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.

PURPOSE/UNASSIGNED:As part of COVID-19 control policy, the Centers for Disease Control and Prevention has advised local jurisdictions to permit the formation of homeless encampments to prevent community disease spread. This new federal public health guidance is in conflict with existing police policies in many jurisdictions to raze or evict homeless encampments upon discovery. However, no empirical research on homeless encampment policy actions exists. METHODS/UNASSIGNED:This study utilized interrupted time series to estimate the impact of the 2017 closure of "the Hole"-a longstanding encampment of homeless people who use drugs in the Bronx, New York City-on crime complaints. Daily crime complaints originating from public spaces within 1 mile of the encampment were captured during the 30-day periods before and after closure. RESULTS/UNASSIGNED:Closure was associated with no short-term changesin complaints [IRR=1.01; 95% CI (0.81-1.27)], with daily complaints remaining at baseline levels during the post-closure period [IRR 0.99; 95% CI (0.98-1.00)]. DISCUSSION/UNASSIGNED:Findings preliminarily suggest that the presence of a homeless encampment may not have been associated with increased levels of crime in the neighborhood where it was located. Future research is necessary to understand the health and social impacts of homeless encampments and inform municipal policymakers.

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.

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.

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.

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.

BACKGROUND:In August 2010, extended-release OxyContin® products, including oxycodone 80 mg, were reformulated and released as abuse-deterrent medications. This paper describes changes in individual prescription filling patterns that followed the reformulation of oxycodone 80 mg. METHODS:Using New York State prescription monitoring program data, we conducted a retrospective analysis of a cohort of New York City residents who had filled at least three consecutive prescriptions for oxycodone 80 mg immediately prior to the reformulation. We classified cohort members into one of three groups (continuers, switchers, and discontinuers) based on prescription filling patterns post-reformulation. Descriptive analyses were conducted to identify prevalence of filling patterns. Differences in median morphine milligram equivalents (MME) pre- and post-reformulation were compared using the Wilcoxon signed-rank sum test. Analyses were completed in 2018. RESULTS:A cohort of 4,098 New York City residents filled continuous prescriptions for oxycodone 80 mg immediately prior to reformulation. Post-reformulation, 14% of the cohort discontinued filling opioid analgesic prescriptions; 46% continued to fill prescriptions for oxycodone 80 mg; and 40% switched to a different opioid analgesic, most commonly oxycodone 30 mg. Post-reformulation, the median MME dose decreased significantly among all three groups: 45 mg among continuers, 150 mg among switchers, and 360 mg among discontinuers. CONCLUSION:Post-reformulation, more than half the cohort changed their filling patterns. Following reformulation, median MME dose decreased significantly among the cohort. We hypothesize that the dramatic decrease in MME dose prompted many to transition to heroin in order to avoid severe withdrawal.