Module 2: Fundamentals of Epidemiology - PowerPoint PPT Presentation

Loading...

PPT – Module 2: Fundamentals of Epidemiology PowerPoint presentation | free to download - id: 7f8f0b-MzcxO



Loading


The Adobe Flash plugin is needed to view this content

Get the plugin now

View by Category
About This Presentation
Title:

Module 2: Fundamentals of Epidemiology

Description:

Title: Slide 1 Author: J Patrick McGee Last modified by: Jeffrey Bethel Created Date: 3/21/2008 1:44:10 PM Document presentation format: On-screen Show (4:3) – PowerPoint PPT presentation

Number of Views:2
Avg rating:3.0/5.0
Slides: 63
Provided by: JPat45
Learn more at: http://c.ymcdn.com
Category:

less

Write a Comment
User Comments (0)
Transcript and Presenter's Notes

Title: Module 2: Fundamentals of Epidemiology


1
Module 2Fundamentals of Epidemiology
Issues of Interpretation in Epidemiologic Studies
Developed through the APTR Initiative to Enhance
Prevention and Population Health Education in
collaboration with the Brody School of Medicine
at East Carolina University with funding from
the Centers for Disease Control and Prevention
2
Acknowledgments
  • APTR wishes to acknowledge the following
    individual that developed this module
  • Jeffrey Bethel, PhD
  • Department of Public Health
  • Brody School of Medicine at East Carolina
    University

This education module is made possible through
the Centers for Disease Control and Prevention
(CDC) and the Association for Prevention Teaching
and Research (APTR) Cooperative Agreement, No.
5U50CD300860. The module represents the opinions
of the author(s) and does not necessarily
represent the views of the Centers for Disease
Control and Prevention or the Association for
Prevention Teaching and Research.
3
Presentation Objectives
  • Describe the key features of selection and
    information bias
  • Identify the ways selection and information bias
    can be minimized or avoided
  • Implement the methods for assessing and
    controlling confounding
  • Identify uses of the Surgeon Generals Guidelines
    for establishing causality

4
Smith, AH. The Epidemiologic Research Sequence.
1984
5
Exposure or Characteristic
Observed Association
Disease or Outcome
6
Exposure or Characteristic
Observed Association Is it biased,
confounded, or causal?
Disease or Outcome
7
Bias
  • Any systematic error in the design, conduct, or
    analysis of a study that results in a mistaken
    estimate of an exposures effect on the risk of
    disease

8
Bias
  • Can create spurious association when there really
    is not one (bias away from the null)
  • Can mask an association when there really is one
    (bias towards the null)
  • Primarily introduced by the investigators or
    study participants
  • Selection and information bias

9
Selection Bias
  • Results from procedures used to study
    participants that lead to a result different from
    what would have been obtained from the entire
    population targeted for study
  • Systematic error made in selecting one or more of
    the study groups to be compared

10
Selection Bias Examples
  • Control selection bias
  • Self-selection bias
  • Differential referral, surveillance, or diagnosis
    bias
  • Loss to follow-up

11
Selection Bias in a Case-Control StudyControl
Selection Bias
Question Do Pap smears prevent cervical cancer?
Cases diagnosed at a city hospital. Controls
randomly sampled from household in same city by
canvassing the neighborhood on foot. Here is the
observed relationship
Cases Controls
Pap Smear 100 100
No Pap Smear 150 150
Total 250 250
OR (100 x 150) / (100 x 50) 1.0 There is
no association between Pap smears and risk of
cervical cancer (40 of cases and 40 of controls
had Pap smears)
12
Selection Bias in a Case-Control StudyControl
Selection Bias
  • Recall Cases from the hospital and controls come
    from the neighborhood around the hospital
  • Now for the bias Only controls who were at home
    during recruitment for the study were actually
    included in the study. Women at home were less
    likely to work and less likely to have regular
    checkups and Pap smears. Therefore, being
    included in the study as a control is not
    independent of the exposure.

13
Selection Bias in a Case-Control Study Control
Selection Bias
Question Do Pap smears prevent cervical cancer?
Cases diagnosed at a city hospital. Controls
randomly sampled from household in same city by
canvassing the neighborhood on foot. Here is the
true relationship
Cases Controls
Pap Smear 100 150
No Pap Smear 150 100
Total 250 250
OR (100)(100) / (150)(150) .44 56
reduced risk of cervical cancer among women who
had Pap smears as compared to women who did not
(40 of cases had Pap smears versus 60 of
controls)
14
Selection Bias in a Case-Control
StudySelf-Selection Bias
  • Refusal or nonresponse by participants that is
    related to both exposure and disease
  • e.g. if exposed cases are more/less likely to
    participate than participants in other categories
  • Best way to avoid is to obtain high participation
    rates

15
Selection Bias in a Case-Control
StudyDifferential Surveillance, Diagnosis or
Referral
  • Example related to exposure
  • CC study venous thromboembolism (VT) and oral
    contraceptive (OC) use
  • Cases 20-44 yo, hospitalized for VT
  • Controls 20-44 yo, hospitalized for acute
    illness or elective surgery at same hospitals
  • Result OR 10.2

16
Selection Bias in a Case-Control
StudyDifferential Surveillance, Diagnosis or
Referral
  • Authors acknowledged high OR might be due to
    bias in the criteria for hospital admission
  • Previous studies linked VT to OC
  • Health care provider more likely to hospitalize
    women with VT symptoms who were taking OC than
    symptomatic women who were not taking OC

17
Selection Bias in a Cohort StudyLoss to Follow-up
  • Compared HIV incidence rates among IVDU in NYC
    from 1992-97 through 10 incidence studies to
    previous years
  • HIV incidence rates (IR) range from 0 to 2.96 per
    100 person-years (py)
  • Well below IR in NYC from late 70s and early 80s
    (13 per 100 py) to mid 80s and early 90s (4.4 per
    100 py)
  • Resulted in funding cuts to drug treatment and
    prevention programs

18
Selection Bias in a Cohort StudyLoss to
Follow-up
  • Was decline real?
  • Follow-up rates in 10 cohorts ranged from 36 to
    95
  • Only 2 reported gt80
  • Sample size ranged from 96 to 1,671
  • Solution minimize loss to follow-up

19
Selection Bias Can We Fix It?
  • No need to avoid it when you design and conduct
    the study
  • For example
  • Use the same criteria for selecting cases and
    controls
  • Obtaining all relevant participant records
  • Obtaining high participation rates
  • Taking in account diagnostic and referral
    patterns of disease

20
Information Bias
  • Arises from a systematic difference in the way
    that exposure or outcome is measured between
    groups
  • Can bias towards or away from the null
  • Occurs in prospective and retrospective studies
  • Includes recall bias and interviewer bias

21
Recall Bias in a Case-Control Study
  • Case-control study of birth defects
  • Controls healthy infants
  • Cases malformed infants
  • Exposure data collected at postpartum interviews
    with infants mothers
  • Controls or cases may have underreported
    exposure, depending on nature of exposure

22
Methods to Minimize Recall Bias
  • Select diseased control group
  • Design structured questionnaire
  • Use self-administered questionnaire
  • Use biological measurements
  • Mask participants to study hypotheses

23
Interviewer Bias
  • Systematic difference in soliciting, recording,
    interpreting information
  • Case-control study exposure information is
    sought when outcome is known
  • Cohort study outcome information is sought when
    exposure is known
  • Solutions Mask interviewers, use standardized
    questionnaires or standardized methods of outcome
    (or exposure) ascertainment

24
Exposure or Characteristic
Observed Association Is it biased,
confounded, or causal?
Disease or Outcome
25
Confounding
  • X
  • A
    B
  • A mixing of effects association between
    exposure and disease is distorted by the effect
    of a third variable that is associated with the
    disease
  • Alternate explanation for observed association
    between an exposure and disease

26
Criteria for Confounding
  • X
  • A
    B
  • In order for a factor (X) to be a confounder, all
    of the following must be TRUE
  • Factor X is associated with Disease B (risk
    factor or preventive factor)
  • Factor X is associated with Factor A (exposure)
  • Factor X is not a result of Factor A (not on
    causal pathway)

27
Example of Confounding
Smoking
Coffee Consumption
Pancreatic Cancer
  • Smoking is associated with pancreatic cancer
  • Smoking is associated with coffee drinking
  • Smoking is not a result of coffee drinking

28
Impact of Confounding
  • Pulls the observed association away from the true
    association
  • Positive confounding
  • Exaggerates the true association
  • True relative risk (RR) 1.0 and confounded RR
    2.0
  • Negative confounding
  • Hides the true association
  • True RR 2.0 and confounded RR 1.0

29
Hypothetical Cohort Study of Obesity and Dementia
Dementia No Dementia TOTAL
Obese 400 600 1,000
Not Obese 100 900 1,000
TOTAL 500 1,500 2,000
Relative Risk
4.0 (crude measure)
400/1,000 100/1,000
30
Is age confounding the association between
obesity and dementia?
31
Dementia and Diabetes Cohort StudyCriterion 1
Is Age Associated with Dementia?
Dementia Dementia TOTAL
Age Yes No
80-99 Years 400 600 1,000
45-79 Years 100 900 1,000
TOTAL 1,000 1,000 2,000
Relative Risk
4.0
400/1,000 100/1,000
32
Dementia and Diabetes Cohort StudyCriterion 2
Is Age Associated with Obesity?
Obese Obese TOTAL
Age Yes No
80-99 Years 900 100 1,000
45-79 Years 100 900 1,000
TOTAL 1,000 1,000 2,000
Relative Risk 9.0

Odds ratio
81.0
900/1000 100/1000
900 x 900 100 x 100
33
Were Criteria for Confounding Satisfied?
  • Age
  • Obesity
    Dementia
  • Age is associated with dementia (RR4.0)
  • Age is associated with obesity (OR81.0)
  • Age is not a result of obesity (not from data)

34
Controlling Confounding
  • Design phase
  • Group or individual matching on the suspected
    confounding factor
  • e.g. matching on age in case-control study
  • Analysis phase
  • Stratification
  • Standardization
  • Adjustment (multivariate analysis)

35
Thoughts on Confounding
  • Not an error in the study
  • Valid finding of relationships between factors
    and disease
  • Failure to take into account confounding IS an
    error and can bias the results!

36
Exposure or Characteristic
Observed Association Is it biased, confounded,
or causal?
Disease or Outcome
37
Epidemiologic Reasoning
  • Determine whether a statistical association
    exists between characteristics or exposures and
    disease
  • Study of group characteristics (ecologic studies)
  • Study of individual characteristics (case-control
    and cohort studies)
  • Derive inferences regarding possible causal
    relationship using pre-determined criteria or
    guidelines

38
Causation
  • Association is not equal to causation
  • Consider the following statement If the rooster
    crows at the break of dawn, then the rooster
    caused the sun to rise
  • Causation implies there is a true mechanism from
    exposure to disease

39
Koch-Henle Postulates (1880s)
  • The organism is always found with the disease
    (regular)
  • The organism is not found with any other disease
    (exclusive)
  • The organism, isolated from one who has the
    disease, and cultured through several
    generations, produces the disease (in
    experimental animals)

40
Koch-Henle Postulates (1880s)
  • Koch added that Even when an infectious disease
    cannot be transmitted to animals, the regular
    and exclusive presence of the organism
    postulates 1 and 2 proves a causal relationship
  • Unknown at the time of Koch-Henle (1840-1880)
  • Carrier state
  • Asymptomatic infection
  • Multifactorial causation
  • Biologic spectrum of disease

41
Understanding Causality
  • Lets say you have determined
  • There is a real association
  • You believe it to be causal (ruled out
    confounding)
  • NOW have you proven CAUSALITY?

42
Surgeon Generals Guidelines for Establishing
Causality
  1. Temporal relationship
  2. Strength of the association
  3. Dose-response relationship
  4. Replication of the findings
  5. Biologic plausibility
  6. Consideration of alternate explanations
  7. Cessation of exposure
  8. Consistency with other knowledge
  9. Specificity of the association

43
Temporal Relationship
  • Exposure to factor must have occurred before
    disease developed
  • Easiest to establish in a prospective cohort
    study
  • Length of interval between exposure and disease
    very important
  • e.g. asbestos and lung cancer
  • Lung cancer followed exposure by 3 or 20 years?

44
Strength of the Association
  • The stronger the association, the more likely the
    exposure is causing the disease
  • Example RR of lung cancer in smokers vs.
    non-smokers 9 RR of lung cancer in heavy vs.
    non-smokers 20

45
Strength of the Association
Which odds ratio (OR) would you be more likely to
infer causation from? OR1 OR 1.4 95 CI
(1.2 - 1.7) OR2 OR 9.8 95 CI (1.8 -
12.3) OR3 OR 6.6 95 CI (5.9 -
8.1)
46
Dose-Response Relationship
  • Persons who have increasingly higher exposure
    levels have increasingly higher risks of disease
  • Example Lung cancer death rates rise with the
    number of cigarettes smoked

47
Age-adjusted mortality rates of bronchogenic
carcinoma by current amount of smoking
48
Replication of Findings
  • The association is observed repeatedly in
    different persons, places, times, and
    circumstances
  • Replicating the association in different samples,
    with different study designs, and different
    investigators gives evidence of causation
  • Example Smoking has been associated with lung
    cancer in dozens of retrospective and prospective
    studies

49
Biologic Plausibility
  • Biological or social model exists to explain the
    association
  • Does not conflict with current knowledge of
    natural history and biology of disease
  • Example Cigarettes contain many carcinogenic
    substances
  • Many epidemiologic studies have identified causal
    relationships before biological mechanisms were
    identified

50
Consideration of Alternate Explanations
  • Did the investigators consider bias and
    confounding?
  • Investigators must consider other possible
    explanations
  • Example Did the investigators consider the
    associations between smoking, coffee consumption
    and pancreatic cancer?

51
Cessation of Exposure
  • Risk of disease should decline when exposure to
    factor is reduced or eliminated
  • In certain cases, the damage may be irreversible
  • Example Emphysema is not reversed with the
    cessation of smoking, but its progression is
    reduced

52
Consistency With Other Knowledge
  • If a relationship is causal, the findings should
    be consistent with other data
  • If lung cancer incidence increased as cigarette
    use was on the decline, need to explain how this
    was consistent with a causal relationship

53
Specificity of the Association
  • A single exposure should cause a single disease
  • Example Smoking is associated with lung cancer
    as well as many other diseases
  • Lung cancer results from smoking as well as other
    exposures
  • When present, provides additional support for
    causal inference
  • When absent, does not preclude a causal
    relationship

54
Uses of Surgeon Generals Guidelines for
Establishing Causality
  • Remembering distinctions between association and
    causation in epidemiologic research
  • Critically reading epidemiologic studies
  • Designing epidemiologic studies
  • Interpreting the results of your own study

55
Does HIV Cause AIDS?
  • Majority of scientists believe HIV causes AIDS
  • Small group believes AIDS is a behavioral rather
    than an infectious disease
  • AIDS is caused by use of recreational drugs and
    antiretroviral drugs in the U.S. and Europe, and
    by malnutrition in Africa

56
Does HIV Cause AIDS?Koch-Henle Postulates
  • Regular and exclusive
  • Gallo et al. routinely found HIV in people with
    AIDS symptoms and failed to find HIV among people
    who either lacked AIDS symptoms or
    AIDS-associated risk factors
  • Experimental model
  • 3 lab workers accidentally infected in early
    1990s with purely molecularly cloned strain of
    HIV
  • One developed pneumonia (AIDS-defining disease)
    before started antiretroviral therapy

57
Does HIV Cause AIDS?
  • Epidemiologic studies have established
  • Temporal relationship
  • Strong association
  • Dose response
  • Replication of findings
  • Biologic plausibility
  • Cessation of exposure (decrease in deaths after
    antiretroviral therapy)
  • Specificity
  • Consistency with other knowledge

58
Causation
  • Associations are observed
  • Causation is inferred
  • It is important to remember that these criteria
    provide evidence for causal relationships
  • All of the evidence must be considered and the
    criteria weighed against each other to infer the
    causal relationship

59
Summary
  • Bias is a systematic error that results in an
    incorrect estimate of association
  • Selection and information bias
  • Confounding is an alternate explanation which can
    be controlled
  • Design and analysis phases
  • Causation must be inferred

60
Collaborating Institutions
  • Center for Public Health Continuing Education
  • University at Albany School of Public Health
  • Department of Community Family Medicine
  • Duke University School of Medicine

61
Advisory Committee
Mike Barry, CAE Lorrie Basnight, MD Nancy
Bennett, MD, MS Ruth Gaare Bernheim, JD,
MPH Amber Berrian, MPH James Cawley, MPH,
PA-C Jack Dillenberg, DDS, MPH Kristine Gebbie,
RN, DrPH Asim Jani, MD, MPH, FACP Denise Koo,
MD, MPH Suzanne Lazorick, MD, MPH Rika Maeshiro,
MD, MPH Dan Mareck, MD Steve McCurdy, MD,
MPH Susan M. Meyer, PhD Sallie Rixey, MD,
MEd Nawraz Shawir, MBBS
62
APTR
  • Sharon Hull, MD, MPH
  • President
  • Allison L. Lewis
  • Executive Director
  • O. Kent Nordvig, MEd
  • Project Representative
About PowerShow.com