Studies have demonstrated seasonal variability in rates of Clostridium difficile infection (CDI). Synthesising all available information on seasonality is a necessary step in identifying large-scale epidemiological patterns and elucidating underlying causes.
Three medical and life sciences publication databases were searched from inception to October 2014 for longitudinal epidemiological studies written in English, Spanish or Portuguese that reported the incidence of CDI. The monthly frequency of CDI were extracted, standardized and weighted according to the number of follow-up months. Cross correlation coefficients (XCORR) were calculated to examine the correlation and lag between the year-month frequencies of reported CDI across hemispheres and continents.
The search identified 13, 5 and 2 studies from North America, Europe, and Oceania, respectively that met the inclusion criteria. CDI had a similar seasonal pattern in the Northern and Southern Hemisphere characterized by a peak in spring and lower frequencies of CDI in summer/autumn with a lag of 8 months (XCORR = 0.60) between hemispheres. There was no difference between the seasonal patterns across European and North American countries.
Citation: Furuya-Kanamori L, McKenzie SJ, Yakob L, Clark J, Paterson DL, Riley TV, et al. (2015) Clostridium difficile Infection Seasonality: Patterns across Hemispheres and Continents – A Systematic Review. PLoS ONE 10(3): e0120730. https://doi.org/10.1371/journal.pone.0120730
Academic Editor: Abhishek Deshpande, Cleveland Clinic, UNITED STATES
Received: October 28, 2014; Accepted: February 6, 2015; Published: March 16, 2015
Copyright: © 2015 Furuya-Kanamori et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Data Availability: Data used in the review were extracted from: * Archibald LK, Banerjee SN, Jarvis WR (2004) Secular trends in hospital-acquired Clostridium difficile disease in the United States, 1987–2001. J Infect Dis 189: 1585–1589. * Gilca R, Hubert B, Fortin E, Gaulin C, Dionne M (2010) Epidemiological patterns and hospital characteristics associated with increased incidence of Clostridium difficile infection in Quebec, Canada, 1998–2006. Infect Control Hosp Epidemiol 31: 939–947. * Jagai J, Naumova E (2009) Clostridium difficile-associated disease in the elderly, United States. Emerg Infect Dis 15: 343–344. * Brown KA, Daneman N, Arora P, Moineddin R, Fisman DN (2013) The co-seasonality of pneumonia and influenza with Clostridium difficile infection in the United States, 1993–2008. Am J Epidemiol 178: 118–125. * Gilca R, Fortin E, Frenette C, Longtin Y, Gourdeau M (2012) Seasonal variations in Clostridium difficile infections are associated with influenza and respiratory syncytial virus activity independently of antibiotic prescriptions: a time series analysis in Quebec, Canada. Antimicrob Agents Chemother 56: 639–646. * Furuya-Kanamori L, Robson J, Soares Magalhaes RJ, Yakob L, McKenzie SJ, et al. (2014) A population-based spatio-temporal analysis of Clostridium difficile infection in Queensland, Australia over a 10-year period. J Infect. * Slimings C, Armstrong P, Beckingham WD, Bull AL, Hall L, et al. (2014) Increasing incidence of Clostridium difficile infection, Australia, 2011–2012. Med J Aust 200: 272–276. * Wong-McClure RA, Guevara-Rodríguez M, Abarca-Gómez L, Solano-Chinchilla A, Marchena-Picado M, et al. (2012) Clostridium difficile outbreak in Costa Rica: control actions and associated factors. Rev Panam Salud Publica 32: 413–418. * Burckhardt F, Friedrich A, Beier D, Eckmanns T Clostridium difficile surveillance trends, Saxony, Germany: Emerg Infect Dis. 2008 Apr;14(4):691–2. doi: 10.3201/eid1404.071023. * Camacho-Ortiz A, Galindo-Fraga A, Rancel-Cordero A, Macias AE, Lamothe-Molina P, et al. (2009) [Factors associated with Clostridium difficile disease in a tertiary-care medical institution in Mexico: a case-control study]. Rev Invest Clin 61: 371–377. * Damani N, Trudy R, Markey M, Wallace S (2011) C. difficile associated diarrhoea-don't blame community or norovirus. BMC Proceedings 5. * Deorari S, McConnell A, Tan KK, Jadavji N, Ma D, et al. (1999) Differential yield of pathogens from stool testing of nosocomial versus community-acquired paediatric diarrhea. Canadian Journal of Infectious Diseases 10: 421–428. * Dubberke ER, Butler AM, Hota B, Khan YM, Mangino JE, et al. (2009) Multicenter study of the impact of community-onset Clostridium difficile infection on surveillance for C. difficile infection. Infect Control Hosp Epidemiol 30: 518–525. * Faires MC, Pearl DL, Ciccotelli WA, Berke O, Reid-Smith RJ, et al. (2014) Detection of Clostridium difficile infection clusters, using the temporal scan statistic, in a community hospital in southern Ontario, Canada, 2006–2011. BMC Infect Dis 14: 1471–2334. * MacDonald KS, McLeod J, Nicolle L (1993) Clostridium difficile enteritis in a Canadian tertiary care hospital. Can J Infect Control 8: 37–40. * Reveles KR, Lee GC, Boyd NK, Frei CR (2014) Regional and seasonal variations in clostridium difficile infections in United States hospitals, 2001 to 2010. Value in Health 17: A267. * Sonnenberg A Seasonal variation of enteric infections and inflammatory bowel disease: Inflamm Bowel Dis. 2009 Jun;15(6):809. doi: 10.1002/ibd.20770.
Funding: LFK is funded by an Endeavour Postgraduate Scholarship (#3781_2014), an Australia National University Higher Degree Scholarship, and a Fondo para la Innovación, Ciencia y Tecnología Scholarship (#095-FINCyT-BDE-2014). ACC is funded by an Australian National Health and Medical Research Council Senior Research Fellowship (#1058878). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Clostridium difficile is the most common cause of antibiotic-associated diarrhea among hospital inpatients . The incidence and severity of C. difficile infection (CDI) have increased worldwide in the last two decades .
Understanding the seasonal patterns of infectious diseases is crucial to identify factors associated with an increased risk of infection and to implement control measures during the time of year when interventions are likely to have the greatest impact . Epidemiological studies have documented a seasonal variation in the frequency of CDI, yet the mechanisms responsible for its variability remain poorly understood. Specifically, in the USA and Canada, the incidence of CDI has been reported to increase during boreal winter months (February–March) [4–6]. Antibiotic exposure is strongly associated with CDI [7–10]; consequently, it has been proposed that the observed CDI seasonality in the Northern Hemisphere is associated with the higher incidence of respiratory infections, which leads to an increase in antibiotic prescriptions during winter months [11,12].
In Australia, even though antibiotic consumption also peaks during winter (August) ; recent epidemiological studies have found that the seasonal pattern of C. difficile is not characterized by an increased number of CDI during winter months [14,15]. This indicates that CDI in Australia may not conform to currently proposed mechanisms of C. difficile seasonality, suggesting that factors in addition to antibiotic exposure might be driving the seasonality. Therefore, the aim of the current review was to pool the existing evidence to describe the global patterns of CDI seasonality and to facilitate improved understanding of underlying mechanisms.
The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed in this systematic review . A systematic search was undertaken in three medical and life sciences databases (PubMed, Embase and Latin American and Caribbean Health Sciences Literature [LILACS]) from their inception to October 1st 2014 for longitudinal epidemiological studies that reported the incidence of CDI. Search terms included were “Clostridium difficile” and “season”, the specific keywords and connectors used in the systematic search strategy for each database are listed in S1.A Search strategy.
The inclusion of studies was restricted to human studies, full-text articles or abstracts written in English, Spanish or Portuguese. Studies with at least 12 months follow-up that reported the incidence of CDI or the proportion of stool specimens examined in which C. difficile was detected, per month or per season, were included. CDI intervention studies were excluded from the review because of the interference that interventions might have on transmission dynamics. Exclusions were also made for studies that reported the number of positive samples detected for C. difficile without reporting the total number of samples that were tested; unless the authors stated that the number of stool samples examined per month was constant across the follow-up period. Corresponding authors were contacted for further information regarding the total number of samples examined per month/season. The characteristics of the excluded studies are listed in S1 Table.
Two authors (LFK and LY) independently examined all the citations by title and abstracts for studies that met the inclusion criteria. Full-text version articles of all potentially relevant studies were retrieved and independently extracted. Data presented in a graphical format were extracted using Plot Digitizer version 2.6.6 (http://plotdigitizer.sourceforge.net/). Data from all the included studies were extracted and summarized in a spreadsheet. The extracted data were cross-checked by the two authors, discrepancies during the selection of studies or data extraction were resolved through discussion and consensus. The quality of the selected studies was assessed independently by the same two authors using the Newcastle-Ottawa scale (NOS) .
The extracted data (incidence of CDI or proportion of positive stool specimens for C. difficile) were standardized to have a mean = 0, a minimum value = −1, and a maximum value = 1 for comparison across studies. A weight between zero and 1 was assigned to each study proportional to the number of months of follow-up. The number of months of follow-up for each study were divided by the number of months of follow-up of the study with the longest follow-up period; this ensured that the study with the longest follow-up period received a weight of 1.
The weighted average of the standardized monthly incidence were then plotted by hemispheres and continents to compare the seasonal patterns of CDI in each setting. An additional plot in which weighted average of the standardized CDI data from the Southern Hemisphere was shifted 6 months to align the meteorological seasons between hemispheres was created for ease of comparison.
Cross correlation coefficients (XCORR) were used to examine the correlation and lag value (in months) between the weighted average of standardized monthly incidence of CDI across hemispheres and continents using the extracted temporal data.
The search identified 244 publications; after screening the publications by title and abstract, 171 publications were excluded. After a full-text review of 41 publications was conducted, 20 studies met the inclusion criteria and were selected for the review (Fig. 1). Of the 20 studies, 18 were conducted in Northern Hemisphere countries and only 2 in the Southern Hemisphere. Among the studies from the Northern Hemisphere, 13 were from North America and 5 from Europe. The 2 studies from the Southern Hemisphere were from Oceania (Australia; Tables 1 and 2). No studies from South America, Africa or Asia were identified despite additional efforts to target these regions in our search strategy (S1.B Search strategy). Using the NOS, all the studies but two were identified as high quality (⩾80% NOS score; Table 1 and S2 Table).
A similar seasonal pattern was observed between the Northern and Southern Hemisphere. In the Northern Hemisphere, CDI rates peaked during March – April (early boreal spring) and were at their lowest during the second half of the year. CDI increased in the Southern Hemisphere during the second half of the year and peaked in the last trimester of the year (October – November, the mid austral spring – Fig. 2A and 2B). The XCORR peaked (0.60) at lags = 8, indicating that the rise in the weighted average of the standardized monthly incidence of CDI in the Southern Hemisphere lagged the Northern Hemisphere by 8 months (i.e. it occurred two months later relative to the onset of spring in the Southern Hemisphere as compared to the Northern Hemisphere). The lowest value was identified (–0.76) at lag = 1, which indicates that at lag = 1 month the weighted average of the standardized monthly incidence of CDI in the Northern Hemisphere decreases while it increases in the Southern Hemisphere.
(b)Weighted average of the standardized monthly incidence of C. difficile infection by hemispheres. For ease of comparison, the Southern Hemisphere plot was moved 6 months (in the x-axis) thus the meteorological seasons align between hemispheres.
When the studies were grouped by continents, a similar trend was observed in the Northern Hemisphere between North American and European countries. This observation was confirmed by the peak of XCORR = 0.69 at lag 0 months. Both presented a higher frequency of CDI during the first half of the year, with peaks of CDI in March and April in Europe and North America, respectively (Fig. 3).
The findings of the current systematic review suggested that the Northern and Southern Hemisphere countries exhibit similar seasonal patterns characterized by CDI peaking in spring and being at their lowest during summer/autumn months. Antibiotic consumption in the community also follows a seasonal pattern. In North American and European countries the consumption of antibiotics mainly peaked in January-February, whereas in Australia antibiotic consumption peaked in August . Hensgens et al. found that after cessation of antibiotic therapy, patients remain at higher risk of CDI for up to 3 months . Therefore, the observed seasonality may indicate a lag of 2–3 months between antibiotic exposure and CDI. It is not surprising that several studies have found co-seasonality of CDI and respiratory tract infection [11,12,19]. In these studies, the respiratory infections often lead CDI by 1 month which could be explained by the corresponding incidence of respiratory tract infection and antibiotic prescription in the community .
Risk factors in addition to antibiotic exposure such as environmental variables (temperature, precipitation, altitude, etc.) could also be involved in the observed seasonality as they have also been demonstrate to affect the dynamics of numerous infectious diseases [3,21]. In a previous study we found that the odds of CDI infection increased by 9% (OR: 1.09; 95%CI: 1.02 to 1.17) per 100 mm increase in monthly rainfall in Queensland, Australia . Respiratory tract infection transmission dynamics are highly dependent on environmental factors ; therefore, caution is advised for future studies drawing an association between CDI and environmental factors because of the possible confounder of co-seasonality in CDI and respiratory infections. Because CDI was traditionally viewed as a nosocomial disease, studies that assess the relationship between environmental factors and CDI are scant and this is a research gap that requires substantial development. The observed difference of two-month lag between the Southern and Northern Hemisphere (relative to the onset of spring) may be explained by the climatic zones where the studies included in the review are located. Australia, which is located in tropical and sub-tropical zones was the only country included in the review from the Southern Hemisphere; whereas the Northern Hemisphere countries included were mainly located in a temperate zone (USA, Canada, Germany, Ireland, and England). Von Boeckel et al. found that countries further from the equator (temperate zone) have a prominent seasonal pattern in antibiotic consumption characterized by peaks during winter, whereas antibiotic consumption is fairly constant across the months in countries located in tropical and sub-tropical zones . Furthermore, Tamerius et al. described a similar one-month lag between the start of influenza epidemic in temperate Northern Hemisphere countries (November, end of boreal autumn) and the start of influenza epidemic in Australia (June, start of austral winter). In both cases, the influenza epidemic starts 3–4 months before the peak of CDI (March – April in Northern Hemisphere and October – November in Southern Hemisphere).
Despite contrasting antibiotic prescribing practices in outpatients between North America and Europe, the results indicate a similar seasonal pattern between European and North American countries. Patrick et al. found that the antibiotic consumption in the community was higher in British Columbia, Canada, than in Sweden, Germany, United Kingdom, Denmark and The Netherlands . Of particular interest is the high consumption rate found in Canada compared to Denmark for some antibiotic classes such as fluoroquinolones (1.44 versus 0.15 defined daily doses [DDDs]/1000 inhabitant-days), macrolides (1.59 versus 0.92 DDDs/1000 inhabitant-days), and cephalosporins (1.86 versus 0.02 DDDs/1000 inhabitant-days) as these antibiotic classes have been associated with an increased risk of community-acquired CDI [7,8]. A similar trend in antibiotic prescribing was observed in children; higher rates of use of cephalosporins (89.1 versus 0.2 prescriptions/1000 children), lincosamides (2.3 versus 0.1 prescriptions/1000 children), macrolides (148.0 versus 42.6 prescriptions/1000 children), and fluoroquinolones (1.4 versus 0.5 prescriptions/1000 children) were reported in Canada compared to Denmark . This finding supports the need to investigate additional factors (other than antibiotic exposure [11,12]) that would contribute towards a broader understanding of CDI seasonality.
Exposure to proton pump inhibitor (PPI)  and glucocorticoid  has been associated with an increased risk of CDI, however no study has yet examined the temporal relationship between monthly PPIs or glucocorticoids prescription rates and CDI seasonality. Additional factors such as the introduction of new strains of the pathogen via trade in livestock, commodities and/or movement of people (asymptomatic colonized patients such as tourists or business travellers, or hospital transfers) across boundaries should be evaluated when assessing possible factors associated with the seasonality of CDI . Rodriguez-Palacios et al. reported a possible seasonality in contamination of retail meat in Canada with higher prevalence of C. difficile in January – February (11.5%) compared to other months of the study (4.0%) . Riley has implicated the importation of onions and garlic from USA and Mexico into Australia in the increase in CDI during October – December in Western Australia .
Although a comprehensive review was carried out, several limitations were noted. First, only two studies were identified that reported the seasonality of CDI in Southern Hemisphere countries [14,15]. Furthermore, both studies were conducted in Australia. This may limit the generalizability of the findings for Southern Hemisphere countries only to Australia. However, the identified gap in information should encourage further investigation particularly in countries in South America, Africa and Asia. Second, there was a small number of studies from countries located between the Tropic of Cancer and the Tropic of Capricorn. The study conducted by Wong-McClure et al.  in Costa Rica was the only study from the Northern Hemisphere located in a tropical zone, precluding the comparison between the seasonality of CDI in temperate and sub-tropical/tropical climates. Despite the documented changes in CDI epidemiology , the increase in community-acquired CDI , and the different risk profiles between community- and hospital-acquired CDI patients , our study was also limited by the inability to compare the community- and hospital-acquired CDI seasonal patterns. Despite the increasing incidence of CDI among the paediatric population  only one study (Deodari et al. ) was identified that described the CDI seasonality in children; therefore, generalizability of the findings may be limited among this population. Potential factors that may contribute to differences in monthly CDI incidence that could not be accounted for in this review, such as hospital characteristics (e.g. staffing, overcrowding), CDI diagnosis ascertainment, severity of underlying illness, infection control practices, and CDI strain need to be assessed in future studies.
As the studies included in the review reported the measures of monthly CDI using different units, the values were standardized to compare the monthly CDI incidence across the studies. By doing so, the magnitude of the seasonality measured by the amplitude between the peak and the trough was lost. Although, the magnitude of the seasonality could be masked, the observed patterns should not be affected by the standardization. Finally, the weight allocated to each study was based on the number of follow-up months and not on the sample size as the number of participants or stool samples examined during the study period was not available for all the studies included in this review.
Understanding the seasonality of an infectious disease and the driving factors are of utmost importance for planning prevention and control strategies [21,35]. Recently, several epidemiological models of CDI have been constructed to inform control strategies for this disease of increasing incidence and severity [36–39]. However, none has yet incorporated the effects of seasonality and this will be difficult to achieve without better understanding of the underlying mechanisms. The current review provided evidence of a similar CDI seasonal pattern across hemispheres which differs from the seasonality that was previously proposed. Further studies are required to identify exposure to medications and environmental factors associated with the observed seasonality.
The authors would like to thank Professor Lutz von Müller, Dr. Alexander Halfmann and Dr. Kelly Reveles for kindly provide us with additional data from their studies.
Conceived and designed the experiments: LY. Analyzed the data: LFK SM AC. Wrote the paper: LFK SM LY JC DP TR AC. Conducted the systematic review: JC.
- 1. Bartlett JG (2002) Antibiotic-Associated Diarrhea. N Engl J Med 346: 334–339. pmid:11821511
- 2. Freeman J, Bauer MP, Baines SD, Corver J, Fawley WN, Goorhuis B, et al. (2010) The changing epidemiology of Clostridium difficile infections. Clin Microbiol Rev 23: 529–549. pmid:20610822
- 3. Grassly NC, Fraser C (2006) Seasonal infectious disease epidemiology. Proc Biol Sci 273: 2541–2550. pmid:16959647
- 4. Archibald LK, Banerjee SN, Jarvis WR (2004) Secular trends in hospital-acquired Clostridium difficile disease in the United States, 1987–2001. J Infect Dis 189: 1585–1589. pmid:15116293
- 5. Gilca R, Hubert B, Fortin E, Gaulin C, Dionne M (2010) Epidemiological patterns and hospital characteristics associated with increased incidence of Clostridium difficile infection in Quebec, Canada, 1998–2006. Infect Control Hosp Epidemiol 31: 939–947. pmid:20677973
- 6. Jagai J, Naumova E (2009) Clostridium difficile-associated disease in the elderly, United States. Emerg Infect Dis 15: 343–344. pmid:19193291
- 7. Brown KA, Khanafer N, Daneman N, Fisman DN (2013) Meta-analysis of antibiotics and the risk of community-associated Clostridium difficile infection. Antimicrob Agents Chemother 57: 2326–2332. pmid:23478961
- 8. Deshpande A, Pasupuleti P, Thota P, Pant C, Rolston DDK, Sferra TJ, et al. (2013) Community-associated Clostridium difficile infection antibiotics: A meta-analysis. J Antimicrob Chemother 68: 1951–1961. pmid:23620467
- 9. Slimings C, Riley TV (2014) Antibiotics and hospital-acquired Clostridium difficile infection: update of systematic review and meta-analysis. J Antimicrob Chemother 69: 881–891. pmid:24324224
- 10. Thomas C, Stevenson M, Riley TV (2003) Antibiotics and hospital-acquired Clostridium difficile-associated diarrhoea: a systematic review. J Antimicrob Chemother 51: 1339–1350. pmid:12746372
- 11. Brown KA, Daneman N, Arora P, Moineddin R, Fisman DN (2013) The co-seasonality of pneumonia and influenza with Clostridium difficile infection in the United States, 1993–2008. Am J Epidemiol 178: 118–125. pmid:23660799
- 12. Gilca R, Fortin E, Frenette C, Longtin Y, Gourdeau M (2012) Seasonal variations in Clostridium difficile infections are associated with influenza and respiratory syncytial virus activity independently of antibiotic prescriptions: a time series analysis in Quebec, Canada. Antimicrob Agents Chemother 56: 639–646. pmid:22106208
- 13. Van Boeckel TP, Gandra S, Ashok A, Caudron Q, Grenfell BT, Levin SA, et al. (2014) Global antibiotic consumption 2000 to 2010: an analysis of national pharmaceutical sales data. Lancet Infect Dis 14: 742–750. pmid:25022435
- 14. Furuya-Kanamori L, Robson J, Soares Magalhaes RJ, Yakob L, McKenzie SJ, Paterson DL, et al. (2014) A population-based spatio-temporal analysis of Clostridium difficile infection in Queensland, Australia over a 10-year period. J Infect 69: 447–455. pmid:24984276
- 15. Slimings C, Armstrong P, Beckingham WD, Bull AL, Hall L, Kennedy KJ, et al. (2014) Increasing incidence of Clostridium difficile infection, Australia, 2011–2012. Med J Aust 200: 272–276. pmid:24641152
- 16. Moher D, Liberati A, Tetzlaff J, Altman DG, PRISMA Group (2009) Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med 6: e1000097. pmid:19621072
- 17. Wells GA, Shea B, O'Connell D, Peterson J, Welch V, Losos M, et al. The Newcastle-Ottawa Scale (NOS) for assessong the quality of nonrandomized studies in meta-analyses. Available: http://www.ohri.ca/programs/clinical_epidemiology/oxford.asp. Accessed 2015 February 2.
- 18. Hensgens MPM, Goorhuis A, Dekkers OM, Kuijper EJ (2012) Time interval of increased risk for Clostridium difficile infection after exposure to antibiotics. J Antimicrob Chemother 67: 742–748. pmid:22146873
- 19. Polgreen PM, Yang M, Bohnett LC, Cavanaugh JE (2010) A time-series analysis of Clostridium difficile and its seasonal association with influenza. Infect Control Hosp Epidemiol 31: 382–387. pmid:20175682
- 20. Fleming DM, Ross AM, Cross KW, Kendall H (2003) The reducing incidence of respiratory tract infection and its relation to antibiotic prescribing. Br J Gen Pract 53: 778–783. pmid:14601353
- 21. Altizer S, Dobson A, Hosseini P, Hudson P, Pascual M, Rohani P (2006) Seasonality and the dynamics of infectious diseases. Ecol Lett 9: 467–484. pmid:16623732
- 22. Tamerius J, Nelson MI, Zhou SZ, Viboud C, Miller MA, Alonso WJ (2011) Global influenza seasonality: reconciling patterns across temperate and tropical regions. Environ Health Perspect 119: 439–445. pmid:21097384
- 23. Patrick DM, Marra F, Hutchinson J, Monnet DL, Ng H, Bowie WR (2004) Per capita antibiotic consumption: how does a North American jurisdiction compare with Europe? Clin Infect Dis 39: 11–17. pmid:15206046
- 24. Marra F, Monnet DL, Patrick DM, Chong M, Brandt CT, Winters M, et al. (2007) A comparison of antibiotic use in children between Canada and Denmark. Ann Pharmacother 41: 659–666. pmid:17374628
- 25. Cunningham R, Dale B, Undy B, Gaunt N (2003) Proton pump inhibitors as a risk factor for Clostridium difficile diarrhoea. J Hosp Infect 54: 243–245. pmid:12855243
- 26. Furuya-Kanamori L, Stone JC, Clark J, McKenzie SJ, Yakob L, Paterson DL, et al. (2015) Comorbidities, Exposure to Medications, and the Risk of Community-Acquired Clostridium difficile Infection: A Systematic Review and Meta-analysis. Infec Control Hosp Epidemiol 36: 131–141.
- 27. Clements AC, Magalhaes RJ, Tatem AJ, Paterson DL, Riley TV (2010) Clostridium difficile PCR ribotype 027: assessing the risks of further worldwide spread. Lancet Infect Dis 10: 395–404. pmid:20510280
- 28. Rodriguez-Palacios A, Reid-Smith RJ, Staempfli HR, Daignault D, Janecko N, Avery BP, et al. (2009) Possible seasonality of Clostridium difficile in retail meat, Canada. Emerg Infect Dis 15: 802–805. pmid:19402975
- 29. Riley TV (2013) Clostridium difficile infection: the Australian experience. Available: http://www.hqsc.govt.nz/assets/Infection-Prevention/CDI-workshop-Feb-2013-Riley.pdf. Accessed 2014 October 30.
- 30. Wong-McClure RA, Guevara-Rodríguez M, Abarca-Gómez L, Solano-Chinchilla A, Marchena-Picado M, Shea M, et al. Clostridium difficile outbreak in Costa Rica: control actions and associated factors. Rev Panam Salud Publica 32: 413–418. pmid:23370184
- 31. Kuntz JL, Chrischilles EA, Pendergast JF, Herwaldt LA, Polgreen PM (2011) Incidence of and risk factors for community-associated Clostridium difficile infection: a nested case-control study. BMC Infect Dis 11: 194. pmid:21762504
- 32. Pituch H (2009) Clostridium difficile is no longer just a nosocomial infection or an infection of adults. Int J Antimicrob Agents 33: S42–45. pmid:19303569
- 33. Khanna S, Baddour LM, Huskins WC, Kammer PP, Faubion WA, Zinsmeister AR, et al. (2013) The epidemiology of Clostridium difficile infection in children: a population-based study. Clin Infect Dis 56: 1401–1406. pmid:23408679
- 34. Deorari S, McConnell A, Tan KK, Jadavji N, Ma D, Church D, et al. (1999) Differential yield of pathogens from stool testing of nosocomial versus community-acquired paediatric diarrhea. Can J Infect Dis 10: 421–428. pmid:22346400
- 35. Pascual M, Dobson A (2005) Seasonal Patterns of Infectious Diseases. PLoS Med 2: e5. pmid:15696215
- 36. Codella J, Safdar N, Heffernan R, Alagoz O (2014) An Agent-based Simulation Model for Clostridium difficile Infection Control. Med Decis Making (Epub ahead of print).
- 37. Lanzas C, Dubberke ER (2014) Effectiveness of Screening Hospital Admissions to Detect Asymptomatic Carriers of Clostridium difficile: A Modeling Evaluation. Infect Control Hosp Epidemiol 35: 1043–1050. pmid:25026622
- 38. Yakob L, Riley T, Paterson D, Clements A (2013) Clostridium difficile exposure as an insidious source of infection in healthcare settings: an epidemiological model. BMC Infect Dis 13: 376. pmid:23947736
- 39. Yakob L, Riley TV, Paterson DL, Marquess J, Clements ACA (2014) Assessing control bundles for Clostridium difficile: a review and mathematical model. Emerg Microbes Infect 3: e43.
- 40. Burckhardt F, Friedrich A, Beier D, Eckmanns T (2008) Clostridium difficile surveillance trends, Saxony, Germany. Emerg Infect Dis 14: 691–692. pmid:18394306
- 41. Camacho-Ortiz A, Galindo-Fraga A, Rancel-Cordero A, Macias AE, Lamothe-Molina P, Ponce de Leon-Garduno A, et al. (2009) [Factors associated with Clostridium difficile disease in a tertiary-care medical institution in Mexico: a case-control study]. Rev Invest Clin 61: 371–377. pmid:20184096
- 42. Damani N, Trudy R, Markey M, Wallace S (2011) C. difficile associated diarrhoea-don't blame community or norovirus. BMC Proc 5(Suppl 6): P186.
- 43. Dubberke ER, Butler AM, Hota B, Khan YM, Mangino JE, Mayer J, et al. (2009) Multicenter study of the impact of community-onset Clostridium difficile infection on surveillance for C. difficile infection. Infect Control Hosp Epidemiol 30: 518–525. pmid:19419269
- 44. Faires MC, Pearl DL, Ciccotelli WA, Berke O, Reid-Smith RJ, Weese JS (2014) Detection of Clostridium difficile infection clusters, using the temporal scan statistic, in a community hospital in southern Ontario, Canada, 2006–2011. BMC Infect Dis 14: 254. pmid:24885351
- 45. MacDonald KS, McLeod J, Nicolle L (1993) Clostridium difficile enteritis in a Canadian tertiary care hospital. Can J Infect Control 8: 37–40. pmid:8400341
- 46. McFarland LV, Clarridge JE, Beneda HW, Raugi GJ (2007) Fluoroquinolone use and risk factors for Clostridium difficile-associated disease within a Veterans Administration health care system. Clin Infect Dis 45: 1141–1151. pmid:17918075
- 47. Reil M, Hensgens MP, Kuijper EJ, Jakobiak T, Gruber H, Kist M, et al. (2012) Seasonality of Clostridium difficile infections in Southern Germany. Epidemiol Infect 140: 1787–1793. pmid:22152928
- 48. Reveles KR, Lee GC, Boyd NK, Frei CR (2014) Regional and seasonal variations in Clostridium difficile infections in United States hospitals, 2001 to 2010. Value in Health 17: A267.
- 49. Sonnenberg A (2008) Seasonal variation of enteric infections and inflammatory bowel disease. Inflamm Bowel Dis 14: 955–959. pmid:18302273
- 50. Von Muller L, Speck K, Herrmann M (2011) Surveillance analysis of C. difficile genotyps demonstrates decreasing frequencies of 027 infections in a tertiary care hospital. Clin Microbiol Infec 17: S577.