The 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.

Participants 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.

Baseline 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 (>0.8) whereas sleep, spasticity pain, vision and bladder had an effect size >0.5. 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).

A 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.