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Predictors of the 10-year direct costs for treating multiple sclerosis.

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Title: Predictors of the 10-year direct costs for treating multiple sclerosis.
Authors: Moccia, M., Palladino, R., Lanzillo, R., Triassi, M., Brescia Morra, V.
Source: Acta Neurologica Scandinavica. May2017, Vol. 135 Issue 5, p522-528. 7p.
Subjects: Multiple sclerosis treatment, Medical care costs, Disease relapse, Regression analysis, Disease management, Economics
Abstract: Objectives Disease-modifying treatments ( DMTs) constitute the largest direct medical cost for multiple sclerosis ( MS). This study aims at investigating predictors of the 10-year economic burden for DMT administration and management. Materials and Methods This study included 537 newly diagnosed, drug naïve relapsing-remitting MS ( RRMS) patients, followed up for 10.1±3.3 years. Costs for DMT administration and management were calculated, and referred to each year of observation (annual costs). Possible predictors of disease evolution were categorized into early predictors (age, gender, disease duration, baseline expanded disability status scale ( EDSS), 1-point EDSS progression within 2 years, and annualized relapse rate - ARR- within 2 years), and long-term predictors (reaching of EDSS 4.0, conversion to secondary progressive - SP-, ARR, number of DMTs, follow-up duration). Association between predictors and study outcome was explored using mixed-effects log-linear regression models. Results A 1-point higher EDSS at diagnosis was associated with 13.21% increase in the annual costs (95% CI=4.16-23.04%). Each additional year of age at diagnosis was associated with a 0.74% decrease in the annual costs (95% CI=−1.43 to-0.04%). Female gender was associated with a 12.43% decrease in the annual costs (95% CI=−22.61 to-0.93%). Converting to SP was associated with a 14.26% decrease in the annual costs (95% CI=−14.26 to-2.94%). Each additional year of follow-up was associated with a 3.05% decrease in the annual costs (95% CI=−4.51 to-1.57%). Conclusions An estimate of the 10-year costs associated with DMT administration and management can be calculated by analyzing different factors, and might be of particular interest for planning resources needed for treating people with MS. [ABSTRACT FROM AUTHOR]
Copyright of Acta Neurologica Scandinavica is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Predictors of the 10-year direct costs for treating multiple sclerosis.
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  Data: <searchLink fieldCode="AR" term="%22Moccia%2C+M%2E%22">Moccia, M.</searchLink><br /><searchLink fieldCode="AR" term="%22Palladino%2C+R%2E%22">Palladino, R.</searchLink><br /><searchLink fieldCode="AR" term="%22Lanzillo%2C+R%2E%22">Lanzillo, R.</searchLink><br /><searchLink fieldCode="AR" term="%22Triassi%2C+M%2E%22">Triassi, M.</searchLink><br /><searchLink fieldCode="AR" term="%22Brescia+Morra%2C+V%2E%22">Brescia Morra, V.</searchLink>
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  Data: <searchLink fieldCode="JN" term="%22Acta+Neurologica+Scandinavica%22">Acta Neurologica Scandinavica</searchLink>. May2017, Vol. 135 Issue 5, p522-528. 7p.
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  Data: <searchLink fieldCode="DE" term="%22Multiple+sclerosis+treatment%22">Multiple sclerosis treatment</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+care+costs%22">Medical care costs</searchLink><br /><searchLink fieldCode="DE" term="%22Disease+relapse%22">Disease relapse</searchLink><br /><searchLink fieldCode="DE" term="%22Regression+analysis%22">Regression analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Disease+management%22">Disease management</searchLink><br /><searchLink fieldCode="DE" term="%22Economics%22">Economics</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Objectives Disease-modifying treatments ( DMTs) constitute the largest direct medical cost for multiple sclerosis ( MS). This study aims at investigating predictors of the 10-year economic burden for DMT administration and management. Materials and Methods This study included 537 newly diagnosed, drug naïve relapsing-remitting MS ( RRMS) patients, followed up for 10.1±3.3 years. Costs for DMT administration and management were calculated, and referred to each year of observation (annual costs). Possible predictors of disease evolution were categorized into early predictors (age, gender, disease duration, baseline expanded disability status scale ( EDSS), 1-point EDSS progression within 2 years, and annualized relapse rate - ARR- within 2 years), and long-term predictors (reaching of EDSS 4.0, conversion to secondary progressive - SP-, ARR, number of DMTs, follow-up duration). Association between predictors and study outcome was explored using mixed-effects log-linear regression models. Results A 1-point higher EDSS at diagnosis was associated with 13.21% increase in the annual costs (95% CI=4.16-23.04%). Each additional year of age at diagnosis was associated with a 0.74% decrease in the annual costs (95% CI=−1.43 to-0.04%). Female gender was associated with a 12.43% decrease in the annual costs (95% CI=−22.61 to-0.93%). Converting to SP was associated with a 14.26% decrease in the annual costs (95% CI=−14.26 to-2.94%). Each additional year of follow-up was associated with a 3.05% decrease in the annual costs (95% CI=−4.51 to-1.57%). Conclusions An estimate of the 10-year costs associated with DMT administration and management can be calculated by analyzing different factors, and might be of particular interest for planning resources needed for treating people with MS. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Acta Neurologica Scandinavica is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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