Impact of Antibiotic Exposure Patterns on Selection of Community

ANTIMICROBIAL AGENTS AND CHEMOTHERAPY, Oct. 2011, p. 4888–4895 Vol. 55, No. 100066-4804/11/$12.00 doi:10.1128/AAC.01626-10Copyright © 2011, American Society for Microbiology. All Rights Reserved.

Impact of Antibiotic Exposure Patterns on Selectionof Community-Associated Methicillin-Resistant

Staphylococcus aureus in Hospital Settings�†Lidia Kardas-Słoma,1,2,3* Pierre Yves Boelle,1,3,4 Lulla Opatowski,5,6 Christian Brun-Buisson,5,7,8

Didier Guillemot,5,6,9,10 and Laura Temime2

UPMC Universite Paris 06, F-75005, Paris, France1; Conservatoire National des Arts et Metiers, Laboratoire Modelisation etSurveillance des Risques pour la Securite Sanitaire, Paris, France2; INSERM, U 707, Paris, France3; AP-HP,

Hopital Saint-Antoine, Paris, France4; INSERM, U 657, Paris, France5; Institut Pasteur, PhEMI, Paris,France6; Faculte de Medecine, Universite Paris-Est, Creteil, France7; AP-HP, Hopital Henri Mondor,

Service de Reanimation Medicale; Creteil, France8; Faculte de Medecine Paris Ile de France Ouest,Universite Versailles Saint Quentin9; and AP-HP, Hopital Raymond Poincare,

Service de Medecine Aigue, Garches, France10

Received 23 November 2010/Returned for modification 16 May 2011/Accepted 17 July 2011

Community-associated methicillin-resistant S. aureus (CA-MRSA) is increasingly common in hospitals, withpotentially serious consequences. The aim of this study was to assess the impact of antibiotic prescriptionpatterns on the selection of CA-MRSA within hospitals, in a context of competition with other circulatingstaphylococcal strains, including methicillin-sensitive (MSSA) and hospital-associated methicillin-resistant(HA-MRSA) strains. We developed a computerized agent-based model of S. aureus transmission in a hospitalward in which CA-MRSA, MSSA, and HA-MRSA strains may cocirculate. We investigated a wide range ofantibiotic prescription patterns in both intensive care units (ICUs) and general wards, and we studied howdifferences in antibiotic exposure may explain observed variations in the success of CA-MRSA invasion in thehospitals of several European countries and of the United States. Model predictions underlined the influenceof antibiotic prescription patterns on CA-MRSA spread in hospitals, especially in the ICU, where the endemicprevalence of CA-MRSA carriage can range from 3% to 20%, depending on the simulated prescription pattern.Large antibiotic exposure with drugs effective against MSSA but not MRSA was found to promote invasion byCA-MRSA. We also found that, should CA-MRSA acquire fluoroquinolone resistance, a major increase inCA-MRSA prevalence could ensue in hospitals worldwide. Controlling the spread of highly community-prevalent CA-MRSA within hospitals is a challenge. This study demonstrates that antibiotic exposure strat-egies could participate in this control. This is all the more important in wards such as ICUs, which may playthe role of incubators, promoting CA-MRSA selection in hospitals.

Methicillin-resistant Staphylococcus aureus (MRSA) is oneof the leading causes of nosocomial infections. Community-associated MRSA (CA-MRSA), which acquire methicillinresistance outside the hospital, are increasingly found inhospitals worldwide. In the United States, CA-MRSA nowrepresents more than 30% of all nosocomial MRSA strains(7). This spread occurs in an environment where a complex,and poorly understood, balance exists between methicillin-sensitive S. aureus (MSSA) and hospital-associated MRSA(HA-MRSA).

The introduction and dissemination of CA-MRSA into hos-pitals are current threats, especially at a time when the imple-mentation of effective policies for control of cross-transmissionin hospitals has led to the reduction of HA-MRSA infections inmany developed countries (21). Indeed, while CA-MRSA

strains are currently susceptible to more antibiotics than HA-MRSA, they are more transmissible and may acquire addi-tional resistances under the high antibiotic selective pressurefound in hospitals (24). In this context, it is important to gaina better understanding of the factors that may promote theselection of CA-MRSA in hospital settings.

Simulation models have long been used to analyze pathogendissemination in hospital settings and control strategies (3, 22).However, the complex functioning of a hospital ward mustoften be overly simplified in these approaches, for example,ignoring spatial detail or structure of contact networks. Suchlimitations may be overcome using stochastic agent-based sim-ulation, as illustrated in the study of pandemic influenza orbioterrorist attacks (10, 12).

Agent-based models are well suited to modeling complexphenomena. In this bottom-up approach, agents (i.e., individ-uals) are described as entities in interaction with others ac-cording to explicit rules. The dynamics of the system as a wholeis studied from the individual level toward the population level(5, 17, 25).

Here, an agent-based, spatially explicit model of pathogentransmission in a hypothetical intensive care unit or generalmedicine ward was described, where agents represented pa-

* Corresponding author. Mailing address: Conservatoire des Arts etMetiers, Laboratoire Modelisation et Surveillance des Risques pour laSecurite Sanitaire, 292 rue Saint Martin, 75141 Paris Cedex 03, France.Phone: 33-153-018-069. Fax: 33-140-272-312. E-mail: [email protected].

† Supplemental material for this article may be found at http://aac.asm.org/.

� Published ahead of print on 25 July 2011.

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tients and staff. The model includes detailed data on antibioticexposure, as well as data on antibiotic susceptibility of allstaphylococcal strains.

First, we use model simulations to investigate the impact ofa wide range of hypothetical antibiotic prescription patterns onCA-MRSA prevalence in two different ward types: an intensivecare unit (ICU) and a general ward (GW). Then, we interpretour findings in the context of several European countries andthe United States.

MATERIALS AND METHODS

Model structure. We developed and used an agent-based, stochastic, discretetime, spatially explicit computerized model to simulate the spread of bacterialstrains in a hypothetical 20-bed hospital ward among patients and health careworkers (HCWs) (35, 36).

In this model, each patient and health care worker (HCW) was represented asan ‘�agent’’ with a specific internal state and a geographical situation. Patientswere characterized by spatial location, length of stay in the hospital, colonizationstatus, and exposure to antibiotics; HCWs were characterized by their dailyschedule and colonization status. Every day, the model simulated the actions ofeach agent, such as patient visits by HCWs and patient admission or discharge.Patients could require low or high levels of care, the latter leading to morefrequent contacts with HCWs. HCW schedules reflected those of nurses or thoseof physicians. Contacts between HCWs and patients and patient admission ordischarge were simulated. Figure S1 in the supplemental material provides aschematic representation of the modeled ward.

Two types of hospital wards were simulated: a GW and an ICU, with differentrates of contacts and antibiotic usage. The main parameters of the model arepresented in Tables 1 and 2. The simulation platform is described in more detailand available for download at http://sites.google.com/site/nososim/.

Staphylococcus aureus colonization. (i) Circulating strains. All three categoriesof S. aureus strains were considered in the simulations. The differences betweenstrains were modeled as differences in susceptibility to antibiotics and transmis-sibility. The prevalence of carriage on hospital admission for MSSA, HA-MRSA,and CA-MRSA was fixed at, respectively, 18%, 5%, and 1% (1, 13, 14).

(ii) Colonization transmission. Transmission of S. aureus to patients occurredonly by contact with transiently colonized HCWs. The probability of S. aureustransmission from a colonized HCW to a patient or from a colonized patient toan HCW was calculated as the product of the transmission rate per minute andthe duration of contact. We denoted the transmission rate per minute as pS forMSSA, pCA for CA-MRSA, and pHA for HA-MRSA. We calibrated pS and pHA

to reproduce an observed prevalence for HA-MRSA of 17% (reported range, 9to 23% [18, 20] and twice as high as for MSSA [11]). The values of pS and pHA

were calibrated independently for GWs and ICUs. We hypothesized that pS �pCA � pHA, as CA-MRSA appears to be more closely related to MSSA and thetransmissibility of HA-MRSA is reduced in comparison with MSSA and CA-MRSA (8).

(iii) Colonization clearance and immunity. We assumed that the mean time todecolonization with S. aureus was 100 days in the absence of antibiotic exposurein patients and that colonization of HCWs was always transient (1 day).

In patients, clearance was followed by a temporary immunity period of 4 days,during which recolonization by the same strain was not allowed (2). Transienthand carriage by HCWs was not associated with a period of immunity.

(iv) Simultaneous carriage and competition. Simultaneous carriage of strainswas allowed. The probability of acquisition of another strain was, however,reduced by 50% in already-colonized individuals to reflect competition betweenstrains (6).

Antibiotic exposure. Patients could be exposed to one or several antibioticsduring their stay. Antibiotic exposure of a colonized patient cleared carriage ofsensitive strains but had no impact on resistant strains. The mean duration ofantibiotic exposure was 8 days (32) for all antibiotic exposures.

(i) Antibiotic class categorizations. We reviewed the systemic antimicrobialsprescribed in hospitals, as found in group J01 of the Anatomical TherapeuticChemical (ATC) classification system (39). The susceptibility of MSSA, CA-MRSA, and HA-MRSA isolates to each of these antimicrobials was assessed.

Then, we split the ATC classes into 4 subgroups, according to their activity oneach of the three S. aureus strains: group A (e.g., J01CA [ampicillin]), to whichall three strains of S. aureus were resistant; group B (e.g., J01CF [methicillin]), towhich MSSA isolates were sensitive and MRSA isolates were resistant; group C(e.g., J01FF [clindamycin]), to which MSSA and CA-MRSA isolates were sen-

sitive but HA-MRSA isolates were resistant; group D (e.g., J01XA [vancomy-cin]), to which all three strains were sensitive. Table S1 in the supplementalmaterial provides the susceptibility data for all ATC classes as well as theclassification into 4 groups of these classes, taking into account uncertainties dueto 5 ATC classes for which variations in susceptibility have been reported.

Baseline scenario. We used as a baseline scenario the best-case scenario forantibiotic efficacy, where CA-MRSA and HA-MRSA are susceptible to antibi-otics for which variations in susceptibility have been reported.

Antibiotic prescription patterns. In order to study the impact of differentpatterns of antibiotic use on CA-MRSA dissemination, we systematically inves-

TABLE 1. Main model parameters

Model parameter Value Source(s)

No. of beds 20 AssumedOccupancy rate (%) 90 AssumedLength of stay (gamma

distribution with mean)of patient with:

Low level of care 5 (shape, 10; scale, 0.5) 29, 33High level of care 14 (shape, 28; scale, 0.5)

Portion of patient with lowlevel of care

0.9 33

Portion of patient with highlevel of care

0.1 33

Fixed length of nurse visit(min) for patient with:

29

Low level of care 20High level of care 100

Fixed length of physicianvisit (min) for patientwith:

16

Low level of care 25High level of care 25

Prevalence (%) of patientscolonized at hospitaladmission with:

MSSA 18 1CA-MRSA 1 13HA-MRSA 5 14

Length (days) of patient’scolonization in absenceof antibiotic exposure

100 (mean ofexponentialdistribution)

4

Length (days) of HCW’ssuperficial colonization

1 (mean of exponentialdistribution)

37

Length of treatment (days) 8 32Antibiotic resistance (%) to: Assumed

Group AMSSA 100CA-MRSA 100HA-MRSA 100

Group BMSSA 0CA-MRSA 100HA-MRSA 100

Group CMSSA 0CA-MRSA 0HA-MRSA 100

Group DMSSA 0CA-MRSA 0HA-MRSA 0

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tigated different hypothetical usage frequencies for the 4 groups (A, B, C, andD), which ranged from 5% to 80% of the overall antibiotic consumption, whichwas fixed. The explored values were 5%, 10%, 15%, 20%, 30%, 40%, 50%, 60%,70%, and 80%, leading to 252 hypothetical antibiotic prescription patterns (e.g.,A � 50%, B � 15%, C � 20%, and D � 15%). Figure S2 in the supplementalmaterial provides all 252 investigated antibiotic prescription patterns.

Antibiotic exposure frequency. We assumed 27% of patients were exposeddaily to antibiotics in the GW, based on data from France (31). As ward-specificdata have shown 2 to 3 times more antibiotic exposure in ICUs than in GWs(31), we assumed a total of 60% of patients were exposed daily to antibioticsin the ICU.

Applications: baseline scenario. We used data from the ESAC study on drugconsumption in European hospitals (9), as well as data from U.S. hospitals (28),in order to locate the antibiotic prescription practices in several countries(United States, Denmark, Finland, France, Poland, and Greece) among the 252investigated patterns. When available, ward-specific data were used (28, 34).

As an example, Table 3 provides the percentages represented by groups A, B,C, and D among the total antibiotic consumption within GWs and ICUs amongFrench hospitals, as well as the corresponding proportion of patients exposeddaily to antibiotics from these 4 groups. Both the baseline scenario and the rangedue to uncertainties in the classification are provided.

Table 4 provides patterns in antibiotic use for all considered countries underthe baseline scenario assumption.

Model simulations. For each possible scenario, we introduced a single initialCA-MRSA-colonized patient within the ward and we simulated the resulting3-strain dynamics for 30 days. Over these 30 days, following this first admittedcolonized patient, 1% of admitted patients were assumed to be colonized withCA-MRSA (13). At the end of the simulated period we calculated the prevalence

of CA-MRSA among all S. aureus patient carriers. This outcome was determinedas the average over 1,000 simulation replicates required to hold stochastic com-ponents of the model constant at their average values.

Investigation of antibiotic prescription patterns. In order to assess the impactof antibiotic prescription patterns on CA-MRSA dissemination in the hospital,we performed a sensitivity analysis based on simulations using our 252 pre-scription patterns. We computed Kendall partial rank correlation coefficients(PRCC) between CA-MRSA endemic prevalence and the exposure to anti-biotic groups A, B, C, and D (expressed as a fraction of total antibioticexposure in the ward).

Sensitivity analyses. We assessed the sensitivity of our predictions in terms ofCA-MRSA colonization prevalence on several model parameters related to S.aureus colonization and transmission. In this analysis, in the pre-CA-MRSA era,we investigated prevalences of HA-MRSA that ranged between 9 and 23%, aswell as prevalences of MSSA between 20 and 40%. We also investigated trans-mission rates per minute for CA-MRSA (pCA), which ranged from pS (transmis-sion rate for MSSA) to pHA (transmission rate for HA-MRSA).

In another analysis, we studied the consequences of future changes in CA-MRSA susceptibility to fluoroquinolone antibiotics. Although CA-MRSA strainsare currently mostly sensitive to fluoroquinolones, previous experience has dem-onstrated that resistance to this class may be selected due to antibiotic selectionpressure. Indeed, recent data from the United States suggests that some CA-MRSA isolates already exhibit a significant decrease in sensitivity to fluoroquino-lones (24). In order to perform this sensitivity analysis, we determined which ofthe 252 investigated prescription patterns best described antibiotic prescriptionpractices in the six countries we studied, assuming that CA-MRSA had becomeresistant to fluoroquinolones.

RESULTS

Model calibration. Using a least square criterion, we cali-brated the transmission rates of MSSA and of HA-MRSA inorder to best reproduce observed carriage prevalence. Trans-mission rates (per minute of contact) for the ICU were 0.0072for pS

ICU and 0.0054 for pHAICU, and rates for GWs were pS

GW

of 0.003 and pHAGW of 0.0028 per minute of contact.

In the following sections, the probability of CA-MRSAtransmission (pCA) was therefore equal to pS

ICU in the ICUand to pS

GW in the GW.Impact of antibiotic prescription patterns. Figure 1 depicts

the range of predicted endemic prevalence of CA-MRSAamong all S. aureus carriers under different antibiotic prescrip-tion patterns in the ICU and in the GW.

Antibiotic prescription patterns had an important impact onCA-MRSA colonization dynamics, especially in the ICU. Thecomputed prevalence varied by a factor of 3.25 in the GW,ranging from 4% to 13%, and by a factor of 6.7 in the ICU,ranging from 3% to 20%.

The sensitivity analysis (see Table S2 in the supplemental

TABLE 2. Specific parameters for the two types of hospital wards

Parameter GW ICU Source(s)

Patient-to-HCW ratio 16, 19Physicians 1:6 1:6Nurse (day shift) 1:4 1:2Nurse (night shift) 1:6 1:2

No. of patient visits with: 15, 16Physicians 2 2Nurse (day shift) 3 3Nurse (night shift) 2 3

Transmission (min ofcontact�1) of:

Calibrated valuesbased onreference 18

MSSA 0.003 0.0072CA-MRSA 0.0028–0.003 0.0054–0.0072HA-MRSA 0.0028 0.0054

Prevalence of antibioticexposure (%)

27 60 31

TABLE 3. Distribution of prescribed antibiotics and dailyfrequency of antibiotic exposure in French hospitals

among the study’s four antibiotic groupsa

Study group

Baseline classification (uncertainty range)

% of total antibioticconsumption

Prevalence (%) of patientsexposed to antibiotics

GW ICU GW ICU

A 21 11 5.7 6.6B 44 (44–72) 63 (63–82) 11.9 (11.9–19.4) 37.8 (37.8–49.2)C 18 (0–30) 17 (0–24) 4.8 (0–8.1) 10.2 (0–14.4)D 17 (5–17) 9 (6–9) 4.6 (1.3–4.6) 5.4 (3.6–5.4)

Total 100 100 27 60

a Based on a classification of detailed drug consumption data from the ESACand the French Coordination Centers to fight Nosocomial Infections (C-CLIN)study (9, 34) in ICUs and GWs. The data represent the baseline values and theranges due to uncertainties in the classification into groups B, C, and D.

TABLE 4. Antibiotic prescription patterns in U.S., French, Danish,Finnish, Polish, and Greek hospitals

Group

% of total antibiotic consumption by drug groupa in:

UnitedStates France

Denmark Finland Poland Greece

GW ICU GW ICU

A 15 11 21 11 45 13 19 14B 57 58 44 63 29 32 46 49C 18 16 18 17 14 15 13 19D 10 15 17 9 12 40 22 18

Total 100 100 100 100 100 100 100 100

a The data represent the baseline classifications for all prescribed antibioticsinto four antibiotic groups, A, B, C, and D. Ward-specific data were availableonly for U.S. and French hospitals (9, 28, 34).

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material) showed that changes in groups B and C had thegreatest influence on the prevalence of CA-MRSA (in theICU, PRCC � 0.81 and PRCC � �0.59, versus PRCC � 0.41and PRCC � �0.26 for groups A and D, respectively). Table

S2 also provides computed PRCCs for the predicted preva-lence rates of MSSA and HA-MRSA.

Figure 2, which depicts the prevalence of CA-MRSA amongcarried staphylococcal strains in the ICU depending on expo-sure to antibiotics from groups A, B, C, and D, highlights theimportance of group B antibiotic exposure for CA-MRSA dis-semination.

Simulations in a GW setting give similar results (see Fig. S3in the supplemental material).

Applications. (i) Baseline scenario. Figure 3 locates the sixantibiotic prescription patterns most similar to observed pre-scription levels in the United States, Denmark, Finland,France, Poland, and Greece among the range of our predic-tions for both the GW and the ICU settings.

High use of antibiotics from group B (e.g., �-lactamase-resistant penicillins) and low use of antibiotics from groupsC (e.g., fluoroquinolones), as in the “Polish-like” prescrip-tion pattern, or D (e.g., glycopeptides), as in the “French-like” or the “U.S.-like” antibiotic prescription patterns,tended to promote dissemination of community strains inhospitals.

On the other hand, high use of antibiotics from group D, asin the “Finland-like” antibiotic prescription pattern, preventedmajor CA-MRSA diffusion.

FIG. 1. Range of predicted endemic prevalence of CA-MRSAamong carried staphylococcal strains under 252 hypothetical differentantibiotic prescription patterns. Each box-plot displays the minimum,first quartile, median, third quartile and the maximum predicted prev-alence in the ward.

FIG. 2. Predicted endemic CA-MRSA prevalence among carried staphylococcal strains in the ICU setting (depicted on a color scale, fromwhite [low prevalence] to red [high prevalence]), depending on relative exposure to antibiotics from groups A, B, and C (a) and B, C, and D (b).(a) Exposure to group A was fixed at 10%, 30%, 50%, and 80%, respectively; exposure to groups B and C varied from 5% to 80% of the overallantibiotic consumption. (b) Exposure to group D was fixed at 10%, 30%, 50%, and 80%, respectively, exposure to groups B and C vary from 5%to 80% of the overall antibiotic consumption.



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Sensitivity analyses. Figure 4 depicts a tornado diagram ofthe effects of transmission and colonization parameter valueson the predicted prevalence of CA-MRSA in the “French-like”scenario as well as on the variance of predicted prevalencesamong the 252 investigated scenarios. The predicted preva-lence decreased, as well as its variance, when a lower trans-missibility of CA-MRSA, lower initial MSSA prevalence, orhigher initial HA-MRSA prevalence was assumed. They in-creased when a higher initial MSSA prevalence or lower initialHA-MRSA prevalence was assumed.

Figure 5 locates the six antibiotic prescription patterns mostsimilar to observed patterns in the United States, Denmark,Finland, France, Poland, and Greece among the range of ourpredictions for both the GW and the ICU settings, assumingthat CA-MRSA has become resistant to fluoroquinolones. Ourresults show that in countries with high levels of fluoroquin-olone use, such as the United States, France, or Greece, suchan evolution in CA-MRSA susceptibility could have a majorimpact on CA-MRSA selection in hospitals, in particular in theICU, where the antibiotic pressure is higher (see Fig. S5 in thesupplemental material).

DISCUSSION

In this study, we examined the impact of antibiotic prescrip-tion patterns on the spread of CA-MRSA and other staphylo-coccal strains among hospitalized patients, using an agent-based model.

We showed that, even at a constant antibiotic consumptionlevel, the selection of prescribed antibiotic classes may have amajor impact on the dynamics of nosocomial spread of micro-organisms. This implies that investigating antibiotic usagestrategies may help provide tools for control. It may also help

explain differences in the hospital epidemiology of CA-MRSAacross different countries.

CA-MRSA epidemiology in the general community. Outpa-tients may play a major role in the community-to-hospitalspread of CA-MRSA. In this study, we assumed an incomingflow of CA-MRSA-colonized patients into health care settingsbut did not include any country-specific observed data on thecommunity prevalence of CA-MRSA.

This means that the application of our results using datafrom 6 countries cannot and should not be interpreted asactual predictions for these countries. Rather, we use country-specific data in order to illustrate the variability among currentpatterns of hospital antibiotic use worldwide and to provideactual examples on how such levels may influence CA-MRSAdissemination in health care settings.

This is underlined by the fact that the “French-like” and the“U.S.-like” prescription patterns have similar impacts on CA-MRSA dissemination in the ICU (Fig. 3), while observed datashow that CA-MRSA is currently more frequent in Americanhospitals than in French hospitals (7, 27).

Model hypotheses. In this study, we used an agent-basedmodel for modeling the spread of S. aureus in a hospitalsetting. This approach allowed for increased realism in thedescription of individual behavior in a small hospital envi-ronment. The simulations demonstrated that invasion ofCA-MRSA in hospital wards could be facilitated depending ontypical antibiotic use. However, our analysis disregards severalpoints which may also be at play in the epidemiology of CA-MRSA, such as community dynamics, strain-specific character-istics, differences in health care system organization, etc.

Despite the applicability of agent-based models for model-ing complex biological phenomena, this approach presents sev-eral limitations. Agent-based modeling requires detailed andreliable data for model building or validation, which is notalways easily available. What is more, the increase in behav-ioral detail provided by agent-based models leads to muchgreater computational intensity and makes carrying out exten-sive sensitivity analyses difficult.

Here, we developed a virtual hospital ward where all S.aureus transmissions between patients occurred via direct con-tacts with HCWs. In the absence of detailed information ontransmission of S. aureus in hospital wards, we assumed equalHCW-to-patient and patient-to-HCW transmissibility rates,and we ignored direct HCW-to-HCW transmissions as well asenvironmental contamination.

Simulations were performed for two different types of hos-pital wards: a GW and an ICU, where overall antibiotic expo-sure was higher and patient-HCW contacts more frequent.Intensive care units have been noted to play an important rolein the selection and spread of antibiotic-resistant bacteriawithin hospitals. Selective pressure of antimicrobials and pres-ence of patients with severe illness combined in a relativelysmall and crowded area promote MRSA spread within ICUs,increasing the risk of MRSA infections (21).

Our results suggest that the chosen antibiotic prescriptionstrategy may have a larger impact on CA-MRSA selection inthe ICU than in GWs. However, this conclusion is important atthe hospital level, as ICU patients are frequently transferredbetween hospitals and wards, thereby increasing the risk forintra- and interhospital dissemination of resistant strains.

FIG. 3. Application of French-like, Greek-like, Danish-like, Finn-ish-like, Polish-like, and U.S.-like patterns of antibiotic use in hospitalsto our predictions in both GW and ICU settings. Levels of antibioticuse were based on a classification of detailed consumption data fromthe ESAC (9), C-CLIN study (34) and from the NNIS system report(28) (Table 4). Other model parameters (e.g., prevalence) were notcountry specific.



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Finally, hospitals are often indicated as a source of emergentresistant strains, but this spread is not unidirectional, as illus-trated by outbreaks of community-acquired MRSA in healthcare facilities. In this study we ignored the community dynam-ics of S. aureus strains and assumed a fixed rate of colonizedpatients on hospital admission. In future studies, it would be

interesting to describe the spread of resistant pathogens fromthe community to hospital settings, as well as the interactionbetween transmission dynamics within a hospital and the sur-rounding community.

Antibiotic efficacy. The efficacy of systemic antibiotics foreliminating carriage of S. aureus strains has been demonstrated

FIG. 4. Tornado diagram of the effects of model parameters on predicted endemic CA-MRSA prevalence among carried staphylococcal strainsin the ICU in the French-like scenario (a) and variance of this predicted prevalence among the 252 investigated scenarios (b). Blue bars areprojections associated with the lower parameter values; red bars show projections associated with the higher parameter values.



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in several trials, but the antibiotics were often used in combi-nation with topical agents (23). In our study, we assumed thatantibiotic exposure of a colonized patient led to completeclearance of carriage for sensitive strains but had no impact onresistant strains.

In order to investigate the impact of this hypothesis, weperformed simulations assuming that 10 to 50% of coloniza-tions with sensitive strains persisted following antibiotic expo-sure. The distribution of predicted CA-MRSA prevalence (seeFig. S4 in the supplemental material) was not significantlydifferent from our baseline predictions (Wilcoxon test, P � 0.2to 0.28).

Data on antibiotic use. In order to apply our predictions, wedetermined which of the 252 investigated antibiotic prescrip-tion patterns best reflected observed levels in antimicrobial usein hospitals from the United States and several Europeancountries, in terms of the distribution of prescribed antibioticclasses. However, recent studies showed wide variations inantimicrobial use in hospital care, both between countries andat the national level (38). This means that the country-levelantibiotic use patterns we used are only a crude description ofactual levels in the countries we considered. This is particularlytrue for large countries, such as the United States, as evidencedby significant differences in reported hospital antibiotic usebetween different American studies (28, 30).

Furthermore, although we performed simulations in both aGW and an ICU setting, we were not always able to use ward-type-specific antibiotic use data, because for several countries(Denmark, Finland, Poland, and Greece) we only had access toaverage hospital antibiotic use patterns.

Finally, the data obtained from the United States was in-complete. The report from the NNIS did not cover use oftetracyclines, macrolides, or aminoglycosides (28). Whetherthis was due to low overall consumption of these agents or toan oversight remains unclear.

Transmission rates. To calibrate transmission rates, we as-sumed that MRSA strains represented 33% of all S. aureusisolates recovered in hospitals, which reflects the mean MRSArate in European hospitals and is close to the French situationin the early 2000s (11).

Applying our model to countries where infection controlprograms are different and where the proportion of MRSAamong all S. aureus isolates carried in hospitals is either muchlarger (e.g., Greece) or much smaller (e.g., Finland) than the33% may lead to errors in the estimation of transmission rates.This in turn may lead to underestimated or overestimatedMRSA prevalence rates in these countries.

However, while this means that our predictions cannot beused to predict accurately CA-MRSA carriage prevalence in agiven country, we feel that it does not impede our capacity toassess qualitatively the impact of antibiotic prescription pat-terns in these countries on the spread of CA-MRSA in hospi-tals.

Epidemiological data suggest that CA-MRSA, like MSSA, ismore transmissible than HA-MRSA (8). For this study, weassumed the transmissibility of CA-MRSA to be as high as thatof MSSA, which is a worst-case scenario for CA-MRSA inva-sion in hospitals. In order to assess the impact of this hypoth-esis, we also performed simulations assuming that CA-MRSAtransmissibility was only equal to that of HA-MRSA. Our mainconclusions still held.

Future evolution of CA-MRSA. Fluoroquinolones areamong the most commonly prescribed classes of antibiotics inthe hospital as well as in the community in some countries (38).Several studies suggest that fluoroquinolone exposure maypredispose patients to infections with or carriage of HA-MRSA, eradicating most susceptible strains. What is more,recent data suggest that CA-MRSA strains may evolve to be-come fluoroquinolone resistant. We performed a sensitivityanalysis to assess the potential consequences of this phenom-enon. Our results suggest that such an evolution in CA-MRSAsusceptibility may have a major impact on its selection withinhospitals of some countries, including France and the UnitedStates (Fig. 5).

Conclusions. Strains of community-acquired MRSA havebeen introduced into hospital settings worldwide and havebecome the most frequent cause of skin and soft tissue infec-tions in the emergency departments in some areas of theUnited States (26). Although routine surveillance and isolationprocedures have proved successful in controlling HA-MRSAin hospitals in some countries, they cannot be efficient in com-munity settings. For this reason, the presence of a communityreservoir from which resistant strains can repeatedly be intro-duced into health care settings to potentially cause secondaryoutbreaks is a growing challenge, and more research is neededto better define optimal control measures. Based on this study,these control measures may include selected antibiotic pat-terns of use and strategies that could minimize the risk ofdissemination within the hospital.

ACKNOWLEDGMENTS

This research program is supported by the European Commission(MOSAR network contract LSHP-CT-2007-037941), by the FrenchAgency for Environmental and Occupational Health Safety (Afsset;SELERA contract ES-2005-028), and by the National Centre for Sci-

FIG. 5. Effects of the acquisition of resistance to fluoroquinolonesin CA-MRSA on their spread in hospitals where antibiotic use patternsare similar to those observed in France, Greece, Denmark, Finland,Poland, and the United States in both GW and ICU settings.



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entific Research, the National Institute for Health and Medical Re-search (INSERM), and the National Institute for Research in Com-puter Science and Control (AREMIS contract SUB-2005-0113-DR16).

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