Evidence map›Paper›PMID 42177497›Full record

ArticleEnvironmental health : a global access science source2026

Climate vulnerability factors for temperature-related respiratory mortality: a nationwide two-stage time-series study from 2008 to 2021.

Hsiao-Yu Yang, Shin-Chieh Chen, Hsi-Yun Chang, Yao-Yuan Wang

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Article in Environmental health : a global access science source, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

4 authors.

Hsiao-Yu YangInstitute of Environmental and Occupational Health Sciences, National Taiwan University College of Public Health, No. 17 Xuzhou Road, Taipei, 10055, Taiwan. hyang@ntu.edu.tw.
Shin-Chieh ChenInstitute of Environmental and Occupational Health Sciences, National Taiwan University College of Public Health, No. 17 Xuzhou Road, Taipei, 10055, Taiwan.
Hsi-Yun ChangInstitute of Environmental and Occupational Health Sciences, National Taiwan University College of Public Health, No. 17 Xuzhou Road, Taipei, 10055, Taiwan.
Yao-Yuan WangInstitute of Environmental and Occupational Health Sciences, National Taiwan University College of Public Health, No. 17 Xuzhou Road, Taipei, 10055, Taiwan.

Funding

Ministry of Education (MOE) in Taiwan NTU-115L9004National Science and Technology Council in Taiwan NSTC 113-2314-B-002-187-MY3
6 · The paper itself

Abstract

backgroundTaiwan is one of the fastest-warming regions globally. As climate change intensifies, understanding how vulnerability influences health outcomes critical. This study aimed to identify regional vulnerability factors for temperature-related respiratory mortality and effective region-specific adaptation policies.

methodsA two-stage time-series study was conducted using daily respiratory mortality counts aggregated by county and day. This study employed a distributed lag non-linear model to estimate the temperature-attributable mortality burden from respiratory diseases across all counties and cities in Taiwan. A two-stage meta-analysis was conducted to estimate temperature-mortality associations and quantify cold- and heat-related mortality burdens by county. Meta-regression was used to identify regional vulnerability factors modifying temperature-related mortality risk, and geographically weighted regression (GWR) was applied to characterize the spatial heterogeneity of these effects across counties.

resultsCold exposure was linked to a higher burden of respiratory disease mortality (attributable fraction [AF]: 2.03%, 95% CI: 1.10-2.95) than heat exposure (AF: 1.02%, 95% CI: 0.65-1.40). For cold-related AFs, higher proportions of Indigenous populations (3.27, 0.79-5.75), low-income populations (2.11, 0.67-3.55), greater population density (2.21, 0.46-3.96), and children (0.98, 0.35-1.61) were significantly associated with increased risk, suggesting vulnerability factors. GWR further showed that hospital bed availability had statistically significant protective effects against cold-related AF in 10 of 19 counties (β = - 5.24 to - 6.78), most pronounced in remote mountainous counties (Hualien, Taitung, Kaohsiung).

conclusionHigher proportions of Indigenous populations, low-income population, and children amplify cold-related respiratory mortality. Hospital bed availability confers the strongest protection against cold-related mortality in remote, mountainous counties. Climate adaptation policies for cold-related respiratory health should therefore be tailored to local vulnerability profiles, prioritizing healthcare expansion in geographically remote counties rather than applying uniform investment across all regions.

Indexed as

Climate ChangeCold TemperatureHot TemperatureRespiratory Tract DiseasesHumansRisk FactorsTaiwanClimate changeDistributed lag non-linear modelGeographically weighted regressionMeta-regressionTemperatureVulnerability

Identifiers

PMID42177497
PMCPMC13390211

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.