Health burden from antimicrobial resistance (AMR)

European Centre for Disease Prevention and Control

Dominique L. Monnet, Alessandro Cassini, for the Antimicrobial Resistance and Healthcare-Associated Infections (ARHAI) Programme, ECDC

EPH pre-conference, Ljubljana, 28 November 2018

Antimicrobial resistance (AMR) is not a disease

Antimicrobial resistance is:

• Multi-factorial (mutation, acquired genes)

• Multi-sectorial (one health, one world)

Varies according to:

• Host

• Microorganism

• Antimicrobial

• Type of infection

Large number of combinations!

Outcome of infections with antimicrobial resistant microorganisms is related to treatment failure

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Objectives

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• To refresh previous estimates (“Time to react”)

and improve methodology

• To provide estimates of:

o the number of infections with antibiotic-resistant

bacteria;

o the number of attributable deaths;

o the attributable length of hospital stay;

o the impact on the health of the population

• Based on EARS-Net data 2015

• All EU/EEA countries

Apples and pears

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Choice of a common currencyto compare impact of diseasesand their sequelae

Disability-adjusted life years (DALYs)

DALYs = YLLs + YLDs

Years of life lost due to death Years of healthy life lost due to disability

= Σ (d x e)d – sum of all fatal casese – remaining life expectancy

at age of death

= Σ (n x t x w)n – number of casest – duration of illnessw – disability weight

Disability-adjusted life years (DALYs)

Attributable deaths and disability-adjusted life-years caused by infections with antibiotic-resistant bacteria in the EU and the European Economic Area in 2015

Cassini A, et al. The Lancet Infectious Diseases 5 November 2018. 6

259 pages!

- Literature review report

- Literature selection grids

- Disease outcome trees

- Methodology protocol to estimate incidence

- GATHER check list

- Further analyses on MRSA trends and on proportion of infection that are healthcare-associated

- Detailed country results

Bacteria and antibiotic resistance categories included in the study

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EARS-NetRe-distribution of

unknowns

Primary data source

• Country specific• Age-group and sex• Microorganism-specific

Cassini A, et al. The Lancet Infectious Diseases. 5 November 2018

Estimating incidence

1. EARS-Net age-group and sex-specific number of cases, per country. Unknown age and sex were re-distributed

2. National designated collaborator provided a country coverage factor for each bacterium

3. Applying a BSI to non-BSI ratio – from PPS 2011-2012 (except for S. pneumoniae)

4. Reduce the number of non-BSIs according to risk of S-BSI (to avoid double-counting)

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Reducing cases

Fourth adjustment

Population coverage

Second adjustment

Other types of infection

Third adjustmentFirst adjustment

Cleaning the data

Cassini A, et al. The Lancet Infectious Diseases. 5 November 2018

Determining attributable mortality and attributable length of stay

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>360 publications

identified

Systematic literature review

• Infection type specific• Bug-drug specific

All papers were scored

Criteria scoring

• Study type• Sample size• Representativeness• Matching • Controlling for confounders

Final disease models

Final decisions

• Built on consensus• Baseline models from HAIs

Staphylococcus

aureus 

Enterococcus

faecalis  and E. faecium

3GCRKP CRKP ColRKP 3GCREC CREC ColREC MDRACI CRACI ColRACI MDRPA CRPA ColRPA MRSA VRE PRSP PMRSP

BSI

Case fatality proportion (%) 7.1-20.3 14.4-19 20-51.3 32-88.8 17.1 (9.5-26) 20-51.3 32-88.8 7.1-32.9 7.1-34 7.1-34 7.1-35.2 7.1-38.7 7.1-38.7 17.9 (14.4-21.8) 22.9 (21.8-23.8) 15.7-20.3 15.7-20.3

Duration (days) 5.87-11.5 9.28 (9.20–9.35) 15-35 15-39.1 6-18.5 15-35 15-39.1 5.87-20.1 5.87-20.1 5.87-20.1 14.87-21.5 14.87-21.5 14.87-21.5 8.99-14.62 6.97-18.3 Baseline Baseline

RESP

Case fatality proportion (%) 3.6 (2.7-4.5) Baseline Baseline Baseline Baseline Baseline Baseline Baseline Baseline Baseline 2.7-9.8 2.7-10.5 2.7-10.5 Baseline Baseline Baseline Baseline

Duration (days) 7-14 13.6-18.1 13.6-18.1 13.6-18.1 Baseline 13.6-18.1 13.6-18.1 Baseline Baseline Baseline 10-17 15-22 15-22 Baseline Baseline Baseline Baseline

UTI

Case fatality proportion (%) 0 Baseline Baseline Baseline Baseline Baseline Baseline Baseline Baseline Baseline Baseline Baseline Baseline Baseline Baseline N/A N/A

Duration (days) 7 (4-11) 7 (5-12) 7.5 (4-14) 7.5 (4-14) 7 (5-12) 7.5 (4-14) 7.5 (4-14) 8 (4-11) 8 (4-11) 8 (4-11) 8 (4-11) 8 (4-11) 8 (4-11) Baseline Baseline N/A N/A

SSI

Case fatality proportion (%) 0.9<65; 3.6>64 Baseline Baseline Baseline Baseline Baseline Baseline Baseline Baseline Baseline Baseline Baseline Baseline Baseline Baseline N/A N/A

Duration (days) 8.5 (0-15.2) Baseline Baseline Baseline Baseline Baseline Baseline Baseline Baseline Baseline Baseline Baseline Baseline Baseline Baseline N/A N/A

OTHER

Case fatality proportion (%) 0 Baseline Baseline Baseline Baseline Baseline Baseline Baseline Baseline Baseline Baseline Baseline Baseline Baseline Baseline 0 0

Duration (days) 6 (3-11) 12 (8-21) 12 (6-27) 12 (6-27) 12 (8-21) 12 (6-27) 12 (6-27) 14.5 (9-19) 14.5 (9-19) 14.5 (9-19) 14.5 (9-19) 14 (9-19) 14.5 (9-19) 12 (8-19) Baseline 5-10 5-10

Streptococcus pneumoniae Baseline model

Klebsiella pneumoniae  Escherichia coli Acinetobacter spp. Pseudomonas aeruginosa

Cassini A, et al. The Lancet Infectious Diseases. 5 November 2018

• 16 bug-drug combinations• 5 types of infection (BSI, UTI, RESP, SSI & OTH)• 80 models per country• 2 400 models for input

= 339 360 variables

Estimated burden of infections with antibiotic-resistant bacteria, EU/EEA, 2015

671 689 infections with antibiotic-resistant bacteria

33 110 attributable deaths

170 DALYs* per 100 000 population

• 63% of cases were healthcare-associated infections, representing 75% of total burden (DALYs)

• 70% due to 4 top-ranking antibiotic-resistant bacteria

• 39% due to carbapenem- and/or colistin resistance

Source: Cassini A, et al. Lancet Infectious Diseases. 5 November 2018.

*DALYS, Disability-adjusted life years

Burden of infections with antibiotic-resistant bacteria is comparable to burden of influenza, TB & HIV/AIDS combined

Adapted from: Cassini A, et al. Eurosurveillance 2018;23(16):pii=17-00454; Cassini A, et al. Lancet Infectious Diseases. 5 November 2018.

*DALYS, Disability-adjusted life years

1. Third-generation cephalosporin-resistant E. coli and K. pneumoniae; aminoglycoside- and fluoroquinolone-resistant Acinetobacter spp.; three or more antimicrobial groups-resistant P. aeruginosa

2. Carbapenem- and/or colistin-resistant E. coli, K. pneumoniae, Acinetobacter spp. and P. aeruginosa3. Meticillin-resistant S. aureus4. Vancomycin-resistant E. faecalis and E. faecium5. Penicillin-resistant and combined penicillin and macrolide-resistant S. pneumoniae6. Other

Burden of infections with antibiotic-resistant bacteria, EU/EEA, 2007

Adapted from Cassini A, et al. The Lancet Infectious Diseases. 5 November 2018 12

No. cases

No. att

ributa

ble

death

s

Diameter of bubbles: No. disability-adjusted life-years (DALYs)

For acronyms of bacterium-antibiotic combinations: see slide 7.

Burden of infections with antibiotic-resistant bacteria, EU/EEA, 2015

Adapted from Cassini A, et al. The Lancet Infectious Diseases. 5 November 2018 13

No. cases

No. att

ributa

ble

death

s

Diameter of bubbles: No. disability-adjusted life-years (DALYs)

For acronyms of bacterium-antibiotic combinations: see slide 7.

Estimated burden of infections with antibiotic-resistant bacteria, age-group standardised, EU/EEA, 2015

Source: Cassini A, et al. Lancet Infectious Diseases. 5 November 2018.

Estimated burden of infections with antibiotic-resistant bacteria, age-group standardised, EU/EEA, 2015

15Cassini A, et al. The Lancet Infectious Diseases. 5 November 2018

Strengths and limitations

High quality of the surveillance data sources• Comprehensive, standardised, multi-country surveillance data

(EARS-Net 2015 and PPS 2011-2012)

Systematic literature reviews • Evidence-based attributable outcomes, but…

• Low or very low quality (when available and when representative)

• Death is related to microorganism, to patient and to therapy (and to delay for the administration of appropriate antibiotic therapy)

Estimation of the incidence• Frequency of susceptibility testing

• Representativeness of participating laboratories (geographical, type of hospital and case-mix of patients)

• Different time span between EARS-Net (2015) and PPS (2011-2012)

• Applying PPS 2011-2012 estimates to community-associated infections

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Conclusions

• The burden of infections with selected antibiotic-resistant bacteria is comparable to the cumulative burden of influenza, tuberculosis and HIV/AIDS.

• This burden is mainly healthcare-associated: antibiotic stewardship and enforced infection prevention and control in hospitals should be the most effective interventions.

• This burden substantially increased between 2007 and 2015, with the largest increases being observed for carbapenem-resistant K. pneumoniae and third-generation cephalosporin-resistant E. coli infections.

• The contribution of various antibiotic-resistant bacteria to the overall burden varies greatly between countries, thus prevention and control strategies should be tailored to the needs of each individual country.

• Future work should focus on improving country coverage estimates, i.e. population and case-mix representativeness, and the frequency of microbiological sampling and antimicrobial susceptibility testing.

• These estimates should be regularly updated and completed with other antimicrobial-resistant pathogens

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A large European consortium

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Muna Abu SinDriss Ait OuakrimBelén AracilAngel AsensioHanna BillströmMichael BorgAna BudimirKaren BurnsManuela CaniçaBoudewijn CatryAlessandro CassiniMichele CecchiniBruno CoignardMélanie Colomb-CotinatTiago Cravo OliveiraGeorge DaikosSabine de GreeffAleksander DeptułaBrecht DevleesschauwerLiselotte Diaz Högberg

Elina DobrevaUga DumpisTim EckmannsPetter ElstrømPaulo André FernandesCarlo GagliottiAchilleas GikasÓlafur GuðlaugssonÁgnes HajduSebastian HallerSusan HopkinsAna HoxhaWaleria HryniewiczIvan IvanovMarina IvanovaJari JalavaAlan JohnsonIrena KlavsMayke KoekJana Kolman

Flora KontopidouAleš KorošecMirjam KretzschmarKarl KristinssonSofie LarssonKatrien LatourSlavka LitvováAgnė LiuimienėOuti Lyytikäinen Vera ManageiroPille MärtinKarl MertensJos MonenDominique MonnetStephen MurchanPaulo Jorge NogueiraNiki PaphitouMaría Pérez-Vázquez Monique PerrinPatrizio Pezzotti

Diamantis PlachourasGabriel PopescuAnnalisa QuattrocchiJacqui ReillyEva SchréterováStefan Schytte OlsenRoxana SerbanGunnar SimonsenSilvija SoprekMária ŠtefkovičováReinhild StraussMarc StruelensThomas StruyfCarl SuetensArjana Tambić AndraševićÁkos TóthSotirios TsiodrasUte Wolff SönksenDorota ŻabickaHelena Žemličková

Thank you

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