Temperature-related neonatal deaths attributable to climate change in 29 low- and middle-income countries

Domain: earth_science
Published: 2024-06-29
Editorial outcome: accept

Abstract
Exposure to high and low ambient temperatures increases the risk of neonatal mortality, but the contribution of climate change to temperature-related neonatal deaths is unknown. We use Demographic and Health Survey (DHS) data (n=40,073) from 29 low- and middle-income countries to estimate the temperature-related burden of neonatal deaths between 2001 and 2019 that is attributable to climate change. We find that across all countries, 4.3% of neonatal deaths were associated with non-optimal temperatures. Climate change was responsible for 32% (range: 19-79%) of heat-related neonatal deaths, while reducing the respective cold-related burden by 30% (range: 10-63%). Climate change has impacted temperature-related neonatal deaths in all study countries, with most pronounced climate-induced losses from increased heat and gains from decreased cold observed in countries in sub-Saharan Africa. Future increases in global mean temperatures are expected to exacerbate the heat-related burden, which calls for ambitious mitigation and adaptation measures to safeguard the health of newborns.

Introduction
The steep increase in atmospheric concentrations of greenhouse gases (GHGs) from human activity since the start of the fossil age has led to increasingly apparent changes in the climate. The last decade has been the warmest on record, with global mean temperatures reaching 0.95°C–1.2°C above pre-industrial levels1. Exposure to high ambient temperatures poses a direct and immediate threat to human health, particularly for populations with low physiological and socioeconomic adaptive capacities2,3.

Exposure to extreme temperatures can be especially detrimental to the health of newborns because of their inherent physiological and anatomical vulnerabilities4,5. Neonates, i.e., infants in the first 28 days of life, especially those that are preterm and have low birth weight, have immature thermoregulatory systems and much narrower optimal body temperature ranges than adults6. Thermoregulation in neonates is also complicated by their higher metabolic rate (more energy expenditure) and lower sweating rate7 and thus lower capacity to dissipate heat. In addition, neonates are characterized by smaller blood volume and higher heart rate, which impact how their heart and blood vessels react to extreme temperatures7,8. Anatomically, neonates, particularly preterm neonates, have a high surface-area-to-mass ratio, which makes them prone to rapid heat loss and consequent hypothermia9. Conversely, this can also lead to higher heat absorption from the environment and an increased risk of dehydration and heat illness. Severe infections during the neonatal period, such as pneumonia and sepsis, can further exacerbate an infant’s physiological susceptibility to heat and cold10. Ambient temperatures might affect very early neonates, i.e., infants in the first 24 h of life, through different physiological pathways compared to later neonates. Preterm birth and complications during childbirth are a leading cause of very early neonatal mortality in low- and middle-income countries (LMICs), while infections are more common among later neonatal deaths11,12. Non-optimal ambient temperatures, particularly heat, have been associated with preterm births and certain pregnancy complications that increase the risk of adverse perinatal outcomes, such as placental abruption, gestational hypertension, and gestational diabetes mellitus13–15.

In 2019, there were an estimated 2.4 million neonatal deaths (95% CI: 2.3–2.7) globally, which accounted for nearly half (47%) of all under-5 child deaths16. The first week of life is especially critical, with 36% of neonatal deaths occurring on the day of birth and 73% occurring during the first week of life17. An overwhelming 91% of total neonatal deaths occur in LMICs, mainly in sub-Saharan Africa and South Asia18,19. Yet, most of the limited literature on ambient temperatures and neonatal mortality is based on populations living in temperate climates and high-income settings20–24, with only two studies reporting effects for LMICs25,26. Furthermore, none of the existing studies on the link between temperature and neonatal mortality have attempted to quantify the impact of climate change. As exposure to high temperatures has become more common with climate change and given that neonates are particularly vulnerable to heat and cold, it is important to determine how much climate change has so far contributed to the burden of neonatal mortality.

Impact attribution studies use formal methods to evaluate the extent to which observed changes in natural and human systems can be attributed to recent climate change as opposed to other potential drivers27. Impact attribution studies can increase awareness of the already occurring impacts of climate change, inform society about related costs and damages, guide adaptation plans, and support climate-related litigation28. Most attribution studies to date have focused on observed impacts on natural systems28, while impacts on human health, especially in children and for LMICs, have received limited attention29,30.

Here, we address this gap by assessing the relative contribution of observed climate change to the burden of temperature-related neonatal deaths in 29 LMICs over the period 2001–2019 (n=40,073). Building on the recent impact attribution framework of the Inter-sectoral Impact Model Intercomparison Project (ISIMIP)31, we combined the largest internationally comparable dataset on neonatal deaths in LMICs32–the Demographic and Health Surveys (DHS)–with three recent temperature reanalysis datasets and corresponding counterfactual datasets in a two-stage analysis.

Results
Epidemiological analysis
The changes in mean annual temperatures over time for each scenario and reanalysis dataset are depicted in Fig. 1a. Across all 29 locations and datasets, the annual mean temperature increased from 21.2°C at the beginning of the 20th century to 22°C in the first decade of the 21st century. During the study period (2001–2019), the annual mean temperature in the factual datasets was, on average, 0.9°C higher than in the counterfactual. The patterns and magnitude of warming were similar for all three factual-counterfactual dataset pairs. There were, however, large differences between study locations in the observed level of warming during 2001–2019, ranging from ~0.5°C in Bangladesh to >1.2°C in Senegal, South Africa, and Mali (Fig. 1b; see Supplementary Fig. 1 for country-specific time series).

The pooled non-linear exposure-response associations of ambient temperature (percentile) with overall and very early neonatal mortality for the three factual temperature datasets are presented in Fig. 2. Two of the factual datasets (20CRV3-W5E5 and GSWP3-W5E5) were identical for the time period of our study and are depicted together (Fig. 2b, d). The exposure-response associations represent the cumulative risk of mortality at every temperature percentile relative to the corresponding optimal temperatures over a 2-day lag period. For both overall and very early neonatal mortality, we observed a U-shaped exposure-response function. However, for the overall neonatal period, the risk of mortality was higher at lower temperatures (Fig. 2a, b), while for the very early neonatal period, the risk increased more steeply at higher temperatures (Fig. 2c, d). The very early neonates exhibited a lower optimal temperature, namely, at the 41.3rd or 40.3rd temperature percentile (depending on the factual dataset), compared to the overall neonatal population, for whom the optimal temperature was at the 51.9th or 53.6th temperature percentile. In absolute values, temperatures of minimum mortality across countries varied between 9°C and 29°C for neonates (see Supplementary Table 2 and Supplementary Fig. 2a) and between 5°C and 28°C for very early neonates (averaged between the three reanalysis datasets; see Supplementary Table 3 and Supplementary Fig. 2b). Higher optimal temperatures were observed in locations with higher mean annual temperatures and those closer to the equator (Supplementary Fig. 3).

In sensitivity analyses, restricting our sample to neonatal deaths reported within a shorter recall period prior to the interview produced very similar results (Supplementary Fig. 4). Using different knot placements also produced broadly similar results, but the number of knots affected the shape of the exposure-response curve (Supplementary Fig. 5). We selected the model configuration that best-fit the data, as determined by the Akaike information criterion (Supplementary Table 4).

Temperature-related neonatal deaths attributable to climate change
Across all study locations, 4.3% (95% uncertainty interval (UI): 1.7–6.8%) of all neonatal deaths in the period 2001–2019 were associated with non-optimal temperatures in the factual scenario. Heat-related deaths accounted, on average, for 1.5% (95% UI: 0.2–2.6%) of the year-round burden of neonatal mortality across all included countries, and cold-related deaths accounted for 2.9% (95% UI: 1.5–4.1%) (See Supplementary Fig. 6 and Supplementary Table 5 for country-specific estimates). The four countries with the highest overall neonatal mortality rates, namely, Pakistan, Mali, Sierra Leone, and Nigeria recorded the highest temperature-related neonatal mortality rates (>160 neonatal deaths per 100,000 live births) (Fig. 3a, b). In terms of temperature ranges, moderately hot and moderately cold temperatures were responsible for the largest fraction of temperature-related neonatal deaths across all locations (Fig. 3b).

After subtracting the burden of temperature-related neonatal deaths in the counterfactual from the factual scenario, we estimated that 32% of the heat-related neonatal deaths (country range: 19–79%) in the period 2001–2019 can be attributed to climate change. In real terms, this amounts to 175,133 additional neonatal deaths (95% UI: 7806–353,516) (See Supplementary Table 6 for location-specific estimates). The heat-related neonatal deaths in the factual scenario represent a 46% increase compared to a counterfactual scenario without climate change. The contribution of climate change to heat-related neonatal mortality was largest in the Philippines (79%), Haiti (79%) and Rwanda (70%) (Fig. 3f). In contrast, we find that in the period 2001–2019, climate change reduced the burden of cold-related neonatal deaths by an average of 30% (country range: 10–63%), equalling 457,384 (95% UI: 170,106–868,519) fewer neonatal deaths in total.

To account for differences in population size, Fig. 3c, d display changes in temperature-related neonatal deaths attributable to climate change as rates. These changes are estimated for the period 2001–2019 by comparing the factual scenario to the counterfactual scenario without climate change. Excess heat due to climate change has affected neonatal mortality rates across all study countries, with the largest increases in heat-related rates (>30 per 100,000) observed in Sierra Leone, Ethiopia, Liberia and Haiti (Fig. 3d). The largest positive effects (>110 per 100,000) from the reduction in cold-related neonatal mortality were observed in Liberia, Ethiopia, Sierra Leone, Uganda and Guinea (Fig. 3c). In terms of fraction of all neonatal deaths, the heat-related burden attributable to climate change ranged from 0.2% in Armenia to 1.1% in Haiti, while the averted burden from cold ranged from 0.3% in Albania, Nepal and Tajikistan to 4.6% in the Philippines (Supplementary Fig. 6 and Supplementary Table 5). Overall, the impacts of climate change on temperature-related neonatal mortality were largest in countries that had relatively high baseline neonatal mortality rates and at the same time experienced large temperature increases due to climate change (Sierra Leone, Ethiopia, Liberia, Mali, Guinea, Benin, Cameroon, Nigeria, Angola, Timor-Leste, Haiti).

For the case of very early neonatal deaths, in the factual scenario over the period 2001–2019, 4.1% (95% UI: 1.6–6.5%) were attributed to heat and 1.9% (95% UI: 0.2–3.5%) to cold, i.e., to temperatures above and below the optimal temperature, respectively (See Supplementary Fig. 7 and Supplementary Table 7 for country-specific estimates). The countries where very early neonatal mortality was most prevalent also recorded some of the highest temperature-related mortality rates, namely, Liberia, Angola, and Timor-Leste (Fig. 4a, b). As with overall neonatal deaths, moderately hot and cold temperatures rather than the extremes dominated the temperature-related very early neonatal mortality burden (Fig. 4b).

Across countries, climate change contributed, on average, to 29% (country range: 8–72%) of the total burden of heat-related very early neonatal mortality, which in real terms equalled 168,835 very early neonatal deaths (95% UI: 48,835–296,467). Similar to overall neonatal deaths, the largest proportions (≥65%) of heat-related very early neonatal deaths attributed to climate change were observed in the Philippines, Haiti and Rwanda (Fig. 4f). We found that climate change reduced cold-related very early neonatal mortality by 35% (country range: 10–69%), amounting to 141,322 fewer neonatal deaths (95% UI: 2377–339,337) (see Fig. 4e and Supplementary Table 8 for country-specific estimates).

The largest increases in heat-related very early neonatal mortality rates induced by climate change (>32 per 100,000) were observed in Liberia, Timor-Leste, Sierra Leone, Ethiopia and Angola (Fig. 4d). Conversely, cold-related very early neonatal mortality rates averted due to climate change exceeded 37 per 100,000 live births in Liberia, Rwanda and Uganda, (Fig. 4c). In relation to all very early neonatal deaths in a country, the heat-related impact from climate change varied from null in Armenia to 3.2% in the Philippines. Concurrently, the cold-related fraction mitigated by climate change ranged from 0.2% in Albania, and Tajikistan to 3.7% in Uganda (Supplementary Fig. 7 and Supplementary Table 7).

Methods
Data on neonatal deaths
We derived data on neonatal deaths from Demographic and Health Surveys (DHS). DHS are large nationally representative, cross-sectional household surveys that provide comparable data across countries for a wide range of demographic and health indicators, including reproductive and child health. Households are selected randomly through a two-stage probability sampling procedure based on an existing sampling frame. All women of reproductive age (15–49 years) from the selected households are interviewed by trained fieldwork staff, including on their birth histories. Since its launch in 1984, the DHS program has conducted surveys in more than 90 low- and middle-income countries (LMICs), systematically collecting data on neonatal deaths, and is thus the largest publicly available source for such data32. We restricted our analysis to surveys with available (i) global positioning system (GPS) data, i.e., latitude and longitude, of primary sampling units (PSUs); (ii) detailed information on the date of birth and death (day, month, and year) and (iii) temperature data for at least 2 of the 3 ISIMIP datasets. In urban areas, a PSU could represent a neighbourhood or a cluster of city blocks; in rural areas, it could represent a village or a group of villages. To ensure respondent confidentiality, GPS coordinates of PSUs are randomly displaced by up to 2km in urban areas and 5km in rural areas, with an additional 1% of PSUs displaced by up to 10km. We included in our analysis only surveys from DHS-VII and DHS-VIII, collected between 2014 and 2021, which include information on the exact date of birth and death compared to earlier surveys, which only recorded the month and year of birth and death. This allowed us to determine a 2-day period prior to the day of birth as our exposure window. The final analysis included data from 32 DHS covering 29 countries (Supplementary Fig. 8). The data are publicly available for academic research and can be accessed on the DHS website (www.dhsprogram.com) upon prior registration.

We retrieved data on neonatal deaths based on the age of death of live-born babies. All newborn deaths that occurred within the first 28 days of life were classified as neonatal deaths, and those that occurred on the day of delivery were classified as very early neonatal deaths. We restricted the neonatal deaths to those reported by the mother within 15 years prior to the interview date.

Temperature factual and counterfactual datasets
We based our analysis on three surface temperature datasets that are part of the ISIMIP3a simulation round, namely, 20CRV3-ERA5, 20CRV3-W5E5, and GSWP3-W5E551. We discarded the fourth ISIMIP3a dataset 20CRV3, as it ends in 2015 and does not have full overlap with the study period (2001–2019). The datasets combine the latest-generation reanalysis data from 1979 to today (ERA5 and W5E5)52,53 with reanalysis data that capture the earlier period from 1900 to 1978 (20CRV3 and GSWP3)54,55. Reanalysis datasets combine a wide range of temperature observations, including from weather stations, aircraft, ships, satellites, and other sources, along with weather forecasting models to generate complete and consistent temperature time series within high-resolution grids, including for world regions where observational temperature data are missing or sparse. The 1900–1978 part is homogenized with the data from ERA5 or W5E5 through bias adjustment56. The combined datasets start in a period when climate change was largely absent and thus allow the construction of counterfactual versions from the dataset without the use of climate model data. The two factual W5E5-based datasets are identical during the period of the study data but differ in their counterfactual that is influenced by the data before 1978. The counterfactual datasets describe ambient temperatures in the absence of climate change. They were generated from the factual datasets by using an innovative detrending method to remove long-term variations linked to global climate change33. The method maintains the internal variability of the observed data by ensuring that temperature observations in the factual and counterfactual for a particular day have the same rank in their respective statistical distributions, which preserves the timing of climate events33. The detrending is not sensitive to the cause of historical climate trends (e.g., anthropogenic emissions, external natural forcing or internal variability), following the impact attribution definition of the IPCC AR634. Both the factual and counterfactual temperature datasets are gridded at 0.5° × 0.5° (~55km × 55km) spatial resolution.

Since we combined data from populations spanning different climate zones, we converted the absolute temperatures into PSU-specific temperature percentiles, following previously described methods57. This approach allowed us to account for population adaptation to their predominant climate. We linked the individual cases of neonatal death with the daily mean ambient temperature data using the geographic coordinates of each PSU.

First stage model: epidemiological analysis
We used a time-stratified case-crossover design to quantify the association between ambient mean temperature exposure, expressed as a cluster-specific temperature percentile, and the risk of neonatal death. The case-crossover design is well established in the literature on the acute health effects of short-term environmental exposures and has been extensively applied in the study of the association between ambient temperatures and adverse birth outcomes14,15,58,59. In this study design each case serves as his/her own control, whereby exposure of the same individual on a case day is assessed against referent exposures on days before or after the case day. The main advantage of this approach is that observed and unobserved time-invariant individual confounders such as age, socioeconomic status, and other factors are controlled for60.

Following the time-stratified strategy, we matched each case day with control days, which were on the same day of the week within the same month and year as the neonatal death. Temperature conditions on the case day were compared with three or four control days. Restricting controls to the same day of the week addresses potential week-varying confounders while selecting controls within the same month and year accounts for long-term trends and seasonality. We applied conditional logistic regression on the pooled neonatal mortality data across all PSUs and countries to estimate the relative risks (RRs) of neonatal deaths at each temperature percentile compared to the optimum temperature, corresponding to the temperature percentile of minimum risk for neonatal mortality. We did not include air pollution in the model because daily data are unavailable. However, the existing literature indicates modest20,61,62 or no63–65 confounding effect of air pollution on the temperature-mortality or -preterm birth associations. We also did not control for relative humidity in the analyses due to minimal observed confounding effects in previous studies66,67. The non-linear and delayed exposure-lag-response relationships between temperature and neonatal death were modelled using distributed lag non-linear models (DLNMs). As per DLNM methodology68, we used two spline functions defined within a cross-basis term to model the bi-dimensional exposure-lag association. We included a lag period of up to two days before the event to examine the delayed effects of low and high ambient temperatures on neonatal deaths. The modelling choice of the lag-neonatal mortality associations was tested with lags up to seven days prior to the event (Supplementary Figs. 9 and 10). We used the Akaike Information Criterion (AIC) model selection to assess a set of possible models with different knot placements for the temperature-neonatal mortality associations (Supplementary Table 4). The best-fit models included a natural spline function with one knot at the 20th percentile of the temperature distribution for the association between temperature and neonatal mortality and at the 10th percentile for the association between temperature and very early neonatal mortality and natural spline functions with one internal knot at equally spaced log-values over two lagged days for the lag-response dimensions. To determine the temperature of minimum mortality, we generated predictions of the RR on a matrix of values of the original predictor (temperature percentiles) and lags using the dlnm package. We then selected as minimum mortality or optimal temperature the temperature percentile value where the predicted RR was minimised. Temperature percentiles below and above that value were considered non-optimal.

We performed several sensitivity analyses to evaluate the robustness and strength of the temperature-neonatal mortality association. First, we tested different configurations of the exposure-response function such as the number and placement of knots (See Supplementary Table 4 for more details). Second, to reduce any potential effect of misreporting or inaccurate reporting of deaths on our results due to problems with recall, we re-estimated the association after restricting the sample to deaths reported within 5 and 10 years prior to the interview.

Second stage model: impact attribution
In the second stage, we used a backward approach to calculate the attributable fraction (AF) of neonatal deaths associated with non-optimum temperature under both the factual and counterfactual scenarios for the period 2001–201969. This method allows to incorporate the added temporal dimension in exposure-response relationships with complex temporal patterns and has been extensively applied in past research using the DLNM framework70,71.

More specifically, for each case day of the sample the attributable fraction AF x,t was calculated using the following formula:

AF x,t = 1 - e^(- \sum_{l=l0}^{L} \beta_{x,t-l,l}) (1)

where the parameter \beta_{x} represents the risk associated with exposure x relative to the reference value of x_{0} and corresponds to logarithm of the odds ratio; l_{0} and L represent the lowest and the highest number of lags considered, respectively; and \sum_{l=l0}^{L} \beta_{x,t-l,l} represents the overall cumulative exposure-response association, which is composed of the sum of contributions \beta_{x} from exposures x_{t-l}, ..., x_{t-L} experienced within the lag period. Thus, AF x,t can be interpreted as the fraction of neonatal mortality at time t attributable to past temperature exposures in the period t-l_{0}, ...t-L compared to a constant exposure x_{0} throughout the same period. These attributable risk fractions can be interpreted as individual’s probability of dying and summed up to derive attributable fraction at the sampled population level. We separated the attributable fractions into components related to hot and cold temperatures by restricting the analysis to temperature ranges above and below the reference temperature percentiles for neonatal mortality. For the factual scenario, we additionally calculated the attributable fractions for five temperature ranges: extremely cold, moderately cold, mildly cold, mildly hot, moderately hot, and extremely hot, with cut-offs corresponding to the 97.5th, 75th, minimum mortality temperature, 25th and 2.5th temperature percentiles. The attributable fractions were calculated for each dataset, country, and scenario. We subsequently obtained the excess fraction of neonatal deaths attributable to climate change by subtracting the hot- and cold-related burdens between the factual and counterfactual scenarios. Due to disparity in the historical temperature series (factual scenario) for two of the three reanalysis products, we estimated the exposure-response associations and the respective temperature-related mortality burdens separately for each dataset (Supplementary Figs. 11 and 12). We computed the climate change-attributable heat- and cold-related neonatal mortality rates per 100,000 live births for each country as the climate attributable fractions multiplied by the respective neonatal mortality rates in the period 2001–2019 retrieved from UNICEF18 (see Supplementary Table 9).

AR_{neonat mortality} = m_{neonat} * AF_{CC} (2)

where m_{neonat} refers to the neonatal mortality rate and AF_{CC} – to the fraction of heat- or cold-related neonatal mortality attributable to climate change. We derived very early neonatal mortality rates for the same period by first estimating the share of overall neonatal deaths for each country using the DHS data and applying this share to the country-specific neonatal mortality statistics from UNICEF. The comparative risk assessment framework that we used did not technically allow us to apply the DHS sampling weights in the estimation of the mortality burdens, but this should not have considerably affected the representativeness of the estimated attributable risk fractions. We quantified the uncertainty in the attributable risk fractions related both to the parameters of the exposure-response functions and the variation in temperature series across the three datasets. Specifically, we generated 10,000 samples of the regression coefficients through Monte Carlo simulation, assuming a multivariate normal distribution for the overall cumulative spline model coefficients for each of the three factual and counterfactual datasets. Point estimates were calculated as the average of the difference between the factual and counterfactual across all sample coefficients for the three temperature datasets. The lower and upper bounds of the empirical confidence intervals were estimated as the 2.5th and 97.5th percentiles of the empirical distribution across coefficient samples. We performed all analyses in R (version 3.6.1) using the packages dlnm and survival.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This study uses time-stratified case-crossover analysis to quantify the non-linear association\nbetween daily ambient temperatures and neonatal mortality and then estimates the attributable\nfraction of neonatal mortality attributed to non-optimal temperature. In addition, the authors also\ncalculated the excess number of neonatal deaths attributable to climate change. These findings\ncould provide valuable evidence for the impact of climate change on neonatal mortality. However,\nthere are still some concerns that should be addressed by the authors.\nMajor comments\n1. Line 295. It is hard to say that the result derived from this study is global evidence since\nsamples are collected from limited countries mainly in Africa. More importantly, almost half of the\ndeaths were located in India, which weakens the representatives of the main findings.\n2. Line 336. What is the distribution of the time gap between the occurrence of neonatal deaths\nand the interview of the mothers? A maximum of 15 years is too long to recall the accurate time of\nneonatal deaths, which may cause substantial recall bias.\n3. Line 361. Indeed, the temperature percentiles may to some extent account for the population\nadaptation to their local climate. However, this method may lose considerable statistical power,\nsince plenty of information on the temperature was omitted. For example, one of the main impacts\nof climate change is the substantial increase in extreme heat events, especially in warm regions.\nHowever, when using temperature percentiles, the difference between extreme temperatures and\nmoderate temperatures may be narrowed.\n4. Line 378. More detailed information about the model specification should be presented. For\nexample, Are any confounding factors, such as air pollution and humidity, adjusted for?\n5. The authors combined data from all countries into one model, which implied a fixed effect of\ntemperature on neonatal deaths for all countries. This assumption may be not appropriate,\nespecially when considering that almost half of the samples were from one country (namely India).\nPlenty of previous epidemiology studies confirmed that the association between temperature and\nmortality varied by region due to different climate conditions, socioeconomic factors, and so on.\nThus, in line with previous studies, I would suggest a two-stage analysis strategy, namely\nmodeling the impact of temperature for each country and then pooling the effects of all countries\nby random effect meta method. Since many countries have limited sample sizes, the BLUP method\ncould be applied to re-estimate the country-specific effect by borrowing information from other\ncountries.\n6. Sensitive analyses are needed to verify the robustness of the main analyses, especially the\nexposure-response association between temperature and death.\n7. Line 386. Why authors choose the lag period of up to two days as the maximum lag period of\ntemperature? Previous studies indicated that the impact of cold on mortality may persist for up to\nweeks. Authors can provide more information about the lagged effect of temperature, especially\nthe difference between two kinds of death since their underly mechanisms may be different. In\naddition, it would provide valuable information for the prevention of neonatal death.\n8. Line 406. Is it the “cut-offs corresponding to the 97.5th, 75.5th, minimum mortality\ntemperature, 25.5th, and 2.5th temperature percentiles”?\n9. The authors calculate the excess fraction of neonatal deaths attributable to climate change by\nsimply subtracting the hot- and cold-related burdens between the factual and counterfactual\nscenarios. This calculation completely ignores human adaptation to temperature changes. As the\nauthors indicated in the manuscript, people can adapt to local climate conditions, so they may also\nadapt to local climate changes. Thus, the excess fraction of neonatal deaths attributable to climate\nchange may be overestimated (for heat).", "reviewer_2": "General comment:\nThis is very well written paper with appropriate methodology applied that links the important topic\nof climate change and neonatal mortality burden in low- and middle-income countries.\nComments\n• It would greatly benefit the readers if the authors explained in detail how ‘non-optimal’ or\nminimum mortality temperature percentile was calculated.\n• I wonder if the authors could access data from the health and demographic surveillance sites in\nLMIC implementing verbal autopsy to corroborate the findings?\n• Why was the data pooled yet country-specify analysis could be done?\no Would the results differ if you did country-specific analysis for the exposure response curves and\nthen combined the results later in a meta regression?\no Would it matter to account for country specify factors that influence neonatal outcomes in a\nmultilevel kind of analysis?\n• You mention, community specific percentiles, what are the communities in this respect?\n• What would be the best mitigation strategies to reduce the burden?\n• I wonder how this study based on the design accounts for other competing risk factors in these\ncommunities such as high malaria burden, health systems etc\n• So many countries are not represented, could this tilt the outcome if there were more countries\nwith DHS data?\n• Can the attribution be extended to the future e.g 2030s ,2050s based on projections data?", "reviewer_3": "Temperature-related neonatal deaths attributable to climate change\nThis is a very interesting study and the findings are meaningful and useful. The results fill in a gap\nin current knowledge about temperature effects on neonates, where empirical data is being used\nto calculate the climate attributable burden of neonatal mortality. Presenting the % change in\ndeaths from heat and cold and associated number of deaths provides policy relevant findings. It’s\ngreat to see countries in Africa and Asia are predominantly featured, where evidence is lacking –\nand the country specific effect estimates are useful for policymaking/guiding uptake of local\nadaptation measures. I thank the authors for publishing this work.\nMy concern is that there is an uneven mix of countries making up the ‘meta-estimate’ grouping\nwhen I stratified the 29 countries across continents (Africa, Asia, Caribbean, Europe), i.e., Europe\nhas one country, while Africa has 18. How was this taken into account in the meta-analysis, as the\nbias in sampling countries may place limitations on comparing effects across continents for the\nmeta-estimates? Can you please explain your approach and how you delt with the heterogeneity in\nterms of providing representative meta-estimates. The study also misses large LMIC\ndemographics, for example, in Latin America. Can you please justify this? This is a limitation and\nshould be talked about in the discussion.\nFurthermore, a country like Jordan is strictly speaking in the middle-east, however, this is not a\ngrouping presented in this study (I understand why you may have grouped it into Asia, where you\nhave more countries). How were the groupings created? Rather than arbitrarily assigning\ngroupings by continent, grouping by a climate classification such as the Koppen Geiger\nclassification can enable effect estimates from countries with more homogeneous climates to be\ncompared and meta-analysed. Can you please discuss your use of grouping by continent, and why\nthis was selected (and what the pros and cons are)?\nI can see in line 321 you give the rationale for including 29 out of 90 countries collecting DHS data\nas “We restricted our analysis to surveys for which global positioning system (GPS) data, i.e.,\nlatitude and longitude, of primary sampling units (PSUs) were available”. Was this the only\nrationale, or was it the availability/completeness of the data?\nA general comment on style: I find active voice to be more effective and engaging for the reader –\nit would be nice if the authors can consider using this in future publications.\nFor example line 225: On the other hand, climate change was estimated to have reduced cold-\nrelated very early neonatal mortality by 35% (range: 10-69%), amounting to 141,322 fewer\nneonatal deaths (95% UI: 2,377-339,337) (see Fig. 4e and Supplementary Table 3 for country-\nspecific estimates).\nCould be rephrased as: We found that climate change reduced cold-related very early neonatal\nmortality by 35% (range: 10-69%), amounting to 141,322 fewer neonatal deaths (95% UI: 2,377-\n339,337) (see Fig. 4e and Supplementary Table 3 for country-specific estimates).\nPlease find my specific comments attached below:\nTitle and abstract\nTitle: In the title please include: (i) sample of neonatal deaths analysed (40,073); (ii) a statement\nsuch as ‘an analysis of 29 low- and middle-income countries’ to indicate the scope.\nLine 11: 29 low- and middle-income countries\nPlease add the following text before 29 so the sentence reads: Demographic and Health Surveys\n(DHS) data from 29 low- and middle-income countries.\nLine 15 between “30%. [insert here.]Countries” please insert effect estimates on the range of\nmortality burden attributable to temperature between individual countries you included (i.e., the\nindividual country that had the highest and lowest burden)?\nLine 15: Countries in sub-Saharan Africa have experienced the most pronounced climate-induced\nlosses from increased heat and gains from decreased cold\nIn the results component of the abstract there is no indication of effect estimates (% deaths)\npresented for region groupings other than Africa (other than ‘all countries’). This could be\ninterpreted as the authors only reporting positive or significant results in the abstract. For Africa,\nplease describe the % of deaths attributable for heat and cold. Please also describe effect\nestimates (% deaths) from meta-analysis of other regional groupings. Furthermore, please clearly\ndescribe how the 29 countries were grouped for meta-analysis (was it by continent, by Koppen\nGeiger classification?). A short sentence on this would add clarity (I see from further reading that\nit’s by continent).\nPlease conduct the meta estimates of the associations. Please include meta estimates in figures 3\nand 4 with the extended figures 2, 3 4 and 5.\nLine 19: the most vulnerable populations.\nConsidering your population of interest are neonates, please be specific and use ‘neonates’ here,\nrather than speaking generally about vulnerable populations.\nIntroduction\nLines 36 to 42: please use consistent terminology - neonates or infants (not interchanging use\nboth):\nLine 71: increase awareness of what?\nLine 72: inform societal responsibility for the cost of damages and losses. I would change this\nsentence to “inform society about costs and damages due to climate change”\nLine 79-81: we combined the largest internationally comparable dataset on neonatal deaths in\nLMICs with three recent temperature reanalysis datasets and corresponding counterfactual\ndatasets in a two-stage analysis.\nPlease state what the empirical ‘largest internationally comparable dataset’ is.\nLine 77: to the burden of temperature-related neonatal deaths in 29 LMICs over the period\nPlease state the total sample size of deaths analysed in the sentence (40,073).\nMethods\nWhat is the source of the empirical data? i.e., did the authors reach out to individual countries\ncollecting DHS data or is there a repository with publicly available data you utilised? Briefly\ndescribe what the frequency and duration of data collection from the various DHS are – i.e.,\nshortest/longest datasets? Is this frequency/duration comparable (consistent) across the 29\ncountries included, or are they different?\nHow complete is the empirical data from the countries (please discuss missingness)? Was any\nimputation necessary?\nA sheltered/secure birthing facility (versus not) may be protective against extreme temperature\nexposures for new-borns. Was information available on the place of birth? If yes, please separate\nout the place of birth and conduct a sensitivity analysis for the very early neonates.\nAnalysis\nIn line 160, it would help if you can provide one sentence to describe how you present the results,\ni.e., first presenting effect estimates from individual countries, and then meta-analysis of pooled\ncountries.\nFigure 2 caption: please say the pooled analysis across ’29 countries’\nDiscussion\nLine 137: However, for the overall neonatal period, the risk of mortality was higher at lower\ntemperatures (Fig. 1a and b), while for the very early neonatal period, the risk increased more\nsteeply at higher temperatures (Fig. 1c and d).\nCan you describe why you observed this across the 29 regions?\nLine 180 and 137: present the absolute numbers of deaths caused by heat and deaths saved from\ncold. To provide context in the discussion, can you elaborate on how these values compare against\nthe overall size of the neonatal population in these countries?\nLine 198: Overall, the impacts of climate change on temperature-related neonatal mortality were\nlargest either in countries with relatively high baseline neonatal mortality rates (e.g., Sierra Leone,\nEthiopia, Liberia, 7 Mali) or those that experienced large temperature increases due to climate\nchange (e.g., the Philippines, Uganda, and Rwanda).\nAre there any other countries that fit into these two categories? Please discuss what relevance this\nhas to the findings.\nLine 248: change to ‘prematurely’\nLine 257: Climate change impacts on neonatal health have been uneven across regions, with the\nlargest losses from increased heat but also gains from decreased cold observed in countries in sub-\nSaharan Africa.\nCan you discuss how this compares to other regions studied, and why these observations are likely\nmanifesting?\nLine 259 to 262: While the decline in the cold-related burden may aid efforts to reduce neonatal\ndeaths, with ongoing climate change, these gains are likely to be outstripped by progressively\nincreasing heat- related mortality in the future.\nCan you add a reference here please?\nLine 266-269: There is also the seminal study of Gasparrini et al, where authors look at exposure\nto heat and cold and show (as you do) that exposure to moderate heat and cold have a greater\nburden than extreme temperatures (in the general population, not neonates). It would be good to\ndiscuss your results in the context of this work (i.e., your findings from lines 167 – 170):\nhttps://www.thelancet.com/journals/lancet/article/PIIS0140-6736(14)62114-0/fulltext\nLine 305: current policies in place, climate change is projected to increase xxx to 2.8°C by the end\nof the…\nplease add the missing words here indicated as xxx: is it ‘ambient temperature’ or ‘surface\ntemperature’?\nExtended Fig 2 and 3: can you please order the countries by continent groupings, rather than\nalphabetically?", "reviewer_author_response": "Response to Reviewers\n\nReviewer #1 (Remarks to the Author):\nThis study uses time-stratified case-crossover analysis to quantify the non-linear association\nbetween daily ambient temperatures and neonatal mortality and then estimates the attributable\nfraction of neonatal mortality attributed to non-optimal temperature. In addition, the authors also\ncalculated the excess number of neonatal deaths attributable to climate change. These findings\ncould provide valuable evidence for the impact of climate change on neonatal mortality. However,\nthere are still some concerns that should be addressed by the authors.\nMajor comments\n1. Line 295. It is hard to say that the result derived from this study is global evidence since samples\nare collected from limited countries mainly in Africa. More importantly, almost half of the deaths\nwere located in India, which weakens the representatives of the main findings.\nReviewer #2 (Remarks to the Author):\nGeneral comment:\nThis is very well written paper with appropriate methodology applied that links the important topic\nof climate change and neonatal mortality burden in low- and middle-income countries.\nReviewer #3 (Remarks to the Author):\nTemperature-related neonatal deaths attributable to climate change\nThis is a very interesting study and the findings are meaningful and useful. The results fill in a gap in\ncurrent knowledge about temperature effects on neonates, where empirical data is being used to\ncalculate the climate attributable burden of neonatal mortality. Presenting the % change in deaths\nfrom heat and cold and associated number of deaths provides policy relevant findings. It’s great to\nsee countries in Africa and Asia are predominantly featured, where evidence is lacking – and the\ncountry specific effect estimates are useful for policymaking/guiding uptake of local adaptation\nmeasures. I thank the authors for publishing this work.\nReviewer #1 (Remarks to the Author):\nThe authors have attempted to address my comments. But here, I have some other comments or\nconcerns for the authors.\n1. For the methodology section, as case-crossover design is used for each individual case and the\nodds ratio is computed by comparing the intensity of temperature exposure during case period and\nduring the reference period, I am quite confused about the calculation of attributable burden of\nneonatal mortality to the temperature exposure. A further explanation and rational support on this\ncalculation is required.\n2. The authors have mentioned “up to seven days of lag” that were used in modelling choice. But it\nseems that only the result of AIC on lag 0-2 days is provided.\n3. Considering the huge heterogeneity of risk of temperature across the country, when calculating\nthe total effect across country, it is better to include the country variable as a random-effect\nvariable in the model.\n4. In comparison with the temperature percentile, absolute temperature is more familiar to the\npublic and policymakers. Therefore, I will suggest some key results using absolute temperature.\nMinor comment:\n1. The “relative risk (RR)” reported in the method and result section should be the “odds ratio (OR)”.\n2. For the period of temperature-related burden estimation, “2000-2019” is more common in\nprevious studies than “2001-2019”. In addition, for the cutoffs to define moderate cold and heat, I\nwill suggest using “25th” and “75th” rather than “25.5th” and “75.5th”.\n3. Line 166, this subtitle should be “Temperature-related neonatal deaths attributable to climate\nchange”\n4. Line 380, surveys were collected only between 2014 and 2021, which seems to conflict periods\n(5 and 10 years prior to the interview) mentioned in Line 461.\n5. Lines 458-459, please specify the information on the number and placement of knots for\nexposure-response function.\n6. Correlation test is required for the Extended data Figure 1.\nReviewer #1 (Remarks on code availability):\nThe authors have not provided the code for reviewing.\nReviewer #2 (Remarks to the Author):\nThe authors have sufficiently addressed my comments.\nReviewer #3 (Remarks to the Author):\nThe authors have adequately addressed my comments and suggestions. Thank you for the work\non improving the manuscript. I recommend the manuscript for publication.\nReviewer #3 (Remarks on code availability):\nThere is no code available to review.\n\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nThe authors have attempted to address my comments. But here, I have some other comments or\nconcerns for the authors.\n1. For the methodology section, as case-crossover design is used for each individual case and the\nodds ratio is computed by comparing the intensity of temperature exposure during case period and\nduring the reference period, I am quite confused about the calculation of attributable burden of\nneonatal mortality to the temperature exposure. A further explanation and rational support on this\ncalculation is required.\nReviewer #1 (Remarks on code availability):\nThe authors have not provided the code for reviewing.\nReviewer #2 (Remarks to the Author):\nThe authors have sufficiently addressed my comments.\nReviewer #3 (Remarks to the Author):\nThe authors have adequately addressed my comments and suggestions. Thank you for the work on\nimproving the manuscript. I recommend the manuscript for publication.\nReviewer #3 (Remarks on code availability):\nThere is no code available to review."}, "author_response": ""}]

Decision letter