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Temperature and cardiovascular mortality in Rio de Janeiro, Brazil: effect modification by individual-level and neighbourhood-level factors


Background Many factors related to susceptibility or vulnerability to temperature effects on mortality have been proposed in the literature. However, there is limited evidence of effect modification by some individual-level factors such as occupation, colour/race, education level and community-level factors. We investigated the effect modification of the temperature–cardiovascular mortality relationship by individual-level and neighbourhood-level factors in the city of Rio de Janeiro, Brazil.

Methods We used a case-crossover study to estimate the total effect of temperature on cardiovascular mortality in Rio de Janeiro between 2001 and 2018, and the effect modification by individual-level and neighbourhood-level factors. Individual-level factors included sex, age, colour/race, education, and place of death. Neighbourhood-level characteristics included social development index (SDI), income, electricity consumption and demographic change. We used conditional Poisson regression models combined with distributed lag non-linear models, adjusted for humidity and public holidays.

Results Our results suggest a higher vulnerability to high temperatures among the elderly, women, non-hospitalised deaths, and people with a lower education level. Vulnerability to low temperatures was higher among the elderly, men, non-white people, and for primary education level. As for neighbourhood-level factors, we identified greater vulnerability to low and high temperatures in places with lower SDI, lower income, lower consumption of electricity, and higher demographic growth.

Conclusion The effects of temperature on cardiovascular disease mortality in Rio de Janeiro vary according to individual-level and neighbourhood-level factors. These findings are valuable to inform policymakers about the most vulnerable groups and places, in order to develop more effective and equitable public policies.

  • Temperature
  • Mortality
  • Cardiovascular disease
  • Time-series
  • Neighborhood/place

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