Please be sure to refer to the book, notes, websites and peer-reviewed articles. Your response should be at least 350 words.

Questions: Provide one example of an environmental health study from a peer-reviewed journal in the School Library. Describe the study design, methods – participant characteristics and data collection methods, and results of the study.

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I attached an article I found in the school library.


Aditi Puri, RDH, MPH, PhD

Learning Objectives

  • Identify differences between various study designs.
  • Infer that the strongest epidemiological conclusions are derived from studies that use large population, and accurate as well as precise measurements of exposures and diseases.
  • Identify the value of epidemiological data in risk assessment, standard setting, and other policy making and dispute resolution, in environmental and occupational health.
  • Interpret the significance of study results free of bias (confounding, selection bias and informational bias).

Chapter 4: Environmental and Occupational Epidemiology

It is now well accepted that several diseases and cancers have multiple causes but their identifications has often proved difficult and the first point in case to be illustrated here is the discovery that smoking caused lung cancer. This discovery was made by Richard Doll and Austin Bradford Hill in a classic paper published in 1950 (see Fig A Bradford Hill and causality. I). This was a major step forward in modern medicine; there was tremendous controversy about this issue – the tobacco industry were much opposed to the idea and argued that increase lung cancer correlated with many other variables and not just with tobacco consumption but Richard Doll and Bradford Hill stuck to their point and produced strong evidence in support of the hypothesis that tobacco was not a confounding factor in the genesis of lung cancer but a causal one.



Epidemiology is the study of the distribution and determinants of health and disease in human populations.

It seeks to make causal inferences about diseases, that is it looks into what causes disease.

  • On the practical level a famous set of criteria set out by Austin Bradford Hill (1965) is commonly used by epidemiologists to judge whether a particular causal hypothesis is plausible, that is, whether the observed association between A and B supports the conclusion that in fact A causes B. Hill set out nine criteria.
  • Only one—the proper temporal relationship—is absolutely required: the exposure must precede the disease.


Bradford Hill and Causality:

Epidemiologists study the causes of disease by looking at temporal relationships, relationships in which one event follows another (apparent cause disease) [most important criteria]. Out of all the criteria only one is absolutely required, which is temporal relationship.

Consistency: the association is repeated in many studies

Effect size: a large effect size, the exposed have much more disease than the non-exposed).

Dose – response relationship: a positive dose-response relationship- more exposure causes more disease.

Biological plausibility: some biological explanation makes it reasonable that A causes B.

A number of agencies, such as the International Agency for Research on Cancer (IARC), the National Toxicology Program (NTP), the Institute of Medicine (IOM) (a part of the National Academies of Sciences, Engineering, and Medicine), and the U.S. Environmental Protection Agency (EPA), regularly review epidemiological evidence and publish summaries in which they evaluate whether associations are likely to be causal.

Agencies such as IARC international agency for research on cancer and NTP national toxicology program consistently review epidemiological evidence to determine if the association is likely to be casual.

Coherence: A proposed mechanism, such as that underlying homeopathy, which requires overturning well-established principles in biology, chemistry, and physics must face a far greater burden of proof to be accepted as valid than a hypothesis consistent with established knowledge.

Analogy: Likewise, while infection may cause a fever, not all fevers are due to infection. Analogies are most useful for suggesting possible relationships, which should then be confirmed or disproven by application of more rigorous criteria.


Kinds of Epidemiological Studies

  • Descriptive Studies- Simply describe disease by factors such as age, sex, time, and geographical region. They do not formulate hypotheses or attempt to make causal inferences. They describe patterns in disease occurrence in terms of broad demographic and other variables.

Descriptive studies can sometimes perform an important role in public policy by determining which diseases are responsible for the greatest burden in different countries. One of the most important efforts in this regard in recent years is the Global Burden of Disease Study.

One of the most important efforts in this regard in recent years is the Global Burden of Disease Study (Lim et al., 2012), a large international effort based on using existing data from a variety of sources, to estimate which diseases in different countries were responsible for the most death and disability.

  • Correlational, or Ecological, Studies-study the correlation between disease rates and some specific exposure, but at the level of groups rather than individuals. Ex: Breast cancer rate is higher in richer, more urbanized countries.
  • Descriptive studies- they provide pattern in disease occurrence, in terms of broad demographic variables. Provides first clues about factors that cause disease. Example: malaria occurs in mainly in warm areas provides clue that warm climate may play a role in transmission.
  • Correlational studies- correlate breast cancer rates in countries around the world, with degree of socio-economic development. Good for studying, time series studies of air pollution levels are correlated with disease rate on a day-day basis. The population is stable the only variable changing is the exposure variable of interest (air pollution).
  • -observational studies: clinical trails are not practical for studying environmental exposure- thus we have the observational studies. As there lack control group, there is a threat of confounding. They are thus viewed as less definitive.


Kinds of Epidemiological Studies

  • Correlational or Ecological Studies: Generally, ecological studies are viewed as weaker than studies of individuals, because across a population, individuals with the risk factors are not necessarily the same individuals who contract the disease. As a result, ecological studies are often called hypothesis-generating studies.
  • Time series studies of air pollution, in which pollution levels are correlated with disease rates on a day-to-day basis. Such studies have the advantage of looking at a population that is presumably stable over time (eliminating most confounding).
  • The only variables changing on a daily basis are the exposure variable of interest (air pollution levels) and the outcome of interest (daily disease rates)- this is an example of an ecological study

Kinds of Epidemiological Studies

  • Etiologic, or Analytical, Studies- studies of individuals in which the investigators seek to test a specific hypothesis about exposure and disease. Divided into two types:
  • Clinical Trials-are in a sense the model for rigorous epidemiological studies. They are often done to compare one medication or treatment to another. They are controlled experiments, because they assign treatment (or exposure) randomly to one group and not another. The treated and untreated groups are therefore likely to be comparable with regard to other variables (such age, weight, sex, and education) that might affect the disease outcome; therefore any difference in subsequent disease rates can be assumed to be due to exposure.
  • In environmental/occupational epidemiology the “treatment” is the exposure of interest. Clinical trials are generally impractical in this setting for ethical reasons. One cannot ethically expose half of a population to a toxin, such as inhaled silica.
  • Therefore the epidemiologist interested in studying suspected occupational and environmental toxins often needs to conduct observational studies.

Kinds of Epidemiological Studies

  • Observational Studies- Observational studies are uncontrolled studies, or natural experiments, of which the epidemiologist takes advantage. For example, the epidemiologist wants to study the effect of lead on cancer risk, so he or she observes a cohort of lead-exposed workers over time and compares their cancer rates to those of the general population.
  • However, the workers and the general population may differ in some important respects, such as smoking habits or diet, that may in turn affect cancer rates (such variables are called confounders).
  • The epidemiologist may be able to adjust or control for the effects of such confounders, but if he or she cannot, these effects may distort the findings about the effect of exposure on disease. For this reason observational studies are viewed as less definitive than clinical trials.


There is growing concern with the increasing prevalence of obesity in industrialised countries, a trend that is more apparent in the poor than in the rich. In an ecological study, the relationship between an area measure of socioeconomic status (SES) and the density of fast-food outlets was examined as one possible explanation for the phenomenon. It was found that there was a dose-response between SES and the density of fast-food outlets, with people living in areas from the poorest SES category having 2.5 times the exposure to outlets than people in the wealthiest category. The findings are discussed.


Obesity; Social determinant; Environmental determinant; Socioeconomic status

An ecological study of the relationship between social and environmental determinants of obesity

Daniel D Reidpath, Cate Burns, Jan Garrard, Mary Mahoney, Mardie Townsend

Types of Observational Studies

  • Cohort Studies
  • Case-Control Studies
  • Cross-Sectional Studies

Cohort Studies

  • Cohort studies start with an exposed group and a non-exposed group, both disease free, and follow them forward in time to observe disease incidence or mortality rates.
  • The observation period in cohort studies may start in the past and move forward to the present ( retrospective studies ), or start in the present and move into the future ( prospective studies ).
  • Retrospective studies are cheaper and quicker, but can be less accurate as a result of their reliance on historical documentation. Ex: study lung cancer among welders and non-welders from 1950 and trace lung cancer mortality to present. The disadvantage of the retrospective approach is having to depend on historical information about exposure levels and about potential confounders (e.g., smoking habits).
  • Prospective studies, Although prospective studies take a long time and are often expensive, they are more appropriate when one wants to measure exposure levels and confounding variables at baseline, or when biological samples such as blood tests are required. Prospective studies may also be needed to study diseases that are difficult to ascertain in retrospect, such as spontaneous abortions (whose occurrence and date of occurrence may be difficult to remember accurately).

Good for rare exposures and common diseases- cohort studies


Cohort studies

  • Cohort studies can consider disease events per person ( cumulative incidence, or risk ) or disease events per person-time ( rates , such as incidence or mortality). Cohort studies are good for rare exposures and common diseases, because one begins with assembling an exposed group and hence can readily assemble an adequate number of exposed subjects (e.g., welders); conversely, when the disease is rare, a very large number of subjects may need to be assembled to yield an appreciable number of cases.

Cumulative incident- study appropriate for short follow-up and fixed cohort. Fixed cohorts are those that can be followed for whole follow-up period.

Incident/mortality- study appropriate for long follow-up period and dynamic cohort. In dynamic cohorts participants can enter anytime and be lost to follow-up at any time. Therefore, followed for different periods of time.

Case-Control Studies

  • In case-control studies the epidemiologist begins with diseased and non-diseased groups and looks backward in time. Ex: bladder cancer patients and those free of cancer. bladder cancer patients (cases) and people free of bladder cancer (controls) can be asked about their past consumption of water treated with chlorine, which results in trihalomethane formation.
  • The investigator determines the odds of exposure in each group and compares them in order to determine the odds ratio.
  • If a is the number exposed, and b is the number nonexposed, then a / b is the odds of exposure. If the odds of exposure are higher among the cases than among the controls, then one judges that the exposure is associated with the disease.
  • These studies are subject to the recall bias of the participants. As cases remember more about the past exposure than the controls.


Cross-Sectional Studies

  • Tend to measure exposure and disease at the same time.
  • For example, lead exposure in relation to performance on tests of intelligence in children may be studied by measuring lead in blood at the time of the neurological testing, or cadmium levels in the urine of smelter workers can be measured at the same time as small protein in the urine (a measure of kidney damage).
  • A typical problem with cross-sectional studies is determining whether exposure in fact preceded the health outcome.

They are usually conducted when the outcome of interest is sub-clinical. They are a snapshot in time and we can divide groups by age, gender etc.



  • Bias refers to the distortion of the true relationship between exposure and disease.
  • The most important sources of bias are selection bias, confounding, and information bias.
  • Bias may effect the internal validity of a study (the certainty that only the independent variable effects the dependent variable) and the external validity of the study (the ability of the results of the study to be generalized to the real world).

Selection Bias

Selection bias occurs when the relationship between exposure and disease in the study population is not representative of the true relation between exposure and disease in the general population because the investigator has selected the study population in a non-representative way.

Ex: only 20% of individual in the target population answer a questionnaire about breast cancer occurrence in a study of Etheyl Oxide. These self-selected participants might differ from the general population. They might have more breast cancer (motivating them to participate).

  • The healthy worker effect- a type of selection bias, that occurs when workers are compared to the general population. Workers are healthier than the general population, so study results will be biased against finding adverse health effects among the workers.

They might have more breast cancer… thus he association between exposure and disease in this population may not be found in the entire population.





  • Confounding is the distortion of the exposure-disease relationship by a third variable that is associated both with exposure and with disease.
  • Ex: in a study of welders in relation to lung cancer, if welders smoke more than non welders do, then smoking (strongly associated with lung cancer) would act as a confounder. Adjustment for the effect of smoking can be made during analysis by stratifying the groups into smokers and nonsmokers, and determining exposure-disease relationship in each group.
  • Confounding can sometimes be corrected by performing a multivariate analysis. Only if you have adequate data on smoking in both exposed and unexposed groups.
  • However, in the case of effect modification, correction is impossible. Effect modification occurs when the third variable modifies the effect of the exposure variable on the variable of interest. Ex: third variable (smoking) modifies the effect of exposure variable of interest (welding).

Strategies to reduce confounding are:

randomization (aim is random distribution of confounders between study groups)

restriction (restrict entry to study of individuals with confounding factors – risks bias in itself)

matching (of individuals or groups, aim for equal distribution of confounders)

stratification (confounders are distributed evenly within each stratum)

adjustment (usually distorted by choice of standard)

multivariate analysis (only works if you can identify and measure the confounders)

Criteria for a confounder


It is a risk factor for the disease, independent of the putative

risk factor (exposure variable or X).


It is associated with putative risk factor (exposure).


It is not in the causal pathway between exposure and disease.

Effect modification example:

Another good example is the effect of smoking on risk of lung cancer. Smoking and exposure to asbestos are both risk factors for lung cancer. Non-smokers exposed to asbestos have a 3-4 fold increased risk of lung cancer, and most studies suggest that smoking increases the risk of lung cancer about 20 times. However, shipyard workers who chronically inhaled asbestos fibers and also smoked had about a 64-fold increased risk of lung cancer. In other words, the effects of smoking and asbestos were not just additive – they were multiplicative. This suggests synergism or interaction, i.e., that the effect of smoking is somehow magnified in people who have also been exposed to asbestos. Multivariable methods can also be used to assess effect modification.


Information Bias

  • Information bias occurs when information obtained about either exposure or disease is incorrect.
  • Information bias usually originates from mismeasurement or misclassification of variables.
  • When exposure is measured incorrectly (for a continuous exposure variable) or misclassified (for a categorical exposure variable), one can expect the exposure-disease association to be distorted.

Independent variables example: Sugar, no exercise.

Dependent variable: Diabetes


Data Analysis

  • Methods of analysis in epidemiology typically depend on whether the exposure variable and the disease variable are continuous variables or categorical variables (Example: blood pressure).
  • Most of the approaches described previously consider disease to be a categorical (yes/no) variable (often called a dichotomous variable).
  • When both exposure and disease variables are dichotomous, then one usually calculates rate ratio or the odds ratio.
  • However, when both the disease and the exposure are continuous variables, typically a regression analysis is conducted (for example, linear regression).

Odds Ratio

Odds Ratio = (A / C) / (B / D) = (AD) / (BC)

2×2 table for calculating Odds Ratio

Disease (Yes) Disease (No)
Exposure (Yes) A B
Exposure (No) C D

Aditi Puri


Yˆ = b0 + b1X1

  • Y is the value dependent variable.
  • a or Alpha, a constant; equals the value of Y when the value of X=0
  • b or Beta, the coefficient of X; the slope of the regression line; how much Y changes for each one-unit change in X.
  • X is the value of independent Variable, what is predicting or explaining the value of Y.
  • A linear regression analysis for a continuous outcome may also be calculated with the exposure variable categorized in the regression.
  • Even when the disease variable is dichotomous, one can employ a type of regression called logistic regression, in which the measure of interest remains the odds ratio and either categorical or continuous variables may be included among the predictors.

Y is the value of the Dependent variable (Y), what is being predicted or explained

a or Alpha, a constant; equals the value of Y when the value of X=0

b or Beta, the coefficient of X; the slope of the regression line; how much Y changes for each one-unit change in X.

X is the value of the Independent variable (X), what is predicting or explaining the value of Y




One important feature of any data analysis is the precision of the estimate of effect.

Large sample sizes confer greater statistical power to detect associations, and lead to high precision.

Precision is often presented by a confidence level, which represents a range of values from measure to effect.


An odds ratio in case-control study of bladder cancer and water supply (public vs. private well) might be, indicating those who use public water have double cancer risk.

If the study is based on 20 cases and 20 controls, its will have low precision. 95% confidence interval for odds ratio of 2 might be (.50 to 8)- indicating wide range of plausible values.

If the study is based on 2000 cases and 2000 controls, the 95% confidence interval might be (1.90 to 2.30)- indicating a narrow range of plausible values.

Statistical Significance

Precision: statistical significance usually means that the estimate of effect is different from null value and the difference is unlikely to have occurred by chance.


Precision is related to statistical significance, a mathematical measure that determines how likely it is that the results obtained could have occurred by chance.

P value

P ≤ .05

P ≤ .01

Environmental Epidemiology

  • Environmental epidemiology concerns environmental agents to which large numbers of people are exposed involuntarily.
  • Ex: Secondhand smoke, radon in homes.
  • Environmental exposures can be thought of as contributing either to epidemics or to endemic diseases.
  • Epidemics are unusual outbreaks of disease clearly above a normal level. Example Cholera epidemic
  • In contrast, endemic diseases exist at constant, low (or background ) levels and may or may not have an environmental cause. Example: led in environment to neurological defects among children.

Occupational Epidemiology

  • Occupational epidemiology is the epidemiological study of illness or injury associated with workplace exposures. Occupational epidemiology often involves relatively high exposures in relatively small numbers of people, often geographically isolated at a worksite.

Ex: Welding and lung cancer, poor ventilation and respiratory illness.

Occupational studies are responsible for discovery of many carcinogens:

Ex: asbestos, silica, arsenic, radon gas

Understanding Clusters

  • A cluster is an apparently elevated number of cases of disease in a limited area over a limited period of time, suggesting some common cause.
  • Ex: Many people staying in a Philadelphia Hotel contracted Legionnaire’s Disease.
  • Typically the number of cases in the cluster is small, on the order of ten or twenty rather than hundreds. Clusters typically come to the attention of public health authorities, who must first determine whether a cluster in fact represents an unusually high occurrence of disease.
  • Typically, though, it is difficult to identify a common cause for a single type of disease experienced by people in close proximity
  • Have more chance of leading to the discovery of a specific cause when the disease in question is rare.







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By Mayo Clinic Staff

Legionnaires’ disease is a severe form of pneumonia — lung inflammation usually caused by infection. Legionnaires’ disease is caused by a bacterium known as legionella.


Measuring Exposure

  • Accurate exposure assessment is essential to detect and quantify dose response relationship.
  • Measuring current exposure is fairly straightforward; however, when looking retrospectively, exposure is much more difficult to determine because of lack of historical information.
  • Epidemiologists have come up with strategies to get around this problem.

For example, using a job-exposure matrix ( JEM), a cross classification of jobs and exposure levels across time.

  • This can be done if industrial hygienists can extrapolate beyond more recent exposure data to make a good guess about exposure further back in time, based on process changes at the plant. Typically plants were dirtier further back in time.
  • A JEM will enable an estimate of cumulative exposure to silica for each worker. Cumulative exposure is often the measure of interest for chronic disease outcomes such as silicosis, lung cancer, or kidney disease.

Retrospective exposure to silica might be difficult, as the data available might only go back 20 years- as that’s when the recording began. However, cohort of workers might have been working in the industry for last 40 to 50 years.

JEM: this can be done if the industrial hygienist can extrapolate beyond the recent exposure data and make a good guess further back in time. All workers in a cohort can be assigned to a level of exposure by JEM. This will enable the hygienist to estimate the cumulative exposure to silica for each worker.



  • External exposure can also be estimated using a biomarker or exposure, for example dioxin in the blood or lead in bone.

problems associated: individual variation, wrong biomarkers in the metabolic pathway that feature several candidate toxins and few biomarkers persist long enough to be useful.

  • Oragnocholine pesticides and PCB are shown to reduce dopamine level in brain, reduced dopamine level is hallmark of Parkinson disease. Some may persist long after the exposure. DDE, a metabolite of DDT can be measured today in serum of most American, even though it was phased out in 1970.
  • Lead, often measured in blood, but also accumulates in the bone. Good indicator of cumulative exposure, even after the exposure ceases. Important in measuring the association between lead and neurological deficient among children.

Risk Assessment

  • The results of occupational and environmental epidemiological studies can affect public health by alerting policymakers to new hazards and possibly by triggering regulations about permissible levels of exposure.
  • Risk assessments are used by scientists and policymakers in order to determine which factors receive attention.
  • Quantitative meta-analyses, which provide weighted averages of quantitative results across studies, are a particularly effective tool for making inferences about risk.
  • Pooled analyses, in which the raw data for each study are obtained and the combined data then reanalyzed, are similarly effective.

Met analysis combine results from many designs, rate ratio from cohort studies and odds ratios from case control studies. Do not require original data and can be conducted from the results found in a study.


Risk Assessment

  • May be based on animal and human data
  • Involves extrapolation from animals to humans
  • Involves considerable amount of uncertainty

US Occupational Safety and Health Administration (OSHA) typically limits risk to 1 in 1,00,000

Future: shift in focus on toxins to, exposure related to job stress, ergonomics, and gene-environment interaction (PCB related to Parkinson’s disease, in presence of certain genetic polymorphism), global climate change.



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