Occupational epidemiology

Introduction

To the order of two million people every year in the UK alone complain of ill-health which is caused or aggravated by work. Epidemiology has an important role notably in:

• Establishing the causes and determinants of this ill-health
• Ensuring adequate recognition and quantification of this
• Determining appropriate occupational exposure limits.

However, occupational epidemiology may be of value beyond the worker and the workplace, for example by contributing to the setting of exposure limits such as air quality guidelines for the population at large.

To gain full benefit from this resource it is important to have an understanding of epidemiologic concepts, notably causal associations, and to be aware of issues which may influence apparent association, notably bias, confounding and chance.

For further details on ‘causal associations’ and ‘chance bias and confounding’, see: Association and cause.

Aim of this resource

To enable an understanding of the role and application of occupational epidemiology.

Learning objectives

You should:

• Understand the purpose and accept the importance of occupational epidemiology in discovering causes, measuring risks, and determining priorities for intervention and evaluation;
• Be able to describe the main types of study design used in occupational epidemiology;
• Be able to illustrate the application of the above with examples.

Drawing analogies between the clinical and epidemiologic approach

The logic including data gathering, data processing, interpretation, intervention and hence change has a lot in common between the clinical and the epidemiologic method, as illustrated in the following table:

Analogies between dealing with individual patients and with populations in occupational and environmental epidemiology*

Structure/Context: Consulting with an individual patient	Structure/Context: Contending with a group of workers or other people
Process: Taking the individual’s symptom history† Taking the individual’s exposure history (and getting information about the work/environment) Carrying out a physical examination and tests Exercising ‘clinical judgement’†	Process: Administering a questionnaire (and collating the replies) As above by a questionnaire, but also by an objective assessment of work/environmental exposures Gathering data from health surveillance tests, etc Analysis of the data
Output: Diagnosis Prognosis Treatment	Output: Description of profile of health and disease Prediction of risk Intervention
Outcome: Change in patient’s condition	Outcome: Change in profile of health and disease

*Adapted with permission from the author’s book Practical Occupational Medicine
†The above analogies can be extended to the history taking process.

Types of studies in occupational epidemiology

Case control studies

The accompanying image shows an occupational cancer - of the nasal sinuses - which is fortunately rarer now than it used to be.

In a case-control approach, ‘cases’ of this disease are compared to a carefully matched reference population of ‘controls’ (or ‘referents’) to determine retrospectively what differences there may have been in their occupational exposure histories.

For example, in one study it was shown that patients with one type of this tumour (an adenocarcinoma) had a disproportionately greater likelihood of having worked in the hardwood industry, when compared to referent patients with other pathology.

(Subsequently cohort studies were conducted - see below - which supported the hypothesis of a causal association by showing that this type of tumour was much commoner in hardwood workers than in other people.)

For further details on causal associations, see: Cancer, work and the environment.

Cross sectional studies

In a cross-sectional study, the prevalence of a particular disease or of a set of symptoms or other indication of ill-health is investigated in a single time-point (or over a relatively narrow period of time). Comparisons can then be made in the frequency of ill-health for example between workers exposed to a particular hazard, and those who are not, or - better still - between workers experiencing different degrees of exposure. (See exposure-response relationships in relation to causal association).

A cross sectional study can determine the prevalence rate, which is defined as the number of EXISTING cases of disease divided by the population at a specified time point.

For example, if a chest X-ray survey of quarry workers is conducted, it might show that workers in quarries with high exposure to quartz (a crystalline form of silica) might have a higher prevalence of pneumoconiosis than those in quarries with little or no such exposure.

Cohort studies

A ‘cohort’ was strictly speaking a fighting unit of the Roman army (roughly comparable to a ‘battalion’ nowadays). The term is now applied epidemiologically to a clearly defined population who prospectively share a common experience - say an occupational exposure. Comparisons in the incidence of ill-health, or in mortality can be made between exposed and non-exposed cohorts or between subsets of the same cohort but with different degrees of exposure.

In the context of a cohort study the term ‘control’ has a different meaning from the meaning in a case-control study (see above). In a cohort study, the ‘controls’ are those people who are not (or have not been) exposed to the agent under investigation.

Some cohort studies measure mortality, others measure incidence of disease. The incidence rate is defined as the number of NEW cases of a disease divided by the population at risk over a given period of time. In occupational and environmental epidemiology it is important to be able to characterise the exposure of the ‘population at risk’.

For example, a study was conducted in which workers were followed up during their employment in a factory bin where they were exposed to benzene. It showed that those categories of workers with a high cumulative exposure to benzene had a higher mortality from certain types of leukaemia than the control population.

Problems in occupational epidemiology

The healthy worker effect

Workers differ from the general population from which they are drawn, and especially from unemployed people, in many ways. This differences can result in serious bias in occupational epidemiology.

Can you consider some of these differences and how they can contribute to bias?

(For example are there socio-economic differences between the employed and the unemployed; can these differences influence health and - if so - in what way?)

Other problems

Various difficulties can affect the design and interpretation of studies in occupational epidemiology.

Can you consider some of these?

(For example how comparable is an occupational population to the general public in terms of age, and sex?)

Bias, confounding and chance are considered briefly elsewhere.

The application of occupational epidemiology

Benefit for the workers

It should be obvious, that epidemiology has a great deal to contribute to the reduction of risks to health from work, through reducing exposure, and in other ways.

For further details on the reduction of risks, see: Ill health and the work environment.

Benefit for the community at large

Various direct and indirect benefits can accrue to the population at large, for example, through the derivation and application of exposure limits. As an example, the recommendations of the Expert Panel on Air Quality Standards in relation to benzene were largely based on occupational epidemiology.

Conclusion

Occupational epidemiology is an important aspect of clinical epidemiology and of occupational hygiene since it provides powerful and practical information to understand the causes and determinants of work related ill-health, to help establish what steps should be taken to reduce those risks, and to evaluate interventions for the benefits of workers, and of the community at large.

Further reading

• Practical Occupational Medicine
  Ch3: ‘Epidemiological Investigation of Occupational Disease.’ pages 61-74 (1st edition).