Heat warnings, mortality, and hospital admissions among older adults in the United States

Temporal changes in temperature-related mortality in relation to the establishment of the heat-health alert system in Victoria, Australia

Extreme heat alerts are the most common form of weather forecasting services used in Australia, yet very limited studies have documented their effectiveness in improving health outcomes. This study aimed to examine the temporal changes in temperature-related mortality in relation to the activation of the heat-health alert and response system (HARS) in the State of Victoria, Australia. We examined the relationship between temperatures and mortality using quasi-Poisson regression and the distributed lag non-linear model (dlnm) and compared the temperature-mortality association between the two periods: period 1- prior-HARS (1992-2009) and period 2- post-HARS (2010-2019). Since the HARS heavily weights heatwave effects, we also compared the main effects of heatwave events between the two periods. The heatwaves were defined for three levels, including 3 consecutive days at 97th, 98th, and 99th percentiles. We also controlled the potential confounding effect of seasonality by including a natural cubic B-spline of the day of the year with equally spaced knots and 8 degrees of freedom per year. The exposure-response curve reveals the temperature mortality was reduced in period 2 in comparison with period 1. The relative risk ratios (RRR) of Period 2 over Period 1 were all less than one and gradually decreased from 0.86 (95% CI, 0.72-1.03) to 0.64 (95% CI, 0.33-1.22), and the differences in attributable risk percent increased from 13.2 to 25.3%. The reduction in the risk of heatwave-related deaths decreased by 3.4% (RRp1 1.068, 95% CI, 1.024-1.112 versus RRp2 1.034, 95% CI, 0.986-1.082) and 10% (RRp1 1.16, 95% CI, 1.10-1.22 versus RRp2 1.06, 95% CI, 1.002-1.119) for all groups of people. The study indicated a decrease in heat-related mortality following the operation of HARS in Victoria under extreme heat and high-intensity heatwaves conditions. Further studies could investigate the extent of changes in mortality among populations of differing socio-economic groups during the operation of the heat-health alert system.

Thermal stress and hospital admissions for cardiorespiratory disease in Brazil

The growing body of scientific literature underscores the intricate relationship between meteorological conditions and human health, particularly in the context of extreme temperatures. However, conventional temperature-centric approaches often fall short in capturing the complexity of thermal stress experienced by individuals. Temperature alone, as a metric, fails to encompass the entirety of the thermal stress individuals face, necessitating a more nuanced understanding. In response to this limitation, climatologists have devised thermal indices-composite measures meticulously crafted to reflect the intricate interplay of meteorological factors influencing human perception of temperature. Recognizing the inadequacy of simplistic temperature-focused methodologies, our study aims to address the multifaceted nature of thermal stress. In this study, we explored the association between thermal indices and hospital admissions for circulatory and respiratory diseases in Brazil. We used an extensive dataset spanning 11 years (2008-2018) from the Brazilian Ministry of Health, encompassing a total of 23,791,093 hospitalizations for circulatory and respiratory diseases. We considered four distinct thermal indices-Discomfort Index (DI), Net Effective Temperature (NET), Humidex (H), and Heat Index (HI). We used an extension of the two-stage design with a case time series to assess this relationship. In the first stage, we applied a distributed lag non-linear modeling framework to create a cross-basis function. We next applied quasi-Poisson regression models adjusted by time-varying confounders. In the second stage, we applied meta-analysis with random effects to estimate the national relative risk (RR). Our findings suggest robust variations among the thermal indices under examination. These variations underscore the intricate nature of associations between temperature and health, with each index capturing distinct aspects of thermal conditions. Our results indicate that extreme thermal conditions, both at the low and high ends, are associated with increased risks of hospital admissions. The diverse impact observed among different indices emphasizes the complex interplay between various meteorological factors and their specific physiological consequences. This underscores the necessity for a comprehensive comprehension of temperature metrics to guide precise public health interventions, recognizing the multifaceted nature of temperature-health relationships.

Beliefs and behaviors associated with the first named heat wave in Seville Spain 2022

Heat waves pose a substantial and increasing risk to public health. Heat health early warning systems (HHEWSs) and response plans are increasingly being adopted to alert people to the health risks posed by days of extreme heat and recommend protective behaviors. However, evidence regarding the effectiveness of HHEWSs remains limited. We examined the impact of heat wave naming on heat-related beliefs and behaviors to ascertain the potential effectiveness of heat wave naming as a heat health risk communication and management tool. Specifically, we surveyed members of the public exposed to the proMETEO Sevilla HHEWS messaging campaign which in the summer of 2022 applied a name to heat waves considered to pose the greatest risk to public health. During the heat season we evaluated, the proMETEO Sevilla HHEWS campaign applied a name to one heat wave, heat wave "Zoe". Our analysis of the post-survey of 2022 adults indicated that the 6% of participants who recalled the name Zoe unaided reported greater engagement in heat wave safety behaviors and more positive beliefs about naming heat waves and their local governments' heat wave response. These results provide initial evidence for potential utility in naming heat waves as part of HHEWSs and HAPs.

A newly proposed heatstroke-induced coagulopathy score in patients with heat illness: A multicenter retrospective study in China

PURPOSE

In patients with heatstroke, disseminated intravascular coagulation (DIC) is associated with greater risk of in-hospital mortality. However, time-consuming assays or a complex diagnostic system may delay immediate treatment. Therefore, the present study proposes a new heatstroke-induced coagulopathy (HIC) score in patients with heat illness as an early warning indicator for DIC.

METHODS

This retrospective study enrolled patients with heat illness in 24 Chinese hospitals from March 2021 to May 2022. Patients under 18 years old, with a congenital clotting disorder or liver disease, or using anticoagulants were excluded. Data were collected on demographic characteristics, routine blood tests, conventional coagulation assays and biochemical indexes. The risk factors related to coagulation function in heatstroke were identified by regression analysis, and used to construct a scoring system for HIC. The data of patients who met the diagnostic criteria for HIC and International Society on Thrombosis and Haemostasis defined-DIC were analyzed. All statistical analyses were performed using SPSS 26.0.

RESULTS

The final analysis included 302 patients with heat illness, of whom 131 (43.4%) suffered from heatstroke, including 7 death (5.3%). Core temperature (OR = 1.681, 95% CI 1.291 - 2.189, p < 0.001), prothrombin time (OR = 1.427, 95% CI 1.175 - 1.733, p < 0.001) and D-dimer (OR = 1.242, 95% CI 1.049 - 1.471, p = 0.012) were independent risk factors for heatstroke, and therefore used to construct an HIC scoring system because of their close relation with abnormal coagulation. A total score ≥ 3 indicated HIC, and HIC scores correlated with the score for International Society of Thrombosis and Hemostasis -DIC (r = 0.8848, p < 0.001). The incidence of HIC (27.5%) was higher than that of DIC (11.2%) in all of 131 heatstroke patients. Meanwhile, the mortality rate of HIC (19.4%) was lower than that of DIC (46.7%). When HIC developed into DIC, parameters of coagulation dysfunction changed significantly: platelet count decreased, D-dimer level rose, and prothrombin time and activated partial thromboplastin time prolonged (p < 0.05).

CONCLUSIONS

The newly proposed HIC score may provide a valuable tool for early detection of HIC and prompt initiation of treatment.

Population Aging and Heat Exposure in the 21st Century: Which U.S. Regions Are at Greatest Risk and Why?

BACKGROUND AND OBJECTIVES

The co-occurring trends of population aging and climate change mean that rising numbers of U.S. older adults are at risk of intensifying heat exposure. We estimate county-level variations in older populations' heat exposure in the early (1995-2014) and mid (2050) 21st century. We identify the extent to which rising exposures are attributable to climate change versus population aging.

RESEARCH DESIGN AND METHODS

We estimate older adults' heat exposure in 3,109 counties in the 48 contiguous U.S. states. Analyses use NASA NEX Global Daily Downscaled Product (NEX-GDDP-CMIP6) climate data and county-level projections for the size and distribution of the U.S. age 69+ population.

RESULTS

Population aging and rising temperatures are documented throughout the United States, with particular "hotspots" in the Deep South, Florida, and parts of the rural Midwest. Increases in heat exposure by 2050 will be especially steep in historically colder regions with large older populations in New England, the upper Midwest, and rural Mountain regions. Rising temperatures are driving exposure in historically colder regions, whereas population aging is driving exposure in historically warm southern regions.

DISCUSSION AND IMPLICATIONS

Interventions to address the impacts of temperature extremes on older adult well-being should consider the geographic distribution and drivers of this exposure. In historically cooler areas where climate change is driving exposures, investments in warning systems may be productive, whereas investments in health care and social services infrastructures are essential in historically hot regions where exposures are driven by population aging.

SHARE-ENV: A Data Set to Advance Our Knowledge of the Environment-Wellbeing Relationship

Climate change interacts with other environmental stressors and vulnerability factors. Some places and, owing to socioeconomic conditions, some people, are far more at risk. The data behind current assessments of the environment-wellbeing nexus is coarse and regionally aggregated, when considering multiple regions/groups; or, when granular, comes from ad hoc samples with few variables. To assess the impacts of climate change, we require data that are granular and comprehensive, both in the variables and population studied. We build a publicly accessible data set, the SHARE-ENV data set, which fulfills these criteria. We expand on EU representative, individual-level, longitudinal data (the SHARE survey), with environmental exposure information about temperature, radiation, precipitation, pollution, and flood events. We illustrate through four simplified multilevel linear regressions, cross-sectional and longitudinal, how full-fledged studies can use SHARE-ENV to contribute to the literature. Such studies would help assess climate impacts and estimate the effectiveness and fairness of several climate adaptation policies. Other surveys can be expanded with environmental information to unlock different research avenues.

Short-term air pollution exposure and mortality in Brazil: Investigating the susceptible population groups

This is the first study to examine the association between ambient air pollution (PM2.5, O3, and NO2) and mortality (in different population groups by sex and age) based on a nationwide death record across Brazil over a 15-year period (2003-2017). We used a time-series analytic approach with a distributed lag model. Our study population includes 2,872,084 records of deaths in Brazil between 2003 and 2017. Men accounted for a higher proportion of deaths, with 58% for all-cause mortality, 54% for respiratory mortality, and 52% for circulatory mortality. Most individuals were over 65 years of age. Our results suggest an association between air pollution and mortality in Brazil. The direction, statistical significance, and effect size of these associations varied considerably by type of air pollutant, region, and population group (sex and age group). In particular, the older population group (>65 years) was most affected. The national meta-analysis for the entire data set (without stratification by sex and age) showed that for every 10 μg/m3 increase in PM2.5 concentration, the risk of death from respiratory diseases increased by 2.93% (95%CI: 1.42; 4.43). For every 10 ppb increase in O3, there is a 2.21% (95%CI: 0.59; 3.83) increase in the risk of all-cause mortality for the group of all people between 46 and 65 years old, and a 3.53% (95%CI: 0.34; 6.72) increase in the risk of circulatory mortality for the group of women, all ages. For every 10 ppb increase in NO2, the risk of respiratory mortality increases by 17.56% (95%CI: 4.44; 30.64) and the risk of all-cause mortality by 5.63% (95%CI: 1.83; 9.44). The results of our study provide epidemiological evidence that air pollution is associated with a higher risk of cardiorespiratory mortality in Brazil. Given the lack of nationwide studies on air pollution in Brazil, our research is an important contribution to the local and international literature that can provide better support to policymakers to improve air quality and public health.

Assessing the causal effects of a stochastic intervention in time series data: are heat alerts effective in preventing deaths and hospitalizations?

The methodological development of this article is motivated by the need to address the following scientific question: does the issuance of heat alerts prevent adverse health effects? Our goal is to address this question within a causal inference framework in the context of time series data. A key challenge is that causal inference methods require the overlap assumption to hold: each unit (i.e., a day) must have a positive probability of receiving the treatment (i.e., issuing a heat alert on that day). In our motivating example, the overlap assumption is often violated: the probability of issuing a heat alert on a cooler day is near zero. To overcome this challenge, we propose a stochastic intervention for time series data which is implemented via an incremental time-varying propensity score (ItvPS). The ItvPS intervention is executed by multiplying the probability of issuing a heat alert on day $t$-conditional on past information up to day $t$-by an odds ratio $\delta_t$. First, we introduce a new class of causal estimands, which relies on the ItvPS intervention. We provide theoretical results to show that these causal estimands can be identified and estimated under a weaker version of the overlap assumption. Second, we propose nonparametric estimators based on the ItvPS and derive an upper bound for the variances of these estimators. Third, we extend this framework to multisite time series using a spatial meta-analysis approach. Fourth, we show that the proposed estimators perform well in terms of bias and root mean squared error via simulations. Finally, we apply our proposed approach to estimate the causal effects of increasing the probability of issuing heat alerts on each warm-season day in reducing deaths and hospitalizations among Medicare enrollees in 2837 US counties.

Population vulnerability to heat: A case-crossover analysis of heat health alerts and hospital morbidity data in Victoria, Australia

OBJECTIVE

From 2010 to 2022, the Victorian Department of Health operated a heat health alert system. We explored whether changes to morbidity occurred during or directly after these alerts, and how this differed for certain population groups.

METHODS

We used a space-time-stratified case-crossover design and conditional logistic regression to examine the associations between heat health alerts and heat-related and all-cause emergency department (ED) presentations and hospital admissions at the state-wide level, with models created for the whole population and subgroups. Data were included for the warm season (November-March) from 2014 to 2021.

RESULTS

Increases occurred in heat-related ED presentations (OR 1.73, 95% CI: 1.53-1.96) and heat-related hospital admissions (OR 1.23, 95% CI: 1.16-1.30) on days on or after heat health alerts. Effect sizes were largest for those 65 years and older, Aboriginal and Torres Strait Islander people, and those living in the most disadvantaged areas.

CONCLUSIONS

We confirm that increases in morbidity occurred in Victoria during heat health alerts and describe which population groups are more likely to require healthcare in a hospital.

IMPLICATIONS FOR PUBLIC HEALTH

These findings can inform responses before and during periods of extreme heat, data-driven adaptation strategies, and the development of heat health surveillance systems.

Development and validation of a prognostic model of survival for classic heatstroke patients: a multicenter study

Classic heatstroke (CHS) is a life-threatening illness characterized by extreme hyperthermia, dysfunction of the central nervous system and multiorgan failure. Accurate predictive models are useful in the treatment decision-making process and risk stratification. This study was to develop and externally validate a prediction model of survival for hospitalized patients with CHS. In this retrospective study, we enrolled patients with CHS who were hospitalized from June 2022 to September 2022 at 3 hospitals in Southwest Sichuan (training cohort) and 1 hospital in Central Sichuan (external validation cohort). Prognostic factors were identified utilizing least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate Cox regression analysis in the training cohort. A predictive model was developed based on identified prognostic factors, and a nomogram was built for visualization. The areas under the receiver operator characteristic (ROC) curves (AUCs) and the calibration curve were utilized to assess the prognostic performance of the model in both the training and external validation cohorts. The Kaplan‒Meier method was used to calculate survival rates. A total of 225 patients (median age, 74 [68-80] years) were included. Social isolation, self-care ability, comorbidities, body temperature, heart rate, Glasgow Coma Scale (GCS), procalcitonin (PCT), aspartate aminotransferase (AST) and diarrhea were found to have a significant or near-significant association with worse prognosis among hospitalized CHS patients. The AUCs of the model in the training and validation cohorts were 0.994 (95% [CI], 0.975-0.999) and 0.901 (95% [CI], 0.769-0.968), respectively. The model's prediction and actual observation demonstrated strong concordance on the calibration curve regarding 7-day survival probability. According to K‒M survival plots, there were significant differences in survival between the low-risk and high-risk groups in the training and external validation cohorts. We designed and externally validated a prognostic prediction model for CHS. This model has promising predictive performance and could be applied in clinical practice for managing patients with CHS.

Low ambient temperature and hospitalization for cardiorespiratory diseases in Brazil

Studies have shown that larger temperature-related health impacts may be associated with cold rather than with hot temperatures. Although it remains unclear the cold-related health burden in warmer regions, in particular at the national level in Brazil. We address this gap by examining the association between low ambient temperature and daily hospital admissions for cardiovascular and respiratory diseases in Brazil between 2008 and 2018. We first applied a case time series design in combination with distributed lag non-linear modeling (DLNM) framework to assess the association of low ambient temperature with daily hospital admissions by Brazilian region. Here, we also stratified the analyses by sex, age group (15-45, 46-65, and >65 years), and cause (respiratory and cardiovascular hospital admissions). In the second stage, we performed a meta-analysis to estimate pooled effects across the Brazilian regions. Our sample included more than 23 million hospitalizations for cardiovascular and respiratory diseases nationwide between 2008 and 2018, of which 53% were admissions for respiratory diseases and 47% for cardiovascular diseases. Our findings suggest that low temperatures are associated with a relative risk of 1.17 (95% CI: 1.07; 1.27) and 1.07 (95% CI: 1.01; 1.14) for cardiovascular and respiratory admissions in Brazil, respectively. The pooled national results indicate robust positive associations for cardiovascular and respiratory hospital admissions in most of the subgroup analyses. In particular, for cardiovascular hospital admissions, men and older adults (>65 years old) were slightly more impacted by cold exposure. For respiratory admissions, the results did not indicate differences among the population groups by sex and age. This study can help decision-makers to create adaptive measures to protect public health from the effects of cold temperature.

Nationwide projections of heat- and cold-related mortality impacts under various climate change and population development scenarios in Switzerland

Climate change and progressive population development (i.e., ageing and changes in population size) are altering the temporal patterns of temperature-related mortality in Switzerland. However, limited evidence exists on how current trends in heat- and cold-related mortality would evolve in future decades under composite scenarios of global warming and population development. Moreover, the contribution of these drivers to future mortality impacts is not well-understood. Therefore, we aimed to project heat- and cold-related mortality in Switzerland under various combinations of emission and population development scenarios and to disentangle the contribution of each of these two drivers using high-resolution mortality and temperature data. We combined age-specific (<75 and ⩾75 years) temperature-mortality associations in each district in Switzerland (1990-2010), estimated through a two-stage time series analysis, with 2 km downscaled CMIP5 temperature data and population and mortality rate projections under two scenarios: RCP4.5/SSP2 and RCP8.5/SSP5. We derived heat and cold-related mortality for different warming targets (1.5 °C, 2.0 °C and 3.0 °C) using different emission and population development scenarios and compared this to the baseline period (1990-2010). Heat-related mortality is projected to increase from 312 (116; 510) in the 1990-2010 period to 1274 (537; 2284) annual deaths under 2.0 °C of warming (RCP4.5/SSP2) and to 1871 (791; 3284) under 3.0 °C of warming (RCP8.5/SSP5). Cold-related mortality will substantially increase from 4069 (1898; 6016) to 6558 (3223; 9589) annual deaths under 2.0 °C (RCP4.5/SSP2) and to 5997 (2951; 8759) under 3.0 °C (RCP8.5/SSP5). Moreover, while the increase in cold-related mortality is solely driven by population development, for heat, both components (i.e., changes in climate and population) have a similar contribution of around 50% to the projected heat-related mortality trends. In conclusion, our findings suggest that both heat- and cold-related mortality will substantially increase under all scenarios of climate change and population development in Switzerland. Population development will lead to an increase in cold-related mortality despite the decrease in cold temperature under warmer scenarios. Whereas the combination of the progressive warming of the climate and population development will substantially increase and exacerbate the total temperature-related mortality burden in Switzerland.

Association of high ambient temperature with daily hospitalization for cardiorespiratory diseases in Brazil: A national time-series study between 2008 and 2018

Further research is needed to examine the nationwide impact of temperature on health in Brazil, a region with particular challenges related to climate conditions, environmental characteristics, and health equity. To address this gap, in this study, we looked at the relationship between high ambient temperature and hospital admissions for circulatory and respiratory diseases in 5572 Brazilian municipalities between 2008 and 2018. We used an extension of the two-stage design with a case time series to assess this relationship. In the first stage, we applied a distributed lag non-linear modeling framework to create a cross-basis function. We next applied quasi-Poisson regression models adjusted by PM_2.5, O₃, relative humidity, and time-varying confounders. We estimated relative risks (RRs) of the association of heat (percentile 99th) with hospitalization for circulatory and respiratory diseases by sex, age group, and Brazilian regions. In the second stage, we applied meta-analysis with random effects to estimate the national RR. Our study population includes 23,791,093 hospital admissions for cardiorespiratory diseases in Brazil between 2008 and 2018. Among those, 53.1% are respiratory diseases, and 46.9% are circulatory diseases. The robustness of the RR and the effect size varied significantly by region, sex, age group, and health outcome. Overall, our findings suggest that i) respiratory admissions had the highest RR, while circulatory admissions had inconsistent or null RR in several subgroup analyses; ii) there was a large difference in the cumulative risk ratio across regions; and iii) overall, women and the elderly population experienced the greatest impact from heat exposure. The pooled national results for the whole population (all ages and sex) suggest a relative risk of 1.29 (95% CI: 1.26; 1.32) associated with respiratory admissions. In contrast, national meta-analysis for circulatory admissions suggested robust positive associations only for people aged 15-45, 46-65, >65 years old; for men aged 15-45 years old; and women aged 15-45 and 46-65 years old. Our findings are essential for the body of scientific evidence that has assisted policymakers to promote health equity and to create adaptive measures and mitigations.

Association of Temperature Thresholds with Heat Illness- and Cardiorespiratory-Related Emergency Visits during Summer Months in Alaska

BACKGROUND

Recent record-breaking hot temperatures in Alaska have raised concerns about the potential human health implications of heat exposure among this unacclimated population.

OBJECTIVES

We estimated cardiorespiratory morbidity associated with days above summer (June-August) heat index (HI, apparent temperature) thresholds in three major population centers (Anchorage, Fairbanks, and the Matanuska-Susitna Valley) for the years 2015-2019.

METHODS

We implemented time-stratified case-crossover analyses of emergency department (ED) visits for International Classification of Diseases, 10^th Revision codes indicative of heat illness and major cardiorespiratory diagnostic codes using data from the Alaska Health Facilities Data Reporting Program. Using conditional logistic regression models, we tested maximum hourly HI temperature thresholds between 21.1°C (70°F) and 30°C (86°F) for a single day, 2 consecutive days, and the absolute number of previous consecutive days above the threshold, adjusting for the daily average concentration of particulate matter ≤2.5μg.

RESULTS

There were increased odds of ED visits for heat illness above a HI threshold as low as 21.1°C (70°F) [odds ratio (OR)=13.84; 95% confidence interval (CI): 4.05, 47.29], and this increased risk continued for up to 4 d (OR=2.43; 95% CI: 1.15, 5.10). Asthma and pneumonia were the only respiratory outcomes positively associated with the HI: ED visits for both were highest the day after a heat event (Asthma: HI>27°C(80°F) OR=1.18; 95% CI: 1.00, 1.39; Pneumonia: HI>28°C(82°F) OR=1.40; 95% CI: 1.06, 1.84). There was a decreased odds of bronchitis-related ED visits when the HI was above thresholds of 21.1-28°C (70-82°F) across all lag days. We found stronger effects for ischemia and myocardial infarction (MI) than for respiratory outcomes. Multiple days of warm weather were associated with an increased risk of health impacts. For each additional preceding day above a HI of 22°C (72°F), the odds of ED visits related to ischemia increased 6% (95% CI: 1%, 12%); for each additional preceding day above a HI of 21.1°C (70°F), the odds of ED visits related to MI increased 7% (95% CI: 1%, 14%).

DISCUSSION

This study demonstrates the importance of planning for extreme heat events and developing local guidance for heat warnings, even in areas with historically mild summertime climates. https://doi.org/10.1289/EHP11363.

Human Health and Well-being in a Warming World

Policy Points After decades of scientific progress and growth in academic literature, there is a recognition that climate change poses a substantial threat to the health and well-being of individuals and communities both in the United States and globally. Solutions to mitigate and adapt to climate change can have important health cobenefits. A vital component of these policy solutions is that they must also take into consideration historic issues of environmental justice and racism, and implementation of these policies must have a strong equity lens.

Short-term air pollution exposure and hospital admissions for cardiorespiratory diseases in Brazil: A nationwide time-series study between 2008 and 2018

The established evidence associating air pollution with health is limited to populations from specific regions. Further large-scale studies in several regions worldwide are needed to support the literature to date and encourage national governments to act. Brazil is an example of these regions where little research has been performed on a large scale. To address this gap, we conducted a study looking at the relationship between daily PM_2.5, NO₂, and O₃, and hospital admissions for circulatory and respiratory diseases across Brazil between 2008 and 2018. A time-series analytic approach was applied with a distributed lag modeling framework. We used a generalized conditional quasi-Poisson regression model to estimate relative risks (RRs) of the association of each air pollutant with the hospitalization for circulatory and respiratory diseases by sex, age group, and Brazilian regions. Our study population includes 23, 791, 093 hospital admissions for cardiorespiratory diseases in Brazil between 2008 and 2018. Among those, 53.1% are respiratory diseases, and 46.9% are circulatory diseases. Our findings suggest significant associations of ambient air pollution (PM_2.5, NO₂, and O₃) with respiratory and circulatory hospital admissions in Brazil. The national meta-analysis for the whole population showed that for every increase of PM_2.5 by 10 μg/m³, there is a 3.28% (95%CI: 2.61; 3.94) increase in the risk of hospital admission for respiratory diseases. For O₃, we found positive associations only for some sub-group analyses by age and sex. For NO₂, our findings suggest that a 10 ppb increase in this pollutant, there was a 35.26% (95%CI: 24.07; 46.44) increase in the risk of hospital admission for respiratory diseases. This study may better support policymakers to improve the air quality and public health in Brazil.

Impact of environmental factors on heat-associated mortalities in an urban desert region

The troubling trend of rising heat-associated mortalities in an urban desert region (Maricopa County, AZ, USA) has motivated us to explore the extent to which environmental factors may contribute to increased heat-health risks. Summertime data from 2010 to 2019 were used to construct a suite of models for daily heat-associated mortalities. The best-performing full model included the following predictors, ordered from strongest to weakest influence: daily average air temperature, average of previous 5 days daily average air temperature, year, day of year, average of previous 5 days daily average dew point temperature, average of previous 5 days daily average PM_2.5, and daily average PM₁₀. This full model exhibited a 5.39% reduction in mean absolute error in daily heat-associated mortalities as compared to the best-performing model that included only air temperature as an environmental predictor. The extent to which issued and modeled excessive heat warnings (from both the temperature only and full models) corresponded with heat-associated mortalities was also examined. Model hindcasts for 2020 and 2021 showed that the models were able to capture the high number of heat-associated mortalities in 2020, but greatly undercounted the highest yet observed number of heat-associated mortalities in 2021. Results from this study lend insights into environmental factors corresponding to an increased number of heat-associated mortalities and can be used for informing strategies towards reducing heat-health risks. However, as the best-performing model was unable to fully capture the observed number of heat-associated mortalities, continued scrutiny of both environmental and non-environmental factors affecting these observations is needed.

Utility of the Heat Index in defining the upper limits of thermal balance during light physical activity (PSU HEAT Project)

Extreme heat events and consequent detrimental heat-health outcomes have been increasing in recent decades and are expected to continue with future climate warming. While many indices have been created to quantify the combined atmospheric contributions to heat, few have been validated to determine how index-defined heat conditions impact human health. However, this subset of indices is likely not valid for all situations and populations nor easily understood and interpreted by health officials and the public. In this study, we compare the ability of thresholds determined from the National Weather Service's (NWS) Heat Index (HI), the Wet Bulb Globe Temperature (WBGT), and the Universal Thermal Climate Index (UTCI) to predict the compensability of human heat stress (upper limits of heat balance) measured as part of the Pennsylvania State University's Heat Environmental Age Thresholds (PSU HEAT) project. While the WBGT performed the best of the three indices for both minimal activities of daily living (MinAct; 83 W·m-2) and light ambulation (LightAmb; 133 W·m-2) in a cohort of young, healthy subjects, HI was likewise accurate in predicting heat stress compensability in MinAct conditions. HI was significantly correlated with subjects' perception of temperature and humidity as well as their body core temperature, linking perception of the ambient environment with physiological responses in MinAct conditions. Given the familiarity the public has with HI, it may be better utilized in the expansion of safeguard policies and the issuance of heat warnings during extreme heat events, especially when access to engineered cooling strategies is unavailable.

Internet searches and heat-related emergency department visits in the United States

Emerging research suggests that internet search patterns may provide timely, actionable insights into adverse health impacts from, and behavioral responses to, days of extreme heat, but few studies have evaluated this hypothesis, and none have done so across the United States. We used two-stage distributed lag nonlinear models to quantify the interrelationships between daily maximum ambient temperature, internet search activity as measured by Google Trends, and heat-related emergency department (ED) visits among adults with commercial health insurance in 30 US metropolitan areas during the warm seasons (May to September) from 2016 to 2019. Maximum daily temperature was positively associated with internet searches relevant to heat, and searches were in turn positively associated with heat-related ED visits. Moreover, models combining internet search activity and temperature had better predictive ability for heat-related ED visits compared to models with temperature alone. These results suggest that internet search patterns may be useful as a leading indicator of heat-related illness or stress.