Socioeconomic level and associations between heat exposure and all-cause and cause-specific hospitalization in 1,814 Brazilian cities: A nationwide case-crossover study

摘要

Background Heat exposure, which will increase with global warming, has been linked to increased risk of a range of types of cause-specific hospitalizations. However, little is known about socioeconomic disparities in vulnerability to heat. We aimed to evaluate whether there were socioeconomic disparities in vulnerability to heat-related all-cause and cause-specific hospitalization among Brazilian cities. Methods and findings We collected daily hospitalization and weather data in the hot season (city-specific 4 adjacent hottest months each year) during 2000–2015 from 1,814 Brazilian cities covering 78.4% of the Brazilian population. A time-stratified case-crossover design modeled by quasi-Poisson regression and a distributed lag model was used to estimate city-specific heat–hospitalization association. Then meta-analysis was used to synthesize city-specific estimates according to different socioeconomic quartiles or levels. We included 49 million hospitalizations (58.5% female; median [interquartile range] age: 33.3 [19.8–55.7] years). For cities of lower middle income (LMI), upper middle income (UMI), and high income (HI) according to the World Bank’s classification, every 5°C increase in daily mean temperature during the hot season was associated with a 5.1% (95% CI 4.4%–5.7%, P 0.001), 3.7% (3.3%–4.0%, P 0.001), and 2.6% (1.7%–3.4%, P 0.001) increase in all-cause hospitalization, respectively. The inter-city socioeconomic disparities in the association were strongest for children and adolescents (0–19 years) (increased all-cause hospitalization risk with every 5°C increase [95% CI]: 9.9% [8.7%–11.1%], P 0.001, in LMI cities versus 5.2% [4.1%–6.3%], P 0.001, in HI cities). The disparities were particularly evident for hospitalization due to certain diseases, including ischemic heart disease (increase in cause-specific hospitalization risk with every 5°C increase [95% CI]: 5.6% [?0.2% to 11.8%], P = 0.060, in LMI cities versus 0.5% [?2.1% to 3.1%], P = 0.717, in HI cities), asthma (3.7% [0.3%–7.1%], P = 0.031, versus ?6.4% [?12.1% to ?0.3%], P = 0.041), pneumonia (8.0% [5.6%–10.4%], P 0.001, versus 3.8% [1.1%–6.5%], P = 0.005), renal diseases (9.6% [6.2%–13.1%], P 0.001, versus 4.9% [1.8%–8.0%], P = 0.002), mental health conditions (17.2% [8.4%–26.8%], P 0.001, versus 5.5% [?1.4% to 13.0%], P = 0.121), and neoplasms (3.1% [0.7%–5.5%], P = 0.011, versus ?0.1% [?2.1% to 2.0%], P = 0.939). The disparities were similar when stratifying the cities by other socioeconomic indicators (urbanization rate, literacy rate, and household income). The main limitations were lack of data on personal exposure to temperature, and that our city-level analysis did not assess intra-city or individual-level socioeconomic disparities and could not exclude confounding effects of some unmeasured variables. Conclusions Less developed cities displayed stronger associations between heat exposure and all-cause hospitalizations and certain types of cause-specific hospitalizations in Brazil. This may exacerbate the existing geographical health and socioeconomic inequalities under a changing climate.