A July spike in fatal medication errors: a possible effect of new medical residents

J Gen Intern Med. 2010 Aug;25(8):774-9. doi: 10.1007/s11606-010-1356-3. Epub 2010 May 29.


Background: Each July thousands begin medical residencies and acquire increased responsibility for patient care. Many have suggested that these new medical residents may produce errors and worsen patient outcomes-the so-called "July Effect;" however, we have found no U.S. evidence documenting this effect.

Objective: Determine whether fatal medication errors spike in July.

Design: We examined all U.S. death certificates, 1979-2006 (n = 62,338,584), focusing on medication errors (n = 244,388). We compared the observed number of deaths in July with the number expected, determined by least-squares regression techniques. We compared the July Effect inside versus outside medical institutions. We also compared the July Effect in counties with versus without teaching hospitals.

Outcome measure: JR = Observed number of July deaths / Expected number of July deaths.

Results: Inside medical institutions, in counties containing teaching hospitals, fatal medication errors spiked by 10% in July and in no other month [JR = 1.10 (1.06-1.14)]. In contrast, there was no July spike in counties without teaching hospitals. The greater the concentration of teaching hospitals in a region, the greater the July spike (r = .80; P = .005). These findings held only for medication errors, not for other causes of death.

Conclusions: We found a significant July spike in fatal medication errors inside medical institutions. After assessing competing explanations, we concluded that the July mortality spike results at least partly from changes associated with the arrival of new medical residents.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Clinical Competence / statistics & numerical data*
  • Death Certificates
  • Hospitals, Teaching / statistics & numerical data
  • Humans
  • Inpatients
  • Internship and Residency / statistics & numerical data*
  • Linear Models
  • Medication Errors / statistics & numerical data*
  • Mortality / trends*
  • Quality of Health Care / statistics & numerical data*
  • Risk Assessment
  • Seasons
  • United States