Will 10 Million People Die a Year due to Antimicrobial ...bfb80df4-b8f7-43a4... · - Antibiotic...

Will 10 Million People Die a Year due to Antimicrobial Resistance by 2050?

Prof. Stephan HarbarthInfection Control Program

Geneva, Switzerland

Thanks for material provided by Marlieke de Kraker & Andrew Stewardson !

Antibiotic resistance: global public health concern

Tackling drug-resistant infections globally (O’Neill report) - Mai 2016

What are clinical implications of antimicrobial resistance?

• Treatment failure due to wrong choice

– Increased morbidity and mortality

• Use of more toxic, more expensive and less efficacious therapeutic alternatives

• Added burden of nosocomial infections

• Risk of explosive outbreaks

Main Conclusions:

- Antibiotic resistance significantly impacts on illness burden in the community.

- Patients with laboratory-confirmed antibiotic-resistant urinary and respiratory-tract infections are more likely to experience delays in clinical recovery after treatment with antibiotics.

• C. H. (71) first woman elected lieutenant governor in South Dakota.

• She had suffered a spinal fracture and 3 broken ribs Oct. 8 while sailing the Adriatic Sea.

• She underwent surgery in Zagreb, Croatia on Oct. 10, then was hospitalized Oct. 19 during a stop in Switzerland on her way back to the US.

• She suffered pneumonia, a bacterial blood infection, and a series of strokes, which claimed her life in Lausanne, Switzerlandon October 25, 2007.

Deadly MRSA Infection

Acinetobacter Outbreak, Lausanne

• Index patient

– Severe burn injuries, transfer from Bali (Oct2002)

– Multi-R Acinetobacter at admission

• Outbreak

– Spread to 2 patients

– 6 months later: 6 new cases

– Closure of the burn unit

• Environnement

– Widespread contamination: 16/161 (10%) positive swabs

Patients Environnement

► Environmental cleaning & disinfection

► Complete replacement of all disposable material

Zanetti G et al. Infect Control Hosp Epidemiol 2007; 28: 723-25

Economic burden of MDROs

• Increased direct costs of providing care to

MDRO-infected patients;

• Indirect costs to patients, caregivers, &

diminished quality of life;

• Infrastructure and productivity costs of

surveillance, screening and isolation;

• Antibiotic treatment costs for therapy or

empiric coverage of MDRO

Projected impact of antimicrobial-resistant neonatal sepsis in India

Apocalypse soon?

Add Raoult, CID headline

AMR burden

“ Data are insufficient to determine full

extent of public health burden associated

with antibacterial resistance.”

US General Accounting Office, 1999Report to U.S. Congress

“ The estimates of the burden caused by

bacterial resistance depend heavily on

unknown parameters.”

Public health burden of drug resistanceC.E. Phelps, Med Care 1989; 27: 194-203

R. Rappuoli. Nat Med 2004.

From Pasteur to genomics: progress and challenges in infectious diseases

Affected people / deaths

Threat level: URGENT

Threat level: SERIOUS

Tackling drug-resistant infections globally (O’Neill report) - Mai 2016

Usability/Business case, O’Neill report

General objective for the AMR review group:

“Defining the steps needed to avoid the AMR crisis”

Objective of this report:

“Determine the health and macro-economic consequences for the world, especially in emerging economies if antimicrobial resistance is not tackled”

Tackling drug-resistant infections globally (O’Neill report) - Mai 2016

De Kraker N, Stewardson A, Harbarth S. PLoS Med 2016; 13: e1002184

Caveats

• Internal validity?

• External validity?

• Assumptions?

• Peer review?

Accurate, with and without random error

In-accurate, with and without random error

De Kraker N, Stewardson A, Harbarth S. PLoS Med 2016; 13: e1002184

Estimating the Burden of Disease (BoD) Related to Antimicrobial Resistance

• Number of infections (I)

• Resistance proportions (R)

• Burden measure: Attributable mortality proportion (M)

BoD= I*R*M

• Future scenarios– Determine coefficient for change (c)

– Future BoD = I*c1 * R*c2 *M*c3

De Kraker N, Stewardson A, Harbarth S. PLoS Med 2016; 13: e1002184

Number of infections (BoD= I*R*M)

EARS-net data: Representativeness?

• Mainly tertiary care hospitals

• Few community/paediatric/LTCF isolates

ECDC, EARS-Net Antimicrobial resistance surveillance report 2013/2014

Resistance proportions (BoD= I*R*M)

EARS-net and WHO data: Representativeness?

• Highly variable blood culture rates

ECDC, EARS-Net Antimicrobial resistance surveillance report 2012/2011*/2009**/2008***/2006

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Extrapolation from bloodstream infections to infections at other sites

Infect Control Hosp Epidemiol 2002; 23: 106-108.

• Fifteen Brooklyn hospitals 1999

•44 ESBL+ K. pneumoniae isolates

• 12 BSIs, 4 SSIs, 14 UTIs, 14 LRTIs:

1 : 0.33 : 1.2 : 1.2

Infect Control Hosp Epidemiol 2006; 27: 1264-1266.

• 396-bed hospital in Spain 2002

•33 MRSA isolates

• 4 BSIs, 17 SSIs, 3 UTIs, 5 LRTIs:

1 : 4.3 : 0.8 : 1.3

Attributable mortality (BoD= I*R*M)

ECDC, The bacterial challenge: Time to react 2009

Methodological challenges --Why is it so difficult to estimate

the attributable mortality of AMR-related infection?

Problem 1:

- Severity of illness and underlying disease

PROBLEM

• High crude mortality in patients with infections caused by multidrug-resistant bacteria

• Carriers of multiresistant bacteria who die in the hospital may die either…– with simple asymptomatic carriage of resistant bacteria

– with infection by resistant bacteria

or

– because of infection (primary cause of death)

Problem 2:

- Appropriateness of antimicrobial therapy

Causal pathways & challenges

Resistant infection Death

Severity of underlying illness

Appropriateness of antibiotics

Mortality prediction in nosocomial bacteremia

McCabe & Jackson. Arch Intern Med. 1962;110:856-864

Freeman J et al. Rev Infect Dis 1988; 10: 1118-1141

Bacteremia Death

Severity of underlying illness:RR = 6.9 – 20.9

Inappropriate antibiotic therapyRR = 1.8 – 4.5

Problem 3:

- Timing of events and time-varying exposures

The importance of correct measurement

When did the antibiotic-resistant infection occur?

Admission

Infected

infection

Non-infected

Discharge

Death

• Cohort study, 2005-2008

• 10 countries, 537 ICUs, 119699 pts

• Sophisticated statistical analyses adjusted for the timing of events and competing outcomes (multistate modelling)

Lambert et al. Lancet Infect Dis 2011

Main findings

• High excess mortality associated with bacteremia and pneumonia acquired in the intensive care unit

• Substantially increased excess length of stay for pneumonia, but not for bloodstream infection

• Pseudomonas aeruginosa: greatest burden (not MRSA)

• AMR: only a small contribution to the overall burden of ICU-acquired infections

Lambert et al. Lancet Infect Dis 2011

Multicenter study (TIMBER)

PopulationPatients with bloodstream infection (BSI) caused by S. aureus or Enterobacteriaceae

Main exposure of interestMethicillin resistance or third-generation cephalosporin resistance

Main comparison groupPatients with infections by susceptible strains

Main outcomesExcess length of stay (LoS) and in-hospital mortality

Stewardson A, …., Harbarth S. EuroSurveillance 2016; 21: 33

= Extended-spectrum betalactamase-producing

Enterobacteriaceae (ESBL-E)(e.g. E.coli, Klebsiella spp)

Methods

• Design: – Multicentre retrospective cohort study

– 10 European hospitals

• Population: – All acute inpatient admissions

– January 2010 – December 2011

• Data collection:– Demographic, clinical, microbiologic & administrative data were extracted electronically

– One investigator from each site trained in standardized data collection during a workshop

Stewardson A, …., Harbarth S. EuroSurveillance 2016; 21: 33

Statistical methods

• Cox proportional hazards analysis– Compute hazards of inpatient mortality

– Multivariable models• Baseline covariates: for age, sex, elective versus emergent admission,

previous hospitalisation, 17 comorbidities

• Time-varying covariates: bloodstream infection, ICU admission or surgery

• Multistate modeling– Compute excess hospital LoS (days) attributable to each type of BSI

– Accounting for competing risks (discharge vs death)

Stewardson A, …., Harbarth S. EuroSurveillance 2016; 21: 33

62

S. aureus analysis

Group NIncidence proportion

(events/100 admissions)Total length of stay

Median (IQR)MortalityCount (%)

MRSA BSI 163 0.03 31 (16–45) 36 (22.1%)

MSSA BSI 885 0.15 23 (13–39) 149 (16.8%)

Non-infected 604797 - 4 (2–7) 10161 (1.7%)

Group nIncidence proportion

(events/100 admissions)Total length of stay

Median (IQR)MortalityCount (%)

3GCR-E BSI 360 0.06 26 (12.75–45) 58 (16.1%)

3GCS-E BSI 2100 0.35 14 (7–28) 212 (10.1%)

Non-infected 603972 - 4 (2–7) 10105 (1.7%)

Enterobacteriaceae analysis

Outcomes (unadjusted)

Stewardson A, …., Harbarth S. EuroSurveillance 2016; 21: 33

In-hospital mortalityAdjusted proportional hazards analysis

63

Interpretation: Risk of death after bloodstream infection (BSI)

Adjusted for age, sex, emergent/elective admission, comorbidities, nights hospitalised in previous 12 months, plus ICU-admission and surgical procedures as time-dependent covariates

Stewardson A, …., Harbarth S. EuroSurveillance 2016; 21: 33

Excess length-of-stayMultistate model

64Stewardson A, …., Harbarth S. EuroSurveillance 2016; 21: 33

Assumptions for future scenarios

KPMG report, the global economic impact of anti-microbial resistance 2014

Number of death certificates in England & Wales mentioning MRSA

Office for National Statistics online 2010

The way forward

• Comprehensive, population-based antimicrobial resistance surveillance

– Paediatric & geriatric infections

– Community-acquired infections

– Low-, middle-, and high-income countries

– Different types of infections

– Morbidity and mortality data

• Accurate & valid analyses

Summary

• Preventing antimicrobial resistance is desirable by patients and society

• Consistency of data regarding the impact of antimicrobial resistance on clinical outcomes and risk of treatment failure

• However:

– Paucity of data regarding the overall impact of antimicrobial resistance on healthservices and societal burden, especially in LMIC

– Due to methodological limitations, we may have overestimated the attributable mortality and excess costs of antimicrobial resistance

83

Situation in 2050

• 10.000.000 people dying due to antibiotic resistance?

• Methodological challenges & flaws of these projections:

– To predict total number of infections

– To predict the proportion of resistance

– To predict the attributable mortality

• Lack of robust data note of caution:“broad brush estimates, not certain forecasts”

De Kraker N, Stewardson A, Harbarth S. PLoS Med 2016; 13: e1002184

What will happen in 2050?

• The first Rhino will be born on the North pole

• Geneva will be renamed Genève-sur-Mer

• Ivanka Trump, the 1st female US president, will be re-elected for the 4th time in a row

• 10.000.000 people will die from AMR

Courtesy: Marc Bonten (Utrecht)

Thanks for your attention !

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