o verview of the field of environmental epidemiology lydia b. zablotska, md, phd associate professor...

OOverview of the field of verview of the field of Environmental EpidemiologyEnvironmental Epidemiology

Lydia B. Zablotska, MD, PhDAssociate ProfessorDepartment of Epidemiology and Biostatistics

ObjectivesObjectives

• Review of study designs• How to choose a study design appropriate for

a specific question• Exposure assessment• Dose modeling

Natural Progression in Epidemiologic Natural Progression in Epidemiologic ReasoningReasoning

1st – Suspicion that a factor influences disease occurrence. Arises from clinical practice, lab research, examining disease patterns by person, place and time, prior epidemiologic studies

2nd – Formulation of a specific hypothesis

3rd – Conduct epidemiologic study to determine the relationship between the exposure and the disease. Need to consider chance, bias, confounding when interpreting the study results.

4th – Judge whether association may be causal. Need to consider other research, strength of association, time directionality

Hypothesis Formation and TestingHypothesis Formation and Testing

• Clues from many sources and imagination lead to hypothesis formation (inductive vs. deductive reasoning)

• Conduct epidemiologic study to test hypothesis

• Classifications by:

• approach to data collection

• goal

• timing and directionality

• unit of analysis

Epidemiological MethodsEpidemiological Methods

• Experimental

–RCTs, field trials, community intervention

and cluster randomized trials

• Quasi-experimental

–natural disaster studies

• Non-experimental or observational

–cohort, case-control, ecological

Classification by approach Classification by approach to data collectionto data collection

Classification by goalClassification by goal

• Descriptive

– ecological correlational studies, case reports, case

series, cross-sectional surveys

• Analytic

– observational studies and intervention studies

(RCTs)

Classification by timing and Classification by timing and directionalitydirectionality

– Directionality: "Which did you observe first, the exposure or the disease?“

– forward (RCT, cohort) – backwards (case-control)

– Timing: “Has the information being studied already occurred before the study actually began?"

– retrospective and prospective cohort studies

Classification by timing and Classification by timing and directionalitydirectionality

past present future

x

RCT

exposed

unexposed

Diseased

Non-diseased

Diseased

Non-diseased

Retrospective cohort study

exposed

unexposed

Diseased

Non-diseased

Diseased

Non-diseased

Classification by unit of analysisClassification by unit of analysis

– What is a unit?

• Observations for which outcome and exposure are

measured

– Individual-level variables are properties of individuals

– ecological variables are properties of groups,

organizations or places

Descriptive EpidemiologyDescriptive Epidemiology

• Describe patterns of disease by person, place, and time

• Person: Who is getting the disease? (for example, what is their age, sex, religion, race, educational level etc?)

Mortality rates per 100,000 from diseases Mortality rates per 100,000 from diseases of the heart by age and sex (2000)of the heart by age and sex (2000)

What hypotheses can you generate from these data?What hypotheses can you generate from these data?

Age (in years) Men Women

25-34 10.3 5.5

35-44 41.6 17.2

45-54 142.7 50.3

55-64 378.6 160.4

65-74 909.2 479.9

75-84 2210.1 1501.5

85+ 6100.8 5740.1

PlacePlace

Where are the rates of disease the highest and lowest? Where are the rates of disease the highest and lowest?

What hypotheses can you generate from this map?What hypotheses can you generate from this map?

Malignant Melanoma of Skin

PlacePlace

What hypotheses can you generate from this map?What hypotheses can you generate from this map?Cancer of the Trachea, Bronchus and Lung

Variation on Place: Migrant StudiesVariation on Place: Migrant Studies

Mortality rates (per 100,000) due to stomach cancer. What Mortality rates (per 100,000) due to stomach cancer. What hypotheses can you generate from these data? hypotheses can you generate from these data?

Japanese in Japan 58.4

Japanese Immigrants to California 29.9

Sons of Japanese Immigrants 11.7

Native Californians (Caucasians) 8.0

Time:Time:

Is the present frequency of disease different from the past?Is the present frequency of disease different from the past?

What hypotheses can you generate from these data? What hypotheses can you generate from these data?

Main Epidemiologic Study Designs for Main Epidemiologic Study Designs for Testing HypothesesTesting Hypotheses

Experimental study Cohort studyCase‑control study

• Each design represents a different way of harvesting information.

• Selection of one over another depends on the particular research question, concerns of about data quality and efficiency, and practical and ethical considerations

Experimental study designs

Defining feature of experimental studies: Investigator assigns exposure to study subjects

A) Experimental studies most closely resemble controlled laboratory experiments and serve as models for the conduct of observational studies.

B) They are the gold standard of epidemiological research. They have high status and validity, and can pick up small and modest effects

Ways to categorize experimental studiesWays to categorize experimental studies

Individual versus community – treatmentallocated to individual OR entire community

Do women with stage I breast cancer given a lumpectomy alone survive as long without recurrence of disease as women given a lumpectomy plus radiation?

Does fluoride in the water supply decrease the frequency of dental caries in a community compared to a similar community without such water treatment?

Ways to categorize experimental studiesWays to categorize experimental studies

Preventive versus therapeutic – prophylactic agent given to healthy or high-risk individual to prevent disease OR treatment given to diseased individual to reduce risk of recurrence, improve survival, quality of life

Does tamoxifen lower the incidence of breast cancer in women with high risk profile compared to high risk women not given tamoxifen?

Do combinations of two or three antiretroviral drugs prolong survival of AIDS patients as well as regimens of single drugs?

Reference Population

Experimental Population

Non-Participants

Participants

Treatment Allocation

Treatment Group Comparison Group

Cooperators Non-Cooperators Cooperators Non-Cooperators

PopulationHierarchy

Issues to be consideredIssues to be considered

A) Size, size, size - not just number of people in the trial, but how many endpoints (outcome under study) are expected

B) Restrictions on who is eligible (eligibility criteria)

– Substantive: What group are you interested in?

– Logistics: What group is accessible? Who will comply with study protocol? How feasible is complete and accurate follow-up on the subjects?

– Characteristics of volunteers - How does study population differ from total experimental population?

Allocation of treatmentAllocation of treatment

A) Should be random assignment • DEFINITION: Each individual has the same chance of

receiving each possible “treatment”

B) Some examples of random allocation• Random number table: as each subject enrolled,

assigned a number from the random number table; assign even numbers to treatment A and odd to treatment B

• Toss a coin for each subject: heads=A, tails=B

C) Some examples of nonrandom allocation• Alternate assignment of treatments• Assignment by day of the week


D) Goal of randomization

– To achieve baseline comparability between compared groups on factors related to outcome

– Essence of good comparison between “treatments” is that the compared groups are the same EXCEPT for the “treatment.”

– Any group of individuals will vary in response to a “treatment” based upon their sex, age, overall health, severity of illness - in short, any factor that is relevant to response to the treatment. The investigator knows some of these (like severity of illness), but there are many unknown factors that are also relevant.


D) Goal of randomization

• The compared groups should have the same distribution of all of these characteristics. That is what randomization can accomplish: the equal distribution of known and unknown factors that are relevant to response to the treatment (confounders)

• The larger the groups, the better randomization works

Use of placebo and blindingUse of placebo and blinding

A) Goals

– Placebos are used to make the groups as comparable as possible (recall laboratory experiment)

– Blinding: subjects do not know if they are receiving treatment or placebo (single blind); neither subjects nor investigators know who is receiving treatment or placebo (double blind).

– Purpose of blinding: To avoid ascertainment bias, i.e. bias in ascertainment of outcome

– Placebo allows study to be blind

Ascertaining the outcomeAscertaining the outcome

A) Goals

I. High follow-up rates: don’t lose people

I. Uniform follow-up for compared groups: must be equally vigilant in follow-up in all compared groups

B) Penalty of non-uniform ascertainment of outcome is BIAS

Important issues in experimental studiesImportant issues in experimental studies

A) Ethical considerations

– Equipoise: Must be genuine doubt about efficacy of treatment yet sufficient belief that it may work

– Stopping rules: What if it becomes apparent, before the trial is over, that the new treatment is beneficial (and should not be withheld from the placebo group) or is toxic (and treatment should be withdrawn)?

Important issues in experimental studiesImportant issues in experimental studies

B) Planning for an informative result. If the study finds no difference between compared treatments, do you believe it? Or was there a difference but the study was not powerful enough to detect it? Initial consideration is study size.

C) Analyzing by intention to treat: As the saying goes… once randomized, always analyzed.

Cohort Studies

1. Randomization of treatment so groups are comparable on known and unknown confounders. Can't randomize in an observational study so select a comparison group as alike as possible to the exposed group

Principles of experimental studies applied to Principles of experimental studies applied to observational cohort studiesobservational cohort studies

2. Use placebo in order to reduce bias. Can’t use placebo in observational studies so you must make the groups as comparable as possible.

Principles of experimental studies applied to Principles of experimental studies applied to observational cohort studiesobservational cohort studies

3. Blinding to avoid bias in outcome ascertainment.

In a cohort study, it is crucial to have high follow-up rates and comparable ascertainment of outcomes in the exposed and comparison groups.

You can blind the investigators conducting follow up and confirming the outcomes.

Principles of experimental studies applied Principles of experimental studies applied to observational cohort studiesto observational cohort studies

Timing of cohort studiesTiming of cohort studies

• Retrospective: both exposure and disease have occurred at start of study

Exposure------------------------Disease

*Study starts


• Prospective: exposure has (probably) occurred, disease has not occurred

Exposure----------------------Disease *Study starts

• Ambi-directional: elements of both


How do you choose between a retrospective and a prospective design?

Retrospective:

• Cheaper, faster• Efficient with diseases with long latent period• Exposure data may be inadequate


How do you choose between a retrospective vs. prospective design?

Prospective:

• More expensive, time consuming• Not efficient for diseases with long latent periods • Better exposure and confounder data• Less vulnerable to bias

Issues in design of cohort studiesIssues in design of cohort studies

Selection of exposed population

Choice depends upon hypothesis under study and feasibility considerations


Examples of exposed populations:

• Occupational groups• Groups undergoing particular medical treatment• Groups with unusual dietary or life style factors• Professional groups (nurses, doctors)• Students or alumni of colleges• Geographically defined areas (e.g. Framingham)


For rare exposures, you need to assemble special cohorts (occupational groups, groups with unusual diets etc.)

Example of special cohort study

• Rubber workers in Akron, Ohio• Exposure: industrial solvent• Outcomes: cancer


If exposure is common, you may want to use a general cohort that will facilitate accurate and complete ascertainment of data (Doctors, nurses, well-defined communities)

Example of general cohort study

• Framingham Study• Exposures: smoking, hypertension, family

history• Outcomes: heart disease, stroke, gout, etc.


Selection of comparison (unexposed) group

Principle: You want the comparison (unexposed) group to be as similar as possible to the exposed group with respect to all other factors except the exposure. If the exposure has no effect on disease occurrence, then the rate of disease in the exposed and comparison groups will be the same.


Selection of comparison (unexposed) group (cont’d)

Counterfactual ideal: The ideal comparison group consists of exactly the same individuals in the exposed group had they not been exposed. Since it is impossible for the same person to be exposed and unexposed simultaneously, epidemiologists much select different sets of people who are as similar as possible.


Three possible sources of comparison group

1. Internal comparison: unexposed members of same cohort

• Ex: Framingham study


Three possible sources of comparison group

2. Comparison cohort: a cohort who is not exposed from another similar population

• Ex: Asbestos textile vs. cotton textile workers


3. General population data: Use pre-existing data from the general population as the basis for comparison. General population is commonly used in occupational studies. Usually find healthy worker effect

• Ex. A study of asbestos and lung cancer with U.S. male population as the comparison group

Which of the three comparison groups Which of the three comparison groups is best?is best?


Sources of exposure information:

* Pre-existing records - inexpensive, data recorded before disease occurrence but level of detail may be inadequate. Also, records may be missing, usually don't contain information on confounders


Sources of exposure information:

• Questionnaires, interviews: good for information not routinely recorded but have potential for recall bias

• Direct physical exams, tests, environmental monitoring may be needed to ascertain certain exposures.


Sources of outcome information:

• Death certificates• Physician, hospital, health plan records• Questionnaires (verify by records)• Medical exams


Goal is to obtain complete follow-up information on all subjects regardless of exposure status. You can use blinding (like an experimental study) to ensure that there is comparable ascertainment of the outcome in both groups.


Approaches to follow-up

• In any cohort study, the ascertainment of outcome data involves tracing or following all subjects from exposure into the future.


Approaches to follow-up

• Resources utilized to conduct follow-up: town lists, Polk directories, telephone books; birth, death, marriage records; driver's license lists, physician and hospital records; relatives, friends.

This is a time consuming process but high losses to follow-up raise

doubts about validity of study

Ex. Tuberculosis treatment and breast cancer study

Classifying Person-TimeClassifying Person-Time

• Each unit of person-time contributed by an individual has its own exposure classification

• Must consider the etiologically relevant exposure• Exposure may change over time

Exposure Disease Initiation

Disease Detection

Induction period Latent period

Classifying Person-Time cont.Classifying Person-Time cont.

• Time at which exposure occurs ≠ time at risk of exposure effects– Radiation from an atomic bomb and risk of cancer

• Only the time at risk for exposure effects should be counted in the denominator of the incidence rate for that level of exposure

• If the induction time is not known, can estimate empirically by calculating the incidence rates for differing categories of time since exposure

Classifying Person-Time cont.Classifying Person-Time cont.• How do you classify person-time contributed by exposed subjects before the

minimum induction time has elapsed or after the maximum induction time has passed?

• Example: – Exposure = Rotavirus vaccine– Outcome = Intussusception– Assume induction period ranges from 1-7 days

Exposure Disease Initiation

Induction period

Classifying ExposureClassifying Exposure

• Exposure may change over time• Ideally, measure exposure constantly and classify each unit

of person-time– A given individual can contribute person-time to one or more

exposure category in the same study!• More often, assume one measure of exposure history is the

only aspect of exposure associated with current disease risk– Current, average, cumulative, etc.

• Lag exposure to account for induction time between exposure and disease initiation

Analysis of cohortAnalysis of cohort studiesstudies

• Basic analysis involves calculation of incidence of disease among exposed and unexposed groups.

• Depending on available data, you can calculate cumulative incidence or incidence rates.

• Recall set up of 2 x 2 tables.

Analysis of cohort studiesAnalysis of cohort studies

Example: Tuberculosis treatment and breast cancer study

Followed 1,047 women who were treated with air collapse therapy and exposed to numerous fluoroscopic examinations (radiation) and 717 who received other treatments. A total of 47,036 woman-years of follow-up were accumulated during which 56 breast cancer cases occurred.

Analysis of cohort studiesBreast Cancer Cases Woman-Years of

follow-up

Exposed 41 28,001

Unexposed 15 19,025

Total 56 47,036

IR1 = 41/28,011 = 1.5/1,000 woman-years IR0 = 15/19,025 = 0.8/1,000 woman-years RR = IR1/IR0 = 1.9

Interpretation: Women exposed to fluoroscopies had 1.9 times the risk of breast cancer compared to unexposed women.

Strengths of Cohort StudiesStrengths of Cohort Studies

• Efficient for rare exposures, diseases with long induction and latent period

• Can evaluate multiple effects of an exposure

• If prospective, good information on exposures, less vulnerable to bias, and clear temporal relationship between exposure and disease

Weaknesses of Cohort StudiesWeaknesses of Cohort Studies

• Inefficient for rare outcomes

• If retrospective, poor information on exposure and other key variables, more vulnerable to bias

• If prospective, expensive and time consuming, inefficient for diseases with long induction and latent period

• Keep these strengths and weaknesses in mind for comparison with case-control studies

Case-control studies

““TROHOC” STUDIESTROHOC” STUDIES

• This disparaging term was given to case-control studies because their logic seemed backwards (trohoc is ?? spelled backwards) and they seemed more prone to bias than other designs.

• No basis for this derogation.• Case-control studies are a logical extension of cohort

studies and an efficient way to learn about associations.

General Definition of a Case-Control

Study

A method of sampling a population in which cases of

disease are identified and enrolled, and a sample of the

population that produced the cases is identified and

enrolled. Exposures are determined for individuals in

each group.

When is it desirable to conduct a case-When is it desirable to conduct a case-

control study?control study? • When exposure data are expensive or difficult to obtain

- Ex: Pesticide and breast cancer study

• When disease has long induction and latent period- Ex: Cancer, cardiovascular disease

• When the disease is rare

– Ex: Studying risk factors for birth defects

• When little is known about the disease

– Ex. Early studies of AIDS

• When underlying population is dynamic

– Ex: Studying breast cancer on Cape Cod

CasesCases

• Criteria for case definition should lead to accurate classification of disease

• Efficient and accurate sources should be used to identify cases: existing registries, hospitals

• What do the cases give you? Think of the standard 2 X 2 table:

Yes (case)

No Total

Yes a ? ?

No c ? ?

Total a+c ? ?

Disease

Exposed

Cases give you the numerators of the rates of disease in exposed and unexposed groups being compared:

•Rate of disease in exposed: a/?

•Rate of disease in unexposed: c/?

What is missing? The denominators! If this were a cohort study, you

would have the total population (if you were calculating cumulative

incidence) or total person-years (if you were calculating incidence

rates) for both the exposed and non exposed groups, which would

provide the denominators for the compared rates.

Where do you get the information for the Where do you get the information for the denominators in a case control study? denominators in a case control study? THE CONTROLS.THE CONTROLS.

• A case-control study can be considered a more efficient form of a cohort study.

• Cases are the same as those that would be included in a cohort study.

• Controls provide a fast and inexpensive means of obtaining the exposure experience in the population that gave rise to the cases.

ControlsControls

• Definition: A sample of the source population that gave rise to the cases.

• Purpose: To estimate the exposure distribution in the source population that produced the cases.

Selecting ControlsSelecting Controls

Advantages of general population controls

Because of selection process, investigator is usually assured that they come from the same base population as the cases.

Disadvantages of general population controls

Time consuming, expensive, hard to contact and get cooperation; may remember exposures differently than cases

Hospital-Based Controls cont.Hospital-Based Controls cont.

• Limit diagnoses for controls to conditions with no association with the exposure– May exclude most potential controls– Exclusion criteria only applies to the cause of the current

hospitalization• Reasonable to exclude categories of potential controls on the

suspicion that a given category might be related to exposure• Imprudent to use only a single diagnostic category as a source

of controls

Deceased ControlsDeceased Controls

• Not members of the source population for the cases• If exposure is associated with mortality, dead controls will

misrepresent exposure distribution in source population• Even if cases are dead, generally better to choose living

controls• Do not need a proxy interview for living controls of dead cases

Comparability of InformationComparability of Information

• Comparability of information is often used to guide control selection and data collection

• BUT– Non-differential exposure measurement error does not

guarantee that bias will be toward the null– Efforts to ensure equal accuracy of exposure data tend to

produce equal accuracy of data on other variables– Overall bias due to non-differential error in confounders and

effect modifiers can be larger than error produced by unequal accuracy of exposure data from cases and controls


Advantages of hospital controls • Same selection factors that led cases to hospital led controls to hospital

• Easily identifiable and accessible (so less expensive than population-based controls)

• Accuracy of exposure recall comparable to that of cases since controls are also sick

• More willing to participate than population-based controls


Disadvantages of hospital controls

• Since hospital based controls are ill, they may not accurately represent the exposure history in the population that produced the cases

• Hospital catchment areas may be different for different diseases


• Special control groups like friends, spouses, siblings, and deceased individuals.

• These special controls are rarely used.• Cases not be able to nominate controls because they have few

appropriate friends, are widowed or are only or adopted children.

• Dead controls are tricky to use because they are more likely than living controls to smoke and drink.

Friend/Family ControlsFriend/Family Controls

• Being named as a friend control may be related to exposure– Reclusive people are less likely to be named

• Investigator dependent on cases for identifying controls

• Friend groups often overlap, so persons with more friends are more likely to be selected as a control

Neighborhood ControlsNeighborhood Controls

• Sample residences

• May individually match cases to one or more controls residing in the same neighborhood

• If neighborhood is associated with exposure, must control for matching in the analysis

• Neighbors may not be the source population of the cases– Cases at a VA hospital

Random Digit DialingRandom Digit Dialing

• Case eligibility should include residence in a house with a telephone

• Probability of calling a number ≠ probability of contacting an eligible control– Households vary in the number of people, amount of time a

person is at home, and the number of operating phones• Method requires a great deal of time and labor

Random Digit Dialing cont.Random Digit Dialing cont.

• Answering machines, voicemail, and caller ID reduce response rates

• Cell phones reduce validity of assuming source population can be randomly sampled using this method

– Recent CDC survey showed 2% increase in binge drinking compared to 2009 data – more cell phone numbers included, and average age of respondents decreased

• May not be able to distinguish business and residential numbers - difficult to estimate proportion of non-responders

Control Sampling SchemesControl Sampling SchemesControl Sampling Method Description Measure of effect

estimated by the OR

Case-cohort Persons at risk of disease at baseline

Risk ratio*Rate ratio

Density sampling Proportional to person-time accumulated by persons at risk of disease during follow-up

Rate Ratio

Cumulative case-control Persons at risk of disease who are non-cases at the end of follow-up

Incidence Odds Ratio Risk Ratio*

* Only need rare disease assumption when estimating the risk ratio from the odds ratio.

Density SamplingDensity Sampling

• Sample controls at a steady rate per unit time over period in which cases are sampled

• Probability of being selected as a control is proportional to amount of time person spends at risk of disease in source population

• Individual may be selected as a control while they are at risk for disease, and subsequently become a case

• Incidence density sampling or “risk set sampling” is a form of density sampling in which you match cases and controls on time

Variations in case-control study Variations in case-control study designsdesigns

• Case-cohort• Nested case-control• Case-control studies without controls

– Traditional case series– Case-crossover – Case-specular

Sampling a cohort population for Sampling a cohort population for controls: nested case-control study controls: nested case-control study 1. Sample the population at risk at the start of the observation period

*-------------------------------------------------------------------------*Start FU End FU ^^

2. Sample population at risk as cases develop*-------------------------------------------------------------------------*Start FU End FU ^ ^ ^ ^^^ ^

3. Sample survivors at the end of the observation period*------------------------------------------------------------------------*Start FU End FU ^^

Strengths case-control studiesStrengths case-control studies

• Efficient for rare diseases and diseases with long induction and latent period.

• Can evaluate many risk factors for the same disease so good for diseases about which little is known

Weaknesses of case-control studiesWeaknesses of case-control studies

• Inefficient for rare exposures• Vulnerable to bias because of retrospective nature of study• May have poor information on exposure because

retrospective• Difficult to infer temporal relationship between exposure and

disease

How do these strengths and weaknesses compare to cohort studies?

Comparisons between Case-control Comparisons between Case-control and Cohort study designand Cohort study design

CharacteristicsCharacteristics Case-controlCase-control Cohort studyCohort studySelect subjects based onSelect subjects based on Disease status Exposure Status

ExposureExposure good for common exposures Good for rare exposures

Cost-effectivenessCost-effectiveness Cheaper and less time consuming

Expensive and time consuming

Disease FrequencyDisease Frequency Good for rare diseases Good for common diseases

Establish temporal orderEstablish temporal order Temporality generally not clear Temporality generally clear

Incidence calculationIncidence calculation Can not calculate incidence/risk/rate

Can calculate incidence risk or rate depending on study design

Study more than one Study more than one outcomeoutcome

No Yes

Examine >1 exposureExamine >1 exposure Yes Generally no

Inherent Study Selection Inherent Study Selection problemproblem

Difficult to ascertain appropriate control group

Not applicable since start with a source population

Subject to biasesSubject to biases Susceptible to more biasesParticularly recall bias

Less subject to biases-except to loss to follow-up (Loss of subjects due to migration, lack of participation, withdrawal & death)

Exposure ClassificationExposure Classification

• Same principles as discussed for cohort studies• Cases’ exposure should be classified as of the time

of diagnosis or disease onset, accounting for induction time hypotheses

• Controls should be classified according to their exposure status at the time of selection, accounting for induction time hypotheses

Timing of Exposure ClassificationTiming of Exposure Classification

• Selection time does not necessarily refer to the time at which a control is first identified– For hospital-based controls, selection time may be date of

diagnosis for the disease that resulted in the current hospitalization

– Date of interview is often used if there is not an event analogous to the cases’ date of diagnosis

• Interviewers should be blinded to case-control status whenever possible

Ecological studies

Main properties of ecological Main properties of ecological studies:studies:

• Units of analysis are groups

• Both exposure and outcome are measured for groups

Measures of exposure in ecological studies:Measures of exposure in ecological studies:

– Aggregate – summaries of observations derived

from individuals in each group

• the proportion of smokers and median family

income

• proportion of the population under the age of 18

and rate of thyroid cancer


– Aggregate – summaries of observations derived from

individuals in each group)

– Environmental – physical characteristics of the place in

which members of each group live or work; with an analog at

the individual level

• air pollution level and hours of sunlight

• well water arsenic concentration and skin lesion rate in

each village in Bangladesh


– Aggregate – summaries of observations derived from individuals in

each group)

– Environmental – physical characteristics of the place in which

members of each group live or work; with an analog at the

individual level

– Global – attributes of groups, organizations or places for which

there is no distinct analogue at the individual level

• population density

• existence of special law or type of health-care system

Measure of association is correlation coefficient, r

– Quantifies the extent to which two variables (exposure and outcome) are

associated

– r varies between –1 and 1

If association is linear…If association is linear…

y = b0 + b1x, where b1 is slope (regression coefficient)

Proportionate increase or decrease in disease frequency for every

unit change in level of exposure

Examples of ecological studiesExamples of ecological studies

• Exploratory studies

• Multiple-group studies

– differences among groups

• Time-trend studies

– changes over time within groups

• Mixed studies

– combination of the above

Example of exploratory ecologicalExample of exploratory ecological

studystudy

Cotterill et al., (2001) Eur J Cancer; 37: 1020-26.

Example of multi-group ecological Example of multi-group ecological studystudy

Prisyazhniuk et al., Lancet (1991); 338: 1334-35.

Strengths of ecological studies:Strengths of ecological studies:1. Low cost and convenience

• Examples of secondary data sources: population registries, vital records, large surveys

2. Ability to overcome measurement limitations of individual-level studies

• When exposures cannot be measured accurately for large numbers of subjects

• When there is too much within-person variability in exposures (e.g., dietary factors)

3. Ability to overcome design limitations of individual-level studies

• When there is not enough variability within the study area

Limitations of ecological studies:Limitations of ecological studies:1. No information on the cross-classification of exposures and outcomes

within groups

2. Lack of ability to control for the effects of possible confounding

variables

• Exposure can be associated with a number of factors that are related to

the elevated risk of disease; it is not possible to separate their effects

using ecological data

? ?

? ?A + B

C +D

Limitations of ecological studies, Limitations of ecological studies, continued:continued:

3. Unclear temporality – we do not know temporality at the individual level

4. Ecological variables do not measure the same thing as individual variables with the same name

• Example:– Association between individual-level income

and mortality– Association between country-level income

and mortality5. Data collected for other purposes6. Ecological bias

Ecological study of use of oral contraceptives in the U.S. and risk of CHD in 1950-76 (Rosenberg, 1979)

Findings: NO association between OC use and risk of fatal CHD

Annual mortality from CHD ~ 800,000

18,000 among women of childbearing age

12,600 CHD deaths

decrease during 1950-76

Historical trend:

while use of OC increased, the risk of CHD among women of

childbearing age decreased by 30%

Analytical studies of use of oral contraceptives in the U.S. and risk of fatal CHD

Findings: a two-fold increase in risk of fatal CHD among OC users compared with non-users

~ 400 increase in CHD deaths attributable to OC use

Ecological fallacy:Ecological fallacy:

• At the group level:- No relationship between OC use and CHD mortality in young women

• At the individual level:- two-fold increase in risk of CHD among OC users compared to nonusers

• Summary:• Impossible to detect from correlational data• Incorrect to assume that no relationship between OC use and CHD mortality

Ecological fallacy:Ecological fallacy:•Fallacy of drawing inferences regarding associations at the individual level based on the group-level data

– The group-level data:

• inverse linear relationship between alcohol consumption and CHD mortality

• Those who consume large quantities of alcohol have the smallest mortality

– The individual-level data:

• relationship is J-shaped

• non-drinkers and those who consume large quantities have higher mortality than those who consume small to moderate amounts of alcohol.

Group level

Individual level

Ecological fallacy, Ecological fallacy, continued:continued:

• This does not mean that every ecological study has ecological fallacy!

• The importance of the ecological fallacy may differ for different research questions

• Potential strategies to reduce ecological fallacy:

• Use smaller units to make groups more homogeneous• Supplement ecological variables with individual-level

variables

– drawing inferences at a higher level from analyses performed at a lower level

– Example: • in a case-control collect information on various possible

exposures but ignore the geographic, spatial, and social context in which a person lives

Atomistic fallacy:

Individual level

Group level

Example:• Infant mortality is influenced by:

• Individual-level characteristics:

– Maternal factors » genes» maternal nutrition » habits

• Community-level variables:

– Contextual factors

» environmental pollution» geographical distance to a health care

facility» housing costs» age of housing» availability of social support

Which study design to choose?Which study design to choose?

In theory, it's possible to use each design to test a hypothesis

Example: Suppose you want to study the relationship between dietary Vitamin A and lung cancer….

Cohort Study OptionCohort Study Option

Subjects are chosen on the basis of exposure status and followed to assess the occurrence of disease

• High Vitamin A consumption ‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑> lung cancer or not

• Low Vitamin A Consumption ‑‑‑‑‑‑‑‑‑‑‑‑‑‑> lung cancer or not

What are the advantages and disadvantages of this option?

Experimental Study OptionExperimental Study Option

Special type of cohort study in which investigator assigns the exposure to individuals, preferably at random

Investigator assigns exposure to:

• High Vit A consumption ‑‑‑‑‑‑‑‑‑‑‑‑‑-‑‑> lung cancer or not

• Low Vit A consumption ‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑> lung cancer or not


Case‑Control Study OptionCase‑Control Study Option

Cases with the disease and controls who generally do not have the disease are chosen and past exposure to a factor is determined

• Prior Vitamin A consumption <‑‑‑‑------- lung cancer cases• Prior Vitamin A consumption <‑‑‑‑‑‑‑‑‑‑ controls


In practice, choice of study design In practice, choice of study design depends on:depends on:

• State of knowledge

• Frequency of exposure and disease

• Time, cost and other feasibility considerations

• Each study design has unique and complementary advantages and disadvantages

Exposure assessmentExposure assessment

• Most environmental exposures are complex, time-varying

• Relevant concepts: dose, burden, markers• Example:

• Absorbed dose – amount of energy imparted to the mass of exposed Absorbed dose – amount of energy imparted to the mass of exposed body or organbody or organ

• Equivalent dose – absorbed dose multiplied by the radiation weighting Equivalent dose – absorbed dose multiplied by the radiation weighting factor; used to compare different types of radiationfactor; used to compare different types of radiation

• Effective dose – equivalent dose averaged over all organs; used in Effective dose – equivalent dose averaged over all organs; used in biomonitoringbiomonitoring

Exposure assessmentExposure assessment

Exposure-dose relationsExposure-dose relations

• Uptake (losses associated with absorption)• Clearance• Compartmentalization

Development of the dosimetric models:• Development of model structure• Estimation of model parameters• Validation and testing of the model, including

sensitivity analyses

Advantages and limitations of dose Advantages and limitations of dose modelingmodeling

• Improve study validity and precision by weighting exposure data in a way that improves the fit of epi models

• Helpful in extrapolating results• Require specific assumptions about the structure of

the dose model and the values of its parameters• Uncertainties in exposure measurements may

exacerbate problems• Shifts attention from environmental quantity to a

physiologic one

Dose-response relationsDose-response relations