Using qualitative system dynamics modeling to understand overdose bystander behavior in the context of Connecticut's Good Samaritan Laws and identify effective policy options.

BACKGROUND: Good Samaritan Laws are a harm reduction policy intended to facilitate a reduction in fatal opioid overdoses by enabling bystanders, first responders, and health care providers to assist individuals experiencing an overdose without facing civil or criminal liability. However, Good Samaritan Laws may not be reaching their full impact in many communities due to a lack of knowledge of protections under these laws, distrust in law enforcement, and fear of legal consequences among potential bystanders. The purpose of this study was to develop a systems-level understanding of the factors influencing bystander responses to opioid overdose in the context of Connecticut's Good Samaritan Laws and identify high-leverage policies for improving opioid-related outcomes and implementation of these laws in Connecticut (CT). METHODS: We conducted six group model building (GMB) workshops that engaged a diverse set of participants with medical and community expertise and lived bystander experience. Through an iterative, stakeholder-engaged process, we developed, refined, and validated a qualitative system dynamics (SD) model in the form of a causal loop diagram (CLD). RESULTS: Our resulting qualitative SD model captures our GMB participants' collective understanding of the dynamics driving bystander behavior and other factors influencing the effectiveness of Good Samaritan Laws in the state of CT. In this model, we identified seven balancing (B) and eight reinforcing (R) feedback loops within four narrative domains: Narrative 1 - Overdose, Calling 911, and First Responder Burnout; Narrative 2 - Naloxone Use, Acceptability, and Linking Patients to Services; Narrative 3 - Drug Arrests, Belief in Good Samaritan Laws, and Community Trust in Police; and Narrative 4 - Bystander Naloxone Use, Community Participation in Harm Reduction, and Cultural Change Towards Carrying Naloxone. CONCLUSIONS: Our qualitative SD model brings a nuanced systems perspective to the literature on bystander behavior in the context of Good Samaritan Laws. Our model, grounded in local knowledge and experience, shows how the hypothesized non-linear interdependencies of the social, structural, and policy determinants of bystander behavior collectively form endogenous feedback loops that can be leveraged to design policies to advance and sustain systems change.

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).

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).

Patient expectation trends on receiving antibiotic prescriptions for respiratory tract infections: A systematic review and meta-regression analysis.

BACKGROUND: Despite a variety of programs developed to control inappropriate antibiotic prescribing for viral infections, antibiotics are still prescribed excessively for Respiratory Tract Infections (RTI). The patient's expectation to receive an antibiotic often influences the clinician's decision and can lead to inappropriate antibiotic prescriptions. Our objective was to investigate the changes in patient expectations over time when presenting with symptoms of a respiratory infection. METHODS: We performed a systematic review of patient's expectation to receive antibiotics for RTIs. Two reviewers independently evaluated the collected studies based on inclusion and exclusion criteria. Our search initially identified 12 070 studies, of which 321 studies were eligible for full text review and 37 articles were selected for final evaluation. Meta-regression analysis was used to evaluate the association between patient expectations and different years. Heterogeneity was evaluated using the Q statistic. RESULTS: Patient expectations (effect size) were pooled using a random effects model. The effect-equality test showed heterogeneity among studies (Q = 3304.23, df = 40, P < 0.0001, k = 40, τ2  = 0.63). Meta-regression results revealed that there is a significant linear negative relationship (B = -1.8374, P < 0.05) between patient expectation and year of data collection, at the global level. A similar finding is observed for the subset of studies conducted outside United States (U.S.) (B = -1.2411, P < 0.1). However, there is no discernible trend for patient expectation in the U.S. or among children and adult subgroups. Also, no significant differences are observed between the patient expectations when considering different age groups. CONCLUSION: The trend of patient expectation for receiving antibiotics for RTIs is declining over time on a global level and also outside the U.S.

Laboratory-Confirmed Metformin-Associated Lactic Acidosis

Introduction Metformin is considered the first line oral hypoglycaemic agent for the treatment of type 2 diabetes. We report three cases of prospectively identified laboratory confirmed metformin-associated lactic acidosis admitted to our intensive care unit. Case 1 72-year-old female presented with lactic acidosis; pH 6.7, lactate 22.6mmol/L with elevated Metformin levels of 4.9mg/L. Case 2 56-year-old female presented with lactic acidosis; pH 7.2 and lactate 14.8mmol/L. Metformin levels elevated at 3.9mg/L. Case 3 72-year-old female presented with lactic acidosis, pH 6.95 and lactate of 27.6mmol/L with elevated Metformin levels of 48.7mg/L. Results All three cases were admitted to the intensive care unit to receive supportive care. Despite CVVHD, two patients died. Discussion Metformin is considered the first line oral hypoglycaemic agent. Confirmation of this diagnosis often proves difficult due to the scarcity of laboratory testing. Our case series highlights the issues of inappropriate prescription in specific patient populations.

Systems thinking in 49 communities related to healthy eating, active living, and childhood obesity.

BACKGROUND: Community partnerships to promote healthy eating and active living in order to prevent childhood obesity face a number of challenges. Systems science tools combined with group model-building techniques offer promising methods that use transdisciplinary team-based approaches to improve understanding of the complexity of the obesity epidemic. This article presents evaluation methods and findings from 49 Healthy Kids, Healthy Communities sites funded to implement policy, system, and environmental changes from 2008 to 2014. METHODS: Through half-day group model-building sessions conducted as part of evaluation site visits to each community between 2010 and 2013, a total of 50 causal loop diagrams were produced for 49 communities (1 community had 2 causal loop diagrams representing different geographic regions). The analysis focused on the following evaluation questions: (1) What were the most prominent variables in the causal loop diagrams across communities? (2) What were the major feedback structures across communities? (3) What implications from the synthesized causal loop diagram can be translated to policy makers, practitioners, evaluators, funders, and other community representatives? RESULTS: A total of 590 individuals participated with an average of 12 participants per session. Participants' causal loop diagrams included a total of 227 unique variables in the following major subsystems: healthy eating policies and environments, active living policies and environments, health and health behaviors, partnership and community capacity, and social determinants. In a synthesized causal loop diagram representing variables identified by at least 20% of the communities, many feedback structures emerged and several themes are highlighted with respect to implications for policy and practice as well as assessment and evaluation. CONCLUSIONS: The application of systems thinking tools combined with group model-building techniques creates opportunities to define and characterize complex systems in a manner that draws on the authentic voice of residents and community partners.

Behavior-over-time graphs: assessing perceived trends in healthy eating and active living environments and behaviors across 49 communities.

CONTEXT: In the evaluation of the Healthy Kids, Healthy Communities initiative, investigators implemented Group Model Building (GMB) to promote systems thinking at the community level. As part of the GMB sessions held in each community partnership, participants created behavior-over-time graphs (BOTGs) to characterize their perceptions of changes over time related to policies, environments, collaborations, and social determinants in their community related to healthy eating, active living, and childhood obesity. OBJECTIVE: To describe the process of coding BOTGs and their trends. DESIGN: Descriptive study of trends among BOTGs from 11 domains (eg, active living environments, social determinants of health, funding) and relevant categories and subcategories based on the graphed variables. In addition, BOTGs were distinguished by whether the variables were positively (eg, access to healthy foods) or negatively (eg, screen time) associated with health. SETTING: The GMB sessions were held in 49 community partnerships across the United States. PARTICIPANTS: Participants in the GMB sessions (n = 590; n = 5-21 per session) included key individuals engaged in or impacted by the policy, system, or environmental changes occurring in the community. MAIN OUTCOME MEASURES: Thirty codes were developed to describe the direction (increasing, decreasing, stable) and shape (linear, reinforcing, balancing, or oscillating) of trends from 1660 graphs. RESULTS: The patterns of trends varied by domain. For example, among variables positively associated with health, the prevalence of reinforcing increasing trends was highest for active living and healthy eating environments (37.4% and 29.3%, respectively), partnership and community capacity (38.8%), and policies (30.2%). Examination of trends of specific variables suggested both convergence (eg, for cost of healthy foods) and divergence (eg, for farmers' markets) of trends across partnerships. CONCLUSIONS: Behavior-over-time graphs provide a unique data source for understanding community-level trends and, when combined with causal maps and computer modeling, can yield insights about prevention strategies to address childhood obesity.

A novel system dynamics model of female obesity and fertility.

OBJECTIVES: Our objective was to create a system dynamics model specific to weight gain and obesity in women of reproductive age that could inform future health policies and have the potential for use in preconception interventions targeting obese women. METHODS: We used our system dynamics model of obesity in women to test various strategies for family building, including ovulation induction versus weight loss to improve ovulation. Outcomes included relative fecundability, postpartum body mass index, and mortality. RESULTS: Our system dynamics model demonstrated that obese women who become pregnant exhibit increasing obesity levels over time with elevated morbidity and mortality. Alternatively, obese women who lose weight prior to pregnancy have improved reproductive outcomes but may risk an age-related decline in fertility, which can affect overall family size. CONCLUSIONS: Our model highlights important public health issues regarding obesity in women of reproductive age. The model may be useful in preconception counseling of obese women who are attempting to balance the competing risks associated with age-related declines in fertility and clinically meaningful weight loss.

Leveraging systems science and design thinking to advance implementation science: moving toward a solution-oriented paradigm.

Many public health challenges are characterized by complexity that reflects the dynamic systems in which they occur. Such systems involve multiple interdependent factors, actors, and sectors that influence health, and are a primary driver of challenges of insufficient implementation, sustainment, and scale of evidence-based public health interventions. Implementation science frameworks have been developed to help embed evidence-based interventions in diverse settings and identify key factors that facilitate or hinder implementation. These frameworks are largely static in that they do not explain the nature and dynamics of interrelationships among the identified determinants, nor how those determinants might change over time. Furthermore, most implementation science frameworks are top-down, deterministic, and linear, leaving critical gaps in understanding of both how to intervene on determinants of successful implementation and how to scale evidence-based solutions. Design thinking and systems science offer methods for transforming this problem-oriented paradigm into one that is solution-oriented. This article describes these two approaches and how they can be integrated into implementation science strategies to promote implementation, sustainment, and scaling of public health innovation, ultimately resulting in transformative systems changes that improve population health.

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.

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.

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.

The impact of COVID-19 on transfusion-dependent thalassemia patients of Karachi, Pakistan: A single-center experience.

OBJECTIVES: With the advent of COVID-19 in Pakistan, the already fragmented blood transfusion services (BTS) received a severe blow, putting the lives of transfusion-dependent thalassemia children on stake. This study aimed to assess the impact of the COVID-19 on blood transfusion therapy (BTT) of thalassemia patients and suggest ways to ensure safe and reliable blood supplies amid such health crises. MATERIAL AND METHODS: A retrospective, cross-sectional study was conducted from October 2019 (before COVID-19) to July 2020 (during COVID-19) based on the data provided by a thalassemia center, named Help International Welfare Trust, Karachi, Pakistan. SPSS version 24.0 was used for the data analysis. Data were described in the form of means and percentages. RESULTS: There was a significant reduction in the consumption of PRBCs bags after the emergence of COVID-19 (P=0.002). Moreover, the number of thalassemia patients receiving BTT was dropped by 10.56% during the pandemic. There was a strong negative correlation observed between the rising cases of COVID-19 in Pakistan and the number of patients missing their therapy sessions (r=-0.914, P=0.030). A considerable decline in the reserves of all Rhesus-negative blood groups amid the COVID-19 outbreak was also observed. CONCLUSION: The COVID-19 pandemic adversely affected the already suboptimal care catered to thalassemia patients in Karachi, Pakistan. The fear of the virus contraction coupled with the lockdown and restricted mobility has disrupted the entire transfusion chain from donor to the recipient. Collaborated efforts by the government and healthcare authorities are essential to ensure sufficient blood for thalassemia patients amid the pandemic.

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.

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.

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.