Epidemiology for Occupational Health Services
Abstract
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.
Introduction
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: 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. 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.
Conceptual subdivisions of epidemiology are descriptive epidemiology, and analytical epidemiology.
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.
Finally, confounding factors (or confounders) are essentially alternative possible explanations of the relationship between risk factors of interest and health outcomes. Confounding factors are associated with both risk factors and health outcomes, but they do not come in the causal pathway linking the two. For example, people working in the asbestos mines and exposed to asbestos fibers are prone to develop lung cancer. Thus, occupational exposure to asbestos is an independent risk factor for lung cancer. However, smoking is also a known independent risk factor for lung cancer. If prevalence of smoking is found to be higher among asbestos miners, then, in studying the association between occupational asbestos exposure and lung cancer, cigarette smoking should be considered a confounding factor. 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). Matching is achieved during the study design phase when participants are selected on the basis of known confounding variables (often with respect to age and gender). Stratification is a data analytical technique where the comparable group statistics are studied after dividing the study populations into different levels depending on the confounding variable (4).
From an epidemiological 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. This is a necessary and logically the most powerful criterion for cause and effect relationship. 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. While these criteria are definitely useful and important in conjunction with other criteria, their logical values as sufficient criteria for causality are open to question (3).
Since one risk factor can give rise to several different diseases (smoking for instance, or exposure to noise or sound above 90 decibels), and likewise, one disease may have multiple plausible risk factors (hypertension for instance), the specificity clause is a weak criterion. Also, for all probable risk factor-outcome pairs, it is not possible to offer biological explanations under the existing paradigms of science. For example, no reliable animal model of carcinogenesis exists for chronic exposure to high levels of inorganic arsenic through drinking water, yet epidemiological evidence suggests that long term exposure to high concentrations of inorganic arsenic can induce several different forms of cancer including those of skin, urinary bladder, and lung. 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. The repetitiveness criterion helps to establish the cause and effect relationship by showing that the relationship is indifferent of the population or other characteristics.
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 epidemiological studies where risk factors and health outcomes are studied at population levels and hence the data is available in aggregates. For example, in studying the effects of air pollution on respiratory health, air pollutants are measured on a daily basis in a city. At the same time, hospital admissions due to respiratory diseases are measured from hospital records in select areas. Finally, information on the levels of air pollutants and the number of hospital admissions are statistically analyzed to derive estimations about the relationship between air pollution and respiratory morbidity. The biggest problem with this type of study is lack of inference regarding association at an individual level. 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).
However, these types of studies are important for generation of hypotheses. Other hypothesis generating studies include case studies, case series, and cross-sectional surveys. 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 data are collected at the level of individuals with respect to health outcomes, and risk factors. This is typically done using a survey questionnaire or some other means of measurement, where a community of individuals is approached and information taken at a point in time. 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.
In comparison, a more systematic approach is comparison group studies, where a comparable group of individuals is included for evaluating the linkage between potential risk factors and the health outcomes. Two common observational epidemiological 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). Both groups are selected by the investigator. These two groups are comparable to each other in every way other than the fact that one of these groups has the illness or health outcome. These groups are evaluated with respect to their relative exposures to the different risk factors of interest. 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. Typically, at the initiation of the cohort study, blood samples are stored for each individual in the cohorts. Then, down time, as a few cases start appearing, these cases and corresponding controls are used in a case control study to assess the relationship between different serum level metabolites or biochemical parameters and select health outcomes (4).
Beyond hypothesis generating studies and grouped comparison studies, meta analyses and systematic reviews generate important information by pooling together results of different epidemiological studies. Systematic reviews are compilations of results of different epidemiological studies that are selected according to fixed criteria, and then the results are pooled together to arrive at summary measures of the relationship between a risk factor and a health outcome. Meta analyses are conceptually similar and have been applied to results of randomized trials (3).
Results from epidemiological 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 epidemiological studies in an occupational setting using questionnaires is the concept of “healthy” worker effect. In an occupational setting, when an epidemiological 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. These activities need to be evidence driven with respect to risk factor-health outcome linkages. 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 guide words. These are consensus driven diagnostic techniques for fault detection and management at the plant operational level and aim to develop a repository of faults and their management plans based on guide word driven scenarios. Guide words in the context of HazOp studies are a set of operational keywords that are used to describe the flow diagram of the production process under different scenarios. Fault tree or event tree analysis is a technique of fault diagnosis where a “bottoms-up” approach is taken to detect system faults, including logic diagrams and flow of information.
Epidemiological data can be integrated in both HazOp studies and Tree based risk analysis studies. Epidemiological 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 epidemiological 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.
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