Overview

Among the diverse and complex hazards associated with climate change, variations in temperature are amongst the most direct. Deviations from usual temperatures cause physiological stress to the human body and consequently increased hospitalization and mortality(1). The capacity of individuals and communities to respond to temperature deviations is tightly linked to physiological, behavioural, infrastructural, and technological factors(2).

A community’s vulnerability to temperature varies over time and space as the underlying factors influencing exposure, sensitivity and adaptation capacity change. For example, access to air conditioning improves the adaptation capacity of communities by reducing their exposure to extreme temperatures (3). In Brazil, sales of residential air conditioners, for example, have doubled between 2010 and 2014 (4). In addition to temporal changes, access to resources and distribution of certain factors is not even over space. One example is greenspaces. They are known to have a local cooling effect(5) as well as a positive impact on a population’s health (6), influencing temperature exposure and population sensitivity, respectively. Their distribution is uneven across a city, often clustered in specific areas. Despite some growing evidence, the spatial variation on the temperature-mortality/hospitalization association and the role of neighbourhood characteristics effects of these (7–14) remain scarce, particularly in low- and middle-income countries.

The spatial and temporal distribution of these factors, and their impact on the temperature health burden, are unique to each city. With over 11.3 million inhabitants over an area of 1,521.1km2, São Paulo is the most populous city in Brazil, and the fourth most populated metropolis in the world(15). This makes it a good case study to investigate intra-city patterns, otherwise impossible to assess due to statistical power constrains. Moreover, São Paulo is a vibrant city with strongly engrained social and urban disparities, which makes it an interesting setting to assess spatial patterns in the effects of temperature on mortality and hospitalization.

Aims and objectives

My PhD aims to describe the spatial and temporal patterns of the association between temperature and mortality and hospitalization in São Paulo (Brazil) between 2000 and 2018, and to identify neighbourhoods1, population groups and time periods with particularly high risk of death or hospitalization. More specifically, my objectives are:

  1. To describe temporal trends in the temperature-mortality association, overall and by population subgroup. Refer to: Chapter 1: Adaptation
  2. To develop a spatiotemporal model to estimate daily mean temperature at high spatial and temporal resolution. Refer to: Chapter 2: Temperature model
  3. To explore spatial patterns in the mortality and hospitalization risk associated to daily mean temperatures. Refer to: Chapter 3: Spatiotemporal patterns of risk
  4. To encourage my PhD results to feed into Public Health programs.

The study area: São Paulo (Brazil)

Study Area

References

  1. WMO, WHO. Heatwaves and Health: Guidance on Warning-System Development. 2015. 114 p.
  2. Hondula DM, Balling RC, Vanos JK, Georgescu M. Rising Temperatures, Human Health, and the Role of Adaptation. Vol. 1, Current Climate Change Reports. 2015. p. 144–54.
  3. Barreca AI, Clay K, Deschenes O, Greenstone M, Shapiro JS. Adapting to Climate Change: The Remarkable Decline in the U.S. Temperature-Mortality Relationship Over the 20th Century. SSRN. 2012.
  4. Kamimura A. Prospective of Brazilian Household Air Conditioning Energy Consumption and Related Carbon Emission : A Non Linear Numerical Approach Prospective of Brazilian Household Air Conditioning Energy Consumption and Related Carbon Emission : A Non Linear Numerica. 2019;(January):1–9.
  5. Aram F, Higueras E, Solgi E, Mansournia S. Urban green space cooling e ff ect in cities. Heliyon. 2019;(March):e01339.
  6. Nieuwenhuijsen MJ, Khreis H, Triguero-mas M, Gascon M, Dadvand P. Fifty Shades of Green. 2017;28(1):63–71.
  7. Xu Y, Dadvand P, Barrera-Gómez J, Sartini C, Marí-Dell’Olmo M, Borrell C, et al. Differences on the effect of heat waves on mortality by sociodemographic and urban landscape characteristics. J Epidemiol Community Health. 2013;67(6):519–25.
  8. Murage P, Kovats S, Sarran C, Taylor J, McInnes R, Hajat S. What individual and neighbourhood-level factors increase the risk of heat-related mortality? A case-crossover study of over 185,000 deaths in London using high-resolution climate datasets. Environ Int. 2019;134(July 2019):105292.
  9. Eisenman DP, Wilhalme H, Tseng C-H, Chester M, English P, Pincetl S, et al. Heat Death Associations with the built environment, social vulnerability and their interactions with rising temperature. Health Place. 2016 Sep;41:89–99.
  10. Ribeiro AI, Krainski ET, Autran R, Teixeira H, Carvalho MS, de Pina M de F. The influence of socioeconomic, biogeophysical and built environment on old-age survival in a Southern European city. Health Place. 2016 Sep 1;41:100–9.
  11. Dang TN, Van DQ, Kusaka H, Seposo XT, Honda Y. Green Space and Deaths Attributable to the Urban Heat Island Effect in Ho Chi Minh City. Am J Public Health. 2018 Apr 1;108(Suppl 2):S137.
  12. Hondula DM, Davis RE, Georgescu M. Clarifying the Connections Between Green Space, Urban Climate, and Heat-Related Mortality. Am J Public Health. 2018 Apr 1;108(Suppl 2):S62.
  13. Gronlund CJ, Berrocal VJ, White-Newsome JL, Conlon KC, O’Neill MS. Vulnerability to extreme heat by socio-demographic characteristics and area green space among the elderly in Michigan, 1990-2007. Environ Res. 2015 Jan;136:449–61.
  14. Pascal M, Goria S, Wagner V, Sabastia M, Guillet A, Cordeau E, et al. Greening is a promising but likely insufficient adaptation strategy to limit the health impacts of extreme heat. Environ Int. 2021 Jun 1;151:106441.
  15. United Nations. World Urbanization Prospects. Vol. 12, Demographic Research. 2018. 197–236 p.