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

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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 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

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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

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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