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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. THE PLAN. - PowerPoint PPT Presentation
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April 3, 2008 1
Back to Basics, 2008POPULATION HEALTH (1):
GENERAL OBJECTIVESN Birkett, MD
Epidemiology & Community Medicine
Based on slides prepared by Dr. R. SpasoffOther resources available on Individual & Population Health web site
April 3, 2008 2
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 3
THE PLAN(2)
• 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 4
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 5
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 6
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 7
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 8
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 9
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 10
Reliability and Validity: the metaphor of target shooting. Here, reliability is represented by consistency, and validity by aim
Reliability Low High
Low
Validity
High
•
••
•
•
•
•
• •
••
•
•••
•••
•• ••••
April 3, 2008 11
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 12
Test Properties (1)Diseased Not diseased
Test +ve 90 5 95
Test -ve 10 95 105
100 100 200
True positives False positives
False negatives True negatives
April 3, 2008 13
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 14
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)
April 3, 2008 15
Test Properties (6)
• Sensitivity = Pr(test positive in a personwith disease)
• Specificity = Pr(test negative in a person without disease)
• Range: 0 to 1– > 0.9: Excellent– 0.8-0.9: Not bad– 0.7-0.8: So-so– < 0.7: Poor
April 3, 2008 16
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 17
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 18
Test Properties (9)Diseased Not diseased
Test +ve 90 5 95
Test -ve 10 95 105
100 100 200
PPV = 0.95
NPV = 0.90
April 3, 2008 19
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
April 3, 2008 20
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 21
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 22
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 23
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 24
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.
April 3, 2008 25
Calibration by hypothetical table
Fill cells in following order:
“Truth”
Disease Disease Total PV
Present Absent
Test Pos 4th 7th 8th 10th
Test Neg 5th 6th 9th 11th
Total 2nd 3rd 1st (10,000)
April 3, 2008 26
Test Properties (12)
Diseased Not diseased
Test +ve 425 50 475
Test -ve 75 450 525
500 500 1,000
Tertiary care: research study. Prev=0.5
PPV = 0.89
Sens = 0.85 Spec = 0.90
April 3, 2008 27
Test Properties (13)
Diseased Not diseased
Test +ve
Test -ve
10,000
Primary care: Prev=0.01
PPV = 0.08
9,900
85
15
100
990
8,910
1,075
8,925
0.01*10000
0.85*100
0.9*9900
April 3, 2008 28
Calibration by Bayes’ Theorem
• You don’t need to learn Bayes’ theorem
• Instead, work with the Likelihood Ratio (+ve).
April 3, 2008 29
Test Properties (9)Diseased Not
diseased
Test +ve
90 5 95
Test -ve
10 95 105
100 100 200 Pre-test odds = 1.00
Post-test odds = 18.0
Likelihood ratio (+ve) = LR(+) = 18.0/1.0 = 18.0
April 3, 2008 30
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 31
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 32
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 33
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 34
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 35
Example of Nomogram (pretest probability 1%, LR+ 45, LR– 0.102)
Pretest Prob. LR Posttest Prob.
1%45
.10231%
0.1%
April 3, 2008 36
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 37
The Government is extremely fond of amassinggreat 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 38
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 39
Definitions of Health
1. A state of complete physical, mental and social well-being 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 40
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 41
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– Reorient health services
April 3, 2008 42
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 43
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
April 3, 2008 44
April 3, 2008 45
April 3, 2008 46
April 3, 2008 47
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 48
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} often similar• Quasi-experimental } or identical• Experimental (Randomized)
April 3, 2008 49
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 50
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 51
CRITICAL APPRAISAL/ MEDICAL ECONOMICS (3)
• “Outline the principles of cost-containment, cost benefit analysis and cost effectiveness”
• Not addressed in UTMCCQE
April 3, 2008 52
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 53
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 (quality-adjusted life years)
April 3, 2008 54
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 55
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 56
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 57
April 3, 2008 58
Age-sex specific MortalityCanada, 1999
Age at death
0 20 40 60 80
Rat
e/10
0,00
0
0
1000
2000
3000
4000
5000
6000
CombinedMalesFemales
April 3, 2008 59
April 3, 2008 60
April 3, 2008 61
April 3, 2008 62
Overall trends in mortality 1976-2005: rates and numbers
April 3, 2008 63
Overall trends in mortality 1976-2005: rates and numbers
April 3, 2008 64
Cancer and AgeAge-Specific Incidence Rates for All Cancers by Sex, Canada, 2003
Surveillance Division, CCDPC, Public Health Agency of Canada
April 3, 2008 65
Cancer and AgeAge-Specific Mortality Rates for All Cancers by Sex, Canada, 2003
Surveillance Division, CCDPC, Public Health Agency of Canada
66
Time trends in incidence - Males
Age-Standardized Incidence Rates (ASIR) for Selected Cancer Sites, Males, Canada, 1978-2007
Surveillance and Risk Assessment Division, CCDPC, Public Health Agency of Canada
1975 1980 1985 1990 1995 2000 2005
0
20
40
60
80
100
120
140
160
Prostate
Lung
Colorectal
Bladder
NHLStomach
Melanoma
Larynx
Liver
Thyroid
Estimated
67
1980 1985 1990 1995 2000 2005
AS
MR
(/1
00
,00
0)
0
20
40
60
80
100
Prostate
Lung
Colorectal
NHL
Stomach
Oral
Larynx
Hodgkin's
Time trends in mortality - Males
Age-Standardized Incidence Rates (ASIR) for Selected Cancer Sites, Males, Canada, 1978-2007
Surveillance and Risk Assessment Division, CCDPC, Public Health Agency of Canada
Estimated
68
1975 1980 1985 1990 1995 2000 2005
0
20
40
60
80
100
120
140
160
Breast
Lung
Colorectal
NHLStomach
Cervix
Larynx
Thyroid
Time trends in incidence - Females
Age-Standardized Incidence Rates (ASIR) for Selected Cancer Sites, Females, Canada, 1978-2007
Surveillance and Risk Assessment Division, CCDPC, Public Health Agency of Canada
Estimated
69
1980 1985 1990 1995 2000 2005
AS
MR
(/1
00,
000)
0
20
40
60
80
100
Breast
Lung
Colorectal
NHL
Stomach
Cervix
Time trends in mortality - Females
Age-Standardized Incidence Rates (ASIR) for Selected Cancer Sites, females, Canada, 1978-2007
Surveillance and Risk Assessment Division, CCDPC, Public Health Agency of Canada
Estimated