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March 26, 2019; 92 (13) Article

Socioeconomic status and disability progression in multiple sclerosis

A multinational study

Katharine E. Harding, Mark Wardle, Robert Carruthers, Neil Robertson, Feng Zhu, Elaine Kingwell, Helen Tremlett
First published February 22, 2019, DOI: https://doi.org/10.1212/WNL.0000000000007190
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Abstract

Objective To examine the association between socioeconomic status (SES) and disability outcomes and progression in multiple sclerosis (MS).

Methods Health administrative and MS clinical data were linked for 2 cohorts of patients with MS in British Columbia (Canada) and South East Wales (UK). SES was measured at MS symptom onset (±3 years) based on neighborhood-level average income. The association between SES at MS onset and sustained and confirmed Expanded Disability Status Scale (EDSS) 6.0 and 4.0 and onset of secondary progression of MS (SPMS) were assessed using Cox proportional hazards models. EDSS scores were also examined via linear regression, using generalized estimating equations (GEE) with an exchangeable working correlation. Models were adjusted for onset age, sex, initial disease course, and disease-modifying drug exposure. Random effect models (meta-analysis) were used to combine results from the 2 cohorts.

Results A total of 3,113 patients with MS were included (2,069 from Canada; 1,044 from Wales). A higher SES was associated with a lower hazard of reaching EDSS 6.0 (adjusted hazard ratio [aHR] 0.90, 95% confidence interval [CI] 0.89–0.91), EDSS 4.0 (aHR 0.93, 0.88–0.98), and SPMS (aHR 0.94, 0.88–0.99). The direction of findings was similar when all EDSS scores were included (GEE: β = −0.13, −0.18 to −0.08).

Conclusions Lower neighborhood-level SES was associated with a higher risk of disability progression. Reasons for this association are likely to be complex but could include factors amenable to modification, such as lifestyle or comorbidity. Our findings are relevant for planning and development of MS services.

Glossary

BCMS=
British Columbia MS;
CI=
confidence interval;
DMT=
disease-modifying therapy;
EDSS=
Expanded Disability Status Scale;
GEE=
generalized estimating equations;
HR=
hazard ratio;
ICD=
International Classification of Diseases;
MS=
multiple sclerosis;
NHS=
National Health Service;
SES=
socioeconomic status;
SPMS=
secondary progressive multiple sclerosis

Socioeconomic status (SES) represents a composite measure of economic and sociologic environmental influences that may be relevant for outcomes in long-term disease, including cardiovascular disease,1,2 cancer,2,3 and stroke.2,4 To date, studies of SES and multiple sclerosis (MS) have largely focused on its relationship with risk of MS.5,6 However, study findings have been variable,5 with some identifying an association of high SES with increased risk of MS,6,7 others finding the opposite,8,–,11 and some reporting no evidence for an association, particularly in egalitarian societies.5 These discrepancies might relate to variability between study designs, especially when adjusting for lifestyle factors, which can change over time. Notably, when these temporal effects were considered, and known risk factors were adjusted for, it was demonstrated in 2 population-based Norwegian studies that high SES (measured by education level) was associated with lower risk of developing MS.10,11

Few studies have examined the association of SES with accumulation of disability, but given the evidence from other diseases, it is feasible that SES is associated with health outcomes in MS. A better understanding of the effects of SES on disability progression in MS may shed light on risk factors for poor prognosis that could potentially be modified. It would also be relevant for planning and development of MS services. In addition, if SES were associated with disability progression, this may be an important factor to take into account when designing studies and interpreting findings, including clinical trials, and particularly when comparing results across different regions.

This was an international study utilizing data from 2 well-established MS databases in British Columbia, Canada, and southeast Wales, United Kingdom, to investigate the relationship between SES at MS onset and disability progression, as measured using the Expanded Disability Status Scale (EDSS)12, and onset of secondary progression.

Methods

This was an observational cohort study using prospectively collected linked clinical and health administrative data, including sociodemographic information, in 2 jurisdictions: British Columbia and southeast Wales.

Clinical databases

We accessed the British Columbia MS (BCMS) clinical database. Established in 1980, it serves a population of 4.6 million and has been estimated to include 80% of the MS population in the province until 2004.13 We included all patients registered with one of the 4 MS clinics in British Columbia to 2004 and included their clinical follow-up to the end of 2008. Clinical data (EDSS scores, disease course, attainment of secondary progression of MS (SPMS), and disease-modifying therapy [DMT] use) for all patients with a definite diagnosis of MS were included, as captured at routine clinical visits by treating neurologists, with the date of MS symptom onset captured at the first contact with an MS clinic.

We also accessed the MS database of southeast Wales, which collates data from patients at the neuroinflammatory clinics of the University Hospital of Wales in Cardiff and the Royal Gwent Hospital in Newport. These clinics serve the cities and counties of Gwent, South Glamorgan, and Rhondda Cynon Taf; the populations of these regions combined represent almost half the population of Wales (1.4 million of a total 3 million population in Wales). The database was initially established in 1985 for a cross-sectional study.14 Prospective longitudinal data including EDSS scores have been collected routinely for all patients since 199915 as follows. Patients are routinely reviewed at least annually. At first contact with the neuroinflammatory clinics, data are collected on demographics and date of onset of MS, and at all clinic appointments current disease course and EDSS score are documented following examination by the treating neurologist. In the United Kingdom, DMTs can only be prescribed by an MS specialist and therefore all DMT prescriptions in southeast Wales are administered through these clinics. We accessed clinical data for all patients registered, and included all follow-up to July 2017.

Standard protocol approvals, registrations, and patient consents

This study was approved by the Research Ethics Boards of South East Wales and the University of British Columbia, which included patient consent. Access to administrative data from British Columbia was approved by the British Columbia Ministry of Health and Data Stewardship Committee, facilitated by Population Data British Columbia.

Linking clinical and health administrative data, including measures of SES

Clinical information from the BCMS database was linked with population-based provincial health administrative data using each individual’s unique, lifelong personal health number. Data included repeated measures of SES over time for each individual, from 1994 onwards. The SES measure was obtained using an algorithm developed by Statistics Canada,16 which uses individual postal codes and census-derived neighborhood income. The mean neighborhood income level for the British Columbia population is grouped by quintiles within geographical areas determined by the Canadian census to provide the SES estimate.17 Hospital and physician encounters, coded using the ICD system, were obtained via the Discharge Abstract Database (Hospital Separations)18 and Medical Services Plan Payment Information Files19,20 for use in additional comorbidity-adjusted models (see complementary analyses).

Clinical data in southeast Wales were linked using each individual's National Health Service (NHS) number to provide residential postcode for each patient annually. The postcode data were then linked to the Welsh Index of Multiple Deprivation21 to obtain markers of SES. This index is the official measure of deprivation used by the Welsh Assembly Government, and is available from 2001 onwards and updated every 3–4 years.21 Wales is divided into areas of approximately 1,500 residents, with each ranked using 8 indices: income, education, employment, health, access to services, physical environment, community safety, and housing quality. To access a comparable SES estimate to that used in British Columbia, we grouped the income index of the Welsh MS cohort into quintiles. As we only had access to relative measures—i.e., ranked income level within census region for British Columbia,17 and ranked income data within Wales for the Welsh cohort21—we do not have information about the absolute income levels within the quintiles for either cohort. Additional complementary metrics—education and the composite score—were assessed to capture some broader aspects of SES in the southeast Wales cohort. The education index was selected because it can be considered a more stable measure of SES (unlike income, which can fluctuate for many reasons) and is typically attained earlier in life and once attained is not reversible. The composite deprivation score combines all 8 indices and provides a measure of the overall deprivation of each area.21 It represents a more comprehensive measure than income alone, and allows comparison with other UK studies. We also assessed the physical environment index as a negative control, hypothesizing that it would have no measurable association with disability progression. This metric captures a combination of air quality, proximity to waste and industrial sites, and flood risk, none of which would be expected to be associated with disability progression (though there is mixed evidence linking air quality/pollution with relapse risk22). These additional metrics were included in complementary analyses (see below).

In order to minimize the risk of bias due to reverse causation (i.e., MS causing a decline in SES), we used the nearest measure of SES to the date of onset of MS (3 years) in both jurisdictions.

Defining disability progression

The primary outcome was time from MS symptom onset to reaching sustained EDSS 6.0, confirmed by a second EDSS score of ≥6.0 at least 6 months later. Secondary outcomes were EDSS 4.0 (sustained and confirmed as for EDSS 6.0) and onset of secondary progression (SPMS), defined clinically by the treating MS neurologist using the internationally recognized definition of SPMS: presence of progressive worsening of disability with or without superimposed relapses, with an initial relapsing-remitting disease course.23 For both cohorts, current disease course was recorded at each visit by the treating clinician. In addition, when the clinician considered that the patient had transitioned to SPMS, the date (to the nearest month) of transition was recorded. This approach has been widely used in previous studies.13,24,–,29

Inclusion criteria

Patients with a definite diagnosis of MS (according to the prevailing criteria at time of diagnosis30,31), symptom onset during or after the year that SES estimates became available (1994 in British Columbia and 2001 in southeast Wales), a documented disease course (relapsing-onset or primary progressive), and at least one EDSS score (for models using EDSS 4.0 or 6.0 as the outcome) were included. For analyses with SPMS as the outcome, only patients with relapsing-onset MS were included.

Statistical analysis

Cox proportional hazards regression was used to analyze associations of SES at MS symptom onset with the time from MS symptom onset to sustained and confirmed EDSS and SPMS outcomes. SES quintiles were included in the models in 2 ways: first treated as a linear variable, then as categories. If a patient had reached EDSS 6.0 or 4.0 but had no further scores to allow confirmation of the endpoint, follow-up was censored at the day before the final EDSS score. End of follow-up was the last recorded EDSS score for models with sustained and confirmed EDSS outcomes, and the last recorded clinic visit for models with SPMS as the outcome. Models were adjusted for age and calendar year at onset, sex, and treatment with DMT as a time-dependent covariate, as well as initial disease course (progressive or relapsing) when EDSS was the outcome. Models were assessed for violation of the proportional hazards assumption (no violations were found). Due to privacy and data sharing restrictions, results from the 2 cohorts were combined in random effects meta-analysis. To ascertain if the associations were consistent for men and women, separate analyses were also conducted by sex.

Three complementary analyses were performed using the same approaches as in the main analysis, including model adjustments except where specified. First, we used all EDSS scores (instead of single milestones), thus making best use of all available disability scores. EDSS was treated as a continuous variable, and a linear model with generalized estimating equations (GEE) was fitted using an exchangeable correlation structure.32 Second, we used alternative SES metrics (southeast Wales only): education, physical environment, and the composite deprivation scores (as described above). Third, we included comorbidity as an additional model adjustment (British Columbia only). Comorbidity was identified using ICD codes from physician encounters19 and hospital admissions18 in the year prior to MS onset, and included ischemic heart disease, cerebrovascular disease, peripheral vascular disease, chronic obstructive pulmonary disease, and cancers of the respiratory tract (table 1). These were selected because of potential association with both SES and disability progression.32,33 Patients were included if they had been resident in British Columbia for at least 270 days before MS onset20 and a comorbidity was considered present if at least one of the relevant diagnostic codes was present in the year prior to MS onset.32 All comorbidities were combined and included in the models as a single binary variable (any comorbidity vs no comorbidity).

View this table:
Table 1

ICD-9 codes used to define comorbidity in the British Columbia cohort

All statistical analysis was performed in R version 3.4.2 (R Foundation for Statistical Computing, Vienna, Austria).34

Data sharing

The health administrative and clinical data from British Columbia that were used in this study were accessed through Population Data British Columbia (popdata.bc.ca/) and reside on a limited access secure research environment. For legal and ethical reasons, the data cannot leave this secure research environment. Similarly, in southeast Wales, the socioeconomic administrative data were accessed via the Welsh Government.21 Clinical data are held on PatientCare within NHS Wales on a secure limited access resource. For ethical and legal reasons, these data cannot leave this secure research environment. The statistical code and related information are available from the corresponding author.

Results

In British Columbia, 2,100 patients had onset in 1994 or later, and in southeast Wales, 1,059 had onset in 2001 or later. Of these, a total of 3,113 patients with MS (2,069 from British Columbia and 1,044 from southeast Wales) met all inclusion criteria; i.e., SES was available at onset ±3 years, disease course was documented, and at least 1 EDSS score was available (table 2). In total, 46 (0.1%) otherwise eligible patients were excluded because of no available SES metric (31 from British Columbia and 15 from southeast Wales). In British Columbia, on average 1.1 EDSS scores were recorded per patient for every year of follow-up in the clinic for the cohort in our study, while in southeast Wales, the average was 0.9 EDSS scores per patient per year of follow-up. The characteristics of the 2 cohorts were largely similar when compared by sex, onset age, initial disease course, and DMT exposure. Follow-up was naturally longer in southeast Wales due to the availability of more recent data.

View this table:
Table 2

Characteristics of the multiple sclerosis cohorts in British Columbia, Canada, and southeast Wales, UK

Primary and secondary endpoints: Disability milestones and onset of SPMS

In the combined cohort, 349 patients had reached EDSS 6.0 before first clinical contact (171 in British Columbia and 178 in southeast Wales) and were not included in this analysis. Similarly, 666 had reached EDSS 4.0 before first clinical contact (314 in British Columbia and 352 in southeast Wales) and were not included in this analysis. After adjustment for relevant covariates, a step to the next (higher) quintile of SES was associated with a 10% lower hazard of reaching EDSS 6.0 (hazard ratio [HR] 0.90, 95% confidence interval [CI] 0.89–0.91). A similar association was observed for EDSS 4.0 (HR 0.93, 95% CI 0.88–0.98, figure 1). When individual SES quintiles were compared, the highest SES group (least deprived) had significantly lower hazard of EDSS 6.0 relative to the lowest SES group (most deprived, table 3). For the analyses separated by sex, all findings were in the same direction for both women and men, although not all reached significance for men (table 4).

Figure 1
Figure 1 Socioeconomic status (SES) and the hazard of reaching disability milestones and secondary progressive multiple sclerosis (SPMS)

The association between SES at multiple sclerosis symptom onset and the subsequent hazard (risk) of reaching the Expanded Disability Status Scale (EDSS) milestones 6.0 (A) and 4.0 (B) and onset of SPMS (C) in British Columbia, Canada; southeast Wales, UK; and the combined cohorts: adjusted hazard ratios with 95% confidence intervals.

View this table:
Table 3

The association between socioeconomic status (SES) at multiple sclerosis (MS) onset and the subsequent hazard (risk) of reaching disability milestones and secondary progressive MS: adjusted Cox proportional regression models in British Columbia, Canada; southeast Wales, UK; and the combined cohort

View this table:
Table 4

The association between socioeconomic status (SES), disability milestones, and onset of secondary progressive multiple sclerosis (SPMS): adjusted Cox proportional hazards regression models in female and male participants separately in British Columbia, Canada; southeast Wales, UK; and the combined cohort

There were 2,818 patients with relapsing-onset MS (1,928 in British Columbia and 890 in southeast Wales), of whom 625 (328 in British Columbia and 297 in southeast Wales) reached SPMS during follow-up. Of these, 122 (21 in British Columbia and 101 in southeast Wales) reached SPMS but at an unknown date, so were not included in the analysis. Higher SES was associated with a 6% lower hazard of reaching SPMS (HR 0.94, 95% CI 0.88–0.99) in the combined cohort. Although a similar pattern was observed across the individual SES quintiles, as for the outcome of EDSS 4.0, none reached significance in relation to SPMS (table 3).

Findings from the complementary analyses were consistent with the main analysis. First, higher SES (less deprived) at MS symptom onset was associated with lower EDSS scores over time (β = −0.13, 95% CI −0.18 to −0.08). Second, both the education index and composite deprivation index were associated with the hazard of reaching EDSS 6.0, with the same direction and similar effect size to the main analysis of the SES income index (figure 2). While the direction of effect was similar for EDSS 4.0 and SPMS, none of those findings reached significance. However, there was no evidence for an association between the physical environment index and hazard of reaching any of the endpoints (figure 2). Third, in the British Columbia cohort, of the 1,957 patients (94.6%) who fulfilled residency criteria, 112 had one or more of the specified comorbidities prior to onset of MS. After adjusting the main model for comorbidity in addition to the other covariates, the direction of effect remained consistent with the main analysis, although only reached significance for EDSS 4.0 (HR 0.91, 95% CI 0.83–0.99) and when SES was treated as a continuous variable (table 5).

Figure 2
Figure 2 Alternative socioeconomic status (SES) indices and the hazard of reaching disability milestones and secondary progressive multiple sclerosis (SPMS)

The association between alternative SES indices: education (A), physical environment (B), and composite deprivation score (C) measured at multiple sclerosis symptom onset and the subsequent hazard (risk) of reaching the Expanded Disability Status Scale (EDSS) milestones (6.0 and 4.0) and onset of SPMS in southeast Wales: adjusted hazard ratios with 95% confidence intervals. Models adjusted for age at onset, sex, calendar year of onset, disease-modifying therapy exposure, and disease course for the models with EDSS 4 and 6 as endpoints.WIMD = Welsh Index of Multiple Deprivation.

View this table:
Table 5

Association between socioeconomic status (SES), disability milestones, and onset of secondary progressive multiple sclerosis (SPMS), with additional adjustment for comorbidity at onset of multiple sclerosis in British Columbia, Canada: adjusted Cox proportional hazards regression models

Discussion

In this multinational study of SES and long-term outcomes in MS, we have demonstrated an association of lower SES (greater deprivation) at onset of MS with an increased hazard of reaching key disability milestones (EDSS 6.0 and EDSS 4.0) and onset of secondary progression. Specifically, a higher neighborhood income–related index of SES was associated with a 10% lower hazard per SES quintile of reaching sustained and confirmed EDSS 6.0. These findings were consistent for both men and women and after accounting for demographic and clinical characteristics. Although SES is likely to represent a combination of multiple factors, findings suggest that there may be modifiable aspects that can benefit outcomes in MS. Further, findings are relevant when comparing and interpreting disability outcomes across jurisdictions and when planning development of MS services. We were also able to show a consistent effect using a complementary measure of neighborhood SES—based on level of education (available in southeast Wales only)—a higher level of which was associated with 9% lower hazard of reaching EDSS 6.0. Level of education might be considered a more robust measure of SES (as compared to income), especially as highest educational attainment is often reached before disease starts. However, as hypothesized, no relationship was found with the physical environment index (based on air quality, proximity to waste and industrial sites, and flood risk) and any of the disability or progression outcomes.

Although few studies have examined the relationship of SES and disability outcomes in MS (and none to the scale or scope of our current study), there is evidence from large-scale studies in other diseases including cardiovascular disease,1,2,35,36 stroke,4 diabetes,1 epilepsy,37 and cancer2,3 that lower SES is associated both with higher frequency of disease onset and poorer outcomes including increased mortality risk. This effect has been found consistently, even by studies that have utilized different measures of SES including employment grade in the UK civil service,1 income level,38 educational attainment,7,39 and neighborhood level SES.3,4,35,–,37 In addition, there is evidence from a study of the US general population based on a supplementary survey to the national census with 0.6% of the US adult population represented that lower income is associated with increased limitations in daily functioning,38 suggesting that poverty is generally associated with disability rather than only in specific disease cohorts. Similar to these observations in disparate settings and diseases, we also show that a lower SES was associated with poorer outcomes in MS.

Although a cross-sectional study in Australia found that higher levels of MS disability were associated with higher levels of unemployment,7 the 2 measures were based on the same point in time, so this finding was attributed to the effect of having MS rather than premorbid SES. A study from Belgium using data gathered from a cross-sectional patient survey through the MS Society (with a 43% response rate) found that participants with more than 12 years of education had a reduced hazard of reaching a self-reported disability milestone (considered equivalent to EDSS 6) compared to those with fewer years of education.40 A lower level of education has also been associated with negative health outcomes including cognitive impairment in a cohort of 419 participants with MS in Portugal.39 We were able to access prospectively collected information for 3,113 participants accrued from 2 jurisdictions and were able to show that low SES at symptom onset (prior to disability accumulation) was associated with a higher risk of reaching 2 important disability milestones and onset of secondary progression. In addition, we were able to dynamically assess EDSS by using GEE models to show that there was an association of SES and disability across different stages of disease.

SES is a complex construct, and it is not clear what the mechanisms are for variation in health outcomes by SES. In order to investigate this effect further, it would be valuable to gain access to data on potential explanatory or confounding lifestyle factors, such as smoking history, exercise, body size, infectious mononucleosis, sunlight exposure, and vitamin D levels; these factors have been shown to be important in previous studies of SES and MS risk.8,–,11 However, it is exceptionally challenging to gain access to high-quality lifestyle-related data combined with long-term disability outcomes. Some of these factors are known to vary by SES, such as smoking41 and comorbidity,42 both of which are typically more prevalent with lower SES. There is evidence that smoking is associated with more rapid accumulation of disability in smokers,43,44 although this was not observed in primary progressive MS.45 Preliminary data (based on cross-sectional information) suggest that smoking cessation may lower the hazard of EDSS 4.0 and 6.0.44 While we did not have access to data on smoking habits, adjustment for comorbidities associated with both smoking and MS outcomes had no effect on the findings. However, the burden of comorbidity was low in our cohort around the time of MS symptom onset, and it remains possible that accumulation of comorbidity after onset could have a modifying or mediating effect on the relationship between SES and disability outcome. It would be of value for future studies to consider these complex relationships, and to assess how and if the relationships between lifestyle factors such as smoking, comorbidity, or physical activity are associated with progression of disability within a given cohort and whether there is an independent effect of SES.

In this study, we used a neighborhood income–based definition of SES, using residential postcodes at onset of MS. While this may not reflect an individual's SES or income, it does represent the broader wealth of the community in which the person resides. Further, this neighborhood-level metric has been used to investigate the association of SES with a number of different diseases,3,4,35,–,37,46 and an independent effect of neighborhood SES has been reported in addition to that of an individual's SES,36 suggesting that it is a relevant metric. Finally, we were able to access additional measures of SES, via the Welsh Index of Multiple Deprivation.21 This index has been successfully used in a number of studies to examine SES and health outcomes,37,46 as has its equivalent in England.3 We were able to show that the education component and overall (combined) index, in addition to the income index alone, were all associated with EDSS 6.0.

Our study has several strengths. First, it is an international study incorporating data from 2 separate MS cohorts, which increases generalizability of the findings to other MS populations. Due to data sharing and privacy restrictions, we combined results using meta-analysis; nonetheless, the findings from each cohort were similar, suggesting that the association of SES with the hazard of reaching disability milestones is not a feature specific to an individual cohort but is a generalizable finding. Both Canada and the United Kingdom have publicly funded universal health care systems, and we recently showed that in the British Columbia (Canadian) cohort, there was no difference in distribution across the SES quintiles between patients who attended MS specialist clinics and those who did not,47 which suggests that differences in access to health care are unlikely to explain our findings. Second, the analysis was based on longitudinal data that had been collected prospectively over a number of years, with the exposure (SES) collated independently of the disability and progression outcomes. These features minimized the potential effects of MS on SES (reverse causation) by enabling access to SES measures at MS symptom onset, prior to accrual of disability, and allowed us to consider multiple endpoints, including SPMS. Finally, we were able to use different analytic approaches (survival and longitudinal modeling) and the similarity of the findings from both approaches strengthens our interpretation of lower SES and a higher risk of disability progression. The advantage offered by the GEE model (for the complementary analyses) was that every available EDSS score could be used. This method has been used successfully in prior studies.48 The inherent limitation of using the ordinal EDSS as a linear scale should be considered when interpreting the findings. For example, the same magnitude of change in the EDSS can have a clinically different meaning depending on where on the scale it occurs, and the probability of a 1-point change can be uneven across the full range of EDSS scores.

Our study also has some limitations. The measurement of SES is based on neighborhood-level income data and is ranked relative to that of other persons in British Columbia at the time of the onset of their MS or, in Wales, relative to other persons in the MS cohort. The same SES quintiles for members of the British Columbia and Welsh MS cohorts do not imply the same income; rather, this measure of SES indicates the relative socioeconomic position within their own community at the time of onset of MS. This approach is consistent with the intended use of these SES quintiles.17,21 Because Canada and the United Kingdom are similar in terms of standard of living and the relationship between income and SES, we assume similar interpretation of the effects of relative SES within the individual and combined cohorts from the 2 countries. One of our outcomes was time to SPMS. Although this is considered a relevant outcome and important point in the disease course of MS,49 it is primarily determined clinically and after retrospective evaluation of disease activity. Nonetheless, it is an approach used by others,13,24,–,29 and reassuringly, the direction of our findings was similar for both the SPMS and EDSS-related outcomes. We used the presence of one ICD in the year prior to MS onset as a marker of comorbidity. Although this is a sensitive method that may tend towards overestimation of comorbidity, we would not expect this to influence our findings unless the overestimate varied by SES or disability outcome. Use of one ICD code has been shown to have a reasonable correlation with comorbidity algorithms with stricter criteria.50

High SES, measured at the neighborhood level, at onset of MS is associated with a 10% lower hazard of reaching EDSS 6.0, and a lower hazard of reaching EDSS 4.0 and SPMS in 2 cohorts from the United Kingdom and Canada. We are not aware of another study that has reported such an association or has compared and combined findings across MS cohorts based in geographically disparate locations. Our findings suggest that there could be an important future opportunity to positively modify health outcomes in MS by targeting aspects of SES.

Author contributions

K. Harding designed the study, obtained funding, collected data in Wales, performed the statistical analysis, interpreted the data, and drafted the manuscript. M. Wardle collected data in Wales, interpreted the data, and drafted the manuscript. R. Carruthers collected data in British Columbia, interpreted the data, and drafted the manuscript. N. Robertson collected data in Wales, interpreted the data, and drafted the manuscript. F. Zhu designed the study, performed statistical analysis, interpreted the data, and drafted the manuscript. E. Kingwell designed the study and drafted the manuscript. H. Tremlett designed the study, obtained funding, and drafted the manuscript.

Study funding

K.H. was funded by an MS of Society Canada Fellowship award.

Disclosure

K. Harding received funding for this project from the MS Society of Canada and has previously received research support from Novartis UK for an unrelated project. M. Wardle reports no disclosures relevant to the manuscript. R. Carruthers is Site Investigator for clinical trials funded by Novartis, Med-Immune, and Roche and receives research support from Teva Innovation Canada, Roche Canada, and Vancouver Coastal Health Research Institute. R.C. has done consulting work and has received honoraria from Roche, EMD Serono, Sanofi, Biogen, Novartis, and Teva. N. Robertson has received honoraria and/or support to attend educational meetings from Biogen, Novartis, Genzyme, Teva, and Roche. His research group has also received research support from Biogen, Novartis, and Genzyme. F. Zhu and E. Kingwell report no disclosures relevant to the manuscript. H. Tremlett is the Canada Research Chair for Neuroepidemiology and Multiple Sclerosis. She currently receives research support from the National Multiple Sclerosis Society, the Canadian Institutes of Health Research, the Multiple Sclerosis Society of Canada, and the Multiple Sclerosis Scientific Research Foundation. In addition, in the last 5 years she has received research support from the Multiple Sclerosis Society of Canada (Don Paty Career Development Award), the Michael Smith Foundation for Health Research (Scholar Award), and the UK MS Trust; and speaker honoraria and/or travel expenses to attend conferences from the Consortium of MS Centres (2013), the National MS Society (2014, 2016), ECTRIMS (2013, 2014, 2015, 2016, 2017), Biogen Idec (2014), and American Academy of Neurology (2013, 2014, 2015, 2016). All speaker honoraria are either declined or donated to an MS charity or to an unrestricted grant for use by her research group. Go to Neurology.org/N for full disclosures.

Acknowledgment

The authors thank the British Columbia Ministry of Health and British Columbia Vital Statistics Agency for approval and support with accessing provincial data and Population Data British Columbia for facilitating approval and use of the data. All inferences, opinions, and conclusions drawn in this epidemiologic study are those of the authors and do not reflect the opinions or policies of the Data Stewards. The authors thank the British Columbia MS Clinic neurologists who contributed to the study through patient examination and data collection (current members at the time of data extraction listed here by primary clinic): UBC MS Clinic: A. Traboulsee, MD, FRCPC (UBC Hospital MS Clinic Director and Head of the UBC MS Programs); A.-L. Sayao, MD, FRCPC; V. Devonshire, MD, FRCPC; S. Hashimoto, MD, FRCPC (UBC and Victoria MS Clinics); J. Hooge, MD, FRCPC (UBC and Prince George MS Clinic); L. Kastrukoff, MD, FRCPC (UBC and Prince George MS Clinic); J. Oger, MD, FRCPC. Kelowna MS Clinic: D. Adams, MD, FRCPC; D. Craig, MD, FRCPC; S. Meckling, MD, FRCPC. Prince George MS Clinic: L. Daly, MD, FRCPC. Victoria MS Clinic: O. Hrebicek, MD, FRCPC; D. Parton, MD, FRCPC; K. Atwell-Pope, MD, FRCPC. The authors thank the neurologists of southeast Wales who contributed to the study through patient examination and data collection: T.P. Pickersgill, MRCP; C.L. Hirst, MRCP, MD; G. Ingram, MRCP, PhD; M.D. Cossburn, MRCP; M.D. Willis, MRCP, PhD; V. Tomassini, PhD; E.C. Tallantyre, MRCP, PhD; J. Hrastelj, MRCP; and O.H. Williams, MRCP. The views expressed in this article do not necessarily reflect the views of the individuals acknowledged.

Footnotes

  • Go to Neurology.org/N for full disclosures. Funding information and disclosures deemed relevant by the authors, if any, are provided at the end of the article.

  • Patient Page page e1536

  • Received September 7, 2018.
  • Accepted in final form November 16, 2018.

References

  1. 1.↵
    1. Chaturvedi N,
    2. Jarrett J,
    3. Shipley MJ,
    4. Fuller JH
    . Socioeconomic gradient in morbidity and mortality in people with diabetes: cohort study findings from the Whitehall study and the WHO Multinational Study of vascular disease in diabetes. BMJ 1998;316:100–105.
    OpenUrlAbstract/FREE Full Text
  2. 2.↵
    1. Smith GD,
    2. Hart C,
    3. Blane D,
    4. Hole D
    . Adverse socioeconomic conditions in childhood and cause specific adult mortality: prospective observational study. BMJ 1998;316:1631–1635.
    OpenUrlAbstract/FREE Full Text
  3. 3.↵
    1. Taib BG,
    2. Oakley J,
    3. Dailey Y, et al
    . Socioeconomic deprivation and the burden of head and neck cancer: regional variations of incidence and mortality in Merseyside and Cheshire, North West, England. Clin Otolaryngol 2018;43:846–853.
    OpenUrl
  4. 4.↵
    1. Aslanyan S,
    2. Weir CJ,
    3. Lees KR,
    4. Reid JL,
    5. McInnes GT
    . Effect of area-based deprivation on the severity, subtype, and outcome of ischemic stroke. Stroke 2003;34:2623–2628.
    OpenUrlAbstract/FREE Full Text
  5. 5.↵
    1. Goulden R,
    2. Ibrahim T,
    3. Wolfson C
    . Is high socioeconomic status a risk factor for multiple sclerosis? A systematic review. Eur J Neurol 2015;22:899–911.
    OpenUrlCrossRefPubMed
  6. 6.↵
    1. Goulden R,
    2. Riise T,
    3. Myhr KM,
    4. Pugliatti M,
    5. Wolfson C
    . Does low socioeconomic status in early life protect against multiple sclerosis? A multinational, case-control study. Eur J Neurol 2016;23:168–174.
    OpenUrl
  7. 7.↵
    1. Hammond SR,
    2. McLeod JG,
    3. Macaskill P,
    4. English DR
    . Multiple sclerosis in Australia: socioeconomic factors. J Neurol Neurosurg Psychiatry 1996;61:311–313.
    OpenUrlAbstract/FREE Full Text
  8. 8.↵
    1. Riise T,
    2. Kirkeleit J,
    3. Aarseth JH, et al
    . Risk of MS is not associated with exposure to crude oil, but increases with low level of education. Mult Scler J 2011;17:780–787.
    OpenUrl
  9. 9.↵
    1. Briggs FBS,
    2. Acua BS,
    3. Shen L, et al
    . Adverse socioeconomic position during the life course is associated with multiple sclerosis. J Epidemiol Community Health 2014;68:622–629.
    OpenUrlAbstract/FREE Full Text
  10. 10.↵
    1. Bjørnevik K,
    2. Riise T,
    3. Cortese M, et al
    . Level of education and multiple sclerosis risk after adjustment for known risk factors: the EnvIMS study. Mult Scler J 2016;22:104–111.
    OpenUrl
  11. 11.↵
    1. Bjørnevik K,
    2. Riise T,
    3. Benjaminsen E, et al
    . Level of education and multiple sclerosis risk over a 50-year period: registry-based sibling study. Mult Scler J 2017;23:213–219.
    OpenUrl
  12. 12.↵
    1. Kurtzke JF
    . Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS). Neurology 1983;33:1444–1452.
    OpenUrlAbstract/FREE Full Text
  13. 13.↵
    1. Tremlett H,
    2. Zhao Y,
    3. Rieckmann P,
    4. Hutchinson M
    . New perspectives in the natural history of multiple sclerosis. Neurology 2010;74:2004–2015.
    OpenUrlAbstract/FREE Full Text
  14. 14.↵
    1. Swingler RJ,
    2. Compston DA
    . The prevalence of multiple sclerosis in south east Wales. J Neurol Neurosurg Psychiatry 1988;51:1520–1524.
    OpenUrlAbstract/FREE Full Text
  15. 15.↵
    1. Hirst C,
    2. Ingram G,
    3. Pickersgill T,
    4. Swingler R,
    5. Compston DAS,
    6. Robertson NP
    . Increasing prevalence and incidence of multiple sclerosis in south east Wales. J Neurol Neurosurg Psychiatry 2009;80:386–391.
    OpenUrlAbstract/FREE Full Text
  16. 16.↵
    1. Wilkins R,
    2. Berthelot JM,
    3. Ng E
    . Trends in mortality by neighbourhood income in urban Canada from 1971 to 1996. Health Rep 2002;13(suppl):1–27.
    OpenUrl
  17. 17.↵
    Statistics Canada. Annual Income Estimates for Census Families and Individuals (T1 Family File). 2014. Available at: www23.statcan.gc.ca/imdb/p2SV.pl?Function=getSurvey&SDDS=4105&lang=en&db=IMDB&dbg=f&adm=8&dis=2. Accessed August 11, 2014.
  18. 18.↵
    Canadian Institute for Health Information [creator] and Population Data BC [publisher]. Discharge Abstract Database (Hospital Separations). V2. Data Extract. Ministry of Health, 2012. Available at: popdata.bc.ca/data. Accessed February 27, 2018.
  19. 19.↵
    British Columbia Ministry of Health [creator] and Population Data BC [publisher]. Medical Services Plan (MSP) Payment Information File. 2012. Available at: popdata.bc.ca/data. Accessed February 27, 2018.
  20. 20.↵
    British Columbia Ministry of Health [creator] and Population Data BC [publisher]. Consolidation File (MSP registration & Premium Billing). 2012. Available at: popdata.bc.ca/data. Accessed February 27, 2018.
  21. 21.↵
    Welsh Assembly Government. Welsh Index of Multiple Deprivation (WIMD). Online. 2014. Available at: gov.wales/statistics-and-research/welsh-index-multiple-deprivation/?lang=en. Accessed January 18, 2018.
  22. 22.↵
    1. Oikonen M,
    2. Laaksonen M,
    3. Laippala P, et al
    . Ambient air quality and occurrence of multiple sclerosis relapse. Neuroepidemiology 2003;22:95–99.
    OpenUrlCrossRefPubMed
  23. 23.↵
    1. Lublin FD,
    2. Reingold SC
    . Defining the clinical course of multiple sclerosis: results of an international survey. Neurology 1996;46:907–911.
    OpenUrlAbstract/FREE Full Text
  24. 24.↵
    European Study Group on Interferon Beta-1b in Secondary Progressive MS. Placebo-controlled multicentre randomised trial of interferon beta-1b in treatment of secondary progressive multiple sclerosis. Lancet 1998;352:1491–1497.
    OpenUrlCrossRefPubMed
  25. 25.↵
    1. Trojano M,
    2. Pellegrini F,
    3. Fuiani A, et al
    . New natural history of interferon-beta-treated relapsing multiple sclerosis. Ann Neurol 2007;61:300–306.
    OpenUrlCrossRefPubMed
  26. 26.↵
    1. Tremlett H,
    2. Zhao Y,
    3. Devonshire V
    . Natural history of secondary-progressive multiple sclerosis. Mult Scler J 2008;14:314–324.
    OpenUrl
  27. 27.↵
    1. Tremlett H,
    2. Zhao Y,
    3. Devonshire V,
    4. Neurologists U
    . Natural history comparisons of primary and secondary progressive multiple sclerosis reveals differences and similarities. J Neurol 2009;256:374–381.
    OpenUrlCrossRefPubMed
  28. 28.↵
    1. Tedeholm H,
    2. Lycke J,
    3. Skoog B, et al
    . Time to secondary progression in patients with multiple sclerosis who were treated with first generation immunomodulating drugs. Mult Scler J 2013;19:765–774.
    OpenUrl
  29. 29.↵
    1. Bergamaschi R,
    2. Quaglini S,
    3. Tavazzi E, et al
    . Immunomodulatory therapies delay disease progression in multiple sclerosis. Mult Scler J 2016;22:1732–1740.
    OpenUrl
  30. 30.↵
    1. Poser CM,
    2. Paty DW,
    3. Scheinberg L, et al
    . New diagnostic criteria for multiple sclerosis: guidelines for research protocols. Ann Neurol 1983;13:227–231.
    OpenUrlCrossRefPubMed
  31. 31.↵
    1. Polman CH,
    2. Reingold SC,
    3. Banwell B, et al
    . Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann Neurol 2011;69:292–302.
    OpenUrlCrossRefPubMed
  32. 32.↵
    1. Zhang T,
    2. Tremlett H,
    3. Zhu F, et al
    . Effects of physical comorbidities on disability progression in multiple sclerosis. Neurology 2018;90:e419–e427.
    OpenUrlAbstract/FREE Full Text
  33. 33.↵
    1. Marrie RA,
    2. Rudick R,
    3. Horwitz R, et al
    . Vascular comorbidity is associated with more rapid disability progression in multiple sclerosis. Neurology 2010;74:1041–1047.
    OpenUrlAbstract/FREE Full Text
  34. 34.↵
    R Core Team. R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing; 2017.
  35. 35.↵
    1. Chaix B,
    2. Rosvall M,
    3. Merlo J
    . Neighborhood socioeconomic deprivation and residential instability: effects on incidence of ischemic heart disease and survival after myocardial infarction. Epidemiology 2007;18:104–111.
    OpenUrlCrossRefPubMed
  36. 36.↵
    1. Nobel L,
    2. Jesdale WM,
    3. Tjia J, et al
    . Neighborhood socioeconomic status predicts health after hospitalization for acute coronary syndromes: findings from TRACE-CORE (Transitions, Risks, and Actions in Coronary Events–Center for Outcomes Research and Education). Med Care 2017;55:1008–1016.
    OpenUrl
  37. 37.↵
    1. Pickrell WO,
    2. Lacey AS,
    3. Bodger OG, et al
    . Epilepsy and deprivation, a data linkage study. Epilepsia 2015;56:585–591.
    OpenUrl
  38. 38.↵
    1. Minkler M,
    2. Fuller-Thomson E,
    3. Guralnik JM
    . Gradient of disability across the socioeconomic spectrum in the United States. New Engl J Med 2006;355:695–703.
    OpenUrlCrossRefPubMed
  39. 39.↵
    1. Martins Da Silva A,
    2. Cavaco S,
    3. Moreira I, et al
    . Cognitive reserve in multiple sclerosis: protective effects of education. Mult Scler J 2015;21:1312–1321.
    OpenUrl
  40. 40.↵
    1. D'hooghe MB,
    2. Haentjens P,
    3. Van Remoortel A,
    4. De Keyser J,
    5. Nagels G
    . Self-reported levels of education and disability progression in multiple sclerosis. Acta Neurol Scand 2016;134:414–419.
    OpenUrl
  41. 41.↵
    1. Corsi DJ,
    2. Boyle MH,
    3. Lear SA,
    4. Chow CK,
    5. Teo KK,
    6. Subramanian SV
    . Trends in smoking in Canada from 1950 to 2011: progression of the tobacco epidemic according to socioeconomic status and geography. Cancer Causes Control 2014;25:45–57.
    OpenUrlCrossRefPubMed
  42. 42.↵
    1. Marrie RA,
    2. Horwitz R,
    3. Cutter G,
    4. Tyry T,
    5. Campagnolo D,
    6. Vollmer T
    . Comorbidity, socioeconomic status and multiple sclerosis. Mult Scler J 2008;14:1091–1098.
    OpenUrl
  43. 43.↵
    1. Manouchehrinia A,
    2. Tench CR,
    3. Maxted J,
    4. Bibani RH,
    5. Britton J,
    6. Constantinescu CS
    . Tobacco smoking and disability progression in multiple sclerosis: United Kingdom cohort study. Brain 2013;136:2298–2304.
    OpenUrlCrossRefPubMed
  44. 44.↵
    1. Tanasescu R,
    2. Constantinescu CS,
    3. Tench CR,
    4. Manouchehrinia A
    . Smoking cessation and the reduction of disability progression in multiple sclerosis: a cohort study. Nicotine Tob Res 2018;20:589–595.
    OpenUrl
  45. 45.↵
    1. Javizian O,
    2. Metz LM,
    3. Deighton S,
    4. Koch MW
    . Smoking does not influence disability accumulation in primary progressive multiple sclerosis. Eur J Neurol 2017;24:624–630.
    OpenUrl
  46. 46.↵
    1. Stephens MR,
    2. Evans M,
    3. Ilham MA,
    4. Marsden A,
    5. Asderakis A
    . The influence of socioeconomic deprivation on outcomes following renal transplantation in the United Kingdom. Am J Transplant 2010;10:1605–1612.
    OpenUrlCrossRefPubMed
  47. 47.↵
    1. McKay KA,
    2. Tremlett H,
    3. Zhu F,
    4. Kastrukoff L,
    5. Marrie RA,
    6. Kingwell E
    . A population-based study comparing multiple sclerosis clinic users and non-users in British Columbia, Canada. Eur J Neurol 2016;23:1093–1100.
    OpenUrl
  48. 48.↵
    1. McKay KA,
    2. Tremlett H,
    3. Fisk JD, et al
    . Psychiatric comorbidity is associated with disability progression in multiple sclerosis. Neurology 2018;90:e1316–e1323.
    OpenUrlAbstract/FREE Full Text
  49. 49.↵
    1. Rovaris M,
    2. Confavreux C,
    3. Furlan R,
    4. Kappos L,
    5. Comi G,
    6. Filippi M
    . Secondary progressive multiple sclerosis: current knowledge and future challenges. Lancet Neurol 2006;5:343–354.
    OpenUrlCrossRefPubMed
  50. 50.↵
    1. Lix L,
    2. Yogendran M,
    3. Burchill C, et al
    . Defining and validating chronic diseases: an administrative data approach. Tech. rep., Manitoba Centre for Health Policy. 2006. Available at: mchp-appserv.cpe.umanitoba.ca/reference/chronic.disease.pdf. Accessed August 23, 2018.

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