Transcript Back to Basics, 2003 POPULATION HEALTH

Back to Basics, 2009
POPULATION HEALTH (3):
CLEO & OTHER TOPICS
N Birkett, MD
Epidemiology & Community Medicine
Based on slides prepared by Dr. R. Spasoff
April 9, 2009
1
THE PLAN(2)
• First class
– mainly lectures
• Other classes
– 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 9, 2009
2
THE PLAN (5)
• Session 3 (April 9)
– CLEO
• Overview of ethical principles
• Organization of Health Care Delivery in Canada
– Other topics
• Intro to Biostatistics
• Brief overview of epidemiological research methods
April 9, 2009
3
CLEO
• You will be having more sessions specifically on
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
April 9, 2009
4
COMMUNICATIONS!!!
April 9, 2009
5
Ethics (1)
• Key Principles
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–
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Autonomy
Beneficence
Justice
Non-Maleficence
April 9, 2009
6
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.
April 9, 2009
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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)
– Capacity is specific for each decision & can change over time
– Failure to agree with medical recommendations does NOT mean
‘lack of capacity’.
– 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)
April 9, 2009
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Capacity assessment ‘aid’
• Patients should:
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–
–
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
April 9, 2009
9
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.
April 9, 2009
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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
– Setting, perception, invitation, knowledge, empathize, strategy
April 9, 2009
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Ethics (7)
• Confidentiality
– PIPEDA (privacy regulations)
– Is not absolute: Can be over-ridden in some cases
•
•
•
•
•
•
•
April 9, 2009
‘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
12
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
– Must give care in emergency situations
– 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
April 9, 2009
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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
April 9, 2009
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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
• 2001: Kirby & Romanow commissions
• 2005: Chaoulli decision (Quebec)
– Controversial interpretation of the CHA in regards to banning of
private clinics.
April 9, 2009
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Organization of Health Care (0A)
• Canada Health Act established five
principles
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–
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–
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Public administration
Comprehensiveness
Universality
Portability
Accessibility
• Bans ‘extra-billing’
April 9, 2009
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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
April 9, 2009
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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)
April 9, 2009
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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.
April 9, 2009
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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
April 9, 2009
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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 to
you
• PAIRO (Professional Assoc of Interns and
Residents of Ontario) has been extremely
effective in negotiating salaries, working
conditions, educational programs
April 9, 2009
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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 cooperative, 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).
April 9, 2009
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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)
– Recent political negotiations may be changing this
April 9, 2009
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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.
April 9, 2009
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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.
April 9, 2009
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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 throughout 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
April 9, 2009
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Requirements for an Independent
Practice certificate (Ontario)
• A medical degree from an accredited Canadian or U.S.
medical school or from an acceptable medical school listed
in the World Directory of Medical Schools.
• Parts 1 and 2 of the Medical Council of Canada Qualifying
Examination.
• Certification by examination by either the Royal College
of Physicians and Surgeons of Canada (RCPSC) or the
College of Family Physicians of Canada (CFPC).
• Completion in Canada of one year of postgraduate training
or active medical practice, or completion of a full clinical
clerkship at an accredited Canadian medical school.
• Canadian Citizenship or permanent resident status.
April 9, 2009
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Organization of Health Care (5)
Medical Officer of Health
• Reports to municipal government.
• Responsible for:
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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.
April 9, 2009
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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
April 9, 2009
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The Coroner
• Notify coroner of deaths in the following cases:
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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.
April 9, 2009
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OTHER TOPICS
Not explicitly mentioned by MCC or
adequately addressed by UTMCCQE,
but important
•Biostatistics
•Epidemiologic methods
April 9, 2009
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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.
April 9, 2009
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BIOSTATISTICS
Core 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.
April 9, 2009
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BIOSTATISTICS
Core 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.
April 9, 2009
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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
April 9, 2009
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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.
April 9, 2009
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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.
April 9, 2009
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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.
April 9, 2009
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BIOSTATISTICS
Inferential 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.
April 9, 2009
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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.
April 9, 2009
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Hypothesis Testing (2)
• Common methods used are:
–
–
–
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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
April 9, 2009
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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’
April 9, 2009
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Hypothesis testing (4)
Actual Situation
No effect
Results
of Stats
Analysis
April 9, 2009
Effect
No effect No error
Type 2 error (β)
Effect
No error
Type 1 error
(α)
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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 chisquare value as large as 5.64 would occur
only 1.8% of the time.
April 9, 2009
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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
April 9, 2009
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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 nonprescription decongestant preparation containing
antihistamine was discontinued. The authors suggest ......
‘that consideration be given to discontinuing all
nonessential medications for patients with cancer.’.
April 9, 2009
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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
April 9, 2009
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The Epidemiological Triad
Agent
Host
Environment
April 9, 2009
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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.
April 9, 2009
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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%
April 9, 2009
50
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
April 9, 2009
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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 cases
IR = --------------------------PT of follow-up
April 9, 2009
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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:
30
April 9, 2009
IR = -------- = 0.065/PY = 65/1,000 PY
460
53
Prevalence & incidence
• As long as conditions are ‘stable’, we have
this relationship:
P=I*d
• That is, prevalence = incidence * disease
duration
April 9, 2009
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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
April 9, 2009
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Case-control (2)
Exp
Disease
ODDS RATIO
YES
NO
YES
a
b
a+b
Odds of exposure in cases = a/c
Odds of exposure in controls = b/d
NO
c
d
c+d
a+c
b+d
N
April 9, 2009
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 cases
Odds ratio (OR) = ----------------------------Exp odds in controls
= (a/c)/(b/d)
ad
= ---------bc
56
Case-control (3)
Disease
Apgar
Yes
No
Low 0-3
42
18
OK 4-6
43
67
85
85
Odds of exp in cases:
Odds of exp in controls:
= 42/43
= 18/67
= 0.977
= 0.269
Odds ratio (OR) = Odds in cases/odds in controls
= 0.977/ 0.269 = (42*67)/(43*18)
= 3.6
April 9, 2009
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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)
Disease
YES
Exp
RISK RATIO
NO
YES
a
b
a+b
NO
c
d
c+d
a+c
b+d
N
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 risk
Risk ratio (RR) = ---------------------Non-exp risk
= (a/(a+b))/(c/(c+d)
April 9, 2009
a/(a+b)
= -------------c/(c+d)
59
Cohorts (3)
Death
Apgar
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
April 9, 2009
= 2.8
60
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
April 9, 2009
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The Confounding Triangle
Outcome
Exposure
Confounder
April 9, 2009
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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)
April 9, 2009
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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)
April 9, 2009
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Mortality data
Three 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.
April 9, 2009
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Summary measures
of 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
April 9, 2009
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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).
April 9, 2009
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Attributable risks (2)
• In exposed subjects
Iexp
RD or
Attributable Risk
RD = AR = Iexp - Iunexp
Iunexp
Iexp – Iunexp
AR(%)=AF= ----------------------Iexp
Unexp
April 9, 2009
Exp
68
Attributable risks (3)
Iexp
Attributable Risk,
population
Ipop
Iunexp
Unexp
April 9, 2009
Population
Exp
69
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
April 9, 2009
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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
April 9, 2009
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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
April 9, 2009
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Population Pyramids
•
•
•
•
Canada, 1901-2001
Newfoundland 1951-2001
Ontario 1951-2001
Nunavut, 1991-2001
April 9, 2009
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