Overview of the field of Environmental Epidemiology

Overview of the field of Environmental Epidemiology
Lydia B. Zablotska, MD, PhD Associate Professor Department of Epidemiology and Biostatistics

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

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

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

Epidemiological Methods
Epidemiological Methods Classifications by: approach to data collection goal timing and directionality unit of analysis So, having devised the hypotheses… we have to design experiments to test these hypotheses in a systematic way. There are various epidemiological methods, and by methods we mean different ways of collecting and analyzing the data.

Classification by approach to data collection
Classification by approach to data collection Experimental RCTs, field trials, community intervention and cluster randomized trials Quasi-experimental natural disaster studies Non-experimental or observational cohort, case-control, ecological Epidemiologic study designs have their roots in the concepts of scientific experimentation. When epidemiologic experiments are feasible we do such study designs as RCT. Experiment: community field trial of water fluoridization and caries in children. Cohort study of lung cancer and smoking.

Classification by goal
Classification by goal Descriptive ecological correlational studies, case reports, case series, cross-sectional surveys Analytic observational studies and intervention studies (RCTs) Goal - description of the distribution of disease or elucidation of determinants

Classification by timing and directionality
Classification by timing and directionality 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 Example: occupational study of radiation exposure in nuclear workers and leukemia.

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

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

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?)

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

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

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

Variation on Place: Migrant Studies Mortality 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

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

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

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 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?

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?

Population Hierarchy Reference Population Experimental Population
Non-Participants Participants Treatment Allocation Treatment Group Comparison Group Cooperators Non-Cooperators Population Hierarchy

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?

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: 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 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 outcome
A) Goals High follow-up rates: don’t 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

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)?

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.

Cohort Studies

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

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.

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.

Timing 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 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 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-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

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.
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 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 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.

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.

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.

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

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

Case-control studies

“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.

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-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

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 ? c a+c 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 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.

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.

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

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 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 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

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

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

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.

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 81

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 82

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 83

Random Digit Dialing cont.
Answering machines, voic , 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 84

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.

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

Variations in case-control study designs
Case-cohort Nested case-control Case-control studies without controls Traditional case series Case-crossover Case-specular

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 ^ ^ ^ ^^^ ^ 3. Sample survivors at the end of the observation period * *

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

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?

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 >1 exposure 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)

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

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

Ecological studies

Main properties of ecological studies:
Main properties of ecological studies: Units of analysis are groups Both exposure and outcome are measured for groups Aggregate measures are summaries of observations derived from individuals in each group (eg., the proportion of smokers and median family income, proportion of the population under the age of 18 and rate of thyroid cancer) Environmental measures are physical characteristics of the place in which members of each group live or work (e.g., air pollution level and hours of sunlight, well water arsenic concentration and skin lesion rate in each village in Bangladesh). Each environmental measure has analogue at the individual level, but is not a summation of individual-level measurements, and these individual exposures (or doses) usually vary among members of each group Global measures are attributes of groups, organizations or places for which there is no distinct analogue at the individual level, unlike aggregate and environmental measures (e.g., population density, the existence of special law, or type of health-care system)

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
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 “R” is a descriptive measure of association. What kind of slope did you observe in the Epiville exercise? Positive increasing slope for the Rothman reservoir, slope =1 for Greenland reservoir.

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 studies
Examples 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 Exploratory – search for spatial or temporal patterns that might suggest environmental etiology or more specific etiologic hypotheses Space-time cluster – interaction between place and time of disease occurrence, such that cases that occur close in space also occur close in time

Example of exploratory ecological study
Example of exploratory ecological study Exploratory study of temporal patterns of thyroid cancer comparing rates for a geographically defined population over a period of at least 20 years Cotterill et al., (2001) Eur J Cancer; 37:

Example of multi-group ecological study
Most frequently done ecological type. Usually done by linking different sources of data. For example, census and tumor registry data. Prisyazhniuk et al., Lancet (1991); 338:

Strengths of ecological studies:
Strengths of ecological studies: 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 The first question in the Discussion section of the Epiville exercise asks you to summarize the advantages and disadvantages of ecological studies.

Limitations of ecological studies:
Limitations of ecological studies: 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 CHD mortality can be caused by smoking or by increased fat intake, or decreased intake of vegetables, or lower SES

Limitations of ecological studies, continued:
Limitations of ecological studies, continued: 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 Question 4 in the Step 6 of the Epiville exercise asks you about the quality of secondary data sources.

Ecological study of use of oral contraceptives in the U. S
Ecological study of use of oral contraceptives in the U.S. and risk of CHD in (Rosenberg, 1979) Findings: NO association between OC use and risk of fatal CHD 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

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 Out of 18,000 annual deaths from CHD among women of childbearing age, only a small portion is attributable to OC use.

Ecological fallacy: At the group level:
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: Group level Individual level
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, continued:
Ecological fallacy, 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

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

Example: Maternal factors Contextual factors genes maternal nutrition
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?
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…. 115

Cohort 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? 116

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

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 <‑‑‑‑ lung cancer cases Prior Vitamin A consumption <‑‑‑‑‑‑‑‑‑‑ controls What are the advantages and disadvantages of this option? 118

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 119

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

Exposure assessment

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

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

Dose-response relations

Overview of the field of Environmental Epidemiology

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