What is it about?
Statistical Process Control (SPC) tools to analyze and monitor the yearly rate of Surgical Site Infections (SSI) in four selected countries from the WHO database: the Czech Republic (CZE), Sweden (SWE), Kazakhstan (KAZ), and Finland (FIN). The core objective was to use SPC as a guiding tool to analyze historical trends, spot current SSI control challenges, and provide insight for future preventative measures in healthcare facilities. The study used two primary types of control charts—the Individual-Moving Range (I-MR) chart and the Laney-modified attribute chart—to compare their outcomes, especially since the data for most countries did not meet the normality prerequisite for standard control charts, which could lead to false interpretations. The analysis showed that SSI rates in CZE and KAZ generally decreased over the study period, with CZE showing the greatest data variability due to six outlier results in its initial years. Conversely, SWE and FIN records showed a slight, gradual increase in SSI rates. A critical methodological finding was that the I-MR and Laney charts were in agreement regarding their control limits and alarming points, validating their combined utility for monitoring this challenging type of public health data.
Featured Image
Photo by Adrian Sulyok on Unsplash
Why is it important?
Surgical Site Infection is one of the most common postoperative complications, making its control a global public health priority. This research is important because it demonstrates the indispensable utility of Statistical Process Control (SPC) in the healthcare sector for continuous monitoring, diagnosis, improvement, and quantitative assessment of infection prevention measures. The study's key methodological contribution is the evidence showing that the Laney-modified attribute chart and the I-MR chart are equivalent in setting control limits and flagging out-of-control conditions, even when the SSI rate data is statistically non-normal. This validation is crucial because standard attribute charts used with non-normal data can result in an elevated false alarm rate. The verified SPC approach offers a validated, simpler tool—without complex data transformation—for practitioners to monitor the stability of their SSI control process and receive early warnings of quality drift before serious infection excursions occur. By comparing the nations, the study also highlights that CZE and KAZ had unstable variation shifts in their processes, unlike the more stable variation in SWE and FIN, underscoring the need for region-specific and continuous improvement measures.
Perspectives
The study's perspective on the SSI challenge is that its control process is dynamic and often unstable. Although SWE and FIN had stable variation, their SSI mean rate showed a rising trend, suggesting that their current "good practices" are no longer sufficient to lower the mean and require re-evaluation and stabilization. Conversely, the unstable process variation in CZE and KAZ indicates that external factors caused the sudden shifts in their infection rates, necessitating the collection of more data and the reconstruction of new, updated control charts to ensure the lower SSI rates are reliably maintained. Methodologically, the research strongly validates the use of Laney-modified control charts when monitoring public health data like SSI rates. This is due to the practical reality that raw SSI data frequently exhibits over-dispersion (failure to meet prerequisite distributions), which would cause ordinary charts to issue false alarms and undermine confidence in the tool. Ultimately, the study advocates for the perspective that SPC control charts are an indispensable, objective tool that relates data distribution and shape to actions taken, allowing healthcare staff to investigate, monitor, and improve the SSI prevention system continuously.
Independent Researcher & Consultant Mostafa Essam Eissa
Read the Original
This page is a summary of: Application of Control Charts in Monitoring of Surgical Site Infection Trending Records Using Statistical Software, Asian Journal of Applied Sciences, March 2019, Science Alert,
DOI: 10.3923/ajaps.2019.76.84.
You can read the full text:
Contributors
The following have contributed to this page
