Possible Smart City Solutions in the Fight against Black Economy

According to international statistics, Hungary has a high ratio of black economy. On December 31th of 2017, the number of registered corporations reached 1.7 million, of which 395 910 were registered in Budapest [1]. There is a clear need for such measures and developments that are aimed to track corporations at the e-government level, and such infocommunicational equipment and services that essentially promote the arrangement of data. Black employment is one of the most easily identified territories of the hidden economy. Employers must register the working hours, and they are also responsible for the factual, real and entire content of the registry. A number of entry systems are available which – besides ensuring electronic protection – are also suitable for registering the working hours. These systems, however, have both advantages and disadvantages, and different types of abuses have become widespread. The purpose of the study is to provide an overview of these systems based on their ability to reduce black employment and the limitations of their applicability from the point of data protection, with particular attention to the introduction of the General Data Protection Regulation of the European Union from 25 May 2018 in all member states. Employers manage data through their legitimate interest on web and telephone usage, control of emails, or even GPS-based location information. Likewise, legitimate interest is also the basis of the introduction of workplace monitoring systems. To reduce the size of the black economy, the use of an electronic system would be the most suitable tool – which would transfer the information extracted from the system to an immediate tax authority – based on the patterns of online cash registers or online billing programs. This, on the one hand, could provide the basis for the necessary identification and work documentation, but on the other hand, it raises the risk of excessive data handling, which is illegal

Comparative effectiveness of autologous hematopoietic stem cell transplant vs fingolimod, natalizumab, and ocrelizumab in highly active relapsing-remitting multiple sclerosis

Importance: Autologous hematopoietic stem cell transplant (AHSCT) is available for treatment of highly active multiple sclerosis (MS). Objective: To compare the effectiveness of AHSCT vs fingolimod, natalizumab, and ocrelizumab in relapsing-remitting MS by emulating pairwise trials. Design, Setting, and Participants: This comparative treatment effectiveness study included 6 specialist MS centers with AHSCT programs and international MSBase registry between 2006 and 2021. The study included patients with relapsing-remitting MS treated with AHSCT, fingolimod, natalizumab, or ocrelizumab with 2 or more years study follow-up including 2 or more disability assessments. Patients were matched on a propensity score derived from clinical and demographic characteristics. Exposure: AHSCT vs fingolimod, natalizumab, or ocrelizumab. Main outcomes: Pairwise-censored groups were compared on annualized relapse rates (ARR) and freedom from relapses and 6-month confirmed Expanded Disability Status Scale (EDSS) score worsening and improvement. Results: Of 4915 individuals, 167 were treated with AHSCT; 2558, fingolimod; 1490, natalizumab; and 700, ocrelizumab. The prematch AHSCT cohort was younger and with greater disability than the fingolimod, natalizumab, and ocrelizumab cohorts; the matched groups were closely aligned. The proportion of women ranged from 65% to 70%, and the mean (SD) age ranged from 35.3 (9.4) to 37.1 (10.6) years. The mean (SD) disease duration ranged from 7.9 (5.6) to 8.7 (5.4) years, EDSS score ranged from 3.5 (1.6) to 3.9 (1.9), and frequency of relapses ranged from 0.77 (0.94) to 0.86 (0.89) in the preceding year. Compared with the fingolimod group (769 [30.0%]), AHSCT (144 [86.2%]) was associated with fewer relapses (ARR: mean [SD], 0.09 [0.30] vs 0.20 [0.44]), similar risk of disability worsening (hazard ratio [HR], 1.70; 95% CI, 0.91-3.17), and higher chance of disability improvement (HR, 2.70; 95% CI, 1.71-4.26) over 5 years. Compared with natalizumab (730 [49.0%]), AHSCT (146 [87.4%]) was associated with marginally lower ARR (mean [SD], 0.08 [0.31] vs 0.10 [0.34]), similar risk of disability worsening (HR, 1.06; 95% CI, 0.54-2.09), and higher chance of disability improvement (HR, 2.68; 95% CI, 1.72-4.18) over 5 years. AHSCT (110 [65.9%]) and ocrelizumab (343 [49.0%]) were associated with similar ARR (mean [SD], 0.09 [0.34] vs 0.06 [0.32]), disability worsening (HR, 1.77; 95% CI, 0.61-5.08), and disability improvement (HR, 1.37; 95% CI, 0.66-2.82) over 3 years. AHSCT-related mortality occurred in 1 of 159 patients (0.6%). Conclusion: In this study, the association of AHSCT with preventing relapses and facilitating recovery from disability was considerably superior to fingolimod and marginally superior to natalizumab. This study did not find evidence for difference in the effectiveness of AHSCT and ocrelizumab over a shorter available follow-up time

Delay from treatment start to full effect of immunotherapies for multiple sclerosis

In multiple sclerosis, treatment start or switch is prompted by evidence of disease activity. Whilst immunomodulatory therapies reduce disease activity, the time required to attain maximal effect is unclear. In this study we aimed to develop a method that allows identification of the time to manifest fully and clinically the effect of multiple sclerosis treatments ('therapeutic lag') on clinical disease activity represented by relapses and progression-of-disability events. Data from two multiple sclerosis registries, MSBase (multinational) and OFSEP (French), were used. Patients diagnosed with multiple sclerosis, minimum 1-year exposure to treatment, minimum 3-year pretreatment follow-up and yearly review were included in the analysis. For analysis of disability progression, all events in the subsequent 5-year period were included. Density curves, representing incidence of relapses and 6-month confirmed progression events, were separately constructed for each sufficiently represented therapy. Monte Carlo simulations were performed to identify the first local minimum of the first derivative after treatment start; this point represented the point of stabilization of treatment effect, after the maximum treatment effect was observed. The method was developed in a discovery cohort (MSBase), and externally validated in a separate, non-overlapping cohort (OFSEP). A merged MSBase-OFSEP cohort was used for all subsequent analyses. Annualized relapse rates were compared in the time before treatment start and after the stabilization of treatment effect following commencement of each therapy. We identified 11 180 eligible treatment epochs for analysis of relapses and 4088 treatment epochs for disability progression. External validation was performed in four therapies, with no significant difference in the bootstrapped mean differences in therapeutic lag duration between registries. The duration of therapeutic lag for relapses was calculated for 10 therapies and ranged between 12 and 30 weeks. The duration of therapeutic lag for disability progression was calculated for seven therapies and ranged between 30 and 70 weeks. Significant differences in the pre- versus post-treatment annualized relapse rate were present for all therapies apart from intramuscular interferon beta-1a. In conclusion we have developed, and externally validated, a method to objectively quantify the duration of therapeutic lag on relapses and disability progression in different therapies in patients more than 3 years from multiple sclerosis onset. Objectively defined periods of expected therapeutic lag allows insights into the evaluation of treatment response in randomized clinical trials and may guide clinical decision-making in patients who experience early on-treatment disease activity. This method will subsequently be applied in studies that evaluate the effect of patient and disease characteristics on therapeutic lag

Delay from treatment start to full effect of immunotherapies for multiple sclerosis

International audienceIn multiple sclerosis, treatment start or switch is prompted by evidence of disease activity. Whilst immunomodulatory therapies reduce disease activity, the time required to attain maximal effect is unclear. In this study we aimed to develop a method that allows identification of the time to manifest fully and clinically the effect of multiple sclerosis treatments (‘therapeutic lag’) on clinical disease activity represented by relapses and progression-of-disability events. Data from two multiple sclerosis registries, MSBase (multinational) and OFSEP (French), were used. Patients diagnosed with multiple sclerosis, minimum 1-year exposure to treatment, minimum 3-year pretreatment follow-up and yearly review were included in the analysis. For analysis of disability progression, all events in the subsequent 5-year period were included. Density curves, representing incidence of relapses and 6-month confirmed progression events, were separately constructed for each sufficiently represented therapy. Monte Carlo simulations were performed to identify the first local minimum of the first derivative after treatment start; this point represented the point of stabilization of treatment effect, after the maximum treatment effect was observed. The method was developed in a discovery cohort (MSBase), and externally validated in a separate, non-overlapping cohort (OFSEP). A merged MSBase-OFSEP cohort was used for all subsequent analyses. Annualized relapse rates were compared in the time before treatment start and after the stabilization of treatment effect following commencement of each therapy. We identified 11 180 eligible treatment epochs for analysis of relapses and 4088 treatment epochs for disability progression. External validation was performed in four therapies, with no significant difference in the bootstrapped mean differences in therapeutic lag duration between registries. The duration of therapeutic lag for relapses was calculated for 10 therapies and ranged between 12 and 30 weeks. The duration of therapeutic lag for disability progression was calculated for seven therapies and ranged between 30 and 70 weeks. Significant differences in the pre- versus post-treatment annualized relapse rate were present for all therapies apart from intramuscular interferon beta-1a. In conclusion we have developed, and externally validated, a method to objectively quantify the duration of therapeutic lag on relapses and disability progression in different therapies in patients more than 3 years from multiple sclerosis onset. Objectively defined periods of expected therapeutic lag allows insights into the evaluation of treatment response in randomized clinical trials and may guide clinical decision-making in patients who experience early on-treatment disease activity. This method will subsequently be applied in studies that evaluate the effect of patient and disease characteristics on therapeutic lag

Comparative effectiveness of autologous hematopoietic stem cell transplant vs fingolimod, natalizumab, and ocrelizumab in highly active relapsing-remitting multiple sclerosis

Importance Autologous hematopoietic stem cell transplant (AHSCT) is available for treatment of highly active multiple sclerosis (MS).Objective To compare the effectiveness of AHSCT vs fingolimod, natalizumab, and ocrelizumab in relapsing-remitting MS by emulating pairwise trials.Design, Setting, and Participants This comparative treatment effectiveness study included 6 specialist MS centers with AHSCT programs and international MSBase registry between 2006 and 2021. The study included patients with relapsing-remitting MS treated with AHSCT, fingolimod, natalizumab, or ocrelizumab with 2 or more years study follow-up including 2 or more disability assessments. Patients were matched on a propensity score derived from clinical and demographic characteristics.Exposure AHSCT vs fingolimod, natalizumab, or ocrelizumab.Main outcomes Pairwise-censored groups were compared on annualized relapse rates (ARR) and freedom from relapses and 6-month confirmed Expanded Disability Status Scale (EDSS) score worsening and improvement.Results Of 4915 individuals, 167 were treated with AHSCT; 2558, fingolimod; 1490, natalizumab; and 700, ocrelizumab. The prematch AHSCT cohort was younger and with greater disability than the fingolimod, natalizumab, and ocrelizumab cohorts; the matched groups were closely aligned. The proportion of women ranged from 65% to 70%, and the mean (SD) age ranged from 35.3 (9.4) to 37.1 (10.6) years. The mean (SD) disease duration ranged from 7.9 (5.6) to 8.7 (5.4) years, EDSS score ranged from 3.5 (1.6) to 3.9 (1.9), and frequency of relapses ranged from 0.77 (0.94) to 0.86 (0.89) in the preceding year. Compared with the fingolimod group (769 [30.0%]), AHSCT (144 [86.2%]) was associated with fewer relapses (ARR: mean [SD], 0.09 [0.30] vs 0.20 [0.44]), similar risk of disability worsening (hazard ratio [HR], 1.70; 95% CI, 0.91-3.17), and higher chance of disability improvement (HR, 2.70; 95% CI, 1.71-4.26) over 5 years. Compared with natalizumab (730 [49.0%]), AHSCT (146 [87.4%]) was associated with marginally lower ARR (mean [SD], 0.08 [0.31] vs 0.10 [0.34]), similar risk of disability worsening (HR, 1.06; 95% CI, 0.54-2.09), and higher chance of disability improvement (HR, 2.68; 95% CI, 1.72-4.18) over 5 years. AHSCT (110 [65.9%]) and ocrelizumab (343 [49.0%]) were associated with similar ARR (mean [SD], 0.09 [0.34] vs 0.06 [0.32]), disability worsening (HR, 1.77; 95% CI, 0.61-5.08), and disability improvement (HR, 1.37; 95% CI, 0.66-2.82) over 3 years. AHSCT-related mortality occurred in 1 of 159 patients (0.6%).Conclusion In this study, the association of AHSCT with preventing relapses and facilitating recovery from disability was considerably superior to fingolimod and marginally superior to natalizumab. This study did not find evidence for difference in the effectiveness of AHSCT and ocrelizumab over a shorter available follow-up time