Introduction to Epidemiology Basic Principles 1

1
Introduction to Epidemiology Basic Principles 1
Martin Frisher Department of Medicines
Management Keele University
2
Learning Objectives

• Review of module 1 (incidence prevalence)
• Risk Exposure and Outcomes
• Cohort and Cross Sectional Studies
• Bias and Confounding
• Standardization
• Sensitivity and Specificity
• Exercises Relative Risk, Standardization
  Sensitivity

3
Epidemiology Definition

• The study of the incidence, distribution and
  determinants of an infection, disease or other
  health-related event in a population.
• In US law, epidemiology alone cannot prove a
  causal association does/does not exist in
  general.
• ref http//en.wikipedia.org/wiki/Epidemiology

4
Epidemiologic Analyses

• Causal agents related to disease
• Nutritional agents diet (fats, carbohydrates,
  food nutrients)
• Biological agents bacteria, viruses, insects
• Chemical agents gases, toxic agents
• Physical agents climate, vegetation, chemical
  pollutants (air, water, food)
• Social agents occupation, stress, social class,
  lifestyle, location of residence
• Ref www.iihe.org/education/lectures/epi1.ppt

5
Preventable Causes of Disease

• BEINGS
• Biological factors and Behavioral Factors
• Environmental factors
• Immunologic factors
• Nutritional factors
• Genetic factors
• Services, Social factors, and Spiritual factors
• JF Jekel, Epidemiology, Biostatistics, and
  Preventive Medicine, 1996 ped1.med.uth.tmc.edu/n
  eo/clinepi1.ppt

6
Prevalence

• Number of existing cases of disease
• Proportion of individuals in a population with
  disease or condition at a specific point of time
• Diabetes prevalence, smoking prevalence
• Provides estimate of the probability or risk that
  one will be affected at a point in time
• Provides an idea of how severe a problem may be
  measures overall extent
• Useful for planning health services (facilities,
  staff)

7
Calculation of proportion
Males undergoing bypass surgery at Hospital
A Total patients undergoing bypass surgery at
Hospital A

352 males undergoing bypass surgery 539 total
patients undergoing bypass surgery
65.3

8
Incidence

• Measure of new cases of disease (or other events
  of interest) that develop in a population during
  a specified period of time
• E.g. Annual incidence, five-year incidence
• Measure of the probability that unaffected
  persons will develop the disease
• Used when examining an outbreak of a health
  problem

9
Formula for cumulative incidence
Number of new cases of disease during a
given time period CI
Total population at risk
70 new cases of breast cancer in a
5 year period CI 3,000
women at risk 0.023 23 cases per 1,000
women during 5 years
10
Incidence Density
11
Cumulative incidence vs Incidence density
Cumulative incidence 2 cases/5 individuals
over a 5-year period 0.4 over a 5 year period
0.08 over a 1 year period 8 per 100 over
a 1 year period Incidence density 2
cases/16.5 person years 12.1/100 person
years of observation
12
Relationship Between Incidence and Prevalence

• Prevalence varies directly with both incidence
  and duration.
• If incidence is low, but duration is long
  (chronic),
• prevalence will be high in relation to
  incidence.
• If prevalence is low because of short duration
  (due to recovery, migration or death),
• prevalence will be small in relation to incidence.

13
Concept of Risk
14
Risk Distribution of cholera deaths, London 1850
Source www.ph.ucla.edu/epi/snow.html
15
Risk - Two by Two Tables
Used to summarize frequencies of disease and
exposure and calculation of association.
16
Relative Risk

• The relative risk (sometimes called the risk
  ratio) compares the probability of death in each
  group.
• For females, the probability of death is 33
  (154/4620.33).
• For males, the probability of death is 83
  (709/8510.83).
• The relative risk of death is 2.5 (0.83/0.33).
  There is a 2.5 greater probability of death for
  males than for females.

17
Risk and Odds

• Risk probability of disease
• Odds the probability of disease to the
  probability of not disease
• Example
• Absolute risk of disease 0.3 (30)
• Odds of disease 0.3 / 0.7 0.43

18
Odds ratio and the relative risk

• Both the odds ratio and the relative risk compare
  the likelihood of an event between two groups.
• Consider the following data on survival of
  passengers on the Titanic.

19
Odds Ratio

• The odds ratio compares the relative odds of
  death in each group.
• For females, the odds were 2 to 1 against dying
  (154/3080.5).
• For males, the odds were almost 5 to 1 in favor
  of death (709/1424.9).
• The odds ratio is 9.9 (4.9/0.5).

20
Breastfeeding StudyWhich measure of risk?
21
Odds Ratio

• Suppose there are two groups, one with a 25
  chance of mortality and the other with a 50
  chance of mortality.
• Most people would say that the latter group has
  it twice as bad. But the odds ratio is 3, which
  seems too big.
• The latter odds are even (1 to 1) and the former
  odds are 3 to 1 against.
• A change from 25 to 75 mortality represents a
  relative risk of 3, but an odds ratio of 9.
• A change from 10 to 90 mortality represents a
  relative risk of 9 but an odds ratio of 81

22
Types of StudyCohort and Case Control

• Cohort Studies
• Subjects are selected on the basis of exposure
  and the outcome of interest is the development of
  disease/death.
• Case-control Studies
• Subjects are selected because they have the
  disease in question (or have died from the
  disease) and control are selected because they
  have not but are broadly similar.

23
Cohort study
Disease
Exposure
No disease
No exposure
Disease
No disease
time
direction of study
24
Example of a prospective cohort study

• Smoking and lung cancer
• Studied determined smoking habits of 34,439 male
  British doctors in 1951 and followed them for 50
  years
• Smokers died on average 10 year earlier compared
  with non-smokers
• Doll et al, BMJ 2004

25
Example of a prospective cohort study (2)

• Moderate Alcohol Consumption and the Risk of
  Breast cancer (Willet et al. 1987).
• Population
• Nurses Health Study (N89,538) RNs
• Ages 34-59
• Follow-up
• Entry in 1980, followed until 1984
• 601 cases of breast cancer by 1984
• Results Relative Risk (RR)
• Alcohol Use RR (95 C.I.)
• None 1.0
• 3-9 Drinks/wk 1.3 (1.1 - 1.7)
• 10 Drinks/wk 1.6 (1.3 - 2.0)

26
Case-control study
Exposure
Disease
No Exposure
No Disease
Exposure
No Exposure
time
direction of study
27
Example of case-control study

• Thalidomide (exposure) and limb defects (outcome)
• Compared mothers of children with malformations
  (cases) with mothers of normal children
  (controls)
• 41/46 mothers of children with malformations had
  taken thalidomide during pregnancy, compared to
  0/300 mothers of children without malformations.
• Mellin and Kalzenstein, NEJM 1962

28
Bias

• Any trend in the collection, analysis,
  interpretation, publication or review of data
  that can lead to conclusions that are
  systematically different from the truth (Last,
  2001)
• Any state of inference tending to produce results
  that depart systematically from the true values
  (Fletcher et al, 1988)
• Systematic error in design or conduct of a study
  (Szklo et al, 2000)

29
Chance vs Bias

• Chance is cased by random error
• Bias is caused by systematic error
• Errors from chance will cancel each other out in
  the long run (large sample size)
• Errors from bias will not cancel each other out
  whatever the sample size
• Chance leads to imprecise results
• Bias leads to inaccurate results

30
Selection Bias
Cohort study Differential loss to
follow-up Especially problematic in cohort
studies Subjects in follow-up study of multiple
sclerosis may differentially drop out due to
disease severity
31
Confounding
To be a confounding factor, two conditions must
be met
Exposure
Outcome
Third variable
A) Confounder must be associated with exposure
- without being the consequence of
exposure B) Confounder must be associated with
outcome - independently of exposure
(not an intermediary)
32
Confounding
Accident Rate
Type of Car
Age of Driver
33
Confounding

• A) association between childs birth rank
  (exposure) and Down syndrome (outcome) - is
  mothers age a confounder?
• B) association between mothers age (exposure)
  and Down syndrome (outcome) -is birth rank a
  confounder?

34
Cases of Down Syndrome by Birth Order
35
Cases of Down Syndrome by Mothers Age Groups
EPIET (www)
36
Cases of Down Syndrome by Birth Order and
Maternal Age
EPIET (www)
37
Confounding coffee drinking, cigarette smoking,
and pre-term birth
EXPOSURE OUTCOME (coffee drinking)
(pre-term birth) CONFOUNDING VARIABLE
(cigarette smoking)
38
Confounding
Coffee drinking Heavy Light Preterm Yes
64 20 84 birth
No 36 180 216
100
200 300 RR 6.4
39
Exposure Smoking and outcome- coffee drinking
Exposure-Smoking and outcome- preterm birth
40
Smoking, pre-term birth and coffee drinking
41
Standardization of Rates

• Used to reduce distortion in comparison of crude
  rates
• Also referred to as adjusting rates

42
Adjusting Rates

• Allows comparison of rates between populations
  that differ by variables that can influence the
  rate (e.g. age)
• Direct and Indirect Method

43
Adjusted Rates

• Advantages
• Summary statements
• Differences in group composition removed allows
  unbiased comparison
• Disadvantages
• Fictional rates
• Absolute magnitude dependent on standard
  population chosen

44
Direct Adjustment of Rates

• Requires a standardized population, to which an
  estimated age specific rate can be applied
• Choice of the standard population may affect the
  magnitude of the age-adjusted rates, but not the
  ranking of the population

45
Crude Mortality Rate by Community and by Age
46
Direct Adjustment by Age and Age-Specific Death
Rates
47
Indirect Adjustment of Rates

• Used if age specific rates cannot be estimated
• Based on applying the age-specific rates of the
  standard population to the population of interest
  to determine the number of expected deaths.

48
Indirect Standardization Community A
(standardcommunity B)
49
Indirect Standardization Community A
SMR A 1781/2032.5 0.876 SMR B 1.0
50
Standardized Mortality Ratio

• If the SMR is greater than 1, more deaths have
  occurred than anticipated
• If the SMR is less than 1, fewer deaths have
  occurred than anticipated

51
Sensitivity and Specificity

• Sensitivity
• the probability that a symptom is present (or
  screening test is positive) given that the person
  has the disease d/(cd) 340 / (40340) .895.
  This is also know as the true positive rate.
• Specificity
• the probability that a symptom is not present (or
  screening test is negative) given that the person
  does not have the disease a/(a b)
• 8660 / (8660 960) .90. This is also known as
  true negative rate.

52
Predictive Values

• Predictive value positive
• the probability that a person has the disease
  given a positive test result
• d / (b d) 340 / (960340) .26
• Predictive value negative
• the probability that a person does not have the
  disease given a negative test a / (a c) 8660 /
  (866040) .995
• http//research.med.umkc.edu/tlwbiostats/sens_spec
  if_predval.html

53
Summary

• Risk Exposure and Outcomes
• Cohort and Cross Sectional Studies
• Bias and Confounding
• Standardization
• Sensitivity and Specificity