Factors associated with longer wait times, admission and reattendances in older patients attending emergency departments: an analysis of linked healthcare data

Background and objective Care for older patients in the ED is an increasingly important issue with the ageing society. To better assess the quality of care in this patient group, we assessed predictors for three outcomes related to ED care: being seen and discharged within 4 hours of ED arrival; being admitted from ED to hospital and reattending the ED within 30 days. We also used these outcomes to identify better-performing EDs. Methods The CUREd Research Database was used for a retrospective observational study of all 1 039 251 attendances by 368 754 patients aged 75+ years in 18 type 1 EDs in the Yorkshire and the Humber region of England between April 2012 and March 2017. We estimated multilevel logit models, accounting for patients’ characteristics and contact with emergency services prior to ED arrival, time variables and the ED itself. Results Patients in the oldest category (95+ years vs 75–80 years) were more likely to have a long ED wait (OR=1.13 (95% CI=1.10 to 1.15)), hospital admission (OR=1.26 (95% CI=1.23 to 1.29)) and ED reattendance (OR=1.09 (95% CI=1.06 to 1.12)). Those who had previously attended (3+ vs 0 previous attendances) were more likely to have long wait (OR=1.07 (95% CI=1.06 to 1.08)), hospital admission (OR=1.10 (95% CI=1.09 to 1.12)) and ED attendance (OR=3.13 (95% CI=3.09 to 3.17)). Those who attended out of hours (vs not out of hours) were more likely to have a long ED wait (OR=1.33 (95% CI=1.32 to 1.34)), be admitted to hospital (OR=1.19 (95% CI=1.18 to 1.21)) and have ED reattendance (OR=1.07 (95% CI=1.05 to 1.08)). Those living in less deprived decile (vs most deprived decile) were less likely to have any of these three outcomes: OR=0.93 (95% CI=0.92 to 0.95), 0.92 (95% CI=0.90 to 0.94), 0.86 (95% CI=0.84 to 0.88). These characteristics were not strongly associated with long waits for those who arrived by ambulance. Emergency call handler designation was the strongest predictor of long ED waits and hospital admission: compared with those who did not arrive by ambulance; ORs for these outcomes were 1.18 (95% CI=1.16 to 1.20) and 1.85 (95% CI=1.81 to 1.89) for those designated less urgent; 1.37 (95% CI=1.33 to 1.40) and 2.13 (95% CI=2.07 to 2.18) for urgent attendees; 1.26 (95% CI=1.23 to 1.28) and 2.40 (95% CI=2.36 to 2.45) for emergency attendees; and 1.37 (95% CI=1.28 to 1.45) and 2.42 (95% CI=2.26 to 2.59) for those with life-threatening conditions. We identified two EDs whose patients were less likely to have a long ED, hospital admission or ED reattendance than other EDs in the region. Conclusions Age, previous attendance and attending out of hours were all associated with an increased likelihood of exceeding 4 hours in the ED, hospital admission and reattendance among patients over 75 years. These differences were less pronounced among those arriving by ambulance. Emergency call handler designation could be used to identify those at the highest risk of long ED waits, hospital admission and ED reattendance.

seen and discharged from the ED within four hours of arrival, calculated by comparing time of arrival in the ED and departure time from the ED. The second was whether they were transferred from the ED and admitted to hospital, determined by seeing if the same patient appeared in the APC dataset within 24 hours of being transferred from the ED. A 24 hour window was applied because only dates not time is recorded in the APC, and some people are transferred overnight. The third was whether the patient re-attended the ED within 30 days of discharge either from the ED or hospital, this being ascertained by looking at the gap between dates of ED discharge and subsequent attendance in the linked data.
The analyses controlled for a set of socio-demographic and clinical characteristics including: the patients' age, categorized into 5-year age bands; sex; the socioeconomic conditions of where they lived using the deciles of Index of Multiple Deprivation (IMD) [2], with IMD=1 indicating the worst-off communities; number of ED attendances in the past year; whether the patient's postcode indicated that they were a care home resident; and the travel time by road between the patient´s residence and the hospital [3]. The analysis of re-attendance was conditioned on the patient surviving the ED or hospital admission, identified from the PAS attendance disposal and APC discharge method variables.
We included a set of variables capturing the patient's emergency and urgent care journey prior to admission. For those that made emergency calls, we accounted for the number and length in minutes of the individual´s emergency (NHS111 and 999) calls. For those conveyed to the ED ambulance, we included variables measuring: the time of the ambulance on scene (arrival to departure); the time taken between calling the ambulance and arrival at the emergency department; and the urgency with which the ambulance was dispatched, assigned by the NHS Pathways triage system based on answers from the caller to scripted questions asked by the call-handler [4].
We included variables accounting for the day of ED attendance, whether or not the ED attendance was out-of-hours (all weekend and weekday from 6.30pm to 8am) and whether this was on a public holiday [5]; and month and financial year variables capturing seasonal effects and annual trends. These variables are not reported in the tables of results or forest plots. We use number of attendances (in 1000s) during the year that the patient attended in order account for the size of the ED. We also construct patient-to-doctor ratios, using annual data constructed from NHS workforce statistics which record the number of full time equivalent senior A&E doctors (referred to as "consultants" in England) in each hospital trust [6]. For trusts with more than one A&E site, doctors were allocated according to the share of attendances. There is evidence that ED performance is related to capacity constraints, notably the availability of beds, in the host hospital [7; 8; 9]. To explore this, we ran analyses that also controlled for the percentage of overnight hospital beds that are occupied using quarterly data reported by NHS England for each hospital Trust [10]. These quarterly data are an imperfect indication of whether or not a bed might be available for any particular person requiring admission from the ED but daily data were unavailable for the period covered by our study.
When including quarterly bed occupancy rate, the model estimating the probability of hospital admission failed to converge unless the year dummies were omitted. Omitting year dummies biases the estimates of most of the other variables in the model away from OR=1, whereas omission of the bed occupancy rate has negligible impact on the other estimates. Hence, the decision was made to retain year dummies and omit bed occupancy rate in the main analysis.
Conclusions about the relative performance of EDs were also robust to omission or inclusion of the bed occupancy rate variable. The same two ED sites were identified as having patients that were significantly less likely than patients in other EDs to wait more than four hours, to be admitted to hospital and to re-attend within 30 days. Similarly, no other ED was found in which patients were significantly less (or more) likely than the national average to have longer waits, be admitted or re-attend when accounting for bed occupancy rate.