Connecting Clinical Capacity and Intervention Sustainability in Resource-Variable Pediatric Oncology Centers in Latin America.

Clinical capacity for sustainability, or the clinical resources needed to sustain an evidence-based practice, represent proximal determinants that contribute to intervention sustainment. We examine the relationship between clinical capacity for sustainability and sustainment of PEWS, an evidence-based intervention to improve outcomes for pediatric oncology patients in resource-variable hospitals. We conducted a cross-sectional survey among Latin American pediatric oncology centers participating in Proyecto Escala de Valoración de Alerta Temprana (EVAT), an improvement collaborative to implement Pediatric Early Warning Systems (PEWS). Hospitals were eligible if they had completed PEWS implementation. Clinicians were eligible to participate if they were involved in PEWS implementation or used PEWS in clinical work. The Spanish language survey consisted of 56 close and open-ended questions about the respondent, hospital, participants' assessment of clinical capacity to sustain PEWS using the clinical sustainability assessment tool (CSAT), and perceptions about PEWS and its use as an intervention. Results were analyzed using a multi-level modeling approach to examine the relationship between individual, hospital, intervention, and clinical capacity determinants to PEWS sustainment. A total of 797 responses from 37 centers in 13 countries were included in the analysis. Eighty-seven percent of participants reported PEWS sustainment. After controlling for individual, hospital, and intervention factors, clinical capacity was significantly associated with PEWS sustainment (OR 3.27, p < .01). Marginal effects from the final model indicate that an increasing capacity score has a positive influence (11% for every additional CSAT point) of predicting PEWS sustainment. PEWS is a sustainable intervention and clinical capacity to sustain PEWS contributes meaningfully to PEWS sustainment.

Introducing SoNHR-Reporting guidelines for Social Networks In Health Research.

OBJECTIVE: The overall goal of this work is to produce a set of recommendations (SoNHR-Social Networks in Health Research) that will improve the reporting and dissemination of social network concepts, methods, data, and analytic results within health sciences research. METHODS: This study used a modified-Delphi approach for recommendation development consistent with best practices suggested by the EQUATOR health sciences reporting guidelines network. An initial set of 28 reporting recommendations was developed by the author team. A group of 67 (of 147 surveyed) experienced network and health scientists participated in an online feedback survey. They rated the clarity and importance of the individual recommendations, and provided qualitative feedback on the coverage, usability, and dissemination opportunities of the full set of recommendations. After examining the feedback, a final set of 18 recommendations was produced. RESULTS: The final SoNHR reporting guidelines are comprised of 18 recommendations organized within five domains: conceptualization (how study research questions are linked to network conceptions or theories), operationalization (how network science portions of the study are defined and operationalized), data collection & management (how network data are collected and managed), analyses & results (how network results are analyzed, visualized, and reported), and ethics & equity (how network-specific human subjects, equity, and social justice concerns are reported). We also present a set of exemplar published network studies which can be helpful for seeing how to apply the SoNHR recommendations in research papers. Finally, we discuss how different audiences can use these reporting guidelines. CONCLUSIONS: These are the first set of formal reporting recommendations of network methods in the health sciences. Consistent with EQUATOR goals, these network reporting recommendations may in time improve the quality, consistency, and replicability of network science across a wide variety of important health research areas.

Bridges and Mechanisms: Integrating Systems Science Thinking into Implementation Research.

We present a detailed argument for how to integrate, or bridge, systems science thinking and methods with implementation science. We start by showing how fundamental systems science principles of structure, dynamics, information, and utility are relevant for implementation science. Then we examine the need for implementation science to develop and apply richer theories of complex systems. This can be accomplished by emphasizing a causal mechanisms approach. Identifying causal mechanisms focuses on the "cogs and gears" of public health, clinical, and organizational interventions. A mechanisms approach focuses on how a specific strategy will produce the implementation outcome. We show how connecting systems science to implementation science opens new opportunities for examining and addressing social determinants of health and conducting equitable and ethical implementation research. Finally, we present case studies illustrating successful applications of systems science within implementation science in community health policy, tobacco control, health care access, and breast cancer screening. Expected final online publication date for the Annual Review of Public Health, Volume 45 is April 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Sustainability determinants of an intervention to identify clinical deterioration and improve childhood cancer survival in Latin American hospitals: the INSPIRE study protocol.

BACKGROUND: More than 90% of children with cancer live in low-resourced settings, where survival is only 20%. Sustainable evidence-based (EB) interventions yielding ongoing beneficial patient outcomes are critical to improve childhood cancer survival. A better understanding of factors promoting intervention sustainability in these settings is urgently needed. The aim of this study is to provide an empirical understanding of how clinical capacity for sustainability, or the resources needed to sustain an intervention, impacts the sustainment of Pediatric Early Warning System (PEWS), an EB intervention that improves pediatric oncology outcomes in low-resource hospitals by detecting clinical deterioration and preventing the need for more intense treatment. METHODS: We will conduct a prospective, longitudinal study of approximately 100 resource-variable hospitals implementing and sustaining PEWS participating in Proyecto EVAT, a quality improvement collaborative of Latin American pediatric oncology centers. Aim 1: We will evaluate how clinical capacity for sustainability changes over time through 5 to 9 prospective measurements of capacity via survey of clinical staff using PEWS (approximately n = 13 per center) during the phases of PEWS adoption, implementation, and sustainability using the Clinical Sustainability Assessment Tool (CSAT). Aim 2: We will determine the relationship between capacity and a) PEWS sustainment and b) clinical deterioration mortality among pediatric oncology patients at centers sustaining PEWS for 2 to 10 years using chart review and an existing patient outcomes registry. Aim 3: We will develop novel strategies to promote sustainability by gaining a deeper understanding of perceived challenges to building capacity and PEWS sustainment. In combination with quantitative outcomes, we will conduct 24 focus groups with staff (doctors, nurses, and administrators) from hospitals with both high (n = 4) and low capacity (n = 4). We will then use implementation mapping to generate theoretically driven, empirically-supported sustainability strategies. DISCUSSION: This study will advance implementation science by providing a theoretically driven, foundational understanding of factors that predict sustainability among a large, diverse cohort of hospitals. We will then use this knowledge to develop sustainability evidence-informed strategies that optimize capacity and promote long-term sustainment of PEWS and improvements in patient outcomes, thus promoting equity in childhood cancer care globally.

CritCom: assessment of quality of interdisciplinary communication around deterioration in pediatric oncologic patients.

Background: High-quality clinical care requires excellent interdisciplinary communication, especially during emergencies, and no tools exist to evaluate communication in critical care. We describe the development of a pragmatic tool focusing on interdisciplinary communication during patient deterioration (CritCom). Methods: The preliminary CritCom tool was developed after a literature review and consultation with a multidisciplinary panel of global experts in communication, pediatric oncology, and critical care to review the domains and establish content validity iteratively. Face and linguistic validity were established through cognitive interviews, translation, and linguistic synthesis. We conducted a pilot study among an international group of clinicians to establish reliability and usability. Results: After reviewing 105 potential survey items, we identified 52 items across seven domains. These were refined through cognitive interviews with 36 clinicians from 15 countries. CritCom was piloted with 433 clinicians (58% nurses, 36% physicians, and 6% other) from 42 hospitals in 22 countries. Psychometric testing guided the refinement of the items for the final tool. CritCom comprised six domains with five items each (30 total). The final tool has excellent reliability (Cronbach's alpha 0.81-0.86), usability (93% agree or strongly agree that the tool is easy to use), and similar performance between English and Spanish tools. Confirmatory factor analysis was used to establish the final 6-domain structure. Conclusions: CritCom is a reliable and pragmatic bilingual tool to assess the quality of interdisciplinary communication around patient deterioration for children in diverse resource levels globally. Critcom results can be used to design and evaluate interventions to improve team communication.

A new measure for multi-professional medical team communication: design and methodology for multilingual measurement development.

Background: As implementation science in global health continues to evolve, there is a need for valid and reliable measures that consider diverse linguistic and cultural contexts. A standardized, reproducible process for multilingual measure development may improve accessibility and validity by participants in global health settings. To address this need, we propose a rigorous methodology for multilingual measurement development. We use the example of a novel measure of multi-professional team communication quality, a determinant of implementation efforts. Methods: The development and translation of this novel bilingual measure is comprised of seven steps. In this paper, we describe a measure developed in English and Spanish, however, this approach is not language specific. Participants are engaged throughout the process: first, an interprofessional panel of experts and second, through cognitive interviewing for measure refinement. The steps of measure development included: (1) literature review to identify previous measures of team communication; (2) development of an initial measure by the expert panel; (3) cognitive interviewing in a phased approach with the first language (English); (4): formal, forward-backward translation process with attention to colloquialisms and regional differences in languages; (5) cognitive interviewing repeated in the second language (Spanish); (6) language synthesis to refine both instruments and unify feedback; and (7) final review of the refined measure by the expert panel. Results: A draft measure to assess quality of multi-professional team communication was developed in Spanish and English, consisting of 52 questions in 7 domains. This measure is now ready for psychometric testing. Conclusions: This seven-step, rigorous process of multilingual measure development can be used in a variety of linguistic and resource settings. This method ensures development of valid and reliable tools to collect data from a wide range of participants, including those who have historically been excluded due to language barriers. Use of this method will increase both rigor and accessibility of measurement in implementation science and advance equity in research and practice.

Understanding Misimplementation in U.S. State Health Departments: An Agent-Based Model.

INTRODUCTION: The research goal of this study is to explore why misimplementation occurs in public health agencies and how it can be reduced. Misimplementation is ending effective activities prematurely or continuing ineffective ones, which contributes to wasted resources and suboptimal health outcomes. METHODS: The study team created an agent-based model that represents how information flow, filtered through organizational structure, capacity, culture, and leadership priorities, shapes continuation decisions. This agent-based model used survey data and interviews with state health department personnel across the U.S. between 2014 and 2020; model design and analyses were conducted with substantial input from stakeholders between 2019 and 2021. The model was used experimentally to identify potential approaches for reducing misimplementation. RESULTS: Simulations showed that increasing either organizational evidence-based decision-making capacity or information sharing could reduce misimplementation. Shifting leadership priorities to emphasize effectiveness resulted in the largest reduction, whereas organizational restructuring did not reduce misimplementation. CONCLUSIONS: The model identifies for the first time a specific set of factors and dynamic pathways most likely driving misimplementation and suggests a number of actionable strategies for reducing it. Priorities for training the public health workforce include evidence-based decision making and effective communication. Organizations will also benefit from an intentional shift in leadership decision-making processes. On the basis of this initial, successful application of agent-based model to misimplementation, this work provides a framework for further analyses.

Draining the tobacco swamps: Shaping the built environment to reduce tobacco retailer proximity to residents in 30 big US cities.

Combining geospatial data on residential and tobacco retailer density in 30 big US cities, we find that a large majority of urban residents live in tobacco swamps - neighborhoods where there is a glut of tobacco retailers. In this study, we simulate the effects of tobacco retail reduction policies and compare probable changes in resident-to-retailer proximity and retailer density for each city. While measures of proximity and density at baseline are highly correlated, the results differ both between effects on proximity and density and across the 30 cities. Context, particularly baseline proximity of residents to retailers, is important to consider when designing policies to reduce retailer concentration.

Designing for Dissemination and Sustainability to Promote Equitable Impacts on Health.

Designing for dissemination and sustainability (D4DS) refers to principles and methods for enhancing the fit between a health program, policy, or practice and the context in which it is intended to be adopted. In this article we first summarize the historical context of D4DS and justify the need to shift traditional health research and dissemination practices. We present a diverse literature according to a D4DS organizing schema and describe a variety of dissemination products, design processes and outcomes, and approaches to messaging, packaging, and distribution. D4DS design processes include stakeholder engagement, participatory codesign, and context and situation analysis, and leverage methods and frameworks from dissemination and implementation science, marketing and business, communications and visualarts, and systems science. Finally, we present eight recommendations to adopt a D4DS paradigm, reflecting shifts in ways of thinking, skills and approaches, and infrastructure and systems for training and evaluation.

Coordination of sustainable financing for evidence-based youth mental health treatments: protocol for development and evaluation of the fiscal mapping process.

BACKGROUND: Sustained delivery of evidence-based treatments (EBTs) is essential to addressing the public health and economic impacts of youth mental health problems, but is complicated by the limited and fragmented funding available to youth mental health service agencies (hereafter, "service agencies"). Strategic planning tools are needed that can guide these service agencies in their coordination of sustainable funding for EBTs. This protocol describes a mixed-methods research project designed to (1) develop and (2) evaluate our novel fiscal mapping process that guides strategic planning efforts to finance the sustainment of EBTs in youth mental health services. METHOD: Participants will be 48 expert stakeholder participants, including representatives from ten service agencies and their partners from funding agencies (various public and private sources) and intermediary organizations (which provide guidance and support on the delivery of specific EBTs). Aim 1 is to develop the fiscal mapping process: a multi-step, structured tool that guides service agencies in selecting the optimal combination of strategies for financing their EBT sustainment efforts. We will adapt the fiscal mapping process from an established intervention mapping process and will incorporate an existing compilation of 23 financing strategies. We will then engage participants in a modified Delphi exercise to achieve consensus on the fiscal mapping process steps and gather information that can inform the selection of strategies. Aim 2 is to evaluate preliminary impacts of the fiscal mapping process on service agencies' EBT sustainment capacities (i.e., structures and processes that support sustainment) and outcomes (e.g., intentions to sustain). The ten agencies will pilot test the fiscal mapping process. We will evaluate how the fiscal mapping process impacts EBT sustainment capacities and outcomes using a comparative case study approach, incorporating data from focus groups and document review. After pilot testing, the stakeholder participants will conceptualize the process and outcomes of fiscal mapping in a participatory modeling exercise to help inform future use and evaluation of the tool. DISCUSSION: This project will generate the fiscal mapping process, which will facilitate the coordination of an array of financing strategies to sustain EBTs in community youth mental health services. This tool will promote the sustainment of youth-focused EBTs.

Assessing the sustainability capacity of evidence-based programs in community and health settings.

Background: Within many public health settings, there remain large challenges to sustaining evidence-based practices. The Program Sustainability Assessment Tool has been developed and validated to measure sustainability capacity of public health, social service, and educational programs. This paper describes how this tool was utilized between January 2014 and January 2019. We describe characteristics of programs that are associated with increased capacity for sustainability and ultimately describe the utility of the PSAT in sustainability research and practice. Methods: The PSAT is comprised of 8 subscales, measuring sustainability capacity in eight distinct conceptual domains. Each subscale is made up of five items, all assessed on a 7-point Likert scale. Data were obtained from persons who used the PSAT on the online website (https://sustaintool.org/), from 2014 to 2019. In addition to the PSAT scale, participants were asked about four program-level characteristics. The resulting dataset includes 5,706 individual assessments reporting on 2,892 programs. Results: The mean overall PSAT score was 4.73, with the lowest and highest scoring subscales being funding stability and program adaptation, respectively. Internal consistency for each subscale was excellent (average Cronbach's alpha = 0.90, ranging from 0.85 to 0.94). Confirmatory factor analysis highlighted good to excellent fit of the PSAT measurement model (eight distinct conceptual domains) to the observed data, with a comparative fit index of 0.902, root mean square error of approximation equal to 0.054, and standardized root mean square residual of 0.054. Overall sustainability capacity was significantly related to program size (F = 25.6; p < 0.001). Specifically, smaller programs (with staff sizes of ten or below) consistently reported lower program sustainability capacity. Capacity was not associated with program age and did not vary significantly by program level. Discussion: The PSAT maintained its excellent reliability when tested with a large and diverse sample over time. Initial criterion validity was explored through the assessment of program characteristics, including program type and program size. The data collected reinforces the ability of the PSAT to assess sustainability capacity for a wide variety of public health and social programs.

Sustainability in pediatric hospitals: An exploration at the intersection of quality improvement and implementation science.

Background: Although new evidence-based practices are frequently implemented in clinical settings, many are not sustained, limiting the intended impact. Within implementation science, there is a gap in understanding sustainability. Pediatric healthcare settings have a robust history of quality improvement (QI), which includes a focus on continuation of change efforts. QI capability and sustainability capacity, therefore, serve as a useful concept for connecting the broader fields of QI and implementation science to provide insights on improving care. This study addresses these gaps in understanding of sustainability in pediatric settings and its relationship to QI. Methods: This is a cross-sectional observational study conducted within pediatric academic medical centers in the United States. Clinicians surveyed worked with one of three evidence-based clinical programs: perioperative antimicrobial stewardship prescribing, early mobility in the intensive care unit, and massive blood transfusion administration. Participants completed two assessments: (1) the Clinical Sustainability Assessment Tool (CSAT) and (2) a 19-question assessment that included demographics and validation questions, specifically a subset of questions from the Change Process Capability Questionnaire, a QI scale. Initial descriptive and bivariate analyses were conducted prior to building mixed-effects models relating perceived QI to clinical sustainability capacity. Results: A total of 181 individuals from three different programs and 30 sites were included in the final analyses. QI capability scores were assessed as a single construct (5-point Likert scale), with an average response of 4.16 (higher scores indicate greater QI capability). The overall CSAT score (7-point Likert scale) was the highest for massive transfusion programs (5.51, SD = 0.91), followed by early mobility (5.25, SD = 0.92) and perioperative antibiotic prescribing (4.91, SD = 1.07). Mixed-effects modeling illustrated that after controlling for person and setting level variables, higher perceptions of QI capabilities were significantly related to overall clinical sustainability. Conclusion: Organizations and programs with higher QI capabilities had a higher sustainability capacity, even when controlling for differences at the individual and intervention levels. Organizational factors that enable evidence-based interventions should be further studied, especially as they relate to sustainability. Issues to be considered by practitioners when planning for sustainability include bedside provider perceptions, intervention achievability, frequency of delivery, and organizational influences.

Point of care tobacco treatment sustains during COVID-19, a global pandemic.

BACKGROUND: Tobacco cessation treatment for cancer patients is essential to providing comprehensive oncologic care. We have implemented a point of care tobacco treatment care model enabled by electronic health record (EHR) modifications in a comprehensive cancer center. Data are needed on the sustainability of both reach of treatment and effectiveness over time, including the COVID-19 pandemic. METHODS: Using EHR data from the pre-implementation (P: 5 months) and post-implementation periods (6 month-blocks, T1-T5 for a total of 30 months), we compared two primary outcomes: 1) reach of treatment among those smoking and 2) effectiveness assessed by smoking cessation among those smoking in the subsequent 6 month period. We analyzed the data using generalized estimation equation regression models. RESULTS: With the point of care tobacco treatment care model, reach of treatment increased from pre to post T5 (3.2 % vs. 48.4 %, RR 15.50, 95 % CI 10.56-22.74, p < 0.0001). Reach of treatment in all post periods (T1-T5 including the COVID-19 pandemic time) remained significantly higher than the pre period. Effectiveness, defined by smoking cessation among those smoking, increased from pre to post T2 before the pandemic (12.4 % vs. 21.4 %, RR 1.57, 95 % CI 1.31-1.87, p < 0.0001). However, effectiveness, while higher in later post periods (T3, T4), was no longer significantly increased compared with the pre period. CONCLUSION: A point of care EHR-enabled tobacco treatment care model demonstrates sustained reach up to 30 months following implementation, even during the COVID-19 pandemic and changes in healthcare prioritization. Effectiveness was sustained for 12 months, but did not sustain through the subsequent 12 months.

Utility of Facebook's Social Connectedness Index in Modeling COVID-19 Spread: Exponential Random Graph Modeling Study.

BACKGROUND: The COVID-19 (the disease caused by the SARS-CoV-2 virus) pandemic has underscored the need for additional data, tools, and methods that can be used to combat emerging and existing public health concerns. Since March 2020, there has been substantial interest in using social media data to both understand and intervene in the pandemic. Researchers from many disciplines have recently found a relationship between COVID-19 and a new data set from Facebook called the Social Connectedness Index (SCI). OBJECTIVE: Building off this work, we seek to use the SCI to examine how social similarity of Missouri counties could explain similarities of COVID-19 cases over time. Additionally, we aim to add to the body of literature on the utility of the SCI by using a novel modeling technique. METHODS: In September 2020, we conducted this cross-sectional study using publicly available data to test the association between the SCI and COVID-19 spread in Missouri using exponential random graph models, which model relational data, and the outcome variable must be binary, representing the presence or absence of a relationship. In our model, this was the presence or absence of a highly correlated COVID-19 case count trajectory between two given counties in Missouri. Covariates included each county's total population, percent rurality, and distance between each county pair. RESULTS: We found that all covariates were significantly associated with two counties having highly correlated COVID-19 case count trajectories. As the log of a county's total population increased, the odds of two counties having highly correlated COVID-19 case count trajectories increased by 66% (odds ratio [OR] 1.66, 95% CI 1.43-1.92). As the percent of a county classified as rural increased, the odds of two counties having highly correlated COVID-19 case count trajectories increased by 1% (OR 1.01, 95% CI 1.00-1.01). As the distance (in miles) between two counties increased, the odds of two counties having highly correlated COVID-19 case count trajectories decreased by 43% (OR 0.57, 95% CI 0.43-0.77). Lastly, as the log of the SCI between two Missouri counties increased, the odds of those two counties having highly correlated COVID-19 case count trajectories significantly increased by 17% (OR 1.17, 95% CI 1.09-1.26). CONCLUSIONS: These results could suggest that two counties with a greater likelihood of sharing Facebook friendships means residents of those counties have a higher likelihood of sharing similar belief systems, in particular as they relate to COVID-19 and public health practices. Another possibility is that the SCI is picking up travel or movement data among county residents. This suggests the SCI is capturing a unique phenomenon relevant to COVID-19 and that it may be worth adding to other COVID-19 models. Additional research is needed to better understand what the SCI is capturing practically and what it means for public health policies and prevention practices.

The Clinical Sustainability Assessment Tool: measuring organizational capacity to promote sustainability in healthcare.

BACKGROUND: Few validated assessment tools are available to increase understanding and measure factors associated with sustainment of clinical practices, an increasingly recognized need among clinicians. We describe the development of the Clinical Sustainability Assessment Tool (CSAT), designed to assess factors that contribute to sustainable practices in clinical settings. METHODS: Sixty-four participants from clinical and research fields participated in concept mapping and were recruited to brainstorm factors that lead to sustained clinical practices. Once repeated factors were removed, participants sorted items based on similarity and rated them by importance and feasibility. Using concept mapping analyses, items were grouped into meaningful domains to develop an initial tool. We then recruited pilot sites and early adopters, for a total of 286 practicing clinicians, to pilot and evaluate the tool. Individuals were recruited from clinical settings across pediatric and adult medical and surgical subspecialties. The data were analyzed using confirmatory factor analysis (CFA) to test hypothesized subscale structure in the instrument. We used root mean square error of approximation (RMSEA) and the standardized root mean square residual (SRMR) to assess fit and thus the ability of CSAT to measure the identified domains. RESULTS: The concept mapping produced sorted statements that were edited into items that could be responded to, resulting in the creation of a tool with seven determinant domains and 47 items. The pilot and CFA testing resulted in a final CSAT instrument made up 35 items, five per domain. CFA results demonstrated very good fit of the seven domain structure of the CSAT (RMSEA = 0.049; SRMR = 0.049). Usability testing indicated the CSAT is brief, easy to use, easy to learn, and does not require extensive training. Additionally, the measure scored highly (18/20) on the Psychometric and Pragmatic Evidence Rating Scale (PAPERS). The seven final CSAT domains were engaged staff and leadership, engaged stakeholders, organizational readiness, workflow integration, implementation and training, monitoring and evaluation, and outcomes and effectiveness. CONCLUSIONS: The CSAT is a new reliable assessment tool which allows for greater practical and scientific understanding of contextual factors that enable sustainable clinical practices over time.

Patterns and correlates of mis-implementation in state chronic disease public health practice in the United States.

BACKGROUND: Much of the disease burden in the United States is preventable through application of existing knowledge. State-level public health practitioners are in ideal positions to affect programs and policies related to chronic disease, but the extent to which mis-implementation occurring with these programs is largely unknown. Mis-implementation refers to ending effective programs and policies prematurely or continuing ineffective ones. METHODS: A 2018 comprehensive survey assessing the extent of mis-implementation and multi-level influences on mis-implementation was reported by state health departments (SHDs). Questions were developed from previous literature. Surveys were emailed to randomly selected SHD employees across the Unites States. Spearman's correlation and multinomial logistic regression were used to assess factors in mis-implementation. RESULTS: Half (50.7%) of respondents were chronic disease program managers or unit directors. Forty nine percent reported that programs their SHD oversees sometimes, often or always continued ineffective programs. Over 50% also reported that their SHD sometimes or often ended effective programs. The data suggest the strongest correlates and predictors of mis-implementation were at the organizational level. For example, the number of organizational layers impeded decision-making was significant for both continuing ineffective programs (OR=4.70; 95% CI=2.20, 10.04) and ending effective programs (OR=3.23; 95% CI=1.61, 7.40). CONCLUSION: The data suggest that changing certain agency practices may help in minimizing the occurrence of mis-implementation. Further research should focus on adding context to these issues and helping agencies engage in appropriate decision-making. Greater attention to mis-implementation should lead to greater use of effective interventions and more efficient expenditure of resources, ultimately to improve health outcomes.

Systems Science Approaches for Global Environmental Health Research: Enhancing Intervention Design and Implementation for Household Air Pollution (HAP) and Water, Sanitation, and Hygiene (WASH) Programs.

BACKGROUND: Two of the most important causes of global disease fall in the realm of environmental health: household air pollution (HAP) and poor water, sanitation, and hygiene (WASH) conditions. Interventions, such as clean cookstoves, household water treatment, and improved sanitation facilities, have great potential to yield reductions in disease burden. However, in recent trials and implementation efforts, interventions to improve HAP and WASH conditions have shown few of the desired health gains, raising fundamental questions about current approaches. OBJECTIVES: We describe how the failure to consider the complex systems that characterize diverse real-world conditions may doom promising new approaches prematurely. We provide examples of the application of systems approaches, including system dynamics, network analysis, and agent-based modeling, to the global environmental health priorities of HAP and WASH research and programs. Finally, we offer suggestions on how to approach systems science. METHODS: Systems science applied to environmental health can address major challenges by a) enhancing understanding of existing system structures and behaviors that accelerate or impede aims; b) developing understanding and agreement on a problem among stakeholders; and c) guiding intervention and policy formulation. When employed in participatory processes that engage study populations, policy makers, and implementers, systems science helps ensure that research is responsive to local priorities and reflect real-world conditions. Systems approaches also help interpret unexpected outcomes by revealing emergent properties of the system due to interactions among variables, yielding complex behaviors and sometimes counterintuitive results. DISCUSSION: Systems science offers powerful and underused tools to accelerate our ability to identify barriers and facilitators to success in environmental health interventions. This approach is especially useful in the context of implementation research because it explicitly accounts for the interaction of processes occurring at multiple scales, across social and environmental dimensions, with a particular emphasis on linkages and feedback among these processes. https://doi.org/10.1289/EHP7010.

Development of a computational modeling laboratory for examining tobacco control policies: Tobacco Town.

A key focus of recent policy efforts to curb tobacco product usage has been the role of place-specifically the density of retail and advertising and the resulting spatial pattern of access and exposure for consumers. Policies can alter the environment by reducing density or shifting distribution of tobacco retail and thus limiting access and exposure. Since little empirical evidence exists for the potential impact of these policies across potentially heterogeneous places, we develop and apply an original spatial computational model to simulate place-based retail tobacco control policies. The model is well-grounded in theory and available empirical evidence. We apply the model in four representative settings to demonstrate the utility of this approach as a policy laboratory, to develop general insights on the relationship between retailer density, retail interventions, and tobacco costs incurred by consumers, and to provide a framework to guide future modeling and empirical studies. Our results suggest that the potential impact on costs of reducing tobacco retailer density are highly dependent on context. Projected impacts are also influenced by assumptions made about agent (smoker) purchasing decision-making processes. In the absence of evidence in this area, we tested and compared three alternative decision rules; these interact with environmental properties to produce different results. Agent properties, namely income and cigarettes per day, also shape purchasing patterns before and after policy interventions. We conclude that agent-based modeling in general, and Tobacco Town specifically, hold much potential as a platform for testing and comparing the impact of various retail-based tobacco policies across different communities. Initial modeling efforts uncover important gaps in both data and theory and can provide guidance for new empirical studies in tobacco control.

Social Network Mapping and Functional Recovery Within 6 Months of Ischemic Stroke.

Objective. Stroke recovery is a multidimensional process influenced by biological and psychosocial factors. To understand the latter, we mapped the social networks of stroke patients, analyzing their changes and effects on physical function at 3 and 6 months after stroke. Methods. We used a quantitative social network assessment tool to map the structure and health habits embedded in patients' personal social networks. The physical function outcome was determined using the National Institutes of Health (NIH) Patient-Reported Outcomes Measurement Information System (PROMIS) Physical Function Scale (0-100, mean 50 for US general population). We used mixed-effects models to assess changes in social network metrics. We used multivariable models to test the association between social networks and physical function, independent of demographics, socioeconomic status, clinical characteristics, comorbidities, cognition, and depression. Results. The cohort consisted of 172 patients, with mostly mild motor-predominant stroke (median NIH Stroke Scale of 2) with retention of 149 at 3 months and 139 at 6 months. An average patient's network over 6 months contracted by 1.25 people and became denser and family oriented. Network composition also became healthier with pruning of ties with people who smoked or did not exercise. The baseline network size, and not density or health habits in the network, was independently associated with 3- and 6-month physical function PROMIS scores. Patients embedded in small kin-based networks reported more negative social interactions. Conclusions. Despite social networks becoming smaller and close-knit after stroke, they also become healthier. Larger baseline social networks are independently associated with better patient-reported physical function after stroke.