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Performance of statistical process control methods for regional surgical site infection surveillance: a 10-year multicentre pilot study
  1. Arthur W Baker1,2,
  2. Salah Haridy3,4,
  3. Joseph Salem3,
  4. Iulian Ilieş3,
  5. Awatef O Ergai3,
  6. Aven Samareh3,
  7. Nicholas Andrianas3,
  8. James C Benneyan3,
  9. Daniel J Sexton1,2,
  10. Deverick J Anderson1,2
  1. 1 Duke Center for Antimicrobial Stewardship and Infection Prevention, Durham, NC, USA
  2. 2 Division of Infectious Diseases, Duke University School of Medicine, Durham, NC, USA
  3. 3 Healthcare Systems Engineering Institute, Northeastern University, Boston, Massachusetts, USA
  4. 4 Department of Industrial Engineering and Engineering Management, College of Engineering, University of Sharjah, Sharjah, United Arab Emirates
  1. Correspondence to Dr Arthur W Baker, Division of Infectious Diseases, Duke University Medical Center, Box 102359, Durham, NC 27710, USA; Arthur.Baker{at}


Background Traditional strategies for surveillance of surgical site infections (SSI) have multiple limitations, including delayed and incomplete outbreak detection. Statistical process control (SPC) methods address these deficiencies by combining longitudinal analysis with graphical presentation of data.

Methods We performed a pilot study within a large network of community hospitals to evaluate performance of SPC methods for detecting SSI outbreaks. We applied conventional Shewhart and exponentially weighted moving average (EWMA) SPC charts to 10 previously investigated SSI outbreaks that occurred from 2003 to 2013. We compared the results of SPC surveillance to the results of traditional SSI surveillance methods. Then, we analysed the performance of modified SPC charts constructed with different outbreak detection rules, EWMA smoothing factors and baseline SSI rate calculations.

Results Conventional Shewhart and EWMA SPC charts both detected 8 of the 10 SSI outbreaks analysed, in each case prior to the date of traditional detection. Among detected outbreaks, conventional Shewhart chart detection occurred a median of 12 months prior to outbreak onset and 22 months prior to traditional detection. Conventional EWMA chart detection occurred a median of 7months prior to outbreak onset and 14 months prior to traditional detection. Modified Shewhart and EWMA charts additionally detected several outbreaks earlier than conventional SPC charts. Shewhart and SPC charts had low false-positive rates when used to analyse separate control hospital SSI data.

Conclusions Our findings illustrate the potential usefulness and feasibility of real-time SPC surveillance of SSI to rapidly identify outbreaks and improve patient safety. Further study is needed to optimise SPC chart selection and calculation, statistical outbreak detection rules and the process for reacting to signals of potential outbreaks.

  • statistical process control
  • adverse events, epidemiology and detection
  • healthcare quality improvement
  • infection control

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  • Handling editor Kaveh G Shojania

  • Contributors AWB designed the study, performed the analysis and wrote the first version of the manuscript. SH, JS, II, AOE, AS, NA and JCB constructed and analysed SPC charts. JCB and DJA additionally designed the study, performed the analysis and revised the manuscript. DJS designed the study and revised the manuscript.

  • Funding This work was supported by the National Center for Advancing Translational Sciences of the National Institutes of Health (grant number UL1TR001117), the Transplant Infectious Disease Interdisciplinary Research Training Grant of the National Institutes of Health (grant number 5T32AI100851-02), the National Science Foundation (grant number IIP-1034990) and the Agency for Healthcare Research and Quality (grant number R01 HS 23821-01).

  • Competing interests None declared.

  • Ethics approval Institutional review board at our organisations.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data sharing statement Unpublished data are available upon request.