Modification of temperature-related human mortality by area-level socioeconomic and demographic characteristics in Latin American cities

Carregando...
Imagem de Miniatura
Citações na Scopus
4
Tipo de produção
article
Data de publicação
2023
Título da Revista
ISSN da Revista
Título do Volume
Editora
PERGAMON-ELSEVIER SCIENCE LTD
Autores
BAKHTSIYARAVA, Maryia
SCHINASI, Leah H.
SANCHEZ, Brisa N.
DRONOVA, Iryna
KEPHART, Josiah L.
JU, Yang
CAIAFFA, Waleska Teixeira
O'NEILL, Marie S.
YAMADA, Goro
Citação
SOCIAL SCIENCE & MEDICINE, v.317, article ID 115526, 11p, 2023
Projetos de Pesquisa
Unidades Organizacionais
Fascículo
Resumo
Background: In Latin America, where climate change and rapid urbanization converge, non-optimal ambient temperatures contribute to excess mortality. However, little is known about area-level characteristics that confer vulnerability to temperature-related mortality. Objectives: Explore city-level socioeconomic and demographic characteristics associated with temperature-related mortality in Latin American cities. Methods: The dependent variables quantify city-specific associations between temperature and mortality: heatand cold-related excess death fractions (EDF, or percentages of total deaths attributed to cold/hot temperatures), and the relative mortality risk (RR) associated with 1 degrees C difference in temperature in 325 cities during 2002-2015. Random effects meta-regressions were used to investigate whether EDFs and RRs associated with heat and cold varied by city-level characteristics, including population size, population density, built-up area, age-standardized mortality rate, poverty, living conditions, educational attainment, income inequality, and residential segregation by education level. Results: We find limited effect modification of cold-related mortality by city-level demographic and socioeconomic characteristics and several unexpected associations for heat-related mortality. For example, cities in the highest compared to the lowest tertile of income inequality have all-age cold-related excess mortality that is, on average, 3.45 percentage points higher (95% CI: 0.33, 6.56). Higher poverty and higher segregation were also associated with higher cold EDF among those 65 and older. Large, densely populated cities, and cities with high levels of poverty and income inequality experience smaller heat EDFs compared to smaller and less densely populated cities, and cities with little poverty and income inequality. Discussion: Evidence of effect modification of cold-related mortality in Latin American cities was limited, and unexpected patterns of modification of heat-related mortality were observed. Socioeconomic deprivation may impact cold-related mortality, particularly among the elderly. The findings of higher levels of poverty and income inequality associated with lower heat-related mortality deserve further investigation given the increasing importance of urban adaptation to climate change.
Palavras-chave
Temperature -related mortality, Urban health, Latin America, Climate change
Referências
  1. Adger WN, 2006, GLOBAL ENVIRON CHANG, V16, P268, DOI 10.1016/j.gloenvcha.2006.02.006
  2. Astrom DO, 2020, SCAND J PUBLIC HEALT, V48, P428, DOI 10.1177/1403494818801615
  3. Avashia V, 2021, LANDSCAPE URBAN PLAN, V212, DOI 10.1016/j.landurbplan.2021.104107
  4. Balbus JM, 2009, J OCCUP ENVIRON MED, V51, P33, DOI 10.1097/JOM.0b013e318193e12e
  5. Barnard Lucy F. Telfar, 2008, Reviews on Environmental Health, V23, P203
  6. Basu R, 2008, AM J EPIDEMIOL, V168, P632, DOI 10.1093/aje/kwn170
  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. Benmarhnia T, 2014, ENVIRON HEALTH-GLOB, V13, DOI 10.1186/1476-069X-13-53
  10. Bilal U, 2021, NAT MED, V27, P463, DOI 10.1038/s41591-020-01214-4
  11. Burkart KG, 2021, LANCET, V398, P685, DOI 10.1016/S0140-6736(21)01700-1
  12. Campbell S, 2018, HEALTH PLACE, V53, P210, DOI 10.1016/j.healthplace.2018.08.017
  13. Cohen F., MORTALITY TEMPERATUR, V81
  14. Gasparrini A, 2012, STAT MED, V31, P3821, DOI 10.1002/sim.5471
  15. Gasparrini A, 2015, LANCET, V386, P369, DOI 10.1016/S0140-6736(14)62114-0
  16. Gasparrini A, 2014, STAT MED, V33, P881, DOI 10.1002/sim.5963
  17. Gasparrini A, 2013, BMC MED RES METHODOL, V13, DOI 10.1186/1471-2288-13-1
  18. Gasparrini A, 2011, J STAT SOFTW, V43, P1, DOI 10.18637/jss.v043.i08
  19. Geirinhas JL, 2020, INT J BIOMETEOROL, V64, P1319, DOI 10.1007/s00484-020-01908-x
  20. Gouveia N, 2003, INT J EPIDEMIOL, V32, P390, DOI 10.1093/ije/dyg077
  21. Green H, 2019, ENVIRON RES, V171, P80, DOI 10.1016/j.envres.2019.01.010
  22. Grimm NB, 2008, SCIENCE, V319, P756, DOI 10.1126/science.1150195
  23. Gronlund Carina J, 2014, Curr Epidemiol Rep, V1, P165
  24. Hajat S, 2007, OCCUP ENVIRON MED, V64, P93, DOI 10.1136/oem.2006.029017
  25. Hajat S, 2010, J EPIDEMIOL COMMUN H, V64, P753, DOI 10.1136/jech.2009.087999
  26. Heaviside Clare, 2017, Curr Environ Health Rep, V4, P296, DOI 10.1007/s40572-017-0150-3
  27. Hsu A, 2021, NAT COMMUN, V12, DOI 10.1038/s41467-021-22799-5
  28. Huang ZJ, 2015, BMJ OPEN, V5, DOI 10.1136/bmjopen-2015-009172
  29. Iceland J., 2002, RACIAL ETHNIC RESIDE, V8
  30. Ingole V, 2017, INT J BIOMETEOROL, V61, P1797, DOI 10.1007/s00484-017-1363-8
  31. Jesdale BM, 2013, ENVIRON HEALTH PERSP, V121, P811, DOI 10.1289/ehp.1205919
  32. Ju Y, 2021, ENVIRON RES LETT, V16, DOI 10.1088/1748-9326/ac2a63
  33. Kephart JL, 2022, NAT MED, V28, P1700, DOI 10.1038/s41591-022-01872-6
  34. Liu SD, 2020, INT J ENV RES PUB HE, V17, DOI 10.3390/ijerph17197326
  35. MASSEY DS, 1988, SOC FORCES, V67, P281, DOI 10.2307/2579183
  36. Medina-Ramon M, 2006, ENVIRON HEALTH PERSP, V114, P1331, DOI 10.1289/ehp.9074
  37. Mielck A, 2014, HEALTH QUAL LIFE OUT, V12, DOI 10.1186/1477-7525-12-58
  38. Munoz-Sabater J, 2021, EARTH SYST SCI DATA, V13, P4349, DOI 10.5194/essd-13-4349-2021
  39. Murage P, 2020, ENVIRON INT, V134, DOI 10.1016/j.envint.2019.105292
  40. Ng CFS, 2016, GLOBAL ENVIRON CHANG, V39, P234, DOI 10.1016/j.gloenvcha.2016.05.006
  41. O'Neill MS, 2005, J URBAN HEALTH, V82, P191, DOI 10.1093/jurban/jti043
  42. Ortigoza AF, 2021, J EPIDEMIOL COMMUN H, V75, P264, DOI 10.1136/jech-2020-215137
  43. Quistberg DA, 2019, J URBAN HEALTH, V96, P311, DOI 10.1007/s11524-018-00326-0
  44. Robine JM, 2008, CR BIOL, V331, P171, DOI 10.1016/j.crvi.2007.12.001
  45. Scheelbeek PFD, 2021, ENVIRON RES LETT, V16, DOI 10.1088/1748-9326/ac092c
  46. Sera F, 2019, INT J EPIDEMIOL, V48, P1101, DOI 10.1093/ije/dyz008
  47. Son JY, 2019, ENVIRON RES LETT, V14, DOI 10.1088/1748-9326/ab1cdb
  48. Son JY, 2016, INT J BIOMETEOROL, V60, P113, DOI 10.1007/s00484-015-1009-7
  49. Tuholske C, 2021, P NATL ACAD SCI USA, V118, DOI 10.1073/pnas.2024792118
  50. World Health Organization, 2021, WHO METHODS DATA SOU
  51. Yu WW, 2010, SCI TOTAL ENVIRON, V408, P3513, DOI 10.1016/j.scitotenv.2010.04.058
  52. Zanobetti A, 2013, EPIDEMIOLOGY, V24, P809, DOI 10.1097/01.ede.0000434432.06765.91
  53. Zhao Q, 2021, LANCET PLANET HEALTH, V5, pE415, DOI 10.1016/S2542-5196(21)00081-4
  54. Zhao Q, 2019, ENVIRON HEALTH PERSP, V127, DOI [10.1289/EHP3889, 10.1289/ehp3889]