Heat, Health, and Infrastructure: Infant Mortality in a Warming South and Southeast Asia (SSEA)

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Using high-resolution climate and demographic data from 1960 to 2023 across eight countries, we examine the impact of in utero heat exposure on infant mortality. We find that each additional day with mean temperatures exceeding 30°C during gestation increases infant mortality by 1.78 deaths per 1,000 live births, relative to a 12-15°C reference range. The effects are more pronounced in rural areas and among populations lacking access to essential services such as healthcare, electricity, and transportation. These findings highlight the urgent need for climate-resilient infrastructure investments targeted at the most vulnerable communities to mitigate the rising burden of heat-related health risks. Earth and environmental sciences/Environmental social sciences/Climate-change mitigation Earth and environmental sciences/Environmental social sciences/Environmental economics Health sciences/Risk factors Climate Change Heat Infant Mortality Infrastructure Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Global temperatures are rising at an unprecedented pace, with 2024 recorded as the hottest year to date – marking the first time the 1.5°C warming threshold has been exceeded. 1 Asia is warming more rapidly than the global average, and South and Southeast Asia (SSEA) stands out as one of the most climate-vulnerable regions. 2 In SSEA, extreme weather events coincide with high population density, rapid urbanization, severe air pollution, and high humidity – factors that collectively heighten the region’s exposure to climate-related risks. 3,4 Recent climatological data reveal a sharp increase in the frequency, intensity, and duration of extreme heat events across the SSEA. Between 1950 and 2014, the average number of days annually with temperatures exceeding 30°C rose from 101 to 121, and projections suggest this number could reach 150 by 2050, according to UN climate forecasts (Figure 1a). While climatic conditions vary across countries in the region, the long-term trend is unequivocal: monthly average temperatures have risen steadily from 1960 to 2023 (Figure S1). In parallel with temperature increases, the number of people exposed to extreme heat is also rising, driven by population growth and accelerating rural-to-urban migration (Figure 1b). By 2080, an estimated 1 billion people in SSEA will be exposed to extreme heat for approximately one month each year, compounding existing health and infrastructure vulnerabilities. 5 The health implications of rising temperatures are profound. A growing body of interdisciplinary work links extreme heat to increased mortality and morbidity across a range of outcomes, including respiratory and cardiovascular illness, occupational injuries, and adverse birth outcomes. 4,6–9 Vulnerable groups such as infants, the elderly, and outdoor workers are especially susceptible. Infants, in particular, have underdeveloped thermoregulatory systems and rely heavily on caregivers and environmental conditions, making them acutely vulnerable to heat exposure. 10 While recent studies have identified strong associations between extreme temperatures and infant mortality in both high- and low-income settings, 11,12 most research remains localized, with limited evidence available at the regional scale for SSEA. Moreover, the extent to which infrastructure can buffer these health risks remains poorly understood. This represents a critical gap, given growing evidence that access to healthcare, transportation, energy, and green space plays a crucial role in shaping environmental exposures and vulnerabilities. 13–15 Although a few studies suggest that infrastructure in high-income contexts can mitigate temperature-related mortality, 13 comparable research in rapidly urbanizing, lower-income settings remains scarce. This study addresses that gap through a comprehensive, cross-country analysis of the relationship between extreme heat and infant mortality across eight SSEA countries. Leveraging high-resolution climate data from the Copernicus Climate Change Service (C3S) and demographic data from the Demographic and Health Surveys (DHS), we estimate the effects of in utero heat exposure using both temperature-bin and cumulative degree days (CDD) models. We then examine the moderating role of local infrastructure, focusing on four domains critical to heat resilience: healthcare access, transportation connectivity, electricity availability, and green space. Infrastructure availability is mapped using demographic data from DHS alongside geospatial data from OpenStreetMap (OSM), and the Leaf Area Index (LAI). Although climate-health research increasingly acknowledges the importance of structural context, few studies systematically evaluate how infrastructure mediates the health impacts of extreme heat. 14–16 In SSEA, infrastructure remains markedly underdeveloped. Healthcare systems are severely constrained, with fewer than two hospital beds per 1,000 people – less than half the OECD average. 17 Large segments of the population lack reliable electricity, functional transportation, or access to public green space. 18–21 Rapid urbanization continues to strain these limited resources, particularly in densely populated areas where adaptation needs are greatest. Our findings show that infrastructure access significantly shapes both exposure and vulnerability to extreme heat. Communities lacking basic infrastructure experience substantially higher rates of infant mortality under similar climate conditions, underscoring the need for targeted infrastructure investments. By integrating climate, demographic, and spatial data at scale, this study provides actionable insights for building climate resilience and safeguarding infant health in one of the world’s most exposed regions. Results 2.1 The Impact of Extreme Heat on Infant Mortality We examine the relationship between in utero exposure to extreme heat and infant mortality using two complementary empirical approaches: a temperature-bin model and a cumulative degree days (CDD) model. Together, these frameworks offer robust evidence that elevated ambient temperatures during gestation significantly compromise early-life survival outcomes. Temperature bin model. Figure 2a presents baseline estimates from the temperature-bin specification, which captures the non-linear relationship between fetal temperature exposure and infant mortality risk. The model uses the 12-15°C interval as the reference category and estimates the marginal effect of additional gestational exposure days across other temperature bins. The results reveal a marked increase in infant mortality at higher temperatures. Specifically, each additional day of fetal exposure to temperatures exceeding 30°C is associated with approximately 1.78 more infant deaths per 1,000 live births, relative to the baseline. This effect size is substantial, and comparable in magnitude to other major environmental risk factors such as air pollution. 22,23 It is also important to highlight that extreme heat can worsen air quality, 24 and that the combined burden of heat and poor air quality is particularly harmful to vulnerable populations. The nonlinearity observed aligns with physiological insights into fetal thermoregulation and heat stress, 10 and complements evidence from both high- and low-income settings linking prenatal temperature shocks to adverse birth outcomes. 25–27 Our findings are consistent with earlier research from India, which showed that hot days during pregnancy significantly increase neonatal and infant deaths, 11 and extend this evidence to a multi-country context in SSEA. Cumulative Degree Days (CDD) Model. To capture the cumulative burden of heat exposure over gestation, we estimate models based on cumulative degree days (CDD), which quantify the total number of temperature exceedances above a specified threshold (30°C) during pregnancy. Table 1a reports results across five specifications with progressively richer sets of controls—including individual-level characteristics, household assets, air pollution, humidity, and fixed effects for geographic location and birth year-month. Across all specifications, the coefficient on CDD remains positive, stable, and statistically significant. Estimated impacts range from 0.025 to 0.029 additional infant deaths per 1,000 live births for each unit increase in CDD above 30°C. These results are robust to a wide set of confounders, increasing confidence in their causal interpretation. The magnitude of these effects suggests that heat-induced heat exposure is a material threat to infant survival in SSEA, comparable in scale to previously studied hazards such as indoor air pollution 13 or water contamination. 28 Robustness and Sensitivity Analyses. To assess the robustness of our findings, we conduct two sets of supplementary analyses. First, we examine the sensitivity of our CDD model to alternative specifications of fixed effects and control variables (Table S2). Second, we explore the implications of varying the temperature threshold used to define cumulative heat exposure (Table S3). Table S2 presents five regressions that progressively incorporate individual-level, household-level, and environmental controls, as well as alternate fixed effects. Across all specifications, the coefficient on cumulative degree days above 30°C remains positive, statistically significant, and stable in magnitude. The inclusion of controls for air pollution and humidity (columns 4 and 5) does not materially attenuate the estimated effects, suggesting that our core results are not driven by correlated environmental confounders. All specifications include birth year-cluster and birth month fixed effects, which help account for seasonal variation and location-specific temporal shocks. In Table S3, we test the sensitivity of our results to different definitions of "extreme heat" by varying the CDD threshold from 18°C to 33°C. This exercise reveals a clear dose–response relationship: coefficients increase as the threshold rises, with statistically significant and robust effects observed only at higher temperature thresholds (27°C and above). In particular, the estimated impact of CDD > 33°C is approximately 0.030–0.032 additional deaths per 1,000 live births—larger in magnitude than the baseline CDD > 30°C results, and consistent with findings from other contexts showing nonlinear increases in heat-related mortality at higher temperatures. 7,29 The absence of statistically significant effects at lower thresholds (e.g., CDD > 18°C or 21°C) suggests that milder temperatures do not pose a comparable risk, reinforcing the interpretation that it is extreme heat, rather than overall warmth, that drives adverse fetal outcomes. Heterogeneity Across Countries. To examine cross-country heterogeneity in the impacts of prenatal heat exposure, we estimate country-specific models using nationally defined thresholds for extreme temperature. For each country, the threshold is set at the 90th percentile of the daily temperature distribution during the 1960–1980 reference period, capturing anomalously hot days relative to historical climatic norms. Table S4 reports the effects of in utero exposure to these country-specific extremes on infant mortality. Despite substantial variation in climatic baselines, health systems, and socioeconomic conditions, we find statistically significant and positive effects of prenatal heat exposure on infant mortality across all eight countries in the sample – including India, Pakistan, and Bangladesh in South Asia, and Myanmar, Cambodia, Timor-Leste, Indonesia, and the Philippines in Southeast Asia. These results demonstrate that the adverse effects of extreme heat are not confined to specific national contexts, but instead represent a broad and region-wide public health risk. By using relative, within-country thresholds, our approach accounts for population-level physiological and behavioral adaptation to local climate conditions. 4,7 This method enables more accurate identification of heat anomalies within each national context, minimizing bias from absolute temperature comparisons across countries with different climate baselines. It is important to note that these estimates are generated from within-country regressions, and therefore treatment and control groups differ by national temperature norms. As such, the observed effects reflect the impact of unseasonably high gestational temperature exposure, rather than heat per se, further reinforcing the biological plausibility of a temperature–mortality link that depends on local acclimatization thresholds. 10,13 Heat-induced Infant Deaths. To better understand the magnitude of this effect, we estimate the absolute number of additional infant deaths caused by heat exposure due to climate change over the past few decades. We define each country’s average temperature from 1960 to 2000 as the reference scenario and construct a counterfactual in which this average temperature remained constant during the period from 2001 to 2020 – that is, assuming no climate change had occurred. The effect of climate change is thus measured as the annual deviation in each country’s average temperature relative to the 1960–2000 baseline. By applying our estimated temperature-mortality relationship, we quantify the number of additional heat-induced infant deaths resulting from these temperature deviations. Using the baseline coefficients presented in Figure 2a, Figure 3 shows the estimated number of heat-induced infant deaths attributable to climate change in SSEA from 2001 to 2020. The total number of heat-induced infant deaths remained consistently high across the two decades. We estimate that between 2001 and 2020, climate change contributed to approximately 656,340 heat-related infant deaths across the entire SSEA region (covering all 19 countries listed in the Supplementary Materials), equivalent to about 32,817 deaths per year. We cross-validate our results with findings from other studies. One recent study estimated approximately 175,000 neonatal deaths attributable to climate change across 29 low- and middle-income countries between 2001 and 2019. While the figures are not directly comparable, a key distinction lies in the definition of outcomes: that study focused on neonatal deaths (within 28 days of birth), whereas our analysis includes all infant deaths (within the first year of life). This broader outcome measure enables us to capture a wider range of heat-related risks extending beyond the neonatal period and thereby complements existing evidence. 2.2 The Role of Infrastructure in Mitigating Heat-related Health Risks When examining the role of infrastructure in mitigating the health impacts of extreme heat, it is essential to first understand how heat affects urban and rural areas differently in order to design effective adaptation strategies. Urban and rural environments vary significantly in their exposure to and vulnerability to heat, influenced by infrastructure availability, environmental conditions, population density, and socioeconomic factors. Disaggregating these differences is crucial for informing more tailored, context-specific interventions. Urban–Rural Disparities in Heat-Related Infant Mortality. Figure 2b illustrates the relationship between temperature exposure and infant mortality separately for urban and rural populations. In urban areas, we find no statistically significant association between temperature and infant mortality across most of the distribution; confidence intervals remain tightly clustered around zero, suggesting a relatively stable baseline risk. By contrast, in rural areas, we observe a clear and statistically significant increase in infant mortality as temperatures exceed 18°C. The magnitude of these effects is consistently larger in rural settings, underscoring the disproportionate vulnerability of rural infants to heat exposure. This disparity likely reflects a combination of infrastructure access and occupational exposure. Although urban areas in SSEA are often affected by the urban heat island effect, they generally benefit from greater access to protective infrastructure—such as healthcare facilities, reliable electricity, paved roads, and climate-controlled environments. Rural areas, on the other hand, frequently lack these services, limiting both preventative care and emergency response capacity during extreme weather events. Infrastructure Dimensions: Health, Electricity, Transport, and Greenness. We further investigate the extent to which access to infrastructure moderates the relationship between ambient temperature and infant mortality. Figure 4 presents results stratified by high (blue) versus low (red) access to four key infrastructure domains: medical facilities (Panel A), electricity (Panel B), transportation (Panel C), and greenness (Panel D). These comparisons provide insight into the role of infrastructure in buffering—or amplifying—the health risks associated with extreme heat. Panel A – Access to Medical Facilities: Healthcare access is a critical determinant of resilience to environmental health shocks. As shown in Figure 4A, the relationship between temperature and infant mortality is substantially weaker among populations with high access to medical services. Across most of the temperature distribution, the estimated coefficients are not statistically significant, even at elevated temperatures. In contrast, among those with limited access to medical facilities, a strong and statistically significant positive relationship emerges at higher temperatures, with mortality risk rising sharply above 21°C. Panel B – Access to Electricity: Access to electricity enables the use of fans, air conditioning, refrigeration, and other cooling mechanisms essential for maternal and infant health during heat events. In Figure 4B, we observe a pronounced divergence in the temperature–mortality gradient between areas with high versus low electricity access. Among households with reliable electricity, the estimated effects of temperature on infant mortality are muted and statistically indistinguishable from zero across most temperature ranges. By contrast, in electricity-poor areas, infant mortality rises significantly with temperature, with effects becoming statistically significant above 21°C. Panel C – Access to Transportation: Transportation infrastructure facilitates access to healthcare, emergency services, and alternative shelter, especially during extreme weather events. As illustrated in Figure 4C, the effects of heat on infant mortality are more muted in areas with high transportation access, where statistical significance emerges only at higher temperature ranges (above 30°C). In contrast, limited transportation access is associated with a steeper and more statistically significant rise in mortality risk as temperatures increase. Panel D – Access to Green Space: Green space moderates urban heat through evapotranspiration and shading, helping to reduce ambient temperatures at the microclimate level. In Figure 4D, the protective effects of greenness – as measured by the Leaf Area Index (LAI) – are less pronounced relative to other infrastructure types. While populations in greener areas appear marginally less vulnerable, confidence intervals frequently overlap, and differences are smaller in magnitude. One plausible explanation is that the LAI reflects existing vegetative cover, but does not capture the counterfactual temperature reductions that green space might have induced. Since our models condition on actual recorded temperatures, the cooling effect of greenness may be underestimated. This limitation echoes prior methodological challenges in estimating the indirect health benefits of nature-based climate solutions. 14,16 Future research incorporating thermal remote sensing and spatial causal inference techniques could help better quantify the role of green space in climate-health resilience. Discussion This study provides the first comprehensive, cross-country analysis of the relationship between extreme heat exposure during gestation and infant mortality across South and Southeast Asia (SSEA), using high-resolution temperature data and nationally representative health surveys from eight countries. Our findings contribute to the growing climate-health literature by demonstrating a robust, statistically significant link between in utero heat exposure and elevated risks of infant death. 10–13,30 The effects are non-linear and intensify sharply at higher temperature thresholds, particularly above 27°C. Each additional day of fetal exposure to temperatures exceeding 30°C is associated with approximately 1.78 more infant deaths per 1,000 live births, relative to the 12-15°C reference range. By applying our estimated temperature–mortality relationship to a counterfactual scenario in which average temperatures remained at 1960-2000 levels (i.e., in the absence of climate change), we estimate that between 2001 and 2020, climate change contributed to approximately 656,340 heat-related infant deaths across the SSEA region – equivalent to about 32,817 deaths per year. We validate our findings with evidence from existing studies. For example, a study in India found that high-temperature exposure during pregnancy was associated with an increase of 2 infant deaths per 1,000 live births, although the effect appeared to be confined to rural areas. 11 Another analysis reported that heat-related deaths accounted for 1.5% of total neonatal deaths across 29 low- and middle-income countries between 2001 and 2019, with Pakistan, Mali, Sierra Leone, and Nigeria recording the highest temperature-related neonatal mortality rates – each exceeding 1.6 neonatal deaths per 1,000 live births. 10 Moreover, the stratified analyses reveal that the effects of extreme heat on infant mortality are not homogeneously distributed across populations. Infants born in rural areas, where access to basic services is limited, face substantially higher risks than those in urban environments. This finding is particularly important, as the prominent focus on the Urban Heat Island (UHI) effect and its role in elevating urban temperatures can sometimes overshadow the heightened vulnerability of rural populations who typically find it harder to reduce their exposure to high temperatures. These disparities reflect deep structural inequities in access to infrastructure, socioeconomic conditions, and occupational exposure. In rural communities across the SSEA region, where many pregnant women engage in outdoor, labor-intensive work, prolonged heat exposure is further compounded by limited access to healthcare and essential services, thereby intensifying their vulnerability. Importantly, our findings further demonstrate that access to critical infrastructure can substantially mitigate the adverse effects of extreme heat. Medical facilities, electricity, and transportation each play distinct but complementary roles in buffering the health risks associated with heat exposure. These infrastructures act through multiple pathways: ensuring timely access to care, enabling use of cooling technologies, and facilitating mobility during heat events. Although the role of green space appears less pronounced in our estimates, this is likely due to limitations in capturing its indirect contribution to microclimate regulation. Future research employing more advanced spatial and counterfactual modeling is needed to better quantify these effects. Taken together, these results underscore an urgent policy imperative: while long-term mitigation of climate change through emissions reductions remains essential, immediate adaptation through investments in climate-resilient infrastructure is critical to protect vulnerable populations. In the SSEA context, where infrastructure deficits remain significant, prioritizing such investments in underserved regions bearing the greatest health burdens from extreme heat is likely to yield high social returns and contribute to reducing inequalities. These efforts have important implications for promoting more balanced and resilient economic growth. Strengthening equitable, adaptive health and infrastructure systems is thus central not only to enhancing climate resilience but also to advancing child survival and public health in a warming world. Declarations Acknowledgments Chunping Xie and Erik Berglöf acknowledge the support of the Asian Infrastructure Investment Bank (AIIB). This research builds on the analysis presented in the report Asian Infrastructure Finance 2025: Infrastructure for Planetary Health (Chapter 4: Heat-Related Health Stress and Infrastructure – Evidence from South and Southeast Asia). Yuhang Pan declares financial support from the National Science and Technology Major Project of the Ministry of Science and Technology of China (No. 2023ZD0503501) and the Young Scientists Fund of the National Natural Science Foundation of China (No. 72403005). This study is supported by the High-Performance Computing Platform of Peking University. The authors declare that they have no relevant or material financial interests that relate to the research described in this paper. References WMO. WMO confirms 2024 as warmest year on record at about 1.55°C above pre-industrial level. (2025). WMO. Climate change and extreme weather impacts hit Asia hard. (2024). Watts, N. et al. Health and climate change: policy responses to protect public health. The lancet 386 , 1861–1914 (2015). Carleton, T. et al. Valuing the global mortality consequences of climate change accounting for adaptation costs and benefits. Q. J. Econ. 137 , 2037–2105 (2022). IPCC. 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Table 1 Table 1: Impact of In Utero Temperature Exposure on Infant Mortality (CDD model) (1) (2) (3) (4) (5) CDD Exceeding 30℃ 0.025*** 0.028*** 0.026*** 0.028** 0.029** (0.007) (0.007) (0.008) (0.012) (0.013) R-Squared 0.761 0.802 0.858 0.864 0.870 Observations 3,360,451 3,360,451 3,360,451 3,360,451 3,360,451 Number of Clusters 64,606 64,606 64,606 64,606 64,606 Individual Controls Y Y Y Y Family Controls Y Y Y Control for Air Pollution Y Y Control for Humidity Y Cluster Fixed Effects Y Y Y Y Y Birth YearMonth Fixed Effects Y Y Y Y Y Note : This paper presents the effect of in utero temperature exposure on infant mortality using cumulative degree days (CDD) model. Standard error clustered at DHS cluster level is reported in parentheses. Significance levels are denoted as: * p<0.10, ** p<0.05, *** p<0.01. Additional Declarations There is NO Competing Interest. Supplementary Files SI.pdf Supplementary Materials for Heat, Health, and Infrastructure: Infant Mortality in a Warming South and Southeast Asia (SSEA) Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6848870","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Social Sciences - Article","associatedPublications":[],"authors":[{"id":471609806,"identity":"8eef01b6-0864-4707-80f4-ff7bea6a043f","order_by":0,"name":"Yuhang 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legend\u0026nbsp;\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6848870/v1/68d5090425cf2b62e790315a.png"},{"id":95671708,"identity":"b6b546ca-efc4-465c-b554-3a8f04d47bc0","added_by":"auto","created_at":"2025-11-11 17:26:55","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":766658,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u0026nbsp;\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6848870/v1/dda2dcabe12b13c9ff542fc7.png"},{"id":95804840,"identity":"8ce692c1-dcaa-428a-8eee-d29316b50675","added_by":"auto","created_at":"2025-11-13 08:39:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2992253,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6848870/v1/8ac3f9f2-b12b-4810-b38f-8572aabb5b02.pdf"},{"id":95798582,"identity":"d1836ec4-bbc5-425c-9790-a0ceec53bff6","added_by":"auto","created_at":"2025-11-13 08:17:13","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":453721,"visible":true,"origin":"","legend":"Supplementary Materials for Heat, Health, and Infrastructure: Infant Mortality in a Warming South and Southeast Asia (SSEA)","description":"","filename":"SI.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6848870/v1/258ed2125dd81f1673159151.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Heat, Health, and Infrastructure: Infant Mortality in a Warming South and Southeast Asia (SSEA)","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlobal temperatures are rising at an unprecedented pace, with 2024 recorded as the hottest year to date \u0026ndash; marking the first time the 1.5\u0026deg;C warming threshold has been exceeded.\u003csup\u003e1\u003c/sup\u003e Asia is warming more rapidly than the global average, and South and Southeast Asia (SSEA) stands out as one of the most climate-vulnerable regions.\u003csup\u003e2\u003c/sup\u003e In SSEA, extreme weather events coincide with high population density, rapid urbanization, severe air pollution, and high humidity \u0026ndash; factors that collectively heighten the region\u0026rsquo;s exposure to climate-related risks.\u003csup\u003e3,4\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRecent climatological data reveal a sharp increase in the frequency, intensity, and duration of extreme heat events across the SSEA. Between 1950 and 2014, the average number of days annually with temperatures exceeding 30\u0026deg;C rose from 101 to 121, and projections suggest this number could reach 150 by 2050, according to UN climate forecasts (Figure 1a). While climatic conditions vary across countries in the region, the long-term trend is unequivocal: monthly average temperatures have risen steadily from 1960 to 2023 (Figure S1). In parallel with temperature increases, the number of people exposed to extreme heat is also rising, driven by population growth and accelerating rural-to-urban migration (Figure 1b). By 2080, an estimated 1 billion people in SSEA will be exposed to extreme heat for approximately one month each year, compounding existing health and infrastructure vulnerabilities.\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eThe health implications of rising temperatures are profound. A growing body of interdisciplinary work links extreme heat to increased mortality and morbidity across a range of outcomes, including respiratory and cardiovascular illness, occupational injuries, and adverse birth outcomes.\u003csup\u003e4,6\u0026ndash;9\u003c/sup\u003e Vulnerable groups such as infants, the elderly, and outdoor workers are especially susceptible. Infants, in particular, have underdeveloped thermoregulatory systems and rely heavily on caregivers and environmental conditions, making them acutely vulnerable to heat exposure.\u003csup\u003e10\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eWhile recent studies have identified strong associations between extreme temperatures and infant mortality in both high- and low-income settings,\u003csup\u003e11,12\u003c/sup\u003e most research remains localized, with limited evidence available at the regional scale for SSEA. Moreover, the extent to which infrastructure can buffer these health risks remains poorly understood. This represents a critical gap, given growing evidence that access to healthcare, transportation, energy, and green space plays a crucial role in shaping environmental exposures and vulnerabilities.\u003csup\u003e13\u0026ndash;15\u003c/sup\u003e Although a few studies suggest that infrastructure in high-income contexts can mitigate temperature-related mortality,\u003csup\u003e13\u003c/sup\u003e comparable research in rapidly urbanizing, lower-income settings remains scarce.\u003c/p\u003e\n\u003cp\u003eThis study addresses that gap through a comprehensive, cross-country analysis of the relationship between extreme heat and infant mortality across eight SSEA countries. Leveraging high-resolution climate data from the Copernicus Climate Change Service (C3S) and demographic data from the Demographic and Health Surveys (DHS), we estimate the effects of in utero heat exposure using both temperature-bin and cumulative degree days (CDD) models. We then examine the moderating role of local infrastructure, focusing on four domains critical to heat resilience: healthcare access, transportation connectivity, electricity availability, and green space. Infrastructure availability is mapped using demographic data from DHS alongside geospatial data from OpenStreetMap (OSM), and the Leaf Area Index (LAI).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlthough climate-health research increasingly acknowledges the importance of structural context, few studies systematically evaluate how infrastructure mediates the health impacts of extreme heat.\u003csup\u003e14\u0026ndash;16\u003c/sup\u003e In SSEA, infrastructure remains markedly underdeveloped. Healthcare systems are severely constrained, with fewer than two hospital beds per 1,000 people \u0026ndash; less than half the OECD average.\u003csup\u003e17\u003c/sup\u003e Large segments of the population lack reliable electricity, functional transportation, or access to public green space.\u003csup\u003e18\u0026ndash;21\u003c/sup\u003e Rapid urbanization continues to strain these limited resources, particularly in densely populated areas where adaptation needs are greatest.\u003c/p\u003e\n\u003cp\u003eOur findings show that infrastructure access significantly shapes both exposure and vulnerability to extreme heat. Communities lacking basic infrastructure experience substantially higher rates of infant mortality under similar climate conditions, underscoring the need for targeted infrastructure investments. By integrating climate, demographic, and spatial data at scale, this study provides actionable insights for building climate resilience and safeguarding infant health in one of the world\u0026rsquo;s most exposed regions.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e2.1\u0026nbsp;\u0026nbsp;The Impact of Extreme Heat on Infant Mortality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe examine the relationship between in utero exposure to extreme heat and infant mortality using two complementary empirical approaches: a temperature-bin model and a cumulative degree days (CDD) model. Together, these frameworks offer robust evidence that elevated ambient temperatures during gestation significantly compromise early-life survival outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTemperature bin model.\u003c/strong\u003e Figure 2a presents baseline estimates from the temperature-bin specification, which captures the non-linear relationship between fetal temperature exposure and infant mortality risk. The model uses the 12-15\u0026deg;C interval as the reference category and estimates the marginal effect of additional gestational exposure days across other temperature bins. The results reveal a marked increase in infant mortality at higher temperatures. Specifically, each additional day of fetal exposure to temperatures exceeding 30\u0026deg;C is associated with approximately 1.78 more infant deaths per 1,000 live births, relative to the baseline. This effect size is substantial, and comparable in magnitude to other major environmental risk factors such as air pollution.\u003csup\u003e22,23\u003c/sup\u003e It is also important to highlight that extreme heat can worsen air quality,\u003csup\u003e24\u003c/sup\u003e and that the combined burden of heat and poor air quality is particularly harmful to vulnerable populations.\u003c/p\u003e\n\u003cp\u003eThe nonlinearity observed aligns with physiological insights into fetal thermoregulation and heat stress,\u003csup\u003e10\u003c/sup\u003e and complements evidence from both high- and low-income settings linking prenatal temperature shocks to adverse birth outcomes.\u003csup\u003e25\u0026ndash;27\u003c/sup\u003e Our findings are consistent with earlier research from India, which showed that hot days during pregnancy significantly increase neonatal and infant deaths,\u003csup\u003e11\u003c/sup\u003e and extend this evidence to a multi-country context in SSEA.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCumulative Degree Days (CDD) Model.\u003c/strong\u003e To capture the cumulative burden of heat exposure over gestation, we estimate models based on cumulative degree days (CDD), which quantify the total number of temperature exceedances above a specified threshold (30\u0026deg;C) during pregnancy. Table 1a reports results across five specifications with progressively richer sets of controls\u0026mdash;including individual-level characteristics, household assets, air pollution, humidity, and fixed effects for geographic location and birth year-month. Across all specifications, the coefficient on CDD remains positive, stable, and statistically significant. Estimated impacts range from 0.025 to 0.029 additional infant deaths per 1,000 live births for each unit increase in CDD above 30\u0026deg;C. These results are robust to a wide set of confounders, increasing confidence in their causal interpretation. The magnitude of these effects suggests that heat-induced heat exposure is a material threat to infant survival in SSEA, comparable in scale to previously studied hazards such as indoor air pollution\u003csup\u003e13\u003c/sup\u003e or water contamination.\u003csup\u003e28\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRobustness and Sensitivity Analyses.\u003c/strong\u003e To assess the robustness of our findings, we conduct two sets of supplementary analyses. First, we examine the sensitivity of our CDD model to alternative specifications of fixed effects and control variables (Table S2). Second, we explore the implications of varying the temperature threshold used to define cumulative heat exposure (Table S3).\u003c/p\u003e\n\u003cp\u003eTable S2 presents five regressions that progressively incorporate individual-level, household-level, and environmental controls, as well as alternate fixed effects. Across all specifications, the coefficient on cumulative degree days above 30\u0026deg;C remains positive, statistically significant, and stable in magnitude. The inclusion of controls for air pollution and humidity (columns 4 and 5) does not materially attenuate the estimated effects, suggesting that our core results are not driven by correlated environmental confounders. All specifications include birth year-cluster and birth month fixed effects, which help account for seasonal variation and location-specific temporal shocks.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn Table S3, we test the sensitivity of our results to different definitions of \u0026quot;extreme heat\u0026quot; by varying the CDD threshold from 18\u0026deg;C to 33\u0026deg;C. This exercise reveals a clear dose\u0026ndash;response relationship: coefficients increase as the threshold rises, with statistically significant and robust effects observed only at higher temperature thresholds (27\u0026deg;C and above). In particular, the estimated impact of CDD \u0026gt; 33\u0026deg;C is approximately 0.030\u0026ndash;0.032 additional deaths per 1,000 live births\u0026mdash;larger in magnitude than the baseline CDD \u0026gt; 30\u0026deg;C results, and consistent with findings from other contexts showing nonlinear increases in heat-related mortality at higher temperatures.\u003csup\u003e7,29\u003c/sup\u003e The absence of statistically significant effects at lower thresholds (e.g., CDD \u0026gt; 18\u0026deg;C or 21\u0026deg;C) suggests that milder temperatures do not pose a comparable risk, reinforcing the interpretation that it is extreme heat, rather than overall warmth, that drives adverse fetal outcomes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHeterogeneity Across Countries.\u0026nbsp;\u003c/strong\u003eTo examine cross-country heterogeneity in the impacts of prenatal heat exposure, we estimate country-specific models using nationally defined thresholds for extreme temperature. For each country, the threshold is set at the 90th percentile of the daily temperature distribution during the 1960\u0026ndash;1980 reference period, capturing anomalously hot days relative to historical climatic norms. Table S4 reports the effects of in utero exposure to these country-specific extremes on infant mortality.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDespite substantial variation in climatic baselines, health systems, and socioeconomic conditions, we find statistically significant and positive effects of prenatal heat exposure on infant mortality across all eight countries in the sample \u0026ndash; including India, Pakistan, and Bangladesh in South Asia, and Myanmar, Cambodia, Timor-Leste, Indonesia, and the Philippines in Southeast Asia. These results demonstrate that the adverse effects of extreme heat are not confined to specific national contexts, but instead represent a broad and region-wide public health risk.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBy using relative, within-country thresholds, our approach accounts for population-level physiological and behavioral adaptation to local climate conditions.\u003csup\u003e4,7\u003c/sup\u003e This method enables more accurate identification of heat anomalies within each national context, minimizing bias from absolute temperature comparisons across countries with different climate baselines. It is important to note that these estimates are generated from within-country regressions, and therefore treatment and control groups differ by national temperature norms. As such, the observed effects reflect the impact of unseasonably high gestational temperature exposure, rather than heat per se, further reinforcing the biological plausibility of a temperature\u0026ndash;mortality link that depends on local acclimatization thresholds.\u003csup\u003e10,13\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHeat-induced Infant Deaths.\u003c/strong\u003e To better understand the magnitude of this effect, we estimate the absolute number of additional infant deaths caused by heat exposure due to climate change over the past few decades. We define each country\u0026rsquo;s average temperature from 1960 to 2000 as the reference scenario and construct a counterfactual in which this average temperature remained constant during the period from 2001 to 2020 \u0026ndash; that is, assuming no climate change had occurred. The effect of climate change is thus measured as the annual deviation in each country\u0026rsquo;s average temperature relative to the 1960\u0026ndash;2000 baseline.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBy applying our estimated temperature-mortality relationship, we quantify the number of additional heat-induced infant deaths resulting from these temperature deviations. Using the baseline coefficients presented in Figure 2a, Figure 3 shows the estimated number of heat-induced infant deaths attributable to climate change in SSEA from 2001 to 2020.\u003c/p\u003e\n\u003cp\u003eThe total number of heat-induced infant deaths remained consistently high across the two decades. We estimate that between 2001 and 2020, climate change contributed to approximately 656,340 heat-related infant deaths across the entire SSEA region (covering all 19 countries listed in the Supplementary Materials), equivalent to about 32,817 deaths per year.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe cross-validate our results with findings from other studies. One recent study estimated approximately 175,000 neonatal deaths attributable to climate change across 29 low- and middle-income countries between 2001 and 2019. While the figures are not directly comparable, a key distinction lies in the definition of outcomes: that study focused on neonatal deaths (within 28 days of birth), whereas our analysis includes all infant deaths (within the first year of life). This broader outcome measure enables us to capture a wider range of heat-related risks extending beyond the neonatal period and thereby complements existing evidence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2\u0026nbsp;\u0026nbsp;The Role of Infrastructure in Mitigating Heat-related Health Risks\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhen examining the role of infrastructure in mitigating the health impacts of extreme heat, it is essential to first understand how heat affects urban and rural areas differently in order to design effective adaptation strategies. Urban and rural environments vary significantly in their exposure to and vulnerability to heat, influenced by infrastructure availability, environmental conditions, population density, and socioeconomic factors. Disaggregating these differences is crucial for informing more tailored, context-specific interventions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUrban\u0026ndash;Rural Disparities in Heat-Related Infant Mortality.\u0026nbsp;\u003c/strong\u003eFigure 2b illustrates the relationship between temperature exposure and infant mortality separately for urban and rural populations. In urban areas, we find no statistically significant association between temperature and infant mortality across most of the distribution; confidence intervals remain tightly clustered around zero, suggesting a relatively stable baseline risk. By contrast, in rural areas, we observe a clear and statistically significant increase in infant mortality as temperatures exceed 18\u0026deg;C. The magnitude of these effects is consistently larger in rural settings, underscoring the disproportionate vulnerability of rural infants to heat exposure.\u003c/p\u003e\n\u003cp\u003eThis disparity likely reflects a combination of infrastructure access and occupational exposure. Although urban areas in SSEA are often affected by the urban heat island effect, they generally benefit from greater access to protective infrastructure\u0026mdash;such as healthcare facilities, reliable electricity, paved roads, and climate-controlled environments. Rural areas, on the other hand, frequently lack these services, limiting both preventative care and emergency response capacity during extreme weather events.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInfrastructure Dimensions: Health, Electricity, Transport, and Greenness.\u0026nbsp;\u003c/strong\u003eWe further investigate the extent to which access to infrastructure moderates the relationship between ambient temperature and infant mortality. Figure 4 presents results stratified by high (blue) versus low (red) access to four key infrastructure domains: medical facilities (Panel A), electricity (Panel B), transportation (Panel C), and greenness (Panel D). These comparisons provide insight into the role of infrastructure in buffering\u0026mdash;or amplifying\u0026mdash;the health risks associated with extreme heat.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePanel A \u0026ndash; Access to Medical Facilities:\u0026nbsp;\u003c/strong\u003eHealthcare access is a critical determinant of resilience to environmental health shocks. As shown in Figure 4A, the relationship between temperature and infant mortality is substantially weaker among populations with high access to medical services. Across most of the temperature distribution, the estimated coefficients are not statistically significant, even at elevated temperatures. In contrast, among those with limited access to medical facilities, a strong and statistically significant positive relationship emerges at higher temperatures, with mortality risk rising sharply above 21\u0026deg;C.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePanel\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u0026ndash; Access to Electricity:\u0026nbsp;\u003c/strong\u003eAccess to electricity enables the use of fans, air conditioning, refrigeration, and other cooling mechanisms essential for maternal and infant health during heat events. In Figure 4B, we observe a pronounced divergence in the temperature\u0026ndash;mortality gradient between areas with high versus low electricity access. Among households with reliable electricity, the estimated effects of temperature on infant mortality are muted and statistically indistinguishable from zero across most temperature ranges. By contrast, in electricity-poor areas, infant mortality rises significantly with temperature, with effects becoming statistically significant above 21\u0026deg;C.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePanel\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u0026ndash; Access to Transportation:\u0026nbsp;\u003c/strong\u003eTransportation infrastructure facilitates access to healthcare, emergency services, and alternative shelter, especially during extreme weather events. As illustrated in Figure 4C, the effects of heat on infant mortality are more muted in areas with high transportation access, where statistical significance emerges only at higher temperature ranges (above 30\u0026deg;C). In contrast, limited transportation access is associated with a steeper and more statistically significant rise in mortality risk as temperatures increase.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePanel\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eD\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u0026ndash; Access to Green Space:\u0026nbsp;\u003c/strong\u003eGreen\u0026nbsp;space\u0026nbsp;moderates urban heat through evapotranspiration and shading, helping to reduce ambient temperatures at the microclimate level. In Figure 4D, the protective effects of greenness\u0026nbsp;\u0026ndash;\u0026nbsp;as measured by the Leaf Area Index (LAI)\u0026nbsp;\u0026ndash;\u0026nbsp;are less pronounced relative to other infrastructure types. While populations in greener areas appear marginally less vulnerable, confidence intervals frequently overlap, and differences are smaller in magnitude. One plausible explanation is that the LAI reflects existing vegetative cover, but does not capture the counterfactual temperature reductions that green space might have induced. Since our models condition on actual recorded temperatures, the cooling effect of greenness may be underestimated. This limitation echoes prior methodological challenges in estimating the indirect health benefits of nature-based climate solutions.\u003csup\u003e14,16\u003c/sup\u003e Future research incorporating thermal remote sensing and spatial causal inference techniques could help better quantify the role of green space in climate-health resilience.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provides the first comprehensive, cross-country analysis of the relationship between extreme heat exposure during gestation and infant mortality across South and Southeast Asia (SSEA), using high-resolution temperature data and nationally representative health surveys from eight countries. Our findings contribute to the growing climate-health literature by demonstrating a robust, statistically significant link between in utero heat exposure and elevated risks of infant death.\u003csup\u003e10\u0026ndash;13,30\u003c/sup\u003e The effects are non-linear and intensify sharply at higher temperature thresholds, particularly above 27\u0026deg;C. Each additional day of fetal exposure to temperatures exceeding 30\u0026deg;C is associated with approximately 1.78 more infant deaths per 1,000 live births, relative to the 12-15\u0026deg;C reference range. By applying our estimated temperature\u0026ndash;mortality relationship to a counterfactual scenario in which average temperatures remained at 1960-2000 levels (i.e., in the absence of climate change), we estimate that between 2001 and 2020, climate change contributed to approximately 656,340 heat-related infant deaths across the SSEA region \u0026ndash; equivalent to about 32,817 deaths per year.\u003c/p\u003e\n\u003cp\u003eWe validate our findings with evidence from existing studies. For example, a study in India found that high-temperature exposure during pregnancy was associated with an increase of 2 infant deaths per 1,000 live births, although the effect appeared to be confined to rural areas.\u003csup\u003e11\u003c/sup\u003e Another analysis reported that heat-related deaths accounted for 1.5% of total neonatal deaths across 29 low- and middle-income countries between 2001 and 2019, with Pakistan, Mali, Sierra Leone, and Nigeria recording the highest temperature-related neonatal mortality rates \u0026ndash; each exceeding 1.6 neonatal deaths per 1,000 live births.\u003csup\u003e10\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eMoreover, the stratified analyses reveal that the effects of extreme heat on infant mortality are not homogeneously distributed across populations. Infants born in rural areas, where access to basic services is limited, face substantially higher risks than those in urban environments. This finding is particularly important, as the prominent focus on the Urban Heat Island (UHI) effect and its role in elevating urban temperatures can sometimes overshadow the heightened vulnerability of rural populations who typically find it harder to reduce their exposure to high temperatures. These disparities reflect deep structural inequities in access to infrastructure, socioeconomic conditions, and occupational exposure. In rural communities across the SSEA region, where many pregnant women engage in outdoor, labor-intensive work, prolonged heat exposure is further compounded by limited access to healthcare and essential services, thereby intensifying their vulnerability.\u003c/p\u003e\n\u003cp\u003eImportantly, our findings further demonstrate that access to critical infrastructure can substantially mitigate the adverse effects of extreme heat. Medical facilities, electricity, and transportation each play distinct but complementary roles in buffering the health risks associated with heat exposure. These infrastructures act through multiple pathways: ensuring timely access to care, enabling use of cooling technologies, and facilitating mobility during heat events. Although the role of green space appears less pronounced in our estimates, this is likely due to limitations in capturing its indirect contribution to microclimate regulation. Future research employing more advanced spatial and counterfactual modeling is needed to better quantify these effects.\u003c/p\u003e\n\u003cp\u003eTaken together, these results underscore an urgent policy imperative: while long-term mitigation of climate change through emissions reductions remains essential, immediate adaptation through investments in climate-resilient infrastructure is critical to protect vulnerable populations. In the SSEA context, where infrastructure deficits remain significant, prioritizing such investments in underserved regions bearing the greatest health burdens from extreme heat is likely to yield high social returns and contribute to reducing inequalities. These efforts have important implications for promoting more balanced and resilient economic growth. Strengthening equitable, adaptive health and infrastructure systems is thus central not only to enhancing climate resilience but also to advancing child survival and public health in a warming world.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChunping Xie and Erik Bergl\u0026ouml;f acknowledge the support of the Asian Infrastructure Investment Bank (AIIB). This research builds on the analysis presented in the report Asian Infrastructure Finance 2025: Infrastructure for Planetary Health (Chapter 4: Heat-Related Health Stress and Infrastructure \u0026ndash; Evidence from South and Southeast Asia). Yuhang Pan declares financial support from the National Science and Technology Major Project of the Ministry of Science and Technology of China (No. 2023ZD0503501) and the Young Scientists Fund of the National Natural Science Foundation of China (No. 72403005). This study is supported by the High-Performance Computing Platform of Peking University. The authors declare that they have no relevant or material financial interests that relate to the research described in this paper.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWMO. 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Sci.\u003c/em\u003e \u003cstrong\u003e114\u003c/strong\u003e, 13447\u0026ndash;13452 (2017).\u003c/li\u003e\n\u003cli\u003eJagnani, M., Barrett, C. B., Liu, Y. \u0026amp; You, L. Within-Season Producer Response to Warmer Temperatures: Defensive Investments by Kenyan Farmers. \u003cem\u003eEcon. J.\u003c/em\u003e \u003cstrong\u003e131\u003c/strong\u003e, 392\u0026ndash;419 (2020).\u003c/li\u003e\n\u003cli\u003eBurgess, R., Deschenes, O., Donaldson, D. \u0026amp; Greenstone, M. Weather, climate change and death in India. \u003cem\u003eUniv. Chic.\u003c/em\u003e 577\u0026ndash;617 (2017).\u003c/li\u003e\n\u003cli\u003eDasgupta, S. \u0026amp; Robinson, E. J. Z. Climate, weather, and child health: quantifying health co-benefits. \u003cem\u003eEnviron. Res. Lett.\u003c/em\u003e \u003cstrong\u003e19\u003c/strong\u003e, 084001 (2024).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003e\u003cstrong\u003eTable 1:\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eImpact of In Utero Temperature Exposure on Infant Mortality (CDD model)\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44.4444%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44.4444%;\"\u003e\n \u003cp\u003eCDD Exceeding 30℃\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e0.025***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e0.028***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e0.026***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e0.028**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e0.029**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44.4444%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e(0.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e(0.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e(0.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e(0.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e(0.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44.4444%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44.4444%;\"\u003e\n \u003cp\u003eR-Squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e0.761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e0.802\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e0.858\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e0.864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e0.870\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44.4444%;\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e3,360,451\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e3,360,451\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e3,360,451\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e3,360,451\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e3,360,451\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44.4444%;\"\u003e\n \u003cp\u003eNumber of Clusters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e64,606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e64,606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e64,606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e64,606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e64,606\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44.4444%;\"\u003e\n \u003cp\u003eIndividual Controls\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44.4444%;\"\u003e\n \u003cp\u003eFamily Controls\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44.4444%;\"\u003e\n \u003cp\u003eControl for Air Pollution\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44.4444%;\"\u003e\n \u003cp\u003eControl for Humidity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44.4444%;\"\u003e\n \u003cp\u003eCluster Fixed Effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44.4444%;\"\u003e\n \u003cp\u003eBirth YearMonth Fixed Effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e: This paper presents the effect of in utero temperature exposure on infant mortality using cumulative degree days (CDD) model. Standard error clustered at DHS cluster level is reported in parentheses. Significance levels are denoted as: * p\u0026lt;0.10, ** p\u0026lt;0.05, *** p\u0026lt;0.01.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Climate Change, Heat, Infant Mortality, Infrastructure","lastPublishedDoi":"10.21203/rs.3.rs-6848870/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6848870/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Climate change is intensifying the frequency and severity of heatwaves, with South and Southeast Asia (SSEA) among the most vulnerable regions. Using high-resolution climate and demographic data from 1960 to 2023 across eight countries, we examine the impact of in utero heat exposure on infant mortality. We find that each additional day with mean temperatures exceeding 30°C during gestation increases infant mortality by 1.78 deaths per 1,000 live births, relative to a 12-15°C reference range. The effects are more pronounced in rural areas and among populations lacking access to essential services such as healthcare, electricity, and transportation. These findings highlight the urgent need for climate-resilient infrastructure investments targeted at the most vulnerable communities to mitigate the rising burden of heat-related health risks.","manuscriptTitle":"Heat, Health, and Infrastructure: Infant Mortality in a Warming South and Southeast Asia (SSEA)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-11 17:26:50","doi":"10.21203/rs.3.rs-6848870/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-climate-change","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"nclimate","sideBox":"Learn more about [Nature Climate Change](http://www.nature.com/nclimate/)","snPcode":"","submissionUrl":"","title":"Nature Climate Change","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Research","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"1b03680d-0de4-4c8c-848e-7edd5f1d37cc","owner":[],"postedDate":"November 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":50079257,"name":"Earth and environmental sciences/Environmental social sciences/Climate-change mitigation"},{"id":50079258,"name":"Earth and environmental sciences/Environmental social sciences/Environmental economics"},{"id":50079259,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-11-11T17:26:50+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-11 17:26:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6848870","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6848870","identity":"rs-6848870","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}