Inappropriate hospital admission: interaction between patient age and co-morbidity

Intern Emerg Med. 2011 Aug;6(4):361-7. doi: 10.1007/s11739-011-0629-0. Epub 2011 Jun 8.

Abstract

The aim of the study is to determine the prevalence of inappropriate admission, and to identify the factors that influence appropriateness of hospital admission. Data were prospectively collected from all 345 consecutive patients admitted during the period of 1 month for acute hospital care at a 110-bed division of internal medicine using socio-demographic and medical information. Statistical analyses included χ2 tests, t tests, and logistic regression analyses. According to the European version of the Appropriateness Evaluation Protocol of hospital admission, 28.1% of medical admissions for acute care in the Central Hospital of Bolzano, Italy, have been classified as inappropriate. Factors that reduced appropriateness included female gender, age and chronic illness that are significantly associated with appropriateness of medical admission, whereas time of day or day of week of the emergency department (ED) visit does not influence appropriateness. In multiple logistic regression analyses, age and co-morbidity are not independently related to appropriateness, however, when tested for interaction, inappropriateness is significantly more frequent at a young age in the absence of co-morbidities, and, numerically most relevant, in elderly patients presenting with co-morbidities. In this evaluation of a single centre North Italian hospital admission, co-morbidity turns out to be an important age-dependent determinant of appropriateness. Although in the young age group, co-morbidity increases the likelihood of being appropriately admitted, the presence of chronic illness in the elderly increases the risk of inappropriate hospital use.

MeSH terms

  • Adolescent
  • Adult
  • Age Factors
  • Aged
  • Aged, 80 and over
  • Chi-Square Distribution
  • Comorbidity
  • Confidence Intervals
  • Cross-Sectional Studies
  • Diagnosis-Related Groups
  • Female
  • Hospitalization / statistics & numerical data*
  • Humans
  • Italy
  • Logistic Models
  • Male
  • Middle Aged
  • Odds Ratio
  • Prospective Studies
  • ROC Curve
  • Risk Assessment / methods*
  • Triage / methods*
  • Young Adult