Epidemiologic Formulas and Terminology Cheat Sheet, Cheat Sheet of Epidemiology

Download Epidemiologic Formulas and Terminology Cheat Sheet and more Cheat Sheet Epidemiology in PDF only on Docsity! Page 1 Epidemiologic Formulas and Terminology Epidemiologic terminology is far from uniform. The chart clarifies some of the terms used in the course. Measure Synonyms (or nearly so) Comment Prevalence Prevalence “rate” (misnomer) Proportion of people with disease at a point in time. Risk Cumulative Incidence Incidence Proportion Probability of Disease Number of disease onsets divided by the number of people exposed to risk. Rate Incidence Density Incidence Rate Central Rate Hazard Rate Force of morbidity / mortality Number of disease onsets divided by sum of person- time. Risk (or Rate) Ratio Relative Risk Incidence Ratio Cumulative Incidence Ratio Incidence Density Ratio Hazard Ratio Ratio of two risks or rates. Provides a relative measure of the effect of the exposure. Risk (or Rate) Difference Cumulative Incidence Difference Incidence Density Difference Difference of two risks or rates. Provides an absolute measure of the effect of the exposure. Attributable Fraction in the Population Etiologic Fraction, Population Expected % reduction in number cases following elimination of the exposure in population. Attributable Fraction in Exposed Cases Etiologic Fraction, Exposed Cases Expected % reduction in number cases following elimination of the exposure in exposed case. Odds ratio Exposure odds ratio Use primarily restricted to case-control studies. Also used in logistic models. Provides an estimate of the rate ratio. Page 2 Risk = Cumulativ e Incidence = no. of disease onsets size of population initially exposed to risk Rate = Incidence density = no. of disease onsets sum of person-time no. of disease onsets≅ ⋅N t∆ Prevalence = no. of existing cases on a specific date no. of people in the population on this date Birth rate per = no. of births average population size m m× Crude death rate per = no. of deaths avg. population size m m× Infant mortality rate per = no. of deaths < 1 yr of age no. of live births m m× Age -specific death rate per = no. of deaths in age group no. of people in age group m m× Chapter 6: Incidence and Prevalence Basics where represents the average (“central”) population at risk and ∆t represents the time of observations (e.g., a one-yearN study). Examples of Specific “Rates” where m is a population multiplier (e.g., per 1000 individuals). Page 5 AF R R Re = −1 0 1 AF R R Rp = − 0 Chapter 8, continued The attributable fraction in the population is: This quantifies the expected proportional reduction in risk if the exposure were eliminated from the population. For example, a population attributable fraction of 50% suggests that eliminating the exposure from the population would eliminate half the cases. The attributable fraction in exposed cases is: This quantifies the expected proportional reduction in cases had the exposed cases not been exposed (it is a liability measure). For example, an attributable fraction in exposed cases of 75% would suggest that three-quarters of the exposed cases would have been avoided had they not been exposed. Page 6 OR p p p p a m b m c m d m a b c d ad bc = − − = = =1 1 0 0 1 1 0 0 1 1 / ( ) / ( ) ( / ) / ( / ) ( / ) / ( / ) / / AFp p OR p OR = − − + 0 0 1 1 1 ( ) ( ) AF OR OR e = −1 Chapter 9: Case-Control Studies Notation for 2-by-2 Cross-Tabulations: Exposed Not Exposed Cases a b m1 Controls c d m0 n1 n0 n The case-control method precludes absolute direct estimation of risk, but allows risk to be estimated in relative terms through a statistic known as the odds ratio: This statistic is equivalent to a rate ratio from a cohort study when density sampling. Therefore, the odds ratio is a measure of relative incidence (not unlike the risk ratio). Thus, an odds ratio of 1 indicates no association between the exposure and disease, an odds ratio of 2 indicates a doubling of the rate, and so on. For example, as case-control study with the following data: Exposed+ Exposed- Case 647 2 Cntl 622 27 has OR =(647)(27)/(622)(2) = 14.0. This indicates that the exposed group has a rate of disease that is 14 times that of the unexposed group (equivalently, a 1300% increase in risk). Attributable fractions in exposed cases can be determined from case-control studies as: For example, when the OR = 14.0, AFe = (14.0 – 1)/(14.0) = .929. The attributable fraction in the population is where p0 represent the exposure proportion in controls, which is equal to p0 = c / m0. For the above data, p0 = 622 / 649 = .9584 and AFp = [(.9584)(14.0 – 1)] / [(.958)(14.0 – 1) + 1] = .926. Page 7 ê ' 2(ad &bc) p1q2% p2q1 Chapter 4: Reproducibility and Validity Reproducibility Statistics Rater B Rater A + ! + a b p1 ! c d q1 p2 q2 N Overall agreement = (a + d) / N Agreement in Subjects w/ At Least One Positive Diagnosis = a / (a + b + c) Kappa Iinterpreting ê: ê . 0 indicates random agreement; ê < .4 represents poor agreement; .4 # ê < .7 represents moderate agreement; ê > .7 represents excellent agreement; ê = 1 indicates perfect agreement. Validity Statistics Disease + Disease ! Test + True Positives (TP) False Positive (FP) n1 Test ! False Negative (FN) True Negative (TN) n2 m1 m2 N SENsitivity = TP / m1 Note: TP = (SEN)(m1) SPECificity = TN / m2 Note: TN = (SPEC)(m2) PVP = (TP) / n1 Bayesian: PVP ' (P)(SEN) (P)(SEN) % (1&SPEC)(1&P) PVN = (TN) / n2 Baysian: PVN ' (1&P)(SPEC) (1&P)(SPEC) % (1&SEN)(P) P = m1 / N where P represents the true prevalence of disease. This allows us to calculate the number of [true] cases, m1 = (P)(N) P* = n1 / N where P* represents the apparent prevalence of disease