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.

A time-series study.

The study was conducted in the Birmingham–Solihull metropolitan area, West Midlands, UK.

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.

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.

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.

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.

Rising demand for emergency care in England is a continuing challenge driven by population ageing and increasing multimorbidity. Ambulatory emergency care (AEC) refers to the provision of same-day acute care for patients who might otherwise require admission. However, the contribution of AEC conditions to demand remains unclear. This study aimed to examine the proportion and nature of patients attending emergency departments (ED) with AEC-related conditions and to describe variation between hospitals in attendances and emergency admissions for AEC conditions.

A retrospective study of routine data from 21 acute hospitals in England, including adult ED attendances and emergency admissions between 1 November 2021 and 31 October 2022. We used a federated approach to ensure data security, applying established AEC definitions to explore variation by age, socioeconomic status and length of stay.

Primary: Proportion of (i) ED attendances and (ii) emergency admissions for AEC conditions. Secondary: (i) Proportion of patients presenting at ED with an AEC condition who were admitted; (ii) proportion of emergency admissions with an AEC condition with a length of stay

We analysed 1 513 480 attendances (median per hospital: 73 125) and 660 105 admissions (median per hospital: 30 425). AEC accounted for 29.6% of attendances and 40.8% of admissions, with substantial inter-hospital variability. Patients aged ≥65 were more likely to present with an AEC, while patients from deprived areas had lower rates. Among AEC-related admissions, 49.3% had a stay of less than 2 days.

Nearly one-third of attendances and two-fifths of admissions were for conditions potentially manageable in AEC or community settings. Variation between hospitals suggests local factors, including service configuration and primary care access, may influence avoidable acute care use. These findings suggest a need for a more nuanced understanding of the drivers behind AEC, or SDEC Services, to better understand their impact on reducing hospital admissions. Analysing these patterns may inform interventions to reduce avoidable hospital utilisation. Further research is needed to identify drivers of variation and to develop scalable strategies for prevention.