Social consequences of multiple sclerosis: clinical and demographic predictors - a historical prospective cohort study.
BACKGROUND
Time to disability pension is one of the endpoints to be used to determine the prognosis of multiple sclerosis (MS) in prospective studies.


OBJECTIVE
To assess the time to cessation of work and receiving disability pension in MS, and how it may depend on gender, type of work and age and symptom at onset.


METHODS
A total of 2240 Danes with onset of definite/probable MS 1980-1989, identified from the Danish MS-Registry, were included. Information on social endpoints was retrieved from Statistics Denmark. Cox regression analyses were used with onset as starting point.


RESULTS
Afferent onset symptoms [hazard ratio (HR 0.57)] and non-physical type of work (HR 0.70) were favourable prognostic factors compared with high age at onset, physical work and efferent symptoms at onset. The mean time to disability pension was 13 years for patients with afferent/brainstem onset symptom but 8.7 years for those with efferent onset symptoms (P < 0.0001). The effect of onset symptom was reduced and the effect of sex became significant when all covariates and age at onset were included in multivariate Cox regression.


CONCLUSIONS
Onset age, type of onset symptom and work are robust predictors of disability pension in MS. Disability pension proves to be a reliable milestone in estimation of the prognosis of MS.
Pfleger
C C H
CC
Department of Neurology, Aarhus University Hospital in Aalborg, Aalborg, Denmark. ccp@rn.dk
Flachs
E M
EM
Koch-Henriksen
N
N
D017668:Age of Onset; D015331:Cohort Studies; D003710:Demography; D003718:Denmark; D004185:Disability Evaluation; D006233:Persons with Disabilities / Q000706:statistics & numerical data; D018450:Disease Progression; D005260:Female; D006801:Humans; D008297:Male; D009103:Multiple Sclerosis / Q000191:economics* / Q000453:epidemiology*; D010415:Pensions / Q000706:statistics & numerical data*; D011237:Predictive Value of Tests; D011336:Probability; D011379:Prognosis; D016016:Proportional Hazards Models; D012179:Retirement / Q000706:statistics & numerical data*