Taking the "Wild" out of Wildfires - Harnessing Data to Predict, Prevent, and Prepare for the Future

Taking the "Wild" out of Wildfires - Harnessing Data to Predict, Prevent, and Prepare for the Future | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Taking the "Wild" out of Wildfires - Harnessing Data to Predict, Prevent, and Prepare for the Future Amna Naeem, Anne Hicks, Ana Lorena Ruano, Jurgen Pilz, Jennifer B Unger, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7004287/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Climate change and human activity are intensifying wildfires, endangering health, ecosystems, and infrastructure, especially in wild-urban interface regions. This study examined the impact of informal communication, public perception, and psychological readiness on preparedness and health outcomes in Los Angeles and Hawaii (USA), Asturias (Spain), and Canada. It also explored the roles of government response, social media, and perceptions of wildfire causes in shaping these outcomes. Methods A cross-sectional survey of 156 participants from wildfire-prone areas was conducted using convenience and snowball sampling. A 77-item questionnaire in English or Spanish assessed health, preparedness, and communication. Data analysis included descriptive statistics, chi-square tests, and Poisson Generalized Linear Mixed Models (GLMM). Chi-square analysis was used to test hypotheses about associations between perceived evacuation safety, communication tools, health impacts, and government response effectiveness. Poisson GLMM examined whether perceived wildfire impact was predicted by social media use, perceived wildfire drivers, and perceived government role. Fuzzy-set Qualitative Comparative Analysis (fsQCA) identified region-specific condition combinations linked to effective wildfire preparedness and governance outcomes. Results Participants rated wildfire preparedness and communication as moderately effective, with only 19% finding evacuation plans very accessible. Over half reported physical (61%) or respiratory (58%) symptoms. Statistical models linked evacuation confidence and air purifier distribution to reduced respiratory issues. Social media information and community engagement increased trust but contributed to confusion. Social media significantly influenced perceived wildfire impact, while perceptions of government response did not. Fuzzy-set analysis revealed context-specific governance patterns, with both institutional and informal strategies shaping effective wildfire responses across regions. Conclusions This study highlights critical gaps in wildfire preparedness, health protection, and emergency communication, especially in high-risk areas. Strengthening multi-channel communication, infrastructure, and community engagement is essential amid escalating climate-driven wildfire threats. Wildfire Public health Emergency preparedness Evacuation behavior Crisis response Disaster communication Risk perception Introduction Wildfires are increasingly frequent as climate change increases heat and prolongs dry seasons 1 . Globally, millions of hectares of trees were lost to wildfires between 2021 & 2025 2 , endangering millions through displacement, property loss, and health issues from smoke exposure 1 . Impacts on natural ecosystems and human infrastructure are exacerbated by ongoing environmental and human activities that continue to raise global temperatures 3 . Catastrophic events stemming from wildfires, amply documented in the western United States, Hawaii, Canada and Spain, represent millions of dollars in lost infrastructure, human and animal life, and damage to local ecosystems 4 – 6 . Effective Communication is paramount during wildfire emergencies; however, research indicates persistent challenges in this domain. In adequate messaging, timing misalignment, or a lack of comprehension can result in confusion, and mistrust among public noncompliance with evacuation or health directives 7 . These issues disproportionately impact vulnerable populations, such as elderly. Robust prevention and response strategies, complemented by addressing the knowledge gap around how communication, psychological preparedness, and institutional efforts in high-risk wildfire regions influence health outcomes, are needed. This study investigates: 1) how decentralized communication, through social media and peer networks, enhances wildfire preparedness; 2) the disconnect between perceived government performance and personal impact shaped by emotion, experience, and information; and 3) how perceived evacuation safety, trust, and psychological readiness influence health during wildfire events in Canada, Spain and the USA. Methodology Participant information and sampling This cross-sectional study collected data from participants across: Ontario, Quebec, Alberta, and British Columbia (Canada: n= 13); Asturias (Spain: n= 15); Maui, Oahu (Hawaii: n=13), and Los Angeles (USA: n= 115). Participants were recruited using convenience and snowball sampling methods (see supplement). Data collection The questionnaire included 77 items, with 57 rated on 5-point Likert scales where anchors for Likert scale varied depending on the question. Questions were related to demographics, wildfire concerns and impacts, causes, evacuation plans, health implications, air quality, prevention and control and open-ended reflections, available in English and Spanish. Variables, chosen for their theoretical relevance and alignment with study objectives, included risk awareness, clarity of communication, preparedness, health impact, and institutional response. Variables were transformed into fuzzy-set scores through the calibration of their qualitative anchors, followed by a configurational analysis to identify region-specific patterns. Data analysis Chi-square test was used to examine associations between perceived evacuation safety, communication tools, health impacts, and government response effectiveness. Poisson GLMM was applied ( glmer function, lme4 package) to assess wildfire impact predictors. A random country effect is controlled for regional response differences. Chi-square and Poisson GLMM were performed on full data set (n= 156). Quantitative analysis is described in the Supplementary Materials. Qualitative Comparative analysis (fsQCA) was performed separately for each region (Canada, Hawaii, Spain and USA), allowing regional-context insights. The dataset had a single outcome variable, the perceived effectiveness of wildfire governance, alongside eight condition measures related to community preparedness, health impacts, and evacuation communication. For fuzzy-set analysis, min-max normalization methodology was applied to raw values to generate fuzzy-set membership scores, ranging from 0 (complete non-membership) to 1 (complete membership). Scores of 0.5 were reassigned to 0.51 to eliminate ambiguity during calibration, in line with fuzzy set research best practices 8 . Missing data was imputed using mean substitution, ensuring dataset completeness to avoid significant bias. Mean imputation was used considering its ability to preserve the comparability of fuzzy-set membership scores and the configuration structure necessary for set-theoretic analysis. This calibration process was necessary to record the presence or absence of each condition and the extent to which each condition was affected, making the subsequent fsQCA specifically responsive to qualitative reasoning and quantitative detail 9 . The fsQCA process was executed using R Studio with relevant QCA packages 10,11 . The conditions and outcomes are shown in Table 1. A necessity analysis was conducted for each research objective to ascertain whether certain conditions were always present whenever effective wildfire governance was observed. Consistency and coverage values were calculated for each condition and its negation. A condition was categorized as essential when its consistency score exceeded the standard 0.87, indicating a high degree of consistency in the presence of the condition across instances where the outcome is positive. Each country case study and objective were analyzed separately, allowing for the identification of cross-regional consistencies and context-dependent differences. Both the presence and absence of each condition were analyzed to determine whether the existence of a condition was conditionally necessary for achieving the outcome. Sufficiency and solution configurations were assessed through generating and minimizing truth tables. Truth tables were constructed based on the calibrated fuzzy data for each case study, illustrating all logically possible combinations of conditions. A configuration was deemed adequate when it achieved a consistency of 0.87 threshold or higher. To drive immediate solutions, simplifying assumptions were applied in accordance with directional expectations whenever feasible. In instances where assumptions could not be implemented (insufficient empirical variability, emergence of mutually inconsistent arrangements), more complex solutions were generated. Solution type (intermediate, complex) was documented for each country independently to account for contextual variability in associations between conditions and outcomes. Results Community perceptions, health impacts, and emergency response Across all regions surveyed, participants rated wildfire preparedness and communication not very effective (29%, 22%). While 45% found local communication timely and clear, only 13% felt evacuation plans were very accessible. Public perception of local wildfire management was not positive, with only 10% agreeing that governments effectively raised risk awareness and 18% citing sufficient individual preparedness support (Table S4). Communication efforts were rated inadequate by 39% of respondents; just 22% viewed them as effective, (Table S4). Drought (63%) and high winds (74%) were the most frequently cited natural causes of wildfires (Table S5), but 62% thought human negligence was very important (Table S5). Health issues were the most reported consequence (Table S6); 61% reported physical symptoms like throat irritation, coughing and 58% reported having respiratory symptoms (Figure S2). Although 56% had evacuated ≥ once, only 44% found evacuation plans effective or communication sufficient (Table S7). Government (56%) and social media (58%) were the most useful communication channels (Table S8). Direct interventions (masks (46%), air filtration (40%)), were rated more effective than long-term strategies (Table S9). Detailed descriptive results are provided in the Supplementary Section. Statistical associations and modeling Participants (n =156) who perceived the evacuation plan as safe were significantly less likely to report respiratory problems (χ² = 43.99, p-value < 0.05). The perceived effectiveness of air purifier distribution to vulnerable populations was associated with reduced respiratory symptoms (χ² = 20.97, p-value < 0.05) (Table 2). Communication clarity was influential in shaping public behavior and trust. Participants who valued social media as a valuable information source were significantly more likely to perceive official communications as clear and straightforward (χ² = 23.00, p-value < 0.05). Community engagement (e.g., discussing with neighbors) was linked to understanding wildfire messaging better. The perceived usefulness of government communication and usefulness of social media were significantly associated (χ² = 6.02, p-value < 0.05). Participants who recognized climate change as a contributing factor to wildfires were more likely to view government initiatives related to health and air quality as effective (χ² = 36.75, p-value < 0.05). However, Poisson GLMM revealed that use of social media as an information during wildfire was significantly associated with participant’s perceived wildfire impact (β = 0.030, p < 0.001) (Table 2), but attributing wildfires to natural causes had a small negative association with perceived impact (β = -0.009, p < 0.05) (Table 3). Perceived positive government performance in managing wildfire-related health and air quality had no statistically significant effect on perceived impact of wildfire-related health and air quality (β = -0.003, p > 0.05), Qualitative comparative analysis results Government Effectiveness Necessity analysis (Tables 4 & S10), indicating more favorable conditions in the environment of the efficient wildfire governance, in particular circumstances. In Los Angeles and Canadian sites, risk awareness and sufficient, timely and clear communication were essential to efficient wildfire governance (Tables 4, S11-14), while in Asturias institutional constraints or reliance on informal systems impacted efficient governance. Sufficiency analysis elucidates the configurational aspects of the wildfire preparedness (Table S11-14). In both Los Angeles and Canada, core conditions that constitute high-consistency configurations include risk awareness, sufficient information, and timely communication. Clear and timely communication consistently emerged as core elements, with Canadian configuration 1 yielding an overall consistency of 0.943 and coverage of 0.61 (Table S12). Peripheral conditions, such as personal preparedness resources and community-wide risk reduction initiatives, were present but not uniformly distributed across the configurations, suggesting they are contextual enhancers without systematic impact. The relative prevalence of configurations exhibiting lower core conditions and higher reliance on peripheral or mixed forms reflects an adaptive characteristic in settings with limited institutional consistency, as evidenced in Hawaii (Table S13) and Asturias (Table S14), which depended on informal preparedness networks, community agency, and responsive coordination. Health impact and air quality Los Angeles and Canada weighed air quality monitoring systems, public health advisories and warnings, and clean air shelters differently compared to each other, reflecting variations in institutional strategies and public health capacities (Tables 5, S15). In regions with strong institutional capacity (such as Canada and Los Angeles), public health and environmental systems were effective in mitigating wildfire-related adverse health effects. However, respondents from these sites agreed on primary institutional responses, including distribution of air purifiers, government-funded research, and emergency response units with filtration systems (Tables S16-S17). Peripheral solutions, such as limiting outdoor activities and the distribution of face masks, did not demonstrate universal presence but contributed to increased capacity. In Hawaii and Asturias, configurations suggested inadequate implementation in resource-scarce, less consolidated contexts. Communication and evacuation In Canada and Los Angeles, official government channels, traditional media, and evacuation communication clarity indicate evacuation plans and multi-platform communication significantly improved preparedness and promoted behavioral compliance (Tables 6, S18). Hawaii exhibited a distinct communication profile, where word of mouth and social media platforms attained the highest consistency scores (Table S18). Discussion This study describes how communication, perception, and governance shape wildfire preparedness and health outcomes. Confidence in evacuation plans was significantly associated with reduced respiratory symptoms, indicating the health benefits of psychological preparedness. In areas like Hawaii and Asturias, where institutional and technological resources such as official alert systems and emergency planning infrastructure are relatively limited, informal communication, particularly social media and word-of-mouth, was considered more reliable than formal government channels. This suggests the need for decentralized emergency communication strategies. Although modeling did not identify a statistically significant correlation between perceived government performance and perceived effectiveness of wildfire response efforts as a whole, the fsQCA findings indicated the importance of government-related conditions in influencing wildfire preparedness in regions such as Hawaii and Asturias. This suggests that the impact of government perception may be context-dependent, potentially becoming more pronounced in environments characterized by weak institutional frameworks. One of the most policy-relevant findings of this study is the high reliability of informal communication systems, particularly social media 12 and word-of-mouth, in less-resourced settings where institutional infrastructure was weaker. This contrasts regions with more robust infrastructure where clear, timely communication enhanced preparedness. In Hawaii, for example, peer-based communication outperformed formal systems, challenging conventional top-down emergency communication strategies. However, while social media platforms like X and Facebook enabled rapid updates during crises, they also created confusion, particularly with inconsistent access,, as seen in Lahaina and Los Angeles 13,14 . Reliance on informal networks in resource-limited areas may be more adaptive than stable 15 , however decentralized, context-specific approaches that prioritize community trust and local engagement clearly provide important ways to bridge infrastructure gaps that deserve further research. Another finding, that social media use heightened perceived wildfire impact, suggests its potential power in increasing wildfire awareness. Our findings underscore the importance of psychological preparedness in shaping health outcomes during wildfire events. The positive associations between perceived institutional initiatives (e.g., safe evacuation, equitable air purifier distribution) and fewer respiratory symptoms suggest that, beyond the physical infrastructure of emergency systems, public confidence plays a crucial role in health protection. Preparedness strategies, transparent risk communication, participatory planning, and community-based drills may enhance trust and encourage protective behavior 16 . We found widespread concerns about infrastructure resilience and personal safety, especially in high-risk evacuation areas 17 . In Los Angeles and Asturias, participants cited gaps in planning and insufficient preparedness resources. Respondents frequently criticized local governments for inadequate communication and limited support for individuals, echoing broader critiques that urban planning rarely prioritizes wildfire resilience 18 . While community-wide initiatives and individual preparedness efforts improved adaptability, they were not sufficient on their own 19 . Developing context-sensitive evacuation strategies should integrate formal systems with trusted informal networks, particularly in regions with limited institutional capacity 20 . Decentralized communication aids governance in low-resource settings, but may reduce coherence, coverage, and reliability compared to institutional systems 21 . Our analysis revealed a counterintuitive but critical insight: perceived government effectiveness in managing wildfire-related health and air quality was not significantly associated with individuals’ sense of being impacted. Instead, subjective factors, including personal experience, social media and interpersonal interactions, were more influential predictors of perceived impact, potentially due to personal narratives and emotionally charged content. These findings challenge the assumption that institutional performance alone mitigates perceived vulnerability 22 . They underscore the need for a dual approach that strengthens institutional response to address emotional and cognitive drivers of risk perception. These findings align with existing literature identifying human activity and climate change as primary drivers of wildfire risk 1 . Wildland-urban interface zones heighten the likelihood of property damage while presenting logistic challenges for emergency response and evacuation 23 . We found that individuals who attributed wildfires to climate-related factors were more likely to believe government health interventions are effective, suggesting that environmental literacy can foster institutional trust. When people view wildfires within a broader ecological and climatic framework, they may interpret institutional actions as more legitimate and impactful 3 . Educational campaigns that clearly connect climate change to wildfire risk could enhance public engagement. Our study found better concordance between direct and tangible protective measures, (masks, air filtration, clean-air shelters), than long-term or systemic interventions. While systemic solutions are essential for long-term resilience, their benefits may not be readily visible to the public during acute crises 24 . This discrepancy suggests a gap between public priorities and institutional investments. Bridging this perception gap requires better communication strategies that clearly link systemic interventions to perceived personal and community health outcomes. Clear communication was essential for institutional trust and effective preparedness. Social media and neighbor-based discussions improved acceptance of official messaging, emphasizing the value of integrated, multimodal communication networks. Wildfire communication strategies that blend digital, interpersonal, and traditional channels may enhance reach and credibility across diverse populations 25 . Health impacts of wildfires were widely reported 26 . Exposure to wildfire smoke is associated with respiratory and cardiovascular risks, especially among vulnerable groups 27 . Post-wildfire runoff introduces contaminants into drinking water 28,29 . Settings with stronger infrastructure could mitigate these effects through coordinated interventions (e.g., air quality monitoring, advisories, clean-air shelters), supported by institutionalized, multisectoral systems 30 . In contrast, less supported areas depend on adaptive, informal or mixed strategies which may lack resilience and scalability 31 . Strengths This study highlights the strength of examining both institutional capacity and community adaptation. Regions with well-developed infrastructure demonstrated timely communication and robust public health systems that were perceived as effective wildfire governance. While regions with less-developed infrastructure did not, mitigating factors including flexible informal networks, were perceived as valuable, underscoring the need for integrated, context-sensitive, multi-channel preparedness strategies. The cross-sectional design captures a timely snapshot of perceptions, revealing critical gaps and strengths in governance and community response. Limitations The sample size varied across regions and relied on self-reported data. The convenience and snowball sampling methods may have introduced selection bias. Not all questionnaires were complete; while validated strategies were used for missing data, errors may have been introduced. Despite these constraints, the findings contribute context-specific knowledge to inform more adaptive and inclusive wildfire management strategies. Conclusion This study provides a multi-scalar, multi-method examination of how public perceptions, communication strategies, and institutional responses shape wildfire preparedness and health outcomes. Our findings underscore the urgency of integrating decentralized, context-specific communication, strengthening psychological preparedness, and aligning institutional interventions with public expectations. Given the increasing frequency and intensity of wildfires under climate change, policymakers must prioritize inclusive, adaptive, and community-informed approaches to emergency management. Investments in social infrastructure, particularly communication networks, trust-building mechanisms, and participatory planning, are as critical as physical infrastructure in mitigating the health and societal impacts of wildfires. Declarations Ethical approval for this study was obtained from the Ethics Review Committee of COMSATS University, Islamabad. 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RMS 19(5):1447–1476 Tables Table 1: Government effectiveness in managing wildfire risk, health impacts, and air quality Outcome Conditions Government Effectiveness on Wildfire Risk Risk Communication Preparedness Info Effectiveness Preparedness Resources Effectiveness Community Initiatives Community Plans Usability Communication Timeliness Communication Clarity Information Sufficiency for Decisions Evacuation Plan Effectiveness Evacuation Communication Health Impact and Air Quality Management Health Impact Response Air Quality Monitoring Health Advisories Air Purifier Distribution Clean Air Shelters Research & Reports Activity Restrictions Emergency Response Equipment Mask Distribution Government Effectiveness on Wildfire Risk Evacuation Plan Effectiveness Evacuation Communication Social media platforms (e.g., X, Facebook) Official Government Channels Traditional Media Word of mouth Table 2: Chi-square Test of Associations Between Wildfire Preparedness, Communication Tools, and Perceived Health and Safety Impacts Variable 1 Variable 2 χ² p- value Evacuation plan safety Respiratory issues 43.99 0.0005 Social media platform Clear communication from authorities 23 0.0005 Distribution of air purifiers to vulnerable populations Respiratory issues 20.97 0.0005 Sufficient information Social media platform 6.015 0.0195 Word of mouth Clear communication from authorities 5.28 0.0225 Climate change Local/government and health impact 36.752 0.0005 Table 3: Poisson Generalized Linear Mixed Model (GLMM) Assessing the Effect of Government Role, Wildfire Drivers, and Social Media Use on Perceived Wildfire Impact Variables Estimate Std. Error Z value Pr(>|z|) ( Intercept) 3.013 0.123 24.464 0.000 Government role -0.003 0.002 -1.135 0.256 Wildfire drivers -0.009 0.004 -2.414 0.016 Social media use 0.030 0.004 6.832 0.000 Table 4: Overall Necessity Consistency Scores for Preparedness Conditions across All Cases Country RA PPI PPR CRI ECCP TCOM COMM SINF Los Angeles, USA 0.91 0.84 0.85 0.88 0.85 0.9 0.89 0.87 Canada 1 0.86 0.86 0.76 0.75 0.65 0.97 0.95 0.93 Hawaii, USA 0.91 0.73 0.64 0.73 0.45 0.82 0.82 0.72 Asturias 0.62 0.62 0.57 0.81 0.67 0.76 0.81 0.72 Risk Awareness= RA; Personal Preparedness information=PPI; Personal Preparedness resources=PPR; Community-wide initiatives related to risk reduction=CRI; Easiness of community-level plans=ECCP; Timely Communication=TCOM; Clear Communication=CCOM; Sufficient Information= SINF 1 Ontario, Quebec, Alberta, and British Columbia Table 5: Overall Necessity Consistency Scores for Health and Air Quality Conditions Country AQMS PHAW DAPVP TSCA GFAQR LOAE ERUAFE DFM Los Angeles, USA 0.87 0.86 0.84 0.85 0.82 0.85 0.8 0.85 Canada 1 0.95 1 0.72 0.92 0.95 0.77 0.85 0.71 Hawaii, USA 0.87 1 0.54 0.51 0.72 0.96 0.56 0.49 Asturias 0.73 0.76 0.79 0.66 0.72 0.71 0.78 0.66 Air quality monitoring systems=AQMS; Public health advisories and warnings=PHAW; Distribution of air purifiers to vulnerable populations=DAPVP; Temporary shelters with clean air systems=TSCA; Government-funded air quality research and reports=GFAQR; Limiting outdoor activities and events=LOAE; Emergency response units with air filtration equipment=ERUAFE; Distribution of face masks=DFM; 1 Ontario, Quebec, Alberta, and British Columbia Table 6: Overall Necessity Consistency Scores for Evacuation and Communication Conditions Country EVP ECC SMP OGC TMED WOM Los Angeles, USA 0.83 0.83 0.84 0.86 0.85 0.82 Canada 1 0.79 0.79 0.79 0.89 0.86 0.89 Hawaii, USA 0.73 0.73 0.91 1 0.82 0.91 Asturias 0.74 0.74 0.7 0.94 0.94 0.85 Evacuation plan safety= EVP; Evacuation related communication clarity= ECC; Social media platforms (e.g., X, Facebook) =SMP; Official government channels (e.g., emergency alerts, websites) =OGC; Traditional media (e.g., TV, radio) =TMED; Word of mouth (e.g., neighbors, friends, community members) =WOM; 1 Ontario, Quebec, Alberta, and British Columbia Additional Declarations The authors declare no competing interests. Supplementary Files SupplementtextsWildfirefinal.docx Supplemetary Text SupplemetaryTablesfinal.docx Supplementary Tables SupplementFiguresfinal.docx Supplementary Figures WildfireQuestionnaireGoogleForms.pdf Questionnaire Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7004287","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":478018829,"identity":"e96e2dcd-c338-412f-97bc-abe26e0decaf","order_by":0,"name":"Amna Naeem","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIiWNgGAWjYFAD9v7vHz4AaTZ2orXwHDBjnAHSwky0FgkHM2YeEIOQFv5pZw9+LqixkdOdwZD22ObXNnk+ZgbGDx9z8Jh9Oy9ZesaxNGOz2w3HjXP7bhu2MTMwS87chsea2zkG0jxshxO33TnYIJ3bc5sRqIWNmRePFvnbOca/ef79r992I5lB2rLntj1BLQa3c8ykedsOJJjdSGOTZvhxO5GgFkOgFmvevmTDbWfOMBv2NtxObmNmbMbrFzmgw27zfLOTNzvew/jgx5/btvPbmw9++IjP+yiAsQ1MNhCrHgT+kKJ4FIyCUTAKRgoAAHVyUXsYFK18AAAAAElFTkSuQmCC","orcid":"","institution":"Quaid-i-Azam University, Islamabad, Pakistan","correspondingAuthor":true,"prefix":"","firstName":"Amna","middleName":"","lastName":"Naeem","suffix":""},{"id":478018830,"identity":"e390ae6b-27df-4f12-b722-aa411e3a2e99","order_by":1,"name":"Anne Hicks","email":"","orcid":"","institution":"Department of Pediatrics, University of Alberta, Edmonton, Alberta, Canada","correspondingAuthor":false,"prefix":"","firstName":"Anne","middleName":"","lastName":"Hicks","suffix":""},{"id":478018831,"identity":"80adfa35-4a80-4e53-8dde-1750ed689b88","order_by":2,"name":"Ana Lorena Ruano","email":"","orcid":"","institution":"Center for International Health, Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway","correspondingAuthor":false,"prefix":"","firstName":"Ana","middleName":"Lorena","lastName":"Ruano","suffix":""},{"id":478018832,"identity":"1ac9f5ff-2e00-4e6a-ad18-7e467cc95772","order_by":3,"name":"Jurgen Pilz","email":"","orcid":"","institution":"Alpen-Adria University of Klagenfurt, Universität’s. 65–67, 9020 Klagenfurt, Austria","correspondingAuthor":false,"prefix":"","firstName":"Jurgen","middleName":"","lastName":"Pilz","suffix":""},{"id":478018833,"identity":"6f638779-73b2-4910-bfc5-732cf763d890","order_by":4,"name":"Jennifer B Unger","email":"","orcid":"","institution":"Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America","correspondingAuthor":false,"prefix":"","firstName":"Jennifer","middleName":"B","lastName":"Unger","suffix":""},{"id":478018834,"identity":"a5f9f226-146d-482d-8028-14027ba3ad5e","order_by":5,"name":"Esther Annan","email":"","orcid":"","institution":"Center for Health and Wellbeing, School of Public and International Affairs, Princeton University, Princeton, NJ, USA","correspondingAuthor":false,"prefix":"","firstName":"Esther","middleName":"","lastName":"Annan","suffix":""},{"id":478018835,"identity":"e6f6fba7-eee7-4ea7-b2b8-e55a7460424c","order_by":6,"name":"June Seok Lee","email":"","orcid":"","institution":"Department of Civil and Environmental Engineering, Manhattan University, Riverdale, New York, USA","correspondingAuthor":false,"prefix":"","firstName":"June","middleName":"Seok","lastName":"Lee","suffix":""},{"id":478018836,"identity":"15dfb588-ee8d-4acd-b00a-aa26dd88fd77","order_by":7,"name":"Wael K. Al-Delaimy","email":"","orcid":"","institution":"Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla, California, United States","correspondingAuthor":false,"prefix":"","firstName":"Wael","middleName":"K.","lastName":"Al-Delaimy","suffix":""},{"id":478018837,"identity":"e3b41bd7-b506-48bd-9e0b-7915af61dc38","order_by":8,"name":"Iftikhar U Sikder","email":"","orcid":"","institution":"Department of Information Systems, Cleveland State University, USA","correspondingAuthor":false,"prefix":"","firstName":"Iftikhar","middleName":"U","lastName":"Sikder","suffix":""},{"id":478018838,"identity":"5c753149-3cab-41f2-a64b-a680e242451b","order_by":9,"name":"Kalim Ullah","email":"","orcid":"","institution":"Department of Meteorology, COMSATS University Islamabad, Islamabad, Pakistan","correspondingAuthor":false,"prefix":"","firstName":"Kalim","middleName":"","lastName":"Ullah","suffix":""},{"id":478018839,"identity":"56966a85-2b9b-47d7-a166-38816ae874e8","order_by":10,"name":"Ubydul Haque","email":"","orcid":"","institution":"Rutgers Global Health Institute, and Department of Biostatistics and Epidemiology, School of Public Health, Rutgers University, Piscataway, NJ, USA","correspondingAuthor":false,"prefix":"","firstName":"Ubydul","middleName":"","lastName":"Haque","suffix":""}],"badges":[],"createdAt":"2025-06-29 18:02:48","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7004287/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7004287/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85753933,"identity":"bf59b4c6-baa0-447f-8ecb-85aa3c80e297","added_by":"auto","created_at":"2025-07-01 10:36:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":980247,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7004287/v1/c1958ed7-47a9-4490-ab88-3f68ff9eebcf.pdf"},{"id":85751982,"identity":"bd6e12c6-96f7-42c6-9579-e9cd0ef4fe09","added_by":"auto","created_at":"2025-07-01 10:20:53","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":21401,"visible":true,"origin":"","legend":"\u003cp\u003eSupplemetary Text\u003c/p\u003e","description":"","filename":"SupplementtextsWildfirefinal.docx","url":"https://assets-eu.researchsquare.com/files/rs-7004287/v1/d9e8269f40ee2200de85e0e0.docx"},{"id":85751430,"identity":"34e393ca-05ba-4f5f-9616-6a18c245deca","added_by":"auto","created_at":"2025-07-01 10:12:53","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":97568,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Tables\u003c/p\u003e","description":"","filename":"SupplemetaryTablesfinal.docx","url":"https://assets-eu.researchsquare.com/files/rs-7004287/v1/a0f1f3e7e01451f7d78e917c.docx"},{"id":85751428,"identity":"9d12dcc5-97a4-41f6-8ff8-500207763194","added_by":"auto","created_at":"2025-07-01 10:12:53","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":52522,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Figures\u003c/p\u003e","description":"","filename":"SupplementFiguresfinal.docx","url":"https://assets-eu.researchsquare.com/files/rs-7004287/v1/5b17afd8946cf556fb186db2.docx"},{"id":85751433,"identity":"3d786eb1-d362-44cd-8452-67e1a9c72fc2","added_by":"auto","created_at":"2025-07-01 10:12:53","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":455585,"visible":true,"origin":"","legend":"\u003cp\u003eQuestionnaire\u003c/p\u003e","description":"","filename":"WildfireQuestionnaireGoogleForms.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7004287/v1/5e5c27529d71b98afa9b2762.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eTaking the \"Wild\" out of Wildfires - Harnessing Data to Predict, Prevent, and Prepare for the Future\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWildfires are increasingly frequent as climate change increases heat and prolongs dry seasons\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Globally, millions of hectares of trees were lost to wildfires between 2021 \u0026amp; 2025\u003csup\u003e2\u003c/sup\u003e, endangering millions through displacement, property loss, and health issues from smoke exposure\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Impacts on natural ecosystems and human infrastructure are exacerbated by ongoing environmental and human activities that continue to raise global temperatures\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Catastrophic events stemming from wildfires, amply documented in the western United States, Hawaii, Canada and Spain, represent millions of dollars in lost infrastructure, human and animal life, and damage to local ecosystems\u003csup\u003e\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e–\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eEffective Communication is paramount during wildfire emergencies; however, research indicates persistent challenges in this domain. In adequate messaging, timing misalignment, or a lack of comprehension can result in confusion, and mistrust among public noncompliance with evacuation or health directives\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. These issues disproportionately impact vulnerable populations, such as elderly. Robust prevention and response strategies, complemented by addressing the knowledge gap around how communication, psychological preparedness, and institutional efforts in high-risk wildfire regions influence health outcomes, are needed.\u003c/p\u003e \u003cp\u003eThis study investigates: 1) how decentralized communication, through social media and peer networks, enhances wildfire preparedness; 2) the disconnect between perceived government performance and personal impact shaped by emotion, experience, and information; and 3) how perceived evacuation safety, trust, and psychological readiness influence health during wildfire events in Canada, Spain and the USA.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eParticipant information and sampling\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis cross-sectional study collected data from participants across: Ontario, Quebec, Alberta, and British Columbia (Canada: n= 13); Asturias (Spain: n= 15); Maui, Oahu (Hawaii: n=13), and Los Angeles (USA: n= 115). Participants were recruited using convenience and snowball sampling methods (see supplement).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData collection\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe questionnaire included 77 items, with 57 rated on 5-point Likert scales where anchors for Likert scale varied depending on the question. Questions were related to demographics, wildfire concerns and impacts, causes, evacuation plans, health implications, air quality, prevention and control and open-ended reflections, available in English and Spanish. Variables, chosen for their theoretical relevance and alignment with study objectives, included risk awareness, clarity of communication, preparedness, health impact, and institutional response. Variables were transformed into fuzzy-set scores through the calibration of their qualitative anchors, followed by a configurational analysis to identify region-specific patterns.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChi-square test was used to examine associations between perceived evacuation safety, communication tools, health impacts, and government response effectiveness. \u0026nbsp;Poisson GLMM was applied (\u003cem\u003eglmer\u003c/em\u003e function, \u003cem\u003elme4\u003c/em\u003e package) to assess wildfire impact predictors. A random country effect is controlled for regional response differences. Chi-square and Poisson GLMM were performed on full data set (n= 156). Quantitative analysis is described in the Supplementary Materials. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eQualitative Comparative analysis (fsQCA) was performed separately for each region (Canada, Hawaii, Spain and USA), allowing regional-context insights. The dataset had a single outcome variable, the perceived effectiveness of wildfire governance, alongside eight condition measures related to community preparedness, health impacts, and evacuation communication. For fuzzy-set analysis, min-max normalization methodology was applied to raw values to generate fuzzy-set membership scores, ranging from 0 (complete non-membership) to 1 (complete membership). Scores of 0.5 were reassigned to 0.51 to eliminate ambiguity during calibration, in line with fuzzy set research best practices \u003csup\u003e8\u003c/sup\u003e. Missing data was imputed using mean substitution, ensuring dataset completeness to avoid significant bias. Mean imputation was used considering its ability to preserve the comparability of fuzzy-set membership scores and the configuration structure necessary for set-theoretic analysis. \u0026nbsp;This calibration process was necessary to record the presence or absence of each condition and the extent to which each condition was affected, making the subsequent fsQCA specifically responsive to qualitative reasoning and quantitative detail \u003csup\u003e9\u003c/sup\u003e. The fsQCA process was executed using R Studio with relevant QCA packages \u003csup\u003e10,11\u003c/sup\u003e. \u0026nbsp;The conditions and outcomes are shown in Table 1.\u003c/p\u003e\n\u003cp\u003eA necessity analysis was conducted for each research objective to ascertain whether certain conditions were always present whenever effective wildfire governance was observed. Consistency and coverage values were calculated for each condition and its negation. A condition was categorized as essential when its consistency score exceeded the standard 0.87, indicating a high degree of consistency in the presence of the condition across instances where the outcome is positive. Each country case study and objective were analyzed separately, allowing for the identification of cross-regional consistencies and context-dependent differences. Both the presence and absence of each condition were analyzed to determine whether the existence of a condition was conditionally necessary for achieving the outcome.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSufficiency and solution configurations\u0026nbsp;\u003c/em\u003ewere assessed through generating and minimizing truth tables. Truth tables were constructed based on the calibrated fuzzy data for each case study, illustrating all logically possible combinations of conditions. A configuration was deemed adequate when it achieved a consistency of 0.87 threshold or higher. To drive immediate solutions, simplifying assumptions were applied in accordance with directional expectations whenever feasible. In instances where assumptions could not be implemented (insufficient empirical variability, emergence of mutually inconsistent arrangements), more complex solutions were generated. Solution type (intermediate, complex) was documented for each country independently to account for contextual variability in associations between conditions and outcomes.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCommunity perceptions, health impacts, and emergency response\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAcross all regions surveyed, participants rated wildfire preparedness and communication not very effective (29%, 22%). While 45% found local communication timely and clear, only 13% felt evacuation plans were very accessible. Public perception of local wildfire management was not positive, with only 10% agreeing that governments effectively raised risk awareness and 18% citing sufficient individual preparedness support (Table S4). Communication efforts were rated inadequate by 39% of respondents; just 22% viewed them as effective, (Table S4). Drought (63%) and high winds (74%) were the most frequently cited natural causes of wildfires (Table S5), but 62% thought human negligence was very important (Table S5). Health issues were the most reported consequence (Table S6); 61% reported physical symptoms like throat irritation, coughing \u0026nbsp; and 58% reported having respiratory symptoms (Figure S2). Although 56% had evacuated \u0026ge; once, only 44% found evacuation plans effective or communication sufficient (Table S7). Government (56%) and social media (58%) were the most useful communication channels (Table S8). Direct interventions (masks (46%), air filtration (40%)), were rated more effective than long-term strategies (Table S9). Detailed descriptive results are provided in the Supplementary Section.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStatistical associations and modeling\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Participants (n =156) who perceived the evacuation plan as safe were significantly less likely to report respiratory problems (\u0026chi;\u0026sup2; = 43.99, p-value \u0026lt; 0.05). The perceived effectiveness of air purifier distribution to vulnerable populations was associated with reduced respiratory symptoms (\u0026chi;\u0026sup2; = 20.97, p-value \u0026lt; 0.05) (Table 2).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Communication clarity was influential in shaping public behavior and trust. \u0026nbsp; Participants who valued social media as a valuable information source were significantly more likely to perceive official communications as clear and straightforward (\u0026chi;\u0026sup2; = 23.00, p-value \u0026lt; 0.05). Community engagement (e.g., discussing with neighbors) was linked to understanding wildfire messaging better. The perceived usefulness of government communication and usefulness of social media were significantly associated (\u0026chi;\u0026sup2; = 6.02, p-value \u0026lt; 0.05).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eParticipants who recognized climate change as a contributing factor to wildfires were more likely to view government initiatives related to health and air quality as effective (\u0026chi;\u0026sup2; = 36.75, p-value \u0026lt; 0.05). \u0026nbsp;However, Poisson GLMM revealed that use of social media as an information during wildfire was significantly associated with participant\u0026rsquo;s perceived wildfire impact (\u0026beta; = 0.030, p \u0026lt; 0.001) (Table 2), but attributing wildfires to natural causes had a small negative association with perceived impact (\u0026beta; = -0.009, p \u0026lt; 0.05) (Table 3). \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePerceived positive government performance in managing wildfire-related health and air quality had no statistically significant effect on perceived impact of wildfire-related health and air quality (\u0026beta; = -0.003, p \u0026gt; 0.05),\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQualitative comparative analysis results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eGovernment Effectiveness \u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNecessity analysis (Tables 4 \u0026amp; S10), indicating more favorable conditions in the environment of the efficient wildfire governance, in particular circumstances. In Los Angeles and Canadian sites, risk awareness and sufficient, timely and clear communication were essential to efficient wildfire governance (Tables 4, S11-14), while in Asturias institutional constraints or reliance on informal systems impacted efficient governance.\u003c/p\u003e\n\u003cp\u003eSufficiency analysis elucidates the configurational aspects of the wildfire preparedness (Table S11-14). In both Los Angeles and Canada, core conditions that constitute high-consistency configurations include risk awareness, sufficient information, and timely communication. Clear and timely communication consistently emerged as core elements, with Canadian configuration 1 yielding an overall consistency of 0.943 and coverage of 0.61 (Table S12).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePeripheral conditions, such as personal preparedness resources and community-wide risk reduction initiatives, were present but not uniformly distributed across the configurations, suggesting they are contextual enhancers without systematic impact.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe relative prevalence of configurations exhibiting lower core conditions and higher reliance on peripheral or mixed forms reflects an adaptive characteristic in settings with limited institutional consistency, as evidenced in Hawaii (Table S13) and Asturias (Table S14), which depended on informal preparedness networks, community agency, and responsive coordination.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eHealth impact and air quality\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLos Angeles and Canada weighed air quality monitoring systems, public health advisories and warnings, and clean air shelters differently compared to each other, reflecting variations in institutional strategies and public health capacities (Tables 5, S15). In regions with strong institutional capacity (such as Canada and Los Angeles), public health and environmental systems were effective in mitigating wildfire-related adverse health effects. However, respondents from these sites agreed on primary institutional responses, including distribution of air purifiers, government-funded research, and emergency response units with filtration systems (Tables S16-S17).\u003c/p\u003e\n\u003cp\u003ePeripheral solutions, such as limiting outdoor activities and the distribution of face masks, did not demonstrate universal presence but contributed to increased capacity. In Hawaii and Asturias, configurations suggested inadequate implementation in resource-scarce, less consolidated contexts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCommunication and evacuation\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn Canada and Los Angeles, official government channels, traditional media, and evacuation communication clarity indicate evacuation plans and multi-platform communication significantly improved preparedness and promoted behavioral compliance (Tables 6, S18). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHawaii exhibited a distinct communication profile, where word of mouth and social media platforms attained the highest consistency scores (Table S18).\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study describes how communication, perception, and governance shape wildfire preparedness and health outcomes. Confidence in evacuation plans was significantly associated with reduced respiratory symptoms, indicating the health benefits of psychological preparedness. In areas like Hawaii and Asturias, where institutional and technological resources such as official alert systems and emergency planning infrastructure are relatively limited, informal communication, particularly social media and word-of-mouth, was considered more reliable than formal government channels. This suggests the need for decentralized emergency communication strategies. Although modeling did not identify a statistically significant correlation between perceived government performance and perceived effectiveness of wildfire response efforts as a whole, the fsQCA findings indicated the importance of government-related conditions in influencing wildfire preparedness in regions such as Hawaii and Asturias. This suggests that the impact of government perception may be context-dependent, potentially becoming more pronounced in environments characterized by weak institutional frameworks.\u003c/p\u003e\n\u003cp\u003eOne of the most policy-relevant findings of this study is the high reliability of informal communication systems, particularly social media\u003csup\u003e12\u003c/sup\u003e and word-of-mouth, in less-resourced settings where institutional infrastructure was weaker. This contrasts regions with more robust infrastructure where clear, timely communication enhanced preparedness. In Hawaii, for example, peer-based communication outperformed formal systems, challenging conventional top-down emergency communication strategies. However, while social media platforms like X and Facebook enabled rapid updates during crises, they also created confusion, particularly with inconsistent access,, as seen in Lahaina and Los Angeles\u003csup\u003e13,14\u003c/sup\u003e. Reliance on informal networks in resource-limited areas may be more adaptive than stable\u003csup\u003e15\u003c/sup\u003e, however \u0026nbsp;decentralized, context-specific approaches that prioritize community trust and local engagement clearly provide important ways to bridge infrastructure gaps that deserve further research. Another finding, that social media use heightened perceived wildfire impact, suggests its potential power in increasing wildfire awareness.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Our findings underscore the importance of psychological preparedness in shaping health outcomes during wildfire events. The positive associations between perceived institutional initiatives (e.g., safe evacuation, equitable air purifier distribution) and fewer respiratory symptoms suggest that, beyond the physical infrastructure of emergency systems, public confidence plays a crucial role in health protection. Preparedness strategies, transparent risk communication, participatory planning, and community-based drills may enhance trust and encourage protective behavior\u003csup\u003e16\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eWe found widespread concerns about infrastructure resilience and personal safety, especially in high-risk evacuation areas\u003csup\u003e17\u003c/sup\u003e. In Los Angeles and Asturias, participants cited gaps in planning and insufficient preparedness resources. Respondents frequently criticized local governments for inadequate communication and limited support for individuals, echoing broader critiques that urban planning rarely prioritizes wildfire resilience\u003csup\u003e18\u003c/sup\u003e. While community-wide initiatives and individual preparedness efforts improved adaptability, they were not sufficient on their own\u003csup\u003e19\u003c/sup\u003e. Developing context-sensitive evacuation strategies should integrate formal systems with trusted informal networks, particularly in regions with limited institutional capacity\u003csup\u003e20\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eDecentralized communication aids governance in low-resource settings, but may reduce coherence, coverage, and reliability compared to institutional systems\u003csup\u003e21\u003c/sup\u003e. Our analysis revealed a counterintuitive but critical insight: perceived government effectiveness in managing wildfire-related health and air quality was not significantly associated with individuals\u0026rsquo; sense of being impacted. Instead, subjective factors, including personal experience, social media and interpersonal interactions, were more influential predictors of perceived impact, potentially due to personal narratives and emotionally charged content. These findings challenge the assumption that institutional performance alone mitigates perceived vulnerability\u003csup\u003e22\u003c/sup\u003e. They underscore the need for a dual approach that strengthens institutional response to address emotional and cognitive drivers of risk perception.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThese findings align with existing literature identifying human activity and climate change as primary drivers of wildfire risk\u003csup\u003e1\u003c/sup\u003e. Wildland-urban interface zones heighten the likelihood of property damage while presenting logistic challenges for emergency response and evacuation\u003csup\u003e23\u003c/sup\u003e. We found that individuals who attributed wildfires to climate-related factors were more likely to believe government health interventions are effective, suggesting that environmental literacy can foster institutional trust. When people view wildfires within a broader ecological and climatic framework, they may interpret institutional actions as more legitimate and impactful\u003csup\u003e3\u003c/sup\u003e. Educational campaigns that clearly connect climate change to wildfire risk could enhance public engagement.\u003c/p\u003e\n\u003cp\u003eOur study found better concordance between direct and tangible protective measures, (masks, air filtration, clean-air shelters), than long-term or systemic interventions. While systemic solutions are essential for long-term resilience, their benefits may not be readily visible to the public during acute crises\u003csup\u003e24\u003c/sup\u003e. This discrepancy suggests a gap between public priorities and institutional investments. Bridging this perception gap requires better communication strategies that clearly link systemic interventions to perceived personal and community health outcomes.\u003c/p\u003e\n\u003cp\u003eClear communication was essential for institutional trust and effective preparedness. Social media and neighbor-based discussions improved acceptance of official messaging, emphasizing the value of integrated, multimodal communication networks. Wildfire communication strategies that blend digital, interpersonal, and traditional channels may enhance reach and credibility across diverse populations\u003csup\u003e25\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHealth impacts of wildfires were widely reported\u003csup\u003e26\u003c/sup\u003e. Exposure to wildfire smoke is associated with respiratory and cardiovascular risks, especially among vulnerable groups\u003csup\u003e27\u003c/sup\u003e. Post-wildfire runoff introduces contaminants into drinking water\u003csup\u003e28,29\u003c/sup\u003e. Settings with stronger infrastructure could mitigate these effects through coordinated interventions (e.g., air quality monitoring, advisories, clean-air shelters), supported by institutionalized, multisectoral systems\u003csup\u003e30\u003c/sup\u003e. In contrast, less supported areas depend on adaptive, informal or mixed strategies which may lack resilience and scalability \u003csup\u003e31\u003c/sup\u003e .\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStrengths\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study highlights the strength of examining both institutional capacity and community adaptation. Regions with well-developed infrastructure demonstrated timely communication and robust public health systems that were perceived as effective wildfire governance. While regions with less-developed infrastructure did not, mitigating factors including flexible informal networks, were perceived as valuable, underscoring the need for integrated, context-sensitive, multi-channel preparedness strategies. The cross-sectional design captures a timely snapshot of perceptions, revealing critical gaps and strengths in governance and community response.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe sample size varied across regions and relied on self-reported data. The convenience and snowball sampling methods may have introduced selection bias. Not all questionnaires were complete; while validated strategies were used for missing data, errors may have been introduced. Despite these constraints, the findings contribute context-specific knowledge to inform more adaptive and inclusive wildfire management strategies.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provides a multi-scalar, multi-method examination of how public perceptions, communication strategies, and institutional responses shape wildfire preparedness and health outcomes. Our findings underscore the urgency of integrating decentralized, context-specific communication, strengthening psychological preparedness, and aligning institutional interventions with public expectations. Given the increasing frequency and intensity of wildfires under climate change, policymakers must prioritize inclusive, adaptive, and community-informed approaches to emergency management. Investments in social infrastructure, particularly communication networks, trust-building mechanisms, and participatory planning, are as critical as physical infrastructure in mitigating the health and societal impacts of wildfires.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthical approval\u003c/h2\u003e \u003cp\u003e for this study was obtained from the Ethics Review Committee of COMSATS University, Islamabad. Members volunteered and the study obtained the informed consent of all respondents.\u003c/p\u003e \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFAO, Wildfires (2010) \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.undrr.org/understanding-disaster-risk/terminology/hips/en0013#:~:text=Wildfires%20are%20\u003c/span\u003e\u003cspan address=\"https://www.undrr.org/understanding-disaster-risk/terminology/hips/en0013#:~:text=Wildfires%20are%20\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e any%20unplanned%20or,adapted%20from %20FAO%2C%202010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlobal. 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RMS 19(5):1447\u0026ndash;1476\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1: Government effectiveness in managing wildfire risk, health impacts, and air quality\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 221px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutcome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 403px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eConditions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"10\" style=\"width: 221px;\"\u003e\n \u003cp\u003eGovernment Effectiveness on Wildfire Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 403px;\"\u003e\n \u003cp\u003eRisk Communication\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 403px;\"\u003e\n \u003cp\u003ePreparedness Info Effectiveness\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 403px;\"\u003e\n \u003cp\u003ePreparedness Resources Effectiveness\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 403px;\"\u003e\n \u003cp\u003eCommunity Initiatives\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 403px;\"\u003e\n \u003cp\u003eCommunity Plans Usability\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 403px;\"\u003e\n \u003cp\u003eCommunication Timeliness\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 403px;\"\u003e\n \u003cp\u003eCommunication Clarity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 403px;\"\u003e\n \u003cp\u003eInformation Sufficiency for Decisions\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 403px;\"\u003e\n \u003cp\u003eEvacuation Plan Effectiveness\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 403px;\"\u003e\n \u003cp\u003eEvacuation Communication\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"9\" style=\"width: 221px;\"\u003e\n \u003cp\u003eHealth Impact and Air Quality Management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 403px;\"\u003e\n \u003cp\u003eHealth Impact Response\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 403px;\"\u003e\n \u003cp\u003eAir Quality Monitoring\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 403px;\"\u003e\n \u003cp\u003eHealth Advisories\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 403px;\"\u003e\n \u003cp\u003eAir Purifier Distribution\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 403px;\"\u003e\n \u003cp\u003eClean Air Shelters\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 403px;\"\u003e\n \u003cp\u003eResearch \u0026amp; Reports\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 403px;\"\u003e\n \u003cp\u003eActivity Restrictions\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 403px;\"\u003e\n \u003cp\u003eEmergency Response Equipment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 403px;\"\u003e\n \u003cp\u003eMask Distribution\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" style=\"width: 221px;\"\u003e\n \u003cp\u003eGovernment Effectiveness on Wildfire Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 403px;\"\u003e\n \u003cp\u003eEvacuation Plan Effectiveness\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 403px;\"\u003e\n \u003cp\u003eEvacuation Communication\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 403px;\"\u003e\n \u003cp\u003eSocial media platforms (e.g., X, Facebook)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 403px;\"\u003e\n \u003cp\u003eOfficial Government Channels\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 403px;\"\u003e\n \u003cp\u003eTraditional Media\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 403px;\"\u003e\n \u003cp\u003eWord of mouth\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\u003cbr\u003e\u003cp\u003e\u003cstrong\u003eTable 2: Chi-square Test of Associations Between Wildfire Preparedness, Communication Tools, and Perceived Health and Safety Impacts\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"613\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 194px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 253px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026chi;\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep- value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 194px;\"\u003e\n \u003cp\u003eEvacuation plan safety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 253px;\"\u003e\n \u003cp\u003eRespiratory issues\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e43.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.0005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 194px;\"\u003e\n \u003cp\u003eSocial media platform\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 253px;\"\u003e\n \u003cp\u003eClear communication from authorities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.0005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 194px;\"\u003e\n \u003cp\u003eDistribution of air purifiers to vulnerable populations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 253px;\"\u003e\n \u003cp\u003eRespiratory issues\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e20.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.0005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 194px;\"\u003e\n \u003cp\u003eSufficient information\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 253px;\"\u003e\n \u003cp\u003eSocial media platform\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e6.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.0195\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 194px;\"\u003e\n \u003cp\u003eWord of mouth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 253px;\"\u003e\n \u003cp\u003eClear communication from authorities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e5.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.0225\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 194px;\"\u003e\n \u003cp\u003eClimate change\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 253px;\"\u003e\n \u003cp\u003eLocal/government and health impact\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e36.752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.0005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3: Poisson Generalized Linear Mixed Model (GLMM) Assessing the Effect of Government Role, Wildfire Drivers, and Social Media Use on Perceived Wildfire Impact\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"626\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 205px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEstimate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Error\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eZ value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePr(\u0026gt;|z|)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 205px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(\u003c/strong\u003eIntercept)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e3.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e24.464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 205px;\"\u003e\n \u003cp\u003eGovernment role\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e-1.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.256\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 205px;\"\u003e\n \u003cp\u003eWildfire drivers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e-2.414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 205px;\"\u003e\n \u003cp\u003eSocial media use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e6.832\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4: Overall Necessity Consistency Scores for Preparedness Conditions across All Cases\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"617\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCountry\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCRI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eECCP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTCOM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCOMM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSINF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eLos Angeles, USA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eCanada\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eHawaii, USA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eAsturias\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eRisk Awareness= RA; Personal Preparedness information=PPI; Personal Preparedness resources=PPR;\u003c/p\u003e\n\u003cp\u003eCommunity-wide initiatives related to risk reduction=CRI; Easiness of community-level plans=ECCP; Timely Communication=TCOM; Clear Communication=CCOM; Sufficient Information= SINF\u003c/p\u003e\n\u003cp\u003e1 Ontario, Quebec, Alberta, and British Columbia\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5: Overall Necessity Consistency Scores for Health and Air Quality Conditions\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"603\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCountry\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAQMS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePHAW\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDAPVP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTSCA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGFAQR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLOAE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eERUAFE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDFM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eLos Angeles, USA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eCanada\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eHawaii, USA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eAsturias\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAir quality monitoring systems=AQMS; Public health advisories and warnings=PHAW; Distribution of air purifiers to vulnerable populations=DAPVP; Temporary shelters with clean air systems=TSCA; Government-funded air quality research and reports=GFAQR; Limiting outdoor activities and events=LOAE; Emergency response units with air filtration equipment=ERUAFE; Distribution of face masks=DFM;\u003c/p\u003e\n\u003cp\u003e1 Ontario, Quebec, Alberta, and British Columbia\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6: Overall Necessity Consistency Scores for Evacuation and Communication Conditions\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"606\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCountry\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEVP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eECC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSMP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOGC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTMED\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWOM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eLos Angeles, USA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eCanada\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eHawaii, USA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eAsturias\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eEvacuation plan safety= EVP; Evacuation related communication clarity= ECC; Social media platforms (e.g., X, Facebook) =SMP; Official government channels (e.g., emergency alerts, websites) =OGC;\u003c/p\u003e\n\u003cp\u003eTraditional media (e.g., TV, radio) =TMED; Word of mouth (e.g., neighbors, friends, community members) =WOM;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e1 Ontario, Quebec, Alberta, and British Columbia\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"None","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Wildfire, Public health, Emergency preparedness, Evacuation behavior, Crisis response, Disaster communication, Risk perception","lastPublishedDoi":"10.21203/rs.3.rs-7004287/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7004287/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eClimate change and human activity are intensifying wildfires, endangering health, ecosystems, and infrastructure, especially in wild-urban interface regions. This study examined the impact of informal communication, public perception, and psychological readiness on preparedness and health outcomes in Los Angeles and Hawaii (USA), Asturias (Spain), and Canada. It also explored the roles of government response, social media, and perceptions of wildfire causes in shaping these outcomes.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA cross-sectional survey of 156 participants from wildfire-prone areas was conducted using convenience and snowball sampling. A 77-item questionnaire in English or Spanish assessed health, preparedness, and communication. Data analysis included descriptive statistics, chi-square tests, and Poisson Generalized Linear Mixed Models (GLMM). Chi-square analysis was used to test hypotheses about associations between perceived evacuation safety, communication tools, health impacts, and government response effectiveness. Poisson GLMM examined whether perceived wildfire impact was predicted by social media use, perceived wildfire drivers, and perceived government role. Fuzzy-set Qualitative Comparative Analysis (fsQCA) identified region-specific condition combinations linked to effective wildfire preparedness and governance outcomes.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eParticipants rated wildfire preparedness and communication as moderately effective, with only 19% finding evacuation plans very accessible. Over half reported physical (61%) or respiratory (58%) symptoms. Statistical models linked evacuation confidence and air purifier distribution to reduced respiratory issues. Social media information and community engagement increased trust but contributed to confusion. Social media significantly influenced perceived wildfire impact, while perceptions of government response did not. Fuzzy-set analysis revealed context-specific governance patterns, with both institutional and informal strategies shaping effective wildfire responses across regions.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study highlights critical gaps in wildfire preparedness, health protection, and emergency communication, especially in high-risk areas. Strengthening multi-channel communication, infrastructure, and community engagement is essential amid escalating climate-driven wildfire threats.\u003c/p\u003e","manuscriptTitle":"Taking the \"Wild\" out of Wildfires - Harnessing Data to Predict, Prevent, and Prepare for the Future","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-01 10:12:48","doi":"10.21203/rs.3.rs-7004287/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"04d8b992-b817-4ba6-8e68-b8acb9ecdca6","owner":[],"postedDate":"July 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-01T10:12:48+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-01 10:12:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7004287","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7004287","identity":"rs-7004287","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}