Conectar
Frequent use of emergency departments (EDs) places a considerable burden on healthcare systems. Although frequent attenders are known to have complex physical, mental health and social needs, national-level evidence on their characteristics and patterns of attendance remains limited. This study aimed to provide a comprehensive, population-level description of frequent ED attendance in England, with a focus on age-based subgroups.
Retrospective cohort study.
EDs in England via the Hospital Episode Statistics and the Emergency Care Dataset data linked with primary care prescribing and mortality data, between March 2016 and March 2021.
The dataset received from National Health Service Digital contained approximately 150 million ED attendances by 30 million adult (>18 years) patients over the time period April 2016 to March 2021. A random sample of 5 million people was used for this analysis.
The primary outcome was the number of attendances in each financial year by frequent attenders compared with the remaining patients, split by age bands. Patients were classified as frequent attenders if they had ≥5 or ≥10 ED attendances within a rolling 12-month period. Secondary outcomes included demographic, diagnostic and prescribing characteristics, as well as the number of different ED sites visited.
A Gaussian mixture model was used to identify age-based subgroups. Descriptive statistics were used to summarise key features; 95% CIs were reported where applicable. Among 3.91 million unique adult ED attenders, there were 8.7 million attendances. Of these, 222 160 individuals (5.7%) had ≥5 attendances in a year, accounting for 12.6% of total attendances. A trimodal age distribution was identified, with three distinct peaks corresponding to ages 18–34, 35–64 and 65+. Frequent attenders were more likely to live in deprived areas and have a history of psychotropic or analgesic prescribing. Mental health diagnoses and polypharmacy were particularly common in the younger and middle-aged groups. Multisite attendance was uncommon, with over 80% of frequent attenders using only one ED site annually.
This national analysis reveals a trimodal age pattern among frequent ED attenders, with differing clinical and socio-demographic profiles across age groups. These findings highlight the need for age-tailored approaches to managing high-intensity ED use and inform targeted service development.
To develop and validate a model to predict cognitive decline within 12 months for home care clients without a diagnosis of dementia.
We included all adults aged ≥ 18 years who had at least two interRAI Home Care assessments within 12 months, no diagnosis of dementia and a baseline Cognitive Performance Scale score ≤ 1. The sample was randomly split into a derivation cohort (75%) and a validation cohort (25%). Significant cognitive decline was defined as an increase (deterioration) in Cognitive Performance Scale scores from ‘0’ or ‘1’ at baseline to a score of ≥ 2 at the follow-up assessment.
Using the derivation cohort, a multivariable logistic regression model was used to predict cognitive decline within 12 months. Covariates included demographics, disease diagnoses, sensory and communication impairments, health conditions, physical and social functioning, service utilisation, informal caregiver status and eight interRAI-derived health index scales. The predicted probability of cognitive decline was calculated for each person in the validation cohort. The c-statistic was used to assess the model's discriminative ability. This study followed the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) reporting guidelines.
A total of 6796 individuals (median age: 82; female: 60.4%) were split into a derivation cohort (n = 5098) and a validation cohort (n = 1698). Logistic regression models using the derivation cohort resulted in a c-statistic of 0.70 (95% CI 0.70, 0.73). The final regression model (including 21 main effects and 8 significant interaction terms) was applied to the validation cohort, resulting in a c-statistic of 0.69 (95% CI 0.66, 0.72).
interRAI data can be used to develop a model for identifying individuals at risk of cognitive decline. Identifying this group enables proactive clinical interventions and care planning, potentially improving their outcomes. While these results are promising, the model's moderate discriminative ability highlights opportunities for improvement.
FreshRSS 