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Back to Basics, 2008POPULATION HEALTH (3): CLEO & OTHER TOPICS

N Birkett, MDEpidemiology & Community Medicine

Based on slides prepared by Dr. R. Spasoff

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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

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THE PLAN (5)

• Session 3 (April 25)– CLEO

• Overview of ethical principles

• Organization of Health Care Delivery in Canada

– Other topics• Intro to Biostatistics

• Brief overview of epidemiological research methods

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CLEO

• You’ve already had 2 days on ethical and legal issues.

• Ethical: ; also very well handled in UTMCCQE

• Legal: very well handled in UTMCCQE

• Organization of Health Care in Canada: well handled in UTMCCQE, but a couple of points require elaboration

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COMMUNICATIONS!!!

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Ethics (1)

• Key Principles– Autonomy– Beneficence– Justice– Non-Maleficence

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Ethics (2)

• Consent– 3 key components

• Disclosure• Capacity• Voluntariness

– Explicit vs. implicit consent– Signed consent forms document consent

process but do not replace need to talk with patient.

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Ethics (3)

• Assessing capacity– Ability to understand relevant information– Ability to appreciate reasonably foreseeable

consequences of a decision– Uncoerced choice (illness, drugs, family)– Age does not determine capacity, even if province has a

minimum ‘age of consent’.– Minors can give consent without parental approval if

they are deemed ‘capable’.– Substitute decision maker (self identified or appointed)

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Capacity assessment ‘aid’

• Patients should:– Understand medical problem

– Understand proposed treatment

– Understand alternatives

– Understand option of refusing/deferring Rx

– Understand reasonably foreseeable consequences of accepting/refusing Rx

– Have decision-making not substantially based on delusions or depression

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Ethics (5)

• ‘Truth Telling’– CPSO policy: Physicians should provide

patients with whatever information that will, from the patient’s perspective, have a bearing on medical decision-making and communicate that information in a way that is comprehensible to the patient.

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Ethics (6)

• Principles of disclosure– Patient decision making

– Patient consent

– Medical error• Bad communication is the number reason for patient

complaints about physicians

• Error ≠ negligence

– Breaking bad news• Approach with care and patient support

• SPIKES protocol

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Ethics (7)

• Confidentiality– PIPEDA (privacy regulations)

– Can be over-ridden in some cases• ‘duty to warn’

• Child abuse

• Fitness to drive

• Reportable diseases (to PHU)

• Legal requirements (coroner, vital stats, court order)

• Telling spouse about partner with HIV/AIDS

• Improper conduct of other physicians

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Ethics (8)

• Physician-Industry relationships– MD’s are often pressured by pharmaceutical companies– Your duty is to place your patient’s interest first

• Doctor-patient relationship– Can not discriminate in accepting patients– In terminating your willingness to give care to a patient

• Give adequate notice• Arrange for alternate care to be provided

– Do not exploit the doctor-patient relationship– Disclose limitations (e.g. personal values) which limit

care

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Ethics (9)

• Some key controversies– Euthanasia/physician assisted suicide

• Illegal in Canada

– Maternal-fetal conflict of rights• Canada supports maternal over fetal rights

– Advanced reproductive technology– Fetal tissue

• Human cloning is strictly prohibited but we are getting into some real gray zones given latest lab advances

– Abortion• Should not be used as alternative to contraception

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Organization of Health Care (0)

• Provincial governments are responsible for Health Care.• 1962: First universal medical care insurance• 1965: Hall commission recommended federal leadership

on medical insurance• 1966: Medical Care Act (federal) established medical

insurance with 50% funding from federal government• 1977: EPFA reducing federal role; led to extra billing

debate• 1984: Canada Health Act• 2005: Chaoulli decision (Quebec)

– Controversial interpretation of the CHA in regards to banning of private clinics.

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Organization of Health Care (0A)

• Canada Health Act established five principles– Public administration– Comprehensiveness– Universality– Portability– Accessibility

• Bans ‘extra-billing’

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Organization of Health Care (0B)

• 2003: total health care expenditures were $3,839/person or about $135billion, 10% of GDP

• 73% from public sector (45% in the USA)• 32% spent on hospitals, 16% on drugs,14%

on MD’s and 12% on other HCP’s• Research shows that private-for-profit care

is more expensive and less effective

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Methods of paying doctors (I&PH link)

• Fee-for-service: unit is services. Incentive to provide many services, especially procedures.

• Capitation: unit is patient. Fixed payment per patient. Incentive to keep people healthy, but not to make yourself accessible.

• Salary: unit is time. Productivity depends on professionalism and institutional controls– Practice plans

• Combinations of above, e.g., "blended funding“– Family networks (Ontario) (I&PH link)

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Methods for paying hospitals

• Line-by-line: separate payments for staff, supplies, etc. Cumbersome, rigid.

• Global budget: fixed payment to be used as hospital sees fit. Fails to recognize differences in case mix.

• Case-Mix weighted: payment for total cost of episode, greater for more complicated cases. Now used in Canada.

• New technology: OHTAC reviews requests. If approved, government pays. If declined, hospitals can pay for it from core budget.

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How good is the Canadian health care system?

• The World Health Report 2000 (from WHO) placed Canada 30th to 35th in the world, slightly above US but well below most of western Europe

• Implies that we should be healthier, given our high levels of income and education

• Methods used by the Report have been highly criticized

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Organization of Health Care (1)Student & Resident Issues

• “The role of student and resident associations in promoting protecting their members’ interests.”

• Student organizations will be familiar• PAIRO (Professional Assoc of Interns and

Residents of Ontario) has been extremely effective in negotiating salaries, working conditions, educational programs

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Organization of Health Care (2)CMPA

• “The role of the CMPA as a medical defence association representing the interests of individual physicians.”

• Canadian Medical Protective Association is a co-operative, replacing commercial malpractice insurance. It advises physicians on threatened litigation (talk to them early), and pays legal fees and court settlements. Fees vary by region and specialty ($500-$75,000/year).

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Organization of Health Care (3) Interprovincial Issues

• “The portability of the medical degree.”• Degrees are portable across North America

• “The non-transferability of provincial medical licences.”

• Provincial Colleges of Physicians and Surgeons set own requirements (with input from provincial governments)

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Organization of Health Care (3b)

• Certification vs. licensing– Medical College of Canada

• Certifies MD’s (LMCC)

– Royal College of Physicians and Surgeons of Canada• Certifies specialists

– College of Family Physicians of Canada• Certifies family physicians

– College of Physicians and Surgeons of Ontario• Issues a licence to practice to MD’s with the LMCC (or

equivalent) and a certificate.

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Organization of Health Care (4a)Physician Organizations

• Medical Council of Canada– Maintains the Canadian Medical Registry

– Does not grant licence to practice medicine

• College of Physicians and Surgeons of Ontario– Responsible for issuing license to practice medicine

– Handles public complaints, professional discipline, etc.

– Does not engage in lobbying on matters such as salaries, working conditions.

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Organization of Health Care (4b)Physician Organizations

• Royal College of Physicians and Surgeons of Canada.– Maintains standards for post-graduate training through-

out Canada.– Sets exams and issues fellowships for specialty training

• Ontario Medical Association– Professional association; lobbies on behalf of

physicians re: fees, working conditions, etc.

• College of Family Physicians of Canada– Voluntary organization certifying/promoting family

practice

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Organization of Health Care (5)Medical Officer of Health

• Reports to municipal government.• Responsible for:

– Food/lodging sanitation– Infectious disease control and immunization– Health promotion, etc.– Family health programmes

• E.g. family planning, pre-natal and pre-school care, Tobacco prevention, nutrition

– Occupational and environmental health surveillance.

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Organization of Health Care (6)Medical Officer of Health

• Powers include ordering people, due to a public health hazard, to take and of these actions:– Vacate home or close business;– Regulate or prohibit sale, manufacture, etc. of

any item– Isolate people with communicable disease– Require people to be treated by MD– Require people to give blood samples

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The Coroner

• Notify coroner of deaths in the following cases:– Due to violence, negligence, misconduct, etc.

– During work at a construction or mining site.

– During pregnancy

– Sudden/unexpected

– Due to disease not treated by qualified MD

– Any cause other than disease

– Under suspicious circumstance or by ‘unfair means’

– Deaths in jails, foster homes, nursing homes, etc.

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OTHER TOPICS

Not explicitly mentioned by MCC or adequately addressed by UTMCCQE,

but important

•Biostatistics•Epidemiologic methods

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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

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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.

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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.

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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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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

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Population Pyramids

• Canada, 1901-2001

• Newfoundland 1951-2001

• Ontario 1951-2001

• Nunavut, 1991-2001