3 Epidemiology and Statistics for IPC Surveillance.ppt

MTI, Khyber Teaching Hospital, Peshawar
Epidemiology and Statistics for
IPC Surveillance
By
Rehmat Ullah
Nursing Director, MTI KTH, Peshawar

Objectives of the Session
 The participants will be able to;
 Define surveillance and explain surveillance systems
 Discuss basic statistical concepts and methods used to
analyze and report infection prevention and control
(IPC) data
 Discuss descriptive statistics used for describing health
care-associated infections (HAIs)
 Importance of sharing IPC data with key staff
 Data visualization methods and techniques for effective
data sharing

What Is Surveillance?
 Centers for Disease Control and Prevention
(CDC): is epidemiologic surveillance is;
 “ongoing systematic collection, analysis, and
interpretation of health data essential to the
planning, implementation, and evaluation of public
health practice, closely integrated with the timely
dissemination of these data to those who need to
know.”

Why Is Surveillance Important?
 Collecting data is merely one step
 Critical goal is to control and/or prevent diseases
 Any data collected must be organized and carefully
examined
 Any results need to be communicated to public health
and medical communities

Why Is Surveillance Important?
 Vital to communicate results
 During potential outbreak so public health and medical
communities can help with disease prevention and
control efforts
 During non-outbreak times to provide information
about baseline levels of disease
 Baseline provides information to public health
officials monitoring health at community level,
serves as reference in future outbreaks

Types of Surveillance-- activities can be
outcome- or process-oriented
 Outcome surveillance: monitoring
of specific HAIs (e.g., SSIs,
catheter-associated urinary tract
infections [CAUTIs], diarrhea), or
 Determine baseline rates of
HAI
 Identify occurrence of
infections above the baseline
(expected) rates
 Detect and report notifiable
diseases to the public health
authorities
 Detect and investigate clusters,
outbreaks, and exposures,
including emerging infectious
diseases
 Process surveillance: monitoring of
patient care practices, including IPC
practices (e.g., compliance with
hand hygiene, timing of prophylactic
antibiotics during surgery, use of
aseptic technique for central line
insertion).
 Observe HCWs’ practices to
ensure compliance with policies
and best practices
 Provide information to help guide
performance improvement
activities
 Assess the effectiveness of IPC
measures
 Meet the safety standards
required by the health
department and other regulatory
agencies

Types of Surveillance
 Surveillance can be continuous or periodic:
 Continuous surveillance: data are collected
continuously on a routine basis, or
 Periodic surveillance: data are collected at intervals,
such as 1 month each quarter or 1 quarter per year.

Surveillance Systems
 Classified as passive or active
 Passive surveillance: local and state health
departments rely on health care providers or
laboratories to report cases of disease
 Primary advantage is efficiency: simple and requires
relatively few resources
 Disadvantage is possibility of incomplete data due to
underreporting
 Majority of public health surveillance systems are
passive

SURVEILLANCE SYSTEMS
 Active surveillance: health department contacts
health care providers or laboratories requesting
information about conditions or diseases to
identify possible cases
 Requires more resources than passive surveillance
 Useful when important to identify all cases
 Example: between 2002 and 2005, active surveillance used to
detect adverse events associated with smallpox vaccine. (2)

Characteristics of Effective Surveillance
 Surveillance is based upon sound epidemiological
and statistical principles.
 Data are properly collected and analyzed.
 Information is shared in a timely manner with
those who can act to improve IPC practices and
the quality of care. Efforts to improve practices
and decrease HAI are a critical part of the
surveillance plan.

Design and Develop a Surveillance
Approach
1. Choose whether to monitor an outcome or a
process measure
2. Select appropriate indicators
 HCWs’ compliance with hand hygiene guidelines (the
proportion of compliant hand hygiene opportunities)
 The SSI rates following C-sections, per 100 C-sections
 The CAUTI rates per 1,000 catheter-days
3. Consider benchmarks and goals

Basic Statistical Concepts
 Populations---
 can be defined based on how permanent their
membership is (Aschengrau and Seage 2008).
1. Fixed populations have permanent members who are
usually defined by a life event. the person will be a
member of this population for life. (Employee of the
Insititute)
2. Dynamic, or open, Populations can also be
transient, with members joining and leaving the
population over time. (Patient and Visitors)

Basic Statistical Concepts
 “At-risk” Population- Patients who are at risk of
developing a specific HAI are called the “at-risk” population
for that HAI

INTRODUCTION TO STATISTICS
 The word statistics came into use more than
200 years ago.
 At that time, statistics referred to a country’s
quantifiable political characteristics—
characteristics such as population, taxes, and
area

INTRODUCTION TO STATISTICS
 Statistics….. Statistics meant “state numbers.”
 Statistical analysis helps make sense of
information
 Statistical procedures enable to;
 summarize, organize, evaluate, interpret, and
communicate information
 Two different techniques are called statistics
which are either
 DESCRIPTIVE statistics or INFERENTIAL statistics

DESCRIPTIVE & INFERENTIAL STATISTICS
 Descriptive Statistics
 Describe the data… produces a number or a figure that
summarizes or describes a set of data
 what the data is?
 what the data Show?
 Enable the researcher to explore identify pattern in the collected
data
 Inferential Statistics
 Used to draw general conclusions about the concerned
population
 Measure a sample to reach conclusions about a larger,
unmeasured population
 Used to INFER conclusion from the data & making
generalization to the population

DESCRIPTIVE STATISTICS
 Used to describe and synthesize data
 Present data by using Graph, tables and chart;
 Tables, Graphs, Histogram & Bar charts, Pie Chart
 Measurement of central tendency;
 Averages/Means, Mode, Median and percentages
 Measures of Dispersion (Variability)
 Range.. Difference B/t Lowest & Highest (11-31)
 Deviation & Variations…. Homogeneity& heterogeneity
 Standard Deviation… Average amount of deviation of values from the mean
 Frequency Distribution
 Shape of Distribution…. Symmetrical/normal, Asymmetrical
distribution/Skewed distribution

PRESENT DATA BY USING CHARTS
 Pie chart.. Show data in
slice… each slice is shown
in relationship to total
 Bar chart… the height of
bar shows the data
 Line Chart… for ratio & interval
data
 Useful to shows changes over
time
Choice of graph depend on data type
Nominal & ordinal.. Pie & bar chart
Scale data… line, histogram

PRESENT DATA BY USING CHARTS
 Histograms -- Histogram … the
bar chart for categorical data
 frequency polygons…
displaying frequency
information graphically

PRESENT DATA BY USING TABLE
 Frequency distributions… Impose order on
raw data, numeric values are ordered from
lowest to highest

DESCRIBING DISTRIBUTIONS
 A distribution is symmetric if its two halves are mirror
images of each other.
 A skewed distribution, by contrast, is asymmetric, with
one tail longer than the other.
 In a positively skewed distribution the long tail points to the
right (e.g., personal income)
 In a negatively skewed distribution the long tail points to the
left (e.g., age at death)

Measurement of central tendency
 Measurement of central tendency… indexes, expressed
as a single number, that represent the average or typical
value of a set of scores
 Mode is the value that occurs most frequently in the distribution
 50 51 51 52 53 53 53 53 54 55 56 Mode=53
 Median is the point above which and below which 50% of the
cases fall… divide into two equal halves
 2 2 3 3 4 5 6 7 8 9 Median= 4.5
 Mean is the arithmetic average of all scores
 85 + 109 + 120 + 135 + 158 + 177 + 181 + 195= total/8= 145
The mean, mode, standard deviation, univariate (one-variable) descriptive statistics

MEASURES OF VARIABILITY
 Measures of variability—spread out of the data
 Range is the distance between the highest and lowest scores (XH-
X L)
 (650 -350=300)
 Semi-quartile range indicates one half of the range of scores
within which the middle 50% of scores lie; (Q3- Q1/2= SQR)
 deviation Score is a raw score minus the mean of the distribution,
whether the distribution is a sample or a population. (X-X)
 Standard deviation indicates how much, on average, scores
deviate from the mean
 The variance is equal to the standard deviation squared (sd2= ex2
/N)

BIVARIATE DISTRIBUTIONS
 Two variables (bivariate data) and the
relationship between them

BIVARIATE DESCRIPTIVE STATISTICS
 two-dimensional frequency distribution
 Relationship of smoking & gender

 Correlation… Relationships between two variables
 Correlation coefficients can be computed with two
variables measured on either the ordinal, interval, or
ratio scale
 To what degree are anxiety test scores and blood pressure
readings related?
 Positive/negative correlation is computed (e.g.Hand hygiene and
infection control)
 Range from -1.00 (perfect negative correlation) through
.00 to 1.00 (perfect positive correlation)

Descriptive Statistics Associated with
Methods & Designs
Design Descriptive Statistics
Survey Studies Percentages, measures of central
tendency and variation
Meta-analysis Effect sizes
Causal comparative studies Measures of central tendency &
variation, percentages, standard scores
Experimental Measures of central tendency &
variation, percentages, standard scores,
effect sizes

Selecting the Appropriate Descriptive
Statistic
Type of Statistic Level of Measurement Statistic
Measures of central tendency Nominal scale Mode
Ordinal scale Median
Interval/ratio scale Mean
Measures of variation Nominal scale No. of categories
Ordinal scale Range
Interval/ratio scale Standard deviation
Tests of relationships Nominal scale Chi-square (X2)
Ordinal scale Spearman rank
Interval/ratio scale Pearson r

INFERENTIAL STATISTICS
 To generalize or predict how a large group will behave
based upon information taken from a part of the group is
called and INFERENCE
 Techniques which tell us how much confidence we can
have when we GENERALIZE from a sample to a
population
 Based on the laws of probability, estimate population
parameters from sample statistics
 A framework for making judgments about their reliability
in a systematic, objective fashion

Estimating parameters Testing hypotheses
 Parameter estimation is used
to estimate a single parameter,
like a mean
 Point estimation provides a
single numeric value
 Interval estimation provides
the upper and lower limits of a
range of values—
 Confidence interval— between
which the population value is
expected to fall, at a specified
probability level
 Establish the degree of
confidence that the population
value lies within this range
 Make objective decisions about
the validity of their hypotheses
 Type I error… null hypothesis is
incorrectly rejected,
 Type II error …null hypothesis
that should be rejected is
accepted
 The risk of making a Type I
 error is controled by establishing a
level of significance (or alpha
(a) level)
 The a.05 level means that there is
only 5 % chance the null hypothesis
will be rejected when it should have
been accepted
Inferential Statistics
consists of two major types of approaches

Examples of Descriptive & Inferential Statistics
Descriptive Statistics
 Graphical
 Arrange data in tables
 Bar graphs and pie charts
 Numerical
 Percentages
 Averages
 Range
 Relationships
 Correlation coefficient
Inferential Statistics
 Confidence interval
 Margin of error
 Compare means of two
samples
 Pre/post scores
 t Test
 Compare means from three
samples
 Pre/post and follow-up
 ANOVA = analysis of
variance

Measuring Disease Occurrence
 IPC surveillance will produce a dataset of raw numbers
(e.g., number of patients with SSIs, number of patients who had an indwelling
urinary catheter and developed a urinary tract infection).
 Incidence and prevalence are the most common ways to
measure disease frequency

Incidence
 Measures new cases of a disease or condition that
occur in a specified population over a given time
period, and thus looks only at new cases

DIFFERENT TYPES OF INCIDENCE RATES CALCULATED IN THE IPC
SETTING

Incidence Density
 A specific type of incidence rate
 The occurrence of new events (e.g., cases of an
infection) that arise during observation of total
person-time at risk.

 The CLABSI incidence density for April 2016 = (# new CLABSIs/#
central line-days) x constant
 = (2/142) x 1,000 central line-days = 14.08.
 The facility had a CLABSI rate of 14.08 infections per 1,000 central line-days in April
2016.
 Incidence rate (as compared to incidence density) in this case would
be 2/9 patients x (1,000) = 222.22 per 1,000 admissions.

Prevalence
 The number of existing cases and it represents the
proportion of the total population that has the
disease or condition. Prevalence accounts for all
existing cases.
 A very effective measure to express burden of
disease in a population
 There are two main types of prevalence:
 Point prevalence
 Period prevalence

Prevalence Calculation

Standardized infection ratio
 Summary measurement that compares the number of
reported HAIs among a group of patients to the number of
predicted or expected infections, based on a standard
population.

Data Feedback and Sharing
 Tables, graphs, and charts are all common ways
to share IPC data
 All results based on data analysis should be
shared soon after the data are collected
 When sharing IPC data, it is important to consider;
 how to best present the data so that the desired
message is most effectively communicated
 Different data should be shown in different ways i.e. use
graphs, tables, chart etc.

3 Epidemiology and Statistics for IPC Surveillance.ppt

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3 Epidemiology and Statistics for IPC Surveillance.ppt