Dissemination Science to Advance the Use of Simulation Modeling: Our Obligation Moving Forward

Translating and disseminating a localised economic model to support implementation of the 'Ending the HIV Epidemic' initiative to public health policymakers

Background

Despite significant progress in HIV treatment and prevention, the US remains far from its goal of 'Ending the HIV Epidemic' by 2030. Economic models using local data can synthesise the evidence to help policymakers allocate HIV resources efficiently, but persistent research-to-practice gaps remain. Little is known about how to facilitate the use of economic modelling data among local public health policymakers in real-world settings.

Aims and objectives

To explore the dissemination of results from a locally-calibrated economic model for HIV prevention and treatment and identify the factors influencing potential uptake of the model for public health decision making at the local level.

Methods

Four virtual focus groups with 26 local health department policymakers in Baltimore, Miami, Seattle, and New York City were held between July 2020 and May 2021. Qualitative content analysis of transcripts identified key themes around using the localised economic model in policy decisions.

Results

Participants were interested in using local data in their decisions to allocate resources for HIV prevention/treatment. Six themes emerged: 1) importance of understanding local policy context; 2) health equity considerations; 3) using evidence to support current priorities; 4) difficulty of changing strategies, even incrementally; 5) bang for the incremental buck (efficiency) vs. previous impact; and 6) community values.

Conclusion and relevance

To optimise acceptance and use of results from economic models, researchers should engage with local community members and public health decision makers early to understand budgetary and community priorities. Participants prioritised evidence that supports their existing strategies, considers budgets and funding streams, and improves health equity; however, real-world budget constraints and conflicting interests serve as barriers to implementing model recommendations and reaching national goals.

Use of Modeling to Inform Decision Making in North Carolina during the COVID-19 Pandemic: A Qualitative Study

Background. The COVID-19 pandemic has popularized computer-based decision-support models, which are commonly used to inform decision making amidst complexity. Understanding what organizational decision makers prefer from these models is needed to inform model development during this and future crises. Methods. We recruited and interviewed decision makers from North Carolina across 9 sectors to understand organizational decision-making processes during the first year of the COVID-19 pandemic (N = 44). For this study, we identified and analyzed a subset of responses from interviewees (n = 19) who reported using modeling to inform decision making. We used conventional content analysis to analyze themes from this convenience sample with respect to the source of models and their applications, the value of modeling and recommended applications, and hesitancies toward the use of models. Results. Models were used to compare trends in disease spread across localities, estimate the effects of social distancing policies, and allocate scarce resources, with some interviewees depending on multiple models. Decision makers desired more granular models, capable of projecting disease spread within subpopulations and estimating where local outbreaks could occur, and incorporating a broad set of outcomes, such as social well-being. Hesitancies to the use of modeling included doubts that models could reflect nuances of human behavior, concerns about the quality of data used in models, and the limited amount of modeling specific to the local context. Conclusions. Decision makers perceived modeling as valuable for informing organizational decisions yet described varied ability and willingness to use models for this purpose. These data present an opportunity to educate organizational decision makers on the merits of decision-support modeling and to inform modeling teams on how to build more responsive models that address the needs of organizational decision makers.

Opportunities and Barriers to the Development and Use of Open Source Health Economic Models: A Survey

OBJECTIVES

Health economic (HE) models are routinely used to support health policy and resource allocation decisions but are often considered "black boxes" that may be prone to error and bias. Open source models (OSMs) have been advocated to increase the transparency, credibility, and reuse of HE models. Previous studies have demonstrated interest in OSMs among the health economics and outcomes research community, but the number of OSMs remains low.

METHODS

We conducted an online survey of ISPOR (the leading professional society for health economics and outcomes research) members' perspectives on the usefulness of OSMs and barriers to their development and implementation.

RESULTS

Respondents (N = 230) included academics (27%), pharmaceutical (or related) industry representatives (23%), health research or consulting representatives (21%), governmental or nonprofit agency representatives (10%), and others (19%). Respondents were generally not familiar with barriers to the development and adoption of OSMs. Most agreed that OSMs would improve transparency (92%), efficiency (76%), and HE model reuse (86%) and promote confidence in using HE models (75%). The use of OSMs by health technology assessment authorities was considered a very important indicator of the usefulness of OSMs by 49% of respondents. Three-quarters of respondents perceived legal concerns and the ability to transfer data as important barriers to the development and use of OSMs.

CONCLUSIONS

Respondents believe that OSMs could increase the transparency, efficiency, and credibility of HE models, but that several barriers hamper their widespread adoption. Our results suggest that fundamental changes may be needed across the health economics and outcomes research community if OSMs are to become widely adopted.

Human Immunodeficiency Virus transmission by HIV Risk Group and Along the HIV Care Continuum: A Contrast of 6 US Cities

BACKGROUND

Understanding the sources of HIV transmission provides a basis for prioritizing HIV prevention resources in specific geographic regions and populations. This study estimated the number, proportion, and rate of HIV transmissions attributable to individuals along the HIV care continuum within different HIV transmission risk groups in 6 US cities.

METHODS

We used a dynamic, compartmental HIV transmission model that draws on racial behavior-specific or ethnic behavior-specific and risk behavior-specific linkage to HIV care and use of HIV prevention services from local, state, and national surveillance sources. We estimated the rate and number of HIV transmissions attributable to individuals in the stage of acute undiagnosed HIV, nonacute undiagnosed HIV, HIV diagnosed but antiretroviral therapy (ART) naïve, off ART, and on ART, stratified by HIV transmission group for the 2019 calendar year.

RESULTS

Individuals with undiagnosed nonacute HIV infection accounted for the highest proportion of total transmissions in every city, ranging from 36.8% (26.7%-44.9%) in New York City to 64.9% (47.0%-71.6%) in Baltimore. Individuals who had discontinued ART contributed to the second highest percentage of total infections in 4 of 6 cities. Individuals with acute HIV had the highest transmission rate per 100 person-years, ranging from 76.4 (58.9-135.9) in Miami to 160.2 (85.7-302.8) in Baltimore.

CONCLUSION

These findings underline the importance of both early diagnosis and improved ART retention for ending the HIV epidemic in the United States. Differences in the sources of transmission across cities indicate that localized priority setting to effectively address diverse microepidemics at different stages of epidemic control is necessary.

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

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.