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Cross-sector collaboration is needed to address root causes of persistent public health challenges. We conducted a systematic literature review to identify studies describing theories, models, frameworks and principles for cross-sector collaboration and synthesized collaboration constructs into the Consolidated Framework for Collaboration Research (CFCR). Ninety-five articles were included in the review. Constructs were abstracted from articles and grouped into seven domains within the framework: community context; group composition; structure and internal processes; group dynamics; social capital; activities that influence or take place within the collaboration; activities that influence or take place within the broader community; and activities that influence or take place both in the collaboration and in the community. Community engagement strategies employed by collaborations are discussed, as well as recommendations for using systems science methods for testing specific mechanisms of how constructs identified in the review influence one another. Researchers, funders, and collaboration members can use the consolidated framework to articulate components of collaboration and test mechanisms explaining how collaborations function. By working from a consolidated framework of collaboration terms and using systems science methods, researchers can advance evidence for the efficacy of cross-sector collaborations.

Anxiety is the most common and widespread mental health disorder impacting youth between the ages of 10–19. Youth of color including Hispanic youth are disproportionately impacted. Fewer than 20% of youth of color who need mental health services are receiving them. However, we know relatively little about how to best engage Hispanic youth to increase their use of mental health services. The aims of this study were to better understand the personal, environmental, and behavioral factors that impact Hispanic adolescents help-seeking behaviors and to identify the important criteria needed to develop an appealing intervention that would increase engagement with mental health services. This study used a Design Thinking process—a participatory research approach that included qualitative and engaged methods. In-depth interviews (n = 8) and a Design Thinking workshop (n = 11 participants) were conducted with Hispanic youth with anxiety residing in the San Francisco Bay Area. In-depth interviews were coded using Social Cognitive Theory to identify key themes that impact an adolescent’s decision to seek help. The 90-minute workshop included ideation and Design Thinking activities including personas, mind-mapping, and analytical problem-solving to identify the most important tools and strategies that could be used to manage anxiety. The study identified several themes that directly impact program design, including barriers to seeking help for anxiety, coping strategies, sources of support, and specific program ideas. The findings revealed that Hispanic youth want a culturally relevant technology-based program that provides easily accessible educational information and coping strategies delivered in an engaging format that also facilitates mental health support with a trusted adult. The results reinforce the need to develop culturally inclusive and innovative programs designed specifically for priority populations to increase youth engagement with mental health services.

Simulation models of tobacco use behavior are useful analytic tools for projecting rates of tobacco use over time and identifying priority areas for intervention. This paper presents the Tobacco Use Individual-level Simulation and Tracking (TwIST) Model, an individual-based simulation model of tobacco use in the adult US population. We describe the model structure, data sources and parameters, and, in addition to future projections, compare modeled estimates of smoking prevalence to those from established surveys. The simulated population and model parameter estimates are informed by the Population Assessment of Tobacco and Health Study and other nationally representative datasets. To simulate tobacco use over time, we estimated 2 nd order Markov models using multinomial logistic regression. To validate the model, we compared model estimates of tobacco use to data from three national surveys. The model estimates adult cigarette smoking rates will decline from a prevalence of 12.4% (95% uncertainty interval (95% UI): 12.2–12.8%) in 2020 to 9.6% (95% UI: 9.3–9.9%), 9.1% (95% UI: 8.9–9.4%), and 8.7% (95% UI: 8.5–9.0%) in 2030, 2040, and 2050, respectively. From 2020 through 2050, adults living in poverty are estimated to have a cigarette smoking rate 2.1–2.3 times higher than individuals living above the poverty line. The prevalence of menthol cigarette use will decline at a slower rate than the prevalence of non-menthol cigarette use (21% vs. 38% decline). Model projections of cigarette smoking prevalence typically fall within the 95% confidence intervals of prevalence estimates across three national surveys. Overall, the TwIST Model projects cigarette smoking prevalence rates that are similar to real-world estimates. If tobacco use continues based on current patterns, income-based disparities in smoking will persist and a growing proportion of individuals who smoke will use menthol cigarettes, which are known to be harder to quit.

Background Economic evaluations of the implementation of health-related evidence-based interventions (EBIs) are conducted infrequently and, when performed, often use a limited set of quantitative methods to estimate the cost and effectiveness of EBIs. These studies often underestimate the resources required to implement and sustain EBIs in diverse populations and settings, in part due to inadequate scoping of EBI boundaries and underutilization of methods designed to understand the local context. We call for increased use of diverse methods, especially the integration of quantitative and qualitative approaches, for conducting and better using economic evaluations and related insights across all phases of implementation. Main body We describe methodological opportunities by implementation phase to develop more comprehensive and context-specific estimates of implementation costs and downstream impacts of EBI implementation, using the Exploration, Preparation, Implementation, Sustainment (EPIS) framework. We focus specifically on the implementation of complex interventions, which are often multi-level, resource-intensive, multicomponent, heterogeneous across sites and populations, involve many stakeholders and implementation agents, and change over time with respect to costs and outcomes. Using colorectal cancer (CRC) screening EBIs as examples, we outline several approaches to specifying the “boundaries” of EBI implementation and analyzing implementation costs by phase of implementation. We describe how systems mapping and stakeholder engagement methods can be used to clarify EBI implementation costs and guide data collection—particularly important when EBIs are complex. In addition, we discuss the use of simulation modeling with sensitivity/uncertainty analyses within implementation studies for projecting the health and economic impacts of investment in EBIs. Finally, we describe how these results, enhanced by careful data visualization, can inform selection, adoption, adaptation, and sustainment of EBIs. Conclusion Health economists and implementation scientists alike should draw from a larger menu of methods for estimating the costs and outcomes associated with complex EBI implementation and employ these methods across the EPIS phases. Our prior experiences using qualitative and systems approaches in addition to traditional quantitative methods provided rich data for informing decision-making about the value of investing in CRC screening EBIs and long-term planning for these health programs. Future work should consider additional opportunities for mixed-method approaches to economic evaluations.

Real-world adherence to colorectal cancer (CRC) screening strategies is imperfect. The CRC-AIM microsimulation model was used to estimate the impact of imperfect adherence on the relative benefits and burdens of guideline-endorsed, stool-based screening strategies. Predicted outcomes of multi-target stool DNA (mt-sDNA), fecal immunochemical tests (FIT), and high-sensitivity guaiac-based fecal occult blood tests (HSgFOBT) were simulated for 40-year-olds free of diagnosed CRC. For robustness, imperfect adherence was incorporated in multiple ways and with extensive sensitivity analysis. Analysis 1 assumed adherence from 0%-100%, in 10% increments. Analysis 2 longitudinally applied real-world first-round differential adherence rates (base-case imperfect rates = 40% annual FIT vs 34% annual HSgFOBT vs 70% triennial mt-sDNA). Analysis 3 randomly assigned individuals to receive 1, 5, or 9 lifetime (9 = 100% adherence) mt-sDNA tests and 1, 5, or 9 to 26 (26 = 100% adherence) FIT tests. Outcomes are reported per 1000 individuals compared with no screening. Each screening strategy decreased CRC incidence and mortality versus no screening. In individuals screened between ages 50-75 and adherence ranging from 10%a-100%, the life-years gained (LYG) for triennial mt-sDNA ranged from 133.1-300.0, for annual FIT from 96.3-318.1, and for annual HSgFOBT from 99.8-320.6. At base-case imperfect adherence rates, mt-sDNA resulted in 19.1% more LYG versus FIT, 25.4% more LYG versus HSgFOBT, and generally had preferable efficiency ratios while offering the most LYG. Completion of at least 21 FIT tests is needed to reach approximately the same LYG achieved with 9 mt-sDNA tests. Adherence assumptions affect the conclusions of CRC screening microsimulations that are used to inform CRC screening guidelines. LYG from FIT and HSgFOBT are more sensitive to changes in adherence assumptions than mt-sDNA because they require more tests be completed for equivalent benefit. At imperfect adherence rates, mt-sDNA provides more LYG than FIT or HSgFOBT at an acceptable tradeoff in screening burden.

Few investigations have explored the potential impact of the Affordable Care Act on health disparity outcomes in states that chose to forgo Medicaid expansion. Filling this evidence gap is pressing as Congress grapples with controversial healthcare legislation that could phase out Medicaid expansion. Colorectal cancer (CRC) is a commonly diagnosed, preventable cancer in the US that disproportionately burdens African American men and has substantial potential to be impacted by improved healthcare insurance coverage. Our objective was to estimate the impact of the Affordable Care Act (increasing insurance through health exchanges alone or with Medicaid expansion) on colorectal cancer outcomes and economic costs among African American and White males in North Carolina (NC), a state that did not expand Medicaid. We used an individual-based simulation model to estimate the impact of ACA (increasing insurance through health exchanges alone or with Medicaid expansion) on three CRC outcomes (screening, stage-specific incidence, and deaths) and economic costs among African American and White males in NC who were age-eligible for screening (between ages 50 and 75) during the study period, years of 2013-2023. Health exchanges and Medicaid expansion improved simulated CRC outcomes overall, though the impact was more substantial among AAs. Relative to health exchanges alone, Medicaid expansion would prevent between 7.1 to 25.5 CRC cases and 4.1 to 16.4 per 100,000 CRC cases among AA and White males, respectively. Our findings suggest policies that expanding affordable, quality healthcare coverage could have a demonstrable, cost-saving impact while reducing cancer disparities.

Background A growing number of older adults (ages 65+) live with Type 1 diabetes. Simultaneously, technologies such as continuous glucose monitoring (CGM) have become standard of care. There is thus a need to understand better the complex dynamics that promote use of CGM (and other care innovations) over time in this age group. Our aim was to adapt methods from systems thinking, specifically a participatory approach to system dynamics modeling called group model building (GMB), to model the complex experiences that may underlie different trajectories of CGM use among this population. Herein, we report on the feasibility, strengths, and limitations of this methodology. Methods We conducted a series of GMB workshops and validation interviews to collect data in the form of questionnaires, diagrams, and recordings of group discussion. Data were integrated into a conceptual diagram of the “system” of factors associated with uptake and use of CGM over time. We evaluate the feasibility of each aspect of the study, including the teaching of systems thinking to older adult participants. We collected participant feedback on positive aspects of their experiences and areas for improvement. Results We completed nine GMB workshops with older adults and their caregivers ( N  = 33). Each three-hour in-person workshop comprised: (1) questionnaires; (2) the GMB session, including both didactic components and structured activities; and (3) a brief focus group discussion. Within the GMB session, individual drawing activities proved to be the most challenging for participants, while group activities and discussion of relevant dynamics over time for illustrative (i.e., realistic but not real) patients yielded rich engagement and sufficient information for system diagramming. Study participants liked the opportunity to share experiences with peers, learning and enhancing their knowledge, peer support, age-specific discussions, the workshop pace and structure, and the systems thinking framework. Participants gave mixed feedback on the workshop duration. Conclusions The study demonstrates preliminary feasibility, acceptability, and the value of GMB for engaging older adults about key determinants of complex health behaviors over time. To our knowledge, few studies have extended participatory systems science methods to older adult stakeholders. Future studies may utilize this methodology to inform novel approaches for supporting health across the lifespan.

Background The process of implementing evidence-based interventions, programs, and policies is difficult and complex. Planning for implementation is critical and likely plays a key role in the long-term impact and sustainability of interventions in practice. However, implementation planning is also difficult. Implementors must choose what to implement and how best to implement it, and each choice has costs and consequences to consider. As a step towards supporting structured and organized implementation planning, we advocate for increased use of decision analysis. Main text When applied to implementation planning, decision analysis guides users to explicitly define the problem of interest, outline different plans (e.g., interventions/actions, implementation strategies, timelines), and assess the potential outcomes under each alternative in their context. We ground our discussion of decision analysis in the PROACTIVE framework, which guides teams through key steps in decision analyses. This framework includes three phases: (1) definition of the decision problems and overall objectives with purposeful stakeholder engagement, (2) identification and comparison of different alternatives, and (3) synthesis of information on each alternative, incorporating uncertainty. We present three examples to illustrate the breadth of relevant decision analysis approaches to implementation planning. Conclusion To further the use of decision analysis for implementation planning, we suggest areas for future research and practice: embrace model thinking; build the business case for decision analysis; identify when, how, and for whom decision analysis is more or less useful; improve reporting and transparency of cost data; and increase collaborative opportunities and training.

Retention in care remains an important issue for prevention of mother-to-child transmission (PMTCT) programs according to WHO guidelines, formerly called the \"Option B+\" approach. The objective of this study was to examine how poverty, gender, and health system factors interact to influence women's participation in PMTCT services. We used qualitative research, literature, and hypothesized variable connections to diagram causes and effects in causal loop models. We found that many factors, including antiretroviral therapy (ART) use, service design and quality, stigma, disclosure, spouse/partner influence, decision-making autonomy, and knowledge about PMTCT, influence psychosocial health, which in turn affects women's participation in PMTCT services. Thus, interventions to improve psychosocial health need to address many factors to be successful. We also found that the design of PMTCT services, a modifiable factor, is important because it affects several other factors. We identified 66 feedback loops that may contribute to policy resistance-that is, a policy's failure to have its intended effect. Our findings point to the need for a multipronged intervention to encourage women's continued participation in PMTCT services and for longitudinal research to quantify and test our causal loop model.

Engaging youth from racial and ethnic minority communities as leaders for change is a potential strategy to mobilize support for addressing childhood obesity, but there are limited curricula designed to help youth understand the complex influences on obesity. Our aim was to develop and pilot test a systems science curriculum to elicit rural African American youth perspectives on childhood obesity and enhance their understanding of and support for obesity prevention solutions. The curriculum was designed so it could be integrated with existing positive youth development curricula that help youth advocate for and implement identified solutions. We conducted four workshop sessions with youth that engaged them in systems learning activities such as guided systems diagramming activities. The participants (n = 21) completed validated surveys presession and postsession that assessed their causal attributions of obesity and support for obesity prevention policies. The youths’ perception that environmental factors cause obesity increased (p < .05), and perceptions that individual behavior and biology cause obesity did not change. Their support for policies that addressed food access and food pricing significantly increased (p < .05). The youths’ system diagrams elucidated links between multilevel factors such as personal attitudes, social influence, and the built environment, which provides important information for designing synergistic solutions. The changes we observed in youths’ perceptions of obesity and support for policy changes have important implications for youths’interest and willingness to advocate for social and environmental changes in their community. The strategies have a promising role in supporting community mobilization to address childhood obesity.