Transcript Surveillance and Epidemiologic Investigation

Surveillance and
Epidemiologic Investigation
Angela Booth-Jones, PhD, RN
Marian Rodgers, MSN, MPH, RN
How we view the world…..
 Pessimist: The
glass is half empty.
 Optimist: The glass
is half full.
 Epidemiologist: As
compared to what?
Epidemiology
EPI
DEMO
Upon,on,befall
People,population,man
LOGOS
the Study of
The study of anything that happens to
people
“That which befalls man”
Clinician
Epidemiologist
Patient’s
diagnostician
 Community’s
diagnostician
Investigations
 Investigations
Diagnosis
 Predict trend
Therapy
 Control
Cure
 Prevention
What is Epidemiology?
 Epi means “over all”
 Demos means “people”
 Epi + Demos = “All of the people”
 Definition: The study of the distribution and
determinants of disease
 Definition: The science behind disease control,
prevention and public health
 Epidemiologists plan, conduct, analyze and
interpret medical research.
Poor Quality Care
Institute of Medicine (IOM) Committee
on the Quality of Health Care in America
 Report: Crossing the Quality Chasm, 2001.

“The current health care system frequently fails to
translate knowledge into practice and to apply new
technology safely and appropriately”
 Established 6 major aims for improving health care. Health
care should be:
 Safe, effective, patient-centered, timely, efficient, and
equitable.
Evidence-based Practice
Research Methodologies for
Cause-and-effect Relationships
Criteria that must be met for a study to
demonstrate a cause-and-effect
relationship:
1. Observed Statistical Association There
must be some statistical evidence of association
between the cause and the effect.
2. Time Precedence
The cause must occur first, followed by the effect.
3. Rule out Alternative Explanations for the
Association
Research Methodologies for
Cause-and-effect Relationships
 The last criterion is the most difficult to
satisfy.
 A "true experiment" is a study design that is
intended to rule out alternative explanations.
 By definition, a "True Experiment" must have
the following characteristics:



A study group and a control group.
Randomly assign of participants to the study
and control groups.
Manipulation of an "independent variable" in
the study group.
Understanding Statistics
 Population
 Description
 Inference
 BIG WORDS
 Significant
 Valid
 No formulas
 Focus on frequency
Types of DATA
 Qualitative Data
 Categorical
 Sex
 Diagnosis
 Anything that’s not a
#
 Rank (1st, 2nd, etc)
 Quantitative Data
 Something you
measure
 Age
 Weight
 Systolic BP
 Viral load
Data Comes from a Population
 In clinical research the population of
interest is typically human.
 The population is who you want to infer to
 We sample the population because we
can’t measure everybody.
 Our sample will not be perfect.


True random samples are extremely rare
Random sampling error
Describing the Population
 Frequencies for categorical
 Central tendency for continuous
 Mean / median / mode
 Dispersion
 SD / range / IQR
 Distribution
 Normal (bell shaped)
 Non-normal (hospital LOS)
 Small numbers / non-normal data
 Non-parametric tests
Statisticians Require Precise
Statement of the Hypothesis
 H0: There is no association between the
exposure of interest and the outcome
 H1: There is an association between the
exposure and the outcome.


This association is not due to chance.
The direction of this association is not
typically assumed.
Basic Inferences
 Correlation
 Pack years of smoking
is positively
associated with
younger age of death.
 (R square)
 Association
 Smokers die, on
average, five years
earlier than nonsmokers.
 Smokers are 8 X more
likely to get lung
cancer than nonsmokers.
Measure of Effect
 Risk Ratio / Odds Ratio / Hazards Ratio
 Not the same thing, but close enough.
 Calculate point estimate and confidence interval of
the ‘risk’ associated with an exposure.


Smoking
Drug X
RR =
Rate of disease among smokers
Rate of disease among non-smokers
 If Rate ratio = 1
 There is no relationship between the exposure and
the outcome
 This is the ‘null’ value (remember null hypothesis?)
Normal Curve
95% confidence interval
normally distributed statistic
sample and measurements are valid
Interpreting Measures of Effect
RR = 1: No Association
RR >1: Risk Factor
RR <1: Protective Factor
Crude vs Adjusted Analyses
 Crude analysis – we only look at
exposure and outcome.
 Adjusted analysis – we adjust for
potential ‘confounding variables’


The existence of confounding obscures
the true relationship between exposure
and outcome.
We can control for confounding by
adjusting for confounding variables using
statistical models.
P value?
 We can make a point estimate and a
confidence interval.
 What’s a p value?




Significant p value is an arbitrary number.
Does NOT measure the strength of
association.
Measures the likelihood that the observed
estimate is due to random sampling error.
P < 0.05 is, by convention, an indication of
‘statistical significance’.
If you have an ILLNESS, which
result do you want?
 Mean = 1.4
 SD = 0.1
 P <0.0005
 Mean = 4
 SD = 1.5
 P = 0.051
Hypothesis testing
 Uses the p value
 Or, does the confidence
a
interval include the null
value?
b
 Looking at a, b, and c –
c
which p value is:
 p = 0.8
 p = 0.047
 p = 0.004
 CI is better than p value.
Figure 1.
Risk of adverse pregnancy
outcomes among women with
asthma.
Types of Data
Discrete Data-limited number of choices

Binary: two choices (yes/no)



Categorical: more than two choices, not
ordered



Dead or alive
Disease-free or not
Race
Age group
Ordinal: more than two choices, ordered


Stages of a cancer
Likert scale for response
 E.G. strongly agree, agree, neither agree or disagree,
etc.
Types of data
Continuous data

Theoretically infinite possible values (within
physiologic limits) , including fractional
values


Can be interval




Height, age, weight
Interval between measures has meaning.
Ratio of two interval data points has no meaning
Temperature in celsius, day of the year).
Can be ratio


Ratio of the measures has meaning
Weight, height
Types of Data
 Why important?
 The type of data defines:
 The summary measures used
 Mean, Standard deviation for
continuous data
 Proportions for discrete data
 Statistics used for analysis:
 Examples:
 T-test for normally distributed continuous
 Wilcoxon Rank Sum for non-normally
distributed continuous
Descriptive Statistics
 Characterize data set
 Graphical
presentation
Histograms
 Frequency distribution
 Box and whiskers plot

 Numeric

description
Mean, median, SD, interquartile
range
Histogram
Continuous Data
No segmentation of data into groups
Frequency Distribution
Segmentation of data into groups
Discrete or continuous data
Sample Mean
 Most commonly used measure of central
tendency
 Best applied in normally distributed
continuous data.
 Not applicable in categorical data
 Definition:
 Sum of all the values in a sample, divided by
the number of values.
Sample Median
 Used to indicate the “average” in a skewed
population
 Often reported with the mean

If the mean and the median are the same,
sample is normally distributed.
 It is the middle value from an ordered listing
of the values


If an odd number of values, it is the middle
value
If even number of values, it is the average of
the two middle values.
 Mid-value in interquartile range
Sample Mode
 Infrequently reported as a value in
studies.
 Is the most common value
 More frequently used to describe the
distribution of data
 Uni-modal,
bi-modal, etc.
MODE
MEAN
Mean,
Median,
Mode &
Tornadoes
MEDIAN
Standard Error
 A fundamental goal of statistical analysis is
to estimate a parameter of a population
based on a sample
 The values of a specific variable from a
sample are an estimate of the entire
population of individuals who might have
been eligible for the study.
 A measure of the precision of a sample in
estimating the population parameter.
Confidence Intervals
 May be used to assess a single point
estimate such as mean or proportion.
 Most commonly used in assessing
the estimate of the difference between
two groups.
P Values
 The probability that any observation is due to
chance alone assuming that the null hypothesis is
true
 Typically, an estimate that has a p value of 0.05
or less is considered to be “statistically
significant” or unlikely to occur due to chance
alone.

The P value used is an arbitrary value



P value of 0.05 equals 1 in 20 chance
P value of 0.01 equals 1 in 100 chance
P value of 0.001 equals 1 in 1000 chance.
P Values and Confidence
Intervals
 P values provide less information than
confidence intervals.


A P value provides only a probability that estimate is
due to chance
A P value could be statistically significant but of
limited clinical significance.
 A very large study might find that a difference of .1
on a VAS Scale of 0 to 10 is statistically significant
but it may be of no clinical significance
 A large study might find many “significant” findings
during multivariable analyses.
“a large study dooms you to statistical
Anonymous Statistician
significance”
Errors
 Type I error
 Claiming a difference between two
samples when in fact there is none.
 Remember there is variability among
samples- they might seem to come
from different populations but they
may not.
 Also called the  error.
 Typically 0.05 is used
Errors
 Type II error
 Claiming there is no difference
between two samples when in fact
there is.
 Also called a  error.
 The probability of not making a Type
II error is 1 - , which is called the
power of the test.
 Hidden error because can’t be
detected without a proper power
analysis
Errors
Truth
Test Result
Null
Hypothesis
H0
Alternative
Hypothesis
H1
Null
Hypothesis
H0
Alternative
Hypothesis
H1
No Error
Type I

Type II

No Error
General Formula
The basic formula is as follows:
Numerator(x)
Measure =
Denominator(y)
Rate
The basic formula for a rate is as follows:
Number of cases or events occurring
during a given time period
Rate =
Population at Risk during the same
time period
Use of Ratios, Proportions, and Rates
Condition
Ratios
Proportions
Rates
Morbidity
(Disease)
Risk Ratio
(Relative Risk)
Rate Ratio
Odds Ratio
Attributable
proportion
Point Prevalence
Incidence rate
Attack rate
Secondary attack rate
Person-time rate
Period Prevalence
Mortality
(Death)
Death-to-case ratio
Maternal Mortality rate
Proportionate mortality
rate
Postneonatal mortality
rate
Proportionate
mortality
Case-fatality rate
Crude mortality rate
Cause-specific
mortality rate
Age-specific mortality rate
Race-specific mortality
rate
Age adjusted mortality
rate
Low birth weight
ratio
Crude birth rate
Crude fertility rate
Crude rate of natural
increase
Natality
(Birth)
Risk Ratio
The formula for Risk Ratio is :
Risk for Group of primary interest
RR =
Risk for Comparison Group
Rate Ratio
The formula for Rate Ratio is :
Rate for Group of primary interest
RR =
Rate for Comparison Group
Odds Ratio
The formula for Odds Ratio is :
Disease/Outcome
+
-
+
a
b
-
c
d
ad
bc
Exposure/Cause
OR =
Attributable Proportion
The formula for attributable proportion is :
Risk for exposed group – Risk for unexposed group
AR =
X 100%
Risk for exposed group
Person-time Rate
The formula for person time rate is :
# cases during observation period
PtR =
X 10 n
Time each person observed, Totaled for all person
Incidence Rate
The formula for incidence rate is :
# new cases of a specified
disease reported during a given time interval
IR =
Average population during time interval
Attack Rate
The formula for attack rate is :
# new cases of a specified diseases
reported during an epidemic period
AR =
Population at start of The epidemic period
Secondary Attack Rate
The formula for secondary attack rate is :
# new cases of a specified
diseases among contacts of known cases
SAR =
Size of contact Population at risk
child attending child care
center
child with Influenza
family member
family member who develops
influenza
Point Prevalence
The formula for point prevalence is :
# current cases, new and old, of a specified disease at a
given point in time
PoP =
Estimated population at the same point in time
Period Prevalence
The formula for period prevalence is :
# current cases, new and old, of a specified disease
identified over a given time interval
PeP =
Estimated population at mid-interval
Frequently Used Measures of Morbidity
Measure
Numerator (x)
Denominator (y)
Expressed per
Number at Risk (10n)
Incidence Rate
# new cases of a specified
disease reported during a
given time interval
# new cases of a specified
diseases reported
during an epidemic
period
Average population
during time
interval
Varies :
10n where
n = 2,3,4,5,6
varies
10n where
n = 2,3,4,5,6
# new cases of a specified
diseases among contacts
of known cases
# current cases, new and
old, of a specified
disease at a given
point in time
# current cases, new and
old, of a specified
disease identified over
a given time interval
Size of contact
Population at risk
Attack Rate
Secondary
Attack Rate
Point
Prevalence
Period
Prevalence
Population at start of
The epidemic period
Estimated population
at the same point
in time
Estimated population
at mid-interval
varies
10n where
n = 2,3,4,5,6
varies
10n where
n = 2,3,4,5,6
varies
10n where
n = 2,3,4,5,6
Example
During the first 9 months of national surveillance
for eosinophilia-myalgia syndrome (EMS), CDC
received 1,068 case reports which specified sex;
893 cases were in females, 175 in males
How do we calculate the female-to-male
ratio for EMS?
Solution
1. Define x and y: x = cases in females
y = cases in males
2. Identify x and y: x = 893
y = 175
3. Set up the ratio x/y: 893/175
4. Reduce the fraction so that either x or y equals 1
893/175 = 5.1 to 1
Summary: there were just over 5 female EMS patients for
each male patient reported to the CDC
Another example
In 1989, 733,151 new cases of gonorrhea were reported
among United States civilian population. The 1989 midyear U.S. civilian population was estimated to by
246,552,000. For these data we will use a value of 105 for
10n. We will calculate the 1989 gonorrhea incidence rate
for the U.S. civilian population using these data.
Data Presentation:
Ten Episodes of an Illness in a population of 20
Solution
Point of clarification: the “attack” chart only shows
those cases that were affected, there were 10
persons not affected.
x = new cases occurring between 10/1/90 and 9/30/91 = 4
y = total population at midpoint - 20 - 2 = 18
x
y
n
 10 
4
18
x100 
22
100
So the one year incident was 22 cases per 100 population.
Understanding Run Charts
 The purpose of chart interpretation is to
help make better decisions by identifying
the two types of variation -- common and
special.
 Processes that consist of just common
causes are more predictable over time.
If you want to maintain predictability,
monitor the process and eliminate
special causes when they occur.
Understanding Run Charts
 To improve the process, change is required.
 To determine if the change is resulting in a
shift in the process, an interpretation
standard is needed.
 Run of seven points(special cause variation).
a.
b.
Seven points in a row above or below the
center line (average or central location).
b. Seven or more points in a row going in one
direction, up or down.
Indications of special causes
 Run of seven points.
 Seven points in a row above or below the center line
(average or central location).
 Seven or more points in a row going in one direction,
up or down.
 Any nonrandom pattern.
 Too close to the average.
 Too far from the average.
 Cycles.
 Any point lying outside the upper or lower control
limits.
 Generally, 20-25 data points are needed to develop
upper and lower limits.
Pareto Chart Analysis
 Quality problems are rarely spread evenly
across the different aspects of patient
care.
 Rather, a few "bad apples" often account
for the majority of problems.
 This principle has come to be known as
the Pareto principle, which basically
states that quality losses are maldistributed in such a way that a small
percentage of possible causes are
responsible for the majority of the quality
problems.
Pareto example…
Considerations for Designing
Surveillance
1. Population served
2. Services provided
3. Regulatory or other requirements
Structure and Data
 Determine data needed to calculate
specific rates
 Establish mechanisms for data collection


Routine
Critical values
 Study types



Outcome vs process
Case/control vs cohort
Experimental
Surveillance Design
Take Away Points
1. Design determines data requirements



Attack rate
Incidence density
Prevalence
2. Concentrate on direct risks (of which,
employee staffing is NOT one)
3. Active and passive systems
Surveillance Definitions
 Set up when designing surveillance
system
 Clinical and surveillance definitions may
not agree
What is a HAI?
 More than a positive culture
 NOTE: THERE IS NO 48 HOUR NOR 3-DAY
RULE FOR DISTINGUISHING BETWEEN A
COMMUNITY ACQUIRED AND HAI
http://www.cdc.gov/nhsn/Training/patient-safety-component/index.html#web
(accessed 9/19/12)
What is an Indwelling Catheter?
A drainage tube that is inserted into the
urinary bladder through the urethra, is left
in place, and is connected to a closed
collection system
Note: There is no minimum period of time
that the catheter must be in place in order
for the UTI to be considered catheterassociated.
What is an SSI?
 Active, patient based, prospective
surveillance
 Varieties



Superficial (not including stitch abcesses)
Deep incisional
Organ/space
 Definition of an operating room
 Role of The Implant….
What is a CLABSI?
Primary BSI that develops in a patient that had a central line within the 48
hours prior to the infection onset.
 Primary or secondary BSI?

CLA or non-CLA?
 Health care associated or community
acquired?
 Pathogen or contaminant?
Outbreak Investigation
1.Verify existence of outbreak
 Confirm reports
 Develop a line listing, outbreak curve
2.Collaborate with experts on case
definition, time frame, case finding
methods
3.Define
 Time,
place, person, AND RISK FACTORS
4.Formulate hypothesis
Outbreak Investigation
1.Implement and evaluate control measures,
including ongoing surveillance
2.Prepare and disseminate reports
Outbreak Investigation
Take Away Points
1. Testing care givers is seldom an effective
approach
2. An epidemic curve is a histogram
3. Common source outbreaks often come
from a single vehicle

Different organisms prefer different
vehicles
Conclusions
 Brief overview of principles related to
epidemiology and surveillance has been
shared
 Baseline statistics and their interpretation
also presented
 Review of run charts and basic
understanding
 Overview of healthcare-associated
infections and the role of the ICP