A NON-PARAMETRIC FRAMEWORK FOR ANALYZING SPATIAL HETEROGENEITY AND CONTAMINATION PATHWAYS IN HEALTHCARE ENVIRONMENTS
Mostafa Essam Eissa
Independent Researcher and Consultant, Cairo, Egypt.
Abstract
Background: The systematic management of microbial bioburden in Class C healthcare cleanrooms is a critical factor in patient safety. Standard environmental monitoring often overlooks the complex spatial and statistical relationships of contamination. This study applies a rigorous statistical framework to a comprehensive environmental monitoring dataset to accurately map contamination risk.
Methods: A cross-sectional analysis was performed on 318 microbial surface samples from 28 distinct operational locations in a Class C facility. Colony Forming Unit (CFU) data were analyzed using non-parametric statistics due to non-normal distribution, confirmed by Shapiro-Wilk tests on all locations with sufficient sample size (n=12). The Kruskal-Wallis test with Dunn's post-hoc analysis was used for group comparisons. Spearman's correlation was used to assess inter-location relationships.
Results: Significant spatial heterogeneity in microbial contamination was confirmed (p<0.0001). Dunn's test identified CP C 11 W as the location with the highest contamination burden (mean CFU=12.17). The most statistically robust contrasts were observed when comparing high-burden sites against the cleanest location, CP C 32 WNme (mean CFU=0.67), which serves as a control benchmark. Multiple high-burden locations, including CP C 11 W and CP C 30 NCu, were found to be significantly more contaminated than this benchmark. No Spearman correlations survived the strict Bonferroni correction; however, the relationship between CP C 11 W and CP C 45 Wif (r=0.882, p<0.05) approached the significance threshold, suggesting a potential pathway requiring further investigation.
Conclusions: Microbial contamination within the facility is spatially patterned, not random. The analysis provides a definitive hierarchy of risk, highlighting CP C 11 W as the primary target for enhanced sanitation. While correlational pathways could not be statistically confirmed, near-significant results provide a clear direction for future, more targeted sampling to validate operational links between zones.
Keywords: Cleanroom, contamination control, environmental monitoring, hotspot analysis, non-parametric statistics, spatial heterogeneity.
INTRODUCTION
In modern healthcare, the use of microbially controlled environments is indispensable for the safe preparation of sterile products and the execution of sensitive medical procedures1. Cleanrooms are engineered spaces designed to limit airborne particulates and microbial contamination to rigorously defined levels, thereby protecting both patients and products2-4. The ISO 14698 standard provides a specific framework for biocontamination control, outlining the principles for monitoring and managing microbial risk in these environments5. Class C cleanrooms (often corresponding to ISO Class 7 or 8) are critical support areas where the threat of contamination transfer into more sterile zones must be meticulously controlled.
Contamination sources are well-established, with personnel being the most significant contributor, alongside materials, equipment, and HVAC systems6,7. Microorganisms deposited on surfaces can create persistent reservoirs, posing a continuous risk of cross-contamination8. An effective environmental monitoring (EM) program is therefore the cornerstone of cleanroom quality assurance. While surface monitoring via contact plates is standard practice, the subsequent data analysis is frequently limited to checking compliance against static action levels. Accordingly, this approach is inherently reactive and often fails to uncover underlying spatial patterns or systematic risks9.
A proactive, risk-based approach requires a more sophisticated application of statistical tools to transform EM data into actionable intelligence10. A critical aspect of this is recognizing that microbiological data are rarely normally distributed, instead displaying skewed profiles with frequent low counts and occasional high excursions11. Therefore, this statistical reality invalidates the use of parametric tests and demands robust, non-parametric methods for accurate analysis12-14. Due to the aforementioned challenges, this study employs such a framework to analyze a large surface contamination dataset from a Class C facility selected as a model example from Bangladesh, Pakistan and India region. Thus, the objectives are to accurately characterize the bioburden distribution, statistically validate contamination hotspots based on the most robust contrasts, and critically assess the significance of potential contamination pathways, providing a precise, data-driven foundation for advanced contamination control.
MATERIALS AND METHODS
Study Design
A retrospective, cross-sectional analysis was performed on a dataset of 318 environmental monitoring results from a single Class C healthcare facility based on Asian country15. The data were collected from 28 functionally distinct operational zones as part of a routine monitoring schedule.
Data Collection
Surface microbial bioburden was quantified using the contact plate method, with results reported in Colony Forming Units (CFU). The methodology is presumed to follow ISO 14698-1 standards, utilizing a general nutritive agar (e.g., Tryptic Soy Agar) with incorporated disinfectant neutralizers (e.g., lecithin, polysorbate 80)5. Standard incubation protocols would typically involve a dual-temperature regimen (e.g., 20-25°C and 30-35°C) to facilitate the recovery of both environmental bacteria and fungi16. Twenty-five locations were sampled 12 times, and three locations (CP C 44, CP C 45, CP C 46) were sampled 6 times.
Spatial Analysis
The spatial interpretation of data reflects the actual operational layout of the facility, which consists of functionally clustered zones rather than a uniform geometric grid16,17. Visualizations and interpretations are based on this organic, process-driven layout.
Statistical Analysis
All analyses used a significance level of α = 0.05, unless otherwise specified.
RESULTS AND DISCUSSION
The statistical analysis provided a revised and highly accurate understanding of the facility's microbial contamination patterns22. A summary of descriptive statistics for all 28 locations is provided in Table 1. This table details parameters such as mean, median, standard deviation, minimum, and maximum Colony Forming Unit (CFU) counts for each site. On the other hand, Figure 1 demonstrates dispersion of microbiological count data using box plot diagram.
Data distribution and contamination heterogeneity
The Shapiro-Wilk test was rigorously applied to assess the normality of the data, confirming that microbial contamination data were predominantly non-normal23. Out of the 25 locations with a sufficient sample size of n=12 for statistical testing, a significant majority 18 locations, representing 72%, failed to meet the assumption of normality (p<0.05). The remaining three locations, which had smaller sample sizes (n=6), were consequently excluded from this specific normality analysis due to insufficient statistical power. Hence, this finding was critical as it firmly validated the necessity and appropriateness of employing non-parametric statistical methods throughout the entirety of this study. Furthermore, the non-parametric Kruskal-Wallis test provided clear statistical confirmation of significant spatial heterogeneity in microbial contamination across the facility (Table 2)24.
The test yielded a statistic of H=104.1 with a highly significant p-value of p<0.0001. This strongly indicates that the risk of contamination is not uniformly distributed but rather varies significantly across different operational zones within the facility. An important observation during this analysis was the presence of an extreme outlier: a reading of 72 CFU recorded at location CP C 11 W. This specific value was intentionally retained within the dataset. Its inclusion underscores its representation of a genuine, high-risk event, emphasizing that effective monitoring programs must possess the capability to detect and respond to such
occurrences, which, in a proactive scenario, would immediately trigger a root-cause investigation.
Hotspot hierarchy and significant differences
Dunn's post-hoc test provided a detailed and nuanced perspective on the hierarchy of contamination risk24. While preliminary assessments might have indicated CP C 11 W as a singular primary concern, this rigorous analysis confirmed its status as the location with the highest absolute bioburden, exhibiting a mean CFU count of 12.17. Crucially, the analysis revealed that the most statistically robust contrasts were observed when comparing these high-burden sites against the facility's cleanest location, CP C 32 WNme. Importantly, the analysis clearly demonstrated that multiple locations, extending beyond just CP C 11 W, were significantly more contaminated than the facility's designated low-burden zones.
Table 2 provides a comprehensive summary of all statistically significant pair wise comparisons. Specifically, high burden locations such as CP C 11 W, CP C 30 NCu, and CP C 38 W consistently showed statistically significant differences when contrasted with low-risk areas, including CP C 32 WNme, CP C 34 WbCu, and CP C 49 WbAL. This pivotal finding significantly broadens the scope of necessary targeted interventions, shifting the focus from a single isolated hotspot to a broader cluster of interconnected high-risk operational zones. Figure 2 diagram illustrates the statistically significant pairwise comparisons between higher-burden (hotspot) locations and lower-burden (cleaner) locations, based on Dunn's post-hoc test results (extended from Table 2 from the statistical analysis).
Evaluation of correlational pathways
A critical re-evaluation of the Spearman correlation data was meticulously conducted with adjustment after initial extrapolation elucidated apparently significant associations25. Following the application of a stringent Bonferroni correction for the 378 multiple comparisons performed, utilizing a corrected alpha threshold of p<0.00013, a crucial clarification emerged: no correlations remained statistically significant (Figure 3). This outcome is vital to understand, as it means that even though some correlation coefficients were numerically high (e.g., r>0.8), the study, after rigorous correction for multiple testing, lacked the statistical power to confirm these observed relationships as definitively non-random events.
Nevertheless, it is noteworthy that the correlation observed between CP C 11 W and CP C 45 Wif yielded a strong Spearman correlation coefficient of r=0.882 and an uncorrected p-value of 0.0004. While this particular p-value did not meet the very stringent Bonferroni threshold established for the study, it strongly suggests a potentially robust underlying relationship between these two locations.
This near-significant finding should not be disregarded. Instead, it offers a clear and valuable direction for future research. It strongly indicates that a more targeted investigation, involving an increased sampling frequency specifically at CP C 11 W and CP C 45 Wif, could provide the necessary statistical power to conclusively validate a true contamination pathway between them.
Limitations of the study
The study's conclusions are drawn from data collected at a single Class C healthcare facility located in the South Asia region. This specificity may limit the generalizability of the findings, as different facilities may exhibit unique contamination patterns due to variations in layout, operational protocols, personnel flow, and environmental conditions. Study needs futuristically to account for potential seasonal fluctuations, long-term trends, or the impact of specific operational changes over time.
CONCLUSIONS
Applying comprehensive non-parametric statistical analysis definitively confirms that surface microbial contamination within the studied Class C healthcare environment is spatially heterogeneous. The analysis has established a clear and statistically validated hierarchy of contamination risk, with CP C 11 W identified as the most contaminated site. Conversely, CP C 32 WNme serves as the most effectively controlled benchmark location within the facility. The findings further delineate a network of several high-risk zones that collectively warrant focused and targeted sanitation efforts. Crucially, this study powerfully underscores the paramount importance of applying appropriate statistical corrections to avoid spurious conclusions. After the application of these corrections, no definitive contamination pathways could be statistically confirmed. However, the identification of a near-significant link between two key sites provides a precise and actionable direction for future, more granular investigations. The primary recommendation derived from this analysis is the immediate implementation of a risk-based monitoring plan, strategically focused on the multiple statistically validated high-burden zones identified.
ACKNOWLEDGEMENTS
The authors acknowledge the facility's quality control personnel for their data collection efforts.
AUTHOR'S CONTRIBUTION
Eissa ME: designed the study, performed the statistical re-analysis, manuscript writing, microbiological interpretation, critically reviewed.
DATA AVAILABILITY
Data will be made available on request.
CONFLICT OF INTEREST
No conflict of interest is associated with this work.
REFERENCES