Development and validation of a community-level social determinants of health index for drug overdose deaths in the HEALing Communities Study.

INTRODUCTION: Social determinants of health (SDoH), such as socioeconomic status, education level, and food insecurity, are believed to influence the opioid crisis. While global SDoH indices such as the CDC's Social Vulnerability Index (SVI) and Area Deprivation Index (ADI) combine the explanatory power of multiple social factors for understanding health outcomes, they may be less applicable to the specific challenges of opioid misuse and associated outcomes. This study develops a novel index tailored to opioid misuse outcomes, tests the efficacy of this index in predicting drug overdose deaths across contexts, and compares the explanatory power of this index to other SDoH indices. METHODS: Focusing on four HEALing Communities Study (HCS) states (Kentucky, Massachusetts, New York and Ohio; encompassing 4269 ZIP codes), we identified multilevel SDoH potentially associated with opioid misuse and aggregated publicly available data for each measure. We then leveraged a random forest model to develop a composite measure that predicts age-adjusted drug overdose mortality rates based on SDoH. We used this composite measure to understand HCS and non-HCS communities in terms of overdose risk across areas of varying racial composition. Finally, we compared variance in drug overdose deaths explained by this index to variance explained by the SVI and ADI. RESULTS: Our composite measure included 28 SDoH measures and explained approximately 89 % percent of variance in age-adjusted drug overdose mortality across HCS states. Health care measures, including emergency department visits and primary care provider availability, were top predictors within the index. Index accuracy was robust within and outside of HCS communities and states. This measure identified high levels of overdose mortality risk in segregated communities. CONCLUSIONS: Existing SDoH indices fail to explain much variation in area-level overdose mortality rates. Having tailored composite indices can help us to identify places in which residents are at highest risk based on their composite contexts. A comprehensive index can also help to develop effective community interventions for programs such as HCS by considering the context in which people live.

Application of group model building in implementation research: A systematic review of the public health and healthcare literature.

BACKGROUND: Group model building is a process of engaging stakeholders in a participatory modeling process to elicit their perceptions of a problem and explore concepts regarding the origin, contributing factors, and potential solutions or interventions to a complex issue. Recently, it has emerged as a novel method for tackling complex, long-standing public health issues that traditional intervention models and frameworks cannot fully address. However, the extent to which group model building has resulted in the adoption of evidence-based practices, interventions, and policies for public health remains largely unstudied. The goal of this systematic review was to examine the public health and healthcare applications of GMB in the literature and outline how it has been used to foster implementation and dissemination of evidence-based interventions. METHODS: We searched PubMed, Web of Science, and other databases through August 2022 for studies related to public health or health care where GMB was cited as a main methodology. We did not eliminate studies based on language, location, or date of publication. Three reviewers independently extracted data on GMB session characteristics, model attributes, and dissemination formats and content. RESULTS: Seventy-two studies were included in the final review. Majority of GMB activities were in the fields of nutrition (n = 19, 26.4%), health care administration (n = 15, 20.8%), and environmental health (n = 12, 16.7%), and were conducted in the United States (n = 29, 40.3%) and Australia (n = 7, 9.7%). Twenty-three (31.9%) studies reported that GMB influenced implementation through policy change, intervention development, and community action plans; less than a third reported dissemination of the model outside journal publication. GMB was reported to have increased insight, facilitated consensus, and fostered communication among stakeholders. CONCLUSIONS: GMB is associated with tangible benefits to participants, including increased community engagement and development of systems solutions. Transdisciplinary stakeholder involvement and more rigorous evaluation and dissemination of GMB activities are recommended.

Identifying opportunities for collective action around community nutrition programming through participatory systems science.

PURPOSE: To apply principles of group model building (GMB), a participatory systems science approach, to identify barriers and opportunities for collective impact around nutrition programming to reduce cancer risk for immigrant communities in an urban environment. METHODS: We convened four in-person workshops applying GMB with nine community partners to generate causal loop diagrams (CLDs)-a visual representation of hypothesized causal relationships between variables and feedback structures within a system. GMB workshops prompted participants to collaboratively identify programmatic goals and challenges related to (1) community gardening, (2) nutrition education, (3) food assistance programs, and (4) community-supported agriculture. Participants then attended a plenary session to integrate findings from all workshops and identify cross-cutting ideas for collective action. RESULTS: Several multilevel barriers to nutrition programming emerged: (1) food policies center the diets and practices of White Americans and inhibit culturally tailored food guidelines and funding for culturally appropriate nutrition education; (2) the lack of culturally tailored nutrition education in communities is a missed opportunity for fostering pride in immigrant food culture and sustainment of traditional food practices; and (3) the limited availability of traditional ethnic produce in food assistance programs serving historically marginalized immigrant communities increases food waste and worsens food insecurity. CONCLUSION: Emergent themes coalesced around the need to embed cultural tailoring into all levels of the food system, while also considering other characteristics of communities being reached (e.g., language needs). These efforts require coordinated actions related to food policy and advocacy, to better institutionalize these practices within the nutrition space.

Hepatitis C virus risk among young people who inject drugs.

Background: Injection drug use (IDU) is the leading risk factor for hepatitis C virus (HCV) transmission in the U.S. While the general risk factors for HCV transmission are known, there is limited work on how these factors interact and impact young people who inject drugs (YPWID). Methods: Project data were drawn from a study of 539 New York City (NYC) residents ages 18-29 who were recruited via Respondent-Driven Sampling and, reported past-month non-medical use of prescription opioids and/or heroin. Analyses are based on a subsample of 337 (62%) who reported injecting any drug in the past 12 months. All variables were assessed via self-report, except HCV status, which was established via rapid antibody testing. Integrating the observed statistical associations with extant literature on HCV risk, we also developed a qualitative system dynamics (SD) model to use as a supplemental data visualization tool to explore plausible pathways and interactions among key risk and protective factors for HCV. Results: Results showed a 31% HCV antibody prevalence with an overall incidence of 10 per 100 person-years. HCV status was independently correlated with having shared cookers with two or more people (AOR = 2.17); injected drugs 4-6 years (AOR = 2.49) and 7 or more years (AOR = 4.95); lifetime homelessness (AOR = 2.52); and having been incarcerated two or more times (AOR = 1.99). These outcomes along with the extant literature on HCV risk were used to develop the qualitative SD model, which describes a causal hypothesis around non-linearities and feedback loop structures underlying the spread of HCV among YPWID. Conclusions: Despite ongoing harm reduction efforts, close to a third of YPWID in the community sample have been exposed to HCV, have risks for injection drug use, and face challenges with structural factors that may be preventing adequate intervention. The qualitative SD model explores these issues and contributes to a better understanding of how these various risk factors interact and what policies could potentially be effective in reducing HCV infections.

Trends and Insights from Transportation Congestion Pricing Policy Research: A Bibliometric Analysis.

Toll-based congestion pricing (CP) policies are increasingly implemented globally for alleviating road traffic congestion. Several interconnected factors affecting or induced by CP implementation include air quality/emissions, travel time, and road user safety. We sought to examine and characterize research output and patterns across several domains (e.g., health, policy acceptability) surrounding toll-based CP policies, in order to identify where research has focused and where gaps exist. We conducted a structured review and identified 2333 relevant publications, using semi-supervised and machine learning strategies combined with manual review. Annual publication counts peaked in 2015 (n = 122). Themes identified from title and abstract terms included policy implementation characteristics, advanced transportation modeling methods and approaches, and public perception and acceptability. Authorship networks indicated a lack of interdisciplinary research. Country analyses identified the US, China, and the UK as the most frequently represented countries, and underrepresentation from low-income countries. Findings indicate that research focused on specific road user types (e.g., pedestrians) and safety impacts, and equity considerations were relatively sparse compared to other topics (e.g., policy economics, public perception). Additional research on these critical topics is necessary to ensure that such policies are designed to promote positive and equitable effects on road user health and safety.

What Predicts a Clinical Discussion About PrEP? Results From Analysis of a U.S. National Cohort of HIV-Vulnerable Sexual and Gender Minorities.

HIV-outcome inequities remain prevalent in the U.S. Medical providers (MPs) are gatekeepers of PrEP, and understanding the dynamics of PrEP assessments is of major interest for public health. We analyzed data from Together 5000, an internet-based U.S. national cohort of sexual and gender minority (SGM) individuals aged 16-49 years and at risk for HIV. Among those eligible for PrEP uptake (n = 6264), we modeled predictors of discussing PrEP with an MP. A third (31%) of participants had spoken to a MP about PrEP. Among those who spoke to a MP, 45% suggested they would initiate PrEP; this outcome was more common among participants older than 24. With a persistent stagnant uptake nationwide, new opportunities to influence PrEP uptake must be explored. An attractive less targeted space is the medical office, specifically ways to support an initial and continued discussion about PrEP between MPs and their patients.

Intersectional Stigma and Prevention Among Gay, Bisexual, and Same Gender-Loving Men in New York City, 2020: System Dynamics Models.

Objectives. To create causal loop diagrams that characterize intersectional stigma experiences among Black, gay, bisexual, same gender-loving, and other men who have sex with men and to identify intervention targets to reduce stigma and increase testing and prevention access. Methods. Between January and July 2020, we conducted focus groups and in-depth interviews with 80 expert informants in New York City, which were transcribed, coded, and analyzed. These qualitative insights were developed iteratively, visualized, and validated in a causal loop diagram (CLD) using Vensim software. Results. The CLD revealed 3 key feedback loops-medical mistrust and HIV transmission, serosorting and marginalization of Black and gay individuals, and family support and internalized homophobia-that contribute to intersectional HIV and related stigmas, homophobia, and systemic racism. On the basis of these results, we designed 2 novel intervention components to integrate into an existing community-level anti-HIV stigma and homophobia intervention. Conclusions. HIV stigma, systemic racism, and homophobia work via feedback loops to reduce access to and uptake of HIV testing, prevention, and treatment. Public Health Implications. The CLD method yielded unique insights into reciprocal feedback structures that, if broken, could interrupt stigmatization and discrimination cycles that impede testing and prevention uptake. (Am J Public Health. 2022;112(S4):S444-S451. https://doi.org/10.2105/AJPH.2022.306725).

Simulating congestion pricing policy impacts on pedestrian safety using a system dynamics approach.

Research on congestion pricing policy (CPP) impacts has generally focused on the economic and congestion-related benefits of CPPs. Few studies have examined safety effects and the interrelated factors that produce safety outcomes for vulnerable road users. We built a novel system dynamics simulation model to explore the potential mechanisms producing pedestrian injuries over time and the impacts of a CPP (and related interventions) on this trend. We found that pedestrian injury trends varied based on important decisions related to how the CPP is designed, including investments in potential safety-related supports for pedestrians. Infrastructure improvements and speed management interventions could help cities achieve both congestion-relieving goals while also improving safety. Additionally, certain CPP configurations (e.g., additional charges on for-hire vehicles) could further reduce daily vehicle trips and congestion but might lead to unintended negative safety consequences of greater pedestrian injuries. This is the first model to provide a holistic and endogenous look at how interconnected processes affecting congestion and CPP impacts also affect vulnerable road user safety. The use of system dynamics models can facilitate a holistic inspection of potential intended and unintended effects across a range of outcomes, prior to policy implementation.

A qualitative system dynamics model for effects of workplace violence and clinician burnout on agitation management in the emergency department.

BACKGROUND: Over 1.7 million episodes of agitation occur annually across the United States in emergency departments (EDs), some of which lead to workplace assaults on clinicians and require invasive methods like physical restraints to maintain staff and patient safety. Recent studies demonstrated that experiences of workplace violence contribute to symptoms of burnout, which may impact future decisions regarding use of physical restraints on agitated patients. To capture the dynamic interactions between clinicians and agitated patients under their care, we applied qualitative system dynamics methods to develop a model that describes feedback mechanisms of clinician burnout and the use of physical restraints to manage agitation. METHODS: We convened an interprofessional panel of clinician stakeholders and agitation experts for a series of model building sessions to develop the current model. The panel derived the final version of our model over ten sessions of iterative refinement and modification, each lasting approximately three to four hours. We incorporated findings from prior studies on agitation and burnout related to workplace violence, identifying interpersonal and psychological factors likely to influence our outcomes of interest to form the basis of our model. RESULTS: The final model resulted in five main sets of feedback loops that describe key narratives regarding the relationship between clinician burnout and agitated patients becoming physically restrained: (1) use of restraints decreases agitation and risk of assault, leading to increased perceptions of safety and decreasing use of restraints in a balancing feedback loop which stabilizes the system; (2) clinician stress leads to a perception of decreased safety and lower threshold to restrain, causing more stress in a negatively reinforcing loop; (3) clinician burnout leads to a decreased perception of colleague support which leads to more burnout in a negatively reinforcing loop; (4) clinician burnout leads to negative perceptions of patient intent during agitation, thus lowering threshold to restrain and leading to higher task load, more likelihood of workplace assaults, and higher burnout in a negatively reinforcing loop; and (5) mutual trust between clinicians causes increased perceptions of safety and improved team control, leading to decreased clinician stress and further increased mutual trust in a positively reinforcing loop. CONCLUSIONS: Our system dynamics approach led to the development of a robust qualitative model that illustrates a number of important feedback cycles that underly the relationships between clinician experiences of workplace violence, stress and burnout, and impact on decisions to physically restrain agitated patients. This work identifies potential opportunities at multiple targets to break negatively reinforcing cycles and support positive influences on safety for both clinicians and patients in the face of physical danger.

Assessing the impact of the Good Samaritan Law in the state of Connecticut: a system dynamics approach.

BACKGROUND: Although Good Samaritan laws (GSLs) have been widely adopted throughout the United States, their efficacy in individual states is often unknown. This paper offers an approach for assessing the impact of GSLs and insight for policy-makers and public health officials who wish to know whether they should expect to see outcomes from similar policy interventions. METHODS: Utilizing a system dynamics (SD) modeling approach, the research team conducted a policy evaluation to determine the impact of GSLs on opioid use disorder (OUD) in Connecticut and evaluated the GSL based upon the following health outcomes: (1) emergency department (ED) visits for overdose, (2) behavioral changes of bystanders, and (3) overdose deaths. RESULTS: The simulation model suggests that Connecticut's GSL has not yet affected overdose deaths but has resulted in bystander behavioral changes, such as increased 911 calls for overdose. ED visits have increased as the number of opioid users has increased. CONCLUSIONS: The simulation results indicate that the number of opioid-related deaths will continue to increase and that the GSL alone cannot effectively control the crisis. However, the SD approach that was used will allow policymakers to evaluate the effectiveness of the GSL over time using a simulation framework. This SD model demonstrates great potential by producing simulations that allow policymakers to assess multiple strategies for combating the opioid crisis and select optimal public health interventions.

Congestion Pricing Policies and Safety Implications: a Scoping Review.

Congestion pricing policies (CPPs) are a common strategy for addressing urban traffic congestion. Research has explored several impacts of these policies (e.g., air quality, equity, congestion relief). The purpose of this review was to synthesize findings from publications examining CPP impacts on road user safety outcomes. We conducted a systematic search of relevant literature in four large research databases (Transport Research International Documentation, Web of Science, PubMed, and Scopus), searching from database inception through January 2021. We identified 18 eligible publications. Safety-related outcomes included overall crashes and injury crashes with stratification by injury severity and road user type (e.g., bicyclist, pedestrian). A majority of the publications examined zone-based CPPs (n = 13) and used observed data involving real policies (n = 10), as compared to a predicted or simulated analysis. Decreases in overall crashes and injuries for some road users were observed (e.g., car occupants). While some studies estimated short-term increases in injuries and crashes for bicyclists and motorcyclists (likely due to shifts from personal vehicle use to other transportation modes and increased exposure), most analyses focused on longer-term impacts and generally found a reversal and eventual decrease in injuries and crashes after a few years. The relative scarcity of safety outcomes in published literature, along with the wide breadth of CPP types, implementation contexts, and outcomes measured, demonstrates that more research on safety outcomes is needed. Cities and regions planning to implement CPPs should consider potential mode shifts and safety supports for all road users (e.g., bicycle and pedestrian infrastructure).

A Differential Threshold of Breakfast, Caffeine and Food Groups May Be Impacting Mental Well-Being in Young Adults: The Mediation Effect of Exercise.

Diet and exercise are known to influence mental health. However, the interaction between diet, dietary practices, and exercise and its impact on the mood of young adults (YA) is poorly understood. YA are inherently at risk for mental distress. They tend to consume a low-quality diet and are generally active. The purpose of the study was to assess these relationships through validating causal loop diagrams (CLD) that describe these connections by using a system dynamic (SD) modeling methodology. Adults 18-29 years were invited to complete the Food-Mood questionnaire. The anonymous questionnaire link was distributed to several institutional listservs and via several social media platforms targeting young adults. A multi-level analysis, including machine learning techniques, was used to assess these relationships. The key findings were then built into gender based CLD, which suggest that a differential repertoire may be needed to optimize diet quality, exercise, and mental well-being. Additionally, a potential net threshold for dietary factors and exercise may be needed to achieve mental well-being in young adults. Moreover, our findings suggest that exercise may boost the enhancing effect of food groups on mental well-being and may lessen the negative impact of dietary impediments of mental well-being.

Using community engagement to implement evidence-based practices for opioid use disorder: A data-driven paradigm & systems science approach.

Community-driven responses are essential to ensure the adoption, reach and sustainability of evidence-based practices (EBPs) to prevent new cases of opioid use disorder (OUD) and reduce fatal and non-fatal overdoses. Most organizational approaches for selecting and implementing EBPs remain top-down and individually oriented without community engagement (CE). Moreover, few CE approaches have leveraged systems science to integrate community resources, values and priorities. This paper provides a novel CE paradigm that utilizes a data-driven and systems science approach; describes the composition, functions, and roles of researchers in CE; discusses unique ethical considerations that are particularly salient to CE research; and provides a description of how systems science and data-driven approaches to CE may be employed to select a range of EBPs that collectively address community needs. Finally, we conclude with scientific recommendations for the use of CE in research. Greater investment in CE research is needed to ensure contextual, equitable, and sustainable access to EBPs, such as medications for OUD (MOUD) in communities heavily impacted by the opioid epidemic. A data-driven approach to CE research guided by systems science has the potential to ensure adequate saturation and sustainability of EBPs that could significantly reduce opioid overdose and health inequities across the US.

Common and differential associations between levels of alcohol drinking, gender-specific neurobehaviors and mental distress in college students.

BACKGROUND: Binge drinking is associated with poor academic behaviors and performance. Excessive alcohol drinking induces molecular changes and neurobehaviors that support use of other substances and alter cognitive functions. The purpose of this study was to compare neurobehaviors and academic effort among college students with low alcohol use with those of high alcohol consumption and build conceptual models that represent the integration of the different variables. METHOD: College students from several U.S colleges were assessed through an anonymous online survey for alcohol use, academic performance, lifestyle factors and mental distress. RESULTS: Our results depicted common neurobehaviors and differential responses to high alcohol use. CONCLUSION: The common responses in young men and women with high alcohol use are reflective of a hyperactive limbic system. The different responses involve cognitive aptitudes, typically controlled by cortical regions and affected by levels of brain connectivity known to be dissimilar between men and women.

Spatial dynamics of access to primary care for the medicaid population.

Primary care (PC) has always been underestimated and underinvested by the United States health system. Our goal was to investigate the effect of Medicaid expansion and the Affordable Care Act (ACA) provisions on PC access in Broome County, NY, a county that includes both rural and urban areas, and can serve as a benchmark for other regions. We developed a spatial system dynamics model to capture different stages of PC access for the Medicaid population by using the health belief model constructs and simulate the effect of several hypothetical interventions on PC utilisation. The government data portals used as data sources for calibrating our model include the New York State Department of Health, the Medicaid Delivery System Reform Incentive Payment (DSRIP) dashboards, and the US census. In our unique approach, we integrated the simulation results within Geographical Information System (GIS) maps, to assess the influence of geospatial factors on PC access. Our results identify hot spot demographic areas that have poor access to PC service facilities due to transportation constraints and a shortage in PC providers. Our decision support tool informs policymakers about programmes with the strongest impact on improving access to care, considering spatial and temporal characteristics of a region.

Analyzing the cost-effectiveness of Lyme disease risk reduction approaches.

Lyme disease (LD) is endemic in many regions of the Northeastern United States. Given the elusive nature of the disease, a systematic approach to identify efficient interventions would be useful for policymakers in addressing LD. We used Markov modeling to investigate the efficiency of interventions. These interventions range from awareness-based to behavioral-based strategies. Targeting animal reservoirs of LD using fungal spray or bait boxes did not prove to be an effective intervention. Results of awareness-based interventions, including distribution of signage, fliers, and presentations, implementable in different geographical scales, suggest that policymakers should focus on these interventions, as they are both cost-effective and have the highest impact on lowering LD risk. Populations may lose focus of LD warnings over time, thus quick succession of these interventions is vital. Our modeling results identify the awareness-based intervention as the most cost-effective strategy to lower the number of LD cases. These results can aid in the establishment of effective LD risk reduction policy at various scales of implementation.

Principal component analysis identifies differential gender-specific dietary patterns that may be linked to mental distress in human adults.

Independent reports describe the structural differences between the human male and female brains and the differential gender-susceptibility to mood disorders. Nutrition is one of the modifiable risk factors that has been reported to impact brain chemistry and affect mental health. Objectives: To study dietary patterns in adult men and women in relation to mental distress. Another aim was to develop evidence-based prototypes using System Dynamic Modeling methodology to better describe our findings. Methods: An anonymous internet-based survey was sent through social media platforms to different social and professional networks. Multivariate analyses were used for data mining. Data were stratified by gender and further by tertiles to capture the latent variables within the patterns of interest. Results: Mental distress in men associated with a consumption of a Western-like diet. In women, mental wellbeing associated with a Mediterranean-like diet and lifestyle. No other patterns in both genders were linked to mental distress. Based on the generated prototypes, men are more likely to experience mental wellbeing until nutritional deficiencies arise. However, women are less likely to experience mental wellbeing until a balanced diet and a healthy lifestyle are followed. In men, dietary deficiencies may have a profound effect on the limbic system; whereas dietary sufficiency in women may potentiate the mesocortical regulation of the limbic system. Discussion and conclusion: Our results may explain the several reports in the literature that women are at a greater risk for mental distress when compared to men and emphasize the role of a nutrient-dense diet in mental wellbeing.

A system dynamics model of infection risk, expectations, and perceptions on antibiotic prescribing in the United States.

RATIONALE, AIMS, AND OBJECTIVES: Inappropriate antibiotic prescribing is still a major concern that can lead to devastating outcomes including antibiotic resistance. This study aimed to simulate the antibiotic prescribing behaviour by providers for acute respiratory tract infections (ARTIs) and to evaluate the impact of patient expectation, provider's perception of patient's expectation to receive a prescription, and patient's risk for bacterial infection, on the decision to prescribe. METHODS: We developed a unique system dynamics (SD) simulation model based on the significant factors that impact the interaction between provider and patient during visits for ARTIs and the decision to prescribe antibiotics. In order to validate the model for different age groups and regions in the United States, we used the sample of 53 000 ARTI patient visits made at outpatient settings between 1993 and 2015, based on the National Ambulatory Medical Care Survey (NAMCS). RESULTS: Simulation results reveal that physician diagnosis for prescribing antibiotics is based on physician's experience from their prior prescribing behaviour, their perception of patient's infection risk, and patient's expectation to receive antibiotics. Also, there are some variations depending on patient's age and residential region. The simulation analysis also depicts the decreasing trend in patient's expectation over the past two decades for most age groups and regions. CONCLUSIONS: Given the high number of unnecessary prescriptions for ARTI, we found that policies are needed to influence provider's prescribing behaviour through patient's expectation and provider's perception regarding those expectations. Our simulation framework can further be used by policymakers to design and evaluate interventions that may modify the interaction between health providers and patients to optimize antibiotic prescriptions among ARTI patients for different regions and age groups.