When the measure comes back for maintenance in 3 years, the developer will have: Explored the possibility of using other all-payer data sources to expand the use of patient-level factors in the risk adjustment model and reduce reliance on facility-level factors.
Annual risk-adjusted standardized infection ratio (SIR) of Clostridioides difficile (CDI) LabID events among adults and pediatrics hospitalized as inpatients at acute care hospitals, critical access hospitals, oncology hospitals, long-term acute care hospitals, and acute care rehabilitation hospitals. SIR is reported annually and is calculated by dividing the number of observed CDIs into the number of predicted CDIs.
Measure Specs
General Information
The use of this measure will promote Clostridioides difficile (CDI) prevention activities that will lead to improved patient outcomes, including reductions of CDI infections, avoidable medical costs, and patient morbidity and mortality, through reduced need for antimicrobials and reduced length of stay.
https://www.cdc.gov/nhsn/psc/cdiff/index.html
http://www.cdc.gov/nhsn/pdfs/pscmanual/12pscmdro_cdadcurrent.pdf
https://www.cdc.gov/nhsn/pdfs/ps-analysis-resources/VarLabelXref-PS_cur…
http://www.cdc.gov/nhsn/PDFs/pscManual/14_Tables_of_Instructions.pdf
https://www.cdc.gov/nhsn/PDFs/pscManual/16pscKeyTerms_current.pdf
https://www.cdc.gov/nhsn/forms/57.128_LabIDEvent_BLANK.pdf
https://www.cdc.gov/nhsn/forms/57.127_MDROMonthlyReporting_BLANK.pdf
https://www.cdc.gov/nhsn/pdfs/ps-analysis-resources/mrsacdi_tips.pdf
NHSN Standard Infection Ratio (SIR) Guide NHSN SIR Guide
https://wwwdev.cdc.gov/nhsn/2022rebaseline/index.html
Numerator
Number of annually observed Clostridioides difficile (CDI) LabID events in hospital inpatients.
- Determine the patients who developed a Clostridioides difficile LabID event
- C. difficile-positive laboratory assay: A positive laboratory test result for C. difficile toxin A and/or B, (includes molecular assays [PCR] and/or toxin assays) tested on an unformed stool specimen (must conform to the container). OR
- A toxin-producing C. difficile organism detected by culture or other laboratory means performed on an unformed stool sample (must conform to the container).
- Determine if the patient is in an inpatient location.
- Determine if a healthcare facility-onset event, defined as an event occurring on or after the 4th day of admission.
- There should be 14 days with no C. difficile toxin-positive laboratory result for the patient and specific location before another C. difficile LabID event is entered into NHSN for the patient and location.
Denominator
Number of annually predicted Clostridioides difficile (CDI) LabID events in hospital inpatients.
Determine the total number of patient days for all inpatient locations.
Determine the total number of patient admissions for all inpatient locations.
The number of predicted infections in NHSN is calculated based on the 2022 national hospital acquired infection (HAI) aggregate data and is adjusted for each facility using variables found to be significant predictors of HAI incidence. The number of predicted CDI LabID events is calculated using a negative binomial regression model.
The general formula for the negative binomial regression model is
log (λ) = α + 𝛽1𝑋1 + 𝛽2𝑋2 + ··· + 𝛽i𝑋i, where:
α = Intercept
βi = Parameter estimate
Xi = Value of risk factor (categorical variables: 1 if present, 0 if not present)
i = Number of predictors
The tables below represent the variables found to be statistically significant predictors of Clostridioides difficile (CDI) LabID events and used in the negative binomial regression model to calculate the number of predicted healthcare facility-onset CDI LabID events in hospital inpatients under the 2022 baseline data.
See 7.1 Supplemental Information Attachment Pages 1-4 for risk models.
Exclusions
Baby based locations such as, neonatal ICU, special care nursery and well-baby nurseries, are excluded from the denominator count. In LDRP locations, moms and babies must each be counted separately (as two patients). Any locations that predominantly house infants, including NICU, SCN, or well-baby locations (for example, nurseries, babies in LDRP) are excluded.
Baby based locations are removed from the total facility denominator.
Measure Calculation
See attachment under 1.18a for details.
The measure is not stratified.
N/A
Supplemental Attachment
Measure Record
Point of Contact
NA
Andrea Benin
Atlanta , GA
United States
Paula Farrell
CDC NHSN
Atlanta, GA
United States
Importance
Evidence
Clostridioides difficile infection (CDI) is the leading cause of antibiotic-associated diarrhea and one of the most common healthcare-associated infections in the United States (CDC, 2024). In 2017, an estimated 223,900 cases of CDI occurred in hospitalized patients, resulting in an estimated 12,800 deaths, and an estimated $1 billion in healthcare costs were attributed to CDI (CDC, 2025). Multiple studies provide strong empirical support for the association between CDI infection prevention practices, such as environmental disinfection, antimicrobial stewardship, hand hygiene, chlorhexidine bathing, bundled approaches, and other interventions, and the reduction of CDI. Also in 2017, the state of Maryland implemented the Statewide Prevention and Reduction of C. difficile collaborative to reduce hospital-wide C. difficile. The state recruited 12 Maryland acute care hospitals that participated in the collaborative and 36 hospitals that participated as control hospitals. The collaborative team created assessment tools and resources to support quality improvement efforts in four domains: infection prevention, environmental cleaning, antimicrobial stewardship, and diagnostic stewardship (Rock, et. al, 2022). The collaborative team assessed the hospitals’ hand hygiene, use of personal protective equipment (PPE), and environmental cleaning and then provided feedback to the participating hospitals (Rock, et. al, 2022). In addition, the collaborative provided webinars and training on antimicrobial stewardship, funding to train pharmacists through the Society of Infectious Diseases Pharmacists’ antimicrobial stewardship certification program, and a peer-to-peer workshop on environmental cleaning and infection prevention (Rock, et. al, 2022). Within the first two quarters of involvement with the collaborative and implementation of the bundle interventions, the 12 intervention hospitals had a greater SIR reduction compared to control hospitals (Rock, et. al, 2022). The Maryland state-wide SIR for hospital-onset C. difficile decreased from 0.92 in 2017 to 0.8 in 2018 during initiation of the collaborative, and then to 0.61 in 2019 while the collaborative was ongoing (Rock, et. al, 2022). A three-hospital system in Florida also implemented a bundle approach to improve their C. difficile SIRs. Specific interventions included providing education to healthcare personnel regarding early detection and isolation of CDI patients, updating the electronic medical record to include a criteria-for-use C. difficile order form, which required providers to confirm appropriate criteria were met prior to testing for (White, et. al, 2020). Antibiotic stewardship interventions included physician education and EHR updates that required prescribers to select an appropriate indication upon order of fluoroquinolones (White, et. al, 2020). The hospital system level SIR decreased from 1.0 to 0.87, while the largest hospital in the system, hospital A, decreased its SIR from 1.03 to 0.84 by implementing this bundle of interventions (White, et.al, 2020). Another study found that implementing a multifaceted approach, including a new laboratory testing policy, enhanced cleaning and disinfection of patient rooms, antibiotic stewardship, use of PPE and proper isolation precautions, reduced CDI rates. Implementing these multiple interventions allowed the facility to reduce their CDI SIR from 1.10 in 2017 to 0.65 in 2018 (Read, et.al, 2020).
- Centers of Disease Control and Prevention. C. diff: Facts for Clinicians, March 2024 https://www.cdc.gov/c-diff/hcp/clinical-overview/index.html. Accessed May 27, 2025.
- Centers of Disease Control and Prevention. 2019 Antibiotic Resistance Threats Report https://www.cdc.gov/antimicrobial-resistance/data-research/threats/inde…. Accessed May 27, 2025.
- Read ME, Olson AJ, Calderwood MS. Front-line education by infection preventionists helps reduce Clostridioides difficile infections. Am J Infect Control. 2020 Feb;48(2):227-229.
- Rock C, Perlmutter R, Blythe D, et al. Impact of Statewide Prevention and Reduction of Clostridioides difficile (SPARC), a Maryland public health-academic collaborative: an evaluation of a quality improvement intervention. BMJ Quality & Safety. 2022 Feb;31(2):153-162.
- White KA, Soe MM, Osborn A, Walling C, Fike LV, Gould CV, Kuhar DT, Edwards JR, Cochran RL. Implementation of the Targeted Assessment for Prevention Strategy in a healthcare system to reduce Clostridioides difficile infection rates. Infect Control Hosp Epidemiol. 2020 Mar;41(3):295-301.
Adherence to recommendations in the clinical guidelines published for the management of C. difficile can result in decreased rates of C. difficile transmission and infection. Decreased rates of infection translate to a lower standardized infection ratio (SIR), which indicates improved performance.
The Infectious Diseases Society of America (IDSA) and Society for Healthcare Epidemiology of America’s (SHEA) Clinical Practice Guidelines for Clostridium difficile Infection in Adults and Children assesses the body of evidence existing in the literature. The two societies convened an expert panel to review and grade existing evidence for control and prevention of CDI. The panel used a standard process that included weighing the quality of evidence for practices that lead to successful diagnosis, treatment, infection prevention and environmental management of CDI in the inpatient setting. A single overall guideline recommendation was not provided. Each recommendation in the guidelines was given a grade.
Grading Scale: The panel followed a process used in the development of other IDSA guidelines, which included a systematic weighting of the strength of recommendation and quality of evidence using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system. http://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1…. The 2018 updated IDSA/SHEA practice guidelines for the management of CDI included results from over 300 studies.
- McDonald LC, Gerding DN, Johnson S, Bakken JS, Carroll KC, Coffin SE, Dubberke ER, Garey KW, Gould CV, Kelly C, Loo V, Shaklee Sammons J, Sandora TJ, Wilcox MH. Clinical Practice Guidelines for Clostridium difficile Infection in Adults and Children: 2017 Update by the Infectious Diseases Society of America (IDSA) and Society for Healthcare Epidemiology of America (SHEA). Clin Infect Dis. 2018 Mar 19;66(7):e1-e48. doi: 10.1093/cid/cix1085. https://pubmed.ncbi.nlm.nih.gov/29462280/
The following guidelines with corresponding grade are related to the surveillance, testing, and prevention of CDI.
EPIDEMIOLOGY
I. How are CDI cases best defined?
Recommendation
1. To increase comparability between clinical settings, use available standardized case definitions for surveillance of (1) healthcare facility-onset (HO) CDI; (2) community-onset, healthcare facility-associated (CO-HCFA) CDI; and (3) community-associated (CA) CDI (good practice recommendation).
II. What is the minimal surveillance recommendation for institutions with limited resources?
Recommendation
- At a minimum, conduct surveillance for HO-CDI in all inpatient healthcare facilities to detect elevated rates or outbreaks of CDI within the facility (weak recommendation, low quality of evidence)
IV. How should CDI surveillance be approached in settings of high endemic rates or outbreaks? Recommendation
1. Stratify data by patient location to target control measures when CDI incidence is above national and/or facility reduction goals or if an outbreak is noted (weak recommendation, low quality of evidence)
DIAGNOSIS
VI. What is the preferred population for C. difficile testing, and should efforts be made to achieve this target?
Recommendation
1. Patients with unexplained and new-onset ≥3 unformed stools in 24 hours are the preferred target population for testing for CDI (weak recommendation, very low quality of evidence).
IX. What is the role of repeat testing, if any? Are there asymptomatic patients in whom repeat testing should be allowed, including test of cure?
Recommendation
1. Do not perform repeat testing (within 7 days) during the same episode of diarrhea and do not test stool from asymptomatic patients, except for epidemiological studies (strong recommendation, moderate quality of evidence).
INFECTION PREVENTION AND CONTROL
Isolation Measures for Patients With CDI
XIII. Should private rooms and/or dedicated toilet facilities be used for isolated patients with CDI?
Recommendations
1. Accommodate patients with CDI in a private room with a dedicated toilet to decrease transmission to other patients. If there is a limited number of private single rooms, prioritize patients with stool incontinence for placement in private rooms (strong recommendation, moderate quality of evidence).
2. If cohorting is required, it is recommended to cohort patients infected or colonized with the same organism(s)—that is, do not cohort patients with CDI who are discordant for other multidrug-resistant organisms such as methicillin-resistant Staphylococcus aureus or vancomycin-resistant Enterococcus (strong recommendation, moderate quality of evidence).
XIV. Should gloves and gowns be worn while caring for isolated CDI patients?
Recommendation
- Healthcare personnel must use gloves (strong recommendation, high quality of evidence) and gowns (strong recommendation, moderate quality of evidence) on entry to a room of a patient with CDI and while caring for patients with CDI.
XV. When should isolation be implemented?
Recommendation
- Patients with suspected CDI should be placed on preemptive contact precautions pending the C. difficile test results if test results cannot be obtained on the same day (strong recommendation, moderate quality of evidence).
XVI. How long should isolation be continued?
Recommendations
1. Continue contact precautions for at least 48 hours after diarrhea has resolved (weak recommendation, low quality of evidence). 2. Prolong contact precautions until discharge if CDI rates remain high despite implementation of standard infection control measures against CDI (weak recommendation, low quality of evidence)
XVII. What is the recommended hand hygiene method (assuming glove use) when caring for patients in isolation for CDI?
Recommendations
1. In routine or endemic settings, perform hand hygiene before and after contact of a patient with CDI and after removing gloves with either soap and water or an alcohol-based hand hygiene product (strong recommendation, moderate quality of evidence).
2. In CDI outbreaks or hyperendemic (sustained high rates) settings, perform hand hygiene with soap and water preferentially instead of alcohol-based hand hygiene products before and after caring for a patient with CDI given the increased efficacy of spore removal with soap and water (weak recommendation, low quality of evidence).
3. Handwashing with soap and water is preferred if there is direct contact with feces or an area where fecal contamination is likely (eg, the perineal region) (good practice recommendation).
XVIII. Should patient bathing interventions be implemented to prevent CDI?
Recommendation
1. Encourage patients to wash hands and shower to reduce the burden of spores on the skin (good practice recommendation).
XIX. Should noncritical devices or equipment be dedicated to or specially cleaned after being used on the isolated patient with CDI?
Recommendation
1. Use disposable patient equipment when possible and ensure that reusable equipment is thoroughly cleaned and disinfected, preferentially with a sporicidal disinfectant that is equipment compatible (strong recommendation, moderate quality of evidence).
XX. What is the role of manual, terminal disinfection using a C. difficile sporicidal agent for patients in isolation for CDI?
Recommendation
- Terminal room cleaning with a sporicidal agent should be considered in conjunction with other measures to prevent CDI during endemic high rates or outbreaks, or if there is evidence of repeated cases of CDI in the same room (weak recommendation, low quality of evidence).
XX. What is the role of manual, terminal disinfection using a C. difficile sporicidal agent for patients in isolation for CDI?
- Terminal room cleaning with a sporicidal agent should be considered in conjunction with other measures to prevent CDI during endemic high rates or outbreaks, or if there is evidence of repeated cases of CDI in the same room (weak recommendation, low quality of evidence).
XXI. Should cleaning adequacy be evaluated?
- Incorporate measures of cleaning effectiveness to ensure quality of environmental cleaning (good practice recommendation).
XXII. What is the role of automated terminal disinfection using a method that is sporicidal against C. difficile?
- There are limited data at this time to recommend use of automated, terminal disinfection using a sporicidal method for CDI prevention (no recommendation).
XXIII. What is the role of daily sporicidal disinfection?
- Daily cleaning with a sporicidal agent should be considered in conjunction with other measures to prevent CDI during outbreaks or in hyperendemic (sustained high rates) settings, or if there is evidence of repeated cases of CDI in the same room (weak recommendation, low quality of evidence).
XXV. What is the role of antibiotic stewardship in controlling CDI rates?
Recommendations
1. Minimize the frequency and duration of high-risk antibiotic therapy and the number of antibiotic agents prescribed, to reduce CDI risk (strong recommendation, moderate quality of evidence).
2. Implement an antibiotic stewardship program (good practice recommendation).
3. Antibiotics to be targeted should be based on the local epidemiology and the C. difficile strains present. Restriction of fluoroquinolones, clindamycin, and cephalosporins (except for surgical antibiotic prophylaxis) should be considered (strong recommendation, moderate quality of evidence)
Measure Impact
The Patient Safety Action Network is a coalition of individuals and organizations consisting of patients who have been medically harmed, their loved ones, and concerned patient safety advocates.
“Please accept these comments from the Patient Safety Action Network regarding the following HAI measures; we are commenting on all of them together:
- Catheter-Associated Urinary Tract Infections (CAUTI)
- Central Line Associated Blood Stream Infections (CLABSI)
- 30-Day Post-Operative Colon Surgery (COLO) and Abdominal Hysterectomy (HYST) Surgical Site Infection (SSI)
- Methicillin-resistant Staphylococcus aureus (MRSA) Bacteremia LabID Event
- Clostridioides difficile (CDI) LabID Event
- Antimicrobial Use Measure
“Fundamentally, each of these measures is important and essential to preventing infections. If we do not measure and publicly report these events in a continuous, standardized way, we cannot truly know or understand when actual progress is made.
There are several target populations for these measures. First, members of the public who may need to use the services of a local hospital at any given point without warning or who have an interest in seeing how their hospital compares to others on hospital acquired infections. The published HAI measures provide that public service. Second, patients being treated at a hospital who are infected might not benefit from the past published HAI measures, but they probably are interested in accountability. One of the first questions many ask is “will my infection be counted?” The next question typically is, “how can we prevent it from happening again to someone else?” To them, these measurements are very important.
The value and meaningfulness of these outcome measures lie in tracking reduction of patient harm over time using individual hospitals’ HAI measures. Progress means fewer infections at each point of measurement with a goal toward no infections. Unfortunately, these measures are rarely presented on a continuum demonstrating whether each hospital has reduced this harm over the years. And they are no longer presented with the actual numbers of infections, which reflect actual infections reported and not an estimate.
We also believe the value of these measures is lowered because of the way they are reported to the public. It appears that the standardization using an SIR of 1.0 as the baseline has established that as the status quo, even though the baseline has been adjusted over time. We wonder how often hospitals accept SIRs of around 1.0 as acceptable. Further, the use of risk adjustment skews the real results in each of these measures, i.e., the patients who got infected. We would rather see a stratified presentation that compares similar hospitals together – without risk adjustments. We believe that would be more meaningful to the public.
Also, the terms used to present the data lead to confusion, such as predicted number of infections and better than/no different/worse than the national benchmark. Many hospitals’ data is “not available,” without context (the hospital failed to report, the hospital does not have enough cases to rate, etc).
Even with these limitations, the measures are important to retain because of their value to patients who expect to be free from preventable harm when hospitalized. You ask about the full meaning of these measures to patients, but that requires some understanding of what happens to them following a hospital acquired infection. These events affect each person in a different way. It can mean a round of antibiotics; a longer stay in the hospital or the need to seek further treatment; continued chronic conditions, including recurrences of the infection; significant medical debt; losing a job due to missing work as a consequence of an infection; losing one’s home due to mounting medical bills and other debts; permanent disability; sepsis that is only survived after intense medical care; and death. This should clearly explain why all these measures are meaningful to patients.
Frankly, we need more infection measures so that all hospital acquired infections are accounted for, like what is done in California. It seems to us that every time federal agencies ask for feedback about these measures, the result is less information to the public.”
The Clostridium difficile (CDI) LabID Event Standardized Infection Ratio serves as an objective measure of healthcare-associated infection (HAI) burden within a hospital. HAI reduction has been a national priority set by U.S. Government going back to 2008 with the U.S. Health and Human Services (HHS) National Action Plan to Prevent Health Care-associated Infections: Roadmap to Elimination.1 The 2016 update to this national action plan has included specific HAIs as targets for benchmarking progress including "Reduce hospital-onset Clostridioides difficile infections (CDI)". While there has been overall progress in reducing these specific HAIs, there is room for improvement in both the surveillance and prevention of hospital-onset Clostridioides difficile infections.
Measuring hospital-onset C.difficile has also been a priority for CMS as indicated by the use of the measure in six CMS Measure Programs, including Hospital Acquired Condition Reduction Program, Hospital Value-Based Purchasing, Hospital Inpatient Quality Reporting, Inpatient Rehabilitation Facility Quality Reporting, Long-Term Care Hospital Quality Reporting and Prospective Payment System-Exempt Cancer Hospital Quality Reporting.2
1U.S. Health and Human Services (HHS) National Action Plan to Prevent Health Care-associated Infections: Roadmap to Elimination. Accessed May 5, 2025 at https://www.hhs.gov/oidp/topics/health-care-associated-infections/hai-a…;
2Centers for Medicare and Medicaid Services Measures Inventory Tool at https://cmit.cms.gov/cmit/#/FamilyView?familyId=462
Performance Gap
In data submitted to NHSN for 1/1/2024 – 12/31/2024, a total of 2,875 acute care hospitals had at least one predicted event and qualified for the measure. The mean CDI SIR across all hospitals was 0.781 with a range of 0-8.73. A total of 334 hospitals had an SIR=0, meaning they had zeroC. difficile events. The decile groups represent 287 or 288 hospitals.. The range of mean performance across the 10 groups ranged from 0 to 2.08, indicating a wide range of performance across hospitals.
A total of 224 critical access hospitals had at least one predicted event and qualified for the measure. The mean CDI SIR across all hospitals was 0.822 with a range of 0-6.32. A total of 81 hospitals had an SIR=0 meaning they had zero C. difficile events. The decile groups represent 22 or 23 hospitals. The range of mean performance across the 10 groups ranged from 0 to 2.73, indicating a wide range of performance across hospitals.
A total of 345 long-term care hospitals had at least one predicted event and qualified for the measure. The mean CDI SIR across all hospitals was 0.779 with a range of 0-5.43. A total of 77 hospitals had an SIR=0, meaning they had zero C. difficile events. The decile groups represent 34 or 35 hospitals. The range of mean performance across the 10 groups ranged from 0 to 2.62, indicating a wide range of performance across hospitals.
A total of 633 inpatient rehabilitation hospitals had at least one predicted event and qualified for the measure . The mean CDI SIR across all hospitals was 0.658 with a range of 0-4.69. A total of 217 hospitals had an SIR=0 meaning they had zero C. difficile events. The decile groups represent 63 or 64 hospitals. The range of mean performance across the 10 groups ranged from 0 to 2.40, indicating a wide range of performance across hospitals.
| Overall | Minimum | Decile_1 | Decile_2 | Decile_3 | Decile_4 | Decile_5 | Decile_6 | Decile_7 | Decile_8 | Decile_9 | Decile_10 | Maximum | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Mean Performance Score | 0.781 | 0 | 0.00 | 0.156 | 0.351 | 0.493 | 0.628 | 0.767 | 0.910 | 1.079 | 1.342 | 2.081 | 8.733 |
| N of Entities | 2875 | 287 | 288 | 287 | 288 | 287 | 288 | 287 | 288 | 287 | 288 | ||
| N of Persons / Encounters / Episodes | 151749866 | 5186641 | 14001734 | 13947429 | 16974075 | 17712744 | 18422534 | 18761071 | 19291832 | 17935621 | 9516185 |
Care Gaps
Closing Care Gaps
This criteria is optional for the Fall 2025 cycle.
Feasibility
Feasibility
This is a maintenance measure, and the measure specifications have not changed. Facilities have not notified NHSN of any feasibility issues within the last year.
All required data elements are routinely generated, in structured fields, and used during care delivery. Facilities can choose to submit this data manually via a web form or via submission of CDA electronic files. NHSN has built-in business rules for mandatory data elements and does not allow for the submission of incomplete records.
Addressing NHSN data quality issues is integral to NHSN’s ability to help facilities collect the data necessary to identify areas needing prevention efforts, measure progress of prevention efforts, and push toward C. difficile elimination. The NHSN Team routinely reviews the data reported to NHSN and will contact facilities to resolve confirmed and suspected data quality flags. Multiple data quality checks are conducted to help confirm the accuracy of the data being reported. These data quality checks include confirming numerator and denominator data, verifying business rules within the application, verifying alerts, and confirming the flags triggered by incomplete data.
NHSN provides facilities with internal validation toolkits, which facilities can choose to use to audit their internal data to identify any potential inaccuracies or problems. The internal validation toolkit also provides recommendations to facilities for implementing quality control processes to ensure data is accurate and complete.
Additionally, NHSN offers external validation toolkits, which can be used by state or local health departments, or other auditors, to perform checks on the data that facilities submit to NHSN. External validation allows for the auditors to identify gaps in understanding of surveillance definitions or other errors and provide education to ensure data reported to NHSN follows the standardized specifications.
Per the Paperwork Reduction Act (PRA) of 1995, federal agencies cannot conduct or sponsor the collection of information unless the Office of Management and Budget (OMB) has reviewed and approved the proposed data collection. Federal agencies must submit a set of documents known as an Information Collection Request (ICR) to request OMB approval of an information collection. The ICR documents describe what information is needed, why it is needed, how it will be collected, and how much time, money, and effort it will cost the respondents to collect the information.
Multiple data collection forms are used to provide surveillance data on CDI LabID events. Below are the OMB-approved estimated total annual burden hours and annual cost for all facilities that complete this data collection.
See 7.1 Supplemental Information Attachment Page 5 for cost and burden details.
While CDC can retrieve data by personal identifier, CDC does not, as a matter of practice or policy, retrieve data in this way. Specifically, the primary practice and policy of CDC regarding NHSN data are to retrieve data by the name of the hospital or another non-personal identifier, not an individual patient, for surveillance and public health purposes. Furthermore, patient identifiers are not necessary for NHSN to operate.
An Assurance of Confidentiality is granted for all data collected under NHSN. NHSN’s Assurance of Confidentiality states the following:
“the voluntarily provided information obtained in this surveillance system that would permit identification of any individual or institution is collected with a guarantee that it will be held in strict confidence, will be used only for the purposes stated, and will not otherwise be disclosed or released without the consent of the individual, or the institution in accordance with Sections 304, 306 and 308(d) of the Public Health Service Act (42 USC 242b, 242k, and 242m(d)).”
This is a maintenance measure, and the measure specifications have not changed.
Proprietary Information
Scientific Acceptability
Testing Data
Reliability Testing:
The dataset used for testing came from CDC NHSN, which collects data about healthcare-associated infections (HAI) from healthcare facilities throughout the United States. Data reported for the period from 1/1/2024 to 12/31/2024 were used for reliability testing.
Validity Testing:
The dataset used for testing came from CDC’s NHSN, which collects HAI data from facilities throughout the United States. Data reported for the period from 1/1/2024 to 12/31/2024 were used for validity testing.
Validation Studies:
Rock C, Perlmutter R, Blythe D, et al. Impact of Statewide Prevention and Reduction of Clostridioides difficile (SPARC), a Maryland public health-academic collaborative: an evaluation of a quality improvement intervention. BMJ Quality & Safety. 2022 Feb;31(2):153-162.
Dates of data used in testing: April 2017 to March 2020
White KA, Soe MM, Osborn A, Walling C, Fike LV, Gould CV, Kuhar DT, Edwards JR, Cochran RL. Implementation of the Targeted Assessment for Prevention Strategy in a healthcare system to reduce Clostridioides difficile infection rates. Infect Control Hosp Epidemiol. 2020 Mar;41(3):295-301.
Dates of data used in testing: January 2015- December 2016
- Garcia Reeves AB, Lewis JW, Trogdon JG, Stearns SC, Weber DJ, Weinberger M. Association between statewide adoption of the CDC's Core Elements of Hospital Antimicrobial Stewardship Programs and rates of methicillin-resistant Staphylococcus aureus bacteremia and Clostridioides difficile infection in the United States. Infect Control Hosp Epidemiol. 2020 Apr;41(4):430-437.
Dates of data used in testing: January 2014-December 2016.
4. Bartles R, Reese S, Gumbar A. Closing the gap on infection prevention staffing recommendations: Results from the beta version of the APIC staffing calculator. Am J of Infect Control. 2024; 52(12): 1345-1350.
Date of data used in testing: December 2023 to June 2024.
Risk Adjustment: The dataset used for the risk adjustment model was derived from CDC’s NHSN, which collects data about HAIs from healthcare facilities throughout the United States. Data reported from 1/1/2022 to 12/31/2022 was used in the risk adjustment analysis. In-plan CDI LabID data and risk factors were derived from facility enrollment information and the annual facility survey.
Reliability Testing:
The dataset used for testing came from CDC NHSN, which collects data about healthcare-associated infections (HAI) from healthcare facilities throughout the United States. Data reported for the period from 1/1/2024 to 12/31/2024 were used for reliability testing.
Validity Testing:
The dataset used for testing came from CDC’s NHSN, which collects HAI data from facilities throughout the United States. Data reported for the period from 1/1/2024 to 12/31/2024 were used for validity testing.
Validation Studies:
Rock C, Perlmutter R, Blythe D, et al. Impact of Statewide Prevention and Reduction of Clostridioides difficile (SPARC), a Maryland public health-academic collaborative: an evaluation of a quality improvement intervention. BMJ Quality & Safety. 2022 Feb;31(2):153-162.
Dates of data used in testing: April 2017 to March 2020
White KA, Soe MM, Osborn A, Walling C, Fike LV, Gould CV, Kuhar DT, Edwards JR, Cochran RL. Implementation of the Targeted Assessment for Prevention Strategy in a healthcare system to reduce Clostridioides difficile infection rates. Infect Control Hosp Epidemiol. 2020 Mar;41(3):295-301.
Dates of data used in testing: January 2015- December 2016
- Garcia Reeves AB, Lewis JW, Trogdon JG, Stearns SC, Weber DJ, Weinberger M. Association between statewide adoption of the CDC's Core Elements of Hospital Antimicrobial Stewardship Programs and rates of methicillin-resistant Staphylococcus aureus bacteremia and Clostridioides difficile infection in the United States. Infect Control Hosp Epidemiol. 2020 Apr;41(4):430-437.
Dates of data used in testing: January 2014-December 2016.
4. Bartles R, Reese S, Gumbar A. Closing the gap on infection prevention staffing recommendations: Results from the beta version of the APIC staffing calculator. Am J of Infect Control. 2024; 52(12): 1345-1350.
Date of data used in testing: December 2023 to June 2024.
Risk Adjustment: The dataset used for the risk adjustment model was derived from CDC’s NHSN, which collects data about HAIs from healthcare facilities throughout the United States. Data reported from 1/1/2022 to 12/31/2022 was used in the risk adjustment analysis. In-plan CDI LabID data and risk factors were derived from facility enrollment information and the annual facility survey.
Reliability Testing:
The dataset used for testing came from CDC’s NHSN, which collects data about HAIs from healthcare facilities throughout the United States. Data reported for the period from 1/1/2024 to 12/31/2024 were used for reliability testing.
Validity Testing:
The dataset used for testing came from CDC’s NHSN, which collects data about HAIs from healthcare facilities throughout the United States. Data reported for the year 2024 were used for validity testing. Facilities with both C. difficile (CDI) and methicillin-resistant Staphylococcus aureus (MRSA) SIRs were included in the analysis (facilities with ≥1 predicted event for both event types were included).
Validation Studies:
- Impact of Statewide Prevention and Reduction of Clostridioides difficile (SPARC), a Maryland public health-academic collaborative: an evaluation of a quality improvement intervention. Facilities participating in the intervention reported CDI SIR data to NHSN from April 2017 to March 2020 and control hospitals reported CDI SIR data from October 2017 to March 2020.
Rock C, Perlmutter R, Blythe D, et al. Impact of Statewide Prevention and Reduction of Clostridioides difficile (SPARC), a Maryland public health-academic collaborative: an evaluation of a quality improvement intervention. BMJ Quality & Safety. 2022 Feb;31(2):153-162.
2.Implementation of the Targeted Assessment for Prevention Strategy in a healthcare system to reduce Clostridioides difficile infection rates. Facilities reported CDI SIR data to NHSN from January 2015 to December 2016.
White KA, Soe MM, Osborn A, Walling C, Fike LV, Gould CV, Kuhar DT, Edwards JR, Cochran RL. Implementation of the Targeted Assessment for Prevention Strategy in a healthcare system to reduce Clostridioides difficile infection rates. Infect Control Hosp Epidemiol. 2020 Mar;41(3):295-301.
3. Association between statewide adoption of the CDC's Core Elements of Hospital Antimicrobial Stewardship Programs and rates of methicillin-resistant Staphylococcus aureus bacteremia and Clostridioides difficile infection in the United States. This study merged 2014–2017 hospital-level data from the Centers for Medicare & Medicaid Services’ (CMS) Hospital Compare data, Provider of Service files, Medicare cost reports, and 2014–2016 state-level data from the CDC’s Patient Safety Atlas website.
Garcia Reeves AB, Lewis JW, Trogdon JG, Stearns SC, Weber DJ, Weinberger M. Association between statewide adoption of the CDC's Core Elements of Hospital Antimicrobial Stewardship Programs and rates of methicillin-resistant Staphylococcus aureus bacteremia and Clostridioides difficile infection in the United States. Infect Control Hosp Epidemiol. 2020 Apr;41(4):430-437.
4. Closing the gap on infection prevention staffing recommendations: Results from the beta version of the APIC staffing calculator. For each facility, SIRs were entered from CMS’s Hospital Compare website for central line-associated bloodstream infection (CLABSI), catheter-associated urinary tract infection (CAUTI), CDI and colon surgical site infection (SSI).
Bartles R, Reese S, Gumbar A. Closing the gap on infection prevention staffing recommendations: Results from the beta version of the APIC staffing calculator. Am J of Infect Control. 2024; 52(12): 1345- 1350.
Risk Adjustment:
The 2022 national aggregate data were reviewed for all potential data quality issues, including outlier values, prior to performing the risk adjustment modeling of the SIR denominator for the CDI LabID model. Based on the surveillance protocol for CDI, data submitted from inpatient rehabilitation locations and inpatient psychiatric locations that have their CMS Certification Number (CCN) were excluded from modeling consideration. NICUs and well-baby units, such as newborn nurseries, are excluded.
See 7.1 Supplemental Information Attachment Pages 6-8 for details.
Reliability Testing:
The C. difficile risk models used to calculate the predicted number of events were developed using facility-level factors and testing practices.
Validity Testing:
The C. difficile risk models used to calculate the predicted number of events were developed using facility-level factors and testing practices.
Validation Studies:
I. Impact of Statewide Prevention and Reduction of Clostridioides difficile (SPARC), a Maryland public health-academic collaborative: an evaluation of a quality improvement intervention. Descriptive characteristics of patients were not available because demographic data on patients is not collected in NHSN.
Rock C, Perlmutter R, Blythe D, et al. Impact of Statewide Prevention and Reduction of Clostridioides difficile (SPARC), a Maryland public health-academic collaborative: an evaluation of a quality improvement intervention. BMJ Quality & Safety. 2022 Feb;31(2):153-162.
II. Implementation of the Targeted Assessment for Prevention Strategy in a healthcare system to reduce Clostridioides difficile infection rates. Descriptive characteristics of patients were not available because demographic data on patients is not collected in the NHSN module.
White KA, Soe MM, Osborn A, Walling C, Fike LV, Gould CV, Kuhar DT, Edwards JR, Cochran RL. Implementation of the Targeted Assessment for Prevention Strategy in a healthcare system to reduce Clostridioides difficile infection rates. Infect Control Hosp Epidemiol. 2020 Mar;41(3):295-301.
III. Association between statewide adoption of the CDC's Core Elements of Hospital Antimicrobial Stewardship Programs and rates of methicillin-resistant Staphylococcus aureus bacteremia and Clostridioides difficile infection in the United States. Descriptive characteristics of patients were not available because demographic data on patients is not collected in the NHSN Module.
Garcia Reeves AB, Lewis JW, Trogdon JG, Stearns SC, Weber DJ, Weinberger M. Association between statewide adoption of the CDC's Core Elements of Hospital Antimicrobial Stewardship Programs and rates of methicillin-resistant Staphylococcus aureus bacteremia and Clostridioides difficile infection in the United States. Infect Control Hosp Epidemiol. 2020 Apr;41(4):430-437.
IV. Results from the beta version of the APIC staffing calculator: Most of the hospitals had an intensive care unit (ICU) (n = 355, 91.0%) and an emergency department (ED) (n = 386, 99.0%), performed surgery (n = 385, 98.7%), and were a part of a system (n = 329, 84.4%) (Table 1). Almost 90% of hospitals with > 500 beds had a burn unit, stem cell transplant unit (SCTU) or an inpatient rehabilitation unit (IRF) (n = 64, 87.7%), compared to about 50% of hospitals with < 200 beds. The case mix index (CMI) increased by hospital size; hospitals with 101 to 200 beds had a lower CMI (median: 1.54, IQR: 0.25) compared to hospitals with > 750 beds (2.27, 0.39).
Bartles R, Reese S, Gumbar A. Closing the gap on infection prevention staffing recommendations: Results from the beta version of the APIC staffing calculator. Am J of Infect Control. 2024; 52(12): 1345-1350.
Risk Adjustment: The risk models used to calculate the predicted number of CDI events were developed using facility-level factors and testing practices.
Reliability
To measure facility-level reliability, we computed the signal-to-noise ratio (SNR).
SNR reliability testing was performed to distinguish measure scores between facilities (Adams J.L. 2009). SNR reliability scores can range from 0 to 1. A reliability of zero implies that all the variability in a measure is attributable to measurement error. Reliability of one implies that all variability is attributable to real difference in performance. The annual standardized infection ratio (SIR) was defined as the sum of observed (O) events at the facility divided by the sum of predicted (P) events calculated from the risk-adjustment model. SNR reliability testing denotes between-facility variance and within-facility variance (Adams J.L. 2009). The SNR for each facility SIR was calculated using both the between- facility and within-facility variance across eligible facilities with a predicted number of CDI events ≥1. The between-facility variance was the total variance of the SIR facility distribution. The within-facility variance of the SIR for each facility was then calculated as Var(O/P) where P is a constant, a nuisance factor with no random variation. The observed (O) was assumed to follow a Poisson distribution with a mean parameter lambda approximated by P. The result was Var(O/P) = Var(O)/P2 = P/P2 = 1/P.
References:
Adams, J. L. (2009). The reliability of provider profiling: a tutorial. RAND.
See attachment under 5.2.3a for reliability testing results for each care setting.
The estimated SNR reliability score was ≥0.6 in 72% of acute care hospitals (ACH), 21% of critical access hospitals (CAH), 84% of long-term acute care facilities (LTACF), and 39% of inpatient rehabilitation facilities (IRF).
The median SNR reliability score was 0.712 among ACH, 0.546 among CAH, 0.73 among LTACF, and 0.571 among IRF.
We calculated the signal-to-noise ratio (SNR) reliability score for each facility that had at least one predicted CDI event. SNR reliability scores vary across facilities from zero to one, with a score of zero indicating that all variation is attributable to noise (variation across patients within facilities) and a score of one indicating that all variation is caused by real differences in performance across facilities. Reliability testing was performed on data reported for the year 2024 from all care settings that report the measure.
The estimated SNR reliability score was ≥0.6 in 72% of acute care hospitals (ACH), 21% of critical access hospitals (CAH), 84% of long-term acute care facilities (LTACF), and 39% of inpatient rehabilitation facilities (IRF). The decile distribution of reliability measurements is presented above in section 5.2.3a.
The median SNR reliability score was 0.712 among ACH, 0.546 among CAH, 0.73 among LTACF, and 0.571 among IRF. The median SNR reliability scores for ACH and LTACH demonstrate substantial reliability, and the scores for CAH and IRF demonstrate moderate reliability. Our interpretation of the results is based on the standards established by Landis and Koch (1977):
< 0 – Less than chance agreement
0 – 0.2 Slight agreement
0.21 – 0.39 Fair agreement
0.4 – 0.59 Moderate agreement
0.6 – 0.79 Substantial agreement
0.8 – 0.99 Almost perfect agreement
1 Perfect agreement
Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. biometrics, 159-174.
Validity
Validity Testing:
A Spearman correlation coefficient was calculated to assess a hypothesized monotonic relationship in the positive direction between annual CDI and MRSA standardized infection ratios (SIR). The annual SIR was defined as the sum of observed (O) events at the facility divided by the sum of predicted (P) events calculated from the risk-adjustment model. Facility that reported both CDI and MRSA data for 2024 with at least 1 predicted event for each were included. Facility that reported only CDI, reported only MRSA, or did not have at least 1 predicted event for both HAIs were excluded from the analysis. Correlation coefficients range from -1 to +1, where a coefficient of -1 implies a perfect negative correlation, 0 implies no correlation, and +1 implies a perfect positive correlation. A significance threshold of 0.05 was used.
We hypothesized that there would be a positive correlation between CDI and MRSA SIRs because there is some overlap in the infection prevention practices that protect against both types of infections (for example, implementing hand hygiene, standard precautions, contact isolation, environmental cleaning, and antimicrobial stewardship). However, there are also differences in prevention practices as well, such as insertion and maintenance practices for vascular access devices for the prevention of MRSA bloodstream infections. Thus, we predicted that while the correlation would be positive, it would be a weak correlation.
Validation Studies:
These studies assess a hypothesized relationship between a reduction in the CDI SIR and implementation of CDI prevention techniques. An SIR > 1.0 represents that more CDIs were observed than predicted, an SIR <1.0 represents that fewer CDIs were observed than predicted, and an SIR =1.0 represents the same number of CDIs were observed as predicted. No published literature that examined prevention activities and reported the results of the NHSN C. difficile (CDI) LabID Event SIR in long-term acute care hospitals or inpatient acute rehabilitation hospitals was identified during a literature review. After consultations with experts in the field, it was determined that healthcare facilities utilize the same strategies to prevent C. diff. regardless of facility type. Therefore, we selected the following articles that address C. diff prevention in acute care hospitals. The studies support the hypothesis that the measure score (CDI SIR) correctly reflects the quality of care provided and adequately identifies differences in quality.
I. Impact of Statewide Prevention and Reduction of Clostridioides difficile (SPARC), a Maryland public health-academic collaborative: an evaluation of a quality improvement intervention.
The Maryland Statewide Prevention and Reduction of C. difficile (SPARC) collaborative was created to decrease rates of C. diff. across the state. Twelve Maryland acute care hospitals participated in the collaborative and 36 hospitals served as controls. The collaborative created assessment tools and resources to support quality improvement efforts across the hospitals in four domains: infection prevention, environmental cleaning, antimicrobial stewardship, and diagnostic stewardship. The SPARC team conducted site visits to observe hand hygiene, use of personal protective equipment (PPE), and environmental cleaning and provided feedback reports to the participating hospitals. Feedback from the site visits was also discussed with hospital leadership. SPARC also provided webinars and training on antimicrobial stewardship, funding to train pharmacists through the Society of Infectious Diseases Pharmacists antimicrobial stewardship certification program and a peer-to-peer workshop on environmental cleaning and infection prevention. The unit of analysis was hospital-quarter. Data used from intervention hospitals included four quarters of pre-intervention and six quarters of post-intervention data from NHSN. For control hospitals, 10 quarters of data from October 2017 to March 2020 were used.
Rock C, Perlmutter R, Blythe D, et al. Impact of Statewide Prevention and Reduction of Clostridioides difficile (SPARC), a Maryland public health-academic collaborative: an evaluation of a quality improvement intervention. BMJ Quality & Safety. 2022 Feb;31(2):153-162.
II. Implementation of the Targeted Assessment for Prevention Strategy in a healthcare system to reduce Clostridioides difficile infection rates.
The Targeted Assessment for Prevention (TAP) strategy was initiated within a three-hospital system in Florida. TAP assessments were administered to staff at each hospital to identify infection control gaps and inform CDI prevention interventions. Based on priority areas identified by the assessments, the healthcare organization worked with their QIO to prioritize opportunities for improvement and CDI prevention interventions. These interventions included providing education to healthcare personnel regarding early detection and isolation of CDI patients, updating the electronic medical record to include a criteria-for-use C. difficile order form requiring providers to confirm that appropriate criteria were met prior to testing for C. difficile, executing antibiotic stewardship interventions including physician education, and implementing an update to the electronic health record that required prescribers to select an appropriate indication upon order of fluoroquinolones. The study hypothesized that identifying opportunities for improvement and implementing CDI prevention interventions in these areas would decrease the hospitals’ CDI SIR. Reductions in CDI events were determined by assessing the system level CDI event SIR and each individual hospital CDI SIR provided by NHSN.
White KA, Soe MM, Osborn A, Walling C, Fike LV, Gould CV, Kuhar DT, Edwards JR, Cochran RL. Implementation of the Targeted Assessment for Prevention Strategy in a healthcare system to reduce Clostridioides difficile infection rates. Infect Control Hosp Epidemiol. 2020 Mar;41(3):295-301.
III. Association between statewide adoption of the CDC's Core Elements of Hospital Antimicrobial Stewardship Programs and rates of methicillin-resistant Staphylococcus aureus bacteremia and Clostridioides difficile infection in the United States.
In 2014, the CDC launched seven core elements for antimicrobial stewardship programs: leadership commitment, accountability, drug expertise, action, tracking, reporting, and education. A study was conducted to examine compliance with the core elements from 2014 - 2016 and the association between statewide adoption of the core elements and hospital MRSA and CDI rates in all US states. The study hypothesized that states with higher percentages of reported compliance with the core elements would have significantly lower MRSA and CDI rates. The study used the CDC’s Patient Safety Atlas website, which provides access to state-level data on hospital-acquired infections, antimicrobial resistance, and antibiotic stewardship programs from acute-care hospitals nationwide. The data presented on the website were collected through the CDC National Healthcare Safety Network (NHSN) Patient Safety Component Annual Hospital Survey. Antibiotic stewardship program data, also collected through NHSN, was used to assess whether facilities met the criteria for each of the 7 recommended core elements. For each year from 2014-2016, between 4,173 and 4,764 acute-care facilities completed the NHSN survey. The study used descriptive statistics to measure state-level variation in the percentage of hospitals meeting the core elements during the study period.
Garcia Reeves AB, Lewis JW, Trogdon JG, Stearns SC, Weber DJ, Weinberger M. Association between statewide adoption of the CDC's Core Elements of Hospital Antimicrobial Stewardship Programs and rates of methicillin-resistant Staphylococcus aureus bacteremia and Clostridioides difficile infection in the United States. Infect Control Hosp Epidemiol. 2020 Apr;41(4):430-437.
IV. Closing the Gap on Infection Prevention Staffing Recommendations: Results from the beta version of the APIC staffing calculator: In this study, a staffing calculator was developed and piloted to provide facilities with customized infection prevention staffing recommendations. The study hypothesized that facilities with lower staffing levels would have higher SIRs for central line-associated bloodstream infection (CLABSI), catheter-associated urinary tract infection (CAUTI), CDI, and colon surgical site infection (SSI).
Bartles R, Reese S, Gumbar A. Closing the gap on infection prevention staffing recommendations: Results from the beta version of the APIC staffing calculator. Am J of Infect Control. 2024; 52(12): 1345-1350.
Validity Testing:
Among 1,906 acute care hospitals, we identified a weak but significant positive correlation between CDI SIR and MRSA SIR (rho= 0.06775, p= 0.0031).
Validation Studies:
- Impact of Statewide Prevention and Reduction of Clostridioides difficile (SPARC), a Maryland public health-academic collaborative: an evaluation of a quality improvement intervention.
Within the first two quarters of involvement with the collaborative, 12 intervention hospitals had a greater standardized infection ratio (SIR) reduction compared to control hospitals. A SIR > 1.0 represents that more CDI events were observed than predicted, a SIR < 1.0 represents that fewer CDI events were observed than predicted, and a SIR= 1.0 represents the same number of CDI events were observed as predicted. The Maryland state-wide SIR for hospital-onset C. difficile decreased from 0.92 in 2017 to 0.8 in 2018 during initiation of the collaborative, and then to 0.61 in 2019 while the collaborative was ongoing.
This study demonstrated that implementation of a state-wide collaborative that focused on prevention activities for C. diff. along with ongoing collaborative support led to a significant reduction in the reported NHSN CDI SIRs. These data support the hypothesis that the measure score correctly reflects the quality of care provided and adequately identifies differences in quality.
Rock C, Perlmutter R, Blythe D, et al. Impact of Statewide Prevention and Reduction of Clostridioides difficile (SPARC), a Maryland public health-academic collaborative: an evaluation of a quality improvement intervention. BMJ Quality & Safety. 2022 Feb;31(2):153-162.
2. Implementation of the Targeted Assessment for Prevention strategy in a healthcare system to reduce Clostridioides difficile infection rates.
The hospital system level SIR decreased from 1.0 to 0.87, while the largest hospital in the system, hospital A, decreased its SIR from 1.03 to 0.84. A SIR > 1.0 represents that more CDI events were observed than predicted, a SIR < 1.0 represents that fewer CDI events were observed than predicted, and a SIR= 1.0 represents the same number of CDI events were observed as predicted. This study demonstrated that the implementation of a targeted assessment and infection control practices that focused on C. diff. prevention led to a reduction in the reported NHSN CDI SIRs. The study and data support the hypothesis that the measure score correctly reflects the quality of care provided and adequately identifies differences in quality.
White KA, Soe MM, Osborn A, Walling C, Fike LV, Gould CV, Kuhar DT, Edwards JR, Cochran RL. Implementation of the Targeted Assessment for Prevention Strategy in a healthcare system to reduce Clostridioides difficile infection rates. Infect Control Hosp Epidemiol. 2020 Mar;41(3):295-301.
3. Association between statewide adoption of the CDC's Core Elements of Hospital Antimicrobial Stewardship Programs and rates of methicillin-resistant Staphylococcus aureus bacteremia and Clostridioides difficile infection in the United States.
The study’s results supported its hypothesis that compliance with the antibiotic stewardship program core elements was associated with a significant 0.3% decrease (P < .01) in CDI SIR in 2016 relative to 2014. A SIR > 1.0 represents that more CDI events were observed than predicted, a SIR < 1.0 represents that fewer CDI events were observed than predicted, and a SIR= 1.0 represents the same number of CDI events were observed as predicted. This study demonstrates that compliance with the CDC’s Antimicrobial Stewardship Programs Core Elements led to a significant reduction in CDI LabID event SIRs. Thus, this data supports the hypothesis that the measure score correctly reflects the quality of care provided and adequately identifies differences in quality.
Garcia Reeves AB, Lewis JW, Trogdon JG, Stearns SC, Weber DJ, Weinberger M. Association between statewide adoption of the CDC's Core Elements of Hospital Antimicrobial Stewardship Programs and rates of methicillin-resistant Staphylococcus aureus bacteremia and Clostridioides difficile infection in the United States. Infect Control Hosp Epidemiol. 2020 Apr;41(4):430-437.
4. Closing the Gap on Infection Prevention Staffing Recommendations: Results from the beta version of the APIC Staffing Calculator
This study showed a significant association between staffing status and higher SIRs for central line-associated bloodstream infections (p=0.02), catheter-associated urinary tract infections (p=0.001), Clostridioides difficile infections (p=0.003), and colon surgical site infections (p=0.0001).
Bartles R, Reese S, Gumbar A. Closing the gap on infection prevention staffing recommendations: Results from the beta version of the APIC staffing calculator. Am J of Infect Control. 2024; 52(12): 1345-1350.
See attachment under 5.3.4a for additional details.
Validity Testing:
The CDI SIR and MRSA SIR are both laboratory-identified healthcare associated infection outcome measures. Implementation of infection prevention strategies, such as hand hygiene, have been shown to decrease the spread of C.difficile and MRSA. Environmental cleaning and disinfection, and appropriate use of contact/isolation precautions are also important prevention practices that can decrease the transmission of both pathogens.
However, other factors or prevention strategies may differ between the two pathogens. For example, facilities may focus antimicrobial stewardship resources to avoiding or limiting antibiotics associated with high CDI risk (such as fluoroqinolones). Alternatively, some facilities may choose to implement decolonization strategies aimed to reduce MRSA bloodstream infections. We hypothesized that there would be a weak positive correlation between the CDI SIR and MRSA SIR. We predicted only a weak correlation between the two measures because some facilities may choose to focus quality improvement on the prevention of a single HAI (CDI or MRSA) due to resource limitations or other factors.
The significant positive correlation between CDI and MRSA SIRs (rho= 0.06775, p= 0.0031) in acute care hospitals demonstrate that the SIRs are valid measures of healthcare quality, as SIRs for both infections are both driven by clinically relevant patient care practices and evidence-based infection prevention strategies implemented by the healthcare facilities.
Validation Studies:
I-III. These studies support the hypothesis that the measure score correctly reflects the quality of care provided and adequately identifies differences in quality.
Rock C, Perlmutter R, Blythe D, et al. Impact of Statewide Prevention and Reduction of Clostridioides difficile (SPARC), a Maryland public health-academic collaborative: an evaluation of a quality improvement intervention. BMJ Quality & Safety. 2022 Feb;31(2):153-162.
White KA, Soe MM, Osborn A, Walling C, Fike LV, Gould CV, Kuhar DT, Edwards JR, Cochran RL. Implementation of the Targeted Assessment for Prevention Strategy in a healthcare system to reduce Clostridioides difficile infection rates. Infect Control Hosp Epidemiol. 2020 Mar;41(3):295-301.
Garcia Reeves AB, Lewis JW, Trogdon JG, Stearns SC, Weber DJ, Weinberger M. Association between statewide adoption of the CDC's Core Elements of Hospital Antimicrobial Stewardship Programs and rates of methicillin-resistant Staphylococcus aureus bacteremia and Clostridioides difficile infection in the United States. Infect Control Hosp Epidemiol. 2020 Apr;41(4):430-437.
IV. This study showed a correlation between staffing levels and infection outcomes (CAUTI, CLABSI, CDI, and colon SSI). Programs with below-expected staffing levels according to the calculator were more likely to have higher SIRs. This finding reinforces the value of well-staffed infection prevention programs in maintaining lower SIRs.
Bartles R, Reese S, Gumbar A. Closing the gap on infection prevention staffing recommendations: Results from the beta version of the APIC staffing calculator. Am J of Infect Control. 2024; 52(12): 1345-1350.
Risk Adjustment
NHSN follows a highly rigorous process while developing risk adjustment models for its measures. The process begins with a thorough clinical and epidemiological review of all eligible potential risk factors that are currently collected in NHSN. The data available in NHSN are a combination facility-level and testing practice risk factors. Experts then recommend risk factors to be evaluated statistically. CDC obtains the risk factors considered for inclusion in the model for predicted events (i.e., denominator) by estimating the parameters, or probability of risk occurrence. The final model is chosen by identifying the optimal parameterizations of all covariates (i.e., risk factors) in linear regression procedures. In other words, risk factors are included in a model if they are determined to significantly impact C. difficile incidence. The model is then double-tested by a reverse process that removes non-significant factors. Each best model is fit-tested, calibrated, and validated using industry standard techniques.
References:
- NHSN's Guide to the 2022 Baseline Standardized Infection Ratios. Centers for Disease Control and Prevention website. https://www.cdc.gov/nhsn/2022rebaseline/sir-guide.pdf.
- Obtaining the Number of Predicted Events for the Standardized Infection Ratio (SIR)
- https://wwwdev.cdc.gov/nhsn/2022rebaseline/index.html
See attachment under 5.4.3a for details.
First, a multidisciplinary team of subject matter experts, database experts, statisticians, and internal leadership specified the variables to move forward for testing as potential risk factors. Each potential risk factor was tested for association with the outcome using Wald, likelihood ratio and type III Chi-square tests at significance level for entry ≤ 0.25. This initial analysis was repeated by adding successive model parameters guided by a statistician that assessed model fit using AIC, BIC, and Deviance and, where possible, evaluated model prediction using the pseudo-adjusted R-squared. Model diagnostics were used to assess potential multicollinearity by variance decomposition and the conditional index. Data points were assessed for high influence and leverage. Linearization and monotonicity were assessed using splines or other regularization methods. Each resulting model from this process was fit using backward elimination (or selection) to detect any possible associations not identified in the former forward stagewise selection process and to obtain additional confirmation of associations. Variables were retained in the final model if p<0.05 in both the forward stagewise and the backward selection approaches. Next, the best model was validated via bootstrap sampling that relied on 1000 replications selected randomly with replacement. Variables for which the confidence interval of the beta estimate for a contained 0 using the 2.5 and 97.5 percentiles were removed from the final model. Finally, the model discrimination was computed with the pseudo-adjusted R-squared.
Below is a list of variables that were initially sent forward for testing by the multidisciplinary group but not included in the final models. Briefly, for the acute care hospital model, two variables were not included in the final model due to collinearity with other variables in the model. For the critical access hospital model, three variables that were not significant on the univariate associations were not included in the final model. For the long-term acute care model, a total of 16 variables were not included in the final model. Of these, seven were non-significant on univariate associations, seven were significant in univariate analysis but not in the multivariable model, and two were collinear with other variables in the final model. For the inpatient rehabilitation facility model, eight variables were not included in the final model. Four of these were non-significant in univariate analysis and four were significant in univariate analysis but not in the multivariable model.
See attachment under 5.4.4a for further details.
Discrimination was assessed for each risk model using the dispersion-based pseudo-R-square, and calibration was visually investigated by dividing the predicted number of events into deciles and plotting the observed number of events. Additionally, the root mean square error (RMSE) was calculated between observed and predicted events.
For the acute care hospital model, the dispersion-based pseudo r-square was 44.0% for acute care hospitals, 37.9% for critical access hospitals, 17.5% for long-term acute care facilities, and 15.1% for inpatient rehabilitation facilities. The RSME for each model was 2.9 for acute care hospitals, 0.45 for critical access hospitals, 1.56 for long-term acute care facilities, and 0.79 for inpatient rehabilitation facilities.
See attachment under 5.4.5a for calibration plots
The final risk adjustment models demonstrated that differences in facility-level factors were adequately accounted for. Variables were retained based on both statistical significance (p < 0.05) and validation through forward stagewise and backward elimination techniques. For the acute care hospital model, all but two variables that were sent forward for testing was retained in the final model. These two variables were removed because of collinearity with other variables. This indicates that every variable tested was independently associated with CDI events and the number of events we had to model. The other 3 models (critical access hospital/long-term acute care hospital/inpatient rehabilitation facilities) were created using fewer hospitals and fewer CDI events so those models are not as robust. A full listing of variables not included in the models are given in section 5.4.4, but briefly three variables were not retained in the critical access hospital model, 16 in long-term acute care facilities model, and eight in the inpatient rehabilitation facility model. The reasons for not being retained included non-significance in univariate analysis, non-significance in the multivariable model, and collinearity. The models were validated using bootstrap sampling, which helped identify and remove any variables with unstable beta estimates, ensuring that the model maintained generalizability. Overall, the modeling approach demonstrated that the retained risk factors sufficiently captured variation in patient case-mix across facility types. The use of model diagnostics such pseudo-R-squared confirmed good model fit and predictive utility. This indicates that outcome comparisons using the risk-adjusted results are fair and not confounded by underlying differences in population or facility. The retained variables meaningfully explain differences in outcome risk, and the exclusion of non-significant variables and variables that were limited in the model helps to avoid unnecessary model complexity.
See 7.1 Supplemental Information Attachment Pages 9-12 for risk models
Use & Usability
Use
NHSN HAI tracking system provides facilities, states, regions, and the nation with data needed to identify problem areas, measure progress of prevention efforts, and ultimately eliminate HAIs.
Healthcare facilities across the US
Facility, Acute, IRF, LTACF
The tool can be used to find and compare different types of Medicare providers.
Over 4,000 Medicare-certified acute-care hospitals, LTAC hospitals and over 1,100 acute IRF in the nation.
Facility, Inpatient/Hospital
Encourages hospitals to improve patient safety and reduce the number of conditions people experience from their time in a hospital.
General acute-care hospitals across the nation.
Facility, Inpatient/Hospital
The program collects quality data from hospitals paid under the Inpatient Prospective Payment System, with the goal of driving quality improvement through measurement and transparency by publicly displaying data to help consumers make more informed decisions about their health care. It is also intended to encourage hospitals and clinicians to improve the quality and cost of inpatient care provided to all patients.
Over 4,000 Medicare-certified acute-care hospitals across the nation.
Facility, Inpatient/Hospital
The program is intended to equip consumers with quality-of-care information to make more informed decisions about healthcare options.
Eleven cancer hospitals across the nation.
Facility, Inpatient/Hospital
The program collects data from IRFs with the goal of driving quality improvement through measurement and transparency by publicly displaying data to help consumers make more informed decisions about their health care.
Over 1,100 acute rehabilitation hospitals across the nation.
Facility, Inpatient/Hospital
The program collects data from LTCHs with the goal of driving quality improvement through measurement and transparency by publicly displaying data to help consumers make more informed decisions about their health care.
Over 350 LTCHs across the nation.
Facility, Inpatient/Hospital
This program adjusts payments to hospitals under the Inpatient Prospective Payment System (IPPS), based on the quality of care they deliver.
Over 3,000 hospitals across the country.
Facility, Inpatient/Hospital
Usability
To improve performance on this measure, facilities should review best practices and available guidelines and recommendations to determine which prevention strategies they can implement. The capability of a facility to implement CDI LabID Event prevention strategies can vary. Success in reducing rates depends on factors such as available resources, leadership support, and staff engagement.
Prevention strategies can include hand washing, performing routine surveillance, implementing enhanced environmental cleaning and patient and healthcare personnel education, assessing for signs or symptoms of infection, and adhering to clinical guidelines. Conducting root cause analysis of increased prevalence or outbreaks of C. difficile can help identify weak points in infection control and guide targeted interventions. Additionally, antimicrobial stewardship programs play a crucial role in preventing CDI LabID Events by promoting the appropriate use of antibiotics. These programs aim to reduce unnecessary antibiotic prescriptions, which are a major risk factor for CDI LabID Events, and to ensure that patients who need antibiotics receive the right drug, dose, and duration.
CDC's Targeted Assessment for Prevention (TAP) strategy provides a structured framework to minimize C. difficile events. This strategy involves assessing infection prevention policies and practices through TAP Facility Assessments and implementing tailored interventions to address gaps and reduce HAIs. It is primarily focused on CAUTIs, CLABSIs, and C. difficile infections.
The CDI LabID event standardized infection ratio (SIR) is an important indicator of C. difficile LabID event prevention efforts.
Facilities provide feedback by generating monthly standardized infection ratio (SIR) analysis reports within CDC NHSN and by using their SIR to determine if process improvement initiatives should be implemented to reduce C. difficile events.
State health departments publicly report facility-level SIRs, which allows patients and families within the state to select high-quality facilities. State health departments also utilize the CDI LabID event SIRs to target specific facilities with higher SIRs for additional support in initiating prevention activities.
Reporting facilities and state health departments send feedback on measure performance and implementation to the CDC NHSN Helpdesk. Additionally, during live training such as ‘Ask the Experts’ webinars and educational sessions, an online survey is provided to attendees to share feedback on the measure.
There was no feedback given by users and there was no change to the measure or implementations strategy.
CDC NHSN teams conduct an annual review of every measure protocol. For any measure revision recommendation received, CDC NHSN follows a standard operational procedure designed to ensure thorough evaluation and implementation if appropriate. The process begins with a preliminary discussion and decision making by the NHSN subject matter expert (SME) team. User inquiries are then assessed to understand the extent of the concern or improvement. A literature review is conducted to determine whether the recommendations align with current guidelines. If supporting evidence is identified, the findings are reviewed collaboratively by the NHSN team and then receive input from branch leadership and clinicians. External experts are consulted on an ad hoc basis.
Since 2015, NHSN has released an annual 'Summary of Updates' that outlines changes to the Patient Safety Component protocol based on the review process. These modifications aim to enhance clarity and address feedback received from measured entities. It is important to note that the measures are not changed every year.
There was no feedback given by users and there was no change to the measure or implementations strategy.
See 7.1 Supplemental Information Attachment Pages 13-15 for details.
Patient medical records and other sources of patient data must be reviewed to determine if the patient meets the necessary criteria for a CDI LabID event. It is possible that reviewers may miss symptoms or fail to identify that a patient meets the criteria. This might result in under-reporting of CDI LabID Events. Data collectors might also intentionally under-report CDI LabID Events. Both of these circumstances would result in an SIR that is calculated to be lower than the actual SIR. Alternatively, patients may be identified as having a CDI LabID Event, when in fact they do not meet CDI LabID Event criteria. This may result in a calculated SIR that is higher than the actual SIR. CDC NHSN reporting tool includes business logic to minimize misclassification of CDI LabID Events and the CDC NHSN system generates SIRs automatically, reducing the possibility of manual error in SIR calculation.
Comments
Staff Preliminary Assessment
CBE #1717 Staff Preliminary Assessment
Importance
Strengths
- A clear logic model is provided, depicting the relationships between inputs (e.g., hospital staff, clinical practice guidelines, and health care personnel education), activities (e.g., assessing signs and symptoms for Clostridioides difficile [CDI] LabID events and implementing infection control practices to reduce CDI LabID events, guiding patient care and infection control practices, and training patient care staff on appropriate infection control practices), and desired outcomes (e.g., reducing facility SIRs, providing optimal patient care, and preventing CDI LabID events). This model demonstrates how the measure's implementation will lead to the anticipated outcomes.
The problem this measure addresses presents a significant public health concern with CDI being the leading cause of antibiotic-associated diarrhea as well as one of the most common health care-associated infections in the United States. In 2017, 223,900 CDI cases occurred in hospitalized patients which resulted in 12,800 deaths and $1 billion in health care costs.
The measure is supported by a comprehensive literature review, including clinical practice guidelines and four high quality empirical studies, demonstrating a clear net benefit in terms of reducing CDI LabID events
In data submitted to NHSN for 1/1/2024 – 12/31/2024, 2,875 acute care hospitals (ACH), 224 critical access hospitals (CAH), 345 long-term care hospitals (LTAC), and 633 inpatient rehabilitation hospitals (IRF) showed performance gaps, with decile ranges from 0 to 2.081 (ACH), 0 to 2.730 (CAH), 0 to 2.618 (LTAC), and from 0 to 0.2395 (IRF) indicating variation in measure performance across the target population.
Description of patient input supports the conclusion that the measured outcome is meaningful with at least moderate certainty. Patient input was obtained from the Patient Safety Action Network, a coalition of individuals and organizations consisting of patients who have been medically harmed, their loved ones, and concerned patient safety advocates. Additionally, tracking the reduction of patient harm over time highlights the value and meaningfulness of this measure.
Limitations
- The quality of evidence for the clinical practice guidelines presented ranged from very low to moderate; however, the clear benefits over harms and the strong public health imperative supported strong recommendations across many interventions.
Rationale
- This maintenance measure meets all criteria for 'Met' for importance due to the significance of the problem it addresses, its robust evidence base, a documented performance gap, and a well-articulated logic model, making it essential for addressing the standardized infection ratio (SIR) of CDI LabID Events There is at least moderate confidence that the business case is adequate, i.e., the anticipated impacts of the measure on patient outcomes justify use of the measure.
Closing Care Gaps
The developer did not address this optional domain.
Feasibility Assessment
Strengths
- All required data elements are routinely generated during care delivery, and required elements are available from digital or electronic sources. The developer indicated there have been no changes to the measure specifications. The developer stated that no feasibility issues were found requiring adjustment of the final measure’s specifications.
The developer described the costs and burden associated with data collection and data entry, validation, and analysis. They discussed the potential for data quality issues that could be encountered in implementing or reporting the measure, which include inaccuracies and incomplete data. They also noted mitigation approaches such as routine data reviews by the National Healthcare Safety Network (NHSN) to identify and resolve data quality flags with facilities and providing facilities with internal validation toolkits to audit their internal data to overcome the barriers identified.
The developer described how all required data elements can be collected without risk to patient confidentiality, including retrieving data by the name of the hospital or other non-personal identifiers.
There are no fees, licensing, or other requirements to use any aspect of the measure (e.g., value/code set, risk model, programming code, algorithm).
Limitations
- None identified.
Rationale
- This maintenance measure meets all criteria for 'Met' for feasibility due to its well-documented feasibility assessment, clear and implementable data collection strategy and transparent handling of patient confidentiality, burden, licensing, and fees. These factors collectively ensure that the measure can be implemented effectively and sustainably in a real-world health care setting.
Scientific Acceptability
Strengths
- The developer performed the required reliability testing for this maintenance measure, namely, they conducted accountable entity-level (“measure score”) reliability testing at the level(s) for which the measure is specified. Data sources used for reliability analysis are adequately described and include CDC NHSN data from over 150.5M patients in 2,875 acute care hospitals, over 1M patients in 224 critical access hospitals, over 4M patients in 345 LTAC facilities, and over 8.8M patients in 633 IRFs during the period of 2024.
The developer conducted signal-to-noise reliability testing at the accountable entity-level. Approximately 70% of the acute care and more than 80% of the LTAC facilities meet the expected threshold of 0.6.
Limitations
- Approximately 25% of the critical access hospitals and 45% of the IRFs were above the expected threshold of 0.6. The developer did not provide an interpretation of nor a rationale for these results.
Rationale
- This maintenance measure is rated as ‘Not Met, but Addressable’ for reliability. The developer performed the required reliability testing for this measure and the results demonstrate sufficient reliability at the accountable entity-level for acute care and LTAC facilities. However, reliability testing results significantly fall below the established thresholds for critical access and IRF entities indicating major issues with the consistency and accuracy of the results across different settings and populations. The developer did not provide an interpretation of nor a rationale for these results. The developer may consider a modification such as increasing the minimum number of predicted events that make critical access or IRF facilities eligible. By addressing this issue, there is potential to enhance the reliability.
Strengths
- The developer performed the required validity testing for this maintenance measure, namely, they conducted accountable entity-level (“measure score”) validity testing at the level for which the measure is specified. The data source used for validity analysis is the Center for Disease Control’s (CDC) National Healthcare Safety Network (NHSN) during calendar year 2024. The 1,906 ACHs included in the analysis had median bed size of 254, were distributed across all four regions, and were approximately two-thirds major vs. non-major facilities.
The developer conducted empirical validity testing using Spearman’s rank-order correlation between the measure and the Methicillin-resistant Staphylococcus Aureus (MRSA) SIR at the accountable entity level. The developer hypothesized a positive, weak correlation between the measure and the MRSA SIR, with the rationale that infection prevention practices for different infections overlap, and that entities may prioritize certain practices. The results showed a significant, weak correlation with the MRSA SIR (rho = 0.068, p = .0031), in line with the hypothesis.
The developer also cited one interventional study that used a non-randomized comparison group to evaluate the impact of Maryland’s Statewide Prevention and Reduction of C. difficile (SPARC) collaborative on CDI incidence rates (Rock et al., 2022). This study estimated that participating hospitals experienced a 45% greater reduction in CDI rates than comparison hospitals during the study period (October 2017 – March 2022).
A well-developed logic model and detailed information on relevant, graded clinical recommendations suggest adequate “ruling in” of mechanisms that can explain the measure focus. Acceptable reliability for ACHs and LTACs also supports an inference of validity in those settings.
The developer conducted statistical risk adjustment, based on a conceptual model, selecting risk factors that have a significant correlation with the outcome. The developer reported pseudo r-squared values of 15.1%-44%, indicating acceptable model fit.
Limitations
- While the developer explained how use of different infection prevention practices could yield weak correlations between measures, results from the entity level validity testing could be strengthened with a rationale for the very small correlation coefficient reported, by referencing the extent of expected overlap in use of such practices for these measures (i.e., a mechanistic explanation). In addition, the same apparent analysis using the same data over the same time frame comparing the CDI SIR with the MRSA SIR was presented for CBE #1716, for which the developer reported a slightly different result (rho = 0.5); perhaps the developer could clarify why there is a difference in these results.
The measure's low reliability for IRFs and CAHs does not support an inference of validity in these settings, because it may indicate that observed relationships are not real.
The risk adjustment model includes only facility-level risk factors to minimize the burden of data collection.
Rationale
- This maintenance measure is rated as ‘Not Met But Addressable’ for validity because the validity testing results partially support an inference of validity for the measure, suggesting that the measure somewhat accurately reflects performance on quality and can distinguish good from poor performance to a limited extent.
The risk adjustment methods used demonstrate variation in the prevalence of risk factors across measured entities and that risk factors contribute to unique variation in the outcome. The models show acceptable fit and calibration.
Use and Usability
Strengths
- The measure is currently used in the CDC National Healthcare Safety Network (NHSN), CMS Care Compare, CMS Hospital-Acquired Condition (HAC) Reduction Program, CMS Hospital Inpatient Quality Reporting Program (HIQR), CMS Prospective Payment System (PPS)-Exempt Cancer Hospital Quality Reporting (PCHQR) Program, CMS Inpatient Rehabilitation Facility (IRF) Quality Reporting Program, and CMS Long-Term Care Hospital (LTCH) Quality Reporting Program.
The developer provided a summary of how accountable entities can use the measure results to improve performance. Specifically, facilities can use best practices and available practice guidelines to identify and implement CDI LabID Event prevention strategies including hand washing, conducting routine surveillance, patient and health care personnel education, and adhering to clinical guidelines. Facilities should conduct root cause analysis of increased prevalence or outbreaks of C. difficile to help identify infection control weak points and guide targeted interventions. They should also engage in antimicrobial stewardship programs to reduce the unnecessary prescription of antibiotics. The developer highlighted the CDC's Targeted Assessment for Prevention (TAP) strategy as a structured framework for reducing C. difficile events.
Facilities provide feedback by generating monthly SIR analysis reports within CDC NHSN and by using their SIR to determine if improvement initiatives are needed to reduce C. difficile events. State health departments publicly report facility-level SIRs and use the CDI LabID event SIRs to target hospitals with higher SIRs for additional support in implementing prevention activities. Facilities and state health departments provide feedback on measure performance and implementation to the CDC NHSN Helpdesk and are also able to complete online surveys sharing feedback during live webinars and educational sessions.
The developer noted that no feedback has been received from users; thus, there were no changes in the measure specifications.
The developer reported changes in performance from 2015 to 2023 across multiple settings, in which the overall performance score (lower SIR indicates better performance) decreased: from 0.993 to 0.420 for acute care hospitals, from 1.008 to 0.736 in critical access hospitals, from 0.944 to 0.313 for Long-Term Acute Care Hospitals (LTAC), and from 1.031 to 0.395 for Inpatient Rehabilitation Facilities (IRF). These findings support the argument that this measure is usable.
While the developer reported no specific unexpected findings, they highlighted circumstances under which CDI LabID Events might be misreported. They noted that CDC NHSN includes tools to minimize misclassification of CDI LabID events and reduce manual error in calculating the SIR.
Limitations
- None identified.
Rationale
- This maintenance measure is rated ‘Met’ for use and usability because it is actively used in at least one accountability application, with a systematic feedback approach that allows for continuous updates based on stakeholder feedback. The measure also demonstrates a positive trend in performance results, affirming its ongoing usability. The developer reported no unexpected findings.
Committee Independent Review
Support-CDI-1717
Importance
Regarding importance, this is a common healthcare-associated infections. Data shows over 200,000 cases resulting in over 12,000 deaths in hospitalized patients and healthcare costs of $1 billion in healthcare costs. Research shows that lab tests, room cleaning, care with antibiotic use, isolation, and PPE (personal protective equipment) reduces CDI (Clostridioides difficile)rates.
Suggestions under infection control include private toilets, contact precautions, etc. I would suggest a test for cure prior to eliminating contact precautions or hospital discharge. I disagree with the suggestion of hand washing or hand sanitizer as alcohol does not kill C-diff. Would also suggest daily cleaning parameters include commonly touched areas such as light switches, bed rails, doorknobs, blinds cords, etc. that maintenance crew may not clean frequently.
Closing Care Gaps
This is listed as optional but I would suggest special vigilance for immunocompromised patients.
Feasibility Assessment
It is noted that this "is a maintenance measure, and the measure specifications have not changed. All required data elements are routinely generated..."
Scientific Acceptability
For both reliability/validity, the "dataset used for testing came from CDC NHSN, which collects data about healthcare-associated infections (HAI)".
For both reliability/validity, the "dataset used for testing came from CDC NHSN, which collects data about healthcare-associated infections (HAI)". It is noted that for risk adjustment, the "dataset used for the risk adjustment model was derived from CDC’s NHSN, which collects data about HAIs".
Use and Usability
This measure will be used for public reporting, public disease surveillance, payment, accreditation, and quality improvement.
Summary
This is an important measure for patient safety, which can be prevented or reduced through appropriate intervention. I would emphasize hand washing, test for cure, and specific cleaning protocols.
Clostridioides difficile (CDI) LabID Event Standardized Infectio
Importance
This measure has been in place for a while. Its importance has been clearly with adequately reference to support the measure's importance.
Closing Care Gaps
Not addressed since this was optional.
Feasibility Assessment
It was carefully presented relying on the ongoing NHSN system of data collection.
Scientific Acceptability
A heavy reliance of published materials on the scientific acceptability of the measure. I would have liked to see a summary of the scientific acceptability by the measure sponsor.
See comment above
Use and Usability
The measure sponsor has listed a wide range of use and useability for the measure for various purposes. Again, a summary of use and usability for the measure would have made the by the sponsor would have enhanced the application.
Summary
- The sponsor provided information that is clear and adequately described in all required areas.
- The cost of monitoring and data collection provided in the supplement of over $2,200 is concerning given that this is only one of several NHSN measures hospitals report on.
Adequate metric but some considerations for CAH and care gaps
Importance
Clostridioides difficile infection (CDI) is the leading cause of antibiotic-associated diarrhea and one of the most common healthcare-associated infections in the United States. Multiple studies provide empirical support for the association between CDI infection prevention practices, such as environmental disinfection, antimicrobial stewardship, hand hygiene, chlorhexidine bathing, bundled approaches, and other interventions, and the reduction of CDI. However, some of the listed recommendations have weak, low quality of evidence and the rationale can be strengthened. There was also feedback on the terms used to present the data, which will lead to confusion in how the measure is interrupted that the developers should consider. There is a demonstrated range of mean performance gap in the metric across different hospital types, suggesting that more monitoring is needed.
Closing Care Gaps
Failure to consider patient-level factors in model could widen care gaps by 'hiding disparities' in subgroup populations who are at greater risk for CDI. Only looking at facility level data will miss opportunities to develop targeted programs for patient groups more likely to have negative outcomes due to C.diff (e.g., greater medical complexity, dual eligible).
Feasibility Assessment
Maintenance measure with no changes since the prior approval. Facilities have not reported issues and all data elements are routinely generated. NHSN provides facilities with internal validation toolkits. It is not a proprietary measure and no costs.
Scientific Acceptability
Signal to noise ratio was ≥0.6 in 72% and 84% of long-term acute care facilities. The SNR was not adequate for critical access hospitals with only 21% of CAH meeting the 0.6 threshold. The median SNR was 0.546, also below the PQM threshold. The developers should comment on and reconsider these settings in the metric.
The developer provides evidence that implementation of the antibiotic stewardship program core elements is associated with a decrease in CDI SIR overtime. They also conducted risk adjustment modeling with selection of variables that were correlated to the outcome. However, some of the data presented for IRF and CAH does not support use of the measure in these settings. However, it is unclear why this repeated analysis from measure CBE #1716 does not yield the same results. As noted in the care gap section, there are also significant limitations to the variables chosen for the risk adjustment model.
Use and Usability
CDC provides multiple avenues to facilities and state health departments to send feedback and get implementation support (e.g., HelpDesk, Ask the Expert webinars and educational sessions). Releases an annual 'Summary of Updates' that outlines changes to the Patient Safety Component protocol based on the review process. No feedback given and no changes have been made.
Summary
Developers should comment on low reliability in CAH settings, and failure to consider patient level factors which could have important implications for the care gap metric in future years.
Support
Importance
The measure is meaningful, respectful, and understandable for patients
Closing Care Gaps
The measure is meaningful, respectful, and understandable for patients
Feasibility Assessment
The measure is meaningful, respectful, and understandable for patients
Scientific Acceptability
The measure is meaningful, respectful, and understandable for patients
The measure is meaningful, respectful, and understandable for patients
Use and Usability
The measure is meaningful, respectful, and understandable for patients
Summary
I am in support of the measure
(No subject)
Importance
-
Closing Care Gaps
This section was not filled out by submitter
Feasibility Assessment
One challenge with this measure is that there can be a high rate of C. diff colonization, which can be up to 21% of hospitalized adults and these colonized patients test positive for the C.diff toxin. There can be cases in which a patient has diarrhea, is tested for C.diff but the diarrhea is actually from a different source. C. diff: Facts for Clinicians | C. diff | CDC
Scientific Acceptability
-
-
Use and Usability
One challenge with this measure is that there can be a high rate of C. diff colonization, which can be up to 21% of hospitalized adults and these colonized patients test positive for the C.diff toxin. There can be cases in which a patient has diarrhea, is tested for C.diff but the diarrhea is actually from a different source. C. diff: Facts for Clinicians | C. diff | CDC
Summary
-
Met but would like some discussion
Importance
The measure development emphasizes that this is an already established measure. However, I am struggling to understand how does this reduce the incidence of C. difficile infections when it is compared to predicted not actual infections. How do we know the measure is doing something when it is based on the predictions for that year and not the prior year incidence? This measure does not seem to have been improved with changes in the healthcare world as well. I believe this is important, but don't agree the measure logic is the best at this time.
Closing Care Gaps
The MD did not submit this optional area.
Feasibility Assessment
No burden evaluation noted. They based this in that this is a maintenance measure and nothing has changed. I believe they should have still done something, as the measure did not change BUT the healthcare environment is continuously changing. In addition, I would like to see the developer surveying facilities regarding feasibility and burden of implementation, rather than using just the collected data.
Scientific Acceptability
Agree with staff assessment and findings. Appreciate the input.
Agree with staff assessment and findings. Appreciate the input.
Use and Usability
I understand this measure has been in use for quite some time and I appreciate it showing that there is an improvement in incidence from prior years. But I do not understand why the use of a complicated denominator is better than a simple historical data that can also show improvement in the individual hospital with its individual interventions.
Summary
Please see above comments.
Overall evaluation
Importance
Clearly an important topic and one which this measure does a nice job of addressing.
Closing Care Gaps
Did not address this optional issue.
Feasibility Assessment
No concerns for feasibility.
Scientific Acceptability
Agree with staff evaluations regarding need for further comment from sponsors regarding reliability testing.
Agree with staff evaluation regarding the needs to improve validity evaluation.
Use and Usability
Seems like usability is appropriate.
Summary
Overall, this is a solid measure which will serve an important purpose.
(No subject)
Importance
no comments
Closing Care Gaps
optional
Feasibility Assessment
no comments
Scientific Acceptability
agree with staff assessment
I would like to hear from developer rationale for excluding infants/NICU settings and how risk model (especially for ACHs) was selected - does not appear to account for patient frailty or immunosuppression and thus seems to risk inadequate case mix adjustment
Use and Usability
no comments
Summary
no comments
(No subject)
Importance
This is a very important measure to me. I want to tell you that one c-dif event is too many. This measure is respectful and easy to understand. I believe it meets the requirements.
Evaluation
Importance
Addresses health concerns especially related to HAIs.
Closing Care Gaps
Not reported
Feasibility Assessment
All required data elements are routinely generated, in structured fields, and used during care delivery. Facilities can choose to submit this data manually via a web form or via submission of CDA electronic files. NHSN has built-in business rules for mandatory data elements and does not allow for the submission of incomplete records.
Scientific Acceptability
Reliability results are adequate for acute and LTAC, however, critical access does not meet the required targets. Interpretation of these results would be helpful.
Needs improved validity to more accurately indicate performance levels.
Use and Usability
Currently used in a variety of programs and evaluated as PI initiatives to improve patient safety and outcomes.
Summary
An important measure but needs minor tweaks/refinement
CBE ID 1717
Importance
Facility-level outcome with known facility-level interventions to improve outcomes.
Closing Care Gaps
Not required
Feasibility Assessment
The measure has been used in public reporting for many years, highlighting its feasibility.
Scientific Acceptability
Reliability results were not good in IRFs and SNFs.
Although the submitters provided a rationale for the weak correlation between the MRSA measure and this one, it was not sound. The correlation analyses results were poor.
Use and Usability
Measure has been in use for many years, and the maintenance process is robust.
Summary
Overall, this is a sound measure that with an aim of reducing this common HAI. I have some concerns about the reliability and validity test results.
Support
Importance
this is met
Closing Care Gaps
not discussed
Feasibility Assessment
this is proven by historic use
Scientific Acceptability
agree with staff assessment, thank you for the detailed review
met
Use and Usability
met
Summary
Agree with the evaluation, particularly the adjustments recommended by staff.
(No subject)
Importance
I agree with the staff preliminary assessment.
Closing Care Gaps
Not addressed
Feasibility Assessment
Agree with staff preliminary review.
Scientific Acceptability
Agree with staff preliminary review. Would also want to know more about what one should make of the 28% of ACH and 16% of LTACF that did not have an SNR score >0.6.
Agree with staff preliminary review
Use and Usability
Agree with staff preliminary review
Summary
-
Public Comments
No Public Comments
No public comments received.