Principles of Epidemiology Dona Schneider, PhD, MPH, FACE

Principles of Epidemiology

Dona Schneider, PhD, MPH, FACE

Epidemiology (Schneider)

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 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)


Uses of Epidemiology Identifying the causes of disease

Legionnaire’s disease

Completing the clinical picture of disease

Tuskegee experiment

Determining effectiveness of therapeutic and preventive measures

Mammograms, clinical trials

Identifying new syndromes

Varieties of hepatitis


Uses of Epidemiology Monitoring the health of a community, region, or nation

Surveillance, accident reports

Identifying risks in terms of probability statements

DES daughters

Studying trends over time to make predictions for the future

Smoking and lung cancer

Estimating health services needs


Life Table of Deaths in LondonAge 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 0Source: Graunt’s Observations 1662


Graunt’s Observations

Excess of male births

High infant mortality

Seasonal variation in mortality


Yearly Mortality Bill for 1632:Top 10 Causes of Death

0 500 1000 1500 2000 2500

Liver GrownChildbed

Convulsion

Dropsie & SwellingBloody Flux, Scowring & Flux

Flox & Small PoxCollick, Stone, Strangury

FeverConsumption

Chrisomes & Infants

Number of deaths


Leading Causes of Death in US: 1900

0 50 100 150 200 250 300

DiptheriaCancer

Diseases of early infancyStroke

Unintentional injuryChronic nephritis

Heart diseaseDiarrhea and enteritis

TuberculosisPneumonia

Death rate per 100,000


0 50 100 150 200 250 300

HIV/AIDS

Liver disease

Suicide

Diabetes

Pneumonia and influenza

Lung diseases

Unintentional injury

Stroke

Cancer

Heart disease

Death Rates per 100,000

Leading Causes of Death in US: 1990


Endemic Vs. Epidemic

Endemic Epidemic

No

. of

Ca

ses

of

a D

isea

se

Time


Population Pyramid


1900

20001980

19601940


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


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


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


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


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


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)


Classification of Data

Qualitative or Quantitative

Qualitative: non-numeric or categorical

Examples: gender, race/ethnicity

Quantitative: numeric

Examples: age, temperature, blood pressure


Classification of DataDiscrete 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


Hint

Qualitative (categorical) data are discrete

Quantitative (numerical) data may be discrete

continuous


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


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


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)


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)

Quantitative Data: Ratio


Review of Descriptive Biostatistics

Mean

Median

Mode and range

Variance and standard deviation

Frequency distributions

Histograms


Mean

Most commonly used measure of central tendency

Arithmetic average

Formula: x = x / n

Sensitive to outliers


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


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


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


Mode

The number which occurs the most frequently in a set

Example: 1, 2, 2, 2, 3, 4, 5, 6, 7, 8

Mode = 2


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


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


Variance

V = [(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


SD = V

Standard Deviation

SD = 2.404


Symmetric and Skewed Distributions

Mean

Median

ModeModeMedian

Mean

Symmetrical Skewed


Frequency Diagrams of Symmetric and Skewed Distributions

Symmetric 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

Score Frequency

1 1

2 1

3 2

4 5

5 3

Total 12


Frequency Diagram for 12 Psychiatric Patients

0

1

2

3

4

5

1 2 3 4 5

Score

Frequency


Accidents at a summer camp requiringER 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


Histogram

0

1

2

3

4

0 1 2 3 4 5 6 7 8 9

Number of accidents per week

Fre

qu

ency


Frequency Polygon

0

1

2

3

4

0 1 2 3 4 5 6 7 8 9


Fre

qu

ency


0

1

2

3

4

0 1 2 3 4 5 6 7 8 9

Frequency Polygon and Histogram


Fre

quen

cy

A

A

B

B

C

C

D

D

Note: area A = A; B = B; C = C; D = D; area under histogram = to area under polygon


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)

Documents

Principles of Epidemiology Dona Schneider, PhD, MPH, FACE