Sickness absence is a major socio-economic problem imposing a huge amount of cost to societies.This issue has been widely concerned over the last few years. Absence from work entails complex conditions and multifactorial etiology. According to previous studies, there are various occupational and personal factors contributing to the incidence of sickness absence.
Long-term sickness absence compels the employer to reassign the tasks to other workers or replace the absent worker. In addition, previous studies indicated that the likelihood of returning to work decreases with longer absence from work. In this light, it is essential to identify the workers at risk of sickness absence, particularly long-term sickness absence.
The ability to stay at work and handling the work demands have been discussed as work ability, which is actually a balance between worker’s resources and work requirements.
The work ability index (WAI) provides a valuable tool to assess the work ability. It can serve research purposes and clinical assessments in occupational health, aimed at evaluating the ability to work during occupational health examinations and environmental monitoring. Furthermore, the WAI helps better identify the workers at risk of long term sickness absence and early work loss.
Various studies have reported that lower WAI scores are correlated with increased incidence of sickness absence and lengthened duration of sickness absence. In a study on construction workers in the Netherlands, Alavinia et al. found that workers with moderate and good work ability are at higher risk of sickness absence than workers with excellent work ability. Moreover, it was revealed that WAI can be a good predictor for sickness absence, especially for prolonged periods of absence from work.
Given the importance of sickness absence and identification of workers at risk of absence from work, this study attempted to evaluate Influence of short term and long term sickness absence on work ability index.
This cross-sectional study was conducted on 806 car accessories production company workers. The questionnaires were handed to workers with, at a minimum, one year of employment volunteering in the research project. Certain occupational data concerning date of employment, working location and shift work status were obtained from the Personnel Selection Department. Informed consent was obtained from all individual participants included in the study .The sickness absence data were extracted from the HSE of the factory, containing the number of days and episodes of sickness absence. Absence from work was divided into two categories: short term(<3day)and long term (≥3days) sickness absence. A questionnaire completed by the workers was employed to collect data on demographic information including age, marital status, education level, smoker/non-smoker and BMI. In terms of age, the subjects were divided into two categories of less than 35 years old and equal or greater than 35 years old. In terms of BMI, they were divided into two categories of less than 25 and equal or greater than 25. Moreover, the education levels included three categories of low, medium, high. The work ability among the workers was measured through the Work Ability Index questionnaire comprising 7 items: 1) current work ability compared with the lifetime best ranging from 0 to 10 points, 2) work ability in relation to the demands of the job ranging from 2 to 10, 3) number of current diseases diagnosed by physician ranging from 1 to 7, 4) estimated work impairment due to diseases ranging from 1 to 6, 5) sick leave during the past year (12 months) ranging from 1 to 5, 6) Work ability in the forthcoming two years covering 1, 4 and 7, and 7) mental resources ranging from 4 and 7. In all items,the highest scores of WAI represent the best work ability. The overall score of WAI obtained by the total points achieved in the seven items ranged from 7 to 49, where higher score indicates a greater work ability.
The WAI scores in workers with short-term and long-term sickness absence from work and the combined sickness absence were compared against those in workers without any sickness absence. In this study, the dependent variable was WAI , while the independent variables included sickness absence and occupational and demographic characteristics. The Chi- square test was used to compare the qualitative variables and the logistic regression analysis to determine the relationship between WAI and sickness absence. In all tests, the significance level was considered to be 0.05 with confidence interval of 95%. The statistical analysis involved SPSS 16.
Of the 956 workers, 850 responded to the questionnaire (response rate of 88.91%). Having applied the exclusions criteria, a total of 806 subjects were analyzed. All the subjects were male ranging from 21 to 63 years old with an average age of 35.04±6.84 years. Moreover, 75.7% of the subjects had shift work, and 76.4% were blue collar . Analysis of data on Sickness absence revealed that 55.3% of subjects had a record of absence from work, of whom 61.9% were short-term (<3 days) and 38.1% were long-term (≥3 days). The mean WAI score was 42.19±4.37. In comparison of the two groups with and without sickness absence in terms of demographic characteristics, the mean age was 35.65±7.32 for the non-absence group and 34.55±6.40 for the absence group, indicating a statistically significant difference (P value=0.025). Furthermore, the mean BMI was 25.90±2.78 in the non-absence group, higher than 25.35±2.62 in the absence group (P value=0.004).
The Chi square test suggested that shift workers and blue collars had more frequent absence from work with (OR=1.651 and P value=0.002) and (OR=2.256 and P value<0.001), respectively. Short-term Sickness absence was higher among shift workers (OR=1.84 and P value=0.011). Moreover, the long-term absence was more frequent among smokers while short-term absence was more frequent among non-smokers (OR=2.12 and P-value=0.002). The two groups with short and long-term absences, however, indicated no significant differences in terms of age and BMI.
The mean WAI was 41.0 ±4.8 in smokers and 42.4±4.2 in non-smokers, indicating a statistically significant difference (P value<0.001). The mean score was 42.5±4.3 in subjects with a BMI less than 25 and 41.9±4.3 in subjects with BMI≥25 (P value=0.049). The WAI scores in the population indicated no significant differences in terms of age, work experience, education, shift work and work group. The mean (SD) of WAI scores in workers with short-term, long-term and total sickness absence were 41.97(3.97) 40.62(5.30) and 41.45(4.56), respectively; and in workers without any sickness absence was 43.09(3.93). The WAI of workers without sickness absence was higher than that of workers with sickness absence (OR=2.79; 95% CI=1.63-4.76 and P-value<0.001); and was higher in workers with short-term sickness absence than those with long-term sickness absence (OR=3.06; 95% CI=1.74-5.36 and P value<0.001). After adjusting the effects of other factors, there was a correlation between WAI and sickness absence in the two groups with and without absences as well as workers with short and long term sickness absence (P-value<0.001 and 0.019, respectively). Furthermore, there was a significant relationship between the work group and absence/non-absence as well as between smoking and short/long-term absence (P value=0.029, <0.001 respectively). This was consistent with results of previous tests for comparison of mean values (t-test and chi-square). In comparison of the WAI subcategories between the absence and non-absence groups, "the current work ability compared with life time best" was greater in the non-absence subjects than the absence subjects (P value=0.013). Moreover, it was greater in the short-term absence subjects than the long-term absence subjects (P value=0.045).
“Work impairment due to disease” was greater in the non-absence group than the absence group (P value<0.001). Moreover, it was greater in the short-term absence group than the long-term absence group (P value<0.001).
“Psychological resources” was greater in the non-absence group than the absence group (P value=0.033). However, there was no significant difference in comparing the two groups with short and long-term absences.
In this study, the subjects were divided in terms of work ability index into two categories of low work ability (including those with low and medium WAI) and high work ability (including those with good and excellent WAI). The analytical results of chi-square test showed that WAI was significantly higher in the non-absence group than the absence group. In other words, sickness absence was considerably lower in the group with high work ability (OR=2.790; CI=1.63-4.76 and P value<0.001).Concerning the subjects with a history of sickness absence, the WAI was greater in the short-term absence group than the long-term absence group (OR=3.060; CI=1.74-5.36 and P value<0.001).
This study intended to assess the relationship between WAI scores, sickness absence and the role of individual and occupational factors among the workers involved in the Iranian automotive industry. The mean WAI score was 42.19, and 40.7% of subjects were categorized as excellent, 49.5% as good, 9.3% as moderate and 0.5% as poor. In this population, there was a significant relationship found between WAI and sickness absence. This was consistent with the results of other studies. Sickness absence in the blue collar group and shift workers was higher. This finding can be explained by the fact that white collars are not in contact with the production-related hazards and are less likely to absent from work. Similarly, shift work disrupts the circadian rhythm, leading to morbidity and risk of many diseases. The overall WAI score was greater in non-absence subjects than absence subjects. Moreover, it was lower in long-term absence subjects than short-term absence subjects. Comparing the two groups with a history of short and long-term absences, it was revealed that short-term absence was more frequent among the shift workers. This finding can be explained by the fact that sick or non-healthy individuals are more likely to prefer jobs not requiring work shift.
According to the results of this study, the lower WAI scores increased the episodes and lengths of sickness absence. The findings suggested that WAI can be correlated with short-term and long-term Sickness absence and can be employed as a simple and useful tool to identify workers at risk of sickness absence.