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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 – – – – 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 7 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 8 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 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 10 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 11 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 13 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 14 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 15 Organization of Health Care (0A) • Canada Health Act established five principles – – – – – Public administration Comprehensiveness Universality Portability Accessibility • Bans ‘extra-billing’ April 9, 2009 16 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 17 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 18 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 19 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 20 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 21 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 22 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 23 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 24 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 25 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 26 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 27 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. April 9, 2009 28 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 29 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. April 9, 2009 30 OTHER TOPICS Not explicitly mentioned by MCC or adequately addressed by UTMCCQE, but important •Biostatistics •Epidemiologic methods April 9, 2009 31 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 32 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 33 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 34 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 35 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 36 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 37 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 38 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 39 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 40 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 April 9, 2009 41 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 42 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) 43 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 44 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 45 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 46 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 47 The Epidemiological Triad Agent Host Environment April 9, 2009 48 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 49 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 51 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 52 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 54 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 55 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 57 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. April 9, 2009 58 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 61 The Confounding Triangle Outcome Exposure Confounder April 9, 2009 62 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 63 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 64 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 65 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 66 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 67 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. April 9, 2009 70 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 71 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 72 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 73 Population Pyramids • • • • Canada, 1901-2001 Newfoundland 1951-2001 Ontario 1951-2001 Nunavut, 1991-2001 April 9, 2009 74