The HEALing (Helping to End Addiction Long-term SM) Communities Study: Protocol for a cluster randomized trial at the community level to reduce opioid overdose deaths through implementation of an integrated set of evidence-based practices

Walsh, Sharon; El‐Bassel, Nabila; Jackson, Rebecca D.; Samet, Jeffrey H.; Aggarwal, Maneesha; Aldridge, Arnie; Baker, Trevor; Barbosa, Carolina; Barocas, Joshua A.; Battaglia, Tracy A.; Beers, Donna; Bernson, Dana; Bowers-Sword, Rachel; Bridden, Carly; Brown, Jennifer L.; Bush, Heather; Bush, Joshua L.; Button, Amy; Campbell, Aimee N.C.; Cerdá, Magdalena; Cheng, Debbie M.; Chhatwal, Jag; Clarke, Thomas; Conway, Kevin P.; Crable, Erika L.; Czajkowski, Andrea; David, James L.; Drainoni, Mari-Lynn; Fanucchi, Laura; Feaster, Daniel J.; Fernández, Soledad; Freedman, Darcy A.; Freisthler, Bridget; Gilbert, Louisa; Glasgow, LaShawn; Goddard-Eckrich, Dawn; Gutnick, Damara; Harlow, Kristin; Helme, Donald W.; Huang, Terry T.‐K.; Huerta, Timothy R.; Hunt, Timothy; Hyder, Ayaz; Kerner, Robin; Keyes, Katherine M.; Knott, Charles; Knudsen, Hannah K.; Konstan, Michael W.; Larochelle, Marc R.; Lefebvre, R. Craig; Levin, Frances R.; Lewis, Nicky; Linas, Benjamin P.; Lofwall, Michelle R.; Lounsbury, David W.; Lyons, Michael S.; Mann, Sarah; Marks, Katherine R.; McAlearney, Ann Scheck; McCollister, Kathryn E.; McCrimmon, Tara; Miles, Jennifer; Miller, Christina M.; Nash, Denis; Nunes, Edward V.; Oga, Emmanuel; Oser, Carrie B.; Plouck, Tracy J; Rapkin, Bruce D.; Freeman, Patricia R.; Rodríguez, Sandra; Root, Elisabeth Dowling; Rosen-Metsch, Lisa; Sabounchi, Nasim S.; Saitz, Richard; Salsberry, Pamela J.; Savitsky, Caroline; Schackman, Bruce R.; Seiber, Eric E.; Slater, Michael D.; Slavova, Svetla; Speer, Drew; Martínez, Linda Sprague; Stambaugh, Leyla; Staton, Michele; Stein, Michael D.; Stevens‐Watkins, Danelle; Surratt, Hilary L.; Talbert, Jeffery; Thompson, Katherine; Toussant, Kim; Vandergrift, Nathan; Villani, Jennifer; Walker, Daniel M.; Walley, Alexander Y.; Walters, Scott T.; Westgate, Philip M.; Winhusen, Theresa; Wu, Elwin; Young, April M.; Young, Greg; Zarkin, Gary A.; Chandler, Redonna K.

doi:10.1016/j.drugalcdep.2020.108335

Cited by 79 publications

(51 citation statements)

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“…The purpose of this paper is to evaluate the performance of model-based tests and permutation tests in the covariate constrained cluster-randomized HEALing (Helping to End Addiction Long-term SM ) Communities Study (HCS). The HCS is a multi-site parallel group cluster randomized wait-list comparison trial of the Communities That Heal (CTH) intervention [14][15][16]. The HCS was designed to evaluate the impact of the CTH intervention compared to usual care on opioid overdose deaths among 67 communities.…”

Section: Discussionmentioning

confidence: 99%

“…As described by Westgate et al, the proposed negative binomial model will utilize small-sample adjusted empirical standard error estimates and degrees of freedom of the t-statistic equal to the number of communities minus the number of regression parameters [16] . The negative binomial regression model that will be used to analyze the number of opioid overdose deaths is: 1) The first model-based test is from the planned primary analysis of the HCS described above, i.e.…”

Section: Planned Hcs Analysis: Negative Binomial Regression With Smal...mentioning

confidence: 99%

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Performance of Model-Based vs. Permutation Tests in the HEALing (Helping to End Addiction Long-TermSM) Communities Study, a Covariate Constrained Cluster Randomized Trial

Tang

Heeren

Westgate

et al. 2022

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Background The HEALing (Helping to End Addiction Long-termSM) Communities Study (HCS) is a multi-site parallel group cluster randomized wait-list comparison trial designed to evaluate the effect of the Communities That Heal (CTH) intervention compared to usual care on opioid overdose deaths. Covariate constrained randomization (CCR) was applied to balance the community-level baseline covariates in the HCS. The purpose of this paper is to evaluate the performance of model-based tests and permutation tests in the HCS setting. We conducted a simulation study to evaluate Type I error rates and power for model-based and permutation tests for the multi-site HCS as well as for a subgroup analysis of a single state (Massachusetts). We also investigated whether the maximum degree of imbalance in the CCR design has an impact on the performance of the tests. Methods The primary outcome, number of opioid overdose deaths, is count data assessed at the community-level that will be analyzed using a negative binomial regression model. We conducted a simulation study to evaluate the Type I error rates and power for 3 tests: 1) Wald-type t-test with small-sample corrected empirical standard error estimates; 2) Wald-type z-test with model-based standard error estimates; and 3) permutation test with test statistics calculated by the difference in average residuals for the two groups. Results Our simulation results demonstrated that Wald-type t-tests with small-sample corrected empirical standard error estimates from the negative binomial regression model maintained proper Type I error. Wald-type z-tests with model-based standard error estimates were anti-conservative. Permutation tests preserved Type I error rates if the constrained space was not too small. For all tests, power was high to detect the hypothesized 40% reduction in opioid overdose deaths for the intervention vs. comparison group both for the overall HCS and the subgroup analysis of Massachusetts (MA). Conclusions Based on the results of our simulation study, the Wald-type t-test with small-sample corrected empirical standard error estimates from a negative binomial regression model is a valid and appropriate approach for analyzing cluster-level count data from the HEALing Communities Study. Trial registration: ClinicalTrials.gov http://www.clinicaltrials.gov; Identifier: NCT04111939

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Section: Discussionmentioning

confidence: 99%

Section: Planned Hcs Analysis: Negative Binomial Regression With Smal...mentioning

confidence: 99%

Performance of Model-Based vs. Permutation Tests in the HEALing (Helping to End Addiction Long-TermSM) Communities Study, a Covariate Constrained Cluster Randomized Trial

Tang

Heeren

Westgate

et al. 2022

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“…1–6 Our motivating example is the HEALing (Helping to End Addiction Long-term SM ) Communities Study, which is an ongoing multi-site (Kentucky, Massachusetts, New York, and Ohio), parallel-group, post-test only cluster randomized trial in which 67 communities are randomized to either an intervention arm or a wait-list control arm. 7–9 The primary goal is to reduce opioid overdose fatalities in the population by 40% 8 through the Communities That HEAL intervention. Details on the HEALing Communities Study and the Communities That HEAL intervention are described in “Introduction to the special issue on the HEALing Communities Study.” 7–17 Communities are unique in that they are very large in size, and opioid-related overdose deaths are expected to be very rare.…”

Section: Introductionmentioning

confidence: 99%

Marginal modeling in community randomized trials with rare events: Utilization of the negative binomial regression model

et al. 2022

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Background/aims This work is motivated by the HEALing Communities Study, which is a post-test only cluster randomized trial in which communities are randomized to two different trial arms. The primary interest is in reducing opioid overdose fatalities, which will be collected as a count outcome at the community level. Communities range in size from thousands to over one million residents, and fatalities are expected to be rare. Traditional marginal modeling approaches in the cluster randomized trial literature include the use of generalized estimating equations with an exchangeable correlation structure when utilizing subject-level data, or analogously quasi-likelihood based on an over-dispersed binomial variance when utilizing community-level data. These approaches account for and estimate the intra-cluster correlation coefficient, which should be provided in the results from a cluster randomized trial. Alternatively, the coefficient of variation or R coefficient could be reported. In this article, we show that negative binomial regression can also be utilized when communities are large and events are rare. The objectives of this article are (1) to show that the negative binomial regression approach targets the same marginal regression parameter(s) as an over-dispersed binomial model and to explain why the estimates may differ; (2) to derive formulas relating the negative binomial overdispersion parameter k with the intra-cluster correlation coefficient, coefficient of variation, and R coefficient; and (3) analyze pre-intervention data from the HEALing Communities Study to demonstrate and contrast models and to show how to report the intra-cluster correlation coefficient, coefficient of variation, and R coefficient when utilizing negative binomial regression. Methods Negative binomial and over-dispersed binomial regression modeling are contrasted in terms of model setup, regression parameter estimation, and formulation of the overdispersion parameter. Three specific models are used to illustrate concepts and address the third objective. Results The negative binomial regression approach targets the same marginal regression parameter(s) as an over-dispersed binomial model, although estimates may differ. Practical differences arise in regard to how overdispersion, and hence the intra-cluster correlation coefficient is modeled. The negative binomial overdispersion parameter is approximately equal to the ratio of the intra-cluster correlation coefficient and marginal probability, the square of the coefficient of variation, and the R coefficient minus 1. As a result, estimates corresponding to all four of these different types of overdispersion parameterizations can be reported when utilizing negative binomial regression. Conclusion Negative binomial regression provides a valid, practical, alternative approach to the analysis of count data, and corresponding reporting of overdispersion parameters, from community randomized trials in which communities are large and events are rare.

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“…The HEALing (Helping to End Addiction Long-term) Communities Study (HCS) is a community engaged multisite, cluster-randomized trial that draws on principles of CBPR to build or partner with existing coalitions in 67 communities across four states [11,ClinicalTrials.gov identi er NCT04111939]. The goal of the HCS is to examine the effectiveness and implementation success of the Communities that Heal Intervention (CTH), a three-component intervention based on expanding evidence-based practices (EBPs) shown to reduce opioid overdose deaths [12]. The three CTH components include: 1) community engagement with coalitions to develop and deploy comprehensive, data-driven plans for EBP selection and implementation across multiple community sectors [13]; 2) the Opioid Reduction Continuum of Care Approach (ORCCA), which consists of a menu of EBPs to be implemented in the areas of overdose education and naloxone distribution (OEND), medication for opioid use disorder (MOUD) treatment and safer opioid prescribing [14]; and 3) community-informed communication campaigns to address stigma and raise awareness about services [15].…”

Section: Introductionmentioning

confidence: 99%

Coalition Building: What Happens When External Facilitators Put the CBPR Principles in Practice? Ethnographic Examples From the Massachusetts HEALing Communities Study

Drainoni

Walt

Martínez

et al. 2022

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Background The HEALing Communities Study (HCS) is a large-scale multisite study testing Community Engagement using coalition facilitation as an approach to addressing the worsening overdose crisis. Within Community Engagement, Community-Based Participatory Research (CBPR) principles guide researchers on best practices for working in partnership with communities, yet these principles have not been well researched in large complex multi-site studies. Methods This paper used ethnographic methods to explore how coalitions operationalized the CBPR principles during early coalition formation. Two coders independently analyzed 101 ethnographies from HCS coalition meetings in eight Massachusetts communities held between November 2019 and December 2020. Themes were developed through consensus between the coders followed by group discussions among the authorship team. Results We found mutual trust, shared goals, addressing power dynamics, meeting structure and attending to the sociopolitical community context could all serve as barriers or facilitators to operationalizing the CBPR principles. Conclusions These findings provide unique suggestions for future community-engaged multisite studies, and demonstrate the importance of research teams to mitigate inherent power imbalances by acknowledging and creating space for community ownership. The findings also highlight the value of a community engaged facilitator role to promote the CBPR principles. Strategies such as transparency, uniting over shared interests and bringing in a wide range of stakeholders were notable facilitators for operationalizing the principles.

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The HEALing (Helping to End Addiction Long-term SM) Communities Study: Protocol for a cluster randomized trial at the community level to reduce opioid overdose deaths through implementation of an integrated set of evidence-based practices

Cited by 79 publications

References 55 publications

Performance of Model-Based vs. Permutation Tests in the HEALing (Helping to End Addiction Long-TermSM) Communities Study, a Covariate Constrained Cluster Randomized Trial

Performance of Model-Based vs. Permutation Tests in the HEALing (Helping to End Addiction Long-TermSM) Communities Study, a Covariate Constrained Cluster Randomized Trial

Marginal modeling in community randomized trials with rare events: Utilization of the negative binomial regression model

Coalition Building: What Happens When External Facilitators Put the CBPR Principles in Practice? Ethnographic Examples From the Massachusetts HEALing Communities Study

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