How simulation modeling can support the public health response to the opioid crisis in North America: Setting priorities and assessing value

Bansback, Nick; Barbosa, Carolina; Barocas, Joshua A.; Bayoumi, Ahmed M.; Behrends, Czarina N.; Chhatwal, Jag; Cipriano, Lauren E.; Coffin, Phillip O.; Goldhaber-Fiebert, Jeremy D.; Hoch, Jeffrey S.; Irvine, Mike; Jalal, Hawre; Krebs, Emanuel; Linas, Benjamin P.; Marshall, Brandon D.L.; Martin, Natasha K.; McCollister, Kathryn E.; Meisel, Zachary F.; Murphy, Sean M.; Nosyk, Bohdan; Schackman, Bruce R.; Zarkin, Gary A.

doi:10.1016/j.drugpo.2020.102726

Cited by 4 publications

(5 citation statements)

References 69 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Simulation modeling will incorporate these results to characterize CTH’s impact better, allowing for extrapolation of results to non-HCS communities to support planning and policy making around similar interventions. The models developed will evaluate combinations of different interventions and consider synergies across interventions in the continuum of OUD prevention, harm reduction, and treatment—an endeavor that no OUD simulation model has yet achieved ( Barbosa et al, 2020 ; Nosyk, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

“…Simulation models can also enhance cost-effectiveness analyses conducted alongside clinical trials by improving the measurement of uncertainty around estimates of economic value, examining causal factors, and characterizing alternative scenarios to inform policy ( Buxton et al, 1997 ). Models with longer-term time horizons can capture the multiplicity of outcomes characteristic of OUD, which often has periods of relapse and recovery accompanied by several comorbid conditions ( Barbosa et al, 2020 ; Nosyk, 2020 ). The HCS presents an ideal platform for developing simulation models through collaboration among research sites and for engaging communities during model development so that model results can be most useful to decision makers.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Health economic design for cost, cost-effectiveness and simulation analyses in the HEALing Communities Study

Aldridge

Barbosa

Barocas

et al. 2020

Drug and Alcohol Dependence

Self Cite

View full text Add to dashboard Cite

Highlights The HEALing Communities Study (HCS) is designed to implement and evaluate the Communities That HEAL (CTH) intervention to reduce opioid overdose deaths. The HCS includes a health economics study. Costs of CTH will be estimated for multiple perspectives. The health economics study includes cost-effectiveness analyses and simulation modeling.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Health economic design for cost, cost-effectiveness and simulation analyses in the HEALing Communities Study

Aldridge

Barbosa

Barocas

et al. 2020

Drug and Alcohol Dependence

Self Cite

View full text Add to dashboard Cite

show abstract

“…Similarly, medication for opioid-use disorder can also provide exceptional value in addition to substantial health benefits [11]. However, there remains a pressing need for data to guide evidence-based decision-making in addressing the North American opioid crisis, particularly for assessing the impact of public health strategies over different timescales, for different populations or settings, and in combination with multiple interventions [12]. Of course, the global COVID-19 pandemic poses the most significant and immediate challenge in public health implementation.…”

Section: Introductionmentioning

confidence: 99%

“…The context in which healthcare services are delivered has been shown to influence the cost-effectiveness of interventions [9,27]. Simulation models that capture the heterogeneity across settings are uniquely positioned to offer guidance on contextually efficient strategies to implement [12,28,29].…”

Section: Introductionmentioning

confidence: 99%

Cost-Effectiveness Analysis in Implementation Science: a Research Agenda and Call for Wider Application

Krebs

Nosyk

2021

Curr HIV/AIDS Rep

Self Cite

View full text Add to dashboard Cite

Purpose of Review Cost-effectiveness analysis (CEA) can help identify the trade-offs decision makers face when confronted with alternative courses of action for the implementation of public health strategies. Application of CEA alongside implementation scientific studies remains limited. We aimed to identify areas for future development in order to enhance the uptake and impact of model-based CEA in implementation scientific research. Recent Findings Important questions remain about how to broadly implement evidence-based public health interventions in routine practice. Establishing population-level implementation strategy components and distinct implementation phases, including planning for implementation, the time required to scale-up programs, and sustainment efforts required to maintain them, can help determine the data needed to quantify each of these elements. Model-based CEA can use these data to determine the added value associated with each of these elements across systems, settings, population subgroups, and levels of implementation to provide tailored guidance for evidence-based public health action. There is a need to integrate implementation science explicitly into CEA to adequately capture diverse real-world delivery contexts and make detailed, informed recommendations on the aspects of the implementation process that provide good value. Summary We describe examples of how model-based CEA can integrate implementation scientific concepts and evidence to help tailor evaluations to local context. We also propose six distinct domains for methodological advancement in order to enhance the uptake and impact of model-based cost-effectiveness analysis in implementation scientific research.

show abstract

“…More discussion about the benefits, weaknesses, and uses of different modeling methods, as well as the type of policy questions they can answer, can be found elsewhere. 18,23,24 Systems modeling has been applied widely to public health issues and epidemics for which insufficient data or high levels of uncertainty make more traditional analytic approaches infeasible. 25−27 Examples include large-scale models developed by multiple teams such as the Cancer Intervention and Surveillance Modeling Network for cancer, 28 Framework for Reconstructing Epidemiological Dynamics for infectious diseases, 29 CommunityFlu for influenza, 30 and projects that have developed models for obesity 31 and post-traumatic stress disorder.…”

Section: Introductionmentioning

confidence: 99%

Data Needs in Opioid Systems Modeling: Challenges and Future Directions

Jalali

Ewing

Bannister

et al. 2021

American Journal of Preventive Medicine

Self Cite

View full text Add to dashboard Cite

Introduction: The opioid crisis is a pervasive public health threat in the U.S. Simulation modeling approaches that integrate a systems perspective are used to understand the complexity of this crisis and analyze what policy interventions can best address it. However, limitations in currently available data sources can hamper the quantification of these models. Methods: To understand and discuss data needs and challenges for opioid systems modeling, a meeting of federal partners, modeling teams, and data experts was held at the U.S. Food and Drug Administration in April 2019. This paper synthesizes the meeting discussions and interprets them in the context of ongoing simulation modeling work. Results: The current landscape of national-level quantitative data sources of potential use in opioid systems modeling is identified, and significant issues within data sources are discussed. Major recommendations on how to improve data sources are to: maintain close collaboration among modeling teams, enhance data collection to better fit modeling needs, focus on bridging the most crucial information gaps, engage in direct and regular interaction between modelers and data experts, and gain a clearer definition of policymakers' research questions and policy goals. Conclusions: This article provides an important step in identifying and discussing data challenges in opioid research generally and opioid systems modeling specifically. It also identifies opportunities for systems modelers and government agencies to improve opioid systems models.

show abstract

How simulation modeling can support the public health response to the opioid crisis in North America: Setting priorities and assessing value

Cited by 4 publications

References 69 publications

Health economic design for cost, cost-effectiveness and simulation analyses in the HEALing Communities Study

Health economic design for cost, cost-effectiveness and simulation analyses in the HEALing Communities Study

Cost-Effectiveness Analysis in Implementation Science: a Research Agenda and Call for Wider Application

Data Needs in Opioid Systems Modeling: Challenges and Future Directions

Contact Info

Product

Resources

About