Sick leave analysis among self-employed Dutch farmers

Transcription

1 Occupational Medicine 2003;53: DOI: /occmed/kqg089 Sick leave analysis among self-employed Dutch farmers E. Hartman 1,2, H. H. E. Oude Vrielink 1, R. B. M. Huirne 2 and J. H. M. Metz 1 Background Agriculture is one of the most physically demanding and risky industries. Aim Method Results Conclusion Key words The objective of this study was to provide baseline data on the diagnoses, occurrence and duration of sick leave of self-employed Dutch farmers. A database of sick leave claims of farmers during the period was analysed. Most of the claims (61%) were for musculo-skeletal injuries and disorders. The mean cumulative incidence (CI) was 10.2 claims per year per 100 farmers and did not change over time. However, it varied per agricultural sector and per age category, being lowest in arable farming and in the youngest age category and highest in mushroom farming and in the oldest age category. The duration of sick leave depended both on diagnosis and age category: the slowest recovery from sick leave was seen in farmers with respiratory diseases and farmers in the oldest age category. The results make it easier to identify groups of farmers to be targeted to prevent sick leave. To reduce the occurrence of sick leave in agriculture, the strategy should be to prevent musculo-skeletal injuries and disorders. Furthermore, a sector-specific approach is recommended, so that preventive actions can be focused on working conditions specifically on workload and work safety. Efforts to shorten the duration of sick leave will also be valuable to obtain a reduction of sick leave. Age; agriculture; injury; musculo-skeletal disorders; survival analysis. Received 12 November 2002 Revised 9 May 2003 Accepted 13 May 2003 Introduction Agriculture is one of the most physically demanding and risky industries [1,2]. A survey in 15 European countries demonstrated that it is one of the industries with the most exposure to heavy physical loads (77% of the respondents) [3]. It also showed that 41% of the respondents considered their health and safety to be at risk because of their work. 1 Institute of Agricultural and Environmental Engineering (IMAG), Wageningen, The Netherlands. 2 Farm Management Group, Institute for Risk Management in Agriculture, Wageningen University, Wageningen, The Netherlands. Correspondence to: Esther Hartman, Farm Management Group, Institute for Risk Management in Agriculture, Wageningen University, PO 9101, 6700 HB Wageningen, The Netherlands. Tel: ; fax: ; esther.hartman@wur.nl For employees in the Dutch agricultural sector, some recent data on health effects are available. In 2001, musculo-skeletal disorders and injuries contributed most to the volume of sick leave, together accounting for 53% of all sick leave days [4]. Self-employed farmers perform the greater part of the work on farms. In 2000 in The Netherlands, the total numbers of man-years worked by self-employed farmers and employees were and , respectively [5]. However, data on the health effects of being a self-employed farmer are lacking. The Dutch agricultural sector includes a wide range of occupations in different subsectors. The sector has changed considerably in recent decades. Not only has there been a significant increase in farm scale (in terms of herd size and area farmed), but also in many sectors the volume of hired labour has increased, from approximately man-years in 1995 to man-years in 2000 Occupational Medicine, Vol. 53 No. 7, Society of Occupational Medicine; all rights reserved 461

2 462 OCCUPATIONAL MEDICINE [5]. The implication is that the nature of work for self-employed farmers is likely to have changed from being primarily physically demanding towards more mentally demanding tasks. Another important factor is that farmers children are less willing to take on the farm; the result is an ageing population of self-employed farmers. These changes may have influenced the sick leave figures for self-employed farmers in the last decade. To be able to develop a strategy to prevent health problems and sick leave in the heterogeneous group of farmers, specific target groups need to be identified. The objective of the study described here, therefore, was to provide baseline data on the diagnoses, occurrence and duration of sick leave of self-employed farmers. Using a unique database from a major agricultural insurance company, the study considered the changes over time and the variation between sectors, with the aim of identifying target groups for sick leave prevention. Materials and methods In The Netherlands, there is no social insurance for selfemployed persons during the first year of sick leave. After 1 year of sick leave, social insurance provides compensation for loss of income to a maximum of 70% of the statutory minimum income. This financial gap can be bridged by an insurance policy. An estimated 63% of self-employed farmers take out an insurance policy with a private insurance company [6], which provides supplementary compensation for loss of income if they are unable to work due to illness or an accident. Database For the present study, the database of a major Dutch insurance company was analysed. It covered about onethird of self-employed Dutch farmers who are insured with one of the private insurance companies, i.e. ~20% of self-employed Dutch farmers. The database represents the portfolio of the insurance company at the end of 2001, which includes personal policies of selfemployed farmers (henceforth referred to as policyholders ). The insurance company s portfolio is dynamic; the total number of policies changes as new policies are taken out, policies expire (for ~90% of the policyholders this occurs at the age of 60; the remainder carry on until the age of 65), the farm is wound up or the policyholder dies. The following information was available for each policyholder in the database: personal policy number, gender, date of birth, type of agricultural sector, starting date of the policy and expected final date of the policy. The sample population The database also included information on sick leave claims from policyholders made within the time period Policyholders with one or more sick leave claims will be referred to as claimants. The claimants constitute the sample population we studied. In total, claims from claimants were recorded. The sample population only partly overlaps the portfolio of the insurance company at the end of Only 7795 of the sample had a policy on that date. The remainder (4832) no longer had a policy by then. In the case of sick leave, the starting date of sick leave was recorded and, if applicable, the final date of the sick leave and the reason for its termination. The diagnosis that necessitated the sick leave was recorded, based on the diagnosis made by the claimant s general practitioner. Only in the case of recovery after at least 6 days of sick leave did the insurance company record claims in the database. These claims were supplied with information on the duration of sick leave, including the first 6 days of sick leave. The main characteristics of the study population are presented in Table 1. The table shows that a substantial part of the claimants had a mixed farm, e.g. pig husbandry in combination with arable farming. However, for most of the claimants the type of sector within agriculture had not been registered and was therefore unknown. In all groups, most of the claimants were male (>88%) and the mean age of the claimants varied between 30.1 years in arable farming and 39.6 years in the category sector unknown. The mean number of claims per claimant varied between 1.3 in arable farming and 1.9 in the categories sector unknown and combination of sectors. In all groups, most of the claimants had recovered from sick leave at the end of Diagnoses of sick leave The ICD-9 codes [7] were used to classify the diagnoses of sick leave into disorders (codes ) and injuries (codes ). Disorders were grouped into the following categories: (1) musculo-skeletal system and connective tissue (henceforth referred to as musculoskeletal disorders and subdivided into back and neck or upper extremity and lower extremity disorders); (2) mental disorders; (3) diseases of the respiratory system; and (4) other disorders (a category including, for example, infections, skin diseases, diseases of the circulatory system and diseases of the nervous system and sense organs). We also assigned multifactorial disorders to this category. In the case of multifactorial disorders, more than one diagnosis was involved, but the precise diagnoses were unknown. The injuries were subdivided into musculo-skeletal injuries (according to the body regions affected) and other injuries, which included poisoning, burns and superficial wounds. The only diagnosis of sick leave excluded from analysis was pregnancy.

3 E. HARTMAN ET AL.: SICK LEAVE ANALYSIS AMONG SELF-EMPLOYED DUTCH FARMERS 463 Table 1. Main characteristics of the study population (n = ). The claimants were grouped according to agricultural sector Livestock farming a Arable farming Other agricultural sectors b Sector unknown Combination of sectors Agriculture (total) Number of persons (%) (n = 3029) (n = 73) (n = 2787) (n = 4642) (n = 2096) (n = ) Percentage of males Mean age (years) at 1 January 1994 (SD) (10.3) (7.8) (11.2) (10.4) (10.1) (10.7) Number of claims (%) (n = 5435) (n = 92) (n = 4616) n = 8786) (n = 3878) (n = ) Mean number of claims per person Status at 31 December 2001 (%) Ill Recovered Other c Unknown a Including pig husbandry, dairy farming, poultry farming and other livestock farming. b Including horticulture, fruit farming, plant nursery, bulb culture and mushroom farming. c Expiration of the policy or death of the claimant. Data analysis Diagnoses The relative occurrence per diagnosis was calculated for sick leave up to 1 year ( 365 days) and longer than 1 year (>365 days). By definition, duration of sick leave could not be calculated for claimants who were still ill at the end of Claims for sick leave 365 days that had the status not recovered on 31 December 2001 were excluded from analysis. The analysis was performed for all first claims (one per person) and all other claims in the study period. Occurrence of claims The occurrence of claims and changes over the years in agriculture as a whole and per sector was determined by calculating the cumulative incidence (CI) per year from 1 January 1994 through to 31 December 2001 (8 full years). CI was calculated, after Beaglehole et al. [8], as follows: CI = ClaimNew/PopRisk 100, where ClaimNew = number of claimants submitting one or more claims in a certain year and PopRisk = population at risk, i.e. number of policyholders in a certain year minus the number of policyholders who had a claim at the beginning of that year. The exact number of policyholders was known only at the end of We estimated the number of policyholders in previous years, using the number of policyholders at the end of 2001 and adjusting this figure for the change in number of farms per sector in The Netherlands per year from 1994 through 2001 [5,9]. Furthermore, the distribution of CI over sick leave diagnoses was calculated to find out whether the relative contribution of different diagnoses (as percentage of total CI) changed over the years. This analysis was also performed for individual sectors that demonstrated a positive or negative trend in CI over the years. Linear regression analysis was used to test the significance of a trend in CI over the years using SPSS (v. 10.0, 2000). The effect of age on CI (total and per diagnosis) was investigated by calculating CI over the whole time period (8 years) per age category. Three age categories were considered: 35 years, years and >45 years. The population at risk per age category was estimated by calculating the age of all policyholders at the end of 1997 (the middle of the study period). Duration of sick leave Kaplan Meier non-parametric survival analysis was used to describe the duration of sick leave per diagnosis [10]. A Kaplan Meier survival curve shows the proportion of claimants still on sick leave related to the time since the start of sick leave. All first claims that started within the study period were included and censored data were also taken into account. Data were censored if sick leave had not been terminated by the final date (31 December 2001). This was the case if claimants were still ill or if their policy had expired by the final date. The category other disorders was excluded from analysis, because it contained all kinds of diagnoses. Per diagnosis, a survival analysis was performed to investigate the effect of age category on the duration of sick leave. Influence of diagnosis and age category was tested using the log rank test [10]. All calculations were carried out using SPSS (v. 10.0, 2000).

4 464 OCCUPATIONAL MEDICINE Results Diagnoses of sick leave The diagnoses of sick leave claims in the study period were calculated relative to the total number of claims. Results were split into first claims and other claims and claims up to 1 year and longer than 1 year (see Table 2). About 87% of the claims were for sick leave shorter than 1 year. The results of first claims and other claims were comparable. For all claims for sick leave up to 1 year, musculo-skeletal disorders and injuries accounted for ~61% of the total number of claims. For all claims for sick leave longer than 1 year, these diagnoses accounted for ~58% of the total number of claims. Note that in the groups of claims lasting more than 1 year, the contribution of musculo-skeletal or other injuries had decreased, whereas that of musculo-skeletal disorders had increased. Furthermore, the contribution of respiratory diseases had increased (1.8 times higher) and the relative contribution of mental disorders had more than doubled. Incidence of claims and time-dependent changes per diagnosis and per sector For agriculture as a whole, the CI of sick leave did not change significantly over the years (Table 3). The mean CI over the study period was 10.2 claims per year per 100 policyholders. Table 3 also shows the CI for the individual sectors of agriculture. Some sectors demonstrated no significant increase or decrease over the years. Of these sectors, CI was highest in mushroom farming (mean CI = 15.1) and lowest in arable farming (mean CI = 2.8). No significant change in CI was also seen in plant nursery (mean CI = 7.3), fruit farming (mean CI = 8.9) and poultry farming (mean CI = 10.2). In dairy farming and pig husbandry, the CI increased significantly, from 2.5 in 1994 to 6.6 in 2001 and from 8.5 in 1994 to 15.3 in 2001, respectively. In horticulture and bulb culture, the CI decreased significantly, from 9.8 in 1994 to 7.4 in 2001 and from 5.9 in 1994 to 4.3 in 2001, respectively. The trend over the years of the relative contribution of various diagnoses (as percentages of total CI of all Table 2. Diagnoses of first claims, other claims and all claims with a duration of 1 year and a duration >1 year ICD-9 code First claim a Other claims b All claims 1 year c 1 year 1 year c 1 year 1 year c 1 year 1. Musculo-skeletal system and connective tissue (%) Back Neck/upper extremity Lower extremity Mental disorders (%) Diseases of the respiratory system (%) Other disorders (%) Injuries 1. Musculo-skeletal injuries (%) Other injuries (%) Total (%) No. of claims a First claims in the period (one per person). b Other claims in the period c Includes claims from claimants with status recovered at the end of Table 3. Cumulative incidence (CI) of first claims in the time period in agriculture as a whole and per sector (the mean number of policyholders per year over the entire study period is given in parentheses) Sector P-value Dairy farming (1186) Pig husbandry (1407) <0.001 Poultry farming (470) Arable farming (333) Horticulture (3223) Bulb culture (1079) Fruit farming (330) Plant nursery (576) Mushroom farming (335) Agriculture total (27 044)

5 E. HARTMAN ET AL.: SICK LEAVE ANALYSIS AMONG SELF-EMPLOYED DUTCH FARMERS 465 Figure 1. Relative cumulative incidence (RCI) as percentage of total CI of all diagnoses per year. diagnoses) is shown in Figure 1. The relative cumulative incidence (RCI) of musculo-skeletal injuries decreased from 27% in 1994 to 20% in 2001 (P = 0.002). A slight increase of the RCI was seen in the case of back disorders (P = 0.022), neck or upper extremity disorders (P = 0.001), lower extremity disorders (P = 0.002) and mental disorders (P = 0.032). In the other diagnoses, no significant change in the RCI was seen during the study period. The changes over time in the RCI within the sectors dairy farming, pig husbandry, horticulture and bulb culture were also analysed. In dairy farming, a significant increase in the RCI was observed for back disorders (from 6% in 1994 to 25% in 2001; P = 0.010) and mental disorders (from 0% in 1994 to 5% in 2001; P =0.013).In pig husbandry, there was also an increase in the RCI for mental disorders, but only from 1994 (4%) to 1998 (10%). Thereafter, there was a decline to 2% in In horticulture and dairy farming, respective decreases in RCI of musculo-skeletal injuries were seen: from 22% in 1994 to 17% in 2001 (P = 0.008) and from 42% in 1994 to 22% in 2001 (P = 0.006). In bulb culture, no significant change in the RCI of sick leave diagnoses was seen. Incidence of claims per age category The effect of age on CI over the whole time period (8 years) for the first claims is shown in Table 4. The table shows that there was an increase of CI with age. The CI of the oldest age group was 1.6 times higher than that of the youngest age group. Musculo-skeletal and other disorders contributed most to this increase. There was also an increase with age in the case of respiratory diseases. No increase of CI with age was observed for musculo-skeletal or other injuries. Table 4. Cumulative incidence (CI) of first claims in the period 1994 through 2001 per age category and diagnosis (the number of policyholders per age category is given in parentheses) ICD-9 code 35 years (10 642) years (6738) >45 years (6388) Musculo-skeletal system and connective tissue Back Neck/upper extremity Lower extremity Mental disorders Diseases of the respiratory system Other disorders Musculo-skeletal injuries Other injuries Total Duration of sick leave The duration of the sick leave claimed was investigated using survival analysis. In Figure 2, Kaplan Meier curves per sick leave diagnosis are presented. The Kaplan Meier curves show the proportion of claimants that had not recovered, as a function of the time since the start of sick leave. Figure 2 shows that the duration of the sick leave strongly depended on the diagnosis and that recovery, especially in the first months after the sick leave began, varied greatly. After 3 months, 64.5% of the claimants with musculo-skeletal injuries had recovered, compared with only 27.7% of the claimants with mental disorders. Figure 2 also shows that after 2 years, ~93% of the claimants with musculo-skeletal or other injuries had recovered. After the same period, the recovery percentages were 89.3% for claimants with neck or upper extremity disorders, 84.8% for claimants with back disorders,

6 466 OCCUPATIONAL MEDICINE Figure 2. Kaplan Meier curves showing the proportion of claimants not having recovered as a function of the duration of sick leave (up to 2 years) for musculo-skeletal injuries ( inj. musc ; n = 3045), other injuries ( inj. other ; n = 999), neck or upper extremity disorders ( up.ex ; n = 957), back disorders ( back ; n = 2576), lower extremity disorders ( low.ex ; n = 889), respiratory diseases ( resp ; n = 210) and mental disorders ( mental ; n = 439). Figure 3. Kaplan Meier curves showing the proportion of claimants not having recovered from back disorders as a function of time (up to 2 years) per age category ( 35 years, n = 800; years, n = 872; >45 years, n = 904). 84.0% for claimants with mental disorders and 82.7% for claimants with lower extremity disorders. Claimants with respiratory diseases recovered most slowly: 76.4% had recovered after 2 years. Since the minimum claim duration of 6 days before a claim is included in the database, all survival curves start off horizontally. The recovery rates of claimants with musculo-skeletal and other injuries, musculo-skeletal disorders or diseases of the respiratory system increased strongly in the first month of sick leave. Thereafter, the recovery rate gradually decreased. In the case of mental disorders, the increase of the recovery rate in the first month was much less than for the other diagnoses and the probability of recovery was also less. We investigated whether the relationship between sick leave diagnosis and sick leave duration was affected by

7 E. HARTMAN ET AL.: SICK LEAVE ANALYSIS AMONG SELF-EMPLOYED DUTCH FARMERS 467 age. It was found that the curves for all diagnoses of sick leave, except mental disorders, were significantly higher for older claimants. As an example, Figure 3 illustrates the influence of age, as divided into three categories, on the duration of sick leave related to back disorders. Each age group differed significantly from the others. After 2 years of sick leave, 77.3% of the oldest age group had recovered, compared with 90.3% of the youngest age group (P < 0.001). The largest difference was shown in diseases of the respiratory system: after 2 years, only 53.4% of the oldest age group had recovered, compared with 93.7% of the youngest age group (P < 0.001). Discussion In the present study, we have shown that among selfemployed Dutch farmers, musculo-skeletal injuries and disorders account for ~61% of the total number of claims related to sick leave. There was a wide variation in incidence of sick leave between the different agricultural sectors. Furthermore, the incidence of sick leave increased concomitantly with age. One of the reasons for this increase was the increase in the incidence of musculoskeletal disorders. The duration of sick leave depended on the sick leave diagnosis and also on the age category: the slowest recovery from sick leave was seen in claimants with respiratory diseases and claimants in the oldest age category. An important point is the representativeness of our findings for self-employed Dutch farmers. The database covers ~20% of all self-employed Dutch farmers; 7% of the policyholders in the present study were women, compared with ~20% among all self-employed Dutch farmers [11]. Furthermore, the policyholders we studied were relatively young: 10% were <30 years old, 90% were between 30 and 65 years old and none were >65 years old; the comparable percentages for all self-employed Dutch farmers are 1, 78 and 21% [11]. We do not think that differences in gender greatly influenced the results and, when describing the results, we took account of the effect of age. It is impossible to determine exactly the representativeness with respect to sectors, because the group of policyholders in the sector unknown is so large. However, it seems that the number of farmers working in pig husbandry or horticulture is higher than the national average. This reflects the fact that most of the clients of the insurance company live in the west and south of The Netherlands, where these sectors predominate. In the description of some of the results, we did take account of the effect of sector. The database we used to analyse the magnitude of sick leave among self-employed Dutch farmers comprised cases that were work-related and cases that were not. Clearly, the work-related cases are most relevant for developing a strategy to prevent sick leave. The social security system of The Netherlands does not distinguish between disorders or injuries that are work-related and those that are not [12] and neither did the insurance company whose database we used. This means that some of the claims included in the analysis may have been wholly or partly unrelated to work. In the case of claims related to injuries, additional research could give insight into the degree of work-relatedness. In the case of disorders, it is more difficult to establish the extent to which health problems are solely attributable to workrelated factors. The World Health Organization (WHO) has defined work-related diseases as characterized by a complex, multifactorial aetiology [13]. This indicates that it is not always possible to identify one risk factor responsible for health problems. Other researchers have also used the term work-related to indicate that the cause of health problems is multifactorial [14,15]. Since it was not possible to make a sound distinction between diagnoses that were work-related and those that were not, the only diagnosis we excluded from our analysis was pregnancy. One of our findings was that the incidence of sick leave related to musculo-skeletal disorders increased with age. This result is important, because the agricultural population in The Netherlands is ageing. Our results are consistent with the finding reported by De Zwart et al. [16] that in physically demanding occupations, musculoskeletal complaints increase concomitantly with age. In our study, we also found that the duration of sick leave increased with age. The data showed that older claimants found it difficult to recover from claims related to musculo-skeletal disorders, musculo-skeletal or other injuries, or respiratory diseases. On the basis of a review of the literature, Turner et al. [17] concluded that age was a predictor of chronic disability. They found that older workers had poorer outcomes with respect to recovery after sick leave. De Zwart et al. [18] mentioned that the capacity for physical work declines with age. It has been suggested that an imbalance between the capacity to do physical work and the physical workload results in longterm effects on health, with chronic musculo-skeletal complaints being one of the major outcomes. McIntosh et al. [19] have stated that it might be biologically more difficult for older workers to recover when they have low back pain. They also stated that workers within a few years of retirement might lack incentives for returning to work. In the present study, which looked at self-employed persons only, expiration of the policy may have played a role in this respect. Another of our findings was that the incidence of sick leave differed strongly between sectors within agriculture. Bernard and colleagues [15] concluded that occupational title could be used as a crude measure of exposure. In many studies, differences in the prevalence of health problems or sick leave have been found between different

8 468 OCCUPATIONAL MEDICINE occupations, on the basis of job titles. In some studies, agriculture has been defined as a single job title [3,20]. However, from the present study it can be concluded that within the occupational title agriculture, there is huge variation between the sectors. The incidence of sick leave in The Netherlands in agriculture as a whole in 2001, for example, was 9.9 new claims per year per 100 policyholders. If only the animal husbandry sectors (dairy farming, pig husbandry, poultry farming) had been included in the study population, the incidence of sick leave would have been 10.9 new claims per year per 100 policyholders. However, if only sectors such as horticulture, fruit farming or plant nursery had been included, the incidence of sick leave would have been 7.3 new claims per year per 100 policyholders. These differences probably reflect differences in physical and mental workload and the differences in safety at work between the sectors in agriculture. A sector-specific approach is recommended in identifying target groups for preventive actions. Furthermore, the job title agriculture as a measure of the exposure to work in agriculture might not be valid. Burdorf [21] also concluded that in some studies grouping by job title is inadequate, because of the variability of exposure among workers within an occupational group. Therefore, it would be better to measure exposure at sector level at least. Concluding remarks This study is a first attempt to provide insight into the diagnoses, occurrence and duration of sick leave among self-employed farmers in The Netherlands. Musculoskeletal disorders and injuries appeared to dominate. To reduce the occurrence of sick leave in agriculture, these diagnoses should have priority in preventive actions. The increasing age of the agricultural population will affect the diagnoses, occurrence and duration of sick leave. The relative contribution of musculo-skeletal disorders is expected to increase in the future. The duration of sick leave is also expected to increase, which is why it is important to try to reduce sick leave. Moreover, our findings suggest that a sector-specific approach should be taken, to focus preventive actions on specific working conditions with respect to workload and safety at work. References 1. Lewis MQ, Sprince NL, Burmeister LF, Whitten PS, Torner JC, Zwerling C. Work-related injuries among Iowa farm operators: an analysis of the Iowa farm family health and hazard surveillance project. Am J Ind Med 1998;33: Von Essen SG, McCurdy SA. Health and safety risks in production agriculture. West J Med 1998;169: Paoli P, Merllié, D. Third European Survey on Working Conditions Dublin: European Foundation for the Improvement of Living and Working Conditions, De Rooij A, Broekema R. Ziekteverzuim in het agrarisch bedrijf. Jaarcijfers Zoetermeer: Stigas Arbodienst, Statistics Netherlands. Agricultural Census Voorburg/ Heerlen: Statistics Netherlands, Research and Marketing. Agrarische vakmonitor Onderzoek betreffende de agrarische markt naar structuur, gebruik bankdiensten, verzekeringen en automatiseringsgraad. Heerlen: Research and Marketing, Commission on Professional and Hospital Activities. The International Classification of Diseases: 9th Revision, Clinical Modification. New York: McGraw-Hill, Beaglehole R, Bonita R, Kjellström T. Basic Epidemiology. Geneva: World Health Organization, Statistics Netherlands. Agricultural Census Voorburg/ Heerlen: Statistics Netherlands, Kleinbaum DG. Survival Analysis: A Self-learning Text. Springer Series in Statistics, Statistics in the Health Sciences. New York: Springer, Statistics Netherlands. Statistical Yearbook Voorburg/ Heerlen: Statistics Netherlands, National Institute of Social Security. Social Security in The Netherlands. Facts and Figures. Amsterdam: LISV, World Health Organization (WHO). Identification and Control of Work-related Diseases. Report of a WHO Expert Committee. Technical Report Series 714. Geneva: WHO, Kuorinka I, Forcier L. Work-related Musculoskeletal Disorders (WMSDs). A Reference Book for Prevention. London: Taylor & Francis, Bernard BP (ed.). Musculoskeletal Disorders and Workplace Factors. A Critical Review of Epidemiologic Evidence for Work-related Musculoskeletal Disorders of the Neck, Upper Extremity and Low Back. US Department of Health and Human Services. Cincinnati: National Institute of Occupational Health, De Zwart BCH, Broersen JPJ, Frings-Dresen MWH, Van Dijk FJH. Musculoskeletal complaints in the Netherlands in relation to age, gender and physical demanding work. Int Arch Occup Environ Health 1997;70: Turner JA, Franklin G, Turk DC. Predictors of chronic disability in injured workers: a systematic literature synthesis. Am J Ind Med 2000;38: De Zwart BCH, Broersen JPJ, Frings-Dresen MWH, Van Dijk FJH. Repeated survey on changes in musculoskeletal complaints relative to age and work demands. Occup Environ Med 1997;54: McIntosh G, Frank J, Hogg-Johnson S, Bombardier C, Hall H. Prognostic factors for time receiving workers compensation benefits in a cohort of patients with low back pain. Spine 2000;25: Islam SS, Velilla AM, Doyle EJ, Ducatman AM. Gender differences in work-related injury/illness: analysis of workers compensation claims. Am J Ind Med 2001;39: Burdorf A. Bias in risk estimates from variability of exposure to postural load on the back in occupational groups. Scand J Work Environ Health. 1993;19:50 54.