Trend and seasonality in hospitalizations for pulmonary embolism: a time-series analysis

J Thromb Haemost. 2015 Jan;13(1):23-30. doi: 10.1111/jth.12772. Epub 2014 Nov 29.

Abstract

Background: The existence of seasonal variability in patients with acute pulmonary embolism (PE) has been debated for years, with contradictory results. The aim of this study was to identify the trend and possible existence of a seasonal pattern in hospitalizations for PE in Spain.

Methods: We analyzed the hospital discharge database of the Spanish National Health System from 2001 to 2010. Patients aged > 14 years diagnosed with PE were selected and a time series was constructed considering mean daily admissions for PE by month. The trend and seasonality factor of the series were determined using time-series analysis, and time-series modeling was used for analysis. Exponential smoothing models and the autoregressive integrated moving average test were used to generate a predictive model.

Results: From 2001 to 2010, there were 162,032 diagnoses of PE (5.07 per 1000 hospitalizations). In 105,168 cases, PE was the reason for admission. The PE diagnosis rate ranged from 4.14 per 1000 in 2001 to 6.56 per 1000 in 2010; and hospital admissions due to PE ranged from 2.67 to 4.28 per 1000 hospital discharges. Time-series analysis showed a linear increase in the incidence and a significant seasonal pattern with 17% more admissions in February and 12% fewer in June-July with respect to the central tendency (difference from February to June, 29%).

Conclusions: The incidence of hospitalizations for PE showed a linear increase and a seasonal pattern, with the highest number of admissions in winter and the lowest number in summer.

Keywords: community health planning; inpatients; pulmonary embolism; seasonal variation; trends.

MeSH terms

  • Databases, Factual
  • Hospitalization / trends*
  • Humans
  • Incidence
  • Linear Models
  • Pulmonary Embolism / diagnosis
  • Pulmonary Embolism / epidemiology*
  • Pulmonary Embolism / therapy
  • Seasons*
  • Spain / epidemiology
  • Time Factors