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General Hospital Psychiatry 37 (2015) 507–512

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General Hospital Psychiatry

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Depression — a common disorder across a broad spectrum of

neurological conditions: a cross-sectional nationally representative survey☆

Andrew G.M. Bulloch, Ph.D. a,b,d,⁎, Kirsten M. Fiest, Ph.D. a,d, Jeanne V.A. Williams, M.Sc. a, Dina H. Lavorato, M.Sc. a, Sandra A. Berzins, Ph.D. a,d, Nathalie Jetté, M.D. a,c, Tamara M. Pringsheim, M.D. c,d, Scott B. Patten, M.D., Ph.D. a,b,d,⁎ a Department of Community Health Sciences, University of Calgary, Canada b Department of Psychiatry, University of Calgary, Canada c Department of Clinical Neurosciences, Hotchkiss Brain Institute and Institute for Public Health, University of Calgary, Canada d Mathison Centre for Mental Health Research & Education, Hotchkiss Brain Institute, University of Calgary, Canada

a b s t r a c ta r t i c l e i n f o

☆ Disclaimer: This research and analysis were based on the opinions expressed do not represent the views of Stat ⁎ Corresponding author. Department of Community He

Hospital Drive NW, Calgary, Canada, T2N 4Z6. Tel.: +1 210-8840.

E-mail address: [email protected] (A.G.M. Bulloch).

http://dx.doi.org/10.1016/j.genhosppsych.2015.06.007 0163-8343/© 2015 Elsevier Inc. All rights reserved.

Article history:

Received 4 March 2015 Revised 19 May 2015 Accepted 8 June 2015

Keywords: Neurological conditions Depressive disorder Depression rating scales Prevalence Community-based study

Objective: To estimate the prevalence of depression across a range of neurological conditions in a nationally rep- resentative sample. Methods: The data source was the Survey of Living with Neurological Conditions in Canada (SLNCC), which ac- crued its sample by selecting participants from the Canadian Community Health Survey. The point prevalence of depression was estimated by assessment of depressive symptoms with the Patient Health Questionnaire, Brief (Patient Health Questionnaire, 9-item). Results: A total of n=4408 participated in the SLNCC. The highest point prevalence of depression (N30%) was seen in those with traumatic brain injury and brain/spinal cord tumors. Depression was also highly prevalent (18–28%) in those with (listed from highest to lowest) Alzheimer’s disease/dementia, dystonia, multiple sclero- sis, Parkinson’s disease, stroke, migraine, epilepsy and spina bifida. The odds ratios for depression, with the ref-

erent group being the general population, were significant (fromhighest to lowest) formigraine, traumatic brain injury, stroke, dystonia and epilepsy. Conclusions: All neurological conditions included in this study are associated with an elevated prevalence of de- pression in community populations. The conditions with the highest prevalence are traumatic brain injury and brain/spinal cord tumors.

© 2015 Elsevier Inc. All rights reserved.

1. Introduction

Major depression is frequently comorbid with a diverse range of chronic medical conditions. Examples of such conditions include pain [1], diabetes [2], heart disease [3], rheumatoid arthritis [4] and Parkinson’s disease [5]. The relationship between depression and chronic conditions is reciprocal. Although depression is often assumed to be a consequence of chronic conditions, depression can increase the risk of a number of chronic conditions including heart disease, arthritis, asthma, back pain, bronchitis, hypertension and migraines [6]. Further- more, higher levels of depression predict faster progression of

data from Statistics Canada, but istics Canada. alth Sciences, TRW 4D67, 3280 -403-220-4586; fax: +1-403-

Parkinson’s disease [7]. Accurate assessment of depression is therefore pertinent both before and after the onset of chronic conditions.

Depression is known to be elevated in of neurological conditions in- cluding epilepsy, multiple sclerosis (MS), Parkinson’s disease and trau- matic brain injury, a finding that has been substantiated by recent reviews [8–11]. However, these systematic reviews have identified con- siderable heterogeneity in the estimates. Such heterogeneity is likely due to different sampling and measurement procedures. For example, different depression assessment tools have been applied, and many of the samples are from clinical populations (see Discussion). This consid- eration leads to uncertainty about the relative prevalence in different neurological populations. Recently, a study called the Survey of Living with Neurological Conditions in Canada (SLNCC) was launched. This study explores a broad range of experiences and outcomes addressing Canadians’ experiences with chronic neurological conditions in a population-based sample. Among a number of important outcomes (i.e., the economic impact of having a neurological condition), the study provides an opportunity to estimate depression in persons with a range of neurological conditions with consistent sampling and

508 A.G.M. Bulloch et al. / General Hospital Psychiatry 37 (2015) 507–512

measurement procedures. This is unique in that this is, to our knowledge, the first time estimates of depression can be generated and compared across many neurological conditions and in a community-based cohort, using a validated depression questionnaire, the Patient Health Question- naire, Brief [Patient Health Questionnaire, 9-item (PHQ-9)] [12].

The PHQ-9 is a commonly used instrument for assessing depressive symptoms and can be scored both categorically and dimensionally. This instrumentmaps directly onto the depressive symptomsof theDiagnos- tic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) that has been validated in a number of settings including the general population [13,14], in patients with epilepsy [15] and in patients with MS [16]. Due to overlap of symptoms between depression and neuro- logical disorders, concerns have been expressed about possible contam- ination of responses to the PHQ-9when used in neurological conditions. For example, for patients with Parkinson’s disorder, the Geriatric De- pression Scale-15 showed a higher sensitivity than the PHQ-9 in assessing depressive symptoms due to its reduced focus on somatic symptoms [17]. In contrast, a study of patients with MS found that ex- clusion of the fatigue and concentration items on the PHQ-9 did not im- pact prevalence estimates [18]. Taken together, these studies suggest that overlap of symptoms may impact depression estimates in some, but not all, neurological conditions.

The importance of understanding the prevalence of depression in neurological disorders has several aspects. For example, depression can exacerbate neurological symptoms [19,20], reduce treatment ad- herence [21,22], erode quality of life [23–26] and interfere with self- management, leading to accelerated disease progression [20]. Of great concern is that depression contributes significantly to elevated suicide rates in neurological patients [27–30].

The objective of the current study was to establish the prevalence of depression across a range of neurological conditions in the general pop- ulation, using the same sampling and assessment methods and using data drawn from the SLNCC.

2. Methods

2.1. Surveys

The SLNCC is a cross-sectional study that adopted a sampling strate- gy linked to a large general health survey called the Canadian Commu- nity Health Survey (CCHS) [31]. The CCHS selected a probability sample of approximately 286,000 residents from 130,000 households in 2010–2011 and used a complex multistage sampling procedure to ob- tain a representative sample of the Canadian population. First, geo- graphical clusters are selected, then households are selected within the clusters and finally one respondent per household is selected. The CCHS includes questions about professionally diagnosed long-term (at least 6 months) medical conditions, as well as a measure of major de- pression (see below). In order to support the SLNCC, the CCHS interview included questions about 18 neurological conditions and participants with affirmative responses were invited to participate in the SLNCC. Survey respondents were also asked whether there were household members with one or more of the same list of conditions, and those identified were also asked to participate. In order to produce the most reliable estimates possible, every household that contained at least one personwith a neurological condition, except for the twomost prev- alent conditions (stroke and migraine), was selected. Then a sample of households containing only persons reported to have either the effects of stroke or migraine headaches was also selected. It was possible that more than one person in a household reported being diagnosedwith a neurolog- ical condition, and it was also possible that some respondents had more than one condition. However, only one person per householdwas selected, giving a higher chance of being selected (i.e., oversampling) to thosewith a more rare condition than to those with stroke or migraine headaches. Oversampling was required to yield sufficiently large samples of those with rare conditions so that reasonably precise estimates could be made.

Individuals who reported having multiple neurological conditions were also given a higher chance of being selected. Exclusions included living in the three territories (Nunavut, Northwest Territories and the Yukon), living on an aboriginal reserve or settlement, being a full-time member of the Canadian Armed Forces, living in certain remote regions (Région du Nunavik and Région des Terres-Cries-de-la-Baie-James) and residing in an institution. In total, these exclusion apply to approximately 3% of the Canadian population 15 years of age and older in the 10 provinces. Data collection interviews for the SLNCC were conducted between September to October 2011 and February to March 2012 and included a total of 8200 (raw sample size) people 15 years of age and older living with neurological conditions. The estimated response rate for the 2011 SLNCCwas 81.6% [31]. Subjects found to be dead, to havemoved to an in- stitution, to havemoved outside Canada or to not actually have the condi- tion reported were classified as “out of scope” and were not included in the calculation of this response rate.

2.1.1. Selected neurological conditions Eighteen neurological conditions were included in the SLNCC, but

some of these could not be included in the analysis reported here due to limited sample size. The excluded conditionswere cerebral palsy, hy- drocephalus (both of which mainly affect young children), muscular dystrophy, Tourette’s syndrome, amyotrophic lateral sclerosis and Huntington’s disease.We excluded spinal cord injurywhose prevalence (0.4%) is likely overestimated perhaps due to inclusion of other spinal conditions such as lower back pain, e.g., see estimate of 0.2% [32]. We also excluded brain injury, which is an imprecise term that respondents would likely include both traumatic brain injury and stroke. The 10 con- ditions that were included were migraine, MS, epilepsy, dystonia, Parkinson’s disease, spina bifida, Alzheimer’s disease and related demen- tia, stroke, brain/spinal cord tumor and traumatic brain injury (Table 1).

The SLNCC included the PHQ-9, a widely scale use to assess depres- sive symptoms [12–14]. This scale asks questions about depressive symptoms in the preceding 2 weeks (i.e., point prevalence) and func- tions as a symptom severity measure; however, it is closely aligned to the DSM-IV definition of major depression. For purposes of prevalence estimation, a cut point of 10 on the PHQ-9 is usually interpreted as indi- cating the presence of depression, scores of 8–11 all showing similar sensitivity and specificity [13]. A metaanalysis showed that, at a cut point of 10, the pooled sensitivity and specificity of the PHQ-9 for de- tecting depression is 85% and 89%, respectively [13]. It is important to note that we cannot conclude that patients have major depression per se without a diagnostic interview; rather, we use the term “depression” to indicate the presence of significant depressive symptoms. The de- pression data and other data collection elements were collected by computer-assisted telephone interviewing after extensive qualitative testing by Statistics Canada’s Questionnaire Design Resource Centre.

Other data collected in the SLNCC included age, sex, province, pro- vincial health care number, preferred language, age at first diagnosis, reasons the neurological disorder is better, years livedwith the disorder, education, formal assistance received, general health, income, health utility index, informal assistance received, medication use for neurolog- ical conditions (not depression), out-of pocket expenses, restriction of activities, stigma, social support and work activities.

2.1.2. Statistical analysis The analysis reported here consisted of estimating the overall prev-

alence of depression with 95% confidence intervals (95% CI) in those with the 10 neurological conditions noted above. In order to account for design effects (includingundersampling of stroke andmigraine), ini- tial samplingweights from the CCHSwere refined for the SLNCC by Sta- tistics Canada and provided to researchers as a set of bootstrapweights. Employment of a bootstrapping procedure results in estimates weight- ed to the general household population and that provide an accurate es- timate of variance.

Table 1 Prevalence of conditions in the CCHS 2010/2011 and demographics of the SLNCC sample.

Condition Prevalencea, % Men, %, 95% CI, N=2000b Women, %, 95% CI, N=2400b Age mean, 95% CI (range)

Migraine 8.3 18.0 82.0 43.4 13.0–22.7 77.3–86.7 41.6–45.2 (15–90)

Stroke 1.0 51.6 48.4 66.0 44.6–58.6 41.4–55.4 63.8–68.1 (17–98)

Alzheimer’s disease/dementiac 0.6 43.4 56.6 78.7 b

36.5–50.2 49.8–63.5 77.6–79.9 (45–98) Epilepsy 0.4 41.0 59.0 45.3

33.2–48.7 51.3–66.8 42.3–48.3 (15–91) Brain injury 0.4 54.7 45.3 47.2

44.9–64.5 35.5–55.1 43.4–51.0 (16–96) MS 0.3 26.7 73.3 51.8

19.5–33.8 66.2–80.5 49.3–54.3 (15–95) Parkinson’s disease 0.2 65.2 34.8 72.8

56.5–73.9 26.1–43.5 71.0–74.6 (43–94) Spina bifida 0.1 38.3 61.7 38.8

24.2–52.3 47.7–75.8 33.7–43.9 (16–79) Brain/spinal cord tumor 0.1 38.2 61.8 51.6

27.2–49.3 50.7–72.8 47.9–55.2 (21–89) Dystonia b0.1 42.4 57.6 54.5

22.8–62.1 37.9–77.2 47.1–62.0 (17–94) Traumatic brain injuryd N/A 53.1 46.9 46.1

42.4–63.8 36.2–57.6 42.0–50.2 (16–96)

N.B.: Estimates reflect those with at least one neurological disorder (not independent). a Sample size reflects added household members with a neurological condition. Statistics Canada— cansim reference – table 105-1300 “Neurological conditions, by age group and sex,

household population aged 0 and over, 2010/2011”, data from the CCHS. b Rounded estimates. c Among those aged 35 years and older. d Not documented as a separate group from brain injury in Statistics Canada table 105-1300.

Table 2 Prevalence of depression by neurological condition.

Condition Prevalence 95% CI

Traumatic brain injury 33.2 24.4–42.0 Brain/spinal cord tumor 32.0 20.8–43.1 Alzheimer’s disease/dementia 28.2 16.6–39.8 Dystonia 27.7 12.9–42.6 MS 26.0 18.9–33.0 Parkinson’s disease 23.4 14.9–31.8 Stroke 22.7 16.9–28.6 Migraine 21.9 16.5–27.3 Epilepsy 21.3 13.3–29.4 Spina bifida 18.6 9.1–28.0

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Additionally, we estimated the odds ratios (ORs) with 95% CI of de- pression for each condition with the baseline group being the general population of the CCHS sample. We also use logistic regression models to produce age and sex adjusted ORs. Since the SLNCC did not include a control group, we restricted this analysis to the CCHS participants that did not report a neurological condition since they were adminis- tered the short form for major depression [Composite International Di- agnostic Interview Short Form for Major Depression (CIDI-SFMD)] [33]. The CIDI-SFMD was not included in all provinces, but this analysis still included 35,544 participants. The CIDI-SFMD has sensitivity and speci- ficity for major depression of 90% and 94% when compared to the CIDI/Diagnostic and Statistical Manual of Mental Disorders, Revised Third Edition diagnoses [33]. This research was approved by the ethics review board of the University of Calgary.

3. Results

Table 1 shows the prevalence of the 10 neurological conditions in the general population as estimated from full CCHS 2010/2011 survey [34] in column 2. As explained in the methods section, oversampling of those with rare conditions (i.e., excluding migraine or stroke) meant that general population estimates could not be extrapolated from the SLNCC sample. The other columns of Table 1 show the demo- graphics of the SLNCC sample. The final sample for the SLNCC analysis included 4400 participants (out-of-scope individuals were removed as described in Discussion) and included a range from 110 to 730 individ- uals with rare conditions, which is sufficient for reasonably precise esti- mates of 95% CI values to be estimated. The SLNCC sample consisted of 45.5% men and 54.5% women with an overall mean age of 47.6 years. As judged by nonoverlap of 95% CI values, the proportion of females was higher for those with migraine, MS, epilepsy and brain/spinal cord tumors. In contrast, the majority of those with Parkinson’s disease were male. The youngest mean age was seen in migraine, epilepsy and traumatic brain injury, whereas the highest mean ages were seen in Alzheimer’s disease/dementia and Parkinson’s disease.

Before estimating depression prevalence, we examined the internal consistency of the 9 individual PHQ-9 ratings in each neurological con- dition. The internal consistency was very good for each condition

(Cronbach’s alpha≥0.82 for every condition). We also examined item- total correlations for each of the 9 PHQ-9 items due to concerns that common symptoms of neurological conditions (e.g., fatigue and cogni- tive difficulties) might correlate poorly with the other items. However, the PHQ-9 items for fatigue and cognitive difficulties consistently displayed item-total correlations between 0.50 and 0.70, comparable to other items. The only item with low item-total correlation was the suicidal ideation item, for which most correlations were between 0.40 and 0.50 with that for epilepsy being the lowest at 0.38.

Complete PHQ-9 ratings were provided by 77.9% of the SLNCC sam- ple. The overall prevalence of depression estimate for all 18 conditions was 25.2% (95% CI: 23.8–26.7). Among the 10 selected conditions, the prevalence was 25.1% (95% CI: 23.5–26.7). The estimated prevalence of depression in the 10 neurological conditions is ranked from highest to lowest in Table 2. The highest prevalence estimates (N30%) were ob- served for traumatic brain injury and brain/spinal cord tumors, respec- tively. All the other 8 conditions had prevalence estimates of N18%.

In the next step of the analysis, we estimated both crude as well as age and sex adjusted ORs of depression in each conditionwith the base- line group being the general population of the CCHS sample. As noted above, the measure of depression in this part of the analysis was the CIDI-SFMD. Significantly elevatedORswere observed formigraine, trau- matic brain injury, stroke and epilepsy (Table 3). The adjusted ORswere not significantly different from the crude ones; i.e., no evidence of

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confounding by age or sex was evident. In some cases (e.g., stroke), the adjusted point estimates differed from the crude ones, but the 95% CI values of the two estimates overlapped. The ORs for the other 6 neurolog- ical conditions are all elevated, but the 95% CI values include 1.0 so these data do not provide evidence against the null hypothesis (i.e., that the odds of depression are not different between those with and without a neurological condition). Due to the smaller number of subjects in this part of the analysis, some of the estimates were imprecise.

4. Discussion

The estimated prevalence of depression from this study in those with neurological disorders ranged from 18.6% to 33.2% (Table 2). These estimates derived from the PHQ-9 using the cutoff scoringmeth- od are considerably higher than our previous estimate from a general population survey conducted in Calgary, Canada, i.e., 8.4% [18]. Our find- ings confirm previous data (see below) indicating a significantly elevat- ed burden of depression in people with neurological conditions but provide an overall comparative perspective for 10 conditions, some of which have received little attention in the literature. Further, we used the same depression symptom assessment scale in the same sample of the general population. When ORs were calculated, the referent group being the general population of Canada as sampled in the CCHS, signif- icantly elevated ORswere observed formigraine, traumatic brain injury, stroke, dystonia and epilepsy (Table 3).

It is important to consider our results from the general population in the context of other disease-specific studies sincemost of these are from clinical samples. For example, the PHQ-9 was used to estimate depres- sion prevalence in a clinical sample (n=173) of thosewithMS in South- ern Alberta [18]. Our estimate of 21.4% depression prevalence in this sample falls into to the range estimated in the current study (Table 2); see also Ref. [9]. In terms of epilepsy, a recent metaanalysis estimated a current or past year depression prevalence of 23.1% [8], i.e., close to our estimated point prevalence of depression in the current study. In a study of 201 patients admitted to long-term care with dementia, 19.9% of patients had depression on admission [35], within the range es- timated in the current study (Table 2).

In terms of movement disorders, two previous studies of community-based clinical populations have estimated depression, using the Beck Depression Inventory (BDI) (that assesses past week de- pression), in those with dystonia at 37% (n=83) and 30% (n=329), re- spectively [36,37]. The former study included Parkinson’s disease patients (n=354), 48% of whom were found to be depressed, a higher proportion than we observed. In another study of n=173 patients with Parkinson’s disease attending a movement disorders clinic, 30.0% were found to have major depression based on the nine symptom- based DSM-IV criteria [38], a proportion within the range of our study. A systematic review of both population and community samples using Diagnostic and Statistical Manual of Mental Disorders criteria estimated

Table 3 ORs of CIDI Short-Form depression by neurological condition.

Condition OR 95% CI OR, age/sex adjusted

95% CI, age/sex adjusted

Migraine 6.1 4.0–9.1 6.0 4.0–9.0 Traumatic brain injury 6.1 3.5–11.0 6.0 3.9–12.3 Spina bifida 3.2 1.0–10.9 3.0 0.9–10.3 Stroke 3.1 1.6–6.0 4.7 2.4–9.4 Dystonia 2.8 0.6–12.1 2.8 0.6–12.3 Epilepsy 2.6 1.4–4.8 2.7 1.4–5.0 Brain/spinal cord tumor 2.3 0.8–6.6 2.5 0.9–7.1 Alzheimer’s disease/dementia 1.8 0.4–7.5 2.7 0.6–11.4 Parkinson’s disease 1.5 0.5–4.8 2.4 0.7–7.8 MS 1.3 0.6–3.0 1.3 0.6–2.9

As assessed by the CIDI-SFMD in a subset of study participants thatwere also CCHS partic- ipants and (in the denominator) the odds of major depressive episode according to the CIDI-SFMD in CCHS participants without a neurological condition.

the point prevalence of depression in Parkinson’s disease as 19% [10], close to our point estimate.

Traumatic brain injury and stroke have received significant attention in the literature. Major depression was found in 6–77% of patients fol- lowing traumatic brain injury as assessed with a variety of methods [39]. In a longitudinal study of traumatic brain injury patients, 26% of participants were found to developmajor orminor depression between 1 and 2 years of follow-up as assessed with the PHQ-9 [40]. In terms of stroke, a review of a large number of studies of clinical samples showed that nearly 30% of stroke patients develop depression in the early or late stages after stroke [41]. These data suggest that our population esti- mates for point prevalence of depression in traumatic brain injury and stroke are reasonable.

Brain tumors have been reported to be associatedwith a range of de- pression prevalence. For example, in a study of n=77 patients assessed before brain tumor surgery in Finland, a relatively small proportion (16%) had depression according to the BDI [42]. Use of the same instru- ment in n=73 patients attending a tertiary cancer center in Canada showed 38% scoring in the depressed range using the BDI [43], and a study in the USA in a similar clinical setting found 28% of depressed pa- tients using DSM-IV criteria [44]. The latter proportions are in the range of the current Canadian study that combined brain and spinal cord tu- mors. To the best of our knowledge, no data are available regarding de- pression in patients with spinal cord tumors as a stand-alone group.

An original contribution of the current study lies in its inclusion of a rare condition (spina bifida) for which no data on depression are avail- able to the best of our knowledge. Although the point estimate for the prevalence of depression is patients with spina bifida that is the lowest of the 11 conditionswe examined, it is still more than 2-fold higher than for the general population (Table 2).

It might be expected that estimates of the prevalence of depression in the general community would be lower than those obtained from clinical samples. However, comparison of Table 2 with the estimates discussed above show that most of the estimates from clinical popula- tions fall within the 95% CI values of our estimates.

Another expectation might be that a difference in the prevalence of depressionmight be evident between disorders characterized bymove- ment impairment versus executive dysfunction. Inspection of Tables 2 and 3, however, does not support a clear demarcation between the dif- ferent neurological disorders in this regard.

An important question that arises is why individuals with such a wide range of different neurological conditions have a high prevalence of depression. Depression is a heterogeneous condition with a number of biopsychosocial determinants; however, neuroinflammation may be a common biological link between depression and a number of neu- rological conditions. The inflammatory (or cytokine) hypothesis for de- pression is increasingly recognized as an important component in the etiology of this condition [45]. A number of studies have shown that neuroinflammation increases neurodegeneration, and it has been pro- posed inflammation may be the biological mechanism that links de- mentia with depression [46]. In terms of stroke, cytokine-driven induced factors appear in the brain following ischaemic damage and in- crease the likelihood of depression [47]. Traumatic brain injury is known to be followed by an extended period of neuroinflammation, and a recent preclinical study showed that brain injury primesmicroglia that, when reactivated, are associatedwith the generation of depressive symptoms [48]. Inflammation in the brain contributes to the generation of seizures via cytokines that are released by glial cells [49], although a connection to depression has not been established to date. Beyond neu- rological conditions, it is thought that inflammation contributes to both depression and adverse cardiac outcomes in patients with coronary heart disease [50]. A complete review of the literature in this area is be- yond the scope of this paper, but this discussion puts our findings into the context of some of the current biological research in this area.

The principle strengths of this study lie in its use of a population- based cohort and assessment of depression across many neurological

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conditions using consistent sampling andmeasurement procedures;we are not aware of any comparable study. A limitation of the SLNCC is a higher than expected out-of-scope proportion within the sample. There are several reasons a person selected for the SLNCC could become out of scope including if they had died, moved to an institution or moved outside Canada. The most common reason a participant was out of scope was that the respondent reported not having any of the 18 neurological conditions of interest, contrary to the CCHS report. This group accounted for 28.7% of all resolved SLNCC cases, the apparent reason being that householdmembersmay not have had accurate infor- mation about the neurological health of other householdmembers. Due to this high number of out-of-scope cases on the SLNCC, Statistics Canada has advised against using the SLNCC for direct estimation of neurological condition prevalence. Adjusted prevalence data have, however, beenmade available by Statistics Canada after appropriate ad- justments were made; see Table 1.

A limitation of the survey is that we do not have information on treatment of depression. This may have led to underestimation of the proportions of patients that had received a depression diagnosis. Anoth- er limitation relates to the issue of symptom overlap between depres- sion and neurological disorders, as well as the potential contamination of PHQ-9 results. The literature reviewed in Introduction on this issue suggests that this may be pertinent to some, but not all, neurological disorders. An additional limitation lies in the reliance of the SLNCC data on self-report. However, as an example, in the context of epilepsy, self-reported disease status is commonly used in epidemiological stud- ies of epilepsy [51], and self-reported lifetime epilepsy is highly sensi- tive (84.2%) and specific (99.2%) when compared to medical record diagnosis [52].

5. Conclusions

In conclusion, we estimate the point prevalence of significant de- pressive symptoms in patients with neurological disorders to be in the range from 33.2% to 18.6%, ranked from highest to lowest in prevalence as traumatic brain injury, brain/spinal cord tumor, Alzheimer’s disease/ dementia, dystonia, MS, Parkinson’s disease, stroke, migraine, epilepsy and spina bifida respectively.

Conflicts of interests

None.

Acknowledgements

This projectwas supported by a grant from theHotchkiss Brain Insti- tute at the University of Calgary. This Institute had no role in study de- sign, in the collection, analysis and interpretation of data, in the writing of the report or in the decision to submit the article for publica- tion. Scott Patten, Nathalie Jette and Kirsten Fiestwere supported by sal- ary awards by Alberta Innovates — Health Solutions. Nathalie Jette also holds a Canada Research Chair Tier 2 in Neuroscience Health Services Research. Sandy Berzins was supported by an operating grant from the Alberta Addiction andMental Health Research Partnership Program and by an endMS Studentship.

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