Socioeconomic inequality in vulnerability to all-cause and cause-specific hospitalisation associated with temperature variability: a time-series study in 1814 Brazilian cities

Abstract

BACKGROUND AND AIM: Exposure to temperature variability has been associated with increased risk of mortality and morbidity. We aimed to evaluate whether the association between short-term temperature variability and hospitalisation was affected by local socioeconomic level in Brazil. METHODS: We collected city-level socioeconomic data, and daily hospitalisation and weather data from 1814 Brazilian cities between Jan 1, 2000, and Dec 31, 2015. All-cause and cause-specific hospitalisation data was from the Hospital Information System of the Unified Health System in Brazil. City-specific daily minimum and maximum temperatures came from a 0·25° × 0·25° Brazilian meteorological dataset. We represented city-specific socioeconomic level using literacy rate, urbanisation rate, average monthly household income per capita and GDP per capita over the study peroid. We used quasi-Poisson regression to do time-series analyses and obtain city-specific associations between temperature variability (TV) and hospitalisation. We pooled city-specific estimates according to different socioeconomic quartiles or levels using random-effect meta-analyses. RESULTS:We included a total of 147 959 243 hospitalisations (59·0% female) during the study period. Overall, we estimated that the hospitalisation risk due to every 1°C increase in the temperature variability in the current and previous day (TV0–1) increased by 0·52% (95% CI 0·50−0·55). For lower-middle-income cities, this risk was 0·63% (95% CI 0·58–0·69), for upper-middle-income cities it was 0·50% (0·47–0·53), and for high-income cities it was 0·39% (0·33–0·46). The socioeconomic inequality in vulnerability to TV0–1 was especially evident for people aged 0–19 years (effect estimate 1·21% for lower-middle income vs 0·52% for high income) and people aged 60 years or older (0·60% vs 0·43%), and for hospitalisation due to infectious diseases (1·62% vs 0·56% ), respiratory diseases (1·32% vs 0·55% ), and endocrine diseases (1·21% vs 0·32% ). CONCLUSIONS:People living in less developed cities in Brazil were more vulnerable to hospitalisation related to temperature variability, which could exacerbate existing health and socioeconomic inequalities in Brazil. KEYWORDS: Temperature extremes and variability, Socio-economic factors (non-chemical stressors), Environmental justice, Environmental disparities