Contributions
Abstract: P913
Type: Poster
Abstract Category: Clinical aspects of MS - Economic burden
Background: Unemployment is a fundamental negative outcome of multiple sclerosis (MS). Currently we do not have any reliable predictors for a change of employment status.
Objectives: Our main aim was to identify the best clinical markers that could distinguish between employed and unemployed patients.
Methods: This was a cross-sectional study with 1226 patients in the original cohort. Every patient was evaluated by the Expanded Disability Status Scale (EDSS), 25 Foot Walk Test (25 FWT), Nine Hole Peg Test (9HPT), Brief International Cognitive Assessment for MS (BICAMS), Paced Auditory Serial Addition Test (PASAT), Beck Depression Inventory (BDI) and Sloan charts (SLOAN). In the first step, we chose patients with full time job (n=787) and unemployed patients with disability pension (n=210). In the second step we matched both groups according to gender, age and education. Logistic regression analysis was used to select the best predictors. The fitted models were compared using the Akaike Information Criterion (AIC), the Nagelkerke R2 (R2), as well as Odds Ratio 95% confidence interval (OR).
Results: The final selected groups accounted for 307 full time job patients (F 248, M 59; mean age 41.2y; mean disease duration 9.64y) and 153 unemployed patients with disability pension (F 127, M 26; mean age 42.21y; mean disease duration 14.36y). Both studied groups were significantly different in all tested variables (p < 0.05). In the univariate analysis the significant predictors of vocational status were EDSS (AIC 364.3; R2 0.54), SDMT + BDI (AIC 474.5; R2 0.31), 9HPT (AIC 489.8; R2 0.27; OR 1.16-1.28) and 25FWT (AIC 471.6; R2 0.30; OR 1.81-2.63). OR for SDMT was 0.91-0.95 and for BDI 1.07-1.1. EDSS was then excluded from a multivariate model because it was not an independent variable (in the Czech Republic a value of EDSS is used for providing a disability pension). In the multivariate model 25FWT+SDMT+BDI was the best combination of predictors (AIC 414.9; R2 0.44; OR 25FWT (1.51 - 2.22), SDMT (0.93 - 0.97), BDI (1.06 - 1.14)).
Conclusions: Walking ability (25FWT) and cognitive performance (SDMT) adjusted for depression are reliable independent predictors of vocational status. Taking them together increased our ability to identify unemployed patients. This study provides the first step towards our main goal: finding predictors for employment status and employability in longitudinal data analysis.
Disclosure: Barbora Benova received compensation for travelling and conference fees from Novartis and Sanofi Genzyme.
Lukas Sobisek received financial support from Novartis.
Tomas Uher received financial support for conference travel and honoraria from Biogen Idec,Novartis, Genzyme and Merck Serono.
Karolina Kucerova: nothing to disclose.
Eva Havrdova received speaker honoraria and consultant fees from Biogen Idec, Merck Serono, Novartis, Genzyme and Teva, as well as support for research activities from Biogen Idec and Merck Serono.
Dana Horakova received compensation for travel, speaker honoraria and consultant fees from Biogen Idec, Novartis, Merck, Bayer, Sanofi Genzyme, and Teva, as well as support for research activities from Biogen Idec.
The project was supported by the Czech Ministry of Education project PRVOUK-P26/LF1/4 and by the Czech Science Foundation GA CR 16-03322S. Funding for biostatistical support was provided by Novartis
Abstract: P913
Type: Poster
Abstract Category: Clinical aspects of MS - Economic burden
Background: Unemployment is a fundamental negative outcome of multiple sclerosis (MS). Currently we do not have any reliable predictors for a change of employment status.
Objectives: Our main aim was to identify the best clinical markers that could distinguish between employed and unemployed patients.
Methods: This was a cross-sectional study with 1226 patients in the original cohort. Every patient was evaluated by the Expanded Disability Status Scale (EDSS), 25 Foot Walk Test (25 FWT), Nine Hole Peg Test (9HPT), Brief International Cognitive Assessment for MS (BICAMS), Paced Auditory Serial Addition Test (PASAT), Beck Depression Inventory (BDI) and Sloan charts (SLOAN). In the first step, we chose patients with full time job (n=787) and unemployed patients with disability pension (n=210). In the second step we matched both groups according to gender, age and education. Logistic regression analysis was used to select the best predictors. The fitted models were compared using the Akaike Information Criterion (AIC), the Nagelkerke R2 (R2), as well as Odds Ratio 95% confidence interval (OR).
Results: The final selected groups accounted for 307 full time job patients (F 248, M 59; mean age 41.2y; mean disease duration 9.64y) and 153 unemployed patients with disability pension (F 127, M 26; mean age 42.21y; mean disease duration 14.36y). Both studied groups were significantly different in all tested variables (p < 0.05). In the univariate analysis the significant predictors of vocational status were EDSS (AIC 364.3; R2 0.54), SDMT + BDI (AIC 474.5; R2 0.31), 9HPT (AIC 489.8; R2 0.27; OR 1.16-1.28) and 25FWT (AIC 471.6; R2 0.30; OR 1.81-2.63). OR for SDMT was 0.91-0.95 and for BDI 1.07-1.1. EDSS was then excluded from a multivariate model because it was not an independent variable (in the Czech Republic a value of EDSS is used for providing a disability pension). In the multivariate model 25FWT+SDMT+BDI was the best combination of predictors (AIC 414.9; R2 0.44; OR 25FWT (1.51 - 2.22), SDMT (0.93 - 0.97), BDI (1.06 - 1.14)).
Conclusions: Walking ability (25FWT) and cognitive performance (SDMT) adjusted for depression are reliable independent predictors of vocational status. Taking them together increased our ability to identify unemployed patients. This study provides the first step towards our main goal: finding predictors for employment status and employability in longitudinal data analysis.
Disclosure: Barbora Benova received compensation for travelling and conference fees from Novartis and Sanofi Genzyme.
Lukas Sobisek received financial support from Novartis.
Tomas Uher received financial support for conference travel and honoraria from Biogen Idec,Novartis, Genzyme and Merck Serono.
Karolina Kucerova: nothing to disclose.
Eva Havrdova received speaker honoraria and consultant fees from Biogen Idec, Merck Serono, Novartis, Genzyme and Teva, as well as support for research activities from Biogen Idec and Merck Serono.
Dana Horakova received compensation for travel, speaker honoraria and consultant fees from Biogen Idec, Novartis, Merck, Bayer, Sanofi Genzyme, and Teva, as well as support for research activities from Biogen Idec.
The project was supported by the Czech Ministry of Education project PRVOUK-P26/LF1/4 and by the Czech Science Foundation GA CR 16-03322S. Funding for biostatistical support was provided by Novartis