SEARCH

SEARCH BY CITATION

Keywords:

  • Patient safety;
  • children;
  • pediatrics;
  • patient safety indicators;
  • hierarchial methods;
  • large administrative databases;
  • medical errors

Objective. To determine the rates, patient, and institutional characteristics associated with the occurrence of patient safety indicators (PSIs) in hospitalized children and the degree of statistical difference derived from using three approaches of controlling for institution level effects.

Data Source. Pediatric Health Information System Dataset consisting of all pediatric discharges (<21 years of age) from 34 academic, freestanding children's hospitals for calendar year 2003.

Methods. The rates of PSIs were computed for all discharges. The patient and institutional characteristics associated with these PSIs were calculated. The analyses sequentially applied three increasingly conservative methods to control for the institution-level effects robust standard error estimation, a fixed effects model, and a random effects model. The degree of difference from a “base state,” which excluded institution-level variables, and between the models was calculated. The effects of these analyses on the interpretation of the PSIs are presented.

Principal Findings. PSIs are relatively infrequent events in hospitalized children ranging from 0 per 10,000 (postoperative hip fracture) to 87 per 10,000 (postoperative respiratory failure). Significant variables associated PSIs included age (neonates), race (Caucasians), payor status (public insurance), severity of illness (extreme), and hospital size (>300 beds), which all had higher rates of PSIs than their reference groups in the bivariable logistic regression results. The three different approaches of adjusting for institution-level effects demonstrated that there were similarities in both the clinical and statistical significance across each of the models.

Conclusions. Institution-level effects can be appropriately controlled for by using a variety of methods in the analyses of administrative data. Whenever possible, resource-conservative methods should be used in the analyses especially if clinical implications are minimal.