High ambient temperatures effects on the anthropometric status of the population: a systematic review and meta-analysis

High ambient temperatures effects on the anthropometric status of the population: a systematic review and meta-analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article High ambient temperatures effects on the anthropometric status of the population: a systematic review and meta-analysis Priscila Ribas de Farias Costa, Rita de Cássia Ribeiro-Silva, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6404122/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background : The effect of high temperatures and heatwaves on several health outcomes is well known, but there is a knowledge in gap about their effects on nutritional status. This systematic review aims to synthesise research on the association between high temperatures and anthropometric indicators. Methodology : A systematic review and meta-analysis was conducted and the protocol registered in PROSPERO (CRD42024555573). The search included relevant databases and was conducted in October 2024, using terms for “high temperatures”, “heatwaves”, and "anthropometric indicators". Data were extracted and qualitative and quantitative synthesis were performed. Results : Nineteen studies were included, encompassing 3,892,838 participants, predominantly children under the age of 5, mainly from African countries. The studies presented inconsistent results, although most identified inverse relationship between high temperatures and anthropometric indicators. In adults, increased temperatures were associated with elevated risk of both underweight and obesity. In children, the meta-analysis revealed significant reduction of 0.06σ in the Z-score of the Weight-for-Height and 0.02σ in the Z-score of the Height-for-Age indicators for every 1°C increase in average temperature. The observed associations were modest, but with important implications for public health, considering the high proportion of population exposed to the climate changes. Further studies addressing this topic are necessary for a better understanding. Health sciences/Diseases/Nutrition disorders Health sciences/Risk factors Earth and environmental sciences/Climate sciences/Climate change ambient temperature anthropometric status climate changes nutritional status Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 INTRODUCTION Malnutrition, which includes both undernutrition (underweight, wasting, weight loss, and stunted growth), and overnutrition (overweight and obesity), is a challenge for the of individuals and populations, a burden for the healthcare system and impact economic productivity 1 . The human and socioeconomic costs of malnutrition are enormous, disproportionately affecting the poorest, particularly women, children, and the elderly. The harmful effects of malnutrition can impact an individual’s health throughout their life, beginning early and persisting into old age. It affects physical, mental, and social well-being, while also increasing the risks of morbidity and mortality 2 , 3 . Emerging research on climate and health suggests a link between high temperatures and malnutrition 2 , 4 , 5 . The pathways via which high temperatures impact health and nutrition are complex, involving short-, medium-, and long-term mechanisms, and may vary by geography, socioeconomic context, and ecosystem 6 . Children are particularly vulnerable to the harmful effects of extreme heat due to an inferior thermoregulatory response (compared to adults) 7 . Moreover, activities such as outdoor play or participating in agricultural work increase their risk of exposure. Heat stress can cause a series of acute health problems, including loss of appetite, poor nutrient retention, and increased diarrhoea and dehydration, hence leading to poor nutrient and calorie absorption and ultimately weight loss 8 . Furthermore, high temperatures can reduce crop yields threatening household food security, and increase water scarcity which contributes to poor sanitation 9 , 10 . They also alter the transmission dynamics of infectious diseases 11 . In addition, they increase the risk of violent conflicts 12 , reduce work productivity, income, and economic growth 13 . These indirect effects of high temperature can, in turn, influence nutritional outcomes among adults and the elderly, although these relationships continue to be poorly understood 14 . High temperatures have also been suggested to alter physical activity levels, favouring a positive calorie balance, thereby directly impacting adult obesity 15 , 16 , 17 , 18 . Many of these adverse health consequences are concentrated in the populations of low- and middle-income countries who are exposed to higher average temperatures, may have a lower adaptive capacity, and where the means of subsistence are more directly dependent on environmental conditions. In recent years, an increasing number of studies suggested an association between high temperatures and nutritional outcomes. However, this evidence has not yet been systematically compiled and evaluated. Therefore, this systematic review aims to synthesise previous research on the association between high ambient temperatures and heatwaves and indicators of nutritional status (anthropometric indicators) in all age groups. By mapping the global literature, our review can inform the development of climate adaptation policies which mitigate the risk of malnutrition associated with extreme heat. METHODS This is a systematic review and meta-analysis which evaluated the evidence available on the association between extreme heat and heatwaves and anthropometric indicators in the general population, whose protocol was submitted to PROSPERO (International prospective register of systematic reviews) and registered under number CRD42024555573. We adopted the methodological recommendations proposed by Cochrane 19 and the wording proposed by PRISMA 20 . Inclusion and exclusion criteria The inclusion and exclusion criteria were defined in accordance with the PECOS acronym (Table 1 ). Studies included in the systematic review must meet the following criteria: (1) population consisting of general population, children, adolescents, adults, pregnant women, or the elderly; (2) use of ambient temperature or heatwaves as an exposure variable; (3) evaluation of anthropometric indicators as outcome variables, including weight-for-age (WAZ), height-for-age (HAZ), weight-for-height (WHZ), BMI-for-age (BAZ), BMI, waist circumference (WC), percentage of body fat (%BF), and percentage of lean mass (%LM); (4) report at least one of the following effect measures: relative risk (RR), prevalence ratio (PR), or odds ratio (OR), with their confidence intervals (CI) for categorical variables, or mean, standard deviation values, β or correlation coefficient, with the respective p values for continuous variables; and (5) observational studies. The following were adopted as exclusion criteria: (1) study population representing a select group of individuals with chronic or high risk diseases, or users of medications which may alter anthropometric measurements or pressure levels, such as nephropathies, neoplasias, chronic liver diseases, lupus, Crohn’s disease, mental illnesses, HIV, and Down’s syndrome, among others; (2) review studies and/or case reports; (3) studies which did not evaluate the outcomes covered in this research; and (4) studies which did not use temperature, or heatwaves, as an exposure variable. Table 1 PECOS criteria for study selection. Parameters Criteria Population General (children, adolescents, pregnant women, adults and elderly people) Exposure High temperatures, extreme heat, heatwaves, and high ambient temperature Comparator Population exposed to lower temperature levels or not exposed to heatwaves Outcomes Anthropometric indicators Setting or study design Observational studies, such as cross-sectional, panel, cohort, case-control, and ecological studies Search strategy We searched for observational studies on the following electronic databases: PUBMED/MEDLINE, EMBASE, BVS (LILACS, IBECS, WHO IRIS, CUMED, BDENF, PAHO, VENTIDEX, ARGMSAL, BINACS, and LIPECS), and WEB OF SCIENCE, in addition to Google Scholar for grey literature published before 23th September 2024. In order to guarantee saturation, we examined the reference lists of studies included or of relevant reviews that were identified manually through research, to include studies that were not indexed on databases but were pertinent for inclusion in this review. No date, language limitations, or search filters were imposed on the search. The exposure and outcome terms and their respective synonyms were used in the search strategy, with the aim of including all studies relevant to this topic. We adopted the Boolean operators “AND” and “OR” for the database searches 19 . We selected the Pubmed/MEDLINE MeSH (Medical Subject Headings) database descriptors. We also opted for sensitivity, with the inclusion of entry terms and non-controlled vocabulary. We developed Boolean combinations of words (separated by outcome) for database searches using MeSH descriptors in PUBMED/MEDLINE, BVS, and Web of Science, and also on Google Scholar. Regarding the LILACS database search, we used selected Virtual Health Library (Biblioteca Virtual em Saúde – BVC) DeCS (Health Science Descriptors) and also developed Boolean expressions of words for this search. Lastly, we used Embase EMTREE (Embase Subject Headings) controlled vocabulary descriptors to construct Boolean expressions of words to search for articles indexed in this database. We also conducted a manual search of the reference lists of the studies included in the review, and relevant reviews identified during the selection process, in order to retrieve those which had not been retained by database searches. A full description of the search strategies can be found in the supplementary material ( Supplement 1 ). We executed a sensitivity test to confirm the search strategy’s ability to capture relevant studies. We selected four sentinel articles, which were primary studies meeting the review eligibility criteria. The search strategy was able to capture 100% of the sentinels, indicating high sensitivity. Selection of articles We exported the citations from each database to the Covidence review software, Veritas Health Innovation, Melbourne, Australia (available at www.covidence.org ), where any duplicates were removed. We executed a pilot test in the initial study selection stage, with the aim of “calibrating” the reviewers’ decisions and, if necessary, improving the clarity of the eligibility criteria. For the test, ten records identified by the search strategy were randomly selected, with two reviewers assessing the titles/abstract using eligibility criteria. The reviewers achieved an agreement rate higher than 75% in the pilot test, indicating the reviewers’ understanding of the eligibility criteria. Following the pilot test, we screened the titles/abstracts (stage I) of all the sources retrieved in the search, following the eligibility criteria. In stage II of the selection, we reviewed the full text of the records selected in stage I, and of those whose eligibility was still uncertain. Two reviewers worked independently, and any inconsistencies in classifying the decisions were discussed with a third reviewer. In stage II, the excluded sources were recorded on Covidence, along with the reason for their exclusion, and the entire study selection process was detailed in a PRISMA flowchart. The research team decided which studies should be included in the final selection for data summary. Data extraction and quality Two independent reviewers systematically executed the data extraction, and any divergences were resolved through discussion with a third reviewer. Following the final article selection, the data were extracted on a form using COVIDENCE software. In order to increase consistency among reviewers, and guarantee validity, we conducted another pilot test of the data extraction form in a random sample of five studies, and a third reviewer confirmed content accuracy. Following the pilot test, information was extracted from all the included studies, which included: the first author; study design and location, which was described in accordance with the country of the population analyzed; year of publication; follow-up period, when applicable; the study population was characterized according to the sample size, sex, age range, and/or average age of participants, and recruitment method; definition of the exposition: extreme temperature or heatwave classification method; exposure data source; year of data collection; statistical approach used, in addition to specifications in relation to instruments, indicators, and methods to identify the outcome; outcome evaluated: anthropometric indicators (WAZ, HAZ, WHZ, BMI, WC, arm circumference); main results found: measures of association (OR, RR, differences in the average outcome values in the exposed and non-exposed groups, and the linear regression β coefficient, or correlation coefficient); and study limitations. The authors of the selected studies were contacted by email to provide incomplete data or for any clarification as to the metrics evaluated that were incomplete or missing. The risk of bias evaluation for each study included in the systematic review (except for the ecological studies, where no tool is available to analyze the risk of bias) was conducted using the tool developed by the Office of Health Assessment and Translation (OHAT) 21 , and is designed specifically for environmental health research 22 . Cohort and cross-sectional studies were evaluated based on five categories (selection, confounding, exclusion/attrition, detection, selective reporting, and other sources of bias) which included seven questions (three classified as key criteria, and four as other criteria), with the following response options for each question: 1) definitively low risk of bias, 2) probably low risk of bias, 3) probably high risk of bias, and 4) definitively high risk of bias 22 . Two evaluators independently classified the studies, and then a consensus decision was achieved through discussion. Applying OHAT guidance, the publications were classified as Tier 1 (evaluated as ‘definitively’ or ‘probably low’ risk of bias in the three key criteria, and as ‘definitively’ or ‘probably low’ in the majority of the other criteria); Tier 3 (evaluated as ‘definitively’ or ‘probably high’ risk of bias in the three key criteria, and as ‘definitively’ or ‘probably high’ risk of bias in the majority of the other criteria); and Tier 2 (when the study does not meet the criteria for Tier 1 or Tier 3). Meta-analysis For all studies included in this review, a narrative data summary was presented. For studies considered combinable 19 , a quantitative data summary was conducted using meta-analysis. Only studies with a child population were combinable in terms of exposure [studies which used the average temperature (monthly or annual), maximum average temperature (monthly or yearly) or high temperatures] and outcome (WAZ, HAZ and WHZ indicators) and therefore a meta-analysis was conducted. Combinable studies were not found for the other age ranges and anthropometric indicators. The extent of the heterogeneity in the meta-analysis heterogeneity was tested using Cochran’s Q test and quantified by the inconsistency test (statistic I 2 ). This statistic determines the magnitude of the heterogeneity by the proportion of the total variation between studies, due to heterogeneity 19 , 23 . The p-value is frequently cited as an indication of the extent of variability in studies. Thus, we used the chi-squared test to evaluate the significance of the heterogeneity. Therefore, we adopted a p-value of < 0.05 as the significance level, with the aim of detecting the heterogeneity of the study results 19 , 23 . We used the metan command for the meta-analyses, with the specification of two variables, assuming this to be the measure of effect (beta coefficient), and its respective standard errors, transformed into a logarithmic scale to stabilize the variances and standardize the distributions. The eform option was specified to convert the summarised measure to the normal scale, improving its interpretability. The summary effect was calculated using random-effect models, applying the restricted maximum likelihood (REML) method 19 , 23 . Considering the limited number of studies included in the meta-analyses, we were not able to investigate the causes of heterogeneity in the studies, whether by subgroup analysis or meta-regression; nor was an analysis of the publication bias conducted through the funnel chart and Egger’s test 19 , 23 . The statistical analysis was conducted using STATA for MAC statistics software (Version 16.0, Stata Corp LP, College Station, Texas). The Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach was used to rate the certainty of the evidence 24 . A Summary of Findings (SoF) table was prepared using the GRADEpro online software (GRADE Working Group, McMaster University) 25 . DESCRIPTION OF THE RESULTS Study and population characteristics The database search identified 5,512 published articles between 2012 and 2023. After removing 283 duplicates, 5,229 titles and abstracts were screened, with 29 articles read in full. A total of 19 studies were included in the systematic review (Fig. 1 ). Of the 19 studies, 14 evaluated the effects of temperature on anthropometric indicators of children under the age of 5 (n = 2,658,752) 4,5,8,26,27,28,29,30,31,32,33,34,35,36 ; one on children and adolescents (n = 700,000) 37 ; one on adolescents and adults (n = 12,509) 38 ; one on adults only (n = 500,000) 39 ; one on adults and the elderly (n = 20,990) 14 ; and one on the elderly (n = 587) 40 , totaling 3,892,838 individuals. Almost all the studies used secondary data from demographic and health surveys (DHS), except one study, which was based on primary data 36 (Kanazawa, 2020) (Table 2). Regarding study design, four were cohort studies; twelve were panel/cross-sectional studies, and three were ecological studies. Geographically, the majority of the studies were developed in countries on the African continent (n = 12), with four of these being multi-country studies, and one was in Japan [n = 1], one in China [n = 1], one in the United States [n = 1], and two in South Asia (one in Bangladesh and one in India, Nepal, Bangladesh, and Pakistan). A further two studies were multi-country, but the countries involved were not individually identified (Table 2). RISK OF BIAS We evaluated 16 out of the 19 publications for risk of bias. Three were not evaluated, since they were ecological studies for which no instrument is available, but they have high risk of bias. Ten publications (62.5%) were classified as Tier 1, meaning they presented either a probably or definitively low risk of bias for all the key criteria of the instrument, as well as for the majority of the other questions. Six studies (37.5%) were classified as Tier 2, i.e., they did not present either a low or high risk of bias for all the key criteria (Fig. 2 ). None of the studies were classified as Tier 3, that means the study presents a probably or definitively high risk of bias for all the key criteria and the majority of the other questions. The key questions of the instrument refer to: a) Is the exposure characterization trustworthy?; b) Is the outcome evaluation trustworthy?; and c) Did the study design or analysis take important confounding and modifying variables into consideration? The main problems presented by the studies refer to incomplete outcome data, with a loss of participants, or exclusion in the analysis (n = 14), inappropriate statistical analysis (n = 03); inadequate selection of study participants (n = 02); and study design or analysis which did not take important confounding and modifying variables into consideration (n = 02). The majority of the studies were well classified in relation to characterization of exposure, outcome, and presentation of the results for all outcomes evaluated (Fig. 2 ). EXPOSURE The measurement of the ambient temperature varied considerably between studies, with some using distinct metrics within the same study. Most studies used mean temperature (daily, monthly, or annual) (n = 10), or maximum temperature (daily or monthly) (n = 4). One study used the El Nino 3.4 index (average monthly value of the index anomaly during the period between May and December); others adopted the concept of temperature anomalies, classified in distinct forms (n = 3); two used heatwaves, classified in different ways; and others adopted temperature categories (which also varied across studies) (n = 3). The temperature data were obtained from different sources, including weather forecasting centres, and air quality or meteorological monitoring stations (Table 2). Considering the exposure window, the studies also showed considerable variation. Four of them assessed the effect of temperature on the anthropometric status of their populations, considering one year of exposure (the year prior to the interview) 14 , 27 , 34 , 40 . Three studies evaluated the effect of temperature from prenatal exposure up to the first or second year of life 4 , 33 , 35 . Two studies examined the effect of temperature over the past 30 years on the anthropometric status of individuals 37 , 39 . Three studies assessed the effect of temperature over lifetime period 8 , 28 , 30 . One study each considered the following exposure windows: temperature over the past 16 years 34 , temperature over the past five years 26 , from one year before birth to lifetime 29 , from the month of conception to lifetime 32 , from the two years prior to the interview 5 , the temperature during the growing season 31 , and in one case, this information was unclear (possibly the year prior to the interview) 38 (Table 2). OUTCOME The majority of the studies focused on children (< 5 years old) evaluated the effects of high temperatures on the HAZ (n = 12), WAZ (n = 5), and WHZ (n = 6) anthropometric indicators (in a continuous and/or categorical form). BMI-for-age (n = 1) and arm circumference (n = 1) indicators were also evaluated. A study including children and adolescents investigated body weight 37 ; and the one including adolescents and adults used the BMI 38 . The two studies conducted with adults only used the BMI to assess the anthropometric status 14 , 39 . Lastly, a study conducted with elderly men evaluated the effect of high temperatures on the waist circumference 40 (Table 2). ASSOCIATIONS BETWEEN TEMPERATURE AND ANTHROPOMETRIC INDICATORS HEIGHT-FOR-AGE (HAZ) For each study, the association between temperature and the anthropometric status is summarised in Table 2. Of the studies that investigated the effect of temperature on linear child growth and stunting, the majority were conducted in countries in the Sub-Saharan Africa [n = 11]. The results of the different studies presented great variation: while four studies suggest negative effects of high temperatures on growth indicators 4 , 28 , 31 , 34 , others (n = 3) identified positive 33 , 35 , 36 or null (n = 4) effect results 8 , 26 , 29 , 30 . Blom, Ortiz-Bobea & Hoddinott (2022) identified that a 2ºC increase in the average temperature was associated with an increase in the prevalence of stunting from 4–7.4% 28 . In Ethiopia, it was observed that the increase of 1°C in temperature is associated with a 0.216 decrease in moderate stunting 31 . Randell, Gray & Grace (2020) found higher temperatures during pregnancy (in uterus), particularly during the first and third trimesters, to be positively associated with serious stunting. They also identified that higher temperatures from birth until the time of the evaluation were associated with a 0.104 reduction in standard deviations in HAZ 4 . It was reported in Nigeria that a 1ºC increase in temperature was associated with a 16.7% increase in the probability of stunting, being more marked in the rural zone 34 (Table 2). McMahon & Gray (2021) identified that an additional unit of heat (or monthly anomaly generated from the average historical standard deviation in each location) decreased the probability of stunting by 3.4% (p = 0.078), and increased the HAZ by 2.7% (p = 0.05) when the heat occurred in the first and second year of life 33 . Rojas, Gray & West (2023) evaluated if exposure to anomalies in high temperatures affects the HAZ and observed that during the pre-natal period [0.037; p < 0.01], first [0.066; p < 0.01] and second year of life [0.028; p < 0.01] the high temperature led to a HAZ increase 35 . Tusting et al. (2020) showed that an average monthly daytime land surface temperature of over 35°C was associated with a reduction in stunting (OR: 0.90, 0.85–0.96; p = 0.00047), compared to a monthly average daytime land surface temperature of less than 30°C 36 (Table 2). In Uganda, Amondo, Nshakira‑Rukundo & Mirzabaev (2023) identified that the occurrence of heatwaves in the past year reduced the HAZ by 0.03, and had increased by 0.02 in the previous 5 years. However, these associations were not statistically significant 26 . Using anthropometric data on 192,000 children from 30 countries in Sub-Saharan Africa and climate data to directly estimate the effect of temperature on the main results of malnutrition, Baker & Anttila-Hughes (2020) did not observe the effect of temperatures on chronic measures of malnutrition, such as HAZ and stunting 8 . Based on nationally representative demographic and health research data on child malnutrition in four South Asian countries (Bangladesh, India, Nepal, and Pakistan), Davenport et al. (2017) indicated a modest negative effects of heating on the child growth deficit, with the decrease of a -0.01 standard deviation in HAZ, while not statistically significant 29 . An increase in the HAZ indicator with the temperature rise [β=-0.0385; p > 0.05] was also verified in Kenya 30 , but not statistically significant (Table 2). WEIGHT-FOR-AGE (WAZ) Five studies evaluated the effect of high temperatures on WAZ and underweight, all identifying an association between temperature (evaluated in different forms) and underweight 8 , 27 , 31 , 34 , 36 . In investigating the effect of weather (variability in the average maximum monthly temperature close to the surface and total monthly rainfall) on stunting and underweight of Nigerian children, Merwe, Clance & Yitbarek (2022) observed an effect of temperature in underweight increase, with these results being more robust when adjusting for sociodemographic and regional characteristics 34 . In Ethiopia, Hagos et al. (2014) observed that the increase of 1 standard deviation in temperature reduced the WAZ by 0.26 (p < 0.01) 31 . Anttila-Hughes, Jina & McCord (2021), estimated that a 1ºC increase in the ENSO (El Niño Southern Oscillation) index is associated with a 0.03 reduction in standard deviation (p = 0.02) in the WAZ average, and a 0.6% increase in the prevalence of underweight (p < 0.05) 27 . The results from the study conducted by Tusting et al. (2020) indicated that a monthly average surface temperature of over 35ºC is associated with an increase in the chance of underweight (1.09, 1.02–1.16; p = 0.0073), when compared with a monthly average surface temperature of under 30ºC 36 . Similarly, Baker & Anttila‑Hughes (2020) verified that the WAZ decreased appreciably with average temperatures over 25ºC in the year before the studied outcome 8 . In Japan, Yokoya and Higuchi (2016) only evaluating the weight variable among children and adolescents, identified a significant negative correlation between the average maximum daily temperature and body weight in all ages 37 (Table 2). WEIGHT-FOR-HEIGHT (WHZ) Six studies included in this review investigated the influence of high temperatures on the WHZ indicator and/or wasting, with four identifying an inverse association, and two did not find any association (Table 2). Thiede and Strube (2020) evaluated this relationship, estimating the effect of temperature on the wasting status of children aged between 0–59 months of age in 16 Sub-Saharan African countries. They identified that an increase of 2 standard deviations above the average temperature in the past 12 months was associated with a 6.7% reduction in the WHZ mean, from − 0.252 to -0.269 5 . Similarly, Tusting et al. (2020) identified that average monthly surface temperatures of over 35ºC are associated with 27% higher chance of wasting (OR = 1.27; 95% CI = 1.16–1.38) 36 . Baker & Anttila‑Hughes (2020), in turn, identified that a 1ºC change in the annual average temperature leads to a decline of approximately 0.08 standard deviations in the WHZ 8 . Blom, Ortiz-Bobea & Hoddinott (2022) observed that a 2ºC increase in the average monthly temperature increases the wasting percentage from 4.1–6.2% 28 . On the other hand, Hagos et al. (2014) observed that high temperatures were not associated with wasting [β=-0.14, p > 0.005] 31 . Similarly, Anttila-Hughes, Jina & McCord (2021) did not identify an association between the 1ºC increase in the ENSO index and wasting 27 (Table 2). ABDOMINAL OBESITY Only one study evaluated the relationship between temperature and abdominal obesity. Wallwork et al. (2017) examined the long-term association of average daily temperature during the year prior to the visit and found no statistically significant association with the risk of metabolic syndrome (HR = 0.99, 95% CI: 0.82, 1.21; P = 0.95) or its components, including abdominal obesity (HR = 1.0; 95% CI: 0.86, 1.16; P = 1.00) among elderly people 40 (Table 2). ARM CIRCUMFERENCE One study evaluated the effect of temperature variability on the nutritional state of children aged between 0 and 3 in Bangladesh 32 . They reported that temperatures between 25°C and 30ºC during the month of the child’s birth negatively affected their nutritional status, evaluated through the arm circumference (β = -1.533; p < 0.01). For temperatures above 30ºC during the month of the child’s birth, the negative effect on the arm circumference was even more pronounced (β = -2.154; p < 0.01) (Table 2). BODY MASS INDEX (BMI) Mueller & Gray (2018) suggested that temperature anomalies increase the probability of underweight in individuals aged between 41 and 60 (F statistic = 0.006; p = 0.003), with the magnitude of the effect being even greater among those aged over 60 (F statistic = 0.011; p = 0.004) in China, between 1989–2011. Extremely hot, dry conditions produce an increase of 3.3 percentage points in the underweight status for the group aged over 60 14 (Table 2). Kanazawa (2020) analysed the effect of atmospheric temperature on BMI and obesity in the United States of America, using the National Longitudinal Study of Adolescent to Adult Health (Add Health) data. They showed that maximum daily temperature was positively associated with BMI (β = 0.036; p < 0.001), weight (β = 0.098; p < 0,001), overweight (β = 0.008; p < 0.001), and obesity (β = 0.011; p < 0.001) 38 (Table 2). Huang & Hong (2024), evaluating the effect of temperature on obesity in 152 countries between 1975 and 2016 using a country-level aggregated data, identified that global warming is associated with an increase in obesity rates in countries located in temperate zones, while it is associated with a reduction in obesity prevalence in a small number of tropical countries. The estimates suggest that a 1°C increase in the annual average temperature is associated with an increase of 79.7 million obese adults (12.3%) globally 39 (Table 2). Table 2. Summary of study characteristics included in the systematic review. Author Country Study Design Age Sample Size Outcomes Exposure (Temperature) Duration of Exposure Results Amondo, Nshakira‑Rukundo & Mirzabaev, 2023 Uganda Panel study Children aged between 6 and 59 months 1,397 HAZ Heatwave (monthly temperatures above 29°C (84.2°F). Last five years HAZ : a heatwave in the main season and the last five years reduced calories, protein, zinc and vitamin A supply. A 10% decrease in zinc supply decreased HAZ by approximately 0.139–0.164 SD, and increased the probability of stunting, ranging from 3.1 to 3.5 percentage points. Anttila-Hughes, Jina & McCord, 2021 51 countries (Not informed) Panel study Children aged under 4 1,253,176 WAZ WHZ BAZ Underweight Wasting NINO 3.4 index of equatorial Pacific sea surface temperature. From May of one year to April of the next year WAZ : a 1°C increase in the ENSO index is associated with 0.03σ (p = 0.02) average decrease in WAZ. Wasting : the risk of wasting is similarly positive, but not significant (0.3 p.p./°C, p = 0.21). Underweight : warmer ENSO increases the prevalence of being significantly underweight, by 0.6 percentage points per 1°C (p < 0.05). Baker & Anttila-Hughes, 2020 30 Sub-Saharan African countries Panel study Children aged between 1 and 5 190,000 WHZ HAZ WAZ Average monthly temperature Annual and lifetime period WHZ : a 1ºC change in annual temperature leads to an approximate 0.08𝜎 decline in WHZ (adding temperature effects across 12 months in the year). A lifetime average temperature from 25 to 30ºC is associated with an approximate 0.5σ decrease in WHZ. HAZ : effect of temperatures not found. WAZ : effect of temperatures not found. Blom, Ortiz-Bobea & Hoddinott, 2022 Benin, Burkina Faso, the Ivory Coast, Ghana, and Togo Panel study Children aged between 3 and 36 months 32,036 HAZ WHZ Average hours per month over the exposure window bins: ≤25ºC, 25–30ºC, 30–35ºC, and > 35ºC HAZ: lifetime exposure WHZ: 90 days prior to the interview date HAZ : exposure to temperatures above 35ºC decreases HAZ, and increases the risk of stunting. HAZ decrease of 18% for each 100 h of exposure above 35ºC. WHZ : decrease by 0.10 SD per 100 h increase in average monthly exposure to temperatures between 30–35ºC Davenport et al., 2017 13 Sub-Saharan African countries Panel study Children aged under 5 60,577 HAZ Number of days where the maximum daytime temperature exceeds 37.7°C One year before birth, until the interview date HAZ : there is a negative effect of warming on child stunting, but it could be mitigated by increasing mothers’ educational status and household access to electricity. Grace et al., 2012 Kenya Cross-sectional study Children aged between 1 and 5 2,255 HAZ Average temperature over the growing season, and the average of these values over the child’s life Lifetime period HAZ : temperature appears to have no significant impact on HAZ [ß= -0.0385; p > 0.05] Hagos et al., 2014 Ethiopia Ecological study Children aged under 5 145 HAZ (stunting) WAZ (underweight) WHZ (wasting) Average temperature over the growing season Growing season Stunting : 1SD increase in temperature resulted in 0.216 SD decrease in moderate stunting. Underweight : 1SD increase in temperature resulted in 0.26 SD decrease in being severely underweight. Wasting : no significant relationship Ahmed Hanifi, Menon & Quisumbing, 2022 Bangladesh Panel study Children aged between 0 and 3 19,357 Upper arm circumference (MUAC) - Monthly average temperature - Three temperature bins: 15–20ºC, 25–30ºC, and > 30ºC Month of conception In utero (by trimester) Month and year of birth Lifetime MUAC : we found that temperatures that exceed 25ºC in the month and year of birth decrease the mid upper arm circumference (MUAC) by 1.476cm (p < 0.01), and an over 30ºC decrease in MUAC to 2.115cm (p < 0.05). Huang & Hong, 2024 152 countries Ecological study Adults 500,000 BMI - Annual average temperature - Temperature bins - Seasonal average temperature - Temperature variation - Temperature shocks 30 years BMI : in general, high temperatures have significantly increased obesity rates in countries located in temperate zones, while only causing a reduction in a small number of tropical countries. The estimates suggest that a 1ºC increase in the annual average temperature would result in a worldwide increase in obese adults of 79.7 million, or 12.3%. Kanazawa, 2020 USA Cohort study Adolescents and adults 12,509 BMI Overweight Obesity - Average number of annual days with temperature higher than 90°F (32.2ºC) - Average maximum daily temperature - Average minimum daily temperature - Average total annual hours of sunshine Not clear (one year before interview?) BMI : high temperatures were significantly associated with a higher BMI, weight, being overweight, and obesity. They found that the daily maximum temperature was positively associated with BMI (β = 0.036; p < 0.001), measured weight (β = 0.098; p < 0.001), being overweight (β = 0.008; p < 0.001), and obesity (β = 0.011; p < 0.001). McMahon & Gray, 2021 Bangladesh, India, Nepal, and Pakistan Panel study Children aged between 2 and 5 222,572 HAZ - Monthly climate anomalies (using the historical average and historical standard deviation in each province) − 9-month anomalies − 12-month anomalies for the first (0–11 months) and second (12–23) year of life During the prenatal period and first 2 years of life HAZ : One additional unit of heat decreases the likelihood of stunting by 3.4% (p = 0.078), and increases HAZ by 2.7% (p = 0.055) when the anomaly occurs during the first and second years, respectively. Merwe, Clance & Yitbarek, 2022 Nigeria Cohort study Children under the age of 5 3,511 HAZ WAZ Monthly average temperature From July (the year prior to the survey) to June, (year of the survey) HAZ : a one-unit (◦C) increase in temperature increases the probability of child stunting by between 18.6% and 22.3% WAZ : the probability of a child being underweight increases by between 7.9% and 15.2% with a one-unit (◦C) increase in temperature Mueller & Gray, 2018 China Cohort study Adults and the elderly 20,990 BMI Temperature anomalies (measured as standardized anomalies, or z-scores, defined as the temperature deviation during the calendar year of interview from the 1981–2010 average temperature, divided by the standard deviation in the temperature measured over the same period) Year of the interview BMI : temperature anomalies increase the probability of being underweight in individuals aged between 41 and 60 (F statistic = 0.006; p = 0.003), and more significantly in those aged over 60 (F statistic = 0.011; p = 0.004). Extremely dry and hot conditions lead to a 3.3 percentage point increase in the underweight status for the group of those aged over 60. Randell, Gray & Grace, 2020 Ethiopia Panel study Children aged between 1 and 5 23,026 HAZ Average maximum daily temperature (°C) Child’s prenatal period and early life (birth and through the time of the survey) HAZ : 1°C increase in average prenatal temperature is associated with a 16% (p < 0.01) and 28% (p < 0.001) increase in the odds of stunting and severe stunting, respectively. And 1°C increase in average early life temperature is associated with a 13% (p < 0.01) and 23% (p < 0.001) increase in the odds of stunting and severe stunting, respectively. Rojas, Gray & West, 2023 Burkina Faso Panel study Children aged between 2 and 5 12,321 HAZ Temperature anomalies (daily average temperature higher than the 95th percentile) Prenatal period, the first and second year of life HAZ : temperature anomalies in the first year were positively associated with HAZ (ß=0.066, p < 0.01). Temperature anomalies in the prenatal period and second year of life were not associated with HAZ. Thiede & Strube, 2020 18 Sub-Saharan African countries Panel study Children under the age of 5 182,272 WHZ Wasting Temperature anomalies (average temperature observed for a given cluster for the 12 months prior to the survey, standardized over all consecutive 12-month periods in the entire climate history for that location) Two years WHZ : an increase in temperatures from average to two standard deviations above average is associated with a 6.7% reduction in predicted WHZ, from 0.252 to 0.269 (p < 0.05). Wasting : the predicted probability of wasting increases by approximately 8.1 percent (from 9.9 to 10.7%) as temperatures increase by two standard deviations over the baseline average (p < 0.01). Tusting et al., 2020 29 Sub-Saharan African countries Panel study Children under the age of 5 656,107 HAZ (stunting) WHZ (wasting) WAZ (underweight) Monthly average daytime LST classified in three temperature bins: 30°C, 30°C to < 35°C, and ≥ 35°C Between 2000 and 2016 Stunting : odds of stunting were 10% lower among children living where the monthly average daytime LST exceeded 35°C, compared with those where monthly average daytime LST was less than 30°C (OR: 0.90, 95% CI 0.85–0.96). Wasting : the odds of wasting were 27% higher among children living where the monthly average daytime LST exceeded 35°C (OR: 1.27, 95% CI 1.16–1.38). Underweight : the odds of being underweight were 9% higher among children living where the monthly average daytime LST exceeded 35°C (OR: 1.09, 95% CI 1.02–1.16). Wallwork et al., 2017 USA Cohort study Elderly men 587 Waist circumference (WC) Average daily temperature The year before each visit (2- to 5- year average) WC : temperature was not significantly associated with both the risk of abdominal obesity (HR = 1.06, 95% CI: 0.86, 1.31 0.58) and metabolic syndrome as a whole (HR = 0.99, 95% CI: 0.82, 1.21) Yokoya & Higuchi, 2016 Japan Ecological study Children and adolescents (aged between 5 and 17) 47 prefectures Weight Average annual temperature (ºC), average daily maximum temperature in August (ºC), and average daily minimum temperature in January (ºC) 30 year period Weight : height and the mean maximum daily temperature in August were significant predictors of weight in most age groups. A negative correlation between the average maximum daily temperature in August and weight with highly significant regression coefficients was observed for most ages. META-ANALYSIS The meta-analysis results are presented in Figs. 3 to 6. We only conducted meta-analyses with studies that were combinable in terms of exposure (i.e., studies that used average temperature - monthly or yearly, or average maximum temperature -monthly or yearly), population (children) and outcome (HAZ, WAZ, and WHZ indicators). When evaluating the effect of average temperature (monthly or yearly) on the HAZ indicator, we identified a 0.02σ reduction in the z-score average of this indicator for every 1ºC increase in average temperature (β=-0.02; 95% CI: -0.04; -0.01, n = 2 studies) (Fig. 3 ). When analyzing the effect of the average maximum temperature (monthly or annual) on HAZ, no statistically significant association was found (β=-0.31; 95% CI: -1.06; 0.44, n = 3 studies) (Fig. 4 ). For the WAZ, no statistically significant association between the maximum average temperature and this indicator (β=-0.80; 95% CI: -2.52; 0.91, n = 2 studies) was identified (Fig. 5 ). Regarding the WHZ, we observed a 0.06σ reduction in the z-score average for this indicator for every 1ºC increase in the average temperature (monthly or annual) (β=-0.06; 95% CI: -0.09; -0.04, n = 2 studies) (Fig. 6). The Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach was applied to the outcomes HAZ, WAZ, WHZ, BMI, WC, arm circumference, and BMI (Table 03). All outcomes started with low certainty, as they were based on observational studies. With the exception of arm circumference, all outcomes underwent at least one downgrade and were classified as having very low certainty, according to GRADE criteria. The domains with the most significant issues were inconsistency (substantial variability among results) and imprecision (uncertain effects and/or small sample sizes). Tabela 03 - Resumo da Classificação de Recomendações Avaliação, Desenvolvimento e Avaliação (GRADE). Certainty assessment Certainty Nº of Studies Study design Risk of bias Inconsistency Indirectness Imprecision Publication Bias HAZ 11 Observational studies Not serious Serious a Not serious Not serious Not serious ⨁⨁◯◯ Very low WAZ 05 Observational studies Not serious Serious a Not serious Not serious Not serious ⨁⨁◯◯ Very low WHZ 06 Observational studies Not serious Moderate Not serious Not serious Not serious ⨁⨁◯◯ Very low BMI 03 Observational studies Not serious Serious Not serious Serious Not serious ⨁⨁⨁◯ Very low WC 01 Observational studies Not serious Not serious Not serious Serious Not serious ⨁⨁◯◯ Very low Arm Circumference 01 Observational studies Not serious Not serious Not serious Not serious Not serious ⨁◯◯◯ Low a. Meta-analyses showed high heterogeneity. DISCUSSION In the current systematic review with meta-analysis, we investigated the effect of high ambient temperatures on anthropometric indicators in various age groups, without any geographical or time limitations. We found that the majority of studies included in this review originated from Sub-Saharan African countries and focused on children under the age of 5. The included studies presented mixed results for the HAZ indicator. For the WHZ and WAZ indicators, the review results showed an almost unanimous inverse association with high temperatures, showing a reduction in WHZ and WAZ mean or an increased probability of wasting and being underweight with rising temperatures. Divergent results were observed in elderly adults, with some studies showing a weight reduction, while others indicated an increase in obesity with temperature increases. In the meta-analysis of combinable studies, we estimated a reduction in the Z-score average for the HAZ and WHZ indicators for every one degree increase in the average temperature (monthly or yearly). Although the results were statistically significant, the strength of these associations was weak, with a 0.02 reduction in the HAZ average and 0.06 reduction in the WHZ average. A significant association was not identified for the WAZ indicator. Our review provides evidence of the effect of high ambient temperatures on both the classical measures of acute (WHZ) and chronic (HAZ) child malnutrition. We highlight that the mechanisms through which high temperatures affect chronic malnutrition may not be the same as those which affect acute malnutrition. Chronic and acute malnutrition are consequences of types of nutritional challenges. Chronic malnutrition reflects prolonged periods of insufficient nutrient intake, with permanent consequences for growth, while acute malnutrition reflects shorter periods of insufficient food intake from which children can recover if the food intake improves. For example, chronic malnutrition could be caused by long-term insufficient calorie intake, as a result of heat-induced crop failure. In contrast, acute malnutrition could result from heat-induced food spoilage, causing short-term illness 28 . The effect of temperature on the metabolic syndrome and abdominal obesity in the elderly was evaluated in one study only. Considering the vulnerability of elderly individuals, the need to evaluate the effect of temperature changes on malnutrition is relevant, since it is a frequent nutritional disorder in this population 41 , 42 . Only three studies were conducted to evaluate the effect of high temperatures on the BMI of adult individuals. One of these identified that temperature anomalies increase the probability of low weight 14 , while a further two observed an increase in BMI for being overweight and obesity for every one degree increase in the maximum daily temperature 38 and a significant increase in obesity rates, with a one degree increase in the average annual temperature 39 . A number of authors suggest that increases in temperature may influence income 15 , the price of food 16 , preferences with food consumption 17 and levels of physical activity 18 , particularly in low- and middle-income countries. In addition, changes in working hours, levels of physical activity, and minimum calorie requirements may also influence calorie intake. Any alteration in calorie intake and expenditure naturally has an impact on obesity. Obesity is one of the greatest global health challenges in recent decades. Thus, knowing the effects of the rising temperature increase on this condition is essential for policymakers and the general population. We emphasize that in the 16 studies evaluated for risk of bias, the majority presented a good classification, and none were classified as Tier 3, which indicates a probably or definitively high risk of bias. We also highlight that the majority used Demographic and Health Survey (DHS) data, which covers large samples representative of the countries’ populations; they involved appropriate study designs to assess associations of interest, robust statistical analysis, accounting for confounding and effect modification, and the exposure and outcome measured appropriately. These results indicate the good quality of the studies that explore the association between high temperatures and the anthropometric status of the general population, making the findings of this review consistent and trustworthy. However, we should also highlight the great heterogeneity identified in the methods of measuring the exposure (temperature) in the different studies. Since there are no gold standards, each study adopted distinct measures, which made comparison difficult, indicating that the body of evidence is still limited and inconsistent for exposure. Thus, the development of studies which seek to define standards of temperature when used as an exposure is urgently required. This systematic review and meta-analysis provide a comprehensive summary of the effects of high ambient temperature on malnutrition, including studies which use demographic and national health surveys with large sample sizes and from various countries around the world, with large sample sizes, which increase the accuracy and representativeness of the results and relevance of the findings. However, we must emphasize several limitations regarding the geographic scope and meteorological variables, which affect generalization. Firstly, most of the studies originated in Africa, with other tropical regions of South America and Asia being under-represented, limiting the application of the findings to these regions. Another important limitation is that the majority of studies included in the review did not address issues of attribution and detection of causal pathways, which would help to identify and understand the underlying mechanisms of this phenomenon. In addition, the great heterogeneity in the studies with regards to the form of assessing the meteorological exposure variable (high temperatures) prevented the performance of a more robust meta-analysis, with a higher number of studies included and more diversified outcomes, as well as subgroup analyses, as intended in the protocol for this review. However, despite the limitations, the meta-analysis performed here provides valuable insights into the relationship between temperature and nutrition, suggesting that elevated temperatures may negatively affect the growth and nutritional status of populations. Therefore, public health strategies aimed at climate adaptation should be prioritized, particularly in tropical and low- and middle-income countries, where vulnerability is greater. From a policy perspective, integrating climate resilience into nutritional and child health programs may help mitigate the adverse effects of rising temperatures. Future studies are needed to better assess the effects of elevated ambient temperature on nutritional outcomes in the general population. The observed inconsistencies in HAZ outcomes, combined with limitations related to age range, geographic distribution, and high heterogeneity in temperature estimation methods, reveal significant gaps in the current body of evidence. Both short- and long-term effects should be investigated across different contexts and population groups, including the identification of potential mediators and effect modifiers of this association, such as droughts and heavy rainfall, to better understand the pathways of these relationships. Conclusion The findings of this review highlight the need for further research on the impact of high ambient temperatures on anthropometric indicators. The inconsistencies observed in HAZ results, along with limitations in age range, geographical coverage, and high heterogeneity in temperature estimation, underscore the gaps in the current literature. A reduction in the Z-score for the HAZ and WHZ indicators for every 1°C increase was identified in the meta-analyses. In practical terms, these findings suggest that high temperatures may harm the anthropometric status of populations, and actions and strategies for adapting to climate changes, in order to minimise their effects should be prioritised, particularly in tropical and low- and middle-income countries, seeking to protect the health of their populations. In addition, given the limitations of the studies included in this review, robust future studies that assess how temperature increases affect obesity and malnutrition rates of the general population are required. These studies will be relevant to scientific knowledge and to formulate targeted public adaptation policies. Declarations Ethical approval Ethical approval was not required since this study does not involve human participants. Declaration of generative AI and AI assisted technologies during the writing process The authors did not use any generative AI or AI assisted technologies during the preparation of this work. Consent for publication Consent was not required since this study does not involve human participants. Declaration of conflicts of interest All other authors declare they have no conflicts of interest related to this work to disclose. Funding This study was funded by CNPQ/DECIT/SECTICS/MS Nº18/2023 - Ciência de dados: mudanças climáticas e impactos para a saúde [444383/2023-9]. E.S.P. is funded by the Wellcome Trust 225925/Z/22/Z. The study also received support from MS-SCTIE-Decit/CNPq [25000.148278/2022-10]. OTR is funded by the Ramón y Cajal program (RYC2023-002923-C) awarded by the Spanish Ministry of Science, Innovation and Universities. The funders did not play any role in the study design or collection, data analysis, and interpretation, writing of the study, or decision to submit the article for publication. Author Contribution PRFC made substantial contributions to the conception of the work; collection, analysis, and interpretation of data; manuscript writing; reviewed and approved the submitted version.RCRS made substantial contributions to the conception of the work; collection, analysis, and interpretation of data; manuscript writing; reviewed and approved the submitted version.ROS made substantial contributions to the collection, analysis, and interpretation of data; manuscript writing; reviewed and approved the submitted version.KBBSM made substantial contributions to the collection, analysis, and interpretation of data; manuscript writing; reviewed and approved the submitted version.ESC made substantial contributions to the collection, analysis, and interpretation of data; manuscript writing; reviewed and approved the submitted version.ASR made substantial contributions to the collection, analysis, and interpretation of data; manuscript writing; reviewed and approved the submitted version.LPL made substantial contributions to the collection, analysis, and interpretation of data; manuscript writing; reviewed and approved the submitted version.LHLR made substantial contributions to the collection, analysis, and interpretation of data; manuscript writing; reviewed and approved the submitted version.OTR made substantial contributions to the conception of the work; collection, analysis, and interpretation of data; manuscript writing; reviewed and approved the submitted version.IS made substantial contributions to the conception of the work; collection, analysis, and interpretation of data; manuscript writing; reviewed and approved the submitted version.ESP made substantial contributions to the conception of the work; collection, analysis, and interpretation of data; manuscript writing; reviewed and approved the submitted version.MP made substantial contributions to the conception of the work; collection, analysis, and interpretation of data; manuscript writing; reviewed and approved the submitted version.MLB made substantial contributions to the conception of the work; collection, analysis, and interpretation of data; manuscript writing; reviewed and approved the submitted version. Data Availability The dataset supporting the findings of this meta-analysis is not publicly available due to restrictions related to copyright and licensing agreements of the original studies included. The data were extracted from published articles that are protected by third-party rights, and sharing the full dataset may violate those terms. Additionally, some studies did not provide raw data and required individual calculations based on figures or summary tables, limiting reproducibility outside the context of this review. However, detailed extraction procedures and summary data used in the analyses can be provided upon reasonable request to the corresponding author PRFC ( [email protected] ). References Phalkey RK, Aranda-Jan C, Marx S, Höfle B, Sauerborn R. Systematic review of current efforts to quantify the impacts of climate change on undernutrition. Proc Natl Acad Sci U S A. 2015 Aug 18;112(33):E4522-9. doi: 10.1073/pnas.1409769112. Epub 2015 Jul 27. PMID: 26216952; PMCID: PMC4547305. Black RE, Allen LH, Bhutta ZA, Caulfield LE, de Onis M, Ezzati M, Mathers C, Rivera J; Maternal and Child Undernutrition Study Group. Maternal and child undernutrition: global and regional exposures and health consequences. Lancet. 2008 Jan 19;371(9608):243-60. doi: 10.1016/S0140-6736(07)61690-0. PMID: 18207566. Schaible UE, Kaufmann SH. Malnutrition and infection: complex mechanisms and global impacts. PLoS Med. 2007 May;4(5):e115. doi: 10.1371/journal.pmed.0040115. PMID: 17472433; PMCID: PMC1858706. Randell H, Gray C, Grace K. Stunted from the start: Early life weather conditions and child undernutrition in Ethiopia. Soc Sci Med. 2020 Sep;261:113234. doi: 10.1016/j.socscimed.2020.113234. Epub 2020 Jul 23. PMID: 32823214; PMCID: PMC7716344. Thiede BC, Strube J. Climate Variability and Child Nutrition: Findings from Sub-Saharan Africa. Glob Environ Change. 2020 Nov;65:102192. doi: 10.1016/j.gloenvcha.2020.102192. Epub 2020 Nov 26. PMID: 34789965; PMCID: PMC8594912. Helldén D, Andersson C, Nilsson M, Ebi KL, Friberg P, Alfvén T. Climate change and child health: a scoping review and an expanded conceptual framework. Lancet Planet Health. 2021 Mar;5(3):e164-e175. doi: 10.1016/S2542-5196(20)30274-6. PMID: 33713617. Smith CJ. Pediatric Thermoregulation: Considerations in the Face of Global Climate Change. Nutrients. 2019 Aug 26;11(9):2010. doi: 10.3390/nu11092010. PMID: 31454933; PMCID: PMC6770410. Baker RE, Anttila-Hughes J. Characterizing the contribution of high temperatures to child undernourishment in Sub-Saharan Africa. Sci Rep. 2020 Nov 2;10(1):18796. doi: 10.1038/s41598-020-74942-9. PMID: 33139856; PMCID: PMC7606522. Zhao C, Liu B, Piao S, Wang X, Lobell DB, Huang Y, Huang M, Yao Y, Bassu S, Ciais P, Durand JL, Elliott J, Ewert F, Janssens IA, Li T, Lin E, Liu Q, Martre P, Müller C, Peng S, Peñuelas J, Ruane AC, Wallach D, Wang T, Wu D, Liu Z, Zhu Y, Zhu Z, Asseng S. Temperature increase reduces global yields of major crops in four independent estimates. Proc Natl Acad Sci U S A. 2017 Aug 29;114(35):9326-9331. doi: 10.1073/pnas.1701762114. Epub 2017 Aug 15. PMID: 28811375; PMCID: PMC5584412. Kunitski M, Eicke N, Huber P, Köhler J, Zeller S, Voigtsberger J, Schlott N, Henrichs K, Sann H, Trinter F, Schmidt LPH, Kalinin A, Schöffler MS, Jahnke T, Lein M, Dörner R. Double-slit photoelectron interference in strong-field ionization of the neon dimer. Nat Commun. 2019 Jan 2;10(1):1. doi: 10.1038/s41467-018-07882-8. PMID: 30602773; PMCID: PMC6315036. Levy K, Smith SM, Carlton EJ. Climate Change Impacts on Waterborne Diseases: Moving Toward Designing Interventions. Curr Environ Health Rep. 2018 Jun;5(2):272-282. doi: 10.1007/s40572-018-0199-7. PMID: 29721700; PMCID: PMC6119235. Hsiang SM, Burke M, Miguel E. Quantifying the influence of climate on human conflict. Science. 2013 Sep 13;341(6151):1235367. doi: 10.1126/science.1235367. Epub 2013 Aug 1. PMID: 24031020. Burke M, Hsiang S, Miguel E. Global non-linear effect of temperature on economic production. Nature 527, 235–239 (2015). https://doi.org/10.1038/nature15725 Mueller V, Gray C. Heat and Adult Health in China. Popul Environ. 2018 Sep;40(1):1-26. doi: 10.1007/s11111-018-0294-6. Epub 2018 Apr 14. PMID: 30349149; PMCID: PMC6195320. Huang, K., & Sim, N. (2020). Adaptation may reduce climate damage in agriculture by two thirds. Agricultural Economics, 51 (3), 289–300. https://doi.org/10.1111/1477-9552.12389 Wheeler T, von Braun J (2013). Climate change impacts on global food security. Science, 341(6145), 508–513. https://doi.org/10.1126/science.1239402 Scheelbeek PFD, Moss C, Kastner T et al. United Kingdom’s fruit and vegetable supply is increasingly dependent on imports from climate-vulnerable producing countries. Nat Food 1, 705–712 (2020). https://doi.org/10.1038/s43016-020-00179-4 Obradovich N, Fowler J. Climate change may alter human physical activity patterns. Nat Hum Behav 1, 0097 (2017). https://doi.org/10.1038/s41562-017-0097 Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, Welch VA (editors). Cochrane Handbook for Systematic Reviews of Interventions version 6.5 (updated August 2024). Cochrane, 2024. Available from www.training.cochrane.org/handbook. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Shamseer L, Tetzlaff JM, Akl EA, Brennan SE, Chou R, Glanville J, Grimshaw JM, Hróbjartsson A, Lalu MM, Li T, Loder EW, Mayo-Wilson E, McDonald S, McGuinness LA, Stewart LA, Thomas J, Tricco AC, Welch VA, Whiting P, Moher D. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021 Mar 29;372:n71. doi: 10.1136/bmj.n71. PMID: 33782057; PMCID: PMC8005924. National Toxicology Program. (2019). Handbook for conducting a literature-based health assessment using OHAT approach for systematic review and evidence integration (p. 96). Office of Health Assessment and Translation (OHAT), Division of the National Toxicology Program, National Institute of Environmental Health Sciences. https://ntp.niehs.nih.gov/sites/default/files/ntp/ohat/pubs/handbookmarch2019_508.pdf Michel SKF, Atmakuri A, von Ehrenstein O S (2024). Systems for rating bodies of evidence used in systematic reviews of air pollution exposure and reproductive and children’s health: A methodological survey. Environmental Health, 23, Article 32. https://doi.org/10.1186/s12940-024-01069-z Deeks JJ, Higgins JPT, Altman DG, eds. Analyzing data and undertaking meta-analyses. In: Higgins JPT, Thomas J, Chandler J, et al. eds. Cochrane Handbook for Systematic Reviews of Interventions version 6.0 (updated July 2019). Barcelona: Centro Cochrane Iberoamericano; 2019. Guyatt GH, Oxman AD, Sch€Unemann HJ, Tugwell P, Knottnerus A. GRADE guidelines: A new series of articles in the Journal of Clinical Epidemiology. Journal of Clinical Epidemiology 64 (2011) 380e382. https://doi:10.1016/j.jclinepi.2010.09.011 Manheimer E. Summary of findings tables: presenting the main findings of Cochrane complementary and alternative medicine-related reviews in a transparent and simple tabular format. Glob Adv Health Med. 2012 Mar;1(1):90-1.b Amondo E I, Nshakira-Rukundo E, Mirzabaev A (2023). The effect of extreme weather events on child nutrition and health. Food Security, 15(3), 571–596. https://doi.org/10.1007/s12571-023-01354-8 Anttila-Hughes JK, Jina AS, McCord GC. ENSO impacts child undernutrition in the global tropics. Nat Commun. 2021 Oct 12;12(1):5785. doi: 10.1038/s41467-021-26048-7. PMID: 34642319; PMCID: PMC8511020. Blom S, Ortiz-Bobea A, & Hoddinott J (2022). Heat exposure and child nutrition: Evidence from West Africa. Journal of Environmental Economics and Management, 115, 102698. https://doi.org/10.1016/j.jeem.2022.102698 Davenport, F., Grace, K., Funk, C., & Shukla, S. (2017). Child health outcomes in sub-Saharan Africa: A comparison of changes in climate and socio-economic factors. Global Environmental Change, 46, 72–87. https://doi.org/10.1016/j.gloenvcha.2017.06.002 Grace, K., Davenport, F., Funk, C., & Lerner, A. M. (2012). Child malnutrition and climate in Sub-Saharan Africa: An analysis of recent trends in Kenya. Applied Geography, 35, 405–413. https://doi.org/10.1016/j.apgeog.2012.02.003 Hagos S, Lunde T, Mariam DH, Woldehanna T, Lindtjørn B. Climate change, crop production and child under nutrition in Ethiopia; a longitudinal panel study. BMC Public Health. 2014 Aug 27;14:884. doi: 10.1186/1471-2458-14-884. PMID: 25163522; PMCID: PMC4158109. Ahmed Hanifi SMM, Menon N, Quisumbing A. The impact of climate change on children's nutritional status in coastal Bangladesh. Soc Sci Med. 2022 Feb;294:114704. doi: 10.1016/j.socscimed.2022.114704. Epub 2022 Jan 10. PMID: 35030394. McMahon K, Gray C. Climate change, social vulnerability and child nutrition in South Asia. Glob Environ Change. 2021 Nov;71:102414. doi: 10.1016/j.gloenvcha.2021.102414. Epub 2021 Nov 13. PMID: 34898861; PMCID: PMC8653856. Merwe E van der, Clance M, Yitbarek E (2022). Climate change and child malnutrition: A Nigerian perspective. Food Policy, 113, 102281. https://doi.org/10.1016/j.foodpol.2022.102281 Rojas AJ Jr, Gray CL, West CT. "Measuring the Environmental Context of Child Growth in Burkina Faso". Popul Environ. 2023 Jun;45(2):3. doi: 10.1007/s11111-023-00414-7. Epub 2023 Mar 24. PMID: 37274602; PMCID: PMC10237046. Tusting LS, Bradley J, Bhatt S, Gibson HS, Weiss DJ, Shenton FC, Lindsay SW. Environmental temperature and growth faltering in African children: a cross-sectional study. Lancet Planet Health. 2020 Mar;4(3):e116-e123. doi: 10.1016/S2542-5196(20)30037-1. PMID: 32220673; PMCID: PMC7232952. Yokoya M, Higuchi Y. Association between summer temperature and body weight in Japanese adolescents and children: An ecological analysis. Am J Hum Biol. 2016 Nov;28(6):789-795. doi: 10.1002/ajhb.22867. Epub 2016 May 25. PMID: 27224001. Kanazawa S. Does global warming contribute to the obesity epidemic? Environ Res. 2020 Mar;182:108962. doi: 10.1016/j.envres.2019.108962. Epub 2019 Dec 6. PMID: 31862545. Huang, K., & Hong, Q. (2024). The impact of global warming on obesity. Journal of Population Economics, 37(1), 59. https://doi.org/10.1007/s00148-024-01039-2 Wallwork RS, Colicino E, Zhong J, Kloog I, Coull BA, Vokonas P, Schwartz JD, Baccarelli AA. Ambient Fine Particulate Matter, Outdoor Temperature, and Risk of Metabolic Syndrome. Am J Epidemiol. 2017 Jan 1;185(1):30-39. doi: 10.1093/aje/kww157. Epub 2016 Dec 7. PMID: 27927620; PMCID: PMC5209587. Corcoran C, Murphy C, Culligan EP, Walton J, Sleator RD. Malnutrition in the elderly. Sci Prog. 2019 Jun;102(2):171-180. doi: 10.1177/0036850419854290. PMID: 31829839; PMCID: PMC10424533. Fávaro-Moreira NC, Krausch-Hofmann S, Matthys C, Vereecken C, Vanhauwaert E, Declercq A, Bekkering GE, Duyck J. Risk Factors for Malnutrition in Older Adults: A Systematic Review of the Literature Based on Longitudinal Data. Adv Nutr. 2016 May 16;7(3):507-22. doi: 10.3945/an.115.011254. PMID: 27184278; PMCID: PMC4863272. Additional Declarations No competing interests reported. Supplementary Files SearchStrategy.docx Cite Share Download PDF Status: Posted 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. 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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-6404122","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":476555254,"identity":"e6b1cf21-edad-4d63-a50e-a7bfdddbe2fc","order_by":0,"name":"Priscila Ribas de Farias 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14:08:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6404122/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6404122/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85844161,"identity":"7da1e431-ad53-4828-821d-ef50e8bdf547","added_by":"auto","created_at":"2025-07-02 09:26:46","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":624580,"visible":true,"origin":"","legend":"\u003cp\u003ePRISMA flowchart describing the study selection process.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6404122/v1/d0157819b180d5cb9ca53a60.jpeg"},{"id":85844165,"identity":"467177a7-add4-43b2-81e1-1ac4faa0cdc0","added_by":"auto","created_at":"2025-07-02 09:26:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":451164,"visible":true,"origin":"","legend":"\u003cp\u003eSummary of the risk of bias evaluation of cross-sectional and cohort studies using the OHAT instrument.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6404122/v1/d0b60cdb4e19ff78e251de12.png"},{"id":85844160,"identity":"eeda19f3-1947-4ad7-94d7-36b051d162a0","added_by":"auto","created_at":"2025-07-02 09:26:46","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":107412,"visible":true,"origin":"","legend":"\u003cp\u003eMeta-analysis of the association between the average temperature (monthly or yearly) and the height-for-age (HAZ) indicator.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6404122/v1/2a2a74cafe10ce5f07f468db.jpeg"},{"id":85842185,"identity":"d30681ed-fab3-4597-af7d-7a8325adcbcc","added_by":"auto","created_at":"2025-07-02 09:18:46","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":199925,"visible":true,"origin":"","legend":"\u003cp\u003eMeta-analysis of the association between maximum temperature (monthly or yearly) and the height-for-age (HAZ) indicator.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6404122/v1/be182caf7d7270f103bbe6bf.jpeg"},{"id":85842182,"identity":"bc80a8cf-9d6b-4d9e-9d93-3d61ef5c509e","added_by":"auto","created_at":"2025-07-02 09:18:46","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":166545,"visible":true,"origin":"","legend":"\u003cp\u003eMeta-analysis of the association between maximum temperature (monthly or yearly) and the weight-for-age (WAZ).\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6404122/v1/ebe6bdf530b2743a8375ce67.jpeg"},{"id":85844166,"identity":"07bb7326-a46a-4a8a-b629-ed8722fcc108","added_by":"auto","created_at":"2025-07-02 09:26:46","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":97384,"visible":true,"origin":"","legend":"\u003cp\u003eMeta-analysis of the association between average temperature (monthly or yearly) and the weight-for-height (WHZ) indicator.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6404122/v1/a3a67884204b5014364e1ead.jpeg"},{"id":89446099,"identity":"77314e68-5f24-40e4-a20f-6df173ae4bc0","added_by":"auto","created_at":"2025-08-20 05:08:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2950368,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6404122/v1/b9b2d9e1-e22a-41ac-a8cb-0e5f9fd800a7.pdf"},{"id":85842188,"identity":"7fb49980-4ed2-41fd-8412-82953f2208fa","added_by":"auto","created_at":"2025-07-02 09:18:46","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":2304805,"visible":true,"origin":"","legend":"","description":"","filename":"SearchStrategy.docx","url":"https://assets-eu.researchsquare.com/files/rs-6404122/v1/e36012ed35c2dc2ce23719da.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"High ambient temperatures effects on the anthropometric status of the population: a systematic review and meta-analysis","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eMalnutrition, which includes both undernutrition (underweight, wasting, weight loss, and stunted growth), and overnutrition (overweight and obesity), is a challenge for the of individuals and populations, a burden for the healthcare system and impact economic productivity\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. The human and socioeconomic costs of malnutrition are enormous, disproportionately affecting the poorest, particularly women, children, and the elderly. The harmful effects of malnutrition can impact an individual\u0026rsquo;s health throughout their life, beginning early and persisting into old age. It affects physical, mental, and social well-being, while also increasing the risks of morbidity and mortality\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eEmerging research on climate and health suggests a link between high temperatures and malnutrition\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. The pathways via which high temperatures impact health and nutrition are complex, involving short-, medium-, and long-term mechanisms, and may vary by geography, socioeconomic context, and ecosystem\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Children are particularly vulnerable to the harmful effects of extreme heat due to an inferior thermoregulatory response (compared to adults)\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Moreover, activities such as outdoor play or participating in agricultural work increase their risk of exposure. Heat stress can cause a series of acute health problems, including loss of appetite, poor nutrient retention, and increased diarrhoea and dehydration, hence leading to poor nutrient and calorie absorption and ultimately weight loss\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFurthermore, high temperatures can reduce crop yields threatening household food security, and increase water scarcity which contributes to poor sanitation\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. They also alter the transmission dynamics of infectious diseases\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. In addition, they increase the risk of violent conflicts\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, reduce work productivity, income, and economic growth\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. These indirect effects of high temperature can, in turn, influence nutritional outcomes among adults and the elderly, although these relationships continue to be poorly understood\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. High temperatures have also been suggested to alter physical activity levels, favouring a positive calorie balance, thereby directly impacting adult obesity\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Many of these adverse health consequences are concentrated in the populations of low- and middle-income countries who are exposed to higher average temperatures, may have a lower adaptive capacity, and where the means of subsistence are more directly dependent on environmental conditions.\u003c/p\u003e \u003cp\u003eIn recent years, an increasing number of studies suggested an association between high temperatures and nutritional outcomes. However, this evidence has not yet been systematically compiled and evaluated. Therefore, this systematic review aims to synthesise previous research on the association between high ambient temperatures and heatwaves and indicators of nutritional status (anthropometric indicators) in all age groups. By mapping the global literature, our review can inform the development of climate adaptation policies which mitigate the risk of malnutrition associated with extreme heat.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003eThis is a systematic review and meta-analysis which evaluated the evidence available on the association between extreme heat and heatwaves and anthropometric indicators in the general population, whose protocol was submitted to PROSPERO (International prospective register of systematic reviews) and registered under number CRD42024555573. We adopted the methodological recommendations proposed by Cochrane\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e and the wording proposed by PRISMA\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eInclusion and exclusion criteria\u003c/h2\u003e \u003cp\u003eThe inclusion and exclusion criteria were defined in accordance with the PECOS acronym (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Studies included in the systematic review must meet the following criteria: (1) population consisting of general population, children, adolescents, adults, pregnant women, or the elderly; (2) use of ambient temperature or heatwaves as an exposure variable; (3) evaluation of anthropometric indicators as outcome variables, including weight-for-age (WAZ), height-for-age (HAZ), weight-for-height (WHZ), BMI-for-age (BAZ), BMI, waist circumference (WC), percentage of body fat (%BF), and percentage of lean mass (%LM); (4) report at least one of the following effect measures: relative risk (RR), prevalence ratio (PR), or odds ratio (OR), with their confidence intervals (CI) for categorical variables, or mean, standard deviation values, β or correlation coefficient, with the respective p values for continuous variables; and (5) observational studies.\u003c/p\u003e \u003cp\u003eThe following were adopted as exclusion criteria: (1) study population representing a select group of individuals with chronic or high risk diseases, or users of medications which may alter anthropometric measurements or pressure levels, such as nephropathies, neoplasias, chronic liver diseases, lupus, Crohn’s disease, mental illnesses, HIV, and Down’s syndrome, among others; (2) review studies and/or case reports; (3) studies which did not evaluate the outcomes covered in this research; and (4) studies which did not use temperature, or heatwaves, as an exposure variable.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePECOS criteria for study selection.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCriteria\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGeneral (children, adolescents, pregnant women, adults and elderly people)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh temperatures, extreme heat, heatwaves, and high ambient temperature\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComparator\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePopulation exposed to lower temperature levels or not exposed to heatwaves\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcomes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnthropometric indicators\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSetting or study design\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObservational studies, such as cross-sectional, panel, cohort, case-control, and ecological studies\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSearch strategy\u003c/h3\u003e\n\u003cp\u003eWe searched for observational studies on the following electronic databases: PUBMED/MEDLINE, EMBASE, BVS (LILACS, IBECS, WHO IRIS, CUMED, BDENF, PAHO, VENTIDEX, ARGMSAL, BINACS, and LIPECS), and WEB OF SCIENCE, in addition to Google Scholar for grey literature published before 23th September 2024. In order to guarantee saturation, we examined the reference lists of studies included or of relevant reviews that were identified manually through research, to include studies that were not indexed on databases but were pertinent for inclusion in this review. No date, language limitations, or search filters were imposed on the search. The exposure and outcome terms and their respective synonyms were used in the search strategy, with the aim of including all studies relevant to this topic. We adopted the Boolean operators “AND” and “OR” for the database searches\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe selected the Pubmed/MEDLINE MeSH (Medical Subject Headings) database descriptors. We also opted for sensitivity, with the inclusion of entry terms and non-controlled vocabulary. We developed Boolean combinations of words (separated by outcome) for database searches using MeSH descriptors in PUBMED/MEDLINE, BVS, and Web of Science, and also on Google Scholar. Regarding the LILACS database search, we used selected Virtual Health Library (Biblioteca Virtual em Saúde – BVC) DeCS (Health Science Descriptors) and also developed Boolean expressions of words for this search.\u003c/p\u003e \u003cp\u003eLastly, we used Embase EMTREE (Embase Subject Headings) controlled vocabulary descriptors to construct Boolean expressions of words to search for articles indexed in this database. We also conducted a manual search of the reference lists of the studies included in the review, and relevant reviews identified during the selection process, in order to retrieve those which had not been retained by database searches. A full description of the search strategies can be found in the supplementary material (\u003cb\u003eSupplement 1\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eWe executed a sensitivity test to confirm the search strategy’s ability to capture relevant studies. We selected four sentinel articles, which were primary studies meeting the review eligibility criteria. The search strategy was able to capture 100% of the sentinels, indicating high sensitivity.\u003c/p\u003e\n\u003ch3\u003eSelection of articles\u003c/h3\u003e\n\u003cp\u003eWe exported the citations from each database to the Covidence review software, Veritas Health Innovation, Melbourne, Australia (available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.covidence.org\u003c/span\u003e\u003cspan address=\"http://www.covidence.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), where any duplicates were removed. We executed a pilot test in the initial study selection stage, with the aim of “calibrating” the reviewers’ decisions and, if necessary, improving the clarity of the eligibility criteria. For the test, ten records identified by the search strategy were randomly selected, with two reviewers assessing the titles/abstract using eligibility criteria. The reviewers achieved an agreement rate higher than 75% in the pilot test, indicating the reviewers’ understanding of the eligibility criteria.\u003c/p\u003e \u003cp\u003eFollowing the pilot test, we screened the titles/abstracts (stage I) of all the sources retrieved in the search, following the eligibility criteria. In stage II of the selection, we reviewed the full text of the records selected in stage I, and of those whose eligibility was still uncertain. Two reviewers worked independently, and any inconsistencies in classifying the decisions were discussed with a third reviewer.\u003c/p\u003e \u003cp\u003eIn stage II, the excluded sources were recorded on Covidence, along with the reason for their exclusion, and the entire study selection process was detailed in a PRISMA flowchart. The research team decided which studies should be included in the final selection for data summary.\u003c/p\u003e\n\u003ch3\u003eData extraction and quality\u003c/h3\u003e\n\u003cp\u003eTwo independent reviewers systematically executed the data extraction, and any divergences were resolved through discussion with a third reviewer.\u003c/p\u003e \u003cp\u003eFollowing the final article selection, the data were extracted on a form using COVIDENCE software. In order to increase consistency among reviewers, and guarantee validity, we conducted another pilot test of the data extraction form in a random sample of five studies, and a third reviewer confirmed content accuracy. Following the pilot test, information was extracted from all the included studies, which included: the first author; study design and location, which was described in accordance with the country of the population analyzed; year of publication; follow-up period, when applicable; the study population was characterized according to the sample size, sex, age range, and/or average age of participants, and recruitment method; definition of the exposition: extreme temperature or heatwave classification method; exposure data source; year of data collection; statistical approach used, in addition to specifications in relation to instruments, indicators, and methods to identify the outcome; outcome evaluated: anthropometric indicators (WAZ, HAZ, WHZ, BMI, WC, arm circumference); main results found: measures of association (OR, RR, differences in the average outcome values in the exposed and non-exposed groups, and the linear regression β coefficient, or correlation coefficient); and study limitations. The authors of the selected studies were contacted by email to provide incomplete data or for any clarification as to the metrics evaluated that were incomplete or missing.\u003c/p\u003e \u003cp\u003eThe risk of bias evaluation for each study included in the systematic review (except for the ecological studies, where no tool is available to analyze the risk of bias) was conducted using the tool developed by the Office of Health Assessment and Translation (OHAT)\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, and is designed specifically for environmental health research\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Cohort and cross-sectional studies were evaluated based on five categories (selection, confounding, exclusion/attrition, detection, selective reporting, and other sources of bias) which included seven questions (three classified as key criteria, and four as other criteria), with the following response options for each question: 1) definitively low risk of bias, 2) probably low risk of bias, 3) probably high risk of bias, and 4) definitively high risk of bias\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Two evaluators independently classified the studies, and then a consensus decision was achieved through discussion. Applying OHAT guidance, the publications were classified as Tier 1 (evaluated as ‘definitively’ or ‘probably low’ risk of bias in the three key criteria, and as ‘definitively’ or ‘probably low’ in the majority of the other criteria); Tier 3 (evaluated as ‘definitively’ or ‘probably high’ risk of bias in the three key criteria, and as ‘definitively’ or ‘probably high’ risk of bias in the majority of the other criteria); and Tier 2 (when the study does not meet the criteria for Tier 1 or Tier 3).\u003c/p\u003e\n\u003ch3\u003eMeta-analysis\u003c/h3\u003e\n\u003cp\u003eFor all studies included in this review, a narrative data summary was presented. For studies considered combinable\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, a quantitative data summary was conducted using meta-analysis. Only studies with a child population were combinable in terms of exposure [studies which used the average temperature (monthly or annual), maximum average temperature (monthly or yearly) or high temperatures] and outcome (WAZ, HAZ and WHZ indicators) and therefore a meta-analysis was conducted. Combinable studies were not found for the other age ranges and anthropometric indicators.\u003c/p\u003e \u003cp\u003eThe extent of the heterogeneity in the meta-analysis heterogeneity was tested using Cochran’s Q test and quantified by the inconsistency test (statistic I\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e). This statistic determines the magnitude of the heterogeneity by the proportion of the total variation between studies, due to heterogeneity\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. The p-value is frequently cited as an indication of the extent of variability in studies. Thus, we used the chi-squared test to evaluate the significance of the heterogeneity. Therefore, we adopted a p-value of \u0026lt; 0.05 as the significance level, with the aim of detecting the heterogeneity of the study results\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe used the \u003cem\u003emetan\u003c/em\u003e command for the meta-analyses, with the specification of two variables, assuming this to be the measure of effect (beta coefficient), and its respective standard errors, transformed into a logarithmic scale to stabilize the variances and standardize the distributions. The \u003cem\u003eeform\u003c/em\u003e option was specified to convert the summarised measure to the normal scale, improving its interpretability. The summary effect was calculated using random-effect models, applying the restricted maximum likelihood (REML) method\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eConsidering the limited number of studies included in the meta-analyses, we were not able to investigate the causes of heterogeneity in the studies, whether by subgroup analysis or meta-regression; nor was an analysis of the publication bias conducted through the funnel chart and Egger’s test\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe statistical analysis was conducted using STATA for MAC statistics software (Version 16.0, Stata Corp LP, College Station, Texas).\u003c/p\u003e \u003cp\u003eThe Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach was used to rate the certainty of the evidence\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. A Summary of Findings (SoF) table was prepared using the GRADEpro online software (GRADE Working Group, McMaster University)\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e "},{"header":"DESCRIPTION OF THE RESULTS","content":"\u003ch2\u003eStudy and population characteristics\u003c/h2\u003e\u003cp\u003eThe database search identified 5,512 published articles between 2012 and 2023. After removing 283 duplicates, 5,229 titles and abstracts were screened, with 29 articles read in full. A total of 19 studies were included in the systematic review (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOf the 19 studies, 14 evaluated the effects of temperature on anthropometric indicators of children under the age of 5 (n = 2,658,752)\u003csup\u003e4,5,8,26,27,28,29,30,31,32,33,34,35,36\u003c/sup\u003e; one on children and adolescents (n = 700,000)\u003csup\u003e37\u003c/sup\u003e; one on adolescents and adults (n = 12,509)\u003csup\u003e38\u003c/sup\u003e; one on adults only (n = 500,000)\u003csup\u003e39\u003c/sup\u003e; one on adults and the elderly (n = 20,990)\u003csup\u003e14\u003c/sup\u003e; and one on the elderly (n = 587)\u003csup\u003e40\u003c/sup\u003e, totaling 3,892,838 individuals. Almost all the studies used secondary data from demographic and health surveys (DHS), except one study, which was based on primary data\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e (Kanazawa, 2020) (Table\u0026nbsp;2).\u003c/p\u003e\u003cp\u003eRegarding study design, four were cohort studies; twelve were panel/cross-sectional studies, and three were ecological studies. Geographically, the majority of the studies were developed in countries on the African continent (n = 12), with four of these being multi-country studies, and one was in Japan [n = 1], one in China [n = 1], one in the United States [n = 1], and two in South Asia (one in Bangladesh and one in India, Nepal, Bangladesh, and Pakistan). A further two studies were multi-country, but the countries involved were not individually identified (Table\u0026nbsp;2).\u003c/p\u003e\u003ch3\u003eRISK OF BIAS\u003c/h3\u003e\u003cp\u003eWe evaluated 16 out of the 19 publications for risk of bias. Three were not evaluated, since they were ecological studies for which no instrument is available, but they have high risk of bias. Ten publications (62.5%) were classified as Tier 1, meaning they presented either a probably or definitively low risk of bias for all the key criteria of the instrument, as well as for the majority of the other questions. Six studies (37.5%) were classified as Tier 2, i.e., they did not present either a low or high risk of bias for all the key criteria (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). None of the studies were classified as Tier 3, that means the study presents a probably or definitively high risk of bias for all the key criteria and the majority of the other questions. The key questions of the instrument refer to: a) Is the exposure characterization trustworthy?; b) Is the outcome evaluation trustworthy?; and c) Did the study design or analysis take important confounding and modifying variables into consideration?\u003c/p\u003e\u003cp\u003eThe main problems presented by the studies refer to incomplete outcome data, with a loss of participants, or exclusion in the analysis (n = 14), inappropriate statistical analysis (n = 03); inadequate selection of study participants (n = 02); and study design or analysis which did not take important confounding and modifying variables into consideration (n = 02). The majority of the studies were well classified in relation to characterization of exposure, outcome, and presentation of the results for all outcomes evaluated (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003ch2\u003eEXPOSURE\u003c/h2\u003e\u003cp\u003eThe measurement of the ambient temperature varied considerably between studies, with some using distinct metrics within the same study. Most studies used mean temperature (daily, monthly, or annual) (n = 10), or maximum temperature (daily or monthly) (n = 4). One study used the El Nino 3.4 index (average monthly value of the index anomaly during the period between May and December); others adopted the concept of temperature anomalies, classified in distinct forms (n = 3); two used heatwaves, classified in different ways; and others adopted temperature categories (which also varied across studies) (n = 3). The temperature data were obtained from different sources, including weather forecasting centres, and air quality or meteorological monitoring stations (Table\u0026nbsp;2).\u003c/p\u003e\u003cp\u003eConsidering the exposure window, the studies also showed considerable variation. Four of them assessed the effect of temperature on the anthropometric status of their populations, considering one year of exposure (the year prior to the interview)\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Three studies evaluated the effect of temperature from prenatal exposure up to the first or second year of life\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Two studies examined the effect of temperature over the past 30 years on the anthropometric status of individuals\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Three studies assessed the effect of temperature over lifetime period\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. One study each considered the following exposure windows: temperature over the past 16 years\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, temperature over the past five years\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, from one year before birth to lifetime\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, from the month of conception to lifetime\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, from the two years prior to the interview\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, the temperature during the growing season\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, and in one case, this information was unclear (possibly the year prior to the interview)\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e (Table\u0026nbsp;2).\u003c/p\u003e\u003ch2\u003eOUTCOME\u003c/h2\u003e\u003cp\u003eThe majority of the studies focused on children (\u0026lt; 5 years old) evaluated the effects of high temperatures on the HAZ (n = 12), WAZ (n = 5), and WHZ (n = 6) anthropometric indicators (in a continuous and/or categorical form). BMI-for-age (n = 1) and arm circumference (n = 1) indicators were also evaluated. A study including children and adolescents investigated body weight\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e; and the one including adolescents and adults used the BMI\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. The two studies conducted with adults only used the BMI to assess the anthropometric status\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Lastly, a study conducted with elderly men evaluated the effect of high temperatures on the waist circumference\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e (Table\u0026nbsp;2).\u003c/p\u003e\u003ch2\u003eASSOCIATIONS BETWEEN TEMPERATURE AND ANTHROPOMETRIC INDICATORS\u003c/h2\u003e\u003ch2\u003eHEIGHT-FOR-AGE (HAZ)\u003c/h2\u003e\u003cp\u003eFor each study, the association between temperature and the anthropometric status is summarised in Table\u0026nbsp;2. Of the studies that investigated the effect of temperature on linear child growth and stunting, the majority were conducted in countries in the Sub-Saharan Africa [n = 11]. The results of the different studies presented great variation: while four studies suggest negative effects of high temperatures on growth indicators\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, others (n = 3) identified positive\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e or null (n = 4) effect results\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Blom, Ortiz-Bobea \u0026amp; Hoddinott (2022) identified that a 2ºC increase in the average temperature was associated with an increase in the prevalence of stunting from 4–7.4%\u003csup\u003e28\u003c/sup\u003e. In Ethiopia, it was observed that the increase of 1°C in temperature is associated with a 0.216 decrease in moderate stunting\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Randell, Gray \u0026amp; Grace (2020) found higher temperatures during pregnancy (in uterus), particularly during the first and third trimesters, to be positively associated with serious stunting. They also identified that higher temperatures from birth until the time of the evaluation were associated with a 0.104 reduction in standard deviations in HAZ\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. It was reported in Nigeria that a 1ºC increase in temperature was associated with a 16.7% increase in the probability of stunting, being more marked in the rural zone\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e (Table\u0026nbsp;2).\u003c/p\u003e\u003cp\u003eMcMahon \u0026amp; Gray (2021) identified that an additional unit of heat (or monthly anomaly generated from the average historical standard deviation in each location) decreased the probability of stunting by 3.4% (p = 0.078), and increased the HAZ by 2.7% (p = 0.05) when the heat occurred in the first and second year of life\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Rojas, Gray \u0026amp; West (2023) evaluated if exposure to anomalies in high temperatures affects the HAZ and observed that during the pre-natal period [0.037; p \u0026lt; 0.01], first [0.066; p \u0026lt; 0.01] and second year of life [0.028; p \u0026lt; 0.01] the high temperature led to a HAZ increase\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Tusting et al. (2020) showed that an average monthly daytime land surface temperature of over 35°C was associated with a reduction in stunting (OR: 0.90, 0.85–0.96; p = 0.00047), compared to a monthly average daytime land surface temperature of less than 30°C\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e (Table\u0026nbsp;2).\u003c/p\u003e\u003cp\u003eIn Uganda, Amondo, Nshakira‑Rukundo \u0026amp; Mirzabaev (2023) identified that the occurrence of heatwaves in the past year reduced the HAZ by 0.03, and had increased by 0.02 in the previous 5 years. However, these associations were not statistically significant\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Using anthropometric data on 192,000 children from 30 countries in Sub-Saharan Africa and climate data to directly estimate the effect of temperature on the main results of malnutrition, Baker \u0026amp; Anttila-Hughes (2020) did not observe the effect of temperatures on chronic measures of malnutrition, such as HAZ and stunting\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Based on nationally representative demographic and health research data on child malnutrition in four South Asian countries (Bangladesh, India, Nepal, and Pakistan), Davenport et al. (2017) indicated a modest negative effects of heating on the child growth deficit, with the decrease of a -0.01 standard deviation in HAZ, while not statistically significant\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. An increase in the HAZ indicator with the temperature rise [β=-0.0385; p \u0026gt; 0.05] was also verified in Kenya\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, but not statistically significant (Table\u0026nbsp;2).\u003c/p\u003e\u003ch2\u003eWEIGHT-FOR-AGE (WAZ)\u003c/h2\u003e\u003cp\u003eFive studies evaluated the effect of high temperatures on WAZ and underweight, all identifying an association between temperature (evaluated in different forms) and underweight\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. In investigating the effect of weather (variability in the average maximum monthly temperature close to the surface and total monthly rainfall) on stunting and underweight of Nigerian children, Merwe, Clance \u0026amp; Yitbarek (2022) observed an effect of temperature in underweight increase, with these results being more robust when adjusting for sociodemographic and regional characteristics\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. In Ethiopia, Hagos et al. (2014) observed that the increase of 1 standard deviation in temperature reduced the WAZ by 0.26 (p \u0026lt; 0.01)\u003csup\u003e31\u003c/sup\u003e. Anttila-Hughes, Jina \u0026amp; McCord (2021), estimated that a 1ºC increase in the ENSO (El Niño Southern Oscillation) index is associated with a 0.03 reduction in standard deviation (p = 0.02) in the WAZ average, and a 0.6% increase in the prevalence of underweight (p \u0026lt; 0.05)\u003csup\u003e27\u003c/sup\u003e. The results from the study conducted by Tusting et al. (2020) indicated that a monthly average surface temperature of over 35ºC is associated with an increase in the chance of underweight (1.09, 1.02–1.16; p = 0.0073), when compared with a monthly average surface temperature of under 30ºC\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Similarly, Baker \u0026amp; Anttila‑Hughes (2020) verified that the WAZ decreased appreciably with average temperatures over 25ºC in the year before the studied outcome\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. In Japan, Yokoya and Higuchi (2016) only evaluating the weight variable among children and adolescents, identified a significant negative correlation between the average maximum daily temperature and body weight in all ages\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e (Table\u0026nbsp;2).\u003c/p\u003e\u003ch2\u003eWEIGHT-FOR-HEIGHT (WHZ)\u003c/h2\u003e\u003cp\u003eSix studies included in this review investigated the influence of high temperatures on the WHZ indicator and/or wasting, with four identifying an inverse association, and two did not find any association (Table\u0026nbsp;2). Thiede and Strube (2020) evaluated this relationship, estimating the effect of temperature on the wasting status of children aged between 0–59 months of age in 16 Sub-Saharan African countries. They identified that an increase of 2 standard deviations above the average temperature in the past 12 months was associated with a 6.7% reduction in the WHZ mean, from − 0.252 to -0.269\u003csup\u003e5\u003c/sup\u003e. Similarly, Tusting et al. (2020) identified that average monthly surface temperatures of over 35ºC are associated with 27% higher chance of wasting (OR = 1.27; 95% CI = 1.16–1.38)\u003csup\u003e36\u003c/sup\u003e. Baker \u0026amp; Anttila‑Hughes (2020), in turn, identified that a 1ºC change in the annual average temperature leads to a decline of approximately 0.08 standard deviations in the WHZ\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Blom, Ortiz-Bobea \u0026amp; Hoddinott (2022) observed that a 2ºC increase in the average monthly temperature increases the wasting percentage from 4.1–6.2%\u003csup\u003e28\u003c/sup\u003e. On the other hand, Hagos et al. (2014) observed that high temperatures were not associated with wasting [β=-0.14, p \u0026gt; 0.005]\u003csup\u003e31\u003c/sup\u003e. Similarly, Anttila-Hughes, Jina \u0026amp; McCord (2021) did not identify an association between the 1ºC increase in the ENSO index and wasting\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e (Table\u0026nbsp;2).\u003c/p\u003e\u003ch2\u003eABDOMINAL OBESITY\u003c/h2\u003e\u003cp\u003eOnly one study evaluated the relationship between temperature and abdominal obesity. Wallwork et al. (2017) examined the long-term association of average daily temperature during the year prior to the visit and found no statistically significant association with the risk of metabolic syndrome (HR = 0.99, 95% CI: 0.82, 1.21; P = 0.95) or its components, including abdominal obesity (HR = 1.0; 95% CI: 0.86, 1.16; P = 1.00) among elderly people\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e (Table\u0026nbsp;2).\u003c/p\u003e\u003ch2\u003eARM CIRCUMFERENCE\u003c/h2\u003e\u003cp\u003eOne study evaluated the effect of temperature variability on the nutritional state of children aged between 0 and 3 in Bangladesh\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. They reported that temperatures between 25°C and 30ºC during the month of the child’s birth negatively affected their nutritional status, evaluated through the arm circumference (β = -1.533; p \u0026lt; 0.01). For temperatures above 30ºC during the month of the child’s birth, the negative effect on the arm circumference was even more pronounced (β = -2.154; p \u0026lt; 0.01) (Table\u0026nbsp;2).\u003c/p\u003e\u003ch2\u003eBODY MASS INDEX (BMI)\u003c/h2\u003e\u003cp\u003eMueller \u0026amp; Gray (2018) suggested that temperature anomalies increase the probability of underweight in individuals aged between 41 and 60 (F statistic = 0.006; p = 0.003), with the magnitude of the effect being even greater among those aged over 60 (F statistic = 0.011; p = 0.004) in China, between 1989–2011. Extremely hot, dry conditions produce an increase of 3.3 percentage points in the underweight status for the group aged over 60\u003csup\u003e14\u003c/sup\u003e (Table\u0026nbsp;2).\u003c/p\u003e\u003cp\u003eKanazawa (2020) analysed the effect of atmospheric temperature on BMI and obesity in the United States of America, using the National Longitudinal Study of Adolescent to Adult Health (Add Health) data. They showed that maximum daily temperature was positively associated with BMI (β = 0.036; p \u0026lt; 0.001), weight (β = 0.098; p \u0026lt; 0,001), overweight (β = 0.008; p \u0026lt; 0.001), and obesity (β = 0.011; p \u0026lt; 0.001)\u003csup\u003e38\u003c/sup\u003e (Table\u0026nbsp;2).\u003c/p\u003e\u003cp\u003eHuang \u0026amp; Hong (2024), evaluating the effect of temperature on obesity in 152 countries between 1975 and 2016 using a country-level aggregated data, identified that global warming is associated with an increase in obesity rates in countries located in temperate zones, while it is associated with a reduction in obesity prevalence in a small number of tropical countries. The estimates suggest that a 1°C increase in the annual average temperature is associated with an increase of 79.7\u0026nbsp;million obese adults (12.3%) globally\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e (Table\u0026nbsp;2).\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"9\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eTable\u0026nbsp;2. Summary of study characteristics included in the systematic review.\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAuthor\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCountry\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eStudy Design\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eSample Size\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eOutcomes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eExposure (Temperature)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eDuration of\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eExposure\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmondo, Nshakira‑Rukundo \u0026amp; Mirzabaev, 2023\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUganda\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePanel study\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChildren aged between 6 and 59 months\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,397\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHAZ\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHeatwave (monthly temperatures above 29°C (84.2°F).\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLast five years\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eHAZ\u003c/b\u003e: a heatwave in the main season and the last five years reduced calories, protein, zinc and vitamin A supply. A 10% decrease in zinc supply decreased HAZ by approximately 0.139–0.164 SD, and increased the probability of stunting, ranging from 3.1 to 3.5 percentage points.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnttila-Hughes, Jina \u0026amp; McCord, 2021\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51 countries (Not informed)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePanel study\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChildren aged under 4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,253,176\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWAZ\u003c/p\u003e \u003cp\u003eWHZ\u003c/p\u003e \u003cp\u003eBAZ\u003c/p\u003e \u003cp\u003eUnderweight\u003c/p\u003e \u003cp\u003eWasting\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNINO 3.4 index of equatorial Pacific sea surface temperature.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFrom May of one year to April of the next year\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eWAZ\u003c/b\u003e: a 1°C increase in the ENSO index is associated with 0.03σ (p = 0.02) average decrease in WAZ.\u003c/p\u003e \u003cp\u003e\u003cb\u003eWasting\u003c/b\u003e: the risk of wasting is similarly positive, but not significant (0.3 p.p./°C, p = 0.21).\u003c/p\u003e \u003cp\u003e\u003cb\u003eUnderweight\u003c/b\u003e: warmer ENSO increases the prevalence of being significantly underweight, by 0.6 percentage points per 1°C (p \u0026lt; 0.05).\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaker \u0026amp; Anttila-Hughes, 2020\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 Sub-Saharan African countries\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePanel study\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChildren aged between 1 and 5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e190,000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWHZ\u003c/p\u003e \u003cp\u003eHAZ\u003c/p\u003e \u003cp\u003eWAZ\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAverage monthly temperature\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAnnual and lifetime period\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eWHZ\u003c/b\u003e: a 1ºC change in annual temperature leads to an approximate 0.08𝜎 decline in WHZ (adding temperature effects across 12\u0026nbsp;months in the year). A lifetime average temperature from 25 to 30ºC is associated with an approximate 0.5σ decrease in WHZ.\u003c/p\u003e \u003cp\u003e\u003cb\u003eHAZ\u003c/b\u003e: effect of temperatures not found.\u003c/p\u003e \u003cp\u003e\u003cb\u003eWAZ\u003c/b\u003e: effect of temperatures not found.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlom, Ortiz-Bobea \u0026amp; Hoddinott, 2022\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBenin, Burkina Faso, the Ivory Coast, Ghana, and Togo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePanel study\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChildren aged between 3 and 36 months\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32,036\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHAZ\u003c/p\u003e \u003cp\u003eWHZ\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAverage hours per month over the exposure window bins: ≤25ºC, 25–30ºC, 30–35ºC, and \u0026gt; 35ºC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHAZ: lifetime exposure\u003c/p\u003e \u003cp\u003eWHZ: 90 days\u003c/p\u003e \u003cp\u003eprior to the interview date\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eHAZ\u003c/b\u003e: exposure to temperatures above 35ºC decreases HAZ, and increases the risk of stunting. HAZ decrease of 18% for each 100 h of exposure above 35ºC.\u003c/p\u003e \u003cp\u003e\u003cb\u003eWHZ\u003c/b\u003e: decrease by 0.10 SD per 100 h increase in average monthly exposure to temperatures between 30–35ºC\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDavenport et al., 2017\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 Sub-Saharan African countries\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePanel study\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChildren aged under 5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60,577\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHAZ\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNumber of days where the maximum daytime temperature exceeds 37.7°C\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOne year before birth, until the interview date\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eHAZ\u003c/b\u003e: there is a negative effect of warming on child stunting, but it could be mitigated by increasing mothers’ educational status and household access to electricity.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrace et al., 2012\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKenya\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChildren aged between 1 and 5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,255\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHAZ\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAverage temperature over the growing season, and\u003c/p\u003e \u003cp\u003ethe average of these values over the child’s life\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLifetime period\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eHAZ\u003c/b\u003e: temperature appears to have no significant impact on HAZ [ß= -0.0385; p \u0026gt; 0.05]\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHagos et al., 2014\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEthiopia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEcological study\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChildren aged under 5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e145\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHAZ (stunting)\u003c/p\u003e \u003cp\u003eWAZ (underweight)\u003c/p\u003e \u003cp\u003eWHZ (wasting)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAverage temperature over the growing season\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGrowing season\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eStunting\u003c/b\u003e: 1SD increase in temperature resulted in 0.216 SD\u003c/p\u003e \u003cp\u003edecrease in moderate stunting.\u003c/p\u003e \u003cp\u003e\u003cb\u003eUnderweight\u003c/b\u003e: 1SD increase in temperature resulted in 0.26 SD decrease in being severely underweight.\u003c/p\u003e \u003cp\u003e\u003cb\u003eWasting\u003c/b\u003e: no significant relationship\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAhmed Hanifi, Menon \u0026amp; Quisumbing, 2022\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBangladesh\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePanel study\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChildren aged between 0 and 3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19,357\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUpper arm circumference (MUAC)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e- Monthly average temperature\u003c/p\u003e \u003cp\u003e- Three temperature bins: 15–20ºC, 25–30ºC, and \u0026gt; 30ºC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMonth of conception\u003c/p\u003e \u003cp\u003eIn utero (by trimester)\u003c/p\u003e \u003cp\u003eMonth and year of birth\u003c/p\u003e \u003cp\u003eLifetime\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eMUAC\u003c/b\u003e: we found that temperatures that exceed 25ºC in the month and year of birth decrease the mid upper arm circumference (MUAC) by 1.476cm (p \u0026lt; 0.01), and an over 30ºC decrease in MUAC to 2.115cm (p \u0026lt; 0.05).\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHuang \u0026amp; Hong, 2024\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e152 countries\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEcological study\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdults\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e500,000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e- Annual average temperature\u003c/p\u003e \u003cp\u003e- Temperature bins\u003c/p\u003e \u003cp\u003e- Seasonal average temperature\u003c/p\u003e \u003cp\u003e- Temperature variation\u003c/p\u003e \u003cp\u003e- Temperature shocks\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e30 years\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e: in general, high temperatures have significantly increased obesity rates in countries located in temperate zones, while only causing a reduction in a small number of tropical countries. The estimates suggest that a 1ºC increase in the annual average temperature would result in a worldwide increase in obese adults of 79.7\u0026nbsp;million, or 12.3%.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKanazawa, 2020\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort study\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdolescents and adults\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12,509\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003cp\u003eObesity\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e- Average number of annual days with temperature higher than 90°F (32.2ºC)\u003c/p\u003e \u003cp\u003e- Average maximum daily temperature\u003c/p\u003e \u003cp\u003e- Average minimum daily temperature\u003c/p\u003e \u003cp\u003e- Average total annual hours of sunshine\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNot clear (one year before interview?)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e: high temperatures were significantly associated with a higher BMI, weight, being overweight, and obesity. They found that the daily maximum temperature was positively associated with BMI (β = 0.036; p \u0026lt; 0.001), measured weight (β = 0.098; p \u0026lt; 0.001), being \u003cb\u003eoverweight\u003c/b\u003e (β = 0.008; p \u0026lt; 0.001), and \u003cb\u003eobesity\u003c/b\u003e (β = 0.011; p \u0026lt; 0.001).\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMcMahon \u0026amp; Gray, 2021\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBangladesh, India, Nepal, and Pakistan\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePanel study\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChildren aged between 2 and 5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e222,572\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHAZ\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e- Monthly climate anomalies (using the historical average and historical standard deviation in each province)\u003c/p\u003e \u003cp\u003e− 9-month anomalies\u003c/p\u003e \u003cp\u003e− 12-month anomalies\u003c/p\u003e \u003cp\u003efor the first (0–11 months) and second (12–23) year of life\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDuring the prenatal period and first 2\u003c/p\u003e \u003cp\u003eyears of life\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eHAZ\u003c/b\u003e: One additional unit of heat decreases the likelihood of stunting by 3.4% (p = 0.078), and increases HAZ by 2.7% (p = 0.055) when the anomaly occurs during the first and second years, respectively.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMerwe, Clance \u0026amp; Yitbarek, 2022\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNigeria\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort study\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChildren under the age of 5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,511\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHAZ\u003c/p\u003e \u003cp\u003eWAZ\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMonthly average temperature\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFrom July (the year prior to the survey) to June, (year of the survey)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eHAZ\u003c/b\u003e: a one-unit (◦C) increase in temperature increases the\u003c/p\u003e \u003cp\u003eprobability of child stunting by between 18.6% and 22.3%\u003c/p\u003e \u003cp\u003e\u003cb\u003eWAZ\u003c/b\u003e: the probability of a child being underweight increases by between 7.9% and 15.2% with a one-unit (◦C) increase in temperature\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMueller \u0026amp; Gray, 2018\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort study\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdults and the elderly\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20,990\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTemperature anomalies (measured as standardized anomalies, or z-scores, defined as the temperature deviation during the calendar year of interview from the 1981–2010 average temperature, divided by the standard deviation in the temperature measured over the same period)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYear of the interview\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e: temperature anomalies increase the probability of being underweight in individuals aged between 41 and 60 (F statistic = 0.006; p = 0.003), and more significantly in those aged over 60 (F statistic = 0.011; p = 0.004). Extremely dry and hot conditions lead to a 3.3 percentage point increase in the underweight status for the group of those aged over 60.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRandell, Gray \u0026amp; Grace, 2020\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEthiopia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePanel study\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChildren aged between 1 and 5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23,026\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHAZ\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAverage maximum daily temperature (°C)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eChild’s prenatal period and early life (birth and through\u003c/p\u003e \u003cp\u003ethe time of the survey)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eHAZ\u003c/b\u003e: 1°C increase in average prenatal temperature is associated with a 16% (p \u0026lt; 0.01) and 28% (p \u0026lt; 0.001) increase in the odds of stunting and severe stunting, respectively.\u003c/p\u003e \u003cp\u003eAnd 1°C increase in average early life temperature is associated with a 13% (p \u0026lt; 0.01) and 23% (p \u0026lt; 0.001) increase in the odds of stunting and severe stunting, respectively.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRojas, Gray \u0026amp; West, 2023\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBurkina Faso\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePanel study\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChildren aged between 2 and 5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12,321\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHAZ\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTemperature anomalies (daily average temperature higher than the 95th percentile)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePrenatal period, the first and second year of life\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eHAZ\u003c/b\u003e: temperature anomalies in the first year were positively associated with HAZ (ß=0.066, p \u0026lt; 0.01). Temperature anomalies in the prenatal period and second year of life were not associated with HAZ.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThiede \u0026amp; Strube, 2020\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 Sub-Saharan African countries\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePanel study\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChildren under the age of 5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e182,272\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWHZ\u003c/p\u003e \u003cp\u003eWasting\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTemperature anomalies (average temperature observed for a given cluster for the 12 months prior to the survey, standardized over all consecutive 12-month periods in the entire climate history for that location)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTwo years\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e\u003ccolgroup cols=\"1\"\u003e\u003c/colgroup\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWHZ\u003c/b\u003e: an increase in temperatures from average to two standard deviations above average is associated with a 6.7% reduction in predicted WHZ, from 0.252 to 0.269 (p \u0026lt; 0.05).\u003c/p\u003e \u003cp\u003e\u003cb\u003eWasting\u003c/b\u003e: the predicted probability of wasting increases by approximately 8.1 percent (from 9.9 to 10.7%) as temperatures increase by two standard deviations over the baseline average (p \u0026lt; 0.01).\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTusting et al., 2020\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 Sub-Saharan African countries\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePanel study\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChildren under the age of 5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e656,107\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHAZ (stunting)\u003c/p\u003e \u003cp\u003eWHZ (wasting)\u003c/p\u003e \u003cp\u003eWAZ (underweight)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMonthly average daytime LST classified in three temperature bins: 30°C, 30°C to \u0026lt; 35°C, and ≥ 35°C\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eBetween 2000 and 2016\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eStunting\u003c/b\u003e: odds of stunting were 10% lower among children\u003c/p\u003e \u003cp\u003eliving where the monthly average daytime LST exceeded 35°C, compared with those where monthly average daytime LST was less than 30°C (OR: 0.90, 95% CI 0.85–0.96).\u003c/p\u003e \u003cp\u003e\u003cb\u003eWasting\u003c/b\u003e: the odds of wasting were 27% higher among children living where the monthly average daytime LST exceeded 35°C (OR: 1.27, 95% CI 1.16–1.38).\u003c/p\u003e \u003cp\u003e\u003cb\u003eUnderweight\u003c/b\u003e: the odds of being underweight were 9% higher among children living where the monthly average daytime LST exceeded 35°C (OR: 1.09, 95% CI 1.02–1.16).\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWallwork et al., 2017\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort study\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eElderly men\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e587\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWaist circumference (WC)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAverage daily temperature\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eThe year before each visit (2- to 5- year average)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eWC\u003c/b\u003e: temperature was not significantly associated with both the risk of abdominal obesity (HR = 1.06, 95% CI: 0.86, 1.31 0.58) and metabolic syndrome as a whole (HR = 0.99, 95% CI: 0.82, 1.21)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYokoya \u0026amp; Higuchi, 2016\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJapan\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEcological study\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChildren and adolescents (aged between 5 and 17)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47 prefectures\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWeight\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAverage annual temperature (ºC), average daily maximum temperature in August (ºC), and average daily minimum\u003c/p\u003e \u003cp\u003etemperature in January (ºC)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e30 year period\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eWeight\u003c/b\u003e: height and the mean maximum daily temperature in August were significant predictors of weight in most age groups. A negative correlation between the average maximum daily temperature in August and weight with highly significant regression coefficients was observed for most ages.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003ch2\u003eMETA-ANALYSIS\u003c/h2\u003e\u003cp\u003eThe meta-analysis results are presented in Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e to 6. We only conducted meta-analyses with studies that were combinable in terms of exposure (i.e., studies that used average temperature - monthly or yearly, or average maximum temperature -monthly or yearly), population (children) and outcome (HAZ, WAZ, and WHZ indicators). When evaluating the effect of average temperature (monthly or yearly) on the HAZ indicator, we identified a 0.02σ reduction in the z-score average of this indicator for every 1ºC increase in average temperature (β=-0.02; 95% CI: -0.04; -0.01, n = 2 studies) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). When analyzing the effect of the average maximum temperature (monthly or annual) on HAZ, no statistically significant association was found (β=-0.31; 95% CI: -1.06; 0.44, n = 3 studies) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFor the WAZ, no statistically significant association between the maximum average temperature and this indicator (β=-0.80; 95% CI: -2.52; 0.91, n = 2 studies) was identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Regarding the WHZ, we observed a 0.06σ reduction in the z-score average for this indicator for every 1ºC increase in the average temperature (monthly or annual) (β=-0.06; 95% CI: -0.09; -0.04, n = 2 studies) (Fig.\u0026nbsp;6).\u003c/p\u003e\u003cp\u003eThe Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach was applied to the outcomes HAZ, WAZ, WHZ, BMI, WC, arm circumference, and BMI (Table\u0026nbsp;03). All outcomes started with low certainty, as they were based on observational studies. With the exception of arm circumference, all outcomes underwent at least one downgrade and were classified as having very low certainty, according to GRADE criteria. The domains with the most significant issues were inconsistency (substantial variability among results) and imprecision (uncertain effects and/or small sample sizes).\u003c/p\u003e\u003cp\u003e \u003cb\u003eTabela 03\u003c/b\u003e - Resumo da Classificação de Recomendações Avaliação, Desenvolvimento e Avaliação (GRADE).\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003ctable float=\"No\" id=\"Tabc\" border=\"1\"\u003e\u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eCertainty assessment\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCertainty\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNº of Studies\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStudy design\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRisk of bias\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInconsistency\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIndirectness\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eImprecision\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePublication Bias\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eHAZ\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObservational studies\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNot serious\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSerious\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNot serious\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNot serious\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNot serious\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e⨁⨁◯◯\u003c/p\u003e \u003cp\u003eVery low\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWAZ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObservational studies\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNot serious\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSerious\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNot serious\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNot serious\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNot serious\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e⨁⨁◯◯\u003c/p\u003e \u003cp\u003eVery low\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWHZ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e06\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObservational studies\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNot serious\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNot serious\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNot serious\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNot serious\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e⨁⨁◯◯\u003c/p\u003e \u003cp\u003eVery low\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObservational studies\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNot serious\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSerious\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNot serious\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSerious\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNot serious\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e⨁⨁⨁◯\u003c/p\u003e \u003cp\u003eVery low\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObservational studies\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNot serious\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot serious\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNot serious\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSerious\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNot serious\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e⨁⨁◯◯\u003c/p\u003e \u003cp\u003eVery low\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eArm Circumference\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObservational studies\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNot serious\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot serious\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNot serious\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNot serious\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNot serious\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e⨁◯◯◯\u003c/p\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003ea. Meta-analyses showed high heterogeneity.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn the current systematic review with meta-analysis, we investigated the effect of high ambient temperatures on anthropometric indicators in various age groups, without any geographical or time limitations. We found that the majority of studies included in this review originated from Sub-Saharan African countries and focused on children under the age of 5. The included studies presented mixed results for the HAZ indicator. For the WHZ and WAZ indicators, the review results showed an almost unanimous inverse association with high temperatures, showing a reduction in WHZ and WAZ mean or an increased probability of wasting and being underweight with rising temperatures. Divergent results were observed in elderly adults, with some studies showing a weight reduction, while others indicated an increase in obesity with temperature increases. In the meta-analysis of combinable studies, we estimated a reduction in the Z-score average for the HAZ and WHZ indicators for every one degree increase in the average temperature (monthly or yearly). Although the results were statistically significant, the strength of these associations was weak, with a 0.02 reduction in the HAZ average and 0.06 reduction in the WHZ average. A significant association was not identified for the WAZ indicator.\u003c/p\u003e \u003cp\u003eOur review provides evidence of the effect of high ambient temperatures on both the classical measures of acute (WHZ) and chronic (HAZ) child malnutrition. We highlight that the mechanisms through which high temperatures affect chronic malnutrition may not be the same as those which affect acute malnutrition. Chronic and acute malnutrition are consequences of types of nutritional challenges. Chronic malnutrition reflects prolonged periods of insufficient nutrient intake, with permanent consequences for growth, while acute malnutrition reflects shorter periods of insufficient food intake from which children can recover if the food intake improves. For example, chronic malnutrition could be caused by long-term insufficient calorie intake, as a result of heat-induced crop failure. In contrast, acute malnutrition could result from heat-induced food spoilage, causing short-term illness\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe effect of temperature on the metabolic syndrome and abdominal obesity in the elderly was evaluated in one study only. Considering the vulnerability of elderly individuals, the need to evaluate the effect of temperature changes on malnutrition is relevant, since it is a frequent nutritional disorder in this population\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Only three studies were conducted to evaluate the effect of high temperatures on the BMI of adult individuals. One of these identified that temperature anomalies increase the probability of low weight\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, while a further two observed an increase in BMI for being overweight and obesity for every one degree increase in the maximum daily temperature\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e and a significant increase in obesity rates, with a one degree increase in the average annual temperature\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. A number of authors suggest that increases in temperature may influence income\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, the price of food\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, preferences with food consumption\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e and levels of physical activity\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, particularly in low- and middle-income countries. In addition, changes in working hours, levels of physical activity, and minimum calorie requirements may also influence calorie intake. Any alteration in calorie intake and expenditure naturally has an impact on obesity. Obesity is one of the greatest global health challenges in recent decades. Thus, knowing the effects of the rising temperature increase on this condition is essential for policymakers and the general population.\u003c/p\u003e \u003cp\u003eWe emphasize that in the 16 studies evaluated for risk of bias, the majority presented a good classification, and none were classified as Tier 3, which indicates a probably or definitively high risk of bias. We also highlight that the majority used Demographic and Health Survey (DHS) data, which covers large samples representative of the countries\u0026rsquo; populations; they involved appropriate study designs to assess associations of interest, robust statistical analysis, accounting for confounding and effect modification, and the exposure and outcome measured appropriately. These results indicate the good quality of the studies that explore the association between high temperatures and the anthropometric status of the general population, making the findings of this review consistent and trustworthy. However, we should also highlight the great heterogeneity identified in the methods of measuring the exposure (temperature) in the different studies. Since there are no gold standards, each study adopted distinct measures, which made comparison difficult, indicating that the body of evidence is still limited and inconsistent for exposure. Thus, the development of studies which seek to define standards of temperature when used as an exposure is urgently required.\u003c/p\u003e \u003cp\u003eThis systematic review and meta-analysis provide a comprehensive summary of the effects of high ambient temperature on malnutrition, including studies which use demographic and national health surveys with large sample sizes and from various countries around the world, with large sample sizes, which increase the accuracy and representativeness of the results and relevance of the findings. However, we must emphasize several limitations regarding the geographic scope and meteorological variables, which affect generalization. Firstly, most of the studies originated in Africa, with other tropical regions of South America and Asia being under-represented, limiting the application of the findings to these regions. Another important limitation is that the majority of studies included in the review did not address issues of attribution and detection of causal pathways, which would help to identify and understand the underlying mechanisms of this phenomenon.\u003c/p\u003e \u003cp\u003eIn addition, the great heterogeneity in the studies with regards to the form of assessing the meteorological exposure variable (high temperatures) prevented the performance of a more robust meta-analysis, with a higher number of studies included and more diversified outcomes, as well as subgroup analyses, as intended in the protocol for this review. However, despite the limitations, the meta-analysis performed here provides valuable insights into the relationship between temperature and nutrition, suggesting that elevated temperatures may negatively affect the growth and nutritional status of populations. Therefore, public health strategies aimed at climate adaptation should be prioritized, particularly in tropical and low- and middle-income countries, where vulnerability is greater. From a policy perspective, integrating climate resilience into nutritional and child health programs may help mitigate the adverse effects of rising temperatures.\u003c/p\u003e \u003cp\u003eFuture studies are needed to better assess the effects of elevated ambient temperature on nutritional outcomes in the general population. The observed inconsistencies in HAZ outcomes, combined with limitations related to age range, geographic distribution, and high heterogeneity in temperature estimation methods, reveal significant gaps in the current body of evidence. Both short- and long-term effects should be investigated across different contexts and population groups, including the identification of potential mediators and effect modifiers of this association, such as droughts and heavy rainfall, to better understand the pathways of these relationships.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe findings of this review highlight the need for further research on the impact of high ambient temperatures on anthropometric indicators. The inconsistencies observed in HAZ results, along with limitations in age range, geographical coverage, and high heterogeneity in temperature estimation, underscore the gaps in the current literature. A reduction in the Z-score for the HAZ and WHZ indicators for every 1\u0026deg;C increase was identified in the meta-analyses. In practical terms, these findings suggest that high temperatures may harm the anthropometric status of populations, and actions and strategies for adapting to climate changes, in order to minimise their effects should be prioritised, particularly in tropical and low- and middle-income countries, seeking to protect the health of their populations. In addition, given the limitations of the studies included in this review, robust future studies that assess how temperature increases affect obesity and malnutrition rates of the general population are required. These studies will be relevant to scientific knowledge and to formulate targeted public adaptation policies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthical approval\u003c/h2\u003e \u003cp\u003eEthical approval was not required since this study does not involve human participants.\u003c/p\u003e \u003cp\u003e\u003cstrong\u003eDeclaration of generative AI and AI assisted technologies during the writing process\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors did not use any generative AI or AI assisted technologies during the preparation of this work.\u003c/p\u003e\u003ch2\u003eConsent for publication\u003c/h2\u003e \u003cp\u003eConsent was not required since this study does not involve human participants.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eDeclaration of conflicts of interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll other authors declare they have no conflicts of interest related to this work to disclose.\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was funded by CNPQ/DECIT/SECTICS/MS N\u0026ordm;18/2023 - Ci\u0026ecirc;ncia de dados: mudan\u0026ccedil;as clim\u0026aacute;ticas e impactos para a sa\u0026uacute;de [444383/2023-9]. E.S.P. is funded by the Wellcome Trust 225925/Z/22/Z. The study also received support from MS-SCTIE-Decit/CNPq [25000.148278/2022-10]. OTR is funded by the Ram\u0026oacute;n y Cajal program (RYC2023-002923-C) awarded by the Spanish Ministry of Science, Innovation and Universities. The funders did not play any role in the study design or collection, data analysis, and interpretation, writing of the study, or decision to submit the article for publication.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003ePRFC made substantial contributions to the conception of the work; collection, analysis, and interpretation of data; manuscript writing; reviewed and approved the submitted version.RCRS made substantial contributions to the conception of the work; collection, analysis, and interpretation of data; manuscript writing; reviewed and approved the submitted version.ROS made substantial contributions to the collection, analysis, and interpretation of data; manuscript writing; reviewed and approved the submitted version.KBBSM made substantial contributions to the collection, analysis, and interpretation of data; manuscript writing; reviewed and approved the submitted version.ESC made substantial contributions to the collection, analysis, and interpretation of data; manuscript writing; reviewed and approved the submitted version.ASR made substantial contributions to the collection, analysis, and interpretation of data; manuscript writing; reviewed and approved the submitted version.LPL made substantial contributions to the collection, analysis, and interpretation of data; manuscript writing; reviewed and approved the submitted version.LHLR made substantial contributions to the collection, analysis, and interpretation of data; manuscript writing; reviewed and approved the submitted version.OTR made substantial contributions to the conception of the work; collection, analysis, and interpretation of data; manuscript writing; reviewed and approved the submitted version.IS made substantial contributions to the conception of the work; collection, analysis, and interpretation of data; manuscript writing; reviewed and approved the submitted version.ESP made substantial contributions to the conception of the work; collection, analysis, and interpretation of data; manuscript writing; reviewed and approved the submitted version.MP made substantial contributions to the conception of the work; collection, analysis, and interpretation of data; manuscript writing; reviewed and approved the submitted version.MLB made substantial contributions to the conception of the work; collection, analysis, and interpretation of data; manuscript writing; reviewed and approved the submitted version.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe dataset supporting the findings of this meta-analysis is not publicly available due to restrictions related to copyright and licensing agreements of the original studies included. The data were extracted from published articles that are protected by third-party rights, and sharing the full dataset may violate those terms. Additionally, some studies did not provide raw data and required individual calculations based on figures or summary tables, limiting reproducibility outside the context of this review. However, detailed extraction procedures and summary data used in the analyses can be provided upon reasonable request to the corresponding author PRFC ([email protected]).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePhalkey RK, Aranda-Jan C, Marx S, H\u0026ouml;fle B, Sauerborn R. Systematic review of current efforts to quantify the impacts of climate change on undernutrition. Proc Natl Acad Sci U S A. 2015 Aug 18;112(33):E4522-9. doi: 10.1073/pnas.1409769112. Epub 2015 Jul 27. PMID: 26216952; PMCID: PMC4547305.\u003c/li\u003e\n\u003cli\u003eBlack RE, Allen LH, Bhutta ZA, Caulfield LE, de Onis M, Ezzati M, Mathers C, Rivera J; Maternal and Child Undernutrition Study Group. Maternal and child undernutrition: global and regional exposures and health consequences. Lancet. 2008 Jan 19;371(9608):243-60. doi: 10.1016/S0140-6736(07)61690-0. PMID: 18207566.\u003c/li\u003e\n\u003cli\u003eSchaible UE, Kaufmann SH. Malnutrition and infection: complex mechanisms and global impacts. PLoS Med. 2007 May;4(5):e115. doi: 10.1371/journal.pmed.0040115. PMID: 17472433; PMCID: PMC1858706.\u003c/li\u003e\n\u003cli\u003eRandell H, Gray C, Grace K. Stunted from the start: Early life weather conditions and child undernutrition in Ethiopia. Soc Sci Med. 2020 Sep;261:113234. doi: 10.1016/j.socscimed.2020.113234. Epub 2020 Jul 23. PMID: 32823214; PMCID: PMC7716344.\u003c/li\u003e\n\u003cli\u003eThiede BC, Strube J. Climate Variability and Child Nutrition: Findings from Sub-Saharan Africa. Glob Environ Change. 2020 Nov;65:102192. doi: 10.1016/j.gloenvcha.2020.102192. Epub 2020 Nov 26. PMID: 34789965; PMCID: PMC8594912.\u003c/li\u003e\n\u003cli\u003eHelld\u0026eacute;n D, Andersson C, Nilsson M, Ebi KL, Friberg P, Alfv\u0026eacute;n T. Climate change and child health: a scoping review and an expanded conceptual framework. Lancet Planet Health. 2021 Mar;5(3):e164-e175. doi: 10.1016/S2542-5196(20)30274-6. PMID: 33713617.\u003c/li\u003e\n\u003cli\u003eSmith CJ. Pediatric Thermoregulation: Considerations in the Face of Global Climate Change. Nutrients. 2019 Aug 26;11(9):2010. doi: 10.3390/nu11092010. PMID: 31454933; PMCID: PMC6770410.\u003c/li\u003e\n\u003cli\u003eBaker RE, Anttila-Hughes J. Characterizing the contribution of high temperatures to child undernourishment in Sub-Saharan Africa. Sci Rep. 2020 Nov 2;10(1):18796. doi: 10.1038/s41598-020-74942-9. PMID: 33139856; PMCID: PMC7606522.\u003c/li\u003e\n\u003cli\u003eZhao C, Liu B, Piao S, Wang X, Lobell DB, Huang Y, Huang M, Yao Y, Bassu S, Ciais P, Durand JL, Elliott J, Ewert F, Janssens IA, Li T, Lin E, Liu Q, Martre P, M\u0026uuml;ller C, Peng S, Pe\u0026ntilde;uelas J, Ruane AC, Wallach D, Wang T, Wu D, Liu Z, Zhu Y, Zhu Z, Asseng S. Temperature increase reduces global yields of major crops in four independent estimates. Proc Natl Acad Sci U S A. 2017 Aug 29;114(35):9326-9331. doi: 10.1073/pnas.1701762114. Epub 2017 Aug 15. PMID: 28811375; PMCID: PMC5584412.\u003c/li\u003e\n\u003cli\u003eKunitski M, Eicke N, Huber P, K\u0026ouml;hler J, Zeller S, Voigtsberger J, Schlott N, Henrichs K, Sann H, Trinter F, Schmidt LPH, Kalinin A, Sch\u0026ouml;ffler MS, Jahnke T, Lein M, D\u0026ouml;rner R. Double-slit photoelectron interference in strong-field ionization of the neon dimer. Nat Commun. 2019 Jan 2;10(1):1. doi: 10.1038/s41467-018-07882-8. PMID: 30602773; PMCID: PMC6315036.\u003c/li\u003e\n\u003cli\u003eLevy K, Smith SM, Carlton EJ. Climate Change Impacts on Waterborne Diseases: Moving Toward Designing Interventions. Curr Environ Health Rep. 2018 Jun;5(2):272-282. doi: 10.1007/s40572-018-0199-7. PMID: 29721700; PMCID: PMC6119235.\u003c/li\u003e\n\u003cli\u003eHsiang SM, Burke M, Miguel E. Quantifying the influence of climate on human conflict. Science. 2013 Sep 13;341(6151):1235367. doi: 10.1126/science.1235367. Epub 2013 Aug 1. PMID: 24031020.\u003c/li\u003e\n\u003cli\u003eBurke M, Hsiang S, Miguel E. Global non-linear effect of temperature on economic production. Nature 527, 235\u0026ndash;239 (2015). https://doi.org/10.1038/nature15725\u003c/li\u003e\n\u003cli\u003eMueller V, Gray C. Heat and Adult Health in China. Popul Environ. 2018 Sep;40(1):1-26. doi: 10.1007/s11111-018-0294-6. Epub 2018 Apr 14. PMID: 30349149; PMCID: PMC6195320.\u003c/li\u003e\n\u003cli\u003eHuang, K., \u0026amp; Sim, N. (2020). Adaptation may reduce climate damage in agriculture by two thirds. \u003cem\u003eAgricultural Economics, 51\u003c/em\u003e(3), 289\u0026ndash;300. https://doi.org/10.1111/1477-9552.12389\u003c/li\u003e\n\u003cli\u003eWheeler T, von Braun J (2013). Climate change impacts on global food security. Science, 341(6145), 508\u0026ndash;513. https://doi.org/10.1126/science.1239402\u003c/li\u003e\n\u003cli\u003eScheelbeek PFD, Moss C, Kastner T et al. United Kingdom\u0026rsquo;s fruit and vegetable supply is increasingly dependent on imports from climate-vulnerable producing countries. Nat Food 1, 705\u0026ndash;712 (2020). https://doi.org/10.1038/s43016-020-00179-4\u003c/li\u003e\n\u003cli\u003eObradovich N, Fowler J. Climate change may alter human physical activity patterns. Nat Hum Behav 1, 0097 (2017). https://doi.org/10.1038/s41562-017-0097\u003c/li\u003e\n\u003cli\u003eHiggins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, Welch VA (editors). Cochrane Handbook for Systematic Reviews of Interventions version 6.5 (updated August 2024). Cochrane, 2024. Available from www.training.cochrane.org/handbook.\u003c/li\u003e\n\u003cli\u003ePage MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Shamseer L, Tetzlaff JM, Akl EA, Brennan SE, Chou R, Glanville J, Grimshaw JM, Hr\u0026oacute;bjartsson A, Lalu MM, Li T, Loder EW, Mayo-Wilson E, McDonald S, McGuinness LA, Stewart LA, Thomas J, Tricco AC, Welch VA, Whiting P, Moher D. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021 Mar 29;372:n71. doi: 10.1136/bmj.n71. PMID: 33782057; PMCID: PMC8005924.\u003c/li\u003e\n\u003cli\u003eNational Toxicology Program. (2019). Handbook for conducting a literature-based health assessment using OHAT approach for systematic review and evidence integration (p. 96). Office of Health Assessment and Translation (OHAT), Division of the National Toxicology Program, National Institute of Environmental Health Sciences. https://ntp.niehs.nih.gov/sites/default/files/ntp/ohat/pubs/handbookmarch2019_508.pdf\u003c/li\u003e\n\u003cli\u003eMichel SKF, Atmakuri A, von Ehrenstein O S (2024). Systems for rating bodies of evidence used in systematic reviews of air pollution exposure and reproductive and children\u0026rsquo;s health: A methodological survey. Environmental Health, 23, Article 32. https://doi.org/10.1186/s12940-024-01069-z\u003c/li\u003e\n\u003cli\u003eDeeks JJ, Higgins JPT, Altman DG, eds. Analyzing data and undertaking meta-analyses. In: Higgins JPT, Thomas J, Chandler J, et al. eds. Cochrane Handbook for Systematic Reviews of Interventions version 6.0 (updated July 2019). Barcelona: Centro Cochrane Iberoamericano; 2019.\u003c/li\u003e\n\u003cli\u003eGuyatt GH, Oxman AD, Sch\u0026euro;Unemann HJ, Tugwell P, Knottnerus A. GRADE guidelines: A new series of articles in the Journal of Clinical Epidemiology. Journal of Clinical Epidemiology 64 (2011) 380e382. https://doi:10.1016/j.jclinepi.2010.09.011 \u003c/li\u003e\n\u003cli\u003eManheimer E. Summary of findings tables: presenting the main findings of Cochrane complementary and alternative medicine-related reviews in a transparent and simple tabular format. Glob Adv Health Med. 2012 Mar;1(1):90-1.b\u003c/li\u003e\n\u003cli\u003eAmondo E I, Nshakira-Rukundo E, Mirzabaev A (2023). The effect of extreme weather events on child nutrition and health. Food Security, 15(3), 571\u0026ndash;596. https://doi.org/10.1007/s12571-023-01354-8 \u003c/li\u003e\n\u003cli\u003eAnttila-Hughes JK, Jina AS, McCord GC. ENSO impacts child undernutrition in the global tropics. Nat Commun. 2021 Oct 12;12(1):5785. doi: 10.1038/s41467-021-26048-7. PMID: 34642319; PMCID: PMC8511020.\u003c/li\u003e\n\u003cli\u003eBlom S, Ortiz-Bobea A, \u0026amp; Hoddinott J (2022). Heat exposure and child nutrition: Evidence from West Africa. Journal of Environmental Economics and Management, 115, 102698. https://doi.org/10.1016/j.jeem.2022.102698 \u003c/li\u003e\n\u003cli\u003eDavenport, F., Grace, K., Funk, C., \u0026amp; Shukla, S. (2017). Child health outcomes in sub-Saharan Africa: A comparison of changes in climate and socio-economic factors. Global Environmental Change, 46, 72\u0026ndash;87. https://doi.org/10.1016/j.gloenvcha.2017.06.002 \u003c/li\u003e\n\u003cli\u003eGrace, K., Davenport, F., Funk, C., \u0026amp; Lerner, A. M. (2012). Child malnutrition and climate in Sub-Saharan Africa: An analysis of recent trends in Kenya. Applied Geography, 35, 405\u0026ndash;413. https://doi.org/10.1016/j.apgeog.2012.02.003 \u003c/li\u003e\n\u003cli\u003eHagos S, Lunde T, Mariam DH, Woldehanna T, Lindtj\u0026oslash;rn B. Climate change, crop production and child under nutrition in Ethiopia; a longitudinal panel study. BMC Public Health. 2014 Aug 27;14:884. doi: 10.1186/1471-2458-14-884. PMID: 25163522; PMCID: PMC4158109.\u003c/li\u003e\n\u003cli\u003eAhmed Hanifi SMM, Menon N, Quisumbing A. The impact of climate change on children\u0026apos;s nutritional status in coastal Bangladesh. Soc Sci Med. 2022 Feb;294:114704. doi: 10.1016/j.socscimed.2022.114704. Epub 2022 Jan 10. PMID: 35030394.\u003c/li\u003e\n\u003cli\u003eMcMahon K, Gray C. Climate change, social vulnerability and child nutrition in South Asia. Glob Environ Change. 2021 Nov;71:102414. doi: 10.1016/j.gloenvcha.2021.102414. Epub 2021 Nov 13. PMID: 34898861; PMCID: PMC8653856.\u003c/li\u003e\n\u003cli\u003eMerwe E van der, Clance M, Yitbarek E (2022). Climate change and child malnutrition: A Nigerian perspective. Food Policy, 113, 102281. https://doi.org/10.1016/j.foodpol.2022.102281 \u003c/li\u003e\n\u003cli\u003eRojas AJ Jr, Gray CL, West CT. \u0026quot;Measuring the Environmental Context of Child Growth in Burkina Faso\u0026quot;. Popul Environ. 2023 Jun;45(2):3. doi: 10.1007/s11111-023-00414-7. Epub 2023 Mar 24. PMID: 37274602; PMCID: PMC10237046.\u003c/li\u003e\n\u003cli\u003eTusting LS, Bradley J, Bhatt S, Gibson HS, Weiss DJ, Shenton FC, Lindsay SW. Environmental temperature and growth faltering in African children: a cross-sectional study. Lancet Planet Health. 2020 Mar;4(3):e116-e123. doi: 10.1016/S2542-5196(20)30037-1. PMID: 32220673; PMCID: PMC7232952.\u003c/li\u003e\n\u003cli\u003eYokoya M, Higuchi Y. Association between summer temperature and body weight in Japanese adolescents and children: An ecological analysis. Am J Hum Biol. 2016 Nov;28(6):789-795. doi: 10.1002/ajhb.22867. Epub 2016 May 25. PMID: 27224001.\u003c/li\u003e\n\u003cli\u003eKanazawa S. Does global warming contribute to the obesity epidemic? Environ Res. 2020 Mar;182:108962. doi: 10.1016/j.envres.2019.108962. Epub 2019 Dec 6. PMID: 31862545.\u003c/li\u003e\n\u003cli\u003eHuang, K., \u0026amp; Hong, Q. (2024). The impact of global warming on obesity. Journal of Population Economics, 37(1), 59. https://doi.org/10.1007/s00148-024-01039-2 \u003c/li\u003e\n\u003cli\u003eWallwork RS, Colicino E, Zhong J, Kloog I, Coull BA, Vokonas P, Schwartz JD, Baccarelli AA. Ambient Fine Particulate Matter, Outdoor Temperature, and Risk of Metabolic Syndrome. Am J Epidemiol. 2017 Jan 1;185(1):30-39. doi: 10.1093/aje/kww157. Epub 2016 Dec 7. PMID: 27927620; PMCID: PMC5209587.\u003c/li\u003e\n\u003cli\u003eCorcoran C, Murphy C, Culligan EP, Walton J, Sleator RD. Malnutrition in the elderly. Sci Prog. 2019 Jun;102(2):171-180. doi: 10.1177/0036850419854290. PMID: 31829839; PMCID: PMC10424533.\u003c/li\u003e\n\u003cli\u003eF\u0026aacute;varo-Moreira NC, Krausch-Hofmann S, Matthys C, Vereecken C, Vanhauwaert E, Declercq A, Bekkering GE, Duyck J. Risk Factors for Malnutrition in Older Adults: A Systematic Review of the Literature Based on Longitudinal Data. Adv Nutr. 2016 May 16;7(3):507-22. doi: 10.3945/an.115.011254. PMID: 27184278; PMCID: PMC4863272.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"ambient temperature, anthropometric status, climate changes, nutritional status","lastPublishedDoi":"10.21203/rs.3.rs-6404122/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6404122/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: The effect of high temperatures and heatwaves on several health outcomes is well known, but there is a knowledge in gap about their effects on nutritional status. This systematic review aims to synthesise research on the association between high temperatures and anthropometric indicators.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethodology\u003c/strong\u003e: A systematic review and meta-analysis was conducted and the protocol registered in PROSPERO (CRD42024555573). The search included relevant databases and was conducted in October 2024, using terms for “high temperatures”, “heatwaves”, and \"anthropometric indicators\". Data were extracted and qualitative and quantitative synthesis were performed.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Nineteen studies were included, encompassing 3,892,838 participants, predominantly children under the age of 5, mainly from African countries. The studies presented inconsistent results, although most identified inverse relationship between high temperatures and anthropometric indicators. In adults, increased temperatures were associated with elevated risk of both underweight and obesity. In children, the meta-analysis revealed significant reduction of 0.06σ in the Z-score of the Weight-for-Height and 0.02σ in the Z-score of the Height-for-Age indicators for every 1°C increase in average temperature. The observed associations were modest, but with important implications for public health, considering the high proportion of population exposed to the climate changes. Further studies addressing this topic are necessary for a better understanding.\u003c/p\u003e","manuscriptTitle":"High ambient temperatures effects on the anthropometric status of the population: a systematic review and meta-analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-02 09:18:41","doi":"10.21203/rs.3.rs-6404122/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f5b9c426-cc32-4bd7-8e49-efd7700df3e4","owner":[],"postedDate":"July 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":50604791,"name":"Health sciences/Diseases/Nutrition disorders"},{"id":50604792,"name":"Health sciences/Risk factors"},{"id":50604793,"name":"Earth and environmental sciences/Climate sciences/Climate change"}],"tags":[],"updatedAt":"2025-08-20T05:08:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-02 09:18:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6404122","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6404122","identity":"rs-6404122","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}