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Antimicrob Agents Chemother. 2009 October; 53(10): 4574–4575.
PMCID: PMC2764173

Relationship between Antibiotic Consumption and Clostridium difficile-Associated Diarrhea: an Epidemiological Note

In a recent study, Aldeyab et al. determined the hospital-wide relationship between antimicrobial drug consumption and occurrence as well as spread of Clostridium difficile-associated diarrhea (CDAD) in a teaching hospital in Belfast, Northern Ireland (1). A major advantage of the methods used (time series analysis) is the establishment of a hospital-wide cause-effect relationship, with a clear direction and determination of the strength of the observed correlation.

We compared the study results reported by Aldeyab et al. with data from a 1,600-bed teaching hospital in Freiburg, Germany, and found comparable dose-response rates for the two studies (1, 4).

The dose-response relationship between antibiotic consumption and emergence of resistance was first described in 1989 by Phelps, who defined this relationship as “the percentage change in the probability of an organism being resistant following a 1% change in the level of antibiotic use” (6). In economic science, the time series analysis is used extensively for analysis of economic relationships (econometrics). In this context, the dose-response rate is termed elasticity and can easily be deduced from the coefficients estimated by time series analyses (3). In the studies conducted by Aldeyab et al., elasticity ECDAD,q is defined as

equation M1

where CDAD(q) stands for the incidence of CDAD, which is a dependent function of the consumption of q. The variable q represents the independent variable used in the time series analysis, i.e., a series associated with the consumption of a class of antimicrobials. Following this concept, the coefficients estimated by Aldeyab et al. can be transformed into elasticities, thus facilitating comparisons between different study settings. First, estimated coefficients are inserted into the first fraction of equation 1 [[partial differential]CDAD(q)/[partial differential]q]. Second, averages of the dependent variable CDAD and the independent variable q (i.e., antibiotic use series) are inserted into the second fraction [q/CDAD(q)].

In contrast, the study conducted in Southern Germany used a log-linear modeling approach for the estimation of the multivariate model (4). According to this, elasticity ECDAD,q is defined as

equation M2

which means that all estimated coefficients may be interpreted as dose-response rates without transformation. For the two studies investigated here, elasticities are shown in Table Table1.1. A 1% increase in the use of fluoroquinolones in Belfast, for instance, was followed by a 0.4% increase in the incidence of CDAD, while a 1% increase in the use of fluoroquinolones in Freiburg was followed by a 1% increase in the incidence of CDAD.

Coefficients estimated by time series analyses for Belfast and Freiburg and their corresponding elasticitiesa

Unfortunately, both studies do not distinguish between community-acquired and health care-associated cases of CDAD. Therefore, both analyses may be biased by a proportion of patients admitted with CDAD whose diarrhea was not caused by prior in-hospital antibiotic use. Differences in this proportion could act as an explanation for differences in the dose-response rates.

An analysis of the influence of Clostridium difficile colonization pressure from outside the hospital that includes the incidence of patients admitted with CDAD as an independent variable would be a challenge for future analyses in the same way as was recently done for methicillin-resistant Staphylococcus aureus and extended-spectrum-β-lactamase-producing strains (2, 4, 5).


Research on the health and economic impacts of antimicrobial resistance by K.K. and U.F. is currently supported by the European Commission (project BURDEN, commissioned by DG SANCO, grant agreement no. 2005203 []).

There are no conflicts of interest related to this work.


Ed. note: The authors of the published article did not respond.


1. Aldeyab, M. A., S. Harbarth, N. Vernaz, M. P. Kearney, M. G. Scott, C. Funston, K. Savage, D. Kelly, M. A. Aldiab, and J. C. McElnay. 2009. Quasiexperimental study of the effects of antibiotic use, gastric acid-suppressive agents, and infection control practices on the incidence of Clostridium difficile-associated diarrhea in hospitalized patients. Antimicrob. Agents Chemother. 53:2082-2088. [PMC free article] [PubMed]
2. Aldeyab, M. A., D. L. Monnet, J. M. Lopez-Lozano, C. M. Hughes, M. G. Scott, M. P. Kearney, F. A. Magee, and J. C. McElnay. 2008. Modelling the impact of antibiotic use and infection control practices on the incidence of hospital-acquired methicillin-resistant Staphylococcus aureus: a time-series analysis. J. Antimicrob. Chemother. 62:593-600. [PubMed]
3. Greene, W. H. 1997. Econometric analysis. Prentice Hall, Upper Saddle River, NJ.
4. Kaier, K., C. Hagist, U. Frank, A. Conrad, and E. Meyer. 2009. Two time-series analyses of the impact of antibiotic consumption and alcohol-based hand disinfection on the incidences of nosocomial methicillin-resistant Staphylococcus aureus infection and Clostridium difficile infection. Infect. Control Hosp. Epidemiol. 30:346-353. [PubMed]
5. Kaier, K., U. Frank, C. Hagist, A. Conrad, and E. Meyer. 2009. The impact of antimicrobial drug consumption and alcohol-based hand rub use on the emergence and spread of extended-spectrum {beta}-lactamase-producing strains: a time-series analysis. J. Antimicrob. Chemother. 63:609-614. [PubMed]
6. Phelps, C. E. 1989. Bug/drug resistance. Sometimes less is more. Med. Care 27:194-203. [PubMed]

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