ARTICLESPractical risk-adjusted quality control charts for infection control*
Section snippets
Surveillance system
The AICE National Database Initiative is a voluntary national consortium of US hospitals ranging in size from 50 to 900 beds. ICPs collect data on 3 types of nosocomial infections: surgical site (SSI), ventilator-associated pneumonia (VAP) and central line–associated bacteremia (CLB). All hospitals in the AICE National Database Initiative project that submitted data for at least 12 consecutive months between 1996 and 1998 were included in this study. The sample included 51 hospitals in 29
Results
Of the 128 flags analyzed, 84 were flagged for suspiciously high rates, 28 for suspiciously low rates, and 16 for excessive month-to-month variability. Among high rates, 28 of 84 (33%) lasted more than 1 month. Among low rates, 18 of 28 (64%) lasted more than 1 month. If the 16 variability flags are included, then 62 of 128 flags (48%) involved more than 1 month of data.
The primary or root causes of all 128 flags were grouped into 5 major categories as shown in Table 2.Sixty-two flags (48%)
Discussion
No control chart or statistic can perfectly predict which nosocomial infections have common causes and which infections have special causes. Thus control charts only benefit hospitals with trained ICPs who know how investigate suspicious clusters once they are identified. Still, professionals at any level of experience should welcome a tool that helps focus their search for preventable special causes.
Whereas classic statistical tests like χ2, Fisher exact, or incidence density are valuable
Acknowledgements
I thank Kathryn Miller, AICE National Database Initiative Project Manager, for her tireless data collection efforts, and Dr Robert Haley for his review and suggestions on the manuscript.
Glossary
GLOSSARY
- Background variation:
- See common cause variation.
- C Chart.
- Control chart that plots counts (infections) over time.
- Common cause variation.
- Normal background variation related to many interacting factors that are difficult to prevent or control.
- CUSUM chart.
- Cumulative Sum control chart plots the cumulative sum of deviations from the mean instead of the continuous variable itself.
- EWMA chart.
- Exponentially Weighted Moving Average control chart plots a moving average of a continuous variable, giving more
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