The global, regional, and national burden of disease attributable to non-optimal temperatures in children

Introduction

The accelerating global climate crisis, characterized by increasing frequency and intensity of extreme temperature events, poses an unprecedented threat to fundamental child health rights worldwide (1). Non-optimal temperatures—spanning both heat and cold—are now established among the leading global risk factors for mortality, accounting for an estimated 2.98% of global deaths in 2019 (2). Children face disproportionate vulnerability due to biological factors (e.g., immature thermoregulation, critical developmental windows) and socio-behavioral dependencies (e.g., reliance on caregivers, outdoor activity patterns) (3-5). Crucially, early-life temperature exposures can induce lifelong physiological alterations, elevating risks for renal, respiratory, and developmental disorders (4,5).

Existing epidemiological evidence confirms severe pediatric health impacts from non-optimal temperatures (6,7). Heat extremes amplify risks of asthma exacerbations, vector-borne diseases (e.g., dengue, malaria), and heat-related mortality among children (8,9). Conversely, cold exposure disrupts respiratory defenses, increasing hospitalizations for bronchiolitis and severe infections (10,11). Critically, these burdens are inequitably distributed: children from marginalized socioeconomic and ethnic groups face heightened exposure (e.g., urban heat islands) and reduced adaptive capacity due to structural barriers in healthcare access and resource allocation (5,12). Climate change thus entrenches preexisting health disparities, with children at the epicenter.

Despite this evidence, reliable, comparable, and geographically complete estimates of the cause-specific mortality and disability burden attributable to non-optimal temperatures across global pediatric populations remain absent. Prior studies lack granular quantification of socioeconomic and geographic disparities, hindering equity-focused interventions. To address these gaps, this study leverages the Global Burden of Diseases Study (GBD) 2021. We provide comprehensive estimates of mortality and disability-adjusted life years (DALYs) attributable to non-optimal temperatures among children aged 0–14 years from 1990 to 2021 across global, regional, and national levels. Additionally, we analyze socioeconomic and geographic inequalities in the distribution of this burden, aiming to offer insights for targeted policy action. We present this article in accordance with the STROBE reporting checklist (available at https://tp.amegroups.com/article/view/10.21037/tp-2026-1-0091/rc).

Methods

Study overview

The Global Burden of Disease Study 2021 (GBD 2021), coordinated by the Institute for Health Metrics and Evaluation (IHME), involves a global network of over 14,000 collaborators from 160+ countries who contributed to data collection, review, and validation. It provides comprehensive estimates of health loss for 371 diseases and 88 risk factors across 204 countries and 811 subnational locations from 1990 to 2021 (13). This study leverages GBD 2021 to quantify mortality and DALYs attributable to non-optimal temperatures (encompassing both high temperature and low temperature exposure) in the pediatric population, defined as children aged 0–14 years. This age range was selected to align with the developmental stages characterized by the highest degree of physiological vulnerability (e.g., immature thermoregulation) and socio-behavioral dependency on caregivers, which are central to our research questions. It also ensures consistency with established GBD pediatric age strata, allowing for a focused analysis distinct from the different risk profiles of older adolescents (15–19 years). Non-optimal temperatures were defined as deviations from the theoretical minimum risk exposure level (TMREL), which represents the temperature range associated with the lowest daily all-cause mortality risk. TMRELs were location- and year-specific, calibrated through age-stratified meta-analyses of global exposure-response functions to account for developmental differences in pediatric thermoregulation (14).

The GBD 2021 framework employs a comprehensive hierarchy for disease and injury risk factors, with non-optimal temperatures categorized under environmental risk factors (15). For cause of death modeling, GBD 2021 utilizes a standardized approach that incorporates multiple data sources and statistical models to estimate mortality attributed to various risk factors, including non-optimal temperatures (16). Non-fatal disease and injury modeling follows a similar rigorous process, considering the incidence, prevalence, and severity of conditions related to non-optimal temperatures in the pediatric population (13).

GBD classifies diseases and injuries into a hierarchy with four levels that include both fatal and non-fatal causes (13,16). In this GBD 2021 study, the scientific evidence of a causal relationship attributable to non-optimal temperatures were judged to be sufficient for 14 level-three disease groups, namely lower respiratory infections, cardiomyopathy and myocarditis, stroke, chronic kidney disease, diabetes mellitus, drowning, animal contact, exposure to forces of nature, exposure to mechanical forces, self-harm, interpersonal violence, road injuries, other transport injuries, and other unintentional injuries (15). Detailed information on the International Classification of Disease codes corresponding to the specific diseases is available in the Table S1. Relevant metrics include mortality rates and DALYs to quantify the overall burden. Subgroup analyses were stratified by age groups (<1 year, 12–23 months, 2–4 years, 5–9 years, 10–14 years), gender, and Socio-demographic Index (SDI) levels (low SDI, low-middle SDI, middle SDI, high-middle SDI, high SDI) to explore age- and gender-specific patterns and socio-demographic disparities (17). GBD 2021 estimates for non-optimal temperatures and their impact on child health were originally produced by the GBD 2021 Collaborators and shared publicly in the GBD Results Tool (https://vizhub.healthdata.org/gbd-results/).

Proportional population attributable fraction (PAF) calculation and burden estimation

To quantify the burden of disease attributable to non-optimal temperatures, we calculated the PAFs, which represent the proportion of health outcomes (mortality and DALYs) that could be theoretically avoided if exposure to non-optimal temperatures were eliminated, assuming a causal relationship. The PAF was computed using the formula: PAF = [Pe × (RR − 1)]/[1 + Pe × (RR − 1)], where Pe is the population exposure proportion to non-optimal temperatures, and RR is the relative risk of the health outcome associated with such exposure (15). Relative risks were derived from meta-analyses of epidemiological studies specific to pediatric populations, accounting for age, gender, and regional variations in exposure-response relationships. These PAFs were then applied to the total burden of relevant diseases (e.g., respiratory infections, heat-related illnesses) to estimate the fraction attributable to non-optimal temperatures (15).

Data sources and processing

Data sources used to produce GBD 2021 estimates are listed in the GBD 2021 Sources Tool (https://ghdx.healthdata.org/gbd-2021/sources); metadata and citations for the input data sources specific to this study are available by selecting “nonfatal health outcomes and mortality” for components, “non-optimal temperatures” for causes/risks, and “global, regional, and national locations” for locations. See the GBD 2021 Collaborators’ publications for details on how input data were processed, including data cleaning, validation, and standardization to ensure consistency across different sources and regions (13,15,16). A key strength of the GBD framework is its approach to handling incomplete or heterogeneous data. It does not rely solely on reported figures. Instead, it synthesizes all available input data—including vital registration, verbal autopsy, survey, and surveillance data—using standardized modeling tools like spatiotemporal Gaussian process regression (ST-GPR) and DisMod-MR 2.1. These tools borrow statistical strength across time, geography, and cause of death patterns to generate consistent and comparable estimates of mortality and morbidity for all locations, including those with sparse data. This process inherently involves modeling and prediction, particularly for low-SDI regions where primary data are often limited.

Calculating uncertainty

Uncertainty was introduced throughout the estimation process. Mean estimates for all metrics reported represent the mean value across 500 draws from the estimate’s distribution, with 95% uncertainty intervals (UI) calculated as the 2.5 and 97.5 percentile values across the draws. This approach is consistent with the methods used in GBD 2021 to account for various sources of uncertainty, including data variability, model assumptions and the imputation of estimates for data-sparse regions (13,16). Consequently, wider UIs in low-SDI regions reflect the greater underlying data uncertainty and the increased reliance on model-based predictions in those settings. For PAF estimates, uncertainty was propagated from both exposure data (Pe) and relative risk (RR) estimates, with 95% UIs derived using the same 500 draws framework to maintain methodological consistency (15).

Statistical analysis

The estimated annual percentage change (EAPC) was calculated to evaluate temporal trends in mortality and DALYs attributed to non-optimal temperatures from 1990 to 2021. EAPC was derived using linear regression, where the natural logarithm of rates was regressed on calendar year. The EAPC provides a summary measure of the trend over the entire period, assuming a constant rate of change. Its primary advantage is that it offers a comparable and stable estimate of the direction and speed of change, which is more informative for comparative analyses than simply examining differences between the start and end years, as it is less influenced by fluctuations in those specific time points. A positive EAPC indicates an increasing trend, and a negative value indicates a decreasing trend; statistical significance was determined by 95% confidence intervals (CI) excluding zero (18). Furthermore, correlation and regression analyses examined the relationship between non-optimal temperature-related child health burden (mortality and DALYs) and SDI, quantifying how burden varies with socio-demographic development (17). To measure socioeconomic inequalities in disease burden, we employed the slope index of inequality (SII) and concentration index. The SII, representing absolute inequality, was calculated via weighted linear regression of health outcomes on the relative rank of SDI. The concentration index, representing relative inequality, quantifies the degree to which the burden is concentrated among specific socioeconomic groups. These metrics characterize distributional inequities and identify groups with disproportionately high burdens (19). All statistical analyses and data visualization were performed using R (version 4.4.2), and statistical significance was determined by a P value less than 0.05. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

GBD researching and reporting practices

GBD 2021 complies with the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) guidelines. The original GBD 2021 analyses were completed using appropriate software versions, and the statistical code used in GBD 2021 is publicly available online (http://ghdx.healthdata.org/gbd-2021/code).

Results

Global burden of disease attributable to non-optimal temperatures in 2021

In 2021, non-optimal temperatures contributed to a substantial global disease burden. The death rate attributable to non-optimal temperatures was 2.14 (95% UI: 0.22–4.12) per 100,000. This burden was driven primarily by high temperature, with a death rate of 1.92 (95% UI: 0.45–3.32) per 100,000, while the death rate for low temperature was 0.25 (95% UI: −0.36 to 1.00) per 100,000. Among specific diseases, the highest death rates attributable to non-optimal temperatures were observed for lower respiratory infections, followed by road injuries, stroke, chronic kidney disease, and cardiomyopathy and myocarditis. In contrast, significant decreases in mortality were observed for drowning, other unintentional injuries, and animal contact (Table 1, Tables S2,S3). A similar pattern was observed for DALYs attributable to non-optimal temperatures (Table 2, Tables S4,S5).

The disease burden varied markedly across SDI regions. For non-optimal temperatures, the mortality burden was highest in low and low-middle SDI regions, with death rates of 4.34 (95% UI: 1.35–8.04) and 3.42 (95% UI: −0.29 to 7.04) per 100,000, respectively. Similarly, the death rates for both high and low temperatures were highest in low SDI regions, followed by low-middle SDI regions, and were lowest in high SDI regions. Significant geographical disparities were also evident at the GBD regional level. The death rate attributable to non-optimal temperatures was highest in Western Sub-Saharan Africa and Central Asia, and lowest in East Asia and Eastern Europe. For high temperature, the death rate was highest in Western Sub-Saharan Africa and South Asia, and lowest in Western Europe and Australasia. For low temperature, the death rate was highest in Central Asia and Oceania, and lowest in East Asia and the Caribbean. At the national level, the death rate attributable to non-optimal temperatures was highest in Chad and Niger, and lowest in Greenland and the Republic of Cabo Verde [Figure 1, supplementary table 1 (available at https://cdn.amegroups.cn/static/public/tp-2026-1-0091-1.xlsx)]. The death rate attributable to high temperature was highest in Chad and Niger, and lowest in Norway and Poland [Figure S1, supplementary table 1 (available at https://cdn.amegroups.cn/static/public/tp-2026-1-0091-1.xlsx)]. The death rate attributable to low temperature was highest in Lesotho and Tajikistan, and lowest in Greenland and Niue [Figure S2, supplementary table 1 (available at https://cdn.amegroups.cn/static/public/tp-2026-1-0091-1.xlsx)]. Similar geographical trends were observed for DALYs attributable to non-optimal, high, and low temperatures [Figures S3-S5; supplementary table 2 (available at https://cdn.amegroups.cn/static/public/tp-2026-1-0091-2.xlsx)].

Figure 1 All-cause death rate attributable to non-optimal temperature in 204 countries and territories in 2021. (A) Death counts. (B) Death rates. (C) EAPC in death rates from 1990 to 2021. EAPC, estimated annual percentage change; NA, not available.

The disease burden exhibited clear variations by sex and age (Tables 1,2; Tables S2-S5; Figure S1). In 2021, the overall mortality rate and attributable fraction for non-optimal temperatures were slightly higher among females than males. Specifically, the death rate attributable to high temperature was higher in males [2.03 (95% UI: 0.49–3.53) per 100,000] than in females [1.80 (95% UI: 0.40–3.14) per 100,000], although the attributable fraction was slightly higher in females [0.44% (95% UI: 0.03–0.83%)] than in males [0.42% (95% UI: 0.03–0.78%)]. For low temperature, the pattern was reversed. The mortality rates for non-optimal, high, and low temperatures were highest among children under one year of age and generally decreased with increasing age. Notably, the peak percentage of deaths attributable to non-optimal temperatures occurred in the 2–4 years age group. Similar patterns by sex and age were observed for DALYs.

Temporal trends of the disease burden from 1990 to 2021

From 1990 to 2021, the global disease burden attributable to non-optimal temperatures declined substantially. The global death rate decreased from 8.49 (95% UI: 3.31–15.17) to 2.14 (95% UI: 0.22–4.12) per 100,000, with an EAPC of −3.46% (95% CI: −3.91% to −3.02%). The death rate attributable to high temperature decreased from 5.25 (95% UI: 1.83–8.98) to 1.92 (95% UI: 0.45–3.32) per 100,000 [EAPC: −2.40% (95% CI: −2.96% to −1.84%)]. The death rate for low temperature showed the most dramatic decline, falling from 3.33 (95% UI: 0.93–6.79) to 0.25 (95% UI: −0.36 to 1.00) per 100,000 [EAPC: −6.56% (95% CI: −7.06% to −6.05%)]. Declining trends were observed for both sexes. The EAPC for high temperature was slightly less pronounced in males than in females, whereas the EAPC for low temperature was substantially greater in males (Tables 1,2; Tables S2-S5; Figure 2).

Figure 2 Temporal trends in the disease burden attributable to non-optimal temperatures, by sex, 1990–2021. (A,B) Deaths and DALYs attributable to non-optimal temperature. (C,D) Deaths and DALYs attributable to high temperature. (E,F) Deaths and DALYs attributable to low temperature. CI, confidence interval; DALYs, disability-adjusted life years.

The rate of decline varied across regions (supplementary tables 1,2 available at https://cdn.amegroups.cn/static/public/tp-2026-1-0091-1.xlsx and https://cdn.amegroups.cn/static/public/tp-2026-1-0091-2.xlsx). At the SDI regional level, the fastest decline in mortality from non-optimal temperatures occurred in the high-middle SDI region [EAPC: −14.85 (95% CI: −17.36 to −12.27)], while the slowest decline was observed in the high SDI region [EAPC: −2.09 (95% CI: −4.89 to 0.79)]. At the GBD regional level, Western Europe [EAPC: 111.07 (95% CI: 40.36–217.40)] and the Caribbean [EAPC: 1.91 (95% CI: −2.45 to 6.46)] were among the few regions that exhibited an increasing trend. At the national level, Luxembourg experienced the most rapid increase [EAPC: 143.48% (95% CI: 2.14–480.42%)], while China experienced the fastest decline [EAPC: −31.14 (95% CI: −38.80 to −22.51)]. The trends for DALYs rates mirrored those for mortality rates.

Among the 14 level-three causes examined, mortality and DALYs rates attributable to non-optimal temperatures declined for all conditions except road injuries and interpersonal violence. The largest decreases were observed for drowning and exposure to mechanical forces, while the smallest decreases were seen for diabetes mellitus and chronic kidney disease. For lower respiratory infections, which carried the heaviest burden, the mortality and DALYs rates declined by −4.06%. Notably, the trends for high and low temperatures differed. Mortality and DALYs rates for chronic kidney disease, diabetes mellitus, and cardiomyopathy and myocarditis attributable to high temperature showed slight increases, whereas all 14 causes attributable to low temperature showed declines, with the largest decreases for lower respiratory infections and stroke, and the smallest for diabetes mellitus and chronic kidney disease (Tables 1,2; Tables S2-S5).

Furthermore, significant gender differences were observed in the time trends (Figure 3 and Figure S6). In terms of overall burden changes, the non-optimal temperature-related interpersonal violence mortality rate among female children showed a slowly increasing trend, which became slightly more pronounced after 2015, while no significant increase was observed in males. The high temperature-related road injury mortality rate increased more rapidly in males than in females, with a further acceleration after 2010. For specific diseases, high temperature-related cardiomyopathy and myocarditis mortality showed no significant decline in females and even increased slightly after 2010, whereas a steady decline was observed in males. The decline in low temperature-related stroke mortality was significantly greater in males, resulting in lower mortality among males than females by 2021. Additionally, between 1990 and 2021, low temperature-related self-harm mortality showed no significant change in females, while a slow decline was observed in males. The decline in non-optimal temperature-related exposure to mechanical forces mortality was also greater in males. The corresponding trends in DALYs rates were consistent with those in mortality rates.

Figure 3 Sex-specific DALYs rates attributable to non-optimal (A,B), high (C,D), and low (E,F) temperatures by cause, 1990–2021. DALYs, disability-adjusted life years.

PAF of the diseases attributable to non-optimal temperatures

The PAFs for mortality and DALYs rates varied significantly across regions. Globally, five level-three diseases had high PAFs for non-optimal temperatures: lower respiratory infections, cardiomyopathy and myocarditis, diabetes mellitus, stroke, and chronic kidney disease. At the SDI regional level, the highest PAFs were primarily found in the low-middle SDI region (Figures S7,S8). At the GBD regional level, high PAFs were concentrated in Western Sub-Saharan Africa and South Asia, with Western Sub-Saharan Africa having higher PAFs for road injuries, diabetes mellitus, and interpersonal violence, and South Asia having higher PAFs for lower respiratory infections and cardiomyopathy and myocarditis (supplementary tables 1,2 available at https://cdn.amegroups.cn/static/public/tp-2026-1-0091-1.xlsx and https://cdn.amegroups.cn/static/public/tp-2026-1-0091-2.xlsx).

For high temperature, the conditions with the highest PAFs were lower respiratory infections and drowning, with the highest PAFs also found in the low-middle SDI region and in Western Sub-Saharan Africa and South Asia at the GBD regional level. For low temperature, the highest PAFs were for cardiomyopathy and myocarditis, chronic kidney disease, and lower respiratory infections. The highest PAFs were observed in the low SDI region, and at the GBD regional level, high PAFs were concentrated in Western Sub-Saharan Africa and Southeast Asia (supplementary tables 1,2 available at https://cdn.amegroups.cn/static/public/tp-2026-1-0091-1.xlsx and https://cdn.amegroups.cn/static/public/tp-2026-1-0091-2.xlsx).

Relationship between SDI and disease burden

The relationship between SDI and temperature-related mortality and DALYs rates was systematically evaluated across 21 GBD regions and 204 countries and territories. The results indicated a significant negative correlation between SDI and temperature-related health burden, although the strength of this association varied by temperature type. A strong negative correlation was observed at the national level for both high temperature (r=−0.54, P<0.001) and non-optimal temperature (r=−0.52, P<0.001). This pattern was most pronounced in low-SDI countries, such as those in Sub-Saharan Africa (e.g., Burkina Faso, Niger, Chad) and South Asia (e.g., Afghanistan, Pakistan), which exhibited the highest mortality and DALYs rates from high temperature globally. In contrast, the association between SDI and low-temperature burden was weak (r=−0.15, P=0.03). Although some Eastern European countries (e.g., Russia, Ukraine) had a relatively high burden from low temperature, they did not demonstrate the strong socioeconomic gradient observed for high temperature (Figure 4). This pattern was consistent for both mortality and DALYs rates. Furthermore, correlations were generally stronger at the national than at the regional level (Figure S9). In summary, the heavier burden of non-optimal temperatures in lower-SDI countries is predominantly driven by exposure to high temperature.

Figure 4 Associations of the disease burden attributable to non-optimal (A,B), high (C,D), and low (E,F) temperatures with the SDI at the national levels. DALYs, disability-adjusted life years; SDI, Socio-demographic Index; Val, value.

Inequality analysis

The analysis of socioeconomic inequality from 1990 to 2021 revealed a consistent pattern: while absolute inequality in mortality and DALYs rates from temperature exposures decreased, relative inequality increased, indicating a profound structural shift in the socioeconomic distribution of this health burden. For non-optimal temperatures, the SII for mortality rate improved substantially from −9.61 to −1.99, and for DALYs rate from −845.6 to −173.4. However, the concentration index increased for both mortality (from 0.46 to 0.58) and DALYs (from 0.46 to 0.57) rates, indicating that the burden became more concentrated among populations with higher socioeconomic status over time (Figure 5A,5B; Figure S10A,S10B).

Figure 5 Mortality inequities attributable to non-optimal, high, and low temperatures. (A,B) Absolute and relative inequality of mortality attributable to non-optimal temperatures. (C,D) Absolute and relative inequality of mortality attributable to high temperature. (E,F) Absolute and relative inequality of mortality attributable to low temperature. SDI, Socio-demographic Index.

A similar pattern was observed for high temperature (Figure 5C,5D; Figure S10C,S10D). The SII for mortality decreased from −4.51 to −1.22, and for DALYs from −395.5 to −104.6. The concentration index for both mortality and DALYs increased slightly from 0.52 to 0.55, suggesting a persistent trend towards a relative concentration of the high-temperature burden among higher socioeconomic groups. The most dramatic transformation occurred for low temperature (Figure 5E,5F; Figure S10E,S10F). The absolute inequality, as measured by the SII, nearly vanished for mortality rate (from −1.89 to −0.14) and greatly decreased for DALYs rate (from −166.8 to −12.4). Concurrently, the concentration index exhibited the most rapid increase, rising for mortality rate from 0.37 to 0.76 and for DALYs rate from 0.36 to 0.69. This reveals a fundamental shift of the low-temperature-related health burden from a more widespread distribution to a high concentration among socioeconomically advantaged populations.

Discussion

This study provides the first systematic global assessment of the distribution and temporal trends of the disease burden attributable to non-optimal temperatures among children aged 0–14 years from 1990 to 2021. The results indicate that although mortality and DALYs rates attributable to non-optimal temperatures showed a declining trend during this period, the distribution of the disease burden exhibited significant cause-specific, regional clustering, and socioeconomic disparities. In terms of disease composition, road injuries, stroke, chronic kidney disease, and cardiomyopathy and myocarditis were the leading causes of death and DALYs loss. Regarding dynamic changes in temporal trends, the burden related to drowning (EAPC: −6.64%) and exposure to mechanical forces (EAPC: −6.07%) decreased substantially, whereas the burden from road injuries (EAPC: 6.23%) and interpersonal violence (EAPC: 2.78%) increased against the overall trend. More importantly, the burden remained highly concentrated among infants under one year of age and in low/low-middle SDI regions. Furthermore, the inequality analysis revealed a fundamental shift: while absolute disparities narrowed, relative inequality intensified, with the low-temperature-related health burden clearly shifting towards socioeconomically advantaged groups. These findings collectively indicate that the threat of non-optimal temperatures to child health is becoming increasingly complex, urgently necessitating more targeted public health response strategies that prioritize health equity.

The overall decline in the global temperature-related disease burden among children over the past three decades is essentially an achievement resulting from the improvement of global public health systems and the widespread adoption of basic health interventions. For instance, the decline in lower respiratory infections (EAPC: −4.06%), which carried the heaviest burden, is likely primarily attributable to the widespread use of vaccines (e.g., pneumococcal vaccine, respiratory syncytial virus vaccine) and improvements in indoor air quality (e.g., effective heating systems and the use of high-efficiency particulate air filters) (20). Secondly, the responsiveness and adaptive capacity of public health systems have been strengthened. For example, most countries (especially middle-high SDI regions) have established child health early warning systems for high temperatures (e.g., school closure policies during heatwaves, community education on heat-related illnesses), and primary healthcare institutions have significantly improved their capacity for rapid treatment of heat-related diseases (21). Furthermore, specific engineering and non-engineering interventions (e.g., improving housing ventilation, implementing child water safety measures and road traffic safety regulations) have effectively reduced children’s direct temperature exposure time and risks (22). It is noteworthy that children’s healthcare systems, as core institutions for health protection, are increasingly considering their own environmental impact within intervention strategies—for instance, Great Ormond Street Hospital (GOSH) in London declared a “climate and health emergency” in 2021, committing to achieve carbon neutrality by 2030 through measures such as phasing out diesel ambulances and promoting renewable energy, thereby both reducing healthcare carbon emissions and improving the efficiency of treating heat-related illnesses during hot seasons (23). However, the significant divergence in trends across different diseases reveals their distinct pathogenic mechanisms and varying intervention effectiveness. For example, the sharp decline in the drowning burden (EAPC: −6.64%) is likely directly related to globally promoted “child water safety interventions”, including improvements in water safety facilities, public education, and swimming lessons, while the reduction in exposure to mechanical forces may be attributed to improved safety regulations in high-SDI regions and increased awareness of protection in low-SDI regions (24,25). In stark contrast, the rising burden of road injuries is closely linked to weak healthcare systems, poor road infrastructure, and a lack of post-accident care resources in low-income countries, leading to disproportionately high mortality and long-term disability rates (26). The increase in the interpersonal violence burden suggests a neglected association: rising sea levels and frequent natural disasters linked to high temperatures are exacerbating poverty and population displacement, which may also act as drivers for increased violence risk (27), highlighting the urgency of expanding climate health risk assessments from traditional infectious diseases to non-traditional areas such as injuries.

Despite favorable global trends, the disease burden remains high in low/low-middle SDI regions and countries such as Western Sub-Saharan Africa, South Asia, Chad, and Niger, with different temperature types dominating for various diseases. The core reason for this phenomenon is not merely an economic gap but reflects global inequity where “countries/groups with the lowest carbon emissions bear the heaviest health costs”—children in low-SDI regions are exposed to extreme temperatures while lacking protective resources (e.g., clean drinking water, basic heating), and this resource imbalance is essentially a result of political power and policy biases (22). The high burden of lower respiratory infections in these regions is a typical “double burden”: under low temperatures, cold stress induced by inhaling cold air leading to a drop in body temperature triggers a pathophysiological response. This response includes vasoconstriction in the respiratory mucosa and suppression of immune responses, both of which increase susceptibility to infection. Furthermore, the lack of household heating and widespread use of biomass fuels significantly increase exposure to wood smoke, which is associated with higher rates of respiratory infections. In high-temperature environments, the stability and transmission rate of respiratory viruses can be affected, increasing the risk of viral spread, coupled with the general shortage of respiratory support equipment for children in primary healthcare facilities, ultimately resulting in high rates of severe illness and mortality (20,28,29). Unlike the general population, the burden of chronic kidney disease in children is primarily driven by high temperatures. Persistent dehydration, renal hyperfiltration, and heat stress induced by high temperatures are particularly detrimental in areas with unstable clean water supplies and poor housing conditions. Lower levels of economic development lead to a larger proportion of the population engaging in outdoor activities, potentially resulting in prolonged exposure to high temperatures and consequently severe chronic kidney disease. These findings suggest that in low-SDI regions, climate adaptation measures alone are insufficient; they must be combined with strengthening basic health systems and ensuring access to essential living resources to effectively reduce the disease burden (30). It is noteworthy that the effect of cold on mortality has a “delayed and persistent” characteristic (peak effect occurs on day 2 post-exposure, excess risk persists for 14 days), while the effect of heat is more immediate (peaking at 0–1 days, with no significant risk after 4 days). This indicates the need for “long-term protection plans” for cold (e.g., continuous guidance on keeping warm) and “immediate response mechanisms” for heat (e.g., short-term heat early warnings) (31).

Infants under one year constituted the most vulnerable subgroup, their high burden closely linked to physiological characteristics specific to their developmental stage. Infants’ narrow airways (diameter only one-third that of adults) make them extremely sensitive to temperature fluctuations; low temperatures easily lead to secretion blockage, while high temperatures reduce the antibacterial capacity of the airway mucosa (32), explaining why the mortality rate from lower respiratory infections in infants (24.43/100,000) is far higher than in older children. Similarly, their immature myocardium makes it more susceptible to temperature stress; low temperatures can lead to insufficient myocardial blood supply, while high temperatures increase cardiac load due to tachycardia. Studies indicate that for every 1 ℃ increase in ambient temperature above the TMREL, cardiovascular diseases increase by 3.4% and cerebrovascular mortality by 1.4%. Conversely, for every 1 ℃ decrease below the TMREL, cardiovascular disease mortality increases by 1.66% (33). More critically, temperature exposure during infancy not only affects current health but can also create “intergenerational health debt” through “developmental programming”—current exposure increases the risk of renal, respiratory, and cardiovascular system diseases in adulthood (e.g., a 37% increased risk of chronic kidney disease in adulthood associated with high temperature exposure during infancy). This lifelong cumulative effect makes temperature protection during infancy not only a short-term health need but also key to ensuring intergenerational health equity (34). These mechanistic insights emphasize that child health policies must not be simply scaled-down versions of adult policies; they must fully consider the specific physiological vulnerabilities at different developmental stages and implement precise protection focused on early life, for example, incorporating “temperature exposure history” into routine infant check-ups and providing individualized protection plans for high-risk infants (e.g., warmth packs, guidance on going out during hot days).

Furthermore, this study found subtle but clear sex differences in the overall burden of disease caused by non-optimal temperatures in children. Overall, the disease burden attributable to non-optimal temperatures was slightly higher among female children than males. This difference was primarily driven by low-temperature exposure, where female children had significantly higher mortality and DALYs burdens related to low temperature; conversely, under high-temperature exposure, the disease burden was slightly higher in male children. These differences result from the combined effects of physiological regulation mechanisms, behavioral perceptual responses, and social exposure factors. At the physiological and perceptual level, females generally exhibit higher cold sensitivity. Experimental studies confirm that in dynamic cooling environments (e.g., from 26 to 24 ℃), females report significantly colder thermal sensations, significantly lower thermal comfort, and lower environmental acceptability (35). Combined with their earlier vasoconstriction response, this leads to lower skin temperature and stronger cold discomfort in low temperatures, potentially exacerbating physiological stress and increasing risks such as respiratory infections and cardiovascular diseases. Under high temperatures, although female sweating capacity may be lower than males, their higher thermal perceptual sensitivity might prompt them to adopt cooling behaviors earlier, forming a protective mechanism (36,37). At the socio-behavioral level, male children may have relatively dulled heat perception and higher participation in outdoor activities due to sociocultural factors, thereby increasing direct exposure and related injury risks under high temperatures (38). Additionally, studies show that males have a wider acceptable temperature range and greater psychological adaptability than females (35). Therefore, future research should strive to explore, at the cause-specific level, the interaction between autonomic nervous regulation (e.g., thermoreceptor function, control of sweating and vasoconstriction) and behavioral thermoregulation underlying these sex differences, in order to develop more targeted temperature-related health protection strategies for children of different sexes.

This study revealed that while absolute inequality generally improved for both high and low-temperature-related disease burdens, relative inequality significantly intensified. Regarding the overall burden of non-optimal temperatures, the improvement in absolute inequality reflects the protective effect of global basic medical and public health interventions for vulnerable groups. However, the intensification of relative inequality reveals a deeper transformation: the distribution mechanism of temperature-related health burdens is undergoing fundamental changes with socioeconomic development. For the high-temperature burden, its inequality pattern still largely follows the traditional health equity gradient. Low-SDI regions continue to bear the heaviest burden due to high heat exposure levels, poor living conditions, and energy insecurity (39). For instance, low-income and minority communities are more likely to face the “heat or eat” dilemma, and indoor temperatures may even exceed outdoor temperatures during heat events, exacerbating health risks. Furthermore, such communities often lack sufficient green cover and shading facilities, and have poor thermal insulation in residential buildings, further amplifying the health risks of heat exposure (40). For the low-temperature burden, its distribution pattern has undergone a structural shift. The near disappearance of absolute inequality indicates significant achievements in basic protective measures in low-SDI regions. For example, studies confirm that insulating houses for low-income households significantly increases winter indoor temperature, reduces humidity, and directly improves residents’ health status (41). However, the sharp rise in relative inequality suggests that the low-temperature burden is gradually concentrating among high-SDI groups. This phenomenon may be related to reduced thermal adaptability in children from high-SDI regions due to long-term exposure to highly controlled thermal environments, while also reflecting more frequent physiological stress from indoor-outdoor temperature differences experienced by children in these regions.

This study has several limitations. First, although GBD 2021 integrates multiple data sources worldwide, death and disease registration data in some low-income regions and conflict areas remain missing or incomplete, potentially affecting the accuracy of estimates. Second, the exposure assessment for non-optimal temperatures relies on meteorological data and the TMREL, without fully considering individual-level actual exposure differences (e.g., indoor microenvironments, clothing behavior). Third, this study only included 14 level-three diseases with sufficient evidence of level within the GBD framework, potentially not covering all child health outcomes affected by temperature, such as certain mental disorders or developmental diseases, neonatal diseases, etc. Additionally, although we quantified estimation error through PAF and UIs, model assumptions (e.g., the linear or nonlinear form of exposure-response relationships) and residual confounding factors may still affect the strength of causal inference. Finally, differences in healthcare access, diagnostic criteria, and coding practices across regions may also introduce bias, affecting the reliability of cross-country and cross-time comparisons. Future research needs to incorporate more micro-level individual exposure data, more comprehensive disease spectra, and more complex confounding control methods to further validate and extend the findings of this study.

Conclusions

In summary, although the global burden of disease related to non-optimal temperatures in children shows a declining trend, its distribution exhibits significant cause-specific variation, regional inequality, and a socioeconomic gradient. High temperature is the predominant current health threat, with its burden highly concentrated in low-SDI regions, while lower respiratory infections, cardiomyopathy and myocarditis, and chronic kidney disease are the conditions carrying the heaviest burden. Furthermore, infants under one year of age constitute the most vulnerable group due to immature physiological development, and their high burden highlights the urgency of temperature protection in early life. Notably, although absolute health inequality has lessened, relative inequality has significantly intensified, particularly as the low-temperature-related burden has become noticeably concentrated among high-SDI groups. In conclusion, to effectively mitigate the current and long-term impacts of non-optimal temperatures on child health, multi-level and targeted intervention strategies are urgently needed: strengthening basic health services and ensuring access to essential living resources in low-SDI regions; focusing on declining adaptability and novel risks in high-SDI regions; and implementing comprehensive public health actions across all regions that focus on early life stages and integrate climate adaptation with health equity.

Acknowledgments

We appreciate the high-quality data provided by the Global Burden of Disease study 2021 collaborators.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://tp.amegroups.com/article/view/10.21037/tp-2026-1-0091/rc

Peer Review File: Available at https://tp.amegroups.com/article/view/10.21037/tp-2026-1-0091/prf

Funding: This study was funded by the National Natural Science Foundation of China (No. 82400854).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tp.amegroups.com/article/view/10.21037/tp-2026-1-0091/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. He YS, Cao F, Hu X, et al. Time Trends in the Burden of Environmental Heat and Cold Exposure Among Children and Adolescents. JAMA Pediatr 2025;179:55-64. [Crossref] [PubMed]
2. Burkart KG, Brauer M, Aravkin AY, et al. Estimating the cause-specific relative risks of non-optimal temperature on daily mortality: a two-part modelling approach applied to the Global Burden of Disease Study. Lancet 2021;398:685-97. [Crossref] [PubMed]
3. Perera F, Nadeau K. Climate Change, Fossil-Fuel Pollution, and Children’s Health. N Engl J Med 2022;386:2303-14. [Crossref] [PubMed]
4. Alvarez-Elias AC, Brenner BM, Luyckx VA. Climate change and its influence in nephron mass. Curr Opin Nephrol Hypertens 2024;33:102-9. [Crossref] [PubMed]
5. Bayram H, Rice MB, Abdalati W, et al. Impact of Global Climate Change on Pulmonary Health: Susceptible and Vulnerable Populations. Ann Am Thorac Soc 2023;20:1088-95. [Crossref] [PubMed]
6. Weeda LJZ, Bradshaw CJA, Judge MA, et al. How climate change degrades child health: A systematic review and meta-analysis. Sci Total Environ 2024;920:170944. [Crossref] [PubMed]
7. Lakhoo DP, Blake HA, Chersich MF, et al. The Effect of High and Low Ambient Temperature on Infant Health: A Systematic Review. Int J Environ Res Public Health 2022;19:9109. [Crossref] [PubMed]
8. Bignier C, Havet L, Brisoux M, et al. Climate change and children’s respiratory health. Paediatr Respir Rev 2025;53:64-73. [Crossref] [PubMed]
9. Romanello M, McGushin A, Di Napoli C, et al. The 2021 report of the Lancet Countdown on health and climate change: code red for a healthy future. Lancet 2021;398:1619-62. [Crossref] [PubMed]
10. Chen PC, Mou CH, Chen CW, et al. Roles of Ambient Temperature and PM(2.5) on Childhood Acute Bronchitis and Bronchiolitis from Viral Infection. Viruses 2022;14:1932. [Crossref] [PubMed]
11. Linssen RS, den Hollander B, Bont L, et al. The Association between Weather Conditions and Admissions to the Paediatric Intensive Care Unit for Respiratory Syncytial Virus Bronchiolitis. Pathogens 2021;10:567. [Crossref] [PubMed]
12. Ahdoot S, Baum CR, Cataletto MB, et al. Climate Change and Children’s Health: Building a Healthy Future for Every Child. Pediatrics 2024;153:e2023065505. [Crossref] [PubMed]
13. Global incidence, prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet 2024;403:2133-61. [Crossref] [PubMed]
14. Zhu S, Zhang J, Liu C, et al. Global burden of non-optimal temperature attributable stroke: The long-term trends, population growth and aging effects. Prev Med 2024;178:107813. [Crossref] [PubMed]
15. Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet 2024;403:2162-203. [Crossref] [PubMed]
16. Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet 2024;403:2100-32. [Crossref] [PubMed]
17. Liu T, Xu Y, Gong Y, et al. The global burden of disease attributable to preterm birth and low birth weight in 204 countries and territories from 1990 to 2019: An analysis of the Global Burden of Disease Study. J Glob Health 2024;14:04109. [Crossref] [PubMed]
18. Zhang K, Kan C, Han F, et al. Global, Regional, and National Epidemiology of Diabetes in Children From 1990 to 2019. JAMA Pediatr 2023;177:837-46. [Crossref] [PubMed]
19. Bai Z, Han J, An J, et al. The global, regional, and national patterns of change in the burden of congenital birth defects, 1990-2021: an analysis of the global burden of disease study 2021 and forecast to 2040. EClinicalMedicine 2024;77:102873. [Crossref] [PubMed]
20. Yu Y, Liu C, Zhou J, et al. Global burden study of lower respiratory infections linked to low temperatures: an analysis from 1990 to 2019. Environ Sci Pollut Res Int 2024;31:11150-63. [Crossref] [PubMed]
21. Vaidyanathan A, Malilay J, Schramm P, et al. Heat-Related Deaths - United States, 2004-2018. MMWR Morb Mortal Wkly Rep 2020;69:729-34. [Crossref] [PubMed]
22. Gauffin K, Spencer N. Climate crisis and child health inequity. BMJ Paediatr Open 2022;6:e001357. [Crossref] [PubMed]
23. McIntosh J, McGreevy KS, Clark W, et al. A call to action: children’s hospitals, child health, and the climate crisis. Lancet Child Adolesc Health 2021;5:774-6. [Crossref] [PubMed]
24. Xie Z, Huang Z, Ran Q, et al. Global burden of drowning and risk factors across 204 countries from 1990 to 2021. Sci Rep 2025;15:10916. [Crossref] [PubMed]
25. Huang Y, Wu Y, Schwebel DC, et al. Disparities in Under-Five Child Injury Mortality between Developing and Developed Countries: 1990-2013. Int J Environ Res Public Health 2016;13:653. [Crossref] [PubMed]
26. Wang K, Li Z. Global, regional, and national burdens of road injuries from 1990 to 2021: Findings from the 2021 Global Burden of Disease Study. Injury 2025;56:112221. [Crossref] [PubMed]
27. Fu L, Liu K, Wang B, et al. Burdens of interpersonal violence in adolescents and young adults. Pediatr Res 2025;98:1290-300. [Crossref] [PubMed]
28. Shi Y, Zhang L, Wu D, et al. Systematic analysis and prediction of the burden of lower respiratory tract infections attribute to non-optimal temperature, 1990-2019. Front Public Health 2024;12:1424657. [Crossref] [PubMed]
29. Huang W, Yin L, Li H, et al. Impact of temperature variations on burden of lower respiratory infections under climate change (1990-2021). BMC Public Health 2025;25:1972. [Crossref] [PubMed]
30. He L, Xue B, Wang B, et al. Impact of high, low, and non-optimum temperatures on chronic kidney disease in a changing climate, 1990-2019: A global analysis. Environ Res 2022;212:113172. [Crossref] [PubMed]
31. Fu SH, Gasparrini A, Rodriguez PS, et al. Mortality attributable to hot and cold ambient temperatures in India: a nationally representative case-crossover study. PLoS Med 2018;15:e1002619. [Crossref] [PubMed]
32. Wang ZX, Cheng YB, Wang Y, et al. Burden of Outpatient Visits Attributable to Ambient Temperature in Qingdao, China. Biomed Environ Sci 2021;34:395-9. [PubMed]
33. Al-Kindi S, Motairek I, Khraishah H, et al. Cardiovascular disease burden attributable to non-optimal temperature: analysis of the 1990-2019 global burden of disease. Eur J Prev Cardiol 2023;30:1623-31. [Crossref] [PubMed]
34. Chen R, Yin P, Wang L, et al. Association between ambient temperature and mortality risk and burden: time series study in 272 main Chinese cities. BMJ 2018;363:k4306. [Crossref] [PubMed]
35. Zhang S, Zhu N. Gender differences in thermal responses to temperature ramps in moderate environments. J Therm Biol 2022;103:103158. [Crossref] [PubMed]
36. Greenfield AM, Alba BK, Giersch GEW, et al. Sex differences in thermal sensitivity and perception: Implications for behavioral and autonomic thermoregulation. Physiol Behav 2023;263:114126. [Crossref] [PubMed]
37. Debray A, Sardar S, Deshayes TA, et al. Sex-related differences in temperature regulation during heat stress from childhood to older age. Auton Neurosci 2025;260:103294. [Crossref] [PubMed]
38. Timonin S, Shartova N, Wen B, et al. The differential effect of ambient temperature on age-specific and sex-specific mortality in the 300 largest cities of Russia, 2000-19: a first national time-series study. Lancet Planet Health 2025;9:e410-20. [Crossref] [PubMed]
39. Howden-Chapman P, Bennett J, Edwards R, et al. Review of the Impact of Housing Quality on Inequalities in Health and Well-Being. Annu Rev Public Health 2023;44:233-54. [Crossref] [PubMed]
40. Meltzer GY, Factor-Litvak P, Herbstman JB, et al. Indoor Temperature and Energy Insecurity: Implications for Prenatal Health Disparities in Extreme Heat Events. Environ Health Perspect 2024;132:35001. [Crossref] [PubMed]
41. Howden-Chapman P, Matheson A, Crane J, et al. Effect of insulating existing houses on health inequality: cluster randomised study in the community. BMJ 2007;334:460. [Crossref] [PubMed]