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Record W2751760100 · doi:10.1289/ehp1026

Heat Wave and Mortality: A Multicountry, Multicommunity Study

2017· article· en· W2751760100 on OpenAlexaff
Yuming Guo, Antonio Gasparrini, Ben Armstrong, Benjawan Tawatsupa, Aurelio Tobı́as, Éric Lavigne, Micheline de Sousa Zanotti Stagliorio Coêlho, Xiaochuan Pan, Ho Kim, Masahiro Hashizume, Yasushi Honda, Yue Leon Guo, Chang‐Fu Wu, Antonella Zanobetti, Joel Schwartz, Michelle L. Bell, Matteo Scortichini, Paola Michelozzi, Kornwipa Punnasiri, Shanshan Li, Linwei Tian, Samuel David Osorio García, Xerxes Seposo, Ala Overcenco, Ariana Zeka, Patrick Goodman, Trần Ngọc Đăng, Do Van Dung, Fatemeh Mayvaneh, Paulo Hilário Nascimento Saldiva, Gail Williams, Shilu Tong

Bibliographic record

VenueEnvironmental Health Perspectives · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsUniversity of OttawaHealth Canada
FundersNational Health and Medical Research CouncilMedical Research CouncilNational Research Foundation of KoreaAustralian Research CouncilNational Research Foundation
KeywordsHeat waveDistributed lagPercentileContext (archaeology)DemographyEnvironmental scienceMedicineGeographyClimate changeMathematicsGeologyStatistics

Abstract

fetched live from OpenAlex

BACKGROUND: Few studies have examined variation in the associations between heat waves and mortality in an international context. OBJECTIVES: We aimed to systematically examine the impacts of heat waves on mortality with lag effects internationally. METHODS: We collected daily data of temperature and mortality from 400 communities in 18 countries/regions and defined 12 types of heat waves by combining community-specific daily mean temperature ≥90th, 92.5th, 95th, and 97.5th percentiles of temperature with duration ≥2, 3, and 4 d. We used time-series analyses to estimate the community-specific heat wave-mortality relation over lags of 0-10 d. Then, we applied meta-analysis to pool heat wave effects at the country level for cumulative and lag effects for each type of heat wave definition. RESULTS: Heat waves of all definitions had significant cumulative associations with mortality in all countries, but varied by community. The higher the temperature threshold used to define heat waves, the higher heat wave associations on mortality. However, heat wave duration did not modify the impacts. The association between heat waves and mortality appeared acutely and lasted for 3 and 4 d. Heat waves had higher associations with mortality in moderate cold and moderate hot areas than cold and hot areas. There were no added effects of heat waves on mortality in all countries/regions, except for Brazil, Moldova, and Taiwan. Heat waves defined by daily mean and maximum temperatures produced similar heat wave-mortality associations, but not daily minimum temperature. CONCLUSIONS: Results indicate that high temperatures create a substantial health burden, and effects of high temperatures over consecutive days are similar to what would be experienced if high temperature days occurred independently. People living in moderate cold and moderate hot areas are more sensitive to heat waves than those living in cold and hot areas. Daily mean and maximum temperatures had similar ability to define heat waves rather than minimum temperature. https://doi.org/10.1289/EHP1026.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0020.007
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.100
GPT teacher head0.383
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations585
Published2017
Admission routes1
Has abstractyes

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