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The Added Value of Sickness Presenteeism to Prediction Models for Sickness Absence
S
ickness presenteeism (SP), defined as going ill to work, is increasingly recognized as an economic and occupational health problem. The costs of SP in terms of reduced worker productivity are higher than the expenses of sickness absence (SA) benefits. Sickness presenteeism is associated with health status, work-related factors as well as personal circumstances and attitudes.1 The pressure to attend work when ill comes from workers themselves rather than colleagues or superiors.2 Women and young workers do not want to burden their colleagues, whereas managers and professionals think that they are indispensable.3 Elstad and Vabø4 reported high SP levels in Nordic eldercare, especially in understaffed centers. Possibly, eldercare professionals refrain from taking sick leave because they realize that their absence could affect the quality of care. Sickness presenteeism is prospectively related to SA5,6 and might therefore be a predictor of future SA. Recently, models including age, prior SA, and self-rated health were found to predict high SA in hospital workers7 as well as office workers.8 Nevertheless, more predictors of high SA are needed to improve the ability of the SA prediction models to discriminate between high- and low-risk workers. We investigated whether SP improved risk discrimination by the SA prediction models. After approval of the Danish Data Protection Agency (2012-41-1290), data were obtained from the Working in Eldercare Survey9 for which 4536 municipal eldercare workers in Aarhus (Denmark) were invited between February and July 2005. A total of 3444 (76%) eldercare workers participated by returning a completed survey questionnaire, which addressed self-rated health by the question Address correspondence to: Corne Roelen, MD, PhD, ArboNed Occupational Health Service, Utrecht, the Netherlands (corne.roelen@ arboned.nl). The authors declare no conflicts of interest. C 2014 by American College of OccupaCopyright tional and Environmental Medicine DOI: 10.1097/JOM.0000000000000219
e58
“In general, would you say your health is” and responses excellent (5), very good (4), good (3), fair (2), or poor (1). SP was addressed by the question “How many days during the past 12 months did you attend work while feeling ill?” Age was calculated from the 10-digit personal identification number assigned to every Danish citizen and SA 1 year before the survey response date was retrieved from employer records.9 These variables were used to predict high (≥30) employer-registered SA days and high (≥3) employer-registered SA episodes in the year after the survey response date. Eldercare workers with high SA at 1-year follow-up were regarded as cases and those without high SA as noncases. Pencina et al10 introduced a novel risk reclassification analysis to assess the added value of new predictor variables to established prediction models. For this purpose, we stratified eldercare workers into high- and low-risk groups by data driven cutoff risks of 15% for high SA days and 40% for high SA episodes. Thus, for example, eldercare workers with a predicted risk 15% or less of high SA days were classified as a low-risk group and those with a predicted risk of more than 15% of high SA days as a high-risk group. Reclassification of workers after adding SP was assessed conditional on the outcome. For cases, upward movement from low risk to high risk is correct, whereas downward movement is incorrect. Alternatively, downward movement from high risk to low risk is correct for noncases, and upward movement is incorrect. The net reclassification improvement (NRI) summarizes these reclassifications and was calculated in R (Project for Statistical Computing) by using the predictABEL package, version 1.2-1. A total of 2357 (96% women) eldercare workers were eligible for reclassification analysis as they were employed in municipal eldercare during the entire study period from 1 year before to 1 year after the survey response date. They were on average 45.6 (standard deviation = 9.7) years of age and worked 32.6 (standard deviation = 3.7) hours per week as health care workers (63%), nurses (13%), activity guiders (7%), facility staff (6%), or administrators (6%); 5% performed other jobs. At 1-year follow-up, there were 283 (12%) cases of high SA days and 801 (34%) cases of high SA episodes; 186 eldercare workers had both high SA days and
episodes. After adding SP to the SA days model, 43 cases (31 correct and 12 incorrect) and 215 noncases (84 correct and 131 incorrect) were reclassified, resulting in NRI = 4.5%, which was nonsignificant (Table 1). Adding SP to the SA episodes model resulted in reclassification of 26 cases (12 correct and 14 incorrect) and 48 noncases (17 correct and 31 incorrect), with nonsignificant NRI = −1.2% (Table 2). These results show that SP did not improve risk classification by models predicting high SA. Participants of the Working in Eldercare Survey had less SA than nonparticipants.9 The prevalence of SP among survey participants was 73%, which is in line with the previously reported prevalence for the Danish workforce.1 Possibly, participants had lower SA levels because they were healthier than nonparticipants. Such a “healthy volunteer effect” could have weakened SA predictions by the models used in this study. Adding SP to the SA prediction models particularly reclassified workers without high SA as being high-risk workers. In other words, adding SP increased the number of false-positives, which implicates higher intervention costs and unnecessary utilization of resources. If we want to limit false-positive rates, SP should not be added to models including age, prior SA, and selfrated health to predict future SA. In general, we recommend careful consideration of the added value of new predictor variables to prediction models, even if variables are prospectively associated with the outcome. Corn´e A. M. Roelen, MD, PhD Department of Health Sciences, Section of Community and Occupational Medicine, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands Chris Jensen, PhD Department of Public Health and General Practice, Norwegian University of Science and Technology, Trondheim, Norway Christina M. Stapelfeldt, PhD Department of Public Health and Quality Improvement, Marcelisborgcentret, Aarhus, Denmark Johan W. Groothoff, PhD Department of Health Sciences, Section of Community and Occupational Medicine, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
JOEM r Volume 56, Number 7, July 2014 Copyright © 2014 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
JOEM r Volume 56, Number 7, July 2014
Letters to the Editor
TABLE 1. Reclassification Table for Sickness Absence Days* Without SP
With SP
Risk ≤15% Cases Noncases Total Risk >15% Cases Noncases Total
≤15% 155 1560 1715
>15% 31 131 162
Total 186 1691 1877
12 84 96
85 299 384
97 383 480
Reclassification
%improvement (95% CI)
Significance
Cases Noncases NRI
6.7% (2.2% to 1.1%) −2.3% (−3.7% to −0.1%) 4.5% (−0.0% to −9.2%)
P < 0.01 P < 0.01 P = 0.07
*The table shows reclassifications after adding sickness presenteeism to the prognostic model predicting high (≥30) sickness absence days at 1-year follow-up. CI, confidence interval; NRI, net reclassification improvement; SP, sickness presenteeism.
TABLE 2. Reclassification Table for Sickness Absence Episodes* Without SP
With SP
Risk ≤40% Cases Noncases Total Risk >40% Cases Noncases Total
≤40% 323 1232 1555
>40% 12 31 43
Total 335 1263 1598
14 17 31
452 276 728
466 293 759
Reclassification
%improvement (95% CI)
Significance
Cases Noncases NRI
−0.3% (−1.5% to 0.1%) −0.9% (−1.8% to −0.0%) −1.2% (−2.7% to 0.4%)
P = 0.69 P = 0.04 P = 0.14
*The table shows reclassifications after adding sickness presenteeism to the prognostic model predicting high (≥3) sickness absence episodes at 1-year follow-up. CI, confidence interval; NRI, net reclassification improvement; SP, sickness presenteeism.
Claus V. Nielsen, MD, PhD Department of Public Health, Section of Clinical Social Medicine and Rehabilitation, Aarhus University, Aarhus, Denmark Ute Bultmann, PhD ¨ Department of Health Sciences, Section of Community and Occupational Medicine, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
REFERENCES 1. Hansen CD, Andersen JH. Going ill to work— what personal circumstances, attitudes and work-related factors are associated with sickness presenteeism? Soc Sci Med. 2008;67:956–964. 2. Roberston I, Leach D, Doerner N, Smeed M. Poor health, but not absent: prevalence, predictors, and outcomes of presen-
3.
4. 5.
6.
7.
teeism. J Occup Environ Med. 2012;54:1344– 1349. Johansen V, Aronsson G, Marklund S. Positive and negative reasons for sickness presenteeism in Norway and Sweden: a cross-sectional survey. BMJ Open. 2014;4:e004123. Elstad JI, Vabø M. Job stress, sickness absence and sickness presenteeism in Nordic elderly care. Scand J Public Health. 2008;36:467–474. Bergstr¨om G, Bodin L, Hagberg J, Aronsson G, Josephson M. Sickness presenteeism today, sickness absenteeism tomorrow? A prospective study on sickness presenteeism and future sickness absenteeism. J Occup Environ Med. 2009;51:629–638. Taloyan M, Aronsson G, Leineweber C, Magnusson Hanson L, Alexanderson K, Westerlund H. Sickness presenteeism predicts suboptimal self-rated health and sickness absence: a nationally representative study of the Swedish working population. PLoS One. 2012;7:e44721. Roelen CA, van Rhenen W, B¨ultmann U, Groothoff JW, van der Klink JJ, Heymans
MW. The development and validation of two prediction models to identify employees with high sickness absence. Eur J Public Health. 2013;23:128–133. 8. Roelen CA, B¨ultmann U, van Rhenen W, van der Klink JJ, Twisk JWR, Heymans MW. External validation of two prediction models identifying employees at risk of high sickness absence: cohort study with 1-year follow-up. BMC Public Health. 2013;13:105. 9. Stapelfeldt CM, Jensen C, Andersen NT, Fleten N, Nielsen CV. Validation of sick leave measures: self-reported sick leave and sickness benefit data from a Danish national register compared to multiple workplaceregistered sick leave spells in a Danish municipality. BMC Public Health. 2012;12: 661. 10. Pencina MJ, d’Agostinho RB Sr, d’Agostinho RB Jr, Vasan RS. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med. 2008;27:157–172.
C 2014 American College of Occupational and Environmental Medicine
Copyright © 2014 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
e59
The Added Value of Sickness Presenteeism to Prediction Models for Sickness Absence
S
ickness presenteeism (SP), defined as going ill to work, is increasingly recognized as an economic and occupational health problem. The costs of SP in terms of reduced worker productivity are higher than the expenses of sickness absence (SA) benefits. Sickness presenteeism is associated with health status, work-related factors as well as personal circumstances and attitudes.1 The pressure to attend work when ill comes from workers themselves rather than colleagues or superiors.2 Women and young workers do not want to burden their colleagues, whereas managers and professionals think that they are indispensable.3 Elstad and Vabø4 reported high SP levels in Nordic eldercare, especially in understaffed centers. Possibly, eldercare professionals refrain from taking sick leave because they realize that their absence could affect the quality of care. Sickness presenteeism is prospectively related to SA5,6 and might therefore be a predictor of future SA. Recently, models including age, prior SA, and self-rated health were found to predict high SA in hospital workers7 as well as office workers.8 Nevertheless, more predictors of high SA are needed to improve the ability of the SA prediction models to discriminate between high- and low-risk workers. We investigated whether SP improved risk discrimination by the SA prediction models. After approval of the Danish Data Protection Agency (2012-41-1290), data were obtained from the Working in Eldercare Survey9 for which 4536 municipal eldercare workers in Aarhus (Denmark) were invited between February and July 2005. A total of 3444 (76%) eldercare workers participated by returning a completed survey questionnaire, which addressed self-rated health by the question Address correspondence to: Corne Roelen, MD, PhD, ArboNed Occupational Health Service, Utrecht, the Netherlands (corne.roelen@ arboned.nl). The authors declare no conflicts of interest. C 2014 by American College of OccupaCopyright tional and Environmental Medicine DOI: 10.1097/JOM.0000000000000219
e58
“In general, would you say your health is” and responses excellent (5), very good (4), good (3), fair (2), or poor (1). SP was addressed by the question “How many days during the past 12 months did you attend work while feeling ill?” Age was calculated from the 10-digit personal identification number assigned to every Danish citizen and SA 1 year before the survey response date was retrieved from employer records.9 These variables were used to predict high (≥30) employer-registered SA days and high (≥3) employer-registered SA episodes in the year after the survey response date. Eldercare workers with high SA at 1-year follow-up were regarded as cases and those without high SA as noncases. Pencina et al10 introduced a novel risk reclassification analysis to assess the added value of new predictor variables to established prediction models. For this purpose, we stratified eldercare workers into high- and low-risk groups by data driven cutoff risks of 15% for high SA days and 40% for high SA episodes. Thus, for example, eldercare workers with a predicted risk 15% or less of high SA days were classified as a low-risk group and those with a predicted risk of more than 15% of high SA days as a high-risk group. Reclassification of workers after adding SP was assessed conditional on the outcome. For cases, upward movement from low risk to high risk is correct, whereas downward movement is incorrect. Alternatively, downward movement from high risk to low risk is correct for noncases, and upward movement is incorrect. The net reclassification improvement (NRI) summarizes these reclassifications and was calculated in R (Project for Statistical Computing) by using the predictABEL package, version 1.2-1. A total of 2357 (96% women) eldercare workers were eligible for reclassification analysis as they were employed in municipal eldercare during the entire study period from 1 year before to 1 year after the survey response date. They were on average 45.6 (standard deviation = 9.7) years of age and worked 32.6 (standard deviation = 3.7) hours per week as health care workers (63%), nurses (13%), activity guiders (7%), facility staff (6%), or administrators (6%); 5% performed other jobs. At 1-year follow-up, there were 283 (12%) cases of high SA days and 801 (34%) cases of high SA episodes; 186 eldercare workers had both high SA days and
episodes. After adding SP to the SA days model, 43 cases (31 correct and 12 incorrect) and 215 noncases (84 correct and 131 incorrect) were reclassified, resulting in NRI = 4.5%, which was nonsignificant (Table 1). Adding SP to the SA episodes model resulted in reclassification of 26 cases (12 correct and 14 incorrect) and 48 noncases (17 correct and 31 incorrect), with nonsignificant NRI = −1.2% (Table 2). These results show that SP did not improve risk classification by models predicting high SA. Participants of the Working in Eldercare Survey had less SA than nonparticipants.9 The prevalence of SP among survey participants was 73%, which is in line with the previously reported prevalence for the Danish workforce.1 Possibly, participants had lower SA levels because they were healthier than nonparticipants. Such a “healthy volunteer effect” could have weakened SA predictions by the models used in this study. Adding SP to the SA prediction models particularly reclassified workers without high SA as being high-risk workers. In other words, adding SP increased the number of false-positives, which implicates higher intervention costs and unnecessary utilization of resources. If we want to limit false-positive rates, SP should not be added to models including age, prior SA, and selfrated health to predict future SA. In general, we recommend careful consideration of the added value of new predictor variables to prediction models, even if variables are prospectively associated with the outcome. Corn´e A. M. Roelen, MD, PhD Department of Health Sciences, Section of Community and Occupational Medicine, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands Chris Jensen, PhD Department of Public Health and General Practice, Norwegian University of Science and Technology, Trondheim, Norway Christina M. Stapelfeldt, PhD Department of Public Health and Quality Improvement, Marcelisborgcentret, Aarhus, Denmark Johan W. Groothoff, PhD Department of Health Sciences, Section of Community and Occupational Medicine, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
JOEM r Volume 56, Number 7, July 2014 Copyright © 2014 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
JOEM r Volume 56, Number 7, July 2014
Letters to the Editor
TABLE 1. Reclassification Table for Sickness Absence Days* Without SP
With SP
Risk ≤15% Cases Noncases Total Risk >15% Cases Noncases Total
≤15% 155 1560 1715
>15% 31 131 162
Total 186 1691 1877
12 84 96
85 299 384
97 383 480
Reclassification
%improvement (95% CI)
Significance
Cases Noncases NRI
6.7% (2.2% to 1.1%) −2.3% (−3.7% to −0.1%) 4.5% (−0.0% to −9.2%)
P < 0.01 P < 0.01 P = 0.07
*The table shows reclassifications after adding sickness presenteeism to the prognostic model predicting high (≥30) sickness absence days at 1-year follow-up. CI, confidence interval; NRI, net reclassification improvement; SP, sickness presenteeism.
TABLE 2. Reclassification Table for Sickness Absence Episodes* Without SP
With SP
Risk ≤40% Cases Noncases Total Risk >40% Cases Noncases Total
≤40% 323 1232 1555
>40% 12 31 43
Total 335 1263 1598
14 17 31
452 276 728
466 293 759
Reclassification
%improvement (95% CI)
Significance
Cases Noncases NRI
−0.3% (−1.5% to 0.1%) −0.9% (−1.8% to −0.0%) −1.2% (−2.7% to 0.4%)
P = 0.69 P = 0.04 P = 0.14
*The table shows reclassifications after adding sickness presenteeism to the prognostic model predicting high (≥3) sickness absence episodes at 1-year follow-up. CI, confidence interval; NRI, net reclassification improvement; SP, sickness presenteeism.
Claus V. Nielsen, MD, PhD Department of Public Health, Section of Clinical Social Medicine and Rehabilitation, Aarhus University, Aarhus, Denmark Ute Bultmann, PhD ¨ Department of Health Sciences, Section of Community and Occupational Medicine, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
REFERENCES 1. Hansen CD, Andersen JH. Going ill to work— what personal circumstances, attitudes and work-related factors are associated with sickness presenteeism? Soc Sci Med. 2008;67:956–964. 2. Roberston I, Leach D, Doerner N, Smeed M. Poor health, but not absent: prevalence, predictors, and outcomes of presen-
3.
4. 5.
6.
7.
teeism. J Occup Environ Med. 2012;54:1344– 1349. Johansen V, Aronsson G, Marklund S. Positive and negative reasons for sickness presenteeism in Norway and Sweden: a cross-sectional survey. BMJ Open. 2014;4:e004123. Elstad JI, Vabø M. Job stress, sickness absence and sickness presenteeism in Nordic elderly care. Scand J Public Health. 2008;36:467–474. Bergstr¨om G, Bodin L, Hagberg J, Aronsson G, Josephson M. Sickness presenteeism today, sickness absenteeism tomorrow? A prospective study on sickness presenteeism and future sickness absenteeism. J Occup Environ Med. 2009;51:629–638. Taloyan M, Aronsson G, Leineweber C, Magnusson Hanson L, Alexanderson K, Westerlund H. Sickness presenteeism predicts suboptimal self-rated health and sickness absence: a nationally representative study of the Swedish working population. PLoS One. 2012;7:e44721. Roelen CA, van Rhenen W, B¨ultmann U, Groothoff JW, van der Klink JJ, Heymans
MW. The development and validation of two prediction models to identify employees with high sickness absence. Eur J Public Health. 2013;23:128–133. 8. Roelen CA, B¨ultmann U, van Rhenen W, van der Klink JJ, Twisk JWR, Heymans MW. External validation of two prediction models identifying employees at risk of high sickness absence: cohort study with 1-year follow-up. BMC Public Health. 2013;13:105. 9. Stapelfeldt CM, Jensen C, Andersen NT, Fleten N, Nielsen CV. Validation of sick leave measures: self-reported sick leave and sickness benefit data from a Danish national register compared to multiple workplaceregistered sick leave spells in a Danish municipality. BMC Public Health. 2012;12: 661. 10. Pencina MJ, d’Agostinho RB Sr, d’Agostinho RB Jr, Vasan RS. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med. 2008;27:157–172.
C 2014 American College of Occupational and Environmental Medicine
Copyright © 2014 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
e59
