Extreme heat and pediatric health in a warming world: a space-time stratified case-crossover investigation in Ontario, Canada

Extreme heat and pediatric health in a warming world: a space-time stratified case-crossover investigation in Ontario, Canada | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Extreme heat and pediatric health in a warming world: a space-time stratified case-crossover investigation in Ontario, Canada Hallah Kassem, Eric Lavigne, Kate Weinberger, Michael Brauer This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4904542/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Jun, 2025 Read the published version in Environmental Health → Version 1 posted 7 You are reading this latest preprint version Abstract Background Globally, climate change is causing frequent and severe extreme heat events (EHEs). A large body of literature links EHEs to multiple health endpoints. While children’s physiology and activity patterns differ from those of adults in ways that are hypothesized to increase susceptibility to such endpoints, research gaps remain regarding the specific impacts of EHEs on child health. This study evaluated pediatric emergency healthcare utilizations associated with EHEs in Ontario. Methods Applying a space-time stratified case-crossover design, associations between EHEs (same-day or lagged exposure to consecutive days of daily maximum temperatures above percentile thresholds) and 15 causes of pediatric emergency healthcare use in Ontario, Canada from 2005–2015 were analysed using conditional quasi-Poisson regression. In primary analyses, EHEs were defined as two or more consecutive days with temperatures above the 99th percentile of temperature within each respective forward sortation area (FSA). Healthcare use was measured using hospital admissions as an indicator of severe outcomes, and emergency department (ED) visits as a sensitive measure of outcomes. Results EHEs increased the risk of pediatric hospital admissions for respiratory illnesses by 26% (95% CI: 14%-40%), asthma by 29% (16%-44%); infectious and parasitic diseases by 36% (24%-50%), lower respiratory infections by 50% (36%-67%), and enteritis by 19% (7%-32%). EHEs also increased the risk of ED visits for lower respiratory infections by 10% (0%-21%), asthma by 18% (7%-29%), heat-related illnesses by 211% (193%-230%), heatstroke by 590% (550%-622%), and dehydration by 35% (25%-46%), but not for other causes. Admissions and ED visits due to injuries and transportation related injuries were negatively associated with EHEs. Neither all-cause hospital admissions nor ED visits were associated with EHEs. Conclusions In Ontario, EHEs decreased the risk of pediatric emergency healthcare utilization for injuries and increased the risk of respiratory illnesses, asthma, heat-related illnesses, heatstroke, dehydration, infectious and parasitic diseases, lower respiratory infections, and enteritis. Tailored policies and programs that reflect the specific heat-related vulnerabilities of children to respiratory and infectious illnesses are warranted in the face of a rapidly warming climate. Environmental epidemiology pediatric health climate change extreme heat hospital admissions emergency department Figures Figure 1 Figure 2 Figure 3 1. Background Climate change threatens the health of populations through a myriad of hazards including wildfires, droughts, famine, drinking water contamination, vector-borne diseases, and extreme heat events (EHEs). According to Canada’s Changing Climate Report, Canada is warming at twice the average rate globally.( 2 ) The annual number of extremely hot days is estimated to double in Canada over the next 30 years.( 3 ) Definitions of an EHE vary by region but are generally defined as 2 or more consecutive days with average temperatures significantly greater than typical for the region.( 4 ) In Canada, this temperature is typically around 30°C (between 28°C to 35°C), although it varies between and within provinces and territories.( 5 ) Heat can directly impact multiple organ systems, worsen existing conditions, and increase the risk of injuries by impacting behaviour.( 6 – 9 ) EHEs can also overwhelm healthcare services and infrastructure such as the supply of electrical power and water, as the rate of resource use exceeds system capacities; this can increase risk of transmission of water- and food-borne illnesses.( 6 , 8 , 10 , 11 ) While the epidemiologic literature on heat has focused primarily on adults, children may exhibit unique effects. Within the literature on EHEs and pediatric health outcomes, studies have typically used coarse age groups for children under 18. Children’s activity patterns and dependence on caregivers may also heighten their susceptibility as they spend more time outside and are unable to respond to heat themselves.( 12 ) Compared to adults, children have a higher surface area to mass ratio, higher temperature at which sweating begins, lower sweating capacity, lower blood volume and lower cardiac output.( 13 , 14 ) These physiological factors combine to increase strain on circulatory systems and decrease ability to thermoregulate.( 13 ). Only two studies have been conducted in Canada which examined solely hospitalizations due to drownings ( 15 ) and emergency department (ED) visits due to respiratory and infectious diseases ( 16 ). In part due to lack of research specific to the impact of EHEs on children, current heat-related public health interventions may not be optimally tailored to the unique needs of children. For example, the temperature thresholds used for Ontario’s heat warning criteria are based on increases in all-cause mortality of the general population.( 5 ) The aim of this study was to identify specific pediatric uses of emergency healthcare associated with EHEs among children in Ontario. 2. Methods 2.1. Population The population in this study was children ages 0–18 years in Ontario who were admitted to a hospital for urgent (i.e. non-elective) care or to an ED during warm months (May-September) between 2005 to 2015. Age was categorized into “0–4”, “5–12” and “13–18” years. These categories were selected to facilitate comparisons with similar studies ( 16 – 20 ). Sex was categorized as male or female; records with missing responses for sex were included in analyses that were not stratified by sex. Residential locations of study participants were examined at the resolution of the Forward Sortation Area (FSA) boundaries. FSAs are the first three digits of a postal code and reflect the part of the province, whether the area is rural or urban and the specific region, though do not reflect the population or geographic size. ( 21 ) 2.2. Outcome measures This study examined two measures of emergency healthcare utilization which may reflect conditions of varying severity: hospital admissions and ED visits. A child with a severe condition requiring longer-term treatment, for example a severe asthma attack, would be admitted to the hospital following an ED visit. Conversely, conditions needing only short-term emergency healthcare assessment, for example receiving a prescription for an antibiotic to treat otitis, would need only to be seen in the emergency department without escalation to hospital admission. The inclusion of both datasets in this study allows for the comparison of type of healthcare utilization by condition. Several causes (all-cause, respiratory, asthma, injury, heat-related, heatstroke, dehydration, renal disease, infectious diseases, otitis, enteritis) were defined a priori based on a) literature ( 12 , 18 , 20 , 22 – 26 ) and b) causes of pediatric emergency healthcare use not previously addressed in a Canadian context (all-cause, asthma, injury, falls, transportation-related injuries, heat, heatstroke, dehydration, renal disease, otitis, enteritis, lower respiratory infections) that we hypothesized as potentially being associated directly or indirectly with EHEs. Deidentified hospital admission data from the Discharge Abstract Database (DAD) and ED visit data from the National Ambulatory Care Reporting System (NACRS), national databases of healthcare utilization by province, were classified using the medical classification list of the World Health Organization’s (WHO) 10th revision of the International Statistical Classification of Disease and Related Health Problems (ICD-10), shown in Table 1 . ICD-10 codes reached complete implementation in Ontario in 2002.( 27 ) The ICD-10 codes of the primary diagnoses were used in defining outcome. However, ICD-10 codes for external causes like drownings and falls are specified in secondary diagnosis fields; so, these external causes were defined using ICD-10 codes in either primary or secondary fields. These data provided counts of ED visits and hospital admissions by cause, date, age, sex, and FSA. No personal identifying information on each presentation were provided therefore it was not possible to link the ED visit and hospital admission data to each other, rather analyses of each were conducted separately. Table 1 Outcome measures and corresponding ICD-10 codes Outcome ICD-10 Code(s) Respiratory J00-J99 Asthma J45 Injury S00-T66, T68-88 Drowning V90, V92, W67-W70, W73, W74 Falls W00-W19 Transportation V01-V99 Heat T67, E86, E87 Heatstroke T67 Dehydration E86-87 Renal N00-N399 Inf/Parasitic A00-B99 Otitis H60, H65-67 Enteritis A00-A09 Lower Resp J12-J18, J20-J22 2.3. Exposure measures EHEs are generally defined as consecutive days with daily temperatures significantly greater than expected for the given location.( 4 ) Commonly, temperature thresholds are set at the regional 95th ( 26 , 28 – 30 ), 97.5th ( 28 ), and/or the 99th ( 30 – 34 ) percentile of the location-specific seasonal temperature distribution. Similarly, this study used percentile-based thresholds of daily maximum temperature averages for each FSA during warm months (May to September) within the study period (2005–2015). The primary analysis defined EHEs as two consecutive days above the 99th percentile, the most restrictive definition of a heatwave. Broader definitions were used in sensitivity analyses using the 97.5th and 95th percentiles of temperature, and 1- or 2-day lag periods since lagged effects have been found in similar studies( 11 , 34 – 38 ). Heat warnings in Ontario are issued by Environment and Climate Change Canada (ECCC) at temperatures akin to the regional 95th percentile of temperature found in this study.( 5 ) Relative humidity inhibits the thermoregulatory effect of sweating by reducing the evaporative capacity of the environment. For this reason, relative humidity, rather than absolute humidity, was evaluated within a sensitivity analysis, as evaluated in similar analyses( 15 , 16 , 25 , 39 , 40 ). Exposure measures of all analyses are summarized below in Table 2 . Table 2 Exposure measures and corresponding definitions Exposure Definition Primary Analysis 99th percentile Lag 0 Same day exposure to 2 consecutive days with daily maximum temperature above the 99th percentile of temperature within an FSA Sensitivity Analyses Relative humidity Same day exposure to 2 consecutive days with daily maximum temperature above the 99th percentile of temperature within an FSA, controlling for relative humidity Lag 1 1 day lagged exposure to 2 consecutive days with daily maximum temperature above the 99th percentile of temperature within an FSA Lag 2 2 day lagged exposure to 2 consecutive days with daily maximum temperature above the 99th percentile of temperature within an FSA 97.5th percentile Same day exposure to 2 consecutive days with daily maximum temperature above the 97.5th percentile of temperature within an FSA 95th percentile Same day exposure to 2 consecutive days with daily maximum temperature above the 95th percentile of temperature within an FSA 2.4. Analytic approach 2.4.1. Study Design This study utilized a space-time-stratified case-crossover study design. Stratification of space was done at the residential FSA level, and stratification of time was done by day-of-week. The case-crossover design, as an alternative to time series regression, is commonly used in environmental epidemiology studies of associations between short-term exposures and outcomes ( 41 ) by comparing outcomes in individuals when exposed and unexposed.( 42 ) This study applied this design by comparing pediatric emergency healthcare utilization counts on EHE days to 3–4 control days on the same day of the week for each week in the same month and year within the same FSA, effectively comparing healthcare utilization within the same population. A visual illustration of the study design can be found in the supplementary material, Additional_File_2 . By matching counts on exposed days to unexposed days drawn from the same populations, confounding by variables that do not vary week to week (ex. age, sex, SES) is eliminated by design.( 43 ) Risk of confounding is thereby limited to variables that change over short periods of time (e.g. humidity). Similarly, since population sizes of FSAs do not significantly change week to week there is no need to incorporate population offsets in the models. In case-crossover studies, control days are often selected unidirectionally (control days are either before or after case day) or bidirectionally (control days are equally split before and after case day). These selection methods, however, introduce risk of biases from time-trends in exposure or outcome.( 44 ) The time-stratified case-crossover design of this study removes patterns in control days (control days may be before and/or after case day), thus avoiding risk of time-trend biases.( 45 , 46 ) 2.4.2. Analysis Daily 1-squared-kilometer gridded estimates of temperature were computed by Daymet, supported by NASA, and were aggregated to the FSA level.( 47 ) A custom QGIS ( 48 ) plugin integrating the Google Earth Engine (GEE) python API was used to automate the extraction of the Daymet grid cell-level data ( 49 ). Daily temperature and water pressure values were averaged across inhabited land within each FSA using the GEE mean reducer algorithm. Given the extensive heat sink coverage of water and forest in Ontario, it was important to identify inhabited land. Inhabited land, defined as having a population density of 0.4 or more people per square kilometre, was identified using ecumene boundaries retrieved from Statistics Canada.( 50 ) After restricting to these ecumene, the resulting polygons were used to define each FSA. The boundary of each FSA’s polygon may include 100% of some gridded cells and only part of other cells. Thus, to calculate the average temperature within an FSA, the Daymet daily temperature in each gridded cell was weighted corresponding to that cell’s pixel fraction inside the FSA polygon using the ee.Reducer.mean() parameter ( 49 ). The resulting dataset contained the FSA (of child’s residence); maximum, minimum and average daily temperatures in °C; water vapour pressure in kPa; and relative humidity (using Bolton’s Equation). Temperature equivalents of the 99th, 97.5th or 95th percentiles were identified in each FSA. A binary (0/1) indicator variable was created to identify days which met the EHE definition for each FSA (see Table 2 ). Days on which the maximum temperature exceeded the threshold of the respective FSA and was preceded by a day on which the maximum temperature also exceeded the threshold was designated with a 1 to indicate it as an EHE day. All other days were considered non-EHE days (designated with a 0). Using a similar binary indicator variable, lag 1- and 2-days were identified as 1 or 2 days after an EHE day. Using date and FSA, the data merge was conducted with 100% linkage. The percentile-specific data frames were fit in generalized nonlinear models (gnm) of conditional quasi-Poisson regression in R using the “gnm” package.( 51 ) Rate ratios (RR) and 95% confidence intervals (CI) were calculated for each outcome comparing risk on EHE days to that on non-EHE days( 41 ). Associations were also estimated by sex and 5-year age groups. 3. Results 284,939 all-cause hospital admissions and 5,875,119 all-cause emergency department visits were included in this study. Of the 5 general causes included in this analysis, injuries and respiratory illnesses were the most common causes of both hospital admissions (14.55% and 15.16%, respectively) and ED visits (32.80% 14.33%, respectively), as shown in Figs. 1 and 2 . To maintain precision of reported results, ED visits due to drowning, and hospital admissions due to drowning, heat, heatstroke, dehydration, and otitis were omitted as these outcomes occurred less than 20 times on EHE days during the study period. In contrast to ED visits, individuals who sought emergency care may be admitted to hospitals if they present with symptoms requiring specialized care or extended observation. The fewer counts of hospital admissions compared to ED visits likely reflects the relative rarity of ailments requiring escalation from an ED visit to an admission. Consequently, the results herein should be interpreted considering both outcome measures and in consideration that differences observed between hospital admissions and ED visits may reflect the nature of the care required. During EHEs, statistically significant increases in risk were observed for hospital admissions due to respiratory illnesses, asthma; infectious and parasitic diseases, lower respiratory infections, and enteritis (Fig. 3 ). Similarly, on EHE days, risk increased for ED visits due to asthma; heat-related illnesses, heatstroke, dehydration, and lower respiratory infections. In contrast, decreases in risk were observed on EHE days for both hospital admissions and ED visits for injuries and transportation-related injuries, as well as ED visits due to falls. Associations observed in the primary analyses were consistent when adjusting for relative humidity (see tables A4 and A5). Associations were generally attenuated in the days following an EHE (lag 1 or 2) for most outcomes, however, hospital admissions and ED visits due to enteritis were highest the day after an EHE (lag 1). Similarly, 2 days after an EHE (lag 2) risk of renal and infectious and parasitic disease hospital admissions was highest. Associations were generally strongest in the primary analysis and approached the null in sensitivity analyses with lower threshold temperatures. For example, asthma admissions were associated with a 29% (95%CI: 16%, 44%) increased risk when EHEs were defined at the 99th percentile of temperature, 8% (2%, 15%) at the 97.5th, and a null effect (-7%, 0%) at the 95th ; conversely, EHEs were associated with a decreased risk of injury admissions by 13% (21%, 4%) at the 99th percentile, 7% (11%, 2%) at the 97.5th and a null effect (4%, -2%) at the 95th. These patterns suggest health risks increase with increasing temperature. As shown in Tables A6 and A7, EHEs had the fewest positive associations with hospital admissions or ED visits in children in the 5-12-year age group. It follows, then, that most all-cause admissions and ED visits were found in the 0–4 age category (46.96% and 34.76%, respectively) and the 13–18 age category (29.46% and 33.42%, respectively), with the fewest in the 5–12 age category (23.58% and 31.82%, respectively). In sex-stratified analyses, presented in Tables A8 and A9, more harmful associations were found for hospital admissions than ED visits, and in females than males. When compared to hospitalizations, ED visits had fewer associations of elevated risk overall and far fewer that were shared by both sexes. During EHEs, both sexes exhibited higher risks of hospitalizations due to respiratory illness, asthma; infectious and parasitic diseases, lower respiratory infections and enteritis, and ED visits due to asthma, heat and heatstroke. The sexes differ, however, in associated risks of injury, falls, transportation-related injury and renal disease for which males’ risk decreased while females’ risk increased or saw no change during EHEs. During EHEs, hospital admissions for injuries and transportation-related injuries increased in females and decreased in males. 4. Discussion Many results of this study align with hypothesized effects and relationships based on biological plausibility, and evidence from similar research on respiratory illness ( 39 , 52 ), asthma ( 38 ), enteritis ( 19 ), and direct heat-related illnesses ( 18 , 19 , 26 , 29 , 35 , 52 ). In the primary analysis, EHEs were found to increase the risk of hospital admissions due to general respiratory illnesses and asthma; general infectious and parasitic diseases, lower respiratory infections, and enteritis. Similarly, risk of ED visits due to asthma; heat-related illnesses, heatstroke, dehydration, and lower respiratory infections were positively associated with EHEs. Interestingly, EHEs were found to reduce risk of hospital admissions and ED visits for general injuries and transportation related injuries, and ED visits due to falls. This novel finding contrasts with those of similar studies in settings including New York City ( 53 , 54 ) and England ( 55 ) in which injuries amongst children were found to increase during EHEs. This may reflect the efficacy of heat warnings in mitigating effects of EHEs on child health through activity modifications like abstaining from sports and physical activities, for example. The same theory may help explain why sex stratification showed that during EHEs males were at a reduced risk of injuries, falls, and transportation-related injuries as well as hospitalizations due to renal disease. This protective effect contrasts with results of a study in New York City which found the highest risk of unintentional injury among males ages 5 to 9 years old.( 53 ) When stratified by age, the 0–4 and 13–18 year age groups exhibited the most positive associations. Previous studies have also found that, when compared to older age groups, children under 5 have higher risks of emergency department visits( 54 ), asthma( 38 ), respiratory illness( 39 , 52 ) and renal diseases( 52 ), This may be due to heightened caution for the 0–4 age group by caregivers and healthcare providers increasing the likelihood of an ED visit patient being admitted to hospital care, and due to the 13–18 age group being more physically active and independent which can heighten susceptibility to EHEs. Adjusting for relative humidity did not alter primary results. Associations, either positive or negative, were often strongest in the primary analysis and approached the null in sensitivity analyses with lower threshold temperatures and lag days from exposure. This monotonically decreasing trend effect demonstrates an exposure-response relationship between heat and risk of pediatric emergency healthcare. However, although risks of most outcomes were generally highest on same-day exposure to EHEs, hospital admissions and ED visits due to enteritis were highest the day after an EHE (lag 1), reflecting typical incubation periods of common foodborne and waterborne pathogens( 56 ) including nontyphoidal Salmonella spp. (12–96 hours)( 57 ), norovirus (12–48 hours)( 58 ), and Campylobacter spp. (2–4 days)( 59 ). Similarly, risks of renal and infectious and parasitic disease hospital admissions were highest 2 days after an EHE (lag 2), possibly reflecting the cumulative effects of days of dehydration on renal and immune system function. EHEs do not have a universal definition. Rather, they vary in threshold, temperature, and length making relative each of the terms “extreme”, “heat” and “event”. This consequently complicates comparisons. When aptly compared to the results of primary or sensitivity analyses herein, similar literature has made similar findings. Pediatric hospital admissions due to respiratory illnesses ( 26 , 39 ), asthma ( 38 ), and heat ( 26 ) were positively associated with EHEs in Australia, the US, and China. It is also important to note that studies used varied age categories for children( 16 , 19 ), including several which used coarse age categories of 0–14 and 15–64 years( 52 ), or 0–4 and 5–65( 18 ). In two Ontario hospitals, Wilk et. al. found pediatric infectious and respiratory diseases ED visits were positively associated with EHEs. This is the only prior study of pediatric ED visits in Canada, and it included only these two outcome measures. Respiratory illnesses ( 52 ), asthma ( 35 ), heat-related illnesses ( 26 , 29 , 44 , 45 ), dehydration ( 18 , 35 , 52 , 60 ), renal diseases ( 26 ), otitis ( 19 ), and bacterial enteritis ( 19 , 35 ) have been positively associated with EHEs in studies outside of Canada. Of these outcomes, our study similarly found that EHEs increased risk of ED visits for asthma, heat-related illnesses, and dehydration in primary analysis, as well as with renal diseases and otitis in 1-day lag, and renal diseases under the 97.5th and 95th percentile definitions. To date, this study was the largest and most comprehensive examination globally of EHEs and pediatric healthcare use, and the first study in Canada to assess associations of EHEs and pediatric hospital admissions. The data spanned the warm months of 10 years, including 284,939 hospital admissions, and 5,875,119 ED visits. The large scale of this analysis increases the power and representativeness of the study, especially for general, broadly defined causes of admissions and ED visits such as respiratory disease and injury. Given that Ontario provides universal free healthcare to residents and reports all admissions and ED visits to NACRS and DAD, the data used in this study is comprehensive and includes all pediatric cases. The space-time stratified case crossover design applied in this analysis controls for characteristics that do not vary over time or vary only slowly (e.g., age, race, sex) and for time-trend and seasonal patterns. Conditional quasi-Poisson regression common in similar studies ( 16 , 61 – 67 ), was applied instead of the traditionally used conditional logistic regression because it is simpler and faster to code, and allows for adjusting for overdispersion and auto-correlation.( 41 ) Poisson distribution models rate counts given exposure, whereas conditional logistic regression models odds of a binary outcome given a one unit increase in exposure. To create a binary outcome to use conditional logistic regression, the data would need to be expanded such that each stratum contains only one case, or semi-expanded such that each stratum contains only one case day and was weighted by the number of cases that day. This study also benefits from the application of Daymet daily estimates of temperature aggregated to residential FSA, which are a more accurate measurement of exposure than the alternative of regional weather station data. Despite the above strengths, since effects were measured over the whole study period conclusions cannot be drawn concerning possible changes in these effects over time. It is possible that the effects may differ year to year or between early and late season. Additionally, the ecological design of this study limits generalizability. Individual level factors, like SES and comorbidities, may modify the effect of EHEs on pediatric emergency healthcare use but were not included in this study. Built environments, housing and neighborhood characteristics may affect susceptibility to EHEs within FSAs.( 68 ) Moreover, children who live in remote areas, areas where ED wait times are long, or who have low SES may not be well represented in this study. However, this is unlikely to have systematically biased results since the distribution of urban and rural FSAs of study participants was comparable to the distribution of urban and rural FSAs of children in Ontario, according to the 2011 census( 69 ). In fact, children with rural FSAs made up a slightly higher proportion (7%) of the distribution within this study than was observed in the 2011 census. ECCC’s heat alert protocols in Canada identify conditions in which increased all-cause mortality of the general population are likely to occur.( 3 ) In Ontario, regional alert criteria are 2 or more consecutive days with daytime maximum temperatures above 29°C in Northern Ontario, or 31°C in South and Southwestern Ontario.( 5 ) These temperatures are most similar to those of the 95th percentile of temperature (see Additional File 1 Table A3). The results of this study may suggest that ECCC’s heat warnings are effective in preventing emergency healthcare use for pediatric injuries, as evidenced by the consistently protective effect of EHEs on injuries observed in all analyses and a contrasting harmful association in adults who are largely unable to change their activities (ex. outdoor labour, commuting to work)( 33 , 34 ). Despite these regional alerts, in analyses using the 95th percentile, EHEs increased risk of pediatric ED visits for heat-related illnesses, heatstroke, dehydration, renal diseases, and otitis; as well as hospital admissions for renal diseases, lower respiratory infections, and bacterial enteritis. It is also important to consider that with increasing intensity and frequency of EHEs due to climate change, today’s 99th percentile temperatures may be the 95th percentile in the near future ( 3 ). Given the suggested efficacy of heat warnings in preventing pediatric injuries, these outcomes may be preventable with more stringent child-specific heat warnings at lower temperatures along with messaging specifying the increased risk of these health impacts. 5. Conclusions Complex relationships were found between EHEs and pediatric emergency healthcare utilization in Ontario. EHEs were positively associated with pediatric hospital admissions due to respiratory illnesses, asthma; infectious and parasitic diseases, lower respiratory infections, and enteritis. EHEs were also positively associated with ED visits due to asthma; heat-related illnesses, heatstroke, dehydration; and lower respiratory infections. Surprisingly, this study found that EHEs had a protective association with injury and transportation-related injury hospital admissions and ED visits. In wake of a quickly warming climate, it is imperative that heat alert protocols, community emergency planning, urban design, and healthcare facility preparedness be tailored to reflect the impacts of EHEs on child health. Critical gaps in research remain in predicting trends, identifying individual-level vulnerabilities, assessing mental health impacts, and identifying thresholds at which heat impacts child health. Abbreviations EHE - Extreme Heat Event FSA - Forward Sortation Area ED - Emergency Department CI - Confidence Interval RR - Rate Ratios DAD - Discharge Abstract Database NACRS - National Ambulatory Care Reporting System WHO - World Health Organization ICD-10 - International Statistical Classification of Disease and Related Health Problems, 10th Revision ECCC - Environment and Climate Change Canada SES - Socioeconomic Status gnm - Generalized Nonlinear Models CIHI - Canadian Institute for Health Information Declarations Acknowledgements This work was conducted on the unceded, ancestral territory of the xwməθkwəy̓əm (Musqueam) People where the University of British Columbia is now situated. Daily estimates of temperature were computed by NASA’s Daymet and were aggregated to the FSA level by Jean-Nicolas Côté of Health Canada, supported by Dr. Eric Lavigne. Authors’ Contributions Health outcome data was collected by the CIHI and restricted and confidential access to the data was granted to Dr. Kate Weinberger by Dr. Eric Lavigne. Dr. Weinberger processed the data into daily timeseries for each FSA using age group boundaries and disease causes that Hallah Kassem selected based on a priori accounts and expectations of effects of heat on cause-specific diagnoses. Stemming from her Master’s thesis (70), Kassem cleaned, merged, analysed, and interpreted the data and wrote this paper which was then proof read by Drs. Michael Brauer, Kate Weinberger and Eric Lavigne. Funding This work was supported by the Banting Research Foundation for the 2020 Discovery Award Competition. The funding source was not involved in conduct of the research or preparation of this article. Ethics approval Prior to study conduct, review and approval were received from the University of British Columbia Office of Research Ethics Behavioural Research Ethics Board (H20-03369). Competing Interests The authors have no competing interests to declare. Consent for publication Not applicable. Availability of data and materials The health outcome data that support the findings of this study are available from Statistics Canada, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of Statistics Canada. The extracted Daymet data used herein are available from the corresponding author on reasonable request. References Kassem H. Effects of extreme heat on child healthcare use [Internet]. Posit: Shinyapps.io. 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Extreme temperatures and emergency department admissions for childhood asthma in Brisbane, Australia. Occup Environ Med. 2013;70(10):730–5. Armstrong BG, Gasparrini A, Tobias A. Conditional Poisson models: a flexible alternative to conditional logistic case cross-over analysis. BMC Med Res Methodol. 2014;14(1). Wu Y, Li S, Guo Y. Space-Time-Stratified Case-Crossover Design in Environmental Epidemiology Study. Heal Data Sci. 2021;2021. Jaakkola JJK. Case-crossover design in air pollution epidemiology. Supplement: In: European Respiratory Journal; 2003. Janes H, Sheppard L, Lumley T. Case-crossover analyses of air pollution exposure data: Referent selection strategies and their implications for bias. Vol. 16, Epidemiology. Epidemiology; 2005. pp. 717–26. Xu R, Xiong X, Abramson MJ, Li S, Guo Y. Ambient temperature and intentional homicide: A multi-city case-crossover study in the US. Environ Int. 2020;143(June):105992. Janes H, Sheppard L, Lumley T. Overlap bias in the case-crossover design, with application to air pollution exposures. Stat Med Stat Med. 2005;24:285–300. Côté J-N. DayMet data extraction methodology. 2022. QGIS. Welcome to the QGIS project [Internet]. QGIS Geographic Information System. Open Source Geospatial Foundation Project. 2022 [cited 2022 Sep 26]. https://qgis.org/en/site/ Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D, Moore R. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens Environ. 2017;202:18–27. Statistics Canada. Population Ecumene Census Division, Cartographic Boundary files – 2016 Census. 2016;(92). Turner H, Firth D. Generalized nonlinear models in R: An overview of the gnm package [Internet]. 2022 [cited 2023 Jun 4]. https://cran.r-project.org/package=gnm van Loenhout JAF, Delbiso TD, Kiriliouk A, Rodriguez-Llanes JM, Segers J, Guha-Sapir D, et al. Heat and emergency room admissions in the Netherlands. BMC Public Health. 2018;18(1):108. Girma B, Liu B, Schinasi LH, Clougherty JE, Sheffield PE. High ambient temperatures associations with children and young adult injury emergency department visits in NYC. Environ Res Heal. 2023;1(3):035004. Sheffield PE, Herrera MT, Kinnee EJ, Clougherty JE. Not so little differences: variation in hot weather risk to young children in New York City. Public Health. 2018;161:119–26. Parsons N, Odumenya M, Edwards A, Lecky F, Pattison G. Modelling the effects of the weather on admissions to UK trauma units: a cross-sectional study. Emerg Med J. 2011;28(10):851–5. Scallan E, Hoekstra RM, Angulo FJ, Tauxe RV, Widdowson MA, Roy SL, et al. Foodborne Illness Acquired in the United States—Major Pathogens - 17, Number 1—January 2011 - Emerging Infectious Diseases journal - CDC. Emerg Infect Dis. 2011;17(1):7–15. Salmonellosis N. | CDC Yellow Book 2024 [Internet]. [cited 2024 Jun 14]. https://wwwnc.cdc.gov/travel/yellowbook/2024/infections-diseases/salmonellosis-nontyphoidal Norovirus | CDC Yellow. Book 2024 [Internet]. [cited 2024 Jun 14]. https://wwwnc.cdc.gov/travel/yellowbook/2024/infections-diseases/norovirus Campylobacteriosis | CDC Yellow. Book 2024 [Internet]. [cited 2024 Jun 14]. https://wwwnc.cdc.gov/travel/yellowbook/2024/infections-diseases/campylobacteriosis van der Linden N, Longden T, Richards JR, Khursheed M, Goddijn WMT, van Veelen MJ et al. The use of an acclimatisation heatwave measure to compare temperature-related demand for emergency services in Australia, Botswana, Netherlands, Pakistan, and USA. PLoS ONE. 2019;14(3). Tobías A, Madaniyazi L, Gasparrini A, Armstrong B. High Summer Temperatures and Heat Stroke Mortality in Spain. Epidemiology. 2023;34(6):892–6. Colonna KJ, Alahmad B, Choma EF, Albahar S, Al-Hemoud A, Kinney PL, et al. Acute exposure to total and source-specific ambient fine particulate matter and risk of respiratory disease hospitalization in Kuwait. Environ Res. 2023;237:117070. Htay ZW, Ng CFS, Kim Y, Lim YH, Iwagami M, Hashizume M. Associations between short-term exposure to ambient temperature and renal disease mortality in Japan during 1979–2019. Environ Epidemiol. 2024;8(1):E293. Lu P, Xia G, Zhao Q, Green D, Lim YH, Li S, et al. Attributable risks of hospitalizations for urologic diseases due to heat exposure in Queensland, Australia, 1995–2016. Int J Epidemiol. 2022;51(1):144–54. Kubo R, Ueda K, Seposo X, Honda A, Takano H. Association between ambient temperature and intentional injuries: A case-crossover analysis using ambulance transport records in Japan. Sci Total Environ. 2021;774:145511. Huang W, Li S, Vogt T, Xu R, Tong S, Molina T, et al. Global short-term mortality risk and burden associated with tropical cyclones from 1980 to 2019: a multi-country time-series study. Lancet Planet Heal. 2023;7(8):e694–705. Ingole V, Sheridan SC, Juvekar S, Achebak H, Moraga P. Mortality risk attributable to high and low ambient temperature in Pune city, India: A time series analysis from 2004 to 2012. Environ Res. 2022;204:112304. Gemmell E, Adjei-Boadi D, Sarkar A, Shoari N, White K, Zdero S, et al. In small places, close to home: Urban environmental impacts on child rights across four global cities. Health Place. 2023;83:103081. Census Profile [Internet]. [cited 2023 Feb 26]. https://www12.statcan.gc.ca/census-recensement/2011/dp-pd/prof/index.cfm?Lang=E&TABID=2 Kassem H. A case-crossover investigation of associations between extreme heat and pediatric health. 2023. Additional Declarations No competing interests reported. Supplementary Files AdditionalFile1.docx Additional_File_1.docx contains the following descriptive and statistical data tables not shown in the manuscript: Table A1. Age and sex distributions of pediatric hospital admissions, N= 284,939 Table A2. Age and sex distributions of pediatric ED visits, N=5,875,119 Table A3. Temperature equivalents of percentiles of maximum daily temperatures by region Table A4. Pediatric hospital admissions and EHEs, RR (95%CI) Table A5. Pediatric emergency department visits and EHEs, RR (95%CI) Table A6. Hospital admissions primary analysis by age category, RR (95%CI) Table A7. ED visits primary analysis by age category RR (95%CI) Table A8. Hospital admissions primary analysis by sex Table A9. ED visits primary analysis by sex AdditionalFile2.pptx Additional_File_2.pptx contains a graphical abstract summarizing the contents of this article in a pictorial form. Cite Share Download PDF Status: Published Journal Publication published 07 Jun, 2025 Read the published version in Environmental Health → Version 1 posted Editorial decision: Revision requested 26 Sep, 2024 Reviews received at journal 19 Sep, 2024 Reviewers agreed at journal 30 Aug, 2024 Reviewers invited by journal 28 Aug, 2024 Editor assigned by journal 13 Aug, 2024 Submission checks completed at journal 13 Aug, 2024 First submitted to journal 13 Aug, 2024 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. 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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-4904542","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":351750864,"identity":"d88a8547-faf7-4044-880e-d7ef1c01c124","order_by":0,"name":"Hallah Kassem","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIiWNgGAWjYBACAwbmxgOMDUAaDCqAmJm5gYAWxgYkLWdAWhhJ0cLYBibxazFnP9hw4OcOBmP+/uMPH92cVxvN3w7U8qNiG04tlj2JDQd7zzCYSdzIMTbO3XY8d8ZhxgbGnjO3cTvsQGLDAd42BhuGGzxs0rnbjuU2ALUwM7bh0XL+YcPBv0At8uePP/+dO+dY7nyCWm4kNhwG2mJmcCDBjDm3oSZ3A2EtDxsOy7ZJGBsC/SKdc+xA7kagloN4/XI++eDDt202hvPOH3/4OaemLnfe+cMHH/yowK0FCiRgjMNg8gAh9cigjhTFo2AUjIJRMEIAAF/8ZNsU1S5jAAAAAElFTkSuQmCC","orcid":"","institution":"University of British Columbia","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Hallah","middleName":"","lastName":"Kassem","suffix":""},{"id":351750865,"identity":"091340c4-33be-49a2-bbeb-1c6ed3c3d3fb","order_by":1,"name":"Eric Lavigne","email":"","orcid":"","institution":"Environmental Health Science and Research Bureau, Health Canada","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Eric","middleName":"","lastName":"Lavigne","suffix":""},{"id":351750866,"identity":"11ea8d4a-67e1-4a96-b413-3089ea028af5","order_by":2,"name":"Kate Weinberger","email":"","orcid":"","institution":"ICF","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kate","middleName":"","lastName":"Weinberger","suffix":""},{"id":351750867,"identity":"fee129b0-8cbb-4e53-b142-df1b59124ad0","order_by":3,"name":"Michael Brauer","email":"","orcid":"","institution":"University of British Columbia","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Michael","middleName":"","lastName":"Brauer","suffix":""}],"badges":[],"createdAt":"2024-08-13 06:29:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4904542/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4904542/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12940-025-01153-y","type":"published","date":"2025-06-07T15:57:49+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":66372522,"identity":"f8d64a93-f455-443d-8621-c59492778e1c","added_by":"auto","created_at":"2024-10-11 04:55:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":15487,"visible":true,"origin":"","legend":"\u003cp\u003eProportion of hospital admissions by cause (N = 284,939)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4904542/v1/a73486ffaf9aaa09300e5ca7.png"},{"id":66373403,"identity":"1471eed6-3a39-4534-b05d-85032abf71c8","added_by":"auto","created_at":"2024-10-11 05:03:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":13846,"visible":true,"origin":"","legend":"\u003cp\u003eProportion of ED visits by cause (N = 5,875,119)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4904542/v1/88aeab7b2acf0c61eb925f0f.png"},{"id":66372523,"identity":"eed5abff-fc03-4a0b-8bdb-9d97d59a3b7c","added_by":"auto","created_at":"2024-10-11 04:55:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":38631,"visible":true,"origin":"","legend":"\u003cp\u003eProportion of ED visits by cause (N = 5,875,119)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4904542/v1/e24e58735f8757dae3ec31c6.png"},{"id":84242633,"identity":"aaa82159-dfc0-4e02-8b19-7b6e6cbc729b","added_by":"auto","created_at":"2025-06-09 16:10:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":688815,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4904542/v1/94149c0b-4c1f-49c6-b418-8f01b58e8798.pdf"},{"id":66373404,"identity":"dc3523e7-978c-4b5a-bd3f-1632cd6e9267","added_by":"auto","created_at":"2024-10-11 05:03:43","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":55249,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAdditional_File_1.docx\u003c/em\u003e contains the following descriptive and statistical data tables not shown in the manuscript:\u003c/p\u003e\n\u003cp\u003eTable A1. Age and sex distributions of pediatric hospital admissions, N= 284,939\u003c/p\u003e\n\u003cp\u003eTable A2. Age and sex distributions of pediatric ED visits, N=5,875,119\u003c/p\u003e\n\u003cp\u003eTable A3. Temperature equivalents of percentiles of maximum daily temperatures by region\u003c/p\u003e\n\u003cp\u003eTable A4. Pediatric hospital admissions and EHEs, RR (95%CI)\u003c/p\u003e\n\u003cp\u003eTable A5. Pediatric emergency department visits and EHEs, RR (95%CI)\u003c/p\u003e\n\u003ch5\u003eTable A6. Hospital admissions primary analysis by age category, RR (95%CI)\u003c/h5\u003e\n\u003ch5\u003eTable A7. ED visits primary analysis by age category RR (95%CI)\u003c/h5\u003e\n\u003ch5\u003eTable A8. Hospital admissions primary analysis by sex\u003c/h5\u003e\n\u003ch5\u003eTable A9. ED visits primary analysis by sex\u003c/h5\u003e","description":"","filename":"AdditionalFile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4904542/v1/da37d90ce6aec0f4d0bffeb3.docx"},{"id":66372525,"identity":"84e0bd49-2f6e-42dd-8b03-0b053921c3ae","added_by":"auto","created_at":"2024-10-11 04:55:43","extension":"pptx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1046251,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAdditional_File_2.pptx\u003c/em\u003e\u003cstrong\u003e \u003c/strong\u003econtains a graphical abstract summarizing the contents of this article in a pictorial form.\u003c/p\u003e","description":"","filename":"AdditionalFile2.pptx","url":"https://assets-eu.researchsquare.com/files/rs-4904542/v1/7ff831a7f8a4a95b850754a1.pptx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Extreme heat and pediatric health in a warming world: a space-time stratified case-crossover investigation in Ontario, Canada","fulltext":[{"header":"1. Background","content":"\u003cp\u003eClimate change threatens the health of populations through a myriad of hazards including wildfires, droughts, famine, drinking water contamination, vector-borne diseases, and extreme heat events (EHEs). According to Canada\u0026rsquo;s Changing Climate Report, Canada is warming at twice the average rate globally.(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) The annual number of extremely hot days is estimated to double in Canada over the next 30 years.(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Definitions of an EHE vary by region but are generally defined as 2 or more consecutive days with average temperatures significantly greater than typical for the region.(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) In Canada, this temperature is typically around 30\u0026deg;C (between 28\u0026deg;C to 35\u0026deg;C), although it varies between and within provinces and territories.(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) Heat can directly impact multiple organ systems, worsen existing conditions, and increase the risk of injuries by impacting behaviour.(\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) EHEs can also overwhelm healthcare services and infrastructure such as the supply of electrical power and water, as the rate of resource use exceeds system capacities; this can increase risk of transmission of water- and food-borne illnesses.(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eWhile the epidemiologic literature on heat has focused primarily on adults, children may exhibit unique effects. Within the literature on EHEs and pediatric health outcomes, studies have typically used coarse age groups for children under 18. Children\u0026rsquo;s activity patterns and dependence on caregivers may also heighten their susceptibility as they spend more time outside and are unable to respond to heat themselves.(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) Compared to adults, children have a higher surface area to mass ratio, higher temperature at which sweating begins, lower sweating capacity, lower blood volume and lower cardiac output.(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) These physiological factors combine to increase strain on circulatory systems and decrease ability to thermoregulate.(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Only two studies have been conducted in Canada which examined solely hospitalizations due to drownings (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) and emergency department (ED) visits due to respiratory and infectious diseases (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn part due to lack of research specific to the impact of EHEs on children, current heat-related public health interventions may not be optimally tailored to the unique needs of children. For example, the temperature thresholds used for Ontario\u0026rsquo;s heat warning criteria are based on increases in all-cause mortality of the general population.(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) The aim of this study was to identify specific pediatric uses of emergency healthcare associated with EHEs among children in Ontario.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Population\u003c/h2\u003e \u003cp\u003eThe population in this study was children ages 0\u0026ndash;18 years in Ontario who were admitted to a hospital for urgent (i.e. non-elective) care or to an ED during warm months (May-September) between 2005 to 2015. Age was categorized into \u0026ldquo;0\u0026ndash;4\u0026rdquo;, \u0026ldquo;5\u0026ndash;12\u0026rdquo; and \u0026ldquo;13\u0026ndash;18\u0026rdquo; years. These categories were selected to facilitate comparisons with similar studies (\u003cspan additionalcitationids=\"CR17 CR18 CR19\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Sex was categorized as male or female; records with missing responses for sex were included in analyses that were not stratified by sex. Residential locations of study participants were examined at the resolution of the Forward Sortation Area (FSA) boundaries. FSAs are the first three digits of a postal code and reflect the part of the province, whether the area is rural or urban and the specific region, though do not reflect the population or geographic size. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Outcome measures\u003c/h2\u003e \u003cp\u003eThis study examined two measures of emergency healthcare utilization which may reflect conditions of varying severity: hospital admissions and ED visits. A child with a severe condition requiring longer-term treatment, for example a severe asthma attack, would be admitted to the hospital following an ED visit. Conversely, conditions needing only short-term emergency healthcare assessment, for example receiving a prescription for an antibiotic to treat otitis, would need only to be seen in the emergency department without escalation to hospital admission. The inclusion of both datasets in this study allows for the comparison of type of healthcare utilization by condition.\u003c/p\u003e \u003cp\u003eSeveral causes (all-cause, respiratory, asthma, injury, heat-related, heatstroke, dehydration, renal disease, infectious diseases, otitis, enteritis) were defined a priori based on a) literature (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan additionalcitationids=\"CR23 CR24 CR25\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) and b) causes of pediatric emergency healthcare use not previously addressed in a Canadian context (all-cause, asthma, injury, falls, transportation-related injuries, heat, heatstroke, dehydration, renal disease, otitis, enteritis, lower respiratory infections) that we hypothesized as potentially being associated directly or indirectly with EHEs. Deidentified hospital admission data from the Discharge Abstract Database (DAD) and ED visit data from the National Ambulatory Care Reporting System (NACRS), national databases of healthcare utilization by province, were classified using the medical classification list of the World Health Organization\u0026rsquo;s (WHO) 10th revision of the International Statistical Classification of Disease and Related Health Problems (ICD-10), shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. ICD-10 codes reached complete implementation in Ontario in 2002.(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e) The ICD-10 codes of the primary diagnoses were used in defining outcome. However, ICD-10 codes for external causes like drownings and falls are specified in secondary diagnosis fields; so, these external causes were defined using ICD-10 codes in either primary or secondary fields. These data provided counts of ED visits and hospital admissions by cause, date, age, sex, and FSA. No personal identifying information on each presentation were provided therefore it was not possible to link the ED visit and hospital admission data to each other, rather analyses of each were conducted separately.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOutcome measures and corresponding ICD-10 codes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICD-10 Code(s)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJ00-J99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsthma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJ45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInjury\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS00-T66, T68-88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrowning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eV90, V92, W67-W70, W73, W74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFalls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eW00-W19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransportation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eV01-V99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT67, E86, E87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeatstroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDehydration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE86-87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN00-N399\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInf/Parasitic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA00-B99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOtitis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eH60, H65-67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnteritis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA00-A09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLower Resp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJ12-J18, J20-J22\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=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Exposure measures\u003c/h2\u003e \u003cp\u003eEHEs are generally defined as consecutive days with daily temperatures significantly greater than expected for the given location.(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) Commonly, temperature thresholds are set at the regional 95th (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e), 97.5th (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), and/or the 99th (\u003cspan additionalcitationids=\"CR31 CR32 CR33\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e) percentile of the location-specific seasonal temperature distribution. Similarly, this study used percentile-based thresholds of daily maximum temperature averages for each FSA during warm months (May to September) within the study period (2005\u0026ndash;2015). The primary analysis defined EHEs as two consecutive days above the 99th percentile, the most restrictive definition of a heatwave.\u003c/p\u003e \u003cp\u003eBroader definitions were used in sensitivity analyses using the 97.5th and 95th percentiles of temperature, and 1- or 2-day lag periods since lagged effects have been found in similar studies(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan additionalcitationids=\"CR35 CR36 CR37\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Heat warnings in Ontario are issued by Environment and Climate Change Canada (ECCC) at temperatures akin to the regional 95th percentile of temperature found in this study.(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) Relative humidity inhibits the thermoregulatory effect of sweating by reducing the evaporative capacity of the environment. For this reason, relative humidity, rather than absolute humidity, was evaluated within a sensitivity analysis, as evaluated in similar analyses(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Exposure measures of all analyses are summarized below in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eExposure measures and corresponding definitions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDefinition\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePrimary Analysis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e99th percentile Lag 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSame day exposure to 2 consecutive days with daily maximum temperature above the 99th percentile of temperature within an FSA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSensitivity Analyses\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelative humidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSame day exposure to 2 consecutive days with daily maximum temperature above the 99th percentile of temperature within an FSA, controlling for relative humidity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLag 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 day lagged exposure to 2 consecutive days with daily maximum temperature above the 99th percentile of temperature within an FSA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLag 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 day lagged exposure to 2 consecutive days with daily maximum temperature above the 99th percentile of temperature within an FSA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e97.5th percentile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSame day exposure to 2 consecutive days with daily maximum temperature above the 97.5th percentile of temperature within an FSA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e95th percentile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSame day exposure to 2 consecutive days with daily maximum temperature above the 95th percentile of temperature within an FSA\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=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Analytic approach\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1. Study Design\u003c/h2\u003e \u003cp\u003eThis study utilized a space-time-stratified case-crossover study design. Stratification of space was done at the residential FSA level, and stratification of time was done by day-of-week. The case-crossover design, as an alternative to time series regression, is commonly used in environmental epidemiology studies of associations between short-term exposures and outcomes (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e) by comparing outcomes in individuals when exposed and unexposed.(\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e) This study applied this design by comparing pediatric emergency healthcare utilization counts on EHE days to 3\u0026ndash;4 control days on the same day of the week for each week in the same month and year within the same FSA, effectively comparing healthcare utilization within the same population. A visual illustration of the study design can be found in the supplementary material, \u003cem\u003eAdditional_File_2\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eBy matching counts on exposed days to unexposed days drawn from the same populations, confounding by variables that do not vary week to week (ex. age, sex, SES) is eliminated by design.(\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e) Risk of confounding is thereby limited to variables that change over short periods of time (e.g. humidity). Similarly, since population sizes of FSAs do not significantly change week to week there is no need to incorporate population offsets in the models. In case-crossover studies, control days are often selected unidirectionally (control days are either before or after case day) or bidirectionally (control days are equally split before and after case day). These selection methods, however, introduce risk of biases from time-trends in exposure or outcome.(\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e) The time-stratified case-crossover design of this study removes patterns in control days (control days may be before and/or after case day), thus avoiding risk of time-trend biases.(\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2. Analysis\u003c/h2\u003e \u003cp\u003eDaily 1-squared-kilometer gridded estimates of temperature were computed by Daymet, supported by NASA, and were aggregated to the FSA level.(\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e) A custom QGIS (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e) plugin integrating the Google Earth Engine (GEE) python API was used to automate the extraction of the Daymet grid cell-level data (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). Daily temperature and water pressure values were averaged across inhabited land within each FSA using the GEE mean reducer algorithm. Given the extensive heat sink coverage of water and forest in Ontario, it was important to identify inhabited land. Inhabited land, defined as having a population density of 0.4 or more people per square kilometre, was identified using ecumene boundaries retrieved from Statistics Canada.(\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e) After restricting to these ecumene, the resulting polygons were used to define each FSA. The boundary of each FSA\u0026rsquo;s polygon may include 100% of some gridded cells and only part of other cells. Thus, to calculate the average temperature within an FSA, the Daymet daily temperature in each gridded cell was weighted corresponding to that cell\u0026rsquo;s pixel fraction inside the FSA polygon using the ee.Reducer.mean() parameter (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). The resulting dataset contained the FSA (of child\u0026rsquo;s residence); maximum, minimum and average daily temperatures in \u0026deg;C; water vapour pressure in kPa; and relative humidity (using Bolton\u0026rsquo;s Equation).\u003c/p\u003e \u003cp\u003eTemperature equivalents of the 99th, 97.5th or 95th percentiles were identified in each FSA. A binary (0/1) indicator variable was created to identify days which met the EHE definition for each FSA (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Days on which the maximum temperature exceeded the threshold of the respective FSA \u003cem\u003eand\u003c/em\u003e was preceded by a day on which the maximum temperature also exceeded the threshold was designated with a 1 to indicate it as an EHE day. All other days were considered non-EHE days (designated with a 0). Using a similar binary indicator variable, lag 1- and 2-days were identified as 1 or 2 days after an EHE day.\u003c/p\u003e \u003cp\u003eUsing date and FSA, the data merge was conducted with 100% linkage. The percentile-specific data frames were fit in generalized nonlinear models (gnm) of conditional quasi-Poisson regression in R using the \u0026ldquo;gnm\u0026rdquo; package.(\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e) Rate ratios (RR) and 95% confidence intervals (CI) were calculated for each outcome comparing risk on EHE days to that on non-EHE days(\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Associations were also estimated by sex and 5-year age groups.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e284,939 all-cause hospital admissions and 5,875,119 all-cause emergency department visits were included in this study. Of the 5 general causes included in this analysis, injuries and respiratory illnesses were the most common causes of both hospital admissions (14.55% and 15.16%, respectively) and ED visits (32.80% 14.33%, respectively), as shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eTo maintain precision of reported results, ED visits due to drowning, and hospital admissions due to drowning, heat, heatstroke, dehydration, and otitis were omitted as these outcomes occurred less than 20 times on EHE days during the study period.\u003c/p\u003e \u003cp\u003eIn contrast to ED visits, individuals who sought emergency care may be admitted to hospitals if they present with symptoms requiring specialized care or extended observation. The fewer counts of hospital admissions compared to ED visits likely reflects the relative rarity of ailments requiring escalation from an ED visit to an admission. Consequently, the results herein should be interpreted considering \u003cem\u003eboth\u003c/em\u003e outcome measures and in consideration that differences observed between hospital admissions and ED visits may reflect the nature of the care required.\u003c/p\u003e \u003cp\u003eDuring EHEs, statistically significant increases in risk were observed for hospital admissions due to respiratory illnesses, asthma; infectious and parasitic diseases, lower respiratory infections, and enteritis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Similarly, on EHE days, risk increased for ED visits due to asthma; heat-related illnesses, heatstroke, dehydration, and lower respiratory infections. In contrast, decreases in risk were observed on EHE days for both hospital admissions and ED visits for injuries and transportation-related injuries, as well as ED visits due to falls.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAssociations observed in the primary analyses were consistent when adjusting for relative humidity (see tables A4 and A5). Associations were generally attenuated in the days following an EHE (lag 1 or 2) for most outcomes, however, hospital admissions and ED visits due to enteritis were highest the day after an EHE (lag 1). Similarly, 2 days after an EHE (lag 2) risk of renal and infectious and parasitic disease hospital admissions was highest. Associations were generally strongest in the primary analysis and approached the null in sensitivity analyses with lower threshold temperatures. For example, asthma admissions were associated with a 29% (95%CI: 16%, 44%) increased risk when EHEs were defined at the 99th percentile of temperature, 8% (2%, 15%) at the 97.5th, and a null effect (-7%, 0%) at the 95th ; conversely, EHEs were associated with a \u003cem\u003edecreased\u003c/em\u003e risk of injury admissions by 13% (21%, 4%) at the 99th percentile, 7% (11%, 2%) at the 97.5th and a null effect (4%, -2%) at the 95th. These patterns suggest health risks increase with increasing temperature.\u003c/p\u003e \u003cp\u003eAs shown in Tables A6 and A7, EHEs had the fewest positive associations with hospital admissions or ED visits in children in the 5-12-year age group. It follows, then, that most all-cause admissions and ED visits were found in the 0\u0026ndash;4 age category (46.96% and 34.76%, respectively) and the 13\u0026ndash;18 age category (29.46% and 33.42%, respectively), with the fewest in the 5\u0026ndash;12 age category (23.58% and 31.82%, respectively).\u003c/p\u003e \u003cp\u003eIn sex-stratified analyses, presented in Tables A8 and A9, more harmful associations were found for hospital admissions than ED visits, and in females than males. When compared to hospitalizations, ED visits had fewer associations of elevated risk overall and far fewer that were shared by both sexes. During EHEs, both sexes exhibited higher risks of hospitalizations due to respiratory illness, asthma; infectious and parasitic diseases, lower respiratory infections and enteritis, and ED visits due to asthma, heat and heatstroke. The sexes differ, however, in associated risks of injury, falls, transportation-related injury and renal disease for which males\u0026rsquo; risk decreased while females\u0026rsquo; risk increased or saw no change during EHEs. During EHEs, hospital admissions for injuries and transportation-related injuries increased in females and decreased in males.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eMany results of this study align with hypothesized effects and relationships based on biological plausibility, and evidence from similar research on respiratory illness (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e), asthma (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e), enteritis (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), and direct heat-related illnesses (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). In the primary analysis, EHEs were found to increase the risk of hospital admissions due to general respiratory illnesses and asthma; general infectious and parasitic diseases, lower respiratory infections, and enteritis. Similarly, risk of ED visits due to asthma; heat-related illnesses, heatstroke, dehydration, and lower respiratory infections were positively associated with EHEs.\u003c/p\u003e \u003cp\u003eInterestingly, EHEs were found to reduce risk of hospital admissions and ED visits for general injuries and transportation related injuries, and ED visits due to falls. This novel finding contrasts with those of similar studies in settings including New York City (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e) and England (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e) in which injuries amongst children were found to increase during EHEs. This may reflect the efficacy of heat warnings in mitigating effects of EHEs on child health through activity modifications like abstaining from sports and physical activities, for example. The same theory may help explain why sex stratification showed that during EHEs males were at a reduced risk of injuries, falls, and transportation-related injuries as well as hospitalizations due to renal disease. This protective effect contrasts with results of a study in New York City which found the highest risk of unintentional injury among males ages 5 to 9 years old.(\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e) When stratified by age, the 0\u0026ndash;4 and 13\u0026ndash;18 year age groups exhibited the most positive associations. Previous studies have also found that, when compared to older age groups, children under 5 have higher risks of emergency department visits(\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e), asthma(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e), respiratory illness(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e) and renal diseases(\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e), This may be due to heightened caution for the 0\u0026ndash;4 age group by caregivers and healthcare providers increasing the likelihood of an ED visit patient being admitted to hospital care, and due to the 13\u0026ndash;18 age group being more physically active and independent which can heighten susceptibility to EHEs.\u003c/p\u003e \u003cp\u003eAdjusting for relative humidity did not alter primary results. Associations, either positive or negative, were often strongest in the primary analysis and approached the null in sensitivity analyses with lower threshold temperatures and lag days from exposure. This monotonically decreasing trend effect demonstrates an exposure-response relationship between heat and risk of pediatric emergency healthcare. However, although risks of most outcomes were generally highest on same-day exposure to EHEs, hospital admissions and ED visits due to enteritis were highest the day after an EHE (lag 1), reflecting typical incubation periods of common foodborne and waterborne pathogens(\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e) including nontyphoidal \u003cem\u003eSalmonella\u003c/em\u003e spp. (12\u0026ndash;96 hours)(\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e), norovirus (12\u0026ndash;48 hours)(\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e), and \u003cem\u003eCampylobacter\u003c/em\u003e spp. (2\u0026ndash;4 days)(\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e). Similarly, risks of renal and infectious and parasitic disease hospital admissions were highest 2 days after an EHE (lag 2), possibly reflecting the cumulative effects of days of dehydration on renal and immune system function.\u003c/p\u003e \u003cp\u003eEHEs do not have a universal definition. Rather, they vary in threshold, temperature, and length making relative each of the terms \u0026ldquo;extreme\u0026rdquo;, \u0026ldquo;heat\u0026rdquo; and \u0026ldquo;event\u0026rdquo;. This consequently complicates comparisons. When aptly compared to the results of primary or sensitivity analyses herein, similar literature has made similar findings. Pediatric hospital admissions due to respiratory illnesses (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e), asthma (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e), and heat (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) were positively associated with EHEs in Australia, the US, and China. It is also important to note that studies used varied age categories for children(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), including several which used coarse age categories of 0\u0026ndash;14 and 15\u0026ndash;64 years(\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e), or 0\u0026ndash;4 and 5\u0026ndash;65(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn two Ontario hospitals, Wilk et. al. found pediatric infectious and respiratory diseases ED visits were positively associated with EHEs. This is the only prior study of pediatric ED visits in Canada, and it included only these two outcome measures. Respiratory illnesses (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e), asthma (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e), heat-related illnesses (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e), dehydration (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e), renal diseases (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e), otitis (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), and bacterial enteritis (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e) have been positively associated with EHEs in studies outside of Canada. Of these outcomes, our study similarly found that EHEs increased risk of ED visits for asthma, heat-related illnesses, and dehydration in primary analysis, as well as with renal diseases and otitis in 1-day lag, and renal diseases under the 97.5th and 95th percentile definitions.\u003c/p\u003e \u003cp\u003eTo date, this study was the largest and most comprehensive examination globally of EHEs and pediatric healthcare use, and the first study in Canada to assess associations of EHEs and pediatric hospital admissions. The data spanned the warm months of 10 years, including 284,939 hospital admissions, and 5,875,119 ED visits. The large scale of this analysis increases the power and representativeness of the study, especially for general, broadly defined causes of admissions and ED visits such as respiratory disease and injury. Given that Ontario provides universal free healthcare to residents and reports all admissions and ED visits to NACRS and DAD, the data used in this study is comprehensive and includes all pediatric cases. The space-time stratified case crossover design applied in this analysis controls for characteristics that do not vary over time or vary only slowly (e.g., age, race, sex) and for time-trend and seasonal patterns. Conditional quasi-Poisson regression common in similar studies (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan additionalcitationids=\"CR62 CR63 CR64 CR65 CR66\" citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e), was applied instead of the traditionally used conditional logistic regression because it is simpler and faster to code, and allows for adjusting for overdispersion and auto-correlation.(\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e) Poisson distribution models rate counts given exposure, whereas conditional logistic regression models odds of a binary outcome given a one unit increase in exposure. To create a binary outcome to use conditional logistic regression, the data would need to be expanded such that each stratum contains only one case, or semi-expanded such that each stratum contains only one case \u003cem\u003eday\u003c/em\u003e and was weighted by the number of cases that day. This study also benefits from the application of Daymet daily estimates of temperature aggregated to residential FSA, which are a more accurate measurement of exposure than the alternative of regional weather station data.\u003c/p\u003e \u003cp\u003eDespite the above strengths, since effects were measured over the whole study period conclusions cannot be drawn concerning possible changes in these effects over time. It is possible that the effects may differ year to year or between early and late season. Additionally, the ecological design of this study limits generalizability. Individual level factors, like SES and comorbidities, may modify the effect of EHEs on pediatric emergency healthcare use but were not included in this study. Built environments, housing and neighborhood characteristics may affect susceptibility to EHEs within FSAs.(\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e) Moreover, children who live in remote areas, areas where ED wait times are long, or who have low SES may not be well represented in this study. However, this is unlikely to have systematically biased results since the distribution of urban and rural FSAs of study participants was comparable to the distribution of urban and rural FSAs of children in Ontario, according to the 2011 census(\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e). In fact, children with rural FSAs made up a slightly higher proportion (7%) of the distribution within this study than was observed in the 2011 census.\u003c/p\u003e \u003cp\u003eECCC\u0026rsquo;s heat alert protocols in Canada identify conditions in which increased all-cause mortality of the general population are likely to occur.(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) In Ontario, regional alert criteria are 2 or more consecutive days with daytime maximum temperatures above 29\u0026deg;C in Northern Ontario, or 31\u0026deg;C in South and Southwestern Ontario.(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) These temperatures are most similar to those of the 95th percentile of temperature (see Additional File 1 Table A3). The results of this study may suggest that ECCC\u0026rsquo;s heat warnings are effective in preventing emergency healthcare use for pediatric injuries, as evidenced by the consistently protective effect of EHEs on injuries observed in all analyses and a contrasting harmful association in adults who are largely unable to change their activities (ex. outdoor labour, commuting to work)(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Despite these regional alerts, in analyses using the 95th percentile, EHEs increased risk of pediatric ED visits for heat-related illnesses, heatstroke, dehydration, renal diseases, and otitis; as well as hospital admissions for renal diseases, lower respiratory infections, and bacterial enteritis. It is also important to consider that with increasing intensity and frequency of EHEs due to climate change, today\u0026rsquo;s 99th percentile temperatures may be the 95th percentile in the near future (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Given the suggested efficacy of heat warnings in preventing pediatric injuries, these outcomes may be preventable with more stringent child-specific heat warnings at lower temperatures along with messaging specifying the increased risk of these health impacts.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eComplex relationships were found between EHEs and pediatric emergency healthcare utilization in Ontario. EHEs were positively associated with pediatric hospital admissions due to respiratory illnesses, asthma; infectious and parasitic diseases, lower respiratory infections, and enteritis. EHEs were also positively associated with ED visits due to asthma; heat-related illnesses, heatstroke, dehydration; and lower respiratory infections. Surprisingly, this study found that EHEs had a protective association with injury and transportation-related injury hospital admissions and ED visits.\u003c/p\u003e \u003cp\u003eIn wake of a quickly warming climate, it is imperative that heat alert protocols, community emergency planning, urban design, and healthcare facility preparedness be tailored to reflect the impacts of EHEs on child health. Critical gaps in research remain in predicting trends, identifying individual-level vulnerabilities, assessing mental health impacts, and identifying thresholds at which heat impacts child health.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eEHE - Extreme Heat Event\u003c/p\u003e\n\u003cp\u003eFSA - Forward Sortation Area\u003c/p\u003e\n\u003cp\u003eED - Emergency Department\u003c/p\u003e\n\u003cp\u003eCI - Confidence Interval\u003c/p\u003e\n\u003cp\u003eRR - Rate Ratios\u003c/p\u003e\n\u003cp\u003eDAD - Discharge Abstract Database\u003c/p\u003e\n\u003cp\u003eNACRS - National Ambulatory Care Reporting System\u003c/p\u003e\n\u003cp\u003eWHO - World Health Organization\u003c/p\u003e\n\u003cp\u003eICD-10 - International Statistical Classification of Disease and Related Health Problems, 10th Revision\u003c/p\u003e\n\u003cp\u003eECCC - Environment and Climate Change Canada\u003c/p\u003e\n\u003cp\u003eSES - Socioeconomic Status\u003c/p\u003e\n\u003cp\u003egnm - Generalized Nonlinear Models\u003c/p\u003e\n\u003cp\u003eCIHI - Canadian Institute for Health Information\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThis work was conducted on the unceded, ancestral territory of the xwmə\u0026theta;kwəy̓əm (Musqueam) People where the University of British Columbia is now situated.\u003c/p\u003e\n\u003cp\u003eDaily estimates of temperature were computed by NASA\u0026rsquo;s Daymet and were aggregated to the FSA level by Jean-Nicolas C\u0026ocirc;t\u0026eacute; of Health Canada, supported by Dr. Eric Lavigne.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHealth outcome data was collected by the CIHI and restricted and confidential access to the data was granted to Dr. Kate Weinberger by Dr. Eric Lavigne. Dr. Weinberger processed the data into daily timeseries for each FSA using age group boundaries and disease causes that Hallah Kassem selected based on a priori accounts and expectations of effects of heat on cause-specific diagnoses. Stemming from her Master\u0026rsquo;s thesis (70), Kassem cleaned, merged, analysed, and interpreted the data and wrote this paper which was then proof read by Drs. Michael Brauer, Kate Weinberger and Eric Lavigne.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Banting Research Foundation for the 2020 Discovery Award Competition. The funding source was not involved in conduct of the research or preparation of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrior to study conduct, review and approval were received from the University of British Columbia Office of Research Ethics Behavioural Research Ethics Board (H20-03369). \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no competing interests to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe health outcome data that support the findings of this study are available from Statistics Canada, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of Statistics Canada. The extracted Daymet data used herein are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKassem H. Effects of extreme heat on child healthcare use [Internet]. Posit: Shinyapps.io. [cited 2023 Oct 17]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://hallah-kassem.shinyapps.io/pediatric_health_impacts_of_heat/\u003c/span\u003e\u003cspan address=\"https://hallah-kassem.shinyapps.io/pediatric_health_impacts_of_heat/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBush E, Flato G. Canada\u0026rsquo;s Changing Climate Report. 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A case-crossover investigation of associations between extreme heat and pediatric health. 2023.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"enhe","sideBox":"Learn more about [Environmental Health](http://ehjournal.biomedcentral.com)","snPcode":"12940","submissionUrl":"https://submission.nature.com/new-submission/12940/3","title":"Environmental Health","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Environmental epidemiology, pediatric health, climate change, extreme heat, hospital admissions, emergency department","lastPublishedDoi":"10.21203/rs.3.rs-4904542/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4904542/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eGlobally, climate change is causing frequent and severe extreme heat events (EHEs). A large body of literature links EHEs to multiple health endpoints. While children\u0026rsquo;s physiology and activity patterns differ from those of adults in ways that are hypothesized to increase susceptibility to such endpoints, research gaps remain regarding the specific impacts of EHEs on child health. This study evaluated pediatric emergency healthcare utilizations associated with EHEs in Ontario.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eApplying a space-time stratified case-crossover design, associations between EHEs (same-day or lagged exposure to consecutive days of daily maximum temperatures above percentile thresholds) and 15 causes of pediatric emergency healthcare use in Ontario, Canada from 2005\u0026ndash;2015 were analysed using conditional quasi-Poisson regression. In primary analyses, EHEs were defined as two or more consecutive days with temperatures above the 99th percentile of temperature within each respective forward sortation area (FSA). Healthcare use was measured using hospital admissions as an indicator of severe outcomes, and emergency department (ED) visits as a sensitive measure of outcomes.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eEHEs increased the risk of pediatric hospital admissions for respiratory illnesses by 26% (95% CI: 14%-40%), asthma by 29% (16%-44%); infectious and parasitic diseases by 36% (24%-50%), lower respiratory infections by 50% (36%-67%), and enteritis by 19% (7%-32%). EHEs also increased the risk of ED visits for lower respiratory infections by 10% (0%-21%), asthma by 18% (7%-29%), heat-related illnesses by 211% (193%-230%), heatstroke by 590% (550%-622%), and dehydration by 35% (25%-46%), but not for other causes. Admissions and ED visits due to injuries and transportation related injuries were negatively associated with EHEs. Neither all-cause hospital admissions nor ED visits were associated with EHEs.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eIn Ontario, EHEs decreased the risk of pediatric emergency healthcare utilization for injuries and increased the risk of respiratory illnesses, asthma, heat-related illnesses, heatstroke, dehydration, infectious and parasitic diseases, lower respiratory infections, and enteritis. Tailored policies and programs that reflect the specific heat-related vulnerabilities of children to respiratory and infectious illnesses are warranted in the face of a rapidly warming climate.\u003c/p\u003e","manuscriptTitle":"Extreme heat and pediatric health in a warming world: a space-time stratified case-crossover investigation in Ontario, Canada","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-11 04:55:38","doi":"10.21203/rs.3.rs-4904542/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-26T21:40:19+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-19T19:22:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"86746016845797027171002205067836470107","date":"2024-08-30T11:22:32+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-29T03:17:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-13T22:02:30+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-13T22:02:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Health","date":"2024-08-13T06:27:43+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"enhe","sideBox":"Learn more about [Environmental Health](http://ehjournal.biomedcentral.com)","snPcode":"12940","submissionUrl":"https://submission.nature.com/new-submission/12940/3","title":"Environmental Health","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"27669a49-9235-445c-91af-bc0fb3ce484b","owner":[],"postedDate":"October 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-09T16:04:19+00:00","versionOfRecord":{"articleIdentity":"rs-4904542","link":"https://doi.org/10.1186/s12940-025-01153-y","journal":{"identity":"environmental-health","isVorOnly":false,"title":"Environmental Health"},"publishedOn":"2025-06-07 15:57:49","publishedOnDateReadable":"June 7th, 2025"},"versionCreatedAt":"2024-10-11 04:55:38","video":"","vorDoi":"10.1186/s12940-025-01153-y","vorDoiUrl":"https://doi.org/10.1186/s12940-025-01153-y","workflowStages":[]},"version":"v1","identity":"rs-4904542","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4904542","identity":"rs-4904542","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}