Nimalie D. Stone, MD, MS Medical Epidemiologist for LTC GA LTC IC course Winter/Spring 2011 Infection surveillance as a tool for infection prevention in long-term care National Center for Emerging and Zoonotic Infectious Diseases Division of Healthcare Quality Promotion

Objectives q To define the goals and methods for performing infection surveillance q To understand how infection surveillance data can be analyzed and reported q To demonstrate how infection surveillance links to infection prevention activities

Interpretive Guidance from CMS CMS Manual System, Pub , Transmittal 51 “Interpretive Guidelines for Long- Term Care Facilities, Tag F441”,

The American Heritage® Medical DictionaryThe American Heritage® Medical Dictionary Copyright © 2010 by Houghton Mifflin Harcourt Publishing Company. Published by Houghton Mifflin Harcourt Publishing Company. All rights reserved. Sur·veil·lance noun 1. Close observation of a person or group, especially one under suspicion. 2. The act of observing or the condition of being observed. 3. The collection, collation, analysis, and dissemination of data. 4. A type of observational study that involves continuous monitoring of disease occurrence within a population.

Purpose of Infection Surveillance q To keep residents (and staff) safe by determining which infections are most common or cause the most harm in your facility §Examples: Urinary tract infections, influenza, C. difficile q To help identify new infections or increasing infections which require further investigation §Knowing your baseline infections helps you see when new changes are happening q To assess the impact of new prevention strategies through policies/practices on the rates of infections in the facility §Example: Will reducing the number of urinary catheters, decrease the number of UTIs in our resident

Lee TB, et al. AJIC 2007; 35:

Seven Core Recommended Surveillance Practices q 1. Assessing the population q 2. Selecting the outcome or process for surveillance q 3. Using surveillance definitions q 4. Collecting surveillance data q 5. Calculating and analyzing surveillance rates q 6. Applying risk stratification methodology q 7. Reporting and using surveillance information Lee TB, et al. AJIC 2007; 35:

1. Assessing the population q The population of residents in LTC is quite diverse q Different facilities will care for different types of residents or provide different services q Residents/services might carry different risks for different infections q Performing a “risk assessment” of the residents in a facility may help determine which infections are most important to track Lee TB, et al. AJIC 2007; 35:

Assessing the population: Questions to Consider q Do we provide mostly post-acute care, post-surgical rehab., custodial care or specialized care (for example, residents with dementia)? q Do we care for residents with different devices (urinary catheters, central lines (e.g. PICC catheters), tracheostomies, etc.)? q What is the most common device we use? q Do we care for residents with wounds (pressure ulcers vs. surgical)? q What infections do we see most often in our residents?

2. Selecting the process or outcome for surveillance q Sometimes, doing surveillance for every outcome makes it challenging to allocate time and resources for prevention q We have to select a surveillance strategy which enables us to balance our limited time q Surveillance can look at processes (hand hygiene, removing catheters, wearing gowns/gloves) or outcomes (new MRSA cases, UTIs, C difficile) q Different outcome surveillance strategies include: q House-wide infection surveillance q Targeted infection surveillance Lee TB, et al. AJIC 2007; 35:

House-wide surveillance: Tracking all infections PROS q Tells the complete picture of all events q Easier to do in a small facility, or one which provides care to a specialized population CONS q Very time consuming q May limit your ability to “drill-down” to specific risks q May limit time to identify opportunities for prevention

Targeted surveillance: Tracking select infections PROS q Focuses your time and resources on a few key problem areas q Increases time to explore causes and implement prevention activities q Makes time doing surveillance more efficient CONS q Limits your knowledge of the scope of infections in your facility q Will need to be reviewed at least every year and updated q If too narrow, you may miss important events

Selecting a surveillance strategy: Questions to Consider q How much time am I spending on infection surveillance (just counting, analyzing and reporting the data)? q Do I have enough time to educate my staff on how to prevent or respond to infections? q Which infections are most frequent in my resident population? q Which infections have the most serious consequences for my residents or staff? q Transfers to the hospital; outbreaks

Process and outcome surveillance: Questions to consider q If I focus my surveillance on 2-3 key infections, what are the processes of care that I can change or implement to prevent them? q What educational or practice changes will I implement to improve the processes of care? q What methods do I have to track whether staff are using those processes?

3. Using surveillance definitions q All data elements in outcome and process surveillance must be well defined and applied in a consistent way q Using standard criteria will ensure accuracy, reproducibility and the ability to compare data over time q Even with different people doing surveillance q With targeted surveillance, you can capture more detailed information about each event to help identify opportunities for prevention Lee TB, et al. AJIC 2007; 35:

Using surveillance definitions: Questions to consider q What criteria do we use to define HAI events in our resident population? q Are there national infection definitions or standards that we can use? q Can we compare our infection data over time or have the criteria been changing? q If someone new took over for me, what resources could I give them to make sure they perform infection surveillance the way I’m doing it?

Infection surveillance definitions for LTC: McGeer criteria Only published infection surveillance definitions for LTC – lead by a Canadian researcher, Allison McGeer Adapted from CDC hospital infection surveillance definitions by a group of experts in the field

Courtesy of Steve Schweon, presented at NADONA annual conference, 6/ 2010

Important points about surveillance definitions q Surveillance definitions may not be the same as clinical criteria used to make treatment decisions q Sometimes diagnosis/treatment decisions are made before all the data is available q Sometimes insufficient documentation is available to demonstrate that surveillance criteria have been met q Surveillance criteria may be more detailed than what is captured by MDS q It may be important to evaluate the discrepancies between surveillance data and clinical/MDS data as a process improvement exercise

4. Collecting surveillance data q Data collection of infections should be performed by individuals who understand the surveillance definitions and process measures q Train personnel and others in data collection methods specific to each surveillance objective (process vs. outcome) q Develop a data collection tool to fit a given surveillance objective (process vs. outcome) q Limit collection to what is needed for specific objectives q Collaborate with IT and use IT resources to support surveillance activities when possible Lee TB, et al. AJIC 2007; 35:

Collecting surveillance data: Questions to consider q Who has been capturing our infection data over the years? q Are we confident that the data has been collected consistently and accurately? q What is our training process to ensure that the person doing HAI data collection understands the criteria used to define the events? q What is the tool we use to collect and record data? q Are there technologic tools that could help us collect and record our data more easily or efficiently?

NameUnitInfection date Infection type Meets McGeer Criteria Antibiotic given Sonny Flowers 1-East10/2/10UTINYes Betty Crocker 1-East10/13/10Gastro- enteritis YNo J Daniels2-West10/21/10PneumoYYes J Beam2- West10/22/10PneumoY Yes Margie Rin 2- East10/24/10UTINYes Infection Surveillance: Line list

5. Calculating and analyzing surveillance data q Surveillance data should be presented in standard numerical measures of the outcomes/processes being monitored q Usually these measures are fractions (numerator/denominator ) q Numerator=event q Denominator=measurement of the population in which the event may occur q Both the numerators and denominators must be captured accurately and consistently over time for data to be comparable Lee TB, et al. AJIC 2007; 35:

Why fractions instead of raw numbers? q Let’s say you have data from two units q Unit A has 5 UTIs and Unit B has 5 UTIs q Does that mean that the prevalence UTIs are the same between the two units? q Not necessarily… q Unit A has 10 residents; Unit B has 20 residents q A = 5/10 x100 = 50% of residents have UTIs q B = 5/20 x 100 = 25% of residents have UTIs q Raw numbers don’t take into account the total number of people at risk

Surveillance math 101 q Most surveillance data are presented as percentages, rates, or ratios q When you have snapshots of information you use percentages/ratios q Sometimes called prevalence data q When you want to describe events during a time period at risk, you use a rate q Sometimes called incidence data Lee TB, et al. AJIC 2007; 35:

Putting math into context q What if you had two people who each lost 10 pounds; Jack started at 200 lbs and Jill started at 100lbs q The raw number of pounds lost would be the same q But, the percent of weight lost would be different q For Jack, 10/200 x100 (5%) ; For Jill, 10/100 x100 (10%) q The impact of weight loss is bigger for the person who starts at a lower weight q The rate of weight loss depends on time: Jack lost the weight in 5 days and Jill lost the weight in 2 days q Jack:10lbs /5 days = 2 lbs/day; Jill: 10 lbs/2 days = 5 lbs/day q Jill lost weight at a faster rate than Jack

Surveillance math 101 (cont) q Proportions are calculated by q # events (numerator)/ number of residents at risk or opportunities (denominator). q A percentage is the same calculation x 100 ( the standard constant, “k”) q Time at risk is not really captured in a percent or proportion

Surveillance math 101 (cont) Calculating percentages q Example: Hand hygiene compliance q 40 instances of performing HH appropriately / 50 total observations = 40/50 x 100 = 80% compliance q Example: Prevalence of urinary catheters among new admissions q 3 admits with a urinary catheter/30 new admissions = 3/30 x 100 = 10% catheter prevalence among new admits

Surveillance math 101 (cont) q Rates are calculated for a specific window of time q # events (numerator)/ number of resident days at risk (denominator). q The days contributed by each individual during that time period is important when calculating a rate q Using a standard constant allows us to compare rates even when the days at risk changes q For most infections, k = 1000, so the rate is expressed as events per 1,000 resident days

Surveillance math 101 (cont) Calculating a UTI rate for April for a unit with 10 residents residing there q Numerator is # of UTIs during that month: q 5 UTIs happen in April q Denominator is the number of days each resident contributes during the month. q If nobody leaves the unit in April, then each resident contributes 30 days of risk. q 10 residents x 30 days in April = 300 resident days. q UTI rate = 5/300 x 1000 = 16.7 UTIs/1000 resident days

Why rates instead of percentages? q Let’s revisit our two units A and B q In April, Unit A has 5 UTIs in 10 residents (50%) and Unit B has 5 UTIs in 20 residents (25%). Is the incidence of UTI 2x higher on Unit A in April? q Not necessarily… q Remember with a rate, we have to consider the amount of time each resident was “at risk” for the UTI

Why rates instead of percentages? q Unit A as 10 residents contributing 30 days each q 10res x 30 days = 300 days q Unit B has 10 residents contributing 30 days each, 5 contributing 15 days each, and 5 contributing 10 days each q 10res x 30d + 5 res x 15d + 5res x 10d = 425 days q So our rates are q Unit A = (5 UTIs /300 days) x1000 (k) = 16.7 UTI/ 1000 resident days q Unit B = (5 UTIs / 425 days) x 1000 (k) = 11.7 UTI/ 1000 resident days q Unit A doesn’t have a rate that twice as high unit B!!

Data analysis impacts how you interpret the numbers Data presentation Unit A with 10 residents Unit B with 20 residents Raw numbers5 UTIs Percentage among residents at risk 5/10 x 100 = 50% UTIs 5/20 x 100 = 25% UTIs Rate per 1,000 residents days 5/300 x1000 = 16.7 UTIs 5/425x1000 = 11.7 UTIs

6. Applying risk stratification methodology q Within a facility, there may be residents with certain characteristics which impact their likelihood of having an infection q For example, a post-acute care resident may have different risk factors compared to a long-stay resident q When you stratify your data, you end up dividing your resident population into groups with similar risk factors, and then calculating infection rates for each group separately. Lee TB, et al. AJIC 2007; 35:

6. Applying risk stratification methodology: q Risk of a UTI is different for residents with an indwelling urinary catheter vs. those without one q Instead of reporting the total rate of UTIs for the facility or a unit you might report two rates q UTI in residents with a urinary catheter q UTI in residents without a catheter q For other infections, you might have different reasons for grouping residents q For example, skin infections in residents with wounds, vs. skin infections in those without

Why risk stratify the rates? q Last trip to units A and B q How might the rates be affected if all 5 UTIs on Unit A were in residents without urinary catheters but all 5 UTIs on Unit B were in residents with urinary catheters? q Now you’ve got two different types of UTI: q Catheter-associated (CA-UTI): Rate based on device exposure for the residents (catheter-days) q UTI: Rate based on resident days

Why risk stratify the rates? q Unit A as 5 UTIs among 10 residents contributing 30 days each; none have catheters q A = 10 x 30 = 300 resident days. Rate = 5/300*1000 = 16.7 UTIs / 1000 resident days; 0 CA-UTIs q Unit B has 5 UTIs among10 residents with urinary catheters contributing 30 days each; the other 10 don’t have a catheter or a UTI. q B = 10x30 = 300 catheter days. Rate = 5/300*1000 = 16.7 CA-UTIs/ 1000 catheter days; 0 UTIs q Rates are the same but the type of UTI is different on each unit

Reporting and using surveillance information q The goal of doing HAI and process surveillance is to impact staff behavior to improve outcomes q Change won’t happen unless you share your data with staff/providers and leadership q Have a strategy for providing monthly or quarterly HAI surveillance results for your facility staff/providers q Present it in a way that they can easily understand q Highlight the processes of care that will improve outcomes q Sharing data in a timely manner will increase awareness and also highlight teaching opportunities for staff/providers Lee TB, et al. AJIC 2007; 35:

Strategies for sharing information q Keeping infection surveillance data in an electronic spreadsheet enables you to create graphs q Can provide rates for specific infections or trends in process measure compliance q Can provide a mixture of unit-specific information or facility-wide data depending on the infection or process

Developing a surveillance plan: C. diff example q Would C. difficile be an important infection to include in a LTCF surveillance program? q C. difficile infection can be more severe and more prone to relapse in individuals over 65 years old q Antibiotic exposure and healthcare exposure are major risk factors of C. difficile acquisition q C. difficile can be transmitted from person to person in healthcare and shared residential settings q Yes, C. difficile would be important to include in a LTCF infection surveillance program.

q What are the appropriate outcome and process measures associated with C. difficile surveillance? q Outcome measures: q New C. difficile infections (incident cases) vs. Recurrent cases q Healthcare or community assoc. vs. LTCF-assoc. q Process measures: q Hand hygiene; implementing and adhering to contact precautions; environmental cleaning practices; antibiotic stewardship Developing a surveillance plan: C. diff example (cont.)

q How will I define C. difficile events in my facility? q Potential criteria include: q Clinical symptoms of gastroenteritis including new or acutely worsening diarrhea assoc with abdominal upset, nausea, loss of appetite +/- fever and elevated WBC count q Positive lab identifying C. difficile toxin in stool specimen q For consistency and ease of surveillance q Collect and track positive lab events for C. difficile Developing a surveillance plan: C. diff example (cont.)

Using national standards for C. diff event definitions q Consider the timing of the positive lab in deciding how to attribute the event and whether to count as an event: q Incident case: Specimen obtained >8 weeks after the most recent lab event q Recurrent case: Specimen obtained >2 weeks and ≤ 8 weeks after most recent Lab event Modified from Infect Control Hosp Epidemiol 2007;28:140-5.

q Healthcare Facility-Onset (HO): Lab specimen collected >3 days after admission to facility (i.e., on or after day 4) q Community-Onset (CO): Lab event collected as an outpatient or an inpatient ≤3 days after admission to the facility (i.e., days 1, 2, or 3 of admission) q Community-Onset Healthcare Facility-Associated (CO-HCFA): CO Lab event collected from a patient who was discharged from a healthcare facility ≤4 weeks prior to date stool specimen collected Using national standards for C. diff event definitions Modified from Infect Control Hosp Epidemiol 2007;28:140-5.

Applying the definitions to characterize the C. difficile event AdmissionDischarge < 4 weeks 4-12 weeks LTCF-OCO- HCFA IndeterminateCA-CDI Time 2 d > 12 weeks * LCTF-O: Long-term care facility (Healthcare)-Onset CO-HCFA: Community-Onset, Healthcare Facility-Associated CA: Community -Associated * Depending upon whether patient was discharged within previous 4 weeks, CO-HCFA vs. CA † Onset defined by lab specimen collection date Modified from Infect Control Hosp Epidemiol 2007;28: Day 1Day 4

Developing a surveillance plan: C. diff example (cont.) q Proposed definition of LCF-onset, incident C. difficile event: q Positive lab specimen obtained from residents in the LTCF q Specimen obtained on or after Day 4 from date of admission q Resident will not have had a prior positive lab for C. difficile in the prior 8 weeks from the time of the current lab event q In order to capture these events, there must be a way for the facility to capture the results of stool specimens tested for C. difficile from their residents within past 8 weeks q Possible that stool studies were obtained at another facility prior to admission

Developing a surveillance plan: C. diff example (cont.) q How will I report my C. difficile incident infection rates? q Numerator (x): # incident infections q Denominator (y): Number of resident days in the facility q Standardized by a constant (k) so the rates are comparable q Calculate your rates using the same time period for the numerator and denominator (e.g., each month, or each quarter) q For C. difficile, the reporting constant (k) = 10,000 resident days q For other HAIs it might be 1,000 resident days, or 1,000 device days

Developing a surveillance plan: C. diff example (cont.) q How will I report my C. difficile LCTF-onset, incident case rates? q What additional information might be informative in understanding your rates? q Are the cases clustering in one unit or location? q Are the rates of C diff. infections higher in recent admits from the hospital (post-acute care) vs. long-stay residents? q Stratifying your rates by location or by resident services to help understand the way C. diff presents in your facility

Reporting HAI rates over time: C. diff example (cont.)

Linking prevention efforts to surveillance data q What are the process measures that can impact C. difficile transmission and infection in my facility? q Hand hygiene q Adherence to contact precautions when residents have active infection with C. difficile q Good environmental and equipment cleaning practices q Antibiotic stewardship

Linking prevention efforts to surveillance data Changed cleaning of equipment on Unit 1 Facility-wide Hand Hygiene education

Points to consider for infection surveillance q Setting up an effective infection surveillance program takes planning, time and resource support q Select a surveillance strategy based on the risk assessment of your facility’s population q Developing a standard processes and tools to perform surveillance will ensure consistency and comparability of the data over time q Linking surveillance data with prevention efforts is the ultimate goal in infection prevention

Unanswered questions about HAI surveillance in LTCFs? What is the true national burden of LTCF HAIs in terms of morbidity, mortality and cost? What is the best method for infection surveillance in LTCFs with limited resources? What are the rates of specific HAIs in LTCFs across the country? Are rates different for based on facility bed size or resident case-mix? What proportion of LTCF HAIs are preventable? What process measures have the most impact on reducing HAIs in LTCFs?

Impact of a national HAI surveillance system for LTCFs q Standardizes and validates surveillance definitions used by all participating in the system q Allows for fair comparison of rates by facility characteristics and/or resident characteristics q Provides national rates for facilities to use as a benchmark for assessing their own rates and prevention efforts q Over time will demonstrate trends in improvements and/or areas of new need for different HAIs tracked in the system

Aggregated data allowed for individual facility comparisons –Multiple ways to analyze the data (percentiles, rates over time, etc.) –Validity of reporting, 79%, –Validity of data collection, generally >80% Stevenson, KB ICHE 2005; 26: Idaho study showing standardized HAI surveillance by 17 LTCFs

National HAI reporting system n CDC managed web-based data system designed for healthcare facility reporting of HAIs n Developed over long-history of surveillance activity with partner hospitals n Standardized definitions for HAIs n Focused primarily on high-risk situations n Device exposure, multi-drug resistant organisms n Expanding to allow other healthcare facilities to participate

On the horizon… …a national surveillance system to support infection prevention in LTC

Thank you!! National Center for Emerging and Zoonotic Infectious Diseases Division of Healthcare Quality Promotion with questions/comments