Download Serratia liquefaciens Bloodstream Infections and Pyrogenic

Basic Investigation
of Outbreaks
Karin Galil, MD MPH
Centers for Disease Control and Prevention
Atlanta, Georgia
Outline
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Identify the outbreak
Investigate the outbreak
Interpret results
Institute control measures
Report results
Identify Potential Outbreaks
 What is an outbreak ?
 How can one detect outbreaks ?
 Why should one look for outbreaks ?
Outbreak: Definition
 An increase in the occurrence of a
complication or disease above the
background rate
 One rare event
 e.g. GAS surgical site infection
 Many episodes of common occurrence
 e.g. MRSA surgical site infections
Background Rate of Disease
 Ongoing surveillance
 Determine rates—compare within and
between institutions
 Trends
 Requires common, accepted case
definitions
 Retrospective review of data
Pitfalls in Rate Estimates
 Case definitions
 Numerator
 Different definition increased or
decreased number
 Population at risk
 Denominator
 Different definition increased or
decreased rate
Who Identifies Potential
Outbreaks ?
 Routine surveillance
 Infection control
 Registries
 Clinical staff
 Laboratory staff
Reasons to Investigate
 Outbreak control
 Increased knowledge
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Pathogen
Risk factors for acquisition
Transmission
Epidemiology
Clusters that Suggest
Nosocomial Transmission
 Similar cases on one unit or among
similar patients
 Cases associated with invasive device
 HCW and patients with same infection
 Typical nosocomial pathogen
 multiply-resistant
 opportunistic
Determining Risk Factors
for Disease
 Known risk factors in hospital-acquired
infections:
 Invasive devices
 Severe illness or underlying disease
 Environmental factors
 Especially immunocompromised patients
(e.g. aspergillosis)
Institute Control Measures
 Immediate control measures needed even
before investigation begun or completed
Simple: e.g. improved handwashing
Complex: cohorting patients, closing unit, halting
use of device or product
Before the Investigation
 Cooperation
 All involved personnel and administration
 Laboratory capacity
 Antimicrobial susceptibility testing, typing
(molecular and nonmolecular methods)
 Resources
 Personnel, supplies, lead investigator,
statistician
The Investigation
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Define “case”
Find cases
Confirm outbreak
Review charts
Describe epidemiology
Generate hypothesis
Test hypothesis
Analyze data
Communicate results
Case Definitions
 “Working” case definition
 Person, place, time
 Clinical, laboratory or diagnostic findings
 Confirmed vs. possible cases
 Case definitions usually change during
the investigation
Example: Case Definition
“A case of multi-drug resistant tuberculosis was
defined as any patient in Hospital X diagnosed
with active tuberculosis from January 1, 1999
to December 31, 1999 whose isolate was
resistant to at least isoniazid and rifampin.”
Case Finding
Use case definition to find other cases in the
source population
 Large potential source population: discharge
diagnoses, microbiology log books, emergency
room visits, use of diagnostic technique
 Small population (unit of hospital): review charts
of entire cohort
Line Listing
Name Age
Sex
Ward
Onset
Outcome
Confirm the Outbreak
 Calculate background rate of disease
 Compare rate during outbreak with
background rate
 Define periods from incubation time
to last case (or present)
Rate Ratio
=
attack rate (outbreak period)
attack rate (background period)
Pseudo-Outbreaks
 Clusters of positive cultures in
patients without evidence of disease
 Perceived increase in infections
 New or enhanced surveillance
 Different laboratory methods
Descriptive Epidemiology
 Line listing of case-patients
(person, place, time)
 Demographic information
 Clinical information
 Epidemic curve
 Point source
 Person-to-person
Point Source Outbreak
 Shorter duration
 Sharp peak in epidemic curve
 Rapid resolution
 May resolve without intervention
Epidemic Curve:
Point Source Outbreak
No. of cases
35
30
25
20
15
10
5
0
Day 1
Day 2
Day 3
Day of Onset
Day 4
Day 5
N=87
Epidemic Curve:
Contaminated Product
30
25
20
15
10
5
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Bloodstream Infections and Pyrogenic Reactions
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Bloodstream infection
Pyrogenic reaction
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Number of Cases
Extrinsic Contamination
Person-to-Person or
Contaminated Equipment
 Poor infection control technique or
contaminated patient equipment
 Long duration
 May not resolve without intervention
 If HCW and patients affected, plot
separately and together to determine
mode of transmission
Clues
 Location
 Tb skin test conversion associated with
outpatient HIV clinicair flow
 Patient characteristics
 Immunocompromised patients
 Persons of a certain age
 Persons with same disease/procedure
Hypotheses
 What caused the outbreak ?
 Available data from the outbreak
 Published literature
 Expert opinion
 Hypothesis testing
Epidemiologic Studies
 Case-control studies
 Cases : disease
 Controls : equal likelihood of
exposure as cases
 Cohort studies
 Cohort selected on the basis of
exposure status
Case-Control Study
 Advantages: small number of cases, better
for rare diseases, diseases with long latency
periods, multiple exposures
 Disadvantages: selection and recall bias,
not good if exposure is rare, cannot
measure disease incidence rate (OR vs. RR)
Cohort Study
 Advantages: can study rare exposures,
can calculate disease incidence rates,
selection bias less likely
 Disadvantages: feasibility, not suited to
rare diseases
Collect Data
 Complete: same data for cases
and controls
 Unbiased: same way to avoid bias
Potential Types of Bias
 Selection bias
 Self-selection
 Diagnostic bias
 Information bias
 Differential vs. misclassification
 Recall bias
Questionnaire
 Design questionnaire
 Demographic information
 Potential risk factors
 Outcomes
 Field test
 Complete for on all patients
Enter and Clean Data
 Line listing
 Statistical program
 EpiInfo, SAS, STATA
 Clean data
 Correct errors
Data Analysis
 Descriptive statistics
 Univariate analysis
 Stratified analysis
 Complex analysis
Descriptive Statistics
 Vital first step
 Describe person, place, time
 Describe frequency of all
variables collected
 Look for errors
 Decide on further analyses
based on these results
Disease
.
Exposure
Yes
Yes
No
No
a
b
a+b
c
d
c+d
a+c b+d N
Risk Estimate
 OR/RR >> 1
 Strong positive association
 OR/RR = 1
 No association
 OR/RR << 1
 Strong negative association
Statistical Significance
 Confidence Intervals
 Include 1
 Exclude 1
 P value
 p > 0.05
 p << 0.05
Univariate Analysis:
Categorical Variables
 Categorical variables (yes/no; young/old)
 Odds Ratio (OR)  case-control study
 Relative Risk (RR)  cohort study
Odds Ratio
 Case-control study
 OR = odds that person with disease
was exposed compared to odds that
a person without disease was not
exposed to risk factor
 OR estimates the relative risk
Odds Ratio
OR = ad / bc
Odds Ratio
Disease
Exposure 14
No
5
exposure
19
No
disease
7
21
8
13
15
34
Calculating the Odds Ratio
OR = ad / bc
OR = (14)(8) / (7)(5)
OR = 3.2
Relative Risk
 Cohort study
 RR = risk ratio = incidence rate
ratio = relative rate
 RR = risk of disease among
exposed compared to risk
among the unexposed
Relative Risk
RR = a(c+d) / c(a+b)
Confidence Intervals
 Sampling  estimates the OR or RR
 95% confidence Intervals—if we
resampled numerous times, our
estimate would fall within these
bounds 95% of the time
 Finite population correction
Statistical Tests for 2x2 Tables
 Chi-square test
 Fisher’s exact test—if value of any cell <5
 P value indicates level of certainty that
association was not due to chance alone
Risk Estimate vs. P Value
 OR or RR –direction & strength of association
 >>1: strong association
 = 1 : no association
 <<1: strong inverse association
 P Value—level of certainty about the estimate
of the association
 <<.05: unlikely to be due to chance
Univariate Analysis:
Continuous Variables
 Continuous variables (e.g. age, bp)
 Distribution
 Normal (bell-shaped)
• Mean and standard deviation
 Not normal
• Median and range
Stratified Analysis
 Simple stratified analysis
 Control for one variable
 Logistic/linear regression models
 Control for multiple variables at once
 Control for confounding and effect
modification
 Non-linear relationships
Microbiologic Investigation
 Alert lab: save all specimens + positive
cultures
 Typing of organisms
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Species identification
Biotyping
Antimicrobial susceptibility testing
Advanced typing (serotyping, plasmid analysis,
phage typing, isoenzyme electrophoresis,
genetic fingerprinting)
Environmental Investigation
 Are inanimate objects linked with the
outbreak ?
 Were infections clustered in one area ?
 Consider infected devices,
medications/products, airflow patterns
Interpret Results
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Is there an association ?
It is statistically significant ?
Was study biased ?
Are the results plausible ?
Did the exposure precede the outcome ?
Are results consistent with other studies ?
Is there a dose-response effect ?
Control the Outbreak
 Routine infection control procedures
 Guidelines for universal precautions
 Specific guidelines for patient-care equipment
 Specific interventions for the ongoing
outbreak
 Clues—person, place, time
Evaluate Control Measures
Did the control measures stop
the outbreak?
 Were there multiple modes of
transmission ?
 Were control measures implemented
properly ?
 Were control measures sufficient ?
Implement Successful
Control Measures
Report Results
 Inform all concerned parties of results
 Hospital staff, consultants, health department
 Contaminated products/devices—
government authorities, manufacturers
 Media — spokesperson
Investigations are:
Challenging
Time - consuming
Imperfect