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Use of optimised dual statistical process control charts for early detection of surgical site infection outbreaks
  1. Arthur W Baker1,2,
  2. Nicole Nehls3,
  3. Iulian Ilieş3,
  4. James C Benneyan3,
  5. Deverick J Anderson1,2
  1. 1 Duke Center for Antimicrobial Stewardship and Infection Prevention, Durham, North Carolina, USA
  2. 2 Division of Infectious Diseases, Duke University School of Medicine, Durham, North Carolina, USA
  3. 3 Healthcare Systems Engineering Institute, Northeastern University, Boston, Massachusetts, USA
  1. Correspondence to Dr Arthur W Baker, Duke Center for Antimicrobial Stewardship and Infection Prevention, Durham, NC 27710, USA; Arthur.Baker{at}

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Surgical site infections (SSI) are common healthcare-associated infections resulting in substantial morbidity, mortality and hospital costs.1–4 However, no standard algorithm for SSI surveillance or outbreak detection exists, and traditional surveillance techniques may fail to provide timely identification of important SSI rate increases.5 6 We previously showed that standard Shewhart and exponentially weighted moving average statistical process control (SPC) charts have potential to provide early detection of SSI outbreaks.7 We then performed a large-scale empirical optimisation study and determined that simultaneous use of two moving average (MA) SPC charts in this application was most effective in identifying clinically important increases in SSI rates, or SSI clusters, that occurred in our network of community hospitals.8 The objective of the current analysis was to evaluate the performance of this optimised combination of control charts when applied to known SSI outbreaks.


We retrospectively applied an optimised pair of MA SPC charts8 to all 30 SSI outbreaks previously identified and investigated from 2007 to 2015 in the Duke Infection Control Outreach Network (DICON), a network of more than 50 community hospitals (online supplementary table).9 We used procedure-specific SSI data from either the entire network or the single outbreak hospital to calculate chart baselines, or expected SSI rates. The baseline window was the time period used to estimate the expected SSI rate on a rolling basis. The lag was the offset (in months) between each evaluated time point and the corresponding baseline window. The MA span was the number of monthly SSI rates that were averaged, weighted by their respective sample sizes, to calculate …

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  • Presented at An abstract containing preliminary data was presented at IDWeek, 3 October 2019, Washington, DC. Presentation No 85.

  • Contributors AWB designed the study, performed the analysis and wrote the first version of the manuscript. NN and II constructed and analysed SPC charts and revised the manuscript. JCB and DJA designed the study and revised the manuscript.

  • Funding This work was supported by the Agency for Healthcare Research and Quality (grant number R01 HS 23821-01).

  • Competing interests None declared.

  • Patient consent for publication Not required.

  • Ethics approval Institutional review boards at Duke University and Northeastern University determined this analysis to be exempt research.

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

  • Data availability statement Data are available upon request.

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