1. Back to Basics, 2010 POPULATION HEALTH (1): Introduction, Health Promotion, Biostats and Epi Methods
N Birkett, MD
Epidemiology & Community Medicine
Based on slides prepared by Dr. R. Spasoff
Other resources available on Individual & Population Health web site
March 30, 2010

2. THE PLAN
These lectures are based around the MCC Objectives for Qualifying Examination
Emphasis is on core ‘need to know’ rather than on depth and justification
Focus is on topics not well covered in the Toronto Notes (UTMCCQE)
Three sessions: General Objectives & Infectious Diseases, Clinical Presentations, Additional Topics
WAVE MCC OBJECTIVES (2 BOOKS).
FEEL FREE TO INTERRUPT; WE HAVE PLENTY OF TIME.
A few additions to the precirculated version.
March 30, 2010

3. THE PLAN(2) First class Other classes
mainly lectures
Other classes
About 2 hours of lectures
Review MCQs for 60 minutes
A 10 minute break about half-way through
You can interrupt for questions, etc. if things aren’t clear.
WAVE MCC OBJECTIVES (2 BOOKS).
FEEL FREE TO INTERRUPT; WE HAVE PLENTY OF TIME.
A few additions to the precirculated version.
March 30, 2010

4. THE PLAN (3) Session 1 (March 30, 1230-1530) Diagnostic tests
Sensitivity, specificity, validity, PPV
Health Promotion
Critical Appraisal
Intro to Biostatistics
Brief overview of epidemiological research methods
WAVE MCC OBJECTIVES (2 BOOKS).
FEEL FREE TO INTERRUPT; WE HAVE PLENTY OF TIME.
A few additions to the precirculated version.
March 30, 2010

5. THE PLAN (4) Session 2 (March 31, 0800-1130) Clinical Presentations
Periodic Health Examination
Immunization
Occupational Health
Health of Special Populations
Disease Prevention
Determinants of Health
Environmental Health
WAVE MCC OBJECTIVES (2 BOOKS).
FEEL FREE TO INTERRUPT; WE HAVE PLENTY OF TIME.
A few additions to the precirculated version.
March 30, 2010

6. THE PLAN (5) Session 3 (April 1, 0800-1200)
Organization of Health Care Delivery in Canada
Elements of Health Economics
Vital Statistics
Overview of Communicable Disease control, epidemics, etc.
WAVE MCC OBJECTIVES (2 BOOKS).
FEEL FREE TO INTERRUPT; WE HAVE PLENTY OF TIME.
A few additions to the precirculated version.
March 30, 2010

7. LMCC New Objectives (1) Population Health
Concepts of Health and Its Determinants (78-1)
Assessing and Measuring Health Status at the Population Level (78-2)
Interventions at the Population Level (78-3)
Administration of Effective Health Programs at the Population Level (78-4)
Outbreak Management (78-5)
Environment (78-6)
Health of Special Populations (78-7)
WAVE MCC OBJECTIVES (2 BOOKS).
FEEL FREE TO INTERRUPT; WE HAVE PLENTY OF TIME.
A few additions to the precirculated version.
March 30, 2010

8. LMCC New Objectives (2) C2LEO (URL to LMCC objective page)
Considerations for
Cultural-Communication, Legal, Ethical and Organizational Aspects of the Practice of Medicine
WAVE MCC OBJECTIVES (2 BOOKS).
FEEL FREE TO INTERRUPT; WE HAVE PLENTY OF TIME.
A few additions to the precirculated version.
March 30, 2010

9. LMCC New Objectives (3)
We won’t be able to cover every objective in detail.
Sessions will be based around objectives, with links identified as appropriate.
Start with some overviews.
WAVE MCC OBJECTIVES (2 BOOKS).
FEEL FREE TO INTERRUPT; WE HAVE PLENTY OF TIME.
A few additions to the precirculated version.
March 30, 2010

10. LMCC New Objectives (4) 78.1: CONCEPTS OF HEALTH AND ITS DETERMINANTS
Define and discuss the concepts of health, wellness, illness, disease and sickness.
Describe the determinants of health and how they affect the health of a population and the individuals it comprises.
Lifecourse/natural history
Illness behaviour
Culture and spirituality
WAVE MCC OBJECTIVES (2 BOOKS).
FEEL FREE TO INTERRUPT; WE HAVE PLENTY OF TIME.
A few additions to the precirculated version.
March 30, 2010

11. LMCC New Objectives (5) 78.1: CONCEPTS OF HEALTH AND ITS DETERMINANTS
Determinants of health include:
Income/social status
Social support networks
Education/literacy
Employment/working conditions
Social environments
Physical environments
Personal health practices/coping skills
Healthy child development
Biology/genetic endowment
Health services
Gender
Culture
WAVE MCC OBJECTIVES (2 BOOKS).
FEEL FREE TO INTERRUPT; WE HAVE PLENTY OF TIME.
A few additions to the precirculated version.
March 30, 2010

12. LMCC New Objectives (6)
78.2: ASSESSING AND MEASURING HEALTH STATUS AT THE POPULATION LEVEL
Describe the health status of a defined population.
Measure and record the factors that affect the health status of a population with respect to the principles of causation
Principles of Epidemiology, critical appraisal, causation, etc.
WAVE MCC OBJECTIVES (2 BOOKS).
FEEL FREE TO INTERRUPT; WE HAVE PLENTY OF TIME.
A few additions to the precirculated version.
March 30, 2010

13. LMCC New Objectives (7) 78.3: INTERVENTIONS AT THE POPULATION LEVEL
Understand three levels of prevention
Concepts of Health Promotion, etc.
Role of physicians at the community level.
Impact of public policy
WAVE MCC OBJECTIVES (2 BOOKS).
FEEL FREE TO INTERRUPT; WE HAVE PLENTY OF TIME.
A few additions to the precirculated version.
March 30, 2010

14. LMCC New Objectives (8)
78.4: ADMINISTRATION OF EFFECTIVE HEALTH PROGRAMS AT THE POPULATION LEVEL
Structure of the Canadian Health Care System
Concepts of economic evaluation
Quality of care assessment
WAVE MCC OBJECTIVES (2 BOOKS).
FEEL FREE TO INTERRUPT; WE HAVE PLENTY OF TIME.
A few additions to the precirculated version.
March 30, 2010

15. LMCC New Objectives (9) 78.5: OUTBREAK MANAGEMENT
Know defining characteristics of an outbreak
Demonstrate essential skills in outbreak control
WAVE MCC OBJECTIVES (2 BOOKS).
FEEL FREE TO INTERRUPT; WE HAVE PLENTY OF TIME.
A few additions to the precirculated version.
March 30, 2010

16. LMCC New Objectives (10) 78.6: ENVIRONMENT
Recognize implications of environmental health at the individual and community levels
Know methods of information gathering
Work collaboratively with other groups
Recommend to patients and groups how they can minimize risk and maximize overall function
WAVE MCC OBJECTIVES (2 BOOKS).
FEEL FREE TO INTERRUPT; WE HAVE PLENTY OF TIME.
A few additions to the precirculated version.
March 30, 2010

17. LMCC New Objectives (11) 78.7: HEALTH OF SPECIAL POPULATIONS
Specific target population include:
First Nations, Inuit, Métis Peoples
Global health and immigration
Persons with disabilities
Homeless persons
Challenges at the extremes of the age continuum
WAVE MCC OBJECTIVES (2 BOOKS).
FEEL FREE TO INTERRUPT; WE HAVE PLENTY OF TIME.
A few additions to the precirculated version.
March 30, 2010

18. LMCC New Objectives (12) C2LEO
Same material as before but re-structured.
Read objectives for the details
WAVE MCC OBJECTIVES (2 BOOKS).
FEEL FREE TO INTERRUPT; WE HAVE PLENTY OF TIME.
A few additions to the precirculated version.
March 30, 2010

19. Getting Started We can’t cover everything.
Will concentrate on topics not well covered in the Toronto notes and material of greatest importance.
Material will ‘jump around’ a bit
Material won’t flow by LMCC objectives but rather by content links.
WAVE MCC OBJECTIVES (2 BOOKS).
FEEL FREE TO INTERRUPT; WE HAVE PLENTY OF TIME.
A few additions to the precirculated version.
March 30, 2010

20. INVESTIGATIONS (1)
78.2
Determine the reliability and predictive value of common investigations
Applicable to both screening and diagnostic tests.
March 30, 2010

21. Reliability
= reproducibility. Does it produce the same result every time?
Related to chance error
Averages out in the long run, but in patient care you hope to do a test only once; therefore, you need a reliable test
March 30, 2010

22. Validity
Whether it measures what it purports to measure in long run, viz., presence or absence of disease
Normally use criterion validity, comparing test results to a gold standard
Link to I&PH web on validity
March 30, 2010

23. Reliability and Validity: the metaphor of target shooting
Reliability and Validity: the metaphor of target shooting. Here, reliability is represented by consistency, and validity by aim
Reliability
Low High • • • • • • • •
Low
Validity • • • • • • • •
High • • • • • • • •
March 30, 2010

24. Gold Standards Possible gold standards:
More definitive (but expensive or invasive) test
Complete work-up
Eventual outcome (for screening tests, when workup of well patients is unethical; in clinical care you cannot wait)
First two depend upon current state of knowledge and available technology
March 30, 2010

25. Test Properties (1) Diseased Not diseased Test +ve 90 5 95 Test -ve 10
105
100
200
True positives
False positives
False negatives
True negatives
March 30, 2010

26. Test Properties (2) Diseased Not diseased Test +ve 90 5 95 Test -ve 10
105
100
200
Sensitivity = 0.90
Specificity = 0.95
March 30, 2010

27. 2x2 Table for Testing a Test
Gold standard
Disease Disease
Present Absent
Test Positive a (TP) b (FP)
Test Negative c (FN) d (TN)
Sensitivity Specificity
= a/(a+c) = d/(b+d)
March 30, 2010

28. Test Properties (6) Sensitivity = Pr(test positive in a person
with disease)
Specificity = Pr(test negative in a person without disease)
Range: 0 to 1
> 0.9: Excellent
: Not bad
: So-so
< 0.7: Poor
March 30, 2010

29. Test Properties (7) Values depend on cutoff point
Generally, high sensitivity is associated with low specificity and vice-versa.
Not affected by prevalence, if severity is constant
Do you want a test to have high sensitivity or high specificity?
Depends on cost of ‘false positive’ and ‘false negative’ cases
PKU – one false negative is a disaster
Ottawa Ankle Rules
March 30, 2010

30. Test Properties (8)
Sens/Spec not directly useful to clinician, who knows only the test result
Patients don’t ask: if I’ve got the disease how likely is it that the test will be positive?
They ask: “My test is positive. Does that mean I have the disease?”
Predictive values.
March 30, 2010

31. Test Properties (9) Diseased Not diseased Test +ve 90 5 95 Test -ve 10
105
100
200
PPV = 0.95
NPV = 0.90
March 30, 2010

32. 2x2 Table for Testing a Test
Gold standard
Disease Disease
Present Absent
Test + a (TP) b (FP) PPV = a/(a+b)
Test - c (FN) d (TN) NPV= d/(c+d)
a+c b+d
March 30, 2010

33. Predictive Values Based on rows, not columns
PPV = a/(a+b); interprets positive test
NPV = d/(c+d); interprets negative test
Depend upon prevalence of disease, so must be determined for each clinical setting
Immediately useful to clinician: they provide the probability that the patient has the disease
March 30, 2010

34. Prevalence of Disease
Is your best guess about the probability that the patient has the disease, before you do the test
Also known as Pretest Probability of Disease
(a+c)/N in 2x2 table
Is closely related to Pre-test odds of disease: (a+c)/(b+d)
March 30, 2010

35. Test Properties (10) Diseased Not diseased Test +ve a b a+b Test -ve c
c+d
a+c
b+d
a+b+c+d =N
prevalence
odds
March 30, 2010

36. Prevalence and Predictive Values
Predictive values for a test dependent on the pre-test prevalence of the disease
Tertiary hospitals see more pathology then FP’s; hence, their tests are more often true positives.
How to ‘calibrate’ a test for use in a different setting?
Relies on the stability of sensitivity & specificity across populations.
March 30, 2010

37. Methods for Calibrating a Test
Four methods can be used:
Apply definitive test to a consecutive series of patients (rarely feasible)
Hypothetical table
Bayes’s Theorem
Nomogram
You need to be able to do one of the last 3. By far the easiest is using a hypothetical table.
March 30, 2010

38. Calibration by hypothetical table
Fill cells in following order:
“Truth”
Disease Disease Total PV
Present Absent
Test Pos 4th th 8th th
Test Neg 5th th 9th th
Total 2nd rd 1st (10,000)
Easiest to understand, since just common sense.
The 10,000 is arbitrary; gets rid of decimals.
March 30, 2010

39. Test Properties (12) Tertiary care: research study. Prev=0.5 Diseased
Not diseased
Test +ve
425 50
475
Test -ve 75
450
525
500
1,000
PPV = 0.89
Sens = 0.85
Spec = 0.90
March 30, 2010

40. Test Properties (13) Primary care: Prev=0.01 Diseased Not diseased
Test +ve
Test -ve
10,000
1,075 85
990
0.85*100
PPV = 0.08 15
8,910
8,925
0.9*9900
9,900
0.01*10000
100
March 30, 2010

41. Calibration by Bayes’ Theorem
You don’t need to learn Bayes’ theorem
Instead, work with the Likelihood Ratio (+ve).
March 30, 2010

42. Test Properties (9) Diseased Not diseased Test +ve 90 5 95 Test -ve 10
105
100
200
Post-test odds = 18.0
Pre-test odds = 1.00
Likelihood ratio (+ve) = LR(+) = 18.0/1.0 = 18.0
March 30, 2010

43. Calibration by Bayes’s Theorem
You can convert sens and spec to likelihood ratios
LR+ = sens/(1-spec)
LR+ is fixed across populations just like sensitivity & specificity.
Bigger is better.
Posttest odds = pretest odds * LR+
Convert to posttest probability if desired…
March 30, 2010

44. Calibration by Bayes’s Theorem
How does this help?
Remember:
Post-test odds = pretest odds * LR (+)
To ‘calibrate’ your test for a new population:
Use the LR+ value from the reference source
Compute the pre-test odds for your population
Compute the post-test odds
Convert to post-test probability to get PPV
March 30, 2010

45. Converting odds to probabilities
Pre-test odds = prevalence/(1-prevalence)
if prevalence = 0.20, then pre-test odds = .20/0.80 = 0.25
Post-test probability = post-test odds/(1+post-test odds)
if post-test odds = 0.25, then prob = .25/1.25 = 0.2
Negative posttest probability is the tricky one.
March 30, 2010

46. Example of Bayes’s Theorem (‘new’ prevalence 1%, sens 85%, spec 90%)
LR+ = .85/.1 = 8.5 (>1, but not that great)
Pretest odds = .01/.99 =
Positive Posttest odds = .0101*8.5 = .0859
PPV = .0859/ = = 7.9%
Compare to the ‘hypothetical table’ method (PPV=8%)
March 30, 2010

47. Calibration with Nomogram
Graphical approach avoids some arithmetic
Expresses prevalence and predictive values as probabilities (no need to convert to odds)
Draw lines from pretest probability (=prevalence) through likelihood ratios; extend to estimate posttest probabilities
Only useful if someone gives you the nomogram!
Easiest of all, IF you have the diagram to hand. Don’t depend on having this one provided in the exam paper.
March 30, 2010

48. Example of Nomogram (pretest probability 1%, LR+ 45, LR– 0.102)
31%
.102
0.1%
Pretest Prob. LR
Posttest Prob.
March 30, 2010

49. INVESTIGATIONS (2)
What is the effect of demographic considerations on the sensitivity and specificity of diagnostic tests?
Generally, assumed to be constant. BUT…..
Sensitivity and specificity usually vary with severity of disease, and may vary with age and sex
Therefore, you can use sensitivity and specificity only if they were determined on patients similar to your own
Spectrum bias
March 30, 2010

50. The Government is extremely fond of amassing
great quantities of statistics. These are raised to
the nth degree, the cube roots are extracted, and
the results are arranged into elaborate and impressive displays. What must be kept ever in mind, however, is that in every case, the figures are first put down by a village watchman, and he puts down anything he damn well pleases!
Sir Josiah Stamp,
Her Majesty’s Collector of Internal Revenue.
March 30, 2010

51. 78.3: HEALTH PROMOTION & MAINTENANCE (1)
Definitions of health
Concepts of Health Promotion
March 30, 2010

52. Definitions of Health
A state of complete physical, mental and social well-being and not merely the absence of disease or infirmity. [The WHO, 1948]
A joyful attitude toward life and a cheerful acceptance of the responsibility that life puts upon the individual [Sigerist, 1941]
The ability to identify and to realize aspirations, to satisfy needs, and to change or cope with the environment. Health is therefore a resource for everyday life, not the objective of living. Health is a positive concept emphasizing social and personal resources, as well as physical capacities. (WHO Europe, 1986]
March 30, 2010

53. HEALTH PROMOTION Distinct from disease prevention.
Focuses on ‘health’ rather than ‘illness’
Broad perspective. Concerns a network of issues, not a single pathology.
Participatory approach. Requires active community involvement.
Partnerships with NGO’s, NPO’s, etc.
March 30, 2010

54. HEALTH PROMOTION Ottawa Charter for Health Promotion (1996)
Five key pillars to action:
Build Healthy Public Policy
Create supportive environments
Strengthen community action
Develop personal skills
Re-orient health services
March 30, 2010

55. HEALTH PROMOTION Health Education Risk reduction strategies
Health Belief model
Stages of Change model
Risk reduction strategies
Social Marketing
Healthy public policy
Tax policy to promote healthy behaviour
Anti-smoking laws, seatbelt laws
Affordable housing
March 30, 2010

56. 78.1: Illness Behaviour
“Describe the concept of illness behaviour and its influence on health care”
Utilization of curative services, coping mechanisms, change in daily activities
Patients may seek care early or may delay (avoidance, denial)
Adherence may increase or decrease
Not well conceptualized or much discussed. Much talk of sick role and of health behaviour.
March 30, 2010

57. March 30, 2010

58. March 30, 2010

59. March 30, 2010

60. 78.2: CRITICAL APPRAISAL (1)
“Evaluate scientific literature in order to critically assess the benefits and risks of current and proposed methods of investigation, treatment and prevention of illness”
UTMCCQE does not present hierarchy of evidence (e.g., as used by Task Force on Preventive Health Services)
March 30, 2010

61. Hierarchy of evidence (lowest to highest quality, approximately)
Expert opinion
Case report/series
Ecological (for individual-level exposures)
Cross-sectional
Case-Control
Historical Cohort
Prospective Cohort
Quasi-experimental
Experimental (Randomized)
}similar/identical
March 30, 2010

62. Consider a precise number: the normal body temperature of 98. 6EF
Consider a precise number: the normal body temperature of 98.6EF. Recent investigations involving millions of measurements have shown that this number is wrong: normal body temperature is actually 98.2EF. The fault lies not with the original measurements - they were averaged and sensibly rounded to the nearest degree: 37EC. When this was converted to Fahrenheit, however, the rounding was forgotten and 98.6 was taken as accurate to the nearest tenth of a degree.
March 30, 2010

63. BIOSTATISTICS Core concepts(1)
Sample: A group of people, animals, etc. which is used to represent a larger ‘target’ population.
Best is a random sample
Most common is a convenience sample.
Subject to strong risk of bias.
Sample size: the number of units in the sample
Much of statistics concerns how samples relate to the population or to each other.
Only statistical formula you need to know.
March 30, 2010

64. BIOSTATISTICS Core concepts(2)
Mean: average value. Measures the ‘centre’ of the data. Will be roughly in the middle.
Median: The middle value: 50% above and 50% below. Used when data is skewed.
Variance: A measure of how spread out the data is. Defined by subtracting the mean from each observation, squaring, adding them all up and dividing by the number of observations.
Standard deviation: square root of the variance.
Only statistical formula you need to know.
March 30, 2010

65. Core concepts (3)
Standard error: SD/n, where n is sample size. Measures the variability of the mean.
Confidence Interval: A range of numbers which tells us where we believe the correct answer lies. For a 95% confidence interval, we are 95% sure that the true value lies in the interval, somewhere.
Usually computed as: mean ± 2 SE
Only statistical formula you need to know.
March 30, 2010

66. Example of Confidence Interval
If sample mean is 80, standard deviation is 20, and sample size is 25 then:
SE = 20/5 = 4. We can be 95% confident that the true mean lies within the range
80 ± (2*4) = (72, 88).
If the sample size were 100, then SE = 20/10 = 2.0, and 95% confidence interval is 80 ± (2*2) = (76, 84). More precise.
March 30, 2010

67. Core concepts (4)
Random Variation (chance): every time we measure anything, errors will occur. In addition, by selecting only a few people to study (a sample), we will get people with values different from the mean, just by chance. These are random factors which affect the precision (sd) of our data but not the validity. Statistics and bigger sample sizes can help here.
Only statistical formula you need to know.
March 30, 2010

68. Core concepts (5)
Bias: A systematic factor which causes two groups to differ. For example, a study uses a collapsible measuring scale for height which was incorrectly assembled (with a 1” gap between the upper and lower section).
Over-estimates height by 1” (a bias).
Bigger numbers and statistics don’t help much; you need good design instead.
Only statistical formula you need to know.
March 30, 2010

69. BIOSTATISTICS Inferential Statistics
Draws inferences about populations, based on samples from those populations. Inferences are valid only if samples are representative (to avoid bias).
Polls, surveys, etc. use inferential statistics to infer what the population thinks based on a few people.
RCT’s used them to infer treatment effects, etc.
95% confidence intervals are a very common way to present these results.
Only statistical formula you need to know.
March 30, 2010

70. Hypothesis Testing Used to compare two or more groups.
We assume that the two groups are the same.
Compute some statistic which, under this null hypothesis (H0), should be ‘0’.
If we find a large value for the statistic, then we can conclude that our assumption (hypothesis) is unlikely to be true (reject the null hypothesis).
Formal methods use this approach by determining the probability that the value you observe could occur (p-value). Reject H0 if that value exceeds the critical value expected from chance alone.
March 30, 2010

71. Hypothesis Testing (2) Common methods used are:
T-test
Z-test
Chi-square test
ANOVA
Approach can be extended through the use of regression models
Linear regression
Toronto notes are wrong in saying this relates 2 variables. It can relates many variables to one dependent variable.
Logistic regression
Cox models
March 30, 2010

72. Hypothesis Testing (3)
Interpretation requires a p-value and understanding of type 1/2 errors.
P-value: the probability that you will observe a value of your statistic which is as bigger or bigger than you found IF the null hypothesis is true.
This is not quite the same as saying the chance that the difference is ‘real’
Power: The chance you will find a difference between groups when there really is a difference (of a given amount). Depends on how big a difference you treat as ‘real’
March 30, 2010

73. Hypothesis testing (4) No effect Effect No error Type 2 error (β)
Actual Situation
No effect
Effect
No error
Type 2 error (β)
Type 1 error (α)
Results of Stats Analysis
March 30, 2010

74. Example of significance test
Association between sex and smoking: 35 of 100 men smoke but only 20 of 100 women smoke
Calculated chi-square is The critical value is 3.84 (from table, for α = 0.05). Therefore reject H0
P= Under H0 (chance alone), a chi-square value as large as 5.64 would occur only 1.8% of the time.
March 30, 2010

75. How to improve your chance of finding a difference
Increase sample size
Improve precision of the measurement tools used
Use better statistical methods
Use better designs
Reduce bias
March 30, 2010

76. Laboratory and anecdotal clinical evidence suggest that some common non-antineoplastic drugs may affect the course of cancer. The authors present two cases that appear to be consistent with such a possibility: that of a 63-year-old woman in whom a high-grade angiosarcoma of the forehead improved after discontinuation of lithium therapy and then progressed rapidly when treatment with carbamezepine was started and that of a 74-year-old woman with metastatic adenocarcinoma of the colon which regressed when self-treatment with a non-prescription decongestant preparation containing antihistamine was discontinued. The authors suggest ‘that consideration be given to discontinuing all nonessential medications for patients with cancer.’.
March 30, 2010

77. Epidemiology overview
Key study designs to examine (I&PH link)
Case-control
Cohort
Randomized Controlled Trial (RCT)
Confounding
Relative Risks/odds ratios
All ratio measures have the same interpretation
1.0 = no effect
< 1.0  protective effect
> 1.0  increased risk
Values over 2.0 are of strong interest
March 30, 2010

78. The Epidemiological Triad
Agent
Host
Random allocation (as in an intervention study) is not the same as random selection (as in a sample survey).
Environment
March 30, 2010

79. Terminology
Incidence: The probability (chance) that someone without the outcome will develop it over a fixed period of time. Relates to new cases of disease.
Prevalence: The probability that a person has the outcome of interest today. Relates to existing cases of disease. Useful for measuring burden of illness.
March 30, 2010

80. Prevalence
On July 1, 2007, 140 graduates from the U. of O. medical school start working as interns.
Of this group, 100 had insomnia the night before.
Therefore, the prevalence of insomnia is:
100/140 = 0.72 = 72%
March 30, 2010

81. Incidence risk
On July 1, 2007, 140 graduates from the U. of O. medical school start working as interns.
Over the next year, 30 develop a stomach ulcer.
Therefore, the incidence risk of an ulcer is:
30/140 = 0.21 = 214/1,000
March 30, 2010

82. Incidence rate (1) # new cases
Incidence rate is the ‘speed’ with which people get ill.
Everyone dies (eventually). It is better to die later  death rate is lower.
Compute with person-time denominator
PT = # people * time of follow-up
# new cases
IR =
PT of follow-up
March 30, 2010

83. Incidence rate (2)
140 U. of O. medical students, followed during their residency
50 did 2 years of residency
90 did 4 years of residency
Person-time = 50 * * 4 = 460 PY’s
During follow-up, 30 developed ‘stress’.
Incidence rate of stress is: 30
IR = = 0.065/PY = 65/1,000 PY
460
March 30, 2010

84. Prevalence & incidence
As long as conditions are ‘stable’, we have this relationship:
That is, prevalence = incidence * disease duration
P = I * d
March 30, 2010

85. Case-control study Selects subjects based on their final outcome.
Select a group of people with the outcome/disease (cases)
Select a group of people without the outcome (controls)
Ask them about past exposures
Compare the frequency of exposure in the two groups
If exposure increase risk, there should be more exposed cases than controls
Compute an Odds Ratio
March 30, 2010

86. Case-control (2) Disease Exp ODDS RATIO
Odds of exposure in cases = a/c
Odds of exposure in controls = b/d
If exposure increases rate of getting disease, you would to find more exposed cases than exposed controls. That is, the odds of exposure for case would be higher (a/c > b/d). This can be assessed by the ratio of one to the other:
Exp odds in cases
Odds ratio (OR) =
Exp odds in controls
= (a/c)/(b/d) ad = bc
YES NO
YES a b a+b
NO c d c+d
a+c b+d N
Exp
March 30, 2010

87. Case-control (3) Disease Apgar Odds of exp in cases: = 42/43 = 0.977
Yes No
Low OK
Apgar
Odds of exp in cases: = 42/ =
Odds of exp in controls: = 18/ =
Odds ratio (OR) = Odds in cases/odds in controls
= 0.977/ = (42*67)/(43*18)
= 3.6
March 30, 2010

88. Cohort study
Selects subjects based on their exposure status. They are followed to determine their outcome.
Select a group of people with the exposure of interest
Select a group of people without the exposure
Can also simply select a group of people and study a range of exposures.
Follow-up the group to determine what happens to them.
Compare the incidence of the disease in exposed and unexposed people
If exposure increases risk, there should be more cases in exposed subjects than unexposed subjects
Compute a relative risk.
March 30, 2010

89. Cohorts (2) Disease Exp RISK RATIO Risk in exposed: = a/(a+b)
Risk in Non-exposed = c/(c+d)
If exposure increases risk, you would expect a/(a+b) to be larger than c/(c+d). How much larger can be assessed by the ratio of one to the other:
Exp risk
Risk ratio (RR) =
Non-exp risk
= (a/(a+b))/(c/(c+d)
a/(a+b) =
c/(c+d)
YES NO
YES a b a+b
NO c d c+d
a+c b+d N
Exp
March 30, 2010

90. Cohorts (3) Death Apgar Risk in exposed: = 42/122 = 0.344
YES NO
Low OK
Apgar
Risk in exposed: = 42/122 = 0.344
Risk in Non-exposed = 43/345 = 0.125
Exp risk
Risk ratio (RR) =
Non-exp risk
= 0.344/0.125
= 2.8
March 30, 2010

91. Confounding
Mixing of effects of two causes. Can be positive or negative
Confounder is an extraneous factor which is associated with both exposure and outcome, and is not an intermediate step in causal pathway
March 30, 2010

92. The Confounding Triangle
Outcome
Exposure
Random allocation (as in an intervention study) is not the same as random selection (as in a sample survey).
Confounder
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93. Confounding (example)
Does heavy alcohol drinking cause mouth cancer? We get OR=3.4 (95% CI: )
Smoking causes mouth cancer
Heavy drinkers tend to be heavy smokers.
Smoking is not part of causal pathway for alcohol.
Therefore, we have confounding.
We do a statistical adjustment (logistic regression is most common): OR=1.3 (95% CI: )
March 30, 2010

94. Standardization
An older method of adjusting for confounding (usually used for differences in age between two populations)
Refers observed events to a standard population, producing hypothetical values
Direct: age-standardized rate
Indirect: standardized mortality ratio (SMR)
March 30, 2010

95. Mortality data Three ways to summarize them
Mortality rates (crude, specific, standardized)
PYLL: subtracts age at death from some “acceptable” age of death. Emphasizes causes that kill at younger ages.
Life expectancy: average age at death if current mortality rates continue. Derived from life table.
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96. Summary measures of population health
Combine mortality and morbidity statistics, in order to provide a more comprehensive population health indicator, e.g., QALY
Years lived are weighted according to quality of life, disability, etc.
Two types:
Health expectancies point up from zero
Health gaps point down from ideal
March 30, 2010

97. Attributable Risk (I&PH link)
Set upper limit on amount of preventable disease. Meaningful only if association is causal.
Tricky area since there are several measures with similar names.
Attributable risk. The amount of disease due to exposure in the exposed subjects. The same as the risk difference.
Can also look at the risk attributed to the exposure in the general population but we won’t do that one (depends on how common the exposure is).
March 30, 2010

98. Attributable risks (2) In exposed subjects Iexp RD or
RD = AR = Iexp - Iunexp
Iunexp
Iexp – Iunexp
AR(%)=AF=
Iexp
Unexp
Exp
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99. Attributable risks (3) Attributable Risk, population Iexp Ipop Iunexp
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100. Randomized Controlled Trials
Basically a cohort study where the researcher decides which exposure (treatment) the subject get.
Recruit a group of people meeting pre-specified eligibility criteria.
Randomly assign some subjects (usually 50% of them) to get the control treatment and the rest to get the experimental treatment.
Follow-up the subjects to determine the risk of the outcome in both groups.
Compute a relative risk or otherwise compare the groups.
March 30, 2010

101. Randomized Controlled Trials (2)
Some key design features
Blinding
Patient
Treatment team
Outcome assessor
Statistician
Monitoring committee
Two key problems
Contamination
Control group gets the new treatment
Co-intervention
Some people get treatments other than those under study
March 30, 2010

102. Randomized Controlled Trials: Analysis
Outcome is an adverse event
RR is expected to be <1
Absolute risk reduction, ARR =
Incidence(control) - Incidence(treatment) (=|attributable risk|)
Relative risk reduction, RRR = ARR/incidence(control) = 1 - RR
Number needed to treat, NNT (to prevent one adverse event) = 1/ARR
March 30, 2010

103. RCT – Example of Analysis
Asthma No Total Inc
attack attack
Treatment
Control
Relative Risk = 0.30/0.50 = 0.60
Absolute Risk Reduction = = 0.20
Relative Risk Reduction = 0.20/0.50 = 40%
Number Needed to Treat = 1/0.20 = 5
March 30, 2010