Surveillance and Epidemiologic Investigation

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Surveillance and Epidemiologic Investigation

• Angela Booth-Jones, PhD, RN
• Marian Rodgers, MSN, MPH, RN

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How we view the world..

• Pessimist The glass is half empty.
• Optimist The glass is half full.
• Epidemiologist As compared to what?

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Epidemiology
EPI DEMO LOGOS Upon,on,befal
l People,population,man the
Study of
The study of anything that happens to
people That which befalls man
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• Clinician Epidemiologist

• Patients diagnostician
• Investigations
• Diagnosis
• Therapy
• Cure

• Communitys diagnostician
• Investigations
• Predict trend
• Control
• Prevention

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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.

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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.

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Evidence-based Practice
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Research Methodologies for Cause-and-effect
Relationships

• Criteria that must be met for a study to
  demonstrate a cause-and-effect relationship
• 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

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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.

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Understanding Statistics

• Population
• Description
• Inference
• BIG WORDS
• Significant
• Valid
• No formulas
• Focus on frequency

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Types of DATA

• Qualitative Data
• Categorical
• Sex
• Diagnosis
• Anything thats not a
• Rank (1st, 2nd, etc)

• Quantitative Data
• Something you measure
• Age
• Weight
• Systolic BP
• Viral load

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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 cant measure
  everybody.
• Our sample will not be perfect.
• True random samples are extremely rare
• Random sampling error

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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

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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.

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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
  non-smokers.
• Smokers are 8 X more likely to get lung cancer
  than non-smokers.

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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
• If Rate ratio 1
• There is no relationship between the exposure and
  the outcome
• This is the null value (remember null
  hypothesis?)

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Normal Curve
95 confidence interval
normally distributed statistic sample and
measurements are valid
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Interpreting Measures of Effect
RR 1 No Association RR gt1 Risk Factor RR
lt1 Protective Factor
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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.

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P value?

• We can make a point estimate and a confidence
  interval.
• Whats 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 lt 0.05 is, by convention, an indication of
  statistical significance.

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If you have an ILLNESS, which result do you want?

• Mean 1.4
• SD 0.1
• P lt0.0005
• Mean 4
• SD 1.5
• P 0.051

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Hypothesis testing

• Uses the p value
• Or, does the confidence interval include the null
  value?
• Looking at a, b, and c which p value is
• p 0.8
• p 0.047
• p 0.004
• CI is better than p value.

a
b
c
Figure 1. Risk of adverse pregnancy outcomes
among women with asthma.
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Types of Data

• Discrete Data-limited number of choices
• Binary two choices (yes/no)
• Dead or alive
• Disease-free or not
• Categorical more than two choices, not ordered
• 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.

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Types of data

• Continuous data
• Theoretically infinite possible values (within
  physiologic limits) , including fractional values
• Height, age, weight
• Can be interval
• 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

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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

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Descriptive Statistics

• Characterize data set
• Graphical presentation
• Histograms
• Frequency distribution
• Box and whiskers plot
• Numeric description
• Mean, median, SD, interquartile range

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HistogramContinuous Data
No segmentation of data into groups
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Frequency Distribution
Segmentation of data into groups Discrete or
continuous data
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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.

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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

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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.

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Mean, Median, Mode Tornadoes
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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.

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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.

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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.

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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
  significance

Anonymous Statistician
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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

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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 cant be detected without a
  proper power analysis

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Errors
Test Result
Null Hypothesis H0 Alternative Hypothesis H1
Null Hypothesis H0 No Error Type I ?
Alternative Hypothesis H1 Type II ? No Error
Truth
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General Formula
The basic formula is as follows

• Numerator(x)

Measure
Denominator(y)
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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
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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
Natality (Birth) Low birth weight ratio Crude birth rate Crude fertility rate Crude rate of natural increase
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Risk Ratio
The formula for Risk Ratio is

• Risk for Group of primary interest

RR
Risk for Comparison Group
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Rate Ratio
The formula for Rate Ratio is

• Rate for Group of primary interest

RR
Rate for Comparison Group
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Odds Ratio
The formula for Odds Ratio is
Disease/Outcome

• ad

-
a b
- c d
bc
OR
Exposure/Cause
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Attributable Proportion
The formula for attributable proportion is

• Risk for exposed group Risk for unexposed group

AR
X 100
Risk for exposed group
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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
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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
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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
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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
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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
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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
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Frequently Used Measures of Morbidity
Measure Numerator (x) Denominator (y) Expressed per Number at Risk (10n)
Incidence Rate Attack Rate Secondary Attack Rate Point Prevalence Period Prevalence new cases of a specified disease reported during a given time interval new cases of a specified diseases reported during an epidemic period 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 Average population during time interval Population at start of The epidemic period Size of contact Population at risk 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 varies 10n where n 2,3,4,5,6 varies 10n where n 2,3,4,5,6
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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?
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Solution

• Define x and y x cases in females
• y cases in males
• Identify x and y x 893
• y 175
• Set up the ratio x/y 893/175
• 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

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Another example
In 1989, 733,151 new cases of gonorrhea were
reported among United States civilian population.
The 1989 mid-year 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.
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Data Presentation Ten Episodes of an Illness in
a population of 20
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Solution
Point of clarification the attack chart only
shows those cases that were affected, there were
10 persons not affected.
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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.

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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).
• 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.

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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. 

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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 mal-distributed in such a way that a
  small percentage of possible causes are
  responsible for the majority of the quality
  problems.

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Pareto example
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Considerations for Designing Surveillance

1. Population served
2. Services provided
3. Regulatory or other requirements

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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

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Surveillance DesignTake Away Points

• Design determines data requirements
• Attack rate
• Incidence density
• Prevalence
• Concentrate on direct risks (of which, employee
  staffing is NOT one)
• Active and passive systems

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Surveillance Definitions

• Set up when designing surveillance system
• Clinical and surveillance definitions may not
  agree

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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-co
mponent/index.htmlweb (accessed 9/19/12)
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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 catheter-associated.

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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.

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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?

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Outbreak Investigation

• Verify existence of outbreak
• Confirm reports
• Develop a line listing, outbreak curve
• Collaborate with experts on case definition, time
  frame, case finding methods
• Define
• Time, place, person, AND RISK FACTORS
• Formulate hypothesis

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Outbreak Investigation

• Implement and evaluate control measures,
  including ongoing surveillance
• Prepare and disseminate reports

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Outbreak InvestigationTake Away Points

• Testing care givers is seldom an effective
  approach
• An epidemic curve is a histogram
• Common source outbreaks often come from a single
  vehicle
• Different organisms prefer different vehicles

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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