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EBM Module 2: Measurement

This module covers measures used in epidemiology to describe disease frequency, population exposure, and association. Topics include prevalence, incidence, risk, rate, odds, and more to quantify disease occurrences. Understanding these measures is crucial in public health research and interventions.

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EBM Module 2: Measurement

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  1. EBM Module 2:Measurement

  2. Objectives • At the completion of this module participants should be able to: • To enumerate and explain the various measures used to describe the frequency of exposure or disease within a population (prevalence, risk, rate, odds) • To enumerate and explain the various measures that are used to compare the frequency of exposure and disease between two populations • To compare and contrast ‘difference’ and ‘ratio’ measures of association • To define the concepts of ‘number needed to treat’ and ‘number needed to harm’

  3. The 2 x 2 Table

  4. The 2 x 2 Table Exposure Variable

  5. The 2 x 2 Table Outcome Variable

  6. Variable Types • Continuous variables • A variable that may assume any value within an interval (e.g. age, height, blood pressure, etc.) • Discrete (categorical) variables • A variable that may assume only values within a discrete set • Dichotomous (yes/no) • Ordinal - multiple ordered categories (e.g. MRC muscle strength scale) • Nominal - multiple non-ordered categories (e.g. race)

  7. Measures of Frequency

  8. Measures of Frequency The frequency of a Good Outcome within the population may be expressed as (a+c)/(a+b+c+d). In our example, this is (239/319) = 0.75 This means 75% of our entire population had a good outcome.

  9. Measures of Frequency The frequency of an Exposure (e.g., steroids) within the population may be expressed as (a+b)/(a+b+c+d). In our example, this is (167/319) = 0.52 This means 52% of our entire population were given steroids.

  10. Measures of Frequency • Prevalence - the amount of disease already present in a population • Risk (cumulative incidence) - the likelihood or probability than an individual will contract disease in a specified time frame • Rate (incidence density) - the speed with which new occurrences of disease arise in a population • Odds - a ratio of the probability than an event occurs divided by the probability that the event does not occur

  11. Prevalence & Incidence • Prevalence and incidence are related concepts • They are connected by disease duration • Annual Incidence of ALS and MS - both ~2 per 100,000 • Prevalence of ALS ~6 per 100,000 • Prevalence of MS ~100 per 100,000

  12. Risk • Describes the probability of some event (cumulative incidence) • It is a measure of the occurrence of new cases in the population • Quantified by measuring the frequency with which unaffected people develop disease • Assumes a value from 0 to 1 • Requires definition of the relevant time period over which risk is quantified • Formally defined:

  13. Problems with Risk • Incident data is required • Necessary to follow subjects over a given period of time and to document the number of new instances of disease • Problem of the changing denominator due to competing risks and loss to follow-up

  14. Rate

  15. Rate • Another concept used to describe incidence (incidence density) • Describes the speed with which new cases develop • Rate is a measure of frequency that accommodates the problem of a changing denominator • In calculating rate, the numerator is the same as in calculating risk • The denominator is a composite of the number of subjects followed and the duration of time over which they are followed

  16. Rate

  17. Rate • Total follow-up time: Patient 1 = 5 years

  18. Rate • Total follow-up time: Patient 1 = 5 years • Total follow-up time: Patient 2 = 3 years

  19. Rate • Total follow-up time: Patient 1 = 5 years • Total follow-up time: Patient 2 = 3 years • Total follow-up time: Patient 3 = 4 years

  20. Rate • Total follow-up time: Patient 1 = 5 years • Total follow-up time: Patient 2 = 3 years • Total follow-up time: Patient 3 = 4 years • Total follow-up time: Patient 4 = 2 years

  21. Rate • Total follow-up time: Patient 1 = 5 years • Total follow-up time: Patient 2 = 3 years • Total follow-up time: Patient 3 = 4 years • Total follow-up time: Patient 4 = 2 years • Total follow-up time: Patient 5 = 3 years

  22. Rate • Total follow-up time: Patient 1 = 5 years • Total follow-up time: Patient 2 = 3 years • Total follow-up time: Patient 3 = 4 years • Total follow-up time: Patient 4 = 2 years • Total follow-up time: Patient 5 = 3 years • TOTAL PATIENT FOLLOW-UP: 17 YEARS • Thus, if 2 new cases develop during this time, then the incidence rate is 2 per 17 person-years • Alternately expressed as 0.11 per person-year or 11 per 100 person-years

  23. Rate • Assumes a value from 0 to infinity • Rate does NOT reflect the proportion of people who develop disease, but rather the speed with which new instances of disease accrue • The value rate assumes depends on the unit of time used in the denominator

  24. A Chicken and a Half If a chicken and a half lay an egg and a half in a day and a half, then how many eggs does a chicken lay in one day? This riddle is essentially a rate problem. See if you can answer the riddle.

  25. A Chicken and a Half Rate = Cases = Eggs = 1.5 = 0.67 eggs/chicken days Persons x Time Chickens x Days 1.5 x 1.5 • The number of eggs represents the number of cases (1.5) • The “person-time” at risk is represented by the 1.5 chickens who lay eggs over a period of 1.5 days (i.e. 1.5 x 1.5 chicken-egg laying days) • The rate of egg laying is 1.5 eggs / (1.5 x 1.5 chicken-egg laying days) = 0.67 • Therefore, a chicken laying eggs at this rate would lay two-third of an egg each day

  26. Odds

  27. Odds • What does it mean to talk of the “odds of polyneuropathy amongst those with a history of statin use”? • How is this different from the “risk of polyneuropathy …”? • Like risk and rate, odds is also a measure of frequency • Unintuitive because it is a ratio rather than a proportion

  28. Odds Probability of Disease (among exposed) = 20 / 25 = 0.8 (i.e., 80%) Probability of No Disease (among exposed) = 5 / 25 = 0.2 (i.e., 20%) Odds of Disease (among exposed) = 80% / 20% = 4

  29. Odds Also expressed as: Odds of Disease (among exposed) = 20 / 5 = 4

  30. Why the need for Odds ? • We are not always able to estimate risk • Measurement of risk requires incident data • When incident data is not available (e.g. case-control study), then we need to rely on odds as a surrogate measure for risk

  31. Measures of Association Risk of Disease (among exposed) = 20 / 25 = 0.8 (i.e., 80%)

  32. Measures of Association Risk of Disease (among non-exposed) = 40 / 100 = 0.4 (i.e., 40%)

  33. Measures of Association Relative Risk of Disease = 0.8 / 0.4 = 2 (i.e., 80%/40% = 2)) Risk Difference of Disease = 0.8 - 0.4 = 0.4 (i.e., 80% - 40% = 40%)

  34. Measures of Association • In order to quantify the determinants of disease we need tools to compare the frequency of exposure or disease between two populations • Two types of measures of association • Difference measures (additive scale) • Example 0.8 - 0.4 = 0.4 (or 80% - 40% = 40%) • Ratio measures (relative scale) • Example 0.8 / 0.4 = 2 (or 80% / 40% = 2)

  35. Risk: Relative and Absolute • This means, the risk of a vascular event over 3 years was: • 5.83% in the group treated with aspirin • 5.32% in the group treated with clopidogrel • Both of these risk measures required incident data (i.e., following patients over the 3 years and recording how many had a vascular event) • Note that the absolute risk reduction is modest: 5.83% - 5.32% = 0.51% • That means the absolute advantage of clopidogrel over aspirin was only 0.51%

  36. Risk: Relative and Absolute • However, the authors reported the relative risk difference: • Relative Risk Reduction = Risk (standard treatment group) – Risk (new treatment group) Risk (standard treatment group) • Presenting the results in terms of relative risk (an 8.7% reduction in this case) is misleading in that it gives the impression of a more marked difference in outcome between the two treatment groups.

  37. Risk: Relative and Absolute In the aspirin / clopidogrel study, the relative risk difference is: 5.32% - 5.83% 5.83% Presenting the results in terms of relative risk (an 8.7% reduction in this case) is misleading in that it gives the impression of a more marked difference in outcome between the two treatment groups. = 0.087 = 8.7%

  38. Number Needed to Treat / Harm (NNT / NNH) • Number needed to treat or harm • How many patients must I treat to see one better (or worse) outcome? • NNT = 1 / absolute risk difference • NNH is 1/absolute difference in risk of adverse event

  39. Number Needed to Treat / Harm (NNT / NNH) • Thus is the clopidogrel vs aspirin trial • The absolute risk difference of a benefit was 0.51% (0.0051) • NNT = 1 / absolute risk difference • NNT = 1 / 0.0051 = 200 • That means, one would need to treat 200 patients with clopidogrel as opposed to aspirin for 3 years to prevent one vascular event.

  40. Number Needed to Treat / Harm (NNT / NNH) • In the same study, the risk of GI hemorrhage was higher in the aspirin group: • 0.52% in the clopidogrel group • 0.72% in the aspirin group • The absolute risk difference was 0.20% (0.0020) • NNH = 1 / absolute risk difference • NNH = 1 / 0.0020 = 500 • That means, one would need to treat 500 patients with aspirin instead of clopidogrel for 3 years to cause one additional GI hemorrhage.

  41. Odds Ratio Odds ratio = Oddspopulation-1/Oddspopulation-2 • Since odds is the ratio of two probabilities, the odds ratio is a ratio of two ratios • The odds ratio and the risk ratio are related, but not the same • Under certain circumstances (when the outcome is rare), the odds ratio provides a close estimate of the risk ratio • Also known as the “cross-products” ratio

  42. Odds of Disease (exposed) = 50:50 = 1 Odds of Disease (not exposed) = 5:95 =0.0526 Odds Ratio = 19 Risk of Disease (exposed) = 50 / 100 = 0.5 Risk of Disease (not exposed) = 5 / 100 = 0.05 Risk Ratio = 10

  43. Odds Disease (exposed) = 5:95 = 0.0526 Odds Disease (not exposed) = 2:98 = 0.0204 Odds Ratio = 2.58 Risk Disease (exposed) = 5 / 100 = 0.05 Risk Disease (not exposed) = 2 / 100 = 0.02 Risk Ratio = 2.5

  44. Summary (1) • Prevalence describes the amount of exposure or disease in a population at a single time • Incidence (risk and rate) describes the occurrence of new disease in a population • Risk and Rate are appropriate measures only when incident data is available

  45. Summary (2) • Odds should be used when incident data is not available (e.g. case-control study) • For example, in module 1, we collected cases of Bell’s palsy from hospital records, and compared the group of patient who received steroids and those who did not. This is a case-control series. In a case control study, we do not know any incident data.

  46. Summary (3) • Risk ratio describes the relative difference in risk between 2 populations • Risk difference describes the difference in risk between 2 populations. Risk difference can be either absolute or relative. Using relative risk, although correct, may be misleading in that it gives the impression of a more marked difference in outcome between the two treatment groups • Odds ratio (a ratio of ratios) describes the relative difference in odds of exposure (or disease) between 2 populations

  47. References • A randomized, blinded, trial of clopidogrel versus aspirin in patients at risk of ischaemic events (CAPRIE). Lancet 1996 348:1329-1339 • The epidemiology of multiple sclerosis in Europe. Eur J Neurol 2006 13:700-722 • The worldwide prevalence of multiple sclerosis. Clin Neurol Neurosurg 2002 104:182-191 • Incidence of multiple sclerosis in the United Kingdom. J Neurol 2007 254:1736-1741 • Epidemiology of motor neuron disease in Northern Sweden. Acta Neurol Scand 1983 68:20-29 • The incidence and survival of amyotrophic lateral sclerosis in Saskatoon, Saskatchewan, Canada. Neurology 2007 69:1224-1229

  48. References • Epidemiological survey of amyotrophic lateral sclerosis in the province of Reggio Emilia, Italy: influence of environmental exposure to lead. Neuroepi 1996 15:301-312 • Medical Epidemiology (Lange Basic Science). Raymond Greenberg, Stephen Daniels, Dana Flanders and John Eley. McGraw Hill, 2001 • Epidemiology: An Introduction. Kenneth Rothman, Oxford University Press, 2002 • Higgins JPT, Green S (Editors). Cochrane Handbook for Systematic Reviews of Interventions 4.2.5 (updated May 2005). In The Cochrane Library, Issue 3, 2005, Chichester, UK. John Wiley & Sons Ltd (http://www.cochrane.dk/cochrane/handbook/hbook.htm)

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