Advanced infection prevention training CIP Consulting LLC
Overview of Intermediate Infection Prevention Training • Adult learning • Change Theory • Components of a successful Infection prevention program • CDC Surveillance Definitions (“Big 4) • Outbreak investigation • Basic NHSN features • IP in the OR • Basic concepts of cleaning and disinfection • Hand hygiene
Surveillance • Surveillance should be based on sound epidemiological and statistical principles • Surveillance methods continue to evolve and should be designed in accordance to current recommended practices and should consist of defined elements • Surveillance plays an important role in identifying outbreaks, emerging infectious diseases, and bioterrorist events.
Components of Surveillance • Surveillance Methods • Facility wide • Periodic (Quarterly) • Targeted • Outbreak Thresholds • Collecting Relevant Data • Managing Data • Analyzing and Interpreting Data • Communicating Results
Surveillance • Facility wide • In whole house surveillance, all HAIs are monitored in the facility. When whole house surveillance is conducted, overall infection rates should not be calculated. Instead calculate specific rates for each HAI. Overall rates are not sensitive enough to identify potential problems. • Most facilities do not have the resources to do this.
Surveillance • Targeted • In the 1990s the CDC shifted away from whole house to targeted surveillance. Targeted programs usually focus on high-risk, high-volume procedures or units. • This give you the most bang for your buck!!
Surveillance • Periodic • Monitoring a selected unit, device or procedure for a specified time period. • Can be useful to monitor for changes in a stable process.
Collecting Relevant Data • Using Definitions for data collection • Determine the population or event to study • Determine the time period for observation • Write your definition or use an established one e.g. CDC NHSN • Apply the definition consistently • Write or find a data collection tool
Data Collection • Concurrent or retrospective data collection • Advantages of concurrent surveillance: • You can interview care gives • Observe patients and patient care • Implement immediate prevention and control measures • Clusters and outbreaks can be identified quickly • Disadvantages of concurrent surveillance: • Very time intensive • Incomplete records
Data Collection • Advantages of retrospective data collection: • Medical record is complete and can be reviewed quickly • Disadvantages of retrospective data collection: • May be a delay in finding outbreaks or clusters.
Collecting Relevant Data • Review your data collection for accuracy and effectiveness • Check for flaws in the data • Check your data sources (patient based, lab based, post discharge surveillance letters, post op calls) • Validate if you make changes • Sources of data
Managing Data • Record data systematically • Be consistent (data collection tool) • Flow sheet or line list • Can others look at the data and understand it • Think about how you may want to manipulate or analyze the data later • Computer system • Software for analysis (Excel)
Analyzing Data • Analyzing is the reason we do surveillance • Analyze promptly to identify needs for intervention • Compare Data • Same definitions • Same patient population, risk group • Proper denominator • Device Days • Patient Days • Surgical Cases
Analyzing Data • Compare or Benchmark • Historically against your own rates • Against other hospitals of similar size • National Rates (Review NHSN report as a group) • Interpretation and Significance • Use of statistics • Data interpretation pit falls • Reporting Data
Statistics • Statistics can summarize and simplify large amounts of numerical data. • Using statistics one can draw conclusions about data. • Statistics can help communicate findings clearly and meaningfully to others. • Statistics can not prove anything- estimates are normally presented in probabilistic terms (e.g. we are 95% sure ...)
Statistics • Statistics may reveal underlying patterns in data not normally observable. • If used correctly, statistics can separate the probable from the possible • Statistics can not make bad data better - "garbage in, garbage out"
Statistics • Infection Preventionist routinely use statistical methods to: • Prepare reports for committee • Identify problems or outbreaks • Monitor the impact of interventions • Identify areas for improvement
Statistics • Some commonly used statistical methods in health care are: • Measure of central tendency • Mean • Median • Mode • Measures of Dispersion • Standard Deviation • Range • Variance
Statistics • Measures of frequency • Incidence rate • Prevalence rate • Ratio • Proportion • Statistical process control • Control Charts
Statistics • Incidence • # of new cases X Constant # at risk • Prevalence • # of existing cases Constant # at risk
Statistics Pitfalls • A high rate does not necessarily indicate a problem • Intensity of surveillance • Small denominator • Sample size usually not less than 25 • Surgical procedures for devices at least 50
Practice • Now let’s calculate the Mean, Median, Mode, and Range for the following: • 7, 9, 6, 7, 8, 5 • 31, 32, 35, 35, 37, 41, 42, 44, 52, 56 • 2, 12, 4, 11, 3, 7, 10, 5, 9, 6
Practice • Using the two previous slides, calculate the incidence of MRSA for the month of May • What is the prevalence of MRSA on 6/1 with the patient days being 425?
Device Related Data • Devices strongly correlated with infection • Urinary catheters • Central lines • Ventilators # of device assoc infections x 1000 # of device days
Central Line BSI Example • 4 BSI Infections • 120 patients • 1420 line days • 4500 Patient days • What is your rate?????
HA MRSA rate calculation • HA MRSA definition is developed to identify an MRSA case as “new”: MRSA isolated from clinical or surveillance culture obtained after the third calendar day of admission to the unit in a patient that had no prior MRSA by culture, molecular test, or by history. • # of new MRSA patients on the unit/month × 1,000 # of patient days on the unit/month = hospital-associated MRSA rate per 1,000 unit patient days • Good references – • APIC MRSA Elimination guide • CDC MDRO guidelines
What do you do with the Data? • Communicate/Report Data • Look for trends (Analysis) • Implement Changes (Action plan) • Monitor, Track and report Effect of Interventions
Communicating Data • What to report • How to report • Chart • Pie Chart • Bar Charts • Graph • Line Graph • Control Chart
Make Things Self-Explanatory • Title • Time Period • Location • Values • Unit Labels • Definitions
SICU Central line associated bacteremia (CLAB)4th Quarter 2010 Analysis: December rate represents one CLAB. Documented compliance with the insertion bundle.
Rapid Sterilization Rate August 2010 – November 2010 Analysis: November rate represents 11 items rapidly sterilized. 1 dropped instrument 9 consignment instruments 1 sterile instrument set unavailable Action Plan: Review consignment policy to ensure it states that vendors bring instruments in for full sterilization Continue to monitor
Surgical Site Infection Rate July 2010 – October 2010 Analysis: October rate translates to 1 infection – see attached case review. Action Plan: Continue monthly monitoring and discussion of prevention measures
Lumbar Interbody Infection Rate July 2010 – October 2010 Analysis: No SSI identified since surveillance began. Action Plan: Continue to do surveillance and discuss prevention measures
Advanced Infection prevention Class Comparing the rates
Rate comparisons • Some questions the Infection Preventionist may be asked to answer in regards to data are: • Are the findings statistically significant • Was the sample size large enough to demonstrate a difference? • Are the groups being compared truly similar?
The Null Hypothesis • When comparing SSI rates, the hypothesis being tested is that the rates are not different. This is called the null hypothesis. • A statistical test can be used to test the hypothesis and obtain a p-value
P-value • What is "Statistical Significance" (p-value)? • The statistical significance of a result is the probability that the observed relationship or a difference in a sample occurred by pure chance ("luck of the draw"), and that in the population from which the sample was drawn, no such relationship or differences exist. Using less technical terms, we could say that the statistical significance of a result tells us something about the degree to which the result is "true" (in the sense of being "representative of the population").
P-value • More technically, the value of the p-value represents a decreasing index of the reliability of a result. P- values range from 0 – 1. The higher the p-value, the less we can believe that the observed relation between variables in the sample is a reliable indicator of the relation between the respective variables in the population.
P-value • Typically, in many sciences, results that yield p .05 are considered borderline statistically significant, but remember that this level of significance still involves a pretty high probability of error (5%). Results that are significant at the p .01 level are commonly considered statistically significant, and p .005 or p .001 levels are often called "highly" significant.