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Energy and Climate

Heat Exposure among Adult Women in Rural Tamil Nadu, India
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  • Aniruddha Deshpande
    Aniruddha Deshpande
    Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia 30322, United States
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  • Noah Scovronick*
    Noah Scovronick
    Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia 30322, United States
    *Email: [email protected]
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    Orcidhttps://orcid.org/0000-0003-1410-3337
  • Thomas F. Clasen
    Thomas F. Clasen
    Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia 30322, United States
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  • Lance Waller
    Lance Waller
    Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia 30322, United States
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  • Jiantong Wang
    Jiantong Wang
    Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia 30322, United States
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  • Vigneswari Aravindalochanan
    Vigneswari Aravindalochanan
    Sri Ramachandra Institute of Higher Education and Research, Chennai 600116, India
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  • Kalpana Balakrishnan
    Kalpana Balakrishnan
    Sri Ramachandra Institute of Higher Education and Research, Chennai 600116, India
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  • Naveen Puttaswamy
    Naveen Puttaswamy
    Sri Ramachandra Institute of Higher Education and Research, Chennai 600116, India
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    Orcidhttps://orcid.org/0000-0002-8221-1682
  • Jennifer Peel
    Jennifer Peel
    Department of Epidemiology, Colorado State University, Aurora, Colorado 80523, United States
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  • Ajay Pillarisetti*
    Ajay Pillarisetti
    Division of Environmental Health Sciences, University of California Berkeley, Berkeley, California 94720, United States
    *Email: [email protected]
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Environmental Science & Technology

Cite this: Environ. Sci. Technol. 2024, 58, 1, 315–322
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https://doi.org/10.1021/acs.est.3c03461
Published December 28, 2023

Copyright © 2023 The Authors. Published by American Chemical Society. This publication is licensed under

CC-BY 4.0 .

Abstract

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Exposure to heat is associated with a substantial burden of disease and is an emerging issue in the context of climate change. Heat is of particular concern in India, which is one of the world’s hottest countries and also most populous, where relatively little is known about personal heat exposure, particularly in rural areas. Here, we leverage data collected as part of a randomized controlled trial to describe personal temperature exposures of adult women (40–79 years of age) in rural Tamil Nadu. We also characterize measurement error in heat exposure assessment by comparing personal exposure measurements to the nearest ambient monitoring stations and to commonly used modeled temperature data products. We find that temperatures differ across individuals in the same area on the same day, sometimes by more than 5 °C within the same hour, and that some individuals experience sharp increases in heat exposure in the early morning or evening, potentially a result of cooking with solid fuels. We find somewhat stronger correlations between the personal exposure measurements and the modeled products than with ambient monitors. We did not find evidence of systematic biases, which indicates that adjusting for discrepancies between different exposure measurement methods is not straightforward.

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Synopsis

This study conducts a comparative assessment between wearable sensors, modeled products, and ground station measurements for heat exposure.

Introduction

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Exposure to hot temperatures is a top environmental risk factor for global mortality. (1,2) In 2019, an estimated 308 000 deaths were attributed to heat exposure; (3) this already substantial burden is expected to increase as the climate continues to warm. (4) Heat is also associated with a substantial morbidity burden, as well as with reductions in labor productivity. (5) Heat exposure is of particular concern in India─a hot country and also the world’s most populous (6)─where a large fraction of the population works outdoors, lives in dwellings that are thermally inefficient, and is unable to access cooling technologies such as fans or air conditioners. (7)
Despite these concerns, relatively little is known about personal exposure to ambient temperatures in India, particularly in rural areas. Ambient monitoring stations are sparse and even where present may not accurately represent individual exposures, as people frequently move between indoor and outdoor environments, both in the sun and in the shade. Improving exposure assessment for temperature can enhance our understanding of the health effects of heat and cold by reducing potential biases and measurement errors associated with ambient monitors and modeled products, which are commonly used in epidemiological and burden of disease studies. (1,8,9) Personal measurements may also highlight opportunities for intervention by identifying high-exposure activities.
In this study, we leverage data collected as part of the Household Air Pollution Intervention Network (HAPIN) randomized controlled trial of cookstove replacement to describe personal temperature exposures of adult women in rural Indian villages in Tamil Nadu. In addition, we compare personal exposure measurements to the nearest identified ambient monitoring stations, as well as to two sources of modeled temperature data often used in health effect studies (due in part to the limited spatial coverage of the ambient monitoring network). (8) Through these comparisons, we assess potential measurement errors when using proxies for personal temperature exposure.

Methods

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HAPIN Trial: Overview, Study Site, and Data Collection

The HAPIN multicountry randomized controlled trial (RCT) evaluated the effect of a liquefied petroleum gas stove and fuel intervention during pregnancy on birth weight, growth, and severe pneumonia in children and on blood pressure among adult women (40–79 years of age). The trial’s research sites are in four diverse low- and middle-income settings: Guatemala, India, Rwanda, and Peru. The study began in 2017; the analysis of trial findings is ongoing. Details of the HAPIN trial have been published elsewhere. (10,11) The trial is registered with ClinicalTrials.gov (Identifier NCT029446282).
Here, we focus exclusively on non-pregnant adult women participants from the Indian site of the HAPIN trial, which consists of two districts, Villupuram, and Nagapattinam, in Tamil Nadu (Figure 1). The hilly Villupuram study site is located at an altitude of approximately 800 m above the sea level, while the Nagapattinam site, a coastal area, is located at an average elevation between 10 and 50 m above the sea level (full details on the sites, and how they were selected, are in Sambandam et al. (12)).

Figure 1

Figure 1. Map of the study villages and ambient monitors overlaid with grids from the two modeled temperature products. The districts of Villupuram (to the north) and Nagapattinam are shaded in pink.

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As part of the trial, participants were asked to wear a vest holding an Enhanced Children’s MicroPEM (ECM, RTI International, North Carolina), a robust, lightweight, and validated nephelometric and gravimetric PM2.5 monitor. Vests were codesigned with community members to minimize discomfort from wearing devices while also ensuring that samplers were properly oriented and placed. (10) The ECM weighs approximately 150 g and is capable of operating continuously for up to 48 h. These instruments measure temperature and humidity to correct real-time estimates of air pollution levels; here, we take advantage of these measurements as a representative of temperatures experienced by participants as they move through space and time. Temperature is logged every 30 s. Participants were asked to wear the vest while awake during the day and to hang the vest nearby when it is not being worn (such as while bathing or sleeping). The study evaluated exposures for 24 h periods on at least three occasions for each participant over the course of the 18-month HAPIN follow-up period.

HAPIN Ambient Monitors

In addition to personal exposure assessment, two ambient PM2.5 monitors (Met One E-Sampler, Grants Pass, Oregon) were installed in the HAPIN study districts in Tamil Nadu to measure outdoor particulate air pollution (Figure 1). They also log meteorological parameters at 5 min resolution. We use these monitors, which we refer to as “HAPIN ambient monitors,” as one point of comparison with personal monitors.

GHCN Ambient Monitoring Stations

As a second point of comparison with personal monitors, we obtained daily contemporaneous temperature measurements from the nearest established ambient monitoring stations available from the archive of the Global Historical Climatology Network (GHCN), accessed via the US National Oceanographic and Atmospheric Administration’s National Centers for Environmental Information. The data were extracted by using the R package rnoaa. The locations of the stations relative to the study locations can be found in Figure 1.

Modeled Temperature Data

As a final point of comparison, we extracted contemporaneous temperature estimates from two modeled data products. The first is the ERA5-LAND product, which is a high-resolution (9 km) reanalysis data set based on the H-TESSEL land surface model. (13) The data set provides hourly estimates at 2 m above the land surface. ERA-5 data have been increasingly used in health effect studies. (1,9,14) The second product is NASA’s GLDAS-2 product, (15) which provides temperature estimates every 3 h at a spatial resolution of 0.25 × 0.25°, a scale coarser than ERA-5 (Figure 1). GLDAS generates its estimates by fusing satellite- and ground-based observational data products, using advanced land surface modeling and data assimilation techniques. (15)

Data Analysis

First, we calculated descriptive statistics summarizing the personal exposure measurements by month and season, including mean temperature across all measurements, empirical distributions by month, and minima and maxima. Next, we assessed the correlation between personal exposures and corresponding estimates from the alternate data sources. For comparison with the temperatures measured by HAPIN ambient monitors, we matched all observations in each district to the closest corresponding station. To identify the closest GHCN station by Euclidean distance, we utilized the rnoaa R package. Finally, for comparison with the two modeled products (ERA5 and GLDAS), we used the GPS coordinates of each participant’s block of residence to assign the relevant grid square. All correlations are based on daily average exposures, as the different data sources provide measurements at varying temporal resolutions.
In order to further summarize the differences between the personal measurements and the alternate data sources, we produced Bland–Altman plots. (16) Bland–Altman plots characterize the agreement between two different data sources or measurement techniques, displaying the variance between the two measurements, the direction of any bias, and if or how the bias changes along the exposure (temperature) distribution. Bland–Altmann analyses provide associations between the bias and the average temperature for the measurement data being compared. This enables the assessment of the strength of agreement between two sources, similar to a correlation coefficient, and how agreement varies across the observed temperature distribution. All Bland–Altmann analyses were generated relative to the personal measurements, again using daily averages for consistency across data sources.
Analyses were performed in R version 4.1.3 (R Foundation for Statistical Computing, Vienna, Austria).

Ethics

The study protocol has been reviewed and approved by institutional review boards (IRBs) and Ethics Committees at Emory University (00089799), Johns Hopkins University (00007403), Sri Ramachandra Institute of Higher Education and Research (IEC-N1/16/JUL/54/49), the Indian Council of Medical Research─Health Ministry Screening Committee (5/8/4-30/(Env)/Indo-US/2016-NCD-I), Universidad del Valle de Guatemala (146-08-2016), Guatemalan Ministry of Health National Ethics Committee (11-2016), Asociación Benefica PRISMA (CE2981.17), the London School of Hygiene and Tropical Medicine (11664-5), the Rwandan National Ethics Committee (No. 357/RNEC/2018), and Washington University in St. Louis (201611159). The study has been registered with ClinicalTrials.gov (Identifier NCT02944682).

Results

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Personal Measurements

A total of 614 measurements (approximately 1.7 million data points) were recorded from 104 different participants, for an average of 5.9 times (SD 2.2, range 1–11) per participant. The first measurement was on 13 June 2018, and the last one available in this data set was recorded on 29 June 2021. The average age was 49 years (SD = 6.5 years), most participants received little formal education, and most worked in agriculture (Table 1). House construction was a mix of traditional (e.g., thatch/ceramic/mud) and modern (e.g., concrete) materials, and no household had air conditioning.
Table 1. Baseline Characteristics of the Study Population
characteristics(N = 104)
participant characteristics
age at screening
mean (SD)49.0(6.5)
highest level of education
no formal education or primary school incomplete99(95%)
primary school complete5(5%)
main occupationa
agriculture87(84%)
household10(10%)
unemployed7(7%)
other9(9%)
household characteristics
household size
mean (SD)4.4(1.3)
roof type in the main home
thatch28(27%)
concrete28(27%)
ceramic/fired tile25(24%)
other23(22%)
wall type in main home
concrete55(53%)
mud39(38%)
other10(10%)
floor type in the main homeb
concrete58(56%)
mud42(40%)
other6(6%)
air cooler/air conditioner
no104(100%)
time to take to go get water and come back (minutes)
mean (SD)34.2(32.9)
categorical household food insecurityc
none (0)83(80%)
mild (1–3)17(16%)
moderate/severe (4–8)4(4%)
baseline exposure
primary fuel type
wood104(100%)
primary heating source
do not use heating91(88%)
traditional cookstove/three-stone fire11(11%)
other2(2%)
a

Eight respondents reported more than one main occupation (7 indicated two occupations, while 1 indicated three).

b

Multiple materials may be reported for the same household, so households may appear more than once.

c

The Food Insecurity Experience Scale─Developed by the Food and Agriculture Organization of the United Nations, http://www.fao.org/3/as583e/as583e.pdf

The density functions of 30 s personal temperature exposure on the HAPIN participants by month for each of the three seasons (winter, summer, and monsoon) are reported in Figure 2. The overall average temperature exposure was 28.4 °C (SD 3.1), with seasonal differences; average exposure was 26.5 °C in winter (SD 2.9), 30.4 °C (SD 2.8) in summer, and 28.3 °C (SD 3.0) during the monsoon.

Figure 2

Figure 2. Density plots of personal exposure by month during the 2018–2021 study period. The vertical line indicates the average temperature over the study period. Dots are monthly average minimum and maximum values. Fill colors are seasons (blue is monsoon, red is summer, and green is winter).

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Figure 3 presents hourly personal exposures from multiple individuals for the same 24 h period, starting at 8:00 am on 25th November 2019. Two heat exposure-related features of interest in the study area are illustrated in this figure. First, temperatures differ across individuals on the same day, sometimes by more than 5 °C within the same hour. Second, the data suggests that some individuals experience sharp increases in heat exposure in the early morning or late afternoon/evening, potentially a result of cooking with solid fuels and, thus, proximity to stoves or other combustion sources (for example, for household heating). During these times, heat exposure for an individual can vary by several degrees within an hour.

Figure 3

Figure 3. Personal exposure of six individuals (three in each district) from 8:00 am on 11/25/2019 to 8:00 am on 11/26/2019. Each individual is represented by a unique color-shape combination, and each small colored shape represents a single temperature measurement during a given hour. Points are slightly jittered to prevent overlap. White points with a black outline are average hourly measurements across all participants within a district.

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Comparison of Exposure Sources

Summary statistics overall and by season for the different temperature sources are listed in Table 2. In general, the personal measurements tend to be intermediate between the lower temperatures reported by the modeled products and the slightly higher temperatures reported by the ambient monitors.
Table 2. Daily Summary Statistics by Data Sources (Overall and by Season)
 all seasonsmonsoonsummerwinter
data sourcemean (SD)rangemean (SD)rangemean (SD)rangemean (SD)range
HAPIN personal28.4 (3.1)19.6–36.328.3 (3.0)20.1–36.330.4 (2.8)22.4–36.326.5 (2.9)19.6–32.8
HAPIN ambient28.5 (3.2)19.1–37.628.2 (3.1)22.1–37.531.0 (3.8)19.9–37.627.8 (2.6)19.1–37.6
GHCN ambient28.9 (2.7)23.7–35.228.9 (2.7)24.2–35.230.3 (2.0)24.2–35.226.4 (1.3)24.2–35.2
ERA525.4 (2.8)19.5–32.525.2 (2.7)19.6–32.127.7 (2.1)19.6–32.123.8 (2.2)19.5–27.9
GLDAS25.3 (3.3)18.0–34.625.2 (3.2)18.1–33.728.0 (2.5)22.2–34.623.2 (2.2)18.4–28.5
Scatterplots and correlations between personal exposures and alternate data sources are shown in Figure 4. Over the full study period, there were somewhat stronger correlations with the modeled products than with the semilocal ambient monitors. Correlations varied across seasons but were uniformly highest in the monsoon season and, with the exception of the HAPIN ambient sampler, were lowest in the summer. An analogous plot to Figure 4 but restricted to extreme heat days (>35 °C) is reported in Supporting Information Figure S1.

Figure 4

Figure 4. Scatterplots and simple correlations comparing personal exposures with ambient monitors and modeled products. Red lines are 1:1 lines; points represent daily average values.

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The Bland–Altmann plots (Figure 5) indicate that the mean bias for both the ERA5-Land and GLDAS products was negative for the majority of the measurements in all seasons. In contrast, the distribution for the GHCN and HAPIN ambient sampler data is centered closer to zero, with a relatively even number of measurements with positive and negative biases. In the all-year analyses for all sources, slopes were ≤ ±0.21, indicating that the bias remained somewhat similar across the temperature distribution. Confidence bands were wider for the monitors compared to the modeled products, indicating more error. An analogous plot to Figure 5 but restricted to extreme heat days (>35 °C) is reported in Supporting Information Figure S2.

Figure 5

Figure 5. Bland–Altman plots comparing personal exposures with ambient monitors and modeled products. Blue sold lines are best-fit regression lines displaying the relationship between bias and mean changes in the daily temperature. Dashed red lines are 95% Wald confidence intervals; solid red lines are mean values.

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Discussion

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We have presented the results of opportunistic monitoring of personal temperature exposures in Tamil Nadu, India, which were collected as part of a large-scale household air pollution intervention study. To our knowledge, this is one of the very few studies of its kind in India, a country highly vulnerable to climate change and extreme heat, and the first conducted in multiple rural districts. We find that personal measurements follow expected seasonal trends but that even within a season or month, individuals experience a wide range of temperature exposure. The average exposure across the study period was 28.4 °C, but in all months, study participants experienced many exposures above 30–35 °C; exposures above 40 °C occurred in most months. We also found that differences of several degrees may be evident across individuals in the same district even within the same hour of the same day and that individuals themselves may have highly variable exposures within a short period of time. In some individuals, the daily pattern of heat exposure is suggestive of cooking or heating with solid fuels and other behaviors that likely impact exposure.
A previous study in peri-urban Telangana, India, compared personal measurements of temperature opportunistically collected from a similar monitor to the one employed in HAPIN with ambient measurements among a population of 50 participants. (17) They noted limited agreement between personal and ambient samplers and suggested that additional factors, like altitude and demographic data, may help explain the discordance between the monitoring types. To the best of our knowledge, that study did not investigate relationships between modeled ambient temperature products and personal exposure as we did here.
We also compared our data from personal monitors with contemporaneous data from the study and government ambient monitors and two gridded meteorological products. In general, the modeled products performed best, having a higher correlation with the personal measurements and smaller mean errors, as shown in the Bland–Altman analyses. This information may be relevant in the choice of exposure data when conducting observational studies on the relationship between temperature and health (or nonhealth) outcomes. Nevertheless, differences were often ≥3–5 degrees in either direction, which may be problematic for the design of interventions to protect against extreme heat. The finding that there seemed to be no clear systematic relationship between the personal measurements and the alternative data sources indicates that adjusting for the discrepancies is not straightforward. The overall implication is that epidemiological studies based on existing options for exposure assessment may introduce exposure misclassification and therefore produce imprecise or inaccurate health effect estimates. Such misclassification could also affect the burden of disease calculations.
This study has several important limitations and raises the need for additional research. One key limitation is that we present data only for adult women, which may not be representative of the study population at large. Even within our population of adult women, more measurements would enhance the robustness of the results, particularly with respect to potential seasonality in correlations with the ambient monitors and modeled products. We also emphasize that the performance of these alternative sources of data in Tamil Nadu does not necessarily hold in other study locations, particularly those with more (and closer) monitoring stations. However, for many rural locations in low- and middle-income countries, we expect that similar discrepancies will be apparent. Additionally, we note that while our measured temperature exposures fell within expected bounds, further evaluation of the use of these types of instruments as temperature monitors is warranted. Future work could compare the use of these opportunistic measures with other instrumentation, such as the well-validated temperature monitors used in occupational health assessments.
Future research can replicate these results with more data in other locations by exploring the drivers of differences between data sources and by explicitly analyzing how potential biases may influence epidemiological or econometric studies on the consequences of heat exposure. We believe that there are many opportunities to leverage existing data to answer these and other questions. Real-time particulate matter sensors have been used in hundreds of settings in dozens of contexts around the world to assess exposure to household air pollution arising from the use of solid fuels for cooking and heating. Because these real-time particulate matter monitors must measure temperature (and often also measure humidity), there is potentially a large amount of existing data that can be analyzed to characterize and describe heat exposures across a broad area of a typically unmonitored population. Furthermore, given the proliferation of these types of sensors around the globe, as part of primarily urban low-cost air monitoring networks, like the Purple Air network, there may be utility in assessing the information they provide on heat and humidity at a finer geographic and temporal scale. Such opportunistic monitoring may enable more advanced epidemiological analyses and provide a better estimation of personal temperature exposure.

Supporting Information

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The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.est.3c03461.

  • Figures showing correlations and Bland–Altman plots for days with a maximum temperature > 35 °C (PDF)

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Author Information

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  • Corresponding Authors
    • Noah Scovronick - Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia 30322, United States;  Orcidhttps://orcid.org/0000-0003-1410-3337;  Email: [email protected]
    • Ajay Pillarisetti - Division of Environmental Health Sciences, University of California Berkeley, Berkeley, California 94720, United States;  Email: [email protected]
  • Authors
    • Aniruddha Deshpande - Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia 30322, United States
    • Thomas F. Clasen - Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia 30322, United States
    • Lance Waller - Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia 30322, United States
    • Jiantong Wang - Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia 30322, United States
    • Vigneswari Aravindalochanan - Sri Ramachandra Institute of Higher Education and Research, Chennai 600116, India
    • Kalpana Balakrishnan - Sri Ramachandra Institute of Higher Education and Research, Chennai 600116, India
    • Naveen Puttaswamy - Sri Ramachandra Institute of Higher Education and Research, Chennai 600116, India;  Orcidhttps://orcid.org/0000-0002-8221-1682
    • Jennifer Peel - Department of Epidemiology, Colorado State University, Aurora, Colorado 80523, United States
  • Notes
    The authors declare no competing financial interest.

Acknowledgments

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The authors would like to express their gratitude to the households that invited them into their homes for this study. The authors also would like to thank the field teams, which worked diligently to collect the data presented here. The HAPIN study was funded in part by the U.S. National Institutes of Health (NIH; cooperative agreement 1UM1HL134590) in collaboration with the Bill & Melinda Gates Foundation (OPP1131279). This analysis was supported by an internal grant from Emory’s Climate and Health Research Incubator.

References

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This article references 17 other publications.

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    Science (Washington, DC, United States) (2017), 356 (6345), 1362-1369CODEN: SCIEAS; ISSN:0036-8075. (American Association for the Advancement of Science)
    Ests. of climate change damage are central to the design of climate policies. Here, we develop a flexible architecture for computing damages that integrates climate science, econometric analyses, and process models. We use this approach to construct spatially explicit, probabilistic, and empirically derived ests. of economic damage in the United States from climate change. The combined value of market and nonmarket damage across analyzed sectors-agriculture, crime, coastal storms, energy, human mortality, and labor-increases quadratically in global mean temp., costing roughly 1.2% of gross domestic product per +1°C on av. Importantly, risk is distributed unequally across locations, generating a large transfer of value northward and westward that increases economic inequality. By the late 21st century, the poorest third of counties are projected to experience damages between 2 and 20% of county income (90% chance) under business-as-usual emissions (Representative Concn. Pathway 8.5).
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    Science of the Total Environment (2017), 586 (), 241-254CODEN: STENDL; ISSN:0048-9697. (Elsevier B.V.)
    The objectives were (i) to conduct an overview of systematic reviews to summarize evidence from and evaluate the methodol. quality of systematic reviews assessing the impact of ambient temp. on morbidity and mortality; and (ii) to reanalyze meta-analyses of cold-induced cardiovascular morbidity in different age groups. The registration no. is PROSPERO-CRD42016047179. PubMed, Embase, the Cochrane Library, Web of Science, the Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Global Health were systematically searched to identify systematic reviews. Two reviewers independently selected studies for inclusion, extd. data, and assessed quality. The Assessment of Multiple Systematic Reviews (AMSTAR) checklist was used to assess the methodol. quality of included systematic reviews. Ests. of morbidity and mortality risk in assocn. with heat exposure, cold exposure, heatwaves, cold spells and diurnal temp. ranges (DTRs) were the primary outcomes. Twenty-eight systematic reviews were included in the overview of systematic reviews. (i) The median (interquartile range) AMSTAR scores were 7 (1.75) for quant. reviews and 3.5 (1.75) for qual. reviews. (ii) Heat exposure was identified to be assocd. with increased risk of cardiovascular, cerebrovascular and respiratory mortality, but was not found to have an impact on cardiovascular or cerebrovascular morbidity. (iii) Reanal. of the meta-analyses indicated that cold-induced cardiovascular morbidity increased in youth and middle-age (RR = 1.009, 95% CI: 1.004-1.015) as well as the elderly (RR = 1.013, 95% CI: 1.007-1.018). (iv) The definitions of temp. exposure adopted by different studies included various temp. indicators and thresholds. In conclusion, heat exposure seemed to have an adverse effect on mortality and cold-induced cardiovascular morbidity increased in the elderly. Developing definitions of temp. exposure at the regional level may contribute to more accurate evaluations of the health effects of temp.
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    Ye Xiaofang; Wolff Rodney; Yu Weiwei; Vaneckova Pavla; Pan Xiaochuan; Tong Shilu
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    OBJECTIVE: In this paper, we review the epidemiological evidence on the relationship between ambient temperature and morbidity. We assessed the methodological issues in previous studies and proposed future research directions. DATA SOURCES AND DATA EXTRACTION: We searched the PubMed database for epidemiological studies on ambient temperature and morbidity of noncommunicable diseases published in refereed English journals before 30 June 2010. Forty relevant studies were identified. Of these, 24 examined the relationship between ambient temperature and morbidity, 15 investigated the short-term effects of heat wave on morbidity, and 1 assessed both temperature and heat wave effects. DATA SYNTHESIS: Descriptive and time-series studies were the two main research designs used to investigate the temperature-morbidity relationship. Measurements of temperature exposure and health outcomes used in these studies differed widely. The majority of studies reported a significant relationship between ambient temperature and total or cause-specific morbidities. However, there were some inconsistencies in the direction and magnitude of nonlinear lag effects. The lag effect of hot temperature on morbidity was shorter (several days) compared with that of cold temperature (up to a few weeks). The temperature-morbidity relationship may be confounded or modified by sociodemographic factors and air pollution. CONCLUSIONS: There is a significant short-term effect of ambient temperature on total and cause-specific morbidities. However, further research is needed to determine an appropriate temperature measure, consider a diverse range of morbidities, and to use consistent methodology to make different studies more comparable.
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    Chua, P. L.; Ng, C. F.; Madaniyazi, L.; Seposo, X.; Salazar, M. A.; Huber, V.; Hashizume, M. Projecting Temperature-Attributable Mortality and Hospital Admissions due to Enteric Infections in the Philippines. Environ. Health Perspect. 2022, 130 (2), 027011  DOI: 10.1289/EHP9324
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    Projecting Temperature-Attributable Mortality and Hospital Admissions due to Enteric Infections in the Philippines
    Chua Paul L C; Ng Chris Fook Sheng; Hashizume Masahiro; Chua Paul L C; Ng Chris Fook Sheng; Madaniyazi Lina; Seposo Xerxes; Hashizume Masahiro; Chua Paul L C; Salazar Miguel Antonio; Madaniyazi Lina; Hashizume Masahiro; Salazar Miguel Antonio; Huber Veronika
    Environmental health perspectives (2022), 130 (2), 27011 ISSN:.
    BACKGROUND: Enteric infections cause significant deaths, and global projection studies suggest that mortality from enteric infections will increase in the future with warmer climate. However, a major limitation of these projection studies is the use of risk estimates derived from nonmortality data to project excess enteric infection mortality associated with temperature because of the lack of studies that used actual deaths. OBJECTIVE: We quantified the associations of daily temperature with both mortality and hospital admissions due to enteric infections in the Philippines. These associations were applied to projections under various climate and population change scenarios. METHODS: We modeled nonlinear temperature associations of mortality and hospital admissions due to enteric infections in 17 administrative regions of the Philippines using a two-stage time-series approach. First, we quantified nonlinear temperature associations of enteric infections by fitting generalized linear models with distributed lag nonlinear models. Second, we combined regional estimates using a meta-regression model. We projected the excess future enteric infections due to nonoptimal temperatures using regional temperature-enteric infection associations under various combinations of climate change scenarios according to representative concentration pathways (RCPs) and population change scenarios according to shared socioeconomic pathways (SSPs) for 2010-2099. RESULTS: Regional estimates for mortality and hospital admissions were significantly heterogeneous and had varying shapes in association with temperature. Generally, mortality risks were greater in high temperatures, whereas hospital admission risks were greater in low temperatures. Temperature-attributable excess deaths in 2090-2099 were projected to increase over 2010-2019 by as little as 1.3% [95% empirical confidence intervals (eCI): [Formula: see text], 6.5%] under a low greenhouse gas emission scenario (RCP 2.6) or as much as 25.5% (95% eCI: [Formula: see text], 48.2%) under a high greenhouse gas emission scenario (RCP 8.5). A moderate increase was projected for temperature-attributable excess hospital admissions, from 0.02% (95% eCI: [Formula: see text], 1.9%) under RCP 2.6 to 5.2% (95% eCI: [Formula: see text], 21.8%) under RCP 8.5 in the same period. High temperature-attributable deaths and hospital admissions due to enteric infections may occur under scenarios with high population growth in 2090-2099. DISCUSSION: In the Philippines, futures with hotter temperatures and high population growth may lead to a greater increase in temperature-related excess deaths than hospital admissions due to enteric infections. Our results highlight the need to strengthen existing primary health care interventions for diarrhea and support health adaptation policies to help reduce future enteric infections. https://doi.org/10.1289/EHP9324.
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    Johnson, M. A.; Steenland, K.; Piedrahita, R.; Clark, M. L.; Pillarisetti, A.; Balakrishnan, K.; Peel, J. L.; Naeher, L. P.; Liao, J.; Wilson, D. Air pollutant exposure and stove use assessment methods for the Household Air Pollution Intervention Network (HAPIN) trial. Environ. Health Perspect. 2020, 128 (4), 047009  DOI: 10.1289/EHP6422
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    Air pollutant exposure and stove use assessment methods for the household air pollution intervention network (HAPIN) trial
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    Environmental Health Perspectives (2020), 128 (4), 047009CODEN: EVHPAZ; ISSN:1552-9924. (U. S. Department of Health and Human Services, National Institutes of Health)
    BACKGROUND: High quality personal exposure data is fundamental to understanding the health implications of household energy interventions, interpreting analyses across assigned study arms, and characterizing exposure-response relationships for household air pollution. This paper describes the exposure data collection for the Household Air Pollution Intervention Network (HAPIN), a multicountry randomized controlled trial of liquefied petroleum gas stoves and fuel among 3,200 households in India, Rwanda, Guatemala, and Peru. OBJECTIVES: The primary objectives of the exposure assessment are to est. the exposure contrast achieved following a clean fuel intervention and to provide data for analyses of exposure-response relationships across a range of personal exposures. METHODS: Exposure measurements are being conducted over the 3-y time frame of the field study. We are measuring fine particulate matter [PM < 2:5 lm in aerodynamic diam. (PM2.5)] with the Enhanced Children's MicroPEMTM (RTI International), carbon monoxide (CO) with the USB-EL-CO (Lascar Electronics), and black carbon with the OT21 transmissometer (Magee Scientific) in pregnant women, adult women, and chil- dren <1 yr of age, primarily via multiple 24-h personal assessments (three, six, and three measurements, resp.) over the course of the 18- month follow-up period using lightwt. monitors. For children we are using an indirect measurement approach, combining data from area monitors and locator devices worn by the child. For a subsample (up to 10%) of the study population, we are doubling the frequency of measurements in order to est. the accuracy of subject-specific typical exposure ests. In addn., we are conducting ambient air monitoring to help characterize potential contributions of PM2.5 exposure from background concn. Stove use monitors (Geocene) are being used to assess compliance with the intervention, given that stove stacking (use of traditional stoves in addn. to the intervention gas stove) may occur. CONCLUSIONS: The tools and approaches being used for HAPIN to est. personal exposures build on previous efforts and take advantage of new technologies. In addn. to providing key personal exposure data for this study, we hope the application and learnings from our exposure assessment will help inform future efforts to characterize exposure to household air pollution and for other contexts.
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    Clasen, T.; Checkley, W.; Peel, J. L.; Balakrishnan, K.; McCracken, J. P.; Rosa, G.; Thompson, L. M.; Barr, D. B.; Clark, M. L.; Johnson, M. A. Design and rationale of the HAPIN study: a multicountry randomized controlled trial to assess the effect of liquefied petroleum gas stove and continuous fuel distribution. Environ. Health Perspect. 2020, 128 (4), 047008  DOI: 10.1289/EHP6407
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    Design and rationale of the HAPIN study: a multicountry randomized controlledtrial to assess the effect of liquefied petroleum gas stove and continuous fueldistribution
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    Environmental Health Perspectives (2020), 128 (4), 047008CODEN: EVHPAZ; ISSN:1552-9924. (U. S. Department of Health and Human Services, National Institutes of Health)
    BACKGROUND: Globally, nearly 3 billion people rely on solid fuels for cooking and heating, the vast majority residing in low-and middle-income countries (LMICs). The resulting household air pollution (HAP) is a leading environmental risk factor, accounting for an estd. 1.6 million premature deaths annually. Previous interventions of cleaner stoves have often failed to reduce exposure to levels that produce meaningful health improvements. There have been no multicountry field trials with liquefied petroleum gas (LPG) stoves, likely the cleanest scalable intervention. OBJECTIVE: This paper describes the design and methods of an ongoing randomized controlled trial (RCT) of LPG stove and fuel distribution in 3,200 households in 4 LMICs (India, Guatemala, Peru, and Rwanda). METHODS: We are enrolling 800 pregnant women at each of the 4 international research centers from households using biomass fuels. We are randomly assigning households to receive LPG stoves, an 18-mo supply of free LPG, and behavioral reinforcements to the control arm. The mother is being followed along with her child until the child is 1 yr old. Older adult women (40 to < 80 years of age) living in the same households are also enrolled and followed during the same period. Primary health outcomes are low birth wt., severe pneumonia incidence, stunting in the child, and high blood pressure (BP)in the older adult woman. Secondary health outcomes are also being assessed. We are assessing stove and fuel use, conducting repeated personal and kitchen exposure assessments of fine particulate matter with aerodynamic diam. ≤2.5 μm (PM2.5), carbonmonoxide (CO), and black carbon (BC), and collecting dried blood spots (DBS)and urinary samples for biomarker anal. Enrollment and data collection began in May 2018 and will continue through August 2021. The trial is registered with Clin. Trials.gov(NCT02944682). CONCLUSIONS: This study will provide evidence to inform national and global policies on scaling up LPG stove use among vulnerable populations.
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    Comparison of weather station and climate reanalysis data for modelling temperature-related mortality
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    Scientific Reports (2022), 12 (1), 5178CODEN: SRCEC3; ISSN:2045-2322. (Nature Portfolio)
    Abstr.: Epidemiol. analyses of health risks assocd. with non-optimal temp. are traditionally based on ground observations from weather stations that offer limited spatial and temporal coverage. Climate reanal. represents an alternative option that provide complete spatio-temporal exposure coverage, and yet are to be systematically explored for their suitability in assessing temp.-related health risks at a global scale. Here we provide the first comprehensive anal. over multiple regions to assess the suitability of the most recent generation of reanal. datasets for health impact assessments and evaluate their comparative performance against traditional station-based data. Our findings show that reanal. temp. from the last ERA5 products generally compare well to station observations, with similar non-optimal temp.-related risk ests. However, the anal. offers some indication of lower performance in tropical regions, with a likely underestimation of heat-related excess mortality. Reanal. data represent a valid alternative source of exposure variables in epidemiol. analyses of temp.-related risk.
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    Most studies use temp. observation data from weather stations near the analyzed region or city as the ref. point for the exposure-response assocn. Climatic reanal. data sets have already been used for climate studies, but are not yet used routinely in environmental epidemiol. We compared the mortality-temp. assocn. using weather station temp. and ERA-5 reanal. data for the 52 provincial capital cities in Spain, using time-series regression with distributed lag non-linear models. The shape of temp. distribution is very close between the weather station and ERA-5 reanal. data (correlation from 0.90 to 0.99). The overall cumulative exposure-response curves are very similar in their shape and risks ests. for cold and heat effects, although risk ests. for ERA-5 were slightly lower than for weather station temp. Reanal. data allow the estn. of the health effects of temp., even in areas located far from weather stations or without any available.
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    Identifying predictors of personal exposure to air temperature in peri-urban India
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    Science of the Total Environment (2020), 707 (), 136114CODEN: STENDL; ISSN:0048-9697. (Elsevier B.V.)
    Characterizing personal exposure to air temp. is crit. to understanding exposure measurement error in epidemiol. studies using fixed-site exposure data and to identify strategies to protect public health. To date, no study evaluating personal air temp. in the general population has been conducted in a low-and-middle income country. We used data from the CHAI study consisting of 50 adults monitored in up to six non-consecutive 24 h sessions in peri-urban south India. We quantified the agreement and assocn. between fixed-site ambient and personal air temp., and identified predictors of personal air temp. based on housing assessment, self-reported, GPS, remote sensing, and wearable camera data. Mean (SD) daytime (6 am-10 pm) av. personal air temp. was 31.2 (2.6) °C and mean nighttime (10 pm-6 am) av. temp. was 28.8 (2.8) °C. Agreement between av. personal air and fixed-site ambient temps. was limited, esp. at night when personal air temps. were underestimated by fixed-site temps. (MBE = -5.6 °C). The proportion of av. personal nighttime temp. variability explained by ambient fixed-site temps. was moderate (R2mar = 0.39); daytime assocns. were stronger for women (R2mar = 0.51) than for men (R2mar = 0.3). Other predictors of av. nighttime personal air temp. included residential altitude, ceiling height, and household income. Predictors of av. daytime personal air temp. included roof materials, GPS-tracked altitude, time working in agriculture (for women), and time travelling (for men). No biomass cooking, urban heat island, or greenspace effects were identified. R2mar between ambient fixed-site and personal air temp. indicate that ambient fixed-site temp. is only a moderately useful proxy of personal air temp. in the context of peri-urban India. Our findings suggest that people living in houses at lower altitude, with lower ceiling height and asbestos roofing sheets might be more vulnerable to heat. We also identified households with higher income, women working in agriculture and men with long commutes as disproportionately exposed to high temps.
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  1. Sijiu Wang, Tianzi Li, Priya Rajagopalan. Impact of heat exposure on health outcomes among older adults in India: an analysis across ten states. International Journal of Environmental Health Research 2025, , 1-13. https://doi.org/10.1080/09603123.2025.2461115
  2. Xi Xia, Ka Hung Chan, Yue Niu, Cong Liu, Yitong Guo, Kin-Fai Ho, Steve Hung Lam Yim, Baihan Wang, Aiden Doherty, Daniel Avery, Pei Pei, Canqing Yu, Dianjianyi Sun, Jun Lv, Junshi Chen, Liming Li, Peng Wen, Shaowei Wu, Kin Bong Hubert Lam, Haidong Kan, Zhengming Chen. Modelling personal temperature exposure using household and outdoor temperature and questionnaire data: Implications for epidemiological studies. Environment International 2024, 192 , 109060. https://doi.org/10.1016/j.envint.2024.109060
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  • Abstract

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    Figure 1

    Figure 1. Map of the study villages and ambient monitors overlaid with grids from the two modeled temperature products. The districts of Villupuram (to the north) and Nagapattinam are shaded in pink.

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    Figure 2

    Figure 2. Density plots of personal exposure by month during the 2018–2021 study period. The vertical line indicates the average temperature over the study period. Dots are monthly average minimum and maximum values. Fill colors are seasons (blue is monsoon, red is summer, and green is winter).

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    Figure 3

    Figure 3. Personal exposure of six individuals (three in each district) from 8:00 am on 11/25/2019 to 8:00 am on 11/26/2019. Each individual is represented by a unique color-shape combination, and each small colored shape represents a single temperature measurement during a given hour. Points are slightly jittered to prevent overlap. White points with a black outline are average hourly measurements across all participants within a district.

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    Figure 4

    Figure 4. Scatterplots and simple correlations comparing personal exposures with ambient monitors and modeled products. Red lines are 1:1 lines; points represent daily average values.

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    Figure 5

    Figure 5. Bland–Altman plots comparing personal exposures with ambient monitors and modeled products. Blue sold lines are best-fit regression lines displaying the relationship between bias and mean changes in the daily temperature. Dashed red lines are 95% Wald confidence intervals; solid red lines are mean values.

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  • References

    This article references 17 other publications.

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      Burkart, K. G.; Brauer, M.; Aravkin, A. Y.; Godwin, W. W.; Hay, S. I.; He, J.; Iannucci, V. C.; Larson, S. L.; Lim, S. S.; Liu, J. Estimating the cause-specific relative risks of non-optimal temperature on daily mortality: a two-part modelling approach applied to the Global Burden of Disease Study. Lancet 2021, 398 (10301), 685– 697,  DOI: 10.1016/S0140-6736(21)01700-1
      1
      Estimating the cause-specific relative risks of non-optimal temperature on daily mortality: a two-part modelling approach applied to the Global Burden of Disease Study
      Burkart Katrin G; Brauer Michael; Aravkin Aleksandr Y; Hay Simon I; Lim Stephen S; Murray Christopher J L; Zheng Peng; Stanaway Jeffrey D; Godwin William W; He Jaiwei; Iannucci Vincent C; Larson Samantha L; Liu Jiangmei; Zhou Maigeng
      Lancet (London, England) (2021), 398 (10301), 685-697 ISSN:.
      BACKGROUND: Associations between high and low temperatures and increases in mortality and morbidity have been previously reported, yet no comprehensive assessment of disease burden has been done. Therefore, we aimed to estimate the global and regional burden due to non-optimal temperature exposure. METHODS: In part 1 of this study, we linked deaths to daily temperature estimates from the ERA5 reanalysis dataset. We modelled the cause-specific relative risks for 176 individual causes of death along daily temperature and 23 mean temperature zones using a two-dimensional spline within a Bayesian meta-regression framework. We then calculated the cause-specific and total temperature-attributable burden for the countries for which daily mortality data were available. In part 2, we applied cause-specific relative risks from part 1 to all locations globally. We combined exposure-response curves with daily gridded temperature and calculated the cause-specific burden based on the underlying burden of disease from the Global Burden of Diseases, Injuries, and Risk Factors Study, for the years 1990-2019. Uncertainty from all components of the modelling chain, including risks, temperature exposure, and theoretical minimum risk exposure levels, defined as the temperature of minimum mortality across all included causes, was propagated using posterior simulation of 1000 draws. FINDINGS: We included 64·9 million individual International Classification of Diseases-coded deaths from nine different countries, occurring between Jan 1, 1980, and Dec 31, 2016. 17 causes of death met the inclusion criteria. Ischaemic heart disease, stroke, cardiomyopathy and myocarditis, hypertensive heart disease, diabetes, chronic kidney disease, lower respiratory infection, and chronic obstructive pulmonary disease showed J-shaped relationships with daily temperature, whereas the risk of external causes (eg, homicide, suicide, drowning, and related to disasters, mechanical, transport, and other unintentional injuries) increased monotonically with temperature. The theoretical minimum risk exposure levels varied by location and year as a function of the underlying cause of death composition. Estimates for non-optimal temperature ranged from 7·98 deaths (95% uncertainty interval 7·10-8·85) per 100 000 and a population attributable fraction (PAF) of 1·2% (1·1-1·4) in Brazil to 35·1 deaths (29·9-40·3) per 100 000 and a PAF of 4·7% (4·3-5·1) in China. In 2019, the average cold-attributable mortality exceeded heat-attributable mortality in all countries for which data were available. Cold effects were most pronounced in China with PAFs of 4·3% (3·9-4·7) and attributable rates of 32·0 deaths (27·2-36·8) per 100 000 and in New Zealand with 3·4% (2·9-3·9) and 26·4 deaths (22·1-30·2). Heat effects were most pronounced in China with PAFs of 0·4% (0·3-0·6) and attributable rates of 3·25 deaths (2·39-4·24) per 100 000 and in Brazil with 0·4% (0·3-0·5) and 2·71 deaths (2·15-3·37). When applying our framework to all countries globally, we estimated that 1·69 million (1·52-1·83) deaths were attributable to non-optimal temperature globally in 2019. The highest heat-attributable burdens were observed in south and southeast Asia, sub-Saharan Africa, and North Africa and the Middle East, and the highest cold-attributable burdens in eastern and central Europe, and central Asia. INTERPRETATION: Acute heat and cold exposure can increase or decrease the risk of mortality for a diverse set of causes of death. Although in most regions cold effects dominate, locations with high prevailing temperatures can exhibit substantial heat effects far exceeding cold-attributable burden. Particularly, a high burden of external causes of death contributed to strong heat impacts, but cardiorespiratory diseases and metabolic diseases could also be substantial contributors. Changes in both exposures and the composition of causes of death drove changes in risk over time. Steady increases in exposure to the risk of high temperature are of increasing concern for health. FUNDING: Bill & Melinda Gates Foundation.
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    2. 2
      Zhao, Q.; Guo, Y.; Ye, T.; Gasparrini, A.; Tong, S.; Overcenco, A.; Urban, A.; Schneider, A.; Entezari, A.; Vicedo-Cabrera, A. M. Global, regional, and national burden of mortality associated with non-optimal ambient temperatures from 2000 to 2019: a three-stage modelling study. Lancet Planet. Health 2021, 5 (7), e415– e425,  DOI: 10.1016/S2542-5196(21)00081-4
      There is no corresponding record for this reference.
    3. 3
      Institute for Health Metrics and Evaluation Global Burden of Disease 2019https://vizhub.healthdata.org/gbd-compare/. (accessed December 23, 2020).
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    4. 4
      Gasparrini, A.; Guo, Y.; Sera, F.; Vicedo-Cabrera, A. M.; Huber, V.; Tong, S.; Coelho, M.; Saldiva, P.; Lavigne, E.; Correa, P. M. Projections of temperature-related excess mortality under climate change scenarios. Lancet Planet. Health 2017, 1 (9), e360– e367,  DOI: 10.1016/S2542-5196(17)30156-0
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    5. 5
      (a) Hsiang, S.; Kopp, R.; Jina, A.; Rising, J.; Delgado, M.; Mohan, S.; Rasmussen, D.; Muir-Wood, R.; Wilson, P.; Oppenheimer, M. Estimating economic damage from climate change in the United States. Science 2017, 356 (6345), 1362– 1369,  DOI: 10.1126/science.aal4369
      5a
      Estimating economic damage from climate change in the United States
      Hsiang, Solomon; Kopp, Robert; Jina, Amir; Rising, James; Delgado, Michael; Mohan, Shashank; Rasmussen, D. J.; Muir-Wood, Robert; Wilson, Paul; Oppenheimer, Michael; Larsen, Kate; Houser, Trevor
      Science (Washington, DC, United States) (2017), 356 (6345), 1362-1369CODEN: SCIEAS; ISSN:0036-8075. (American Association for the Advancement of Science)
      Ests. of climate change damage are central to the design of climate policies. Here, we develop a flexible architecture for computing damages that integrates climate science, econometric analyses, and process models. We use this approach to construct spatially explicit, probabilistic, and empirically derived ests. of economic damage in the United States from climate change. The combined value of market and nonmarket damage across analyzed sectors-agriculture, crime, coastal storms, energy, human mortality, and labor-increases quadratically in global mean temp., costing roughly 1.2% of gross domestic product per +1°C on av. Importantly, risk is distributed unequally across locations, generating a large transfer of value northward and westward that increases economic inequality. By the late 21st century, the poorest third of counties are projected to experience damages between 2 and 20% of county income (90% chance) under business-as-usual emissions (Representative Concn. Pathway 8.5).
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      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhtVKgsb%252FL&md5=d4711e683baba5d1af38e0dabacfcf7e
      (b) Song, X.; Wang, S.; Hu, Y.; Yue, M.; Zhang, T.; Liu, Y.; Tian, J.; Shang, K. Impact of ambient temperature on morbidity and mortality: An overview of reviews. Sci. Total Environ. 2017, 586, 241– 254,  DOI: 10.1016/j.scitotenv.2017.01.212
      5b
      Impact of ambient temperature on morbidity and mortality: An overview of reviews
      Song, Xuping; Wang, Shigong; Hu, Yuling; Yue, Man; Zhang, Tingting; Liu, Yu; Tian, Jinhui; Shang, Kezheng
      Science of the Total Environment (2017), 586 (), 241-254CODEN: STENDL; ISSN:0048-9697. (Elsevier B.V.)
      The objectives were (i) to conduct an overview of systematic reviews to summarize evidence from and evaluate the methodol. quality of systematic reviews assessing the impact of ambient temp. on morbidity and mortality; and (ii) to reanalyze meta-analyses of cold-induced cardiovascular morbidity in different age groups. The registration no. is PROSPERO-CRD42016047179. PubMed, Embase, the Cochrane Library, Web of Science, the Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Global Health were systematically searched to identify systematic reviews. Two reviewers independently selected studies for inclusion, extd. data, and assessed quality. The Assessment of Multiple Systematic Reviews (AMSTAR) checklist was used to assess the methodol. quality of included systematic reviews. Ests. of morbidity and mortality risk in assocn. with heat exposure, cold exposure, heatwaves, cold spells and diurnal temp. ranges (DTRs) were the primary outcomes. Twenty-eight systematic reviews were included in the overview of systematic reviews. (i) The median (interquartile range) AMSTAR scores were 7 (1.75) for quant. reviews and 3.5 (1.75) for qual. reviews. (ii) Heat exposure was identified to be assocd. with increased risk of cardiovascular, cerebrovascular and respiratory mortality, but was not found to have an impact on cardiovascular or cerebrovascular morbidity. (iii) Reanal. of the meta-analyses indicated that cold-induced cardiovascular morbidity increased in youth and middle-age (RR = 1.009, 95% CI: 1.004-1.015) as well as the elderly (RR = 1.013, 95% CI: 1.007-1.018). (iv) The definitions of temp. exposure adopted by different studies included various temp. indicators and thresholds. In conclusion, heat exposure seemed to have an adverse effect on mortality and cold-induced cardiovascular morbidity increased in the elderly. Developing definitions of temp. exposure at the regional level may contribute to more accurate evaluations of the health effects of temp.
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      (c) Ye, X.; Wolff, R.; Yu, W.; Vaneckova, P.; Pan, X.; Tong, S. Ambient temperature and morbidity: a review of epidemiological evidence. Environ. Health Perspect. 2012, 120 (1), 19– 28,  DOI: 10.1289/ehp.1003198
      5c
      Ambient temperature and morbidity: a review of epidemiological evidence
      Ye Xiaofang; Wolff Rodney; Yu Weiwei; Vaneckova Pavla; Pan Xiaochuan; Tong Shilu
      Environmental health perspectives (2012), 120 (1), 19-28 ISSN:.
      OBJECTIVE: In this paper, we review the epidemiological evidence on the relationship between ambient temperature and morbidity. We assessed the methodological issues in previous studies and proposed future research directions. DATA SOURCES AND DATA EXTRACTION: We searched the PubMed database for epidemiological studies on ambient temperature and morbidity of noncommunicable diseases published in refereed English journals before 30 June 2010. Forty relevant studies were identified. Of these, 24 examined the relationship between ambient temperature and morbidity, 15 investigated the short-term effects of heat wave on morbidity, and 1 assessed both temperature and heat wave effects. DATA SYNTHESIS: Descriptive and time-series studies were the two main research designs used to investigate the temperature-morbidity relationship. Measurements of temperature exposure and health outcomes used in these studies differed widely. The majority of studies reported a significant relationship between ambient temperature and total or cause-specific morbidities. However, there were some inconsistencies in the direction and magnitude of nonlinear lag effects. The lag effect of hot temperature on morbidity was shorter (several days) compared with that of cold temperature (up to a few weeks). The temperature-morbidity relationship may be confounded or modified by sociodemographic factors and air pollution. CONCLUSIONS: There is a significant short-term effect of ambient temperature on total and cause-specific morbidities. However, further research is needed to determine an appropriate temperature measure, consider a diverse range of morbidities, and to use consistent methodology to make different studies more comparable.
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      (d) Zhao, M.; Lee, J. K. W.; Kjellstrom, T.; Cai, W. Assessment of the economic impact of heat-related labor productivity loss: a systematic review. Clim. Change 2021, 167 (1), 1– 16,  DOI: 10.1007/s10584-021-03160-7
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      United Nations Department of Economic and Social Affairs. World Population Prospects 2022https://population.un.org/wpp/.
      There is no corresponding record for this reference.
    7. 7
      McKinsey Global Health Institute. Will India Get Too Hot To Work? 2020.
      There is no corresponding record for this reference.
    8. 8
      (a) Banerjee, R.; Maharaj, R. Heat, infant mortality, and adaptation: Evidence from India. J. Dev. Econ. 2020, 143, 102378  DOI: 10.1016/j.jdeveco.2019.102378
      There is no corresponding record for this reference.
      (b) Fu, S. H.; Gasparrini, A.; Rodriguez, P. S.; Jha, P. Mortality attributable to hot and cold ambient temperatures in India: a nationally representative case-crossover study. PLoS Med. 2018, 15 (7), e1002619  DOI: 10.1371/journal.pmed.1002619
      There is no corresponding record for this reference.
    9. 9
      Chua, P. L.; Ng, C. F.; Madaniyazi, L.; Seposo, X.; Salazar, M. A.; Huber, V.; Hashizume, M. Projecting Temperature-Attributable Mortality and Hospital Admissions due to Enteric Infections in the Philippines. Environ. Health Perspect. 2022, 130 (2), 027011  DOI: 10.1289/EHP9324
      9
      Projecting Temperature-Attributable Mortality and Hospital Admissions due to Enteric Infections in the Philippines
      Chua Paul L C; Ng Chris Fook Sheng; Hashizume Masahiro; Chua Paul L C; Ng Chris Fook Sheng; Madaniyazi Lina; Seposo Xerxes; Hashizume Masahiro; Chua Paul L C; Salazar Miguel Antonio; Madaniyazi Lina; Hashizume Masahiro; Salazar Miguel Antonio; Huber Veronika
      Environmental health perspectives (2022), 130 (2), 27011 ISSN:.
      BACKGROUND: Enteric infections cause significant deaths, and global projection studies suggest that mortality from enteric infections will increase in the future with warmer climate. However, a major limitation of these projection studies is the use of risk estimates derived from nonmortality data to project excess enteric infection mortality associated with temperature because of the lack of studies that used actual deaths. OBJECTIVE: We quantified the associations of daily temperature with both mortality and hospital admissions due to enteric infections in the Philippines. These associations were applied to projections under various climate and population change scenarios. METHODS: We modeled nonlinear temperature associations of mortality and hospital admissions due to enteric infections in 17 administrative regions of the Philippines using a two-stage time-series approach. First, we quantified nonlinear temperature associations of enteric infections by fitting generalized linear models with distributed lag nonlinear models. Second, we combined regional estimates using a meta-regression model. We projected the excess future enteric infections due to nonoptimal temperatures using regional temperature-enteric infection associations under various combinations of climate change scenarios according to representative concentration pathways (RCPs) and population change scenarios according to shared socioeconomic pathways (SSPs) for 2010-2099. RESULTS: Regional estimates for mortality and hospital admissions were significantly heterogeneous and had varying shapes in association with temperature. Generally, mortality risks were greater in high temperatures, whereas hospital admission risks were greater in low temperatures. Temperature-attributable excess deaths in 2090-2099 were projected to increase over 2010-2019 by as little as 1.3% [95% empirical confidence intervals (eCI): [Formula: see text], 6.5%] under a low greenhouse gas emission scenario (RCP 2.6) or as much as 25.5% (95% eCI: [Formula: see text], 48.2%) under a high greenhouse gas emission scenario (RCP 8.5). A moderate increase was projected for temperature-attributable excess hospital admissions, from 0.02% (95% eCI: [Formula: see text], 1.9%) under RCP 2.6 to 5.2% (95% eCI: [Formula: see text], 21.8%) under RCP 8.5 in the same period. High temperature-attributable deaths and hospital admissions due to enteric infections may occur under scenarios with high population growth in 2090-2099. DISCUSSION: In the Philippines, futures with hotter temperatures and high population growth may lead to a greater increase in temperature-related excess deaths than hospital admissions due to enteric infections. Our results highlight the need to strengthen existing primary health care interventions for diarrhea and support health adaptation policies to help reduce future enteric infections. https://doi.org/10.1289/EHP9324.
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    10. 10
      Johnson, M. A.; Steenland, K.; Piedrahita, R.; Clark, M. L.; Pillarisetti, A.; Balakrishnan, K.; Peel, J. L.; Naeher, L. P.; Liao, J.; Wilson, D. Air pollutant exposure and stove use assessment methods for the Household Air Pollution Intervention Network (HAPIN) trial. Environ. Health Perspect. 2020, 128 (4), 047009  DOI: 10.1289/EHP6422
      10
      Air pollutant exposure and stove use assessment methods for the household air pollution intervention network (HAPIN) trial
      Johnson, Michael A.; Steenland, Kyle; Piedrahita, Ricardo; Clark, Maggie L.; Pillarisetti, Ajay; Balakrishnan, Kalpana; Peel, Jennifer L.; Naeher, Luke P.; Liao, Jiawen; Wilson, Daniel; Sarnat, Jeremy; Underhill, Lindsay J.; Burrowes, Vanessa; McCracken, John P.; Rosa, Ghislaine; Rosenthal, Joshua; Sambandam, Sankar; de Leon, Oscar; Kirby, Miles A.; Kearns, Katherine; Checkley, William; Clasen, Thomas
      Environmental Health Perspectives (2020), 128 (4), 047009CODEN: EVHPAZ; ISSN:1552-9924. (U. S. Department of Health and Human Services, National Institutes of Health)
      BACKGROUND: High quality personal exposure data is fundamental to understanding the health implications of household energy interventions, interpreting analyses across assigned study arms, and characterizing exposure-response relationships for household air pollution. This paper describes the exposure data collection for the Household Air Pollution Intervention Network (HAPIN), a multicountry randomized controlled trial of liquefied petroleum gas stoves and fuel among 3,200 households in India, Rwanda, Guatemala, and Peru. OBJECTIVES: The primary objectives of the exposure assessment are to est. the exposure contrast achieved following a clean fuel intervention and to provide data for analyses of exposure-response relationships across a range of personal exposures. METHODS: Exposure measurements are being conducted over the 3-y time frame of the field study. We are measuring fine particulate matter [PM < 2:5 lm in aerodynamic diam. (PM2.5)] with the Enhanced Children's MicroPEMTM (RTI International), carbon monoxide (CO) with the USB-EL-CO (Lascar Electronics), and black carbon with the OT21 transmissometer (Magee Scientific) in pregnant women, adult women, and chil- dren <1 yr of age, primarily via multiple 24-h personal assessments (three, six, and three measurements, resp.) over the course of the 18- month follow-up period using lightwt. monitors. For children we are using an indirect measurement approach, combining data from area monitors and locator devices worn by the child. For a subsample (up to 10%) of the study population, we are doubling the frequency of measurements in order to est. the accuracy of subject-specific typical exposure ests. In addn., we are conducting ambient air monitoring to help characterize potential contributions of PM2.5 exposure from background concn. Stove use monitors (Geocene) are being used to assess compliance with the intervention, given that stove stacking (use of traditional stoves in addn. to the intervention gas stove) may occur. CONCLUSIONS: The tools and approaches being used for HAPIN to est. personal exposures build on previous efforts and take advantage of new technologies. In addn. to providing key personal exposure data for this study, we hope the application and learnings from our exposure assessment will help inform future efforts to characterize exposure to household air pollution and for other contexts.
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    11. 11
      Clasen, T.; Checkley, W.; Peel, J. L.; Balakrishnan, K.; McCracken, J. P.; Rosa, G.; Thompson, L. M.; Barr, D. B.; Clark, M. L.; Johnson, M. A. Design and rationale of the HAPIN study: a multicountry randomized controlled trial to assess the effect of liquefied petroleum gas stove and continuous fuel distribution. Environ. Health Perspect. 2020, 128 (4), 047008  DOI: 10.1289/EHP6407
      11
      Design and rationale of the HAPIN study: a multicountry randomized controlledtrial to assess the effect of liquefied petroleum gas stove and continuous fueldistribution
      Clasen, Thomas; Checkley, William; Peel, Jennifer L.; Balakrishnan, Kalpana; McCracken, John P.; Rosa, Ghislaine; Thompson, Lisa M.; Barr, Dana Boyd; Clark, Maggie L.; Johnson, Michael A.; Waller, Lance A.; Jaacks, Lindsay M.; Steenland, Kyle; Jaime Miranda, J.; Chang, Howard H.; Kim, Dong-Yun; McCollum, Eric D.; Davila-Roman, Victor G.; Papageorghiou, Aris; Rosenthal, Joshua P.
      Environmental Health Perspectives (2020), 128 (4), 047008CODEN: EVHPAZ; ISSN:1552-9924. (U. S. Department of Health and Human Services, National Institutes of Health)
      BACKGROUND: Globally, nearly 3 billion people rely on solid fuels for cooking and heating, the vast majority residing in low-and middle-income countries (LMICs). The resulting household air pollution (HAP) is a leading environmental risk factor, accounting for an estd. 1.6 million premature deaths annually. Previous interventions of cleaner stoves have often failed to reduce exposure to levels that produce meaningful health improvements. There have been no multicountry field trials with liquefied petroleum gas (LPG) stoves, likely the cleanest scalable intervention. OBJECTIVE: This paper describes the design and methods of an ongoing randomized controlled trial (RCT) of LPG stove and fuel distribution in 3,200 households in 4 LMICs (India, Guatemala, Peru, and Rwanda). METHODS: We are enrolling 800 pregnant women at each of the 4 international research centers from households using biomass fuels. We are randomly assigning households to receive LPG stoves, an 18-mo supply of free LPG, and behavioral reinforcements to the control arm. The mother is being followed along with her child until the child is 1 yr old. Older adult women (40 to < 80 years of age) living in the same households are also enrolled and followed during the same period. Primary health outcomes are low birth wt., severe pneumonia incidence, stunting in the child, and high blood pressure (BP)in the older adult woman. Secondary health outcomes are also being assessed. We are assessing stove and fuel use, conducting repeated personal and kitchen exposure assessments of fine particulate matter with aerodynamic diam. ≤2.5 μm (PM2.5), carbonmonoxide (CO), and black carbon (BC), and collecting dried blood spots (DBS)and urinary samples for biomarker anal. Enrollment and data collection began in May 2018 and will continue through August 2021. The trial is registered with Clin. Trials.gov(NCT02944682). CONCLUSIONS: This study will provide evidence to inform national and global policies on scaling up LPG stove use among vulnerable populations.
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    12. 12
      Sambandam, S.; Mukhopadhyay, K.; Sendhil, S.; Ye, W.; Pillarisetti, A.; Thangavel, G.; Natesan, D.; Ramasamy, R.; Natarajan, A.; Aravindalochanan, V. Exposure contrasts associated with a liquefied petroleum gas (LPG) intervention at potential field sites for the multi-country household air pollution intervention network (HAPIN) trial in India: results from pilot phase activities in rural Tamil Nadu. BMC Public Health 2020, 20 (1), 1– 13,  DOI: 10.1186/s12889-020-09865-1
      There is no corresponding record for this reference.
    13. 13
      ERA5-Land Hourly Data From 1950 to Present. https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=overview (accessed 2021 November).
      There is no corresponding record for this reference.
    14. 14
      (a) Mistry, M. N.; Schneider, R.; Masselot, P.; Royé, D.; Armstrong, B.; Kyselý, J.; Orru, H.; Sera, F.; Tong, S.; Lavigne, É.; Urban, A. Comparison of weather station and climate reanalysis data for modelling temperature-related mortality. Sci. Rep. 2022, 12 (1), 5178  DOI: 10.1038/s41598-022-11769-6
      14a
      Comparison of weather station and climate reanalysis data for modelling temperature-related mortality
      Mistry, Malcolm N.; Schneider, Rochelle; Masselot, Pierre; Roye, Dominic; Armstrong, Ben; Kysely, Jan; Orru, Hans; Sera, Francesco; Tong, Shilu; Lavigne, Eric; Urban, Ales; Madureira, Joana; Garcia-Leon, David; Ibarreta, Dolores; Ciscar, Juan-Carlos; Feyen, Luc; de Schrijver, Evan; de Sousa Zanotti Stagliorio Coelho, Micheline; Pascal, Mathilde; Tobias, Aurelio; Multi-Country Multi-City Collaborative Research Network; Guo, Yuming; Vicedo-Cabrera, Ana M.; Gasparrini, Antonio
      Scientific Reports (2022), 12 (1), 5178CODEN: SRCEC3; ISSN:2045-2322. (Nature Portfolio)
      Abstr.: Epidemiol. analyses of health risks assocd. with non-optimal temp. are traditionally based on ground observations from weather stations that offer limited spatial and temporal coverage. Climate reanal. represents an alternative option that provide complete spatio-temporal exposure coverage, and yet are to be systematically explored for their suitability in assessing temp.-related health risks at a global scale. Here we provide the first comprehensive anal. over multiple regions to assess the suitability of the most recent generation of reanal. datasets for health impact assessments and evaluate their comparative performance against traditional station-based data. Our findings show that reanal. temp. from the last ERA5 products generally compare well to station observations, with similar non-optimal temp.-related risk ests. However, the anal. offers some indication of lower performance in tropical regions, with a likely underestimation of heat-related excess mortality. Reanal. data represent a valid alternative source of exposure variables in epidemiol. analyses of temp.-related risk.
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      (b) Royé, D.; Íñiguez, C.; Tobías, A. Comparison of temperature–mortality associations using observed weather station and reanalysis data in 52 Spanish cities. Environ. Res. 2020, 183, 109237  DOI: 10.1016/j.envres.2020.109237
      14b
      Comparison of temperature-mortality associations using observed weather station and reanalysis data in 52 Spanish cities
      Roye, Dominic; Iniguez, Carmen; Tobias, Aurelio
      Environmental Research (2020), 183 (), 109237CODEN: ENVRAL; ISSN:0013-9351. (Elsevier)
      Most studies use temp. observation data from weather stations near the analyzed region or city as the ref. point for the exposure-response assocn. Climatic reanal. data sets have already been used for climate studies, but are not yet used routinely in environmental epidemiol. We compared the mortality-temp. assocn. using weather station temp. and ERA-5 reanal. data for the 52 provincial capital cities in Spain, using time-series regression with distributed lag non-linear models. The shape of temp. distribution is very close between the weather station and ERA-5 reanal. data (correlation from 0.90 to 0.99). The overall cumulative exposure-response curves are very similar in their shape and risks ests. for cold and heat effects, although risk ests. for ERA-5 were slightly lower than for weather station temp. Reanal. data allow the estn. of the health effects of temp., even in areas located far from weather stations or without any available.
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      Bland, J. M.; Altman, D. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1986, 327 (8476), 307– 310,  DOI: 10.1016/S0140-6736(86)90837-8
      There is no corresponding record for this reference.
    17. 17
      Milà, C.; Curto, A.; Dimitrova, A.; Sreekanth, V.; Kinra, S.; Marshall, J. D.; Tonne, C. Identifying predictors of personal exposure to air temperature in peri-urban India. Sci. Total Environ. 2020, 707, 136114  DOI: 10.1016/j.scitotenv.2019.136114
      17
      Identifying predictors of personal exposure to air temperature in peri-urban India
      Mila, Carles; Curto, Ariadna; Dimitrova, Asya; Sreekanth, V.; Kinra, Sanjay; Marshall, Julian D.; Tonne, Cathryn
      Science of the Total Environment (2020), 707 (), 136114CODEN: STENDL; ISSN:0048-9697. (Elsevier B.V.)
      Characterizing personal exposure to air temp. is crit. to understanding exposure measurement error in epidemiol. studies using fixed-site exposure data and to identify strategies to protect public health. To date, no study evaluating personal air temp. in the general population has been conducted in a low-and-middle income country. We used data from the CHAI study consisting of 50 adults monitored in up to six non-consecutive 24 h sessions in peri-urban south India. We quantified the agreement and assocn. between fixed-site ambient and personal air temp., and identified predictors of personal air temp. based on housing assessment, self-reported, GPS, remote sensing, and wearable camera data. Mean (SD) daytime (6 am-10 pm) av. personal air temp. was 31.2 (2.6) °C and mean nighttime (10 pm-6 am) av. temp. was 28.8 (2.8) °C. Agreement between av. personal air and fixed-site ambient temps. was limited, esp. at night when personal air temps. were underestimated by fixed-site temps. (MBE = -5.6 °C). The proportion of av. personal nighttime temp. variability explained by ambient fixed-site temps. was moderate (R2mar = 0.39); daytime assocns. were stronger for women (R2mar = 0.51) than for men (R2mar = 0.3). Other predictors of av. nighttime personal air temp. included residential altitude, ceiling height, and household income. Predictors of av. daytime personal air temp. included roof materials, GPS-tracked altitude, time working in agriculture (for women), and time travelling (for men). No biomass cooking, urban heat island, or greenspace effects were identified. R2mar between ambient fixed-site and personal air temp. indicate that ambient fixed-site temp. is only a moderately useful proxy of personal air temp. in the context of peri-urban India. Our findings suggest that people living in houses at lower altitude, with lower ceiling height and asbestos roofing sheets might be more vulnerable to heat. We also identified households with higher income, women working in agriculture and men with long commutes as disproportionately exposed to high temps.
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    • Figures showing correlations and Bland–Altman plots for days with a maximum temperature > 35 °C (PDF)


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