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Risk factors associated with heatwave mortality in Chinese adults over 65 years

Nature Medicine (2024)Cite this article

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Abstract

Aging populations are susceptible to heat-related mortality because of physiological factors and comorbidities. However, the understanding of individual vulnerabilities in the aging population is incomplete. In the Chinese Longitudinal Healthy Longevity Survey, we assessed daily heatwave exposure individually for 13,527 participants (median age = 89 years) and 3,249 summer mortalities during follow-up from 2008 to 2018. The mortality risk during heatwave days according to relative temperature is approximately doubled (hazard ratio (HR) range = 1.78–1.98). We found that heatwave mortality risks were increased for individuals with functional declines in mobility (HR range = 2.32–3.20), dependency in activities of daily living (HR range = 2.22–3.27), cognitive impairment (HR = 2.22) and social isolation reflected by having nobody to ask for help during difficulties (HR range = 2.14–10.21). Contrary to current understanding, older age was not predictive of heatwave mortality risk after accounting for individual functional declines; no statistical differences were detected according to sex. Beyond age as a risk factor, our findings emphasize that functional aging is an underlying factor in enhancing heatwave resilience. Assessment of functional decline and implementing care strategies are crucial for targeted prevention of mortality during heatwaves.

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Fig. 1: HRs of heatwaves using the CMA, WMO and NOAA definitions among the study population.
Fig. 2: Heatwave vulnerability analysis stratified according to functional aging characteristics.
Fig. 3: Heatwave vulnerability analysis stratified according to the ADL subscales.
Fig. 4: Heatwave vulnerability analysis stratified according to the IADL subscales.
Fig. 5: Heatwave vulnerability analysis stratified according to the MMSE subscales.

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Data availability

Researchers interested in using the CLHLS data are subject to data use agreement, available from the official repository located at https://doi.org/10.18170/DVN/WBO7LK. The environmental exposure datasets, including ERA5-Land (https://doi.org/10.24381/CDS.E2161BAC), MOD09GA v.061 (https://doi.org/10.5067/MODIS/MOD09GA.061) and ChinaHighPM2.5 (https://doi.org/10.5281/zenodo.6398971)44, are accessible.

Code availability

The code for the environmental assessment and cohort study analysis can be obtained from https://github.com/johnjiresearchlab/CLHLS_heatwave_vulnerability (ref. 46).

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Acknowledgements

The CLHLS data collection was jointly supported by the National Key R&D Program of China (no. 2018YFC2000400), the National Natural Sciences Foundation of China (no. 72061137004) and the U.S. National Institute of Aging and National Institutes of Health (no. P01AG031719) to Y.Z. We thank all scholars, survey supervisors, staff members, local medical personnel and interviewers who contributed to the longitudinal follow-up of the survey. We thank the interviewees and their families for their voluntary participation in this study. J.S.J. was supported by the National Natural Science Foundation of China (no. 82250610230), Natural Science Foundation of Beijing (no. IS23105) and Tsinghua University Vanke School of Public Health Research Fund (no. 2021PY001).

Author information

Authors and Affiliations

  1. Vanke School of Public Health, Tsinghua University, Beijing, China

    Di Xi, Linxin Liu, Min Zhang, Cunrui Huang & John S. Ji

  2. Institute for Health Metric and Evaluation, University of Washington, Seattle, WA, USA

    Katrin G. Burkart

  3. Center for Health and the Global Environment, University of Washington, Seattle, WA, USA

    Kristie Ebi

  4. Center for Healthy Aging and Development Studies, National School of Development, Peking University, Beijing, China

    Yi Zeng

  5. Center for the Study of Aging and Human Development, Duke Medical School, Durham, NC, USA

    Yi Zeng

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Contributions

J.S.J. conceived the research hypothesis that focused on the heatwave vulnerability analysis; he designed the study, secured the necessary resources, applied the statistical models, interpreted the results and took the lead role in writing and revising the manuscript. D.X. was responsible for all data management, data cleaning and analysis. She set up the computing resources, leveraged the appropriate software for the analyses, oversaw the statistical evaluations and carried out the exposure assessments using remote sensing satellite techniques. In addition, she was the lead author and drafted and revised the manuscript, and also oversaw all aspects of the research. L.L. was responsible for ensuring data integrity, conducted rigorous cross-checking on data use and evaluated the exposure–outcome relationships. K.G.B., K.E. and C.H. interpreted the results, provided insights on climate change risk and outcome relationships, and actively participated in revising the manuscript. M.Z. provided specialized statistical input on the Cox proportional hazards model and semiparametric modeling, interpreted the results, met the statistical assumptions and ensured the robustness of the model specification. Y.Z. oversaw the creation of the CLHLS, ensuring the follow-up and comprehensive data collection pertaining to health and mortality. All authors reviewed the manuscript and gave their approval for its final version.

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Correspondence to John S. Ji.

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Nature Medicine thanks Agustin Ibanez, Josiah Kephart and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Ming Yang, in collaboration with the Nature Medicine team.

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Extended data

Extended Data Fig. 1 Distribution of study population and annual peak temperature in 2008 and 2018.

a. Geographical spread of the study participants (N = 13,527) in 23 provinces in China. b. Annual peak daily air temperatures in China for the year 2008. c. Annual peak daily air temperatures in China for the year 2018.

Extended Data Fig. 2 Selection procedure of the study population.

Out of 16,954 individuals, we excluded 379 individuals under 65 years of age and 105 individuals due to missing data on key variables such as education (50), occupation (22), marital status (46), and ADL status (1), with some individuals having multiple missing values. Additionally, 2,931 individuals were lost to follow-up after the first survey, contributing person-time to the study. The final analysis included 13,527 participants, of whom 2,341 were still alive at the end of follow up. A total of 1,806 participants were lost in subsequent surveys but had contributed person-time until their loss. There were 3,249 deaths during the summer months (May to September) and 6,131 deaths outside of the summer season.

Extended Data Fig. 3 Population attributable fractions (PAF) of heatwave using CMA, WMO, and NOAA definitions.

Note: The bar plots indicate the weighted PAF of heatwave days among the overall study population (N = 13,527). Communality of environmental covariates (relative humidity, PM2.5, NDVI), basic demographic factors (age, sex) and physical and cognitive health status (ADL, IADL, MMSE) was considered and included as the adjustment. The error bars indicate the lower and upper boundaries of 95%CI.

Extended Data Fig. 4 Heatwave vulnerability analysis stratified by age group and sex.

Note: Stratification analysis by age group was adjusted for sex, while stratification analysis by sex was adjusted for age. The stratified Cox model allowed for separate baseline hazard functions for residential provinces. The correlation of the daily observations within each individual is properly accounted for by specifying their unique IDs.

Extended Data Table 1 Heatwave definitions accrding to the CMA, WMO and NOAA guidelines based on air temperature or heat index, threshold and duration
Full size table
Extended Data Table 2 Hazard ratios (HRs) and population attributable fractions (PAFs) of heatwave under all ten CMA, WMO and NOAA definitions
Full size table

Supplementary information

Supplementary Information

Supplementary Discussion, Methods Questionnaire Figs. 1–6, and Fig. 1.

Reporting Summary

Supplementary Tables

Supplementary Tables 1–16.

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Xi, D., Liu, L., Zhang, M. et al. Risk factors associated with heatwave mortality in Chinese adults over 65 years. Nat Med (2024). https://doi.org/10.1038/s41591-024-02880-4

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