Prevalence, treatment and correlates of depression in multiple sclerosis

BackgroundThe prevalence of depression in Multiple Sclerosis (MS) is often assessed by administering patient reported outcome measures (PROMs) examining depressive symptomatology to population cohorts; a recent review summarised 12 such studies, eight of which used the Hospital Anxiety and Depression Scale-Depression (HADS-D). In clinical practice, depression is diagnosed by an individual structured clinical interview; diagnosis often leads to treatment options including antidepressant medication. It follows that an MS population will include those whose current depressive symptoms meet threshold for depression diagnosis, plus those who previously met diagnostic criteria for depression and have been treated such that depressive symptoms have improved below that threshold. We examined a large MS population to establish a multi-attribute estimate of depression, taking into account probable depression on HADS-D, as well as anti-depressant medication use and co-morbidity data reporting current treatment for depression. We then studied associations with demographic and health status measures and the trajectories of depressive symptoms over time.MethodsParticipants were recruited into the UK-wide Trajectories of Outcome in Neurological Conditions-MS (TONiC-MS) study, with demographic and disease data from clinical records, PROMs collected at intervals of at least 9 months, as well as co-morbidities and medication. Interval level conversions of PROM data followed Rasch analysis. Logistic regression examined associations of demographic characteristics and symptoms with depression. Finally, a group-based trajectory model was applied to those with depression.ResultsBaseline data in 5633 participants showed the prevalence of depression to be 25.3% (CI: 24.2-26.5). There were significant differences in prevalence by MS subtype: relapsing 23.2% (CI: 21.8- 24.5), primary progressive 25.8% (CI: 22.5-29.3), secondary progressive 31.5% (CI: 29.0-34.0); disability: EDSS 0-4 19.2% (CI: 17.8-20.6), EDSS ≥4.5 31.9% (CI: 30.2-33.6); and age: 42-57 years 27.7% (CI: 26.0-29.3), above or below this range 23.1% (CI: 21.6-24.7). Fatigue, disability, self-efficacy and self esteem correlated with depression with a large effect size (>.8) whereas sleep, spasticity pain, vision and bladder had an effect size >.5. The logistic regression model (N=4938) correctly classified 80% with 93% specificity: risk of depression was increased with disability, fatigue, anxiety, more comorbidities or current smoking. Higher self-efficacy or self esteem and marriage reduced depression. Trajectory analysis of depressive symptoms over 40 months in those with depression (N=1096) showed three groups: 19.1% with low symptoms, 49.2% with greater symptoms between the threshold of possible and probable depression, and 31.7% with high depressive symptoms. 29.9% (CI: 27.6-32.3) of depressed subjects were untreated, conversely of those treated, 26.1% still had a symptom level consistent with a probable case (CI: 23.5-28.9).ConclusionA multi-attribute estimate of depression in MS is essential because using only screening questionnaires, diagnoses or antidepressant medication all under-estimate the true prevalence. Depression affects 25.3% of those with MS, almost half of those with depression were either untreated or still had symptoms indicating probable depression despite treatment. Services for depression in MS must be pro-active and flexible, recognising the heterogeneity of outcomes and reaching out to those with ongoing symptoms

Mobility and Cognition in Seniors. Report from the 2008 Institute of Aging (CIHR) Mobility and Cognition Workshop

Background The annual Scientific Meeting of the Canadian Association on Gerontology was held on October 24 and 25, 2008 in London, Ontario. Prior to the annual meeting, mobility and cognition experts met on October 23, 2008 to engage in a pre-conference workshop. Methods Discussions during the workshop addressed novel areas of research and knowledge and research gaps pertaining to the interaction between mobility and cognition in seniors. Results Workshop presenters moved from the neuromuscular, biomechanics, and neurology of gait impairments, and falls through the role of cognition and mood on mobility regulation to the whole person in the environment. Research gaps were identified. Conclusions Despite a consensus that mobility and cognition are increasingly correlated as people age, several gaps in our understanding of mechanisms and how to assess the interaction were recognized. The gaps originally identified in 2008 are still pertinent today. Common and standardized assessments for “mobility and cognition” are still not in place in current practice. Interventions that target mobility and cognitive decline as a single entity are still lacking

Can comprehensive specialised end-of-life care be provided at home? Lessons from a study of an innovative consultant-led community service in the UK

The Midhurst Macmillan Specialist Palliative Care Service (MMSPCS) is a UK, medical consultant-led, multidisciplinary team aiming to provide round-the-clock advice and care, including specialist interventions, in the home, community hospitals and care homes. Of 389 referrals in 2010/11, about 85% were for cancer, from a population of about 155 000. Using a mixed method approach, the evaluation comprised: a retrospective analysis of secondary-care use in the last year of life; financial evaluation of the MMSPCS using an Activity Based Costing approach; qualitative interviews with patients, carers, health and social care staff and MMSPCS staff and volunteers; a postal survey of General Practices; and a postal survey of bereaved caregivers using the MMSPCS. The mean cost is about 3000 GBP (3461 EUR) per patient with mean cost of interventions for cancer patients in the last year of life 1900 GBP (2192 EUR). Post-referral, overall costs to the system are similar for MMSPCS and hospice-led models; however, earlier referral avoided around 20% of total costs in the last year of life. Patients and carers reported positive experiences of support, linked to the flexible way the service worked. Seventy-one per cent of patients died at home. This model may have application elsewhere

The Oncology Data Network (ODN): Methodology, Challenges, and Achievements

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