Emergency department visits in older people: pattern of use, contributing factors, geographical differences and outcomes

Aging Clin Exp Res. 2017 Apr;29(2):319-326. doi: 10.1007/s40520-016-0550-5. Epub 2016 Mar 1.

Abstract

Aims: To assess the pattern of use of Emergency Departments (EDs), factors contributing to the visits, geographical distribution and outcomes in people aged 65 years or more living in the Italian Lombardy Region in 2012.

Methods: Based on an administrative database the study population was divided into groups according to the number of ED visits. A multinomial logistic regression model was performed to compare the characteristics of each group. The Getis-Ord's G statistic was used to evaluate the clusters of high and low visit prevalence odd ratios (OR) at district level. To estimate the severity of the disease leading to ED attendance, visits were stratified based on the level of emergency and outcome.

Results: About 2 million older people were included in the analyses: 78 % had no ED visit, 15 % only 1, 7 % 2 or more. Male sex, age 85 years or more, high number of drugs, ED visits and hospital admissions in the previous year and the location of an ED within 10 km from the patient's place were all factors associated with a higher risk to have more ED visits. Clusters of high and low prevalence of visits were found for occasional users. Overall, 83 % of ED visit with a low emergency triage code at admission had as visit outcome discharge at home.

Conclusions: In older people several variables were associated with an increased risk to have a high number of ED visits. Most of the visits were done for non-urgent problems and significant geographic differences were observed for occasional users.

Keywords: Emergency departments; Older people; Outcomes; Spatial autocorrelation; Triage code.

MeSH terms

  • Aged
  • Emergencies / epidemiology*
  • Emergency Service, Hospital* / statistics & numerical data
  • Female
  • Hospitalization / statistics & numerical data*
  • Humans
  • Italy / epidemiology
  • Logistic Models
  • Male
  • Odds Ratio
  • Patient Discharge / statistics & numerical data
  • Spatio-Temporal Analysis
  • Triage / statistics & numerical data