Epidemiology involves measuring the occurrence of disease and quantifying associations between diseases and exposures.

Measures of Disease Occurrence

Disease occurrence can be measured by frequencies (counts) but is better described by rates, which are composed of three elements: the number of people affected (numerator), the number of people in the source or base population (i.e., the population at risk) from which the affected persons come, and the time period covered. The denominator of the rate is the total person-time experienced by the source population. Rates allow more informative comparisons between populations of different sizes than counts alone. Risk, the probability of an individual developing disease within a specified time period, is a proportion, ranging from 0 to 1, and is not a rate per se. Attack rate, the proportion of people in a population who are affected within a specified time period, is technically a measure of risk, not a rate.

Disease-specific morbidity includes incidence, which refers to the number of persons who are newly diagnosed with the disease of interest. Prevalence refers to the number of existing cases. Mortality refers to the number of persons who die.

Incidence is defined as the number of newly diagnosed cases within a specified time period, whereas the incidence rate is this number divided by the total person-time experienced by the source population (table 1). For cancer, rates are usually expressed as annual rates per 100,000 people. Rates for other more common diseases may be expressed per a smaller number of people. For example, birth defect rates are usually expressed per 1,000 live births. Cumulative incidence, the proportion of people who become cases within a specified time period, is a measure of average risk for a population. 

Table 1. Measures of disease occurrence: Hypothetical population observed for a five-year period

*To simplify the calculations, this example assumes that all deaths occurred at the end of the five-year period so that all 100 persons in the population were alive for the full five years.

Prevalence includes point prevalence, the number of cases of disease at a point in time, and period prevalence, the total number of cases of a disease known to have existed at some time during a specified period.

Mortality, which concerns deaths rather than newly diagnosed cases of disease, reflects factors that cause disease as well as factors related to the quality of medical care, such as screening, access to medical care, and availability of effective treatments. Consequently, hypothesis-generating efforts and aetiological research may be more informative and easier to interpret when based on incidence rather than on mortality data. However, mortality data are often more readily available on large populations than incidence data.

The term death rate is generally accepted to mean the rate for deaths from all causes combined, whereas mortality rate is the rate of death from one specific cause. For a given disease, the case-fatality rate (technically a proportion, not a rate) is the number of persons dying from the disease during a specified time period divided by the number of persons with the disease. The complement of the case-fatality rate is the survival rate. The five-year survival rate is a common benchmark for chronic diseases such as cancer.

The occurrence of a disease may vary across subgroups of the population or over time. A disease measure for an entire population, without consideration of any subgroups, is called a crude rate. For example, an incidence rate for all age groups combined is a crude rate. The rates for the individual age groups are the age-specific rates. To compare two or more populations with different age distributions, age-adjusted (or, age-standardized) rates should be calculated for each population by multiplying each age-specific rate by the per cent of the standard population (e.g., one of the populations under study, the 1970 US population) in that age group, then summing over all age groups to produce an overall age-adjusted rate. Rates can be adjusted for factors other than age, such as race, gender or smoking status, if the category-specific rates are known.

Surveillance and evaluation of descriptive data can provide clues to disease aetiology, identify high-risk subgroups that may be suitable for intervention or screening programmes, and provide data on the effectiveness of such programmes. Sources of information that have been used for surveillance activities include death certificates, medical records, cancer registries, other disease registries (e.g., birth defects registries, end-stage renal disease registries), occupational exposure registries, health or disability insurance records and workmen’s compensation records.

Measures of Association

Epidemiology attempts to identify and quantify factors that influence disease. In the simplest approach, the occurrence of disease among persons exposed to a suspect factor is compared to the occurrence among persons unexposed. The magnitude of an association between exposure and disease can be expressed in either absolute or relative terms. (See also "Case Study: Measures").

Absolute effects are measured by rate differences and risk differences (table 2). A rate difference is one rate minus a second rate. For example, if the incidence rate of leukaemia among workers exposed to benzene is 72 per 100,000 person-years and the rate among non-exposed workers is 12 per 100,000 person-years, then the rate difference is 60 per 100,000 person-years. A risk difference is a difference in risks or cumulative incidence and can range from -1 to 1. 

Table 2. Measures of association for a cohort study

Rate Difference (RD) = 500/100,000 - 250/100,000

= 250/100,000 per year

(146.06/100,000 - 353.94/100,000)*

Rate ratio (or relative risk) (RR) =  

Attributable risk in the exposed (AR_e) = 100/20,000 - 200/80,000

= 250/100,000 per year

Attributable risk per cent in the exposed (AR_e%) =

 Population attributable risk (PAR) = 300/100,000 - 200/80,000

= 50/100,000 per year

Population attributable risk per cent (PAR%) =

 * In parentheses 95% confidence intervals computed using the formulas in the boxes.

Relative effects are based on ratios of rates or risk measures, instead of differences. A rate ratio is the ratio of a rate in one population to the rate in another. The rate ratio has also been called the risk ratio, relative risk, relative rate, and incidence (or mortality) rate ratio. The measure is dimensionless and ranges from 0 to infinity. When the rate in two groups is similar (i.e., there is no effect from the exposure), the rate ratio is equal to unity (1). An exposure that increased risk would yield a rate ratio greater than unity, while a protective factor would yield a ratio between 0 and 1. The excess relative risk is the relative risk minus 1. For example, a relative risk of 1.4 may also be expressed as an excess relative risk of 40%.

In case-control studies (also called case-referent studies), persons with disease are identified (cases) and persons without disease are identified (controls or referents). Past exposures of the two groups are compared. The odds of being an exposed case is compared to the odds of being an exposed control. Complete counts of the source populations of exposed and unexposed persons are not available, so disease rates cannot be calculated. Instead, the exposed cases can be compared to the exposed controls by calculation of relative odds, or the odds ratio (table 3). 

Table 3. Measures of association for case-control studies: Exposure to wood dust and adenocarcinoma of the nasal cavity and paranasal sinues

Relative odds (odds ratio) (OR) =

Attributable risk per cent in the exposed () =

Population attributable risk per cent (PAR%) =

where = proportion of exposed controls = 55/195 = 0.28

* In parentheses 95% confidence intervals computed using the formulas in the box overleaf.

Source: Adapted from Hayes et al. 1986.

Relative measures of effect are used more frequently than absolute measures to report the strength of an association. Absolute measures, however, may provide a better indication of the public health impact of an association. A small relative increase in a common disease, such as heart disease, may affect more persons (large risk difference) and have more of an impact on public health than a large relative increase (but small absolute difference) in a rare disease, such as angiosarcoma of the liver.

Significance Testing

Testing for statistical significance is often performed on measures of effect to evaluate the likelihood that the effect observed differs from the null hypothesis (i.e., no effect). While many studies, particularly in other areas of biomedical research, may express significance by p-values, epidemiological studies typically present confidence intervals (CI) (also called confidence limits). A 95% confidence interval, for example, is a range of values for the effect measure that includes the estimated measure obtained from the study data and that which has 95% probability of including the true value. Values outside the interval are deemed to be unlikely to include the true measure of effect. If the CI for a rate ratio includes unity, then there is no statistically significant difference between the groups being compared.

Confidence intervals are more informative than p-values alone. A p-value’s size is determined by either or both of two reasons. Either the measure of association (e.g., rate ratio, risk difference) is large or the populations under study are large. For example, a small difference in disease rates observed in a large population may yield a highly significant p-value. The reasons for the large p-value cannot be identified from the p-value alone. Confidence intervals, however, allow us to disentangle the two factors. First, the magnitude of the effect is discernible by the values of the effect measure and the numbers encompassed by the interval. Larger risk ratios, for example, indicate a stronger effect. Second, the size of the population affects the width of the confidence interval. Small populations with statistically unstable estimates generate wider confidence intervals than larger populations.

The level of confidence chosen to express the variability of the results (the “statistical significance”) is arbitrary, but has traditionally been 95%, which corresponds to a p-value of 0.05. A 95% confidence interval has a 95% probability of containing the true measure of the effect. Other levels of confidence, such as 90%, are occasionally used.

Exposures can be dichotomous (e.g., exposed and unexposed), or may involve many levels of exposure. Effect measures (i.e., response) can vary by level of exposure. Evaluating exposure-response relationships is an important part of interpreting epidemiological data. The analogue to exposure-response in animal studies is “dose-response”. If the response increases with exposure level, an association is more likely to be causal than if no trend is observed. Statistical tests to evaluate exposure-response relationships include the Mantel extension test and the chi-square trend test.

Standardization

To take into account factors other than the primary exposure of interest and the disease, measures of association may be standardized through stratification or regression techniques. Stratification means dividing the populations into homogenous groups with respect to the factor (e.g., gender groups, age groups, smoking groups). Risk ratios or odds ratios are calculated for each stratum and overall weighted averages of the risk ratios or odds ratios are calculated. These overall values reflect the association between the primary exposure and disease, adjusted for the stratification factor, i.e., the association with the effects of the stratification factor removed.

A standardized rate ratio (SRR) is the ratio of two standardized rates. In other words, an SRR is a weighted average of stratum-specific rate ratios where the weights for each stratum are the person-time distribution of the non-exposed, or referent, group. SRRs for two or more groups may be compared if the same weights are used. Confidence intervals can be constructed for SRRs as for rate ratios.

The standardized mortality ratio (SMR) is a weighted average of age-specific rate ratios where the weights (e.g., person-time at risk) come from the group under study and the rates come from the referent population, the opposite of the situation in a SRR. The usual referent population is the general population, whose mortality rates may be readily available and based on large numbers and thus are more stable than using rates from a non-exposed cohort or subgroup of the occupational population under study. Using the weights from the cohort instead of the referent population is called indirect standardization. The SMR is the ratio of the observed number of deaths in the cohort to the expected number, based on the rates from the referent population (the ratio is typically multiplied by 100 for presentation). If no association exists, the SMR equals 100. It should be noted that because the rates come from the referent population and the weights come from the study group, two or more SMRs tend not to be comparable. This non-comparability is often forgotten in the interpretation of epidemiological data, and erroneous conclusions can be drawn.

Healthy Worker Effect

It is very common for occupational cohorts to have lower total mortality than the general population, even if the workers are at increased risk for selected causes of death from workplace exposures. This phenomenon, called the healthy worker effect, reflects the fact that any group of employed persons is likely to be healthier, on average, than the general population, which includes workers and persons unable to work due to illnesses and disabilities. The overall mortality rate in the general population tends to be higher than the rate in workers. The effect varies in strength by cause of death. For example, it appears to be less important for cancer in general than for chronic obstructive lung disease. One reason for this is that it is likely that most cancers would not have developed out of any predisposition towards cancer underlying job/career selection at a younger age. The healthy worker effect in a given group of workers tends to diminish over time.

Proportional Mortality

Sometimes a complete tabulation of a cohort (i.e., person-time at risk) is not available and there is information only on the deaths or some subset of deaths experienced by the cohort (e.g., deaths among retirees and active employees, but not among workers who left employment before becoming eligible for a pension). Computation of person-years requires special methods to deal with person-time assessment, including life-table methods. Without total person-time information on all cohort members, regardless of disease status, SMRs and SRRs cannot be calculated. Instead, proportional mortality ratios (PMRs) can be used. A PMR is the ratio of the observed number of deaths due to a specific cause in comparison to the expected number, based on the proportion of total deaths due to the specific cause in the referent population, multiplied by the number of total deaths in the study group, multiplied by 100.

Because the proportion of deaths from all causes combined must equal 1 (PMR=100), some PMRs may appear to be in excess, but are actually artificially inflated due to real deficits in other causes of death. Similarly, some apparent deficits may merely reflect real excesses of other causes of death. For example, if aerial pesticide applicators have a large real excess of deaths due to accidents, the mathematical requirement that the PMR for all causes combined equal 100 may cause some one or other causes of death to appear deficient even if the mortality is excessive. To ameliorate this potential problem, researchers interested primarily in cancer can calculate proportionate cancer mortality ratios (PCMRs). PCMRs compare the observed number of cancer deaths to the number expected based on the proportion of total cancer deaths (rather than all deaths) for the cancer of interest in the referent population multiplied by the total number of cancer deaths in the study group, multiplied by 100. Thus, the PCMR will not be affected by an aberration (excess or deficit) in a non-cancer cause of death, such as accidents, heart disease or non-malignant lung disease.

PMR studies can better be analysed using mortality odds ratios (MORs), in essence analysing the data as if they were from a case-control study. The “controls” are the deaths from a subset of all deaths that are thought to be unrelated to the exposure under study. For example, if the main interest of the study were cancer, mortality odds ratios could be calculated comparing exposure among the cancer deaths to exposure among the cardiovascular deaths. This approach, like the PCMR, avoids the problems with the PMR which arise when a fluctuation in one cause of death affects the apparent risk of another simply because the overall PMR must equal 100. The choice of the control causes of death is critical, however. As mentioned above, they must not be related to the exposure, but the possible relationship between exposure and disease may not be known for many potential control diseases.

Attributable Risk

There are measures available which express the amount of disease that would be attributable to an exposure if the observed association between the exposure and disease were causal. The attributable risk in the exposed (AR_e) is the disease rate in the exposed minus the rate in the unexposed. Because disease rates cannot be measured directly in case-control studies, the AR_e is calculable only for cohort studies. A related, more intuitive, measure, the attributable risk percent in the exposed (AR_e%), can be obtained from either study design. The AR_e% is the proportion of cases arising in the exposed population that is attributable to the exposure (see table 2 and table 3 for formula). The AR_e% is the rate ratio (or the odds ratio) minus 1, divided by the rate ratio (or odds ratio), multiplied by 100.

The population attributable risk (PAR) and the population attributable risk per cent (PAR%), or aetiological fraction, express the amount of disease in the total population, which is comprised of exposed and unexposed persons, that is due to the exposure if the observed association is causal. The PAR can be obtained from cohort studies (table 28.3 ) and the PAR% can be calculated in both cohort and case-control studies (table 2 and table 3).

Representativeness

There are several measures of risk that have been described. Each assumes underlying methods for counting events and in the representatives of these events to a defined group. When results are compared across studies, an understanding of the methods used is essential for explaining any observed differences.

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Aims and Principles

Biomechanics is a discipline that approaches the study of the body as though it were solely a mechanical system: all parts of the body are likened to mechanical structures and are studied as such. The following analogies may, for example, be drawn:

• bones: levers, structural members

• flesh: volumes and masses

• joints: bearing surfaces and articulations

• joint linings: lubricants

• muscles: motors, springs

• nerves: feedback control mechanisms

• organs: power supplies

• tendons: ropes

• tissue: springs

• body cavities: balloons.

The main aim of biomechanics is to study the way the body produces force and generates movement. The discipline relies primarily on anatomy, mathematics and physics; related disciplines are anthropometry (the study of human body measurements), work physiology and kinesiology (the study of the principles of mechanics and anatomy in relation to human movement).

In considering the occupational health of the worker, biomechanics helps to understand why some tasks cause injury and ill health. Some relevant types of adverse health effect are muscle strain, joint problems, back problems and fatigue.

Back strains and sprains and more serious problems involving the intervertebral discs are common examples of workplace injuries that can be avoided. These often occur because of a sudden particular overload, but may also reflect the exertion of excessive forces by the body over many years: problems may occur suddenly or may take time to develop. An example of a problem that develops over time is “seamstress’s finger”. A recent description describes the hands of a woman who, after 28 years of work in a clothing factory, as well as sewing in her spare time, developed hardened thickened skin and an inability to flex her fingers (Poole 1993). (Specifically, she suffered from a flexion deformity of the right index finger, prominent Heberden’s nodes on the index finger and thumb of the right hand, and a prominent callosity on the right middle finger due to constant friction from the scissors.) X-ray films of her hands showed severe degenerative changes in the outermost joints of her right index and middle fingers, with loss of joint space, articular sclerosis (hardening of tissue), osteophytes (bony growths at the joint) and bone cysts.

Inspection at the workplace showed that these problems were due to repeated hyperextension (bending up) of the outermost finger joint. Mechanical overload and restriction in blood flow (visible as a whitening of the finger) would be maximal across these joints. These problems developed in response to repeated muscle exertion in a site other than the muscle.

Biomechanics helps to suggest ways of designing tasks to avoid these types of injuries or of improving poorly designed tasks. Remedies for these particular problems are to redesign the scissors and to alter the sewing tasks to remove the need for the actions performed.

Two important principles of biomechanics are:

1. Muscles come in pairs. Muscles can only contract, so for any joint there must be one muscle (or muscle group) to move it one way and a corresponding muscle (or muscle group) to move it in the opposite direction. Figure 1 illustrates the point for the elbow joint.
2. Muscles contract most efficiently when the muscle pair is in relaxed balance. The muscle acts most efficiently when it is in the midrange of the joint it flexes. This is so for two reasons: first, if the muscle tries to contract when it is shortened, it will pull against the elongated opposing muscle. Because the latter is stretched, it will apply an elastic counterforce that the contracting muscle must overcome. Figure 2 shows the way in which muscle force varies with muscle length.

Figure 1. Skeletal muscles occur in pairs in order to initiate or reverse a movement

Figure 2. Muscle tension varies with muscle length

Second, if the muscle tries to contract at other than the midrange of the movement of the joint, it will operate at a mechanical disadvantage. Figure 3 illustrates the change in mechanical advantage for the elbow in three different positions.

Figure 3. Optimal positions for joint movement

An important criterion for work design follows from these principles: Work should be arranged so that it occurs with the opposing muscles of each joint in relaxed balance. For most joints, this means that the joint should be at about its midrange of movement.

This rule also means that muscle tension will be at a minimum while a task is performed. One example of the infringement of the rule is the overuse syndrome (RSI, or repetitive strain injury) which affects the muscles of the top of the forearm in keyboard operators who habitually operate with the wrist flexed up. Often this habit is forced on the operator by the design of the keyboard and workstation.

Applications

The following are some examples illustrating the application of biomechanics.

The optimum diameter of tool handles

The diameter of a handle affects the force that the muscles of the hand can apply to a tool. Research has shown that the optimum handle diameter depends on the use to which the tool is put. For exerting thrust along the line of the handle, the best diameter is one that allows the fingers and thumb to assume a slightly overlapping grip. This is about 40 mm. To exert torque, a diameter of about 50-65 mm is optimal. (Unfortunately, for both purposes most handles are smaller than these values.)

The use of pliers

As a special case of a handle, the ability to exert force with pliers depends on the handle separation, as shown in figure 4.

Figure 4. Grip strength of pliers jaws exerted by male and female users as a function of handle separation

Seated posture

Electromyography is a technique that can be used to measure muscle tension. In a study of the tension in the erector spinae muscles (of the back) of seated subjects, it was found that leaning back (with the backrest inclined) reduced the tension in these muscles. The effect can be explained because the backrest takes more of the weight of the upper body.

X-ray studies of subjects in a variety of postures showed that the position of relaxed balance of the muscles that open and close the hip joint corresponds to a hip angle of about 135º. This is close to the position (128º) naturally adopted by this joint in weightless conditions (in space). In the seated posture, with an angle of 90º at the hip, the hamstring muscles that run over both the knee and hip joints tend to pull the sacrum (the part of the vertebral column that connects with the pelvis) into a vertical position. The effect is to remove the natural lordosis (curvature) of the lumbar spine; chairs should have appropriate backrests to correct for this effort.

Screwdriving

Why are screws inserted clockwise? The practice probably arose in unconscious recognition that the muscles that rotate the right arm clockwise (most people are right-handed) are larger (and therefore more powerful) that the muscles that rotate it anticlockwise.

Note that left-handed people will be at a disadvantage when inserting screws by hand. About 9% of the population are left-handed and will therefore require special tools in some situations: scissors and can openers are two such examples.

A study of people using screwdrivers in an assembly task revealed a more subtle relation between a particular movement and a particular health problem. It was found that the greater the elbow angle (the straighter the arm), the more people had inflammation at the elbow. The reason for this effect is that the muscle that rotates the forearm (the biceps) also pulls the head of the radius (lower arm bone) onto the capitulum (rounded head) of the humerus (upper arm bone). The increased force at the higher elbow angle caused greater frictional force at the elbow, with consequent heating of the joint, leading to the inflammation. At the higher angle, the muscle also had to pull with greater force to effect the screwing action, so a greater force was applied than would have been required with the elbow at about 90º. The solution was to move the task closer to the operators to reduce the elbow angle to about 90º.

The cases above demonstrate that a proper understanding of anatomy is required for the application of biomechanics in the workplace. Designers of tasks may need to consult experts in functional anatomy to anticipate the types of problems discussed. (The Pocket Ergonomist (Brown and Mitchell 1986) based on electromyographical research, suggests many ways of reducing physical discomfort at work.)

Manual Material Handling

The term manual handling includes lifting, lowering, pushing, pulling, carrying, moving, holding and restraining, and encompasses a large part of the activities of working life.

Biomechanics has obvious direct relevance to manual handling work, since muscles must move to carry out tasks. The question is: how much physical work can people be reasonably expected to do? The answer depends on the circumstances; there are really three questions that need to be asked. Each one has an answer that is based on scientifically researched criteria:

1. How much can be handled without damage to the body (in the form, for example, of muscle strain, disc injury or joint problems)? This is called the biomechanical criterion.
2. How much can be handled without overexerting the lungs (breathing hard to the point of panting)? This is called the physiological criterion.
3. How much do people feel able to handle comfortably? This is called the psychophysical criterion.

There is a need for these three different criteria because there are three broadly different reactions that can occur to lifting tasks: if the work goes on all day, the concern will be how the person feels about the task—the psychophysical criterion; if the force to be applied is large, the concern would be that muscles and joints are not overloaded to the point of damage—the biomechanical criterion; and if the rate of work is too great, then it may well exceed the physiological criterion, or the aerobic capacity of the person.

Many factors determine the extent of the load placed on the body by a manual handling task. All of them suggest opportunities for control.

Posture and Movements

If the task requires a person to twist or reach forward with a load, the risk of injury is greater. The workstation can often be redesigned to prevent these actions. More back injuries occur when the lift begins at ground level compared to mid-thigh level, and this suggests simple control measures. (This applies to high lifting as well.)

The load.

The load itself may influence handling because of its weight and its location. Other factors, such as its shape, its stability, its size and its slipperiness may all affect the ease of a handling task.

Organization and environment.

The way work is organized, both physically and over time (temporally), also influences handling. It is better to spread the burden of unloading a truck in a delivery bay over several people for an hour rather than to ask one worker to spend all day on the task. The environment influences handling—poor light, cluttered or uneven floors and poor housekeeping may all cause a person to stumble.

Personal factors.

Personal handling skills, the age of the person and the clothing worn also can influence handling requirements. Education for training and lifting are required both to provide necessary information and to allow time for the development of the physical skills of handling. Younger people are more at risk; on the other hand, older people have less strength and less physiological capacity. Tight clothing can increase the muscle force required in a task as people strain against the tight cloth; classic examples are the nurse’s smock uniform and tight overalls when people do work above their heads.

Recommended Weight Limits

The points mentioned above indicate that it is impossible to state a weight that will be “safe” in all circumstances. (Weight limits have tended to vary from country to country in an arbitrary manner. Indian dockers, for example, were once “allowed” to lift 110 kg, while their counterparts in the former People’s Democratic Republic of Germany were “limited” to 32 kg.) Weight limits have also tended to be too great. The 55 kg suggested in many countries is now thought to be far too great on the basis of recent scientific evidence. The National Institute for Occupational Safety and Health (NIOSH) in the United States has adopted 23 kg as a load limit in 1991 (Waters et al. 1993).

Each lifting task needs to be assessed on its own merits. A useful approach to determining a weight limit for a lifting task is the equation developed by NIOSH:

RWL = LC x HM x VM x DM x AM x CM x FM

Where

RWL = recommended weight limit for the task in question

HM = the horizontal distance from the centre of gravity of the load to the midpoint between the ankles (minimum 15 cm, maximum 80 cm)

VM = the vertical distance between the centre of gravity of the load and the floor at the start of the lift (maximum 175 cm)

DM = the vertical travel of the lift (minimum 25 cm, maximum 200 cm)

AM = asymmetry factor–the angle the task deviates from straight out in front of the body

CM = coupling multiplier–the ability to get a good grip on the item to be lifted, which is found in a reference table

FM = frequency multipliers–the frequency of the lifting.

All variables of length in the equation are expressed in units of centimetres. It should be noted that 23 kg is the maximum weight that NIOSH recommends for lifting. This has been reduced from 40 kg after observation of many people doing many lifting tasks has revealed that the average distance from the body of the start of the lift is 25 cm, not the 15 cm assumed in an earlier version of the equation (NIOSH 1981).

Lifting index.

By comparing the weight to be lifted in the task and the RWL, a lifting index (LI) can be obtained according to the relationship:

LI=(weight to be handled)/RWL.

Therefore, particularly valuable use of the NIOSH equation is the placing of lifting tasks in order of severity, using the lifting index to set priorities for action. (The equation has a number of limitations, however, that need to be understood for its most effective application. See Waters et al. 1993).

Estimating Spinal Compression Imposed by the Task

Computer software is available to estimate the spinal compression produced by a manual handling task. The 2D and 3D Static Strength Prediction Programs from the University of Michigan (“Backsoft”) estimate spinal compression. The inputs required to the program are:

• the posture in which the handling activity is performed

• the force exerted

• the direction of the force exertion

• the number of hands exerting the force

• the percentile of the population under study.

The 2D and 3D programs differ in that the 3D software allows computations applying to postures in three dimensions. The program output gives spinal compression data and lists the percentage of the population selected that would be able to do the particular task without exceeding suggested limits for six joints: ankle, knee, hip, first lumbar disc-sacrum, shoulder, and elbow. This method also has a number of limitations that need to be fully understood in order to derive maximum value out of the program.

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The functions of the immune system are to protect the body from invading infectious agents and to provide immune surveillance against arising tumour cells. It has a first line of defence that is non-specific and that can initiate effector reactions itself, and an acquired specific branch, in which lymphocytes and antibodies carry the specificity of recognition and subsequent reactivity towards the antigen.

Immunotoxicology has been defined as “the discipline concerned with the study of the events that can lead to undesired effects as a result of interaction of xenobiotics with the immune system. These undesired events may result as a consequence of (1) a direct and/or indirect effect of the xenobiotic (and/or its biotransformation product) on the immune system, or (2) an immunologically based host response to the compound and/or its metabolite(s), or host antigens modified by the compound or its metabolites” (Berlin et al. 1987).

When the immune system acts as a passive target of chemical insults, the result can be decreased resistance to infection and certain forms of neoplasia, or immune disregulation/stimulation that can exacerbate allergy or auto-immunity. In the case that the immune system responds to the antigenic specificity of the xenobiotic or host antigen modified by the compound, toxicity can become manifest as allergies or autoimmune diseases.

Animal models to investigate chemical-induced immune suppression have been developed, and a number of these methods are validated (Burleson, Munson, and Dean 1995; IPCS 1996). For testing purposes, a tiered approach is followed to make an adequate selection from the overwhelming number of assays available. Generally, the objective of the first tier is to identify potential immunotoxicants. If potential immunotoxicity is identified, a second tier of testing is performed to confirm and characterize further the changes observed. Third-tier investigations include special studies on the mechanism of action of the compound. Several xenobiotics have been identified as immunotoxicants causing immunosuppression in such studies with laboratory animals.

The database on immune function disturbances in humans by environmental chemicals is limited (Descotes 1986; NRC Subcommittee on Immunotoxicology 1992). The use of markers of immunotoxicity has received little attention in clinical and epidemiological studies to investigate the effect of these chemicals on human health. Such studies have not been performed frequently, and their interpretation often does not permit unequivocal conclusions to be drawn, due for instance to the uncontrolled nature of exposure. Therefore, at present, immunotoxicity assessment in rodents, with subsequent extrapolation to man, forms the basis of decisions regarding hazard and risk.

Hypersensitivity reactions, notably allergic asthma and contact dermatitis, are important occupational health problems in industrialized countries (Vos, Younes and Smith 1995). The phenomenon of contact sensitization was investigated first in the guinea pig (Andersen and Maibach 1985). Until recently this has been the species of choice for predictive testing. Many guinea pig test methods are available, the most frequently employed being the guinea pig maximization test and the occluded patch test of Buehler. Guinea pig tests and newer approaches developed in mice, such as ear swelling tests and the local lymph node assay, provide the toxicologist with the tools to assess skin sensitization hazard. The situation with respect to sensitization of the respiratory tract is very different. There are, as yet, no well-validated or widely accepted methods available for the identification of chemical respiratory allergens although progress in the development of animal models for the investigation of chemical respiratory allergy has been achieved in the guinea pig and mouse.

Human data show that chemical agents, in particular drugs, can cause autoimmune diseases (Kammüller, Bloksma and Seinen 1989). There are a number of experimental animal models of human autoimmune diseases. Such comprise both spontaneous pathology (for example systemic lupus erythematosus in New Zealand Black mice) and autoimmune phenomena induced by experimental immunization with a cross-reactive autoantigen (for example the H37Ra adjuvant induced arthritis in Lewis strain rats). These models are applied in the preclinical evaluation of immunosuppressive drugs. Very few studies have addressed the potential of these models for assessment of whether a xenobiotic exacerbates induced or congenital autoimmunity. Animal models that are suitable to investigate the ability of chemicals to induce autoimmune diseases are virtually lacking. One model that is used to a limited extent is the popliteal lymph node assay in mice. Like the situation in humans, genetic factors play a crucial role in the development of autoimmune disease (AD) in laboratory animals, which will limit the predictive value of such tests.

The Immune System

The major function of the immune system is defence against bacteria, viruses, parasites, fungi and neoplastic cells. This is achieved by the actions of various cell types and their soluble mediators in a finely tuned concert. The host defence can be roughly divided into non-specific or innate resistance and specific or acquired immunity mediated by lymphocytes (Roitt, Brostoff and Male 1989).

Components of the immune system are present throughout the body (Jones et al. 1990). The lymphocyte compartment is found within lymphoid organs (figure 1). The bone marrow and thymus are classified as primary or central lymphoid organs; the secondary or peripheral lymphoid organs include lymph nodes, spleen and lymphoid tissue along secretory surfaces such as the gastrointestinal and respiratory tracts, the so-called mucosa-associated lymphoid tissue (MALT). About half of the body’s lymphocytes are located at any one time in MALT. In addition the skin is an important organ for the induction of immune responses to antigens present on the skin. Important in this process are epidermal Langerhans cells that have an antigen-presenting function.

Figure 1. Primary and secondary lymphoid organs and tissues

Phagocytic cells of the monocyte/macrophage lineage, called the mononuclear phagocyte system (MPS), occur in lymphoid organs and also at extranodal sites; the extranodal phagocytes include Kupffer cells in the liver, alveolar macrophages in the lung, mesangial macrophages in the kidney and glial cells in the brain. Polymorphonuclear leukocytes (PMNs) are present mainly in blood and bone marrow, but accumulate at sites of inflammation.

Non-specific defence

A first line of defence to micro-organisms is executed by a physical and chemical barrier, such as at the skin, the respiratory tract and the alimentary tract. This barrier is helped by non-specific protective mechanisms including phagocytic cells, such as macrophages and polymorphonuclear leukocytes, which are able to kill pathogens, and natural killer cells, which can lyse tumour cells and virus-infected cells. The complement system and certain microbial inhibitors (e.g., lysozyme) also take part in the non-specific response.

Specific immunity

After initial contact of the host with the pathogen, specific immune responses are induced. The hallmark of this second line of defence is specific recognition of determinants, so-called antigens or epitopes, of the pathogens by receptors on the cell surface of B- and T-lymphocytes. Following interaction with the specific antigen, the receptor-bearing cell is stimulated to undergo proliferation and differentiation, producing a clone of progeny cells that are specific for the eliciting antigen. The specific immune responses help the non-specific defence presented to the pathogens by stimulating the efficacy of the non-specific responses. A fundamental characteristic of specific immunity is that memory develops. Secondary contact with the same antigen provokes a faster and more vigorous but well-regulated response.

The genome does not have the capacity to carry the codes of an array of antigen receptors sufficient to recognize the number of antigens that can be encountered. The repertoire of specificity develops by a process of gene rearrangements. This is a random process, during which various specificities are brought about. This includes specificities for self components, which are undesirable. A selection process that takes place in the thymus (T cells), or bone marrow (B cells) operates to delete these undesirable specificities.

Normal immune effector function and homeostatic regulation of the immune response is dependent upon a variety of soluble products, known collectively as cytokines, which are synthesized and secreted by lymphocytes and by other cell types. Cytokines have pleiotropic effects on immune and inflammatory responses. Cooperation between different cell populations is required for the immune response—the regulation of antibody responses, the accumulation of immune cells and molecules at inflammatory sites, the initiation of acute phase responses, the control of macrophage cytotoxic function and many other processes central to host resistance. These are influenced by, and in many cases are dependent upon, cytokines acting individually or in concert.

Two arms of specific immunity are recognized—humoral immunity and cell-mediated or cellular immunity:

Humoral immunity. In the humoral arm B-lymphocytes are stimulated following recognition of antigen by cell-surface receptors. Antigen receptors on B-lymphocytes are immunoglobulins (Ig). Mature B cells (plasma cells) start the production of antigen-specific immunoglobulins that act as antibodies in serum or along mucosal surfaces. There are five major classes of immunoglobulins: (1) IgM, pentameric Ig with optimal agglutinating capacity, which is first produced after antigenic stimulation; (2) IgG, the main Ig in circulation, which can pass the placenta; (3) IgA, secretory Ig for the protection of mucosal surfaces; (4) IgE, Ig fixing to mast cells or basophilic granulocytes involved in immediate hypersensitivity reactions and (5) IgD, whose major function is as a receptor on B-lymphocytes.

Cell-mediated immunity. The cellular arm of the specific immune system is mediated by T-lymphocytes. These cells also have antigen receptors on their membranes. They recognize antigen if presented by antigen presenting cells in the context of histocompatibility antigens. Hence, these cells have a restriction in addition to the antigen specificity. T cells function as helper cells for various (including humoral) immune responses, mediate recruitment of inflammatory cells, and can, as cytotoxic T cells, kill target cells after antigen-specific recognition.

Mechanisms of Immunotoxicity

Immunosuppression

Effective host resistance is dependent upon the functional integrity of the immune system, which in turn requires that the component cells and molecules which orchestrate immune responses are available in sufficient numbers and in an operational form. Congenital immunodeficiencies in humans are often characterized by defects in certain stem cell lines, resulting in impaired or absent production of immune cells. By analogy with congenital and acquired human immunodeficiency diseases, chemical-induced immunosuppression may result simply from a reduced number of functional cells (IPCS 1996). The absence, or reduced numbers, of lymphocytes may have more or less profound effects on immune status. Some immunodeficiency states and severe immunosuppression, as can occur in transplantation or cytostatic therapy, have been associated in particular with increased incidences of opportunistic infections and of certain neoplastic diseases. The infections can be bacterial, viral, fungal or protozoan, and the predominant type of infection depends on the associated immunodeficiency. Exposure to immunosuppressive environmental chemicals may be expected to result in more subtle forms of immunosuppression, which may be difficult to detect. These may lead, for example, to an increased incidence of infections such as influenza or the common cold.

In view of the complexity of the immune system, with the wide variety of cells, mediators and functions that form a complicated and interactive network, immunotoxic compounds have numerous opportunities to exert an effect. Although the nature of the initial lesions induced by many immunotoxic chemicals have not yet been elucidated, there is increasing information available, mostly derived from studies in laboratory animals, regarding the immunobiological changes which result in depression of immune function (Dean et al. 1994). Toxic effects might occur at the following critical functions (and some examples are given of immunotoxic compounds affecting these functions):

•  development and expansion of different stem cell populations (benzene exerts immunotoxic effects at the stem cell level, causing lymphocytopenia)
•  proliferation of various lymphoid and myeloid cells as well as supportive tissues in which these cells mature and function (immunotoxic organotin compounds suppress the proliferative activity of lymphocytes in the thymic cortex through direct cytotoxicity; the thymotoxic action of 2,3,7,8-tetrachloro-dibenzo-p-dioxin (TCDD) and related compounds is likely due to an impaired function of thymic epithelial cells, rather than to direct toxicity for thymocytes)
•  antigen uptake, processing and presentation by macrophages and other antigen-presenting cells (one of the targets of 7,12-dimethylbenz(a)anthracene (DMBA) and of lead is antigen presentation by macrophages; a target of ultraviolet radiation is the antigen-presenting Langerhans cell)
•  regulatory function of T-helper and T-suppressor cells (T-helper cell function is impaired by organotins, aldicarb, polychlorinated biphenyls (PCBs), TCDD and DMBA; T-suppressor cell function is reduced by low-dose cyclophosphamide treatment)
•  production of various cytokines or interleukins (benzo(a)pyrene (BP) suppresses interleukin-1 production; ultraviolet radiation alters production of cytokines by keratinocytes)
•  synthesis of various classes of immunoglobulins IgM and IgG is suppressed following PCB and tributyltin oxide (TBT) treatment, and increased after hexachlorobenzene (HCB) exposure).
•  complement regulation and activation (affected by TCDD)
•  cytotoxic T cell function (3-methylcholanthrene (3-MC), DMBA, and TCDD suppress cytotoxic T cell activity)
•  natural killer (NK) cell function (pulmonary NK activity is suppressed by ozone; splenic NK activity is impaired by nickel)
•  macrophage and polymorphonuclear leukocyte chemotaxis and cytotoxic functions (ozone and nitrogen dioxide impair the phagocytic activity of alveolar macrophages).

Allergy

Allergy may be defined as the adverse health effects which result from the induction and elicitation of specific immune responses. When hypersensitivity reactions occur without involvement of the immune system the term pseudo-allergy is used. In the context of immunotoxicology, allergy results from a specific immune response to chemicals and drugs that are of interest. The ability of a chemical to sensitize individuals is generally related to its ability to bind covalently to body proteins. Allergic reactions may take a variety of forms and these differ with respect to both the underlying immunological mechanisms and the speed of the reaction. Four major types of allergic reactions have been recognized: Type I hypersensitivity reactions, which are effectuated by IgE antibody and where symptoms are manifest within minutes of exposure of the sensitized individual. Type II hypersensitivity reactions result from the damage or destruction of host cells by antibody. In this case symptoms become apparent within hours. Type III hypersensitivity, or Arthus, reactions are also antibody mediated, but against soluble antigen, and result from the local or systemic action of immune complexes. Type IV, or delayed-type hypersensitivity, reactions are effected by T-lymphocytes and normally symptoms develop 24to 48hours following exposure of the sensitized individual.

The two types of chemical allergy of greatest relevance to occupational health are contact sensitivity or skin allergy and allergy of the respiratory tract.

Contact hypersensitivity. A large number of chemicals are able to cause skin sensitization. Following topical exposure of a susceptible individual to a chemical allergen, a T-lymphocyte response is induced in the draining lymph nodes. In the skin the allergen interacts directly or indirectly with epidermal Langerhans cells, which transport the chemical to the lymph nodes and present it in an immunogenic form to responsive T-lymphocytes. Allergen- activated T-lymphocytes proliferate, resulting in clonal expansion. The individual is now sensitized and will respond to a second dermal exposure to the same chemical with a more aggressive immune response, resulting in allergic contact dermatitis. The cutaneous inflammatory reaction which characterizes allergic contact dermatitis is secondary to the recognition of the allergen in the skin by specific T-lymphocytes. These lymphocytes become activated, release cytokines and cause the local accumulation of other mononuclear leukocytes. Symptoms develop some 24 to 48 hours following exposure of the sensitized individual, and allergic contact dermatitis therefore represents a form of delayed-type hypersensitivity. Common causes of allergic contact dermatitis include organic chemicals (such as 2,4-dinitrochlorobenzene), metals (such as nickel and chromium) and plant products (such as urushiol from poison ivy).

Respiratory hypersensitivity. Respiratory hypersensitivity is usually considered to be a Type I hypersensitivity reaction. However, late phase reactions and the more chronic symptoms associated with asthma may involve cell-mediated (Type IV) immune processes. The acute symptoms associated with respiratory allergy are effected by IgE antibody, the production of which is provoked following exposure of the susceptible individual to the inducing chemical allergen. The IgE antibody distributes systemically and binds, via membrane receptors, to mast cells which are found in vascularized tissues, including the respiratory tract. Following inhalation of the same chemical a respiratory hypersensitivity reaction will be elicited. Allergen associates with protein and binds to, and cross-links, IgE antibody bound to mast cells. This in turn causes the degranulation of mast cells and the release of inflammatory mediators such as histamine and leukotrienes. Such mediators cause bronchoconstriction and vasodilation, resulting in the symptoms of respiratory allergy; asthma and/or rhinitis. Chemicals known to cause respiratory hypersensitivity in man include acid anhydrides (such as trimellitic anhydride), some diisocyanates (such as toluene diisocyanate), platinum salts and some reactive dyes. Also, chronic exposure to beryllium is known to cause hypersensitivity lung disease.

Autoimmunity

Autoimmunity can be defined as the stimulation of specific immune responses directed against endogenous “self” antigens. Induced autoimmunity can result either from alterations in the balance of regulatory T-lymphocytes or from the association of a xenobiotic with normal tissue components such as to render them immunogenic (“altered self”). Drugs and chemicals known to incidentally induce or exacerbate effects like those of autoimmune disease (AD) in susceptible individuals are low molecular weight compounds (molecular weight 100 to 500) that are generally considered to be not immunogenic themselves. The mechanism of AD by chemical exposure is mostly unknown. Disease can be produced directly by means of circulating antibody, indirectly through the formation of immune complexes, or as a consequence of cell-mediated immunity, but likely occurs through a combination of mechanisms. The pathogenesis is best known in immune haemolytic disorders induced by drugs:

•  The drug can attach to the red-cell membrane and interact with a drug-specific antibody.
•  The drug can alter the red-cell membrane so that the immune system regards the cell as foreign.
•  The drug and its specific antibody form immune complexes that adhere to the red-cell membrane to produce injury.
•  Red-cell sensitization occurs due to the production of red-cell autoantibody.

A variety of chemicals and drugs, in particular the latter, have been found to induce autoimmune-like responses (Kamüller, Bloksma and Seinen 1989). Occupational exposure to chemicals may incidentally lead to AD-like syndromes. Exposure to monomeric vinyl chloride, trichloroethylene, perchloroethylene, epoxy resins and silica dust may induce scleroderma-like syndromes. A syndrome similar to systemic lupus erythematosus (SLE) has been described after exposure to hydrazine. Exposure to toluene diisocyanate has been associated with the induction of thrombocytopenic purpura. Heavy metals such as mercury have been implicated in some cases of immune complex glomerulonephritis.

Human Risk Assessment

The assessment of human immune status is performed mainly using peripheral blood for analysis of humoral substances like immunoglobulins and complement, and of blood leukocytes for subset composition and functionality of subpopulations. These methods are usually the same as those used to investigate humoral and cell-mediated immunity as well as nonspecific resistance of patients with suspected congenital immunodeficiency disease. For epidemiological studies (e.g., of occupationally exposed populations) parameters should be selected on the basis of their predictive value in human populations, validated animal models, and the underlying biology of the markers (see table 1). The strategy in screening for immunotoxic effects after (accidental) exposure to environmental pollutants or other toxicants is much dependent on circumstances, such as type of immunodeficiency to be expected, time between exposure and immune status assessment, degree of exposure and number of exposed individuals. The process of assessing the immunotoxic risk of a particular xenobiotic in humans is extremely difficult and often impossible, due largely to the presence of various confounding factors of endogenous or exogenous origin that influence the response of individuals to toxic damage. This is particularly true for studies which investigate the role of chemical exposure in autoimmune diseases, where genetic factors play a crucial role.

Table 1. Classification of tests for immune markers

As adequate human data are seldom available, the assessment of risk for chemical-induced immunosuppression in humans is in the majority of cases based upon animal studies. The identification of potential immunotoxic xenobiotics is undertaken primarily in controlled studies in rodents. In vivo exposure studies present, in this regard, the optimal approach to estimate the immunotoxic potential of a compound. This is due to the multifactoral and complex nature of the immune system and of immune responses. In vitro studies are of increasing value in the elucidation of mechanisms of immunotoxicity. In addition, by investigating the effects of the compound using cells of animal and human origin, data can be generated for species comparison, which can be used in the “parallelogram” approach to improve the risk assessment process. If data are available for three cornerstones of the parallelogram (in vivo animal, and in vitro animal and human) it may be easier to predict the outcome at the remaining cornerstone, that is, the risk in humans.

When assessment of risk for chemical-induced immunosuppression has to rely solely upon data from animal studies, an approach can be followed in the extrapolation to man by the application of uncertainty factors to the no observed adverse effect level (NOAEL). This level can be based on parameters determined in relevant models, such as host resistance assays and in vivo assessment of hypersensitivity reactions and antibody production. Ideally, the relevance of this approach to risk assessment requires confirmation by studies in humans. Such studies should combine the identification and measurement of the toxicant, epidemiological data and immune status assessments.

To predict contact hypersensitivity, guinea pig models are available and have been used in risk assessment since the 1970s. Although sensitive and reproducible, these tests have limitations as they depend on subjective evaluation; this can be overcome by newer and more quantitative methods developed in the mouse. Regarding chemical-induced hypersensitivity induced by inhalation or ingestion of allergens, tests should be developed and evaluated in terms of their predictive value in man. When it comes to setting safe occupational exposure levels of potential allergens, consideration has to be given to the biphasic nature of allergy: the sensitization phase and the elicitation phase. The concentration required to elicit an allergic reaction in a previously sensitized individual is considerably lower than the concentration necessary to induce sensitization in the immunologically naïve but susceptible individual.

As animal models to predict chemical-induced autoimmunity are virtually lacking, emphasis should be given to the development of such models. For the development of such models, our knowledge of chemical-induced autoimmunity in humans should be advanced, including the study of genetic and immune system markers to identify susceptible individuals. Humans that are exposed to drugs that induce autoimmunity offer such an opportunity.

Back

The epidemiologist is interested in relationships between variables, chiefly exposure and outcome variables. Typically, epidemiologists want to ascertain whether the occurrence of disease is related to the presence of a particular agent (exposure) in the population. The ways in which these relationships are studied may vary considerably. One can identify all persons who are exposed to that agent and follow them up to measure the incidence of disease, comparing such incidence with disease occurrence in a suitable unexposed population. Alternatively, one can simply sample from among the exposed and unexposed, without having a complete enumeration of them. Or, as a third alternative, one can identify all people who develop a disease of interest in a defined time period (“cases”) and a suitable group of disease-free individuals (a sample of the source population of cases), and ascertain whether the patterns of exposure differ between the two groups. Follow-up of study participants is one option (in so-called longitudinal studies): in this situation, a time lag exists between the occurrence of exposure and disease onset. One alternative option is a cross-section of the population, where both exposure and disease are measured at the same point in time.

In this article, attention is given to the common study designs—cohort, case-referent (case-control) and cross-sectional. To set the stage for this discussion, consider a large viscose rayon factory in a small town. An investigation into whether carbon disulphide exposure increases the risk of cardiovascular disease is started. The investigation has several design choices, some more and some less obvious. A first strategy is to identify all workers who have been exposed to carbon disulphide and follow them up for cardiovascular mortality.

Cohort Studies

A cohort study encompasses research participants sharing a common event, the exposure. A classical cohort study identifies a defined group of exposed people, and then everyone is followed up and their morbidity and/or mortality experience is registered. Apart from a common qualitative exposure, the cohort should also be defined on other eligibility criteria, such as age range, gender (male or female or both), minimum duration and intensity of exposure, freedom from other exposures, and the like, to enhance the study’s validity and efficiency. At entrance, all cohort members should be free of the disease under study, according to the empirical set of criteria used to measure the disease.

If, for example, in the cohort study on the effects of carbon disulphide on coronary morbidity, coronary heart disease is empirically measured as clinical infarctions, those who, at the baseline, have had a history of coronary infarction must be excluded from the cohort. By contrast, electrocardiographic abnormalities without a history of infarction can be accepted. However, if the appearance of new electrocardiographic changes is the empirical outcome measure, the cohort members should also have normal electrocardiograms at the baseline.

The morbidity (in terms of incidence) or the mortality of an exposed cohort should be compared to a reference cohort which ideally should be as similar as possible to the exposed cohort in all relevant aspects, except for the exposure, to determine the relative risk of illness or death from exposure. Using a similar but unexposed cohort as a provider of the reference experience is preferable to the common (mal)practice of comparing the morbidity or mortality of the exposed cohort to age-standardized national figures, because the general population falls short on fulfilling even the most elementary requirements for comparison validity. The Standardized Morbidity (or Mortality) Ratio (SMR), resulting from such a comparison, usually generates an underestimate of the true risk ratio because of a bias operating in the exposed cohort, leading to the lack of comparability between the two populations. This comparison bias has been named the “Healthy Worker Effect”. However, it is really not a true “effect”, but a bias from negative confounding, which in turn has arisen from health-selective turnover in an employed population. (People with poor health tend to move out from, or never enter, “exposed” cohorts, their end destination often being the unemployed section of the general population.)

Because an “exposed” cohort is defined as having a certain exposure, only effects caused by that single exposure (or mix of exposures) can be studied simultaneously. On the other hand, the cohort design permits the study of several diseases at the same time. One can also study concomitantly different manifestations of the same disease—for example, angina, ECG changes, clinical myocardial infarctions and coronary mortality. While well-suited to test specific hypotheses (e.g., “exposure to carbon disulphide causes coronary heart disease”), a cohort study also provides answers to the more general question: “What diseases are caused by this exposure?”

For example, in a cohort study investigating the risk to foundry workers of dying from lung cancer, the mortality data are obtained from the national register of causes of death. Although the study was to determine if foundry dust causes lung cancer, the data source, with the same effort, also gives information on all other causes of death. Therefore, other possible health risks can be studied at the same time.

The timing of a cohort study can either be retrospective (historical) or prospective (concurrent). In both instances the design structure is the same. A full enumeration of exposed people occurs at some point or period in time, and the outcome is measured for all individuals through a defined end point in time. The difference between prospective and retrospective is in the timing of the study. If retrospective, the end point has already occurred; if prospective, one has to wait for it.

In the retrospective design, the cohort is defined at some point in the past (for example, those exposed on 1 January 1961, or those taking on exposed work between 1961 and 1970). The morbidity and/or mortality of all cohort members is then followed to the present. Although “all” means that also those having left the job must be traced, in practice a 100 per cent coverage can rarely be achieved. However, the more complete the follow-up, the more valid is the study.

In the prospective design, the cohort is defined at the present, or during some future period, and the morbidity is then followed into the future.

When doing cohort studies, enough time must be allowed for follow-up in order that the end points of concern have sufficient time to manifest. Sometimes, because historical records may be available for only a short period into the past, it is nevertheless desirable to take advantage of this data source because it means that a shorter period of prospective follow-up would be needed before results from the study could be available. In these situations, a combination of the retrospective and the prospective cohort study designs can be efficient. The general layout of frequency tables presenting cohort data is shown in table 1.

Table 1. The general layout of frequency tables presenting cohort data

The observed proportion of diseased in the exposed cohort is calculated as:

and that of the reference cohort as:

The rate ratio then is expressed as:

N₀ and N₁ are usually expressed in person-time units instead of as the number of people in the populations. Person-years are computed for each individual separately. Different people often enter the cohort during a period of time, not at the same date. Hence their follow-up times start at different dates. Likewise, after their death, or after the event of interest has occurred, they are no longer “at risk” and should not continue to contribute person-years to the denominator.

If the RR is greater than 1, the morbidity of the exposed cohort is higher than that of the reference cohort, and vice versa. The RR is a point estimate and a confidence interval (CI) should be computed for it. The larger the study, the narrower the confidence interval will become. If RR = 1 is not included in the confidence interval (e.g., the 95% CI is 1.4 to 5.8), the result can be considered as “statistically significant” at the chosen level of probability (in this example, α = 0.05).

If the general population is used as the reference population, c₀ is substituted by the “expected” figure, E(c₁ ), derived from the age-standardized morbidity or mortality rates of that population (i.e., the number of cases that would have occurred in the cohort, had the exposure of interest not taken place). This yields the Standardized Mortality (or Morbidity) Ratio, SMR. Thus,

Also for the SMR, a confidence interval should be computed. It is better to give this measure in a publication than a p-value, because statistical significance testing is meaningless if the general population is the reference category. Such comparison entails a considerable bias (the healthy worker effect noted above), and statistical significance testing, originally developed for experimental research, is misleading in the presence of systematic error.

Suppose the question is whether quartz dust causes lung cancer. Usually, quartz dust occurs together with other carcinogens—such as radon daughters and diesel exhaust in mines, or polyaromatic hydrocarbons in foundries. Granite quarries do not expose the stone workers to these other carcinogens. Therefore the problem is best studied among stone workers employed in granite quarries.

Suppose then that all 2,000 workers, having been employed by 20 quarries between 1951 and 1960, are enrolled in the cohort and their cancer incidence (alternatively only mortality) is followed starting at ten years after first exposure (to allow for an induction time) and ending in 1990. This is a 20- to 30-year (depending on the year of entry) or, say, on average, a 25-year follow-up of the cancer mortality (or morbidity) among 1,000 of the quarry workers who were specifically granite workers. The exposure history of each cohort member must be recorded. Those who have left the quarries must be traced and their later exposure history recorded. In countries where all inhabitants have unique registration numbers, this is a straightforward procedure, governed chiefly by national data protection laws. Where no such system exists, tracing employees for follow up purposes can be extremely difficult. Where appropriate death or disease registries exist, the mortality from all causes, all cancers and specific cancer sites can be obtained from the national register of causes of death. (For cancer mortality, the national cancer registry is a better source because it contains more accurate diagnoses. In addition, incidence (or, morbidity) data can also be obtained.) The death rates (or cancer incidence rates) can be compared to “expected numbers”, computed from national rates using the person-years of the exposed cohort as a basis.

Suppose that 70 fatal cases of lung cancer are found in the cohort, whereas the expected number (the number which would have occurred had there been no exposure) is 35. Then:

c₁ = 70, E(c₁) = 35

Thus, the SMR = 200, which indicates a twofold increase in risk of dying from lung cancer among the exposed. If detailed exposure data are available, the cancer mortality can be studied as a function of different latency times (say, 10, 15, 20 years), work in different types of quarries (different kinds of granite), different historical periods, different exposure intensities and so on. However, 70 cases cannot be subdivided into too many categories, because the number falling into each one rapidly becomes too small for statistical analysis.

Both types of cohort designs have advantages and disadvantages. A retrospective study can, as a rule, measure only mortality, because data for milder manifestations usually are lacking. Cancer registries are an exception, and perhaps a few others, such as stroke registries and hospital discharge registries, in that incidence data also are available. Assessing past exposure is always a problem and the exposure data are usually rather weak in retrospective studies. This can lead to effect masking. On the other hand, since the cases have already occurred, the results of the study become available much sooner; in, say, two to three years.

A prospective cohort study can be better planned to comply with the researcher’s needs, and exposure data can be collected accurately and systematically. Several different manifestations of a disease can be measured. Measurements of both exposure and outcome can be repeated, and all measurements can be standardized and their validity can be checked. However, if the disease has a long latency (such as cancer), much time—even 20 to 30 years—will need to pass before the results of the study can be obtained. Much can happen during this time. For example, turnover of researchers, improvements in techniques for measuring exposure, remodelling or closure of the plants chosen for study and so forth. All these circumstances endanger the success of the study. The costs of a prospective study are also usually higher than those of a retrospective study, but this is mostly due to the much greater number of measurements (repeated exposure monitoring, clinical examinations and so on), and not to more expensive death registration. Therefore the costs per unit of information do not necessarily exceed those of a retrospective study. In view of all this, prospective studies are more suited for diseases with rather short latency, requiring short follow-up, while retrospective studies are better for disease with a long latency.

Case-Control (or Case-Referent) Studies

Let us go back to the viscose rayon plant. A retrospective cohort study may not be feasible if the rosters of the exposed workers have been lost, while a prospective cohort study would yield sound results in a very long time. An alternative would then be the comparison between those who died from coronary heart disease in the town, in the course of a defined time period, and a sample of the total population in the same age group.

The classical case-control (or, case-referent) design is based on sampling from a dynamic (open, characterized by a turnover of membership) population. This population can be that of a whole country, a district or a municipality (as in our example), or it can be the administratively defined population from which patients are admitted to a hospital. The defined population provides both the cases and the controls (or referents).

The technique is to gather all the cases of the disease in question that exist at a point in time (prevalent cases), or have occurred during a defined period of time (incident cases). The cases thus can be drawn from morbidity or mortality registries, or be gathered directly from hospitals or other sources having valid diagnostics. The controls are drawn as a sample from the same population, either from among non-cases or from the entire population. Another option is to select patients with another disease as controls, but then these patients must be representative of the population from which the cases came. There may be one or more controls (i.e., referents) for each case. The sampling approach differs from cohort studies, which examine the entire population. It goes without saying that the gains in terms of the lower costs of case-control designs are considerable, but it is important that the sample is representative of the whole population from which the cases originated (i.e., the “study base”)—otherwise the study can be biased.

When cases and controls have been identified, their exposure histories are gathered by questionnaires, interviews or, in some instances, from existing records (e.g., payroll records from which work histories can be deduced). The data can be obtained either from the participants themselves or, if they are deceased, from close relatives. To ensure symmetrical recall, it is important that the proportion of dead and live cases and referents be equal, because close relatives usually give a less detailed exposure history than the participants themselves. Information about the exposure pattern among cases is compared to that among controls, providing an estimate of the odds ratio (OR), an indirect measure of the risk among the exposed to incur the disease relative to that of the unexposed.

Because the case-control design relies on the exposure information obtained from patients with a certain disease (i.e., cases) along with a sample of non-diseased people (i.e., controls) from the population from which the cases originated, the connection with exposures can be investigated for only one disease. By contrast, this design allows the concomitant study of the effect of several different exposures. The case-referent study is well suited to address specific research questions (e.g., “Is coronary heart disease caused by exposure to carbon disulphide?”), but it also can help to answer the more general question: “What exposures can cause this disease?”

The question of whether exposure to organic solvents causes primary liver cancer is raised (as an example) in Europe. Cases of primary liver cancer, a comparatively rare disease in Europe, are best gathered from a national cancer registry. Suppose that all cancer cases occurring during three years form the case series. The population base for the study is then a three-year follow-up of the entire population in the European country in question. The controls are drawn as a sample of persons without liver cancer from the same population. For reasons of convenience (meaning that the same source can be used for sampling the controls) patients with another cancer type, not related to solvent exposure, can be used as controls. Colon cancer has no known relation to solvent exposure; hence this cancer type can be included among the controls. (Using cancer controls minimizes recall bias in that the accuracy of the history given by cases and controls is, on average, symmetrical. However, if some presently unknown connection between colon cancer and exposure to solvents were revealed later, this type of control would cause an underestimation of the true risk—not an exaggeration of it.)

For each case of liver cancer, two controls are drawn in order to achieve greater statistical power. (One could draw even more controls, but available funds may be a limiting factor. If funds were not limited, perhaps as many as four controls would be optimal. Beyond four, the law of diminishing returns applies.) After obtaining appropriate permission from data protection authorities, the cases and controls, or their close relatives, are approached, usually by means of a mailed questionnaire, asking for a detailed occupational history with special emphasis on a chronological list of the names of all employers, the departments of work, the job tasks in different employment, and the period of employment in each respective task. These data can be obtained from relatives with some difficulty; however, specific chemicals or trade names usually are not well recalled by relatives. The questionnaire also should include questions on possible confounding data, such as alcohol use, exposure to foodstuffs containing aflatoxins, and hepatitis B and C infection. In order to obtain a sufficiently high response rate, two reminders are sent to non-respondents at three-week intervals. This usually results in a final response rate in excess of 70%. The occupational history is then reviewed by an industrial hygienist, without knowledge of the respondent’s case or control status, and exposure is classified into high, medium, low, none, and unknown exposure to solvents. The ten years of exposure immediately preceding the cancer diagnosis are disregarded, because it is not biologically plausible that initiator-type carcinogens can be the cause of the cancer if the latency time is that short (although promoters, in fact, could). At this stage it is also possible to differentiate between different types of solvent exposure. Because a complete employment history has been given, it is also possible to explore other exposures, although the initial study hypothesis did not include these. Odds ratios can then be computed for exposure to any solvent, specific solvents, solvent mixtures, different categories of exposure intensity, and for different time windows in relation to cancer diagnosis. It is advisable to exclude from analysis those with unknown exposure.

The cases and controls can be sampled and analysed either as independent series or matched groups. Matching means that controls are selected for each case based on certain characteristics or attributes, to form pairs (or sets, if more than one control is chosen for each case). Matching is usually done based on one or more such factors, as age, vital status, smoking history, calendar time of case diagnosis, and the like. In our example, cases and controls are then matched on age and vital status. (Vital status is important, because patients themselves usually give a more accurate exposure history than close relatives, and symmetry is essential for validity reasons.) Today, the recommendation is to be restrictive with matching, because this procedure can introduce negative (effect-masking) confounding.

If one control is matched to one case, the design is called a matched-pair design. Provided the costs of studying more controls are not prohibitive, more than one referent per case improves the stability of the estimate of the OR, which makes the study more size efficient.

The layout of the results of an unmatched case-control study is shown in table 2.

Table 2. Sample layout of case-control data

From this table, the odds of exposure among the cases, and the odds of exposure among the population (the controls), can be computed and divided to yield the exposure odds ratio, OR. For the cases, the exposure odds is c₁ / c₀, and for the controls it is n₁ / n₀. The estimate of the OR is then:

If relatively more cases than controls have been exposed, the OR is in excess of 1 and vice versa. Confidence intervals must be calculated and provided for the OR, in the same manner as for the RR.

By way of a further example, an occupational health centre of a large company serves 8,000 employees exposed to a variety of dusts and other chemical agents. We are interested in the connection between mixed dust exposure and chronic bronchitis. The study involves follow-up of this population for one year. We have set the diagnostic criteria for chronic bronchitis as “morning cough and phlegm production for three months during two consecutive years”. Criteria for “positive” dust exposure are defined before the study begins. Each patient visiting the health centre and fulfilling these criteria during a one-year period is a case, and the next patient seeking medical advice for non-pulmonary problems is defined as a control. Suppose that 100 cases and 100 controls become enrolled during the study period. Let 40 cases and 15 controls be classified as having been exposed to dust. Then

c₁ = 40, c₀ = 60, n₁ = 15, and n₀ = 85.

Consequently,

In the foregoing example, no consideration has been given to the possibility of confounding, which may lead to a distortion of the OR due to systematic differences between cases and controls in a variable like age. One way to reduce this bias is to match controls to cases on age or other suspect factors. This results in a data layout depicted in table 3.

Table 3. Layout of case-control data if one control is matched to each case

The analysis focuses on the discordant pairs: that is, “case exposed, control unexposed” (f_+–); and “case unexposed, control exposed” (f_–+). When both members of a pair are exposed or unexposed, the pair is disregarded. The OR in a matched-pair study design is defined as

In a study on the association between nasal cancer and wood dust exposure, there were all together 164 case-control pairs. In only one pair, both the case and the control had been exposed, and in 150 pairs, neither the case nor the control had been exposed. These pairs are not further considered. The case, but not the control had been exposed in 12 pairs, and the control, but not the case, in one pair. Hence,

and because unity is not included in this interval, the result is statistically significant—that is, there is a statistically significant association between nasal cancer and wood dust exposure.

Case-control studies are more efficient than cohort studies when the disease is rare; they may in fact provide the only option. However, common diseases also can be studied by this method. If the exposure is rare, an exposure-based cohort is the preferable or only feasible epidemiological design. Of course, cohort studies also can be carried out on common exposures. The choice between cohort and case-control designs when both the exposure and disease are common is usually decided taking validity considerations into account.

Because case-control studies rely on retrospective exposure data, usually based on the participants’ recall, their weak point is the inaccuracy and crudeness of the exposure information, which results in effect-masking through non-differential (symmetrical) misclassification of exposure status. Moreover, sometimes the recall can be asymmetrical between cases and controls, cases usually believed to remember “better” (i.e., recall bias).

Selective recall can cause an effect-magnifying bias through differential (asymmetrical) misclassification of exposure status. The advantages of case-control studies lie in their cost-effectiveness and their ability to provide a solution to a problem relatively quickly. Because of the sampling strategy, they allow the investigation of very large target populations (e.g., through national cancer registries), thereby increasing the statistical power of the study. In countries where data protection legislation or lack of good population and morbidity registries hinders the execution of cohort studies, hospital-based case-control studies may be the only practical way to conduct epidemiological research.

Case-control sampling within a cohort (nested case-control study designs)

A cohort study also can be designed for sampling instead of complete follow-up. This design has previously been called a “nested” case-control study. A sampling approach within the cohort sets different requirements on cohort eligibility, because the comparisons are now made within the same cohort. This should therefore include not only heavily exposed workers, but also less-exposed and even unexposed workers, in order to provide exposure contrasts within itself. It is important to realize this difference in eligibility requirements when assembling the cohort. If a full cohort analysis is first carried out on a cohort whose eligibility criteria were on “much” exposure, and a “nested” case-control study is done later on the same cohort, the study becomes insensitive. This introduces effect-masking because the exposure contrasts are insufficient “by design” by virtue of a lack of variability in exposure experience among members of the cohort.

However, provided the cohort has a broad range of exposure experience, the nested case-control approach is very attractive. One gathers all the cases arising in the cohort over the follow-up period to form the case series, while only a sample of the non-cases is drawn for the control series. The researchers then, as in the traditional case-control design, gather detailed information on the exposure experience by interviewing cases and controls (or, their close relatives), by scrutinizing the employers’ personnel rolls, by constructing a job exposure matrix, or by combining two or more of these approaches. The controls can either be matched to the cases or they can be treated as an independent series.

The sampling approach can be less costly compared to exhaustive information procurement on each member of the cohort. In particular, because only a sample of controls is studied, more resources can be devoted to detailed and accurate exposure assessment for each case and control. However, the same statistical power problems prevail as in classical cohort studies. To achieve adequate statistical power, the cohort must always comprise an “adequate” number of exposed cases depending on the magnitude of the risk that should be detected.

Cross-sectional study designs

In a scientific sense, a cross-sectional design is a cross-section of the study population, without any consideration given to time. Both exposure and morbidity (prevalence) are measured at the same point in time.

From the aetiological point of view, this study design is weak, partly because it deals with prevalence as opposed to incidence. Prevalence is a composite measure, depending both on the incidence and duration of the disease. This also restricts the use of cross-sectional studies to diseases of long duration. Even more serious is the strong negative bias caused by the health-dependent elimination from the exposed group of those people more sensitive to the effects of exposure. Therefore aetiological problems are best solved by longitudinal designs. Indeed, cross-sectional studies do not permit any conclusions about whether exposure preceded disease, or vice versa. The cross-section is aetiologically meaningful only if a true time relation exists between the exposure and the outcome, meaning that present exposure must have immediate effects. However, the exposure can be cross-sectionally measured so that it represents a longer past time period (e.g., the blood lead level), while the outcome measure is one of prevalence (e.g., nerve conduction velocities). The study then is a mixture of a longitudinal and a cross-sectional design rather than a mere cross-section of the study population.

Cross-sectional descriptive surveys

Cross-sectional surveys are often useful for practical and administrative, rather than for scientific, purposes. Epidemiological principles can be applied to systematic surveillance activities in the occupational health setting, such as:

• observation of morbidity in relation to occupation, work area, or certain exposures

• regular surveys of workers exposed to known occupational hazards

• examination of workers coming into contact with new health hazards

• biological monitoring programmes

• exposure surveys to identify and quantify hazards

• screening programmes of different worker groups

• assessing the proportion of workers in need of prevention or regular control (e.g., blood pressure, coronary heart disease).

It is important to choose representative, valid, and specific morbidity indicators for all types of surveys. A survey or a screening programme can use only a rather small number of tests, in contrast to clinical diagnostics, and therefore the predictive value of the screening test is important. Insensitive methods fail to detect the disease of interest, while highly sensitive methods produce too many falsely positive results. It is not worthwhile to screen for rare diseases in an occupational setting. All case finding (i.e., screening) activities also require a mechanism for taking care of people having “positive” findings, both in terms of diagnostics and therapy. Otherwise only frustration will result with a potential for more harm than good emerging.

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