Download Principles of Epidemiology CORE 5520 (32:832:520)

Principles of Epidemiology
Dona Schneider, PhD, MPH, FACE
Epidemiology Defined

Epi + demos + logos = “that which
befalls man”

The study of the distribution and
determinants of disease frequency in
human populations (MacMahon and
Pugh, 1970)
Epidemiology (Schneider)
Epidemiology Defined

The study of the distribution and
determinants of health-related states
or events in specified populations
and the application of this study to
the control of health problems (John
Last, 1988)
Epidemiology (Schneider)
Uses of Epidemiology

Identifying the causes of disease


Completing the clinical picture of disease


Tuskegee experiment
Determining effectiveness of therapeutic and
preventive measures


Legionnaire’s disease
Mammograms, clinical trials
Identifying new syndromes

Varieties of hepatitis
Epidemiology (Schneider)
Uses of Epidemiology

Monitoring the health of a community, region,
or nation


Identifying risks in terms of probability
statements


Surveillance, accident reports
DES daughters
Studying trends over time to make predictions
for the future

Smoking and lung cancer

Estimating health services needs
Epidemiology (Schneider)
Life Table of Deaths in London
Age
Deaths
Survivors
0
--
100
6
36
64
16
24
40
26
15
25
36
9
16
46
6
10
56
4
6
66
3
3
76
2
1
80
1
0
Source: Graunt’s Observations 1662
Epidemiology (Schneider)
Graunt’s Observations

Excess of male births

High infant mortality

Seasonal variation in mortality
Epidemiology (Schneider)
Yearly Mortality Bill for 1632:
Top 10 Causes of Death
Chrisomes & Infants
Consumption
Fever
Collick, Stone, Strangury
Flox & Small Pox
Bloody Flux, Scowring & Flux
Dropsie & Swelling
Convulsion
Childbed
Liver Grown
0
500
1000
1500
Number of deaths
Epidemiology (Schneider)
2000
2500
Leading Causes of Death in US: 1900
Pneumonia
Tuberculosis
Diarrhea and enteritis
Heart disease
Chronic nephritis
Unintentional injury
Stroke
Diseases of early infancy
Cancer
Diptheria
0
50
100
150
200
Death rate per 100,000
Epidemiology (Schneider)
250
300
Leading Causes of Death in US: 1990
Heart disease
Cancer
Stroke
Unintentional injury
Lung diseases
Pneumonia and influenza
Diabetes
Suicide
Liver disease
HIV/AIDS
0
50
100
150
200
Death Rates per 100,000
Epidemiology (Schneider)
250
300
No. of Cases of a Disease
Endemic Vs. Epidemic
Endemic
Time
Epidemiology (Schneider)
Epidemic
Population Pyramid
Epidemiology (Schneider)
1900
1980
Epidemiology (Schneider)
1940
1960
2000
Statistics

Statistics: A branch of applied
mathematics which utilizes procedures
for condensing, describing, analyzing
and interpreting sets of information

Biostatistics: A subset of statistics used
to handle health-relevant information
Epidemiology (Schneider)
Statistics (cont.)


Descriptive statistics: Methods of
producing quantitative summaries of
information

Measures of central tendency

Measures of dispersion
Inferential statistics: Methods of making
generalizations about a larger group
based on information about a subset
(sample) of that group
Epidemiology (Schneider)
Populations and Samples

Before we can determine what
statistical test to use, we need to know
if our information represents a
population or a sample

A sample is a subset which should be
representative of a population
Epidemiology (Schneider)
Samples

A sample should be representative if
selected randomly (i.e., each data point
should have the same chance for
selection as every other point)

In some cases, the sample may be
stratified but then randomized within the
strata
Epidemiology (Schneider)
Example
We want a sample that will reflect a
population’s gender and age:
1. Stratify the data by gender
2. Within each strata, further stratify by age
3. Select randomly within each gender/age strata
so that the number selected will be proportional
to that of the population
Epidemiology (Schneider)
Populations and Samples

You can tell if you are looking at
statistics on a population or a sample

Greek letters stand for population
parameters (unknown but fixed)

Arabic letters stand for statistics
(known but random)
Epidemiology (Schneider)
Classification of Data
Qualitative or Quantitative

Qualitative: non-numeric or categorical


Examples: gender, race/ethnicity
Quantitative: numeric

Examples: age, temperature, blood
pressure
Epidemiology (Schneider)
Classification of Data
Discrete or Continuous

Discrete: having a fixed number of values


Examples: marital status, blood type,
number of children
Continuous: having an infinite number of
values

Examples: height, weight, temperature
Epidemiology (Schneider)
Hint

Qualitative (categorical) data are discrete

Quantitative (numerical) data may be

discrete

continuous
Epidemiology (Schneider)
Qualitative Data: Nominal

Data which fall into mutually exclusive categories
(discrete) for which there is no natural order

Examples:

Race/ethnicity

Gender

Marital status

ICD-10 codes

Dichotomous data such as HIV+ or HIV-; yes or no
Epidemiology (Schneider)
Qualitative Data: Ordinal

Data which fall into mutually exclusive
categories (discrete data) which have a rank
or graded order

Examples:

Grades

Socioeconomic status

Stage of disease

Low, medium, high
Epidemiology (Schneider)
Quantitative Data: Interval

Data which are measured by standard units

The scale measures not only that one data
point is different than another, but by how
much

Examples

Number of days since onset of illness
(discrete)

Temperature in Fahrenheit or Celsius
(continuous)
Epidemiology (Schneider)
Quantitative Data: Ratio

Data which are measured in standard
units where a true zero represents
total absence of that unit

Examples

Number of children (discrete)

Temperature in Kelvin (continuous)
Epidemiology (Schneider)
Review of Descriptive Biostatistics

Mean

Median

Mode and range

Variance and standard deviation

Frequency distributions

Histograms
Epidemiology (Schneider)
Mean

Most commonly used measure of central
tendency

Arithmetic average


Formula: x =  x / n
Sensitive to outliers
Epidemiology (Schneider)
Example: Number of accidents per week
8, 5, 3, 2, 7, 1, 2, 4, 6, 2
x = (8+5+3+2+7+1+2+4+6+2) / 10
= 40 / 10 = 4
Epidemiology (Schneider)
Median

The value which divides a ranked set
into two equal parts

Order the data

If n is even, take the mean of the two middle
observations

If n is odd, the median is the middle
observation
Epidemiology (Schneider)
Given an even number of observations (n=10):
Example: 1, 2, 2, 2, 3, 4, 5, 6, 7, 8
Median = (3+4) / 2 = 3.5
Given an odd number of observations (n=11):
Example: 1, 2, 2, 2, 3, 4, 5, 6, 7, 8, 10
Median = 4
(n+1)/2 = (11+1)/2 = 6th observation
Epidemiology (Schneider)
Mode

The number which occurs the most
frequently in a set

Example: 1, 2, 2, 2, 3, 4, 5, 6, 7, 8

Mode = 2
Epidemiology (Schneider)
Range

The difference between the largest and
smallest values in a distribution

Example: 1, 2, 2, 2, 3, 4, 5, 6, 7, 8

Range = 8-1 = 7
Epidemiology (Schneider)
Variance and Standard Deviation

Measures of dispersion (or scatter) of the
values about the mean

If the numbers are near the mean, variance
is small

If numbers are far from the mean, the
variance is large
Epidemiology (Schneider)
Variance
V = [S(x-x)2] / (n-1)
V = [(8-4) 2 +(5-4) 2 +(3-4) 2 +(2-4) 2 +(7-4) 2 +(1-4) 2 +
(2-4) 2 +(4-4) 2 +(6-4) 2 +(2-4) 2] / (10-1) =
V = 5.7777
Epidemiology (Schneider)
Standard Deviation
SD = V
SD = 2.404
Epidemiology (Schneider)
Symmetric and Skewed Distributions
Symmetrical
Skewed
Mean
Median
Mode
Mean
Median
Mode
Epidemiology (Schneider)
Frequency Diagrams of Symmetric and
Skewed Distributions
Symmetric
Epidemiology (Schneider)
Skewed
12 Patients’ 5-point Anxiety Scale Scores
Patient
1
2
3
4
5
6
7
8
9
10
11
12
Anxiety
score
4
3
5
1
4
4
2
5
4
3
4
5
Epidemiology (Schneider)
Score
Frequency
1
1
2
1
3
2
4
5
5
3
Total
12
Frequency Diagram for
12 Psychiatric Patients
5
4
3
2
1
0
1
2
3
Score
Epidemiology (Schneider)
4
5
Accidents at a summer camp requiring
ER treatment
Week
Frequency
Percent
1
1
10
2
3
30
3
1
10
4
1
10
5
1
10
6
1
10
7
1
10
8
1
10
Epidemiology (Schneider)
Histogram
4
Frequency
3
2
1
0
0
1
2
3
4
5
6
Number of accidents per week
Epidemiology (Schneider)
7
8
9
Frequency Polygon
4
Frequency
3
2
1
0
0
1
2
3
4
5
6
Number of accidents per week
Epidemiology (Schneider)
7
8
9
Frequency Polygon and Histogram
Note: area A = A; B = B; C = C; D = D; area under histogram
= to area under polygon
Frequency
4
3
B
C
2
B
1
C
A
D
A
0
0
D
1
2
3
4
5
6
7
Number of accidents per week
Epidemiology (Schneider)
8
9
Descriptive Statistics

Used as a first step to look at health-related
outcomes

Examine numbers of cases to identify an
increase (epidemic)

Examine patterns of cases to see who gets sick
(demographic variables) and where and when
they get sick (space/time variables)
Epidemiology (Schneider)