Matthew Wise, MPH, PhD Epidemiologist, Office of Prevention Research and Evaluation

1. Developing a Framework for Estimation of Healthcare-Associated Infection Burden at the National and State Level Matthew Wise, MPH, PhD Epidemiologist, Office of Prevention Research and Evaluation CSTE Annual Conference June 4, 2012 National Center for Emerging and Zoonotic Infectious Diseases Division of Healthcare Quality Promotion

2. Importance of HAI Burden Estimates • Defining the public health impact of HAIs • Morbidity, mortality, and cost • Where is burden greatest? • How should public health resources be allocated? • How has burden changed with implementation of prevention programs or policies? • Useful communications tool • Policymakers may relate better to numbers than rates • Can aid in advocating for resources

3. Major Healthcare-Associated Infection (HAI) Types • Device-associated infections: • Bloodstream infections in patients with central lines (CLABSI) • Urinary tract infections in patients with catheters (CAUTI) • Pneumonias in ventilated patients (VAP) • Surgical site infections (SSI): • Superficial and complex infections following surgical procedures • Multidrug-resistant and other important pathogens: • Methicillin-resistant Staphylococcus aureus(MRSA) • Clostridium difficleinfections (CDI)

4. Previous HAI Burden Estimates

5. Expanding on Previous Burden Estimates • Ability to project to the state level • Focus on HAI types that are targets of prevention efforts • Take advantage of more robust HAI data

6. What’s Changed? • HAI surveillance is much more comprehensive • From hundreds of facilities in the 1990s to thousands of facilities currently • Data collected on a larger number of infection types • Greater access to National Healthcare Safety Network data at the state level • State reporting requirements • Group user function

7. CMS Reporting Incentive Timeline

8. Median State-Specific Percent of Acute Care Facilities Participating in HAI Surveillance

9. A Common Approach • CDC and some states already producing HAI burden estimates or exploring burden estimation • Benefits of a common (or at least coordinated) approach: • (Relatively) comparable estimates across states • Internally consistent estimates (e.g., sum to the ~national total) • Greater efficiency by developing standard methods and data sources

10. What is needed to produce HAI burden estimates? • Is there a source of data on the frequency of infections that is generalizable to the population I want to calculate burden for? • Example: “Do I have information on the rate of CLABSIs in hospitalized critical care patients in the United States?” • Do data exist to define the entire population at risk for the outcome of interest? • Example: “Do I know the total number of central line-days in hospitalized critical care patients in the United States?”

11. Simple Approach to HAI Burden Estimation Define the denominator: Patient-days Device-days Procedures Estimate infection rates: CDI CLABSI/CAUTI/VAP SSI Multiply Number of infections

12. Simple Approach to HAI Burden Estimation Define the denominator: Patient-days Device-days Procedures Estimate infection rates: CDI CLABSI/CAUTI/VAP SSI Multiply Number of infections

13. Defining the Denominator: Data Sources • AHRQ Healthcare Cost and Utilization Project • State hospital discharge data • CMS Healthcare Cost Reports

14. Defining the Denominator: AHRQ Healthcare Cost and Utilization Project • Source of national data on non-Federal short-stay community hospital discharges • Also state-specific data available for 35 states • Information can be used to estimate patient-days and surgical procedure denominators • HCUPnet web query system http://hcupnet.ahrq.gov/

15. Defining the Denominator: State Hospital Discharge Data • “Raw” state-specific discharge data files that HCUP uses to create its databases • Data on patient-days and surgical procedures • Ability to design more complex queries • Can be difficult/cumbersome to access in some states

16. Defining the Denominator: CMS Healthcare Cost Reports • Filed by all Medicare-eligible hospitals, nursing homes, dialysis facilities, hospice, and home health agencies • Publicly available, but files difficult to work with • Patient-day data stratified by hospital type and critical care status http://www.cms.gov/Research-Statistics-Data-and-Systems/Files-for-Order/CostReports/Cost-Reports-by-Fiscal-Year.html

17. Defining the Denominator: Complications • General issues • Most data sources exclude Federal facilities • Administrative data can lag by 1-3 years • Device-associated infections • Need to stratify patient-day denominators by critical care status • Must take device utilization into account • Surgical site infections • NHSN procedures may not map directly to ICD-9-CM procedure codes used in hospital discharge data

18. An Example of Estimating Burden:CLABSIs in Critical Care Patients, US, 2010 Estimate critical care patient-days from CMS Hospital Cost Reports and inflate by 4% to account for Federal hospitals 20.8 million*1.04=21.7 million total patient-days

19. An Example of Estimating Burden:CLABSIs in Critical Care Patients, US, 2010 Obtain device utilization ratio from NHSN and convert patient-days to central line-days 21.7 million*0.50 = 10.8 million central line-days 21.7 million US critical care patient-days

20. Simple Approach to HAI Burden Estimation Define the denominator: Patient-days Device-days Procedures Estimate infection rates: CDI CLABSI/CAUTI/VAP SSI Multiply Number of infections

21. Estimating Infection Rates: Data Sources • Hospital discharge data • Emerging Infections Program • National Healthcare Safety Network (NSHN)

22. Estimating Infection Rates: Discharge Data • Few HAIs can be accurately identified using administrative data sources • CDI • ICD-9-CM code 008.45 does a reasonable (but not perfect) job of identifying CDI • Primary diagnosis correlated with community-onset infection • Secondary diagnosis correlated with hospital-onset infection • Some surgical site infections • Example: Some success in identifying post-CABG mediastinitis using a combination of ICD-9-CM diagnosis and procedure codes

23. Estimating Infection Rates: Emerging Infections Program • Captures infections occurring in community and healthcare settings • Rates generally calculated per 100,000 population • Active Bacterial Core Surveillance (ABCs) • Invasive MRSA surveillance • Healthcare-Associated Infections-Community Interface • CDI surveillance • HAI and antimicrobial use prevalence survey of hospitalized patients

24. Estimating Infection Rates: National Healthcare Safety Network • Voluntary, incentivized, and mandatory reporting of HAIs to CDC by healthcare facilities and organizations • Outcomes under surveillance (selected): • Hospital-onset CLABSI, CAUTI, and VAP rates per 1,000 device-days • Surgical site infections per 1,000 procedures (40 different procedure types) • Dialysis events (IV antimicrobials, BSI, access infection) per 100 patient-months by vascular access type • Multidrug-resistant organism and CDI rates based on patient-days or admissions

25. Estimating Infection Rates: Complications • Discharge data is useful in only specific circumstances • EIP data only collected from (at most) ten geographic areas and may not represent the locality for which estimates are being generated • NHSN • The units/facilities participating in surveillance may be systematically different than non-participants • Reported data from participants may not represent “ground truth” • Primarily captures infections with onset in hospitals and other inpatient healthcare facilities (some exceptions)

26. Simple Approach to HAI Burden Estimation Define the denominator: Patient-days Device-days Procedures Estimate infection rates: CDI CLABSI/CAUTI/VAP SSI Multiply Number of infections

27. An Example of Estimating Burden:CLABSIs in Critical Care Patients, US, 2010 Multiply critical care CLABSI rate by central line-days to estimate infections: *10.8 million*(1.46/1000) ~16,000 critical care CLABSIs in 2010 10.8 million US central line-days 21.7 million US critical care patient-days

28. Additional Considerations • For point estimates • Is infection data representative of in my entire jurisdiction • Are there reasons the data might not represent “ground truth”? • When examining trends • Definition and surveillance system changes • Changes in the types of units/facilities participating in surveillance • Growing “at risk” population  may need a counterfactual comparison • Uncertainty • Sensitivity analyses • Monte Carlo simulation

29. Summary • HAI infection rate data is increasingly robust enough to produce estimates at the state level • More infection types • Greater number of settings • Numerous supplemental (and often publicly available) data sources exist to facilitate extrapolation of infection rates to estimate burden at the state level

30. Future Burden Estimation Efforts • When can we just start counting infections reported to NHSN? • How can reliable estimates be produced for less populous areas? • Can we produce more comprehensive HAI burden estimates (i.e., less piecemeal)? • Could state-specific HAI denominators (e.g., patient-days, device-days, procedures) be made publicly available?

31. Contact Information: Matthew Wise, MPH, PhD Prevention and Response Branch Division of Healthcare Quality Promotion, CDC cxx4@cdc.gov For more information please contact Centers for Disease Control and Prevention 1600 Clifton Road NE, Atlanta, GA 30333 Telephone: 1-800-CDC-INFO (232-4636)/TTY: 1-888-232-6348 E-mail: cdcinfo@cdc.gov Web: http://www.cdc.gov The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention. National Center for Emerging and Zoonotic Infectious Diseases Division of Healthcare Quality Promotion