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Occupational Epidemiology

Occupational Epidemiology

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Occupational Epidemiology

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  1. Occupational Epidemiology ดร. อรพิน กฤษณเกรียงไกร คณะสาธารณสุขศาสตร์ มหาวิทยาลัยนเรศวร

  2. Occupational epidemiology = study of the frequency and the causes of work-related diseases and injuries • Branch of epidemiology that is defined by the exposure rather than the outcome. • Helps in studying exposures and outcomes that are rare in the general population. • Helps in devising occupational exposure guidelines. • Helps in deciding what remedial measures to recommend.

  3. Examples of the development of occupational epidemiology

  4. Identifying Occupational Hazards • Recognize a disease cluster among workers from particular occupations or industries. • Conduct a survey in the industry to determine the magnitude of the problem. • Consider other diseases which may occur at excess. • Determine exposure to a known hazardous agent or to another agent not yet known to be hazardous. Or Start out with a particular exposure and conduct medical surveillance.

  5. Characterizing the workplace environment(exposure assessment) • Identify agents likely to be toxic. This may be easy (e.g. asbestos exposure) or very difficult (i.e. mixtures of chemicals). Can use information from prior research or consult toxicologists. Can try to gain information by determining the part of the manufacturing process that seems be most hazardous, or by looking at the type of illness caused by the unknown agent. • Establish the most relevant routes of exposure for the agents of concern. • Measure the exposure.

  6. Measuring Exposure • Type of exposure data • Quantified personal measurements • Quantified area/job specific data • Ordinal ranked jobs/tasks • Duration of employment in the industry at large • Ever/never employed in the industry best worst

  7. Question: Should we measure • Point exposure • Cumulative exposure • Highest exposure • Length of exposure This depends on a variety of issues including the particular agent and the availability data.

  8. Collecting exposure data • Direct measurement over a period of time Problems: • Important exposures in the past are missed. • May lead to overestimation when the exposure is only measured in areas where it is assumed to be highest. • May lead to underestimation when the measuring device is placed away from the work area in order not to interrupt the work. • Wearing a measuring device may alter a person’s behavior.

  9. Compiling an inventory of existing data and determining which data are most complete and useable for the study. a. Historical exposure reconstruction b. Concurrent and prospective exposure estimation Problems: a. - Past data may not exist or may be incomplete. - It may not be possible to combine data from different time periods. (different measurement techniques may have been used; similar job categories may have meant different exposures.) b. – No useable data may exist For both, historical exposure reconstruction and concurrent and prospective exposure estimation, we can get information from…

  10. Industrial hygiene data (May lead to overestimation when the exposure is only measured in areas where it is assumed to be highest; may lead to underestimation when the measuring device is placed away from the work area in order not to interrupt the work; wearing a measuring device may alter a person’s behavior) • Process descriptions (can be used to identify and localize potential agents) • Plant production records (can be used to determine the introduction and removal of chemicals and to detect seasonal variations)

  11. Inspection/accident reports (can be used to detect unusual exposures and to distinguish between routine and excess exposure) • Engineering control/protective equipment documentation (can be used to determine if the workers were fully exposed or if they were protected) • Biological monitoring results (e.g. blood, urine,…monitoring; the usefulness depends on the agent) • Could use a scheme such as high, moderate, low, possible, no exposure and use information from personnel records (e.g. job title, pay code, dates of employment,…) to assign workers to the different groups (Assigning jobs/work areas to different exposure levels is difficult; misclassification is likely.)

  12. For concurrent and prospective exposure estimation we get additional information by • Updating personnel files • Collecting additional exposure data (toss-up between “measuring and recording everything” and “starting to monitor only when a significant health hazard is noticed”; it is sometimes suggested to routinely take a sample of measurements) Conducting ecologic studies (compare disease rates and industrial activities between different areas) Problem is the ecological fallacy

  13. Combining exposure data from various sources • Times • Work areas • Industries • Countries

  14. Purist approach Only workers with the most detailed measurement values can be used. Problem Measurement error is reduced and validity is increased, but sample size and thus precision and drastically reduced.

  15. Take everybody approach Take everybody who has a minimum of useable information. Problem • Difficult to combine site/times with measured concentrations and sites/times with nothing but job information. • Job classifications may differ between different times, industries, or countries.

  16. Study Designs • Case series Identification and reporting of a disease cluster. The cluster might be found among the work force as a whole or among some segment of the work force. Case series can be very useful to start an epi-investigation, especially when the disease is extremely rare and the causal factors are unknown. Disease clusters can be misleading, however, since they could be entirely due to chance.

  17. Study Designs • Cohort Studies Most accepted study design since it most closely resembles the experimental setting (exposure  disease). The study includes the entire available and disease-free study population. 2.1 Prospective cohort study 2.2 Historical cohort study 2.3 Sub-cohort analyses

  18. 2.1 Prospective cohort study The cohort is enumerated at the time of the study, cohort members are followed into the future. The rates of disease occurrence are usually compared to the rates in the national or regional population to determine which diseases occur more or less frequently among the workers. SMR’s or SIR’s are calculated. Prospective cohort studies are rarely used, since they take too long, are too expensive, and are not appropriate for rare diseases. However, they are appropriate for consequences of an occupational exposure that occur within a brief time span (approximately 5 years or less). They are also useful in medical surveillance, where the cohort is followed into the future and the workers’ health status and the occurrence of disease in the cohort is determined. The focus of a medical surveillance program may be very narrow or wide.

  19. 2.2 Historical cohort study Past records are used to enumerate the cohort. The cohort is then followed into the present. Historical cohort studies are cheaper and take less time than prospective cohort studies; SMR’s and SIR’s can be calculated. However, records on the outcome may not be available. Thus, historical cohort studies are mostly used for fatal diseases so that death certificates can be used to determine the type of illness and the time of death. Data on non-fatal diseases are only available when special efforts have been made to collect them (e.g. cancer registries).

  20. 2.3 Sub-cohort analyses Comparisons are made between subgroups (e.g. high/medium/low exposure) rather than between the workers and the general population. Sub-cohort analyses can be conducted in prospective or historical cohort studies, but direct age adjustment must be used and SRR’s (standardized rate ratios) must be calculated. SRR = expected cases in the reference population based on the rates in the exposed group observed cases in the reference population Since sub-cohort analyses are expensive they are generally only performed for diseases with an overall mortality or morbidity only performed for diseases with an overall mortality or morbidity excess and for diseases of special interest.

  21. 3. Case-control studies Smaller sample size, shorter time frame, and thus reduced cost. OR’s are calculated. 3.1 Nested case-control study 3.2 Registry based case-control study

  22. 3.2 Nested case-control study A nested case-control study is a case-control study embedded in a cohort study. It is useful for workplace hazards of particular interest that cannot be studied efficiently with a cohort or sub-cohort analysis. Example: Solvents  Leukemia It would be a huge task to reconstruct the exposures of a large cohort of workers over a long period of time. Instead leukemia cases are identified during follow-up and are used as the cases. Leukemia free workers are used as controls.

  23. 3.2 Nested case-control study Sometimes an occupational cohort cannot be enumerated (e.g. farmers, auto mechanics). In this case a registry can be used to define cases and controls. (E.g.: cancer registry, hospital admissions, insurance claims, disability pension awards,…) The cases can be taken from the registry. The controls can be taken from registrants with other diseases or from the source population for the registry.

  24. 3.1 Nested case-control study Most registry based case-control studies lack detailed exposure data. Often only the type of industry or the job title are known. Therefore, since they are less informative than a nested case-control studies, registry based case-control studies are mostly used for screening hypotheses.

  25. 4. Proportionate mortality studies A proportionate mortality study is conducted when information on occurrence of disease or death exists, but it is impossible to enumerate the cohort. Ex.: Death certificates are available, but personnel information is unavailable or incomplete. We can compare the proportional distributions of causes of death among the workers with the corresponding proportions in the reference population. This gives us an indication of the relative disease frequency.

  26. 4. Proportionate mortality studies Advantage: Quick and inexpensive Disadvantage: The identified deaths may not be representative of all deaths that would have been identified had the cohort been enumerated and followed. Example: Sick people may have causes of death must add up to 100%. Therefore, an excess of deaths from one cause necessarily leads to a deficiency of deaths from one or more other causes. Thus, a deficiency of deaths from one cause of death does not imply that the exposure is protective against this disease.

  27. 5. Cross-sectional studies Disadvantage: Retirees, transferred workers, laid-off workers, dead workers and workers who quit for health reasons are missed. Thus the potentially most important workers are missed.

  28. Study Validity Selection bias Ex.: • Higher response rate among the most heavily exposed people with the disease. • Healthy worker effect (healthy workers are more likely to gain and remain in employment). • Note: As the cohort is followed over time the effect of the healthy worker effect on the study results decrease • Note: The healthy worker effect can be minimized by choosing other active workers rather than the general population as the comparison group.

  29. Information bias Non-differential: The likelihood of misclassification is the same for the compared groups. Ex.: • The study outcome is not well defined and includes a wide range of etiologically unrelated outcomes. This may obscure the effect of the exposure on one specific outcome (a large increase in this outcome may only produce a small increase in the overall group of outcomes studied). • The exposure of interest is not well defined (i.e. an exposure occurring shortly before the diagnosis may be incorrectly included). This bias is of particular concern in studies that show no association between the exposure and the outcome.

  30. Differential: The likelihood of misclassification of the exposure is different for the diseased and the non-diseased. The likelihood of misclassification of the disease is different for the exposed and the non-exposed. Note: It is sometimes worth decreasing the sample size (and thus increasing random error) if the increased accuracy we can achieve on fewer study subjects greatly decreases misclassification due to information bias.

  31. Recall bias Note: studies have been performed to determine how well current workers recall their work history (Baumgarten et al., 1983; Brisson et al., 1988). They found that approximately 80% of the person years were correctly identified (identification was more accurate for the past 12 years and less accurate for years lying further back). Recall did depend on the number of jobs workers held (the more jobs the less accurate the recall), but did not depend on age or level of education.

  32. Confounding The following confounders are often considered in occupational studies • Gender • Ethnicity • Smoking • SES • Time related factors

  33. Time related factors • Length of follow-up (time of hire until disease onset, death, or end of study) • Duration of employment (time of hire until termination of employment; strongly associated with cumulative exposure) • Age at hire • Age at risk (age at any point during follow-up) • Calendar year These factors are associated with the outcome either directly or through their influence on the healthy worker effect.

  34. Time related factors • Length of follow-up (time of hire until disease onset, death, or end of study) • Duration of employment (time of hire until termination of employment; strongly associated with cumulative exposure) • Age at hire • Age at risk (age at any point during follow-up) • Calendar year These factors are associated with the outcome either directly or through their influence on the healthy worker effect.

  35. Examples • The older the workers the more likely they are to get ill or to die. Thus age at risk is associated with the outcome. • Disease incidence may change over time. Thus calendar year may be associated with the outcome. • The healthy worker effect is most pronounced immediately after the workers are hired (i.e. when they are healthy enough to be employed). Around 15 years after they were hired the healthy worker effect almost disappears. Thus, length of follow-up influences the healthy worker effect and therefore the outcome.

  36. Mortality is lowest (and thus the healthy worker effect is strongest) among those with the longest duration of employment. Thus, duration of employment influences the healthy worker effect and therefore the outcome. • The healthy worker effect is stronger among workers hired at an older age than among young workers. Thus, age at hire influences the healthy worker effect and therefore the outcome.

  37. If the time related factors are also associated with the exposure they act as confounders. Examples • Older workers may have been exposed to different chemicals in the past. • Workers hired at an older age may be assigned to different jobs. • Workers with a long duration of employment may have different jobs.

  38. Reference • Annette Bachand, Introduction to Epidemiology: Colorado State University, Department of Environmental Health • Leslie Gross Portney and Mary P. Watkins (2000). Foundations of Clinical Research: Applications to Practice. Prentice-Hall, Inc. New Jersey, USA