Does hourly variation in ambient nitrogen dioxide increase the risk of acute asthma hospitalisations? A time-series study of Birmingham and Solihull, UK.

OBJECTIVE: This study aims to investigate the association between hourly nitrogen dioxide (NO2) concentrations and emergency hospital admissions for adult patients with asthma in the Birmingham-Solihull metropolitan area, West Midlands, UK. DESIGN: A time-series study. SETTING: The study was conducted in the Birmingham-Solihull metropolitan area, West Midlands, UK. PARTICIPANTS: Adult patients with asthma (aged ≥16 years at the time of admission) from 1 June 2016 to 31 May 2022 residing in Birmingham and Solihull, West Midlands. OUTCOME MEASURES: We analysed the hourly rate of emergency hospital admissions for acute asthma. A Poisson generalised additive model combined with the distributed lag non-linear model was applied to assess the association between hourly NO2 concentrations and hourly counts of hospital admissions for acute asthma. Furthermore, the effect modification of the NO2-asthma association in specific participant groups, such as sex, deprivation index and ethnicity, was explored in stratified analyses. RESULTS: The study included 18 943 adults with asthma exacerbations who attended the four acute hospitals in the study area during the study period. The study observed a significant positive association between hospital admissions for acute asthma and hourly NO2 concentrations. The mean NO2 exposure (18 µg/m³) over a lag of 0-24 hours was associated with a 13% (RR=1.13, 95% CI 1.01 to 1.26) increase in the risk of daytime emergency hospital admissions for acute asthma in comparison with no exposure. The results show a significant association between NO2 exposure and hospital admissions for a lag of 3-6 hours. Furthermore, subgroup-specific analysis indicated a higher positive risk associated with hourly NO2 among patients living in the most deprived areas. CONCLUSIONS: This study strengthens the evidence for the importance of using high-temporal resolution air pollution data to examine impacts on health, particularly where there are marked temporal patterns in exposure. Understanding these exposure-response relationships will improve healthcare preparedness and resource allocation in relation to air pollution levels.