Assessing Direct and Spillover Effects of Intervention Packages in Network-randomized Studies.

BACKGROUND: Intervention packages may result in a greater public health impact than single interventions. Understanding the separate impact of each component on the overall package effectiveness can improve intervention delivery. METHODS: We adapted an approach to evaluate the effects of a time-varying intervention package in a network-randomized study. In some network-randomized studies, only a subset of participants in exposed networks receive the intervention themselves. The spillover effect contrasts average potential outcomes if a person was not exposed to themselves under intervention in the network versus no intervention in a control network. We estimated the effects of components of the intervention package in HIV Prevention Trials Network 037, a Phase III network-randomized HIV prevention trial among people who inject drugs and their risk networks using marginal structural models to adjust for time-varying confounding. The index participant in an intervention network received a peer education intervention initially at baseline, then boosters at 6 and 12 months. All participants were followed to ascertain HIV risk behaviors. RESULTS: There were 560 participants with at least one follow-up visit, 48% of whom were randomized to the intervention, and 1,598 participant visits were observed. The spillover effect of the boosters in the presence of initial peer education training was a 39% rate reduction (rate ratio = 0.61; 95% confidence interval = 0.43, 0.87). CONCLUSIONS: These methods will be useful for evaluating intervention packages in studies with network features.

Assessing Direct and Spillover Effects of Intervention Packages in Network-Randomized Studies.

Intervention packages may result in a greater public health impact than single interventions. Understanding the separate impact of each component in the overall package effectiveness can improve intervention delivery. We adapted an approach to evaluate the effects of a time-varying intervention package in a network-randomized study. In some network-randomized studies, only a subset of participants in exposed networks receive the intervention themselves. The spillover effect contrasts average potential outcomes if a person was not exposed themselves under intervention in the network versus no intervention in a control network. We estimated effects of components of the intervention package in HIV Prevention Trials Network 037, a Phase III network-randomized HIV prevention trial among people who inject drugs and their risk networks using Marginal Structural Models to adjust for time-varying confounding. The index participant in an intervention network received a peer education intervention initially at baseline, then boosters at 6 and 12 months. All participants were followed to ascertain HIV risk behaviors. There were 560 participants with at least one follow-up visit, 48% of whom were randomized to the intervention, and 1,598 participant-visits were observed. The spillover effect of the boosters in the presence of initial peer education training was a 39% rate reduction (Rate Ratio = 0.61; 95% confidence interval= 0.43, 0.87). These methods will be useful to evaluate intervention packages in studies with network features.

Modified-Weight Healthy Eating Index-2015 Scores Are More Strongly Associated With Mortality Risk Than Standard Scores.

BACKGROUND: Various diet quality scores are consistently and similarly associated with mortality risk. Emerging evidence suggests that individual diet quality components are differentially associated with mortality risk, but it is unclear whether or not modified weights reflective of relative component differences would strengthen mortality associations. OBJECTIVE: This study examined whether Healthy Eating Index 2015 (HEI-2015) scores with modified (vs standard) component weights are differentially associated with mortality risk. DESIGN: This was a longitudinal analysis of the National Health and Nutrition Examination Survey III (1988-94) with 23 years of mortality follow-up. The HEI-2015 and modified-weight scores were calculated from one 24-hour recall. The a priori Key Facets HEI was a subset score equivalently weighting fruits, vegetables, whole grains, and seafood and plant proteins. In the least absolute shrinkage and selection operator regression-weighted HEI, components were assigned weights reflecting relative mortality risk associations. PARTICIPANTS/SETTING: Analyses included 10,789 US adults (aged 20 years and older) who were not pregnant and without prior diabetes, cancer, cardiovascular disease, or chronic kidney disease diagnoses. MAIN OUTCOME MEASURES: All-cause and cardiovascular disease mortality risk were the primary outcomes examined. STATISTICAL ANALYSES PERFORMED: Three energy-adjusted HEI scores were assigned to quintiles; covariate-adjusted sex-stratified Cox models with age as the timescale assessed associations between and trends across quintiles of HEI scores and all-cause and cardiovascular disease mortality risk. RESULTS: Modified-weight HEI scores were associated with 23% to 38% reduced all-cause mortality risk in the highest vs lowest quintile, whereas comparisons were only significant for women (Key Facets P = 0.02 and least absolute shrinkage and selection operator regression-weighted P = 0.001; for men P = 0.06 on both scores), trends across quintiles of modified-weight scores were significant for men and women. The HEI-2015 was not significantly associated with all-cause mortality risk, and none of the scores were associated with cardiovascular disease mortality risk. CONCLUSIONS: Only modified-weight HEI scores were associated with reduced all-cause mortality risk. Findings suggest modified diet quality weighting schemes warrant further examination to determine their replicability.

Overall, Direct, Spillover, and Composite Effects of Components of a Peer-Driven Intervention Package on Injection Risk Behavior Among People Who Inject Drugs in the HPTN 037 Study.

We sought to disentangle effects of the components of a peer-education intervention on self-reported injection risk behaviors among people who inject drugs (n = 560) in Philadelphia, US. We examined 226 egocentric groups/networks randomized to receive (or not) the intervention. Peer-education training consisted of two components delivered to the intervention network index individual only: (1) an initial training and (2) "booster" training sessions during 6- and 12-month follow up visits. In this secondary data analysis, using inverse-probability-weighted log-binomial mixed effects models, we estimated the effects of the components of the network-level peer-education intervention upon subsequent risk behaviors. This included contrasting outcome rates if a participant is a network member [non-index] under the network exposure versus under the network control condition (i.e., spillover effects). We found that compared to control networks, among intervention networks, the overall rates of injection risk behaviors were lower in both those recently exposed (i.e., at the prior visit) to a booster (rate ratio [95% confidence interval]: 0.61 [0.46-0.82]) and those not recently exposed to it (0.81 [0.67-0.98]). Only the boosters had statistically significant spillover effects (e.g., 0.59 [0.41-0.86] for recent exposure). Thus, both intervention components reduced injection risk behaviors with evidence of spillover effects for the boosters. Spillover should be assessed for an intervention that has an observable behavioral measure. Efforts to fully understand the impact of peer education should include routine evaluation of spillover effects. To maximize impact, boosters can be provided along with strategies to recruit especially committed peer educators and to increase attendance at trainings. Clinical Trials Registration Clinicaltrials.gov NCT00038688 June 5, 2002.

RESUMEN: Intentamos desenmarañar los efectos de los componentes de una intervención de educación entre pares sobre los comportamientos de inyección de riesgo autorreportados entre personas que se inyectan drogas (n = 560; 226 grupos/redes egocéntricos(as)) aleatorizados(as) a recibir (o no) la intervención en Filadelfia, EUA. Dos componentes fueron administrados a índices de redes de intervención: una capacitación inicial y sesiones de "refuerzo" durante visitas de seguimiento. Usando modelos log-binomial de efectos mixtos ponderados por probabilidad inversa, estimamos los efectos de dichos componentes sobre los comportamientos de riesgo posteriores. Encontramos que en comparación con las redes control, en las redes de intervención, las tasas generales de comportamientos de inyección de riesgo fueron más bajas en ambas aquellas expuestas recientemente a un refuerzo (razón de tasas [intervalo de confianza del 95%]: 0.61 [0.46­0.82]) y aquellas no expuestas recientemente (0.81 [0.67­0.98]). Solamente los refuerzos tuvieron efectos derrame (i.e., contraste de las tasas de resultados si es miembro [no índice] de una red en una red con exposición reciente versus bajo la condición control) significativos (p. ej., 0.59 [0.41­0.86] para la exposición reciente).

Methods for Assessing Spillover in Network-Based Studies of HIV/AIDS Prevention among People Who Use Drugs.

Human Immunodeficiency Virus (HIV) interventions among people who use drugs (PWUD) often have spillover, also known as interference or dissemination, which occurs when one participant's exposure affects another participant's outcome. PWUD are often members of networks defined by social, sexual, and drug-use partnerships and their receipt of interventions can affect other members in their network. For example, HIV interventions with possible spillover include educational training about HIV risk reduction, pre-exposure prophylaxis, or treatment as prevention. In turn, intervention effects frequently depend on the network structure, and intervention coverage levels and spillover can occur even if not measured in a study, possibly resulting in an underestimation of intervention effects. Recent methodological approaches were developed to assess spillover in the context of network-based studies. This tutorial provides an overview of different study designs for network-based studies and related methodological approaches for assessing spillover in each design. We also provide an overview of other important methodological issues in network studies, including causal influence in networks and missing data. Finally, we highlight applications of different designs and methods from studies of PWUD and conclude with an illustrative example from the Transmission Reduction Intervention Project (TRIP) in Athens, Greece.

Evaluation of the association between health insurance status and healthcare utilization and expenditures among adult cancer survivors in the United States.

BACKGROUND: Health care expenditures for cancer care has increased significantly over the past decade and is further projected to rise. This study examined the associations between health insurance status and total direct health care expenditures and health care utilization among cancer survivors living in the United States. METHODS: A cross-sectional study of cancer survivors aged ≥18 years, identified from the Medical Expenditures Panel Survey (MEPS) during 2017 using International Classification of Diseases, Tenth Revision codes specific for cancer. Health insurance was categorized into Private, Medicare, Medicaid, and uninsured. Multivariable ordinary least squares regression was used to examine the association between log expenditures and health insurance. Negative binomial regression with log link was used to obtain adjusted incident rate ratios (AIRR) for health care utilization. Survey weights were used to produce nationally representative estimates of the US population. RESULTS: A total of 1140 (weighted = 13.9 million) cancer survivors were identified. Compared to the adjusted mean annual health care expenditures for the private group ($14,265; 95% confidence interval (CI): $12,645 to $16,092), the adjusted mean annual health care expenditures for the Medicare group were higher ($15,112; 95%CI: $13,361 to $17,092). As compared to the private group, the average annual expenditures for uninsured cancer survivors ($2315; 95%CI:1038 to $3501) was significantly lower and so was their health care utilization. Adjusted rates of ER visits for Medicaid were twice (AIRR:2.04; SE:0.28; p = 0.001) as compared to privately insured. CONCLUSIONS: A difference in the average total direct expenditures between uninsured and privately insured patients was found. Uninsured had the lowest health care utilization while Medicaid reported significantly higher number of ER visits. Despite differences in program structures, health care expenditures across insurance types were similar. Lower utilization of health care services among uninsured suggests cost maybe a barrier to accessing care.

Finding influential subjects in a network using a causal framework.

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.

Contingency Management and Pre-Exposure Prophylaxis Adherence Support Services (CoMPASS): A hybrid type 1 effectiveness-implementation study to promote HIV risk reduction among people who inject drugs.

BACKGROUND: HIV disproportionally affects persons who inject drugs (PWID), but engagement with HIV pre-exposure prophylaxis (PrEP) is low. We describe the rationale and study design for a new study, "Contingency Management and Pre-Exposure Prophylaxis (PrEP) Adherence Support Services (CoMPASS)," a hybrid type 1 effectiveness-implementation trial to promote HIV risk reduction among PWID. METHODS: In four community-based programs in the northeastern United States, PrEP-eligible PWID (target n = 526) are randomized to treatment as usual or Contingency Management (CM) and, as indicated, stepped up to PrEP Adherence Support Services (CoMPASS) over 24 weeks. During CM sessions, participants receive timely tangible rewards for verifiable activities demonstrating 1) PrEP initiation and adherence, and 2) engagement with medications for opioid use disorder (MOUD) and other OUD-related care. Participants who do not have high levels of biomarker-confirmed PrEP adherence at week 12 will be stepped up to receive PrEP Adherence Support Services (PASS) consisting of strengths-based case management over 12 weeks. Interventions are delivered by trained PrEP navigators, staff embedded within the respective sites. The primary outcome is sustained PrEP adherence by dried blood spot testing at 24 weeks. To inform future implementation, we are conducting implementation-focused process evaluations throughout the clinical trial. CONCLUSIONS: Results from this protocol are anticipated to yield novel findings regarding the impact and scalability of CoMPASS to promote HIV prevention among PWID in partnership with community-based organizations. http://ClinicalTrials.gov identifier: NCT04738825.

The effect of antiretroviral therapy with high central nervous system penetration on HIV-related cognitive impairment: a systematic review and meta-analysis.

Chronic complications are a significant concern for people living with HIV/AIDS (PLWHA) infection. HIV-associated neurocognitive disorders (HAND) are prevalent in PLWHA. Yet, the efficacy of medications that penetrate the central nervous system (CNS) at preventing or slowing the progression of HAND remains largely unknown. The objective of this study was to determine whether high CNS penetration effectiveness (CPE) regimens improve neurocognitive test scores in PLWHA on combined antiretroviral therapy (cART). Primary literature evaluating cognitive outcomes based on CPE score of cART regimens in PLWHA was assembled from PubMed/Medline and EMBASE. Both randomized controlled trials and observational studies with at least 12 weeks of follow-up were included. A meta-analysis was conducted to calculate the standardized mean difference. Eight trials including a total of 3,303 patients with 13,103 person-years of follow-up were included in the systematic review. Four trials (n = 366 patients) met our inclusion criteria and were included in the meta-analysis. In the meta-analysis, HIV regimens with a high CPE score did not affect NPZ-4 or GDS scores (standardized mean difference (SMD) 0.10, 95% CI -0.19, 0.38; I2 = 26%). Future studies with larger sample sizes are warranted to prospectively evaluate the relationship between CPE and progression of HAND.

Estimating Causal Effects of HIV Prevention Interventions with Interference in Network-based Studies among People Who Inject Drugs.

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.

Spillover benefit of pre-exposure prophylaxis for HIV prevention: evaluating the importance of effect modification using an agent-based model.

We developed an agent-based model using a trial emulation approach to quantify effect measure modification of spillover effects of pre-exposure prophylaxis (PrEP) for HIV among men who have sex with men (MSM) in the Atlanta-Sandy Springs-Roswell metropolitan area, Georgia. PrEP may impact not only the individual prescribed, but also their partners and beyond, known as spillover. We simulated a two-stage randomised trial with eligible components (≥3 agents with ≥1 HIV+ agent) first randomised to intervention or control (no PrEP). Within intervention components, agents were randomised to PrEP with coverage of 70%, providing insight into a high PrEP coverage strategy. We evaluated effect modification by component-level characteristics and estimated spillover effects on HIV incidence using an extension of randomisation-based estimators. We observed an attenuation of the spillover effect when agents were in components with a higher prevalence of either drug use or bridging potential (if an agent acts as a mediator between ≥2 connected groups of agents). The estimated spillover effects were larger in magnitude among components with either higher HIV prevalence or greater density (number of existing partnerships compared to all possible partnerships). Consideration of effect modification is important when evaluating the spillover of PrEP among MSM.

Generalizing trial evidence to target populations in non-nested designs: Applications to AIDS clinical trials.

Comparative effectiveness evidence from randomized trials may not be directly generalizable to a target population of substantive interest when, as in most cases, trial participants are not randomly sampled from the target population. Motivated by the need to generalize evidence from two trials conducted in the AIDS Clinical Trials Group (ACTG), we consider weighting, regression and doubly robust estimators to estimate the causal effects of HIV interventions in a specified population of people living with HIV in the USA. We focus on a non-nested trial design and discuss strategies for both point and variance estimation of the target population average treatment effect. Specifically in the generalizability context, we demonstrate both analytically and empirically that estimating the known propensity score in trials does not increase the variance for each of the weighting, regression and doubly robust estimators. We apply these methods to generalize the average treatment effects from two ACTG trials to specified target populations and operationalize key practical considerations. Finally, we report on a simulation study that investigates the finite-sample operating characteristics of the generalizability estimators and their sandwich variance estimators.

Behavior-based diabetes management: impact on care, hospitalizations, and costs.

OBJECTIVES: To (1) examine the impact of the Diabetes Care Rewards (DCR) program on adherence to care standards and (2) evaluate the economic impact of adherence to care standards. STUDY DESIGN: A retrospective observational cohort study design with propensity matching. Additional covariates adjustment was used to minimize residual imbalance. METHODS: Utilization and cost data were compared between individuals enrolled vs individuals eligible for but not enrolled in the DCR program using a standard mean difference. Individuals were employees or their dependents from self-insured companies throughout the United States. Outcomes included adherence to the care standards, service utilization, and costs. RESULTS: A total of 3318 propensity-matched participants were included. Primary analysis revealed that enrolled members increased adherence to semiannual glycated hemoglobin, annual lipid, and annual urine albumin-creatinine ratio testing. Additionally, enrolled members experienced less utilization of high-acuity services and increased rates of physician visits. In a secondary analysis, the enrolled group was associated with greater pharmaceutical costs but lower medical costs. CONCLUSIONS: A behavioral science- and incentive-based diabetes management program was associated with greater rates of adherence to recommended diabetes monitoring care standards, increased routine clinic visits, decreased hospital admissions, and decreased inpatient days. Anticipated increases in pharmaceutical expenditures were offset by overall lower medical expenditures. Results indicate the economic benefits of adherence to evidence-based standards for diabetes care.

Emulating Target Trials to Improve Causal Inference From Agent-Based Models.

Agent-based models are a key tool for investigating the emergent properties of population health settings, such as infectious disease transmission, where the exposure often violates the key "no interference" assumption of traditional causal inference under the potential outcomes framework. Agent-based models and other simulation-based modeling approaches have generally been viewed as a separate knowledge-generating paradigm from the potential outcomes framework, but this can lead to confusion about how to interpret the results of these models in real-world settings. By explicitly incorporating the target trial framework into the development of an agent-based or other simulation model, we can clarify the causal parameters of interest, as well as make explicit the assumptions required for valid causal effect estimation within or between populations. In this paper, we describe the use of the target trial framework for designing agent-based models when the goal is estimation of causal effects in the presence of interference, or spillover.

Self-Injurious Thoughts and Behaviors Among College Students: A Latent Class Analysis.

This study explored emerging typologies of self-injurious thoughts and behaviors and how they relate to known psychological and cognitive correlates. Latent class analysis was employed to identify subgroups of college students (n = 626, mean age = 20, 73% female, and 83% White) based on history of self-injurious thoughts and behaviors. Differences among classes were tested to examine their associations with suicidal ideation, fearlessness of death, depression/hopelessness, thwarted belongingness, problems with alcohol or drugs, behavioral inhibition, and emotional control. Results of the latent class analysis supported a 3-class solution, with students classified as being likely to have (1) no history of any self-injurious thoughts and behaviors (78%); (2) a history of all self-injurious thoughts and behaviors (8%); or (3) a history of suicidal ideation, plan, and non-suicidal self-injurious thoughts and behaviors, but not suicide attempt (13%). Findings examining correlates of self-injurious thoughts and behaviors highlight the importance of fearlessness of death for distinguishing suicide attempters from non-attempters. Attention to mental health and suicide prevention initiatives should remain a critical priority across college campuses.

Initial Patterns of Prescription Opioid Supply and Risk of Mortality Among Insured Adults in the United States.

OBJECTIVE: To examine the association between initial patterns of prescription opioid supply (POS) and risk of all-cause mortality among an insured opioid-naïve patient population in the United States (US). METHODS: This retrospective observational cohort study used de-identified, administrative health care claims data from a large national insurer (Optum Clinformatics Data Mart) from 2010 to 2015. Participants included insured, cancer-free adults prescribed opioid analgesics. Prescription opioids received during the first 6 months of therapy were used to categorize initial patterns of POS as daily or nondaily. Cox regression was used to estimate the association of initial patterns of POS with all-cause mortality within one year of follow-up, adjusting for baseline covariates to control for confounding. RESULTS: A total of 4,054,417 patients were included, of which 2.75% had incident daily POS; 54.8% were female; median age was 50 years; mean Charlson comorbidity index (CCI) was 0.21 (standard deviation = 0.77); and mean daily morphine milligram equivalent was 34.61 (95% confidence intervals: 34.59, 34.63). There were 2068 more deaths per 100,000 person-years among patients who were prescribed opioids daily than nondaily. After adjusting for baseline covariates, the hazard of all-cause mortality among patients with incident daily POS was nearly twice that among those prescribed nondaily (hazard ratio [HR] = 1.94; 95% confidence intervals: 1.84, 2.04). CONCLUSIONS: Among insured adult patients with noncancer pain, incident chronic POS was associated with a significantly increased risk of all-cause mortality over at most 1 year of follow-up. Because these results may be susceptible to bias, more research is needed to establish causality.

Disseminated Effects in Agent-Based Models: A Potential Outcomes Framework and Application to Inform Preexposure Prophylaxis Coverage Levels for HIV Prevention.

Preexposure prophylaxis (PrEP) for prevention of human immunodeficiency virus (HIV) infection may benefit not only the person who uses it but also their uninfected sexual risk contacts. We developed an agent-based model using a novel trial emulation approach to quantify disseminated effects of PrEP use among men who have sex with men in Atlanta, Georgia, from 2015 to 2017. Model components (subsets of agents connected through partnerships in a sexual network but not sharing partnerships with any other agents) were first randomized to an intervention coverage level or the control group; then, within intervention components, eligible agents were randomized to receive or not receive PrEP. We calculated direct and disseminated (indirect) effects using randomization-based estimators and report corresponding 95% simulation intervals across scenarios ranging from 10% coverage in the intervention components to 90% coverage. A population of 11,245 agents was simulated, with an average of 1,551 components identified. When comparing agents randomized to no PrEP in 70% coverage components with control agents, there was a 15% disseminated risk reduction in HIV incidence (risk ratio = 0.85, 95% simulation interval: 0.65, 1.05). Persons not on PrEP may receive a protective benefit by being in a sexual network with higher PrEP coverage. Agent-based models are useful for evaluating possible direct and disseminated effects of HIV prevention modalities in sexual networks.

Study Designs for Extending Causal Inferences From a Randomized Trial to a Target Population.

In this article, we examine study designs for extending (generalizing or transporting) causal inferences from a randomized trial to a target population. Specifically, we consider nested trial designs, where randomized individuals are nested within a sample from the target population, and nonnested trial designs, including composite data-set designs, where observations from a randomized trial are combined with those from a separately obtained sample of nonrandomized individuals from the target population. We show that the counterfactual quantities that can be identified in each study design depend on what is known about the probability of sampling nonrandomized individuals. For each study design, we examine identification of counterfactual outcome means via the g-formula and inverse probability weighting. Last, we explore the implications of the sampling properties underlying the designs for the identification and estimation of the probability of trial participation.

Rx for Addiction and Medication Safety (RAMS-PEER): Evaluation of an Education and Peer Program on Opioid Misuse.

The Rx (prescription) for Addiction and Medication Safety (RAMS) program was developed during the 2017 through 2018 academic year to educate students from 6 selected Rhode Island public high schools about opioid misuse, overdose, and recovery. During 2016, 3 schools participated in the RAMS program and returned for RAMS-PEER in 2017; 3 schools were newly recruited in 2016. Tenth graders returned from schools that participated during RAMS in 2016, and all ninth graders were new. Our study's aim was to evaluate the overall effect and spillover benefit of the RAMS-PEER intervention from tenth to ninth graders by surveying students both before and after the education program. Survey questions were modified from the 2015 Youth Risk Behavior Survey and the 2015 Ontario Study Survey. Student responses were matched for preintervention and postintervention analysis using a unique identifier. We observed an improvement in knowledge of opioid misuse; however, we found no evidence of a significant spillover benefit.

Defining a recovery-oriented cascade of care for opioid use disorder: A community-driven, statewide cross-sectional assessment.

BACKGROUND: In light of the accelerating and rapidly evolving overdose crisis in the United States (US), new strategies are needed to address the epidemic and to efficiently engage and retain individuals in care for opioid use disorder (OUD). Moreover, there is an increasing need for novel approaches to using health data to identify gaps in the cascade of care for persons with OUD. METHODS AND FINDINGS: Between June 2018 and May 2019, we engaged a diverse stakeholder group (including directors of statewide health and social service agencies) to develop a statewide, patient-centered cascade of care for OUD for Rhode Island, a small state in New England, a region highly impacted by the opioid crisis. Through an iterative process, we modified the cascade of care defined by Williams et al. for use in Rhode Island using key national survey data and statewide health claims datasets to create a cross-sectional summary of 5 stages in the cascade. Approximately 47,000 Rhode Islanders (5.2%) were estimated to be at risk for OUD (stage 0) in 2016. At the same time, 26,000 Rhode Islanders had a medical claim related to an OUD diagnosis, accounting for 55% of the population at risk (stage 1); 27% of the stage 0 population, 12,700 people, showed evidence of initiation of medication for OUD (MOUD, stage 2), and 18%, or 8,300 people, had evidence of retention on MOUD (stage 3). Imputation from a national survey estimated that 4,200 Rhode Islanders were in recovery from OUD as of 2016, representing 9% of the total population at risk. Limitations included use of self-report data to arrive at estimates of the number of individuals at risk for OUD and using a national estimate to identify the number of individuals in recovery due to a lack of available state data sources. CONCLUSIONS: Our findings indicate that cross-sectional summaries of the cascade of care for OUD can be used as a health policy tool to identify gaps in care, inform data-driven policy decisions, set benchmarks for quality, and improve health outcomes for persons with OUD. There exists a significant opportunity to increase engagement prior to the initiation of OUD treatment (i.e., identification of OUD symptoms via routine screening or acute presentation) and improve retention and remission from OUD symptoms through improved community-supported processes of recovery. To do this more precisely, states should work to systematically collect data to populate their own cascade of care as a health policy tool to enhance system-level interventions and maximize engagement in care.