Developing a Framework for Estimation of Healthcare-Associated Infection

Burden at the National and State Level

Matthew Wise, MPH, PhDEpidemiologist, Office of Prevention Research and Evaluation

CSTE Annual ConferenceJune 4, 2012

National Center for Emerging and Zoonotic Infectious DiseasesDivision of Healthcare Quality Promotion

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

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 difficle infections (CDI)

Previous HAI Burden Estimates

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

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

CMS Reporting Incentive TimelineHAI type

Setting/description Date implemented

CLABSI Acute care hospital critical care units

January 2011

CAUTI Acute care hospital critical care units

January 2012

SSI Acute care hospitals: COLO and HYST

January 2012

Dialysis Events

Outpatient dialysis centers: IV antimicrobial starts, BSI, access

infection

January 2012

CLABSI Long-term acute care hospitals October 2012CAUTI Long-term acute care hospitals October 2012CAUTI Inpatient rehabilitation facilities October 2012MRSA

BSIAcute care hospitals: LabID event January 2013

CDI Acute care hospitals: LabID event January 2013

2009 2011

CLABSI

9% 61%

CAUTI 6% 27%

SSI 4% 18%

VAP 7% 15%

CDI 0% 3%

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

Surveillance

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

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?”

Simple Approach to HAI Burden Estimation

Define the denominator:

Patient-daysDevice-daysProcedures

Estimateinfection

rates:CDI

CLABSI/CAUTI/VAPSSI

Multiply

Number of infections

Simple Approach to HAI Burden Estimation

Define the denominator:

Patient-daysDevice-daysProcedures

Estimateinfection

rates:CDI

CLABSI/CAUTI/VAPSSI

Multiply

Number of infections

Defining the Denominator: Data Sources

AHRQ Healthcare Cost and Utilization Project

State hospital discharge data

CMS Healthcare Cost Reports

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/

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

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

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

An Example of Estimating Burden:CLABSIs in Critical Care Patients, US,

2010

20.8 million*1.04=21.7 million total patient-days

Estimate critical care patient-days from CMS Hospital Cost Reports and inflate by 4% to account for Federal hospitals

An Example of Estimating Burden:CLABSIs in Critical Care Patients, US,

2010

21.7 million US critical care patient-days

21.7 million*0.50 = 10.8 million central line-days

Obtain device utilization ratio from NHSN and convert patient-days to central line-days

Simple Approach to HAI Burden Estimation

Define the denominator:

Patient-daysDevice-daysProcedures

Estimateinfection

rates:CDI

CLABSI/CAUTI/VAPSSI

Multiply

Number of infections

Estimating Infection Rates: Data Sources

Hospital discharge data

Emerging Infections Program

National Healthcare Safety Network (NSHN)

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

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

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

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)

Simple Approach to HAI Burden Estimation

Define the denominator:

Patient-daysDevice-daysProcedures

Estimateinfection

rates:CDI

CLABSI/CAUTI/VAPSSI

Multiply

Number of infections

An Example of Estimating Burden:CLABSIs in Critical Care Patients, US,

2010

21.7 million US critical care patient-days

10.8 million US central line-days

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

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

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

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?

For more information please contact Centers for Disease Control and Prevention

1600 Clifton Road NE, Atlanta, GA 30333Telephone: 1-800-CDC-INFO (232-4636)/TTY: 1-888-232-6348E-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 DiseasesDivision of Healthcare Quality Promotion

Contact Information:Matthew Wise, MPH, PhDPrevention and Response BranchDivision of Healthcare Quality Promotion, CDCcxx4@cdc.gov