1 International Agency for Research on Cancer, Unit of Environmental Cancer Epidemiology, Lyon, France.
2 Utrecht University, Institute for Risk Assessment Sciences, Environmental and Occupational Health Division, Utrecht, The Netherlands.
Correspondence:
Andrea t Mannetje, IARC, Unit of Environmental Cancer Epidemiology, 150 Cours Albert Thomas, 79008 Lyon, France. E-mail:
mannetje{at}iarc.fr
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Abstract |
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Accepted 15 November 2002
Occupation and industry classifications categorize occupations and industries into clearly defined groups. As such they provide a common basis for collecting, presenting, and comparing of labour statistics. Occupational classifications group people based on job and tasks performed, and are commonly used in sociology and population studies. Industry classifications group people based on the sector of economic activity in which they are employed and are mainly used for economic analysis.
Although not primarily developed for use in epidemiological studies, occupation classifications, and to a lesser extent industry classifications, are often used in this field. Population-based epidemiological studies frequently include questions about job title and specific tasks, after which the information is coded using either national or international classifications.
Reviewing the literature in the British Medical Journal, American Journal of Epidemiology, and International Journal of Epidemiology (published between 1995 and 2000) indicated that information on occupation in epidemiological studies (n = 129), was mostly used as an indicator for social class (38%). In 27% of the studies, occupation was studied directly in relation to disease, and in 24% occupation was used to infer occupational exposure. In the remaining 11% of the studies occupation was treated as a confounding factor or used to describe the study population.
Although widely applied in epidemiological studies, only limited methodological information is available on the use of job and industry classifications. The lack of a theoretical basis may hamper full exploitation of occupation information within epidemiology and limits the potential to optimize its reliability.
In this paper we review the potential for occupation and industry classifications in epidemiology. The main classifications available are reviewed and different methods for coding occupation and industry are discussed. In addition, we will address issues of reliability of the coding process.
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Options for analysis |
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For the US, national statistics are also available in the National Industry-Occupation Employment Matrix based on SIC (Standard Industrial Classification) and SOC (Standard Occupational Classification), presenting employment statistics for over 240 industries and 500 occupations.2
Social class
In epidemiology, occupation is most often used for distinguishing between socioeconomic groups. Whereas in fields such as sociology social class is often treated as an outcome, in epidemiology it is more often considered as a risk factor or confounder, since many diseases such as cancer appear to have a social class gradient.3 Measures of social class can be based on occupation, education, income, or a composite of these.4 Different scales for occupational class have been developed, of which the British Registar Generals Scale is the most widely used.4 This scale has proven to be highly predictive of inequalities in morbidity and mortality, especially among employed men. Its five categories (I professional; II managerial and technical; IIIN skilled non-manual; IIIM skilled manual; IV partly skilled; V unskilled) are based on a graded hierarchy of occupations ranked according to skill.5 Some more recently revised occupational classifications already include a rating for occupation-related characteristics such as skill, status, or education. The ISCO-88 (International Standard Classification of Occupations) includes for each occupation a reference to one of four broad levels of formal education via the International Standard Classification of Education (ISCED).6 O*NET, the US Occupational Information Network,7 provides links between the newest SOC and knowledge, skills, abilities, educational levels, and work values. Some links between social class indicators and occupation have been made ad hoc for ISCO 68 and ISCO 88.810
Prevalence of confounders
Death certificate or census-based studies typically lack information on variables such as smoking and alcohol consumption, which can act as confounders in studies of tobacco and alcohol-related diseases. In absence of individual-level data, the prevalence of these variables in each occupation can be used for adjustment.11 Data on cigarette smoking prevalence by occupation are generally available,12 although most data are based on smoking patterns in the US. Country, culture, age, and sex differences as well as differences in trends over time need to be taken into account when using these data.
Occupation and industry as a risk factor
Occupations can be regarded as a proxy for exposure to a substance, a mixture of substances or other workplace characteristics. In occupational epidemiology the risk for a disease has often been analysed using occupation or industry information, leading to useful hypotheses with respect to more specific exposures. Besides calculating risks for each possible occupation/industry, epidemiologists may use clusters of high-risk occupations. Ahrens et al.13 proposed lists, A and B, for high-risk occupations for lung cancer, based on ISCO 68 and ISIC Rev. 2. List A consists of occupations and industries known to be associated with lung cancer, and list B consists of occupations and industries that are suspected to be associated with the disease. A similar approach was used for a bladder cancer case-control study.14
Occupational exposure databases
If the occupational profile of a study population is available, the prevalence of exposure to occupational exposures can be inferred using databases, such as CAREX (International Information System on Occupational Exposure to Carcinogens).15 CAREX contains estimates of the numbers of workers occupationally exposed to carcinogens by industry in 15 countries of the European Union 19901993. Statistics are available for 55 industrial categories of the ISIC Rev. 2 classification.16
A similar database is available for the US: NOES (National Occupational Exposure Survey),17 containing, besides carcinogens, also other occupational exposures, and providing statistics for two-digit 1972 SIC codes.
The advantages and limitations of these databases have been discussed15,17 and data collection methods need to be considered when using the databases.
Also available are several national databases in which quantitative workplace measurements have been assembled. For each measurement, information is included such as the sampling strategy applied, the time and place the sample was taken, the purpose and origin of the measurement, a description of the workplace and codes of the job title, and industry the sample was taken from. Although these data are not necessarily representative for a whole occupational group or industry, these databases can be used for exposure modelling and as a source for individual quantitative exposure estimates. Examples of such databases are MEGA from Germany,18 NOEDB from UK,19 ATABAS and BIOBAS from Denmark,19 EXPO from Norway,19 and the international database WAUNC.20 The majority of these databases use the ISIC and ISCO codes for industries and occupations respectively.
Job-exposure matrices
Since the 1980s job-exposure matrices (JEM) have enhanced the value of coding by occupational classifications in epidemiological studies. Job-exposure matrices are cross-classifications of occupation and exposure. When linked with the occupation and industry codes of the study subjects, JEM place subjects from different industryoccupation combinations in the same exposure category.21 Its automatic application avoids recall bias and differential misclassification of exposure. Job-exposure matrices can be categorized in general population JEM (GPJEM) and industry-specific JEM (ISJEM). The ISJEM22 cross-classify exposures only for a limited group of occupations and tasks within one certain industry, and often include more-detailed definitions on the exposure axis. In a GPJEM,23 all possible occupations that can occur in a population are represented and standard or national classifications are often adopted.
Kromhout and Vermeulen24 presented an overview of all presently available GPJEM. In total 19 GPJEM were reviewed, 5 of which used ISCO and ISIC or a direct derivation of these classifications2529 and 5 used SIC/SOC combinations (any revision).21,3033 Nine GPJEM were based on national or ad hoc classifications.3442 Most GPJEM cross-classify occupation and industry with occupational (chemical) agents, while some are made for psychosocial risk factors such as occupational stress28 or exposures related to asthma.43
The first consideration before using a GPJEM is the exposure of interest since each available GPJEM includes a limited list of occupational exposures. A second consideration would be the occupation and industry classification used in the GPJEM and the study. To make optimal use of the information in the JEM, the level of detail of the occupational classification in the study should be equal or higher compared with the level of detail in the JEM. Most of the time when applying an external GPJEM to a study population, a certain amount of recoding of occupational codes is needed. Recoding based on the original task description would be most ideal, but often the only feasible option is a direct conversion. Some conversion keys may be available in the literature or may be obtained through the Internet. The effect of recoding occupational codes to another classification for the application of a JEM was studied by Kromhout and Vermeulen.24 Agreement was measured between codes obtained from a direct conversion key and those from new coding based on the full job descriptions. Recoding to a highly similar classification resulted in an agreement of 84% between direct codes and recodes of the same jobs. Recoding to a dissimilar coding scheme resulted in an agreement of only 49%. This low agreement needs to be considered when using direct conversion keys for multicentre studies. However, the agreement in exposures resulting from the application of a JEM was not effected by the low agreement of the job codes, indicating that although coded with different occupational codes, subjects were nonetheless assigned a similar exposure by the JEM.
In the absence of more sophisticated methods of occupational exposure assessment, JEM provide an easy and low-cost way to assess exposure based on occupation and industry title alone, but the occurrence of non-differential exposure misclassification should not be underestimated when using a JEM.44,45
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Standard classification systems in use |
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Hoffmann and Chamie47 distinguish different types of classifications i.e. reference, derived, or related classifications. Reference classifications are a product of international agreements approved by the United Nations Statistical Commission or another competent intergovernmental board. ISCO (International Standard Classification of Occupations, ILO)48 and ISIC (International Standard Industrial Classification of All Economic Activities, UN) are reference classifications and recognized as such in the family of international economic classifications adopted at the Second Meeting of the Expert Group in International Classifications.49 Reference classifications are used as guidelines for the preparation of national classifications and for international comparison. Derived classifications are based on reference classifications using the same structure, but in defining detailed categories they will go beyond the existing reference structure. For example, NACE Rev. 1 (Statistical Classification of Economic Activities in The European Community, Eurostat)50 is derived from ISIC Rev. 3.51 Related classifications might follow part of the reference classifications structure, but are associated with the reference classification at specific levels of the structure only.
Occupation classifications
Table 1 includes the main standardized classification systems used in epidemiological studies. The main standardized occupation classification used in Europe and other countries, besides the US, is the ISCO classification. It was first developed in 1958 by the International Labour Office (ILO). The most recent version is the 1988 revision48,52 and in epidemiology the 1968 edition53 has been used frequently. The aim of the new 1988 revision was to become the international standard for occupation classification. To make the classification applicable for other regions with specific requirements, ILO has provided advice for the development of three common regional classifications based on ISCO-88:46 the European Union variant of ISCO-88 (ISCO-88(COM)),47,54 a commonwealth variant (ISCO-88(CIS))55 and an Asian variant (ISCO-88(OCWM)56).
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Industry classifications
The main international industry classifications used in epidemiology are presented in Table 2. The ISIC is the most widely used for statistics of economic activities and a new revision of ISIC is envisaged for 2007.49 In the European Union, NACE Rev. 150 is widely used, which was designed as a more detailed version of ISIC Rev. 351 targeted towards the European circumstances.
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International comparability
The preceding paragraphs list the main standardized classifications for industry and occupation, but many countries have developed their own classification, often based on a standard classification.
Table 3 lists some of the national classifications currently in use in several European countries (not necessarily used in epidemiology), and summarizes how they compare to the standard classification ISCO-88 (COM). Ten out of 15 countries use a classification that compares well or reasonably well with ISCO-88. For other countries outside the European Union such as Japan, Mexico, and the US, the mapping of their national classifications tends to be complex resulting in poor comparability. The US SOC was not based on the ISCO-88 classification since the latter was not thought to be flexible enough for use in the US.64 For Australia, Canada, and New Zealand the comparability with ISCO-88 is good.
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For the national industry classifications used in Europe the comparability tends to be better, because NACE Rev. 1 is the official format in which to send statistical data to Eurostat, and therefore used in most European countries. Table 4 lists the national industry classifications used in European countries and how they refer to standard classifications. Many countries in Africa and Asia have based their national classification on ISIC Rev. 3,65 which compares well with NACE Rev. 1. The classification used in North America (NAICS) is comparable with NACE Rev. 1 and ISIC only on the two-digit level.47,66
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The choice of a standard classification for the use in an epidemiological study |
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Hierarchical structure
A classification with a hierarchical structure provides codes for detailed as well as aggregated groups. A hierarchical structure gives the coder the possibility to assign a more aggregated code to a subject, in case the choice between the more detailed descriptions is difficult to make. Furthermore, in case the detailed groups do not contain enough subjects for separate analysis, the detailed groups can be easily collapsed to more general groups.
Availability of a full version and translations
Old classifications are often non-hierarchical listings of occupation or industry title, without a detailed description for each title. To date, occupational classifications include a description of the tasks performed and industry classifications include a description of the products manufactured or services provided, to improve the interpretation of each title.
The availability of exact translations to other languages besides English will facilitate the use of the classification in multicentre studies. When using a translation, it needs to be checked to see if the version is an exact translation of the original standard classification, or a translation adjusted to the local situation. For example, the Brazilian classification for occupations Classificação Brasileira de Ocupaçóes (CBO)67 is highly similar to ISCO 68, but many codes do not correspond.
The possibility of linkage with other data
Standardized classifications for occupation and industry can often be linked with other data or information (see paragraph on options for analysis). Which classification to apply depends on the classification that is used, for example in the GPJEM or social class scale that will be used. Application of a JEM will generally require an occupation as well as industry classification while occupational class only considers occupation.
Also the level of detail needed will depend on the intended use, with social class indicators needing less detail than for example GPJEM.
However, in order not to lose any potential information it is recommended that the coding be as specific as the crude data (the job description) will allow,68 so that the possibility of future comparison or linkage with other data will be maximized.
The use of a standard reference international classification such as ISCO and ISIC will generally give most flexibility in terms of comparing and linking possibilities, and will facilitate multicentre studies, while in the US the use of SOC and SIC can have certain advantages.
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Methods for coding of occupation and industry and reliability |
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Self-classification
The study subject (or proxy) indicates the code or category of occupations to which the subject belongs. Although costs of this coding method are low, the use of it is limited, because a respondent will have difficulty in choosing between categories that are not clearly distinguishable without training. Detailed classification can only be done through one of the following methods.
Clerical coding
A coding expert finds the most applicable job title or economic activity title and code, based on what the study subject or proxy reports, by choosing between the different job descriptions given in a classification book. For most epidemiological studies, the coding for occupation and industry has been done through clerical coding based on questionnaire information.
Computer-assisted coding
A computer-based classification book generates some alternatives for job titles, based on keywords in the job description through word matching and algorithms. The coding expert chooses the most applicable job title. To date, more interviews are computer-assisted (CAPI: computer-assisted personal interview or CATI: computer-assisted telephone interview), enabling computer-assisted coding directly at time of interview.69,70
An evaluation of computer-assisted coding compared with clerical coding,71 showed that computer-assisted coding of occupation by interviewers did not necessarily improve the quality of coding; however, it did reduce the coding time by 1323%. For large batch operations, such as for census data, fully computerized coding is often applied. One study indicated that approximately two-thirds of the job title information provided by the respondents may be classified in a valid and reliable fashion by fully automated coding methods.6 The presence of coding errors through computer-assisted and automated coding needs to be considered when using occupational codes from census data in an epidemiological study.
The reliability of the coding of occupation and industry, defined as the degree to which the results can be replicated, will depend on the following two parts of data collection. (1) The collection of the occupational information on which the coding will be based, for which the reliability depends on the design of the questionnaire, the interviewer, the recall of the subject etc. (2) The translation of this occupational information to a single code, for which the reliability depends on the coding experts familiarity with the coding book, logical structure of the coding book, availability of clear coding rules, training of the coding experts, etc.
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The study of Kromhout and Vermeulen24 indicated that clear instructions on decision-making for the coders can improve the reliability of the coding of both occupation and industry codes (agreement for occupation improved from 69% to 89% and for industry from 92% to 98%). A study by Ahrens78 indicated that coders show a learning curve and perform better after good familiarity with the classification is achieved and regular feedback on coding errors is given (agreement for occupation improved from 82% to 92%).
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Discussion |
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The reliability studies reported here indicated that job information can be translated reliably to an occupation and industry code given that coders are trained, have access to the full version of a classification book and guidelines for its use. Therefore, it is recommended that regular evaluations of coding work are implemented as part of the study, until the learning curve is completed. Any study using occupation and industry classifications should aim at an agreement rate between coders of at least 75% at the three-digit level.
Here we discussed the criteria that need to be considered when choosing an occupation or industry classification for use in population-based studies. The most important criteria are probably that the classification has a hierarchical structure and can be linked easily to other information systems such as labour statistics, socioeconomic indicators, JEM, and occupational exposure databases. The reference classifications of ISCO for occupation and ISIC/NACE for industry meet these needs, as do the American classifications SIC/NAICS and SOC. Exact translations into different languages are available for these classifications (except SOC), making them suitable for international use and comparisons.
Many national classifications are directly based on these reference classifications, and for many of these classifications links or crosswalks are available that can re-code in case one wants to compare or pool results or data from different studies. These links are, however, not always available for the most detailed level of the reference classifications, and re-coding of dissimilar classifications has shown to lead to considerable misclassification,24 an evident shortcoming in epidemiology, which is a field that relies on valid comparisons of results. Before using a national classification it is therefore recommended that the level at which links have been established with reference classifications is verified. If national classifications do not give any additional advantage within the epidemiological study, the use of a reference classification is recommended.
Occupation and industry classifications have been, and will continue to be, an important tool in population-based epidemiological studies that study work-related risk factors, since this easily obtainable information can be put to different uses in population-based epidemiological studies. Their full exploitation in this field will, however, depend on a valid choice and a valid application of the classification.
KEY MESSAGES
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