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CDC

Antimicrobial Resistance

Multidrug-resistant Klebsiella

% Resistant in 2024

16.9% Resistant

Number of Resistant Isolates of the Total Number of Isolates Tested In 2024

1,291 Resistant

/ 7,620 Tested

% Resistant by Patient Age in 2024

Pediatric:
11.7% Resistant
Adult:
17.3% Resistant

Pathogen Profile

Multidrug-resistant Klebsiella

Klebsiella spp. cause pneumonia, urinary tract infections, and bloodstream infections in hospitalized patients, as well as in patients in nursing homes and other healthcare facilities. These strains of Klebsiella spp. have become very resistant to several, if not all, antibiotics and are therefore described as multidrug-resistant.

Current Threat Report

Threat LevelNot Currently Rated

Source: COVID-19: U.S. Impact on Antimicrobial Resistance, Special Report 2024

Years Included
2011 - 2024
Resources

Geographic Locations

Antimicrobial resistance varies by location, driven by a number of factors, including antibiotic use and infection control practices in individual healthcare facilities, the underlying health and age of the patient population, and regional spread from nearby locations.

Percent Multidrug resistance Among Klebsiella spp. by State Map

This map shows the variation in % multidrug resistance among Klebsiella spp. causing all event types in 2024.

Percent Multidrug resistance Among Klebsiella spp. by State

Percent Multidrug resistance Among by State
Locationvalue
Alabama15.1
AlaskaInsufficient Data
Arizona24.6
Arkansas11.6
California17.3
Colorado20.3
Connecticut18.8
Delaware18.8
District of Columbia27.1
Florida18.9
Georgia17.3
Hawaii5.9
Idaho4.3
Illinois17.5
Indiana10.6
Iowa15
Kansas3
Kentucky14
Louisiana11.3
MaineInsufficient Data
Maryland20.8
Massachusetts19.7
Michigan24.1
Minnesota8.1
Mississippi12.2
Missouri12.4
MontanaInsufficient Data
Nebraska15.6
Nevada30
New HampshireInsufficient Data
New Jersey14.8
New Mexico12.9
New York19.4
North Carolina12.1
North DakotaInsufficient Data
Ohio14.9
Oklahoma10
Oregon1.8
Pennsylvania11.4
Puerto Rico72.3
Rhode Island19.2
South Carolina18
South Dakota4.2
Tennessee13.3
Texas15
Utah22.2
VermontInsufficient Data
Virginia20.8
Washington9
West Virginia15.1
Wisconsin10.3
WyomingInsufficient Data

Percent Multidrug resistance Among Klebsiella spp. by State List

Geography

Geography
statevalue
United States16.9%
Alabama15.1%
AlaskaNo Data
Arizona24.6%
Arkansas11.6%
California17.3%
Colorado20.3%
Connecticut18.8%
Delaware18.8%
District of Columbia27.1%

Changes Over Time in Multidrug resistance Among Klebsiella spp.

This graph displays percent antimicrobial resistance from 2011 to 2024 for all event types. Blank areas of the chart represent 0% resistance, indicating that zero resistant pathogens were reported for the selected year, phenotype, and HAI type. “Insufficient data” indicates that less than 20 pathogens were isolated and tested for resistance.

Due to definition changes, caution should be used when reviewing this phenotype over time.

Changes Over Time in Multidrug resistance Among Klebsiella spp.
Year% Resistant
201115.7
201213.8
201315
201412.8
201514.6
201613.7
201712.5
201812.2
201912.7
202013.3
202113.9
202215
202315.9
202416.9

Healthcare Facility Type

The setting or type of healthcare facility can influence the types of procedures and prevention measures that are related to healthcare-associated infections and antimicrobial resistance.

Multidrug resistance Among Klebsiella spp. by Hospital Type

This graph displays percent antimicrobial resistance by hospital type in 2024.

Percent resistant Among Klebsiella spp. by Hospital Type

Percent resistant Among Klebsiella spp. by Hospital Type
Hospital Type% Resistant
General Acute Care Hospitals16
Long Term Acute Care Hospitals35.4
Inpatient Rehabilitation Facilities10

Footnotes

  • HAIs include Catheter-associated Urinary Tract Infections (CAUTI), Central Line-associated Bloodstream Infections (CLABSI), and Surgical Site Infections (SSI).
  • CI (Confidence Interval) - The national, regional, and state-level data included in Antimicrobial Resistance information are displayed with 95% confidence intervals around the percent resistance, which were calculated using a mid-P exact test and are an indication of precision.
  • Map legends are classified using the Jenks Natural Breaks method.
  • The definition for this phenotype underwent changes in 2021. For more information, refer to the AR&PSP Phenotype Definitions.