The team assembled an extraordinary dataset. From inpatient discharge records spanning 2019 to 2024, they identified 2,598,149 cardiovascular admission events among 8,876,140 older adults, generating more than 11.4 million case-day and control-day records for analysis. Each participant was linked to a geocoded primary healthcare institution, which served as a proxy for where the person lived and was exposed to the weather. Environmental exposures were then assigned using ERA5-Land, a high-resolution land-surface reanalysis product developed by the European Centre for Medium-Range Weather Forecasts, which reconstructs daily mean air temperature, dew point, relative humidity, and fine particulate matter concentrations at fine spatial scales.

The study design was a time-stratified case-crossover analysis, an approach that is particularly well suited to transient triggers of acute events. In this framework, each person who experienced a cardiovascular admission serves as their own control: the weather on the day of admission is compared with the weather on nearby days of the same day of the week within the same calendar month, when that same individual was not hospitalized. This design automatically strips out confounding from stable personal characteristics such as age, sex, smoking history, and chronic disease status, because each person is compared only with themselves. It also neutralizes long-term seasonal trends, since comparisons are confined to narrow time windows.

To capture how temperature effects unfold over time, the researchers employed distributed lag non-linear models, a statistical framework that simultaneously estimates how the association between exposure and outcome varies with the intensity of the exposure and with the delay between exposure and response. The analysis examined cumulative associations over lag days 0 through 14, meaning the combined effect of temperature exposure over a two-week window following any given day. This is critical for cardiovascular outcomes, which can respond to cold exposure days or even weeks after the fact, as cold stress gradually promotes vasoconstriction, elevated blood pressure, increased blood viscosity, and the rupture of vulnerable atherosclerotic plaques.

The results reveal a strikingly non-linear relationship between temperature and cardiovascular risk. The fitted cumulative exposure-response curve reached its minimum at 28.5 degrees Celsius, the temperature at which admission risk was lowest. Relative to that optimum, the odds ratio for cardiovascular admission was 1.032 at minus 3.6 degrees Celsius, 1.064 at 0.2 degrees Celsius, and 1.015 at 32.0 degrees Celsius. But the most dramatic finding was at the other end of the mild range: the fitted maximum of the curve occurred at approximately 14.2 degrees Celsius, where the odds ratio climbed to 1.194. In other words, cool but not frigid temperatures in the mid-teens were associated with the highest relative increase in cardiovascular admissions among this population of older adults.

The lag structure of these associations differed markedly between temperature extremes. At 0.2 degrees Celsius, near freezing, the elevated risk emerged after approximately two days and persisted through lag day 14, a slow-burning effect consistent with the cumulative physiological burden of sustained cold exposure. At 32.0 degrees Celsius, the association was smaller and more immediate, appearing quickly and then fading, which aligns with the known acute cardiovascular stress of extreme heat, including dehydration, increased cardiac output, and heightened coagulability. This temporal asymmetry has practical implications: cold-related cardiovascular risk is a prolonged hazard that builds over days, while heat-related risk is a sharp spike concentrated in the hours and days immediately following exposure.

Beyond absolute temperature, the study broke new ground by examining directional temperature change, meaning whether the temperature was rising or falling over a short window, independent of the absolute level. After adjusting for concurrent mean temperature, relative humidity, and fine particulate matter, the researchers found that warming trends within the lag 0 to 3-day window were independently associated with cardiovascular admissions, with an odds ratio of 1.030 at a temperature rise of 8.5 degrees Celsius. This suggests that rapid upward swings in temperature, even from a comfortable baseline, impose an additional cardiovascular burden that cannot be explained by the temperature level alone. The authors are careful to characterize this finding as hypothesis-generating rather than definitive, noting that the effect size is small and requires confirmation in other populations and climates.

The physiological plausibility of these findings rests on well-established cardiovascular mechanisms. Cold exposure triggers peripheral vasoconstriction, raising blood pressure and increasing cardiac workload. It also promotes hemoconcentration and increases fibrinogen levels, tipping the blood toward a prothrombotic state. Sudden temperature changes may add an additional layer of stress by disrupting autonomic regulation of the cardiovascular system, particularly in older adults whose baroreflex sensitivity and thermoregulatory capacity are already diminished. Rapid warming, paradoxically, may trigger vasodilation and changes in blood pressure that destabilize vulnerable plaques or provoke arrhythmias in susceptible individuals.

The scale of the dataset lends considerable weight to the conclusions. With nearly 8.9 million older adults and more than 2.5 million cardiovascular admission events, the study has statistical power that few prior investigations of temperature and cardiovascular risk can match. The use of geocoded primary healthcare institutions as exposure proxies, rather than broad regional averages, reduces exposure misclassification. The linkage to elderly health management rosters, part of China’s Essential Public Health Services program, ensures that the cohort represents the full population of older adults enrolled in primary care across Henan Province rather than a selected sample.

Nevertheless, the authors are candid about the limitations of their work. Because the study estimated relative odds rather than absolute admission risk, the clinical and public health significance of the observed associations remains uncertain. The odds ratios, while statistically robust, are modest in magnitude, and the population-level burden of temperature-attributable cardiovascular admissions was not quantified. The directional warming finding, in particular, requires replication before it can inform clinical guidance or early warning systems. Still, the study adds to a growing body of evidence that climate variability, not just extreme heat or cold, poses measurable risks to cardiovascular health in aging populations, and it underscores the need for weather-sensitive public health strategies that account for both the level and the pace of temperature change.

Subject of Research: Short-term ambient temperature and directional temperature change and cardiovascular hospital admissions among older adults

Article Title: Associations of short-term ambient temperature and directional temperature change with cardiovascular admissions among older adults: a time-stratified case-crossover study

Article References: Zhao, Q., Guo, X., Dai, N., Zhao, Z., Ma, M., Wan, H., Zhao, Y., Miao, Y., Fan, J., Jin, T., Cao, T., Sang, Y., Ma, Z., Wu, J., Guo, Z., & Liu, R. (2026). Associations of short-term ambient temperature and directional temperature change with cardiovascular admissions among older adults: a time-stratified case-crossover study. BMC Medicine. https://doi.org/10.1186/s12916-026-05322-9

Image Credits: AI Generated

DOI: 10.1186/s12916-026-05322-9

Keywords: ambient temperature, cardiovascular disease, hospital admissions, older adults, case-crossover study, distributed lag non-linear models, temperature change, climate change, heat stress, cold exposure, public health, China

The study, published in Environmental Advances, drew on electronic health records from the Medical Research Council Unit The Gambia clinic in Keneba, a facility that has provided free healthcare to residents of the surrounding West Kiang region for decades. Between January 2010 and December 2022, the clinic recorded 11,331 visits for diarrheal diseases and 29,420 visits for skin infections. Roughly seven in ten diarrheal visits involved children under five, while skin infections were more evenly distributed across age groups. The researchers aggregated these visits into weekly counts and matched them against weekly rainfall and temperature data from the European Centre for Medium-Range Weather Forecasts ERA5-Land reanalysis dataset, extracted at roughly ten kilometre resolution for the grid cell containing the clinic.

Because the relationship between weather and disease is rarely linear, the team employed distributed lag non-linear models, a statistical framework that can capture both curved exposure-response relationships and effects that unfold over time. Weekly counts of clinic visits were highly overdispersed, so negative binomial regression was used, with natural cubic splines of time flexibly adjusting for long-term trends and seasonality. The models allowed rainfall effects to emerge across a zero-to-three-week lag window, chosen to encompass the incubation periods of the major pathogens involved as well as the time needed for environmental exposure, pathogen replication and the decision to seek care.

The headline finding is a threshold effect. For diarrheal disease, the cumulative risk of a clinic visit rose steadily as weekly rainfall increased, peaking somewhere between 35 and 40 millimetres of rain per week, after which the risk plateaued and drifted back toward null. A week with 20 millimetres of rainfall, roughly the 75th percentile of the full series, was associated with a 20 percent increase in diarrheal visits compared with dry weeks, after adjusting for temperature. Without that temperature adjustment, the apparent risk was 41 percent higher, a difference that proved to be one of the study’s most methodologically important observations.

Skin infections told a similar but not identical story. Risk climbed with rainfall up to a threshold of about 30 millimetres per week, then declined at heavier totals. Twenty millimetres of weekly rainfall was associated with a 39 percent increase in skin infection visits once temperature was accounted for, and the effect persisted across lags of one, two and three weeks, longer than the one-to-two-week window over which diarrheal risks appeared. The researchers suggest this may reflect different underlying mechanisms: diarrheal pathogens are typically ingested after relatively rapid contamination of water sources, while skin infections may follow slower routes of environmental exposure, crowding during wet weather and changes in hygiene practices when water becomes scarce or contaminated.

Stratifying the data by pathogen category revealed a striking asymmetry. Bacterial diarrheal visits carried the strongest rainfall association, with a 66 percent increase in risk at 20 millimetres of weekly rainfall after temperature adjustment, whereas viral diarrheal visits showed no clear evidence of a rainfall effect at all. A parallel pattern emerged for skin disease: bacterial skin infections were the most rainfall-sensitive category, though fungal and clinically classified local skin infections also showed elevated risks. This aligns with a growing body of evidence from other settings that rainfall and temperature shape the transmission of bacterial and viral enteric pathogens in fundamentally different ways, with wetter conditions generally favouring bacterial spread.

Age modified the risks in unexpected directions. For diarrheal disease, the rainfall association was stronger among patients aged five and over, with a 63 percent increase in risk at 20 millimetres of weekly rainfall, than among children under five, for whom no significant association was detected after temperature adjustment. The authors note that the vast majority of under-five diarrheal visits were classified as viral, which may partly explain the difference. For skin infections the pattern reversed: children under five bore the greater rainfall-associated burden, with a 77 percent increase in visits at 20 millimetres of rain, consistent with the idea that young children crawling and playing in contaminated standing water face heightened exposure. Sex-stratified analyses suggested slightly higher risks among females for both outcomes, though the evidence for modification by sex was weaker than for age.

Perhaps the most counterintuitive result concerns heavy rainfall events, defined as days receiving more than 21.05 millimetres of rain, the 95th percentile of rainy days between 2000 and 2022. In unadjusted models, weeks containing at least one such event were associated with substantially increased risks of both diarrheal and skin infection visits. But once maximum temperature was included in the models, those associations vanished entirely. And when total weekly rainfall was additionally controlled for, a heavy rainfall event was actually associated with a modest decrease in diarrheal risk. The authors propose that competing mechanisms may be at work: heavy rain can flush and dilute pathogens in water sources, while also improving water availability for hygiene, offsetting the contamination and infrastructure damage such events can cause.

The confounding role of temperature deserves emphasis, because many prior studies of rainfall and diarrheal disease have not adjusted for it at all. In The Gambia, temperature and rainfall are strongly inversely related, with the hottest weeks falling just before the onset of the rainy season. Failing to account for this coupling can therefore inflate or distort apparent rainfall effects. The sensitivity analyses were reassuring on other fronts: results held up under alternative methods of seasonal adjustment, different spline configurations, shorter and longer lag windows, and explicit adjustment for the sharp decline in diarrheal visits after rotavirus vaccine introduction in 2014. Crucially, rainfall showed no comparable association with clinic visits for routine child health checks, toothache or hypertension, outcomes that lie outside any plausible rainfall-transmission pathway, suggesting the findings are not simply an artefact of weather-driven changes in health-seeking behaviour.

The study has limitations the authors acknowledge. Clinic visits are a proxy for disease incidence rather than a direct measure, pathogen classifications rest on clinical assessment rather than laboratory confirmation, and reanalysis weather data averaged over ten-kilometre grid cells may smooth out the local extremes of rainfall that matter most. Even so, the work offers one of the most detailed pictures to date of how rainfall shapes water-related disease in West Africa, a region where such evidence has been sparse and where climate projections point toward greater rainfall variability and more intense wet-season events. As the authors conclude, the non-linear risks they document, together with the anticipated shifts in West African rainfall, argue for strengthening the climate resilience of water, sanitation and hygiene services, and for studying rainfall and temperature jointly rather than in isolation if early-warning systems for climate-sensitive diseases are to become a reality.

Subject of Research: The association between weekly rainfall, temperature and clinic visits for diarrheal diseases and skin infections in rural Gambia

Article Title: The impact of weekly rainfall on water-related diseases in The Gambia: a time-series analysis of health facility data

Article References: Bose, I., Part, C., Moirano, G., Murray, K. A., Green, R., Cerami, C., Mistry, M. N., & Kovats, S. (2026). The impact of weekly rainfall on water-related diseases in The Gambia: a time-series analysis of health facility data. Environmental Advances, 26, Article 100757. https://doi.org/10.1016/j.envadv.2026.100757

Image Credits: AI Generated

DOI: 10.1016/j.envadv.2026.100757

Keywords: rainfall, diarrheal disease, skin infections, The Gambia, time-series analysis, climate and health, heavy rainfall events, distributed lag non-linear models, water-related diseases, WASH, temperature confounding, West Africa

The study’s design is a textbook example of modern environmental epidemiology. The team drew on the Ministry of Health Mortality Collection within Stats NZ’s Integrated Data Infrastructure, capturing every registered death in the country from 1 January 1999 to 30 April 2025. Each death was matched to daily heat exposure in the person’s Territorial Authority of residence, reconstructed from weather-station observations in the National Climate Database maintained by the National Institute of Water and Atmospheric Research. To estimate the acute effect of heat, the researchers used a time-stratified case-crossover design, a self-matched method in which each person’s exposure on the day of death is compared with exposure on nearby referent days falling on the same weekday within the same calendar month. Because every individual serves as their own control, time-invariant personal characteristics, seasonality, long-term trends and day-of-week effects are absorbed by the design itself, leaving a clean estimate of the short-term heat-mortality association.

What distinguishes this study from most of its predecessors is the choice of exposure metric. Alongside conventional air-temperature summaries, the team computed the wet-bulb globe temperature, or WBGT, a composite heat-stress index that integrates air temperature with humidity, radiation and wind speed. Developed in the 1950s to prevent heat illness among US military recruits and later codified in occupational health standards such as ISO 7243, WBGT captures the combined thermal load on the human body far better than a thermometer reading alone. The researchers applied the Liljegren physical model to translate standard meteorological measurements into daily WBGT indices based on mean, maximum and minimum temperatures, creating three parallel exposure measures for every district and day. Missing radiation and wind inputs in a small fraction of district-days were filled using multiple imputation by chained equations, a statistically rigorous approach that draws on contemporaneous and recent meteorological information.

The headline results are striking in their consistency. In the all-cause, full-year sample, the odds of death rose by 0.44 percent for every 1 degree Celsius increase in WBGT based on mean daily temperature, and by 0.39 percent per degree of mean air temperature. Restricting the analysis to the warm season, from October to March, sharpened the associations to 0.57 percent and 0.51 percent respectively. Mean-based measures outperformed maximum- and minimum-based measures within both metric families, suggesting that sustained daily heat load, rather than brief afternoon peaks, is what matters most for short-term mortality. When the team compared models statistically, WBGT-only models fit better than temperature-only models, with lower Akaike information criterion values and significant likelihood-ratio improvements, indicating that humidity, radiation and wind carry genuine additional information about lethal heat beyond what a thermometer can see.

To translate these odds ratios into human terms, the researchers performed a simple scaling exercise. A uniform 1 degree Celsius increase in mean WBGT across the country would correspond to roughly 140 additional all-cause deaths per year under full-year assumptions, and about 110 additional non-injury deaths. Restricted to the warm season alone, the same shift implies approximately 85 excess all-cause deaths annually. These are illustrative figures that abstract from spatially differentiated warming and behavioural adaptation, but they give policymakers a tangible benchmark: in a nation of about five million people, even a single degree of additional heat stress claims a measurable annual toll.

The heterogeneity analyses reveal where the burden falls. When Territorial Authorities were grouped by their mean warm-season WBGT into cool, moderate and hot climate tertiles, the per-degree odds increase climbed monotonically from 0.17 percent in the coolest districts to 0.43 percent in moderate ones and 0.67 percent in the hottest. This gradient is consistent with an emerging international pattern in which populations in historically cooler regions may be less adapted, yet the strongest associations here appeared in the warmest districts, hinting that sustained exposure compounds risk. Women showed consistently higher point estimates than men, with a 0.53 percent odds increase per degree of WBGT-Tmean compared with 0.34 percent for men, and the clearest age-specific signal emerged among adults aged 60 and over, where women again faced higher estimates than men. These findings align with physiological expectations that reduced thermoregulatory reserve in older adults heightens vulnerability, and they carry direct implications for aged-care planning and community health services.

Beyond the same-day estimates, the team deployed distributed lag non-linear models to trace how heat effects unfold over the following week. The cumulative exposure-response curves were broadly U-shaped, with fitted minima around 11 to 12 degrees Celsius in the full-year models and rising cumulative odds on the warm side, most clearly supported across the upper-middle of the exposure range. The warm-season curves shifted rightward and became right-skewed, but the qualitative pattern held across metrics, lag windows of 0 to 3, 0 to 7 and 0 to 14 days, and alternative bin thresholds. Robustness checks, including a symmetric 28-day referent window, shifted bin cut points, calendar-period splits and the exclusion of districts with imputed meteorological inputs, all left the main conclusions intact, a testament to the care invested in the empirical design.

The study arrives amid unmistakable evidence that New Zealand’s climate is changing. The country’s seven-station temperature series records a 0.91 degree Celsius rise over the twentieth century, 2024 was the nation’s tenth-warmest year on record, and government projections anticipate that hot days above 25 degrees Celsius could become two to four times more frequent by 2090. The unprecedented 2017-18 austral summer heatwave, with land temperature anomalies of about 2.2 degrees Celsius and sea-surface anomalies reaching 3.7 degrees Celsius in the eastern Tasman Sea, offered a preview of this future. Previous New Zealand evidence had been fragmentary: a Christchurch time-series study from 2000, a Canterbury heatwave pilot, and an expert assessment estimating roughly 14 heat-related deaths per year among older adults in Auckland and Christchurch. The new national analysis replaces these fragments with a coherent, powered, individual-level picture.

The limitations are candidly acknowledged. Exposure was assigned at the district-day level, smoothing over within-district variation that matters in topographically diverse and coastal areas, and the WBGT indices were built from daily summaries rather than hourly data, potentially blurring the timing of peak heat stress. Non-differential exposure error of this kind most likely attenuates rather than inflates the estimated associations, meaning the true burden could be larger. Cause-of-death coding was available only through 2020 for the non-injury analyses, and the case-crossover design, centred on deaths, cannot yet model non-fatal outcomes. The authors flag an individual-day population panel as the next step. Even so, the core message stands firm: measurable heat-mortality associations exist in a temperate maritime climate, they are stronger in the warm season, sharpest among older adults and women, and steepest in the hottest districts. As New Zealand’s climate continues to warm, heat-health warning systems, seasonal preparedness and locally calibrated thresholds will no longer be optional extras but essential infrastructure.

Subject of Research: The association between short-term heat exposure, measured by wet-bulb globe temperature and air temperature, and mortality in Aotearoa New Zealand.

Article Title: Heat exposure and mortality in Aotearoa New Zealand: a time-stratified case-crossover study

Article References: Lu, S., Noy, I., & Stone, D. (2026). Heat exposure and mortality in Aotearoa New Zealand: a time-stratified case-crossover study. Regional Environmental Change, 26(3), Article 184. https://doi.org/10.1007/s10113-026-02667-y

Image Credits: AI Generated

DOI: 10.1007/s10113-026-02667-y

Keywords: heat exposure, mortality, wet-bulb globe temperature, New Zealand, case-crossover study, climate change, heat stress, public health, distributed lag non-linear models, warm season, older adults, temperature

The study, led by environmental epidemiologist Amruta Nori-Sarma of Harvard T.H. Chan School of Public Health in collaboration with colleagues at Truveta, Harvard, and Boston University, exploited a technological capability that earlier generations of heat researchers lacked: continuously refreshed electronic health record data. Rather than waiting months for claims data to be processed and aggregated, the team drew on state-day counts of emergency department visits from 19 eastern states, allowing them to quantify the health burden of the summer 2025 heatwaves while the season was still underway. In total, the analysis encompassed 8,169,637 emergency department visits recorded during the warm seasons of 2023 through 2025, providing a robust historical baseline against which the 2025 episodes could be compared.

The two heatwaves under scrutiny were stark in their timing and severity. The first gripped the region from 22 to 25 June 2025, and the second returned from 10 to 13 August 2025, engulfing the eastern United States in conditions that triggered National Weather Service heat alerts across much of the study area. To estimate how many visits these episodes caused, the researchers compared observed visit counts during the heatwave windows with counterfactual predictions of what visits would have been under typical temperature conditions, using temperature-health associations estimated from the prior three warm seasons.

Technically, the analysis rested on a conditional Poisson model, a statistical framework well suited to time-series data on disease counts. The researchers stratified time by year, calendar month, and day of week, with stratum-specific fixed effects that absorb routine patterns in healthcare utilization, such as weekday and seasonal variation. This design effectively compares hot days with cooler days occurring under otherwise similar conditions, a strategy closely related to the time-stratified case-crossover approach widely used in environmental epidemiology. Temperature exposure was modeled using distributed lag non-linear models, or DLNMs, which capture two crucial features of heat’s health effects simultaneously: the non-linear dose-response relationship between temperature and risk, and the fact that effects unfold over multiple days, with heat stress on one day influencing emergency visits over a window of zero to four days afterward.

The headline numbers were striking. During the June 2025 heatwave, the researchers estimated that 9.2 percent of all-cause emergency department visits were attributable to heat over the zero-to-four-day lag window, with a 95 percent confidence interval of 6.2 to 11.6 percent. During the August heatwave, the attributable fraction was 8.3 percent, with a confidence interval of 6.0 to 10.1 percent. In plain terms, roughly one in every eleven or twelve people who walked into an emergency department in these nineteen states during those weeks was there, at least in part, because of extreme heat. Similar patterns emerged when the team examined cumulative lags and cause-specific outcomes, indicating that the association was not an artifact of a single modeling choice.

Because the underlying data included diagnostic information, the researchers could decompose the burden by cause. Beyond direct heat-related illness diagnoses, they tracked emergency visits for kidney disease and for mental health conditions, both of which have well-documented physiological links to heat exposure. Kidney disease is acutely sensitive to dehydration and thermal strain, while a growing body of literature connects high ambient temperatures to psychiatric crises, including exacerbations of mood disorders and substance-related emergencies. The lag-specific analyses showed that the association between the 99th percentile of daily maximum temperature, about 36.9 degrees Celsius, and the sample median of 27 degrees Celsius was strongest on the same day for direct heat illness, while kidney and mental health outcomes followed somewhat different lag structures, consistent with the diverse physiological pathways through which heat injures the human body.

Those pathways are worth spelling out. Extreme heat forces the cardiovascular system to work harder, shunting blood toward the skin to dissipate warmth and increasing cardiac output, changes that laboratory studies involving more than 400 controlled heat exposure experiments have documented in detail. In people with preexisting heart disease, this added strain can precipitate decompensation. Dehydration concentrates the blood and promotes the formation of kidney stones and acute kidney injury, particularly in older adults and in people taking common medications that impair thermoregulation or fluid balance. Meanwhile, heat disrupts sleep, alters neurotransmitter function, and can increase impulsivity and aggression, mechanisms that plausibly underlie the observed rise in mental health emergency visits during hot weather.

The heterogeneity analyses added an important dimension to the findings. By fitting subgroup models for six age groups, three US census regions, and both sexes, the researchers probed whether the temperature-emergency visit association varied across populations. The results, visualized in forest plots comparing rate ratios at lag zero, showed differences by age, region, and sex, echoing decades of evidence that the very young, older adults, and residents of areas unaccustomed to extreme heat bear disproportionate risk. The state-specific exposure-response functions also revealed geographic variation: states with historically milder summers tended to show steeper increases in emergency visits per degree of warming, a pattern consistent with the idea that populations adapt, physiologically and infrastructurally, to the temperatures they usually experience, and that anomalous heat is most damaging where it is least familiar.

Methodological sensitivity checks strengthened the study’s credibility. The team replicated the primary analysis using daily average temperature instead of daily maximum temperature, and separately extended the lag window from four to seven days; both variations reproduced the central findings. Exposure data were drawn from the Open-Meteo weather API and National Weather Service records, and heat alert dates by state were compiled from National Weather Service advisories, allowing the investigators to anchor their heatwave definitions in the same official warnings that reached the public during those weeks. All analytic code, including the data extraction scripts, DLNM fitting routines, meta-analysis, and attributable burden estimation, has been made publicly available on GitHub, an unusual and welcome degree of transparency for a rapidly produced surveillance analysis.

The broader significance of the study lies less in any single percentage than in what it demonstrates about the future of climate-health surveillance. Traditional epidemiology delivers its verdicts long after the disaster has passed, when the findings can inform the next heat season but not the current one. By contrast, a near-real-time pipeline built on continuously refreshed electronic health records can tell public health officials, while a heatwave is still unfolding, how many excess patients are arriving, which conditions are driving the surge, and where resources are most strained. The authors argue that this capability should become a standing component of heat preparedness, complementing heat action plans and warning systems whose effectiveness, demonstrated in cities from Philadelphia to Montreal, depends on knowing when and where the danger peaks. As climate change renders summers like 2025 more frequent and more intense, the study suggests that the emergency department itself, properly instrumented, can serve as an early-warning sensor for the health impacts of a warming world, and that the true toll of extreme heat is far larger than the narrow category of heatstroke diagnoses would suggest. Nearly a tenth of all emergency care during those scorching weeks was heat’s shadow, and counting it in real time may be the first step toward preventing it.

Subject of Research: Attribution of summer 2025 US heatwave emergency department visits using electronic health records

Article Title: Impact of the summer 2025 heatwaves on emergency department visits in the USA

Article References: Nori-Sarma, A., Cartwright, B. M., Zanobetti, A., Pastwa, A., Stucky, N., & Willis, M. D. (2026). Impact of the summer 2025 heatwaves on emergency department visits in the USA. Nature Health. https://doi.org/10.1038/s44360-026-00194-y

Image Credits: AI Generated

DOI: 10.1038/s44360-026-00194-y

Keywords: heatwaves, emergency department visits, electronic health records, extreme heat, public health surveillance, climate change, epidemiology, distributed lag non-linear models, heat-related illness, kidney disease, mental health, United States