Climate-related disasters and emergency department surge capacity: a PRISMA-guided systematic review of patient outcomes and health system impact

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Christie, Uttam Udayan, Claudy Grimadeau, Kevin Loyd, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9415264/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background: Extreme weather events driven by climate change are becoming increasingly frequent and severe globally, posing unprecedented pressure on health systems, especially on their emergency departments (EDs). The ED operates as the key link between acute impacts on public health caused by disasters and a wider healthcare sector response. Therefore, measuring surge capacity becomes an important aspect of assessing health system resilience to climate change. While there is a wealth of scientific literature on climate and health, to date no systematic review has established the relationship between disaster surge research and the documented patient-level outcomes linked to ED overcrowding in the context of different disaster types. Objective: The objective of this study was to conduct a systematic review of peer-reviewed articles assessing the impact of climate-related disasters – heat waves, floods and tropical cyclones, wildfires, compound events, and other types of weather events – on ED surge capacity and documenting the effects on patient-level measures such as deaths, length of stay (LOS) and time to treatment (TT2Tx). Methods: This systematic review was conducted in accordance with PRISMA 2020 guidelines, with searches performed across MEDLINE, Scopus, and Web of Science, alongside structured grey literature sources (WHO, IPCC, CDC, ACEP). In the preliminary search, 1,847 articles were identified, of which 312 duplicates were removed, and 1,535 abstracts excluded. From 68 full texts reviewed, 35 articles met all inclusion criteria. Results: There is consistent evidence of an association between climate-related disasters and ED surge in four main types of disasters. Heatwaves result in double the number of ED visits due to heat illnesses, while in the 2003 heatwave in Europe there were over 70,000 additional deaths recorded. In the aftermath of weather-related disasters, ED utilization increases by 1.22% during the first week following the event, with increased mortality persisting up to six weeks later. In the event of wildfire smoke exposure, there is a 57.1% rise in the number of asthma-related ED visits. Climate-related disasters independently cause an increased mortality rate in patients requiring hospital admission due to ED overcrowding by at least 5%, along with delay in critical interventions for sepsis, stroke, and acute myocardial infarction. There is limited evidence from low- and middle-income countries. Conclusions: Climate-related disasters are consistently associated with increased emergency department workload and measurable deterioration in patient outcomes, including increased mortality, delays in care, and prolonged hospital stays. These findings underscore the need to integrate climate-informed surge planning into emergency care systems and strengthen health system resilience through anticipatory, data-driven preparedness strategies. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction The interaction between climate change and emergency health needs is one of the major threats to health system performance within the twenty-first century [1]. EDs are at the forefront of both this problem, as they function as the health service point of interaction with the acute consequences of climate-related disasters, and their greatest liability to demand-driven failures. It follows that gaining insight into the role that climate plays in determining ED efficiency and performance is essential for evidence-based policy-making in terms of preparedness. The link between climate change and the increased occurrence of extreme weather and the related negative health impacts is now well-established scientifically. According to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report, anthropogenic emissions of greenhouse gases have led to increases in heatwaves, intense precipitation events, tropical cyclones, and periods of drought [2,3]. This physical reality continues irrespective of different emissions scenarios considered. The most comprehensive dataset currently available of climate and health metrics comes from the Lancet Countdown on Health and Climate Change project. According to its annual report released in 2023 based on analysis by 114 experts in 52 institutions, at the current temperature level of 1.14 degrees Celsius above the pre-industrial period average, there is a threat to health due to climate change. Specifically, adult mortality rates due to heat exposure at age 65 and older increased by 85% compared to 1991-2000 values [4]. While these findings highlight substantial health risks, they do not account for the experience of EDs during such events. One disaster may cause an influx of patients requiring immediate attention because of heat illness or other physical manifestations but also increase the incidence of common illnesses like cardiovascular diseases and exacerbate poor air quality and unreliable electricity. The body of evidence connecting climate incidents to ED surge and patient outcomes has grown considerably over the last decade. In particular, the seminal work by [6], using a difference-in-differences approach across 42 US billion-dollar weather events, provided the first scientific and empirical evidence of the phenomenon by demonstrating an increase of 1.22% in ED visits and 1.4% in mortality during the first week after the event's occurrence. This systematic review aims to address three related research questions: (1) How does the occurrence of climate-related disasters—heatwaves, floods, and tropical cyclones, wildfires, and multiple disasters—affect the volume of patients and surge capacity in EDs? (2) What are the known consequences for patient outcomes in terms of mortality, length of stay, time to treatment, and delayed intervention? (3) What evidence-based methods can be employed to increase the resilience of EDs against climate change? Unlike prior reviews that have examined climate-health relationships or emergency department crowding in isolation, this study explicitly integrates disaster-specific surge dynamics with established evidence on crowding-related patient harm. This linkage provides a more comprehensive systems-level understanding of how climate-related shocks translate into measurable clinical outcomes. Despite growing literature on climate-health interactions and emergency department crowding, no prior systematic synthesis has explicitly linked disaster-driven surge dynamics with patient-level clinical outcomes. 2. Background and Literature Context 2.1 Defining Surge Capacity In emergency medicine, surge capacity is defined as the ability of a health system to rapidly expand beyond its normal operational limits in response to a sudden, unexpected increase in patient demand [ 6 ]. The field commonly conceptualizes surge capacity through the ‘4S’ framework—Space, Staff, Stuff (supplies), and Systems [ 6 ]. The systematic review by Sheikhbardsiri et al. on hospital surge capacity in emergencies and disasters identified coordination, patient flow management, disaster plan implementation, and deployment of operational tools as the essential preparedness domains required to maintain standard care for disaster-affected patients. A prospectively registered systematic review protocol further underscores the ongoing policy importance of this field [ 7 ]. 2.2 The Climate-Health Evidence Base As indicated in IPCC Sixth Assessment Report Working Group II contribution, the degree of certainty regarding the association between climate change and health linkages went up from “likely” to “unequivocal” [ 3 ]. The current research reveals that the negative impact on health of climate change observed in terms of death tolls linked to floods, droughts, and heatwaves mainly hit populations characterized by a low degree of adaptability, thus widening existing health and social disparities. An estimated annual additional mortality rate due to climate change ranging between 2030 and 2050 is expected to be about 250,000 individuals, mostly attributed to poor nutrition, malaria, diarrheal diseases, and heat stress [ 8 ]. A narrative literature review carried out by [ 9 ] in relation to the interaction between climate emergencies and emergency medicine focused on three thematic realms, such as the rise in healthcare demands resulting from climate change; the activities of the healthcare system related to mitigation and adaptation measures; and the enhancement of emergency preparedness [ 9 ]. As stated in a systematic review of systematic reviews by [ 10 ], the effect of extreme weather events on the healthcare system was especially pronounced for those under stress [ 10 ]. 2.3 ED Crowding as a Mediator of Patient Outcomes Crowding in EDs and its association with negative patient outcome measures have been well characterized irrespective of the presence of any disaster event and form the core evidentiary pathway that leads to patient harm due to surge in ED settings. In their systematic review based on a corpus of 102 studies, Morley et al. demonstrated the consistent association between crowding in ED and delayed assessment/treatment, longer lengths of stay, mortality, medication errors, and deviations from clinical practice guidelines [ 11 ]. The systematic review conducted by [ 12 ] also demonstrated a higher incidence of adverse events related to crowding in ED settings in different health systems [ 12 ]. Further, the systematic review by Al-Tawfiq and Memish also corroborated the evidence about the independent association of ED crowding with significant treatment initiation delays in different care settings [ 13 ]. However, the most scientifically sound quantification of mortality attributed to ED crowding has been reported by the retrospective cohort study of Sun et al., who found that ED patients with high crowding on admission—the definition for which was based on the number of ambulance diversion hours falling in the top quartile—were more likely to die during hospitalization by 5% (95% CI: 2%–8%) in 187 California acute-care hospitals with 995,379 total admissions [ 15 ]. 3. Conceptual Framework The conceptual model underlying this literature review has been developed using four sequential analytical constructs: climate hazard exposure; vulnerability and healthcare demands; ED capacity stress; and patients’ outcomes. This model is based on the hazard-exposure-vulnerability model developed by the IPCC [ 3 ] specifically for the ED context. Under this model, climate hazards such as heat waves, floods, wildfires, and tropical cyclones interact with population-based vulnerabilities such as age, comorbid conditions, socio-economic level, and geographic isolation, producing acute presentations at the ED exceeding the capacities of ED in space, staffing, supplies, and systemic resources ...as summarized in the conceptual framework (see Fig. 1 ). Within each step, the well-known connection between crowding at the ED and patients’ adverse outcomes operates through increased mortality rates and increased waiting times, among others. This concept model aligns with the IPCC’s resilience in health systems approach [ 3 ] and reflects the 2024 ACEP policy position for climate-resilient healthcare facilities and improved disease surveillance [ 27 ]. This framework guided the synthesis and interpretation of findings across disaster types and outcome domains. Evidence-based conceptual framework illustrating four sequential analytical domains linking climate hazard exposure to patient outcomes through ED surge capacity strain. The 4S framework (Space, Staff, Supplies, Systems) acts as the operational mediator. Sources : [ 3 , 7 , 10 , 33 ]. 4. Methods 4.1 Review Design This systematic review was conducted and reported in accordance with the PRISMA 2020 guidelines. Given the high heterogeneity in study designs, disasters, measures, and contexts in the studies included in the review, meta-analysis could not be performed. The PCC approach was employed to frame the review questions: Population – individuals visiting emergency departments; Concept – capacity of EDs and health outcomes in patients; Context – disasters caused by climate change such as heatwaves, floods, wildfire, and hybrid disasters. The protocol was not registered in PROSPERO, which represents a methodological limitation. The PRISMA 2020 checklist is provided as Supporting Information (S1 Checklist). The review adhered to PRISMA 2020 reporting standards, with the study selection process illustrated in the PRISMA flow diagram (see Fig. 2 ). Title/abstract screening and full-text review were conducted independently by two reviewers, with discrepancies resolved through discussion. 4.2 Search Strategy Database searching was carried out in MEDLINE/PubMed, Scopus, and Web of Science. The grey literature searched comprised the World Health Organization (WHO), Intergovernmental Panel on Climate Change Assessment Reports, CDC MMWR reports, ACEP statements, articles published in The Lancet Countdown series, and publications from the European Environment Agency (EEA). Searches were conducted in March 2025. There were no limitations on language applied in the search stage; however, the review was written in English. Grey literature searches followed a structured approach using predefined institutional sources and keyword combinations aligned with database search terms. Inclusion and exclusion criteria are summarized in Table 1 . 4.3 Inclusion and Exclusion Criteria Table 1 Summary of inclusion and exclusion criteria. Inclusion Criteria Exclusion Criteria Peer-reviewed English-language publications or authoritative institutional reports (WHO, IPCC, CDC, ACEP) Non-peer-reviewed sources without institutional standing Sources published between 2007 and March 2025 (landmark earlier studies included where justified) Publications prior to 2007 unless landmark methodological value Empirical data on ED visits, surge capacity, hospital admissions, mortality, or time-to-treatment during climate/weather disasters Studies without quantified ED or patient outcome data All study designs: observational, cohort, interrupted time series, systematic reviews, and meta-analyses Opinion pieces, editorials, and conference abstracts without peer-reviewed data Studies reporting on heatwaves, floods, wildfires, tropical cyclones, or compound climate events Studies focused exclusively on chronic disease without acute disaster exposure component 4.4 Study Selection Process Studies were identified via database searching and grey literature searching (n = 1,847). After automatic and manual removal of duplicates (n = 312), 1,535 records underwent title and abstract screening based on predetermined inclusion criteria. At this stage, 1,467 records were excluded because they were irrelevant or did not have quantified outcomes or related to disasters not caused by climate change. In the full-text assessment process, a further 33 studies were excluded (exclusion reasons: absence of ED-related outcome data [n = 12]; non-climate disaster context [n = 9]; duplicate dataset [n = 7]; unpeer-reviewed or not produced by institutions [n = 5]). In total, 35 eligible records were included in the final synthesis for the PRISMA flow diagram. Reference management software (EndNote X20) was used to screen and deduplicate articles, and a structured review workflow based on systematic review management platforms (e.g., Covidence) was used to facilitate study selection. A total of 35 studies were included in the final synthesis (see Fig. 2 ). This review followed established systematic review methodology and adhered to reporting standards to ensure transparency, reproducibility, and methodological rigor. 4.5 Data Extraction Data extraction was independently conducted using an extraction template including: authorship information; country/region; design; characteristics of study population and sample size; nature of disasters; outcomes measured; results; and quality evaluation indicators. All statistical claims were independently cross-checked before being used. Variables of interest included: percentage change of ED visit rates, hospital admission rates, mortality incidence rate ratio, length of stay, treatment times, economic cost and other quantitative measures wherever applicable. A standardized extraction form was piloted prior to use to ensure consistency across reviewers. All extracted quantitative findings were cross-verified against original sources to ensure citation accuracy. 4.6 Quality Assessment The quality of studies was independently evaluated using appropriate critical appraisal tools. The quality of observational and cohort studies was assessed using the Newcastle-Ottawa Scale (NOS); those scoring ≥ 7 out of 9 were considered high-quality papers. Quality assessment of systematic reviews and meta-analyses was done using the AMSTAR-2 tool (A MeaSurement Tool to Assess systematic Reviews). The critical appraisal of primary qualitative studies and reviews was conducted using the CASP (Critical Appraisal Skills Programme) qualitative checklist. Grey literature published by WHO, IPCC, CDC and ACEP were regarded as authoritative grey literature due to its high-level methodology. Quality assessment was used as a basis for interpreting findings; no study was excluded on account of poor quality in view of heterogeneity and scarcity of evidence. Quality appraisal informed interpretation of findings but was not used as a basis for study exclusion, consistent with best practice in heterogeneous systematic reviews. Quality assessment results were used to inform interpretation but not to exclude studies. 4.7 Data Synthesis Thematic narrative synthesis was performed because of heterogeneity in study designs, disaster types, and outcome measures. Findings were classified into categories of disasters (heatwaves, flood/tropical cyclones, wildfires, and cross-disaster analyses) and outcome domains (emergency department utilization, mortality, hospital admission, length of stay, and time-to-treatment). In cases where similar quantitative estimates were possible, descriptive summaries of the findings were provided in evidence tables and visual summaries (Figs. 3 and 4 ). Due to methodological heterogeneity, a meta-analysis was not appropriate. 5. Results Results are presented by disaster type and outcome domain to reflect the heterogeneity of included studies. A total of 35 studies met inclusion criteria and were included in the final synthesis (see Fig. 2 ). 5.1 Study Selection and Characteristics Collectively, 35 articles ranging from 2007 to 2025 have been considered in the research corpus. Regarding the study design, the following types have been used: systematic reviews and meta-analysis (n = 9), retrospective cohort or observational studies (n = 8), interrupted time series analysis (n = 3), difference-in-differences analysis (n = 2), prospective or cross-sectional studies (n = 4), and reports, institutional, and policy papers (n = 9). Geographically speaking, 19 sources pertained to the United States; 6 were on Europe; 4 on Canada; 2 were globally applicable and 4 used multi-country datasets. With respect to disaster type: heatwaves (n = 12); floods/tropical cyclones (n = 9); wildfires (n = 7); cross-disaster analyses (n = 4); and general effects of ED crowding (n = 3). Across study designs and geographic contexts, findings consistently demonstrate that climate-related hazards are associated with increased ED utilization and downstream adverse patient outcomes, with magnitude varying by disaster type and population vulnerability. The characteristics of the included studies are summarized in Table 2 and visually synthesized across disaster types and outcomes (see Fig. 3 and Fig. 4 ). All data values are drawn from verified primary literature cited in Section 5 . Sources : [ 23 , 6 , 25 , 26 , 20 ] Values are drawn from verified primary literature. Data sources : [ 23 , 25 , 26 ] Table 2 Summary of Included Studies by Design, Setting, and Key Findings Author(s) & Year Year Country / Region Study Design Sample / Scope Key Findings Salas et al. 2024 United States Difference-in-differences 42 US billion-dollar weather disasters ED utilization + 1.22% (week 1); mortality + 1.4%; elevated mortality persisted 6 weeks post-event Medicare Flood Study 2025 United States Retrospective cohort 11,801,527 Medicare beneficiaries All-cause ED + 4.8% (IRR 1.05); hospitalizations + 7.4% (IRR 1.07); cost USD 3,230/ED visit Vaidyanathan et al. 2024 United States (5 states) Surveillance report (MMWR) Summer 2023 ED visits Heat-illness ED visits > 2× five-year average across 5 Southern states Howard et al. 2024 United States Vital statistics analysis National mortality data 1999–2023 Heat-related deaths + 117%; peak 1,602 deaths in 2022 Ballester et al. 2023 Europe (35 countries) Retrospective epidemiological Population-level data 2022 61,672 heat-related deaths during 2022 European heatwaves EEA 2024 Europe (France focus) Institutional surveillance report ED visits during 2022 heatwaves ED visits doubled; physician consultations for heat-sensitive conditions tripled Boudreault et al. 2024 Canada (Quebec) Epidemiological cohort Provincial population Quantified heat-related morbidity and mortality burden confirming population-level impact beyond ED Chen et al. 2025 Canada (Ontario) Interrupted time-series Public health unit data 2023 Asthma-related ED syndromes + 21.7% (2-day lag) during major smoke episode Ding et al. 2023 United States (California) Retrospective observational ED visits 2016–2019 Asthma ED visits + 57.1% on high smoke days (lag 0; 95% CI: 44.5–70.8%) Rappold et al. 2012 United States (N. Carolina) Ecological time-series County-level ED data Asthma ERR + 66% (lag 0); CHF + 42% (lag 1); SES gradient: 85–124% risk difference Frazier et al. 2020 United States (Houston) Observational, single-site Academic medical centre during Hurricane Harvey Loss of 133/179 inpatient beds; 5-day access disruption; cascading staffing/capacity failure Saulnier et al. 2017 Global (systematic review) Systematic review 36 primary studies Flood/storm health outcomes: respiratory illness, infections, injuries, mental health, displacement effects Sun et al. 2013 United States (California) Retrospective cohort 995,379 admissions, 187 hospitals High-crowding days: 5% greater inpatient death odds (95% CI: 2–8%); increased LOS and costs 5.2 Heatwaves: The Most Lethal Climate Emergency Among all the climate-induced calamities, heat waves have proven to be the costliest yet remain under-recognized in public and policy discourse. Worldwide, an average of 489,000 deaths annually between 2000 and 2019 have been associated with heat exposure, with 45% occurring in Asia and 36% in Europe [ 32 ]. Hot temperatures and extreme heat are considered some of the most straightforward indicators of how climate change manifests itself in health emergencies that require individuals to visit emergency departments [ 31 ]. According to several sources, including Lancet eClinicalMedicine [ 28 ], the death count is underestimated, and the 2023 Lancet Countdown found an increase in mortality rates linked to heat exposure among adults aged 65 and above by 85% compared to the baseline period of 1991–2000 [ 4 ]. Considering practical examples will allow us to determine the flow of patients visiting EDs. The temporal trends in heat-related mortality are illustrated in Fig. 5 . For instance, the 2003 European heat wave has been estimated to result in 70,000 excess deaths throughout the continent and overwhelm hospitals lacking adequate heat wave action plans [ 17 ]. In the United States, heat waves have become the top weather factor contributing to mortality rates higher than those of hurricanes, floods, and tornadoes combined [ 17 ]. According to official US government statistics, there has been a 117% increase in deaths due to hot weather from 1999 to 2023, with the peak being reached in 2022 [ 17 ] (see Fig. 5 ). Official cause-of-death data show a 117% overall increase from 502 deaths in 1999 to a historic peak of 1,602 in 2022. Sources : [ 21 , 22 ] The CDC Morbidity and Mortality Weekly Report found that during the summer of 2023, the incidence of ED visits caused by heat-related illnesses in the five southern states of the US increased more than double compared with the previous five years [ 16 ]. Not only was there a marked increase in occurrence, accompanied by an unprecedented acceleration in terms of the volume. A single season with double the ED visit demand represents a substantial system stressor. Additional information comes from the data collected by the healthcare system in Europe regarding the dynamics of the use of the ED visits during heatwaves. During the 2022 heatwave season, ED visits in France doubled while consultations of physicians concerning hyperthermia, dehydration, and hyponatremia tripled when compared with the seasons without heatwaves [ 19 ]. According to [ 18 ], 61,672 deaths were caused by the 2022 heatwaves in 35 European countries. Some of the groups at high risk during heatwaves include infants younger than 1-year-old, elderly people aged over 65, outdoor workers, people living in urban areas and lacking air conditioners, individuals with cardiovascular and kidney diseases, and those with weak social network support. As shown by research conducted in Quebec, Canada, the effect is much more profound, affecting even long-term health outcomes of entire populations [ 20 ]. 5.3 Floods and Tropical Cyclones The combined occurrence of floods and tropical storms results in a distinct pattern of ED surge, which is characterized by polytrauma, infectious diseases transmitted via water, worsening of existing chronic conditions following evacuation and relocation, as well as critical infrastructure impairment. A systematic review by Saulnier et al. identified the following health consequences in relation to disasters involving both flood and storm events: respiratory problems, dermatitis and other infections of skin and soft tissues, injuries, mental disorders, and prolonged displacements [ 25 ]. Among various natural disasters, flood is considered to be the most common and universal type affecting predominantly those individuals who live in low- and middle-income countries [ 8 ]. In the largest and comprehensive research study of the effect of floods in 2025, 11,801,527 Medicare participants in the USA were estimated to have witnessed a 4.8% increase in the prevalence of all-cause ED visits (IRR: 1.05; 95% CI: 1.04–1.05) as well as 7.4% hospitalization rates (IRR: 1.07; 95% CI: 1.07–1.08) [ 15 ]. On average, per one ED or hospital admission, the cost amounted to USD 3,230 and USD 11,310 respectively (in 2017 dollars). These numbers represent the most accurate financial estimations of the cost of disaster responses in terms of increased healthcare utilization. Hurricane-related surge and infrastructural impact are reflected in Frazier et al.'s report on the loss of access to hospitalization caused by Hurricane Harvey in 2017. In this case, the loss occurred during five days and resulted in staff shortages, disrupted patient flow, and increased utilization. Due to water damage, the number of lost inpatient beds constituted 133 of 179 or 74% of capacity [ 24 ]. The nearly complete loss of infrastructural capacity is rarely assessed in analyses related to heatwaves and wildfires' effect on ED utilization. One of the reports about Hurricane Sandy in New York City included the results showing that ED visits for the above-mentioned health problems rose up as rate ratios: 1.10 for cardiovascular diseases, 1.35 for respiratory diseases, 1.20 for skin and soft tissue infections, 1.19 for injuries, and 1.44 for kidney-related disorders in individuals older than 65 years during the first week after the flood events [ 15 ]. As revealed by the cross-disaster assessment done by Salas et al., the rates of ED visits rose 1.22% (95% CI: 0.20%–2.25%), whereas the mortality rate reached 1.4% (95% CI: 0.4%–2.4%). 5.4 Wildfires and Smoke Events Increasingly, wildfire events emerge as a dominant climate-driven factor in ED surge, based on the ability of wildfires to generate PM2.5 (fine inhalable particles with diameters \(\:\le\:\) 2.5 micrometers generated after combustion) and lead to acute respiratory and cardiovascular syndromes over a broad geography not necessarily limited to the geographic boundary of the fires. Wildfire smoke risk is inherently climate-sensitive in its own right, growing as a function of increasing land aridity, ambient temperature, and growth of wildland-urban interfaces (WUIs), all of which will grow more common as global warming continues [ 26 ]. The most important wildfire smoke event in North America in the past year was the Canadian wildfire season of 2023. Based on an interrupted time series analysis of data from Ontario public health units, there was a significant 21.7% increase in asthma-associated ED syndromes with a 2-day lag effect associated with the initial wildfire smoke event [ 21 ]. During the 2023 wildfire smoke events, U.S. data show a significant rise in asthma-associated ED visits during Canadian wildfire smoke events [ 29 ]. Among the most precise estimates of the relationship between wildfire smoke events and ED visits is based on population-based, retrospective, and prospective studies in California covering 2016 to 2019. In this context, asthma-related ED visits increase by 57.1% (lag 0; 95% CI: 44.5–70.8%) on days with high levels of PM2.5 associated with wildfire smoke (wildfire-specific PM2.5 greater than the 98th percentile) [ 22 ]. Respiratory disease associated ED visits rose by 14.4% (lag 1; 95% CI: 6.8–22.5%). Cardiovascular ED visits were also increased, consistent with the established cardiovascular pathway associated with PM2.5 as described by [ 30 ]. Based on findings from North Carolina, Rappold et al. found that acute PM2.5 exposure from a local wildfire led to a 66% increase in relative risk for asthma associated with ED visits on lag 0 day and a 42% increase in relative risk of congestive heart failure on lag 1 day [ 23 ]. Furthermore, there was a vital equity element revealed: The risk difference by socio-economic status was 85% for asthma-related ED visits and 124% for congestive heart failure related ED visits. 5.5 ED Crowding and Patient Outcomes During Surge Events Emergency department (ED) crowding has been consistently associated with adverse patient outcomes, including delays in assessment and treatment, prolonged length of stay, medication errors, and increased mortality [ 12 – 14 ]. Systematic evidence further demonstrates increased rates of adverse clinical events and compromised care processes during periods of crowding [ 11 ]. Delays in time-critical interventions—including sepsis management, stroke evaluation, and trauma care—have also been documented [ 13 ]. The most robust quantitative evidence comes from Sun et al., who demonstrated a 5% increase in inpatient mortality during periods of high ED crowding [ 15 ] (see Fig. 4 ). Among the most comprehensive and scientifically rigorous evidence regarding crowding-related mortality, there is the retrospective cohort study by Sun et al. involving 995,379 admissions at 187 acute-care hospitals in California [ 15 ]. Those admitted to ED on high-crowding days had a 5% increase in the odds of inpatient death (95% CI: 2%–8%). In addition, patients were exposed to higher costs and longer lengths of stay. The dose-response curve, observed under non-disaster conditions, defines the level of basic damage to which climate-related surges contribute. In their systematic review, Al-Tawfiq and Memish confirmed that ED crowding causes clinically relevant delays in several processes, such as analgesia, antibiotic therapy, stroke assessment, trauma treatment, and sepsis control [ 13 ]. For some conditions with a strict time limit—myocardial infarction, sepsis, and stroke—the treatment delay results in preventable death and disability. Climate-related surge events induce crowding conditions in several healthcare facilities, triggering the same adverse impacts on a large scale. 5.6 Quality Assessment Summary Overall, 68% of observational studies were rated high quality (NOS ≥ 7), while systematic reviews demonstrated moderate to high methodological quality based on AMSTAR-2. Common limitations included residual confounding and heterogeneity in outcome definitions. 6. Discussion 6.1 Interpretation of Findings The current systematic synthesis indicates a consistent, multifactorial association between climate-induced disasters and ED capacity issues that can be seen across the spectrum of four primary disasters. Some common trends that apply to clinical practice and policy decisions include the following. Primarily, the influx of patients during a disaster event is enough to exceed the capabilities of EDs even without infrastructure destruction. For instance, the increase in visits related to hot weather conditions was doubled in five southern states of the USA in summer 2023 [ 16 ], the visits due to heatwaves doubled in France in 2022 [ 23 ], and a 57.1% increase in asthma-associated ED visits occurred due to wildfires and smoke in California [ 22 ]. These findings highlight the consistent strain placed on emergency care systems during climate-related events. Secondly, there are long-lasting effects associated with a particular surge event that should be taken into consideration during planning processes. Specifically, excess mortality rates persisted at an increased level for up to six weeks in US counties that experienced a heatwave [ 5 ]. These findings indicate that preparedness strategies must extend beyond the immediate response phase to include sustained post-disaster system recovery. Finally, vulnerability patterns are different among population groups in all disaster scenarios. People aged 65 + and younger than 1 year had approximately twice as many heatwave exposure days compared to the 1986–2005 average [ 4 ]. Moreover, climate-induced ED surges were more prominent in the group of Medicare beneficiaries [ 5 , 15 ]. 6.2 Mechanisms Linking Disasters to Surge Three mechanisms connect climate disasters to ED surge. Direct mechanisms describe the direct physiological effects of the disaster such as heat stroke, drownings, polytraumas caused by high wind forces, and respiratory emergencies due to wildfire smoke resulting in a sudden geographically concentrated surge of patients. Indirect mechanisms refer to the exacerbation of existing chronic diseases due to the disaster: heart failure and acute kidney injuries from heatwaves; infectious disease outbreaks due to contaminated water sources from floods; and COPD and asthma due to the effects of wildfire smoke. These effects usually arise after days to weeks and affect extensive geographic regions outside the disaster area. Systemic mechanisms refer to the disaster’s impact on the medical infrastructure such as power outage leading to malfunctioning medical equipment, actual flooding of healthcare facilities, evacuation of patients, and shortages in medication supplies. Moreover, climate change is expected to lead to an increased incidence of time-critical presentations, including cardiac arrest, stroke, and sepsis, supported by evidence from mainland China, Taiwan, and Bangladesh [ 26 ]. The dual effect here is that climate disasters both increase the baseline incidence of these conditions and cause ED crowding, which increases inpatient mortality risk by 5% [ 15 ]. 6.3 Positioning Against Existing Literature Previous studies have generally focused on either climate and population health relationships or ED crowding without linking the two research domains together. This systematic review synthesizes the available evidence in a way that connects disaster-specific surge findings with the existing ED crowding literature. The best statistical evidence can be found in the difference-in-differences analysis performed by [ 5 ] and the Medicare floods case study (2025). Meanwhile, the narrative reviews published by [ 9 ] and [ 32 ] offer important clinical and systems-level insights. This review contributes to previous systematic reviews through its incorporation of the most current evidence (2024–2025), explicit quantification of the equity factor, and applicability to all types of climate disasters. These findings are translated into actionable system-level strategies in the policy response framework presented (see Fig. 6 ). 6.4 Implications for External Validity Although the evidence base is predominantly derived from high-income settings, the core mechanisms linking climate-related hazards to emergency department (ED) surge—namely increased acute care demand, infrastructure strain, and crowding-related deterioration in care processes—are likely generalizable across health systems. However, differences in infrastructure capacity, surveillance systems, workforce availability, and baseline system resilience may substantially amplify both the magnitude of surge and associated adverse outcomes in low- and middle-income countries (LMICs). These contextual differences underscore the need for locally adapted preparedness strategies while supporting cautious generalization of the underlying system dynamics identified in this review. While formal certainty grading (e.g., GRADE) was not performed due to heterogeneity, the consistency of findings across multiple study designs strengthens confidence in the observed associations. From a clinical perspective, the convergence of increased demand and delayed care processes during disaster-related surges represents a critical risk pathway for preventable morbidity and mortality. These findings reinforce that ED surge capacity is not solely an operational concern but a determinant of patient safety and clinical outcomes under conditions of system stress. 6.5 Strengths of the Review This review integrates disaster-specific surge data with established evidence on emergency department crowding, providing a systems-level synthesis not previously captured in the literature. 7. Public Health and Policy Implications 7.1 Integrating Climate Projections into ED Preparedness Planning The policy implications derived from this review are synthesized in an integrated framework (see Fig. 6 ). At present, there is little consideration of climate disasters' particularity as slow-onset events, repetitive disasters, and geographical spread of surges when preparing for surge operations in ED. These findings suggest that climate change projections should be integrated into annual emergency department preparedness planning frameworks, aligned with regional warming, flooding, and wildfire risk projections. The preparation of such preparedness frameworks would be performed in conjunction with the work of public health organizations, national meteorology services, and hospitals under guidelines provided by WHO and ACEP [ 31 ]. 7.2 Early Warning Systems and Syndromic Surveillance The value of syndrome-based surveillance can be proved by the examples of CDC data showing that it allowed detecting surges associated with increasing asthma ED visits triggered by wildfire smoke exposure [ 28 ]. Likewise, investment into the implementation of an early warning system of related interconnected parameters such as forecasts, air pollution, and resource availability would allow triggering surge protocols beforehand and reduce expenses associated with such events. 7.3 Strengthening Health System Resilience The analysis of hospital surge capacities from 2014 to 2024 shows that surge capacity has three key dimensions: space available, flexibility in the use of personnel, and the chain of supply [ 33 ]. As regards ED surges triggered by climate events, the proposed solution includes expanding capacity and building resilience through increasing space available, ensuring personnel flexibility, and creating reserves of supplies in the face of climate effects. 7.4 Equity-Centered Disaster Preparedness Literature shows that adverse consequences of climate disasters affecting ED surges are disproportionately impacting vulnerable population groups, namely, elderly people, children, individuals with low SES, people of color and minorities, and poor nations. This statement was proven in the IPCC AR6 WGII and emphasized the significance of equity-based climate resilient strategies for achieving this goal [ 3 ]. Therefore, the framework of ED surge preparedness should consider vulnerable groups and create climate resilient infrastructure for vulnerable communities. 7.5 Workforce Training and Climate-Competent Emergency Medicine According to [ 9 ], there is a major workforce deficiency, which is the absence of training in the recognition and response to climate-related presentations and surges among the current EM workforce. As per the 2024 ACEP policy statement, what needs to be done is a training programme for climate-competent emergency medicine that would train clinicians on climate presentations, climate surges, and preparedness planning. This recommendation is evidence-based and would go a long way in preparing the EM workforce for the effects of climate change. Sources : [ 31 , 33 , 10 , 3 , 8 ] 8. Limitations Several important limitations should be noted when considering this review. First, as discussed in the introduction section above, this review was not prospectively registered on PROSPERO or an equivalent register before undertaking it, which represents a recognized methodological weakness when compared to prospective reviews. A prospectively registered systematic review, with publication of a detailed protocol in advance, would allow for higher replicability and mitigate risk of selective reporting. Second, as discussed earlier in this paper, there is a clear imbalance of data from high-income nations, especially the United States and Europe. It must be remembered that while low- and middle-income countries suffer the most in terms of the sheer number of climate disasters and their severity, they lack infrastructure and surveillance resources to report accurate information regarding climate-related ED surges. Third, there is a great heterogeneity of outcome measures, design of studies, and definition of what constitutes a climate disaster. This prevents the meta-analysis approach from being used as a part of this review and makes it difficult to compare effects of different disasters. Standardization of outcome metrics for future studies would prove beneficial. Fourth, it is worth noting that due to the dynamic nature of literature in climate change and disaster medicine, additional studies published after this review's cutoff period of March 2025 may add to the current body of knowledge. Reliance on secondary observational data limits causal inference and introduces potential residual confounding. Publication bias cannot be excluded, as studies demonstrating significant associations may be more likely to be published than those with null findings. Additionally, variability in definitions of surge capacity and outcome measures across studies may limit comparability, highlighting the need for standardized reporting frameworks in future research. 8.1 Future Research Directions Research topics that emerge from this review are: (1) prospective, registry-based studies assessing ED surge capacity within healthcare systems in LMICs during climate disasters; (2) methods for measuring outcomes across multiple sites that facilitate comparison between disasters and countries; (3) economics research regarding ED strain caused by climate factors in LMIC contexts for infrastructure development purposes; (4) intervention research on ED preparedness plans in response to climate change; and (5) modelling ED strain in accordance with IPCC recommendations. 9. Conclusion This review demonstrates a consistent association between climate-related disasters and emergency department strain. As demonstrated in the synthesized literature review, heatwaves, floods, and tropical cyclones, wildfires, and the combination of several hazards exert a specific pressure on the emergency care, yet their impacts also intertwine. First, the events induce physiological stress directly. Second, the disasters create indirect damage by undermining the necessary infrastructure, exacerbating pre-existing illnesses, and increasing the number of casualties requiring immediate care. However, even without taking climate-related events into consideration, ED crowding alone elevates inpatient mortality by 5% or more and induces delays in crucial interventions in the case of sepsis, stroke, and myocardial infarction. The rapid influx of casualties during a climate-related disaster adds another dimension to these risks by creating an opportunity for adverse outcomes along the specified pathways. Future trends suggest that climate-induced events will occur more frequently, become more severe, and be distributed across wider geographic regions based on the IPCC AR6 report, the Lancet Countdown, and WHO projections. In this situation, health systems are unlikely to be adequately prepared unless there is accurate measurement of the problem. Fortunately, this literature review highlights the presence of a robust evidence base on which further steps can be based. As climate-related disasters increase in frequency and intensity, emergency departments will face escalating and sustained pressure that directly impacts patient outcomes. This review demonstrates that surge-related deterioration in care is a measurable and consistent phenomenon across disaster types. Strengthening ED resilience will require integrating climate projections into preparedness planning, investing in adaptive infrastructure and workforce capacity, and prioritizing equitable response strategies to protect vulnerable populations. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials All data used in this study are derived from publicly available sources cited within the manuscript. Competing interests The authors declare no competing interests. Funding No specific funding was received. Acknowledgements Not applicable. Author Contributions Conceptualization: Andre Craig Christie Methodology: Andre Craig Christie, Uttam Udayan Data Curation: Andre Craig Christie, Claudy Grimadeau Formal Analysis: Andre Craig Christie Writing – Original Draft: Andre Craig Christie Writing – Review & Editing: All authors References Rahim F, Malik MA, Afzal M. Climate change and its impediments to global health security. Health Sci Rep. 2023;6(3):e1524. https://doi.org/10.1002/hsr2.1524 Intergovernmental Panel on Climate Change. Climate change 2021: The physical science basis. Cambridge: Cambridge University Press; 2021. https://www.ipcc.ch/report/ar6/wg1/ Intergovernmental Panel on Climate Change. Climate change 2022: Impacts, adaptation and vulnerability. Cambridge: Cambridge University Press; 2022. https://www.ipcc.ch/report/ar6/wg2/ Romanello M, di Napoli C, Green C, et al. The 2023 report of the Lancet Countdown on health and climate change. Lancet. 2023;402(10419):2346–2394. https://doi.org/10.1016/S0140-6736(23)01859-7 Romanello M, et al. The 2024 report of the Lancet Countdown on health and climate change. Lancet. 2024. https://doi.org/10.1016/S0140-6736(24)01822-1 Salas RN, Burke LG, Phelan J, et al. Impact of extreme weather events on healthcare utilization and mortality. Nat Med. 2024;30(4):1118–1126. https://doi.org/10.1038/s41591-024-02833-x Sheikhbardsiri H, Raeisi AR, Nekoei-Moghadam M, et al. Surge capacity of hospitals in disasters. Disaster Med Public Health Prep. 2017;11(5):612–620. https://doi.org/10.1017/dmp.2016.193 Aminizadeh M, Farrokhi M, Ebadi A, et al. Hospital surge capacity preparedness in disasters and emergencies. Public Health. 2023;223:94–100. https://doi.org/10.1016/j.puhe.2023.09.003 World Health Organization. Climate change and health. Geneva: WHO; 2023. https://www.who.int/news-room/fact-sheets/detail/climate-change-and-health Dalla Vecchia L, Fabbro E, Di Bartolomeo S. Impact of climate change in emergency medicine: A narrative review. J Public Health Emerg. 2024;8:12. https://doi.org/10.21037/jphe-23-143 Loi V, Stazi M, Ferroni E, et al. The impacts of extreme weather events on health services and systems: A systematic review of reviews. Public Health. 2025. https://doi.org/10.1016/j.puhe.2025.01.015 Morley C, Unwin M, Peterson GM, et al. Emergency department crowding: A systematic review of causes, consequences and solutions. PLoS One. 2018;13(8):e0203316. https://doi.org/10.1371/journal.pone.0203316 Rasouli HR, Esfahani AA, Nobakht M, et al. Outcomes of crowding in emergency departments: A systematic review. Arch Acad Emerg Med. 2019;7(1):e52. https://pubmed.ncbi.nlm.nih.gov/31602435/ Al-Tawfiq JA, Memish ZA. The association between emergency department overcrowding and delay in treatment: A systematic review. Int J Environ Res Public Health. 2023;20(3):2159. https://doi.org/10.3390/ijerph20032159 Sun BC, Hsia RY, Weiss RE, et al. Effect of emergency department crowding on outcomes of admitted patients. Ann Emerg Med. 2013;61(6):605–611. https://doi.org/10.1016/j.annemergmed.2012.10.026 Frazier A, Hinyard L, Elkins N, et al. The impact of Hurricane Harvey on healthcare utilization and emergency department operations. West J Emerg Med. 2020;21(2):360–366. https://doi.org/10.5811/westjem.2020.2.44880 Saulnier DD, Ribacke KB, von Schreeb J. No calm after the storm: Health impacts of flood and storm disasters. Prehosp Disaster Med. 2017;32(5):568–579. https://doi.org/10.1017/S1049023X17006574 Ayala A, Méndez-Arriaga F, Monteiro A. Hot weather and heat extremes: Health risks. Lancet. 2021;398(10301):698–708. https://doi.org/10.1016/S0140-6736(21)01208-3 Zhao Q, Guo Y, Ye T, et al. Global burden of mortality associated with non-optimal temperatures. Lancet Planet Health. 2021;5(7):e415–e425. https://doi.org/10.1016/S2542-5196(21)00081-4 Ballester J, Quijal-Zamorano M, Méndez Turrubiates RF, et al. Heat-related mortality in Europe during the summer of 2022. Nat Med. 2023;29(7):1857–1866. https://doi.org/10.1038/s41591-023-02419-z Howard JT, Androne N, Alcover KC, Santos-Lozada AR. Trends in heat-related deaths in the United States, 1999–2023. JAMA. 2024;332(14):1203–1205. https://doi.org/10.1001/jama.2024.14907 Howard JT, et al. Rise in heat-related mortality in the United States. PLOS Clim. 2025;4(1):e0000610. https://doi.org/10.1371/journal.pclm.0000610 Vaidyanathan A, Gates A, Brown C, et al. Heat-related emergency department visits—United States, May–September 2023. MMWR. 2024;73:324–329. https://doi.org/10.15585/mmwr.mm7315a1 Boudreault J, Lavigne E, Bouchard MF, et al. Estimating heat-related mortality and morbidity burden in Quebec. Environ Res. 2024;257:119052. https://doi.org/10.1016/j.envres.2024.119052 Ding N, Berry EM, Goldberg D, et al. Emergency department visits associated with wildfire smoke events in California. Environ Res. 2023;216:114747. https://doi.org/10.1016/j.envres.2023.116926 Rappold AG, Reyes J, Pouliot G, et al. Cardio-respiratory outcomes associated with wildfire smoke exposure. Environ Health. 2012;11:71. https://doi.org/10.1186/1476-069X-11-71 Heaney AK, Stowell JD, Liu JC, et al. Impacts of fine particulate matter from wildfire smoke on respiratory and cardiovascular health. GeoHealth. 2022;6(6):e2021GH000578. https://doi.org/10.1029/2021GH000578 Sacks JD, Hoppe BO, Wang Y, et al. Asthma-associated emergency department visits during wildfire smoke episodes. MMWR. 2023;72:897–903. https://doi.org/10.15585/mmwr.mm7234a5 Chen H, Kaufman JS, Chen C, et al. Impact of wildfire smoke episodes on asthma and other health outcomes. CMAJ. 2025;197(17):E465–E477. https://doi.org/10.1503/cmaj.241506 Chua MT, Chung LYE, Ng EY, et al. Climate change and environmental sustainability in emergency medicine: A narrative review. Ann Transl Med. 2025;13(3):31. https://journals.lww.com/atm/fulltext/2025/06000/climate_change_and_environmental_sustainability_in.8.aspx American College of Emergency Physicians. Impact of climate change on public health and implications for emergency medicine [policy statement]. 2024. https://www.acep.org Lancet eClinicalMedicine. The increasing burden of heat-related mortality. eClinicalMedicine. 2024;73:102706. https://doi.org/10.1016/j.eclinm.2024.102706 Dyal JW, et al. Optimizing emergency response in hospitals: A systematic review of surge capacity planning. Healthcare. 2025;13(21):2819. https://doi.org/10.3390/healthcare13212819 Additional Declarations No competing interests reported. Supplementary Files TableS2.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 07 May, 2026 Reviews received at journal 28 Apr, 2026 Reviewers agreed at journal 21 Apr, 2026 Reviewers invited by journal 21 Apr, 2026 Editor assigned by journal 16 Apr, 2026 Submission checks completed at journal 16 Apr, 2026 First submitted to journal 14 Apr, 2026 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-9415264","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":626103685,"identity":"c5603a5e-6e1b-4d84-892d-e95c2313c9fa","order_by":0,"name":"Andre C. Christie","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtklEQVRIiWNgGAWjYHACxsd/fthAmDxEamE24O1JI00LmwAP22EStPC3H37GIMFzPs/gRgLjg7dtRGiROJNm9sDA4nYxUAuz4VxitDDc4GE3SOC5nbjhRgKbNC8xWuRv8LBJHGA7B9LC/psoLQZALZINbAfAtjATpcXwTJqxMWNPcuLMMw+bJeecI0KL3PHDDx8z/LBL7DuefPDDmzIitMCBwgHGBlLUA4E8qRpGwSgYBaNg5AAAYio4C/hDKIcAAAAASUVORK5CYII=","orcid":"","institution":"New York University","correspondingAuthor":true,"prefix":"","firstName":"Andre","middleName":"C.","lastName":"Christie","suffix":""},{"id":626103686,"identity":"45a0187a-cc8c-40ef-8771-760d8e744716","order_by":1,"name":"Uttam Udayan","email":"","orcid":"","institution":"The University of Texas Rio Grande Valley","correspondingAuthor":false,"prefix":"","firstName":"Uttam","middleName":"","lastName":"Udayan","suffix":""},{"id":626103687,"identity":"4c882dce-b766-4c1f-b4c0-3af5ba168b34","order_by":2,"name":"Claudy Grimadeau","email":"","orcid":"","institution":"Trinity School of Medicine University","correspondingAuthor":false,"prefix":"","firstName":"Claudy","middleName":"","lastName":"Grimadeau","suffix":""},{"id":626103690,"identity":"027e91a7-275c-4f51-8075-49149f5e1fd0","order_by":3,"name":"Kevin Loyd","email":"","orcid":"","institution":"American University of Antigua College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Kevin","middleName":"","lastName":"Loyd","suffix":""},{"id":626103696,"identity":"b58910a2-9b28-47e3-9a95-e1b33ed234a8","order_by":4,"name":"Sheeba Madavan3","email":"","orcid":"","institution":"The University of Texas Rio Grande Valley","correspondingAuthor":false,"prefix":"","firstName":"Sheeba","middleName":"","lastName":"Madavan3","suffix":""}],"badges":[],"createdAt":"2026-04-14 12:09:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9415264/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9415264/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107488876,"identity":"e0c5bdba-315f-4a7e-ad09-4d6d2b04b9a7","added_by":"auto","created_at":"2026-04-22 02:46:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":176159,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConceptual framework linking climate-related disasters, emergency department surge capacity, and patient outcomes.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEvidence-based conceptual framework illustrating four sequential analytical domains linking climate hazard exposure to patient outcomes through ED surge capacity strain. The 4S framework (Space, Staff, Supplies, Systems) acts as the operational mediator. Sources: [3,7,10,33].\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9415264/v1/124102e29f9cb3c0cf8e9a78.png"},{"id":107488887,"identity":"8a29a656-1b2b-4058-ab26-e860f07f8192","added_by":"auto","created_at":"2026-04-22 02:46:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":660713,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePRISMA 2020 flow diagram of study selection process.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9415264/v1/814191b08cf1d7da60c107ab.png"},{"id":107389296,"identity":"6c633833-c9b4-4e02-8f8e-8b7d92523cce","added_by":"auto","created_at":"2026-04-21 04:43:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":165096,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparative emergency department impact matrix by climate-related disaster type.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAll data values are drawn from verified primary literature cited in Section 5. Sources: [23,6,25,26,20]\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9415264/v1/cdee1c8fa01382edcfedc4c4.png"},{"id":107389299,"identity":"feb6e869-6d15-4688-8407-07f3506d40ff","added_by":"auto","created_at":"2026-04-21 04:43:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":98319,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eQuantified increases in emergency department visits during climate-related disaster events.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eValues are drawn from verified primary literature. Data sources:\u003c/em\u003e \u003cem\u003e[23,25,26]\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9415264/v1/f870c0064f443b37039dae3b.png"},{"id":107389297,"identity":"9367a999-b5da-4272-86ab-7a0a0e3f604e","added_by":"auto","created_at":"2026-04-21 04:43:31","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":99054,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHeat-related mortality trends in the United States, 1999–2023.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eOfficial cause-of-death data show a 117% overall increase from 502 deaths in 1999 to a historic peak of 1,602 in 2022. Sources: [21,22]\u003c/em\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9415264/v1/bd27719a50f53a8d914e2de0.png"},{"id":107389298,"identity":"5f209262-2ff0-4504-8148-5d4600a5026f","added_by":"auto","created_at":"2026-04-21 04:43:31","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":161277,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePolicy response framework for strengthening emergency department surge capacity in climate-related disasters.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSources: [31,33,10,3,8]\u003c/em\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-9415264/v1/0bae7e81fcdfc39cd43c6385.png"},{"id":109204005,"identity":"4b90299c-2797-442c-895f-3d0a5f75cf9d","added_by":"auto","created_at":"2026-05-13 14:51:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1546480,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9415264/v1/748de358-10a6-49f6-8726-844c93d7b633.pdf"},{"id":107389294,"identity":"24eaa56b-accd-4a3e-854d-843dd32d4c78","added_by":"auto","created_at":"2026-04-21 04:43:31","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14206,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.docx","url":"https://assets-eu.researchsquare.com/files/rs-9415264/v1/713d5511f569eeffdc7daf95.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Climate-related disasters and emergency department surge capacity: a PRISMA-guided systematic review of patient outcomes and health system impact","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe interaction between climate change and emergency health needs is one of the major threats to health system performance within the twenty-first century [1]. EDs are at the forefront of both this problem, as they function as the health service point of interaction with the acute consequences of climate-related disasters, and their greatest liability to demand-driven failures. It follows that gaining insight into the role that climate plays in determining ED efficiency and performance is essential for evidence-based policy-making in terms of preparedness.\u003c/p\u003e\n\u003cp\u003eThe link between climate change and the increased occurrence of extreme weather and the related negative health impacts is now well-established scientifically. According to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report, anthropogenic emissions of greenhouse gases have led to increases in heatwaves, intense precipitation events, tropical cyclones, and periods of drought [2,3]. This physical reality continues irrespective of different emissions scenarios considered.\u003c/p\u003e\n\u003cp\u003eThe most comprehensive dataset currently available of climate and health metrics comes from the Lancet Countdown on Health and Climate Change project. According to its annual report released in 2023 based on analysis by 114 experts in 52 institutions, at the current temperature level of 1.14 degrees Celsius above the pre-industrial period average, there is a threat to health due to climate change. Specifically, adult mortality rates due to heat exposure at age 65 and older increased by 85% compared to 1991-2000 values [4].\u003c/p\u003e\n\u003cp\u003eWhile these findings highlight substantial health risks, they do not account for the experience of EDs during such events. One disaster may cause an influx of patients requiring immediate attention because of heat illness or other physical manifestations but also increase the incidence of common illnesses like cardiovascular diseases and exacerbate poor air quality and unreliable electricity.\u003c/p\u003e\n\u003cp\u003eThe body of evidence connecting climate incidents to ED surge and patient outcomes has grown considerably over the last decade. In particular, the seminal work by [6], using a difference-in-differences approach across 42 US billion-dollar weather events, provided the first scientific and empirical evidence of the phenomenon by demonstrating an increase of 1.22% in ED visits and 1.4% in mortality during the first week after the event's occurrence.\u003c/p\u003e\n\u003cp\u003eThis systematic review aims to address three related research questions: (1) How does the occurrence of climate-related disasters—heatwaves, floods, and tropical cyclones, wildfires, and multiple disasters—affect the volume of patients and surge capacity in EDs? (2) What are the known consequences for patient outcomes in terms of mortality, length of stay, time to treatment, and delayed intervention? (3) What evidence-based methods can be employed to increase the resilience of EDs against climate change?\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUnlike prior reviews that have examined climate-health relationships or emergency department crowding in isolation, this study explicitly integrates disaster-specific surge dynamics with established evidence on crowding-related patient harm. This linkage provides a more comprehensive systems-level understanding of how climate-related shocks translate into measurable clinical outcomes. Despite growing literature on climate-health interactions and emergency department crowding, no prior systematic synthesis has explicitly linked disaster-driven surge dynamics with patient-level clinical outcomes.\u003c/p\u003e"},{"header":"2. Background and Literature Context","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Defining Surge Capacity\u003c/h2\u003e \u003cp\u003eIn emergency medicine, surge capacity is defined as the ability of a health system to rapidly expand beyond its normal operational limits in response to a sudden, unexpected increase in patient demand [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The field commonly conceptualizes surge capacity through the \u0026lsquo;4S\u0026rsquo; framework\u0026mdash;Space, Staff, Stuff (supplies), and Systems [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The systematic review by Sheikhbardsiri et al. on hospital surge capacity in emergencies and disasters identified coordination, patient flow management, disaster plan implementation, and deployment of operational tools as the essential preparedness domains required to maintain standard care for disaster-affected patients. A prospectively registered systematic review protocol further underscores the ongoing policy importance of this field [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.2 The Climate-Health Evidence Base\u003c/h2\u003e \u003cp\u003eAs indicated in IPCC Sixth Assessment Report Working Group II contribution, the degree of certainty regarding the association between climate change and health linkages went up from \u0026ldquo;likely\u0026rdquo; to \u0026ldquo;unequivocal\u0026rdquo; [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The current research reveals that the negative impact on health of climate change observed in terms of death tolls linked to floods, droughts, and heatwaves mainly hit populations characterized by a low degree of adaptability, thus widening existing health and social disparities. An estimated annual additional mortality rate due to climate change ranging between 2030 and 2050 is expected to be about 250,000 individuals, mostly attributed to poor nutrition, malaria, diarrheal diseases, and heat stress [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA narrative literature review carried out by [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] in relation to the interaction between climate emergencies and emergency medicine focused on three thematic realms, such as the rise in healthcare demands resulting from climate change; the activities of the healthcare system related to mitigation and adaptation measures; and the enhancement of emergency preparedness [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. As stated in a systematic review of systematic reviews by [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], the effect of extreme weather events on the healthcare system was especially pronounced for those under stress [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.3 ED Crowding as a Mediator of Patient Outcomes\u003c/h2\u003e \u003cp\u003eCrowding in EDs and its association with negative patient outcome measures have been well characterized irrespective of the presence of any disaster event and form the core evidentiary pathway that leads to patient harm due to surge in ED settings. In their systematic review based on a corpus of 102 studies, Morley et al. demonstrated the consistent association between crowding in ED and delayed assessment/treatment, longer lengths of stay, mortality, medication errors, and deviations from clinical practice guidelines [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The systematic review conducted by [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] also demonstrated a higher incidence of adverse events related to crowding in ED settings in different health systems [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Further, the systematic review by Al-Tawfiq and Memish also corroborated the evidence about the independent association of ED crowding with significant treatment initiation delays in different care settings [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, the most scientifically sound quantification of mortality attributed to ED crowding has been reported by the retrospective cohort study of Sun et al., who found that ED patients with high crowding on admission\u0026mdash;the definition for which was based on the number of ambulance diversion hours falling in the top quartile\u0026mdash;were more likely to die during hospitalization by 5% (95% CI: 2%\u0026ndash;8%) in 187 California acute-care hospitals with 995,379 total admissions [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Conceptual Framework","content":"\u003cp\u003eThe conceptual model underlying this literature review has been developed using four sequential analytical constructs: climate hazard exposure; vulnerability and healthcare demands; ED capacity stress; and patients\u0026rsquo; outcomes. This model is based on the hazard-exposure-vulnerability model developed by the IPCC [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] specifically for the ED context.\u003c/p\u003e \u003cp\u003eUnder this model, climate hazards such as heat waves, floods, wildfires, and tropical cyclones interact with population-based vulnerabilities such as age, comorbid conditions, socio-economic level, and geographic isolation, producing acute presentations at the ED exceeding the capacities of ED in space, staffing, supplies, and systemic resources ...as summarized in the conceptual framework (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Within each step, the well-known connection between crowding at the ED and patients\u0026rsquo; adverse outcomes operates through increased mortality rates and increased waiting times, among others.\u003c/p\u003e \u003cp\u003eThis concept model aligns with the IPCC\u0026rsquo;s resilience in health systems approach [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] and reflects the 2024 ACEP policy position for climate-resilient healthcare facilities and improved disease surveillance [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. This framework guided the synthesis and interpretation of findings across disaster types and outcome domains.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eEvidence-based conceptual framework illustrating four sequential analytical domains linking climate hazard exposure to patient outcomes through ED surge capacity strain. The 4S framework (Space, Staff, Supplies, Systems) acts as the operational mediator. Sources\u003c/em\u003e: [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e"},{"header":"4. Methods","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e4.1 Review Design\u003c/h2\u003e\n \u003cp\u003eThis systematic review was conducted and reported in accordance with the PRISMA 2020 guidelines. Given the high heterogeneity in study designs, disasters, measures, and contexts in the studies included in the review, meta-analysis could not be performed. The PCC approach was employed to frame the review questions: Population \u0026ndash; individuals visiting emergency departments; Concept \u0026ndash; capacity of EDs and health outcomes in patients; Context \u0026ndash; disasters caused by climate change such as heatwaves, floods, wildfire, and hybrid disasters. The protocol was not registered in PROSPERO, which represents a methodological limitation. The PRISMA 2020 checklist is provided as Supporting Information (S1 Checklist). The review adhered to PRISMA 2020 reporting standards, with the study selection process illustrated in the PRISMA flow diagram (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Title/abstract screening and full-text review were conducted independently by two reviewers, with discrepancies resolved through discussion.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e4.2 Search Strategy\u003c/h2\u003e\n \u003cp\u003eDatabase searching was carried out in MEDLINE/PubMed, Scopus, and Web of Science. The grey literature searched comprised the World Health Organization (WHO), Intergovernmental Panel on Climate Change Assessment Reports, CDC MMWR reports, ACEP statements, articles published in The Lancet Countdown series, and publications from the European Environment Agency (EEA). Searches were conducted in March 2025. There were no limitations on language applied in the search stage; however, the review was written in English. Grey literature searches followed a structured approach using predefined institutional sources and keyword combinations aligned with database search terms. Inclusion and exclusion criteria are summarized in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e4.3 Inclusion and Exclusion Criteria\u003c/h2\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSummary of inclusion and exclusion criteria.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eInclusion Criteria\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eExclusion Criteria\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePeer-reviewed English-language publications or authoritative institutional reports (WHO, IPCC, CDC, ACEP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNon-peer-reviewed sources without institutional standing\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSources published between 2007 and March 2025 (landmark earlier studies included where justified)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003ePublications prior to 2007 unless landmark methodological value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eEmpirical data on ED visits, surge capacity, hospital admissions, mortality, or time-to-treatment during climate/weather disasters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eStudies without quantified ED or patient outcome data\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAll study designs: observational, cohort, interrupted time series, systematic reviews, and meta-analyses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eOpinion pieces, editorials, and conference abstracts without peer-reviewed data\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eStudies reporting on heatwaves, floods, wildfires, tropical cyclones, or compound climate events\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eStudies focused exclusively on chronic disease without acute disaster exposure component\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e4.4 Study Selection Process\u003c/h2\u003e\n \u003cp\u003eStudies were identified via database searching and grey literature searching (n\u0026thinsp;=\u0026thinsp;1,847). After automatic and manual removal of duplicates (n\u0026thinsp;=\u0026thinsp;312), 1,535 records underwent title and abstract screening based on predetermined inclusion criteria. At this stage, 1,467 records were excluded because they were irrelevant or did not have quantified outcomes or related to disasters not caused by climate change. In the full-text assessment process, a further 33 studies were excluded (exclusion reasons: absence of ED-related outcome data [n\u0026thinsp;=\u0026thinsp;12]; non-climate disaster context [n\u0026thinsp;=\u0026thinsp;9]; duplicate dataset [n\u0026thinsp;=\u0026thinsp;7]; unpeer-reviewed or not produced by institutions [n\u0026thinsp;=\u0026thinsp;5]). In total, 35 eligible records were included in the final synthesis for the PRISMA flow diagram. Reference management software (EndNote X20) was used to screen and deduplicate articles, and a structured review workflow based on systematic review management platforms (e.g., Covidence) was used to facilitate study selection. A total of 35 studies were included in the final synthesis (see Fig. \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This review followed established systematic review methodology and adhered to reporting standards to ensure transparency, reproducibility, and methodological rigor.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e4.5 Data Extraction\u003c/h2\u003e\n \u003cp\u003eData extraction was independently conducted using an extraction template including: authorship information; country/region; design; characteristics of study population and sample size; nature of disasters; outcomes measured; results; and quality evaluation indicators. All statistical claims were independently cross-checked before being used. Variables of interest included: percentage change of ED visit rates, hospital admission rates, mortality incidence rate ratio, length of stay, treatment times, economic cost and other quantitative measures wherever applicable. A standardized extraction form was piloted prior to use to ensure consistency across reviewers. All extracted quantitative findings were cross-verified against original sources to ensure citation accuracy.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e4.6 Quality Assessment\u003c/h2\u003e\n \u003cp\u003eThe quality of studies was independently evaluated using appropriate critical appraisal tools. The quality of observational and cohort studies was assessed using the Newcastle-Ottawa Scale (NOS); those scoring\u0026thinsp;\u0026ge;\u0026thinsp;7 out of 9 were considered high-quality papers. Quality assessment of systematic reviews and meta-analyses was done using the AMSTAR-2 tool (A MeaSurement Tool to Assess systematic Reviews). The critical appraisal of primary qualitative studies and reviews was conducted using the CASP (Critical Appraisal Skills Programme) qualitative checklist. Grey literature published by WHO, IPCC, CDC and ACEP were regarded as authoritative grey literature due to its high-level methodology. Quality assessment was used as a basis for interpreting findings; no study was excluded on account of poor quality in view of heterogeneity and scarcity of evidence. Quality appraisal informed interpretation of findings but was not used as a basis for study exclusion, consistent with best practice in heterogeneous systematic reviews. Quality assessment results were used to inform interpretation but not to exclude studies.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e4.7 Data Synthesis\u003c/h2\u003e\n \u003cp\u003eThematic narrative synthesis was performed because of heterogeneity in study designs, disaster types, and outcome measures. Findings were classified into categories of disasters (heatwaves, flood/tropical cyclones, wildfires, and cross-disaster analyses) and outcome domains (emergency department utilization, mortality, hospital admission, length of stay, and time-to-treatment). In cases where similar quantitative estimates were possible, descriptive summaries of the findings were provided in evidence tables and visual summaries (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Due to methodological heterogeneity, a meta-analysis was not appropriate.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"5. Results","content":"\u003cp\u003eResults are presented by disaster type and outcome domain to reflect the heterogeneity of included studies. A total of 35 studies met inclusion criteria and were included in the final synthesis (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Study Selection and Characteristics\u003c/h2\u003e \u003cp\u003eCollectively, 35 articles ranging from 2007 to 2025 have been considered in the research corpus. Regarding the study design, the following types have been used: systematic reviews and meta-analysis (n\u0026thinsp;=\u0026thinsp;9), retrospective cohort or observational studies (n\u0026thinsp;=\u0026thinsp;8), interrupted time series analysis (n\u0026thinsp;=\u0026thinsp;3), difference-in-differences analysis (n\u0026thinsp;=\u0026thinsp;2), prospective or cross-sectional studies (n\u0026thinsp;=\u0026thinsp;4), and reports, institutional, and policy papers (n\u0026thinsp;=\u0026thinsp;9). Geographically speaking, 19 sources pertained to the United States; 6 were on Europe; 4 on Canada; 2 were globally applicable and 4 used multi-country datasets. With respect to disaster type: heatwaves (n\u0026thinsp;=\u0026thinsp;12); floods/tropical cyclones (n\u0026thinsp;=\u0026thinsp;9); wildfires (n\u0026thinsp;=\u0026thinsp;7); cross-disaster analyses (n\u0026thinsp;=\u0026thinsp;4); and general effects of ED crowding (n\u0026thinsp;=\u0026thinsp;3). Across study designs and geographic contexts, findings consistently demonstrate that climate-related hazards are associated with increased ED utilization and downstream adverse patient outcomes, with magnitude varying by disaster type and population vulnerability.\u003c/p\u003e \u003cp\u003eThe characteristics of the included studies are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e and visually synthesized across disaster types and outcomes (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eAll data values are drawn from verified primary literature cited in\u003c/em\u003e Section \u003cspan refid=\"Sec14\" class=\"InternalRef\"\u003e5\u003c/span\u003e. \u003cem\u003eSources\u003c/em\u003e: [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eValues are drawn from verified primary literature. Data sources\u003c/em\u003e: [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of Included Studies by Design, Setting, and Key Findings\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAuthor(s) \u0026amp; Year\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCountry / Region\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStudy Design\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSample / Scope\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eKey Findings\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSalas et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDifference-in-differences\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42 US billion-dollar weather disasters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eED utilization\u0026thinsp;+\u0026thinsp;1.22% (week 1); mortality\u0026thinsp;+\u0026thinsp;1.4%; elevated mortality persisted 6 weeks post-event\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedicare Flood Study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRetrospective cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11,801,527 Medicare beneficiaries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAll-cause ED\u0026thinsp;+\u0026thinsp;4.8% (IRR 1.05); hospitalizations\u0026thinsp;+\u0026thinsp;7.4% (IRR 1.07); cost USD 3,230/ED visit\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVaidyanathan et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnited States (5 states)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSurveillance report (MMWR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSummer 2023 ED visits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHeat-illness ED visits\u0026thinsp;\u0026gt;\u0026thinsp;2\u0026times; five-year average across 5 Southern states\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHoward et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVital statistics analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNational mortality data 1999\u0026ndash;2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHeat-related deaths\u0026thinsp;+\u0026thinsp;117%; peak 1,602 deaths in 2022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBallester et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEurope (35 countries)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRetrospective epidemiological\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePopulation-level data 2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e61,672 heat-related deaths during 2022 European heatwaves\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEEA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEurope (France focus)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInstitutional surveillance report\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eED visits during 2022 heatwaves\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eED visits doubled; physician consultations for heat-sensitive conditions tripled\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoudreault et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCanada (Quebec)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEpidemiological cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eProvincial population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQuantified heat-related morbidity and mortality burden confirming population-level impact beyond ED\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChen et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCanada (Ontario)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInterrupted time-series\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePublic health unit data 2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAsthma-related ED syndromes\u0026thinsp;+\u0026thinsp;21.7% (2-day lag) during major smoke episode\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDing et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnited States (California)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRetrospective observational\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eED visits 2016\u0026ndash;2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAsthma ED visits\u0026thinsp;+\u0026thinsp;57.1% on high smoke days (lag 0; 95% CI: 44.5\u0026ndash;70.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRappold et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnited States (N. Carolina)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEcological time-series\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCounty-level ED data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAsthma ERR\u0026thinsp;+\u0026thinsp;66% (lag 0); CHF\u0026thinsp;+\u0026thinsp;42% (lag 1); SES gradient: 85\u0026ndash;124% risk difference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrazier et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnited States (Houston)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eObservational, single-site\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAcademic medical centre during Hurricane Harvey\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLoss of 133/179 inpatient beds; 5-day access disruption; cascading staffing/capacity failure\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSaulnier et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGlobal (systematic review)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSystematic review\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36 primary studies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFlood/storm health outcomes: respiratory illness, infections, injuries, mental health, displacement effects\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSun et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnited States (California)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRetrospective cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e995,379 admissions, 187 hospitals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHigh-crowding days: 5% greater inpatient death odds (95% CI: 2\u0026ndash;8%); increased LOS and costs\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Heatwaves: The Most Lethal Climate Emergency\u003c/h2\u003e \u003cp\u003eAmong all the climate-induced calamities, heat waves have proven to be the costliest yet remain under-recognized in public and policy discourse. Worldwide, an average of 489,000 deaths annually between 2000 and 2019 have been associated with heat exposure, with 45% occurring in Asia and 36% in Europe [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Hot temperatures and extreme heat are considered some of the most straightforward indicators of how climate change manifests itself in health emergencies that require individuals to visit emergency departments [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. According to several sources, including Lancet eClinicalMedicine [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], the death count is underestimated, and the 2023 Lancet Countdown found an increase in mortality rates linked to heat exposure among adults aged 65 and above by 85% compared to the baseline period of 1991\u0026ndash;2000 [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eConsidering practical examples will allow us to determine the flow of patients visiting EDs. The temporal trends in heat-related mortality are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. For instance, the 2003 European heat wave has been estimated to result in 70,000 excess deaths throughout the continent and overwhelm hospitals lacking adequate heat wave action plans [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In the United States, heat waves have become the top weather factor contributing to mortality rates higher than those of hurricanes, floods, and tornadoes combined [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. According to official US government statistics, there has been a 117% increase in deaths due to hot weather from 1999 to 2023, with the peak being reached in 2022 [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] (see Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eOfficial cause-of-death data show a 117% overall increase from 502 deaths in 1999 to a historic peak of 1,602 in 2022. Sources\u003c/em\u003e: [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThe CDC Morbidity and Mortality Weekly Report found that during the summer of 2023, the incidence of ED visits caused by heat-related illnesses in the five southern states of the US increased more than double compared with the previous five years [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Not only was there a marked increase in occurrence, accompanied by an unprecedented acceleration in terms of the volume. A single season with double the ED visit demand represents a substantial system stressor.\u003c/p\u003e \u003cp\u003eAdditional information comes from the data collected by the healthcare system in Europe regarding the dynamics of the use of the ED visits during heatwaves. During the 2022 heatwave season, ED visits in France doubled while consultations of physicians concerning hyperthermia, dehydration, and hyponatremia tripled when compared with the seasons without heatwaves [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. According to [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], 61,672 deaths were caused by the 2022 heatwaves in 35 European countries.\u003c/p\u003e \u003cp\u003eSome of the groups at high risk during heatwaves include infants younger than 1-year-old, elderly people aged over 65, outdoor workers, people living in urban areas and lacking air conditioners, individuals with cardiovascular and kidney diseases, and those with weak social network support. As shown by research conducted in Quebec, Canada, the effect is much more profound, affecting even long-term health outcomes of entire populations [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Floods and Tropical Cyclones\u003c/h2\u003e \u003cp\u003eThe combined occurrence of floods and tropical storms results in a distinct pattern of ED surge, which is characterized by polytrauma, infectious diseases transmitted via water, worsening of existing chronic conditions following evacuation and relocation, as well as critical infrastructure impairment. A systematic review by Saulnier et al. identified the following health consequences in relation to disasters involving both flood and storm events: respiratory problems, dermatitis and other infections of skin and soft tissues, injuries, mental disorders, and prolonged displacements [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Among various natural disasters, flood is considered to be the most common and universal type affecting predominantly those individuals who live in low- and middle-income countries [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the largest and comprehensive research study of the effect of floods in 2025, 11,801,527 Medicare participants in the USA were estimated to have witnessed a 4.8% increase in the prevalence of all-cause ED visits (IRR: 1.05; 95% CI: 1.04\u0026ndash;1.05) as well as 7.4% hospitalization rates (IRR: 1.07; 95% CI: 1.07\u0026ndash;1.08) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. On average, per one ED or hospital admission, the cost amounted to USD 3,230 and USD 11,310 respectively (in 2017 dollars). These numbers represent the most accurate financial estimations of the cost of disaster responses in terms of increased healthcare utilization.\u003c/p\u003e \u003cp\u003eHurricane-related surge and infrastructural impact are reflected in Frazier et al.'s report on the loss of access to hospitalization caused by Hurricane Harvey in 2017. In this case, the loss occurred during five days and resulted in staff shortages, disrupted patient flow, and increased utilization. Due to water damage, the number of lost inpatient beds constituted 133 of 179 or 74% of capacity [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The nearly complete loss of infrastructural capacity is rarely assessed in analyses related to heatwaves and wildfires' effect on ED utilization.\u003c/p\u003e \u003cp\u003eOne of the reports about Hurricane Sandy in New York City included the results showing that ED visits for the above-mentioned health problems rose up as rate ratios: 1.10 for cardiovascular diseases, 1.35 for respiratory diseases, 1.20 for skin and soft tissue infections, 1.19 for injuries, and 1.44 for kidney-related disorders in individuals older than 65 years during the first week after the flood events [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. As revealed by the cross-disaster assessment done by Salas et al., the rates of ED visits rose 1.22% (95% CI: 0.20%\u0026ndash;2.25%), whereas the mortality rate reached 1.4% (95% CI: 0.4%\u0026ndash;2.4%).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e5.4 Wildfires and Smoke Events\u003c/h2\u003e \u003cp\u003eIncreasingly, wildfire events emerge as a dominant climate-driven factor in ED surge, based on the ability of wildfires to generate PM2.5 (fine inhalable particles with diameters \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\le\\:\\)\u003c/span\u003e\u003c/span\u003e2.5 micrometers generated after combustion) and lead to acute respiratory and cardiovascular syndromes over a broad geography not necessarily limited to the geographic boundary of the fires. Wildfire smoke risk is inherently climate-sensitive in its own right, growing as a function of increasing land aridity, ambient temperature, and growth of wildland-urban interfaces (WUIs), all of which will grow more common as global warming continues [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe most important wildfire smoke event in North America in the past year was the Canadian wildfire season of 2023. Based on an interrupted time series analysis of data from Ontario public health units, there was a significant 21.7% increase in asthma-associated ED syndromes with a 2-day lag effect associated with the initial wildfire smoke event [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. During the 2023 wildfire smoke events, U.S. data show a significant rise in asthma-associated ED visits during Canadian wildfire smoke events [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAmong the most precise estimates of the relationship between wildfire smoke events and ED visits is based on population-based, retrospective, and prospective studies in California covering 2016 to 2019. In this context, asthma-related ED visits increase by 57.1% (lag 0; 95% CI: 44.5\u0026ndash;70.8%) on days with high levels of PM2.5 associated with wildfire smoke (wildfire-specific PM2.5 greater than the 98th percentile) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Respiratory disease associated ED visits rose by 14.4% (lag 1; 95% CI: 6.8\u0026ndash;22.5%). Cardiovascular ED visits were also increased, consistent with the established cardiovascular pathway associated with PM2.5 as described by [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBased on findings from North Carolina, Rappold et al. found that acute PM2.5 exposure from a local wildfire led to a 66% increase in relative risk for asthma associated with ED visits on lag 0 day and a 42% increase in relative risk of congestive heart failure on lag 1 day [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Furthermore, there was a vital equity element revealed: The risk difference by socio-economic status was 85% for asthma-related ED visits and 124% for congestive heart failure related ED visits.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e5.5 ED Crowding and Patient Outcomes During Surge Events\u003c/h2\u003e \u003cp\u003eEmergency department (ED) crowding has been consistently associated with adverse patient outcomes, including delays in assessment and treatment, prolonged length of stay, medication errors, and increased mortality [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Systematic evidence further demonstrates increased rates of adverse clinical events and compromised care processes during periods of crowding [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Delays in time-critical interventions\u0026mdash;including sepsis management, stroke evaluation, and trauma care\u0026mdash;have also been documented [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The most robust quantitative evidence comes from Sun et al., who demonstrated a 5% increase in inpatient mortality during periods of high ED crowding [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] (see Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAmong the most comprehensive and scientifically rigorous evidence regarding crowding-related mortality, there is the retrospective cohort study by Sun et al. involving 995,379 admissions at 187 acute-care hospitals in California [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Those admitted to ED on high-crowding days had a 5% increase in the odds of inpatient death (95% CI: 2%\u0026ndash;8%). In addition, patients were exposed to higher costs and longer lengths of stay. The dose-response curve, observed under non-disaster conditions, defines the level of basic damage to which climate-related surges contribute.\u003c/p\u003e \u003cp\u003eIn their systematic review, Al-Tawfiq and Memish confirmed that ED crowding causes clinically relevant delays in several processes, such as analgesia, antibiotic therapy, stroke assessment, trauma treatment, and sepsis control [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. For some conditions with a strict time limit\u0026mdash;myocardial infarction, sepsis, and stroke\u0026mdash;the treatment delay results in preventable death and disability. Climate-related surge events induce crowding conditions in several healthcare facilities, triggering the same adverse impacts on a large scale.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e5.6 Quality Assessment Summary\u003c/h2\u003e \u003cp\u003eOverall, 68% of observational studies were rated high quality (NOS\u0026thinsp;\u0026ge;\u0026thinsp;7), while systematic reviews demonstrated moderate to high methodological quality based on AMSTAR-2. Common limitations included residual confounding and heterogeneity in outcome definitions.\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Discussion","content":"\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e6.1 Interpretation of Findings\u003c/h2\u003e \u003cp\u003eThe current systematic synthesis indicates a consistent, multifactorial association between climate-induced disasters and ED capacity issues that can be seen across the spectrum of four primary disasters. Some common trends that apply to clinical practice and policy decisions include the following.\u003c/p\u003e \u003cp\u003ePrimarily, the influx of patients during a disaster event is enough to exceed the capabilities of EDs even without infrastructure destruction. For instance, the increase in visits related to hot weather conditions was doubled in five southern states of the USA in summer 2023 [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], the visits due to heatwaves doubled in France in 2022 [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], and a 57.1% increase in asthma-associated ED visits occurred due to wildfires and smoke in California [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. These findings highlight the consistent strain placed on emergency care systems during climate-related events.\u003c/p\u003e \u003cp\u003eSecondly, there are long-lasting effects associated with a particular surge event that should be taken into consideration during planning processes. Specifically, excess mortality rates persisted at an increased level for up to six weeks in US counties that experienced a heatwave [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. These findings indicate that preparedness strategies must extend beyond the immediate response phase to include sustained post-disaster system recovery.\u003c/p\u003e \u003cp\u003eFinally, vulnerability patterns are different among population groups in all disaster scenarios. People aged 65\u0026thinsp;+\u0026thinsp;and younger than 1 year had approximately twice as many heatwave exposure days compared to the 1986\u0026ndash;2005 average [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Moreover, climate-induced ED surges were more prominent in the group of Medicare beneficiaries [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e6.2 Mechanisms Linking Disasters to Surge\u003c/h2\u003e \u003cp\u003eThree mechanisms connect climate disasters to ED surge. Direct mechanisms describe the direct physiological effects of the disaster such as heat stroke, drownings, polytraumas caused by high wind forces, and respiratory emergencies due to wildfire smoke resulting in a sudden geographically concentrated surge of patients. Indirect mechanisms refer to the exacerbation of existing chronic diseases due to the disaster: heart failure and acute kidney injuries from heatwaves; infectious disease outbreaks due to contaminated water sources from floods; and COPD and asthma due to the effects of wildfire smoke. These effects usually arise after days to weeks and affect extensive geographic regions outside the disaster area. Systemic mechanisms refer to the disaster\u0026rsquo;s impact on the medical infrastructure such as power outage leading to malfunctioning medical equipment, actual flooding of healthcare facilities, evacuation of patients, and shortages in medication supplies.\u003c/p\u003e \u003cp\u003eMoreover, climate change is expected to lead to an increased incidence of time-critical presentations, including cardiac arrest, stroke, and sepsis, supported by evidence from mainland China, Taiwan, and Bangladesh [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The dual effect here is that climate disasters both increase the baseline incidence of these conditions and cause ED crowding, which increases inpatient mortality risk by 5% [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e6.3 Positioning Against Existing Literature\u003c/h2\u003e \u003cp\u003ePrevious studies have generally focused on either climate and population health relationships or ED crowding without linking the two research domains together. This systematic review synthesizes the available evidence in a way that connects disaster-specific surge findings with the existing ED crowding literature. The best statistical evidence can be found in the difference-in-differences analysis performed by [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] and the Medicare floods case study (2025). Meanwhile, the narrative reviews published by [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] and [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] offer important clinical and systems-level insights. This review contributes to previous systematic reviews through its incorporation of the most current evidence (2024\u0026ndash;2025), explicit quantification of the equity factor, and applicability to all types of climate disasters. These findings are translated into actionable system-level strategies in the policy response framework presented (see Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e6.4 Implications for External Validity\u003c/h2\u003e \u003cp\u003eAlthough the evidence base is predominantly derived from high-income settings, the core mechanisms linking climate-related hazards to emergency department (ED) surge\u0026mdash;namely increased acute care demand, infrastructure strain, and crowding-related deterioration in care processes\u0026mdash;are likely generalizable across health systems. However, differences in infrastructure capacity, surveillance systems, workforce availability, and baseline system resilience may substantially amplify both the magnitude of surge and associated adverse outcomes in low- and middle-income countries (LMICs). These contextual differences underscore the need for locally adapted preparedness strategies while supporting cautious generalization of the underlying system dynamics identified in this review. While formal certainty grading (e.g., GRADE) was not performed due to heterogeneity, the consistency of findings across multiple study designs strengthens confidence in the observed associations.\u003c/p\u003e \u003cp\u003eFrom a clinical perspective, the convergence of increased demand and delayed care processes during disaster-related surges represents a critical risk pathway for preventable morbidity and mortality. These findings reinforce that ED surge capacity is not solely an operational concern but a determinant of patient safety and clinical outcomes under conditions of system stress.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e6.5 Strengths of the Review\u003c/h2\u003e \u003cp\u003eThis review integrates disaster-specific surge data with established evidence on emergency department crowding, providing a systems-level synthesis not previously captured in the literature.\u003c/p\u003e \u003c/div\u003e"},{"header":"7. Public Health and Policy Implications","content":"\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e7.1 Integrating Climate Projections into ED Preparedness Planning\u003c/h2\u003e \u003cp\u003eThe policy implications derived from this review are synthesized in an integrated framework (see Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). At present, there is little consideration of climate disasters' particularity as slow-onset events, repetitive disasters, and geographical spread of surges when preparing for surge operations in ED. These findings suggest that climate change projections should be integrated into annual emergency department preparedness planning frameworks, aligned with regional warming, flooding, and wildfire risk projections. The preparation of such preparedness frameworks would be performed in conjunction with the work of public health organizations, national meteorology services, and hospitals under guidelines provided by WHO and ACEP [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e7.2 Early Warning Systems and Syndromic Surveillance\u003c/h2\u003e \u003cp\u003eThe value of syndrome-based surveillance can be proved by the examples of CDC data showing that it allowed detecting surges associated with increasing asthma ED visits triggered by wildfire smoke exposure [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Likewise, investment into the implementation of an early warning system of related interconnected parameters such as forecasts, air pollution, and resource availability would allow triggering surge protocols beforehand and reduce expenses associated with such events.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section2\"\u003e \u003ch2\u003e7.3 Strengthening Health System Resilience\u003c/h2\u003e \u003cp\u003eThe analysis of hospital surge capacities from 2014 to 2024 shows that surge capacity has three key dimensions: space available, flexibility in the use of personnel, and the chain of supply [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. As regards ED surges triggered by climate events, the proposed solution includes expanding capacity and building resilience through increasing space available, ensuring personnel flexibility, and creating reserves of supplies in the face of climate effects.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003e7.4 Equity-Centered Disaster Preparedness\u003c/h2\u003e \u003cp\u003eLiterature shows that adverse consequences of climate disasters affecting ED surges are disproportionately impacting vulnerable population groups, namely, elderly people, children, individuals with low SES, people of color and minorities, and poor nations. This statement was proven in the IPCC AR6 WGII and emphasized the significance of equity-based climate resilient strategies for achieving this goal [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Therefore, the framework of ED surge preparedness should consider vulnerable groups and create climate resilient infrastructure for vulnerable communities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003e7.5 Workforce Training and Climate-Competent Emergency Medicine\u003c/h2\u003e \u003cp\u003eAccording to [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], there is a major workforce deficiency, which is the absence of training in the recognition and response to climate-related presentations and surges among the current EM workforce. As per the 2024 ACEP policy statement, what needs to be done is a training programme for climate-competent emergency medicine that would train clinicians on climate presentations, climate surges, and preparedness planning. This recommendation is evidence-based and would go a long way in preparing the EM workforce for the effects of climate change.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eSources\u003c/em\u003e: [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e"},{"header":"8. Limitations","content":"\u003cp\u003eSeveral important limitations should be noted when considering this review. First, as discussed in the introduction section above, this review was not prospectively registered on PROSPERO or an equivalent register before undertaking it, which represents a recognized methodological weakness when compared to prospective reviews. A prospectively registered systematic review, with publication of a detailed protocol in advance, would allow for higher replicability and mitigate risk of selective reporting.\u003c/p\u003e \u003cp\u003eSecond, as discussed earlier in this paper, there is a clear imbalance of data from high-income nations, especially the United States and Europe. It must be remembered that while low- and middle-income countries suffer the most in terms of the sheer number of climate disasters and their severity, they lack infrastructure and surveillance resources to report accurate information regarding climate-related ED surges.\u003c/p\u003e \u003cp\u003eThird, there is a great heterogeneity of outcome measures, design of studies, and definition of what constitutes a climate disaster. This prevents the meta-analysis approach from being used as a part of this review and makes it difficult to compare effects of different disasters. Standardization of outcome metrics for future studies would prove beneficial.\u003c/p\u003e \u003cp\u003eFourth, it is worth noting that due to the dynamic nature of literature in climate change and disaster medicine, additional studies published after this review's cutoff period of March 2025 may add to the current body of knowledge.\u003c/p\u003e \u003cp\u003eReliance on secondary observational data limits causal inference and introduces potential residual confounding. Publication bias cannot be excluded, as studies demonstrating significant associations may be more likely to be published than those with null findings. Additionally, variability in definitions of surge capacity and outcome measures across studies may limit comparability, highlighting the need for standardized reporting frameworks in future research.\u003c/p\u003e \u003cdiv id=\"Sec34\" class=\"Section2\"\u003e \u003ch2\u003e8.1 Future Research Directions\u003c/h2\u003e \u003cp\u003eResearch topics that emerge from this review are: (1) prospective, registry-based studies assessing ED surge capacity within healthcare systems in LMICs during climate disasters; (2) methods for measuring outcomes across multiple sites that facilitate comparison between disasters and countries; (3) economics research regarding ED strain caused by climate factors in LMIC contexts for infrastructure development purposes; (4) intervention research on ED preparedness plans in response to climate change; and (5) modelling ED strain in accordance with IPCC recommendations.\u003c/p\u003e \u003c/div\u003e"},{"header":"9. Conclusion","content":"\u003cp\u003eThis review demonstrates a consistent association between climate-related disasters and emergency department strain. As demonstrated in the synthesized literature review, heatwaves, floods, and tropical cyclones, wildfires, and the combination of several hazards exert a specific pressure on the emergency care, yet their impacts also intertwine. First, the events induce physiological stress directly. Second, the disasters create indirect damage by undermining the necessary infrastructure, exacerbating pre-existing illnesses, and increasing the number of casualties requiring immediate care.\u003c/p\u003e \u003cp\u003eHowever, even without taking climate-related events into consideration, ED crowding alone elevates inpatient mortality by 5% or more and induces delays in crucial interventions in the case of sepsis, stroke, and myocardial infarction. The rapid influx of casualties during a climate-related disaster adds another dimension to these risks by creating an opportunity for adverse outcomes along the specified pathways.\u003c/p\u003e \u003cp\u003eFuture trends suggest that climate-induced events will occur more frequently, become more severe, and be distributed across wider geographic regions based on the IPCC AR6 report, the Lancet Countdown, and WHO projections. In this situation, health systems are unlikely to be adequately prepared unless there is accurate measurement of the problem. Fortunately, this literature review highlights the presence of a robust evidence base on which further steps can be based.\u003c/p\u003e \u003cp\u003eAs climate-related disasters increase in frequency and intensity, emergency departments will face escalating and sustained pressure that directly impacts patient outcomes. This review demonstrates that surge-related deterioration in care is a measurable and consistent phenomenon across disaster types. Strengthening ED resilience will require integrating climate projections into preparedness planning, investing in adaptive infrastructure and workforce capacity, and prioritizing equitable response strategies to protect vulnerable populations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003cbr\u003e\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003cbr\u003e\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003cbr\u003e\u003c/strong\u003eAll data used in this study are derived from publicly available sources cited within the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003cbr\u003e\u003c/strong\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003cbr\u003e\u003c/strong\u003eNo specific funding was received.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: Andre Craig Christie\u003cbr\u003e\u0026nbsp;Methodology: Andre Craig Christie, Uttam Udayan\u003cbr\u003e\u0026nbsp;Data Curation: Andre Craig Christie, Claudy Grimadeau\u003cbr\u003e\u0026nbsp;Formal Analysis: Andre Craig Christie\u003cbr\u003e\u0026nbsp;Writing – Original Draft: Andre Craig Christie\u003cbr\u003e\u0026nbsp;Writing – Review \u0026amp; Editing: All authors\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eRahim F, Malik MA, Afzal M. Climate change and its impediments to global health security. Health Sci Rep. 2023;6(3):e1524. https://doi.org/10.1002/hsr2.1524\u003c/li\u003e\n \u003cli\u003eIntergovernmental Panel on Climate Change. Climate change 2021: The physical science basis. Cambridge: Cambridge University Press; 2021. https://www.ipcc.ch/report/ar6/wg1/\u003c/li\u003e\n \u003cli\u003eIntergovernmental Panel on Climate Change. Climate change 2022: Impacts, adaptation and vulnerability. Cambridge: Cambridge University Press; 2022. https://www.ipcc.ch/report/ar6/wg2/\u003c/li\u003e\n \u003cli\u003eRomanello M, di Napoli C, Green C, et al. The 2023 report of the Lancet Countdown on health and climate change. Lancet. 2023;402(10419):2346\u0026ndash;2394. https://doi.org/10.1016/S0140-6736(23)01859-7\u003c/li\u003e\n \u003cli\u003eRomanello M, et al. The 2024 report of the Lancet Countdown on health and climate change. Lancet. 2024. https://doi.org/10.1016/S0140-6736(24)01822-1\u003c/li\u003e\n \u003cli\u003eSalas RN, Burke LG, Phelan J, et al. Impact of extreme weather events on healthcare utilization and mortality. Nat Med. 2024;30(4):1118\u0026ndash;1126. https://doi.org/10.1038/s41591-024-02833-x\u003c/li\u003e\n \u003cli\u003eSheikhbardsiri H, Raeisi AR, Nekoei-Moghadam M, et al. Surge capacity of hospitals in disasters. Disaster Med Public Health Prep. 2017;11(5):612\u0026ndash;620. https://doi.org/10.1017/dmp.2016.193\u003c/li\u003e\n \u003cli\u003eAminizadeh M, Farrokhi M, Ebadi A, et al. Hospital surge capacity preparedness in disasters and emergencies. Public Health. 2023;223:94\u0026ndash;100. https://doi.org/10.1016/j.puhe.2023.09.003\u003c/li\u003e\n \u003cli\u003eWorld Health Organization. Climate change and health. Geneva: WHO; 2023. https://www.who.int/news-room/fact-sheets/detail/climate-change-and-health\u003c/li\u003e\n \u003cli\u003eDalla Vecchia L, Fabbro E, Di Bartolomeo S. Impact of climate change in emergency medicine: A narrative review. J Public Health Emerg. 2024;8:12. https://doi.org/10.21037/jphe-23-143\u003c/li\u003e\n \u003cli\u003eLoi V, Stazi M, Ferroni E, et al. The impacts of extreme weather events on health services and systems: A systematic review of reviews. Public Health. 2025. https://doi.org/10.1016/j.puhe.2025.01.015\u003c/li\u003e\n \u003cli\u003eMorley C, Unwin M, Peterson GM, et al. Emergency department crowding: A systematic review of causes, consequences and solutions. PLoS One. 2018;13(8):e0203316. https://doi.org/10.1371/journal.pone.0203316\u003c/li\u003e\n \u003cli\u003eRasouli HR, Esfahani AA, Nobakht M, et al. Outcomes of crowding in emergency departments: A systematic review. Arch Acad Emerg Med. 2019;7(1):e52. https://pubmed.ncbi.nlm.nih.gov/31602435/\u003c/li\u003e\n \u003cli\u003eAl-Tawfiq JA, Memish ZA. The association between emergency department overcrowding and delay in treatment: A systematic review. Int J Environ Res Public Health. 2023;20(3):2159. https://doi.org/10.3390/ijerph20032159\u003c/li\u003e\n \u003cli\u003eSun BC, Hsia RY, Weiss RE, et al. Effect of emergency department crowding on outcomes of admitted patients. Ann Emerg Med. 2013;61(6):605\u0026ndash;611. https://doi.org/10.1016/j.annemergmed.2012.10.026\u003c/li\u003e\n \u003cli\u003eFrazier A, Hinyard L, Elkins N, et al. The impact of Hurricane Harvey on healthcare utilization and emergency department operations. West J Emerg Med. 2020;21(2):360\u0026ndash;366. https://doi.org/10.5811/westjem.2020.2.44880\u003c/li\u003e\n \u003cli\u003eSaulnier DD, Ribacke KB, von Schreeb J. No calm after the storm: Health impacts of flood and storm disasters. Prehosp Disaster Med. 2017;32(5):568\u0026ndash;579. https://doi.org/10.1017/S1049023X17006574\u003c/li\u003e\n \u003cli\u003eAyala A, M\u0026eacute;ndez-Arriaga F, Monteiro A. Hot weather and heat extremes: Health risks. Lancet. 2021;398(10301):698\u0026ndash;708. https://doi.org/10.1016/S0140-6736(21)01208-3\u003c/li\u003e\n \u003cli\u003eZhao Q, Guo Y, Ye T, et al. Global burden of mortality associated with non-optimal temperatures. Lancet Planet Health. 2021;5(7):e415\u0026ndash;e425. https://doi.org/10.1016/S2542-5196(21)00081-4\u003c/li\u003e\n \u003cli\u003eBallester J, Quijal-Zamorano M, M\u0026eacute;ndez Turrubiates RF, et al. Heat-related mortality in Europe during the summer of 2022. Nat Med. 2023;29(7):1857\u0026ndash;1866. https://doi.org/10.1038/s41591-023-02419-z\u003c/li\u003e\n \u003cli\u003eHoward JT, Androne N, Alcover KC, Santos-Lozada AR. Trends in heat-related deaths in the United States, 1999\u0026ndash;2023. JAMA. 2024;332(14):1203\u0026ndash;1205. https://doi.org/10.1001/jama.2024.14907\u003c/li\u003e\n \u003cli\u003eHoward JT, et al. Rise in heat-related mortality in the United States. PLOS Clim. 2025;4(1):e0000610. https://doi.org/10.1371/journal.pclm.0000610\u003c/li\u003e\n \u003cli\u003eVaidyanathan A, Gates A, Brown C, et al. Heat-related emergency department visits\u0026mdash;United States, May\u0026ndash;September 2023. MMWR. 2024;73:324\u0026ndash;329. https://doi.org/10.15585/mmwr.mm7315a1\u003c/li\u003e\n \u003cli\u003eBoudreault J, Lavigne E, Bouchard MF, et al. Estimating heat-related mortality and morbidity burden in Quebec. Environ Res. 2024;257:119052. https://doi.org/10.1016/j.envres.2024.119052\u003c/li\u003e\n \u003cli\u003eDing N, Berry EM, Goldberg D, et al. Emergency department visits associated with wildfire smoke events in California. Environ Res. 2023;216:114747. https://doi.org/10.1016/j.envres.2023.116926\u003c/li\u003e\n \u003cli\u003eRappold AG, Reyes J, Pouliot G, et al. Cardio-respiratory outcomes associated with wildfire smoke exposure. Environ Health. 2012;11:71. https://doi.org/10.1186/1476-069X-11-71\u003c/li\u003e\n \u003cli\u003eHeaney AK, Stowell JD, Liu JC, et al. Impacts of fine particulate matter from wildfire smoke on respiratory and cardiovascular health. GeoHealth. 2022;6(6):e2021GH000578. https://doi.org/10.1029/2021GH000578\u003c/li\u003e\n \u003cli\u003eSacks JD, Hoppe BO, Wang Y, et al. Asthma-associated emergency department visits during wildfire smoke episodes. MMWR. 2023;72:897\u0026ndash;903. https://doi.org/10.15585/mmwr.mm7234a5\u003c/li\u003e\n \u003cli\u003eChen H, Kaufman JS, Chen C, et al. Impact of wildfire smoke episodes on asthma and other health outcomes. CMAJ. 2025;197(17):E465\u0026ndash;E477. https://doi.org/10.1503/cmaj.241506\u003c/li\u003e\n \u003cli\u003eChua MT, Chung LYE, Ng EY, et al. Climate change and environmental sustainability in emergency medicine: A narrative review. Ann Transl Med. 2025;13(3):31. https://journals.lww.com/atm/fulltext/2025/06000/climate_change_and_environmental_sustainability_in.8.aspx\u003c/li\u003e\n \u003cli\u003eAmerican College of Emergency Physicians. Impact of climate change on public health and implications for emergency medicine [policy statement]. 2024. https://www.acep.org\u003c/li\u003e\n \u003cli\u003eLancet eClinicalMedicine. The increasing burden of heat-related mortality. eClinicalMedicine. 2024;73:102706. https://doi.org/10.1016/j.eclinm.2024.102706\u003c/li\u003e\n \u003cli\u003eDyal JW, et al. Optimizing emergency response in hospitals: A systematic review of surge capacity planning. Healthcare. 2025;13(21):2819. https://doi.org/10.3390/healthcare13212819\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-emergency-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emmd","sideBox":"Learn more about [BMC Emergency Medicine](http://bmcemergmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/emmd","title":"BMC Emergency Medicine","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9415264/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9415264/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground:\u003c/p\u003e\n\u003cp\u003eExtreme weather events driven by climate change are becoming increasingly frequent and severe globally, posing unprecedented pressure on health systems, especially on their emergency departments (EDs). The ED operates as the key link between acute impacts on public health caused by disasters and a wider healthcare sector response. Therefore, measuring surge capacity becomes an important aspect of assessing health system resilience to climate change. While there is a wealth of scientific literature on climate and health, to date no systematic review has established the relationship between disaster surge research and the documented patient-level outcomes linked to ED overcrowding in the context of different disaster types.\u003c/p\u003e\n\u003cp\u003eObjective:\u003c/p\u003e\n\u003cp\u003eThe objective of this study was to conduct a systematic review of peer-reviewed articles assessing the impact of climate-related disasters – heat waves, floods and tropical cyclones, wildfires, compound events, and other types of weather events – on ED surge capacity and documenting the effects on patient-level measures such as deaths, length of stay (LOS) and time to treatment (TT2Tx).\u003c/p\u003e\n\u003cp\u003eMethods:\u003c/p\u003e\n\u003cp\u003eThis systematic review was conducted in accordance with PRISMA 2020 guidelines, with searches performed across MEDLINE, Scopus, and Web of Science, alongside structured grey literature sources (WHO, IPCC, CDC, ACEP). In the preliminary search, 1,847 articles were identified, of which 312 duplicates were removed, and 1,535 abstracts excluded. From 68 full texts reviewed, 35 articles met all inclusion criteria.\u003c/p\u003e\n\u003cp\u003eResults:\u003c/p\u003e\n\u003cp\u003eThere is consistent evidence of an association between climate-related disasters and ED surge in four main types of disasters. Heatwaves result in double the number of ED visits due to heat illnesses, while in the 2003 heatwave in Europe there were over 70,000 additional deaths recorded. In the aftermath of weather-related disasters, ED utilization increases by 1.22% during the first week following the event, with increased mortality persisting up to six weeks later. In the event of wildfire smoke exposure, there is a 57.1% rise in the number of asthma-related ED visits. Climate-related disasters independently cause an increased mortality rate in patients requiring hospital admission due to ED overcrowding by at least 5%, along with delay in critical interventions for sepsis, stroke, and acute myocardial infarction. There is limited evidence from low- and middle-income countries.\u003c/p\u003e\n\u003cp\u003eConclusions:\u003c/p\u003e\n\u003cp\u003eClimate-related disasters are consistently associated with increased emergency department workload and measurable deterioration in patient outcomes, including increased mortality, delays in care, and prolonged hospital stays. These findings underscore the need to integrate climate-informed surge planning into emergency care systems and strengthen health system resilience through anticipatory, data-driven preparedness strategies.\u003c/p\u003e","manuscriptTitle":"Climate-related disasters and emergency department surge capacity: a PRISMA-guided systematic review of patient outcomes and health system impact","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-21 04:43:27","doi":"10.21203/rs.3.rs-9415264/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"267438657370279452868212308510306772527","date":"2026-05-07T13:27:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-28T22:23:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"785069782431384233751109804972990529","date":"2026-04-21T13:24:52+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-21T11:40:26+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-16T06:19:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-16T06:19:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Emergency Medicine","date":"2026-04-14T11:57:58+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-emergency-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emmd","sideBox":"Learn more about [BMC Emergency Medicine](http://bmcemergmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/emmd","title":"BMC Emergency Medicine","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a15f6d06-f104-49b0-9ce8-3ed16402e2ce","owner":[],"postedDate":"April 21st, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"267438657370279452868212308510306772527","date":"2026-05-07T13:27:19+00:00","index":27,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-21T11:53:19+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-21 04:43:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9415264","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9415264","identity":"rs-9415264","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}