erasmus summer program 2010 august 16-20 2010 history of...
TRANSCRIPT
ERASMUS, A. Morabia, 06/07/10 (2 pages)
Erasmus Summer Program 2010 August 16-20 2010 "History of epidemiologic ideas” Prof Alfredo Morabia, MD, PhD, Center for the Biology of Natural Systems Queens College – CUNY, 163-03 Horace Harding Expressway Flushing NY 11365, USA TEL (direct): 718-670-4226, FAX: 718-670-4165, Skype: alfredo_cbns, www.epidemiology.ch, EMAIL: [email protected] Course description This is a methodology course, which focuses on the historical evolution of methods (e.g., study designs) and concepts (e.g., confounding, bias, interaction and causal inference) that constitute today’s epidemiology. For each topic, we review and discuss the historical contexts and some landmark studies that led to specific innovations in terms of performance of group comparisons, population thinking and framing of hypotheses. Some of the discussions are supported by exercises. We finally discuss the historical conditions for the emergence of epidemiology as a scientific discipline, the phases it went through and its potential, future developments (25 hours). Course Format The course meets every day from 1:00-6 :00pm. Lectures - Lectures will be given by A. Morabia. Exercises can be done in class during the last hours and are about articles that it is recommended the student reads before coming to class, in the order indicated in the timetable below. Textbook and Articles: Textbook Morabia A (2004) History of epidemiological methods and concepts. Basel: Birkhauser Articles
1. Copeland KT, Checkoway H, McMichael AJ, Holbrook RH (1977) Bias due to misclassification in the estimation of relative risk. Am J Epidemiol 105: 488-495
2. Cornfield J (1951) A method of estimating comparative rates from clinical data; applications to cancer of the lung, breast, and cervix. J Natl Cancer Inst 11: 1269-1275
3. Hammond EC, Selikoff IJ, Seidman H (1979) Asbestos exposure, cigarette smoking and death rates. Ann N Y Acad Sci 330: 473-490
4. Hill AB (1965) Environment and disease: association or causation? Proc Royal Soc Med 58: 295-300
5. Mantel N, Haenszel W (1959) Statistical aspects of the analysis of data from retrospective studies of disease. J Natl Cancer Inst 22: 719-748
ERASMUS, A. Morabia, 06/07/10 (2 pages)
2 Detailed Syllabus: "History of epidemiologic ideas" Time Monday Tuesday Wednesday Thursday Friday Preformal Early Classic Modern Overview 1:15-2:00 1.What is this thing
called epidemiology(1)
4.Early PT 7.Classic Group Comparisons(1)
10.Modern PT 13.Causal Inference
2:15-3:00 1.What is this thing called epidemiology(2)
5.Early GC (1)
8.Classic Group Comparison (2)
11.Modern GC 14. Genetic epistemology (in class exercise)
3:15-4:00 2.Preformal Group Comparisons
5.Early GC (2)
9.Classic Concepts (1) 12.Modern Concepts (1)
14. Genetic epistemology (Lecture)
4:15-5:00 3.Preformal Concepts
6.Early Concepts 9.Classic Concepts (2)
12. Modern Concepts (2)
5:15-6:00 In-Class exercises
1.Cornfield 2.Mantel&Haenszel 3.Hammond
4.Risk&Rates 5.Miettinen 6.Misclassification
Historical Papers
1.Cornfield 2.Mantel&Haenszel 3.Hammond
4. Copeland 5.Hill
Title History of epidemiologic ideas
2
Lecture 1:
What is this thing called
epidemiology?
Roles of Epidemiology
A. Taming epidemicsB. Distinguishing
health knowledge from health beliefs
3 4
A. Epidemiology - Definition•Epidemiology: Epi [upon] –demos [population]- logos [science]
•Science of what falls upon populations.
•Originally : study of epidemics of infectious diseases
Epidemic• Rapid change in the frequency of a
health event over time in the same population
• Health event can be:– an infectious disease (e.g., cholera, HIV)– a non communicable disease (e.g., lung
cancer, scurvy)– a health related behavior (e.g., cigarette
smoking)
5
Epidemic of HIV in the US
6
Adult Per Capita Cigarette Consumption and Major Smoking and Health Events – United States, 1900-2005
0
1000
2000
3000
4000
5000
1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000YEAR
Num
ber
of C
igar
ette
s
Source: United States Department of Agriculture; Centers for Disease Control and Prevention
End of WW II
US & UK Case control Studies
Fairness Doctrine Messages on TV
and Radio
Non-Smokers Rights Movement
Begins
Federal Cigarette Tax Doubles
Surgeon General’s Report on ETS
1st Surgeon General’s Report
Broadcast Ad Ban
1st Great American Smoke-out
OTC Nicotine Medications
Master Settlement Agreement
Great Depression
No Need for Epidemiology if no Epidemics
• When did epidemics start?• How did they impact human health
before epidemiology (i.e., before circa1850)?
• How did medicine and public health respond to epidemics?
8
Evolution of Human Societies1) Foraging: subsists from gathering,
hunting and fishing. No food production. Small bands/clans of about 20-100. Up to 10,000BCE.
2) Agrarian: subsists mainly from agriculture and/or animal husbandry. Most people live in the countryside. 10,000 BCE to 1800
9
3) Industrial: mechanized food production. Most people live in cities. Since 1800.
No epidemics among foragers• Nomads• Small clans (20 – 100 people)• No trade• No domesticated animals (except
the dog in the latest period)• Cannot sustain rapid transmission
of pathogens to susceptible hosts
10
Health of foraging populations
11 12
Epidemic in Agrarian Societies
• Two conditions for epidemics to occur:
1) Population density: > 0.5 million2) Animal domestication• Conditions first met in Sumer
(Mesopotamia, Southern Iraq), 2000 BCE
• Also, Book of Exodus (1000-500 BCE)
13
Historical Region of Sumer
14
Epidemics and Daily Life
• How frequent were epidemics in agrarian societies ?
• Continuous monitoring of epidemic outbreak usually not available
• Exception: Chinese Empire (243 BCE to 1911): gazetteers, dynastic histories, Great Encyclopedia of the Qing
15
North and South
16
Evolution of Epidemics during the Chinese Empire
Morabia, Epid Inf, 2009
Centuries
Num
ber o
f Out
bre
aks
0
10
20
30
40
50
60
70
80
-3 -2 -1 0 1 2 3 4 5 6 7 8 91
01
11
21
31
41
51
61
71
81
9
50
100
150
200
250
300
350
Population
Outbreaks
17Morabia, Epid Inf, 2009 18
04
8
First Century
Calendar Year
N
(-5,-4] (3,4] (11,12] (20,21] (29,30] (38,39] (47,48] (56,57] (65,66] (74,75] (83,84] (92,93] (103,104]
04
8
Sixth Century
Calendar Year
N
(495,496] (507,508] (519,520] (531,532] (543,544] (555,556] (567,568] (579,580] (591,592] (603,604]
04
8
Sixteenth Century
Calendar Year
N
(1495,1496] (1510,1511] (1525,1526] (1540,1541] (1555,1556] (1570,1571] (1585,1586] (1600,1601]
04
8
Nineteenth Century
Calendar Year
N
(1795,1796] (1810,1811] (1825,1826] (1840,1841] (1855,1856] (1870,1871] (1885,1886]
Epidemic Density during the Chinese Empire
Why were agrarian societies
powerless against e
pidemics?
Epidemics Before Epidemiology• If Chinese case can be extrapolated
to other regions of the world:• Began about 2000 BCE• Grew slowly in intensity until about
1200 CE• Exponential increase after 1200 CE
because of population growth and adaptation to many pathogens (measles, smallpox, pertussis, mumps)
19
Individual Thinking• Clinical thinking focusing on a single
patient• Doctors learn by trial and error, one
patient at a time• Not two cases of disease are
considered alike• Never look at aggregates of patients• No quantification, no population
studies
20
21
Medical systems in agrarian societies aimed to explain each individual case separately
Holism leads to Individual Thinking
100
25 75
6 18
2 4
1 1
Spring
Moon Quarter
Choleric
Southern Wind
All
23
Traditional Holistic Medicine
• Everything is connected, body and universe
• True for Egyptian, Greco-Roman, Indian, Chinese, and Meso-American medicines
• Incompatible with epidemiology because every health event is explained by a different set of causes
Population Thinking (1)• Approach to knowledge focusing on
populations or groups of people• Knowledge is acquired by group
comparisons• Cases of diseases are categorized
and grouped• Study of aggregates of patients• Quantification and population studies
24
Population Thinking (2)
• Monitor evolution of health events in populations: surveillance
• Simplify hypothesis: look at one cause at a time
• Starts with monitoring of causes of deaths in England in 1603: Bills of mortality
• Main scourge: Plague
25 26
Context of First Population Thinking Ever: Epidemics of Plague
• The plague had recurred in Europe since about 1350 (The Black Death)
• Transmitted to humans by fleas or by direct exposure to infected tissues or respiratory droplets.
• Fever, chills, headache, malaise, prostration.
• Regional lymphadenitis (bubonic plague)
27
The plague – Black Death
28
Bubonic plague
Avoid Bubonic Plague: Kill rats and mice; Get rid of fleas
William Petty (1623-1687)
30
John Graunt (1620-1694).
• “National and Political Observations... upon the Bills of Mortality”
• Determine, “in general terms, the uniformity and predictability of many important phenomena taken in the mass”
• First example of population thinking applied to human health
POPULATION TH
INKING
Graunt's Tables of Burrials and Christnings in London
YEARS
N
1610 1620 1630 1640 1650 1660
0
5000
10000
15000
20000
25000
30000
35000
---- Deaths, not plague---- Plague deathsººº Births
Environmental origin of the plague (J. Graunt)• Is plague due to “change of the Air”
(environment) or “the constitution of men’s bodies”?
• Plague makes “sudden jumps” from 118/week to 927/week, and back again from 993/week to 258/week
• Graunt attributes plague to “change of the air”
• Plague outbreaks end around 1700
32
Roles of Epidemiology
A. Taming epidemicsB. Distinguishing
health knowledge from health beliefs
33 34
Cholera, 19th Century Pandemic
How to avoid Cholera: Get a vaccine; don't drink non-boiled water; food has to be clean
35
Cholera (“Blue Death”), before 1880
• Clinical presentation: Massive watery diarrhea and vomiting. Death within hours by severe dehydration, capillary hemorrhage, renal failure and shock.
• Treatment: “calomel” (toxic doses of mercury), bleeding and laudanum (opium).
• Incubation period: 0-5 days. • Prognosis: 50% of sick die, but most
people infected with V. cholera do not become ill, however, the bacterium is present in the feces for 7-14 days following infection.
36
Miasma (or Pollution) Belief
Decomposing organic matter
Inhaled in the lungs
(Adapted from Vinten-Johansen, 2003, p. 175)
Miasma
Disease
Humoral/constitutional predisposition/Unique
37
Miasma Belief(2)• Miasma theory: diseases are caused by
putrefaction gases or noxious fumes from decaying organisms
– e.g., unhealthiness of swamp regions, cesspools, excrements, etc
– “Harlem flats have sufficient supply of rotting filth to generate fetid gases to poisoning of half the population” (NYC Bureau OH 1885)
• Multicausal: miasma and individual predisposition
38
Miasmatic Theory
New York, 1832 epidemic
39
Five Points, NY, 1827
40
Horatio Bartley Pamphlet
• Apothecary of New York • Description of cholera cases at one
of the city's cholera hospitals • Of the 410 patients admitted to
this hospital, 179 died• The attempted remedies included
mercury, camphor, and pouring boiling water on the victims.
41
Cholera Case Series
42
Mercury, sulphur. Death
Shock. Death
Camphor. Peppermint. Death
Camphor. Mercury. Cured
Camphor. Mercury. Died.
INDIVIDUAL THINKING. TR
IAL
AND ERROR
43
Bartley’s Case Series•Individual thinking•Trial and error, one patient at a time.
•Weak approach to causality
•None of the treatments are still valid today
Individual Thinking
44
Contagionist Belief (1)
Germ
Orally ingested
Rice-water stools (+vomit)
Direct contamination: Hand to mouth spread
Indirect contamination: Drinking water (contaminated) with cholera evacuations)
(Adapted from Vinten-Johansen, 2003, p.203)
45
Contagionist Belief (2)
• Also the Germ theory of disease
• Mono-causal
• John Snow was a contagionist
• Had few early believers until Robert Koch discovered the bacteria of anthrax (1876), of tuberculosis (1882) and of cholera (1883)
46
Contagionist’s Model
47
The Cause of CholeraWilliam Farr (1807-1883) John Snow (1813-1858)
Miasmatist
Contagionist48
London, 1850, water was supplied by private companies, incl. the Southwarkand Vauxhall, and the Lambeth.
49
Thames
SouthwarkandVauxhall(polluted water)
SewageofLondon
sea
1849
1853-4
(polluted water)
(clean water)
Lambeth
Before-After Comparison
Parish Company 1849 1853
Christ-church
Lambeth ~160/10,000 43/100,000
St. Saviour
Southwark & Vauxhall
153/10,000 227/ 100,000
50
Cholera deaths in two parishes, 1849 vs. 1853
51
POPULATION TH
INKING.
GROUP COMPARISON
[Concurrent] Group comparison
Population thinking
1854 Cholera Epidemic in South London
Cholera deaths per 10,000 households in 1854
53
POPULATION TH
INKING.
GROUP COMPARISON
54
Individual vs. Population thinking• Where hundred of thousands of individual
trials and errors had failed,• A single population-based experiment
identified a possible way of preventing cholera.
• Snow’s conclusion is still valid today: cholera is transmitted by contaminated water
• Group comparison is a powerful approach
Video
• http://www.youtube.com/watch?v=cZrA1Wk4Yxo, 1:12-6:00
55
D. What is epidemiology?
Epidemiology = Population Thinking +
Group Comparison56
Role of Population studies• Answer questions that have no
answer at the Individual level• Does the tx work?• Is this exposure deleterious or
protective?• Epidemiologic methods are used for
most of the health information you read about or you know about.
57 58
Principles of epidemiology1. Population thinking (measuring
occurrence and evolution of events in population)
2. Group comparison (Experiment)3. Concepts related to population thinking
and group comparison :1. confounding, 2. interaction, 3. bias, 4. causal inference
Data to draw the epidemic curve of cholera.Hamburg, 1892
59
month day cases8 16 28 17 48 18 128 19 318 20 668 21 1138 22 2498 23 3388 24 3588 25 6088 26 9038 27 10248 28 9368 29 9258 30 10088 31 8509 1 8639 2 8439 3 7329 4 6649 5 6279 6 4469 7 4309 8 3629 9 3379 10 3519 11 3259 12 3179 13 2419 14 286
month day cases9 15 2949 16 3449 17 2859 18 2219 19 2379 20 1929 21 1719 22 1609 23 1349 24 1099 25 919 26 709 27 699 28 589 29 369 30 46
10 1 2310 2 2610 3 1510 4 1710 5 1310 6 610 7 910 8 1510 9 810 10 110 11 1410 12 210 13 510 14 8
Homework Epidemic Curve
60
Cholera cases in Hamburg, August-October 1892
Date
Nu
mb
er
of C
ase
s
Au
g 1
6
21
26
31
Se
pt 5 10
15
20
25
Oct
1 5
10
15
0
100
200
300
400
500
600
700
800
900
1000
Characteristics of Epidemic Curves• Distribution of time of onset • Distribution of incubation periods• Lognormal shape if outbreak
caused by a single agent (e.g., cholera bacillus) and transmitted by the same vehicle (e.g., drinking water)
• Outbreak recesses when number of susceptible people diminishes
61
Median incubation time (dates on a log scale)
62
Cholera cases in Hamburg, August-October 1892
Date
Nu
mb
er
of C
ase
s
Au
g 1
6
21
26
31
Se
pt 5 10
15
20
25
Oct
1 51
01
5
0
100
200
300
400
500
600
700
800
900
1000
Using Software Packages to Create Epi Curves
• Epi curves may be done by hand or with software such as Microsoft Excel, Microsoft PowerPoint or Epi Info
• To create an epi curve in Microsoft Excel:– Click the “Chart wizard” on the tool bar– Choose “Column” as the chart type– Click “Next” twice and specify the chart
options– Click “Next”– Click “Finish”
Homework
–Change the “Gap width” to “0”to get the bars to touch
R0 - Reproduction Number
• Mean number of secondary cases a typical single infected case will cause in a population with no immunity to the disease
• Measles: R0 = 12-18 • HIV/AIDS: R0 = 2-5• Influenza: R0 = 2-3
64
Title History of epidemiologic ideas
2
Lecture 2
Preformal Group Comparisons
History of Epidemiologic Ideas
3
Outline
• 18th Century England: Lind• Paris 1830: Louis• Vienna 1848: Semmelweis• Limitations of Preformal Group
Comparisons
4
First epidemiologic group comparison
•James Lind (1716-1794)
•A Treaty of Scurvy
5
Scurvy
Swollen & bleeding gumsPurpura/dermal
hemorrhage6
The Salisbury trial “On the 20th of May 1747, I took twelve patients in the scurvy on board the Salisbury at sea. Their cases were as similar as I could have them ..”
They laid together in one place …
Had one common diet for all …
7
Treatment Tx effect
1. Electuary (garlic, mustard seed,
balsam of Peru, etc.) 2. Quarter of cider a day3. Elixir vitriol (3x 25 gttes/d) 4. Vinegar (3x2 spoonfuls/d)5. Sea water (1/2 pint/d)6. 2 Oranges and 1 lemon/d
None
NoneNone
Cured after 6 days
Improved after 20 days
Slight improvement
May 20th 1747, on the Salisburyat sea, 6 pairs of seamen
8
Lind’s conclusion• Lemons and oranges can cure
scurvy• Why didn’t people believe him?• Took another 50 years for the Royal
Navy to give lime juice to its seamen who became known as “limeys”
http://www.jameslindlibrary.org/trial_records/17th_18th_Century/lind/lind_other.php
9
Paris, 1830 •Pierre-Charles-
Alexandre LOUIS,•Researches on the
effects of bloodletting in some inflammatory diseases. (1828)
• Is bloodletting an effective treatment of pneumonia?
“The sophisticated bloodsucker”
http://video.nytimes.com/video/2006/02/06/science/1194817115698/defending-the-leech.html
11
Pierre-Charles-Alexandre Louis (1787-1872)Bloodletting
François Joseph Victor Broussais (1772-1838)
14
PCA Louis’s study• 77 Cases of pneumonia from La Charité
27 died
All bled, but at different times after disease onset
15
Louis’s hypothesis• If bloodletting is effective, patients who are
bled during the first 4 days of the disease should do better than those who are bled later
Thus…
• People who died from pneumonia must have been bled in average later in the course of the disease than those who survived
16
P.C.A. Louis: bleeding day and deaths from pneumonia
Dead Survived(27) (50)
%Bled on day:1 - 4th 67% 46%5 - 9th 33% 54%
“Researches on the effects of bloodletting in some inflammatory diseases”
17
Louis’s dataPatients with pneumonia
Died27
Bled day1-467%
Bled day5-933%
Survived50
Bled day1-446%
Bled day5-954%
18
Ignaz Philipp Semmelweis
1818-1865
Semmelweis
19
Mortality from Puerperal Fever in Vienna General Hospital
MD’s
20
Semmelweis’s hypothesis
0
2
4
6
8
10
12
14
16
18
1841 1842 1843 1844 1845 1846
%Clinic 1
Clinic 2
MD’s
Midwives
Overall mortality in two clinics. Vienna, 1841-1846
21
Overall mortality rates in two clinics. Vienna, 1841-1848
0
2
4
6
8
10
12
14
16
18
1841 1842 1843 1844 1845 1846 1847 1848
%
Clinic 1
Clinic 2
May 1847, chlorine hand washing
Modern analysis of the Vienna General Hospital Data
22
Noakes et al, Epid Inf, 2008
Reanalysis of the Vienna General Hospital data
23
Noakes et al, Epid Inf, 2008
24
Noakes et al, Epid Inf, 2008
25
Major Limits of Pre-Formal Epidemiology1. Weakness of study
designs: cause to effect or effect to cause?
2. Group comparability
26
Directionality of 19th Century Group Comparisons
• The direction of the analysis (from cause to effect or effect to outcome) is not fixed by design.
• Associations can be looked at both ways
27
Two main types of group comparison
1.Compare disease occurrence
2.Compare past exposure
28
Early Bleeding
Dead Survived
Late Bleeding
Dead Survived
Patients with pneumoniaCase series
Louis’s clinical study
29
Death
Early bleeding
Latebleeding
Survival
Early bleeding
Latebleeding
Louis’s clinical study
30
ThamesSouthwark and Vauxhall(polluted water)
Lambeth(clean water)
Sewage of London
sea
After (1853-54)
Thames
Southwark and Vauxhall(polluted water)
Lambeth(polluted water)
Sewage of London
sea
Snow’s Natural Experiment: before (about 1849)
31
1) Compare disease occurrence
• Exposed vs. non exposed
• Southwark and Vauxhall vs. Lambeth => compare mortality from cholera
32
Lambeth
Dead fromCholera
(Measured)
Not dead from cholera
(estimatedFrom
number of clients)
Southwark and Vauxhall
Dead fromCholera(measured)
Not dead from cholera
(estimatedFrom
number of clients)
Population of London
Snow’s natural experiment
33
2) Compare past exposure
• Affected vs. non affected
• Died of cholera vs. did not die from cholera => compare water companies
34
Death (measured)
Lambeth S & W
Survival(estimated
Fromnumber of
clients)
Lambeth S & W
Snow’s natural experiment
35
Group Comparisons before 1900
• The direction of the analysis – From cause to effect – From effect to outcome
is not fixed by design.
• Associations can be looked at both ways• Does not appear rigorous as in an
experimental setting
36
Criticisms of Snow, Louis and Semmelweis
•Groups are not comparable!
37
Farr’s criticism …
“To measure the effect of bad or good water supply, it is requisite to find two classes of inhabitants
• living at the same level [elevation], moving in equal space, enjoying an equal share of the mean of subsistence, engaged in the same pursuits,
• but differing in this respect, - that one drinks water from Battersea [supposedly polluted water], the other from Kew ….
• But of such experimenta crucis the circumstances of London do not admit ….”
(William Farr, November 19, 1853)
ARE THE GROUPS COMPARABLE
for their exposure to miasma?
38
… Snow’s facts“ No fewer than 300,000 people
• of both sexes, of every age and occupation, and of every rank and station, from gentlefolks down to the very poor,
• were divided into two groups without their choice, and, in most cases, without their knowledge;
• one group being supplied with water containing the sewage of London, and, amongst it, whatever might have come from the cholera patients, the other group having water quite free from such impurity.”
(Snow, 1855,pp.46-47)
THE GROUPS ARE COMPARABLE
39
Farr’s Theory of Elevation Snow’s Facts
“the fact of the most elevated towns in this kingdom having suffered excessively from this disease on several occasions, is opposed to [Farr’s] view, as is also the circumstance of several buildings (…) having nearly or altogether escaped cholera, though situated on a very low level.”
40
GROUPS ARE NOT COMPARABLE
for their access to clean water
41
Where are the FACTS?
• What was the evidence that the groups were comparable?
• No theory to formally address the criticism of non comparability
• Epidemiologists can only propose logical reasons to rule out the effect of a third factor
42
Back to Louis’s study
43
PCA Louis: bleeding day and deaths from pneumonia
Dead Survived(27) (50)
% Bled on day:
1 - 4 67% 46%5 - 9 33% 54%
44
Criticism of Louis’s study design
• Why is Louis allowed to compare those who died with those who survived?
• Imagine two people who get pneumonia in 1830, but with different degrees of severityAre their clinical histories comparable?
45
Patient selection?• Patient n°1, very severe pneumonia,
early admission, desperate bloodletting, dies
• Patient n°2, benign form, stays home during the most acute phase, already better when admitted, late bloodletting, survives
• How can this patient selection influence the results?
46
Selection bias?• Dead patients are bled earlier because
they have the severe form of the disease• Survivors are bled later because they
have the benign form of the disease• The study may not test the effect of
bloodletting because the two groups are not comparable with respect to exposure to treatment
47
PCA Louis’s study(+) Compared groups had same
population of origin (all came to same hospital – La Charité - for the same disease)
(-) Subjects were not sampled independently of the severity of disease
=> Not a real case-control study. We cannot use the proportions of early bloodletting to estimate a relative risk
Title History of epidemiologic ideas
2
Lecture 3
Preformal Concepts
History of Epidemiologic Ideas
3
Complete interaction: Two causes need to be simultaneously present
Hemolytic anemia
G6PD-deficient
Fava Beans
4
Fava bean, G6PD and Pythagoras
5
Interaction between fava bean, hemolytic anemia and G6PD deficiency
• Fava beans produces hemolytic anemia only in people with G6PD-Def.
• Hemolytic anemia occurs in G6PD-Def. only if they are exposed to fava beans
No interaction: Same effect of G6PD for the whole population
Population
Risk in G6PD+
Risk in G6PD-
Interaction: Different effects of G6PD in subgroups of the population
Population
Fava bean +
Fava bean -
Risk in G6PD+
Risk in G6PD-
Risk in G6PD+
Risk in G6PD-
8
The 19th century cholera debate
Germ or miasma?
9
Contagionist Theory (1)Germ
Orally ingested
Rice-water stools (+vomit)
Direct contamination: Hand to mouth spread
Indirect contamination: Drinking water (contaminated) with cholera evacuations)
(Adapted from Vinten-Johansen, 2003, p.203)
10
Miasma (Pollution) TheoryDecomposing organic matter
Inhaled in the lungs
(Adapted from Vinten-Johansen, 2003, p. 175)
Miasma
Disease
Humoral/constitutional predisposition
11
No theory could explain all observations about cholera • Miasma theory could not explain why
cholera epidemics followed the routes of human travel and, in a locality, went from ship to harbor and markets before reaching areas more inland as a contagious agent would.
• Contagionists could not explain why doctors and nurses treating cholera patients did not always get cholera.
12
Max von Pettenkofer (1818-1901)
• German hygienist. • Around 1850 he shifted
his interest from chemistry to hygiene of food, clothing, air, water and sewage.
• “Evidence-based public health”
• First Professor of Hygiene and Chief of the Hygiene Institute of Munich, Germany.
13
“Ground-Water Level Theory”
• For Max von Pettenkofer, cholera epidemics involved both the cholera germ and the environment.
• His “ground-water level theory” blended both the contagionist and the miasmatic theories
Cholera germin human excrements
X=
Putrefaction
Z = Cholera miasma
Y = Decay in soil
Ground
Smell
Infection
Ground water level
Ground water level theory
15
Ground-Water Level Theory
• Soil factor: Low, porous and humid soils
• Seasonal factor: summer, when the ground-water level was low.
• Data showed an inverse correlation between ground water levels and incidence of cholera in several European cities.
Pettenkofer’s slide
Cases of cholera (histograms on top), underground distance to water level (in meters, curve), and daily rainfall in millimeters (histograms at the bottom) from October 1873 to May1874 in Munich, Germany.
17
Pettenkofer’s slide
Cases of cholera (histograms on top), underground distance to water level (in meters, curve), and daily rainfall in millimeters (histograms at the bottom) from June 1873 to Mai 1874 in Munich, Germany.
18
Ground-Water Level Theory
• “Neither x nor y can produce cases of Asiatic cholera; only z.
• The specific nature (quality) of z is determined by the specific germ x; its amount (quantity) by the amount of y.
• The nature of x, y and z is so far unknown but one may assume, with a scientific probability bordering on certainty that all three are organic of nature and that x, at least, is an organized body or germ”
(von Pettenkofer, 1869)
19
Pettenkofer’s germ-environment interaction
Cholera miasma (z)
Cholera germ (x)
Ground level water low (y)
Cholera disease
20
Interaction: heterogeneity of effect of cholera germ on cholera disease
Population
Porous Soil
Rocky Soil
Risk in Germ+
Risk in Germ-
Risk in Germ+
Risk in Germ-
21
GWLT consistent with Farr’s elevation theory
22
GWLT consistent with Pasteur’s theory of fermentation.
23
Pasteur’sexperiment
24
GWLT was compatible Koch’s discovery of the “comma bacillus”
Robert Koch (1843-1910), his wife, her hat, and vibrio cholerae
25
GWLT was consistent with effects of public hygiene and sanitation
• Drainage, sewage systems, clean water-supply and dwellings appeared to prevent contacts of the cholera germ-bearing excrements with susceptible soils
• Also explained why cholera followed human travels but produced outbreaks in certain localities only.
26
Weaknesses of the GWL hypothesis• The theory implied that cholera did not put
populations at risk as long as the germ did not get in contact with appropriate soils.
Pettenkofer therefore preached that:
• Quarantine and isolation were ineffective against cholera. Cholera patients could go to markets, festivals and gatherings.
• Populations were wise to migrate away from the epidemic.
• Water did not have to be sand-filtered even though it was an effective way of eliminating the cholera germ.
27
The deadly experiment• 1892, cholera contamination of
water supply of Hamburg and Altona, two adjacent German cities
• Altona sand-filtered its water and had few cholera deaths
• Hamburg did not filter water and 8,606 people died from cholera (1.3% of its population).
28
Cases Deaths
Cholera on the Hamburg-Altona boundary, August-November 1892. From: RJ Evans, 2005
Epidemic Curve, Hamburg 1892
29
Cholera cases in Hamburg, August-October 1892
Date
Nu
mb
er
of C
ase
s
Au
g 1
6
21
26
31
Se
pt 5 10
15
20
25
Oct
1 5
10
15
0
100
200
300
400
500
600
700
800
900
1000
30
Fate of the ground water level hypothesis
• Pettenkofer was mocked and reviled, even after having survived drinking a cholera broth in public.
• Later committed suicide (direct relation not established).
• Pettenkofer’s theory were not formally refuted.
• E.g., nobody found it necessary to demonstrate that the ground water level theory was confounded by other factors, which favored the dissemination of the germ.
31
Was Pettenkofer wrong?
• Specifics of GWLT were wrong.• What about his multicausal mode of
thinking?
• Isn’t Pettenkofer’s mode of thinking more appropriate to frame hypotheses about the causes of cholera epidemics than the germ theory alone?
–How can we prevent cholera pandemics?
32
Cholera pandemics
• Since 1883, there have been at least two more cholera pandemics affecting Europe.
• The seventh pandemic lasted from 1961 to 1971, even though we knew by then how to treat individual cases of cholera
• The presence of the bacillus alone cannot explain why these outbreaks hit certain communities and not others.
33
Modern theory of cholera -environment “interactions” (Colwell, 1996)
• Warm precipitations (e.g., monsoons), in India
• Fresh water mobilizes nutrients in the estuary and starts cholera’s growth cycle.
• Cholera germ replicates on copepod’s egg sack and then gets dispersed into the water.
• Crabs, clams, and oysters concentrate the cholera bacteria in their gut tracts.
• Humans ingest contaminated seafood in springtime, get cholera, and disseminate it.
Title History of epidemiologic ideas
2
Lecture 4:
Early Population Thinking
3
315/37~8
1. Can you identify a potential source of bias in the way Snow reported the results of his survey?
4
Deaths to households RATIORatio = a ÷ b (where a and b are distinct quantities)
a = deaths from cholerab = number of households
Imagine that the Southwark and Vauxhall households were more crowded than the Lambeth’s. How would this affect Snow’s interpretation?
5
2) This table reflects the way William Farr decided to look at the results? What are the advantages and disadvantages relative to the way Snow used in the previous slide?
6
Proportion of deathsProportion = a ÷ (a+b)
a = deaths from choleraa + b = number of people exposed to a
water supplierNote: probability and risk are also
proportions.Using census data in the denominator
was better than ratio, but not quite a probability
7
3) A great theoretical contribution of Farr was to propose a way to provide a quantified answer to the following paradox:
• “Phthisis [i.e., tuberculosis] is more dangerous than cholera”
•Why does “cholera excite the greatest terror”? (William Farr, 1838)
On the basis of the table on the next slide, can you find out how Farr was able to show that the paradox was only apparent?
8
William Farr’s « On Prognosis »
46 /100/week50 /100/yr
9
Farr’s Answer
Rate, force of mortality
Risk
50% chance of dying in a week impresses more than 100% chances of dying in two years
Why does “cholera excite the greatest terror”?
“Phthisis is more dangerous than cholera”
“Cholera destroys in a week more than phthisis consumes in a year.”
Risky Rates (2415/100 sick-yrs)
It seems that in a certain military theater it was policy to send home, at the end of each month, any soldier contracting venereal disease, and to replace him instantly with a disease-free soldier, thus maintaining a constant strength of 100,000 men. Each month, 10 per cent of the number present contracted venereal disease, so that, in one year's time, 120,000 new cases of venereal disease developed, leading to an incidence rate of 120,000 per 100,000 per year. The unfortunate statistician computing these results was rebuked by his superiors, who knew that "rate" and "proportion“ were synonymous and that a proportion could not exceed unity. When the statistician persisted in his calculation, he was sent as a disease-free replacement to the theater in question. It is not known, however, whether he maintained his disease-free status.
10
Cornfield, AJE, 1976
Was epidemiology the handmaid of bacteriology?
• Koch – Anthrax Bacillus• Yersin – Yersinia Pestis• Epidemiologists become
dectectives: Identify the infectious culprit often without the help of bacteriology labs
• E.g. Foodborne outbreaks
11 12
Typhoid (enteric) fever
• 1900 England…. Foodborne disorders– TBC, trichinosis, etc.– 1885 Salmonella enteridis
• Do oysters cause enteric fever?– “Worshipful Company of Fishmongers” say
no, of course– Reports of cases following oyster
consumption
• British Local Govt Board needs evidence from population data
Salmonella Typhi.
The cause of typhoid fever, a disease that sickens 21 million people and kills 200,000 worldwide every year.
13
Asymptomatic carriers played an essential role in the evolution and global transmission of Typhi.Typhoid Mary = Mary Mallon
14
The Winchester outbreak
• Banquet to honor departing mayor of Winchester in 1902–134 guests –10 cases of enteric fever (9 guests
and 1 waiter)–4 deaths
• Epidemiologic inspector H. Timbrell Bulstrode sent to investigate
15
H. Timbrell Bulstrode and Winchester City Hall
16
Winchester Banquet
17
Menu approach – Attack rate• HTB sent the menu to all
participants asking them which food they had eaten and whether they got sick–Oysters eaten by 61% of sick and
100% of enteric fever–9 out of 113 oyster eating guests got
enteric fever (attack rate=8%)–4 out of 10 cases (9 guests and 1
waiter) died (case fatality rate=40%)
18
Strengths and limitations• Conclusion of HTB based on consumption
and attack rate among the sick: 10% of oyster eaters got enteric fever and 100% of enteric fever cases ate oysters. No other food item competed with oysters
• Limitation: did not try to compare the attack rate between the sick and the non-sick even though he had the data, which he reported in a non-aggregated form
• 52% of the non-sick had eaten oysters
“’O Oysters,’ said the Carpenter / ‘You've had a pleasant run! / Shall we be trotting home again?' / But answer came there none-- /And this was scarcely odd, because / They'd eaten every one.”
Through the Looking-Glass, and what Alice Found There (1872).
Mary Mallon (1869-1938)Fifty-one original cases of typhoid and three deaths were directly attributed to her (countless more were indirectly attributed) in New York City, although she herself was immune to the typhoid bacillus (Salmonella typhi).
20
21
Almroth Wright and anti-typhoid vaccine • 1896: AW develops anti-typhoid
vaccine• Tried among volunteers of British
Army• Concludes that the inoculation is
effective for preventing typhoid• Recommends Army routinely
inoculates soldiers with vaccine
22
Typhoid Vaccine Controversy
23
Antityphoid inoculation. British Army in India, 1901
Inocul-ation
Cases Deaths Total Risk /1000
Deaths /1000
Yes 32 3 4,883 6.6 0.6
No 744 199 55,955 13.3 3.6
Wright AE. Anti-typhoid inoculation, 1904:56-7
24
Karl Pearson vs. Almroth Wright• Pearson reviews data negatively (not a
randomized trial) and recommends more research.
• Letter exchanges between KP and AW in BMJ 1904
• Test: Monitoring vaccine use in soldiers between 1904-1908
• Question: how should the data be analyzed as all soldiers are not inoculated at the beginning of the survey?
• Two solutions: Army vs. Greenwood and Yule
25Karl Pearson (1857-1936) Almroth E. Wright (1861-1947)
Pearson and Wright
26
Modes of analysis of inoculation data
Start End
Cases
Inoculation waves
Inoculation status at the start
Inoculation status at the end
ArmyGreenwood and Yule
27
Anti-typhoid inoculation. British Army, 1904-1908
Inoculation Cases Total Risk/1000(army)
Risk/1000(G&Y)
Yes 56 10,000 5.4 8.0
No 272 9,000 30.4 23.0
Leishman, J R Army Med Corps, 1909
28
Army report’s mode of analysis (Hypoth risk diff=.64-.33=.31)
1234567891011121314151617181920
C - - - - - - - -
C - - - - -
2 cases,
6 inoculated
Risk=33%
&
9 cases,
14 uninoculated
Risk= 64%
--------- CII---------------------------
C------------------ I
I
I
----------------------------------I
C
C
C
C
Define inoc status
C
CC
Army report’s mode of analysis
CI--------------------------- Define inoc
status
Disease-free,
uninoculated,
misclassified
Disease-free,
inoculated inoculated
30
Limitation of Army’s mode of analysis
Exposure at the end of risk period. • Army mis-attributes some disease-
free non-inoculated time to the inoculated
• Increasing apparent protection of inoculated=> increases apparent efficacy of vaccine (risk difference)
Greenwood and Yule’s mode of analysis (hypoth RD=.56-.50=.06)
1234567891011121314151617181920
C - - - - - - - -
C - - - - -
1 case,
2 inoculated
Risk=50%
&
10 cases,
18 non inoculated
Risk= 56%
--------- CII---------------------------
C
------------------ I
I
I
----------------------------------I
C
C
C
C
Define inoc status
CC
C
Greenwood and Yule’s mode of analysis
CI---------------------------
Disease-free,
uninoculated
Disease-free,
inoculated,
misclassified
Define inoc status
uninoculated
33
Limitation of G&Y mode of analysis
Exposure at the beginning of risk period.
• G&Yule attribute some inoculated time to the non-inoculated
• Decreases risk in non-inoculated=> decreases apparent vaccine efficacy (risk difference).
34
Karl Pearson vs. Almroth Wright: Epilogue
• None of the solutions is satisfactory, but both show protective effect of inoculation.
• Routine anti-typhoid inoculation introduced in British Army following the 1904-1908 survey.
• Added: personal, unit and cookhouse hygiene and water purification
• Status of controversy in 2005: “Injectable anti-typhoid vaccines have long been available and provide 70 to 90 percent protection for up to 7 years. Because they tend to cause fever, pain and swelling at the injection site, and a general feeling of being unwell in about one in four vaccines, these vaccines are poor public health tools”.
35 36
Austin Bradford Hill (1897 - 1991)
•Austin Bradford Hill proposes an alternative mode of analysis
37
Austin Bradford Hill (1897- 1991)• Pilot in the First World War• Degree in economics by correspondence • 1922: Associated with Major Greenwood and
attended lectures by Karl Pearson• 1933: Reader in Epidemiology and Vital
Statistics• 1947 Professor of Medical Statistics • Textbook, Principles of Medical Statistics• P. Armitage: "to anyone involved in medical
statistics, epidemiology or public health, Bradford Hill was quite simply the world’s leading medical statistician."
38
Hill’s example of the fallacy
39
Hill’s “Fallacious Comparison”(Army’s mode of analysis)
Ratio of attacks to final population of group.
Inoculated 110/1,000 = 11.0 %
Non-inoculated 890/4,000 = 22.3 %
Conclusion: inoculation protects
40
Person-times
• Weigh each observation according to the time it was observed:
300 persons, each observed for a quarter of a year = 300 x 0.25 = 75 person-years
41
Hill’s “True comparison”
Ratio of attacks to person-years of exposure.
Inoculated :
110/ [(300 x ¼) + (900 x ¼) + 1,000 x ¼)] = 20 %
Non-inoculated :
890/(5,000 x ¼) + (4,700 x ¼) + (4,100 x ¼) + (4,000 x ¼) = 20 %
No effect of vaccine42
Three modes of analysis of inoculation data
Start End
Cases
Inoculation wavesGreenwood & Yule’s analysis
Army’s analysis
Hill’s analysis
43
Conclusion: the usage of person-times•Rates having person-time in the denominator accurately describe disease occurrence in populations with incomplete follow-up or comparisons of dynamic populations
Title History of epidemiologic ideas
2
Lecture 5:Early Group
Comparisons
3
Group Comparisons before 1900
• The direction of the analysis – From cause to effect – From effect to outcome
is not fixed by design.
• Associations can be looked at both ways• Does not appear rigorous as in an
experimental setting
4
Group Comparisons after1900
•Emergence of cohort studies takes place in a perturbing phase of the history of public health.
•Western society’s, “big threat” had changed between the 1800s and 1900
5
1800 Threat: Famine and Collapse
Thomas Malthus (1766-1834) 6
Last quarter of 19th century• The economic development in Europe
demonstrated the falseness of Malthusian theories
• Populations grew but societies did not collapse
• Means of production and wealth had grown at an even faster pace.
• Higher wages, social legislation, insurances.
7
From Malthus to Darwin
• The new big fear that replaced Malthus’s came from the Darwinian theory of evolution.
• Degeneration of the species replaced lack of food as the great menace.
8
Social Darwinism • Social scientists apply the concept of
“survival of the fittest” to human populations.
• Progress of medicine and social legislation lead to the survival of less-than-fit people
• Dilution of the quality of the species• Eventually, disappearance of the human
species as a whole. • Solution is “eugenism”: control procreation
of less fit, e.g., malformed, invalid, feeble-minded, diseased, etc.
9
Eugenism in epi and stat• Eugenism became very popular in
statistics and epidemiology at the turn of the 20th century.
• The English school of statistics (Galton, Pearson and Fisher), more mathematical and less “statist” than Farr, supports the eugenic movement and gives it scientific legitimacy. Galton (1822-1911)
Pearson (1857-1936)
Fisher (1890-1962)
11
Racial Hygiene (Germany)German version of Social Darwinism•1895. Alfred Ploetz coined the term “Rassenhygiene”•Society for Racial Hygiene created in 1905. In 1910, Stuttgart branch created by W. Weinberg•Before WWI, RH is no more racist or anti-Semitic than typical Western society ideology.•RH movement infiltrated by nazi after WWI and manipulated after 1933.
12
Racial Hygiene• German eugenics: compulsory
sterilization, commitment of ‘defective asocials’ in psychiatric institutions, etc.
• German social medicine and health reformers (e.g., Alfred Grotjahn) were pro-eugenics
13
Tuberculosis• TB is a source of concern for RH• TB is the major killer in Western
societies. • TB people have families and children. • Do TB people reproduce at a higher
rate than healthy Germans and hence represent a menace to the German people?
14
Koch and bacillus (1882)
Robert Koch (1843-1910)
15
Tuberculosis
• “consumption” vs. sanatoria16
First large cohort study ever • First LARGE cohort study performed to
assess the TB menace for the German race.
• Follow-up for 20 years and comparison on overall and TB mortality of 28,000 children (from 7000 parents) differing by TB exposure
• Conducted by Wilhelm Weinberg, in Germany, and published in 1913
17
Wilhelm Weinberg (1862-1937)•42 years of private practice
•“doctor to the poor”
•3500 deliveries
•Founder and leader of the Stuttgart Society for Racial Hygiene
•Wrote 160 papers
•Worked alone
•Died poor
Kinder der Tuberkuloesen (Cover page)
“The mathematics of Weinberg seemed abstruse”.
19
A. Morabia, R. GutholdWilhelm Weinberg’s 1913 large
retrospective cohort study: a rediscovery
American Journal of Epidemiology 2007 165(7):727-733
Publication
20
Main objectives of Weinberg’s study1)whether children of people
with TB had a higher mortality from all causes and from TB;
2)whether people with TB had a higher fertility.
21
Sources of Data• The Mortality Register of Stuttgart listed
all deaths from TB (life table)• The Family Registry of Stuttgart
provided all familial bonds (life table)• Annual abstract of statistics of
Statistical State Agency of the State Baden-Württemberg (Stuttgart) (SMR)
22
Method1. Retrieved the names of those who died from TB
between 1873 and 1902 from the Mortality Register, 2. Identified their children and whether they had
emigrated in the Family Register3. Going back to the Mortality Register, he established
whether the children had died before their 20th birthday but not later than 1909
4. The maximum duration of follow-up was 20 years, that is, up to the child’s 20th birthday
5. For children who emigrated, Weinberg obtained information from new municipalities of residence
23
Cohorts• Exposure = being the child of a person who
died of TBExposed cohort• N=18,052 children born from 3,246 fathers
and 2,022 mothers who died from TB between 1873 and 1889
Unexposed cohort• N= 7,574 children born from 1,830 parents
who died from other causes, then TB in 1876, 1879, and 1886 (which WW considered as “representative” of all deaths between 1873 and 1889)
24
Children of TB
Dead BeforeAge 20
Survive up To
Age 20
Children of non TB
Dead BeforeAge 20
Survive up To
Age 20
Parents died of TB
Weinberg’s cohort study
Parents did not die of TB
25
Modes of dealing with large sample
• Family cards• Grundtabellen• Urtabellen• Hilfstabellen
26
Family card
27
Grundtabelle
28
Urtabelle
29
Hilfstabelle
Parent TB 20y risk (%)
Average life expectancy
(yr)
Mortality rate
(%/yr)
A Up to age 20 (B) (A/B)
Father Yes 46.82 11.56 4.07
No 40.27 12.73 3.17
Mother Yes 48.11 11.40 4.24
No 40.17 12.76 3.15
TB Mortality (life-table) Risks and Rates
Risk (Father TB+)
• Risk = deaths / cohort over 20y2960/6322=0.47=> 47% over 20 years
• Does this mean:47%/20y = 2.35%/y?
• Risk over 20 years assumes all children lived for 20 years
31
Risk vs. Rate (Father TB+)• But average life expectancy is 11.6y=> Many children did not survive 20y• Use person times: cohort x life
expectancy, 6,322 children x 11.6y= 73,082 person years
• Rate = deaths /PT = 2960 / 73,082=0.04/y, or 4%/y(Hint: like average speed)
32
Reanalysis of Weinberg’s data
0.40
0.60
0.80
1.00
0 5 10 15 20age
Non_TB_dad Non_TB_momTB_dad TB_mon
Kaplan-Meier survival estimates by TB and sex
TB mother
Non TBTB
FertilityAverage number
Gender TB Non TB
of children Crude Age-adjusted
N Boys 3.44 3.87
N Girls 3.33 4.41
Surviving to 20y Fathers 1.77 2.63 1.90
Mothers 1.78 2.31 1.67
35
Mortality risk by age 20 and social class (Kinder der TB)
0
10
20
30
40
50
60
Aalpha,High
Abeta B C, Low
Social class
Mor
talit
y by
age
20
(%)
TB dadTB mom
Weinberg Hardy
-
Hardy-Weinberg Equilibrium
38
Children of TB
Dead BeforeAge 20
Survive up To
Age 20
Children of non TB
Dead BeforeAge 20
Survive up To
Age 20
Parents died of TB
Weinberg’s cohort study
Parents did not die of TB
Had Weinberg performed a case-control study …• He would have done very differently• Cases : Identify all deaths before
age 20 over 20 years• Controls: Take one person still alive
at the time of death of each case• Compare the proportion of parents
who died from TB for the cases and or the controls
39
Very different
40
History of case-control studiesJanet Lane-Claypon
(1877-1967)First woman who enters
the epidemiology portrait gallery for her innovations in epidemiologic methodsPioneering applications
of retrospective cohort studies and case-control studies.
41
Women scientists around 1900
• Marie Curie (1867-1934): only scientist to receive Nobel Prize twice (physics in 1903 and chemistry in 1911).
• 1909, Selma Lagerlöf (1858-1940) :first female Nobel Prize winner in Literature.
• The Polish born Rosa Luxemburg (1870-1919), leader of the German Social-Democratic Party, debated Edouard Bernstein and Vladimir Illich Lenin on strategy, economy and nationalism.
42
Lane-Claypon’s 1926 case-control study• London and Glasgow• 500 hospitalized breast cancer cases• 500 controls with non-cancerous
illnesses• Standard interviews• Cases had a smaller number of
children than controls
43
Breast cancer
nulliparous parous500
Controls
nulliparous parous500
Case-control studyComparison of people having the outcome (cases) with people free of that outcome (controls)No possible “risk”Compare prevalence of past exposure in cases and controls
Had Lane-Claypon performed a cohort study …• She would have done very
differently• Exposed: nulliparous women
followed for many years• Unexposed: parous women followed
for many years• Compare rate of breast cancer
among nuliparous and parous women 44
Very different
45
Lane-Claypon and Recall bias“ … in the event of any divergence between
the two series showing a higher incidence among the cancer series, objections might fairly be raised on psychological grounds. It is evident that a woman who has suffered from a trouble so serious as to require the removal of the breast and the surrounding tissues will be likely to search in her memory for some antecedent causative agent, or event.”
(Lane-Claypon, 1926)46
Case-control study• Design performed to assess determinants of
disease– E.g. causes of breast cancer
• How do you go from comparison of prevalence of exposure …– Cases are more often nulliparous than controls
• … to causal inference– Nulliparity increases the risk of breast cancer
• Solution to this question will hamper the acceptance of case-control study as a valid study design.
47
Group Comparisons after1900• The direction of the analysis (from cause
to effect or effect to outcome) is fixed by design.– A cohort study has fixed exposure and
variable outcome – A case-control study has fixed
outcome and variable exposure• Looking at the association both ways is
either impossible or makes no sense.
48
Wade Hampton Frost (1880-1938)
•Johns Hopkins School of Public Health, Baltimore•1919: Lecturer•1930: First Prof of Epidemiology
49
Frost’s study in Kingsport
• Small town in Tennessee
• 556 persons living in 132 families, interviewed and examined clinically and radiographically
• 238 ex-members of the families, alive or dead
• Nearly 10,000 person-years, counting each person who entered or left the household in any given year for half a year
• Population divided according to history of family contact with pulmonary tuberculosis
• In the presence of family contact the attack rate, standardized for age, was 12.9 per 1000 against 6.8 per 1000 in the absence of such contact
50
Weinberg and Frost• “The risk of dying from
tuberculosis was found to be twice as high among families exposed to a tuberculous member than among families not so exposed, a finding very similar to those of Weinberg in an earlier prospective study in Germany.” Weinberg W. Die Kinder der Tuberkulosen. Leipzig: Hirzel, 1913. [Comstock, 2004]
51
First Cohort Studies• First epi cohort studies were retrospective
• Design independently discovered by Janet Lane-Claypon (1912, UK), Wilhelm Weinberg (1913, Germ) and Wade Hampton Frost (1933, US)
• Contrary to belief, Weinberg’s study was large and used sophisticated analytical methods
• “Children of the Tuberculous” is a major early epidemiologic work; probably the most important before Goldberger & Sydenstricker work in South Carolina
Joseph Goldberger.
52
•Medicine believed pellagra was an infectious disease.•Goldberger demonstrated it was caused by the dietary deficiency of a vitamin.•Nominated in 1929 (vitamin Nobels) but died before decision.
53
3 D’s: Dermatitis, Dementia Diarrhea
Pellagra
54
55
Early combination of “cohort”and “case-control” design
• Golberger et al, 1920, Survey of cotton mill workers in South Carolina
• Compared pellagra risks by income or diet
• Compared diet between villages with high (“cases”) or no (“controls”) pellagra incidence
56
Cohort study design
57
• Compared the diet of two poor villages with extreme incidence rates of pellagra: 0 per 1,000 in NY (unaffected) and 64.6 per 1,000 in IN (affected).
• In the unaffected village of NY, 58.1% of the household had reported having purchased fresh meat twice or more in the 15 day record, whereas this proportion was only 8.5% in the affected village of IN.
Case-control study design
Title History of epidemiologic ideas
2
Lecture 5:Early Concepts
Etymology of “Confounding”
• “these extraneous but unavoidable factors called, aptly, ‘confounders’”(Pollan, NYT 2007)
• Con-fundere: to mix• Confounding: Mixing of effects
producing spurious associations (i.e., amplifies, attenuates or reverses true associations).
3 4
ARTIFACTS
Early 20th century:Confounding as a multicausal fallacy
G Udny Yule
5
Statistician at Cambridge
(1871–1951)President of the Royal Statistical Society in 1924-26.
6
Yule’s Fallacy - Fathers & SonsFathers
Sons Attribute present
Attribute absent
Attribute present 25 25
Attribute absent 25 25
% with attribute 50% 50%
Yule, 1903
7
Yule’s Fallacy - Mothers & Daughters
Mothers
Daughters Attribute present
Attribute absent
Attribute present 1 9
Attribute absent 9 81
% with attribute 10% 10%
8
Yule’s Fallacy - AllParents
Children Attribute present
Attribute absent
Attribute present 26 34
Attribute absent 34 106
% with attribute 43% 24%
9
Major Greenwood (1880-1949)
First Professor of Medical Statistics and Epidemiology at the London School of Hygiene and Tropical Medicine:
10
Greenwood, 1935Outcome
Group 1 Group 2 All
Inoculated
Non Inoculat
ed
Inoculated
Non Inoculat
ed
Inoculated
Non Inoculat
ed
Dead 50 500 50 5 100 505
Alive 50 500 950 95 1,000 595
% Dead
50 50 5 5 9 46
Epidemics and Crowd Diseases, 1935
11
Austin Bradford Hill (1939)
Outcome
Males Females All
Treated Non Treated
Treated Non Treated
Treated Non Treated
Dead 16 6 16 24 32 30
Alive 64 24 24 36 88 60
% Dead
20 20 40 40 27 33
Principles of medical statistics, 1939
Yule, Greenwood and Hill
• Described the fallacy of mixing strata of a third factor, but did not associate it explicitly with group comparability
• None of them stressed how common the issue was in epidemiologic research
• Seems to have been no follow-up on their work 12
Find compelling designs or technique to make groups comparable
• Randomization• Stratification• Restriction• Case-control studies• Retrospective cohort studies=>But the first theory of
confounding will not stem from this line of efforts.
13
14
Random allocation
The idea of random allocation of exposure to produce identical groups was in the air since the mid-19th
centuryIts first practical applications took place in the 1930’s
15
Randomized trials are “in principle,” free of confounding
• Participants do not choose whether they will receive the treatment or its control
• The random procedure (i.e., toss of a coin, alternate day allocation) generates in the long run identical groups
• But randomization only applies to the evaluation of potentially protective treatments.
16
MRC trial 1930-33• Patients received, alternatively according to
the order of admission, either a serum treatment or the conventional treatment, which served as control.
1st patient: serum2nd patient:conventional Tx3rd patient: serum Etc.
17
MRC Trial results Effect of serotherapy for Type I, age 20-40
CFR difference = 11.2 – 5.7 = 4.5%⇒Excess CFR if no serotherapy
CFR ratio = 5.7 / 11.2 = 0.5=> Half the CFR if serotherapy
18
19
Joseph Asbury Bell’s 1941 RCT• Bell JA (1941). Pertussis prophylaxis with two
doses of alum-precipitated vaccine. Public Health Reports 56:1535-1546.
• Earliest methodologically detailed account of a randomized trial. 7 years earlier than streptomycin in pulmonary tuberculosis MRC trial
• “The only practical approach appeared to rest in the selection of two groups, each of which is a random sample of the combined groups in the exact sense of the term.”
• Contact with WH Frost at Hopkins (MPH, 1937)
20
Early Epidemiology• 1880 to 1945• Early epidemiologists: Weinberg, Lane-Claypon,
Greenwood, Goldberger, Frost, Hill, etc.
Population thinking:1. Prevalence vs. incidence
Group comparison:1. First (retrospective) cohort studies 2. First case-control studies3. First randomized therapeutic trials4. First theory of confounding
21
Academic evolution of (early) epidemiology
In the UK and US, there were FIRST
•Professors of epidemiology•Definitions of epidemiology•Textbooks of epidemiology
22
1880-1949 WH Frost 1880-1938
First Professors of Epidemiology: Greenwood (UK) and Frost (US)
23
First Definitions of Epidemiology•“The natural history of the infectious diseases, with special reference to the circumstances and conditions which determine their occurrence in nature" (Frost, 1919).
•"It is ... good usage to speak of the epidemiology of tuberculosis; ... and also to apply the term to the mass-phenomena of such noninfectious diseases such as scurvy, but not to those of the so-called constitutional diseases, such as arteriosclerosis and nephritis" (Frost, 1924).
•“the study of disease, any disease, as a mass phenomenon […] it forms a general picture, an average of what is happening” (Greenwood, 1935)
24
First textbooks of epidemiology
•M. Greenwood, 1935“Epidemics and Crowd-
Diseases“
•WH Frost, 1941“Epidemiology”
25
Historical Contributions of Early Epidemiology
• Laid down the theoretical foundations for rigorous observational research,
•Created the necessary conditions for the rapid expansion of epidemiology after the second World War.
Title History of epidemiologic ideas
2
Lecture 7:Classic Group
Comparisons (1)
Selling newspapers, 1910
3
Raymond Pearl (1879-1940). Statistician at Johns Hopkins, 1938 “In our opinion the increase in
smoking with the universal custom of inhaling is probably a responsible factor, as the inhaled smoke, constantly repeated over a long period of time, undoubtedly is a source of chronic irritation to the bronchial mucosa” (1939)4
NO data on sm
oking !
Obituary DeBakey, age 99
“ While working at Tulane University in New Orleans in 1939, Dr. DeBakey and Dr. Alton Ochsner made one of the first links between cigarette smoking and lung cancer. Many prominent doctors derided the concept. Then, in 1964, the surgeon general documented the link.”
5
New York Times, 7/13/20086
INDIV
IDUAL THIN
KING. T
RIAL
AND ERROR
Ochsner, DeBakey and Dixon, 1947
7 Doctors smoke camels
Do you inhale, Mr. Reagan?
Smoking & lung cancer mortality in the US
11
Medical literature• 1942: “It is doubtful whether the higher
incidence of cancer of the lung observed in recent years is real or only apparent” British Medical Journal
• 1950: “The increase [of lung cancer incidence] may, of course, be only apparent”Med Res Council of The UK, 1950
• Three case-control studies (2 US and 1 UK)
• 1952: “There is little doubt that the increase is both real and numerically important”Lancet
WWII
Smoking & lung cancer mortality in the US
13
Death rates from cancer of the lung and tobacco consumption in the UK
14
US Studies
Ernst Wynder
Dietrich Hoffman
15
Austin Bradford Hill (1897-1991)
Richard Doll (1912-2005)
British case-controls study
16
Case-Control Study (Doll & Hill, 1950)
SAMPLE
Lung Cancer Cases(649)
Smokers(647)
Non-smokers
(2)
Controls free of lung cancer
(649)
Smokers(622)
Non-smokers
(27)
17
D&H study: design
Cases: lung cancer in 20 hospitals of London
Controls: Age-hospital matched controls
Exposure: questionnaire on duration, dates of starting and quitting of smoking, amount smoked, type of tobacco
Doll & Hill, 195018
D&H Study: Simplest Results
SmokingCases Controls
Yes
No
n
647
2
%
99.7
0.3
n
622
27
%
95.8
4.3
Doll & Hill, 1950
19
05
1015202530354045
non smoke
rs <5
5 to 1
4
15 to
24
25 to 4
950
+
Cigarettes /day
%
casescontrols
D&H Study: cig/days
20
D&H: Direct interpretation
•Lung cancer cases have smoked more cigarettes in the past than controls
•But does smoking increase the risk of lung cancer?
21
D&H’s transformation of exposure proportions into rates
• Assume distribution of smoking in controls is representative of inhabitants of Greater London
• Get “n’s” for smoking strata => denominator for population at risk
• “Ratio” = cases ÷ “n”• Not risk but proportional to risk
22From Doll and Hill, 1950
“Risks” (=ratios) derived from the case-control study
23From Doll and Hill, 1950
“Relative Risk” 1.0 2.8 14.8 20.0 35.8 55.6
“Relative Risk, ”age 45-54y
1.0 6 19 26 49 65Overall RR
24
Notes on Doll and Hill’s Case-Control Study
• Performed a case-control study but were interested in the relative risk of smoking and lung cancer
• No odds ratios• Relation of odds ratio to risk
ratio published in 1951
25
Austin Bradford Hill (1897-1991)
Richard Doll (1912-2005)
British Cohort study
26
Rationale for a new study design
" Further retrospective studies would seem unlikely to advance our knowledge materially ... If there were any undetected flaw it would be exposed only by some entirely new approach. That approach we consider should be 'prospective' "
Doll and Hill, 1954
27
Doctor’s Study•October 1951: questionnaire sent to 59,600 doctors of the United Kingdom about their smoking habits
•40,564 valid responses
•Death certificates (since William Farr …)
•10 years of follow-up, 4,597 deaths, of which 212 resulted from lung cancer
28
1951 1961
Exposure and risk periods
Risk period: counting new cases
Exposure to tobacco
29
Cigarettes per day and lung cancer. British Doctor Study (Doll & Hill, 1964)
0
0.5
1
1.5
2
2.5
3
3.5
Nonsmokers
1 to 9 10 to 14 15 to 19 20 to 24 25 to 34 35+
Cigarettes / day
deat
hs/1
000/
yr
30
Years since cessation and lung cancer. British Doctor Study,1964)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Currentsmokers
<5 5 to 9 10 to 19 10 to + NeverSmokers
Years since cessation
deat
hs/1
000/
yr
31
Comparison of cohort and case control study results in Doll and Hill 1954
Effect
32
Other Cohort studies• American Cancer Society’s
“Hammond and Horn Study”:–Launched in January 1952, convenience
sample of about 200,000 subjects• U.S. Veteran’s cohort study
– “Dorn study”–300,000 men holder of active life
insurance policies from the Veteran administration identified in 1953
33
Absolute and relative effects •Cancer Prevention Study 1 – 1959 to 1972
•68,000 volunteer “researchers”
•1,000,000 study participants
•Researcher’s job: enroll ten families with any persons 45+ yr old
• Report any deaths during next 6 yrs
Personal recruitment – confidential questionnaire 1964 – First Surgeon-General’s Report
1965 – Mandatory warning labels 1982 – Cancer Prevention Study 2 launched
Volunteer researcher’s kit Male and female questionnaires
Completed bundles by family or household Opening the confidential envelopes
Pre-processing – transfer to folders Assigning ID numbers: Div – Unit – Group – Researcher – Family - Person
Packing boxes for shipment to data entry firm CGW, Inc. – Upper Darby, PA
Keying data one screen at a time Quality control checks
Post-processing quality control Where do you file a million questionnaires?
Phase one of follow-up: Alive, dead, or unknown? Phase 2 of follow-up: coding death certificates
High-tech storage of the 80’s: 9-track tape drives
Adult Per Capita Cigarette Consumption and Major Smoking and Health Events – United States, 1900-2005
0
1000
2000
3000
4000
5000
1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000YEAR
Num
ber
of C
igar
ette
s
Source: United States Department of Agriculture; Centers for Disease Control and Prevention
End of WW II
US & UK Case control Studies
Fairness Doctrine Messages on TV
and Radio
Non-Smokers Rights Movement Begins
Federal Cigarette Tax Doubles
Surgeon General’s Report on ETS
1st Surgeon General’s Report
Broadcast Ad Ban
1st Great American Smoke-out
OTC Nicotine Medications
Master Settlement Agreement
Great Depression
Title History of epidemiologic ideas
2
Lecture 8:Classic Group
Comparisons (2)
3
Using the “rare disease assumption”
Jerome (Jerry) Cornfield(1912-1979)
Approximation of the RR in case-control studies
4
Jerome Cornfield (1912-1979)• Chairman, Department of Biostatistics at
The Johns Hopkins University • Director of Biostatistics Center at The
George Washington University• President, American Epidemiologic Society
(1972) • President, American Statistical Association
(1974)
5
Context: first CCS of smoking and lung cancer
• Finding: Lung cancer cases have smoked more cigarettes than control cases.
•But does smoking increase the risk of lung cancer?
6
Schrek R, Baker LA, Ballard GP, and Dolgoff S (1950). Tobacco smoking as an etiologic factor in disease. I. Cancer. Cancer Res 10: 49–58.
• 5,003 male admissions, Hines, IL VA hospital, 1941 - 1948,
• Smoking history upon admission, a standard form
• Case groups: lung cancer, other respiratory cancers, upper digestive cancers
• Control groups: all other diseases, all other cancers
7
Form used by Schrek et al
In book
From Paneth et al, 2004
8
Schrek et al’s 1950 data. White males, 40-49y Cigarettes per day
Lung cancer cases
Controls(no lungcancer)
> 10 P1 = 77% P2 = 58%
Never, and light smokers
1-P1 = 23% 1-P2 =42%
N 35 171
9
Interpretation• Cases with lung cancer are more
often smokers of 10 or more cigarettes per day (77%) than controls without cancer of the lung (58%)
Cornfield 1951 asks:• “Can we estimate from these data
the frequency with which cancer of the lung occurs among smokers and non smokers? ” (p. 1270)
10
Cornfield’s paper, 1951• A method of estimating comparative rates
from clinical data. Applications to cancer of the lung, breast and cervix. JNCI 1951; 11:1269-1275
• Optimal design would be prospective: “select representative groups of persons having and not having the characteristics and determine the percentage in each group who have or develop the disease during the time period”
• This design is “rarely if ever used”
11
Exposure frequencies and risk
• “Actual practice in the field is to take two groups presumed to be representative of persons who do or do not have the disease and determine the percentage in each group who have the characteristic”
• “The difference in the magnitude of the relative [exposure] frequencies does not indicate the strength of the association, however.”
Cornfield, 195112
Rates from exposure frequencies?
• “We are consequently interested in whether it is possible to deduce the rates from knowledge of the relative [exposure] frequencies ” (p. 1269)
• Example: Schrek et al’s case-control study• Uses Bayes Theorem
Cornfield, 1951
13
Notation
)|(
)(
)|(
)|(
2
1
++=++=
−+=++=
SDPSinRateDPR
DSPpDSPp
S=smokers, D= disease, R= ‘rate’
14
“Effect of smoking”
)|()|( −+++ SDPSDP
Rate Ratio =
15
Rate for smokers 10+cpd
])[(][
)]()|([)()|(
)()|(
)(/),()|(
2121 XpppXpCornfield
DPDSPDPDSPDPDSP
SPSDPSDP
×−+×==
−×−+++×+++×++
=
+++=++
We need P(D+), which Cornfield calls “Annual Prevalence Rate” and denotes X, from external source
16
Rate for non-smokers
])[()1()1(][
)]()|([)()|()()|(
)(/),()|(
2121 XpppXpCornfield
DPDSPDPDSPDPDSP
SPSDPSDP
×−−−×−==
−×−−++×+−+×+−
=
−−+=−+
17
Rate Ratio if P(D) is “rare”
][)21(
)1(
))(1()(
)21)(()21(
)()1(
)(
21
1
211
1
ratioOddsppx
pp
xpDPDxPpppDPpx
DxPpDxPpRR
=−
−=
−+−−−
−=
=0 =1
=0
18
Incidence rates in Schrek et al’s CC study
Cigarettes per day
Formula Incidence rates (/100K/yr)
> 10 (P1 x P(D+)/ [p2 +P(D+) x (p1-p2)]
(0.77 x0.000155)/[0.58 +0.000155 x(0.77-0.58)] = 20.5 x 10-5
Never, and light smokers
(1-p1)xP(D+) / [(1-p2)–P(D+) x(p1-p2)]
(0.23 x0.000155)/[0.42 -0.000155 x(0.77-0.58)] = 8.6x 10-5
Rate Ratio = 20.5 : 8.6 = 2.38
19
Assume disease is rare: “prevalence” close to zero
Cigarettes per day
Formula Incidence rates (/100K/yr)
> 10 (P1 * Prev)/ [p2 +Prev* (p1-p2)]
(0.77 *0.000155)/[0.58 +0.00003 = 20.5
Never, and light smokers
(1-p1)*Prev / [(1-p2)–Prev*(p1-p2)]
(0.23 *0.000155)/[0.42 -0.00003 = 8.6
20
[Odds ratio] based on Schrek’s 1950 data
Cigarettes perday
Lung cancer cases Controls (no lungcancer)
> 10 P1 = 77% P2 = 58%
Never, andlight smokers
1-P1 = 23% 1-P2 =42%
N 35 171
[OR=] (.77/.23) / (.58/.42) = 2.42(not in JC’s)
21
Implications of the rare disease assumption• If prevalence of disease is small relative to
prevalence of smoking in cases (p2) or non-smoking (1-p2) in controls, P(D+)x(p1-p2) is trivial and may be neglected
• Cases need to be representative of all cases and controls of all subjects free of disease in the target population from which the population risk would be derived
22
Cohort and case-control studies are related designs
“Retrospective studies might on the surface appear to supply only estimates of the proportion of persons with and without the disease who possess the characteristic and not to estimate relative risk. Such an estimate can easily be derived, however.”
(Cornfield and Haenszel, 1960)
Title History of epidemiologic ideas
2
Lecture 9:Classic Concepts
Yule, Greenwood and Hill
• Described the fallacy of mixing strata of a third factor, but did not associate it explicitly with group comparability
• None of them stressed how common the issue was in epidemiologic research
• Seems to have been no follow-up on their work
3
Origin of the word “confounding”
• In 1926, RA Fisher coined the verb “confound” in statistics
• In factorial designs, discarding some interactions mixes (and therefore confounds) some treatment effects
4
Confounded factorial design
Treatment A- A+
B- 0 AB+ B A&B
5
A&B = A + B + A*BConfounded A&B = A + B
6
E.H. Simpson (1951): Interpreting interactions
Outcome Males Females All
Treated Non Treated
Treated Non Treated
Treated Non Treated
Dead 5 3 15 3 20 6
Alive 8 4 12 2 20 6
% Dead 38 43 56 60 50 50
OR=0.8 OR=0.8 OR=1.0
7
Conditions for Simpson’s paradox
Second-order interactions CANNOT be ignored when the third variable is: •dependent of exposure among the non-diseased, and•dependent of the outcome variable among the unexposed
Sociology detour?
8
• Identified a class of variables, of which the effects, if not controlled by the study design or analysis, could be “confounded” with that of the “explanatory” variable of interest
• Kish may in turn have influenced Brian MacMahon, Olli Miettinen, and Mervyn Susser
• Sociologist, Leslie Kish, may have linked Fisher’s expression “confound”with Simpson’s paradox in 1959
McMahon & Pugh, 1970• “Epidemiology. Principles
and Methods” (p. 244)
9
“… confounding variables. The latter are variables that may introduce differences between cases and controls which do not reflect differences in the variables of primary interest (study variables)”
Mervyn Susser, 1973
“In an observed association between two variables, a confounding variable is an uncontrolled third variable that is associated with both the independent and the dependent variables.”
(p. 95).10
11
Diagram representing potential connections between variables that may lead to confounding
Susser M. Causal thinking in the health sciences (1973)
12
What is missing in this picture?
-
13
2x2 Table: Notation
Disease
Exposure Present Absent Total
Yes A B A+B
No C D C+D
Total A+C B+D N
OR = (AxD):(BxC)
14
Case-control study: Pooled
Lung Cancer
Smoking Present Absent Total
1+ ppd 18 13 31
No 46 236 282
Total 64 249 313
ORED =r= (18x236):(46x13) = 7.1
Mantel&Haenszel, 1959
Interpretation of the pooled OR
• Lung cancer cases are 7.1 times more likely to smoke a pack than controls without cancer of the lung
• Those who smoke a pack or more are 7.1 times more at risk of developing lung cancer than nonsmokers
16
Stratum <45y of age confounder
Lung Cancer
Smoking Present Absent Total
1+ ppd 4 5 9
No 2 23 25
Total 6 28 34
ORED|<45y=(4x23):(2x5) = 9.2
Mantel&Haenszel, 1959
17
Stratum <45y of age confounder
Lung Cancer
Smoking Present Absent Total
1+ ppd 4 5 9
No 2 23 25
Total 6 28 34
ORED|<45y=(4x23):(2x5) = 9.2
Mantel&Haenszel, 1959
18
Stratum 45-54y of age confounder
Lung Cancer
Smoking Present Absent Total
1+ ppd 8 4 12
No 8 54 62
Total 16 58 74
ORED|45-54y=(8x54):(8x4) = 13.5
Mantel&Haenszel, 1959
19
Stratum 55-64y of age confounder
Lung Cancer
Smoking Present Absent Total
1+ ppd 5 3 8
No 16 91 107
Total 21 94 115
ORED|55-64y=(5x91):(16x3) = 9.5
Mantel&Haenszel, 1959
20
Stratum 65y+ of age confounder
Lung Cancer
Smoking Present Absent Total
1+ ppd 1 1 2
No 20 68 88
Total 21 69 90
ORED|65+y=(1x68):(20x1) = 3.4
Mantel&Haenszel, 1959
21
Stratification reveals confounding
• Stratification creates strata of homogeneous exposure to the confounder
⇒ Separates the effect of exposure from that of the confounder
22
Mantel-Haenszel Adjusted OR
= weighted average of stratum-specific OR
ORMH=[A1D1/N1] + [A0D0/N0]
[B1C1/N1] + [B0C0/N0]
23
Mantel-Haenszel adjusted OR= weighted average of stratum-specific OR
ORMH=R
[4x23/34]+[8x54/74]+[5x91/115]+[1x68/90]
= 9.8
[2x5/34]+[8x4/74]+[16x3/115]+[20x1/90]
Mantel&Haenszel, 1959
24
600
120
60 100
100
200
300
400
500
600
Mo
rtal
ity
rate
(/
100,
000/
year
)
Present Absent Absent
Present
Smoking
Asbestos
Smoking, asbestos and lung cancer
25
Health effects of exposure to asbestos
26
Cohort study [Hammond et al]Exposed cohort : members of
International Association of Heat and Frost Insulators and Asbestos Workers. N = 12,051. Exposed to asbestos > 20 yrs
Non-exposed cohort : workers exposed to dust, fumes, etc. from the American Cancer Society Study (CPS-I) of causes of cancer (N = 73,763)
Outcome : mortality rate from lung cancer
27
Hammond, Selikoff & Seidman’s Table 8
28
Mortality difference associated with asbestos in the non-smokers?
a. RDA,- ? = RA,- - R-,-
= 58.4 – 11.3 = 47.1
29
What is the death rate in the smokers who are not exposed to asbestos?
b. R-,T ?
= 122.6
30
If the mortality difference associated with asbestos was the same in the smokers and non-smokers, what would the death rate be in the smokers who are exposed to asbestos?
d. Expected RA,T ?= R-,T + RDA,-
= 122.6 + 47.1 =169.7
31
What was the observed death rate of the smokers exposed to asbestos?
c. RA,T ?
= 601.6
32
Additive interaction?
e. Observed RA,T vs. expected RA,T ?
601.6 >>>> 169.8Synergy of Rate Differences= additive synergy= additive interaction
33
Multiplicative interaction?
e. Observed RA,T vs. expected RA,T ?RRA,- ? = RA,- : R-,- = 58.4 : 11.3 = 5.2Expected RA,T ? = R-,T x RRA,- = 122.6 x
5.2 = 637.5601.6 === 637.5 ,
no multiplicative interaction.(if 601.6 < 637.5, then “multiplicative
antagonism”).
34
Sackett’s List of Biases
• Reflects practice of singling out specific sources of bias
• Sackett DL. Bias in analytic research. J Chron Dis. 1979;32:51
35
List of biases (D Sackett, 1979)
Six categories :1. bias in “reading-up” on the field2. in specifying and selecting the study
sample3. in executing the experimental manoeuvre4. in measuring exposures and outcomes5. in analysing the data6. in interpreting the analysis.
36
Miettinen (1976)
Most biases fall under two broad categories (not including confounding):1. selection 2. information Found later in most recent textbooks (Kupper, Kleinbaum and Morgenstern, Rothman, Rothman and Greenland, etc.)
37
1. Selection bias
• Differential follow-up of exposed and unexposed cohorts ...
• Differential recruitment of cases and controls …
… with respect to outcome-related factors
•Model = Berskon’s bias
Joseph Berkson
• 1899-1982• M.A. degree (Physics) from
Columbia University in 1922 and
• two doctoral degrees from Johns Hopkins, an M.D. in 1927 and a Dr. Sc. in statistics in 1928.
• Head of the Biometry and Medical Statistics Division at the Mayo Clinic
38
39
Joseph Berkson’s selection bias• MD, ScD. (1899-1982). Head, Section of
Biometry and Medical Statistics of the Mayo Foundation for Medical Education and Research in Rochester, Minnesota
• Hospitals only admit a fraction of all cases in the population
• The association of smoking and lung cancer observed in hospital-based studies is biased because of the process of selection of cases and controls
40
“Limitations of the application of four-fold table analysis to hospital data”, 1946 • Mathematical demonstration showing that, if
exposed cases were more likely to be hospitalized than exposed controls, then a comparison of hospitalized cases and controls would find an association between smoking and lung cancer even if no such association existed in their population.
• Uses the hypothetical example of a hospital-based case-control study of the association of diabetes (outcome) and cholecystitis (exposure). Controls are ophthalmology patients who came to the clinic to get glasses because of refractive errors.
Biometrics 1946;2:47-53
41
“Berkson’s bias”
• Under specific assumptions, the case-control study may spuriously observe an excess of cholecystitis of 2.32% (+0.5%) in patients with diabetes but not among controls while there is no such association in the whole population from which cases and controls originate (Berkson, 1946).
42
Rational of Berkson’s BiasTable 22: Population frequency, referral rates, and hospital frequency for
exposed and unexposed cases and controls. Source: Table 5.2. in (Schlesselman, 1982) p.129.
Exposure Disease Population frequency
Proportion referred
Hospital frequency
Yes Case A s1 s1A
Control B s2 s2B
No Case C s3 s3C
Control D s4 s4D
Population odds ratio: ψ = AD÷ BCHospital odds ratio: ψ' = [(s1s4)÷(s2s3)] ψ = bias × ψ
43
Table 23: Example of a hypothetical hospital-based case-control study of the association of diabetes with cholecystitis, in which cases suffer from diabetes and controls from ocular refractive errors requiring glasses. The proportions referred are: 0.05 for diabetes, 0.2 for refractive errors and 0.15 for cholecystitis. All forces of hospitalization are independent of each other. Source: (Berkson, 1946)
Exposure Disease Population frequency Proportion referred*
Hospital frequency
Yes Case A= 3,000 s1= 0.2087 626
Control B=29,700 s2=0.32 9504
No Case C=97,000 s3=0.069 6693
Control D=960,300 s4=0.20 192,060
Odds ratio** ψ = AD÷BC = (3,000 × 960,300) ÷(29,700 × 97,000) = 1
ψ' = [(s1s4) ÷ (s2s3)] ×ψ = [(0.2087 ×0.20) ÷ (0.32 ×0.069)] × 1 =1.89 × 1 = 1.89
* The reader should refer to the Berkson’s paper to understand how these probabilities were computed.
** Computed using the equations of Table 22 44
Achilles Tendon of Berkson’s bias
• Berkson bias occurs only if exposure itself is an independent cause of hospitalization
• If not, S1=S3 and S2=S4 (no selection bias)
• Back then, smokers were not admitted to hospitals only because they smoked
45
Empirical example of Berkson’s bias
Roberts RS, Spitzer, WO, Delmore T, Sackett, DL.
An empirical demonstration of Berkson’s bias
J Chronic Dis 1978; 31: 119-28
46
2. Misclassification bias
Lilienfeld & Graham. JNCI 1958;21:713-20
47
Circumcision (removal of some or all of the foreskin from the penis) and cervical cancer (Wynder, AJOG, 1954)
Abraham Lilienfeld Ernst L Wynder48
Lilienfeld and Graham’s study
1) Asked 192 men whether they were circumcised
2) MD clinically examined the 192 men and diagnosed circumcision or not
3) Compared self-report and clinical exam
Lilienfeld & Graham. JNCI 1958;21:713-2050
Quantification of “errors” in Lilienfeld and Graham, 1958.
“Of the 84 patients who the physicians felt were circumcised, 47, or 56%, stated they were not circumcised”
=> Percent of …..“And of the 108 patients determined as not being circumcised, 19 or 18 percent, stated they were circumcised”
=> Percent of …..
51
Modern assessment of validity of self-report among clinically circumcised
Sensitivity = 37/84 = 0.44 Specificity = 89/108 = 0.82
52
Classic definition of epidemiology
“The study of the distribution and determinants of health-related states or events in specified populations, and the application of this study to the control of health problems”
(Last, 2001)
53
Classic epidemiology Texts• Jeremy N. Morris: Uses of Epidemiology
(1957)
• Brian MacMahon: Epidemiology (1970)
• Mervin Susser: Causal Thinking (1973)
• Abe Lilienfeld: Foundations of Epidemiology (1976)
Lilienfeld
Susser
Morris
MacMahon
55
Stellman Wynder Breslow
Cornfield Austin
1970’s epidemiologists
Doll
Upton
56
Classic epidemiology
• Provided the methods and concepts needed to test the tobacco and lung cancer association in populations
• Close link between theory and practice
• Lack of mathematical rigor?
Title History of epidemiologic ideas
2
Lecture 10:Modern Population
Thinking
3
Unified Theory of Risks and Rates
Olli S. Miettinen
4
Farr’s rate and probability
•“In prognosis patients may be considered in two lights:
•in collective masses, whereby general results can be predicted with certainty;
•separately when the question becomes one of probability.”
5
“Estimability and estimation in case-referent studies” (1976)
“With reference to populations it is desired to learn about rates of occurrence of the illness in relation to the exposure (possibly in causal terms) (…); and for individuals the concern is with risks (for various time periods) of the development of the illness in relation to the exposure …”
6
Estimability, Fig 1
7
Incidence Density
Inc density =cases per person times=
ID (exposed)= a’’ / C (t’’-t’)
C = Candidate pool of exposed
ID = incidence rate, risk per unit of time, instantaneous risk
8
Cumulative Incidence
Cum Incid (up to time j) = Σ (1 to j) IDi
Cum Incid (up to time Δtj) = Σ (Δt1 to Δtj) IDi x Δti
Divide (t’’-t’) in j short sub-period
CI = risk, probability
9
Morgenstern et al, 1980
EXERCISE QUESTIONS
10
5yr risk[35-39y] ?
Rate/year = 90:180,000 = 50/100,000/yr or 0.05/100/yr
0.05/100/yr x 5y =0.250 /100 over5 years
11
20yr risk[35-54y]?
Sum of 5-year CI across 4 age groups
Cum Incid (up to time Δtj) = Σ (Δt1 to Δtj) IDi x Δti
= 0.250 + 0.399+0.550+0.686
≈ 1.871 per 100
Title History of epidemiologic ideas
2
Lecture 11:Modern Group Comparisons
3
Estimability and estimation in case-referent studies
“The concept that case-referent studies provide for the estimation of ‘relative risk’only if the illness is ‘rare’andthat the rates and risks themselves are inestimableare overly superficial and restrictive”O. Miettinen, AJE, 1976
4
Estimability, Fig 1
5
Incidence density ratioIn a case-referent study involving incident rather
than prevalent cases, • the cases (a exposed and b non-exposed) provide
for the estimation of the case ratio A/B (as a/b), and
• C/D is estimable from the reference series (as c/d, the ratio of the sample numbers of exposed and non-exposed referents from the total pool of candidates for illness).
• Consequently, the incidence density ratio [=Rate ratio] is estimable from such studies;
• and in particular no rarity-assumption is required for this” (Estimability p. 228)
6
Cumulative incidence ratio
If “The instantaneous risk ratio is estimable (through the exposure odds ratio of incident cases) without any rarity assumption in reference to either incidence density or prevalence” (p. 229),
then the cumulative incidence ratio [=Risk Ratio] is also estimable as “a weighted average of age-specific incidence density ratios … somewhat complicated”
7
Theoretical developments from “Estimability …”• Greenland S, Thomas D. On the need for the rare
disease assumption in case control studies. Am J Epidemiol 1982;116:547-53
• Greenland S, Thomas D, Morgenstern H. The rare disease assumption revisited. A critique of “estimators of relative risk for case-control studies.”Am J Epidemiol 1986;124:869-76
Simpl(ified) explanation: Cohort of 20 subjects
1234567891011121314151617181920
X - - - - - -
X - - - - - - - - - -X - - - - -
X - - - - - - - -
X - - - - -
5 cases,
3 exposed
&
15 non-cases,
4 exposed
Time
9
Nested Case-Control Study
• Case-control study designed within an enumerated cohort study
• Design = cohort study• Analysis = Case-control study• Advantage: perform some
expensive measurements on a fraction of non-cases
10
Miettinen’s logic
Consider a nested case-control study:
odds of exposure in cases =
Case E+ /Case E- =3/2
Odds of exposure in controls=
?
11
Control sampling schemes
Schemes When in risk period?
traditional end concurrent throughout case-based beginning
1234567891011121314151617181920
X - - - - - -
X - - - - - - - - - -X - - - - -
X - - - - - - - -
X - - - - -
Time
CASE-BASE CONCURRENT TRADITIONAL
13
Traditional sampling scheme
1234567891011121314151617181920
X - - - - - -
X - - - - - - - - - -X - - - - -
X - - - - - - - -
X - - - - -
5 cases,
3 exposed
&
5 controls,
1 exposed
xx
xx
xTime 14
Traditional sampling scheme
Cases Non-cases
Exposed 3 1
Unexposed 2 4
15
Traditional sampling scheme
• Odds of exposure in cases =
Case E+ /Case E- =3/2
• Odds of exposure in controls =
C - E+ /C- E- =1/4
• Odds ratio = (3/2)/(1/4) = 6
Case-base sampling scheme1234567891011121314151617181920
X - - - - - -
X - - - - - - - - - -X - - - - -
X - - - - - - - -
X - - - - -
5 cases,
3 exposed
&
20 in cohort,
7 exposed
Time
17
Case-base sampling scheme
Cases Cohort
Exposed 3 7
Unexposed 2 13
18
Case-base sampling scheme
• odds of exposure in cases
= Case E+ /Case E- =3/2
• Odds of exposure in controls
= Cohort E+ /Cohort E- =7/13
• Odds ratio = (3/2)/(7/13)= 2.8
Concurrent sampling scheme
1234567891011121314151617181920
X - - - - - -
X - - - - - - - - - -X - - - - -
X - - - - - - - -
X - - - - -
5 cases,
3 exposed
&
? controls,
? exposed
2.5 4.8 5.3 6 6.3
Time
time units
20
Concurrent sampling scheme
1. Choose one or more controls among non-diseased subjects at the time of diagnosis of index case
2. Count each control as “person-times”
Concurrent sampling scheme
1234567891011121314151617181920
X - - - - - -
X - - - - - - - - - -X - - - - -
X - - - - - - - -
X - - - - -
[2.5] [4.8][5.3]
[6][6.3]
Time
time unitsAt time of case 1:
6 exposed subjects
11 unexposedsubjects
At time of case 2:
4 exposed subjects
8 unexposedsubjects
Etc.22
Concurrent sampling scheme
odds of exposure in cases =
Case E+ /Case E- =3/2
Odds of exposure in controls= PT E+ /PT E- =12/38
Odds ratio = (3/2)/(12/38)= 4.75
23
Concurrent sampling scheme: why PT?Controls should be representative of
• Person-years exposed = (6 x2.5) + (4x4.8) + (2x5.3) + (2x6) + (1x6.3) = 63.1
• Person-years unexposed = (11x2.5) + (8x4.8) + (8x5.3) + (7x6) + (6x6.3) = 188.1
24
Concurrent sampling scheme
Cases Person-times
Exposed 3 63.1
Unexposed 2 188.1
25
Concurrent sampling scheme
Odds of exposure in cases =
Case E+ /Case E- =3/2
Odds of exposure in controls =
PT E+ /PT E- = [63.1/188.1]
Odds ratio = (3/2)/([63.1/188.1])= 4.5
26
Summary of Odds ratios from different control sampling schemes
Odds ratio
traditional 6.0 concurrent 4.5 case-base 2.8
27
Traditional odds ratio
(CaseE+/CaseE-) /(C -E+ /C-E-)
= Odds ratioNB: Cornfield’s 1951 deals with
traditional control sampling
28
Concurrent odds ratio
(CaseE+/CaseE-) /(PTE+/PTE-)
= (CaseE+/PTE+)/(CaseE-/PTE-)
= Rate Ratio
29
Case-base odds ratio
(CaseE+/CaseE-)/(CohE+ /CohE-)
=(CaseE+/CohE+)/(CaseE-/CohE-)
= Risk Ratio
30
Rarity assumption
•When outcome is rare (<10% over risk period), all OR ’s are identical
Title History of epidemiologic ideas
2
Lecture 12:Modern Concepts
Refinement of the theory of confounding
• Formally distinguished from “effect modification” (Miettinen, 1974)
• Data-based definition of confounding makes it dependent on the type of parameterconsidered (RD, RR, OR) (Miettinen and Cook, 1981)
3 4
Refinement of the theory of confounding(2)
• Matching neutralizes confounding in cohort studies
• Matching introduces confounding in case-control studies if data are not analyzed as matched data
Confounding: essence and detection (Miettinen & Cook, 1981)
5
After 1980, word “confounder” is used in all textbooks
Hats Table 1 Table 2 All TablesColor Black Gray Black Gray Black Gray
Fit 9 17 3 1 12 18
Not Fit
1 3 17 9 18 12
% Fit 90 85 15 10 40 60
Rothman, Modern Epidemiology, 1986
“Mixing” definition: 1903-1986
• “A fallacy caused by the mixing of records”(Yule, 1903)
• “On the simplest level, confounding may be considered as a mixing of effects”
([Rothman, 1986], p.89)
6
7
(Strong) conditions for confounding fallacyThird variable is:1. (statistically) related to exposure
in the base population (or controls)
2. a cause (or related to a cause) of the disease in unexposed
3. not an intermediate step in the causal pathway between exposure and disease
8
Fallacy-based definition is parameter-dependent
“It is commonplace to use the data-basedcriterion: control of the extraneous factors changes the estimate of effect”
• Or “the extraneous factor (must) be associated in the data, with both the exposure and the illness”
• “This makes the confounding dependent on the type of parameter considered”(RD, RR, OR)
Confounding: essence and detection (Miettinen & Cook, 1981)
9
• Confounding factors are defined a priori• In cohort studies, a confounder is a
predictor of disease among non-exposed (this “a priori property” is more relevant than its data-based manifestation)
• In case-control studies, “a priori”confounders are correlates of exposure in the joint source population of cases and controls
Confounding in cohorts vs. case-control studies
Confounding: essence and detection (Miettinen & Cook, 1981)10
Matching in cohort studies
Makes covariate uncorrelated with exposure
• “exposure-criterion for confounding will not be satisfied”
• Matching neutralizes confounding
Confounding: essence and detection (Miettinen & Cook, 1981)
11
Matching* in cohort studies
Uncor
relat
ed
Genetic factor
Smoking Lung cancer
Smoking effect*e.g., twins with discordant exposures 12
Matching in case-control studies
Mode of “differential selection according to the health outcome of members of the source population”
• introduces a link between covariate and outcome that is absent in the source population
• Matching introduces confounding if data are not analyzed as matched data.
Confounding: essence and detection (Miettinen & Cook, 1981)
13
Matching* in case-control studies
Distorted down
by selection
Genetic factor
Smoking Lung cancerSmoking effect
*e.g., Controls matched to cases on NAT2 acetylation14
Historical example of strong confounder: Poppers and Kaposi’s Sarcoma
• Kaposi's sarcoma is a cancer that causes patches of abnormal tissue to grow under the skin, in the lining of the mouth, nose, and throat or in other organs.
• Before the AIDS epidemic, KS usually developed slowly. In AIDS patients, though, the disease moves quickly.
15
Poppers• Chemicals: Alkyl Nitrites, these include
Butyl Nitrite, Amyl Nitrite and Isobutyl Nitrite.
• Street names: Rush, Thrust, Liquid Gold, Ram, Rock Hard, TNT, Amyl and others.
•Taken by inhaling the vapor•Used as aphrodisiacs
16
Case-control Study of Kaposi’s Sarcoma and Poppers, 1982
Odds ratio = 8.5
17
HIV
Poppers Kaposi’s Sarcoma
Confounded effect?
?
1983: Discovery of viral cause of AIDS (now known as HIV)
18
Stratification by HIV
HIV adjusted Odds ratio = 3.9Morabia, 1995
19
HHV-8
HIV Kaposi’s Sarcoma
Confounded effect?
?
1994: Discovery of Kaposi's sarcoma-associated herpesvirus (KSHV or HHV-8)
Potential outcome causal models
The problem in measuring y(Exposed)-y(Control)
is that we can never observe both y(Exposed) and y(Control) in the same individual since we cannot return back in time to give the other treatment (Neyman, 1923, Rubin, 1974, 690).
20
21
Potential outcomes and causal types
Greenland S, Robins JM (1986) Identifiability, exchangeability, and epidemiological confounding. Int J Epidemiol 15: 413-419
22
Imagine a centenarian woman who is vaccinated against the flu. She may get the flu anyway:• Because she does not respond to vaccine
(“doomed” to get the flu)• Vaccine caused the flu (attenuated virus)
(“causative”)May not get the flu because:• Vaccine worked (“preventive”)• Insensitive to flu (“immune”)
Causal types and their outcomes according to two potential outcomes if exposed.
Types n Description Vaccinated Not vaccinated
Doomed d Does not respond to vaccine
Flu Flu
Preventive p Responds to vaccine No Flu Flu
Causative c Vaccine induces Flu Flu No Flu
Immune i Naturally insensitive to flu
No Flu No Flu
23 24
“Identifiability”
To “identify” the effect of vaccine in this centenarian woman
• We would need to see how she would have reacted had she not been exposed to the vaccine
• This is impossible• Causal effects cannot be identified in
single individuals
25
Effect identification for populations• We can select two groups of people
with similar characteristics, who, on average, have the same risk of getting the flu
• Probabilistic model • RCTs provide optimal conditions• What does this mean in terms of
causal types?
26
Group composition
Vaccinated, R(vaccinated) = (d + c)/N
Not vaccinated, R(Not vaccinated) = (d + p)/N
d = number “doomed”p = number “preventive”c = number “causative”i = number “immune”N = total number
27
Assume that both groups have identical composition (=exchangeable)
Risk difference = R(Vaccine -)-R(Vaccine +)= [(d + p) – (d + c)]/N = (p-c)/N
If vaccine contains only split viral particles, c=0 and
RD = p/N
28
If groups are not identical (=not exchangeable) => confounding
Risk difference = R(Vaccine -)-R(Vaccine +)= (d + p – d* – c)/N
If vaccine contains only split viral particles, p=0 and
RD = (d + p – d*)/N
Historical importance of potential outcome definition of confounding
• Reconnected theory of confounding with its historical roots: a problem of non-comparability between groups.
• Facilitates the understanding of the concept (commonsensical)
• Subsumes the fallacy definition of confounding
29
Correspondences between PO and techniques to deal with confounding
1)structural equations, 2)Directed acyclic graphs3)G estimation
30
31
Pollution
Treatment
Bronchial reactivity
Sex
Asthma
Equivalent to Figure 5 in Greenland et al, 1999
Directed Acyclic Graph: Should you adjust for Bronchial Reactivity?
The short but winding history of confounding19TH C 1904-
19391926-1959
1970-1986
1986 -now
Comparability YuleGreenwoodHill
Fisher
Simpson Neyman
Kish MacMahonSusserMiettinenRothman
Rubin
Greenland and Robins
32
33
Word “interaction” in the modern epidemiology literature
0
2
4
6
8
10
12
14
16
18
1974 1975 1980 1990 2000 2006
Am J EpidemiolInt J Epidemiol
Rothman, 1974, Synergy and antagonism in cause-effect relationships 34
Landmark Epidemiologic Papers
• Rothman KJ. Synergy and antagonism in cause-effect relationships. Am J Epidemiol. 1974;99:385-8.
• Rothman KJ. Causes. Am J Epidemiol 1976;104:587-92.• Kupper LL, Hogan MD. Interaction in epidemiologic
studies. Am J Epidemiol. 1978;108:447-53.• Hammond EC, Selikoff IJ, Seidman H. Asbestos exposure,
cigarette smoking and death rates. Ann N Y Acad Sci 1979;330: 473-490
• Rothman KJ, Greenland S, Walker AM. Concepts of interaction. Am J Epidemiol 1980;112:467-70.
35
Interaction and component causesRothman’s pies (1976)
Component causes
36
Additional developments
• Parameter-dependence of interaction assessment
• Relative interaction magnitude• Case-only studies (reciprocity of interaction)• Subgroup analysis • Parallelism• …. But little consequences for public health
and medicine
ExerciseEvolution of Epidemiologic Ideas
Misclassification Bias
Copeland KT, Checkoway H, McMichael AJ, Holbrook RH (1977) Bias due to misclassification in the estimation of
relative risk. Am J Epidemiol 105: 488-495
Copyright: Alfredo Morabia@2008
37
Question 1: simplifying assumptions
1) Misclassification of one variable only
2) No selection bias3) Dichotomous variables
38
Question 2a: Main determinants
1) Specificity 2) Magnitude of risk
Question 2b: Sensitivity=Specificity=0.5
RR=1.0
39
Question 2c: apparent RR if sensitivity=0.9 and specificity=0.7
RR=1.1
Question 2d: apparent RR if sensitivity=0.7 and specificity=0.9
RR=1.2
40
Question 2e: Most important of sensitivity or specificity?
Specificity
Question 2f: If specificity=0.9, apparent RR for risks of a)0.2 and 0.1? B) Of 0.05 and 0.025?
a)RR=1.4b)RR <1.2
41
Question 2g: Most important for rare or common diseases?
Rare
Question 3: 2x2 table compatible with the true relative risk of Figure 1.
Disease +
Disease -
Cohorts
Population A
10 90 100
Population B
5 95 100
42
True RR = 0.1/0.05=2.0
43
Non Diff. Misclassification
•Similar validity of outcome measure for exposed and unexposed•True RR = 2.0•Sensitivity = 0.90•Specificity = 0.96•Apparent RR?
44
Sens= 0.90, Spec = 0.96
Group True N
Misclassification Obs N
A, D+ 10 10 x 0.90 + 90 x 0.04 12.6
B, D+, 5 5 x 0.90 + 95 x 0.04 8.3
A, D- Not needed
B, D- Not needed
RR 2.0 1.5
Question 3b: Checking RR=1.5 on figure 1
45 46
Modern Epidemiology Textbooks
• Groundbreaker– Miettinen: Theoretical Epidemiology (1985)
• Clarification (but not only).– Kleinbaum, Kupper, Morgenstern: Epidemiologic
Research (1982) – Rothman: Modern Epidemiology (1986)– Rothman & Greenland: Modern Epidemiology (1998)– Szklo and Nieto. Epidemiology, Beyond the Basic (2000).
• Specialized epidemiology– Many texts
47
Modern epidemiology
• Texts cover all main methods and conceptsBut• More abstract• More mathematical• Not always directly relevant for everyday
practice
48
K.J. Rothman
O. Miettinen
Modern epidemiologists
S. Greenland
M. Szklo
49
‘Modern’ definition of epidemiology
“The ultimate goal of mostepidemiological research is the elaboration of causesthat can explain patterns of disease occurrence”
(Rothman and Greenland, 1998, p. 29)50
Criticisms of classic/modern epidemiology
• “Prisoner of the proximate”• Single level of organization• Preference for distant risk factors and
blindness to underlying causal pathways
• Point vs. lifecourse exposures• Complexity vs. parsimony
(reductionism)
51
Emerging attempts to move beyond modern epidemiology
• Dig deeper into pathways structure and mechanisms– Levels of organization (from cell to society)– Causal Pathways
• Refine inferential process– Causal models– Bayesian approaches
Title History of epidemiologic ideas
2
Lecture 13:Evolution of Causal
Inference
Ωασ Ηιπποχρατεσ αν επιδεμιολογιστ?Οριγιν οφ επιδεμιολογιχ μετηοδσ ανδ χονχεπτσ
4
Hippocrates (BC460-BC377)
• Independent and ambulatory physician, born on the Island of Cos, between current Greece and Turkey.
5
Hippocrates (BC460-BC377)
• Hippocratic treatises, written by Hippocrates and others after him
Relate diseases to environmental or other natural causes
No religion or magic involved in etiology or treatment.
Propose empirical treatments such as surgery, diet, herbal remedies, etc.
6
Is Hippocrates the “father of epidemiology?”Major Greenwood, 1935, p.18:
“Although Hippocrates was before all else a clinician, he was also a student of preventive medicine and epidemiology, of the doctrine of disease as a mass phenomenon, the units not individuals but groups”
Also: MacMahon et al., 1970; Lilienfeld and Lilienfeld, 1980; Pan American Health Organization, 1988
7
Hunting for Fathers
“The very popular hunting for ‘Fathers’ of every branch of medicine and every treatment is rather foolish; it is unfair not only to the mothers and ancestors but also to the obstetricians and midwives”
Henry Sigerist, 1951
8
Environment on health in Hippoc treatises
The traveling physician arriving to a foreign place has to take into account:
• 1) “the seasons of the year • 2) “the winds, the hot and the cold,
especially such as are common to all countries, and then such as are peculiar to each locality.”
• 3) “qualities of the waters”
“On Airs, Waters and Places”
9
Example: Epidemic of mumps
“The whole constitution of the season being thus inclined to the southerly, and with droughts early in the spring, from the preceding opposite and northerly state, ardent fevers occurred in a few instances, and these very mild, being rarely attended with hemorrhage, and never proving fatal.
Swellings appeared about the ears, in many on either side, and in the greatest number on both sides, being unaccompanied by fever so as not to confine the patient to bed”
~ The Book of Prognostics10
Causal Thinking
“The other races in Europe differ from one another, both as to stature and shape, owing to the changes of the seasons …
for vitiations of the semen occur in its coagulation more frequently during frequent changes of the seasons, than where they are alike and equable.”
Hippocrates, Airs Waters and Places
TMK, None of it valid today
11
• Cases are described one by one• Never in an aggregated form• No quantification of event
occurrence in populations or groups.
Thus:
Individual thinking
No population thinkingTherefore no group comparisons
12
Birth of epidemiology Group comparison and population thinking appear very late in human history (> 17th
century, England).So does epidemiology
Epistemological (philosophical) obstacle: infer causality on the basis of probabilistic, non deterministic statements
But in the 17th century, physics is already 2000 years old
13
1739
David Hume (1711-1776)
“Rules by which to judge of causes and effects"
And HUME: inference is shifted from the individual to the group. • “It appears, then, that this idea of a necessary connection
among events arises from a number of similar instances, which occur, of the constant conjunction of these events; nor can that idea ever be suggested by anyone of these instances, surveyed in all possible lights and positions. But there is nothing in a number of instances, different from every single instance, which is supposed to be exactly similar; except only, that after a repetition of similar instances, the mind is carried by habit, upon the appearance of one event, to expect its usual attendant, and to believe, that it will exist. This connection, therefore, which we feel in the mind, this customary transition of the imagination from one object to its usual attendant, is the sentiment or impression, from which we form the idea of power or necessary connection. ”
An Enquiry … 1748
14
15
Ometeotl’s battle for the sun to rise
The Mexica thought that the sun could only shine when the strife among Ometeotl’s sons quieted, but in addition it had to battle the stars and moon every day as it rose in the sky – struggle of light against darkness. The Mexica expected the sun to one day inevitably lose.
16
Hume’s Rules 1 and 2
"The cause must be prior to the effect”
Temporality
17
Hume’s Rule 7
"When any object increases or diminishes with the increase or diminution of its cause, 'tis to be regarded as a compounded effect”
Dose-Response
18
Hume’s Rule 4
"Same cause always produces the same effect, and the same effect never arises but from the same cause"
Specificity
19
Hume’s premises of the catalog of Rules
"Multiplicity of resembling instances constitutes the very essence of power or connexion“
Consistency
20
Hume’s Rules 5 and 6
"Where several different objects produce the same effect, it must be by means of some quality, which we discover to be common amongst them"
"Like effects imply like causes"Analogy
21
Hume’s rule 7"There must be a constant union betwixt
the cause and effect. 'Tis chiefly this quality, that constitutes the relation" (Rule 3)
“This constant conjunction sufficiently proves that the one part is the cause of the other"
Strength of association?22
Theory of group comparison?
John Stuart Mill(1806-1873)A System of Logic
23
Mill’s System of Logic= theory of comparison
• Method of agreement: “the only one circumstance in common”
Method of Difference: when“every circumstances in common save one”, the latter is the cause
Method of concomitant variation:∼correlation
24
Hume
Mill
Hammond
Relevance of Hume and Mill for epidemiology?
25
Early systematic expression of epidemiologic causality - 1955
E. Cuyler Hammond (1912-1986)26
EC Hammond, Cause & Effect, 1955Principles of epidemiology: “ In epidemiologic research the problem is to
discover causative factors which increase the probability of human disease ”
Methods: “ prospective, cause to effect” and
“retrospective studies, effect to cause”designs
Population Thinking
Group comparison
27
EC Hammond, Cause & Effect, 1955 (2)Criteria to rule out “third factor” (= confounding)• Temporality• Specificity of cause or of effect• Strength of association• Experimental evidence• Biological plausibility• Consistency (time trends and ecologic studies)
28
Hume
Mill
Hammond
Hill
29
Hill ’s « criteria » (1965)• Strength of association• Temporality• Dose-response Gradient**• Specificity of effect or cause• Consistency **• Experimental evidence• Plausibility• Analogy **
**Not or different in Hammond, 1955
30
Heritage
Hume's rules sound reasonable to us
Hammond and Hill’s criteria would have sounded reasonable to Hume.
31
Causal inference in epidemiology
Checking the logical coherence of observed associations.
Completely absent in Hippocrates’s work
Rule out contradictions between these associations and what we think we already know.
Principles expressed by Hume and Mill essentially unchanged for 250 years
32
Why Hippocrates could not be an epidemiologistEpidemiology appears very late in human history (2000 years after physics) because:
=> Genetic epistemology
1. Could not be present before there existed a theory of probability (around 1650)
2. Its basic concepts (risk/rates, cohort and case-control studies, confounding, interaction) are not intuitive. For many (most?) people, epidemiology is a mysterious discipline, understood by only a few
Title History of epidemiologic ideas
2
Lecture 14:Genetic Epistemology
of Epidemiology
3
Ex# 7: Genetic epistemology
Jean Piaget, (1896-1980)
4
Piaget’s Genetic Epistemology
• The historical evolution of sciences follows a path resembling that of human intelligence
• Early disciplines (e.g., mechanics) are based on common sense concepts (e.g., trial & error)
• More recent disciplines (e.g., epidemiology) rely on concepts (e.g., speed) that are understood by older children or adults only
• Scientific disciplines themselves evolve away from common sense (e.g., quantum ph., modern vs. classic epidemiology)
5
1.a. How Do Scientific disciplines evolve?
• Common sense experiments, solve simple problems
• Formalized (w/ concepts, theories) experiments, solve more complex problems
• Phenomena addressed growth in complexity and the move away from common sense
• Understanding advanced methods and concepts requires more expertise
• Methods at a given time reflect the state of evolution of the discipline
6
1.b. How do physicists acquire new knowledge?
Physics•Very old science•Learning from action/reaction
(trial/error) is a very primitive mode of acquiring knowledge
7
1.c. History of Physics
•Archimedes: common sense experiments
•Galileo, less common sense•Newton, predicts non-intuitive
phenomena•Einstein, Bohr, beyond common sense
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2.a. How do epidemiologists acquire new knowledge?
• Action = group comparison, which requires population thinking.
• Very elaborated and abstract mode of knowledge acquisition
• Epidemiology is a young science
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2.b. What kind of evolution do you see between Snow, Goldberger, Frost, MacMahon, and Miettinen?
• Pre-formal epidemiology (Snow): Common sense experiments, solve simple problems
• Early epidemiology (Goldberger, Frost): Formalized (w/ concepts, theories) experiments, solve more complex problems
• Classic epidemiology (MacMahon): Phenomena addressed growth in complexity and the move away from common sense
• Modern epidemiology (Miettinen): Understanding advanced methods and concepts requires more expertise
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3.a. Evolution of Population Thinking:
• Preformal: intuitive risk & rates• Early :formal risk and rates,
person-times• Classic: prevalence and incidence• Modern: CI = Σ ID
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3.b. Evolution of group comparison1. Pre-formal comparison (Louis)2. Early: Formal comparison
(Weinberg, Lane-Claypon), separation of study designs
3. Classic: Relation of case control study to cohort study
4. Modern: Theoretical unity of study designs
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3.c. Evolution of confounding1. Pre-formal: Intuition – Non-
comparability2. Early: Fallacy (Yule, etc.), RCT3. Classic: Formal definitions, analytic and
fallacy-based4. Modern: a) Confounding in cohort vs
case-control studies; b) Definition based on potential outcomes.
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3.d. Evolution of interaction1. Preformal – Complex thinking2. Early: ?? (probably none)3. Classic: Departure from additive
or multiplicative models 4. Modern: Parallelism, definition
based on potential outcomes
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Course Conclusions (1)1) Epidemiology is characterized by the
combination of population thinking and group comparisons aimed at discovering the determinants of human health.
2) The set of methods (study design) and concepts (measures of disease occurrence, confounding, interaction, bias) have evolved since the 17th century. This evolution is consistent with Piaget’s theory of genetic epistemology.
Course Conclusions (2)3) In this evolution, we can identify four phases
characterized by quantitative leaps in formalization and abstraction of methods and concepts. After a preformal phase, in which epidemiology was discovered intuitively by scientists, epidemiology has gone through an early, a classic and a modern period.
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4) The fifth phase (Complex epidemiology? Causal epidemiology?) is emerging
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Book