Integrating Observational and Modelled Data to Advance the Understanding of Heat Stress Effects on Pregnant Subsistence Farmers in The Gambia

Integrating Observational and Modelled Data to Advance the Understanding of Heat Stress Effects on Pregnant Subsistence Farmers in The Gambia | 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 Integrating Observational and Modelled Data to Advance the Understanding of Heat Stress Effects on Pregnant Subsistence Farmers in The Gambia Carole Bouverat, Jainaba Badjie, Tida Samateh, Tida Saidy, Kris A Murray, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3931205/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Oct, 2024 Read the published version in Scientific Reports → Version 1 posted 8 You are reading this latest preprint version Abstract Studies on the effect of heat stress on pregnant women are scarce, particularly in highly vulnerable populations. To support the risk assessment of pregnant subsistence farmers in The Gambia, we conducted a study on the pathophysiological effects of extreme heat stress and assessed the applicability of heat stress indices. We added location-specific modelled solar radiation from ERA5 climate reanalysis to datasets from a previous observational cohort study involving on-site measurements of 92 women working in the heat. Associations between physiological and environmental variables were assessed through Pearson correlation coefficient analysis, mixed effect linear models with random intercepts per participant and confirmatory composite analysis. We found low to moderate associations (0 < r < 0.54) and robust estimates for independent effects of environmental variables on skin- and tympanic temperature, but not on heart rate and core temperature. Skin temperature increased more significantly in conditions above a 50% relative humidity threshold, demonstrating interactive effects between air temperature and relative humidity. Pregnant women experienced stronger pathophysiological effects of heat stress in their third than in their second trimester. In conclusion, environmental heat stress significantly altered maternal heat strain, particularly under humid conditions. Based on our results, we recommend including UTCI or WBGT in local heat-health warning systems. Earth and environmental sciences/Climate sciences/Climate change/Climate change impacts/Environmental health Earth and environmental sciences/Environmental social sciences/Climate change impacts/Environmental health Health sciences/Risk factors Figures Figure 1 Figure 2 Figure 3 1. Introduction Pregnant women who work outdoors are particularly vulnerable to heat stress and face an elevated risk of heat-related adverse health outcomes 1, 2, 3 . Heat stress indices offer a way for stakeholders to interpret, communicate, and potentially prevent the health impacts of climate change in the workplace 4 . However, more than 100 indices have been developed to model heat stress (defined by air temperature and its interplay with humidity, solar radiation and air velocity) and defining their appropriateness for a given study setting is key to producing robust and reliable predictions 5 . In addition, heat strain (the physiological response to heat stress) is defined by changes in certain physiological parameters (heart rate, core temperature, etc.) known to be related to exposure to above-optimal temperatures. It is unclear which environmental factors act singularly or in combination to affect maternal physiology leading to heat strain as this area has been under-researched to date 6, 7 . Therefore, a more thorough understanding of the effects of heat stress on maternal physiology, particularly in highly vulnerable populations, could support the adoption of specific heat stress indices that would allow accurate public health messaging and so reduce the health risks of working in the heat. In West Africa, rapid and widespread changes in climate exacerbate the frequency, duration, and severity of extreme heat 8 , whereby the resulting health inequalities are projected to become even more pronounced in the future 9 . Additionally, adverse pregnancy outcomes are disproportionally frequent in low-income countries 10 with 42% of stillbirths and 66% of maternal deaths worldwide occurring in sub-Saharan Africa 11 . This burden is attributable not only to healthcare system deficiencies or limited access to resources but also to environmental factors including heat stress 12 . The mechanisms by which heat stress affects human physiology are well documented in the literature 13 . To reduce excess heat storage, thermoregulatory responses are activated and cool the body through convection, radiation, or evaporation 14, 15 . When thermoregulation is unable to compensate for heat stress, negative health effects can arise, ranging from dizziness, dehydration, thermal fatigue, heat syncope, muscle cramps, and rashes to organ damage and heat stroke 12, 16 . A growing body of scientific evidence suggests that pregnancy represents a vulnerable time to the effects of extreme heat because fetal development is sensitive to alterations in the internal environment and because of the added heat burden of fetal growth, due to increased metabolism 12, 6 . Various studies indicate that exposure to heat during pregnancy affects placental and endocrine functions, and increases the probability of adverse pregnancy outcomes, including pre-eclampsia, premature birth, stillbirth, and prolonged labour 3, 2 . Nonetheless, the effect of environmental factors, both separately and jointly, on the physiological parameters of pregnant women and the applicability of heat stress indices in pregnancy are unclear. To address these gaps, we used data previously collected by Bonell et al. 1 from 92 pregnant farmers in West Kiang, The Gambia in West Africa. First, we aimed to identify the separate and independent effects of air temperature, air velocity, relative humidity, black globe temperature, and modelled solar radiation on the physiological parameters of agricultural workers during pregnancy, while considering the potential confounding influence of general fitness status, gestational age, and metabolic rate. The physiological parameters (heart rate, skin temperature, tympanic temperature and core temperature) indicate the level of heat strain observed in study participants and thus the internal response of the human body when exposed to extreme heat 13 . We also compared the applicability of a range of heat indices (the Heat Index, Apparent Temperature, Wet-bulb Globe Temperature (WBGT), and Universal Thermal Climate Index (UTCI)) in our study context, to determine the potential added value of integrating these heat stress indices in local heat-health warning systems. The second aim was to investigate through confirmatory composite analysis the compound effects of exposure to heat stress (as an indicator for simultaneous changes in environmental variables) on heat strain (as an indicator for simultaneous changes in physiological variables). This study incorporated two types of data, namely (i) observational data, which contain both physiological and environmental on-site measurements of 92 pregnant women working in the heat in West Kiang, The Gambia, collected by the observational cohort study of Bonell et al. 1 , and (ii) modelled solar radiation data which was extracted from the ERA5 climate reanalysis 17 . 2. Results 2.1. Associations between environmental and physiological variables Overall, Pearson product moment correlation coefficient analysis revealed interlinkages both within and between the pools of physiological and environmental variables. Visualized through the dendrogram lines at the left side of the heatmap ( Fig. 1 ) , we observed that, in terms of similarity in correlation pattern, the variables were not clustered in the initial two pools of environmental and physiological variables ( Table 1 ) . Instead, skin temperature and tympanic temperature were nested within the cluster of environmental variables, indicating that their associations followed patterns that were more similar to those of environmental variables than to those of other physiological variables. Other physiological variables, such as core temperature estimate, the Physiological Strain Index, and heart rate, together with air velocity, formed the second cluster of variables. Within the pool of environmental variables, Pearson correlation coefficients ranged from negligible to very high strength (-0.08 < r < 0.80 ) ( Fig. 1 , Supplementary Table 2) 18 . Specifically, negative correlations were observed between relative humidity and air temperature (r=-0.44) , black globe temperature (r=-0.46) , and air velocity (r=-0.35) , while positive correlations were found between solar radiation and air temperature (r = 0.42) , and between black globe temperature and air temperature (r = 0.80) . The heat stress indices, namely the UTCI, WBGT, the Heat Index, and Apparent Temperature, correlated with moderate to very strong strength (0.65 < r < 0.94) , indicating their convergent validity (Supplementary Table 2) . Convergent validity measures how closely an index is related to another index that measures the same concept 19 . Within the pool of physiological variables, Pearson correlation coefficients ranged from negligible to very high strength (-0.01 < r < 0.99) ( Fig. 1 , Supplementary Table 2) . The tympanic temperature of pregnant women was positively associated with skin temperature (r = 0.50) . Very high positive correlations were found between core temperature estimates and heart rate (r = 0.97) , between the Physiological Strain Index and heart rate (r = 0.99) , and between the Physiological Strain Index and the core temperature estimate (r = 0.99) . This high correlation is related to the calculation methods used for the Physiological Strain Index and the core temperature estimate based on other physiological variables. Between the pools of environmental and health variables, Pearson correlation coefficients of low to moderate strength were calculated (0.00 < r < 0.54) ( Fig. 1 , Supplementary Table 2) . Relative humidity was negatively associated with heart rate (r=-0.30) , while positive correlations were found between skin temperature and air temperature (r = 0.37) , between skin temperature and solar radiation (r = 0.34) , and between tympanic temperature and relative humidity (r = 0.30) . The heat stress indices showed similar positive associations with skin temperature (0.43 < r < 0.54) and tympanic temperature (0.26 < r < 0.33) supporting the construct validity of the heat stress indices (Supplementary Table 2) . Construct validity refers to how well an index measures an intended concept 19 . Nevertheless, the construct validity of heat stress indices was only partial, given that the negative associations of heat stress indices with heart rate (-0.22 < r < -0.35) , core temperature estimate (-0.14 < r < -0.30) , and the Physiological Strain Index (-0.18 < r < -0.32) were found. Overall, heat stress indices, as well as air temperature, black globe temperature, and solar radiation correlated most strongly with skin temperature. Table 1 Descriptive statistics table of merged datasets with two main pools of variables. Variable pool Variable type Variable Unit N Mean Std. Dev. Min Pctl. 25 Pctl. 75 Max Environmental variables Observed Air velocity m/s 407 1.3 0.83 0.1 0.7 1.7 5.2 Air temperature °C 407 33 3.8 22 31 36 45 Relative humidity % 407 28 23 0 9.9 41 88 Black globe temperature °C 407 38 4.9 15 34 41 52 Modelled Solar radiation J/m 2 407 6e 6 4.37e 6 3.29e 5 2.27e 6 8.33e 6 2e 7 Calculated Universal thermal climate index °C 407 33 3.9 20 31 35 51 Apparent temperature °C 407 33 4.4 19 31 36 51 Heat index °C 407 33 4.5 21 30 36 71 Wet bulb globe temperature °C 407 24 3.4 15 22 27 35 Physiological variables Observed Heart rate bpm 353 106 13 65 97 115 147 Skin temperature °C 353 37 1.1 32 36 38 40 Calculated Core temperature °C 299 38 0.31 37 37 38 39 Tympanic temperature °C 89 37 0.35 36 37 37 38 Physiological strain index NA 299 3.8 1 1.4 3.1 4.5 7.1 Observed Gestational age weeks 407 27 6.9 12 23 33 41 Fitness status, 6 min walking test m 407 498 74 135 461 544 687 Estimated Metabolic rate kcal/kg/h 357 3.2 0.85 1.9 2.3 3.8 5.2 2.2. Independent and compound effects of heat stress on heat strain The abovementioned relatively strong correlation of environmental variables with skin temperature also became apparent when assessing the independent effect of single environmental variables and the compound effects of multiple environmental variables. First, we assessed the independent association of each environmental factor, adjusted one by the other, on the physiological variables, using mixed effect linear models with random intercepts by each study participant. Here, increases in air temperature, relative humidity, and solar radiation led to highly robust increases in skin temperature, ceteris paribus , but not in air velocity, black globe temperature or metabolic rate ( Table 2 - Model 2A) . Further, air temperature and relative humidity were associated with tympanic temperature ( Table 2 - Model 4A) , and with core temperature and the Physiological Strain Index as output variables, but the estimates were more imprecise ( Table 2 - Model 3A, 5A) . However, we found no robust estimates for the joint association of environmental variables with heart rate ( Table 2 - Model 1A) . We found a robust estimate for solar radiation in association with heart rate through sensitivity analysis, whereby we rematched the highest 5-minute average heart rate within the 1-hour interval prior to each environmental datapoint (Supplementary Table 5) . Further interactive effects were found between air temperature and relative humidity in association with skin temperature ( Table 2 - Model 2B) . Increases in skin temperature were more rapid when the air temperature rose under conditions of relative humidity above the 50% threshold ( Fig. 2 ) . No robust evidence for this interaction was found for the association with heart rate, core temperature estimates, or tympanic temperature ( Table 2 - Model 1B, 3B, 4B, 5B) . Sensitivity analysis showed that the inclusion of fitness status as an additional model parameter did not considerably change the estimates (Supplementary Table 6) . Nor did gestational age behave as a confounder (Supplementary Table 7) . However, we found an interaction effect of gestational age with air temperature on all models ( Table 2 - Model 1C, 2C, 3C, 4C) . With increasing temperature, women in their third trimester of pregnancy experienced greater increases in heart rate, skin temperature, core temperature, and tympanic temperature than women in the second trimester of pregnancy did. This interaction effect of gestational age also became evident in the model in which the Physiological Strain Index served as an output variable ( Table 2 - Model 5D) . Table 2 Mixed effect linear models with random intercepts per participant. Model A assessed the independent effect of each environmental variable on physiological variables (i.e. single models with environmental variables against each physiological variable). Model B assessed the interactive effect of air temperature and relative humidity on physiological variables, with air temperature as a continuous variable and relative humidity as a dummy variable at a 50% relative humidity threshold. Model C assessed the interactive effect of air temperature and gestational age on physiological variables, with air temperature as a continuous variable and gestational age as a dummy variable at a threshold of 27 gestational weeks, separating the second from the third trimester of pregnancy. P-values are in the Supplementary Table 4. Model 1: Heart rate Model 2: Skin temperature Model 3: Core temperature Model 4: Tympanic temperature Model 5: Physiological Strain Index Estimate (98% CI) Estimate (98% CI) Estimate (98% CI) Estimate (98% CI) Estimate (98% CI) Model A – environmental parameters and physiological parameters Air temperature 0.59 (-0.33 ; 1.50) 0.20 (0.14 ; 0.27) 1.91e − 2 (-4.92e − 3 ; 4.28e − 2 ) 0.05 (-0.01 ; 0.11) 0.05 (-0.03 ; 0.13) Relative humidity -0.08 (-0.20 ; 0.04) 0.02 (0.01 ; 0.03) -2.21e − 3 (-5.19e − 3 ; 7.69e − 4 ) 0.01 (2.36e − 3 ; 0.01) -0.01 (-0.02 ; 1.91e − 3 ) Air velocity -0.60 (-2.26 ; 1.37) -0.05 (-0.19 ; 0.09) -2.66e − 2 (-0.07 ; 2.20e − 2 ) 0.01 (-0.08 ; 0.11) -0.08 (-0.24 ; 0.08) Black globe temperature 0.20 (-0.39 ; 0.78) -0.01 (-0.05 ; 0.03) 2.38e − 3 (-0.01 ; 1.75e − 2 ) -0.02 (-0.07 ; 0.02) 0.01 (-0.04 ; 0.06) Solar radiation / 100.000 -0.02 (-0.07 ; 0.02) 0.01 (2.78e − 3 ; 0.01) -5.28e − 5 (-1.22e − 3 ; 1.12e − 3 ) 7.02e − 4 (-1.48e − 3 ; 2.92e − 3 ) -1.27e − 3 (-0.01 ; 2.59e − 3 ) Metabolic rate -0.03 (-2.52 ; 2.45) -0.05 (-0.22 ; 0.12) 1.38e − 2 (-0.06 ; 8.30e − 2 ) 0.02 (-0.09 ; 0.13) 0.06 (-0.17 ; 0.28) Model B – interaction between air temperature and relative humidity Air temperature 0.81 (0.35 ; 1.28) 0.20 (0.17 ; 0.23) 0.02 (0.01 ; 0.04) 0.02 (-0.01 ; 0.05) 0.06 (0.02 ; 0.10) Relative humidity -9.75 (-60.49 ; 41.05) -2.59 (-6.39 ; 1.24) -0.39 (-1.62 ; 0.85) 3.46 (-0.96 ; 7.91) -1.64 (-5.71 ; 2.45) Air temperature · relative humidity 0.29 (-1.32 ; 1.89) 0.11 (0.01 ; 0.23) 0.01 (-0.03 ; 0.05) -0.10 (-0.24 ; 0.04) 0.05 (-0.08 ; 0.18) Model C – interaction between air temperature and gestational age Air temperature 0.55 (-0.09 ; 1.20) 0.19 (0.14 ; 0.24) 0.02 (4.80e − 4 ; 0.03) -0.03 (-0.07 ; 0.01) 0.04 (-0.02 ; 0.10) Gestational age -16.36 (-45.38 ; 13.05) -0.69 (-2.97 ; 1.60) -0.52 (-1.29 ; 0.26) -2.22 (-4.22 ; -0.30) -1.84 (-4.39 ; 0.73) Air temperature · gestational age 0.53 (-0.34 ; 1.39) 0.01 (-0.06 ; 0.07) 0.02 (-6.88e − 3 ; 0.04) 0.06 (3.12e − 3 ; 0.12) 0.06 (-0.02 ; 0.13) Through composite confirmatory analysis 20 , we assessed the simultaneous influence of environmental parameters on the conjunction of observed physiological parameters. We constructed a model with air temperature, relative humidity, black globe temperature, solar radiation, air velocity, and metabolic rate as observable indicators of the composite artefact of heat stress, and with heart rate and skin temperature as observable indicators of the composite artefact of maternal heat strain. We found a factor loading of 0.71 between heat stress and maternal heat strain ( Fig. 3 ) . Model estimations showed that the highest indirect effects of environmental variables were from changes in air temperature, relative humidity, and solar radiation, with loadings equal to 0.45 , 0.46 , and 0.55 respectively. Black globe temperature yielded a loading of 0.17, and negative loadings were observed for air velocity (-0.45) and metabolic rate (-0.45) . This negative value of metabolic rate is in line with a behavioural response to reduce the level of physical activity at rising temperature. The indirect effects on maternal heat strain were positive for skin temperature (0.87) and negative for heart rate (-0.50) , whereby the heart rate was also influenced by behavioural change when working in the heat, which might not sufficiently be covered by estimations of metabolic rate. Even though the fit indices of our model lie within the optimal ranges ( Table 3 ) , the p-values indicate only robust estimates for the loadings of air temperature, solar radiation, and skin temperature (Supplementary Table 7) . Table 3 Indices showing the model fit of the confirmatory composite analysis. The table structure is based on the study of Yazdanirad et al. 21 . Assessment of model fit Indices Optimal fitness Obtained value Chi square (X2/df) 1–3 1.28 Comparative fit index (CFI) > 0.9 0.99 Root mean squared error of approximation (RMSEA) 0.9 0.95 Normed fit index (NFI) > 0.9 0.97 Incremental fit index (IFI) 0–1 0.99 3. Discussion Taken together, our findings demonstrate that physiological indicators of heat strain in pregnant women are significantly influenced by environmental conditions while working in the heat, particularly in humid conditions and in the third trimester of pregnancy compared to the second. This study provides information on the pathophysiological effects of heat stress on pregnant women and not on the respective health outcomes of mothers or babies. We applied a series of methods, namely the Pearson product moment correlation method, mixed effect linear models with random intercepts, and confirmatory composite analysis to assess the association between each environmental and physiological indicator. This approach enabled us to disentangle the independent effects of each of the environmental variables on a set of different physiological variables and assess the potential compound effects between climate variables, while taking into consideration variance inflation factors. Overall, our findings provide an input to quantify the risk of pregnant women experiencing pathophysiological effects from working in the heat and can thus support further research on adaptive measures to alleviate heat strain. Our methodology allowed us to demonstrate that the heat strain of pregnant women was more severe under conditions above a relative humidity of 50%, whereby the impact of rising temperature on skin temperature increased at a greater rate. This is due to the limited capacity of the human body to thermoregulate in hot and humid environments, because the proportion of evaporated sweat, and thus the sweating efficiency, decreases with rising relative humidity above the 35°C threshold 15 . The existence of the 35°C temperature threshold as a human adaptability limit to heat stress 22 was found to be even lower by the recent study of Vecciello et al 23 in non-pregnant individuals. Furthermore, we found robust evidence for the increase in susceptibility to heat strain throughout pregnancy at rising temperatures across all physiological indicators, namely skin temperature, heart rate, tympanic temperature and the thereby estimated core temperature. More precisely, women in their third trimester of pregnancy experienced higher heat strain than women in their second trimester of pregnancy did. It would be interesting for future research to compare these findings with data from early pregnancy, given that a previous study has shown that the risk of heat-related stillbirth might be particularly high in early pregnancy 24 , whilst a global analysis and meta-analysis detected an increased risk of preterm birth at exposure to extreme heat during the last seven days of gestation 12, 25 . Our study demonstrated the convergent validity of heat stress indices through correlations between each other as well as their construct validity through their correlations with skin temperature and tympanic temperature. The Heat Index, UTCI, Apparent Temperature and Wet Bulb Globe Temperature were strongly associated with each other, even though they incorporate different input variables such as relative humidity, solar radiation, black globe temperature and air velocity, in addition to air temperature. The underlying reason for the associations found is the strong weight that each of the heat stress indices assigns to air temperature as an input variable. Furthermore, the selected heat stress indices showed similar associations with both skin temperature and tympanic temperature, which supports their applicability to our study setting. Even though heat stress indices performed similarly in our assessment, we recommend including indices such as the UTCI or WBGT in heat-health warning systems, given that these indices incorporate air temperature, relative humidity and solar radiation, which showed associations to physiological variables in our study (Section 2.2) . These indices could potentially be included in heat-health warning systems as a preventive measure for heat strain. Heat stress indices would need to be communicated in a targeted way, such as through simply understandable risk levels, as to allow for the public to respond appropriately. There are five key limitations to our study. First, we tested the applicability of four out of more than 100 existing heat stress indices 26 . However, the Heat Index, Apparent Temperature, WBGT, and UTCI are indices that are often referred to in the literature 13 and require input variables covered with our datasets, together with additional solar radiation modelling. We also tried to overcome this limitation by assessing the separate effects of environmental variables on maternal physiology, which increases the generalizability of the results. Second, given data availability constraints, our study design omitted variables such as clothing thermal insulation, systolic blood pressure, and diastolic blood pressure, which have been included in other studies 16, 21 . Likewise, water intake or cloud cover could have been added to the models as confounders. Third, we acknowledge the potential imprecision of results sourced from the matching of datasets with different temporal resolutions, especially for heart rate measurements, which fluctuate over time. We reached no clear conclusions about the effects of heat stress on heart rate and could not determine construct validity of heat stress indices on heart rate, core temperature estimates or the physiological strain index, possibly due to behavioural adaptations to heat not captured in estimated metabolic rate. Even while applying a moving average merging technique (Supplementary Table 5) and confirmatory composite analysis (Supplementary Table 9) , we found no robust estimates for the compound effects of air temperature and relative humidity on heart rate, possibly also due to non-accounted behavioural responses. This limitation might be resolved in future studies while measuring metabolic rate more effectively to disentangle the effects of activity and heat stress. Fourth, we did not account for individual differences in our confirmatory composite analysis; therefore, our results do not allow us to make inferences about repeated measurements of the same study participants. Such individual-level differences were only included in our mixed effect models with random intercepts. Fifth, we considered a linear association between variables and did not account for non-linearity, based on previous assessments with this data 1 . Despite these limitations, our understanding of the interplay between environmental variables and the physiology of pregnant women in The Gambia first and foremost underlines the necessity of climate change mitigation and adaptation to protect one of the most vulnerable population subgroups from heat strain. We suggest developing adequate and targeted adaptation strategies that increase the resilience of pregnant women to climatic changes, given the evidence of high levels of physiological impacts while working in hot environments. To protect the maternal health of agricultural workers, particularly during the third trimester of pregnancy, when vulnerability to heat strain increases, measures that reduce exposure to heat stress, including work schedules adapted to climatic conditions, or measures that reduce the impacts of heat stress, such as protective clothing or microclimate cooling equipment, could be tested in future research 27 . The establishment of heat-health warning systems with tested heat stress indices will be crucial for the implementation of adaptive measures 4 . Cherisch et al. 3 suggest interventions to protect maternal and newborn health in Africa at the individual level through behavioral change, at the infrastructural level through changes in health systems and services, at the structural level through policy and financing options, or at the environmental level through nature-based solutions. For optimal policy design and implementation, adaptive measures should be developed in close collaboration with the local community, and investigations of the respective measures should be undertaken through a participatory research approach 28 . 4. Methods The study population consisted of 92 pregnant women from West Kiang, The Gambia, who had been recruited by Bonell et al. 1 through the local antenatal clinic and provided informed consent. Participants worked either on a small-scale farm, in agriculture or in a garden for more than 3 hours per day. Acutely ill participants, including those diagnosed with pre-eclampsia, eclampsia, gestational diabetes, or who had a history of heart disease were excluded from the study. A more detailed overview of the demographics, physical characteristics, and birth outcomes of the study participants is available in Supplementary Table 1 . The study setting covers 9 villages within the West Kiang region: Jali, Janneh Kunda, Jiffarong, Kantong Kunda, Karantaba, Keneba, Kuli Kunda, Mandina, Manduar and Tankular. West Kiang is a district located in the Lower River Division of The Gambia and populated by 14,846 inhabitants. The main mode of subsistence is manual farming. Women work on average between 4.5 and 7.5 hours per day during pregnancy 29 . Observational data (i) were collected in the study of Bonell et al. 1 , in which both environmental and physiological indicators were measured at different time points during pregnancy while the study participants performed daily agricultural tasks. The black globe temperature, dew point temperature, relative humidity, air temperature, and WBGT were collected using an HT200: heat stress WGBT meter. The wind speed was recorded with a portable Extech AN100 Thermo-Anemometer. Skin temperature and maternal heart rate were measured with an Equivital LifeMonitor. Core temperature and tympanic temperature were estimated based on skin temperature and heart rate. Metabolic rates were estimated through observed levels of activity 1 . The resolution of the datasets varied: Environmental parameters were available at hourly intervals, while physiological parameters were available at 5-minute intervals. Based on the environmental data points, we calculated heat stress indices with the R packages weathermetrics 30 , rBiometeo 31 and HeatStress 32 . The study of Bonell et al. (2022), on which this research project is based, has been approved by The Gambian Government and the Medical Research Council Unit The Gambia Joint Ethics Committee and the London School of Hygiene & Tropical Medicine Ethics Advisory Board in accordance with the Declaration of Helsinki 33, 1 . The retrieved modelled data (ii) from the ERA5 hourly land reanalysis dataset contains hourly surface net solar radiation for the geocodes of Jali, Janneh Kunda, Jiffarong, Kantong Kunda, Karantaba, Keneba, Kuli Kunda, Mandina, Manduar and Tankular 17 . In total, we extracted 80 subsets, each of which covered the modelled solar radiation for one village and month within the study period from August 2019 until March 2020. The datasets are temporally resolved at 1-hour intervals and spatially resolved on 0.09° horizontal and vertical grids. The surface net solar radiation is indicated in units of joules per square meter (J/m 2 ) and represents the difference between the solar radiation reaching the Earth’s surface and the solar radiation reflected from the surface through the albedo effect 17 . Given the varying time formats and resolutions of the abovementioned datasets (i-ii), we created uniform time stamps in POSIX.ct format and minimized the time differences between the data points to construct the final merged dataset. Every data point from the environmental dataset was matched with the average of each physiological variable over the previous hourly interval. The influence of external heart rate fluctuations on the results was tested by rematching heart rate values with environmental data points through a 5-minute moving average interval. Observations were matched with the respective study participants’ gestational age, fitness status – measured through a 6-minute walking test – and the geographical location. While calculating work shift lengths, we added respective estimated metabolic rates for both halves of the work shift. Each datapoint was matched with the modelled solar radiation from the ERA5 land datasets of the respective location and closest in time. The final merged dataset consisted of observational (i) and modelled data (ii). The data distribution was verified through descriptive statistics, more specifically, through summary statistics tables, boxplots, probability density functions, QQ plots, and Kolmogorov-Smirnov tests, serving as a basis for outlier detection. We removed outliers of heart rate below 60 bpm, above 200 bpm, or below a confidence interval of 85%, as well as of skin temperature, which was three standard deviations away from the mean. The environmental data points remained complete. Both separate and compound associations between environmental and health variables were investigated. The separate relationships between variables were assessed through the Pearson product moment correlation method and visualized through scatterplots. This methodology is in line with previous studies that have validated heat stress indices in the context of male farmers and mine workers in Iran 34, 16 . We assessed not only associations of environmental parameters with separate health variables, but also with a modified version of the physiological strain index – a composite index based on skin temperature, tympanic temperature, and heart rate. Core temperature was not measured due to practical as well as safety constraints associated with pregnancy 35, 1 . The Pearson product moment correlation analysis allowed us to assess both the convergent and construct validity of the heat stress indices. The independent effects of the environmental variables on the physiological variables were assessed through mixed effect models with random intercepts. With this methodology, we accounted for individual differences between study participants. The variance inflation factor was computed to detect multicollinearity 36 . Sensitivity analysis was applied to determine the potential confounding factors of gestational age and fitness status. While integrating an interaction term between air temperature and relative humidity as well as between air temperature and gestational age, we tested for compound effects. Ultimately, the results from the mixed effect models with random intercepts were compared to those from the confirmatory composite analysis to enhance the robustness of our findings and assess the interplay between multiple environmental and physiological variables simultaneously 20 . Declarations Acknowledgments We would like to acknowledge the communities in West Kiang, especially the pregnant participants enrolled in the observational study. The original project was funded by the Wellcome Trust through the Wellcome Trust Global Health PhD Fellowship awarded to AB (216336/Z/19/Z). The funders had no role in study design, data collection, analysis, manuscript writing or decision to submit. AMVC acknowledges funding from the Swiss National Science Foundation (TMSGI3_211626). Author Contributions Conceptualization: CB, AMVC, AB Data collection: AB Data analysis: CB, AMVC, AB Methodology: CB, AMVC, AB Visualization: CB, AMVC, AB Writing: CB, AMVC, AB Revision: CB, AMVC, AB, AH, NM, JB, TS, TS, KAM, AMP Data Availability Anonymized data will be made available by the corresponding author upon reasonable request. Additional Information The authors declare no competing interests. References Bonell, A. et al. Environmental heat stress on maternal physiology and fetal blood flow in pregnant subsistence farmers in The Gambia, west Africa: an observational cohort study. The Lancet Planetary Health 6 , e968–e976 (2022). Ha, S. The Changing Climate and Pregnancy Health. Curr Envir Health Rpt 9 , 263–275 (2022). Chersich, M. et al. Climate change impacts on maternal and new-born health in Africa: Intervention options. WJCM 4 , 169 (2022). Casanueva, A. et al. Escalating environmental summer heat exposure—a future threat for the European workforce. 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Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel On Climate Change. [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. (Cambridge University Press, 2021). Intergovernmental Panel on Climate Change (IPCC). Africa. [Trisos, C.H., I.O.Adelekan, E.Totin, A.Ayanlade, J.Efitre, A.Gemeda, K.Kalaba, C.Lennard, C.Masao, Y.Mgaya, G. Ngaruiya, D. Olago, N.P. Simpson, and S. Zakieldeen] In: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. [H.- O.Pörtner, D.C.Roberts, M.Tignor, E.S.Poloczanska, K.Mintenbeck, A.Alegría, M.Craig, S. Langsdorf, S. Löschke, V. Möller, A. Okem, B. Rama (eds.)]. 1285-1455 (Cambridge University Press, 2023). Tamirat, K. S., Sisay, M. M., Tesema, G. A. & Tessema, Z. T. Determinants of adverse birth outcome in Sub-Saharan Africa: analysis of recent demographic and health surveys. BMC Public Health 21 , 1092 (2021). Graham, W. et al. Diversity and divergence: the dynamic burden of poor maternal health. The Lancet 388 , 2164–2175 (2016). McElroy, S., Ilango, S., Dimitrova, A., Gershunov, A. & Benmarhnia, T. Extreme heat, preterm birth, and stillbirth: A global analysis across 14 lower-middle income countries. Environment International 158 , 106902 (2022). Havenith, G. & Fiala, D. Thermal Indices and Thermophysiological Modeling for Heat Stress. in Comprehensive Physiology (ed. Prakash, Y. S.) 255–302 (Wiley, 2015). Morris, N. B. et al. The HEAT-SHIELD project — Perspectives from an inter-sectoral approach to occupational heat stress. Journal of Science and Medicine in Sport 24 , 747–755 (2021). Baldwin, J. W. et al. Humidity’s Role in Heat-Related Health Outcomes: A Heated Debate. Environ Health Perspect 131 , 055001 (2023). Zare, S. et al. A comparison of the correlation between heat stress indices (UTCI, WBGT, WBDT, TSI) and physiological parameters of workers in Iran. Weather and Climate Extremes 26 , 100213 (2019). Muñoz Sabater, J. ERA5-Land hourly data from 1950 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (2019). Mukaka, M. M. A guide to appropriate use of Correlation coefficient in medical research. Malawi Medical Journal 24 , 69–71 (2012). Krabbe, P. F. M. Chapter 7 - Validity. in The Measurement of Health and Health Status (ed. Krabbe, P. F. M.) 113–134 (Academic Press, San Diego, 2017). Schuberth, F., Henseler, J. & Dijkstra, T. K. Confirmatory Composite Analysis. Frontiers in Psychology 9 , (2018). Yazdanirad, S., Golbabaei, F., Foroushani, A. R., Monazzam, M. R. & Dehghan, H. Development and validation of an environmental heat strain risk assessment (EHSRA) index using structural equation modeling based on empirical relations. Environ Health Prev Med 25 , 63 (2020). Sherwood, S. C. & Huber, M. An adaptability limit to climate change due to heat stress. Proc. Natl. Acad. Sci. U.S.A. 107 , 9552–9555 (2010). Vecellio, D. J., Wolf, S. T., Cottle, R. M. & Kenney, W. L. Evaluating the 35°C wet-bulb temperature adaptability threshold for young, healthy subjects (PSU HEAT Project). Journal of Applied Physiology 132 , 340–345 (2022). Wang, J., Tong, S., Williams, G. & Pan, X. Exposure to Heat Wave During Pregnancy and Adverse Birth Outcomes: An Exploration of Susceptible Windows. Epidemiology 30 , S115 (2019). Chersich, M. F. et al. Associations between high temperatures in pregnancy and risk of preterm birth, low birth weight, and stillbirths: systematic review and meta-analysis. BMJ m3811 (2020). Ioannou, L. G. et al. Indicators to assess physiological heat strain – Part 1: Systematic review. Temperature 9 , 227–262 (2022). Gao, C., Kuklane, K., Östergren, P.-O. & Kjellstrom, T. Occupational heat stress assessment and protective strategies in the context of climate change. Int J Biometeorol 62 , 359–371 (2018). Ethics and the ethnography of medical research in Africa. Social Science & Medicine 67 , 685–695 (2008). Bonell, A. et al. A protocol for an observational cohort study of heat strain and its effect on fetal wellbeing in pregnant farmers in The Gambia. Wellcome Open Res 5 , 32 (2020). Anderson, G. B., Bell, M. L. & Peng, R. D. Methods to Calculate the Heat Index as an Exposure Metric in Environmental Health Research. Environ Health Perspect 121 , 1111–1119 (2013). Morabito ACM. rBiometeo: biometeorological functions in R. 2016. https://rdrr.io/github/alfcrisci/rBiometeo/ (accessed 10.12.23). Casanueva, A. HeatStress function in R. 2019. https://github.com/anacv/HeatStress (accessed 10.12.23). World Medical Association Declaration of Helsinki. Ethical principles for medical research involving human subjects. Bull World Health Organ 79 , 373–374 (2001). Zamanian, Z., Sedaghat, Z., Hemehrezaee, M. & Khajehnasiri, F. Evaluation of environmental heat stress on physiological parameters. J Environ Health Sci Engineer 15 , 24 (2017). Moran, D. S. et al. Evaluation of the environmental stress index for physiological variables. Journal of Thermal Biology 28 , 43–49 (2003). Shrestha, N. Detecting Multicollinearity in Regression Analysis. American Journal of Applied Mathematics and Statistics 8 , 39–42 (2020). Additional Declarations No competing interests reported. Supplementary Files SupplementaryFiguresTables.docx Cite Share Download PDF Status: Published Journal Publication published 23 Oct, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 05 Apr, 2024 Reviews received at journal 14 Mar, 2024 Reviewers agreed at journal 04 Mar, 2024 Reviewers invited by journal 04 Mar, 2024 Editor assigned by journal 04 Mar, 2024 Editor invited by journal 27 Feb, 2024 Submission checks completed at journal 27 Feb, 2024 First submitted to journal 05 Feb, 2024 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-3931205","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":275231279,"identity":"50c5e003-ef3b-4cef-97b1-c81834692e65","order_by":0,"name":"Carole Bouverat","email":"","orcid":"","institution":"Oeschger Center for Climate Change Research, University of Bern","correspondingAuthor":false,"prefix":"","firstName":"Carole","middleName":"","lastName":"Bouverat","suffix":""},{"id":275231280,"identity":"41addc71-a4cc-4804-a225-afea08a7749f","order_by":1,"name":"Jainaba Badjie","email":"","orcid":"","institution":"Medical Research Unit The Gambia, London School of Hygiene and Tropical Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jainaba","middleName":"","lastName":"Badjie","suffix":""},{"id":275231281,"identity":"073d810c-dc55-487f-9487-b36f599d016c","order_by":2,"name":"Tida Samateh","email":"","orcid":"","institution":"Medical Research Unit The Gambia, London School of Hygiene and Tropical Medicine","correspondingAuthor":false,"prefix":"","firstName":"Tida","middleName":"","lastName":"Samateh","suffix":""},{"id":275231282,"identity":"f0937bdb-919e-4f9b-8910-f75838be75f6","order_by":3,"name":"Tida Saidy","email":"","orcid":"","institution":"Medical Research Unit The Gambia, London School of Hygiene and Tropical Medicine","correspondingAuthor":false,"prefix":"","firstName":"Tida","middleName":"","lastName":"Saidy","suffix":""},{"id":275231283,"identity":"1e5cc4d5-9a31-4504-9d4a-2df9c0b3d231","order_by":4,"name":"Kris A Murray","email":"","orcid":"","institution":"Medical Research Unit The Gambia, London School of Hygiene and Tropical Medicine","correspondingAuthor":false,"prefix":"","firstName":"Kris","middleName":"A","lastName":"Murray","suffix":""},{"id":275231284,"identity":"33a3d43f-befb-4ac4-aae4-f3cf93c4ca91","order_by":5,"name":"Andrew M Prentice","email":"","orcid":"","institution":"Medical Research Unit The Gambia, London School of Hygiene and Tropical Medicine","correspondingAuthor":false,"prefix":"","firstName":"Andrew","middleName":"M","lastName":"Prentice","suffix":""},{"id":275231285,"identity":"06b5d82c-08f6-4538-bc63-38934d47f453","order_by":6,"name":"Neil Maxwell","email":"","orcid":"","institution":"Environmental Extremes Lab, University of Brighton","correspondingAuthor":false,"prefix":"","firstName":"Neil","middleName":"","lastName":"Maxwell","suffix":""},{"id":275231286,"identity":"14011b3c-660a-45ce-81bf-310ff21201a1","order_by":7,"name":"Andy Haines","email":"","orcid":"","institution":"Centre on Climate Change and Planetary Health, London School of Hygiene and 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Bonell","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIie3PPWrDMBTAcYUH8iLVq7KkV3jBB+hBurgEksWGjtkSCLw1F+gh2q2jjSCTDyBIBptApgxaDR0qFTqEYDXZOugPEkjw0wdjsdg/jLsB1k0PCdTVaP27Xw2T1BPlLfDcEfybjN25IH6IwNsI7jctqOVh4oit+8+vZ5boFkQTIIcdgm1OGQf5rmWD5VrMEYQJEJMjjEm/kCcjcoQV7ql2mDyZhQVJekUg2rr3JD2HCZoCPcndX1glPVH+luDDiletGj0l4KglZSWpE9Zvoe+bxcfRLvVjmmy6rqdJuU1nXXveDRNfdbHiVzuxWCwWu7tv+GNUfgh1BhkAAAAASUVORK5CYII=","orcid":"","institution":"Medical Research Unit The Gambia, London School of Hygiene and Tropical Medicine","correspondingAuthor":true,"prefix":"","firstName":"Ana","middleName":"","lastName":"Bonell","suffix":""}],"badges":[],"createdAt":"2024-02-05 14:29:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3931205/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3931205/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-74614-y","type":"published","date":"2024-10-23T15:58:10+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":51821620,"identity":"d02f6d77-e3fb-4bea-9885-dcb2669a4569","added_by":"auto","created_at":"2024-02-29 16:10:45","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":164376,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap indicating the strength of the Pearson correlation coefficients between variables as colour gradients. Denoted with (E) for environmental variables and (P) for physiological variables. P-values are contained in Supplementary Table 3. The dendrogram at the left side orders variables according to the similarity of their correlation with other variables.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3931205/v1/67321d611d81289f4f20e6f2.jpg"},{"id":51821618,"identity":"c9794096-b76e-4029-80aa-92f697b6f2b3","added_by":"auto","created_at":"2024-02-29 16:10:44","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":96183,"visible":true,"origin":"","legend":"\u003cp\u003eInteraction between air temperature and relative humidity in association with skin temperature at a threshold of 50% relative humidity. The skin temperature increases more rapidly with increasing temperature under conditions in which the relative humidity is above the 50% threshold.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3931205/v1/a9b0a94a5ff9fad830a1ed26.jpg"},{"id":51822671,"identity":"450f05ed-806b-43a1-862b-f1c4d4a7f4e9","added_by":"auto","created_at":"2024-02-29 16:18:45","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":211272,"visible":true,"origin":"","legend":"\u003cp\u003eComposite confirmatory analysis with respective loadings of path coefficients between composite artefacts (heat stress and heat strain) and observable indicators (air temperature, relative humidity, black globe temperature, solar radiation, air velocity, metabolic rate, heart rate, skin temperature).\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3931205/v1/560b8a628f9a5a4b90e054d2.jpg"},{"id":67681987,"identity":"c4ae9543-dd9c-49ac-8f08-dbd86d15c616","added_by":"auto","created_at":"2024-10-28 16:12:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1221150,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3931205/v1/9928f74e-b8ed-4efa-b4aa-585dc1caa475.pdf"},{"id":51821617,"identity":"d7c21db4-b6cf-41cf-93cf-15d802487ab8","added_by":"auto","created_at":"2024-02-29 16:10:44","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1317956,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFiguresTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-3931205/v1/5cfb586c7c90f5d562b992d5.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrating Observational and Modelled Data to Advance the Understanding of Heat Stress Effects on Pregnant Subsistence Farmers in The Gambia","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003ePregnant women who work outdoors are particularly vulnerable to heat stress and face an elevated risk of heat-related adverse health outcomes \u003csup\u003e1, 2, 3\u003c/sup\u003e. Heat stress indices offer a way for stakeholders to interpret, communicate, and potentially prevent the health impacts of climate change in the workplace \u003csup\u003e4\u003c/sup\u003e. However, more than 100 indices have been developed to model heat stress (defined by air temperature and its interplay with humidity, solar radiation and air velocity) and defining their appropriateness for a given study setting is key to producing robust and reliable predictions \u003csup\u003e5\u003c/sup\u003e. In addition, heat strain (the physiological response to heat stress) is defined by changes in certain physiological parameters (heart rate, core temperature, etc.) known to be related to exposure to above-optimal temperatures. It is unclear which environmental factors act singularly or in combination to affect maternal physiology leading to heat strain as this area has been under-researched to date \u003csup\u003e6, 7\u003c/sup\u003e. Therefore, a more thorough understanding of the effects of heat stress on maternal physiology, particularly in highly vulnerable populations, could support the adoption of specific heat stress indices that would allow accurate public health messaging and so reduce the health risks of working in the heat.\u003c/p\u003e \u003cp\u003eIn West Africa, rapid and widespread changes in climate exacerbate the frequency, duration, and severity of extreme heat \u003csup\u003e8\u003c/sup\u003e, whereby the resulting health inequalities are projected to become even more pronounced in the future \u003csup\u003e9\u003c/sup\u003e. Additionally, adverse pregnancy outcomes are disproportionally frequent in low-income countries \u003csup\u003e10\u003c/sup\u003e with 42% of stillbirths and 66% of maternal deaths worldwide occurring in sub-Saharan Africa \u003csup\u003e11\u003c/sup\u003e. This burden is attributable not only to healthcare system deficiencies or limited access to resources but also to environmental factors including heat stress \u003csup\u003e12\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe mechanisms by which heat stress affects human physiology are well documented in the literature \u003csup\u003e13\u003c/sup\u003e. To reduce excess heat storage, thermoregulatory responses are activated and cool the body through convection, radiation, or evaporation \u003csup\u003e14, 15\u003c/sup\u003e. When thermoregulation is unable to compensate for heat stress, negative health effects can arise, ranging from dizziness, dehydration, thermal fatigue, heat syncope, muscle cramps, and rashes to organ damage and heat stroke \u003csup\u003e12, 16\u003c/sup\u003e. A growing body of scientific evidence suggests that pregnancy represents a vulnerable time to the effects of extreme heat because fetal development is sensitive to alterations in the internal environment and because of the added heat burden of fetal growth, due to increased metabolism \u003csup\u003e12, 6\u003c/sup\u003e. Various studies indicate that exposure to heat during pregnancy affects placental and endocrine functions, and increases the probability of adverse pregnancy outcomes, including pre-eclampsia, premature birth, stillbirth, and prolonged labour \u003csup\u003e3, 2\u003c/sup\u003e. Nonetheless, the effect of environmental factors, both separately and jointly, on the physiological parameters of pregnant women and the applicability of heat stress indices in pregnancy are unclear.\u003c/p\u003e \u003cp\u003eTo address these gaps, we used data previously collected by Bonell et al. \u003csup\u003e1\u003c/sup\u003e from 92 pregnant farmers in West Kiang, The Gambia in West Africa. First, we aimed to identify the separate and independent effects of air temperature, air velocity, relative humidity, black globe temperature, and modelled solar radiation on the physiological parameters of agricultural workers during pregnancy, while considering the potential confounding influence of general fitness status, gestational age, and metabolic rate. The physiological parameters (heart rate, skin temperature, tympanic temperature and core temperature) indicate the level of heat strain observed in study participants and thus the internal response of the human body when exposed to extreme heat \u003csup\u003e13\u003c/sup\u003e. We also compared the applicability of a range of heat indices (the Heat Index, Apparent Temperature, Wet-bulb Globe Temperature (WBGT), and Universal Thermal Climate Index (UTCI)) in our study context, to determine the potential added value of integrating these heat stress indices in local heat-health warning systems. The second aim was to investigate through confirmatory composite analysis the compound effects of exposure to heat stress (as an indicator for simultaneous changes in environmental variables) on heat strain (as an indicator for simultaneous changes in physiological variables). This study incorporated two types of data, namely (i) observational data, which contain both physiological and environmental on-site measurements of 92 pregnant women working in the heat in West Kiang, The Gambia, collected by the observational cohort study of Bonell et al. \u003csup\u003e1\u003c/sup\u003e, and (ii) modelled solar radiation data which was extracted from the ERA5 climate reanalysis \u003csup\u003e17\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"2. Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Associations between environmental and physiological variables\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eOverall, Pearson product moment correlation coefficient analysis revealed interlinkages both within and between the pools of physiological and environmental variables. Visualized through the dendrogram lines at the left side of the heatmap \u003cem\u003e(\u003c/em\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cem\u003e)\u003c/em\u003e, we observed that, in terms of similarity in correlation pattern, the variables were not clustered in the initial two pools of environmental and physiological variables \u003cem\u003e(\u003c/em\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cem\u003e)\u003c/em\u003e. Instead, skin temperature and tympanic temperature were nested within the cluster of environmental variables, indicating that their associations followed patterns that were more similar to those of environmental variables than to those of other physiological variables. Other physiological variables, such as core temperature estimate, the Physiological Strain Index, and heart rate, together with air velocity, formed the second cluster of variables.\u003c/p\u003e \u003cp\u003eWithin the pool of environmental variables, Pearson correlation coefficients ranged from negligible to very high strength \u003cem\u003e(-0.08\u0026thinsp;\u0026lt;\u0026thinsp;r\u0026thinsp;\u0026lt;\u0026thinsp;0.80 ) (\u003c/em\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cem\u003eSupplementary Table\u0026nbsp;2)\u003c/em\u003e \u003csup\u003e18\u003c/sup\u003e. Specifically, negative correlations were observed between relative humidity and air temperature \u003cem\u003e(r=-0.44)\u003c/em\u003e, black globe temperature \u003cem\u003e(r=-0.46)\u003c/em\u003e, and air velocity \u003cem\u003e(r=-0.35)\u003c/em\u003e, while positive correlations were found between solar radiation and air temperature \u003cem\u003e(r\u0026thinsp;=\u0026thinsp;0.42)\u003c/em\u003e, and between black globe temperature and air temperature \u003cem\u003e(r\u0026thinsp;=\u0026thinsp;0.80)\u003c/em\u003e. The heat stress indices, namely the UTCI, WBGT, the Heat Index, and Apparent Temperature, correlated with moderate to very strong strength \u003cem\u003e(0.65\u0026thinsp;\u0026lt;\u0026thinsp;r\u0026thinsp;\u0026lt;\u0026thinsp;0.94)\u003c/em\u003e, indicating their convergent validity \u003cem\u003e(Supplementary Table\u0026nbsp;2)\u003c/em\u003e. Convergent validity measures how closely an index is related to another index that measures the same concept \u003csup\u003e19\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWithin the pool of physiological variables, Pearson correlation coefficients ranged from negligible to very high strength \u003cem\u003e(-0.01\u0026thinsp;\u0026lt;\u0026thinsp;r\u0026thinsp;\u0026lt;\u0026thinsp;0.99) (\u003c/em\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cem\u003eSupplementary Table\u0026nbsp;2)\u003c/em\u003e. The tympanic temperature of pregnant women was positively associated with skin temperature \u003cem\u003e(r\u0026thinsp;=\u0026thinsp;0.50)\u003c/em\u003e. Very high positive correlations were found between core temperature estimates and heart rate \u003cem\u003e(r\u0026thinsp;=\u0026thinsp;0.97)\u003c/em\u003e, between the Physiological Strain Index and heart rate \u003cem\u003e(r\u0026thinsp;=\u0026thinsp;0.99)\u003c/em\u003e, and between the Physiological Strain Index and the core temperature estimate \u003cem\u003e(r\u0026thinsp;=\u0026thinsp;0.99)\u003c/em\u003e. This high correlation is related to the calculation methods used for the Physiological Strain Index and the core temperature estimate based on other physiological variables.\u003c/p\u003e \u003cp\u003eBetween the pools of environmental and health variables, Pearson correlation coefficients of low to moderate strength were calculated \u003cem\u003e(0.00\u0026thinsp;\u0026lt;\u0026thinsp;r\u0026thinsp;\u0026lt;\u0026thinsp;0.54) (\u003c/em\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cem\u003eSupplementary Table\u0026nbsp;2)\u003c/em\u003e. Relative humidity was negatively associated with heart rate \u003cem\u003e(r=-0.30)\u003c/em\u003e, while positive correlations were found between skin temperature and air temperature \u003cem\u003e(r\u0026thinsp;=\u0026thinsp;0.37)\u003c/em\u003e, between skin temperature and solar radiation \u003cem\u003e(r\u0026thinsp;=\u0026thinsp;0.34)\u003c/em\u003e, and between tympanic temperature and relative humidity \u003cem\u003e(r\u0026thinsp;=\u0026thinsp;0.30)\u003c/em\u003e. The heat stress indices showed similar positive associations with skin temperature \u003cem\u003e(0.43\u0026thinsp;\u0026lt;\u0026thinsp;r\u0026thinsp;\u0026lt;\u0026thinsp;0.54)\u003c/em\u003e and tympanic temperature \u003cem\u003e(0.26\u0026thinsp;\u0026lt;\u0026thinsp;r\u0026thinsp;\u0026lt;\u0026thinsp;0.33)\u003c/em\u003e supporting the construct validity of the heat stress indices \u003cem\u003e(Supplementary Table\u0026nbsp;2)\u003c/em\u003e. Construct validity refers to how well an index measures an intended concept \u003csup\u003e19\u003c/sup\u003e. Nevertheless, the construct validity of heat stress indices was only partial, given that the negative associations of heat stress indices with heart rate \u003cem\u003e(-0.22\u0026thinsp;\u0026lt;\u0026thinsp;r \u0026lt; -0.35)\u003c/em\u003e, core temperature estimate \u003cem\u003e(-0.14\u0026thinsp;\u0026lt;\u0026thinsp;r \u0026lt; -0.30)\u003c/em\u003e, and the Physiological Strain Index \u003cem\u003e(-0.18\u0026thinsp;\u0026lt;\u0026thinsp;r \u0026lt; -0.32)\u003c/em\u003e were found. Overall, heat stress indices, as well as air temperature, black globe temperature, and solar radiation correlated most strongly with skin temperature.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\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\u003eDescriptive statistics table of merged datasets with two main pools of variables.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\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=\"char\" char=\".\" 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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable pool\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStd. Dev.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePctl. 25\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003ePctl. 75\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003eEnvironmental\u003c/p\u003e \u003cp\u003evariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eObserved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAir velocity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003em/s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e5.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAir temperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRelative humidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBlack globe temperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModelled\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSolar radiation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eJ/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6e\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.37e\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.29e\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.27e\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8.33e\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2e\u003csup\u003e7\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eCalculated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUniversal thermal climate index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eApparent temperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHeat index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWet bulb globe temperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003ePhysiological \u003c/p\u003e \u003cp\u003evariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eObserved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHeart rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ebpm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e147\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSkin temperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCalculated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCore temperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTympanic temperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhysiological strain index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e7.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eObserved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGestational age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eweeks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFitness status, 6 min walking test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003em\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e687\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMetabolic rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ekcal/kg/h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e5.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Independent and compound effects of heat stress on heat strain\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe abovementioned relatively strong correlation of environmental variables with skin temperature also became apparent when assessing the independent effect of single environmental variables and the compound effects of multiple environmental variables. First, we assessed the independent association of each environmental factor, adjusted one by the other, on the physiological variables, using mixed effect linear models with random intercepts by each study participant. Here, increases in air temperature, relative humidity, and solar radiation led to highly robust increases in skin temperature, \u003cem\u003eceteris paribus\u003c/em\u003e, but not in air velocity, black globe temperature or metabolic rate \u003cem\u003e(\u003c/em\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cem\u003e- Model 2A)\u003c/em\u003e. Further, air temperature and relative humidity were associated with tympanic temperature \u003cem\u003e(\u003c/em\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cem\u003e- Model 4A)\u003c/em\u003e, and with core temperature and the Physiological Strain Index as output variables, but the estimates were more imprecise \u003cem\u003e(\u003c/em\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cem\u003e- Model 3A, 5A)\u003c/em\u003e. However, we found no robust estimates for the joint association of environmental variables with heart rate \u003cem\u003e(\u003c/em\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cem\u003e- Model 1A)\u003c/em\u003e. We found a robust estimate for solar radiation in association with heart rate through sensitivity analysis, whereby we rematched the highest 5-minute average heart rate within the 1-hour interval prior to each environmental datapoint \u003cem\u003e(Supplementary Table\u0026nbsp;5)\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eFurther interactive effects were found between air temperature and relative humidity in association with skin temperature \u003cem\u003e(\u003c/em\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cem\u003e- Model 2B)\u003c/em\u003e. Increases in skin temperature were more rapid when the air temperature rose under conditions of relative humidity above the 50% threshold \u003cem\u003e(\u003c/em\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cem\u003e)\u003c/em\u003e. No robust evidence for this interaction was found for the association with heart rate, core temperature estimates, or tympanic temperature \u003cem\u003e(\u003c/em\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cem\u003e- Model 1B, 3B, 4B, 5B)\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eSensitivity analysis showed that the inclusion of fitness status as an additional model parameter did not considerably change the estimates \u003cem\u003e(Supplementary Table\u0026nbsp;6)\u003c/em\u003e. Nor did gestational age behave as a confounder \u003cem\u003e(Supplementary Table\u0026nbsp;7)\u003c/em\u003e. However, we found an interaction effect of gestational age with air temperature on all models \u003cem\u003e(\u003c/em\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cem\u003e- Model 1C, 2C, 3C, 4C)\u003c/em\u003e. With increasing temperature, women in their third trimester of pregnancy experienced greater increases in heart rate, skin temperature, core temperature, and tympanic temperature than women in the second trimester of pregnancy did. This interaction effect of gestational age also became evident in the model in which the Physiological Strain Index served as an output variable \u003cem\u003e(\u003c/em\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cem\u003e- Model 5D)\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMixed effect linear models with random intercepts per participant. Model A assessed the independent effect of each environmental variable on physiological variables (i.e. single models with environmental variables against each physiological variable). Model B assessed the interactive effect of air temperature and relative humidity on physiological variables, with air temperature as a continuous variable and relative humidity as a dummy variable at a 50% relative humidity threshold. Model C assessed the interactive effect of air temperature and gestational age on physiological variables, with air temperature as a continuous variable and gestational age as a dummy variable at a threshold of 27 gestational weeks, separating the second from the third trimester of pregnancy. P-values are in the Supplementary Table\u0026nbsp;4.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1:\u003c/p\u003e \u003cp\u003eHeart rate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2: Skin\u003c/p\u003e \u003cp\u003etemperature\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 3: Core\u003c/p\u003e \u003cp\u003etemperature\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 4: Tympanic temperature\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModel 5: Physiological\u003c/p\u003e \u003cp\u003eStrain Index\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003cp\u003e(98% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003cp\u003e(98% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003cp\u003e(98% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003cp\u003e(98% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003cp\u003e(98% CI)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eModel A \u0026ndash; environmental parameters and physiological parameters\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAir temperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003cp\u003e(-0.33 ; 1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003cp\u003e(0.14 ; 0.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.91e\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(-4.92e\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e ; 4.28e\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003cp\u003e(-0.01 ; 0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003cp\u003e(-0.03 ; 0.13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelative humidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003cp\u003e(-0.20 ; 0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003cp\u003e(0.01 ; 0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.21e\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(-5.19e\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e ; 7.69e\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003cp\u003e(2.36e\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e ; 0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003cp\u003e(-0.02 ; 1.91e\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAir velocity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.60\u003c/p\u003e \u003cp\u003e(-2.26 ; 1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003cp\u003e(-0.19 ; 0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.66e\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(-0.07 ; 2.20e\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003cp\u003e(-0.08 ; 0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003cp\u003e(-0.24 ; 0.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack globe temperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003cp\u003e(-0.39 ; 0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003cp\u003e(-0.05 ; 0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.38e\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(-0.01 ; 1.75e\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003cp\u003e(-0.07 ; 0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003cp\u003e(-0.04 ; 0.06)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSolar radiation / 100.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003cp\u003e(-0.07 ; 0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003cp\u003e(2.78e\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e ; 0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-5.28e\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(-1.22e\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e ; 1.12e\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.02e\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(-1.48e\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e ; 2.92e\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.27e\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(-0.01 ; 2.59e\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetabolic rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003cp\u003e(-2.52 ; 2.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003cp\u003e(-0.22 ; 0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.38e\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(-0.06 ; 8.30e\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003cp\u003e(-0.09 ; 0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003cp\u003e(-0.17 ; 0.28)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eModel B \u0026ndash; interaction between air temperature and relative humidity\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAir temperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003cp\u003e(0.35 ; 1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003cp\u003e(0.17 ; 0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003cp\u003e(0.01 ; 0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003cp\u003e(-0.01 ; 0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003cp\u003e(0.02 ; 0.10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelative humidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-9.75\u003c/p\u003e \u003cp\u003e(-60.49 ; 41.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.59\u003c/p\u003e \u003cp\u003e(-6.39 ; 1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.39\u003c/p\u003e \u003cp\u003e(-1.62 ; 0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.46\u003c/p\u003e \u003cp\u003e(-0.96 ; 7.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.64\u003c/p\u003e \u003cp\u003e(-5.71 ; 2.45)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAir temperature \u0026middot; relative humidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003cp\u003e(-1.32 ; 1.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003cp\u003e(0.01 ; 0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003cp\u003e(-0.03 ; 0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.10\u003c/p\u003e \u003cp\u003e(-0.24 ; 0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003cp\u003e(-0.08 ; 0.18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eModel C \u0026ndash; interaction between air temperature and gestational age\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAir temperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003cp\u003e(-0.09 ; 1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003cp\u003e(0.14 ; 0.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003cp\u003e(4.80e\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e ; 0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003cp\u003e(-0.07 ; 0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003cp\u003e(-0.02 ; 0.10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGestational age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-16.36\u003c/p\u003e \u003cp\u003e(-45.38 ; 13.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.69\u003c/p\u003e \u003cp\u003e(-2.97 ; 1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.52\u003c/p\u003e \u003cp\u003e(-1.29 ; 0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.22\u003c/p\u003e \u003cp\u003e(-4.22 ; -0.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.84\u003c/p\u003e \u003cp\u003e(-4.39 ; 0.73)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAir temperature \u0026middot; gestational age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003cp\u003e(-0.34 ; 1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003cp\u003e(-0.06 ; 0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003cp\u003e(-6.88e\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e ; 0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003cp\u003e(3.12e\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e ; 0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003cp\u003e(-0.02 ; 0.13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThrough composite confirmatory analysis \u003csup\u003e20\u003c/sup\u003e, we assessed the simultaneous influence of environmental parameters on the conjunction of observed physiological parameters. We constructed a model with air temperature, relative humidity, black globe temperature, solar radiation, air velocity, and metabolic rate as observable indicators of the composite artefact of heat stress, and with heart rate and skin temperature as observable indicators of the composite artefact of maternal heat strain. We found a factor loading of \u003cem\u003e0.71\u003c/em\u003e between heat stress and maternal heat strain \u003cem\u003e(\u003c/em\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cem\u003e)\u003c/em\u003e. Model estimations showed that the highest indirect effects of environmental variables were from changes in air temperature, relative humidity, and solar radiation, with loadings equal to \u003cem\u003e0.45\u003c/em\u003e, \u003cem\u003e0.46\u003c/em\u003e, and \u003cem\u003e0.55\u003c/em\u003e respectively. Black globe temperature yielded a loading of 0.17, and negative loadings were observed for air velocity \u003cem\u003e(-0.45)\u003c/em\u003e and metabolic rate \u003cem\u003e(-0.45)\u003c/em\u003e. This negative value of metabolic rate is in line with a behavioural response to reduce the level of physical activity at rising temperature. The indirect effects on maternal heat strain were positive for skin temperature \u003cem\u003e(0.87)\u003c/em\u003e and negative for heart rate \u003cem\u003e(-0.50)\u003c/em\u003e, whereby the heart rate was also influenced by behavioural change when working in the heat, which might not sufficiently be covered by estimations of metabolic rate. Even though the fit indices of our model lie within the optimal ranges \u003cem\u003e(\u003c/em\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cem\u003e)\u003c/em\u003e, the p-values indicate only robust estimates for the loadings of air temperature, solar radiation, and skin temperature \u003cem\u003e(Supplementary Table\u0026nbsp;7)\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIndices showing the model fit of the confirmatory composite analysis. The table structure is based on the study of Yazdanirad et al. \u003csup\u003e21\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAssessment of model fit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndices\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOptimal fitness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eObtained value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChi square (X2/df)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComparative fit index (CFI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRoot mean squared error of approximation (RMSEA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGoodness-of-fit index (GFI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormed fit index (NFI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncremental fit index (IFI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026ndash;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Discussion","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTaken together, our findings demonstrate that physiological indicators of heat strain in pregnant women are significantly influenced by environmental conditions while working in the heat, particularly in humid conditions and in the third trimester of pregnancy compared to the second. This study provides information on the pathophysiological effects of heat stress on pregnant women and not on the respective health outcomes of mothers or babies. We applied a series of methods, namely the Pearson product moment correlation method, mixed effect linear models with random intercepts, and confirmatory composite analysis to assess the association between each environmental and physiological indicator. This approach enabled us to disentangle the independent effects of each of the environmental variables on a set of different physiological variables and assess the potential compound effects between climate variables, while taking into consideration variance inflation factors. Overall, our findings provide an input to quantify the risk of pregnant women experiencing pathophysiological effects from working in the heat and can thus support further research on adaptive measures to alleviate heat strain.\u003c/p\u003e \u003cp\u003eOur methodology allowed us to demonstrate that the heat strain of pregnant women was more severe under conditions above a relative humidity of 50%, whereby the impact of rising temperature on skin temperature increased at a greater rate. This is due to the limited capacity of the human body to thermoregulate in hot and humid environments, because the proportion of evaporated sweat, and thus the sweating efficiency, decreases with rising relative humidity above the 35\u0026deg;C threshold \u003csup\u003e15\u003c/sup\u003e. The existence of the 35\u0026deg;C temperature threshold as a human adaptability limit to heat stress \u003csup\u003e22\u003c/sup\u003e was found to be even lower by the recent study of Vecciello et al \u003csup\u003e23\u003c/sup\u003e in non-pregnant individuals.\u003c/p\u003e \u003cp\u003eFurthermore, we found robust evidence for the increase in susceptibility to heat strain throughout pregnancy at rising temperatures across all physiological indicators, namely skin temperature, heart rate, tympanic temperature and the thereby estimated core temperature. More precisely, women in their third trimester of pregnancy experienced higher heat strain than women in their second trimester of pregnancy did. It would be interesting for future research to compare these findings with data from early pregnancy, given that a previous study has shown that the risk of heat-related stillbirth might be particularly high in early pregnancy \u003csup\u003e24\u003c/sup\u003e, whilst a global analysis and meta-analysis detected an increased risk of preterm birth at exposure to extreme heat during the last seven days of gestation \u003csup\u003e12, 25\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur study demonstrated the convergent validity of heat stress indices through correlations between each other as well as their construct validity through their correlations with skin temperature and tympanic temperature. The Heat Index, UTCI, Apparent Temperature and Wet Bulb Globe Temperature were strongly associated with each other, even though they incorporate different input variables such as relative humidity, solar radiation, black globe temperature and air velocity, in addition to air temperature. The underlying reason for the associations found is the strong weight that each of the heat stress indices assigns to air temperature as an input variable. Furthermore, the selected heat stress indices showed similar associations with both skin temperature and tympanic temperature, which supports their applicability to our study setting. Even though heat stress indices performed similarly in our assessment, we recommend including indices such as the UTCI or WBGT in heat-health warning systems, given that these indices incorporate air temperature, relative humidity and solar radiation, which showed associations to physiological variables in our study \u003cem\u003e(Section 2.2)\u003c/em\u003e. These indices could potentially be included in heat-health warning systems as a preventive measure for heat strain. Heat stress indices would need to be communicated in a targeted way, such as through simply understandable risk levels, as to allow for the public to respond appropriately.\u003c/p\u003e \u003cp\u003eThere are five key limitations to our study. First, we tested the applicability of four out of more than 100 existing heat stress indices \u003csup\u003e26\u003c/sup\u003e. However, the Heat Index, Apparent Temperature, WBGT, and UTCI are indices that are often referred to in the literature \u003csup\u003e13\u003c/sup\u003e and require input variables covered with our datasets, together with additional solar radiation modelling. We also tried to overcome this limitation by assessing the separate effects of environmental variables on maternal physiology, which increases the generalizability of the results. Second, given data availability constraints, our study design omitted variables such as clothing thermal insulation, systolic blood pressure, and diastolic blood pressure, which have been included in other studies \u003csup\u003e16, 21\u003c/sup\u003e. Likewise, water intake or cloud cover could have been added to the models as confounders. Third, we acknowledge the potential imprecision of results sourced from the matching of datasets with different temporal resolutions, especially for heart rate measurements, which fluctuate over time. We reached no clear conclusions about the effects of heat stress on heart rate and could not determine construct validity of heat stress indices on heart rate, core temperature estimates or the physiological strain index, possibly due to behavioural adaptations to heat not captured in estimated metabolic rate. Even while applying a moving average merging technique \u003cem\u003e(Supplementary Table\u0026nbsp;5)\u003c/em\u003e and confirmatory composite analysis \u003cem\u003e(Supplementary Table\u0026nbsp;9)\u003c/em\u003e, we found no robust estimates for the compound effects of air temperature and relative humidity on heart rate, possibly also due to non-accounted behavioural responses. This limitation might be resolved in future studies while measuring metabolic rate more effectively to disentangle the effects of activity and heat stress. Fourth, we did not account for individual differences in our confirmatory composite analysis; therefore, our results do not allow us to make inferences about repeated measurements of the same study participants. Such individual-level differences were only included in our mixed effect models with random intercepts. Fifth, we considered a linear association between variables and did not account for non-linearity, based on previous assessments with this data \u003csup\u003e1\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDespite these limitations, our understanding of the interplay between environmental variables and the physiology of pregnant women in The Gambia first and foremost underlines the necessity of climate change mitigation and adaptation to protect one of the most vulnerable population subgroups from heat strain. We suggest developing adequate and targeted adaptation strategies that increase the resilience of pregnant women to climatic changes, given the evidence of high levels of physiological impacts while working in hot environments. To protect the maternal health of agricultural workers, particularly during the third trimester of pregnancy, when vulnerability to heat strain increases, measures that reduce exposure to heat stress, including work schedules adapted to climatic conditions, or measures that reduce the impacts of heat stress, such as protective clothing or microclimate cooling equipment, could be tested in future research \u003csup\u003e27\u003c/sup\u003e. The establishment of heat-health warning systems with tested heat stress indices will be crucial for the implementation of adaptive measures \u003csup\u003e4\u003c/sup\u003e. Cherisch et al. \u003csup\u003e3\u003c/sup\u003e suggest interventions to protect maternal and newborn health in Africa at the individual level through behavioral change, at the infrastructural level through changes in health systems and services, at the structural level through policy and financing options, or at the environmental level through nature-based solutions. For optimal policy design and implementation, adaptive measures should be developed in close collaboration with the local community, and investigations of the respective measures should be undertaken through a participatory research approach \u003csup\u003e28\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"4. Methods","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe study population consisted of 92 pregnant women from West Kiang, The Gambia, who had been recruited by Bonell et al. \u003csup\u003e1\u003c/sup\u003e through the local antenatal clinic and provided informed consent. Participants worked either on a small-scale farm, in agriculture or in a garden for more than 3 hours per day. Acutely ill participants, including those diagnosed with pre-eclampsia, eclampsia, gestational diabetes, or who had a history of heart disease were excluded from the study. A more detailed overview of the demographics, physical characteristics, and birth outcomes of the study participants is available in \u003cem\u003eSupplementary Table\u0026nbsp;1\u003c/em\u003e. The study setting covers 9 villages within the West Kiang region: Jali, Janneh Kunda, Jiffarong, Kantong Kunda, Karantaba, Keneba, Kuli Kunda, Mandina, Manduar and Tankular. West Kiang is a district located in the Lower River Division of The Gambia and populated by 14,846 inhabitants. The main mode of subsistence is manual farming. Women work on average between 4.5 and 7.5 hours per day during pregnancy \u003csup\u003e29\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eObservational data (i) were collected in the study of Bonell et al. \u003csup\u003e1\u003c/sup\u003e, in which both environmental and physiological indicators were measured at different time points during pregnancy while the study participants performed daily agricultural tasks. The black globe temperature, dew point temperature, relative humidity, air temperature, and WBGT were collected using an HT200: heat stress WGBT meter. The wind speed was recorded with a portable Extech AN100 Thermo-Anemometer. Skin temperature and maternal heart rate were measured with an Equivital LifeMonitor. Core temperature and tympanic temperature were estimated based on skin temperature and heart rate. Metabolic rates were estimated through observed levels of activity \u003csup\u003e1\u003c/sup\u003e. The resolution of the datasets varied: Environmental parameters were available at hourly intervals, while physiological parameters were available at 5-minute intervals. Based on the environmental data points, we calculated heat stress indices with the R packages weathermetrics \u003csup\u003e30\u003c/sup\u003e, rBiometeo \u003csup\u003e31\u003c/sup\u003e and HeatStress \u003csup\u003e32\u003c/sup\u003e. The study of Bonell et al. (2022), on which this research project is based, has been approved by The Gambian Government and the Medical Research Council Unit The Gambia Joint Ethics Committee and the London School of Hygiene \u0026amp; Tropical Medicine Ethics Advisory Board in accordance with the Declaration of Helsinki \u003csup\u003e33, 1\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe retrieved modelled data (ii) from the ERA5 hourly land reanalysis dataset contains hourly surface net solar radiation for the geocodes of Jali, Janneh Kunda, Jiffarong, Kantong Kunda, Karantaba, Keneba, Kuli Kunda, Mandina, Manduar and Tankular \u003csup\u003e17\u003c/sup\u003e. In total, we extracted 80 subsets, each of which covered the modelled solar radiation for one village and month within the study period from August 2019 until March 2020. The datasets are temporally resolved at 1-hour intervals and spatially resolved on 0.09\u0026deg; horizontal and vertical grids. The surface net solar radiation is indicated in units of joules per square meter (J/m\u003csup\u003e2\u003c/sup\u003e) and represents the difference between the solar radiation reaching the Earth\u0026rsquo;s surface and the solar radiation reflected from the surface through the albedo effect \u003csup\u003e17\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eGiven the varying time formats and resolutions of the abovementioned datasets (i-ii), we created uniform time stamps in POSIX.ct format and minimized the time differences between the data points to construct the final merged dataset. Every data point from the environmental dataset was matched with the average of each physiological variable over the previous hourly interval. The influence of external heart rate fluctuations on the results was tested by rematching heart rate values with environmental data points through a 5-minute moving average interval. Observations were matched with the respective study participants\u0026rsquo; gestational age, fitness status \u0026ndash; measured through a 6-minute walking test \u0026ndash; and the geographical location. While calculating work shift lengths, we added respective estimated metabolic rates for both halves of the work shift. Each datapoint was matched with the modelled solar radiation from the ERA5 land datasets of the respective location and closest in time. The final merged dataset consisted of observational (i) and modelled data (ii). The data distribution was verified through descriptive statistics, more specifically, through summary statistics tables, boxplots, probability density functions, QQ plots, and Kolmogorov-Smirnov tests, serving as a basis for outlier detection. We removed outliers of heart rate below 60 bpm, above 200 bpm, or below a confidence interval of 85%, as well as of skin temperature, which was three standard deviations away from the mean. The environmental data points remained complete.\u003c/p\u003e\u003cp\u003eBoth separate and compound associations between environmental and health variables were investigated. The separate relationships between variables were assessed through the Pearson product moment correlation method and visualized through scatterplots. This methodology is in line with previous studies that have validated heat stress indices in the context of male farmers and mine workers in Iran \u003csup\u003e34, 16\u003c/sup\u003e. We assessed not only associations of environmental parameters with separate health variables, but also with a modified version of the physiological strain index \u0026ndash; a composite index based on skin temperature, tympanic temperature, and heart rate. Core temperature was not measured due to practical as well as safety constraints associated with pregnancy \u003csup\u003e35, 1\u003c/sup\u003e. The Pearson product moment correlation analysis allowed us to assess both the convergent and construct validity of the heat stress indices.\u003c/p\u003e\u003cp\u003eThe independent effects of the environmental variables on the physiological variables were assessed through mixed effect models with random intercepts. With this methodology, we accounted for individual differences between study participants. The variance inflation factor was computed to detect multicollinearity \u003csup\u003e36\u003c/sup\u003e. Sensitivity analysis was applied to determine the potential confounding factors of gestational age and fitness status. While integrating an interaction term between air temperature and relative humidity as well as between air temperature and gestational age, we tested for compound effects. Ultimately, the results from the mixed effect models with random intercepts were compared to those from the confirmatory composite analysis to enhance the robustness of our findings and assess the interplay between multiple environmental and physiological variables simultaneously \u003csup\u003e20\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to acknowledge the communities in West Kiang, especially the pregnant participants enrolled in the observational study.\u003c/p\u003e\n\u003cp\u003eThe original project was funded by the Wellcome Trust through the Wellcome Trust Global Health PhD Fellowship awarded to AB (216336/Z/19/Z). The funders had no role in study design, data collection, analysis, manuscript writing or decision to submit. AMVC acknowledges funding from the Swiss National Science Foundation (TMSGI3_211626).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: CB, AMVC, AB\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData collection: AB\u003c/p\u003e\n\u003cp\u003eData analysis: CB, AMVC, AB\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethodology: CB, AMVC, AB\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eVisualization: CB, AMVC, AB\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWriting: CB, AMVC, AB\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRevision: CB, AMVC, AB, AH, NM, JB, TS, TS, KAM, AMP\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnonymized data will be made available by the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBonell, A. \u003cem\u003eet al.\u003c/em\u003e Environmental heat stress on maternal physiology and fetal blood flow in pregnant subsistence farmers in The Gambia, west Africa: an observational cohort study. \u003cem\u003eThe Lancet Planetary Health\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, e968\u0026ndash;e976 (2022).\u003c/li\u003e\n\u003cli\u003eHa, S. The Changing Climate and Pregnancy Health. \u003cem\u003eCurr Envir Health Rpt \u003c/em\u003e\u003cstrong\u003e9\u003c/strong\u003e, 263\u0026ndash;275 (2022).\u003c/li\u003e\n\u003cli\u003eChersich, M. \u003cem\u003eet al.\u003c/em\u003e Climate change impacts on maternal and new-born health in Africa: Intervention options. \u003cem\u003eWJCM\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, 169 (2022).\u003c/li\u003e\n\u003cli\u003eCasanueva, A. \u003cem\u003eet al.\u003c/em\u003e Escalating environmental summer heat exposure\u0026mdash;a future threat for the European workforce. \u003cem\u003eReg Environ Change\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 40 (2020).\u003c/li\u003e\n\u003cli\u003eBeshir, M. \u0026amp; Ramsey, J. D. 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Detecting Multicollinearity in Regression Analysis. \u003cem\u003eAmerican Journal of Applied Mathematics and Statistics\u003c/em\u003e\u003cstrong\u003e8\u003c/strong\u003e, 39\u0026ndash;42 (2020).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3931205/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3931205/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eStudies on the effect of heat stress on pregnant women are scarce, particularly in highly vulnerable populations. To support the risk assessment of pregnant subsistence farmers in The Gambia, we conducted a study on the pathophysiological effects of extreme heat stress and assessed the applicability of heat stress indices. We added location-specific modelled solar radiation from ERA5 climate reanalysis to datasets from a previous observational cohort study involving on-site measurements of 92 women working in the heat. Associations between physiological and environmental variables were assessed through Pearson correlation coefficient analysis, mixed effect linear models with random intercepts per participant and confirmatory composite analysis. We found low to moderate associations \u003cem\u003e(0\u0026thinsp;\u0026lt;\u0026thinsp;r\u0026thinsp;\u0026lt;\u0026thinsp;0.54)\u003c/em\u003e and robust estimates for independent effects of environmental variables on skin- and tympanic temperature, but not on heart rate and core temperature. Skin temperature increased more significantly in conditions above a 50% relative humidity threshold, demonstrating interactive effects between air temperature and relative humidity. Pregnant women experienced stronger pathophysiological effects of heat stress in their third than in their second trimester. In conclusion, environmental heat stress significantly altered maternal heat strain, particularly under humid conditions. Based on our results, we recommend including UTCI or WBGT in local heat-health warning systems.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e","manuscriptTitle":"Integrating Observational and Modelled Data to Advance the Understanding of Heat Stress Effects on Pregnant Subsistence Farmers in The Gambia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-29 16:10:39","doi":"10.21203/rs.3.rs-3931205/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-04-05T07:59:01+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-03-14T04:57:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"9835615e-ce1d-4e83-9522-8618c71ea7f7","date":"2024-03-04T07:44:49+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-03-04T07:13:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-04T06:38:48+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-02-27T17:11:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-02-27T17:06:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-02-05T14:20:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1744a865-6afd-46b1-90e4-da21ca20cfa4","owner":[],"postedDate":"February 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":29011030,"name":"Earth and environmental sciences/Climate sciences/Climate change/Climate change impacts/Environmental health"},{"id":29011031,"name":"Earth and environmental sciences/Environmental social sciences/Climate change impacts/Environmental health"},{"id":29011032,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2024-10-28T16:05:00+00:00","versionOfRecord":{"articleIdentity":"rs-3931205","link":"https://doi.org/10.1038/s41598-024-74614-y","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-10-23 15:58:10","publishedOnDateReadable":"October 23rd, 2024"},"versionCreatedAt":"2024-02-29 16:10:39","video":"","vorDoi":"10.1038/s41598-024-74614-y","vorDoiUrl":"https://doi.org/10.1038/s41598-024-74614-y","workflowStages":[]},"version":"v1","identity":"rs-3931205","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3931205","identity":"rs-3931205","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}