Our data source is the LIS Project, a research and databank project for the compilation of household income and labour force surveys across participating countries in Europe, America, Asia and Oceania. The surveys collect nationally representative information on a range of labour force and sociodemographic indicators, such as occupation, employment status, earnings, industry and educational attainment. With funding mainly provided by the national science and social science research foundations of its member countries, LIS (in conjunction with its companion Luxembourg Employment Study project) compiles microdata sets for sample surveys that have already been collected by the countries' Central Statistical Offices and transforms them to a common variable structure. While the surveys themselves are diverse and the types of data not necessarily uniform in nature, a process of data harmonization is undertaken to enhance comparability for public use [9
]. We used information from surveys with occupational data that permitted distinction of health occupations.
Data on health occupations were available for 18 countries with surveys conducted between 1989 and 1997. Twelve of the countries were characterized with developed market economies: Austria, Canada, Denmark, Finland, France, Germany, Netherlands, Norway, Spain, Switzerland, United Kingdom and the United States of America. Six were countries with economies in transition: Czech Republic, Hungary, Poland, Russian Federation, Slovakia and Slovenia. Moreover, for 11 countries two or more surveys were accessible that allowed identification of health occupations, enabling us to conduct time-trend analyses. (The LIS project had compiled surveys for nine other countries that were not used here because the occupational data did not enable differentiation of the health workforce.)
The standardization of classification of health occupations was facilitated through the International Labour Office's latest revision, in 1988, of the International Standard Classification of Occupations (ISCO-88). This internationally comparable classification pools occupational titles into a hierarchical four-digit system, which can be aggregated to progressively broader groups, representing a value set describing the different tasks and duties of jobs [10
]. Within ISCO-88, occupations are essentially organized according to two dimensions: skill level and skill specialization [11
]. The former refers to the complexity of skills required for the job (but not necessarily the way the skills were acquired). Skill specialization is related more to areas of knowledge required, such as subject matter, services produced or equipment used. Different user areas may have different degrees of interest in the various elements, so classification structures may vary nationally. Many national statistical agencies participating in the LIS project mapped their occupational classifications to ISCO-88 for data dissemination. Otherwise, where possible, the project provided ISCO-88 classification codes by reconciling national classifications through standardized mapping techniques of occupational status scales (for example, techniques cited in [12
Among the 10 major ISCO-88 occupational groups, two were of interest here: group 2 "professionals" (generally well-trained workers in jobs that normally require a university or advanced-level degree for recruitment) and group 3 "technicians and associate professionals" (generally requiring skills at a non-university educational qualification level). Identification of the health workforce is possible when the classification is coded to a degree of detail that minimally corresponds to the three-digit level, and preferably to the four-digit level for distinction of practitioner specializations. The professional major group includes physicians, nursing and midwifery professionals and other health professionals, such as dentists, pharmacists and veterinarians (see Table ). Classified as associate professionals are modern health associate professionals (except in nursing), nursing and midwifery associate professionals and traditional medicine practitioners. The former encompass medical assistants, dental assistants, pharmaceutical assistants, opticians, veterinary assistants, physiotherapists, sanitarians and others. Traditional medicine practitioners include herbalists and faith healers.
Selected health occupations in the International Standard Classification of Occupations (1988 Revision)
We performed basic analyses on characteristics of the health workforce where occupational data were standardized at the three-digit ISCO-88 level or equivalent. Further in-depth analyses were conducted where identification of health occupations was possible at the four-digit ISCO-88 level or equivalent. In either instance, occupational categories were aggregated to reflect national classifications or sample size limitations for some surveys. It should be noted that despite efforts to standardize, the definition of certain categories of health occupations may have varied across surveys; for example, in some cases the classification of nurses and midwives did not distinguish between professionals and associate professionals. The precision of mappings to ISCO-88 would have largely depended on the level of detail in the national classifications. Moreover, while certain related occupations aside from medical and nursing practitioners are identifiable at the four-digit ISCO-88 classification level – in particular, medical equipment operators (code 3133), health and safety inspectors (code 3152), and institution-based personal care workers (code 5132) – they were excluded from the present analysis to maintain comparability with data where the selection of occupations was possible only at the three-digit level or equivalent.
The surveys' sampling designs and sizes were not homogeneous: while most sampling frames drew on stratified random selections of private households, some datasets were based on income tax or other administrative records of government agencies. Although several different data types may have been available in many countries, only selected surveys were retained by the LIS project based in part on comparability of information on income sources or other labour market indicators. Non-response rates, for the entire interview or per item, varied and were treated differently across countries [9
]. No attempt was made in the present analysis to further adjust the data for coverage or completeness of information.
To monitor the relative allocation of human resources to the health system, all samples were limited to the population of economically active age (15 years and over) declaring an occupation. The numbers in the samples with health-related occupations ranged from 60 to 12,248 (see Table ). Our study includes profiles of the health workforce by selected sociodemographic and labour force characteristics, including sex, age, migration status, education, income and industry. Such information can offer valuable insight into specific aspects of HRH as an input to assessing health systems performance [7
Sources and sample sizes of health occupations in LIS datasets
Standardization of indicators was ensured to the extent the available data permitted. In terms of migration of health workers, an audit of human resources can show movement between localities (e.g. rural to urban), between sectors (e.g. public to private), or between countries. Relying on information on immigration status available from the LIS datasets, we defined migrants as non-native born. Education was assessed by university-level attainment versus secondary schooling at most, as a gauge of the skill distribution of health care personnel. Depending on the source, this was captured by either the individual's highest level of general education or vocational training, total length of education, or age when the highest level was obtained. The indicator for income – information of value when discussing countries' health care financing options – was measured through either net or gross occupational wages. Workforce industry encapsulated the economic activity of the main job establishment. However, the classification of health services only sometimes distinguished the various health care activities, such as hospitals or practitioners' clinics versus veterinary or pharmaceutical services.
Gender issues were emphasized as being important not only for assessing equity in human resources, but also for health services planning. Studies have shown that increased participation of women in the medical field may be accompanied by differences in working patterns; in particular, female physicians are likely to work fewer hours than their male counterparts [13
], and to present different styles of care provision that may be reflected in the levels of patient participation [15
The statistical methods used were primarily descriptive. First we sketched a general profile of the health workforce for 18 countries based on LIS surveys. Next, where data were available, we compared trends over time in the profile of health occupations; in particular, an overview of the share, mix and demographics of health occupations was drawn. We then undertook an in-depth study of the demographic and labour force characteristics for five countries for which time-trend data were available at the four-digit ISCO-88 occupational classification level or equivalent. All results presented here were compiled using remote submission procedures for microdata-processing programmed in the SPSS statistical software package [16
], and have been weighted to account for survey sampling designs.