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Bulletin of Occupational & Environmental Health

Occupational Health Services – Epidemiological perspective.

Author(s): Arin Basu, Assistant Director, Fogarty International Training Program on Environmental and Occupational Health, IPGMER, Kolkata, West Bengal

Vol. 1, No. 1 (2004-01 - 2004-06)

Introduction

Occupational health services have emerged as an interdisciplinary effort within the setting of a workplace. Using a team approach, occupational health services aim to address healthcare aspects of workplace safety, prevention of worker illnesses, health promotion, and clinical care for workers, early diagnosis, treatment and rehabilitation of employees as a group. Occupational health services are organized around the principles of prevention of illnesses and promotion of health among employees with facilities for prevention, For public health in general and preventive health care in particular, epidemiology is considered as a core component (1).

Epidemiology: brief overview

Epidemiology is the study of distribution and determinants of diseases in populations (2). The focus of epidemiology, unlike clinical medicine, is on populations rather than individuals. Thus, epidemiology aims to describe all human illnesses in terms of persons involved, places where they occur, and times through which illnesses evolve in populations; additionally, it provides information-based tools for analysis of the patterns of such distributions of illnesses.

In Epidemiology, statistical concepts of rates, ratios, proportion measures are used to describe illnesses in populations; the information is organized around prevalence, incidence, crude rates, adjustments for various parmeters (age, gender, socioeconomic status and others), and standardized rates and finally in generating hypotheses about cause and effect relationships. Two basic concepts in epidemiology are tests of association and criteria for causality. Tests of association examine to what extent the observed relationships between the risk factor and the health outcome among the study subjects rule out the following three counter arguments - play of chance, role of bias, and effects of confounding factors. The play of chance in evaluating a valid association between a risk factor and a disease is commonly improved by including a larger sample size, and a careful selection of study design. From an epidemiological perspective, bias is a systematic error in measurement of either the risk factors or indicators of the health outcomes (3). Finally, confounding factors (or confounders) are essentially alternative possible explanations of the relationship between risk factors of interest and health outcomes. Two common methods for controlling the effects of confounding factors in the risk factor-health outcome linkage are matching, and stratification for the levels of the confounding variables (4).

From an epidemiologic perspective, causality is subjective. While a statistically valid association does not necessarily imply a cause-and-effect relationship, the relationship is commonly evaluated by using multiple criteria. These include strength of association, temporality, dose-response relationships, specificity, biological plausibility, and replicability of the findings (5). Strength of association implies the magnitude of effect size of the risk factor on the outcome. The stronger the effect, the higher is the likelihood of a cause-effect relationship. Temporal precedence indicates that the risk factor must precede the outcome. Dose-reponse relationship indicates that as the amount of the risk factor increases, there occurs a corresponding increment in the magnitude of the effect. Specificity and biological plausibility are softer criteria. Specificity indicates a single risk factor for a single health outcome, and biological plausibility indicates that a sound biological explanation based on existing paradigms should be offered for an observed relationship. Finally, replicability indicates that if studies were to be conducted to evaluate the association between a suspected risk factor and a health outcome for different populations and under differing circumstances, the results would be similar or at least close for these different studies conducted in different populations and circumstances using similar methods.

Several types of research study designs are used in epidemiology. The purposes are either a) to generate hypothesis, b) to describe relevant risk factors and health outcomes, or c) to study causal linkages between risk factors and diseases. Study designs that help to generate hypotheses include ecological studies, case studies, and case series. Ecological studies are epidemiologic studies where risk factors and health outcomes are studied at population levels and hence the data is available in aggregates. Any conclusion arrived at about the cause-effect relationships on the basis of an ecological study is therefore open to ecological fallacy – that conclusion about an individual cannot be derived from population level aggregated data (6). Case studies are descriptions of individual cases of diseases and case series are descriptions of a series of similar cases. Cross sectional surveys are “snapshots” of risk factors, health outcomes, and other relevant factors in a population. The problem with these studies from the perspective of causal linkages is lack of comparison groups. While case studies and case series provide information about possible linkages, they cannot control for the effects of alternative explanations or confounding variables. However, cross sectional surveys provide limited information about comparison groups, at the time of data analysis.

Two common observational epidemiologic study designs are case control studies, and cohort studies. In case control studies, individuals with health outcomes (cases) are compared with those with no evidence of health outcome (controls). For a case control study, effect sizes are calculated as ratios of likelihood (Odds Ratios) of exposure to the risk factors (4). In a cohort study, the investigator begins with two groups – both the groups are initially free of the health outcome. One of the groups is exposed to the risk factor of interest, while the other group is not. The incidences of health outcomes are followed prospectively. The incidence rates of the health outcomes are then compared among the exposed and non-exposed individuals. The effect size is expressed as the ratio (Relative Risk or Rate Ratio) of the incidence of disease among exposed versus incidence of disease among non-exposed (4).

A case control study is shorter and less expensive. It provides possibility of studying different exposures for rare diseases. However, from the perspective of deriving causal inferences, it is less reliable compared to a cohort study design in that it cannot account for temporal sequence, and case control studies are open to different types of biases. On the other hand, a cohort study, although a powerful design, is more expensive, and is open to problems of loss of study participants to follow up. A cohort study is not suitable for studying rare diseases or diseases that take a long time to develop. However, given a set of exposures, multiple outcomes can be studied using a cohort study design (3, 4). In nested case control study designs, a case control study is embedded (“nested”) within a longitudinal prospective cohort study.

Results from epidemiologic studies and effect sizes are used in different ways in formulating strategies for prevention and public health approaches. Prevalence and incidence measures are essential to quantify the health outcomes or diseases. Relative risk estimates from cohort studies can be used to identify the impact of the exposure on the outcome by calculating absolute risk reduction scores, relative risk reduction scores, and numbers needed to treat scores to translate the results into public health actions.

While epidemiological study designs can be widely used for investigating disease processes at workplaces and work sites, the primary limitations of epidemiologic studies in an occupational setting using questionnaires is the concept of “healthy worker effect”. In an occupational setting, when an epidemiologic investigation is conducted, the measurements are readily available on workers who are healthier and therefore present for duties. Measurements of health outcomes are missed or inadequately represented for workers who are too sick to attend their duties, or workers who degree of illness is low enough to enable them attend their job responsibilities.

Epidemiology, occupational health services, and risk assessment

Occupational health services (OHS) are a set of preventive and promotive healthcare services for the employees in an organization. Organized as an interdisciplinary team approach, the occupational health service teams consist of several professionals including occupational physicians, industrial hygienists, occupational health nurses, safety engineers, ergonomists, data analysts, and epidemiologists. The core activities of OHS include pre-employment health checkups to ensure that the job is appropriate for the candidate, periodic health examinations to provide early diagnosis and treatment for selected health risks for a given work unit, and regular employee training programs to identify and prevent adverse health events secondary to occupational exposures to risk factors. Consequently, several data analytical processes are involved at various stages, including hazard identification at the workplace, characterization of employee health risks involved in the production process, and estimation of the time interval for periodic health examinations. In all these processes, epidemiology as a quantitative & qualitative study of health effects can play a pivotal role, more so when integrated with available risk assessment techniques. Epidemiology can be combined with either top down risk analytic techniques such as Hazop studies (Hazard and Operability studies), or risk analytical techniques that build scenarios bottoms-up like Fault Tree or Event Tree Analysis. Hazop studies take into consideration flow charts of industrial processes and use of specific guidewords.

Epidemiological data can be integrated in both Hazop studies and Tree based risk analysis studies. Epidemiologic information could be used to evaluate processes that pose significant risk to human health. In event tree analysis or fault tree analysis, where specific discrete events are investigated using specific guide symbols and logic gates, specific health outcomes or diseases can be included in places of words describing plant operations, or illness descriptors can be used as separate nodes, and epidemiologic data can be used to arrive at possible sources and management plans for the control of health hazards.

Epidemiology has emerged as the basic science for public health, prevention and health promotion. With respect to occupational health, it may play a crucial role in linking the human health aspects in the existing risk analysis paradigms.

References

1 Hennekens, CH, Buring JE, Mayrent SL. Epidemiology in Medicine. 1st Edition. Little Brown and Company, Boston, USA. 1987
2 Last J. A Dictionary of Epidemiology. 3rd Edition. Oxford University Press. Oxford, UK, 1995
3 Pearce, N. A Short Introduction to Epidemiology. Centre for Public Health Research, Wellington, NZ, 2003
4 Kelsey, JL, Whittemore, AS, Evans, AS, Thompson, WD. Methods in Observational Epidemiology, 2nd Edition, Oxford University Press, New York, 1996.
5 Hill AB. The environment and disease: Association or causation. Proc Royal Soc Med 1965
6 Baker, D, Kjellstorm, T, Calderon, R, Pastides, H. Environmental Epidemiology: A Textbook on Study Methods and Public Health Applications, World Health Organization, Geneva, 1999.

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