March 30, 2010 1

Back to Basics, 2010POPULATION HEALTH (1):

Introduction, Health Promotion, Biostats and Epi

MethodsN Birkett, MD

Epidemiology & Community Medicine

Based on slides prepared by Dr. R. SpasoffOther 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

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THE PLAN(2)

• First class– 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.

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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

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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

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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.

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)

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

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.

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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

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

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.

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

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

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LMCC New Objectives (9)

78.5: OUTBREAK MANAGEMENT

• Know defining characteristics of an outbreak

• Demonstrate essential skills in outbreak control

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

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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

March 30, 2010 18

LMCC New Objectives (12)

C2LEO

• Same material as before but re-structured.

• Read objectives for the details

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.

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INVESTIGATIONS (1)

• 78.2– Determine the reliability and predictive value of

common investigations– Applicable to both screening and diagnostic

tests.

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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

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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

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Reliability and Validity: the metaphor of target shooting. Here, reliability is represented by consistency, and validity by aim

Reliability Low High

Low

Validity

High

•

••

•

•

•

•

• •

••

•

•••

•••

•• ••••

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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

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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

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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

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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)

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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: Excellent– 0.8-0.9: Not bad– 0.7-0.8: So-so– < 0.7: Poor

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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

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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.

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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

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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

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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

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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)

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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

odds

prevalence

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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.

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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.

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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)

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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

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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

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Calibration by Bayes’ Theorem

• You don’t need to learn Bayes’ theorem

• Instead, work with the Likelihood Ratio (+ve).

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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

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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…

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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 post-test probability to get PPV

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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

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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 = 0.0101

• Positive Posttest odds = .0101*8.5 = .0859

• PPV = .0859/1.0859 = 0.079 = 7.9%

• Compare to the ‘hypothetical table’ method (PPV=8%)

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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!

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Example of Nomogram (pretest probability 1%, LR+ 45, LR– 0.102)

Pretest Prob. LR Posttest Prob.

1%45

.10231%

0.1%

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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

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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.

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78.3: HEALTH PROMOTION & MAINTENANCE (1)

• Definitions of health

• Concepts of Health Promotion

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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]

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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.

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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

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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

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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

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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)

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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 98.6F. Recent investigations involving millions of measurements have shown that this number is wrong: normal body temperature is actually 98.2F. The fault lies not with the original measurements - they were averaged and sensibly rounded to the nearest degree: 37C. 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.

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BIOSTATISTICSCore 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.

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BIOSTATISTICSCore 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.

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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

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 range80 ± (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.

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.

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BIOSTATISTICSInferential 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.

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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.

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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

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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’

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Hypothesis testing (4)

No effect Effect

No effect No error Type 2 error (β)

Effect Type 1 error (α)

No error

Actual Situation

Results of Stats Analysis

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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 5.64. The critical value is 3.84 (from table, for α = 0.05). Therefore reject H0

• P=0.018. Under H0 (chance alone), a chi-square value as large as 5.64 would occur only 1.8% of the time.

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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

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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.’.

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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

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The Epidemiological Triad

Host Agent

Environment

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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.

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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%

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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

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Incidence rate (1)• 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 casesIR = --------------------------- PT of follow-up

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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 * 2 + 90 * 4 = 460 PY’s

• During follow-up, 30 developed ‘stress’.• Incidence rate of stress is:

30IR = -------- = 0.065/PY = 65/1,000 PY 460

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Prevalence & incidence

• As long as conditions are ‘stable’, we have this relationship:

• That is, prevalence = incidence * disease duration

P = I * d

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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

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Case-control (2)

YES NO

YES a b a+b

NO c d c+d

a+c b+d N

Disease

Exp

ODDS RATIO

Odds of exposure in cases = a/cOdds 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 casesOdds ratio (OR) = ----------------------------- Exp odds in controls= (a/c)/(b/d)

ad= ---------- bc

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Yes No

Low 0-3 42 18

OK 4-6 43 67

85 85

Apgar

Odds of exp in cases: = 42/43 = 0.977Odds of exp in controls: = 18/67 = 0.269

Odds ratio (OR) = Odds in cases/odds in controls

= 0.977/ 0.269 = (42*67)/(43*18)

= 3.6

Case-control (3)Disease

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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.

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Cohorts (2)

YES NO

YES a b a+b

NO c d c+d

a+c b+d N

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 riskRisk ratio (RR) = ---------------------- Non-exp risk

= (a/(a+b))/(c/(c+d)

a/(a+b)= -------------- c/(c+d)

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Cohorts (3)

YES NO

Low 0-3 42 80 122

OK 4-6 43 302 345

85 382 467

Death

Apgar

Risk in exposed: = 42/122 = 0.344Risk in Non-exposed = 43/345 = 0.125

Exp riskRisk ratio (RR) = ---------------------- Non-exp risk

= 0.344/0.125

= 2.8

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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

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The Confounding Triangle

Exposure Outcome

Confounder

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Confounding (example)

• Does heavy alcohol drinking cause mouth cancer? We get OR=3.4 (95% CI: 2.1-4.8)

• 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: 0.92-1.83)

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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)

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Mortality dataThree 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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Summary measuresof 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

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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).

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• In exposed subjects

Attributable risks (2)

ExpUnexp

RD or Attributable Risk

Iexp

Iunexp

RD = AR = Iexp - Iunexp

Iexp – Iunexp

AR(%)=AF= -----------------------

Iexp

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Attributable risks (3)

ExpUnexp

Attributable Risk,population

Iexp

Iunexp

Population

Ipop

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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.

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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

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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

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RCT – Example of Analysis

Asthma No Total Inc

attack attack

Treatment 15 35 50 .30

Control 25 25 50 .50

Relative Risk = 0.30/0.50 = 0.60

Absolute Risk Reduction = 0.50-0.30 = 0.20

Relative Risk Reduction = 0.20/0.50 = 40%

Number Needed to Treat = 1/0.20 = 5