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


Back to Basics, 2008 POPULATION HEALTH (3): CLEO & OTHER TOPICS N Birkett, MD Epidemiology & Community Medicine Based on s prepared by Dr. R. Spasoff – PowerPoint PPT presentation

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Title: Back to Basics, 2008 POPULATION HEALTH (3): CLEO

Back to Basics, 2008POPULATION HEALTH (3) CLEO
  • N Birkett, MD
  • Epidemiology Community Medicine
  • Based on slides prepared by Dr. R. Spasoff

  • 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
    arent clear

  • 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

  • Youve already had 2 days on ethical and legal
  • 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

Ethics (1)
  • Key Principles
  • Autonomy
  • Beneficence
  • Justice
  • Non-Maleficence

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.

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

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

Ethics (5)
  • Truth Telling
  • CPSO policy Physicians should provide patients
    with whatever information that will, from the
    patients perspective, have a bearing on medical
    decision-making and communicate that information
    in a way that is comprehensible to the patient.

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

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
  • Telling spouse about partner with HIV/AIDS
  • Improper conduct of other physicians

Ethics (8)
  • Physician-Industry relationships
  • MDs are often pressured by pharmaceutical
  • Your duty is to place your patients interest
  • Doctor-patient relationship
  • Can not discriminate in accepting patients
  • In terminating your willingness to give care to a
  • 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

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

Organization of Health Care (0)
  • Provincial governments are responsible for Health
  • 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
  • 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.

Organization of Health Care (0A)
  • Canada Health Act established five principles
  • Public administration
  • Comprehensiveness
  • Universality
  • Portability
  • Accessibility
  • Bans extra-billing

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 MDs
    and 12 on other HCPs
  • Research shows that private-for-profit care is
    more expensive and less effective

Methods of paying doctors (IPH 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) (IPH link)

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.

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

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

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).

Organization of Health Care (3) Interprovincial
  • The portability of the medical degree.
  • Degrees are portable across North America
  • The non-transferability of provincial medical
  • Provincial Colleges of Physicians and Surgeons
    set own requirements (with input from provincial

Organization of Health Care (3b)
  • Certification vs. licensing
  • Medical College of Canada
  • Certifies MDs (LMCC)
  • Royal College of Physicians and Surgeons of
  • Certifies specialists
  • College of Family Physicians of Canada
  • Certifies family physicians
  • College of Physicians and Surgeons of Ontario
  • Issues a licence to practice to MDs with the
    LMCC (or equivalent) and a certificate.

Organization of Health Care (4a)Physician
  • 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
  • Handles public complaints, professional
    discipline, etc.
  • Does not engage in lobbying on matters such as
    salaries, working conditions.

Organization of Health Care (4b)Physician
  • Royal College of Physicians and Surgeons of
  • Maintains standards for post-graduate training
    through-out Canada.
  • Sets exams and issues fellowships for specialty
  • 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

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

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

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
  • Deaths in jails, foster homes, nursing homes,

  • Not explicitly mentioned by MCC or adequately
    addressed by UTMCCQE, but important
  • Biostatistics
  • Epidemiologic methods

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

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.

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,
  • Usually computed as mean 2 SE

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 (24)
    (72, 88).
  • If the sample size were 100, then SE 20/10
    2.0, and 95 confidence interval is 80 (22)
    (76, 84). More precise.

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.

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 dont help much
    you need good design instead.

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.
  • RCTs used them to infer treatment effects, etc.
  • 95 confidence intervals are a very common way to
    present these results.

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
  • 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.

Hypothesis Testing (2)
  • Common methods used are
  • T-test
  • Z-test
  • Chi-square test
  • 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

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
  • 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

Hypothesis testing (4)
Actual Situation
No effect Effect
No effect No error Type 2 error (ß)
Effect Type 1 error (a) No error
Results of Stats Analysis
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 a 0.05).
    Therefore reject H0
  • P0.018. Under H0 (chance alone), a chi-square
    value as large as 5.64 would occur only 1.8 of
    the time.

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

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
Epidemiology overview
  • Key study designs to examine (IPH link)
  • Case-control
  • Cohort
  • Randomized Controlled Trial (RCT)
  • Confounding
  • Relative Risks/odds ratios
  • All ratio measures have the same interpretation
  • 1.0 no effect
  • lt 1.0 ? protective effect
  • gt 1.0 ? increased risk
  • Values over 2.0 are of strong interest

  • 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

  • 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
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
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 cases IR ----------------------
----- PT of follow-up
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 PYs
  • During follow-up, 30 developed stress.
  • Incidence rate of stress is

30 IR -------- 0.065/PY 65/1,000
PY 460
Prevalence incidence
  • As long as conditions are stable, we have this
  • That is, prevalence incidence disease duration

P I d
Case-control study
  • Selects subjects based on their final outcome.
  • Select a group of people with the outcome/disease
  • Select a group of people without the outcome
  • Ask them about past exposures
  • Compare the frequency of exposure in the two
  • If exposure increase risk, there should be more
    exposed cases than controls
  • Compute an Odds Ratio

Case-control (2)
ODDS RATIO Odds of exposure in cases a/c Odds
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 gt b/d). This can be assessed by the
ratio of one to the other
Exp odds in cases Odds ratio (OR)
Exp odds in controls (a/c)/(b/d)
ad ---------- bc
YES NO YES a b ab NO
c d cd ac bd
Case-control (3)
Yes No Low 0-3 42
18 OK 4-6 43 67 85
Odds of exp in cases 42/43
0.977 Odds of exp in controls 18/67
0.269 Odds ratio (OR) Odds in cases/odds in
controls 0.977/ 0.269 (4267)/(4318) 3.6
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
  • 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
  • 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.

Cohorts (2)
RISK RATIO Risk in exposed a/(ab) Risk in
Non-exposed c/(cd) If exposure increases
risk, you would expect a/(ab) to be larger than
c/(cd). How much larger can be assessed by the
ratio of one to the other
Exp risk Risk ratio (RR)
Non-exp risk (a/(ab))/(c/(cd)
a/(ab) -------------- c/(cd)
YES NO YES a b ab NO
c d cd ac bd
Cohorts (3)
YES NO Low 0-3 42 80
122 OK 4-6 43 302 345 85
382 467
Risk in exposed 42/122 0.344 Risk in
Non-exposed 43/345 0.125
Exp risk Risk ratio (RR)
Non-exp risk 0.344/0.125 2.8
  • 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

The Confounding Triangle
Confounding (example)
  • Does heavy alcohol drinking cause mouth cancer?
    We get OR3.4 (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
  • Therefore, we have confounding.
  • We do a statistical adjustment (logistic
    regression is most common) OR1.3 (95 CI

  • An older method of adjusting for confounding
    (usually used for differences in age between two
  • Refers observed events to a standard population,
    producing hypothetical values
  • Direct age-standardized rate
  • Indirect standardized mortality ratio (SMR)

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

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

Attributable Risk (IPH 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 wont
    do that one (depends on how common the exposure

Attributable risks (2)
  • In exposed subjects

RD or Attributable Risk
RD AR Iexp - Iunexp
Iexp Iunexp AR()AF
Attributable risks (3)
Attributable Risk, population
Randomized Controlled Trials
  • Basically a cohort study where the researcher
    decides which exposure (treatment) the subject
  • 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

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

Randomized Controlled Trials Analysis
  • Outcome is an adverse event
  • RR is expected to be lt1
  • Absolute risk reduction, ARR
  • Incidence(control) - Incidence(treatment)
    (attributable risk)
  • Relative risk reduction, RRR ARR/incidence(contr
    ol) 1 - RR
  • Number needed to treat, NNT (to prevent one
    adverse event) 1/ARR

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

Population Pyramids
  • Canada, 1901-2001
  • Newfoundland 1951-2001
  • Ontario 1951-2001
  • Nunavut, 1991-2001