City-level impact of extreme temperatures and mortality in Latin America

Imagem de Miniatura
Arquivos
art_KEPHART_Citylevel_impact_of_extreme_temperatures_and_mortality_in_2022.PDF (3.34 MB)
Citações na Scopus
52
Tipo de produção
article
Data de publicação
2022
DOI
Scopus
Web of Science
PubMed
Título da Revista
ISSN da Revista
Título do Volume
Editora
NATURE PORTFOLIO
Autores
KEPHART, Josiah L.
SANCHEZ, Brisa N.
MOORE, Jeffrey
SCHINASI, Leah H.
BAKHTSIYARAVA, Maryia
JU, Yang
CAIAFFA, Waleska T.
DRONOVA, Iryna
ARUNACHALAM, Saravanan
Citação
NATURE MEDICINE, v.28, n.8, p.1700-+, 2022
Projetos de Pesquisa
Unidades Organizacionais
Fascículo
Resumo
Climate change and urbanization are rapidly increasing human exposure to extreme ambient temperatures, yet few studies have examined temperature and mortality in Latin America. We conducted a nonlinear, distributed-lag, longitudinal analysis of daily ambient temperatures and mortality among 326 Latin American cities between 2002 and 2015. We observed 15,431,532 deaths among approximate to 2.9 billion person-years of risk. The excess death fraction of total deaths was 0.67% (95% confidence interval (CI) 0.58-0.74%) for heat-related deaths and 5.09% (95% CI 4.64-5.47%) for cold-related deaths. The relative risk of death was 1.057 (95% CI 1.046-1.067%) per 1 degrees C higher temperature during extreme heat and 1.034 (95% CI 1.028-1.040%) per 1 degrees C lower temperature during extreme cold. In Latin American cities, a substantial proportion of deaths is attributable to nonoptimal ambient temperatures. Marginal increases in observed hot temperatures are associated with steep increases in mortality risk. These risks were strongest among older adults and for cardiovascular and respiratory deaths. An ecological analysis of 326 cities in 9 countries across Latin America found that changes in ambient temperature have a substantial contribution to all-cause mortality, with small increases in extreme heat associated with steep increases in mortality risk.
Palavras-chave
URI
https://observatorio.fm.usp.br/handle/OPI/53045
Referências
  1. Abbafati C, 2020, LANCET, V396, P1204, DOI 10.1016/S0140-6736(20)30925-9
  2. [Anonymous], 2021, INT STAT CLASS DIS R
  3. [Anonymous], 2020, STAT CLIM GLOB CLIM
  4. [Anonymous], 2020, WHO METHODS DATA SOU
  5. Arbuthnott K, 2016, ENVIRON HEALTH-GLOB, V15, DOI 10.1186/s12940-016-0102-7
  6. Armstrong BG, 2014, BMC MED RES METHODOL, V14, DOI 10.1186/1471-2288-14-122
  7. Bell ML, 2008, INT J EPIDEMIOL, V37, P796, DOI 10.1093/ije/dyn094
  8. Benmarhnia T, 2015, EPIDEMIOLOGY, V26, P781, DOI 10.1097/EDE.0000000000000375
  9. Buckley JP, 2014, EPIDEMIOLOGY, V25, P242, DOI 10.1097/EDE.0000000000000051
  10. Chen K, 2018, ENVIRON INT, V116, P186, DOI 10.1016/j.envint.2018.04.021
  11. Ebi KL, 2021, LANCET, V398, P698, DOI 10.1016/S0140-6736(21)01208-3
  12. Feron S, 2019, SCI REP-UK, V9, DOI 10.1038/s41598-019-44614-4
  13. Gasparrini A, 2012, STAT MED, V31, P3821, DOI 10.1002/sim.5471
  14. Gasparrini A, 2015, LANCET, V386, P369, DOI 10.1016/S0140-6736(14)62114-0
  15. Gasparrini A, 2017, LANCET PLANET HEALTH, V1, pE360, DOI 10.1016/S2542-5196(17)30156-0
  16. Gasparrini A, 2014, STAT MED, V33, P881, DOI 10.1002/sim.5963
  17. Gasparrini A, 2011, J STAT SOFTW, V43, P1, DOI 10.18637/jss.v043.i08
  18. Gouveia N, 2003, INT J EPIDEMIOL, V32, P390, DOI 10.1093/ije/dyg077
  19. Gouveia N, 2021, SCI TOTAL ENVIRON, V772, DOI 10.1016/j.scitotenv.2021.145035
  20. Green H, 2019, ENVIRON RES, V171, P80, DOI 10.1016/j.envres.2019.01.010
  21. Guo YM, 2014, EPIDEMIOLOGY, V25, P781, DOI 10.1097/EDE.0000000000000165
  22. Hersbach H., 2018, ERA5 HOURLY DATA SIN, DOI [10.24381/cds.adbb2d47, DOI 10.24381/CDS.ADBB2D47, DOI 10.24381/CDS.ADBB2D47,2018]
  23. Jesdale BM, 2013, ENVIRON HEALTH PERSP, V121, P811, DOI 10.1289/ehp.1205919
  24. Lay CR, 2021, LANCET PLANET HEALTH, V5, pE338, DOI 10.1016/S2542-5196(21)00058-9
  25. Lofgren E, 2007, J VIROL, V81, P5429, DOI 10.1128/JVI.01680-06
  26. Magrin GO, 2014, CLIMATE CHANGE 2014: IMPACTS, ADAPTATION, AND VULNERABILITY, PT B: REGIONAL ASPECTS, P1499
  27. Martinez-Solanas E, 2021, LANCET PLANET HEALTH, V5, pE446, DOI 10.1016/S2542-5196(21)00150-9
  28. Mendez-Lazaro PA, 2018, INT J BIOMETEOROL, V62, P699, DOI 10.1007/s00484-016-1291-z
  29. Munoz-Sabater J, 2021, EARTH SYST SCI DATA, V13, P4349, DOI 10.5194/essd-13-4349-2021
  30. Pozos RS, 2002, TXB MILITARY MED MED, V1, P351
  31. Quistberg DA, 2019, J URBAN HEALTH, V96, P311, DOI 10.1007/s11524-018-00326-0
  32. Reid CE, 2012, ENVIRON HEALTH PERSP, V120, P1627, DOI 10.1289/ehp.1205251
  33. Roux AVD, 2019, GLOB CHALL, V3, DOI 10.1002/gch2.201800013
  34. ROWELL LB, 1983, CIRC RES, V52, P367, DOI 10.1161/01.RES.52.4.367
  35. Scheelbeek PFD, 2021, ENVIRON RES LETT, V16, DOI 10.1088/1748-9326/ac092c
  36. Son JY, 2016, INT J BIOMETEOROL, V60, P113, DOI 10.1007/s00484-015-1009-7
  37. Steenland K, 2022, J EXPO SCI ENV EPID, V32, P590, DOI 10.1038/s41370-021-00393-7
  38. Tuholske C, 2021, P NATL ACAD SCI USA, V118, DOI 10.1073/pnas.2024792118
  39. Turner H., 2020, GEN NONLINEAR MODELS
  40. UN, 2019, WORLD URBANIZATION P
  41. United Nations, 2019, WORLD POP PROSP
  42. Weinberger KR, 2020, ENVIRON EPIDEMIOL, V4, DOI 10.1097/EE9.0000000000000096
  43. Weinberger Kate R, 2019, Environ Epidemiol, V3, pe072, DOI 10.1097/EE9.0000000000000072
  44. Zeng WL, 2022, ENVIRON RES, V203, DOI 10.1016/j.envres.2021.111834
  45. Zhao L, 2014, NATURE, V511, P216, DOI 10.1038/nature13462
  46. Zhao Q, 2021, LANCET PLANET HEALTH, V5, pE415, DOI 10.1016/S2542-5196(21)00081-4

Coleções
Artigos e Materiais de Revistas Científicas - FM/MPR
Artigos e Materiais de Revistas Científicas - LIM/39
Artigos e Materiais de Revistas Científicas - ODS/11