mental Hygiene pilot tested an awareness campaign in 23 NYC nightlife venues. Although 87% of venue owners/managers were aware of fentanyl, no participating venues had naloxone on premises prior to the intervention. The campaign's rapid dissemination reached people at potential risk of opioid overdose in a short period of time following the identification of fentanyl in the cocaine supply. Public health authorities in states with high rates of opioid-involved overdose death should consider similar campaigns to deliver overdose prevention education in the context of a drug supply containing fentanyl.

The positive effects of increased diversity and inclusion in scientific research and practice are well documented. In this issue, DeVilbiss et al. (Am J Epidemiol. 2020;189(10):998-1010) present findings from a survey used to collect information to characterize diversity among epidemiologists and perceptions of inclusion in the epidemiologic profession. They capture identity across a range of personal characteristics, including race, gender, socioeconomic background, sexual orientation, religion, and political leaning. In this commentary, we assert that the inclusion of political leaning as an axis of identity alongside the others undermines the larger project of promoting diversity and inclusion in the profession and is symptomatic of the movement for "ideological diversity" in higher education. We identify why political leaning is not an appropriate metric of diversity and detail why prioritizing ideological diversity counterintuitively can work against equity building initiatives. As an alternative to ideological diversity, we propose that epidemiologists take up an existing framework for research and practice that centers the voices and perspectives of historically marginalized populations in epidemiologic work.