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Back to Basics, 2008 POPULATION HEALTH (1): GENERAL OBJECTIVES

Back to Basics, 2008 POPULATION HEALTH (1): GENERAL OBJECTIVES. N Birkett, MD Epidemiology & Community Medicine Based on slides prepared by Dr. R. Spasoff Other resources available on Individual & Population Health web site. THE PLAN.

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Back to Basics, 2008 POPULATION HEALTH (1): GENERAL OBJECTIVES

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  1. Back to Basics, 2008POPULATION HEALTH (1):GENERAL OBJECTIVES N Birkett, MD Epidemiology & Community Medicine Based on slides prepared by Dr. R. Spasoff Other resources available on Individual & Population Health web site April 3, 2008

  2. THE PLAN • We will follow MCC Objectives for Qualifying Examination (in italics) • Focus is on topics not well covered in the Toronto Notes (UTMCCQE) • Three sessions: General Objectives & Infectious Diseases, Clinical Presentations, Additional Topics April 3, 2008

  3. 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 April 3, 2008

  4. THE PLAN (3) • Session 1 (April 3) • Diagnostic tests • Sensitivity, specificity, validity, PPV • Health Promotion • Critical Appraisal (more on April 19) • Elements of Health Economics • Vital Statistics • Overview of Communicable Disease control, epidemics, etc. April 3, 2008

  5. THE PLAN (4) • Session 2 (April 18, 1300-1600) • Clinical Presentations • Periodic Health Examination • Immunization • Occupational Health • Health of Special Populations • Disease Prevention • Determinants of Health • Environmental Health April 3, 2008

  6. THE PLAN (5) • Session 3 (April 25, 0800-1100) • CLEO • Overview of Ethical Principles • Organization of Health Care Delivery in Canada • Other topics • Intro to Biostatistics • Brief overview of epidemiological research methods April 3, 2008

  7. INVESTIGATIONS (1) • “Determine the reliability and predictive value of common investigations” • MCCQE doesn’t address reliability, or show how to estimate predictive value April 3, 2008

  8. Reliability • = reproducibility. Does it produce the same result every time? • Related to chance error • Averages out in the long run, but in patient care you hope to do a test only once; therefore, you need a reliable test April 3, 2008

  9. Validity • Whether it measures what it purports to measure in long run, viz., presence or absence of disease • Normally use criterion validity, comparing test results to a gold standard • Link to I&PH web on validity April 3, 2008

  10. Reliability and Validity: the metaphor of target shooting.Here, reliability is represented by consistency, and validity by aim Reliability LowHigh • • • • • • • • Low Validity • • • • • • • • High • • • • • • • • April 3, 2008

  11. Gold Standards • Possible gold standards: • More definitive (but expensive or invasive) test • Complete work-up • Eventual outcome (for screening tests, when workup of well patients is unethical; in clinical care you cannot wait) • First two depend upon current state of knowledge and available technology April 3, 2008

  12. Test Properties (1) True positives False positives False negatives True negatives April 3, 2008

  13. Test Properties (2) Sensitivity = 0.90 Specificity = 0.95 April 3, 2008

  14. 2x2 Table for Testing a Test Gold standard Disease Disease Present Absent Test Positive a (TP) b (FP) Test Negative c (FN) d (TN) Sensitivity Specificity = a/(a+c) = d/(b+d) April 3, 2008

  15. Test Properties (6) • Sensitivity = Pr(test positive in a person with disease) • Specificity = Pr(test negative in a person without disease) • Range: 0 to 1 • > 0.9: Excellent • 0.8-0.9: Not bad • 0.7-0.8: So-so • < 0.7: Poor April 3, 2008

  16. Test Properties (7) • Values depend on cutoff point • Generally, high sensitivity is associated with low specificity and vice-versa. • Not affected by prevalence, if severity is constant • Do you want a test to have high sensitivity or high specificity? • Depends on cost of ‘false positive’ and ‘false negative’ cases • PKU – one false negative is a disaster • Ottawa Ankle Rules April 3, 2008

  17. Test Properties (8) • Sens/Spec not directly useful to clinician, who knows only the test result • Patients don’t ask: if I’ve got the disease how likely is it that the test will be positive? • They ask: “My test is positive. Does that mean I have the disease?” • Predictive values. April 3, 2008

  18. Test Properties (9) PPV = 0.95 NPV = 0.90 April 3, 2008

  19. 2x2 Table for Testing a Test Gold standard Disease Disease Present Absent Test + a (TP) b (FP) PPV = a/(a+b) Test - c (FN) d (TN) NPV= d/(c+d) a+c b+d April 3, 2008

  20. Predictive Values • Based on rows, not columns • PPV = a/(a+b); interprets positive test • NPV = d/(c+d); interprets negative test • Depend upon prevalence of disease, so must be determined for each clinical setting • Immediately useful to clinician: they provide the probability that the patient has the disease April 3, 2008

  21. Prevalence of Disease • Is your best guess about the probability that the patient has the disease, before you do the test • Also known as Pretest Probability of Disease • (a+c)/N in 2x2 table • Is closely related to Pre-test odds of disease: (a+c)/(b+d) April 3, 2008

  22. Test Properties (10) April 3, 2008

  23. Prevalence and Predictive Values • Predictive values for a test dependent on the pre-test prevalence of the disease • Tertiary hospitals see more pathology then FP’s; hence, their tests are more often true positives. • How to ‘calibrate’ a test for use in a different setting? • Relies on the stability of sensitivity & specificity across populations. April 3, 2008

  24. Methods for Calibrating a Test Four methods can be used: • Apply definitive test to a consecutive series of patients (rarely feasible) • Hypothetical table • Bayes’s Theorem • Nomogram You need to be able to do one of the last 3. By far the easiest is using a hypothetical table. April 3, 2008

  25. Calibration by hypothetical table Fill cells in following order: “Truth” Disease Disease Total PV Present Absent Test Pos 4th 7th 8th 10th Test Neg 5th 6th 9th 11th Total 2nd 3rd 1st (10,000) April 3, 2008

  26. Test Properties (12) Tertiary care: research study. Prev=0.5 PPV = 0.89 Sens = 0.85 Spec = 0.90 April 3, 2008

  27. Test Properties (13) Primary care: Prev=0.01 1,075 85 990 0.85*100 PPV = 0.08 15 8,910 8,925 0.9*9900 9,900 100 0.01*10000 April 3, 2008

  28. Calibration by Bayes’ Theorem • You don’t need to learn Bayes’ theorem • Instead, work with the Likelihood Ratio (+ve). April 3, 2008

  29. Test Properties (9) Post-test odds = 18.0 Pre-test odds = 1.00 Likelihood ratio (+ve) = LR(+) = 18.0/1.0 = 18.0 April 3, 2008

  30. Calibration by Bayes’s Theorem • LR+ is fixed across populations just like sensitivity & specificity. • You can convert sens and spec to likelihood ratios • LR+ = sens/(1-spec) • Bigger is better. • Posttest odds = pretest odds * LR (+ or -) • Convert to posttest probability if desired… April 3, 2008

  31. Calibration by Bayes’s Theorem • How does this help? • Remember: • Post-test odds = pretest odds * LR (+) • To ‘calibrate’ your test for a new population: • Use the LR+ value from the reference source • Compute the pre-test odds for your population • Compute the post-test odds • Convert to posttest probability to get PPV April 3, 2008

  32. Converting odds to probabilities • Pre-test odds = prevalence/(1-prevalence) • if prevalence = 0.20, then pre-test odds = .20/0.80 = 0.25 • Post-test probability = post-test odds/(1+post-test odds) • if post-test odds = 0.25, then prob = .25/1.25 = 0.2 April 3, 2008

  33. Example of Bayes’s Theorem(prevalence 1%, sens 85%, spec 90%) • Pretest odds = .01/.99 = 0.0101 • LR+ = .85/.1 = 8.5 (>1, but not that great) • Positive Posttest odds = .0101*8.5 = .0859 • PPV = .0859/1.0859 = 0.079 = 7.9% • Compare to the ‘hypothetical table’ method (PPV=8%) April 3, 2008

  34. Calibration with Nomogram • Graphical approach avoids some arithmetic • Expresses prevalence and predictive values as probabilities (no need to convert to odds) • Draw lines from pretest probability (=prevalence) through likelihood ratios; extend to estimate posttest probabilities • Only useful if someone gives you the nomogram! April 3, 2008

  35. Example of Nomogram(pretest probability 1%, LR+ 45, LR– 0.102) 1% 45 31% .102 0.1% LR Pretest Prob. Posttest Prob. April 3, 2008

  36. INVESTIGATIONS (2) • “State the effect of demographic considerations on the sensitivity and specificity of diagnostic tests” • Generally, assumed to be constant. BUT….. • Sensitivity and specificity usually vary with severity of disease, and may vary with age and sex • Therefore, you can use sensitivity and specificity only if they were determined on patients similar to your own • Spectrum bias April 3, 2008

  37. The Government is extremely fond of amassing great quantities of statistics. These are raised to the nth degree, the cube roots are extracted, and the results are arranged into elaborate and impressive displays. What must be kept ever in mind, however, is that in every case, the figures are first put down by a village watchman, and he puts down anything he damn well pleases! Sir Josiah Stamp, Her Majesty’s Collector of Internal Revenue. April 3, 2008

  38. HEALTH PROMOTION & MAINTENANCE (1) • “Formulate preventive measures into their management strategies” • “Communicate with the patient, the patient’s family and concerned others with regard to risk factors and their modification where appropriate” • “Describe programs for the promotion of health including screening for, and the prevention of, illness” Covered in UTMCCQE and 077F (April 18) April 3, 2008

  39. Definitions of Health • A state of complete physical, mental and social well-being and not merely the absence of disease or infirmity. [The WHO, 1948] • A joyful attitude toward life and a cheerful acceptance of the responsibility that life puts upon the individual [Sigerist, 1941] • The ability to identify and to realize aspirations, to satisfy needs, and to change or cope with the environment. Health is therefore a resource for everyday life, not the objective of living. Health is a positive concept emphasizing social and personal resources, as well as physical capacities. (WHO Europe, 1986] April 3, 2008

  40. HEALTH PROMOTION • Distinct from disease prevention. • Focuses on ‘health’ rather than ‘illness’ • Broad perspective. Concerns a network of issues, not a single pathology. • Participatory approach. Requires active community involvement. • Partnerships with NGO’s, NPO’s, etc. April 3, 2008

  41. HEALTH PROMOTION • Ottawa Charter for Health Promotion (1996) • Five key pillars to action: • Build Healthy Public Policy • Create supportive environments • Strengthen community action • Develop personal skills • Reorient health services April 3, 2008

  42. HEALTH PROMOTION • Health Education • Health Belief model • Stages of Change model • Risk reduction strategies • Social Marketing • Healthy public policy • Tax policy to promote healthy behaviour • Anti-smoking laws, seatbelt laws • Affordable housing April 3, 2008

  43. HEALTH PROMOTION & MAINTENANCE (2)Illness Behaviour • “Describe the concept of illness behaviour and its influence on health care” • Utilization of curative services, coping mechanisms, change in daily activities • Patients may seek care early or may delay (avoidance, denial) • Adherence may increase or decrease April 3, 2008

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  47. CRITICAL APPRAISAL/ MEDICAL ECONOMICS (1) • “Evaluate scientific literature in order to critically assess the benefits and risks of current and proposed methods of investigation, treatment and prevention of illness” • Most will be covered in session on April 25 • UTMCCQE does not present hierarchy of evidence (e.g., as used by Task Force on Preventive Health Services) April 3, 2008

  48. Hierarchy of evidence(lowest to highest quality, approximately) • Expert opinion • Case report/series • Ecological (for individual-level exposures) • Cross-sectional • Case-Control • Historical Cohort • Prospective Cohort } often similar • Quasi-experimental } or identical • Experimental (Randomized) April 3, 2008

  49. CRITICAL APPRAISAL/ MEDICAL ECONOMICS (2) • “Define the socio-economic rationales, implications and consequences of medical care” • Medical care costs society financial and other resources. • This objective aims to raise awareness of these types of issues. April 3, 2008

  50. CRITICAL APPRAISAL/ MEDICAL ECONOMICS (2a) • Is there a net financial benefit from medical care? • How do we value non-fiscal benefits such as quality of life, ‘health’, not being dead? • Should resources be spent on health or other societal objectives? • How do we value non-traditional expenditures, etc which impact on health (Healthy Public Policy). April 3, 2008

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