provides the rate of PSI events by PSI group for the 385,157 discharges in 34 academic Children's Hospitals in 2003. A total of 4,939 PSI events occurred during these hospitalizations. The number of discharges varied by PSI based upon the inclusion criteria for each PSI. The number of events in a PSI grouping ranged from 0 (postoperative hip fracture) to 1,395 (infection due to medical care). The rate of PSI events ranged from 0 (postoperative hip fracture) to 87 (postoperative respiratory failure) per 10,000 discharges.
The Occurrence and Rates of Patient Safety Indicators in PHIS in 2003
“Postoperative Respiratory Failure” and “Failure to Rescue” were the most common reported PSIs with 87 and 80 events per 10,000 discharges, respectively (). Other more common PSIs included infections due to medical care (44 events per 10,000), decubitus ulcer (37 events per 10,000), and postoperative deep venous thrombosis or pulmonary embolism (35 events per 10,000).
Patient and Institutional Characteristics
examines the bivariate relationships between the occurrence of a PSI and individually considered patient and organizational-level characteristics. There were significant differences in the rate of PSIs among discharges considered by patient age, race, payor, severity, disposition, and outcome (). Neonates had the highest rate of PSIs (1.8 percent), while preschool (ages 1–5 years) and school age (6–12 years) cases had significantly lower odds of experiencing a PSI (odds ratio [OR] 0.58 and 0.61, respectively). Black cases were 16 percent less likely than white cases to experience a PSI, but there were no differences in other racial groups. Privately insured cases and self-pay cases were less likely to experience a PSI during hospitalization than those cases that were government insured (OR 0.77 and 0.52, CI 0.65–0.91 and 0.40–0.67, respectively, both p<.05). There was a linear trend between illness severity and PSIs with increasing severity being associated with a greater likelihood of events (Cochran–Armitage Test for Trend, p<.01). Cases discharged to home were less likely to have experienced a PSI than those who were transferred to another facility, received home care following discharge, or have other discharge needs (). Larger organizations had a higher risk of a PSI during a patient's hospitalization. Specifically, hospitals with >300 beds were more likely to have patients that experienced a PSI than those with <200 beds (OR 1.55, CI 1.20–1.99; p<.01). However, the “busyness” of a hospital as measured by average daily census did not appear to affect the PSI occurrences. To test for an association between bedsize and average daily census, we tested their correlation and found that they were highly related to one another (r2=0.83, p<.001). Because it was unrelated in bivariate analyses and to avoid multicollinearity in the model, we removed average daily census as a variable from further analysis. Patients discharged from hospitals in the North Central region had a significantly lower likelihood of a PSI than patients discharged from hospitals in the Northeast (OR 0.66; CI 0.51–0.86), although other geographic regions were not statistically different in their rates of occurrence.
Population and Institutional Characteristics, Percent with any Patient Safety Indicators (PSI), and Bivariable Logistic Regression Results
After examining the bivariate relationships between PSIs and various patient and hospital characteristics, the next important step was to investigate the associations in a multivariable model. This approach was used to determine which of the independent variables were associated with the occurrence of a PSI. There were statistically significant between-hospital differences in the likelihood of a PSI, with the odds ratios ranging from 0.23 to 1.22. However, hospital alone did not meaningfully explain variation in the likelihood of a PSI (R2=0.01).
displays the results of the “base case” and three other models a robust standard error estimation model, a fixed-effects model, and a random-effects models, with the results being virtually identical. This implies that the different approaches nearly equivalently adjusted for the effects of clustering on variance estimates at the hospital level related to the occurrence of a PSI. A multivariable model without the institutional covariates (base case) was added for reference to allow for a fair comparison. The relationship of age to the occurrence of a PSI persisted after adjustment with all age groups having a higher likelihood of experiencing a PSI than neonates. Infants, preschoolers, and school age children had an OR of a PSI of 1.76, 1.63, and 1.76, respectively (). Adolescents were twice as likely as neonates to have an event (OR: 2.12; CI: 1.66-2.71). As in the bivariable analysis, African American patients continued to be at lower risk of PSIs than white patients. Payor status in the “Other/Self-pay” group was protective compared with public insurance and private insurance. Severity level was also independently related to a higher risk of a PSI. For patients who died, there was still a higher risk of experiencing a PSI (OR: 7.3, CI: 5.96–8.90, ).
Modeling Patient Safety Indicators (PSI) Occurrences, Including Patient and Hospital Characteristics, Using Three Different Methodological Approaches
Importantly, regardless of the method used, the relationships at the hospital level were the same. There were some notable differences when compared with the unadjusted models. For example, hospital bedsize of >300 beds now had a higher likelihood of being associated with a PSI (). Further, after controlling for hospital characteristics, the North Central, South, and West regions become more similar to one another than was true in the bivariable analysis, however, these three regions are still relatively spared compared with the Northeast ().