Epidemiology the basic science of public health – is defined as the study of distribution and determinants of diseases in populations. Epidemiology, when conducted in an occupational setting, can provide vital clues about the etiology of several occupational disorders and can be successfully used to design disease prevention and health promotion activities. From an organizational perspective, occupational health services is an interdisciplinary approach by several groups of professionals to provide disease prevention and health promotion in the workplace. Hazard identification and health risk characterization (health risk analysis) form the core components of occupational health services. Risk analysis at the workplace is essentially a team-based approach, driven by algorithms and flow charts to identify specific risks involved in the production process in the industry concerned. In this review, we first provide a short overview of epidemiology and then propose frameworks in which epidemiology can be integrated in the risk analytical processes, in order to develop an evidence based occupational health services in an organization.
Occupational health services has 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. Risk and safety in the context of workplace connote assessment and minimization of harm to human health. Occupational health services are organized around the principles of prevention of illnesses and promotion of health among employees with facilities for prevention, early diagnosis, treatment and rehabilitation of employees as a group. For public health in general and preventive health care in particular, epidemiology is considered as a core component (1). Hence, to achieve optimum effectiveness, occupational health services should link risk analysis in the workplace with epidemiological knowledge base of the illnesses that occur in the workplace. Here, we provide a brief overview of epidemiology and propose an epidemiological perspective in organizing occupational health services and industry wide risk analysis programs.
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. The formal definition of epidemiology suggests two complementary approaches; one that deals with the distribution of human illnesses in populations and the other, identification of factors that account for such distributions. 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.
Descriptive epidemiology organizes information about diseases in terms of people, place and time. In descriptive 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 parameters (age, gender, socioeconomic status and others), and standardized rates. Descriptive epidemiology is important for identifying patterns in available data, and in generating hypotheses about cause and effect relationships.
Analytical epidemiology, on the other hand, is the study of relationships between risk factors and health outcomes. Two basic concepts in analytical epidemiology are tests of association and criteria for causality. Tests of association examine whether the relationship between a risk factor and a health outcome or disease is statistically significant. Statistical significance indicates the extent to which 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. Beyond statistical significance, substantive significance – whether the relationship can be rationally explained and whether this can importantly impact public health related decision making – is evaluated using criteria for causality.
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. Two other issues are the roles of bias, and effects of confounding factors. From an epidemiological perspective, bias is a systematic error in measurement of either the risk factors or indicators of the health outcomes (3). Since epidemiological studies by nature are observational, where randomization of comparison groups is not possible, controlling for bias is an important issue for study designs. Typically, in evaluating an association between a risk factor and a health outcome, two comparable groups of individuals are considered. One of the two groups is either known to be exposed to the risk factor under study (while the comparison group is not), or alternatively, develop the disease (while the comparison group is disease-free). The groups are alike in every way except for the exposure (or health outcome) of interest. When the extent of measurements errors are similar in both the groups, the resulting biases are termed as random biases, and impact on the effect size is predictable (the effect size moves towards the null or that of no effect). Nonrandom biases (where the extent of measurement errors are dissimilar between the groups compared) can create more intractable problems in assessing the association between the risk factor and the disease, since the impact on the effect size become unpredictable.