Prevalence of Clostridioides difficile contamination in healthcare and non-healthcare environments: a global systematic review and meta-analysis

TL;DR

This study finds that Clostridioides difficile contamination is widespread in healthcare and non-healthcare environments globally, highlighting the need for better disinfection strategies.

Contribution

The study provides a global systematic review and meta-analysis of C. difficile contamination prevalence from 2014 to 2024.

Findings

The overall prevalence of C. difficile contamination was estimated at 0.26 (95% CI 0.23 to 0.28).

Contamination was higher in non-healthcare environments compared to healthcare settings.

PCR detection methods showed higher prevalence than culture tests.

Abstract

Clostridioides difficile infection is a significant public health challenge. This study aimed to determine the overall prevalence of C. difficile contamination in healthcare and non-healthcare environments around the world from 2014 to 2024. In this systematic review, we searched five international databases. Random and fixed-effects models were used to estimate the pooled prevalence with 95% CIs. Among the 49 eligible studies and 20 779 environmental samples, 4507 samples were positive for C. difficile contamination. The overall prevalence was estimated as 0.26 (95% CI 0.23 to 0.28). The pooled prevalence according to the detection method was 0.26 (95% CI 0.21 to 0.32) and 0.62 (95% CI 0.50 to 0.74) using a culture test and PCR, respectively. The overall prevalence was 0.18 (95% CI 0.16 to 0.20) and 0.36 (95% CI 0.30 to 0.42) in the healthcare and non-healthcare environment, respectively. The prevalence in non-healthcare environments was 0.42, 0.38, 0.33 and 0.04 on farms, in public places, in the home and veterinary hospitals, respectively. The findings reveal a significant prevalence of C. difficile contamination across various environments worldwide. This alarming trend underscores the pressing need to prioritize environmental factors and implement effective disinfection strategies as part of comprehensive control programs aimed at tackling this disease.

Figures

PRISMA flowchart presenting the selection of articles analyzed in this systematic review and meta-analysis. PRISMA: Preferred Reporting Items for Systematic reviews and Meta-Analyses.

Forest plot of the pooled prevalence of C. difficile contamination in healthcare and non-healthcare environments.

Forest plots of the pooled prevalence of C. difficile contamination in healthcare and non-healthcare environments according to (A) diagnostic test, (B) setting and (C) year.

Forest plot of the pooled prevalence of C. difficile contamination in healthcare and non-healthcare environments according to region.

Tables

Table 1.. Characteristics of the studies included in the meta-analysis

Study number	First author	Year	Country	Setting	Diagnostic methods	Sample size number	Positive number	Reference
1	Aliramezani	2019	Iran	Healthcare	Culture/other test	1435	2	9
2	Büchler	2022	Switzerland	Healthcare	Culture/other test	206	37	10
3	Doughari	2021	Nigeria	Healthcare	Culture	150	18	11
4	Islam	2019	Bangladesh	Non-healthcare	Culture	94	11	12
5	Janezic	2018	Slovenia	Non-healthcare	Culture/other test	90	31	13
6	Janezic	2020	Slovenia	Non-healthcare	Culture	25	12	14
7	Jinhee	2022	USA	Healthcare and non-healthcare	-	6966	1794	15
			Mexico, Peru, Guatemala, Brazil, France, Germany, Italy, Taiwan, India	Non-healthcare	-	891	194
8	Kabała	2022	Poland	Healthcare	Culture/other test	116	6	16
9	Kiersnowska	2022	Poland	Healthcare	Culture	116	6	17
10	Lemiech-Mirowska	2023	Poland	Healthcare	Culture	100	7	18
11	Lim	2020	Australia	Healthcare	Culture	302	159	19
12	Perumalsamy	2019	Australia	Healthcare	Culture	159	96	20
13	Reigadas	2020	Spain	Healthcare	Culture/other test	476	73	21
14	Riley	2018	Australia	Non-healthcare	PCR	145	86	22
15	Shivaperumal	2021	Australia	Non-healthcare	Culture	97	65	23
16	Rodriguez	2019	Belgium	Non-healthcare	Culture/other test	112	45	24
17	Sofjan	2020	Bangladesh	Healthcare	Culture	400	44	25
18	Srinivasa	2019	USA	Healthcare	Culture/other test	101	14	26
19	Villagomez-Estrada	2019	Spain	Non-healthcare	Culture/other test	100	4	27
20	Weppner	2021	USA	Healthcare	Culture	76	17	28
21	Zhou	2021	China	Healthcare	Culture	608	12	29
22	Blau	2023	Germany	Non-healthcare	Culture and other test	79	30	30
23	O’Shaughnessy	2019	Canada	Non-healthcare	Culture/other test	350	144	31
24	Simecka	2019	USA	Healthcare	Culture/other test	630	24	32
25	Khun	2023	Vietnam	Healthcare and non-healthcare	Culture	199	65	33
26	Warren	2022	USA	Healthcare	Culture	758	121	34
27	Lim	2022	Australia	Healthcare	Culture	600	185	35
28	Endres	2018	USA	Healthcare	Culture/other test	50	14	36
29	Tsai	2022	Taiwan	Non-healthcare	Culture/other test	29	7	37
30	Moradigaravand	2018	UK	Non-healthcare	Culture/other test	20	18	38
31	Garey	2020	USA	Healthcare	Culture	659	55	39
32	Shoaei	2019	Iran	Healthcare	Culture/other test	92	9	40
33	Odoyo	2020	Kenya	Healthcare	Culture	57	43	41
					PCR	57	43
34	Brown	2018	Canada	Healthcare	PCR	96	50	42
					Culture	48	21
35	Moono	2017	Australia	Non-healthcare	Culture/other test	311	182	43
36	Alam	2017	USA	Healthcare and non-healthcare	-	2538	418	44
37	Orden	2018	Spain	Non-healthcare	Culture/other test	40	21	45

Table 1.. Continued

Study number	Author	Year	Country	Setting	Diagnostic methods	Sample size number	Positive number	Reference
38	Taha	2022	Egypt	Non-healthcare	Culture	200	38	46
39	Alves	2022	Portugal	Non-healthcare	Culture/other test	60	35	47
40	Redding	2021	USA	Non-healthcare	Culture/other test	92	28	48
41	Usui	2017	Japan	Non-healthcare	Culture/other test	14	5	49
42	Marcos	2021	Ireland	Non-healthcare	Culture/other test	180	68	50
43	Alam	2014	USA	Non-healthcare	Culture/other test	127	41	51
44	Leitner	2020	Austria	Healthcare	Culture	92	0	52
45	Ali	2021	India	Non-healthcare	Culture/other test	6	1	53
46	Lim	2020	Australia	Non-healthcare	-	142	32	54
47	Janezic	2016	Slovenia	Non-healthcare	Culture/other test	183	44	55
48	Baghani	2020	Iran	Non-healthcare	Culture/other test	72	1	56
49	Shaughnessy	2016	USA	Non-healthcare	Culture/other test	233	31	57

Table 2.. Pooled prevalence of C. difficile contamination in healthcare and non-healthcare environments

			Total			Heterogeneity			Heterogeneity
	Number	Sample	Number	Pooled					between
Independent variable	of studies	size	positive	prevalence	95% CI	Q statistic	I2 (%)	p	subgroup bias
Diagnostic test
Culture	19	4740	975	0.26	0.21–0.32	1709.52	98.95	0.00	0.00
Culture and other test	26	5204	915	0.27	0.21–0.33	1655.63	98.49	0.00
PCR	3	298	179	0.62	0.50–0.74	0	0	0.00
Region
Africa	2	264	104	0.31	0.27–0.36	-	-	-	0.00
Eastern Mediterranean	4	1799	50	0.07	0.01–0.13	56.58	94.70	0.00
America	13	12 724	2772	0.23	0.17–0.29	813.01	98.52	0.00
South-East Asia	3	500	56	0.11	0.08–0.14	-	-	-
Western Pacific	11	2606	894	0.41	0.23–0.58	1365.82	99.27	0.00
Europe	16	1995	437	0.28	0.20–0.36	815.86	98.16	0.00
Year
Before 2020	21	6968	1311	0.31	0.25–0.38	2294.83	99.13	0.00	0.11
After 2020	28	13 811	3196	0.24	0.19–0.30	4415.21	99.39	0.00
Setting
Healthcare	25	11 475	1962	0.18	0.16–0.20	2929.39	99.18	0.00	0.00
Non-healthcare	30	9304	2545	0.36	0.30–0.41	1157.38	97.49	0.00
Sample type
Soil	8	530	310	0.59	0.47–0.71	60.19	88.37	0.00	0.00
Shoe	8	2084	813	0.34	0.23–0.45	156.27	95.52	0.00

Table 3.. Pooled prevalence of C. difficile contamination in healthcare and non-healthcare environments

						Heterogeneity
Independent variable	Number of studies	Sample size	Total number positive	Pooled prevalence	95% CI	Q statistic	I2 (%)	p	Heterogeneity between subgroup bias
Healthcare
Region
Africa	2	264	104	0.31	0.27–0.36	-	-	-	0.00
Eastern Mediterranean	2	1527	11	0.00	0.00–0.00	-	-	-
America	9	6359	1193	0.19	0.12–0.26	449.47	98.22	0.00
South-East Asia	1	400	44	0.11	0.08–0.14	-	-	-
Western Pacific	5	1819	481	0.33	0.10–0.56	695.26	99.42	0.00
Europe	6	1106	129	0.08	0.02–0.15	149.67	96.66	0.00
Sample type
Bed	7	447	43	0.09	0.04–0.15	53.52	88.79	0.00	0.00
Chair	2	246	16	0.03	0.01–0.05	-	-	-
Computer	3	108	7	0.04	0.00–0.09	-	-	-
Door handle	2	32	1	0.00	0.00–0.01	-	-	-
Light switch	2	98	0	0.00	0.00–0.00	-	-	-
Shoe	3	643	205	0.34	0.17–0.51	-	-	-
Soil	2	189	121	0.66	0.59–0.73	-	-	-
Table	3	76	9	0.10	0.03–0.17	-	-	-
Toilet or bathroom	5	413	97	0.14	0.01–0.28	98.88	95.95	0.00
Wheelchair	2	40	5	0.12	0.02–0.22	-	-	-
Window	2	37	4	0.01	0.00–0.04	-	-	-
Non-healthcare
Location
Public	16	6797	1869	0.38	0.28–0.48	705.04	98.01	0.00	0.00
Home	6	1607	345	0.33	0.20–0.45	138.87	96.32	0.00
Farm	7	800	327	0.42	0.31–0.53	54.79	89.05	0.00
Veterinary hospital	1	100	4	0.04	0.02–0.10	-	-	-

Full text

Introduction

Clostridium difficile or Clostridioides difficile is an anaerobic Gram-positive bacterium that is found as a flora of healthy infants. This bacterium can cause disease with severe diarrhea and inflammation of the colon or colitis, and overuse of antibiotics facilitate its occurrence.^1,2^ The bacterium produces two exotoxins, toxin A (TcdA) and toxin B (TcdB), which are associated with the severity of the disease. Toxin A (308 kDa) causes inflammation, resulting in the secretion of intestinal fluids and colonic mucosal damage. Toxin B (270 kDa) also causes mucosal damage. Furthermore, C. difficile produces CD, including a cell binding component (CDTb) and an enzymatic component (CDTa) with ADP ribosyltransferase activity, leading to cytoskeleton disorganization. Other virulence factors include endospore, S-layers, flagella and fimbriae, as well as other virulence factors.^3^

The occurrence of the disease known as C. difficile infection (CDI) is associated with clinical symptoms that include watery diarrhea, abdominal pain, dehydration, fever, nausea and weight loss. In other words, the disease can cause a range of gastrointestinal issues from mild to severe that can be life-threatening.^4^ Considering the importance of the disease, Centers for Disease Control and Prevention (CDC) classified CDI as an urgent public health issue in 2013; there were 250 000 infections and 14 000 deaths reported by the CDC in 2013. The crude incidence rate of CDI was reported as 130.28 per 100 000 persons by the CDC in the CDI Surveillance report in 2017. According to the latest update of the report, the crude incidence rate of CDI in 2022 was 116.1 cases per 100 000 persons.^5^

It has been proven that, besides asymptomatic carriers, the environment plays a crucial role in the transmission of CDI, particularly within healthcare settings. Contaminated surfaces in hospitals can harbor C. difficile spores, which are highly resistant to environmental stressors and can persist for months or even years. These spores can be transferred to patients through direct contact with contaminated surfaces or via healthcare workers, contributing to the spread of infection. Enhanced cleaning protocols, including the use of sporicidal disinfectants, are essential to mitigate the risk.^6,7^

To date, several researchers have reported the contamination rate in various healthcare and non-healthcare environmental settings. Despite this, no attempt has been made to summarize the reported contamination rate worldwide in recent years. Therefore, to fill this gap and improve our understanding of the current global prevalence of contamination, this study was conducted to evaluate the overall prevalence of C. difficile contamination in both healthcare and non-healthcare environments in the last decade (2014–2024).

Methods

Search strategy

We conducted a global systematic review by searching five international databases, namely, PubMed, Science Direct, Springer, CABI Direct and Google Scholar. The search covered the period from January 2014 to January 2024 and utilized the following keywords: ‘Clostridium spp.’, ‘difficile AND environment’, ‘difficile AND healthcare’, ‘difficile AND equipment’ and ‘difficile AND public’. We searched for these combinations in the titles, keywords and abstracts of published papers in the aforementioned databases. The study protocol was registered in the open science framework (OSF) registries for systematic reviews with the following doi: https://doi.org/10.17605/OSF.IO/G8BTQ.

Study selection process

Studies with insufficient data, irrelevant and duplicated data were excluded from the analysis. Scientist evaluated studies related to C. difficile contamination by screening their titles and abstracts, then reviewed the full text of papers for quality assessment after eliminating duplicates. In addition, the references of eligible papers were searched to find additional eligible articles. Scientist conducted separate assessments of the articles' quality.

Eligibility criteria

Articles were considered eligible if they met the following inclusion criteria: (i) they were conducted on environmental samples; and (ii) they reported the prevalence of C. difficile contamination. However, papers that met the following criteria were excluded from the analysis: (i) they were conducted before 2014; (ii) they did not report the prevalence of C. difficile contamination; or (iii) they were case reports or experimental studies.

Data extraction

The investigators used a form specifically designed to extract data from eligible papers. Data from the papers were extracted into summary tables containing information on author(s) name(s), publication year, detection methods, country, region, setting, sample type, total number of samples and the total number of confirmed C. difficile samples.

Quality assessment

The authors reviewed eligible research articles using Critical Appraisal Skills Programme (CASP) Checklists to evaluate cross-sectional studies. This tool has special items to evaluate the quality of a paper, using 10–12 items for each checklist. These checklists evaluate the strengths and weaknesses of articles, the quality of the study design and the applicability of studies. The precise checklist was applied to each paper according to the type of study. For each item, the possible answers were ‘Yes’, ‘No’ or ‘Can’t tell’. Scientist assessed the articles’ quality separately, and discrepancies were resolved via group discussion. For each item, an answer of ‘Yes’ was scored as 1, while answers of ‘No’ or ‘Can’t tell’ were set as 0. Therefore, the maximum score of cross-sectional studies in this systematic review was 11. Articles with scores of ≥75% of the total score were classified as good quality. Articles with scores of 25–75% of the total score were classified as moderate quality articles and articles with scores of <25% of the total score were classified as poor quality articles.^8^

Statistical analysis

We employed both random and fixed-effects models to calculate the pooled prevalence and 95% CIs. To evaluate heterogeneity among the studies, we utilized Cochran’s Q test and the I^2^ index. A random-effects model was selected for estimation when the I^2^ index was >50% and the p-value from Cochran’s Q test was <0.1. Forest plots illustrate the proportions of individual studies, the pooled prevalence and the heterogeneity across studies. The meta-analysis was performed using Stata version 14.

Results

Search results and eligibility

Initially, 3678 articles were obtained from the electronic database search. However, 3629 (98.86%) were excluded due to irrelevancy, duplicated documents, lack of access to the full text or insufficient data. Finally, 49 (1.33%) were deemed eligible and subjected to analysis.^6–54^ A flowchart illustrating the screening of papers is presented in Figure 1. Extracted data from studies included in the meta-analysis (up to 1 January 2024) are presented in Table 1.

PRISMA flowchart presenting the selection of articles analyzed in this systematic review and meta-analysis. PRISMA: Preferred Reporting Items for Systematic reviews and Meta-Analyses.

Table 1.: Characteristics of the studies included in the meta-analysis

<table><thead><tr><th align="left" rowspan="1" colspan="1">Study number</th><th align="center" rowspan="1" colspan="1">First author</th><th align="center" rowspan="1" colspan="1">Year</th><th align="center" rowspan="1" colspan="1">Country</th><th align="center" rowspan="1" colspan="1">Setting</th><th align="center" rowspan="1" colspan="1">Diagnostic methods</th><th align="center" rowspan="1" colspan="1">Sample size number</th><th align="center" rowspan="1" colspan="1">Positive number</th><th align="center" rowspan="1" colspan="1">Reference</th></tr></thead><tbody><tr><td rowspan="1" colspan="1">1</td><td rowspan="1" colspan="1">Aliramezani</td><td rowspan="1" colspan="1">2019</td><td rowspan="1" colspan="1">Iran</td><td rowspan="1" colspan="1">Healthcare</td><td rowspan="1" colspan="1">Culture/other test</td><td rowspan="1" colspan="1">1435</td><td rowspan="1" colspan="1">2</td><td rowspan="1" colspan="1"> ^{<xref>9</xref>} </td></tr><tr><td rowspan="1" colspan="1">2</td><td rowspan="1" colspan="1">Büchler</td><td rowspan="1" colspan="1">2022</td><td rowspan="1" colspan="1">Switzerland</td><td rowspan="1" colspan="1">Healthcare</td><td rowspan="1" colspan="1">Culture/other test</td><td rowspan="1" colspan="1">206</td><td rowspan="1" colspan="1">37</td><td rowspan="1" colspan="1"> ^{<xref>10</xref>} </td></tr><tr><td rowspan="1" colspan="1">3</td><td rowspan="1" colspan="1">Doughari</td><td rowspan="1" colspan="1">2021</td><td rowspan="1" colspan="1">Nigeria</td><td rowspan="1" colspan="1">Healthcare</td><td rowspan="1" colspan="1">Culture</td><td rowspan="1" colspan="1">150</td><td rowspan="1" colspan="1">18</td><td rowspan="1" colspan="1"> ^{<xref>11</xref>} </td></tr><tr><td rowspan="1" colspan="1">4</td><td rowspan="1" colspan="1">Islam</td><td rowspan="1" colspan="1">2019</td><td rowspan="1" colspan="1">Bangladesh</td><td rowspan="1" colspan="1">Non-healthcare</td><td rowspan="1" colspan="1">Culture</td><td rowspan="1" colspan="1">94</td><td rowspan="1" colspan="1">11</td><td rowspan="1" colspan="1"> ^{<xref>12</xref>} </td></tr><tr><td rowspan="1" colspan="1">5</td><td rowspan="1" colspan="1">Janezic</td><td rowspan="1" colspan="1">2018</td><td rowspan="1" colspan="1">Slovenia</td><td rowspan="1" colspan="1">Non-healthcare</td><td rowspan="1" colspan="1">Culture/other test</td><td rowspan="1" colspan="1">90</td><td rowspan="1" colspan="1">31</td><td rowspan="1" colspan="1"> ^{<xref>13</xref>} </td></tr><tr><td rowspan="1" colspan="1">6</td><td rowspan="1" colspan="1">Janezic</td><td rowspan="1" colspan="1">2020</td><td rowspan="1" colspan="1">Slovenia</td><td rowspan="1" colspan="1">Non-healthcare</td><td rowspan="1" colspan="1">Culture</td><td rowspan="1" colspan="1">25</td><td rowspan="1" colspan="1">12</td><td rowspan="1" colspan="1"> ^{<xref>14</xref>} </td></tr><tr><td rowspan="1" colspan="1">7</td><td rowspan="1" colspan="1">Jinhee</td><td rowspan="1" colspan="1">2022</td><td rowspan="1" colspan="1">USA</td><td rowspan="1" colspan="1">Healthcare and non-healthcare</td><td rowspan="1" colspan="1">-</td><td rowspan="1" colspan="1">6966</td><td rowspan="1" colspan="1">1794</td><td rowspan="1" colspan="1"> ^{<xref>15</xref>} </td></tr><tr><td rowspan="1" colspan="1"/><td rowspan="1" colspan="1"/><td rowspan="1" colspan="1"/><td rowspan="1" colspan="1">Mexico, Peru, Guatemala, Brazil, France, Germany, Italy, Taiwan, India</td><td rowspan="1" colspan="1">Non-healthcare</td><td rowspan="1" colspan="1">-</td><td rowspan="1" colspan="1">891</td><td rowspan="1" colspan="1">194</td><td rowspan="1" colspan="1"/></tr><tr><td rowspan="1" colspan="1">8</td><td rowspan="1" colspan="1">Kabała</td><td rowspan="1" colspan="1">2022</td><td rowspan="1" colspan="1">Poland</td><td rowspan="1" colspan="1">Healthcare</td><td rowspan="1" colspan="1">Culture/other test</td><td rowspan="1" colspan="1">116</td><td rowspan="1" colspan="1">6</td><td rowspan="1" colspan="1"> ^{<xref>16</xref>} </td></tr><tr><td rowspan="1" colspan="1">9</td><td rowspan="1" colspan="1">Kiersnowska</td><td rowspan="1" colspan="1">2022</td><td rowspan="1" colspan="1">Poland</td><td rowspan="1" colspan="1">Healthcare</td><td rowspan="1" colspan="1">Culture</td><td rowspan="1" colspan="1">116</td><td rowspan="1" colspan="1">6</td><td rowspan="1" colspan="1"> ^{<xref>17</xref>} </td></tr><tr><td rowspan="1" colspan="1">10</td><td rowspan="1" colspan="1">Lemiech-Mirowska</td><td rowspan="1" colspan="1">2023</td><td rowspan="1" colspan="1">Poland</td><td rowspan="1" colspan="1">Healthcare</td><td rowspan="1" colspan="1">Culture</td><td rowspan="1" colspan="1">100</td><td rowspan="1" colspan="1">7</td><td rowspan="1" colspan="1"> ^{<xref>18</xref>} </td></tr><tr><td rowspan="1" colspan="1">11</td><td rowspan="1" colspan="1">Lim</td><td rowspan="1" colspan="1">2020</td><td rowspan="1" colspan="1">Australia</td><td rowspan="1" colspan="1">Healthcare</td><td rowspan="1" colspan="1">Culture</td><td rowspan="1" colspan="1">302</td><td rowspan="1" colspan="1">159</td><td rowspan="1" colspan="1"> ^{<xref>19</xref>} </td></tr><tr><td rowspan="1" colspan="1">12</td><td rowspan="1" colspan="1">Perumalsamy</td><td rowspan="1" colspan="1">2019</td><td rowspan="1" colspan="1">Australia</td><td rowspan="1" colspan="1">Healthcare</td><td rowspan="1" colspan="1">Culture</td><td rowspan="1" colspan="1">159</td><td rowspan="1" colspan="1">96</td><td rowspan="1" colspan="1"> ^{<xref>20</xref>} </td></tr><tr><td rowspan="1" colspan="1">13</td><td rowspan="1" colspan="1">Reigadas</td><td rowspan="1" colspan="1">2020</td><td rowspan="1" colspan="1">Spain</td><td rowspan="1" colspan="1">Healthcare</td><td rowspan="1" colspan="1">Culture/other test</td><td rowspan="1" colspan="1">476</td><td rowspan="1" colspan="1">73</td><td rowspan="1" colspan="1"> ^{<xref>21</xref>} </td></tr><tr><td rowspan="1" colspan="1">14</td><td rowspan="1" colspan="1">Riley</td><td rowspan="1" colspan="1">2018</td><td rowspan="1" colspan="1">Australia</td><td rowspan="1" colspan="1">Non-healthcare</td><td rowspan="1" colspan="1">PCR</td><td rowspan="1" colspan="1">145</td><td rowspan="1" colspan="1">86</td><td rowspan="1" colspan="1"> ^{<xref>22</xref>} </td></tr><tr><td rowspan="1" colspan="1">15</td><td rowspan="1" colspan="1">Shivaperumal</td><td rowspan="1" colspan="1">2021</td><td rowspan="1" colspan="1">Australia</td><td rowspan="1" colspan="1">Non-healthcare</td><td rowspan="1" colspan="1">Culture</td><td rowspan="1" colspan="1">97</td><td rowspan="1" colspan="1">65</td><td rowspan="1" colspan="1"> ^{<xref>23</xref>} </td></tr><tr><td rowspan="1" colspan="1">16</td><td rowspan="1" colspan="1">Rodriguez</td><td rowspan="1" colspan="1">2019</td><td rowspan="1" colspan="1">Belgium</td><td rowspan="1" colspan="1">Non-healthcare</td><td rowspan="1" colspan="1">Culture/other test</td><td rowspan="1" colspan="1">112</td><td rowspan="1" colspan="1">45</td><td rowspan="1" colspan="1"> ^{<xref>24</xref>} </td></tr><tr><td rowspan="1" colspan="1">17</td><td rowspan="1" colspan="1">Sofjan</td><td rowspan="1" colspan="1">2020</td><td rowspan="1" colspan="1">Bangladesh</td><td rowspan="1" colspan="1">Healthcare</td><td rowspan="1" colspan="1">Culture</td><td rowspan="1" colspan="1">400</td><td rowspan="1" colspan="1">44</td><td rowspan="1" colspan="1"> ^{<xref>25</xref>} </td></tr><tr><td rowspan="1" colspan="1">18</td><td rowspan="1" colspan="1">Srinivasa</td><td rowspan="1" colspan="1">2019</td><td rowspan="1" colspan="1">USA</td><td rowspan="1" colspan="1">Healthcare</td><td rowspan="1" colspan="1">Culture/other test</td><td rowspan="1" colspan="1">101</td><td rowspan="1" colspan="1">14</td><td rowspan="1" colspan="1"> ^{<xref>26</xref>} </td></tr><tr><td rowspan="1" colspan="1">19</td><td rowspan="1" colspan="1">Villagomez-Estrada</td><td rowspan="1" colspan="1">2019</td><td rowspan="1" colspan="1">Spain</td><td rowspan="1" colspan="1">Non-healthcare</td><td rowspan="1" colspan="1">Culture/other test</td><td rowspan="1" colspan="1">100</td><td rowspan="1" colspan="1">4</td><td rowspan="1" colspan="1"> ^{<xref>27</xref>} </td></tr><tr><td rowspan="1" colspan="1">20</td><td rowspan="1" colspan="1">Weppner</td><td rowspan="1" colspan="1">2021</td><td rowspan="1" colspan="1">USA</td><td rowspan="1" colspan="1">Healthcare</td><td rowspan="1" colspan="1">Culture</td><td rowspan="1" colspan="1">76</td><td rowspan="1" colspan="1">17</td><td rowspan="1" colspan="1"> ^{<xref>28</xref>} </td></tr><tr><td rowspan="1" colspan="1">21</td><td rowspan="1" colspan="1">Zhou</td><td rowspan="1" colspan="1">2021</td><td rowspan="1" colspan="1">China</td><td rowspan="1" colspan="1">Healthcare</td><td rowspan="1" colspan="1">Culture</td><td rowspan="1" colspan="1">608</td><td rowspan="1" colspan="1">12</td><td rowspan="1" colspan="1"> ^{<xref>29</xref>} </td></tr><tr><td rowspan="1" colspan="1">22</td><td rowspan="1" colspan="1">Blau</td><td rowspan="1" colspan="1">2023</td><td rowspan="1" colspan="1">Germany</td><td rowspan="1" colspan="1">Non-healthcare</td><td rowspan="1" colspan="1">Culture and other test</td><td rowspan="1" colspan="1">79</td><td rowspan="1" colspan="1">30</td><td rowspan="1" colspan="1"> ^{<xref>30</xref>} </td></tr><tr><td rowspan="1" colspan="1">23</td><td rowspan="1" colspan="1">O’Shaughnessy</td><td rowspan="1" colspan="1">2019</td><td rowspan="1" colspan="1">Canada</td><td rowspan="1" colspan="1">Non-healthcare</td><td rowspan="1" colspan="1">Culture/other test</td><td rowspan="1" colspan="1">350</td><td rowspan="1" colspan="1">144</td><td rowspan="1" colspan="1"> ^{<xref>31</xref>} </td></tr><tr><td rowspan="1" colspan="1">24</td><td rowspan="1" colspan="1">Simecka</td><td rowspan="1" colspan="1">2019</td><td rowspan="1" colspan="1">USA</td><td rowspan="1" colspan="1">Healthcare</td><td rowspan="1" colspan="1">Culture/other test</td><td rowspan="1" colspan="1">630</td><td rowspan="1" colspan="1">24</td><td rowspan="1" colspan="1"> ^{<xref>32</xref>} </td></tr><tr><td rowspan="1" colspan="1">25</td><td rowspan="1" colspan="1">Khun</td><td rowspan="1" colspan="1">2023</td><td rowspan="1" colspan="1">Vietnam</td><td rowspan="1" colspan="1">Healthcare and non-healthcare</td><td rowspan="1" colspan="1">Culture</td><td rowspan="1" colspan="1">199</td><td rowspan="1" colspan="1">65</td><td rowspan="1" colspan="1"> ^{<xref>33</xref>} </td></tr><tr><td rowspan="1" colspan="1">26</td><td rowspan="1" colspan="1">Warren</td><td rowspan="1" colspan="1">2022</td><td rowspan="1" colspan="1">USA</td><td rowspan="1" colspan="1">Healthcare</td><td rowspan="1" colspan="1">Culture</td><td rowspan="1" colspan="1">758</td><td rowspan="1" colspan="1">121</td><td rowspan="1" colspan="1"> ^{<xref>34</xref>} </td></tr><tr><td rowspan="1" colspan="1">27</td><td rowspan="1" colspan="1">Lim</td><td rowspan="1" colspan="1">2022</td><td rowspan="1" colspan="1">Australia</td><td rowspan="1" colspan="1">Healthcare</td><td rowspan="1" colspan="1">Culture</td><td rowspan="1" colspan="1">600</td><td rowspan="1" colspan="1">185</td><td rowspan="1" colspan="1"> ^{<xref>35</xref>} </td></tr><tr><td rowspan="1" colspan="1">28</td><td rowspan="1" colspan="1">Endres</td><td rowspan="1" colspan="1">2018</td><td rowspan="1" colspan="1">USA</td><td rowspan="1" colspan="1">Healthcare</td><td rowspan="1" colspan="1">Culture/other test</td><td rowspan="1" colspan="1">50</td><td rowspan="1" colspan="1">14</td><td rowspan="1" colspan="1"> ^{<xref>36</xref>} </td></tr><tr><td rowspan="1" colspan="1">29</td><td rowspan="1" colspan="1">Tsai</td><td rowspan="1" colspan="1">2022</td><td rowspan="1" colspan="1">Taiwan</td><td rowspan="1" colspan="1">Non-healthcare</td><td rowspan="1" colspan="1">Culture/other test</td><td rowspan="1" colspan="1">29</td><td rowspan="1" colspan="1">7</td><td rowspan="1" colspan="1"> ^{<xref>37</xref>} </td></tr><tr><td rowspan="1" colspan="1">30</td><td rowspan="1" colspan="1">Moradigaravand</td><td rowspan="1" colspan="1">2018</td><td rowspan="1" colspan="1">UK</td><td rowspan="1" colspan="1">Non-healthcare</td><td rowspan="1" colspan="1">Culture/other test</td><td rowspan="1" colspan="1">20</td><td rowspan="1" colspan="1">18</td><td rowspan="1" colspan="1"> ^{<xref>38</xref>} </td></tr><tr><td rowspan="1" colspan="1">31</td><td rowspan="1" colspan="1">Garey</td><td rowspan="1" colspan="1">2020</td><td rowspan="1" colspan="1">USA</td><td rowspan="1" colspan="1">Healthcare</td><td rowspan="1" colspan="1">Culture</td><td rowspan="1" colspan="1">659</td><td rowspan="1" colspan="1">55</td><td rowspan="1" colspan="1"> ^{<xref>39</xref>} </td></tr><tr><td rowspan="1" colspan="1">32</td><td rowspan="1" colspan="1">Shoaei</td><td rowspan="1" colspan="1">2019</td><td rowspan="1" colspan="1">Iran</td><td rowspan="1" colspan="1">Healthcare</td><td rowspan="1" colspan="1">Culture/other test</td><td rowspan="1" colspan="1">92</td><td rowspan="1" colspan="1">9</td><td rowspan="1" colspan="1"> ^{<xref>40</xref>} </td></tr><tr><td rowspan="1" colspan="1">33</td><td rowspan="1" colspan="1">Odoyo</td><td rowspan="1" colspan="1">2020</td><td rowspan="1" colspan="1">Kenya</td><td rowspan="1" colspan="1">Healthcare</td><td rowspan="1" colspan="1">Culture</td><td rowspan="1" colspan="1">57</td><td rowspan="1" colspan="1">43</td><td rowspan="1" colspan="1"> ^{<xref>41</xref>} </td></tr><tr><td rowspan="1" colspan="1"/><td rowspan="1" colspan="1"/><td rowspan="1" colspan="1"/><td rowspan="1" colspan="1"/><td rowspan="1" colspan="1"/><td rowspan="1" colspan="1">PCR</td><td rowspan="1" colspan="1">57</td><td rowspan="1" colspan="1">43</td><td rowspan="1" colspan="1"/></tr><tr><td rowspan="1" colspan="1">34</td><td rowspan="1" colspan="1">Brown</td><td rowspan="1" colspan="1">2018</td><td rowspan="1" colspan="1">Canada</td><td rowspan="1" colspan="1">Healthcare</td><td rowspan="1" colspan="1">PCR</td><td rowspan="1" colspan="1">96</td><td rowspan="1" colspan="1">50</td><td rowspan="1" colspan="1"> ^{<xref>42</xref>} </td></tr><tr><td rowspan="1" colspan="1"/><td rowspan="1" colspan="1"/><td rowspan="1" colspan="1"/><td rowspan="1" colspan="1"/><td rowspan="1" colspan="1"/><td rowspan="1" colspan="1">Culture</td><td rowspan="1" colspan="1">48</td><td rowspan="1" colspan="1">21</td><td rowspan="1" colspan="1"/></tr><tr><td rowspan="1" colspan="1">35</td><td rowspan="1" colspan="1">Moono</td><td rowspan="1" colspan="1">2017</td><td rowspan="1" colspan="1">Australia</td><td rowspan="1" colspan="1">Non-healthcare</td><td rowspan="1" colspan="1">Culture/other test</td><td rowspan="1" colspan="1">311</td><td rowspan="1" colspan="1">182</td><td rowspan="1" colspan="1"> ^{<xref>43</xref>} </td></tr><tr><td rowspan="1" colspan="1">36</td><td rowspan="1" colspan="1">Alam</td><td rowspan="1" colspan="1">2017</td><td rowspan="1" colspan="1">USA</td><td rowspan="1" colspan="1">Healthcare and non-healthcare</td><td rowspan="1" colspan="1">-</td><td rowspan="1" colspan="1">2538</td><td rowspan="1" colspan="1">418</td><td rowspan="1" colspan="1"> ^{<xref>44</xref>} </td></tr><tr><td rowspan="1" colspan="1">37</td><td rowspan="1" colspan="1">Orden</td><td rowspan="1" colspan="1">2018</td><td rowspan="1" colspan="1">Spain</td><td rowspan="1" colspan="1">Non-healthcare</td><td rowspan="1" colspan="1">Culture/other test</td><td rowspan="1" colspan="1">40</td><td rowspan="1" colspan="1">21</td><td rowspan="1" colspan="1"> ^{<xref>45</xref>} </td></tr></tbody></table>

Table 1.: Continued

<table><thead><tr><th align="left" rowspan="1" colspan="1">Study number</th><th align="center" rowspan="1" colspan="1">Author</th><th align="center" rowspan="1" colspan="1">Year</th><th align="center" rowspan="1" colspan="1">Country</th><th align="center" rowspan="1" colspan="1">Setting</th><th align="center" rowspan="1" colspan="1">Diagnostic methods</th><th align="center" rowspan="1" colspan="1">Sample size number</th><th align="center" rowspan="1" colspan="1">Positive number</th><th align="center" rowspan="1" colspan="1">Reference</th></tr></thead><tbody><tr><td rowspan="1" colspan="1">38</td><td rowspan="1" colspan="1">Taha</td><td rowspan="1" colspan="1">2022</td><td rowspan="1" colspan="1">Egypt</td><td rowspan="1" colspan="1">Non-healthcare</td><td rowspan="1" colspan="1">Culture</td><td rowspan="1" colspan="1">200</td><td rowspan="1" colspan="1">38</td><td rowspan="1" colspan="1"> ^{<xref>46</xref>} </td></tr><tr><td rowspan="1" colspan="1">39</td><td rowspan="1" colspan="1">Alves</td><td rowspan="1" colspan="1">2022</td><td rowspan="1" colspan="1">Portugal</td><td rowspan="1" colspan="1">Non-healthcare</td><td rowspan="1" colspan="1">Culture/other test</td><td rowspan="1" colspan="1">60</td><td rowspan="1" colspan="1">35</td><td rowspan="1" colspan="1"> ^{<xref>47</xref>} </td></tr><tr><td rowspan="1" colspan="1">40</td><td rowspan="1" colspan="1">Redding</td><td rowspan="1" colspan="1">2021</td><td rowspan="1" colspan="1">USA</td><td rowspan="1" colspan="1">Non-healthcare</td><td rowspan="1" colspan="1">Culture/other test</td><td rowspan="1" colspan="1">92</td><td rowspan="1" colspan="1">28</td><td rowspan="1" colspan="1"> ^{<xref>48</xref>} </td></tr><tr><td rowspan="1" colspan="1">41</td><td rowspan="1" colspan="1">Usui</td><td rowspan="1" colspan="1">2017</td><td rowspan="1" colspan="1">Japan</td><td rowspan="1" colspan="1">Non-healthcare</td><td rowspan="1" colspan="1">Culture/other test</td><td rowspan="1" colspan="1">14</td><td rowspan="1" colspan="1">5</td><td rowspan="1" colspan="1"> ^{<xref>49</xref>} </td></tr><tr><td rowspan="1" colspan="1">42</td><td rowspan="1" colspan="1">Marcos</td><td rowspan="1" colspan="1">2021</td><td rowspan="1" colspan="1">Ireland</td><td rowspan="1" colspan="1">Non-healthcare</td><td rowspan="1" colspan="1">Culture/other test</td><td rowspan="1" colspan="1">180</td><td rowspan="1" colspan="1">68</td><td rowspan="1" colspan="1"> ^{<xref>50</xref>} </td></tr><tr><td rowspan="1" colspan="1">43</td><td rowspan="1" colspan="1">Alam</td><td rowspan="1" colspan="1">2014</td><td rowspan="1" colspan="1">USA</td><td rowspan="1" colspan="1">Non-healthcare</td><td rowspan="1" colspan="1">Culture/other test</td><td rowspan="1" colspan="1">127</td><td rowspan="1" colspan="1">41</td><td rowspan="1" colspan="1"> ^{<xref>51</xref>} </td></tr><tr><td rowspan="1" colspan="1">44</td><td rowspan="1" colspan="1">Leitner</td><td rowspan="1" colspan="1">2020</td><td rowspan="1" colspan="1">Austria</td><td rowspan="1" colspan="1">Healthcare</td><td rowspan="1" colspan="1">Culture</td><td rowspan="1" colspan="1">92</td><td rowspan="1" colspan="1">0</td><td rowspan="1" colspan="1"> ^{<xref>52</xref>} </td></tr><tr><td rowspan="1" colspan="1">45</td><td rowspan="1" colspan="1">Ali</td><td rowspan="1" colspan="1">2021</td><td rowspan="1" colspan="1">India</td><td rowspan="1" colspan="1">Non-healthcare</td><td rowspan="1" colspan="1">Culture/other test</td><td rowspan="1" colspan="1">6</td><td rowspan="1" colspan="1">1</td><td rowspan="1" colspan="1"> ^{<xref>53</xref>} </td></tr><tr><td rowspan="1" colspan="1">46</td><td rowspan="1" colspan="1">Lim</td><td rowspan="1" colspan="1">2020</td><td rowspan="1" colspan="1">Australia</td><td rowspan="1" colspan="1">Non-healthcare</td><td rowspan="1" colspan="1">-</td><td rowspan="1" colspan="1">142</td><td rowspan="1" colspan="1">32</td><td rowspan="1" colspan="1"> ^{<xref>54</xref>} </td></tr><tr><td rowspan="1" colspan="1">47</td><td rowspan="1" colspan="1">Janezic</td><td rowspan="1" colspan="1">2016</td><td rowspan="1" colspan="1">Slovenia</td><td rowspan="1" colspan="1">Non-healthcare</td><td rowspan="1" colspan="1">Culture/other test</td><td rowspan="1" colspan="1">183</td><td rowspan="1" colspan="1">44</td><td rowspan="1" colspan="1"> ^{<xref>55</xref>} </td></tr><tr><td rowspan="1" colspan="1">48</td><td rowspan="1" colspan="1">Baghani</td><td rowspan="1" colspan="1">2020</td><td rowspan="1" colspan="1">Iran</td><td rowspan="1" colspan="1">Non-healthcare</td><td rowspan="1" colspan="1">Culture/other test</td><td rowspan="1" colspan="1">72</td><td rowspan="1" colspan="1">1</td><td rowspan="1" colspan="1"> ^{<xref>56</xref>} </td></tr><tr><td rowspan="1" colspan="1">49</td><td rowspan="1" colspan="1">Shaughnessy</td><td rowspan="1" colspan="1">2016</td><td rowspan="1" colspan="1">USA</td><td rowspan="1" colspan="1">Non-healthcare</td><td rowspan="1" colspan="1">Culture/other test</td><td rowspan="1" colspan="1">233</td><td rowspan="1" colspan="1">31</td><td rowspan="1" colspan="1"> ^{<xref>57</xref>} </td></tr></tbody></table>

Characteristics of eligible studies

In general, 49 studies measured C. difficile contamination in healthcare and non-healthcare settings. Among these, 25 (51.02%) measured contamination in healthcare settings. Non-healthcare settings consisted of farms, public places, homes and veterinary hospitals. Of the 49 eligible studies, 21 (42.85%) were published before 2020. Based on WHO regions, two were conducted in Africa, while 13, 16, four, three and 11 were conducted in America, Europe, Eastern Mediterranean, South-East Asia and Western Pacific, respectively.

Quality of the eligible studies

Of the 49 original articles, considering the total CASP scores, 31 (63.26%) met all the approval criteria; 46 (93.87%) were classified as good quality and three (6.13%) were classified as moderate quality. No articles were classified as being of poor quality.

Prevalence of C. difficile contamination

Overall, 20 779 environmental samples were investigated in healthcare and non-healthcare settings. Among these, 4507 samples were positive for C. difficile contamination using different detection methods. Meta-analysis indicated that the pooled prevalence of C. difficile contamination using random effects was estimated as 0.26 (95% CI 0.23 to 0.28). There was a high degree of heterogeneity in the prevalence estimates between different studies. It was observed that the Q statistic was 6767.31 (df=48), p<0.01 and I^2^=99.29% (Table 2 and Figure 2).

Forest plot of the pooled prevalence of C. difficile contamination in healthcare and non-healthcare environments.

Table 2.: Pooled prevalence of C. difficile contamination in healthcare and non-healthcare environments

<table><thead><tr><td rowspan="1" colspan="1"/><td rowspan="1" colspan="1"/><td rowspan="1" colspan="1"/><th align="center" rowspan="1" colspan="1">Total</th><td rowspan="1" colspan="1"/><td rowspan="1" colspan="1"/><th align="center" colspan="3" rowspan="1">Heterogeneity</th><th align="center" rowspan="1" colspan="1">Heterogeneity</th></tr><tr><td rowspan="1" colspan="1"/><th align="center" rowspan="1" colspan="1">Number</th><th align="center" rowspan="1" colspan="1">Sample</th><th align="center" rowspan="1" colspan="1">Number</th><th align="center" rowspan="1" colspan="1">Pooled</th><td rowspan="1" colspan="1"/><td rowspan="1" colspan="1"/><td rowspan="1" colspan="1"/><td rowspan="1" colspan="1"/><th align="center" rowspan="1" colspan="1">between</th></tr><tr><th align="left" rowspan="1" colspan="1">Independent variable</th><th align="center" rowspan="1" colspan="1">of studies</th><th align="center" rowspan="1" colspan="1">size</th><th align="center" rowspan="1" colspan="1">positive</th><th align="center" rowspan="1" colspan="1">prevalence</th><th align="center" rowspan="1" colspan="1">95% CI</th><th align="center" rowspan="1" colspan="1">Q statistic</th><th align="center" rowspan="1" colspan="1">I² (%)</th><th align="center" rowspan="1" colspan="1">p</th><th align="center" rowspan="1" colspan="1">subgroup bias</th></tr></thead><tbody><tr><td colspan="10" rowspan="1">Diagnostic test</td></tr><tr><td rowspan="1" colspan="1"> Culture</td><td rowspan="1" colspan="1">19</td><td rowspan="1" colspan="1">4740</td><td rowspan="1" colspan="1">975</td><td rowspan="1" colspan="1">0.26</td><td rowspan="1" colspan="1">0.21–0.32</td><td rowspan="1" colspan="1">1709.52</td><td rowspan="1" colspan="1">98.95</td><td rowspan="1" colspan="1">0.00</td><td rowspan="1" colspan="1">0.00</td></tr><tr><td rowspan="1" colspan="1"> Culture and other test</td><td rowspan="1" colspan="1">26</td><td rowspan="1" colspan="1">5204</td><td rowspan="1" colspan="1">915</td><td rowspan="1" colspan="1">0.27</td><td rowspan="1" colspan="1">0.21–0.33</td><td rowspan="1" colspan="1">1655.63</td><td rowspan="1" colspan="1">98.49</td><td rowspan="1" colspan="1">0.00</td><td rowspan="1" colspan="1"/></tr><tr><td rowspan="1" colspan="1"> PCR</td><td rowspan="1" colspan="1">3</td><td rowspan="1" colspan="1">298</td><td rowspan="1" colspan="1">179</td><td rowspan="1" colspan="1">0.62</td><td rowspan="1" colspan="1">0.50–0.74</td><td rowspan="1" colspan="1">0</td><td rowspan="1" colspan="1">0</td><td rowspan="1" colspan="1">0.00</td><td rowspan="1" colspan="1"/></tr><tr><td colspan="10" rowspan="1">Region</td></tr><tr><td rowspan="1" colspan="1"> Africa </td><td rowspan="1" colspan="1">2</td><td rowspan="1" colspan="1">264</td><td rowspan="1" colspan="1">104</td><td rowspan="1" colspan="1">0.31</td><td rowspan="1" colspan="1">0.27–0.36</td><td rowspan="1" colspan="1">-</td><td rowspan="1" colspan="1">-</td><td rowspan="1" colspan="1">-</td><td rowspan="1" colspan="1">0.00</td></tr><tr><td rowspan="1" colspan="1"> Eastern Mediterranean</td><td rowspan="1" colspan="1">4</td><td rowspan="1" colspan="1">1799</td><td rowspan="1" colspan="1">50</td><td rowspan="1" colspan="1">0.07</td><td rowspan="1" colspan="1">0.01–0.13</td><td rowspan="1" colspan="1">56.58</td><td rowspan="1" colspan="1">94.70</td><td rowspan="1" colspan="1">0.00</td><td rowspan="1" colspan="1"/></tr><tr><td rowspan="1" colspan="1"> America</td><td rowspan="1" colspan="1">13</td><td rowspan="1" colspan="1">12 724</td><td rowspan="1" colspan="1">2772</td><td rowspan="1" colspan="1">0.23</td><td rowspan="1" colspan="1">0.17–0.29</td><td rowspan="1" colspan="1">813.01</td><td rowspan="1" colspan="1">98.52</td><td rowspan="1" colspan="1">0.00</td><td rowspan="1" colspan="1"/></tr><tr><td rowspan="1" colspan="1"> South-East Asia </td><td rowspan="1" colspan="1">3</td><td rowspan="1" colspan="1">500</td><td rowspan="1" colspan="1">56</td><td rowspan="1" colspan="1">0.11</td><td rowspan="1" colspan="1">0.08–0.14</td><td rowspan="1" colspan="1">-</td><td rowspan="1" colspan="1">-</td><td rowspan="1" colspan="1">-</td><td rowspan="1" colspan="1"/></tr><tr><td rowspan="1" colspan="1"> Western Pacific </td><td rowspan="1" colspan="1">11</td><td rowspan="1" colspan="1">2606</td><td rowspan="1" colspan="1">894</td><td rowspan="1" colspan="1">0.41</td><td rowspan="1" colspan="1">0.23–0.58</td><td rowspan="1" colspan="1">1365.82</td><td rowspan="1" colspan="1">99.27</td><td rowspan="1" colspan="1">0.00</td><td rowspan="1" colspan="1"/></tr><tr><td rowspan="1" colspan="1"> Europe </td><td rowspan="1" colspan="1">16</td><td rowspan="1" colspan="1">1995</td><td rowspan="1" colspan="1">437</td><td rowspan="1" colspan="1">0.28</td><td rowspan="1" colspan="1">0.20–0.36</td><td rowspan="1" colspan="1">815.86</td><td rowspan="1" colspan="1">98.16</td><td rowspan="1" colspan="1">0.00</td><td rowspan="1" colspan="1"/></tr><tr><td colspan="10" rowspan="1">Year</td></tr><tr><td rowspan="1" colspan="1"> Before 2020</td><td rowspan="1" colspan="1">21</td><td rowspan="1" colspan="1">6968</td><td rowspan="1" colspan="1">1311</td><td rowspan="1" colspan="1">0.31</td><td rowspan="1" colspan="1">0.25–0.38</td><td rowspan="1" colspan="1">2294.83</td><td rowspan="1" colspan="1">99.13</td><td rowspan="1" colspan="1">0.00</td><td rowspan="1" colspan="1">0.11</td></tr><tr><td rowspan="1" colspan="1"> After 2020</td><td rowspan="1" colspan="1">28</td><td rowspan="1" colspan="1">13 811</td><td rowspan="1" colspan="1">3196</td><td rowspan="1" colspan="1">0.24</td><td rowspan="1" colspan="1">0.19–0.30</td><td rowspan="1" colspan="1">4415.21</td><td rowspan="1" colspan="1">99.39</td><td rowspan="1" colspan="1">0.00</td><td rowspan="1" colspan="1"/></tr><tr><td colspan="10" rowspan="1">Setting</td></tr><tr><td rowspan="1" colspan="1"> Healthcare</td><td rowspan="1" colspan="1">25</td><td rowspan="1" colspan="1">11 475</td><td rowspan="1" colspan="1">1962</td><td rowspan="1" colspan="1">0.18</td><td rowspan="1" colspan="1">0.16–0.20</td><td rowspan="1" colspan="1">2929.39</td><td rowspan="1" colspan="1">99.18</td><td rowspan="1" colspan="1">0.00</td><td rowspan="1" colspan="1">0.00</td></tr><tr><td rowspan="1" colspan="1"> Non-healthcare</td><td rowspan="1" colspan="1">30</td><td rowspan="1" colspan="1">9304</td><td rowspan="1" colspan="1">2545</td><td rowspan="1" colspan="1">0.36</td><td rowspan="1" colspan="1">0.30–0.41</td><td rowspan="1" colspan="1">1157.38</td><td rowspan="1" colspan="1">97.49</td><td rowspan="1" colspan="1">0.00</td><td rowspan="1" colspan="1"/></tr><tr><td colspan="10" rowspan="1">Sample type</td></tr><tr><td rowspan="1" colspan="1"> Soil</td><td rowspan="1" colspan="1">8</td><td rowspan="1" colspan="1">530</td><td rowspan="1" colspan="1">310</td><td rowspan="1" colspan="1">0.59</td><td rowspan="1" colspan="1">0.47–0.71</td><td rowspan="1" colspan="1">60.19</td><td rowspan="1" colspan="1">88.37</td><td rowspan="1" colspan="1">0.00</td><td rowspan="1" colspan="1">0.00</td></tr><tr><td rowspan="1" colspan="1"> Shoe</td><td rowspan="1" colspan="1">8</td><td rowspan="1" colspan="1">2084</td><td rowspan="1" colspan="1">813</td><td rowspan="1" colspan="1">0.34</td><td rowspan="1" colspan="1">0.23–0.45</td><td rowspan="1" colspan="1">156.27</td><td rowspan="1" colspan="1">95.52</td><td rowspan="1" colspan="1">0.00</td><td rowspan="1" colspan="1"/></tr></tbody></table>

According to diagnostic method, the pooled prevalence of C. difficile contamination by culture test was 0.26 (95% CI 0.21 to 0.32). The pooled prevalence using PCR was 0.62 (95% CI 0.50 to 0.74). Meanwhile, the pooled prevalence using a mixed method was 0.27 (95% CI 0.21 to 0.33) (Table 2 and Figure 3).

Forest plots of the pooled prevalence of C. difficile contamination in healthcare and non-healthcare environments according to (A) diagnostic test, (B) setting and (C) year.

The pooled prevalence of C. difficile contamination was 0.31 (95% CI 0.25 to 0.38) and 0.24 (95% CI 0.19 to 0.30) before and after 2020, respectively (Figure 3).

The pooled prevalence of C. difficile contamination according to region indicated that the highest prevalence was in the Western Pacific with a prevalence of 0.41 (95% CI 0.23 to 0.58). The lowest prevalence was in the Eastern Mediterranean with a prevalence of 0.07 (95% CI 0.01 to 0.13). The pooled prevalence in the other regions was 0.40, 0.31, 0.23 and 0.11 in Europe, Africa, America and South-East Asia, respectively. Complete results by region are shown in Table 2 and Figure 4.

Forest plot of the pooled prevalence of C. difficile contamination in healthcare and non-healthcare environments according to region.

The pooled prevalence of C. difficile contamination according to setting was 0.18 (95% CI 0.16 to 0.20) in the healthcare environment and 0.36 (95% CI 0.30 to 0.42) in the non-healthcare environment (Figure 3).

The pooled prevalence of C. difficile contamination according to sample type was 0.59 (95% CI 0.47 to 0.71) in soil samples and 0.34 in shoe samples (95% CI 0.32 to 0.45).

Prevalence of C. difficile contamination in healthcare environments

In total, among 11 475 environmental samples in healthcare settings, 1962 were positive for C. difficile contamination. The pooled prevalence of C. difficile contamination among environmental samples in healthcare settings was 0.18 (95% CI 0.16 to 0.20).

The pooled prevalence of C. difficile contamination in healthcare environments according to region indicated that the highest prevalence was found in the Western Pacific (0.33; 95% CI 0.10 to 0.56), followed by Africa (0.31; 95% CI 0.27 to 0.36). The prevalence for all regions is shown in Table 3.

Table 3.: Pooled prevalence of C. difficile contamination in healthcare and non-healthcare environments

<table><thead><tr><td rowspan="1" colspan="1"/><td rowspan="1" colspan="1"/><td rowspan="1" colspan="1"/><td rowspan="1" colspan="1"/><td rowspan="1" colspan="1"/><td rowspan="1" colspan="1"/><th align="center" colspan="3" rowspan="1">Heterogeneity</th><td rowspan="1" colspan="1"/></tr><tr><th align="left" rowspan="1" colspan="1">Independent variable</th><th align="center" rowspan="1" colspan="1">Number of studies</th><th align="center" rowspan="1" colspan="1">Sample size</th><th align="center" rowspan="1" colspan="1">Total number positive</th><th align="center" rowspan="1" colspan="1">Pooled prevalence</th><th align="center" rowspan="1" colspan="1">95% CI</th><th align="center" rowspan="1" colspan="1">Q statistic</th><th align="center" rowspan="1" colspan="1">I² (%)</th><th align="center" rowspan="1" colspan="1">p</th><th align="center" rowspan="1" colspan="1">Heterogeneity between subgroup bias</th></tr></thead><tbody><tr><td colspan="10" rowspan="1"> <bold>Healthcare</bold> </td></tr><tr><td colspan="10" rowspan="1">Region</td></tr><tr><td rowspan="1" colspan="1"> Africa </td><td rowspan="1" colspan="1">2</td><td rowspan="1" colspan="1">264</td><td rowspan="1" colspan="1">104</td><td rowspan="1" colspan="1">0.31</td><td rowspan="1" colspan="1">0.27–0.36</td><td rowspan="1" colspan="1">-</td><td rowspan="1" colspan="1">-</td><td rowspan="1" colspan="1">-</td><td rowspan="1" colspan="1">0.00</td></tr><tr><td rowspan="1" colspan="1"> Eastern Mediterranean</td><td rowspan="1" colspan="1">2</td><td rowspan="1" colspan="1">1527</td><td rowspan="1" colspan="1">11</td><td rowspan="1" colspan="1">0.00</td><td rowspan="1" colspan="1">0.00–0.00</td><td rowspan="1" colspan="1">-</td><td rowspan="1" colspan="1">-</td><td rowspan="1" colspan="1">-</td><td rowspan="1" colspan="1"/></tr><tr><td rowspan="1" colspan="1"> America</td><td rowspan="1" colspan="1">9</td><td rowspan="1" colspan="1">6359</td><td rowspan="1" colspan="1">1193</td><td rowspan="1" colspan="1">0.19</td><td rowspan="1" colspan="1">0.12–0.26</td><td rowspan="1" colspan="1">449.47</td><td rowspan="1" colspan="1">98.22</td><td rowspan="1" colspan="1">0.00</td><td rowspan="1" colspan="1"/></tr><tr><td rowspan="1" colspan="1"> South-East Asia </td><td rowspan="1" colspan="1">1</td><td rowspan="1" colspan="1">400</td><td rowspan="1" colspan="1">44</td><td rowspan="1" colspan="1">0.11</td><td rowspan="1" colspan="1">0.08–0.14</td><td rowspan="1" colspan="1">-</td><td rowspan="1" colspan="1">-</td><td rowspan="1" colspan="1">-</td><td rowspan="1" colspan="1"/></tr><tr><td rowspan="1" colspan="1"> Western Pacific </td><td rowspan="1" colspan="1">5</td><td rowspan="1" colspan="1">1819</td><td rowspan="1" colspan="1">481</td><td rowspan="1" colspan="1">0.33</td><td rowspan="1" colspan="1">0.10–0.56</td><td rowspan="1" colspan="1">695.26</td><td rowspan="1" colspan="1">99.42</td><td rowspan="1" colspan="1">0.00</td><td rowspan="1" colspan="1"/></tr><tr><td rowspan="1" colspan="1"> Europe </td><td rowspan="1" colspan="1">6</td><td rowspan="1" colspan="1">1106</td><td rowspan="1" colspan="1">129</td><td rowspan="1" colspan="1">0.08</td><td rowspan="1" colspan="1">0.02–0.15</td><td rowspan="1" colspan="1">149.67</td><td rowspan="1" colspan="1">96.66</td><td rowspan="1" colspan="1">0.00</td><td rowspan="1" colspan="1"/></tr><tr><td colspan="10" rowspan="1">Sample type</td></tr><tr><td rowspan="1" colspan="1"> Bed</td><td rowspan="1" colspan="1">7</td><td rowspan="1" colspan="1">447</td><td rowspan="1" colspan="1">43</td><td rowspan="1" colspan="1">0.09</td><td rowspan="1" colspan="1">0.04–0.15</td><td rowspan="1" colspan="1">53.52</td><td rowspan="1" colspan="1">88.79</td><td rowspan="1" colspan="1">0.00</td><td rowspan="1" colspan="1">0.00</td></tr><tr><td rowspan="1" colspan="1"> Chair</td><td rowspan="1" colspan="1">2</td><td rowspan="1" colspan="1">246</td><td rowspan="1" colspan="1">16</td><td rowspan="1" colspan="1">0.03</td><td rowspan="1" colspan="1">0.01–0.05</td><td rowspan="1" colspan="1">-</td><td rowspan="1" colspan="1">-</td><td rowspan="1" colspan="1">-</td><td rowspan="1" colspan="1"/></tr><tr><td rowspan="1" colspan="1"> Computer</td><td rowspan="1" colspan="1">3</td><td rowspan="1" colspan="1">108</td><td rowspan="1" colspan="1">7</td><td rowspan="1" colspan="1">0.04</td><td rowspan="1" colspan="1">0.00–0.09</td><td rowspan="1" colspan="1">-</td><td rowspan="1" colspan="1">-</td><td rowspan="1" colspan="1">-</td><td rowspan="1" colspan="1"/></tr><tr><td rowspan="1" colspan="1"> Door handle</td><td rowspan="1" colspan="1">2</td><td rowspan="1" colspan="1">32</td><td rowspan="1" colspan="1">1</td><td rowspan="1" colspan="1">0.00</td><td rowspan="1" colspan="1">0.00–0.01</td><td rowspan="1" colspan="1">-</td><td rowspan="1" colspan="1">-</td><td rowspan="1" colspan="1">-</td><td rowspan="1" colspan="1"/></tr><tr><td rowspan="1" colspan="1"> Light switch</td><td rowspan="1" colspan="1">2</td><td rowspan="1" colspan="1">98</td><td rowspan="1" colspan="1">0</td><td rowspan="1" colspan="1">0.00</td><td rowspan="1" colspan="1">0.00–0.00</td><td rowspan="1" colspan="1">-</td><td rowspan="1" colspan="1">-</td><td rowspan="1" colspan="1">-</td><td rowspan="1" colspan="1"/></tr><tr><td rowspan="1" colspan="1"> Shoe</td><td rowspan="1" colspan="1">3</td><td rowspan="1" colspan="1">643</td><td rowspan="1" colspan="1">205</td><td rowspan="1" colspan="1">0.34</td><td rowspan="1" colspan="1">0.17–0.51</td><td rowspan="1" colspan="1">-</td><td rowspan="1" colspan="1">-</td><td rowspan="1" colspan="1">-</td><td rowspan="1" colspan="1"/></tr><tr><td rowspan="1" colspan="1"> Soil</td><td rowspan="1" colspan="1">2</td><td rowspan="1" colspan="1">189</td><td rowspan="1" colspan="1">121</td><td rowspan="1" colspan="1">0.66</td><td rowspan="1" colspan="1">0.59–0.73</td><td rowspan="1" colspan="1">-</td><td rowspan="1" colspan="1">-</td><td rowspan="1" colspan="1">-</td><td rowspan="1" colspan="1"/></tr><tr><td rowspan="1" colspan="1"> Table</td><td rowspan="1" colspan="1">3</td><td rowspan="1" colspan="1">76</td><td rowspan="1" colspan="1">9</td><td rowspan="1" colspan="1">0.10</td><td rowspan="1" colspan="1">0.03–0.17</td><td rowspan="1" colspan="1">-</td><td rowspan="1" colspan="1">-</td><td rowspan="1" colspan="1">-</td><td rowspan="1" colspan="1"/></tr><tr><td rowspan="1" colspan="1"> Toilet or bathroom</td><td rowspan="1" colspan="1">5</td><td rowspan="1" colspan="1">413</td><td rowspan="1" colspan="1">97</td><td rowspan="1" colspan="1">0.14</td><td rowspan="1" colspan="1">0.01–0.28</td><td rowspan="1" colspan="1">98.88</td><td rowspan="1" colspan="1">95.95</td><td rowspan="1" colspan="1">0.00</td><td rowspan="1" colspan="1"/></tr><tr><td rowspan="1" colspan="1"> Wheelchair</td><td rowspan="1" colspan="1">2</td><td rowspan="1" colspan="1">40</td><td rowspan="1" colspan="1">5</td><td rowspan="1" colspan="1">0.12</td><td rowspan="1" colspan="1">0.02–0.22</td><td rowspan="1" colspan="1">-</td><td rowspan="1" colspan="1">-</td><td rowspan="1" colspan="1">-</td><td rowspan="1" colspan="1"/></tr><tr><td rowspan="1" colspan="1"> Window</td><td rowspan="1" colspan="1">2</td><td rowspan="1" colspan="1">37</td><td rowspan="1" colspan="1">4</td><td rowspan="1" colspan="1">0.01</td><td rowspan="1" colspan="1">0.00–0.04</td><td rowspan="1" colspan="1">-</td><td rowspan="1" colspan="1">-</td><td rowspan="1" colspan="1">-</td><td rowspan="1" colspan="1"/></tr><tr><td colspan="10" rowspan="1"> <bold>Non-healthcare</bold> </td></tr><tr><td colspan="10" rowspan="1">Location</td></tr><tr><td rowspan="1" colspan="1"> Public</td><td rowspan="1" colspan="1">16</td><td rowspan="1" colspan="1">6797</td><td rowspan="1" colspan="1">1869</td><td rowspan="1" colspan="1">0.38</td><td rowspan="1" colspan="1">0.28–0.48</td><td rowspan="1" colspan="1">705.04</td><td rowspan="1" colspan="1">98.01</td><td rowspan="1" colspan="1">0.00</td><td rowspan="1" colspan="1">0.00</td></tr><tr><td rowspan="1" colspan="1"> Home</td><td rowspan="1" colspan="1">6</td><td rowspan="1" colspan="1">1607</td><td rowspan="1" colspan="1">345</td><td rowspan="1" colspan="1">0.33</td><td rowspan="1" colspan="1">0.20–0.45</td><td rowspan="1" colspan="1">138.87</td><td rowspan="1" colspan="1">96.32</td><td rowspan="1" colspan="1">0.00</td><td rowspan="1" colspan="1"/></tr><tr><td rowspan="1" colspan="1"> Farm</td><td rowspan="1" colspan="1">7</td><td rowspan="1" colspan="1">800</td><td rowspan="1" colspan="1">327</td><td rowspan="1" colspan="1">0.42</td><td rowspan="1" colspan="1">0.31–0.53</td><td rowspan="1" colspan="1">54.79</td><td rowspan="1" colspan="1">89.05</td><td rowspan="1" colspan="1">0.00</td><td rowspan="1" colspan="1"/></tr><tr><td rowspan="1" colspan="1"> Veterinary hospital</td><td rowspan="1" colspan="1">1</td><td rowspan="1" colspan="1">100</td><td rowspan="1" colspan="1">4</td><td rowspan="1" colspan="1">0.04</td><td rowspan="1" colspan="1">0.02–0.10</td><td rowspan="1" colspan="1">-</td><td rowspan="1" colspan="1">-</td><td rowspan="1" colspan="1">-</td><td rowspan="1" colspan="1"/></tr></tbody></table>

The pooled prevalence of C. difficile contamination according to sample type indicated that the highest contamination rates were in soil and shoes, with a prevalence of 0.66 (95% CI 0.59 to 0.73) and 0.34 (95% CI 0.17 to 0.51), respectively. This was followed by toilet or bathroom with a prevalence of 0.14 (95% CI 0.01 to 0.28), then wheelchair with a prevalence of 0.12 (95% CI 0.02 to 0.22). Complete results due to the prevalence of various sample types in healthcare settings are shown in Table 3.

Prevalence of C. difficile contamination in non-healthcare environments

Overall, among 9304 non-healthcare environmental samples, 2545 were positive for C. difficile contamination. The pooled prevalence was 0.36 (95% CI 0.30 to 0.41). The prevalence in the non-healthcare environment according to location was 0.42, 0.38, 0.33 and 0.04 on farms, in public places, in the home and veterinary hospital, respectively.

Discussion

Clostridioides difficile is a Gram-positive, spore-forming anaerobic bacterium that is the cause of antibiotic-associated diarrhea, toxic megacolon and pseudomembranous colitis, which may be life-threatening.^58^ The bacterium is resistant to a wide range of bactericide substances due to the endospore, which can survive for months in harsh environments. This study is the first global comprehensive systematic review concerning C. difficile contamination in healthcare and non-healthcare environments and summarizes its prevalence over the last decade (2014–2024).

In the current study, the pooled prevalence of C. difficile contamination in all environmental samples was estimated as 0.26, but was different for various levels of other variables such as diagnostic test and setting. This result indicates the notable presence of C. difficile in diverse environments across the world in the last decade. On the other hand, research data suggest that C. difficile can persist on surfaces for months. Also, the bacterium has shown resistance to various chemical agents and disinfectants in recent years. Therefore, to effectively control CDI rates, it is crucial to implement a standardized surveillance program with attention paid to the role of the environment in the transmission of the disease. In recent years, more attention and effort have been dedicated to controlling CDI, especially healthcare-associated CDI, and some countries have established guidelines for the detection and control of infection. However, there is a need to implement these programs and guidelines on a global scale, taking account of the role of the environment in the spread of the disease.

Based on our results, the pooled prevalence was 0.31 (95% CI 0.25 to 0.38) and 0.24 (95% CI 0.19 to 0.30) before and after 2020, respectively, which indirectly shows the decrease in CDI cases in recent years. Research indicates that patients treated with certain antibiotics are less likely to contaminate their surroundings, demonstrating a direct link between infection management and environmental hygiene.^59^ Therefore, the decreased contamination rate in the environment in recent years can be attributed to implementing a control program of CDI in several countries, especially in the healthcare system. For example, there is increasingly promising evidence that CDI rates have declined in the healthcare systems of Europe and the USA that should be implemented in other countries.

Screening is an important method for disease control, for reducing symptoms and costs. Choosing screening-accurate tools to identify contaminated samples should be considered. Assessments of diagnostic testing, extracted from studies, were included in this study. Our results based on the diagnostic method utilized indicated that the pooled prevalence was an estimated 26% (95% CI 21 to 32%) using a culture test, approximately equal to those found with a culture test plus another method, attributable to other tests being used less than culture in a large number of studies, although it was an estimated 62% (95% CI 50 to 74%) for PCR only. Most studies found that the molecular-based method is more sensitive and more specific than the culture test; therefore, it appears that PCR is suitable for screening.

Our findings indicated a pooled prevalence of 18% within the healthcare system, whereas the pooled prevalence for the non-healthcare system was 36%. However, we cannot definitively conclude that the prevalence is lower in healthcare compared with non-healthcare settings. This is because a significant portion of the non-healthcare samples were associated with public places, such as those from wastewater treatment plants and agricultural sources, including animal feces, manure and compost, which influenced our results. In any case, environmental contamination in healthcare plays an important role in the dissemination of C. difficile infection between hospital staff and patients, leading to an increase in hospital-acquired cases.

Our results for the healthcare environment indicate that shoes, toilet or bathroom and wheelchairs have the highest contamination rates. Shoe soles of healthcare personnel and community-dwelling individuals can become colonized by microorganisms through direct or indirect transmission routes. Thus, shoe soles can be recognized as agents for the spread of C. difficile spores. Exposure of shoe soles to the ultraviolet-C (UVC) device significantly decreased the contamination rate of bacteria. It is recommended that UVC devices are utilized in healthcare environments to lower the number of colony-forming units of bacteria on shoe soles, which will consequently reduce the colonization of other surfaces.

The results showed that the non-healthcare environment has a remarkable contamination rate for C. difficile. The contamination rate on farms and in the public environment was slightly higher than in the home environment. This can be attributed to the fact that, in the home environment, surfaces are continually being disinfected. It is important to note that the overall contamination rate in home environments could be significantly lower than reported, because most of the home samples were taken from shoes, soil and toilets.

Among non-healthcare environments, farms have the highest prevalence. The bacterium has been isolated from various surfaces in farms, including soil, sand and mud. It was suggested that contaminated animals could be a reservoir of C. difficile for transmission to humans.^58^ The genetic similarity of C. difficile strains found in animals and humans reinforces the idea that animal farms can serve as reservoirs for human infections.^60,61^ On the other hand, studies have shown that animals, especially pigs, cattle and poultry, can shed the bacterium in their feces, contributing to its presence in the farm environment.^58^ It seems that the transmission chain between animals and the community has a role in the increase of community-acquired cases. This result highlights the importance of monitoring and controlling the presence of C. difficile in agricultural settings to mitigate the risk of infection in the human population.

In addition to farms, our results indicates the presence of the bacterium in various public places, such as wastewater treatment plants, where it was detected at various stages of the treatment process. This indicates that the bacterium can endure the treatment processes typically employed at these facilities, raising concerns about its potential release into the environment and subsequent risk to public health. The presence of C. difficile in wastewater suggests that these environments could serve as reservoirs for community-associated CDI. Hence, wastewater environments are critical in the transmission dynamics of C. difficile, necessitating enhanced monitoring and treatment strategies to mitigate the risks associated with this pathogen in public health contexts.

Overall, we report a notable global contamination rate for C. difficile among diverse environments over the last decade. Based on evidence, the risk of acquired environmental infection with C. difficile was similar to the risk of contact with an infected person.^62^ Therefore, environmental contamination with C. difficile plays an important role in increasing the number of reservoirs of C. difficile spores, leading to an increase in community-acquired cases. Therefore, there is a need to implement programs and guidelines on a global scale, taking account of the role of the environment in the spread of the disease.

The strengths of this study include its large total sample size and extraction of detailed data. However, this study has some limitations, and the results presented here should be interpreted with regard to these limitations. One primary limitation of this review is the possibility that our search strategy missed some studies. To address this limitation, we utilized PubMed and Google Scholar, both of which cover a wide range of scientific journals. Additionally, we conducted a thorough search of the references cited in eligible papers to identify any further relevant studies. Another limitation concerns the uneven distribution of studies across different strata, such as region. After stratifying the results by various variables, some strata had relatively few studies, which may have impacted the reliability of the prevalence estimates for those strata. We have transparently described the characteristics and number of studies per strata in the current article to ensure readers are aware of this limitation. Finally, it is important to highlight that the type of environmental sample is the key variable likely affecting prevalence comparisons across different strata. Nonetheless, we have tried to interpret the results with this factor in mind to offer a more nuanced understanding of the findings.

Conclusions

The findings of our study over the past decade reveal a significant prevalence of C. difficile contamination across various environments worldwide. This alarming trend underscores a pressing need to prioritize environmental factors and implement effective disinfection strategies as part of comprehensive prevention and control programs aimed at tackling this challenging disease. By addressing the environmental reservoirs of C. difficile, we can take proactive steps to mitigate the spread of this pathogen and improve public health outcomes.