Trends and weekly and seasonal cycles in the rate of errors in the clinical management of hospitalized patients

Chronobiol Int. 2012 Aug;29(7):947-54. doi: 10.3109/07420528.2012.672265. Epub 2012 Jun 4.


Studies on the rate of adverse events in hospitalized patients seldom examine temporal patterns. This study presents evidence of both weekly and annual cycles. The study is based on a large and diverse data set, with nearly 5 yrs of data from a voluntary staff-incident reporting system of a large public health care provider in rural southeastern Australia. The data of 63 health care facilities were included, ranging from large non-metropolitan hospitals to small community and aged health care facilities. Poisson regression incorporating an observation-driven autoregressive effect using the GLARMA framework was used to explain daily error counts with respect to long-term trend and weekly and annual effects, with procedural volume as an offset. The annual pattern was modeled using a first-order sinusoidal effect. The rate of errors reported demonstrated an increasing annual trend of 13.4% (95% confidence interval [CI] 10.6% to 16.3%); however, this trend was only significant for errors of minor or no harm to the patient. A strong "weekend effect" was observed. The incident rate ratio for the weekend versus weekdays was 2.74 (95% CI 2.55 to 2.93). The weekly pattern was consistent for incidents of all levels of severity, but it was more pronounced for less severe incidents. There was an annual cycle in the rate of incidents, the number of incidents peaking in October, on the 282 nd day of the year (spring in Australia), with an incident rate ratio 1.09 (95% CI 1.05 to 1.14) compared to the annual mean. There was no so-called "killing season" or "July effect," as the peak in incident rate was not related to the commencement of work by new medical school graduates. The major finding of this study is the rate of adverse events is greater on weekends and during spring. The annual pattern appears to be unrelated to the commencement of new graduates and potentially results from seasonal variation in the case mix of patients or the health of the medical workforce that alters health care performance. These mechanisms will need to be elucidated with further research.

MeSH terms

  • Databases, Factual
  • Hospitalization*
  • Humans
  • Medical Errors* / statistics & numerical data
  • Medical Errors* / trends
  • Models, Statistical
  • New South Wales / epidemiology
  • Periodicity*
  • Seasons*
  • Severity of Illness Index