Transcript Epidemiology and Community Medicine (1)

Back to Basics, 2008
POPULATION HEALTH (1):
GENERAL OBJECTIVES
N Birkett, MD
Epidemiology & Community Medicine
Based on slides prepared by Dr. R. Spasoff
Other resources available on Individual & Population Health web site
April 3, 2008
1
THE PLAN
• We will follow MCC Objectives for
Qualifying Examination (in italics)
• Focus is on topics not well covered in the
Toronto Notes (UTMCCQE)
• Three sessions: General Objectives &
Infectious Diseases, Clinical Presentations,
Additional Topics
April 3, 2008
2
THE PLAN(2)
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•
•
•
About 1.5-2 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
April 3, 2008
3
THE PLAN (3)
• Session 1 (April 3)
– Diagnostic tests
• Sensitivity, specificity, validity, PPV
–
–
–
–
–
Health Promotion
Critical Appraisal (more on April 19)
Elements of Health Economics
Vital Statistics
Overview of Communicable Disease control,
epidemics, etc.
April 3, 2008
4
THE PLAN (4)
• Session 2 (April 18, 1300-1600)
– Clinical Presentations
•
•
•
•
•
•
•
Periodic Health Examination
Immunization
Occupational Health
Health of Special Populations
Disease Prevention
Determinants of Health
Environmental Health
April 3, 2008
5
THE PLAN (5)
• Session 3 (April 25, 0800-1100)
– CLEO
• Overview of Ethical Principles
• Organization of Health Care Delivery in Canada
– Other topics
• Intro to Biostatistics
• Brief overview of epidemiological research methods
April 3, 2008
6
INVESTIGATIONS (1)
• “Determine the reliability and predictive
value of common investigations”
• MCCQE doesn’t address reliability, or show
how to estimate predictive value
April 3, 2008
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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
April 3, 2008
8
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
April 3, 2008
9
Reliability and Validity: the metaphor of
target shooting. Here, reliability is represented by
consistency, and validity by aim
Reliability
Low
Low
•
•
•
Validity
High
•••
•••
•
•
•
•
High •
•
•
•
• April 3, 2008
•••
••
•
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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
April 3, 2008
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Test Properties (1)
Test +ve
Diseased
Not diseased
90
5
True positives
Test -ve
10
95
False positives
95
False negatives
100
True negatives
100
April 3, 2008
105
200
12
Test Properties (2)
Diseased
Not diseased
Test +ve
90
5
95
Test -ve
10
95
105
100
100
200
Sensitivity = 0.90
Specificity = 0.95
April 3, 2008
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2x2 Table for Testing a Test
Test Positive
Test Negative
Gold standard
Disease Disease
Present
Absent
a (TP)
b (FP)
c (FN)
d (TN)
Sensitivity Specificity
= a/(a+c) = d/(b+d)
April 3, 2008
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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:
0.8-0.9:
0.7-0.8:
< 0.7:
Excellent
Not bad
So-so
PoorApril 3, 2008
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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
April 3, 2008
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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.
April 3, 2008
17
Test Properties (9)
Diseased
Not diseased
Test +ve
90
5
95
Test -ve
10
95
105
100
100
200
April 3, 2008
PPV =
0.95
NPV =
0.90
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2x2 Table for Testing a Test
Gold standard
Disease
Disease
Present
Absent
Test + a (TP) b (FP)
Test - c (FN) d (TN)
a+c
b+d
April 3, 2008
PPV = a/(a+b)
NPV= d/(c+d)
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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
April 3, 2008
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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)
April 3, 2008
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Test Properties (10)
Diseased
Not diseased
Test +ve
a
b
a+b
Test -ve
c
d
c+d
a+c
b+d
a+b+c+d
=N
April 3, 2008
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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.
April 3, 2008
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Methods for Calibrating a Test
Four methods can be used:
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–
–
–
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.
April 3, 2008
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Calibration by hypothetical table
Fill cells in following order:
“Truth”
Disease Disease
Present
Absent
Test Pos
4th
7th
Test Neg
5th
6th
Total
2nd
3rd
April 3, 2008
Total
PV
8th 10th
9th 11th
1st (10,000)
25
Test Properties (12)
Tertiary care: research study. Prev=0.5
Test +ve
Diseased
Not diseased
425
50
475
PPV = 0.89
Test -ve
75
450
525
500
500
1,000
Sens = 0.85
Spec = 0.90
April 3, 2008
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Test Properties (13)
Primary care: Prev=0.01
Diseased
Test +ve
Test -ve
Not diseased
85
0.85*100
990
15
8,910
0.9*9900
0.01*10000
100
9,900
April 3, 2008
1,075
PPV = 0.08
8,925
10,000
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Calibration by Bayes’ Theorem
• You don’t need to learn Bayes’ theorem
• Instead, work with the Likelihood Ratio
(+ve).
April 3, 2008
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Test Properties (9)
Diseased Not
diseased
90
5
95
Test
+ve
Test - 10
ve
100
95
105
100
200
Post-test odds =
18.0
Pre-test odds =
1.00
Likelihood ratio (+ve) = LR(+) = 18.0/1.0 = 18.0
April 3, 2008
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Calibration by Bayes’s Theorem
• LR+ is fixed across populations just like
sensitivity & specificity.
• You can convert sens and spec to likelihood
ratios
– LR+ = sens/(1-spec)
• Bigger is better.
• Posttest odds = pretest odds * LR (+ or -)
– Convert to posttest probability if desired…
April 3, 2008
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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 posttest probability to get PPV
April 3, 2008
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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
April 3, 2008
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Example of Bayes’s Theorem
(prevalence 1%, sens 85%, spec 90%)
•
•
•
•
Pretest odds = .01/.99 = 0.0101
LR+ = .85/.1 = 8.5 (>1, but not that great)
Positive Posttest odds = .0101*8.5 = .0859
PPV = .0859/1.0859 = 0.079 = 7.9%
• Compare to the ‘hypothetical table’ method
(PPV=8%)
April 3, 2008
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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!
April 3, 2008
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Example of Nomogram
(pretest probability 1%, LR+ 45, LR– 0.102)
1%
45
31%
.102
0.1%
Pretest Prob.
LR
April 3, 2008
Posttest Prob.
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INVESTIGATIONS (2)
• “State 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
April 3, 2008
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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.
April 3, 2008
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HEALTH PROMOTION &
MAINTENANCE (1)
• “Formulate preventive measures into their
management strategies”
• “Communicate with the patient, the patient’s
family and concerned others with regard to risk
factors and their modification where appropriate”
• “Describe programs for the promotion of health
including screening for, and the prevention of,
illness”
Covered in UTMCCQE and 077F (April 18)
April 3, 2008
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Definitions of Health
1.
A state of complete physical, mental and social wellbeing and not merely the absence of disease or infirmity.
[The WHO, 1948]
2.
A joyful attitude toward life and a cheerful acceptance of
the responsibility that life puts upon the individual
[Sigerist, 1941]
3.
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]
April 3, 2008
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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.
April 3, 2008
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HEALTH PROMOTION
• Ottawa Charter for Health Promotion
(1996)
• Five key pillars to action:
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–
–
–
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Build Healthy Public Policy
Create supportive environments
Strengthen community action
Develop personal skills
Reorient health services
April 3, 2008
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HEALTH PROMOTION
• Health Education
– 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
April 3, 2008
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HEALTH PROMOTION &
MAINTENANCE (2)
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
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CRITICAL APPRAISAL/
MEDICAL ECONOMICS (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”
• Most will be covered in session on April 25
• UTMCCQE does not present hierarchy of
evidence (e.g., as used by Task Force on
Preventive Health Services)
April 3, 2008
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Hierarchy of evidence
(lowest to highest quality, approximately)
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•
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•
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•
•
•
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Expert opinion
Case report/series
Ecological (for individual-level exposures)
Cross-sectional
Case-Control
Historical Cohort
Prospective Cohort
} often similar
Quasi-experimental } or identical
Experimental (Randomized)
April 3, 2008
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CRITICAL APPRAISAL/
MEDICAL ECONOMICS (2)
• “Define the socio-economic rationales,
implications and consequences of medical
care”
• Medical care costs society financial and
other resources.
• This objective aims to raise awareness of
these types of issues.
April 3, 2008
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CRITICAL APPRAISAL/
MEDICAL ECONOMICS (2a)
• Is there a net financial benefit from medical
care?
• How do we value non-fiscal benefits such
as quality of life, ‘health’, not being dead?
• Should resources be spent on health or other
societal objectives?
• How do we value non-traditional
expenditures, etc which impact on health
(Healthy Public Policy).
April 3, 2008
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CRITICAL APPRAISAL/
MEDICAL ECONOMICS (3)
• “Outline the principles of cost-containment,
cost benefit analysis and cost effectiveness”
• Not addressed in UTMCCQE
April 3, 2008
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Principles of cost-containment
• Eliminate ineffective care
• Reduce costs of effective care
– Substitute cheaper but equally effective care,
e.g., day surgery for hospital admission, nurse
practitioners for some primary care, generic
drugs
– Reduce unit costs, e.g., reduce salaries (risk of
reduced effectiveness) or fees (but quantity
provided may increase)
April 3, 2008
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Types of economic analysis
[Costs always expressed in dollars]
• Cost-minimization: assume equal outcomes
• Cost-benefit: outcomes in dollars
• *Cost-effectiveness: outcomes in natural
units (deaths, days of care or disability, etc.)
• *Cost-utility: outcomes in QALYs (qualityadjusted life years)
April 3, 2008
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LAW & ETHICS
• “Discuss the principles of law, biomedical
ethics and other social aspects related to
common practice situations.”
• UTMCCQE very thorough; nil to add
• Make sure to read the CLEO section at the
front of the book.
• More on April 19
April 3, 2008
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VITAL STATISTICS
INFORMATION
• What are the key causes of illness or death
in Canada? Common things are common –
using epidemiology can help you run a
better clinical practice
• How have disease incidence and mortality
change in Canada in the past 20 years?
– Little good information on disease incidence
except for cancer (cancer registries)
April 3, 2008
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VITAL STATISTICS (2)
• Leading causes of death
– ‘Cardiovascular disease’: 37%
• Heart disease: 20%
• ‘Other circulatory disease’: 10%
• ‘Stroke’ 7%
– ‘Cancer’: 28%
• Lung cancer: 9% (M); 6% (W)
• Breast cancer: 4% (W)
• Prostate cancer: 4% (M)
–
–
–
–
Respiratory Disease: 10%
Injuries: 6%
Diabetes: 3%
Alzheimer’s: 1%
April 3, 2008
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Age-sex specific Mortality
Canada, 1999
6000
5000
Rate/100,000
4000
Combined
Males
Females
3000
2000
1000
0
0
20
40
60
80
Age at death
April 3, 2008
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Overall trends in mortality 1976-2005: rates and numbers
April 3, 2008
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Overall trends in mortality 1976-2005: rates and numbers
April 3, 2008
63
Cancer and Age
Age-Specific Incidence Rates for All Cancers by Sex, Canada, 2003
April 3, 2008
Surveillance Division, CCDPC, Public Health Agency of Canada
64
Cancer and Age
Age-Specific Mortality Rates for All Cancers by Sex, Canada, 2003
April 3, 2008
Surveillance Division, CCDPC, Public Health Agency of Canada
65
Time trends in incidence - Males
160
Estimated
140
Prostate
120
Lung
100
80
Colorectal
60
40
Bladder
Stomach
NHL
20
Melanoma
0
1975
Larynx
Liver
Thyroid
1980
1985
1990
1995
2000
2005
Age-Standardized Incidence Rates (ASIR) for Selected Cancer Sites, Males, Canada, 1978-2007
Surveillance and Risk Assessment Division, CCDPC, Public Health Agency of Canada
66
Time trends in mortality - Males
100
Estimated
Lung
80
ASMR (/100,000)
60
40
Colorectal
Prostate
20
Stomach
NHL
Oral
Larynx
Hodgkin's
0
1980
1985
1990
1995
2000
2005
Age-Standardized Incidence Rates (ASIR) for Selected Cancer Sites, Males, Canada, 1978-2007
Surveillance and Risk Assessment Division, CCDPC, Public Health Agency of Canada
67
Time trends in incidence - Females
160
Estimated
140
120
100
Breast
80
60
Colorectal
40
Lung
20
Thyroid
Stomach
NHL
Cervix
Larynx
0
1975
1980
1985
1990
1995
2000
2005
Age-Standardized Incidence Rates (ASIR) for Selected Cancer Sites, Females, Canada, 1978-2007 68
Surveillance and Risk Assessment Division, CCDPC, Public Health Agency of Canada
Time trends in mortality - Females
100
Estimated
80
ASMR (/100,000)
60
40
Lung
Breast
Colorectal
20
Stomach
NHL
Cervix
0
1980
1985
1990
1995
2000
2005
Age-Standardized Incidence Rates (ASIR) for Selected Cancer Sites, females, Canada, 1978-2007 69
Surveillance and Risk Assessment Division, CCDPC, Public Health Agency of Canada