# The health and economic burden of bloodstream infections caused by antimicrobial-susceptible and non-susceptible Enterobacteriaceae and Staphylococcus aureus in European hospitals, 2010 and 2011: a multicentre retrospective cohort study.

A.J. Stewardson, Arthur Allignol, Jan Beyersmann, Nicholas Graves, Martin Schumacher, Rodolphe Meyer, Evelina Tacconelli, Giulia de Angelis, Claudio Farina, Fabio Pezzoli, et al.

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## To cite this version:

A.J. Stewardson, Arthur Allignol, Jan Beyersmann, Nicholas Graves, Martin Schumacher, et al. The health and economic burden of bloodstream infections caused by antimicrobial-susceptible and non-susceptible Enterobacteriaceae and Staphylococcus aureus in European hospitals, 2010 and 2011: a multicentre retrospective cohort study.. Eurosurveillance, 2016, 21 (33), 10.2807/1560-7917.ES.2016.21.33.30319. [hal-01506821](https://hal.science/hal-01506821)

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# The health and economic burden of bloodstream infections caused by antimicrobial-susceptible and non-susceptible Enterobacteriaceae and *Staphylococcus aureus* in European hospitals, 2010 and 2011: a multicentre retrospective cohort study

AJ Stewardson<sup>1,2</sup>, A Allignol<sup>3,4</sup>, J Beyersmann<sup>3</sup>, N Graves<sup>5</sup>, M Schumacher<sup>4</sup>, R Meyer<sup>6</sup>, E Tacconelli<sup>7,8</sup>, G De Angelis<sup>7</sup>, C Farina<sup>9</sup>, F Pezzoli<sup>9</sup>, X Bertrand<sup>10</sup>, H Gbaguidi-Haore<sup>10</sup>, J Edgeworth<sup>11</sup>, O Tosas<sup>11</sup>, JA Martinez<sup>12</sup>, MP Ayala-Blanco<sup>12</sup>, A Pan<sup>13</sup>, A Zoncada<sup>13</sup>, CA Marwick<sup>14</sup>, D Nathwani<sup>14</sup>, H Seifert<sup>15,16</sup>, N Hos<sup>15</sup>, S Hagel<sup>17</sup>, M Pletz<sup>17</sup>, S Harbarth<sup>1</sup>, the TIMBER Study Group<sup>18</sup>

1. Infection Control Program, University of Geneva Hospitals and Faculty of Medicine, Geneva, Switzerland

2. Department of Medicine, University of Melbourne, Melbourne, Australia

3. Institute of Statistics, Ulm University, Ulm, Germany

4. Institute of Medical Biometry and Medical Informatics, University Medical Center Freiburg, Freiburg, Germany

5. Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia

6. Information Technology, University of Geneva Hospitals and Faculty of Medicine, Geneva, Switzerland

7. Division of Infectious Diseases, Agostino Gemelli Hospital, Rome, Italy

8. Division of Infectious Diseases, DZIF TTU-HAARBI, University Hospital Tübingen, Tübingen, Germany

9. Papa Giovanni XXIII Hospital, Bergamo, Italy

10. Centre hospitalier régional et universitaire (CHRU) Besançon, Besançon, France

11. Department of Infectious Diseases, Kings College London, London, United Kingdom

12. Hospital Clinic de Barcelona, Barcelona, Spain

13. Istituti Ospitalieri di Cremona, Cremona, Italy

14. Department of Infection and Immunodeficiency, Ninewells Hospital and Medical School, Dundee, United Kingdom

15. Uniklinik Köln, Cologne, Germany

16. German Centre for Infection Research (DZIF), Braunschweig, Germany

17. Center for Infectious Diseases and Infection Control, University Hospital Jena, Jena, Germany

18. The members of the group are listed at the end of the article

Correspondence: Andrew J. Stewardson (andrew.stew@rdson.net)

**Citation style for this article:**

Stewardson AJ, Allignol A, Beyersmann J, Graves N, Schumacher M, Meyer R, Tacconelli E, De Angelis G, Farina C, Pezzoli F, Bertrand X, Gbaguidi-Haore H, Edgeworth J, Tosas O, Martinez JA, Ayala-Blanco MP, Pan A, Zoncada A, Marwick CA, Nathwani D, Seifert H, Hos N, Hagel S, Pletz M, Harbarth S, the TIMBER Study Group. The health and economic burden of bloodstream infections caused by antimicrobial-susceptible and non-susceptible Enterobacteriaceae and Staphylococcus aureus in European hospitals, 2010 and 2011: a multicentre retrospective cohort study. Euro Surveill. 2016;21(33):pii=30319. DOI: http://dx.doi.org/10.2807/1560-7917.ES.2016.21.33.30319

Article submitted on 23 September 2015 / accepted on 20 April 2016 / published on 18 August 2016

We performed a multicentre retrospective cohort study including 606,649 acute inpatient episodes at 10 European hospitals in 2010 and 2011 to estimate the impact of antimicrobial resistance on hospital mortality, excess length of stay (LOS) and cost. Bloodstream infections (BSI) caused by third-generation cephalosporin-resistant Enterobacteriaceae (3GCRE), meticillin-susceptible (MSSA) and -resistant *Staphylococcus aureus* (MRSA) increased the daily risk of hospital death (adjusted hazard ratio (HR) = 1.80; 95% confidence interval (CI): 1.34–2.42, HR = 1.81; 95% CI: 1.49–2.20 and HR = 2.42; 95% CI: 1.66–3.51, respectively) and prolonged LOS (9.3 days; 95% CI: 9.2–9.4, 11.5 days; 95% CI: 11.5–11.6 and 13.3 days; 95% CI: 13.2–13.4, respectively). BSI with third-generation cephalosporin-susceptible Enterobacteriaceae (3GCSE) significantly increased LOS (5.9 days; 95% CI: 5.8–5.9) but not hazard of death (1.16; 95% CI: 0.98–1.36). 3GCRE significantly increased the hazard of death (1.63; 95%

CI: 1.13–2.35), excess LOS (4.9 days; 95% CI: 1.1–8.7) and cost compared with susceptible strains, whereas meticillin resistance did not. The annual cost of 3GCRE BSI was higher than of MRSA BSI. While BSI with *S. aureus* had greater impact on mortality, excess LOS and cost than Enterobacteriaceae per infection, the impact of antimicrobial resistance was greater for Enterobacteriaceae.

## Introduction

Antimicrobial resistance (AMR) represents a significant global threat [1,2]. Response to this threat requires coordinated international interventions likely to involve commitment of substantial resources [3]. It is useful to obtain accurate estimates of the health and economic burden of AMR as these illustrate opportunities to improve health and reduce costs. Comprehensive data remain scarce; a recent World Health Organization (WHO) systematic review identified a "lack of properly
designed and conducted economic studies comparing
the resource use associated with resistant versus non-
resistant pathogens" [1].

Studies to determine health outcomes of infections
with community and hospital onset must adequately
account for confounding, the timing of infection (time
dependency) and simultaneous impact on risk of death
and discharge (competing risks), but also analyse a
sample of sufficient size to produce precise estimates
[4,5]. Furthermore, although the major determinant
of the economic burden of such infections from the
hospital perspective is the number of bed-days they
consume, it is challenging to produce an appropriate
economic valuation of each marginal bed-day [6].

Given the widespread dissemination of meticillin
resistance among *Staphylococcus aureus* and resist-
ance to third-generation cephalosporins among
Enterobacteriaceae [7], we focused on these bacteria
and resistance phenotypes. We examined bloodstream
infections (BSI) because of their relatively high inci-
dence, clinical impact and diagnostic certainty. We
were interested in costs from the hospital perspective
because this is the perspective from which decisions
must be made to allocate resources to interven-
tions such as antimicrobial stewardship and infection
control.

Objectives

We sought to apply state-of-the-art methods to obtain
unbiased and adjusted estimates of the excess length
of stay (LOS), hospital mortality, and cost (from the
hospital perspective) attributable to BSI caused by S.
aureus and Enterobacteriaceae in European hospitals,
and to compare the impact of antimicrobial non-sus-
ceptible versus susceptible strains.

Methods

Study design

We performed a multicentre, retrospective cohort study.
The cohort consisted of all acute-care admissions at 10
European hospitals from 1 January 2010 to 31 December
2011. BSI were the time-varying exposure of interest
and their impact on hospital mortality, LOS and cost
was evaluated. Independent analyses were performed
for BSI due to S. aureus and Enterobacteriaceae.

This report was formulated in accordance with the
STROBE Statement [8].

Setting

A convenience sample of 10 European hospitals partici-
pated: three from Italy, two each from Germany and the
United Kingdom, and one each from France, Spain and
Switzerland. These participants were selected from a
list of interested sites using a questionnaire address-
ing microbiological methods and clinical informatics.
Hospitals were eligible if able to extract the required

data from institutional databases. All eligible hospitals
were included.

Participants

We retrospectively identified all inpatient acute-care
episodes lasting more than one calendar day that
started during the study period. We excluded ambu-
latory, hospital-in-the-home and non-acute care epi-
sodes as well as emergency consultations without
consequent hospital admission. There was no age
limit. For patients with multiple admissions during the
study period, only the first admission was included.

Exposures

We considered four exposures defined by causative
bacteria and antimicrobial susceptibility. *Escherichia
coli*, *Klebsiella* spp. or *Proteus* spp. strains causing
BSI were classified as third-generation cephalosporin-
susceptible Enterobacteriaceae (3GCSE) or third-
generation cephalosporin-non-susceptible (3GCRE).
Non-susceptibility to third-generation cephalosporins
was defined as intermediate susceptibility or resist-
ance to ceftazidime and/or one of cefotaxime, ceftriax-
one or cefpodoxime. *S. aureus* strains causing BSI were
classified as meticillin-susceptible (MSSA) or meticil-
lin-resistant (MRSA). BSI was defined by one or more
blood cultures with growth of the relevant bacteria.

Outcomes

The two primary outcomes were hospital mortality and
excess LOS in hospital. Excess LOS was used to esti-
mate costs from the hospital perspective.

Covariates

Baseline variables considered as potential confounders
were age, sex, location prior to episode, elective/emer-
gent admission, nights hospitalised in the previous 12
months in the same institution and 17 comorbidities
[9]. The Charlson Comorbidity Index was computed for
descriptive purposes, but comorbidities were included
in the analyses as individual covariates. Two time-var-
ying covariates were considered while patients were at
risk for BSI: admission to an intensive care unit (ICU)
and surgical procedure. To estimate the total impact of
infection and avoid controlling for intermediates on the
causal pathway, we did not adjust for events occurring
after BSI onset, such as antibiotic exposure.

BSI were categorised as hospital-onset if detected
after the first three inpatient calendar days [10], if
the patient was transferred from a non-acute ward
or another hospital, or if the patient was born during
the current admission. All others were categorised as
community-onset.

Data collection

One investigator from each site was trained in stand-
ardised data collection. Information technicians from
each participating hospital extracted data from the
hospital databases. Comorbidities were extracted
using a validated algorithm based on ICD-9-CM and
FIGURE 1

Multistate model adopted for the analysis of the burden of
bloodstream infections caused by antimicrobial resistance,
2010–2011

<figure><img src="image_3.png" /></figure>

Patients entered the initial state on admission to acute care unless the infection date was before or equal to the admission date, in which case the patient was assigned directly to the appropriate intermediate infected state. Patients exited by entering one of two competing absorbing states (death or discharge alive), with or without passing through one of two intermediate states (bloodstream infection caused by susceptible or non-susceptible pathogens). *Escherichia coli*, *Klebsiella* spp. or *Proteus* spp. were classified as susceptible or non-susceptible to third-generation cephalosporins. *Staphylococcus aureus* was classified as susceptible or non-susceptible to meticillin.

ICD-10 codes [11]. Each dataset was reviewed for inter-
nal consistency and external plausibility by the cen-
tral coordinating team, with potential errors triggering
review by the local investigators.

Microbiological methods

Antimicrobial susceptibility testing was performed as per routine laboratory methods at each hospital. All laboratories participated in national or international quality assurance programmes and adhered to contemporary guidelines from the following bodies: Clinical and Laboratory Standards Institute (CLSI) for seven sites), European Committee on Antimicrobial Susceptibility Testing (EUCAST) for three sites, Antibiogram Committee of the French Microbiology Society (CA-SFM) for one site, British Society for Antimicrobial Chemotherapy (BSAC) for one site, and Deutsche Industrie Norm (DIN)-Medizinische Mikrobiologie for one site. Three sites used more than one guideline during the study period. Nine sites performed one or more MRSA confirmatory tests: oxacillin minimum inhibitory concentration (MIC) test (n = 6), mecA PCR (n = 4), and penicillin binding protein 2a (PBP2a) agglutination (n = 4). The site that did not perform these tests used disc diffusion (BSAC protocol) and the VITEK2 system with the AST-P578 panel (bioMérieux, Lyon, France). Confirmatory testing for extended-spectrum beta-lactamase (ESBL) production was performed by seven sites but not included in our definition of third-generation cephalosporin susceptibility.

Sample size

The sample size calculation was based on the esti-
mated excess LOS for ESBL-positive BSI, informed by
estimates from a pilot study [12]. We wished to find the
number of infections such that, with a power of 80%
and a equal to 5%, we could conclude that excess LOS
was greater than excess LOS/2, an estimate of preci-
sion, i.e. to have sufficient power to detect a lower
confidence limit of at least half of the point estimate.
On the basis of incidence data from participating hos-
pitals, we expected to include approximately 1,250
patients with BSI caused by 3GCRE, allowing estimates
with good precision for an excess LOS of four days or
more.

Statistical analysis

Descriptive statistics

Continuous variables are summarised as median with 25%-75% percentile, ordinal variables as count with percentage. BSI incidence density was computed by dividing the number of events by the number of patient-days at risk.

Estimation of mortality and excess length of stay

Two important characteristics of this dataset were
the inclusion of time-varying exposures (BSI, surgery
and ICU admission) and competing risks (death and
discharge alive). We adopted the multistate model
illustrated in Figure 1 to explicitly account for these
characteristics [4]. Patients entered the initial state
on admission to acute care and exited by entering one
of two competing absorbing states (hospital death or
discharge alive), with or without passing through one
of two intermediate states (susceptible or non-suscep-
tible BSI). Admissions were artificially right-censored
at day 45 to reduce the influence of outliers. We rea-
soned that patients with such prolonged admissions
were likely to remain hospitalised for other reasons not
influenced by BSI.

Cox proportional hazards models were used to compare the daily risk (hazard) of reaching the endpoint, i.e. hospital death, discharge alive, and the combined end-of-stay endpoint (hospital death or discharge alive), between the three groups of patients (non-susceptible BSI, susceptible BSI and uninfected subjects). We fitted three models for each possible pairwise comparison between these three groups. Model 1 only included infection status as time-dependent variable. Model 2 adjusted for age, sex, emergent/elective admission, nights hospitalised in the previous 12 months and comorbidities. Age was centred at the cohort mean and divided by 10. Each comorbidity was included as a separate indicator variable. Model 3 additionally adjusted for two time-dependent variables while patients were at risk for BSI: ICU admission and surgical procedure. All Cox models were stratified by hospital to account for the multicentre nature of this study by allowing for clustering effects and site-specific heterogeneity in baseline hazards. The proportional
FIGURE 2

Results of multistate models to determine excess length of stay attributable to bloodstream infection caused by different combinations of bacteria and susceptibility, 10 European hospitals, 2010–2011 (n = 606,649)

<figure><img src="image_4.png" /></figure>

LOS: length of stay; MRSA/MSSA: meticillin-resistant/susceptible *Staphylococcus aureus*; 3GCRE/3GCSE: third-generation cephalosporin-resistant/susceptible Enterobacteriaceae.

The upper half in each panel (A-D) illustrates the relationship between the expected change in LOS associated with a BSI (computed daily by subtracting the LOS of patients that had not experienced BSI on that day from those who had) and timing of BSI onset (in days from admission). The lower half of each panel presents the weights used to compute the summary excess LOS, calculated using the observed relative frequency BSI onset each day. For all types of infections, early BSI was associated with the greatest difference in LOS.

hazards assumption was checked by inspection of the Schoenfeld residuals. No major deviations were found.

Multistate models describe the *instantaneous* (in this case, daily) risk of transition between health states. The excess LOS associated with an infection was derived as a function of these transition probabilities [4]. We used the Aalen-Johansen estimators as a nonparametric estimator for the matrix of transition probabilities for all observed transition times [13]. The expected LOS (in days) was then computed by a function of the Aalen-Johansen estimator for the matrix of transition probabilities [4]. The expected change in LOS for each of the four BSI phenotypes was computed for each day of admission as the difference between the estimated

LOS, given that BSI (the intermediate state) had or had not occurred up to that day. The overall change in LOS was computed as a weighted average of these quantities, with weighting determined by the observed distribution of time to BSI onset. The expected difference in LOS between susceptible and resistant infections was produced similarly, by computing for each day the difference between the estimated LOS, given that the susceptible or resistant BSI had occurred up to that day, then computing a weighted average of these quantities determined by the observed distribution of day of BSI onset. Standard errors and confidence intervals were derived by bootstrap re-sampling runs.
TABLE 1

<table><caption>Characteristics of participating hospitals, analysis of the burden of bloodstream infections caused by antimicrobial resistance, 2010–2011 (n = 10)</caption><thead><tr><th rowspan="2">Hospital</th><th rowspan="2">Country</th><th rowspan="2">Type</th><th rowspan="2">Acute beds</th><th colspan="2">Study cohort</th></tr><tr><th>Admissions</th><th>Bed-days</th></tr></thead><tbody><tr><td>1</td><td>France</td><td>Tertiary</td><td>1,200</td><td>64,071</td><td>424,361</td></tr><tr><td>2</td><td>Germany</td><td>Tertiary</td><td>1,200</td><td>67,094</td><td>456,564</td></tr><tr><td>3</td><td>Germany</td><td>Tertiary</td><td>1,332</td><td>59,517</td><td>431,384</td></tr><tr><td>4</td><td>Italy</td><td>Tertiary</td><td>1,100</td><td>89,401</td><td>677,788</td></tr><tr><td>5</td><td>Italy</td><td>Tertiary</td><td>1,050</td><td>53,947</td><td>373,665</td></tr><tr><td>6</td><td>Italy</td><td>Secondary</td><td>555</td><td>27,975</td><td>183,707</td></tr><tr><td>7</td><td>Switzerland</td><td>Tertiary</td><td>900</td><td>58,541</td><td>382,012</td></tr><tr><td>8</td><td>Spain</td><td>Tertiary</td><td>711</td><td>50,065</td><td>336,253</td></tr><tr><td>9</td><td>United Kingdom</td><td>Tertiary</td><td>1,050</td><td>92,569</td><td>423,534</td></tr><tr><td>10</td><td>United Kingdom</td><td>Tertiary</td><td>936</td><td>43,469</td><td>265,392</td></tr></tbody><tfoot><tr><td colspan="2">Total</td><td></td><td>10,034</td><td>606,649</td><td>3,954,660</td></tr></tfoot></table>

Note: Admissions and bed-days relate to acute-care episodes from 2010 and 2011 included in the analysis. For patients with multiple admissions during the study period, only the first admission was included.

We adjusted excess LOS for the baseline covariates included in Model 2 using pseudo-observations [14]. Excess LOS was estimated for all possible subsets of the entire cohort created by removing a single patient. In each case, the excess LOS estimate was compared to the estimate derived from the full cohort; this difference or pseudo-observation contained information on the way in which patient-level covariates affected the LOS estimate. The pseudo-observations were then included in a generalised linear model with identity link and independent working covariance matrix to model the effect of covariates on the excess LOS. In practice, the regression coefficients were estimated using the generalized estimating equations approach with robust variance estimator to account for hospital-level clustering [15]. Time-dependent covariates (Model 3) were not included because this would have been difficult to implement and interpret. To reduce the influence of outliers, the original pseudo-observations were transformed using the cubic root function, similar to the common log transformation of LOS data but allowing for negative excess LOS.

## Cost estimation

For each combination of bacterium and susceptibility, we computed the attributable cost of a single BSI from the hospital perspective as the product of excess LOS and the value of a bed-day [6]. We performed a Monte Carlo simulation with 10,000 samples to account for parameter uncertainty [16]. We used gamma probability distributions to represent the excess LOS associated with each BSI, fitting these distributions to the unadjusted estimate from the current study (Model 1) to best reflect our patient mix. We used log-normal distributions for two contrasting bed-day values, both obtained from the study hospitals as previously reported: an economic estimate of the opportunity cost

of a bed-day obtained by contingent valuation and the accounting cost derived by dividing the total annual hospital budget by the number of bed-days supplied during the same period [17].

To estimate the annual hospital costs of each BSI, these marginal costs were multiplied by the expected number of BSI cases per year, as estimated for a hospital with 450,000 bed-days using incidence densities calculated in the analysis here below. Results are presented as median with 95% credible interval, to two significant figures. A full description of data sources and probability distributions can be obtained from the corresponding author.

The cost estimation was implemented in OpenBUGS, version 3.2.3. Other statistical analyses were performed using R, version 3.1.0 (R Foundation for Statistical Computing) including the etm, mvna, and survival packages [18].

## Ethics statement

This study was approved, with a waiver for individual informed consent, by the human research ethics committee at each institution.

## Results

### Participants

Ten public hospitals provided a cohort of 867,977 acute-care episodes involving 606,649 patients (Table 1). Each patient's first episode was included in the analysis.

Median patient age at admission was 49 years (interquartile range (IQR): 28–69); 53% were female. Median LOS was four days (IQR: 2–7), and 588,118 (97%)
TABLE 2A

<table><caption>Characteristics of patients in *Staphylococcus aureus* and Enterobacteriaceae analyses, stratified by exposure to bloodstream infection in 10 European hospitals, 2010–2011 (n = 606,649<sup>a</sup>)</caption><thead><tr><th rowspan="3">Characteristic</th><th colspan="6">Staphylococcus aureus analysis</th><th colspan="6">Enterobacteriaceae analysis</th></tr><tr><th colspan="2">MRSA BSI</th><th colspan="2">MSSA BSI</th><th colspan="2">Non-infected</th><th colspan="2">3GCRE BSI</th><th colspan="2">3GCSE BSI</th><th colspan="2">Non-infected</th></tr><tr><th>n = 163</th><th>%</th><th>n = 885</th><th>%</th><th>n = 604,797</th><th>%</th><th>n = 360</th><th>%</th><th>n = 2,100</th><th>%</th><th>n = 603,972</th><th>%</th></tr></thead><tbody><tr><td colspan="14"><strong>Demographics</strong></td></tr><tr><td>Male sex</td><td>98</td><td>60.1</td><td>529</td><td>59.8</td><td>286,857</td><td>47.4</td><td>206</td><td>57.2</td><td>1,051</td><td>50.0</td><td>286,492</td><td>47.4</td></tr><tr><td>Median age at enrolment (IQR)</td><td>71<br>(59–81)</td><td></td><td>63<br>(45–76)</td><td></td><td>49<br>(28–69)</td><td></td><td>70<br>(58–78)</td><td></td><td>70<br>(56–80)</td><td></td><td>49<br>(27–69)</td><td></td></tr><tr><td colspan="14"><strong>Hospitalisation in the previous 12 months</strong></td></tr><tr><td>Two or more admissions</td><td>15</td><td>9.2</td><td>40</td><td>4.5</td><td>15,708</td><td>2.6</td><td>23</td><td>6.4</td><td>112</td><td>5.3</td><td>15,660</td><td>2.6</td></tr><tr><td>Two or more nights hospitalised</td><td>24</td><td>14.7</td><td>72</td><td>8.1</td><td>37,468</td><td>6.2</td><td>46</td><td>12.8</td><td>229</td><td>10.9</td><td>37,354</td><td>6.2</td></tr><tr><td colspan="14"><strong>Admission details</strong></td></tr><tr><td>Emergent admission</td><td>111</td><td>68.1</td><td>639</td><td>72.2</td><td>282,382</td><td>46.7</td><td>217</td><td>60.3</td><td>1,588</td><td>75.6</td><td>281,844</td><td>46.7</td></tr><tr><td colspan="14"><strong>Provenance</strong></td></tr><tr><td>Home</td><td>134</td><td>82.2</td><td>734</td><td>82.9</td><td>500,693</td><td>82.8</td><td>303</td><td>84.2</td><td>1,848</td><td>88.0</td><td>499,942</td><td>82.8</td></tr><tr><td>Transfer from other hospital</td><td>16</td><td>9.8</td><td>73</td><td>8.2</td><td>16,930</td><td>2.8</td><td>21</td><td>5.8</td><td>75</td><td>3.6</td><td>16,927</td><td>2.8</td></tr><tr><td>Transfer from non-acute ward</td><td>2</td><td>1.2</td><td>11</td><td>1.2</td><td>2,722</td><td>0.5</td><td>1</td><td>0.3</td><td>26</td><td>1.2</td><td>2,713</td><td>0.4</td></tr><tr><td>Born this episode</td><td>3</td><td>1.8</td><td>7</td><td>0.8</td><td>47,414</td><td>7.8</td><td>3</td><td>0.8</td><td>29</td><td>1.4</td><td>47,397</td><td>7.8</td></tr><tr><td colspan="14"><strong>Comorbidities</strong></td></tr><tr><td>Cardiovascular disease</td><td>8</td><td>4.9</td><td>50</td><td>5.6</td><td>15,192</td><td>2.5</td><td>10</td><td>2.8</td><td>97</td><td>4.6</td><td>15,160</td><td>2.5</td></tr><tr><td>Congestive heart failure</td><td>29</td><td>17.8</td><td>109</td><td>12.3</td><td>22,935</td><td>3.8</td><td>32</td><td>8.9</td><td>195</td><td>9.3</td><td>22,886</td><td>3.8</td></tr><tr><td>Peripheral vascular disease</td><td>17</td><td>10.4</td><td>65</td><td>7.3</td><td>16,515</td><td>2.7</td><td>9</td><td>2.5</td><td>89</td><td>4.2</td><td>16,509</td><td>2.7</td></tr><tr><td>Cerebrovascular disease</td><td>14</td><td>8.6</td><td>77</td><td>8.7</td><td>22,908</td><td>3.8</td><td>27</td><td>7.5</td><td>165</td><td>7.9</td><td>22,836</td><td>3.8</td></tr><tr><td>Dementia</td><td>9</td><td>5.5</td><td>14</td><td>1.6</td><td>5,759</td><td>1.0</td><td>7</td><td>1.9</td><td>64</td><td>3.0</td><td>5,743</td><td>1.0</td></tr><tr><td>COPD</td><td>7</td><td>4.3</td><td>40</td><td>4.5</td><td>25,701</td><td>4.2</td><td>16</td><td>4.4</td><td>112</td><td>5.3</td><td>25,647</td><td>4.2</td></tr><tr><td>Connective tissue disease</td><td>2</td><td>1.2</td><td>19</td><td>2.1</td><td>5,123</td><td>0.8</td><td>2</td><td>0.6</td><td>31</td><td>1.5</td><td>5,124</td><td>0.8</td></tr><tr><td>Peptic ulcer disease</td><td>1</td><td>0.6</td><td>15</td><td>1.7</td><td>2,527</td><td>0.4</td><td>7</td><td>1.9</td><td>27</td><td>1.3</td><td>2,513</td><td>0.4</td></tr><tr><td>Mild liver disease</td><td>9</td><td>5.5</td><td>76</td><td>8.6</td><td>12,541</td><td>2.1</td><td>31</td><td>8.6</td><td>150</td><td>7.1</td><td>12,478</td><td>2.1</td></tr><tr><td>Diabetes without end-organ damage</td><td>23</td><td>14.1</td><td>127</td><td>14.4</td><td>38,004</td><td>6.3</td><td>28</td><td>7.8</td><td>255</td><td>12.1</td><td>37,937</td><td>6.3</td></tr><tr><td>Diabetes with end-organ damage</td><td>7</td><td>4.3</td><td>37</td><td>4.2</td><td>7,901</td><td>1.3</td><td>7</td><td>1.9</td><td>60</td><td>2.9</td><td>7,886</td><td>1.3</td></tr><tr><td>Haemiplegia or paraplegia</td><td>6</td><td>3.7</td><td>44</td><td>5.0</td><td>7,374</td><td>1.2</td><td>12</td><td>3.3</td><td>55</td><td>2.6</td><td>7,363</td><td>1.2</td></tr><tr><td>Renal disease</td><td>26</td><td>16.0</td><td>110</td><td>12.4</td><td>21,309</td><td>3.5</td><td>30</td><td>8.3</td><td>233</td><td>11.1</td><td>21,231</td><td>3.5</td></tr><tr><td>Neoplasia</td><td>13</td><td>8.0</td><td>79</td><td>8.9</td><td>43,830</td><td>7.2</td><td>45</td><td>12.5</td><td>277</td><td>13.2</td><td>43,641</td><td>7.2</td></tr><tr><td>Metastatic cancer</td><td>2</td><td>1.2</td><td>35</td><td>4.0</td><td>16,393</td><td>2.7</td><td>24</td><td>6.7</td><td>119</td><td>5.7</td><td>16,302</td><td>2.7</td></tr><tr><td>Liver diseases</td><td>2</td><td>1.2</td><td>31</td><td>3.5</td><td>3,047</td><td>0.5</td><td>11</td><td>3.1</td><td>49</td><td>2.3</td><td>3,030</td><td>0.5</td></tr><tr><td>HIV</td><td>0</td><td>0.0</td><td>12</td><td>1.4</td><td>1,277</td><td>0.2</td><td>1</td><td>0.3</td><td>17</td><td>0.8</td><td>1,271</td><td>0.2</td></tr><tr><td>Age-adjusted Charlson comorbidity index, median (IQR)</td><td>4<br>(3–5)</td><td></td><td>3<br>(1–5)</td><td></td><td>1<br>(0–3)</td><td></td><td>4<br>(2–5)</td><td></td><td>4<br>(2–5)</td><td></td><td>1<br>(0–3)</td><td></td></tr><tr><td colspan="14"><strong>Primary diagnosis category</strong></td></tr><tr><td>Certain infectious and parasitic diseases</td><td>20</td><td>12.3</td><td>132</td><td>14.9</td><td>13,216</td><td>2.2</td><td>53</td><td>14.7</td><td>411</td><td>19.6</td><td>13,060</td><td>2.2</td></tr><tr><td>Neoplasms</td><td>10</td><td>6.1</td><td>61</td><td>6.9</td><td>56,345</td><td>9.3</td><td>41</td><td>11.4</td><td>225</td><td>10.7</td><td>56,165</td><td>9.3</td></tr><tr><td>Blood and blood-forming organs and certain disorders involving the immune mechanism</td><td>1</td><td>0.6</td><td>6</td><td>0.7</td><td>4,400</td><td>0.7</td><td>4</td><td>1.1</td><td>9</td><td>0.4</td><td>4,399</td><td>0.7</td></tr><tr><td>Endocrine, nutritional and metabolic diseases</td><td>7</td><td>4.3</td><td>16</td><td>1.8</td><td>14,320</td><td>2.4</td><td>4</td><td>1.1</td><td>23</td><td>1.1</td><td>14,320</td><td>2.4</td></tr><tr><td>Mental and behavioural disorders</td><td>1</td><td>0.6</td><td>5</td><td>0.6</td><td>6,270</td><td>1.0</td><td>7</td><td>1.9</td><td>17</td><td>0.8</td><td>6,255</td><td>1.0</td></tr><tr><td>Nervous system, eye and adnexa, ear and mastoid process</td><td>3</td><td>1.8</td><td>34</td><td>3.8</td><td>40,844</td><td>6.8</td><td>5</td><td>1.4</td><td>24</td><td>1.1</td><td>40,848</td><td>6.8</td></tr></tbody></table>

BSI: bloodstream infection; COPD: chronic obstructive pulmonary disease; HIV: human immunodeficiency virus; ICU: intensive care unit; IQR: interquartile range; MRSA/MSSA: meticillin-resistant/susceptible *Staphylococcus aureus*; NA: not applicable; 3GCRE/3GCSE: third-generation cephalosporin-resistant/susceptible Enterobacteriaceae.

<footnote>ª Patients experiencing BSI caused by Enterobacteriaceae were censored from the *S. aureus* analysis on the day of the Enterobacteriaceae BSI. Patients experiencing BSI caused by Enterobacteriaceae on the day of admission were therefore excluded from the *S. aureus* analysis. The inverse applies for the Enterobacteriaceae analysis.</footnote>
TABLE 2B

Characteristics of patients in *Staphylococcus aureus* and Enterobacteriaceae analyses, stratified by exposure to bloodstream infection in 10 European hospitals, 2010–2011 (n = 606,649ª)

<table><thead><tr><th rowspan="3">Characteristic</th><th colspan="4">Staphylococcus aureus analysis</th><th colspan="4">Enterobacteriaceae analysis</th></tr><tr><th colspan="2">MRSA BSI</th><th colspan="2">MSSA BSI</th><th colspan="2">Non-infected</th><th colspan="2">3GCRE BSI</th><th colspan="2">Non-infected</th></tr><tr><th>n = 163</th><th>%</th><th>n = 885</th><th>%</th><th>n = 604,797</th><th>%</th><th>n = 360</th><th>%</th><th>n = 2,100</th><th>%</th></tr></thead><tbody><tr><td>Circulatory system</td><td>35</td><td>21.5</td><td>201</td><td>22.7</td><td>80,361</td><td>13.3</td><td>53</td><td>14.7</td><td>221</td><td>10.5</td><td>80,324</td><td>13.3</td></tr><tr><td>Respiratory system</td><td>5</td><td>3.1</td><td>34</td><td>3.8</td><td>33,426</td><td>5.5</td><td>21</td><td>5.8</td><td>88</td><td>4.2</td><td>33,377</td><td>5.5</td></tr><tr><td>Digestive system</td><td>13</td><td>8.0</td><td>51</td><td>5.8</td><td>44,350</td><td>7.3</td><td>45</td><td>12.5</td><td>322</td><td>15.3</td><td>44,175</td><td>7.3</td></tr><tr><td>Skin and subcutaneous tissue</td><td>3</td><td>1.8</td><td>29</td><td>3.3</td><td>9,597</td><td>1.6</td><td>1</td><td>0.3</td><td>18</td><td>0.9</td><td>9,607</td><td>1.6</td></tr><tr><td>Musculoskeletal system and connective tissue</td><td>14</td><td>8.6</td><td>91</td><td>10.3</td><td>33,452</td><td>5.5</td><td>7</td><td>1.9</td><td>21</td><td>1.0</td><td>33,513</td><td>5.5</td></tr><tr><td>Genitourinary system</td><td>9</td><td>5.5</td><td>28</td><td>3.2</td><td>30,257</td><td>5.0</td><td>37</td><td>10.3</td><td>365</td><td>17.4</td><td>30,107</td><td>5.0</td></tr><tr><td>Pregnancy, childbirth and the puerperium</td><td>0</td><td>0.0</td><td>8</td><td>0.9</td><td>54,785</td><td>9.1</td><td>3</td><td>0.8</td><td>42</td><td>2.0</td><td>54,758</td><td>9.1</td></tr><tr><td>Certain conditions originating in the perinatal period</td><td>2</td><td>1.2</td><td>6</td><td>0.7</td><td>18,641</td><td>3.1</td><td>2</td><td>0.6</td><td>28</td><td>1.3</td><td>18,624</td><td>3.1</td></tr><tr><td>Congenital malformations, deformations and chromosomal abnormalities</td><td>2</td><td>1.2</td><td>7</td><td>0.8</td><td>12,357</td><td>2.0</td><td>1</td><td>0.3</td><td>16</td><td>0.8</td><td>12,350</td><td>2.0</td></tr><tr><td>Symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classified</td><td>3</td><td>1.8</td><td>33</td><td>3.7</td><td>23,985</td><td>4.0</td><td>9</td><td>2.5</td><td>80</td><td>3.8</td><td>23,963</td><td>4.0</td></tr><tr><td>Injury, poisoning and certain other consequences of external causes</td><td>34</td><td>20.9</td><td>127</td><td>14.4</td><td>79,622</td><td>13.2</td><td>63</td><td>17.5</td><td>169</td><td>8.0</td><td>79,565</td><td>13.2</td></tr><tr><td>External causes of morbidity and mortality</td><td>0</td><td>0.0</td><td>3</td><td>0.3</td><td>85</td><td>0.0</td><td>0</td><td>0.0</td><td>1</td><td>0.0</td><td>87</td><td>0.0</td></tr><tr><td>Factors influencing health status and contact with health services</td><td>1</td><td>0.6</td><td>2</td><td>0.2</td><td>47,551</td><td>7.9</td><td>4</td><td>1.1</td><td>15</td><td>0.7</td><td>47,536</td><td>7.9</td></tr><tr><td colspan="13">Epidemiological classification of BSI</td></tr><tr><td>Hospital onset</td><td>101</td><td>62.0</td><td>434</td><td>49.0</td><td>NA</td><td></td><td>214</td><td>59.4</td><td>780</td><td>37.1</td><td>NA</td><td></td></tr><tr><td>Community onset</td><td>62</td><td>38.0</td><td>451</td><td>51.0</td><td>NA</td><td></td><td>146</td><td>40.6</td><td>1,320</td><td>62.9</td><td>NA</td><td></td></tr><tr><td colspan="13">Interventions prior to BSI</td></tr><tr><td>Surgical procedure</td><td>39</td><td>23.9</td><td>243</td><td>27.5</td><td>246,485</td><td>40.8</td><td>112</td><td>31.1</td><td>462</td><td>22.0</td><td>246,180</td><td>40.8</td></tr><tr><td>ICU admission</td><td>52</td><td>31.9</td><td>258</td><td>29.2</td><td>43,307</td><td>7.2</td><td>112</td><td>31.1</td><td>434</td><td>20.7</td><td>43,068</td><td>7.1</td></tr></tbody></table>

BSI: bloodstream infection; COPD: chronic obstructive pulmonary disease; HIV: human immunodeficiency virus; ICU: intensive care unit; IQR: interquartile range; MRSA/MSSA: meticillin-resistant/susceptible *Staphylococcus aureus*; NA: not applicable; 3GCRE/3GCSE: third-generation cephalosporin-resistant/susceptible Enterobacteriaceae.

patients were discharged alive. Of the remaining cohort, 10,419 (1.7%) died and 8,112 (1.3%) remained in hospital at the end of the study period (and underwent administrative censoring). Baseline characteristics are presented in Table 2.

BSI incidence

Of the 1,048 admissions during which *S. aureus* BSI were detected, 885 (84%) and 163 (16%) were due to MSSA and MRSA, respectively. The incidence density of *S. aureus* BSI was 0.269 episodes per 1,000 patient-days at risk: 0.227 and 0.042 episodes per 1,000 patient-days at risk for MSSA and MRSA BSI, respectively.

Of the 2,460 admissions during which Enterobacteriaceae BSI were detected, 2,100 (85%) and 360 (15%) were due to 3GCSE and 3GCRE, respectively. The incidence density of BSI due to Enterobacteriaceae was 0.631 episodes per 1,000 patient-days at risk: 0.538 and 0.092 episodes per 1,000 patient-days at risk for 3GCSE and 3GCRE BSI, respectively.

Hospital mortality and discharge alive

In the *S. aureus* analysis, 149 (16.8%) and 36 (22.1%) patients with MSSA and MRSA BSI died in hospital, respectively, compared with 10,161 (1.7%) non-infected patients. In the Enterobacteriaceae analysis, 212 (10.1%) and 58 (16.1%) patients with 3GCSE and 3GCRE died in hospital, respectively, compared with 10,105 (1.7%) non-infected patients.

<footnote>ª Patients experiencing BSI caused by Enterobacteriaceae were censored from the *S. aureus* analysis on the day of the Enterobacteriaceae BSI. Patients experiencing BSI caused by Enterobacteriaceae on the day of admission were therefore excluded from the *S. aureus* analysis. The inverse applies for the Enterobacteriaceae analysis.</footnote>
TABLE 3

Results of proportional hazards models for hospital mortality and discharge alive, 10 European hospitals, 2010–2011 (n = 606,649)

<table><thead><tr><th rowspan="2">Comparison</th><th rowspan="2">Exposure</th><th rowspan="2">Population</th><th colspan="3">Mortality HR (95% CI)</th><th colspan="3">Discharge alive HR (95% CI)</th></tr><tr><th>Model 1</th><th>Model 2</th><th>Model 3</th><th>Model 1</th><th>Model 2</th><th>Model 3</th></tr></thead><tbody><tr><td>MSSA BSI vs non-infected</td><td>MSSA BSI</td><td>Hospitalised patients</td><td>2.58 (2.19–3.04)</td><td>2.41 (2.05–2.84)</td><td>1.81 (1.49–2.20)</td><td>0.34 (0.31–0.37)</td><td>0.38 (0.35–0.41)</td><td>0.54 (0.50–0.60)</td></tr><tr><td>MRSA BSI vs non-infected</td><td>MRSA BSI</td><td>Hospitalised patients</td><td>3.18 (2.29–4.42)</td><td>2.61 (1.88–3.63)</td><td>2.42 (1.66–3.51)</td><td>0.25 (0.20–0.32)</td><td>0.30 (0.24–0.38)</td><td>0.45 (0.36–0.58)</td></tr><tr><td>MRSA BSI vs MSSA BSI</td><td>Meticillin resistance</td><td>Patients with <i>S. aureus</i> BSI</td><td>1.19 (0.81–1.75)</td><td>1.20 (0.82–1.76)</td><td>1.26 (0.82–1.94)</td><td>0.74 (0.58–0.94)</td><td>0.73 (0.57, 0.94)</td><td>0.80 (0.61, 1.05)</td></tr><tr><td>3GCSE BSI vs non-infected</td><td>3GCSE BSI</td><td>Hospitalised patients</td><td>2.25 (1.96–2.58)</td><td>1.74 (1.51–1.99)</td><td>1.16 (0.98–1.36)</td><td>0.52 (0.49–0.54)</td><td>0.61 (0.58–0.64)</td><td>0.80 (0.75–0.84)</td></tr><tr><td>3GCRE BSI vs non-infected</td><td>3GCRE BSI</td><td>Hospitalised patients</td><td>2.88 (2.22–3.74)</td><td>2.25 (1.73–2.92)</td><td>1.80 (1.34–2.42)</td><td>0.37 (0.32–0.43)</td><td>0.43 (0.38–0.50)</td><td>0.57 (0.49–0.67)</td></tr><tr><td>3GCRE BSI vs 3GCSE BSI</td><td>3GC resistance</td><td>Patients with Enterobacteriaceae BSI</td><td>1.39 (1.02–1.90)</td><td>1.43 (1.05–1.96)</td><td>1.63 (1.13–2.35)</td><td>0.63 (0.55–0.73)</td><td>0.65 (0.56–0.75)</td><td>0.68 (0.57–0.81)</td></tr></tbody></table>

BSI: bloodstream infection; CI: confidence interval; HR: hazard ratio; MRSA/MSSA: meticillin-resistant/susceptible *Staphylococcus aureus*; 3GC: third-generation cephalosporins; 3GCRE/3GCSE: third-generation cephalosporin-resistant/susceptible Enterobacteriaceae.

Model 1: Susceptible and resistant BSI as time-dependent covariates (univariable analysis).

Model 2: As model 1 plus adjustment for age, sex, emergent/elective admission, nights hospitalised in the previous 12 months and comorbidities.

Model 3: As model 2 plus admission to intensive care and surgical procedures as time-dependent covariates.

Results from the Cox proportional hazards analyses for death and discharge alive should be interpreted together (Table 3) [19].

When adjusted for potential confounders, all BSI except 3GCSE significantly increased the hazard of hospital death compared with non-infected patients. This effect was greater for BSI due to *S. aureus* than BSI due to Enterobacteriaceae. Moreover, all BSI strongly reduced the hazard of discharge alive after adjustment for confounders, meaning that patients with BSI stayed longer in hospital (discharge alive HR less than 1) and were exposed to an increased daily risk of death throughout this prolonged period (mortality HR greater than 1).

Among patients with BSI due to Enterobacteriaceae, third-generation cephalosporin resistance significantly increased the hazard of death compared with third-generation cephalosporin susceptibility (adjusted hazard ratio (aHR): 1.63; 95%CI: 1.13–2.35). In contrast, the trend for meticillin resistance to increase hazard of death amongst patients with *S. aureus* BSI did not reach statistical significance (aHR: 1.26; 95%CI: 0.82–1.94). Similarly, while third-generation cephalosporin resistance significantly decreased the hazard of discharge alive amongst patients with BSI due to Enterobacteriaceae, meticillin resistance showed only a trend in this direction among patients with BSI due to *S. aureus*.

Excess length of stay

Table 4 presents the impact of BSI on the combined end-of-stay endpoint (end-LOS HR) and excess LOS (in days) when compared with patients without BSI.

All BSI reduced the daily all-cause hazard of discharge or death, i.e. led to prolonged hospital stay. This prolonging effect was greater for BSI due to *S. aureus* than for BSI due to Enterobacteriaceae, regardless of antimicrobial susceptibility status. For all types of BSI, diagnosis early during admission was associated with the greatest difference in LOS (Figure 2).

Table 4 also presents the end-LOS HR and excess LOS for BSI caused by resistant versus susceptible pathogens. While third-generation cephalosporin resistance significantly prolonged LOS amongst patients with BSI due to Enterobacteriaceae, meticillin resistance did not for the cohort of patients with *S. aureus* BSI.

The adjusted excess LOS estimate (Model 2) was taken from the model intercept, and should therefore be interpreted as the excess LOS caused by infection in a female patient with age equal to the mean age in the cohort, who has no comorbidities, has not been in hospital for the previous year, and was admitted electively. Increasing age, emergency admission, male sex, and all comorbidities except myocardial infarction decreased the excess length of stay associated with all four BSI types.
<caption>**TABLE 4**</caption>

Results of proportional hazards analysis for all-cause end-length of stay and excess length of stay estimates from multistate models, 10 European hospitals, 2010–2011 (n = 606,649)

<table><thead><tr><th rowspan="2">Comparison</th><th rowspan="2">Exposure</th><th rowspan="2">Population</th><th colspan="3">All-cause end-LOS<br>HR (95% CI)</th><th colspan="2">Excess LOS<br>days (95% CI)</th></tr><tr><th>Model 1</th><th>Model 2</th><th>Model 3</th><th>Model 1</th><th>Model 2</th></tr></thead><tbody><tr><td>MSSA BSI vs<br>non-infected</td><td>MSSA BSI</td><td>Hospitalised patients</td><td>0.42<br>(0.39–0.45)</td><td>0.46<br>(0.43–0.49)</td><td>0.64<br>(0.59–0.69)</td><td>10.35<br>(9.44–11.26)</td><td>11.54<br>(11.45–11.63)</td></tr><tr><td>MRSA BSI vs<br>non-infected</td><td>MRSA BSI</td><td>Hospitalised patients</td><td>0.36<br>(0.30–0.44)</td><td>0.42<br>(0.35–0.51)</td><td>0.61<br>(0.50–0.75)</td><td>12.22<br>(9.89–14.55)</td><td>13.33<br>(13.23–13.42)</td></tr><tr><td>MRSA BSI vs MSSA<br>BSI</td><td>Meticillin<br>resistance</td><td>Patients with <i>S. aureus</i><br>BSI</td><td>0.84<br>(0.68–1.03)</td><td>0.83<br>(0.67–1.02)</td><td>0.89<br>(0.71–1.12)</td><td>1.77<br>(-0.51–4.05)</td><td>2.54<br>(-3.19–8.27)</td></tr><tr><td>3GCSE BSI vs<br>non-infected</td><td>3GCSE BSI</td><td>Hospitalised patients</td><td>0.57<br>(0.54–0.60)</td><td>0.66<br>(0.63–0.69)</td><td>0.84<br>(0.80–0.89)</td><td>4.36<br>(3.91–4.81)</td><td>5.87<br>(5.82–5.93)</td></tr><tr><td>3GCRE BSI vs<br>non-infected</td><td>3GCRE BSI</td><td>Hospitalised patients</td><td>0.46<br>(0.41–0.52)</td><td>0.53<br>(0.47–0.60)</td><td>0.69<br>(0.60–0.79)</td><td>7.91<br>(6.66–9.16)</td><td>9.28<br>(9.20–9.35)</td></tr><tr><td>3GCRE BSI vs 3GCSE<br>BSI</td><td>3GC<br>resistance</td><td>Patients with<br>Enterobacteriaceae BSI</td><td>0.72<br>(0.63–0.82)</td><td>0.73<br>(0.64–0.83)</td><td>0.78<br>(0.67–0.90)</td><td>3.53<br>(2.08–4.98)</td><td>4.89<br>(1.11–8.68)</td></tr></tbody></table>

BSI: bloodstream infection; CI: confidence interval; LOS: length of stay; MRSA/MSSA: meticillin-resistant/susceptible *Staphylococcus aureus*; 3GC: third-generation cephalosporins; 3GCRE/3GCSE: third-generation cephalosporin-resistant/susceptible Enterobacteriaceae.

Model 1: Susceptible and resistant BSI as time-dependent covariates (univariable analysis).

Model 2: As model 1 plus adjustment for age, sex, emergent/elective admission, nights hospitalised in the previous 12 months and comorbidities.

Model 3: As model 2 plus admission to intensive care and surgical procedures as time-dependent covariates.

## Cost

The cost, from a hospital perspective, of each BSI and its annual cumulative incidence is presented in Table 5. While 3GCSE BSI was associated with the lowest per-infection cost (EUR 320; 95% credible interval (Crl): 80–1,300; or EUR 4,000; 95% Crl 2,400–6,700, using economic and accounting valuations, respectively), their relative frequency resulted in equal highest annual cost with MSSA (EUR 77,000; 95% Crl: 19,000–300,000; or EUR 970,000; 95% Crl: 590,000–1,600,000, using economic and accounting valuations, respectively).

## Discussion

Per infection, *S. aureus* BSI had a greater effect on mortality, LOS and cost than BSI due to Enterobacteriaceae. Meticillin resistance, however, did not significantly increase the hazard of death or further prolong the excess LOS amongst patients with *S. aureus* BSI. This contrasts with BSI due to Enterobacteriaceae, where third-generation-cephalosporin-resistance increased both the hazard of mortality and excess LOS compared with susceptible strains. Furthermore, the annual cost, from a hospital perspective, of BSI due Enterobacteriaceae was equivalent to the cost of *S. aureus* BSI because the higher incidence of the former balanced the greater per-infection impact of the latter.

This study incorporated several novel methodological approaches to the recently described challenges when estimating the impact of AMR [5]. Multistate modelling is an extension of survival analysis that permits explicit modelling of time-varying exposures and competing outcomes [4], but previous applications to hospital

epidemiology have not addressed confounding. We employed the flexible pseudo-observation regression technique to adjust these estimates for time-invariant potential confounders [14]. We also formally computed the excess LOS due to infections caused by non-susceptible compared with susceptible pathogens [20] rather than heuristically extrapolating this as the difference between excess LOS associated with each infection type compared with non-infected controls. Inclusion of the entire cohort of acute inpatients from 10 hospitals over two years facilitated precise estimates and avoided selection bias at patient-level, a risk when using matched cohorts.

We used a previously reported economic valuation of the opportunity cost of hospital bed-days to translate excess LOS to cost of BSI from the hospital perspective [17], employing a Monte Carlo simulation to preserve uncertainty in this estimation. Substantially higher cost estimates were produced using an accounting bed-day value in order to demonstrate the importance of the costing approach used. Accounting values are readily obtained but only show what has historically been spent on a bed-day. As the majority of hospital costs are fixed, this figure does not represent an amount that could be recouped should the infection be avoided. We contend that economic values, based on the opportunity cost of occupied bed-days, are appropriate for making decisions from the hospital perspective about future resource allocation for infection control programmes [21]. The lower cost of BSI, and also of AMR, obtained using the economic valuation provides insight into the financial challenges faced by hospital leadership when considering such
TABLE 5

<table><caption>Monte Carlo simulation results using economic and accounting bed-day values to estimate the cost of bloodstream infections, 10 European hospitals, 2010–2011 (n = 606,649)</caption><thead><tr><th rowspan="2">Exposure</th><th rowspan="2">Population</th><th rowspan="2">Excess LOS per BSI days (95% CrI) <sup>a</sup></th><th rowspan="2">Expected annual cumulative incidence per hospital <sup>b</sup></th><th colspan="2">Estimated cost per infection EUR (95% CrI)</th><th colspan="2">Estimated cost per hospital-year EUR 1,000 (95% CrI)</th></tr><tr><th>Economic costing <sup>c</sup></th><th>Accounting costing <sup>d</sup></th><th>Economic costing <sup>c</sup></th><th>Accounting costing <sup>d</sup></th></tr></thead><tbody><tr><td>MSSA BSI</td><td>Hospitalised patients</td><td>10.3 (9.3–11.5)</td><td>102</td><td>760 (190–3,000)</td><td>9,500 (5,800–16,000)</td><td>77 (19–300)</td><td>970 (590–1,600)</td></tr><tr><td>MRSA BSI</td><td>Hospitalised patients</td><td>12.2 (9.9–14.7)</td><td>19</td><td>890 (220–3,600)</td><td>11,000 (6,600–19,000)</td><td>17 (4.1–67)</td><td>210 (130–360)</td></tr><tr><td>Meticillin resistance</td><td>Patients with S. aureus BSI</td><td>1.9 (–0.7 TO 4.6)</td><td>NA</td><td>120 (–60 TO 740)</td><td>1,600 (–700 TO 5,000)</td><td>NA</td><td>NA</td></tr><tr><td>3GCSE BSI</td><td>Hospitalised patients</td><td>4.4 (3.9–4.9)</td><td>242</td><td>320 (80–1,300)</td><td>4,000 (2,400–6,700)</td><td>77 (19–300)</td><td>970 (590–1,600)</td></tr><tr><td>3GCRE BSI</td><td>Hospitalised patients</td><td>7.9 (6.6–9.4)</td><td>41</td><td>560 (140–2,300)</td><td>7,300 (4,300–12,000)</td><td>24 (5–94)</td><td>300 (180–510)</td></tr><tr><td>3GC resistance</td><td>Patients with Enterobacteriaceae BSI</td><td>3.5 (2.1–5.1)</td><td>NA</td><td>250 (60–1,100)</td><td>3,200 (1,600–6,000)</td><td>NA</td><td>NA</td></tr></tbody></table>

BSI: bloodstream infection; CrI: credible interval; LOS: length of stay; MRSA/MSSA: meticillin-resistant/susceptible *Staphylococcus aureus*; NA: not applicable; 3GC: third-generation cephalosporins; 3GCRE/3GCSE: third-generation cephalosporin-resistant/susceptible Enterobacteriaceae.

a Output from probabilistic sensitivity analysis based on input distributions, reproduced to demonstrate consistency with estimates from the current study.

b Estimated for a hospital with 450,000 bed-days annually (95% CrI not displayed because precision from the study cohort is such that no additional uncertainty is added to the model).

c Employs the bed-day valuation derived from a contingent valuation survey that estimated the opportunity cost of each bed-day consumed by a patient with BSI.

d Employs the bed-day valuation computed by dividing total hospital budget for one year by the number of bed-days supplied during the same period. Refer to [8] for further details. All costs are displayed at two significant figures.

interventions under existing funding arrangements. While we used the unadjusted excess LOS for this estimation to best reflect the patient mix in our cohort, the adjusted results and covariate coefficients could be used to transfer our excess LOS estimate to settings with different patient mix.

A recent WHO systematic review of the published scientific literature on the health and economic impact of AMR concluded that the quality of evidence on hospital LOS and mortality was ‘very low’ in most cases [1]. It also identified a paucity of studies comparing hospital costs incurred by infection with resistant versus susceptible isolates of *E. coli* and *K. pneumoniae*. Our results are consistent with this review’s finding that third-generation cephalosporin resistance is associated with increased risk of mortality among patients infected with *E. coli* or *K. pneumoniae*. We found that third-generation cephalosporin resistance increased the hospital LOS associated with BSI caused by Enterobacteriaceae, while previous reports were ‘inconsistent’ for *K. pneumoniae* BSI and found no excess LOS for *E. coli* BSI. Our results do not support the review’s finding that infection with MRSA is associated with increased mortality and LOS compared with MSSA. Potential explanations include more appropriate empiric antibiotic therapy during our study compared with older studies and inflated estimation of

excess LOS in previous studies due to time-dependent bias [5,6]. In addition, daily risk (or hazard) of death, as estimated here, can be expected to be smaller than the cumulative risks reported in the review. Although seemingly in contrast to older literature, our findings are consistent with another recent, large European multicentre study that found that meticillin resistance had no significant impact on mortality (adjusted hazard ratio (aHR), 1.1; 95% CI: 0.7–1.8) or excess LOS (0.6 days; 95% CI: –3.7 to 5.3), whereas third-generation cephalosporin resistance increased both risk of death (aHR: 2.9; 95% CI: 1.2–6.9) and excess LOS (5.0 days; 95% CI: 0.4–10.2) [22,23]. A similarly modest impact of AMR has been reported in the European ICU setting [24].

These data should be interpreted within the context of the study design. The dataset was extracted retrospectively from existing databases. Concerns regarding the quality of ICD coding data have been well described [25], although the Charlson comorbidity index derived from administrative databases has elsewhere proven superior to chart review [26]. We relied on routine antimicrobial susceptibility results performed by local laboratories using guidelines from five different organisations. However, for MRSA and 3GCRE, there should not be a major misclassification bias. We were unable to detect community-onset healthcare-acquired infection,
however our primary results do not depend on this dis-
tinction. In addition, we could not include antibiotic
exposure data. However, we consider delayed appropri-
ate antimicrobial therapy to be on the causal pathway
between antimicrobial resistance and the outcomes
of interest [27], so exclusion of this information from
our analysis is appropriate. We were unable to follow
up patients post discharge, thus cannot report 30-day
mortality or longer-term sepsis outcomes [28]. As with
any observational study, we cannot exclude residual
confounding. Our research question, however, is not
amenable to an experimental study, and by accounting
for time-dependent bias and important confounders,
these results add to the existing literature. Finally, our
study was designed to evaluate cost from the hospital
perspective and addressed neither societal costs, mac-
roeconomic indicators, nor the global health-economic
implications of a post-antibiotic future [29,30].

This multicentre study, conducted in 10 European hos-
pitals, could cautiously be extrapolated to large hospi-
tals in other high-income settings, although the burden
of BSI will clearly vary depending on incidence, treat-
ment and hospital funding schemes. However, the cur-
rent study did not address the lack of data in this field
from low- and middle-income countries, where limited
diagnostic and therapeutic resources, combined with
lower proportion of gross domestic product available
for healthcare, are likely to translate to a greater bur-
den of disease.

Our data demonstrate the substantial health and eco-
nomic burden imposed by BSI in European hospitals.
Per infection, BSI caused by non-susceptible strains
were associated with higher mortality risk and cost
than susceptible strains. Given that BSI due to non-
susceptible S. aureus and Enterobacteriaceae strains
are likely to add to rather than replace those due to
susceptible strains [31,32], the additional impact of
AMR is substantial. However, the higher incidence of
BSI due to susceptible strains means that these cur-
rently represent a greater health and economic burden
than non-susceptible strains, emphasising the impor-
tance of surveillance and infection control policies that
target *infections* rather than *resistance*.

TIMBER Study group members

In addition to the named authors of this work, the TIMBER
study group includes Cristina Masuet-Aumatell, PhD,
and Marta Banqué Navarro, PhD (Hospital Universitari de
Bellvitge, L'Hospitalet de Llobregat, Spain), and Chiara
Falcone, MS (Papa Giovanni XXIII Hospital, Bergamo, Italy).

Acknowledgements

We gratefully acknowledge Professor Hajo Grundmann
and Dr Marlieke de Kraker for their contribution to the de-
sign of this study, and thank all individuals involved with
data extraction at each participating site for their collabo-
ration. This work was performed during the PhD of A.S. at
the Department of Medicine, University of Melbourne. This

was an investigator-initiated project supported by an un-
restricted research grant provided by Pfizer Europe. The
sponsor had no role in study design, in the collection, anal-
ysis, and interpretation of data, in the writing of the report
or in the decision to submit the paper for publication. The
corresponding author had full access to all the data in the
study and had final responsibility for the decision to sub-
mit for publication.

Conflict of interest

HS is supported by research grants from The German
Center for Infection Research (DZIF), the European
Union (MagicBullet, Grant Agreement 278232), Novartis
and Pfizer, has received speaking fees from Astellas,
AstraZeneca, Gilead, MSD, Novartis, Oxoid and Pfizer,
and is an advisory Board Member or consultant to
AstraZeneca, Basilea, Cubist, FAB-Pharma, Novartis, SOBI,
The Medicines Company, Theravance, and ThermoFischer.
S. Hagel reports having received lecture fees from Pfizer,
MSD, and Astra Zeneca. S. Harbarth reports having re-
ceived investigator-initiated research grants funded by
Pfizer and B. Braun; he is also a member of the advisory
boards of Destiny Pharma, bioMerieux, Novartis and
DaVolterra. Other authors: no conflicts to declare.

Authors’ contributions

AS, AA, JB, NG and SH designed the study. AS, RM, ET, GDA, CF, FP, XB, HGH, JE, OT, JM, MAB, AP, AZ, CM, DN, HS, NH, S Hagel and MP obtained the data. AS cleaned the data. AA and AS analysed the data. AS wrote the first draft. All authors revised the report and approved the final report. SH raised the funding. SH, NG and JB supervised the study.

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