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Qual Manag Health Care. 2019 Jan/Mar;28(1):39-44. doi: 10.1097/QMH.0000000000000201.

Statistical Process Control Charts for Monitoring Staphylococcus aureus Bloodstream Infections in Australian Health Care Facilities.

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Victorian Healthcare Associated Infection Surveillance System (VICNISS) Coordinating Centre, The Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia.



Staphylococcus aureus bloodstream infection (SAB) in health care settings contributes significantly to mortality, and improved processes are associated with reduced burden of infection. In Australia, health care-associated SAB (HA-SAB) rates are reported as a health care performance indicator, but standardized methods for analyzing longitudinal data are not applied. Our objective was to evaluate the utility of statistical process control chart methodology for reporting HA-SAB and flagging higher than expected rates.


A real-world test data set was defined as HA-SAB surveillance data collected by 155 Australian health care facilities between June 1, 2015, and June 30, 2017. This included 788 HA-SAB events, corresponding to an overall rate of 0.7 HA-SAB events per 10 000 occupied bed-days. The u-chart was selected as an appropriate tool, given the need for reporting natural units (HA-SAB rates) to a range of stakeholders. Facility-level data were plotted as u-charts, applying warning and control limits (2- and 3-SD thresholds, respectively).


Sixty-eight of the 155 participating facilities (43.9%) observed at least 1 HA-SAB event during the studied period. Using the traditional method of Poisson modeling, 56 of these 68 facilities demonstrated overdispersion with variance-to-mean ratio spanning 1.03 to 42.82. Modeling by negative binomial (NB) distribution was therefore applied to enhance functionality.


The u-chart is an accessible method for monitoring HA-SAB, interpretable by a range of stakeholders. We demonstrate the benefit of NB modeling to account for overdispersion, providing an effective tool to avoid inappropriate flags while maintaining early detection of out-of-control systems throughout a wide range of health care settings.

[Indexed for MEDLINE]

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