Overview of the field of Environmental Epidemiology

1
Overview of the field of Environmental
Epidemiology

• Lydia B. Zablotska, MD, PhD
• Associate Professor
• Department of Epidemiology and Biostatistics

2
Objectives

• Review of study designs
• How to choose a study design appropriate for a
  specific question
• Exposure assessment
• Dose modeling

3
Natural Progression in Epidemiologic Reasoning

• 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

4
Hypothesis Formation and Testing

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

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Epidemiological Methods
0

• Classifications by
• approach to data collection
• goal
• timing and directionality
• unit of analysis

6
Classification by approach to data collection
0

• Experimental
• RCTs, field trials, community intervention
• and cluster randomized trials
• Quasi-experimental
• natural disaster studies
• Non-experimental or observational
• cohort, case-control, ecological

7
Classification by goal
0

• Descriptive
• ecological correlational studies, case reports,
  case series, cross-sectional surveys
• Analytic
• observational studies and intervention studies
  (RCTs)

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Classification by timing and directionality
0

• 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

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Classification by timing and directionality
Retrospective cohort study
Diseased Non-diseased
exposed
Diseased Non-diseased
unexposed
x
past
present
future
Diseased Non-diseased
exposed
Diseased Non-diseased
unexposed
RCT
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Classification 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

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Descriptive 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?)

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

• Where are the rates of disease the highest and
  lowest?
• What hypotheses can you generate from this map?
• Malignant Melanoma
  of Skin

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Place

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

15
Variation on Place Migrant StudiesMortality
rates (per 100,000) due to stomach cancer. What
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
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Time

• Is the present frequency of disease different
  from the past?
• What hypotheses can you generate from these data?
  

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Main Epidemiologic Study Designs for Testing
Hypotheses

• Experimental study
• Cohort study
• Case-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

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• Experimental study designs

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• 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

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Ways to categorize experimental studies

• Individual versus community treatment
• allocated 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?

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Ways 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?

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Population Hierarchy
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Issues 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?

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

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Allocation of treatment

• 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.

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Allocation of treatment

• 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

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Use 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

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Ascertaining the outcome

• A) Goals
• High follow-up rates dont lose people
• 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

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Important issues in experimental studies

• 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)?

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Important issues in experimental studies

• 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.
• Analyzing by intention to treat As the saying
  goes once randomized, always analyzed.

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• Cohort Studies

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Principles of experimental studies applied to
observational 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
•  
•  

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Principles of experimental studies applied to
observational cohort studies

•  
• 2. Use placebo in order to reduce bias. Cant
  use placebo in observational studies so you must
  make the groups as comparable as possible.
•  

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Principles of experimental studies applied to
observational 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.

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Timing of cohort studies

• Retrospective both exposure and disease have
  occurred at start of study
• Exposure------------------------?Disease
• 
  Study starts
• 
  

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Timing of cohort studies

• Prospective exposure has (probably) occurred,
  disease has not occurred
• Exposure----------------------?Disease
• Study starts
• Ambi-directional elements of both

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Timing of cohort studies

• 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

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Timing of cohort studies

• 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

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Issues in design of cohort studies

• Selection of exposed population
• Choice depends upon hypothesis under study and
  feasibility considerations

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Issues in design of cohort studies

• 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)

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Issues in design of cohort studies

• 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

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Issues in design of cohort studies

• 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)

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• Example of general cohort study
• Framingham Study
• Exposures smoking, hypertension, family history
• Outcomes heart disease, stroke, gout, etc.

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Issues in design of cohort studies

• 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.

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Issues in design of cohort studies

• Selection of comparison (unexposed) group
  (contd)
• 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.

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Issues in design of cohort studies

• Three possible sources of comparison group
• 1. Internal comparison unexposed members of same
  cohort
• Ex Framingham study

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Issues in design of cohort studies

• 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

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Issues in design of cohort studies

• 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

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Which of the three comparison groups is best?
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Issues in design of cohort studies

• 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

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Issues in design of cohort studies

• 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.

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Issues in design of cohort studies

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

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Issues in design of cohort studies

• 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.

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Issues in design of cohort studies

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

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Issues in design of cohort studies

• 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

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Ex. Tuberculosis treatment and breast cancer
study
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Classifying 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
Latent period
Induction period
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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

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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
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Classifying 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

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Analysis of cohort studies

• 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.

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Analysis 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.

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Analysis of cohort studies
Breast 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.
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Strengths 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

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Weaknesses 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

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• Case-control studies

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TROHOC 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.

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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.
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When is it desirable to conduct a case-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

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Cases

• 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

Disease
Yes (case) No Total
Yes a ? ?
No c ? ?
Total ac ? ?
Exposed
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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.
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Where do you get the information for the
denominators in a case control study? 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.

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Controls

• 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.

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Selecting 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

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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

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Deceased 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

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Comparability 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

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Selecting 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

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Selecting 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

80
Selecting Controls

• 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.

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Friend/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

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Neighborhood 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

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Random 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

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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

85
Control Sampling Schemes
Control 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.
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Density 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

87
Variations in case-control study designs

• Case-cohort
• Nested case-control
• Case-control studies without controls
• Traditional case series
• Case-crossover
• Case-specular

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Sampling a cohort population for 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
• 
  

89
Strengths 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

90
Weaknesses 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?

91
Comparisons between Case-control and Cohort study
design
Characteristics Case-control Cohort study
Select subjects based on Disease status Exposure Status
Exposure good for common exposures Good for rare exposures
Cost-effectiveness Cheaper and less time consuming Expensive and time consuming
Disease Frequency Good for rare diseases Good for common diseases
Establish temporal order Temporality generally not clear Temporality generally clear
Incidence calculation Can not calculate incidence/risk/rate Can calculate incidence risk or rate depending on study design
Study more than one outcome No Yes
Examine gt1 exposure Yes Generally no
Inherent Study Selection problem Difficult to ascertain appropriate control group Not applicable since start with a source population
Subject to biases Susceptible to more biases Particularly recall bias Less subject to biases-except to loss to follow-up (Loss of subjects due to migration, lack of participation, withdrawal death)
92
Exposure 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

93
Timing 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

94

• Ecological studies

95
Main properties of ecological studies
0

• Units of analysis are groups
• Both exposure and outcome are measured for groups

96
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

97
Measures of exposure in ecological studies

• 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

98
Measures of exposure in ecological studies

• 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

99
0

• 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

100
If association is linear
0

• y b0 b1x, where b1 is slope (regression
  coefficient)
• Proportionate increase or decrease in disease
  frequency for every unit change in level of
  exposure

101
Examples of ecological studies
0

• Exploratory studies
• Multiple-group studies
• differences among groups
• Time-trend studies
• changes over time within groups
• Mixed studies
• combination of the above

102
Example of exploratory ecological study
0
Cotterill et al., (2001) Eur J Cancer 37
1020-26.
103
0
104
Example of multi-group ecological study
0
Prisyazhniuk et al., Lancet (1991) 338 1334-35.
105
Strengths of ecological studies
0

• Low cost and convenience
• Examples of secondary data sources population
  registries, vital records, large surveys
• 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)
• Ability to overcome design limitations of
  individual-level studies
• When there is not enough variability within the
  study area

106
Limitations of ecological studies
0

• No information on the cross-classification of
  exposures and outcomes within groups
• 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
107
Limitations of ecological studies, continued
0

• Unclear temporality
• we do not know temporality at the individual
  level
• 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
  mortality
• Data collected for other purposes
• Ecological bias

108

• 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

0
Annual mortality from CHD 800,000
Historical trend while use of OC increased, the
risk of CHD among women of childbearing age
decreased by 30
18,000 among women of childbearing age
12,600 CHD deaths
decrease during 1950-76
109
0

• 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
110
Ecological fallacy
0

• 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

111
Ecological fallacy
0

• 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

Group level
Individual level
112
Ecological fallacy, continued
0

• 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

113
0
Atomistic fallacy

• 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

Group level
Individual level
114
0

• 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

115
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.

116
Cohort Study Option

• Subjects are chosen on the basis of exposure
  status and followed to assess the occurrence of
  disease
• High Vitamin A consumption ---------------gt lung
  cancer or not
• Low Vitamin A Consumption --------------gt lung
  cancer or not
• What are the advantages and disadvantages of this
  option?

117
Experimental 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 ----------------gt lung
  cancer or not
• Low Vit A consumption ----------------gt lung
  cancer or not
• What are the advantages and disadvantages of this
  option?

118
Case-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 lt----------- lung
  cancer cases
• Prior Vitamin A consumption lt---------- controls
  
• What are the advantages and disadvantages of this
  option?

119
In practice, choice of study design 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

120
Exposure 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 body or organ
• Equivalent dose absorbed dose multiplied by the
  radiation weighting factor used to compare
  different types of radiation
• Effective dose equivalent dose averaged over
  all organs used in biomonitoring

121
Exposure assessment
122
Exposure-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

123
Advantages and limitations of dose modeling

• 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

124
Dose-response relations