Faculty Bibliography

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: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.

Locating undiagnosed HIV infections is important for limiting transmission. However, there is limited evidence about how best to do so. In South Africa, men have been particularly challenging to reach for HIV testing due, in part, to stigma. We pilot-tested two versions of a network-based case-finding and care-linkage intervention. The first, TRIP, asked "seeds" (original participants) to recruit their sexual and/or injection partners. The second, TRIPLE, aimed to circumvent some stigma-related issues by asking seeds to recruit anyone they know who might be at risk of being HIV-positive-unaware. We recruited 11 (18% male) newly diagnosed HIV-positive (NDP) seeds from two clinics in KwaZulu-Natal, South Africa and randomly assigned them to either TRIP or TRIPLE. Network members were recruited two steps from each seed. The TRIP arm recruited 12 network members; the TRIPLE arm recruited 62. Both arms recruited NDPs at higher rates than local clinic testing, with TRIP (50.0%) outperforming (p = 0.012) TRIPLE (14.5%). However, TRIPLE (53.2%) was far superior to clinics (27.8%) and to TRIP (25.0%) at recruiting men. Given challenges around testing and treating men for HIV in this context, these findings suggest that the TRIPLE expanded network-tracing approach should be tested formally among larger samples in multiple settings.

Buprenorphine is a highly effective treatment for opioid use disorder (OUD) and a critical tool for addressing the worsening US overdose crisis. However, multiple barriers to treatment-including stringent federal regulations-have historically made this medication hard to reach for many who need it. In 2020, under the COVID-19 public health emergency, federal regulators substantially changed access to buprenorphine by allowing prescribers to initiate patients on buprenorphine via telehealth without first evaluating them in person. As the public health emergency has been set to expire in May of 2023, Congress and federal agencies can leverage extensive evidence from studies conducted during the wake of the pandemic to make evidence-based decisions on the regulation of buprenorphine going forward. To aid policy makers, this narrative review synthesizes and interprets peer-reviewed research on the effect of buprenorphine flexibilities on the uptake and implementation of telehealth, and its impact on OUD patient and prescriber experiences, access to treatment, and health outcomes. Overall, our review finds that many prescribers and patients took advantage of telehealth, including the audio-only option, with a wide range of benefits and few downsides. As a result, federal regulators-including agencies and Congress-should continue nonrestricted use of telehealth for buprenorphine initiation.

BACKGROUND/UNASSIGNED:Approximately 1800 opioid treatment programs (OTPs) in the US dispense methadone to upwards of 400,000 patients with opioid use disorder (OUD) annually, operating under longstanding highly restrictive guidelines. OTPs were granted novel flexibilities beginning March 15, 2020, allowing for reduced visit frequency and extended take-home doses to minimize COVID exposure with great variation across states and sites. We sought to use electronic health records to compare retention in treatment, opioid use, and adverse events among patients newly entering methadone maintenance in the post-reform period in comparison with year-ago, unexposed, controls. METHODS/UNASSIGNED:Retrospective observational cohort study across 9 OTPs, geographically dispersed, in the National Institute of Drug Abuse (NIDA) Clinical Trials Network. Newly enrolled patients between April 15 and October 14, 2020 (post-COVID, reform period) v. March 15-September 14, 2019 (pre-COVID, control period) were assessed. The primary outcome was 6-month retention. Secondary outcomes were opioid use and adverse events including emergency department visits, hospitalizations, and overdose. FINDINGS/UNASSIGNED: INTERPRETATION/UNASSIGNED:Policies allowing for extended take-home schedules were not associated with worse retention or adverse events despite slightly elevated rates of measured opioid use while in care. Relaxed guidelines were not associated with measurable increased harms and findings could inform future studies with prospective trials. FUNDING/UNASSIGNED:USDHHSNIDACTNUG1DA013035-15.

INTRODUCTION/BACKGROUND:Hospitals are an ideal setting to stage opioid-related interventions with patients who are hospitalized due to overdose or other substance use-related complications. Transitional opioid programs-which initiate care and provide linkages upon discharge, such as screening, initiation of medications for opioid use disorder, and addiction consult services-have become the gold standard, but implementation has been uneven. The purpose of this study was to assess disparities in the availability of hospital-based transitional opioid programs, across rural and urban hospital settings in the United States. METHODS:Using hospital administrative data paired with county-level demographic data, we conducted bivariate and regression analyses to assess rural-urban differences in the availability of transitional opioid services including screening, addiction consult services, and MOUD in U.S general medical centers, controlling for hospital- and community-level factors. Our sample included 2846 general medical hospitals that completed the 2021 American Hospital Association (AHA) Annual Survey of Hospitals. Our primary outcomes were five self-reported measures: whether the hospital provided screening in the ED; provided screening in the inpatient setting; whether the hospital provided addiction consult services in the ED; provided addiction consult services in the inpatient setting; and whether the hospital provided medications for opioid use disorder. RESULTS:Rural hospitals did not have lower odds of screening for OUD or other SUDs than urban hospitals, but both micropolitan rural counties and noncore rural counties had significantly lower odds of having addiction consult services in either the ED (OR: 0.74, 95 % CI: 0.58, 0.95; OR: 0.68, 95 % CI: 0.50, 0.91) or inpatient setting (OR: 0.76, 95 % CI: 0.59, 0.97; OR: 0.68, 95 % CI: 0.50, 0.93), respectively, or of offering MOUD (OR: 0.69, 95 % CI: 0.52, 0.90; OR: 0.52, 95 % CI: 0.37, 0.74). CONCLUSIONS:Our study suggests that evidence-based interventions, such as medications for opioid use disorder and addiction consult services, are less often available in rural hospitals, which may contribute to rural-urban disparities in health outcomes secondary to OUD. A priority for population health improvement should be developing implementation strategies to support rural hospital adoption of transitional opioid programs.

Researchers across a wide array of disciplines are interested in finding the most influential subjects in a network. In a network setting, intervention effects and health outcomes can spill over from one node to another through network ties, and influential subjects are expected to have a greater impact than others. For this reason, network research in public health has attempted to maximize health and behavioral changes by intervening on a subset of influential subjects. Although influence is often defined only implicitly in most of the literature, the operative notion of influence is inherently causal in many cases: influential subjects are those we should intervene on to achieve the greatest overall effect across the entire network. In this work, we define a causal notion of influence using potential outcomes. We review existing influence measures, such as node centrality, that largely rely on the particular features of the network structure and/or on certain diffusion models that predict the pattern of information or diseases spreads through network ties. We provide simulation studies to demonstrate when popular centrality measures can agree with our causal measure of influence. As an illustrative example, we apply several popular centrality measures to the HIV risk network in the Transmission Reduction Intervention Project and demonstrate the assumptions under which each centrality can represent the causal influence of each participant in the study.

OBJECTIVE:The COVID-19 pandemic contributed to healthcare disruptions for patients with chronic pain. Following initial disruptions, national policies were enacted to expand access to long-term opioid therapy (LTOT) for chronic pain and opioid use disorder (OUD) treatment services, which may have modified risk of opioid overdose. We examined associations between LTOT and/or OUD with fatal and non-fatal opioid overdoses, and whether the pandemic moderated overdose risk in these groups. METHODS:We analyzed New York State Medicaid claims data (3/1/2019-12/31/20) of patients with chronic pain (N = 236,391). We used generalized estimating equations models to assess associations between LTOT and/or OUD (neither LTOT or OUD [ref], LTOT only, OUD only, and LTOT and OUD) and the pandemic (03/2020-12/2020) with opioid overdose. RESULTS:The pandemic did not significantly (ns) affect opioid overdose among patients with LTOT and/or OUD. While patients with LTOT (vs. no LTOT) had a slight increase in opioid overdose during the pandemic (pre-pandemic: aOR:1.65, 95% CI:1.05, 2.57; pandemic: aOR:2.43, CI:1.75,3.37, ns), patients with OUD had a slightly attenuated odds of overdose during the pandemic (pre-pandemic: aOR:5.65, CI:4.73, 6.75; pandemic: aOR:5.16, CI:4.33, 6.14, ns). Patients with both LTOT and OUD also experienced a slightly reduced odds of opioid overdose during the pandemic (pre-pandemic: aOR:5.82, CI:3.58, 9.44; pandemic: aOR:3.70, CI:2.11, 6.50, ns). CONCLUSIONS:Findings demonstrated no significant effect of the pandemic on opioid overdose among people with chronic pain and LTOT and/or OUD, suggesting pandemic policies expanding access to chronic pain and OUD treatment services may have mitigated the risk of opioid overdose.

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.

BACKGROUND:The national overdose crisis is often quantified by overdose deaths, but understanding the traumatic impact for those who witness and respond to overdoses can help elucidate mental health needs and opportunities for intervention for this population. Many who respond to overdoses are people who use drugs. This study adds to the literature on how people who use drugs qualitatively experience trauma resulting from witnessing and responding to overdose, through the lens of the Trauma-Informed Theory of Individual Health Behavior. METHODS:We conducted 60-min semi-structured, in-depth phone interviews. Participants were recruited from six states and Washington, DC in March-April 2022. Participants included 17 individuals who witnessed overdose(s) during the COVID-19 pandemic. The interview guide was shaped by theories of trauma. The codebook was developed using a priori codes from the interview guide; inductive codes were added during content analysis. Transcripts were coded using ATLAS.ti. RESULTS:A vast majority reported trauma from witnessing overdoses. Participants reported that the severity of trauma varied by contextual factors such as the closeness of the relationship to the person overdosing or whether the event was their first experience witnessing an overdose. Participants often described symptoms of trauma including rumination, guilt, and hypervigilance. Some reported normalization of witnessing overdoses due to how common overdoses were, while some acknowledged overdoses will never be "normal." The impacts of witnessing overdose on drug use behaviors varied from riskier substance use to increased motivation for treatment and safer drug use practices. CONCLUSION/CONCLUSIONS:Recognizing the traumatic impact of witnessed overdoses is key to effectively addressing the full range of sequelae of the overdose crisis. Trauma-informed approaches should be central for service providers when they approach this subject with clients, with awareness of how normalization can reduce help-seeking behaviors and the need for psychological aftercare. We found increased motivation for behavior change after witnessing, which presents opportunity for intervention.