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Epidemiology E2200b
Dr. John Koval
Professor of Biostatistics
Department of Epidemiology & Biostatistics
University of Western Ontario
With thanks to
Dr. Mark Speechley
Professor of Epidemiology
Department of Epidemiology & Biostatistics
Course Objectives
You will be able to:• understand methodological foundations of applied
human health research• critically appraise original articles about things that are
claimed to be ‘good’ and ‘bad’ for us• perform fundamental calculations using published data• discuss why studies of the same question can get
different answers (and why this doesn’t mean the science is flawed)
• list the bases for criticisms and misunderstandings of the science of epidemiology (know the true rather than the imagined limitations)
Objectives of Lectures 1&2
You will learn definitions, key concepts, history, modern applications
You will be able to:• correctly use some terminology• describe historical roots, evolution of modern
epidemiology• recognize epidemiology as a basic science
for clinical medicine, public health, health services research, outcomes research, etc.
Epidemiology: Informal definition
The branch of medical science that helps us identify factors that:
– Keep us healthy (part of ‘health promotion’)– Make us sick (etiologic research)– Help us get better again (therapeutic
research)
“Identifying factors” is NOT the same as “understanding causal mechanisms”
Some Results
Epidemiological methods have discovered numerous
causal factors of health outcomesThese findings underlie:
massive behavioural change after 1950evidence-based health care & health policy
Disagreements among studies are inevitable and do not signify weaknesses of the methodology.
Identify factors that:
• Keep us healthy: physical activity, fruits and vegetables in diet, vitamins and minerals, clean air and water, vaccines
• Make us sick: deficiencies of the above factors; smoking; (some) bacteria, viruses, parasites
• Help us get better again: pharmaceuticals, surgery, rehabilitation
Putative (potential) causal factors precede causal mechanisms
• Often begins with a clinical observation
1774 Dr. Percival Pott – noticed cancer of the scrotum in chimneysweeps, implicated ‘something in soot’
• Mechanistic knowledge takes years to develop;
we now know that soot contains polycyclic aromatic hydrocarbons that lead to squamous-cell carcinoma
Chimney Sweeps (con'd)
Chimney Sweeps Act :
Sweeps must be at least 8 years old
Sweeps must be provided suitable clothes
Other causal risk factors that began with clinical hunches
Exposure• Cigarette smoke
Disease/outcome• Lung cancer (1940s)
Significance? Lung cancer was once very rare. Beginning of epidemic observed among soldiers who had started smoking in WWI. Became the leading cancer death.
rubella
Exposure
Maternal rubella
(red measles)
Viruses not previously known to cause birth defects
(‘teratogenic’). All women planning pregnancy now immunized
Disease/outcome
• Birth defects (1940s)
What is ‘causation’?Experiment 1:Fred is exposed to A ….. [time passes] …… Fred gets Disease B [turn back the clock, hold everything else constant]
Experiment 2:Fred is not exposed to A … [same time passes] … Fred does not get Disease B
We can define a causal exposure as i) one that is followed by a disease outcome ii) that would not have occurred had the exposure not occurred iii) all else held constant.
√ The perfect research design. √ Proves 100% causal certainty in individuals. (Unfortunately, we cannot reverse time.)
Dr. Mark’s Magic Potion(A late night infomercial)
• Hi, Friend. Want to ace your grades in university? Well, Dr. Mark has been teaching for years and has concocted a Magic Study Potion in his kitchen laboratory. If it doesn’t increase your marks by one full letter grade, return the unused portion of the product and I’ll cheerfully refund the unspent portion of your money!….And that’s not all!!....Order now and you’ll receive absolutely free….
Evaluating causal claims
• The Magic Potion claims to causally increase students’ grades.
.
• Is there a way to prove with 100% certainty that any student’s grade was or was not affected by the Magic Potion?
Causal Certainty Necessity and Sufficiency Criteria
Necessary Cause: The Magic Potion is necessary for increased grades: only students who took my potion increased their marks by a full letter grade; none others did.
Sufficient Cause: The Magic Potion is sufficient for increased grades: every student who took my potion increased their grades.
A perfect correlation!
Took potion
Increased one letter grade Yes No
Yes Sufficiency(all exposed have outcome)
n/a
No n/a Necessity (no unexposed have outcome)
How many biological, psychological or sociological causes can you name that meet both necessity and sufficiency criteria?
How many can you name that meet even one of these criteria?
Causation and Correlation
Causation occurs when factor A leads to (or causes) factor B
Correlation happens when factor A and factor B are related, so that when factor A is present, factor B often is present,
and visa versa
“You can’t prove causation with correlation”
• True, but:– You don't need to know the exact cause before doing
something– We don’t need to understand a causal mechanism to
act to reduce exposure– Dozens of examples exist where epidemiologic
associations have subsequently been demonstrated to be causal.
– If an association is causal, every day we fail to act out of scientific prejudice, people will needlessly get ill or even die.
We will be wrong sometimes.
For example, in the investigation of an Hepatitis A outbreak hot dog sausages were implicated.
However, the source of the bacteria was actually the relish.
Since the whole consignment was thrown out, people were spared from the disease, although the actual mechanism was not clear.
“You can’t prove causation with correlation” is true, but…
• You can’t prove causation without correlation either.
• All identified causes began with observed correlations.
The problem isn’t correlation, it’s failure to control for CONFOUNDING – other explanations that could account for the correlation.
How to prove causation?ApproachesBest: expose Person A,
observe; go back in time, remove exposure, observe and compare
2nd Best: random assignment to exposure (Experimental)
3rd Best: observe people in different exposure groups
(Observational)
LimitationsCan’t do time travel:“counterfactual”
Unethical with negative outcomes
Often impractical (time)
Potential for confounding*
*Latin, confundere (pour together; confuse)
2nd best:The RCT (Random Controlled Trial)• Randomize students to Magic or Placebo Potion:
All known and unknown factors are distributed by chance
• Collect data on factors that could affect grades, compare two groups at baseline (e.g. “Table 1” in a paper), should be similar as the sample size increases
• If imbalanced, can statistically adjust final estimates• Observe between-group difference in grades
3rd best: Observational Designs• Are not true experiments• People select themselves into exposures • Unknown or unmeasured factors
(confounders) could be the true cause of any observed difference
• As our theory improves (as we can explain a larger portion of the variation in outcomes) so does our ability to estimate the true causal effect of any single factor
The role of confounding
Cigarette Smoking
DiseaseCoffee consumption
Non-causalassociation:heavy smokerstend to be heavy coffeedrinkers
True causal effect
Spurious association
Smoking, a true cause of disease, will confound (bias) the association between coffee and disease. The apparent association
with coffee is due to the correlation between coffee and smoking.
Confounding (con'd)If you measure association of smoking and
cancer in the presence of a measurement of coffee consumption, the true effect of smoking will be diminished
Coffee consumption is a confounder of the Smoking – lung cancer relationship
Determination of actual risk factor and actual confounder depends on other (clinical) studies
“study” can be:
• Surveillance – (e.g. mandatory disease reporting)
• Descriptive (hypothesis generating)– (e.g. proportion of pregnancies that end in
miscarriage/stillbirth, by characteristics of person, place and time)
• Analytic (hypothesis testing)– (estimates of X-Y association from
observational studies)
• Experiments (clinical trials)
“distribution” (Porta, 2008)“The complete summary of the frequencies of the
values or categories of a measurement made on a group of persons. The distribution tells either how many or what proportion of the group was found to have each value (or each range of values) out of all the possible values that the quantitative measure can have”.
Usually presented broken down by characteristics such as person, place, and time.
Age distribution of percentage of pregnancies ending in miscarriage/stillbirth, by age of women at
end of pregnancy, Canada, 1974 and 1992
0
5
10
15
20
25
Allages
15-19 20-24 25-29 30-34 35-39 40-44
1974
1992
Source: Health Reports, Summer 1996, 8:13
%
“determinants”
“any factor that brings about change in a health condition or other defined characteristic. (Porta, 2008).
Identifying possible (and probable) causal factors is not the same as explaining causal mechanisms
If a factor is causal, reducing exposure will reduce outcome even if we don’t understand the mechanism
Analytic Epidemiology: Primary role is etiologic*
Exposures ('determinants')
For example,– Physical (ionizing
radiation)– Chemical (lead)– Biological (needlesticks)
– Social: educational attainment, poverty
– Behaviours: tobacco, diet
Outcomes (‘health related states and events’)
For example,• Diseases with biological
models
• Illnesses without biological models
• Injuries• Birth outcomes• Psychological states such as
QOL (Quality of Life)*Greek, aitia (cause)
Key concept: Reliable case definition
• Case definition: A set of criteria (not necessarily diagnostic criteria) that must be fulfilled in order to identify a person as a case of a particular disease (Porta, 2008:32)
– Clinical or Laboratory criteria or both– Scoring systems with points that match disease
features (e.g. Multiple sclerosis)
• Reliability: The degree to which the results obtained by a measurement or procedure can be replicated (Porta, 2008:214)
Key concept: Risk
RISK(def): The probability that an event will occur, e.g., that an individual will become ill or die within a stated period of time or age. (Porta, 2008:217)
Major aim of Epidemiology is to quantify the risk of developing disease or other negative health state posed by various exposures (molecules, microorganisms, environments, behaviors).
Probability
• Causation of health and illness is extremely complex
• Even widely agreed upon causes fail to meet necessity and sufficiency:– “Grandma smoked a pack a day and died peacefully
in her sleep at 110, and Uncle Elmo got lung cancer and never smoked”.
• We need to rely on probability statements: the probability of an outcome is 2, 3, 4.. times higher among exposed than unexposed
Observed versus predicted probability
Average (predicted) risks estimated from groups, used to advise individual patients: (e.g. risk of adverse surgical outcome; risk of cancer recurrence)
But! individuals will either have (risk = 100%) or not have (risk = 0%) an outcome over a specified time period (you can’t have ‘32% of a stoke’).
– Estelle, 28, never-smoker, former Varsity volleyball player, has a stroke. Observed individual risk of stroke for that year = 100%
– Jerome, 75, high blood pressure, smoker, does not have a stroke. His observed individual risk for that year = 0%
People like Estelle face a very low predicted risk; people like Jerome face a much higher predicted risk
History of Epidemiology
Epidemiology is a young science with ancient roots in the study of epidemics (def: “The occurrence in a community or region of cases of an illness, specific health related behavior, or other health-related events, clearly in excess of
normal expectancy.” Porta, 2008:79) “Clearly in excess” differs by disease and time
frame (e.g. H1N1 or Lung Cancer)
Began with communicable diseases; methods have been adapted for chronic diseases and other health states and events (injuries, birth outcomes, etc)
Demons, Miasms* and Germs
Epidemiologic insights (e.g. events are not random) are clear in the writings of Hippocrates 2500 years ago.
Millenia passed before we had the intellectual foundation to scientifically test 2 competing hypotheses about the causes of epidemic diseases
Key period: 1850s England: Drs. John Snow (cholera) and William Budd (typhoid fever)
*From Greek, miainein (to pollute).
2 theories of epidemic disease
Miasmatic (miasma)• Air has a ‘bad quality’• Rotting organic matter• ‘Miasma’ could be
passed from cases to susceptibles in contagious diseases
Contagion• Invisible entities• Spread through direct
contact, droplet spread or contaminated fomites
Most physicians supported miasma; it explained the facts better:• didn’t know that asymptomatic people could be infectious
(‘well carriers’)• Didn’t know about immunity
Malaria (‘bad air’): A classic case of confounded association
Swamps (musty air) Malaria
Highlands (fresh air)No
Malaria
Confounder____________
True cause
Spurious association
Solution; leave swanp, What’s the true cause (vector) of malaria?
1850s England: Urbanization, industrialization, poverty, crowding,
filth and epidemic disease
Increasingly scientific medical profession continued to favour miasmatic theory over contagion:
• London, 1854: > 500 cholera fatalities within 250 yards of Cambridge and Broad Streets in a 10 day period. (Probably a greater epidemic than previous plague outbreaks).
Lack of Sanitation
“Miasms”
GermsDisease
William Farr
Table 1-4. Deaths from Cholera in 10,000 Inhabitants by Elevation of Residence above Sea Level, London, 1848-1849
Elevation above Sea Level (ft) Number of Deaths
<20 120
20-40 65
40-60 34
60-80 27
80-100 22
100-120 17
340-360 8
Data from Farr W: Vital Statistics: A Memorial Volume of Selections from the Reports and Writings of William Farr (edited for the Sanitary Institute of Great Britain by Noel A. Humphreys). London, The Sanitary Institute, 1885.
John Snow, M.D. (1813-1858)
www.ph.ucla.edu/epi/snow.html
• 1847- theory that cholera is communicable and waterborne
• Used spot maps of cases’ residences, compared to location of public water pumps
• Eventually convinced Parish authorities to remove Broad Street pump handle during August-September 1854 epidemic (new cases increased, slightly)
Snow's diagram
Modern day view
The pump on
Broadwick street
Plus the Pump Pub
Cases of Cholera by date of onset, London, Aug. 19 – Sept. 19, 1854
0
20
40
60
80
100
120
140
160
Fatal attacksDeaths
Epidemic curve adapted from Roht et al, 1982:300
Pump handleremoved
August September
f
“Natural Experiment”London England, ~1853
• 2 major water suppliers: Lambeth, and Southwark & Vauxhall
• Lambeth moved their intake to a cleaner section up river
• Interviewed household members to ascertain which of two companies supplied their water
• Compared 1853 cholera cases according to water company (retrospective study)
Cholera mortality by water supply, 1st seven weeks of epidemic
(Roht et al, 1982:304)
Water Supply
# houses Cholera Deaths
Deaths/10,000 houses
Southwark & Vauxhall
40,046 1,263 315.4(p = .03154)
Lambeth Co.
26,107 98 37.5(p = .00375)
Rest of London
256,423 1,522 59.4
Dr. Snow expressed cases per 10,000 (risk)
Epidemiologic measures of association: Relative Risk*
One form of Relative Risk = Risk Ratio
Deaths/10,000 exposed (S&V) = 315.4 = 8.4Deaths/10,000 unexposed (Lambeth) 37.5
Mortality was 8.4 times more common in S&V houses than in Lambeth houses.
Based on these non-experimental (non-randomized) findings, who here would choose S&V?
*Relative risk is a generic term encompassing several measures of association between exposure and outcome; see Notes.
The Establishment reacts:
• The Lancet: “not by any means conclusive”• Royal College of Physicians: “theory as a whole is
untenable” … continued to support “foul or damp air” as the cause
• Board of Health Medical Inspectors: “We see no reason to adopt this belief” (1854)
• “far-fetched doctrine”(Chapman, 1866)• 1884, Robert Koch (Nobel, 1905) identified Vibrio
Cholerae, made no mention of Snow’s work
This is, unfortunately, not uniqueMany epidemiologic findings, even after multiple
replications and systematic testing and rejection of bias explanations, are stubbornly resisted. Why?
• economic self-interest • resistance to behavioral change • unwillingness to admit past practices killed people
Unfortunately, isolated first findings are often given the most sensationalistic media coverage
55 Contributions of Epidemiology to Public Health:
Diana Pettiti, MD: “55 Triumphs of Epidemiology” 10 Exposure categories:
Alcohol, viruses, bacteria, nutrition, occupation, environment, drugs & devices, hormones, genetics and ‘miscellaneous’.
Ref: www.epimonitor.net/EpiMonday/ Triumph62501/.htm
Inclusion criteria for this list:
2. the initial hypothesis was derived from an epidemiologic study (sometimes incidentally) and subsequently confirmed as causal in a clinical trial or observational study,
3. an initial clinical observation was made or a cluster was noted and subsequent epidemiologic studies were able to explain the initial observations to discover or establish the risk or protective factor.
1. Widespread agreement that the association is causal, 1. Widespread agreement that the association is causal, ANDAND
OR
Exposure category 1: Alcohol
Alcohol interacts with smoking to cause esophageal cancer
Exposure 2 : Viruses (and prions)
DiseaseLiver cancerBurkitt’s lymphomaKaposi’s sarcomaCervical cancerNasopharyngeal cancerYellow feverCreutzfeldt-Jacob
VirusHepatitis BEpstein BarrHerpes Simplex Type 8Human Papilloma VirusEpstein Barr“Mosquitoes” (arbovirus)
Prions int. with genotype
Exposure 3: Bacteria
DiseaseCholera
Peptic Ulcer
Puerperal feverDr. IP Semmelweis: Discovery 1847Book 1861
Died in asylum 1865, age 47 yrs
Bacterium“something in the water”
(Vibrio cholerae)
Helicobacter pylori
"something on doctors’ hands“ (Streptococcus B)
(see Gordis, Chapter 1)
Exposure 4: Nutrition (note: Protective
effect of nutrient; or risk effect of deficiency) Disease/Birth Outcome
Pellagra
Neural tube defect
Oral clefts
Nutrient
"something in food” (lack of Niacin – vitamin B)
Folic acid (vitamin B12)
Folic acid
Exposure 5: Occupation
Disease Lung CA
Bladder CA
Mesothelioma
Angiosarcoma
Male infertility
Nasal CA
Lung CA
ExposureAsbestos (int. with smoking)
Aniline dye
Asbestos (int. with smoking)
Vinyl chloride
DBCP (soil fumigant)
Nickel
“something in uranium mines” (int. with smoking)
Exposure 6: Environment
Disease
Cancer
Dental caries(HT Dean, 1934)
Exposure
Arsenic
Fluoride (protective)
Fluoride (ppm)
Caries Fluorosis
0 1 2 3
(Illustration not to scale)
Exposure 7: Drugs & Devices
Disease
Myocardial infarction
Micrognathia
Pelvic inflammatory disease, Septic abortion
Drug or device
Aspirin (protective)
Isotretinoin in pregnancy
Dalkon Shield IUD
Exposure 8: Hormones (P) = protective
DiseaseClear cell vaginal
adenocarcinomaVenous thromboembolism
Endometrial CA
Ovarian CAIron deficiency anemiaBenign breast diseaseMyocardial infarction
Ischemic stroke
HormoneDiethylstilbestrol
Estrogen/progestin OC, postmenopausal estrogenEstrogen/progestin OC (P),
postmenopausal estrogenOral contraceptives (P)Oral contraceptives (P)Oral contraceptives (P)Oral contraceptives (int.
w/smoking OC (interaction with
hypertension, mod. by dose)
Exposure 9: Genetics
Disease
Breast CA
Ovarian CA
Colon CA
Mutation“something genetic”
(BRCA1 and 2)
“something genetic” (BRCA1)
“something genetic” (APC1)
Exposure 9: Smoking “leading single preventable cause of premature death and
disability”RiskLung CA, coronary
heart disease, hemorrhagic and ischemic stroke, abdominal aortic aneurysm, peripheral vascular disease, laryngeal CA, intrauterine growth retardation*
Protective
Parkinson’s Disease, ulcerative colitis, toxemia/pre-eclampsia*
(*smoking during pregnancy)
Exposure 9: Smoking “leading single preventable cause of premature death and
disability”RiskLung CA, coronary
heart disease, hemorrhagic and ischemic stroke, abdominal aortic aneurysm, peripheral vascular disease, laryngeal CA, intrauterine growth retardation*
Protective
Parkinson’s Disease, ulcerative colitis, toxemia/pre-eclampsia*
(*smoking during pregnancy)
Exposure 10: Miscellaneous
Disease / outcome
Toxic shock syndrome*
SIDS
Reye’s syndrome*
Exposure
Super-absorbent tampons
Prone sleep position
Aspirin (interacts with infection)
*First studies were case-control studies, an extremely efficientdesign well suited to quickly uncovering strong effects.
Aspirin and Reye’s Syndrome*: •Pediatrics, 1980 66(6):859-864• 1. Cases: 7 patients with Reye’s syndrome
• 2. Controls: 16 classmates
“all 7 patients took salicylates whereas only 8 of 16 controls did, p
< 0.05.” JAMA. 1982 Aug 13;248(6):687-91
1. Cases: 97, aspirin use in 94
2. Controls: 156 (matched for age, race, sex, geog. location, time and type of illness), aspirin use in 110
*A neurological condition with swelling of the brain and massive accumulations of fat in the liver. For more see www.ninds.nih.gov/disorders/reyes_syndrome/reyes_syndrome.htm
Estimate of Association: Odds Ratio
Aspirin Cases Controls
+ 94 110
- 3 46
OR = (94 x 46) / (110 x 3) = 13.1 (4.0 – 67.5)
Interpretation: The odds of exposure to aspirin was 13.1 times higher in cases than in controls
From JAMA. 1982 Aug 13;248(6):687-91
Does aspirin treatment for viral illness ‘cause’ Reye’s syndrome?
• Early 80s, US Surgeon General mandated warning labels on aspirin
• The precise cause(s) and role of aspirin remain unknown
A reasonable interpretation is thata true cause was identified using the case-control design.
What do physicians now recommend for children with viral illness?
Simmelweis and puerperal fever
Childbed fever – 25% mortality
Treated by doctors or midwives
Doctors came from autopsies of victims of childbed fever
Comparision of two treatment groups
Simmelweiss (con'd)
Insisted that doctors wash their hands after autopsies
Results of change
Semmelweiss (fin)
Doctors refused to believe that they could be causing disease
•Claimed that washing hands took too long•Semmelweiss was not “diplomatic”•
•He died in asylum in 1965•- fever caused by Streptococcus B•
•Doctors still fail to wash hands!!•
Jenner and Smallpox
Smallpox killed 400,000 per year, and caused blindness
Survivors could not be re-infected
Inoculated others (variolation), but some still died or infected others
Saw that dairy maids infected with cowpox did not get smallpox
“Vaccinated” others with cowpox, and they did not bet smallpox
1980 – smallpox erradicated from the earth
Fluoride and dental carries
Natural Experiment
Controlled trial
The end of fluouride?
Several Ontario communities (Waterloo, Windsor) have discontinued fluoridation
- some people object to “poison” in their drinking water
- although no ill effect has ever been shown
People don't trust science??
Outbreak (def):
• [A]n incident in which two or more individuals have the same disease, have similar symptoms, or excrete the same pathogens; and there is a time, place, and/or person association between these individuals.
http://www.fda.gov/ora/inspect_ref/iom/ChapterText/8_3.html#SUB8.3
Three main classifications of Epidemic Curves1. Point source
http://www.cdc.gov/cogh/descd/modules/MiniModules/Epidemic_Curve/page06.htm
One source of exposure over a limited time period, usually within one incubation period.
What might explain these ‘outliers’?
2. Continuous Common Source
Exposure over an extended period of time, possibly >1 incubation period.
Continuous source (con'd)
Downslope may be steep if exposure is removed, or gradual if the outbreak is allowed to exhaust itself.
3. Propagated (Progressive source)
Index case of disease serves as source of infection for subsequent cases who in turn infect more cases.
Usually contains series of successively larger peaks until the pool of susceptibles is exhausted or control measures are implemented. Incubation period of measles
Typical 10 daysRange 7-18 days
E. coli, Walkerton, ON May-June, 2000
http://ftp.cdc.gov/pub/infectious_diseases/iceid/2002/pdf/ellis_ver2.pdf
Points about epidemic curves
• Rapid increase in cases (steep left curve) suggests common source (e.g. water, food, air)
• Gradual increase suggests propagated (person to person) spread
• They’re either going up, or they’re going down: Interventions are often applied after the peak
• based on Cholera and E coli curves, interventions that we take to be efficacious were followed by slight increases in the number of cases!
• Both interventions possibly prevented second spikes, but could be interpreted incorrectly by people unfamiliar with epidemiology and the totality of facts as ‘making the problem worse’
1 Outbreak, 2 distinct phases: SARS 1 and SARS 2
Knowing what we know about the transmission of SARS, which form would you expect the epidemic curve to take?
• Examine the epidemic curves named “SARS 1” and “SARS 2” and classify then as Point Source, Continuous Common Source, or Propagated Source
“SARS 1” “SARS 2”
Epidemic curves of SARS 1&2
• What factors might account for the different left-hand slopes of the 2 curves?
• Hints: – What was the major clinical feature of SARS?
(severe acute respiratory syndrome)– Where were they treated?– How is respiratory distress treated?
What was the cause of …?
2000 Walkerton outbreak
SARS 1?
a) Rainfall?b) Infected cattle?c) Drinking on the job?d) E Coli?e) All of the above
f) International travel?g) A ‘new’ coronavirusa) Hospitalization?b) (to some extent, all of
the above)
The point: Even microbial epidemics are multifactorial: several ‘causes’ operating at several levels.
Outbreak investigations (Summary)
• Simple graphical data summaries can be very useful
• Visual correlation can identify a ‘cause’ (e.g. rainfall in Walkerton) but can also lead us astray (e.g. post-intervention ‘rebounds’)
Summary: Exposures and Outcomes
Epidemiology specializes in
1. obtaining the most precise estimates possible of the association between a variety of exposures and a variety of health outcomes in free living community populations, and
2. in ascertaining which associations are likely causal and which are due to confounding or other forms of bias
Main variants: Population and Clinical Epidemiology
POPULATION• focus on applying
results to improve the health of a population
A population is more than the sum of its parts; individuals affect each other; risks cluster
CLINICAL• focus on applying
results to improve the health of individuals
Grew from realization that scientific basis of health care was weak esp. regarding outcomes
Conclusions I
• Epidemiology can integrate evidence from the molecular to the population level.
• Methods are applicable to any exposures and outcomes that can be measured reliably.
• Can identify risk and protective factors.
• Population & Clinical epidemiology share many methods but have different goals.
Conclusions II
• Epidemiology has identified numerous causal factors decades before their mechanisms were understood
• Findings are resisted, often beyond a reasonable standard of prudent skepticism, given the particular costs of acting (if the findings are spurious) versus the costs of not acting (if the findings are correct).
• We will be wrong, from time to time.
Epidemiology* (definition)
• “the study of the occurrence and distribution of health-related states or events in specified populations, including the study of determinants influencing such states, and the application of this study to control health problems” (Porta M. A Dictionary of Epidemiology, 5th ed, 2008:81). (emphases added)
*From Greek; epi (upon) dēmos (people), logos (word, reason)
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