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Targeted Assessment for Prevention of Healthcare-Associated Infections: A New Prioritization Metric

Supporting Files
File Language:
English


Details

  • Alternative Title:
    Infect Control Hosp Epidemiol
  • Personal Author:
  • Description:
    OBJECTIVE

    To develop a method for calculating the number of healthcare-associated infections (HAIs) that must be prevented to reach a HAI reduction goal and identifying and prioritizing healthcare facilities where the largest reductions can be achieved.

    SETTING

    Acute care hospitals that report HAI data to the Centers for Disease Control and Prevention’s National Healthcare Safety Network.

    METHODS

    The cumulative attributable difference (CAD) is calculated by subtracting a numerical prevention target from an observed number of HAIs. The prevention target is the product of the predicted number of HAIs and a standardized infection ratio goal, which represents a HAI reduction goal. The CAD is a numeric value that if positive is the number of infections to prevent to reach the HAI reduction goal. We calculated the CAD for catheter-associated urinary tract infections for each of the 3,639 hospitals that reported such data to National Healthcare Safety Network in 2013 and ranked the hospitals by their CAD values in descending order.

    RESULTS

    Of 1,578 hospitals with positive CAD values, preventing 10,040 catheter-associated urinary tract infections at 293 hospitals (19%) with the highest CAD would enable achievement of the national 25% catheter-associated urinary tract infection reduction goal.

    CONCLUSION

    The CAD is a new metric that facilitates ranking of facilities, and locations within facilities, to prioritize HAI prevention efforts where the greatest impact can be achieved toward a HAI reduction goal.

  • Subjects:
  • Source:
    Infect Control Hosp Epidemiol. 36(12):1379-1384
  • Pubmed ID:
    26310913
  • Pubmed Central ID:
    PMC4658253
  • Document Type:
  • Funding:
  • Name as Subject:
  • Place as Subject:
  • Volume:
    36
  • Issue:
    12
  • Collection(s):
  • Main Document Checksum:
    urn:sha256:b7b89821bd776229466cee83f635472f33403987c5ba7d5be90c7a0136ae78c4
  • Download URL:
  • File Type:
    Filetype[PDF - 239.20 KB ]
File Language:
English
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