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When we look at a single age-specific rate for a given year, we have no indication of the extent to which that rate reflects the influences of chronological age, calendar time-associated changes in the social and physical environment, or characteristics of the cohort that happens to be passing through that age during that year. Even if we look at a given age interval across a span of calendar years or at multiple ages in a given year, there is no way for us to know whether what appear to be changes associated with aging or the passage of time are really reflections of the characteristics of different cohorts (i.e., characteristics acquired due to environmental experiences at a formative period of life, such as exposure to lead in infancy or to radiation in adolescence). Attempts to disentangle the interwoven effects of age, secular time, and cohort are referred to as “age-period-cohort” analyses. The most straightforward approach involves assembling data from more than one period and from a broad range of ages, and then examining the data in relation to age, period, and cohort. For example: Age-period-cohort analysis of mean serum cholesterol (mg/dL, hypothetical data) 60-69 200 A 50-59 205 B 40-49 240 C 30-39 225 D 20-29 210 E Birth cohorts: 210 B 230 C 230 D 215 E 200 F 235 C 235 D 220 E 205 F 190 G 240 D 225 E 210 F 195 G 180 H 230 E 215 F 200 G 185 H 170 I 1950-59 1960-69 1970-79 1980-89 1990-96 A - 1890-1899 D - 1920-1929 G - 1950-1959 B - 1900-1909 E - 1930-1939 (underlined) H - 1960-1969 C - 1910-1919 F - 1940-1949 I - 1970-1979 From the columns (calendar decades), it appears that serum cholesterol increases by 15 mg/dL per decade of age. If we had only one calendar decade of data, this observation is all that we can make, leading us to overstate the relationship between age and cholesterol. With the full data, we can follow the birth cohorts longitudinally, which reveals that for a given cohort cholesterol rises by 5 mg/dL per decade of age, but that also each new cohort has 10 mg/dL lower average cholesterol than the previous one. This observation can be labeled a “cohort effect” and has the capability to confuse interpretation of cross-sectional (one point in time) data. (The reason that the 15 mg/dL increase does not continue at the older ages in the earlier decades is that I decided to precede the secular decline in cholesterol with a secular rise, so that the earliest cohorts had lower cholesterol levels than the ones that came afterwards.) _____________________________________________________________________________________________ www.epidemiolog.net, © Victor J. Schoenbach 1999, 2000 3. Studying populations - basic demography - 50 rev. 1/17/2000, 8/16/2000, 3/9/2001
Thought question: Professors typically comment that with each entering class (i.e., cohort), students seem to be younger. Is this an effect of age, secular time, or cohort? (See bottom of page for the answer.) Bibliography Colton, Theodore. in Statistics in Medicine, pp. 237-250. De Vita, Carol J. The United States at mid-decade. Population Bulletin, Vol. 50, No. 4 (Washington, D.C.: Population Reference Bureau, March 1996). Falkenmark, Malin and Carl Widstrand. Population and water resources: a delicate balance. Population Bulletin, Vol. 47, No. 3 (Washington, D.C.: Population Reference Bureau, November 1992). Lutz, Wolfgang. The future of world population. Population Bulletin, Vol. 49, No. 1, June 1994. McFalls, Joseph A., Jr. Population: a lively introduction. Population Bulletin, Vol. 46, No. 2 (Washington, D.C.: Population Reference Bureau, October 1991) McMichael, Anthony J. Planetary overload. Global environmental change and the health of the human species. NY, Cambridge, 1993. Mosley, W. Henry and Peter Cowley. The challenge of world health. Population Bulletin, Vol. 46, No. 4 (Washington, D.C.: Population Reference Bureau, December 1991). Petersen, William. Population 2 nd ed. Macmillan, London,1969 Remington, Richard D. and M. Anthony Schork. Statistics with applications to the biological and health sciences. Englewood Cliffs, NJ, Prentice-Hall, 1970. Roudi, Farzaneh. Iran's revolutionary approach to family planning. Population Today, July/August 1999; 27(7): 4-5. (Washington, D.C.: Population Reference Bureau) Web sites: Data and publications are now widely available over the World Wide Web. A list of useful sites (e.g., www.ameristat.org, www.cdc.gov and www.cdc.gov/nchswww, www.who.int) is available at www.sph.unc.edu/courses/epid168/. Answer: Age - the aging of the professors! _____________________________________________________________________________________________ www.epidemiolog.net, © Victor J. Schoenbach 1999, 2000 3. Studying populations - basic demography - 51 rev. 1/17/2000, 8/16/2000, 3/9/2001
Page 1 and 2: Understanding the Fundamentals of E
Page 3 and 4: Preface Introductory epidemiology c
Page 5 and 6: Table of Contents Chapter (in Acrob
Page 7 and 8: 1. Introduction Definition, charact
Page 9 and 10: problems (Schwartz and Carpenter, 1
Page 11 and 12: HIV - a new or newly-recognized vir
Page 13 and 14: d = principal investigator's degree
Page 15 and 16: Analytic studies typically involve
Page 17 and 18: Observational sciences especially a
Page 19 and 20: McGavran EG. What is public health?
Page 21 and 22: 2. An evolving historical perspecti
Page 23 and 24: Time line for the history of public
Page 25 and 26: as a one-room bacteriology laborato
Page 27 and 28: − Health surveys − Research fun
Page 29 and 30: his scientific peers of the correct
Page 31 and 32: Kuhn, Thomas S. The structure of sc
Page 33 and 34: 3. Studying populations - basic dem
Page 35 and 36: The demographic balancing equation
Page 37 and 38: ates. Similarly, bulges in the repr
Page 39 and 40: When the baby boom cohort retires 1
Page 41 and 42: The pervasiveness, strength, viciou
Page 43 and 44: Total fertility rate (TFR) Standard
Page 45 and 46: Life expectancy The technique, of u
Page 47 and 48: Excerpt from the U.S. 1993 abridged
Page 49 and 50: At the other end of the life table,
Page 51: to change, hopefully to decline. If
Page 55 and 56: Descriptive studies and surveillanc
Page 57 and 58: 4. The Phenomenon of Disease Concep
Page 59 and 60: Classification is the foundation As
Page 61 and 62: As we gain more sophisticated under
Page 63 and 64: To recapitulate the above discussio
Page 65 and 66: Classifying cause of death Since mo
Page 67 and 68: Laboratory and other objectively me
Page 69 and 70: distribution but are not diabetic (
Page 71 and 72: program in the general population,
Page 73 and 74: Infectious disease Incubation "time
Page 75 and 76: the direct effects of carcinogenic
Page 77 and 78: patient outcomes. The design and ev
Page 79 and 80: detected by screening will appear b
Page 81 and 82: Morrison, Alan S. Screening in chro
Page 83 and 84: e relevant. Compromises might be fo
Page 85 and 86: educational attainment by the numbe
Page 87 and 88: Distributions - the fuller picture
Page 89 and 90: Per capita income: Should health ca
Page 91 and 92: Deaths of children < 1 year of age
Page 93 and 94: No. of persons with senile dementia
Page 95 and 96: Incidence Immigration Relationship
Page 97 and 98: Examples: arthritis, cholelithiasis
Page 99 and 100: Cohort - entrance into the populati
Page 101 and 102: Sample calculation: 200 people free
Page 103 and 104:
• The units of time must be state
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Calculation of person-time in a coh
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Original Calculation of person-time
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average risk for a member of the co
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However: Nurses: 601 cases/89,538 w
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treated. If the rate at which patie
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Incidence and prevalence in a popul
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small prevalence. So if the seropre
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Appendix on weighted averages Becau
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Appendix on exponents and logarithm
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and epidemiology. The notation ln(x
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3. For the following hypothetical d
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c. ________________ 6 fatalities /
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Measuring disease and exposure - As
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5. a. Flow Diagram Population of si
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New cases Q ID = ------------------
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Overview 6. Standardization of rate
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differences between the groups in f
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∑ (stratum-specific rates × stan
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especially important when comparing
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copy this cell to the other columns
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where the RR k are the stratum-spec
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and the observed number of deaths i
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Directly standardized rate for A =
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5,022 Indirectly standardized = —
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the situation among older age group
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Appendix on Standardized Mortality
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across age strata, then the damage
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d. How would you feel about the con
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Cases per 100,000 Mean annual incid
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d. dt Indirectly standardized rates
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The second ingredient for an standa
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Group US rate /100,000 County pop.
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Crude rates are comparable because
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The “Big Picture” 7. Relating r
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CHD and oral contraceptives in Brea
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Some basic measures Before diving i
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esophageal cancer? First, if we did
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ought in here as well. When we cont
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Exposure Disease Yes No Total Yes a
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Relation of the odds ratio to the r
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the case-control study estimates th
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If the data had come from a cross-s
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Excess risk = CIR - 1 = 1.0 (i.e.,
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strength of the relationship (e.g.,
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drawn on ordinary graph paper. Howe
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Measures of Impact Concept Relative
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Attributable risk proportion The AR
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show that a large proportion (i.e.,
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Incidence Diagrammatic representati
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1. high frequency of disease 2. pow
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With this diagram and notation we c
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vaguer) “attributed to”. Incide
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Summary There are three categories
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CIR can be directly estimated if th
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I0 I = ——————— 1 + P1
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PD|e PE (RR - 1) PD|e PE (RR - 1) P
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Walter, Stephen D. Choice of effect
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a. Quit rates were measured as the
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1. Definitions: Relating risk facto
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There is an extremely strong associ
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8. Analytic study designs The archi
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Progression of types of studies In
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Cross-sectional A cross-sectional s
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Follow-up studies Along with case-c
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of interest community-level influen
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ecological approach may be the appr
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What measures can be estimated from
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Ability to estimate risk Ascertainm
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individuals, this relationship does
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Odds Ratio (OR) Exposure odds in ca
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Simple randomization - assignment o
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The relationship of an attribute to
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(a / m1) / (b / m1) ad ORe (Exposur
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Validity The validity of a case-con
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Since the strategic advantage of th
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c = f0N1 = number of exposed noncas
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experienced the event under study.
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approach, the odds ratio computatio
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Exposure Yes No Total Cases A B M1
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Miettinen, Olli S. The clinical tri
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Mantel, N. and Haenszel, W.: Statis
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Peto, R., Pike, M.C., Armitage, P.,
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4. The authors state that "There we
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5. In contrast to many earlier inve
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In this study the use of hospitaliz
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Follow-up must be longer for any gi
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While acknowledging the very import
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world. Even as late as the 1950's,
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Causal Inference Direct observation
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data on the effects of radiation on
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But excluding an explanation based
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Strength of the association Pronou
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Experimental evidence Certain types
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To issue an indictment, the grand j
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10. Sources of error A systematic f
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the data in the table. (Note that t
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associations. So the same terms may
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"Negative bias" - The observed meas
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Classifying sources of bias Inspite
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The study population is a subset of
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This estimate of the prevalence of
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unmatched control groups will presu
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The odds ratio for this table is 3.
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Selection bias in cohort studies Se
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Information bias Information bias r
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following chapter). The concept of
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P h y s i c i a n A Comparison of d
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item). Alpha values of 0.80 are are
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Hypothetical scenario showing effec
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presumably classified as informatio
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Appendix 1 Formulas to see the effe
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Algebraically, _ Pr(E|D') = — the
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Gregorio, David L.; James R. Marsha
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Sources of error - Assignment 1. Th
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Alpha (α) is the probability by wh
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Sources of error - Assignment solut
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3. ii) Selective survival--if OC us
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a. Flow diagram: 1960-1962 1967-196
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Introduction 11. Multicausality: Co
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estimate of what the outcomes would
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A hypothetical example (with apolog
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Questions to ask: There are many as
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Confounding arises from unequal dis
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CHD Incidence by Behavior Pattern a
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In this way, the 1,129 Type A's wit
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exposure. If we assume a baseline r
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Further insight can be gained by co
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Table 7a Colon cancer and drinking
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atio of the observed number of expo
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ural-specific exposure prevalences,
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eing infected, and the more of them
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studies deal with a delimited geogr
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University of Massachusetts at Amhe
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similarly distributed in cases and
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"There have been many important ste
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MAIN POINTS Confounding is a disto
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Greenland, S. and Neutra R.: Contro
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Appendix The following discussion i
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i n1i wi = ----- If exposure is unr
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Multicausality: Confounding - Assig
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4. The following table, published i
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1. Multicausality: Confounding - As
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link between HCS and MI to ensure t
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Multicausality 12. Multicausality:
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Interdependent effects The precedin
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epidemiologic data. Put simply, the
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BP 0 These two lines are parallel;
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ean intake and B representing genet
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Fourth, proportion of disease attri
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Incidence and incidence ratios of f
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% with evidence of impaired red blo
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cancer rate among the light smokers
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Age standardized lung cancer rates
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R11 - R00 = R10 - R00 + R01 - R00 (
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and R111 = R100 × R010 × R001 / (
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R110 = R100 × R010 / R000 (multipl
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Risk ln(risk) 0.19 -1.67 0.13 -2.01
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have clinical or public health sign
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Baseline Characteristics Associated
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Weiss, Noel S. Accounting for the m
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A. Synergism apparently exists in t
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5. Walker (International Journal of
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(or equivalently, the rate for pers
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impact. Synergism in this sense imp
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13. Multicausality - analysis appro
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very far on instruments but needs t
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Summarizing the relationships Often
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We saw in the chapter on effect mod
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So the multiplicative model for joi
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misleading summary. These circumsta
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w1 CID1 + w2 CID2 Summary CID = —
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c d bcn0 + adn1 Var(ln(CIR)) ≈
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Kleinbaum, Kupper, and Morgenstern;
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1 + 1 + 1 3 ORMH = —————
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Risk = R00 + Obesity effect Obesity
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two (or more) factors that "interac
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OR = exp(β3) β3 is the difference
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Other regression models [Optional f
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Know the epidemiologic meaning of t
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14. Data analysis and interpretatio
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and questionable data. Monitoring p
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presence of particular proteins (e.
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4. Count - the number of entities,
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Algorithms - A procedure that uses
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Imputation causes the least distort
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Prenatal exposure to diethylstilbes
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Possible outcomes (Colors of pairs
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In a strict decision-making mode, t
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C = area between -z and +z D = area
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two-tailed 5% significance level).
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If the p-value is not small (i.e.,
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OR we observed was greater than 1.0
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Large trials (e.g., 250 deaths) Tru
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The concept behind the confidence i
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Formulas for confidence intervals f
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Woolf SH, Battista RN, Anderson GM,
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Stallones, Reuel A. The use and abu
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C - Clustered observations, which d
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For example, suppose that n, the sa
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* See previous table Margin of erro
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Even in randomized trials problems
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Pr(H|T) is therefore a function of
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That information gives you pause. C
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Part II 1. In table 2 from the Rose
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If the OR for Current OC use were t
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6. There are no interaction (produc
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24. Submit manuscript from previous
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Cooperative agreement A cooperative
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Assurances by the applicant and her
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The new element is the grant award.
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1. What needs to be done? 2. How lo
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Postaward Final report What to do
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2. maintain close supervision of th
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Publication Epidemiology Working Gr
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Weed, Douglas L. Between science an
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1.3 Specific Objectives of Data Man
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engender a basic conflict between t
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1.4.2 Integration Integrate the dat
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2. Consistency of the protocol's im
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2. Coding of a sample of data forms
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2.3 The steps in the Editing proces
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Although it is rare to use more tha
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The first stages of data analysis s
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have less power to exclude the stat
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greater nonresponse by heavy drinke
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Imputation is then carried out as f
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(normal) distribution in the popula
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Appendix **************************
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___________________________________
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Public health approach The public h
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health, particularly at the world l
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Causative or preventive agents Scur
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Human behavior is also biology The
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crowded, under-resourced urbanized
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Bibliography Annas, George J.; Leon
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18. Overview and Conclusion A look
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Epidemiology is expanding In recen
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More reliance on "soft money" - res
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__________ This is not a new challe
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