The microbiome of an outpatient rehabilitation clinic and predictors of contamination: A pilot study

Abstract

Background: Understanding sources of microbial contamination in outpatient rehabilitation (REHAB) clinics is important to patients and healthcare providers.

Purpose: The purpose of this study was to characterize the microbiome of an outpatient REHAB clinic and examine relationships between clinic factors and contamination.

Methods: Forty commonly contacted surfaces in an outpatient REHAB clinic were observed for frequency of contact and swiped using environmental sample collection kits. Surfaces were categorized based on frequency of contact and cleaning and surface type. Total bacterial and fungal load was assessed using primer sets specific for the 16S rRNA and ITS genes, respectively. Bacterial samples were sequenced using the Illumina system and analyzed using Illumina-utils, Minimum Entropy Decomposition, QIIME2 (for alpha and beta diversity), LEfSe and ANCOM-BC for taxonomic differential abundance and ADONIS to test for differences in beta diversity (p<0.05).

Results: Porous surfaces had more bacterial DNA compared to non-porous surfaces (median non-porous = 0.0016ng/μL, 95%CI = 0.0077-0.00024ng/μL, N = 15; porous = 0.0084 ng/μL, 95%CI = 0.0046-0.019 ng/μL, N = 18. p = 0.0066,DNA. Samples clustered by type of surface with non-porous surfaces further differentiated by those contacted by hand versus foot. ADONIS two-way ANOVA showed that the interaction of porosity and contact frequency (but neither alone) had a significant effect on 16S communities (F = 1.7234, R2 = 0.0609, p = 0.032).

Discussion: Porosity of surfaces and the way they are contacted may play an underestimated, but important role in microbial contamination. Additional research involving a broader range of clinics is required to confirm results. Results suggest that surface and contact-specific cleaning and hygiene measures may be needed for optimal sanitization in outpatient REHAB clinics.

Fig 1. Map of the outpatient rehabilitation clinic.

The sites that were observed for contact and swiped for collection of bacterial and fungal DNA are numbered and indicated. The clinic was approximately 14.5 M x 9 M in size and served approximately 10–15 patients/day during the data collection period. Typically, two physical therapists were present and treating patients and one administrative staff member was present. The clientele represented a wide range of diagnoses including low back and neck pain, knee, shoulder, ankle injuries, neurologic conditions and general medical conditions.

Fig 2. Bacterial contamination and clinic factors.

Total bacterial DNA based on 16S rRNA gene quantification was analyzed. A. Total bacterial DNA comparing surface frequency of contact as low or high. B. Total bacterial DNA comparing surfaces identified as non-porous versus porous. C. Total bacterial DNA comparing surfaces with low, moderate and high frequency of cleaning. * indicates statistically significant difference. Abbreviations used are as follows: ng, nanograms; μL, microliters.

Fig 3. Fungal contamination and clinic factors.

Total fungal DNA based on ITS gene region quantification. A. Total fungal DNA comparing surfaces with low or high frequency of contact. B. Total fungal DNA comparing surfaces identified as non-porous versus porous. C. Total fungal DNA comparing surfaces with low, moderate and high frequency of cleaning. * indicates statistically significant difference. Abbreviations used are as follows: Abbreviations used are as follows: ng, nanograms; μL, microliters.

Fig 4. Clinic surface bacterial taxa.

A. Major phyla. B. Family and genus. Bacterial taxa were determined by 16s RNA gene sequencing.

Fig 5. Alpha diversity based on Shannon Diversity Index of clinic surface microbiome.

A. Shannon Diversity for surfaces with low or high frequency of contact. B. Shannon Diversity for non-porous and porous surfaces. C. Shannon Diversity for surfaces based on low, moderate and high frequency of cleaning. No differences were detected based on any of these groupings. Abbreviations used are as follows: Mod = moderate.

Fig 6. Beta diversity of clinic surface microbiome.

A. Beta diversity on a two-dimensional principle coordinate analysis plot with all surfaces samples marked individually. No clustering can be detected based on subjective analysis of the plot. B. Beta diversity on a three-dimensional plot with surfaces coded based on classification of surfaces as non-porous-hand contacted, non-porous-foot contacted and porous. In this plot there is separation of the samples coded red (porous), blue (non-porous, foot contacted) and yellow (non-porous, hand-contacted) along the X-axis. Only 28 of the 36 samples were include and samples 6, 17, 24, 27, 28, 29, 31, and 33 could not be analyzed due to insufficient reads. Abbreviations are as follows: PC = principal coordinate; PCoA = principal coordinate analysis.

Fig 7

A. Cladogram representing the taxonomy of 16S rRNA amplicons found in this study differentiated by porous versus non-porous surface type. Green indicates taxa significantly more abundant in porous samples, whereas red indicates taxa more abundant in non-porous samples. Prominent taxa are identified as nodes and indicated in the figure or key. B. Bar chart showing the oligotypes identified by LEfSe analysis that were enriched in porous (green) and non-porous samples (red). Asterisks indicate taxa that were found to be significantly differentiated by LEfSe. Abbreviations are as follows: LDA, linear discriminate analysis score.

Fig 8

A. Cladogram representing the taxonomy of 16S rRNA amplicons found in this study differentiated by hand versus foot-contacted surfaces. Green indicates relationships for hand-contacted and red indicates foot-contacted surfaces. B. Bar chart showing the oligotypes identified by LEfSe analysis that were enriched in hand (red) and foot-contacted surfaces (green). Abbreviations are as follows: LDA, linear discriminate analysis score. Asterisks indicate taxa that were found to be significantly differentiated by both LEfSe and ANCOM-BC.