Understanding the Fundamentals of Epidemiology an evolving text

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Bibliography Rothman, Kenneth - Modern Epidemiology, Chapter 5 and pages 227-2290. Lilienfeld and Lilienfeld - Foundations of epidemiology, Measures of mortality, pp. 71-80; Mausner & Kramer - Epidemiology: an introductory text, pp. 338-344. Inskip, Hazel; Valerie Beral, and Patricia Fraser. Methods for age adjustment of rates. Statistics in Medicine 1983; 2:455-4660. Gaffey WR: A critique of the standardized mortality ratio. J Occup Med 18:157-160, 1976. Schoenborn, Charlotte; Richard Klein, Virginia Fried. Age adjustment of National Center for Health Statistics data using the 2000 projected U.S. population with emphasis on survey data systems. Division of Health Interview Statistics, NCHS/CDC. To appear in the Healthy People Statistical Note series (http://www.cdc.gov/nchs/products/pubs/workpap/ageadjust.htm, 9/10/00). Thomas, James C.; Alice L. Kulik, Victor J. Schoenbach. Syphilis in the South: rural rates surpass urban rates in North Carolina. Am J Public Health 1995; 85:1119-1122 Tsai SP, Hardy RJ, Wen CP. The standardized mortality ratio and life expectancy. Am J Epidemiol 1992; 135:824-831. _____________________________________________________________________________________________ www.sph.unc.edu/courses/EPID168, © Victor J. Schoenbach 1999 6. Standardization - 148 rev. 9/14/1999, 12/22/1999, 1/13/2000, 9/10/2000
Appendix on Standardized Mortality Ratios (courtesy of Raymond Greenberg, M.D.,Ph.D. I. DEFINITION. The Standardized Mortality Ratio (SMR) is a measure of mortality in a study population, relative to mortality in a reference population. The SMR answers the following question: "How does the number of observed deaths compare with the expected number of deaths, if our study group had the age-specific mortality rates of the reference population during these study years?" II. CALCULATION. In principle, any reference population yielding sufficiently precise rates can be used to obtain the expected death number, but it is customary to use the general population. The SMR is given by the following expression: Observed deaths in study population SMR = ——————————————— Expected deaths in study population The SMR is usually scaled up by multiplying it by 100. An SMR over 100 indicates that more deaths were observed than expected (i.e., the study population had a relatively poor outcome). An SMR less than 100 means that fewer deaths were observed than expected (i.e., the study population had a relatively favorable outcome). Obviously, the value of the SMR will depend on the choice of the reference population used for the comparison mortality rates. If the reference population is healthy, they will have low mortality rates and thereby increase the SMR. Conversely, if the reference population is unhealthy, they will have high mortality rates and thereby decrease the SMR. It is therefore crucial to choose an appropriate reference population or at least to know in which direction the reference population differs from an appropriate one. III. HEALTHY WORKER EFFECT. The SMR is frequently used to examine mortality in an industrial plant or industry. However, when workers are compared to the general population, it is common to find lower mortality rates in the workers (SMR less than 100). The reason is thought to be that the general population includes people who are too sick to work. The elevated mortality in such people raises the mortality rate of the general population, so that mortality in the general worker population is lower. This phenomenon is called the healthy worker effect. The healthy worker effect is an important consideration primarily for mortality from diseases, such as cardiovascular disease, where an extended period of physical limitation or disability frequently precedes death and thus affects entrance into and remaining in the workforce. _____________________________________________________________________________________________ www.sph.unc.edu/courses/EPID168, © Victor J. Schoenbach 1999 6. Standardization - 149 rev. 9/14/1999, 12/22/1999, 1/13/2000, 9/10/2000
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Understanding the Fundamentals of E
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Preface Introductory epidemiology c
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Table of Contents Chapter (in Acrob
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1. Introduction Definition, charact
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problems (Schwartz and Carpenter, 1
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HIV - a new or newly-recognized vir
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d = principal investigator's degree
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Analytic studies typically involve
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Observational sciences especially a
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McGavran EG. What is public health?
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2. An evolving historical perspecti
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Time line for the history of public
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as a one-room bacteriology laborato
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− Health surveys − Research fun
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his scientific peers of the correct
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Kuhn, Thomas S. The structure of sc
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3. Studying populations - basic dem
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The demographic balancing equation
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ates. Similarly, bulges in the repr
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When the baby boom cohort retires 1
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The pervasiveness, strength, viciou
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Total fertility rate (TFR) Standard
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Life expectancy The technique, of u
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Excerpt from the U.S. 1993 abridged
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At the other end of the life table,
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to change, hopefully to decline. If
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Thought question: Professors typica
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Descriptive studies and surveillanc
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4. The Phenomenon of Disease Concep
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Classification is the foundation As
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As we gain more sophisticated under
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To recapitulate the above discussio
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Classifying cause of death Since mo
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Laboratory and other objectively me
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distribution but are not diabetic (
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program in the general population,
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Infectious disease Incubation "time
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the direct effects of carcinogenic
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patient outcomes. The design and ev
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detected by screening will appear b
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Morrison, Alan S. Screening in chro
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e relevant. Compromises might be fo
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educational attainment by the numbe
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Distributions - the fuller picture
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Per capita income: Should health ca
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Deaths of children < 1 year of age
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No. of persons with senile dementia
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Incidence Immigration Relationship
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Examples: arthritis, cholelithiasis
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Cohort - entrance into the populati
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Sample calculation: 200 people free
Page 103 and 104: • The units of time must be state
Page 105 and 106: Calculation of person-time in a coh
Page 107 and 108: Original Calculation of person-time
Page 109 and 110: average risk for a member of the co
Page 111 and 112: However: Nurses: 601 cases/89,538 w
Page 113 and 114: treated. If the rate at which patie
Page 115 and 116: Incidence and prevalence in a popul
Page 117 and 118: small prevalence. So if the seropre
Page 119 and 120: Appendix on weighted averages Becau
Page 121 and 122: Appendix on exponents and logarithm
Page 123 and 124: and epidemiology. The notation ln(x
Page 125 and 126: 3. For the following hypothetical d
Page 127 and 128: c. ________________ 6 fatalities /
Page 129 and 130: Measuring disease and exposure - As
Page 131 and 132: 5. a. Flow Diagram Population of si
Page 133 and 134: New cases Q ID = ------------------
Page 135 and 136: Overview 6. Standardization of rate
Page 137 and 138: differences between the groups in f
Page 139 and 140: ∑ (stratum-specific rates �× s
Page 141 and 142: especially important when comparing
Page 143 and 144: copy this cell to the other columns
Page 145 and 146: where the RR k are the stratum-spec
Page 147 and 148: and the observed number of deaths i
Page 149 and 150: Directly standardized rate for A =
Page 151 and 152: 5,022 Indirectly standardized = —
Page 153: the situation among older age group
Page 157 and 158: across age strata, then the damage
Page 159 and 160: d. How would you feel about the con
Page 161 and 162: Cases per 100,000 Mean annual incid
Page 163 and 164: d. dt Indirectly standardized rates
Page 165 and 166: The second ingredient for an standa
Page 167 and 168: Group US rate /100,000 County pop.
Page 169 and 170: Crude rates are comparable because
Page 171 and 172: The "Big Picture" 7. Relating risk
Page 173 and 174: Consider first the table on the lef
Page 175 and 176: The epidemiologist first arranges t
Page 177 and 178: (I1 - I0)/I0 = (0.01200-0.00800 / 0
Page 179 and 180: Descriptive adjectives for magnitud
Page 181 and 182: Odds ratio The odds ratio (OR) is a
Page 183 and 184: Duncan C. Thomas, "On the need for
Page 185 and 186: 6. Odds of exposure in controls Pro
Page 187 and 188: CID = CI1 - CI0 = .20 - .10 = .10 (
Page 189 and 190: When both disease and exposure have
Page 191 and 192: variables is linear. An r of zero m
Page 193 and 194: 2. In HIV-discordant couples in whi
Page 195 and 196: Incidence Diagrammatic representati
Page 197 and 198: PAR = AR × P1 = (I1 - I0)P1 = I -
Page 199 and 200: expresses the prevented cases as a
Page 201 and 202: Incidence Diagrammatic representati
Page 203 and 204: The result, (NHIH)/(NLIL+NHIH), is
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Proportion with = 0.5 ( 22 --- ) +
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CI may be preferred: � if the hea
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Appendix — Relating risk factors
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By definition, oddsr = risk / (1 -
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Bibliography Measures of effect: Te
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Relating risk factors to health - A
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4. In a study of adverse effects of
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number of "permanent" quits (define
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= I1n1- I0n1 + I0n0 - I0n0 ——
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2. Imprecision in determining the t
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Sometimes the appearance of a new s
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Case-control (case-referent, etc.)
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ealistic setting and results may th
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"ecologic fallacy", the erroneous i
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can vary substantially among indivi
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e estimated from that study since t
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Risk ratio Consider the example of
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Dietary Salt Intake and hypertensio
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Intervention trials can include tes
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Treatment allocation can be 1) fixe
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Example Cases Case-control studies
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Cases Controls ____________________
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population and that the controls we
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Estrogen users (N1) Nonusers (N0) N
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In the 3 × 10 6 unexposed women-ye
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similar to that in the case group t
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A modern perspective In general, de
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of exposed to unexposed persons sam
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Case-control studies: Armenian, Har
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Wingo, Phyllis A.; Howard W. Ory, P
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Part I Analytic study designs - Ass
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1. Doll and Hill state (page 1451,
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Part I Analytic study designs - Ass
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� Possible motivation to change b
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9. Causal inference * The desire to
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1955:35). Quarantine of sick person
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The mother who replaced the burned-
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As presented in Kleinbaum, Kupper,
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Popper maintains that it is fundame
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1. Is the association real or artef
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Temporality First exposure, then di
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confer upon us a freedom to ignore
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Lave, Lester B.; E.P. Seskin. Epide
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Precision The presence of random va
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the point estimate is based and thu
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methodological literature. Validity
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disease detection may be mistakenly
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In cohort studies, the primary sour
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present, then the D&C would detect
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Controls in an experiment In a true
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Southern endometrial cancer and est
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Cases ` The ` "detection bias" hypo
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from ETS, then a cohort study of ha
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employing lead exposure measures fr
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which the both measures increase in
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coefficient (slope) is to 1.0 and t
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_ _ Se = Pr(D'|D) Sp = PR(D'|D) _ _
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Differential misclassification, how
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subject plus or minus a random nois
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Appendix 2 More on the concern to a
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Bibliography Henneckens and Buring.
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Sackett, D.L.: Bias in analytic res
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e. OPTIONAL: If the reported cumula
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Results from the Evans County Cardi
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2. I1 = incidence in the exposed P1
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makes most sense to view detection
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Sensitivity × cases + (1 - Specifi
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In the factual world, however, we c
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Hypertensive SI participants were t
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3,154 male managers and white colla
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Table 2 Relationship of CHD to Beha
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a group did indeed have higher CHD
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Confounding - comparison of weighte
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Type A smokers) are not very meanin
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Different control groups for hypoth
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Confounding, then, is a function of
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Since the OR is the crossproduct ra
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Case-control study In a case-contro
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First and foremost, a potential con
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is defined in relation to the count
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poor. A scientific basis linking ra
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One way to obtain a better balance
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Stratified analysis is intuitively
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attributed to the exposure of inter
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Bibliography Rothman and Greenland
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Senior P, Bhopal RS. Ethnicity as a
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Some of the relationships just pres
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n1i _______________________________
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2. A 20-year retrospective cohort s
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No. of urinary tract infections Tab
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2. completely attributable to obesi
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5. a. C -- the adjusted relative ri
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of dietary antioxidants. However, c
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epidemiologic research is to identi
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Since the indicator variable (POP)
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Over two decades ago (Causes, Am J
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In this diagram, G and H exhibit ab
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In this situation, the first suffic
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Crude rates as weighted averages Re
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Lung cancer rates by smoking and as
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{B0} Smk Smk Asb. Asb. {B1} {B2} {B
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Notation for joint effects R1 risk
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and R111 = R000 + RD100 + RD010 + R
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RR11 = RR10 × RR01 [End of aside]
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multiplicative model supports the u
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Effect modification as a reflection
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4. Defining subgroups for preventiv
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Bibliography Rothman and Greenland,
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Multicausality: Effect modification
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4. Several studies have shown a syn
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1. Multicausality: Effect modificat
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In these data, or, Expected ROC,SMK
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to drinking in the absence of smoki
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we determine that confounding is pr
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Association between reserpine and b
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R11 R10 × R01 R10 —— = ——
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Hypothetical data on incidence of m
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1. Data conform to an additive mode
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Difference measure - the CID The va
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Distribution of CIR, IDR, OR 0 +1 M
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1 wi = ---------------------------
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5 Y n B 3 n Y C 8 n Y C 4 Y Y D 7 Y
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R11 - R00 = (R10 - R00) + (R01 - R0
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You may well wonder what is the val
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Moreover, the logistic model, we wi
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Key epidemiologic assumptions in th
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a + b1X1 + b2X2 + b3X3 + …) on th
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Wilcosky, Timothy C. and Lloyd E. C
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Data analysis and interpretation Ep
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Range checks Range checks compare e
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� Decisions deferred have to be m
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� Simpler is better � Avoid ext
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educe bias and tend to introduce ad
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data. Perhaps too often generalitie
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When this "analogous experiment" is
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tests are based (e.g., see Sander G
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The probability associated with bei
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conclude that the association is su
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Exposed to diethylstilbesterol? Yes
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For example, suppose that we are co
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The idea behind small studies bias
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Confidence intervals address the qu
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Computing a confidence interval for
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Bibliography General Ahlbom, Anders
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Thompson, W. Douglas. Statistical c
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Appendix Estimating sample size to
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p1 (1 - p1) p2 (1 - p2) σ 2 1 σ 2
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Margin of error (rounded) Point Sam
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Meditations on hypothesis testing a
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Independent replication is the corn
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More meditations on interpreting st
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Part I Data analysis and interpreta
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Part I 1. (a) Restriction Data anal
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9. ORCIG,HYP,OC [multiplicative mod
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15. Practical aspects of epidemiolo
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(CDC, which includes the National I
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cardiovascular area -- notably the
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Disease Control (CDC) decided again
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After submission but before the awa
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Journal of Medicine, Jan 14, 1999,
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Explore the data � Look at data (
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Recently, standardization has come
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Riesenberg, Don; Goerge D. Lundberg
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16. Data management and data analys
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implications, or suspicions or char
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1.4.1.2 Consistency Consistency is
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1.4.5.1.2 Series Runs in series are
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Obviously it will be easier to asse
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2.1.2.1 Statistical There are forma
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2.3.5 Bivariate displays If the sam
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errors. Related to this concern is
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3.3 Expected values Perhaps, the si
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Despite the best efforts, however,
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Complete data cases % distribution
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3.5 Outliers Outliers are now exami
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Bibliography Davidson, Fred. Princi
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attrib ckd length=$1 label='Check d
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17. Epidemiology and public health
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� Geneva - " . . . IFV and allied
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public health, since the best oppor
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Individual-level versus societal le
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14%, so that the burden of the envi
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1. exhausting various non-renewable
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O'Hare WP, Pollard KM, Mann TL, Ken
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� Health for all What contributes
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Challenges in the environment for e
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� What kind of population is requ
Page 579:
Bibliography American College of Ep
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