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)