Faculty Bibliography

BACKGROUND:Fentanyl is a pressing concern in the current drug supply. Social media data can provide access to near real-time understanding of drug trends that may complement official mortality data. DESIGN/METHODS:The total number of fentanyl-related posts and the total number of posts for eight drug subreddit categories (alcohol, cannabis, hallucinogens, multi-drug, opioids, over the counter, sedatives, stimulants) were collected from 2013 to 2021 using the Pushshift Reddit dataset. The proportion of fentanyl-related posts as a fragment of total subreddit posts was examined. Linear regressions described the rate of change in post volume over time. RESULTS:Overall, fentanyl-related content increased across drug-related subreddits from 2013 to 2021 (1292% increase, linear trend p ≤ 0.001). Opioid subreddits (30.62 per 1000 posts, linear trend p ≤ 0.001) had the most fentanyl-related content during the examined time period. Multi-drug (5.95 per 1000; p ≤ 0.01), sedative (3.23 per 1000, p ≤ 0.01), and stimulant (1.60 per 1000, p ≤ 0.01) subreddits also had substantial increases in fentanyl-related content. The greatest increases occurred in the multi-drug (1067% 2013:2021) and stimulant (1862% 2014:2021) subreddits. CONCLUSION/CONCLUSIONS:Fentanyl-related posts on Reddit trended upward, with the fastest rate of change for multi-substance and stimulant subreddits. Beyond opioids, harm reduction and public health messaging should ensure inclusion of individuals who use other drugs.

Prior applications of machine learning to population health have relied on conventional model assessment criteria, limiting the utility of models as decision supports for public health practitioners. To facilitate practitioner use of machine learning as decision support for area-level intervention, this study developed and applied four practice-based predictive model evaluation criteria (implementation capacity, preventive potential, health equity, and jurisdictional practicalities). We used a case study of overdose prevention in Rhode Island to illustrate how these criteria could inform public health practice and health equity promotion. We used Rhode Island overdose mortality records from January 2016 to June 2020 (N=1,408) and neighborhood-level Census data. We learned two disparate machine learning models, Gaussian process and random forest, to illustrate the comparative utility of our criteria to guide interventions. Our models predicted 7.5-36.4% of overdose deaths during the test period, illustrating the preventive potential of overdose interventions assuming 5-20% statewide implementation capacities for neighborhood-level resource deployment. We described the health equity implications of predictive modeling to guide interventions along urbanicity, racial/ethnic composition, and poverty. In sum, our study discussed considerations to complement predictive model evaluation criteria and inform the prevention and mitigation of spatially dynamic public health problems across the breadth of practice.

The Russian war in Ukraine poses many risks for the spread of HIV, TB and associated conditions, including possible increases in the numbers of people who inject drugs or engage in sex work in the years ahead. Ukrainian civil society and volunteer efforts have been able to maintain and at times expand services for HIV Key Populations. The extent of mutual-aid and volunteer efforts as well as the continued strength and vitality of harm reduction organizations such as the Alliance for Public Health and the rest of civil society will be crucial resources for postwar efforts to assist Key Populations and prevent the spread of HIV, TB and other diseases. The postwar period will pose great economic and political difficulties for Ukrainians, including large populations of people physically and/or psychically damaged and in pain who might become people who inject drugs. Local and international support for public health and for harm reduction will be needed to prevent potentially large-scale increases in infectious disease and related mortality.

In the United States, cannabis is increasingly used to manage chronic pain. Veterans Health Administration (VHA) patients are disproportionately affected by pain and may use cannabis for symptom management. Because cannabis use increases the risk of cannabis use disorders (CUDs), we examined time trends in CUD among VHA patients with and without chronic pain, and whether these trends differed by age. From VHA electronic health records from 2005 to 2019 (∼4.3-5.6 million patients yearly), we extracted diagnoses of CUD and chronic pain conditions (International Classification of Diseases [ICD]-9-CM, 2005-2014; ICD-10-CM, 2016-2019). Differential trends in CUD prevalence overall and age-stratified (<35, 35-64, or ≥65) were assessed by any chronic pain and number of pain conditions (0, 1, or ≥2). From 2005 to 2014, the prevalence of CUD among patients with any chronic pain increased significantly more (1.11%-2.56%) than those without pain (0.70%-1.26%). Cannabis use disorder prevalence increased significantly more among patients with chronic pain across all age groups and was highest among those with ≥2 pain conditions. From 2016 to 2019, CUD prevalence among patients age ≥65 with chronic pain increased significantly more (0.63%-1.01%) than those without chronic pain (0.28%-0.47%) and was highest among those with ≥2 pain conditions. Over time, CUD prevalence has increased more among VHA patients with chronic pain than other VHA patients, with the highest increase among those age ≥65. Clinicians should monitor symptoms of CUD among VHA patients and others with chronic pain who use cannabis, and consider noncannabis therapies, particularly because the effectiveness of cannabis for chronic pain management remains inconclusive.

BACKGROUND:In the United States, methadone treatment for opioid use disorder is only available at opioid treatment programs (OTPs). In addition to federal regulations, states can enact laws which shape access to OTPs. We aimed to define classes of states according to restrictiveness of state OTP laws and examine population characteristics associated with class membership. METHODS:A set of laws was extracted from a database of statutes and regulations governing OTPs in 49 states and the District of Columbia as of June 2021. Latent class analysis of laws was used to estimate the probability of class membership for each state. Class-weighted multinomial logistic regression analysis assessed state-level correlates of class membership and adjusted Relative Risk Ratio (aRRR) and 95% confidence intervals (95%CI) were generated. RESULTS:States (n = 50) were assigned to three classes; Class 1) High restrictiveness on patient experience, low restrictiveness on access to service (n = 13); Class 2) Medium restrictiveness on patient experience, high restrictiveness on access to service (n = 14); Class 3) Low restrictiveness on patient experience, low restrictiveness on access to service (n = 23). States with a higher probability of membership in Classes with higher restrictiveness had higher rates of unemployment (Class 1 vs Class 3, aRRR:1.24; 95%CI:1.06-1.45), and Black residents (Class 2 vs Class 3, aRRR:1.10; 95%CI:1.04-1.15), and lower likelihood of Medicaid coverage of methadone (Class 1 vs Class 3, aRRR:0.25; 95%CI:0.07-0.88). States with a higher probability of membership in Classes with higher restrictiveness also had higher rates of potential indicators for opioid use disorder treatment need, including rates of opioid dispensing (Class 1 vs Class 3, aRRR:1.06; 95%CI:1.02-1.10, Class 2 vs Class 3, aRRR:1.07; 95%CI:1.03-1.11) and HIV diagnoses attributed to injection (Class 1 vs Class 3, aRRR:3.92; 95%CI:1.25-12.22). CONCLUSIONS:States with indicators of greater potential need for opioid use disorder treatment have the most restrictions, raising concerns about unmet treatment need.

Evaluating causal effects in the presence of interference is challenging in network-based studies of hard-to-reach populations. Like many such populations, people who inject drugs (PWID) are embedded in social networks and often exert influence on others in their network. In our setting, the study design is observational with a non-randomized network-based HIV prevention intervention. Information is available on each participant and their connections that confer possible HIV risk through injection and sexual behaviors. We considered two inverse probability weighted (IPW) estimators to quantify the population-level spillover effects of non-randomized interventions on subsequent health outcomes. We demonstrated that these two IPW estimators are consistent, asymptotically normal, and derived a closed-form estimator for the asymptotic variance, while allowing for overlapping interference sets (groups of individuals in which the interference is assumed possible). A simulation study was conducted to evaluate the finite-sample performance of the estimators. We analyzed data from the Transmission Reduction Intervention Project, which ascertained a network of PWID and their contacts in Athens, Greece, from 2013 to 2015. We evaluated the effects of community alerts on subsequent HIV risk behavior in this observed network, where the connections or links between participants were defined by using substances or having unprotected sex together. In the study, community alerts were distributed to inform people of recent HIV infections among individuals in close proximity in the observed network. The estimates of the risk differences for spillover using either IPW estimator demonstrated a protective effect. The results suggest that HIV risk behavior could be mitigated by exposure to a community alert when an increased risk of HIV is detected in the network.

BACKGROUND:Despite evidence that the U.S. "War on Drugs" is associated with increases in drug-related harm and other negative outcomes, all U.S. states have long criminalized most drug possession. In early 2021, both Oregon and Washington became exceptions to this rule when they fully (Oregon) or partially (Washington) decriminalized possession of small amounts of all drugs. METHODS:We obtained arrest data for 2019 to 2021 for intervention states (Oregon and Washington) and control states (Colorado, Idaho, Montana, and Nevada). We calculated monthly rates for arrests overall and for violent crimes, drug possession, equipment possession, non-drug crimes, and a set of low-level crimes termed displaced arrests. Using an interrupted time series analysis, we examined changes in monthly arrest rates after the implementation of policy change in Oregon and Washington compared to control states. RESULTS:In Oregon, there were 3 fewer drug possession arrests per 100,000 in the month after the policy change; the rate decreased throughout the post-implementation period. In Washington, there were almost 5 fewer drug possession arrests per 100,000 in the month following policy change, and the rate remained stable thereafter. Both declines were significantly greater than in comparison states. There were also statistically significant reductions in arrests for possession of drug equipment in Washington and a significant increase in displaced arrests in Oregon. There were no significant changes in overall arrests, non-drug arrests or arrests for violent crime in either state, relative to controls. CONCLUSION:This analysis demonstrates that it is possible for state drug decriminalization policies to dramatically reduce arrests for drug possession without increasing arrests for violent crimes, potentially reducing harm to people who use drugs and their communities. Additional research is needed to determine whether these legal reforms were associated with changes in overdose rates and other drug-related harms.

Trauma, violence, and their consequences for population health are shaped by complex, intersecting forces across the life span. We aimed to illustrate the strengths of agent-based modeling (ABM), a computational approach in which population-level patterns emerge from the behaviors and interactions of simulated individuals, for advancing trauma research; Method: We provide an overview of agent-based modeling for trauma research, including a discussion of the model development process, ABM as a complement to other causal inference and complex systems approaches in trauma research, and past ABM applications in the trauma literature; Results: We use existing ABM applications to illustrate the strengths of ABM for trauma research, including incorporating interactions between individuals, simulating processes across multiple scales, examining life-course effects, testing alternate theories, comparing intervention strategies in a virtual laboratory, and guiding decision making. We also discuss the challenges of applying ABM to trauma research and offer specific suggestions for incorporating ABM into future studies of trauma and violence; Conclusion: Agent-based modeling is a useful complement to other methodological advances in trauma research. We recommend a more widespread adoption of ABM, particularly for research into patterns and consequences of individual traumatic experiences across the life course and understanding the effects of interventions that may be influenced by social norms and social network structures. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

The AIDS and COVID-19 pandemics demonstrated that nations at similar economic development levels varied widely in their capacity to protect the health of their residents. For AIDS, Britain and Australia brought gay representatives into official counsels and adopted harm reduction far more rapidly than the United States or Spain, and East African countries responded more effectively than South Africa or the Democratic Republic of the Congo. National responses to COVID-19 varied widely, with New Zealand, China, and Vietnam more effective than Italy, Brazil, or the United States. Further, as phylogenetic research has demonstrated, these pandemics spread from one country to another, with those that responded poorly acting as sources for mutations and potentially sources of transmission to countries with more effective responses. Many observers expressed surprise at the poor responses of the United States to COVID-19, but in retrospect the cutbacks in public health funding at state and national levels made it clear that this was a predictable weakness even in addition to the political vacillations that crippled the US and Brazilian responses. In a time of global sociopolitical and climate instability, it is important to measure and conduct research into spatial and time variations in 1. public health and medical funding, 2. social influence networks, social cohesion and trust, and stigmatization, 3. income inequality, 4. social conflict, and 5. other factors that affect responsiveness to pandemics.