Determinants of accident and emergency attendances and emergency admissions in infants: birth cohort study

Abstract

Background: There is limited understanding of the drivers of increasing infant accident and emergency (A&E) attendances and emergency hospital admissions across England. We examine variations in use of emergency hospital services among infants by local areas in England and investigate the extent to which infant and socio-economic factors explain these variations.

Methods: Birth cohort study using linked administrative Hospital Episode Statistics data in England. Singleton live births between 1-April-2012 and 31-March-2019 were followed up for 1 year; from 1-April-2013 (from the discharge date of their birth admission) until their first birthday, death or 31-March-2019. Mixed effects negative binomial models were used to calculate incidence rate ratios for A&E attendances and emergency admissions and mixed effects logistic regression models estimated odds ratio of conversion (the proportion of infants subsequently admitted after attending A&E). Models were adjusted for individual-level factors and included a random effect for local authority (LA).

Results: The cohort comprised 3,665,414 births in 150 English LAs. Rates of A&E attendances and emergency admissions were highest amongst: infants born < 32 weeks gestation; with presence of congenital anomaly; and to mothers < 20-years-old. Area-level deprivation was positively associated with A&E attendance rates, but not associated with conversion probability. A&E attendance rates were highest in the North East (916 per 1000 child-years, 95%CI: 911 to 921) and London (876 per 1000, 95%CI: 874 to 879), yet London had the lowest emergency admission rates (232 per 1000, 95%CI: 231 to 234) and conversion probability (25% vs 39% in South West). Adjusting for individual-level factors did not significantly affect variability in A&E attendance and emergency admission rates by local authority.

Conclusions: Drivers of A&E attendances and emergency admissions include individual-level factors such being born premature, with congenital anomaly and from socio-economically disadvantaged young parent families. Support for such vulnerable infants and families should be provided alongside preventative health care in primary and community care settings. The impact of these services requires further investigation. Substantial geographical variations in rates were not explained by individual-level factors. This suggests more detailed understanding of local and underlying service-level factors would provide targets for further research on mechanisms and policy priority.

Keywords: Emergency admissions; Emergency care; Hospital episode statistics; Infant health; Local authority; Variations.

Fig. 1

Flow chart of linkage between HES APC, HES A&E and birth cohort data. Notes: ^a Linking date was discharge date of A&E attendance (from HES A&E dataset) and date of hospital emergency admission (from HES APC dataset). ^b Quality control was conducted on linked A&E and APC records according to methodology guidelines from NHS digital. 90% were strong links, 10% good links and only 0.01% poor links (n = 67) which were removed as recommended by the guidelines. Emergency admissions via A&E department that did not link to an A&E attendance record showed 17% were coded as 21 (A&E department) and 83% as code 28 (other means). We prioritised information from APC dataset to indicate emergency admission via A&E department. ^c Direct emergency admissions were admission method codes 22 (GP,92%), 23 (bed bureau, 2%), & 24 (Consultant clinic, 6%) from HES APC dataset. These were removed prior to linkage and appended in afterwards. ^d All of the A&E attendances that were not linked to an APC emergency admission were assumed to be an attendance without an admission. ^e Of observations that did not link with UCL birth cohort, the majority were HESID’s from A&E data (63%). Whereas, 26% were HESID’s found in the A&E and APC linked data, and 11% from APC data only (direct admissions). 48% of the non-linking records were from 2012/13 and therefore may have been records from older infants that were not in the birth cohort (i.e. born before March 2012). This suggests that those not linked might be due to HESID data quality issues in the A&E dataset and HES records from older infants not in the birth cohort for the first year. ^f 701,680 infants had missing data on any risk factor (16%) and were excluded from the analysis sample; missing values were slightly more frequent in later study years (OR: 1.025, 95%CI: 1.024–1.046). ^g As the first year 2012/13 did not have full follow-up data from infant born in previous year, we dropped attendances and admissions in 2012/13. The rest of the financial years consisted of full follow-up

Fig. 2

A&E attendances and emergency admissions for infants < 1 years old in England by Region of residence

Fig. 3

Adjusted IRRs for A&E attendences, Emergency Admissions (EA) and ORs for conversion (including 95% CI). Acronyms: IRR – Incidence Rate Ratio; OR – Odds Ratio; CI – Confidence Interval

Fig. 4

Maps showing adjusted rates (per 1000 child-years) of A&E attendences (A), emergency admissions (B) and converstion probabilities (C) by local authorities in England between finacial years 2013/14 – 2018/19

Fig. 5

Funnel plots of unadjusted and adjusted A&E attendance (A) and emergency admission (B) rates by local authorities in England between financial years 2013/14 – 2018/19. A: Funnel plot of A&E attendance rates for infants by local authority. B: Funnel plot of emergency admission rates for infants by local authority. FE – Fixed effects, RI – Random Intercept