Understanding the Fundamentals of Epidemiology an evolving text

More documents

Recommendations

Info

Calculation of total fertility rate (TFR) For 1000 women from age 15 through age 45 years Age Births 15 110 16 110 17 110 (average annual fertility 18 110 from ages 15-19 = 110/1000) 19 110 20 180 21 180 22 180 (average annual fertility . . . from ages 20-29 = 180/1000) 29 180 30 80 31 80 (average annual fertility . . . from ages 30-45 = 80/1000) 44 80 45 80 ———— 3,630 or about 3.6 children born to each woman. (This TFR could also be calculated more compactly as 110 x 5 + 180 x 10 + 80 x 16 = 3,630) Note that the TFR is a hypothetical measure based on the assumption that the age-specific fertility rates do not change until the cohort has aged beyond them. The TFR is a projection, not a prediction—essentially, a technique for summarizing a set of age-specific rates into an intuitively meaningful number. _____________________________________________________________________________________________ www.epidemiolog.net, © Victor J. Schoenbach 1999, 2000 3. Studying populations - basic demography - 42 rev. 1/17/2000, 8/16/2000, 3/9/2001
Life expectancy The technique, of using current data for people across a range of ages to project what will happen to a person or population who will be passing through those ages, is also the basis for a widely-cited summary measure, life expectancy. Life expectancy is the average number of years still to be lived by a group of people at birth or at some specified age. Although it pretends to foretell the future, life expectancy is essentially a way of summarizing of a set of age-specific death rates. It thus provides a convenient indicator of the level of public health in a population and also a basis for setting life insurance premiums and annuity payments. In order to understand life expectancy and TFR's, it is important to appreciate the difference between these demographic summary measures and actual predictions. A prediction involves judgment about what will happen in the future. Life expectancy and TFR’s are simply ways of presenting the current experience of a population. Thus, my prediction is most of us will live beyond our life expectancy! The explanation for this apparent paradox is that life expectancy is a representation of age-specific death rates as they are at the present time. If age-specific death rates do not change during the rest of our lives, then our life expectancy today will be an excellent estimate of the average number of years we will live. However, how likely are today’s age-specific death rates to remain constant? First, we can anticipate improvements in knowledge about health, medical care technology, and conditions of living to bring about reductions in death rates. Second, today’s death rates for 40-90 year-olds represent the experience of people who were born during about 1900-1960. Today’s over-forties Americans lived through some or all of the Great Depression of the 1930s, two world wars, the Korean War, the Vietnam War, atmospheric nuclear bomb testing, unrestrained DDT use, pre-vaccine levels of mumps, polio, measles, rubella, chicken pox, pre-antibiotic levels of mycobacterium tuberculosis, syphilis, and other now-treatable diseases, varying levels of exposure to noxious environmental and workplace substances, a system of legally-enforced apartheid in much of the nation, limited availability of family planning, and lower general knowledge about health promotive practices, to list just a smattering of the events and conditions that may have affected subsequent health and mortality. Although changes in living conditions are not always for the better (death rates in Russia and some other countries of the former Soviet Union have worsened considerably since the breakup), the United States, Western Europe, Japan, and many countries in the developing world can expect that tomorrow’s elderly will be healthier and longer-lived than the elderly of the previous generation. For these reasons life expectancy, computed from today’s agespecific death rates, probably underestimates the average length of life remaining to those of us alive today. Since it is a summary of a set of age-specific mortality rates, life expectancy can be computed from any particular age forward. Life expectancy at birth summarizes mortality rates across all ages. Life expectancy from age 65 summarizes mortality rates following the conventional age of retirement. Accordingly, life expectancy at birth can be greatly influenced by changes in infant mortality and child survival. The reason is that reductions in early life mortality typically add many more years of life than reductions in mortality rates for the elderly. The importance of knowing the age from which life expectancy is being computed is illustrated by the following excerpt from a column _____________________________________________________________________________________________ www.epidemiolog.net, © Victor J. Schoenbach 1999, 2000 3. Studying populations - basic demography - 43 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: Total fertility rate (TFR) Standard
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 and 52: to change, hopefully to decline. If
Page 53 and 54: Thought question: Professors typica
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
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 and 154:
the situation among older age group
Page 155 and 156:
Appendix on Standardized Mortality
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
Page 205 and 206:
Proportion with = 0.5 ( 22 --- ) +
Page 207 and 208:
CI may be preferred: � if the hea
Page 209 and 210:
Appendix — Relating risk factors
Page 211 and 212:
By definition, oddsr = risk / (1 -
Page 213 and 214:
Bibliography Measures of effect: Te
Page 215 and 216:
Relating risk factors to health - A
Page 217 and 218:
4. In a study of adverse effects of
Page 219 and 220:
number of "permanent" quits (define
Page 221 and 222:
= I1n1- I0n1 + I0n0 - I0n0 ——
Page 223 and 224:
2. Imprecision in determining the t
Page 225 and 226:
Sometimes the appearance of a new s
Page 227 and 228:
Case-control (case-referent, etc.)
Page 229 and 230:
ealistic setting and results may th
Page 231 and 232:
"ecologic fallacy", the erroneous i
Page 233 and 234:
can vary substantially among indivi
Page 235 and 236:
e estimated from that study since t
Page 237 and 238:
Risk ratio Consider the example of
Page 239 and 240:
Dietary Salt Intake and hypertensio
Page 241 and 242:
Intervention trials can include tes
Page 243 and 244:
Treatment allocation can be 1) fixe
Page 245 and 246:
Example Cases Case-control studies
Page 247 and 248:
Cases Controls ____________________
Page 249 and 250:
population and that the controls we
Page 251 and 252:
Estrogen users (N1) Nonusers (N0) N
Page 253 and 254:
In the 3 × 10 6 unexposed women-ye
Page 255 and 256:
similar to that in the case group t
Page 257 and 258:
A modern perspective In general, de
Page 259 and 260:
of exposed to unexposed persons sam
Page 261 and 262:
Case-control studies: Armenian, Har
Page 263 and 264:
Wingo, Phyllis A.; Howard W. Ory, P
Page 265 and 266:
Part I Analytic study designs - Ass
Page 267 and 268:
1. Doll and Hill state (page 1451,
Page 269 and 270:
Part I Analytic study designs - Ass
Page 271 and 272:
� Possible motivation to change b
Page 273 and 274:
9. Causal inference * The desire to
Page 275 and 276:
1955:35). Quarantine of sick person
Page 277 and 278:
The mother who replaced the burned-
Page 279 and 280:
As presented in Kleinbaum, Kupper,
Page 281 and 282:
Popper maintains that it is fundame
Page 283 and 284:
1. Is the association real or artef
Page 285 and 286:
Temporality First exposure, then di
Page 287 and 288:
confer upon us a freedom to ignore
Page 289 and 290:
Lave, Lester B.; E.P. Seskin. Epide
Page 291 and 292:
Precision The presence of random va
Page 293 and 294:
the point estimate is based and thu
Page 295 and 296:
methodological literature. Validity
Page 297 and 298:
disease detection may be mistakenly
Page 299 and 300:
In cohort studies, the primary sour
Page 301 and 302:
present, then the D&C would detect
Page 303 and 304:
Controls in an experiment In a true
Page 305 and 306:
Southern endometrial cancer and est
Page 307 and 308:
Cases ` The ` "detection bias" hypo
Page 309 and 310:
from ETS, then a cohort study of ha
Page 311 and 312:
employing lead exposure measures fr
Page 313 and 314:
which the both measures increase in
Page 315 and 316:
coefficient (slope) is to 1.0 and t
Page 317 and 318:
_ _ Se = Pr(D'|D) Sp = PR(D'|D) _ _
Page 319 and 320:
Differential misclassification, how
Page 321 and 322:
subject plus or minus a random nois
Page 323 and 324:
Appendix 2 More on the concern to a
Page 325 and 326:
Bibliography Henneckens and Buring.
Page 327 and 328:
Sackett, D.L.: Bias in analytic res
Page 329 and 330:
e. OPTIONAL: If the reported cumula
Page 331 and 332:
Results from the Evans County Cardi
Page 333 and 334:
2. I1 = incidence in the exposed P1
Page 335 and 336:
makes most sense to view detection
Page 337 and 338:
Sensitivity × cases + (1 - Specifi
Page 339 and 340:
In the factual world, however, we c
Page 341 and 342:
Hypertensive SI participants were t
Page 343 and 344:
3,154 male managers and white colla
Page 345 and 346:
Table 2 Relationship of CHD to Beha
Page 347 and 348:
a group did indeed have higher CHD
Page 349 and 350:
Confounding - comparison of weighte
Page 351 and 352:
Type A smokers) are not very meanin
Page 353 and 354:
Different control groups for hypoth
Page 355 and 356:
Confounding, then, is a function of
Page 357 and 358:
Since the OR is the crossproduct ra
Page 359 and 360:
Case-control study In a case-contro
Page 361 and 362:
First and foremost, a potential con
Page 363 and 364:
is defined in relation to the count
Page 365 and 366:
poor. A scientific basis linking ra
Page 367 and 368:
One way to obtain a better balance
Page 369 and 370:
Stratified analysis is intuitively
Page 371 and 372:
attributed to the exposure of inter
Page 373 and 374:
Bibliography Rothman and Greenland
Page 375 and 376:
Senior P, Bhopal RS. Ethnicity as a
Page 377 and 378:
Some of the relationships just pres
Page 379 and 380:
n1i _______________________________
Page 381 and 382:
2. A 20-year retrospective cohort s
Page 383 and 384:
No. of urinary tract infections Tab
Page 385 and 386:
2. completely attributable to obesi
Page 387 and 388:
5. a. C -- the adjusted relative ri
Page 389 and 390:
of dietary antioxidants. However, c
Page 391 and 392:
epidemiologic research is to identi
Page 393 and 394:
Since the indicator variable (POP)
Page 395 and 396:
Over two decades ago (Causes, Am J
Page 397 and 398:
In this diagram, G and H exhibit ab
Page 399 and 400:
In this situation, the first suffic
Page 401 and 402:
Crude rates as weighted averages Re
Page 403 and 404:
Lung cancer rates by smoking and as
Page 405 and 406:
{B0} Smk Smk Asb. Asb. {B1} {B2} {B
Page 407 and 408:
Notation for joint effects R1 risk
Page 409 and 410:
and R111 = R000 + RD100 + RD010 + R
Page 411 and 412:
RR11 = RR10 × RR01 [End of aside]
Page 413 and 414:
multiplicative model supports the u
Page 415 and 416:
Effect modification as a reflection
Page 417 and 418:
4. Defining subgroups for preventiv
Page 419 and 420:
Bibliography Rothman and Greenland,
Page 421 and 422:
Multicausality: Effect modification
Page 423 and 424:
4. Several studies have shown a syn
Page 425 and 426:
1. Multicausality: Effect modificat
Page 427 and 428:
In these data, or, Expected ROC,SMK
Page 429 and 430:
to drinking in the absence of smoki
Page 431 and 432:
we determine that confounding is pr
Page 433 and 434:
Association between reserpine and b
Page 435 and 436:
R11 R10 × R01 R10 —— = ——
Page 437 and 438:
Hypothetical data on incidence of m
Page 439 and 440:
1. Data conform to an additive mode
Page 441 and 442:
Difference measure - the CID The va
Page 443 and 444:
Distribution of CIR, IDR, OR 0 +1 M
Page 445 and 446:
1 wi = ---------------------------
Page 447 and 448:
5 Y n B 3 n Y C 8 n Y C 4 Y Y D 7 Y
Page 449 and 450:
R11 - R00 = (R10 - R00) + (R01 - R0
Page 451 and 452:
You may well wonder what is the val
Page 453 and 454:
Moreover, the logistic model, we wi
Page 455 and 456:
Key epidemiologic assumptions in th
Page 457 and 458:
a + b1X1 + b2X2 + b3X3 + …) on th
Page 459 and 460:
Wilcosky, Timothy C. and Lloyd E. C
Page 461 and 462:
Data analysis and interpretation Ep
Page 463 and 464:
Range checks Range checks compare e
Page 465 and 466:
� Decisions deferred have to be m
Page 467 and 468:
� Simpler is better � Avoid ext
Page 469 and 470:
educe bias and tend to introduce ad
Page 471 and 472:
data. Perhaps too often generalitie
Page 473 and 474:
When this "analogous experiment" is
Page 475 and 476:
tests are based (e.g., see Sander G
Page 477 and 478:
The probability associated with bei
Page 479 and 480:
conclude that the association is su
Page 481 and 482:
Exposed to diethylstilbesterol? Yes
Page 483 and 484:
For example, suppose that we are co
Page 485 and 486:
The idea behind small studies bias
Page 487 and 488:
Confidence intervals address the qu
Page 489 and 490:
Computing a confidence interval for
Page 491 and 492:
Bibliography General Ahlbom, Anders
Page 493 and 494:
Thompson, W. Douglas. Statistical c
Page 495 and 496:
Appendix Estimating sample size to
Page 497 and 498:
p1 (1 - p1) p2 (1 - p2) σ 2 1 σ 2
Page 499 and 500:
Margin of error (rounded) Point Sam
Page 501 and 502:
Meditations on hypothesis testing a
Page 503 and 504:
Independent replication is the corn
Page 505 and 506:
More meditations on interpreting st
Page 507 and 508:
Part I Data analysis and interpreta
Page 509 and 510:
Part I 1. (a) Restriction Data anal
Page 511 and 512:
9. ORCIG,HYP,OC [multiplicative mod
Page 513 and 514:
15. Practical aspects of epidemiolo
Page 515 and 516:
(CDC, which includes the National I
Page 517 and 518:
cardiovascular area -- notably the
Page 519 and 520:
Disease Control (CDC) decided again
Page 521 and 522:
After submission but before the awa
Page 523 and 524:
Journal of Medicine, Jan 14, 1999,
Page 525 and 526:
Explore the data � Look at data (
Page 527 and 528:
Recently, standardization has come
Page 529 and 530:
Riesenberg, Don; Goerge D. Lundberg
Page 531 and 532:
16. Data management and data analys
Page 533 and 534:
implications, or suspicions or char
Page 535 and 536:
1.4.1.2 Consistency Consistency is
Page 537 and 538:
1.4.5.1.2 Series Runs in series are
Page 539 and 540:
Obviously it will be easier to asse
Page 541 and 542:
2.1.2.1 Statistical There are forma
Page 543 and 544:
2.3.5 Bivariate displays If the sam
Page 545 and 546:
errors. Related to this concern is
Page 547 and 548:
3.3 Expected values Perhaps, the si
Page 549 and 550:
Despite the best efforts, however,
Page 551 and 552:
Complete data cases % distribution
Page 553 and 554:
3.5 Outliers Outliers are now exami
Page 555 and 556:
Bibliography Davidson, Fred. Princi
Page 557 and 558:
attrib ckd length=$1 label='Check d
Page 559 and 560:
17. Epidemiology and public health
Page 561 and 562:
� Geneva - " . . . IFV and allied
Page 563 and 564:
public health, since the best oppor
Page 565 and 566:
Individual-level versus societal le
Page 567 and 568:
14%, so that the burden of the envi
Page 569 and 570:
1. exhausting various non-renewable
Page 571 and 572:
O'Hare WP, Pollard KM, Mann TL, Ken
Page 573 and 574:
� Health for all What contributes
Page 575 and 576:
Challenges in the environment for e
Page 577 and 578:
� What kind of population is requ
Page 579:
Bibliography American College of Ep
show all

Understanding the Fundamentals of Epidemiology an evolving text

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?