- Email: [email protected]
The incidence and prevalence of fibromyalgia are higher in multiple sclerosis than the general population: A population-based study
Multiple Sclerosis and Related Disorders 1 (2012) 162–167
Contents lists available at SciVerse ScienceDirect
Multiple Sclerosis and Related Disorders journal homepage: www.elsevier.com/locate/msard
The incidence and prevalence of fibromyalgia are higher in multiple sclerosis than the general population: A population-based study Ruth Ann Marrie a,b,n, Bo Nancy Yu b, Stella Leung b, Lawrence Elliott b, Sharon Warren c, Christina Wolfson d,e, Helen Tremlett f, James Blanchard b, John D. Fisk g, for the CIHR Team in the Epidemiology and Impact of Comorbidity in Multiple Sclerosis a
Department of Internal Medicine, University of Manitoba, Winnipeg, Canada Department of Community Health Sciences, University of Manitoba, Winnipeg, Canada c Faculty of Rehabilitation Medicine, University of Alberta, Edmonton, Canada d Department of Epidemiology and Biostatistics, McGill University, Montreal, Canada e Research Institute of the McGill University Health Centre, Montreal, Canada f Department of Medicine (Neurology), University of British Columbia, Vancouver, Canada g Departments of Psychiatry and Medicine, Dalhousie University, Halifax, Canada b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 24 March 2012 Received in revised form 9 May 2012 Accepted 12 June 2012
Objective: Fibromyalgia adversely affects quality of life, yet little is known of the epidemiology of this condition in MS. We aimed to validate and apply administrative case definitions for fibromyalgia in MS. Methods: Using administrative health data we identified persons with MS and an age-, sex- and geographically-matched general population (GP) cohort. Case definitions for fibromyalgia were developed using ICD-9/10 codes, validated against medical records, and applied to evaluate the incidence and prevalence of fibromyalgia. Results: The case definition for fibromyalgia with Z 5 hospital or physician claims in 3 years had a sensitivity of 60%, specificity of 98%, and agreed moderately with medical records (k ¼ 0.48). In 2005, the age-standardized prevalence of fibromyalgia was 6.82% (95% CI: 5.91–7.72) in the MS population and 3.04% (95% CI: 2.77–3.32) in the GP. After adjustment for age, sex and year, the incidence of fibromyalgia was 44% higher in the MS than the GP (IRR 1.44; 95% CI: 1.01–2.07). The incidence of fibromyalgia increased slightly over time in both populations. Conclusion: The incidence and prevalence of fibromyalgia are higher in the MS population than the general population. Fibromyalgia should be considered in the management of pain in persons with MS. & 2012 Elsevier B.V. All rights reserved.
Keywords: Multiple sclerosis Fibromyalgia Cohort studies Validation Prevalence Incidence
1. Introduction Fibromyalgia is a condition of unknown etiology that is characterized by persistent, wide-ranging pain involving multiple tender points (Branco et al., 2010; Clauw et al., 2011; Reisine et al., 2004; Wolfe et al., 1995). Familial aggregation of fibromyalgia and genetic association studies suggest that genetic factors play a role (Arnold et al., 2004; Light et al., 2012), while environmental factors such as psychosocial stressors and physical trauma have been proposed as precipitating factors. Fibromyalgia is estimated to affect approximately 2–5% of the general population, though it is much more prevalent among women, is associated with mood disorders, cognitive complaints, fatigue and sleep disturbance, and adversely
n Correspondence to: Health Sciences Center, GF-533, 820 Sherbrook Street, Winnipeg, MB, Canada R3A 1R9. Tel.: þ204 787 4951; fax: þ 204 787 1486. E-mail address: [email protected] (R.A. Marrie).
2211-0348/$ - see front matter & 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.msard.2012.06.001
impacts quality of life (QOL) (Branco et al., 2010; Clauw et al., 2011; Fuller-Thomson et al., 2011; Reisine et al., 2004; Wolfe et al., 1995). Recent studies have described the prevalence of fibromyalgia in the general population (Branco et al., 2010; Perrot et al., 2011), but incidence data are limited (Weir et al., 2006). Fibromyalgia shares many features with multiple sclerosis (MS) including an etiology secondary to both genetic and environmental factors, predominance among women, associations with depression and anxiety, and adverse effects on QOL (Pugliatti et al., 2002; Siegert and Abernethy, 2005; Weir et al., 2006). MS is a chronic, inflammatory, demyelinating disease of the central nervous system (CNS) estimated to affect more than 2.5 million persons worldwide (Beck et al., 2005; Dean, 1994). Comorbid conditions are increasingly recognized to be common and to impact clinical outcomes in MS (Marrie et al., 2009, 2010a,b); however, data on the epidemiology of fibromyalgia among persons with MS are limited. Among 8822 respondents from The North American Research Committee on Multiple
R.A. Marrie et al. / Multiple Sclerosis and Related Disorders 1 (2012) 162–167
Sclerosis (NARCOMS) Registry, a self-report registry for persons with MS, nearly five percent (428) reported a diagnosis of fibromyalgia (Marrie et al., 2008), and those who did, reported significantly lower physical and mental QOL, as measured by the Short Form-12 (Marrie et al., 2011). Because of the suggestion that comorbid fibromyalgia may add to the already substantial physical, psychosocial and economic impact of MS, we aimed to develop and validate administrative case definitions for fibromyalgia that could be used to estimate the incidence and prevalence of fibromyalgia in the MS and general populations.
2. Materials and methods 2.1. Study setting We conducted this study in Manitoba, a central Canadian province with a population of approximately 1.2 million (Health Information Management Branch, 2008). Data sources included provincial administrative (health) data and medical records data. 2.2. Administrative data With the approval of the Manitoba Health Information Privacy Committee we obtained access to anonymized administrative data from Manitoba Health (MH), a provincial government department which delivers health care services to 98% of the population (Health Information Management Branch, 2008). Since 1984, all provincial residents have been assigned a unique personal health identification number (PHIN) which is attached to all hospital, physician and prescription claims submitted to MH. Hospital discharge abstracts (claims) include the dates of admission and discharge and up to 16 discharge diagnoses using International Classification of Disease (ICD)-10-CA codes. Before 2004, discharge diagnoses were captured using five-digit ICD-9CM codes. Physician claims include the dates of service, and the three-digit ICD-9-CM code for one physician-assigned diagnosis. A population registry is maintained by MH, and is updated when an individual moves into or out of Manitoba, changes marital or family status, or dies. 2.3. Study populations We first developed and validated an administrative case definition for MS, as detailed elsewhere (Marrie et al., 2010a,b). This definition was then used to identify all persons with MS in Manitoba. After excluding other persons with a ICD-9/10 diagnostic code for any demyelinating disease, we identified a non-MS (control) cohort from the general population, individually matched on sex, year of birth and region of residence (postal code) to the MS cohort; we identified up to 5 matches for each case (Marrie et al., 2010a,b). As reported previously, 430 persons with definite MS who had consented to linkage of their medical records and administrative data, comprised our validation cohort (Marrie et al., 2012). A trained abstractor used a standardized data collection form to capture comorbidities from the medical records of these participants. Fibromyalgia was considered to be present if it was recorded as a diagnosis in the medical record; insufficient data were available to permit verification of diagnoses according to the American College of Rheumatology criteria (Wolfe et al., 1990). All data linkage was performed via scrambled PHIN, using anonymized versions of the administrative databases in order to protect participants’ confidentiality.
163
2.4. Development and validation of administrative case definitions We selected ICD-9/10 codes for fibromyalgia (729.1, M79.7). As noted above, hospital claims prior to 2004 used 4 digit ICD-9 codes, following which ICD-10 codes were used. Physician claims are coded using only 3 digits making the code used 729 (other disorders of soft tissue). We did not use prescription claims because we could not identify any medications specific to the treatment of fibromyalgia. From these hospital discharge and physician billing data, we developed several administrative case definitions, varying the number of physician and hospital claims required and the number of years used, to classify a person as having fibromyalgia. In the validation cohort, we compared the classification of fibromyalgia cases according to the administrative case definitions to medical records review by estimating sensitivity (Se), specificity (Sp), and positive predictive value (PPV) and negative predictive value (NPV). We also calculated a k statistic for the agreement between administrative and medical records data. We interpreted agreement using k as follows: slight (0–0.20), fair (0.21–0.40), moderate (0.41–0.60), substantial (0.61–0.80), and almost perfect (0.81–1.0) (Landis and Koch, 1977). 2.5. Prevalence and incidence We applied the case definitions which maximized kappa to the MS and general population cohorts. Once a person met the case definition, he or she was defined as affected in all subsequent years while resident in Manitoba (National Diabetes Surveillance System, 2003). We determined the point prevalence on October 1 each year using the mid-year population figures from the MH population registry for denominators in the calculations. To estimate incidence we required a conservative 5 year run-in period preceding the first fibromyalgia claim to ensure that cases were truly incident. For comparability with other studies, we used the direct method to age-standardize the results to the 2001 Canadian population (Rothman and Greenland, 1998), and calculated 95% confidence intervals (CI) assuming a Poisson distribution. In a matched cohort design a matched analysis is not needed (Rothman and Greenland, 1998), and adjustment is not needed to control for confounding due to the matching variables if followup time is the same in both cohorts. However, if follow-up time is not the same due to differential survival for example, as in our study, then adjustment is needed. Using negative binomial regression to account for overdispersion we calculated prevalence ratios (PR) and incidence rate ratios (IRR) and 95% CI between the MS and control groups, adjusting for sex, age, and year. Cell sizesr5 were suppressed. Ethics approval was obtained from the University of Manitoba Health Research Ethics Board. Written informed consent was obtained from all participants in the validation cohort (according to the Declaration of Helsinki). Statistical analyses used SAS 9.2 (SAS Institute Inc., Cary NC).
3. Results 3.1. Participants The study population included 4192 persons with MS (71.7% women) and 20,940 persons from the general population. As reported previously, most of the 430 persons in the validation cohort were white (391, 91.6%), and women (331, 77.0%) (Marrie et al., 2012). They had a mean (SD) age at symptom onset of 33.2 (11.1) years. Based on medical records review the frequency of fibromyalgia was 2.4%, and based on self-report was 3.5%.
164
R.A. Marrie et al. / Multiple Sclerosis and Related Disorders 1 (2012) 162–167
Table 1 Prevalence (Prev) of fibromyalgia per 100 population in 2006 in Manitoba with 95% confidence interval (CI) according to age and sex. Agea (years)
Multiple sclerosis population
General population
Females
20–44 45–59 Z60 Age-adjusted
Females
Males
Prev
95% CI
Prev
95% CI
Prev
95% CI
Prev
95% CI
5.77 9.67 10.1 7.78
4.34, 8.00, 7.99, 6.66,
3.11 7.05 3.88 4.32
1.48, 4.87, 2.30, 2.93,
2.25 4.68 5.03 3.52
1.83, 4.14, 4.34, 3.17,
1.68 1.74 2.36 1.85
1.06, 1.24, 1.76, 1.46,
7.68 11.7 12.8 8.91
6.53 10.2 6.55 5.72
2.77 5.29 5.83 3.86
2.68 2.44 3.16 2.25
Age groups collapsed because cell sizes o 5 suppressed.
9 8
MS Women GP Women
7
MS Men
6
GP Men
5 4 3 2 1
04
02
20
00
20
98
20
19
96
94
19
92
19
90
19
88
19
19
19
19
86
0
84
Age-standardized prevalence per 100 population
a
Males
As the Poisson model suggested an interaction between age group and population (po0.0001), we stratified the regression analysis by age group. Among persons aged 20–44 years, the prevalence of fibromyalgia was 2-fold higher in the MS population than the general population (PR 2.56; 95% CI: 2.25–2.91). The same was true for persons aged 45–59 years (PR 2.28; 95% CI: 2.05–2.54). Although the prevalence of fibromyalgia was still higher in the MS population among persons aged 60 years and older, this difference was less pronounced (PR 1.72; 95% CI: 1.56–1.90). Over the interval 1996–2005, prevalence of fibromyalgia rose 9% per year (PR 1.09; 95% CI: 1.08–1.10) after adjusting for age group, sex, population and the interaction between age group and population (Fig. 1). There was no interaction between population and year.
Year
3.4. Incidence Fig. 1. Age-standardized prevalence of fibromyalgia in the multiple sclerosis (MS) and general populations (GP) per 100 population according to year and sex.
Agreement between self-report and medical records for fibromyalgia was ‘‘substantial’’ with an estimated k ¼0.78 (95% CI: 0.59–0.97). 3.2. Case definitions Several administrative case definitions (labeled A to AD) were tested for fibromyalgia (Supplemental Table 1). Definitions that used only one year of data were relatively insensitive, with a maximum sensitivity of 40% when only one hospital or physical claims was required. They were, however, highly specific with the lowest specificity being 95.5% for the most sensitive definition of one hospital or physician claim. The maximum sensitivity of 80% was seen with multiple case definitions including those using 2, 4 and 5 years of data. Agreement between the administrative case definitions and medical records varied from slight to moderate (k ¼0.09–0.48). The highest level of agreement was reached for definitions ‘R’ ( Z5 hospital or physician claims in 3 years, k ¼0.48) and ‘X’ ( Z5 hospital or physician claims in 4 years, k ¼0.46). Both definitions had moderate sensitivity (60%) and high specificity (97–98%). 3.3. Prevalence To estimate the prevalence of fibromyalgia in the MS and general populations we applied definition ‘R’. In 2005, the ageadjusted prevalence of fibromyalgia was 3.04% (95% CI: 2.77– 3.32) in the general population and 6.82% (95% CI: 5.91–7.72) in the MS population. The prevalence of fibromyalgia was higher in women than men in both populations (Table 1) and adjusting for age group, year and population, the prevalence of fibromyalgia was two-fold higher among women (PR 2.16; 95% CI: 2.01–2.32).
Again applying definition ‘R’, the average annual incidence of fibromyalgia per 100,000 persons per year after onset of MS was 123.7, while it was 91.4 in the general population (Table 2). After adjustment for age, sex and year, the incidence of fibromyalgia was 44% higher in the MS than the general population (IRR 1.44; 95% CI: 1.01–2.07). In both populations, the incidence of fibromyalgia was two-fold higher in women than men (IRR 2.10; 95% CI: 1.44–3.06). As compared to persons aged 20–44 years, the incidence in persons aged 45–60 years was two-fold higher (IRR 2.06; 95% CI: 1.37–3.10), and the incidence in persons aged 60 years and older was four-fold higher (IRR 4.24; 95% CI: 1.88– 9.59). Over the interval from 1989 to 2005, the incidence of fibromyalgia increased slightly in both populations (IRR 1.08; 95% CI: 1.05–1.12) (Fig. 2). Among incident cases, the mean age of onset of fibromyalgia in the MS population was 53.1 (13.4) years, and in the general population was 52.6 (13.2) years (p¼ 0.80).
4. Discussion Fibromyalgia causes widespread pain, generally accompanied by fatigue and non-restorative sleep (Clauw et al., 2011). In the general population, fibromyalgia adversely impacts quality of life and employment, and confers a substantial economic burden (Reisine et al., 2004). In MS, comorbidities causing pain, such as migraine and fibromyalgia, have independent negative effects on QOL (Marrie et al., 2011; Villani et al., 2011). In the NARCOMS population, individuals with MS who self-report health information, fibromyalgia most impacted physical QOL among the 14 comorbidities evaluated (Marrie et al., 2011). Pain affects 45–79% of individuals with MS (Stenager et al., 1991; Svendsen et al., 2003). To better understand the potential impact of pain on this already debilitating disease, we must improve our understanding of the occurrence of fibromyalgia in MS.
R.A. Marrie et al. / Multiple Sclerosis and Related Disorders 1 (2012) 162–167
165
Table 2 Average annual incidence (Inc) of fibromyalgia per 100,000 persons per year in the multiple sclerosis and general populations. Age (years)
Females
Multiple sclerosis 20–44 45–59 Z60 Age-adjusted
Inc
95% CI
n
Inc
95% CI
a
65.2, 82.6, 156.2, 103.7,
a
60
98.2 131.1 244.8 133.6
17
58.4 161.8 69.8 98.1
21.9, 87.0, 22.5, 61.0,
155.7 300.7 216.6 157.9
74 108 58 240
64.4 160.8 144.6 108.0
51.3, 80.9 133.17, 194.2 111.8, 187.0 95.2, 122.6
13 15 14 42
38.8 49.4 62.4 48.6
22.5, 29.8, 37.0, 36.0,
66.8 81.9 105.4 65.8
a
147.7 208.0 383.8 172.0
a a
n
Inc
95% CI
27 28 22 77
89.2 140.6 182.5 123.7
61.2, 97.1, 120.2, 98.9,
130.0 203.6 277.1 154.7
87 123 72 282
58.6 126.1 115.1 91.4
47.5, 105.7, 91.4, 81.3,
72.3 150.5 145.0 102.7
Suppressed due to small cell sizes.
Age-standardized incidence per 100,000 persons per year
a
Both
n
a
General population 20–44 45–59 Z60 Age-adjusted
Males
700 MS
600
GP
500 400 300 200 100 0
1989
1991
1993
1995
1997 Year
1999
2001
2003
Fig. 2. Age-standardized incidence of fibromyalgia in the multiple sclerosis (MS) and general populations (GP) per 100,000 persons per year.
Developing our case definition for fibromyalgia, which required at least five hospital or physician claims in three years, proved challenging. Physician claims in Manitoba use only three digit ICD-9-CM codes and at this level the code for fibromyalgia (729.1) becomes 729, that is, other disorders of soft tissues. Thus it encompasses rheumatism unspecified, neuralgia, neuritis and radiculitis unspecified, panniculitis unspecified, fasciitis unspecified, pain in limb, residual foreign body in soft tissue, nontraumatic compartment syndrome, other musculoskeletal symptoms referable to limbs, other and unspecified disorders of soft tissue, in addition to the desired myalgia and myositis not otherwise specified. This made it critical to require multiple diagnostic claims, and to validate the definition against medical records review. Nonetheless, we were able to identify highly specific definitions that agreed ‘moderately’ with medical records, and prevalence in the general population was consistent with expectations (Perrot et al., 2011; Wolfe et al., 1995). However, given the nonspecific ICD-9 code that was used, this definition may not perform as well in non-MS populations who might have recurrent uses of other codes encompassed by 729; we recommend further evaluation of this definition in other populations. In jurisdictions where 4-digit codes are available comparison of the incidence and prevalence of fibromyalgia for case definitions using 729 versus 729.1 would be valuable. In jurisdictions which use ICD-10 codes for hospital and physician claims the specific code for fibromyalgia (M79.7) may also facilitate the development of superior case definitions. The availability of an administrative case definition provides another potential method for identifying fibromyalgia, along with self-report and records review (Horton et al., 2010).
Until recently, limited data described the incidence and prevalence of fibromyalgia in the general population. Our findings of a general population prevalence of 3%, with a higher prevalence in women (3.5%) than men (1.9%) are consistent with those reported elsewhere. For example, based on a self-administered questionnaire and physical examination, 2% of persons in Wichita (United States) had fibromyalgia (Wolfe et al., 1995). Based on a telephone-administered questionnaire 2.9–4.7% of the general population in five European countries have fibromyalgia (Branco et al., 2010). We found the average annual incidence in the general population was 91.4 per 100,000 persons per year, and that disease incidence increased slightly over more than 15 years. An American study using administrative claims data with one ICD-9 code of 729.1 reported age-adjusted incidence rates per 1000 person-years of 11.28 cases for women and 6.88 cases for men (Weir et al., 2006), consistent with our findings. A Norwegian study reported an average annual incidence of 583 cases/100,000 females aged 20–49 years (Forseth et al., 1997). Neither study examined temporal trends in incidence. In North America, 4.8% of NARCOMS participants self-reported physician-diagnosed fibromyalgia with a mean age of diagnosis of 43.0 years (Marrie et al., 2008). For the first time, we used a population-based design to estimate the incidence and prevalence of fibromyalgia in the MS population. In 2005, the prevalence was 6.8% while the average annual incidence was 123.7 per 100,000 persons per year. Both the incidence and prevalence of fibromyalgia were higher in the MS than the general population. Several possible explanations exist for this finding. First, the presence of one chronic condition may increase the chances of another condition being diagnosed due to increased health services use. Second, MS and fibromyalgia could share common etiologic factors. Third, fibromyalgia is believed to be of neurogenic origin, due to abnormal processing of painful stimuli in the CNS (Clauw et al., 2011), sometimes described as a central sensitivity syndrome (Yunus, 2012), raising the possibility that fibromyalgia could be a consequence of MS in some individuals. Fibromyalgia occurs more often in persons with other postulated central sensitivity syndromes such as irritable bowel syndrome (Yunus, 2012), and in persons with other immune-mediated diseases such as rheumatoid arthritis (Weir et al., 2006; Yunus, 2012). Further examination of the overlap of these conditions with fibromyalgia may improve our understanding of the mechanisms of pain in fibromyalgia and ultimately result in improved treatment. Due to practical and ethical issues we could not review the medical records of all providers caring for our validation cohort, thus we potentially underestimated the prevalence of fibromyalgia based on medical records review. The validation cohort
166
R.A. Marrie et al. / Multiple Sclerosis and Related Disorders 1 (2012) 162–167
included only 10 persons with fibromyalgia, contributing to wide confidence intervals regarding the sensitivity of our case definition and limiting us to examining incidence and prevalence using only three age groups. Agreement between the case definition and medical records was only moderate; however, our findings regarding the prevalence of fibromyalgia in the general population were consistent with the literature. Despite these issues, this study had several strengths. The administrative case definition was validated in a cohort with similar characteristics to the population in which it was ultimately applied, the design was novel and population-based.
5. Conclusions Fibromyalgia affected 3% of the general population, and nearly twice as many persons in the MS population (over one in twenty in MS patients). Moreover, the incidence of fibromyalgia was 44% higher in the MS population than in the general population though the incidence rose only slightly in both populations. While these findings need to be replicated in other jurisdictions, further evaluation of the mechanisms underlying comorbid fibromyalgia and MS is warranted; particularly given the potential cumulative effect of these conditions. Recognition of the occurrence of fibromyalgia in MS is also important to optimize management of both conditions.
Disclosures Ruth Ann Marrie receives research funding from: Canadian Institutes of Health Research, Public Health Agency of Canada, Manitoba Health Research Council, Health Sciences Centre Foundation, Multiple Sclerosis Society of Canada, Multiple Sclerosis Scientific Foundation, Rx & D Health Research Foundation, and has conducted clinical trials funded by Bayer Inc. and SanofiAventis. Nancy Yu receives research support from the Canadian International Development Agency, the Multiple Sclerosis Society of Canada, CIHR, and Manitoba Health and Healthy Living. Stella Leung reports no disclosures. Lawrence Elliott receives research support from the Canadian Institutes of Health Research, Health Sciences Centre Foundation, Public Health Agency of Canada, and the Multiple Sclerosis Society of Canada. Sharon Warren receives research funding from the CIHR, the Canadian Health Services Research Foundation, Alberta Health Services and SSHRC. Christina Wolfson receives research funding from the Multiple Sclerosis Society of Canada, Canadian Institutes of Health Research, Canada Foundation for Innovation, and Public Health Agency of Canada. Helen Tremlett currently receives funding from the Multiple Sclerosis Society of Canada [Don Paty Career Development Award]; US National MS Society [#RG 4202-A-2 (PI)]; Canadian Institutes of Health Research [MOP: #190898 (PI) and MOP93646 (PI)]; Michael Smith Foundation for Health Research and is the Canada Research Chair for Neuroepidemiology and Multiple Sclerosis. Other funding is as follows: she has received speaker honoraria and/or travel expenses to attend conferences from: the Consortium of MS Centers, US National MS Society, Swiss Multiple Sclerosis Society, the University of British Columbia Multiple Sclerosis Research Program, Teva Pharmaceuticals and Bayer Pharmaceutical (honoraria declined) and ECTRIMS (2011), UK MS Trust and the Chesapeake Health Education Program, US Veterans Affairs (2012, honorarium declined). Unless otherwise
stated, all speaker honoraria are either donated to an MS charity or to an unrestricted grant for use by her research group. John Fisk is the Director of the endMS Atlantic Regional Research and Training Center which is funded by the Multiple Sclerosis Society of Canada. He receives research funding from the Canadian Institutes of Health Research (CIHR) and in the past has received grants, honoraria and consultation fees from AstraZeneca, Bayer, Biogen-Idec Canada, Heron Evidence Development Limited, Hoffmann-La Roche, MAPI Research Trust, Novartis, Sanofi-Aventis, Serono Canada, and Quality Metric Incorporated. James Blanchard receives research support from the Multiple Sclerosis Society of Canada, CIHR, Bill & Melinda Gates Foundation, Canadian International Development Agency and the United States Agency for International Development.
Acknowledgment/funding source This study was supported (in part) by Don Paty Career Development and Operating Grants from the Multiple Sclerosis Society of Canada, Manitoba Health Research Council, Canadian Institutes of Health Research, Public Health Agency of Canada, and Rx & D Health Research Foundation. The funding source(s) had neither a role in the study design, collection, analysis or interpretation of the data, nor in the decision to submit the article for publication. The results and conclusions presented are those of the authors. No official endorsement by Manitoba Health is intended or should be inferred. We thank John Hanly, M.D., for his helpful comments on an earlier version of this manuscript. CIHR Team in the Epidemiology and Impact of Comorbidity on Multiple Sclerosis (by site): University of Manitoba (James Blanchard, M.D., Ph.D.; Patricia Caetano, Ph.D.; Lawrence Elliott, M.D., M.Sc.; Stella Leung, M.Sc.; Ruth Ann Marrie, M.D., Ph.D.; Bo Nancy Yu, M.D., Ph.D.) Dalhousie University (Virender Bhan, M.B.B.S.; John D. Fisk, Ph.D.), University of Alberta (Joanne Profetto-McGrath, Ph.D.; Sharon Warren, Ph.D.; Larry Svenson, B.Sc.); McGill University (Christina Wolfson, Ph.D.); University of British Columbia (Helen Tremlett, Ph.D.); University of Calgary (Scott Patten, M.D.).
Appendix A. Supplementary materials Supplementary data associated with this article can be found in the online version at doi:10.1016/j.msard.2012.06.001.
References Arnold LM, Hudson JI, Hess EV, Ware AE, Fritz DA, Auchenbach MB, et al. Family study of fibromyalgia. Arthritis & Rheumatism 2004;50:944–52. Beck CA, Metz LM, Svenson LW, Patten SB. Regional variation of multiple sclerosis prevalence in Canada. Multiple Sclerosis 2005;11:516–9. Branco JC, Bannwarth B, Failde I, Abello Carbonell J, Blotman F, Spaeth M, et al. Prevalence of fibromyalgia: a survey in five European countries. Seminars in Arthritis and Rheumatism 2010;39:448–53. Clauw DJ, Arnold LM, McCarberg BH. The science of fibromyalgia. Mayo Clinic Proceedings 2011;86:907–11. Dean G. How many people in the world have multiple sclerosis. Neuroepidemiology 1994;13:1–7. Forseth K, Gran J, Husby G. A population study of the incidence of fibromyalgia among women aged 26–55 yr. Rheumatology 1997;36:1318–23. Fuller-Thomson E, Nimigon-Young J, Brennenstuhl S. Individuals with fibromyalgia and depression: findings from a nationally representative Canadian survey. Rheumatology International 2011 [January 8, Epub ahead of print]. Health Information Management Branch. Population report. Winnipeg, Manitoba: Manitoba Health and Healthy Living; 2008. Horton M, Rudick RA, Hara-Cleaver C, Marrie RA. Validation of a self-report comorbidity questionnaire for multiple sclerosis. Neuroepidemiology 2010;35:83–90. Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics 1977;33:159–74.
R.A. Marrie et al. / Multiple Sclerosis and Related Disorders 1 (2012) 162–167
Light K, White A, Tadler S, Iacob E, Light A. Genetics and gene expression involving stress and distress pathways in fibromyalgia with and without comorbid chronic fatigue syndrome. Pain Research and Treatment 2012:427869. Marrie R, Yu B, Leung S, Elliott L, Caetano P, Warren S, et al. Rising prevalence of vascular comorbidities in MS: validation of administrative definitions for diabetes, hypertension, hyperlipidemia. Multiple Sclerosis Journal 2012, http://dx.doi.org/10.1177/1352458512437814 [Epub ahead of print. February 10]. Marrie RA, Horwitz R, Cutter G, Tyry T. Cumulative impact of comorbidity on quality of life in MS. Acta Neurologica Scandinavica 2011, http://dx.doi.org/ 10.1111/j.1600-0404.2011.01526.x. Marrie RA, Horwitz R, Cutter G, Tyry T, Campagnolo D, Vollmer T. Comorbidity, socioeconomic status, and multiple sclerosis. Multiple Sclerosis 2008;14:1091–8. Marrie RA, Horwitz RI, Cutter G, Tyry T, Campagnolo D, Vollmer T. Comorbidity delays diagnosis and increases disability at diagnosis in MS. Neurology 2009;72:117–24. Marrie RA, Rudick R, Horwitz R, Cutter G, Tyry T, Campagnolo D, et al. Vascular comorbidity is associated with more rapid disability progression in multiple sclerosis. Neurology 2010a;74:1041–7. Marrie RA, Yu N, Blanchard JF, Leung S, Elliott L. The rising prevalence and changing age distribution of multiple sclerosis in Manitoba. Neurology 2010;74:465–71. National Diabetes Surveillance System. Responding to the challenge of diabetes in Canada. Ottawa: Health Canada; 2003. Perrot S, Vicaut E, Servant D, Ravaud P. Prevalence of fibromyalgia in France: a multi-step study research combining national screening and clinical confirmation: the DEFI study (determination of epidemiology of fibromyalgia). BMC Musculoskeletal Disorders 2011;12:224. Pugliatti M, Sotgiu S, Rosati G. The worldwide prevalence of multiple sclerosis. Clinical Neurology and Neurosurgery 2002;104:182–91.
167
Reisine S, Fifield J, Walsh S, Dauser D. Employment and quality of life outcomes among women with fibromyalgia compared to healthy controls. Women & Health 2004;39:1–20. Rothman KJ, Greenland S, editors. Modern epidemiology. Philadelphia, PA: Lippincott Williams & Wilkins; 1998. Siegert RJ, Abernethy DA. Depression in multiple sclerosis: a review. Journal of Neurology, Neurosurgery and Psychiatry 2005;76:469–75. Stenager E, Knudsen L, Jensen K. Acute and chronic pain syndromes in multiple sclerosis. Acta Neurologica Scandinavica 1991;84:197–200. Svendsen KB, Jensen TS, Overvad K, Hansen HJ, Koch-Henriksen N, Bach FW. Pain in patients with multiple sclerosis. A population-based study. Archives of Neurology 2003;60:1089–94. Villani V, Prosperini L, Pozzilli C, Salvetti M, Sette G. Quality of life of multiple sclerosis patients with comorbid migraine. Neurological Sciences 2011;32:149–51. Weir PT, Harlan GA, Nkoy FL, Jones SS, Hegmann KT, Gren LH, et al. The incidence of fibromyalgia and its associated comorbidities: a population-based retrospective cohort study based on International Classification of Diseases, 9th Revision codes. Journal of Clinical Rheumatology 2006;12:124–8. Wolfe F, Ross K, Anderson J, Russell J, Hebert L. The prevalence and characteristics of fibromyalgia in the general population. Arthritis & Rheumatism 1995;38: 19–28. Wolfe F, Smythe H, Yunus M, Bennett R, Bombardier C, Goldenberg D, et al. The American College of Rheumatology 1990 criteria for the classification of fibromyalgia. Report of the Multicenter Criteria Committee. Arthritis & Rheumatism 1990;33:160–72. Yunus M. The prevalence of fibromyalgia in other chronic pain conditions. Pain Research and Treatment 2012;584573 [8 p.].
Contents lists available at SciVerse ScienceDirect
Multiple Sclerosis and Related Disorders journal homepage: www.elsevier.com/locate/msard
The incidence and prevalence of fibromyalgia are higher in multiple sclerosis than the general population: A population-based study Ruth Ann Marrie a,b,n, Bo Nancy Yu b, Stella Leung b, Lawrence Elliott b, Sharon Warren c, Christina Wolfson d,e, Helen Tremlett f, James Blanchard b, John D. Fisk g, for the CIHR Team in the Epidemiology and Impact of Comorbidity in Multiple Sclerosis a
Department of Internal Medicine, University of Manitoba, Winnipeg, Canada Department of Community Health Sciences, University of Manitoba, Winnipeg, Canada c Faculty of Rehabilitation Medicine, University of Alberta, Edmonton, Canada d Department of Epidemiology and Biostatistics, McGill University, Montreal, Canada e Research Institute of the McGill University Health Centre, Montreal, Canada f Department of Medicine (Neurology), University of British Columbia, Vancouver, Canada g Departments of Psychiatry and Medicine, Dalhousie University, Halifax, Canada b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 24 March 2012 Received in revised form 9 May 2012 Accepted 12 June 2012
Objective: Fibromyalgia adversely affects quality of life, yet little is known of the epidemiology of this condition in MS. We aimed to validate and apply administrative case definitions for fibromyalgia in MS. Methods: Using administrative health data we identified persons with MS and an age-, sex- and geographically-matched general population (GP) cohort. Case definitions for fibromyalgia were developed using ICD-9/10 codes, validated against medical records, and applied to evaluate the incidence and prevalence of fibromyalgia. Results: The case definition for fibromyalgia with Z 5 hospital or physician claims in 3 years had a sensitivity of 60%, specificity of 98%, and agreed moderately with medical records (k ¼ 0.48). In 2005, the age-standardized prevalence of fibromyalgia was 6.82% (95% CI: 5.91–7.72) in the MS population and 3.04% (95% CI: 2.77–3.32) in the GP. After adjustment for age, sex and year, the incidence of fibromyalgia was 44% higher in the MS than the GP (IRR 1.44; 95% CI: 1.01–2.07). The incidence of fibromyalgia increased slightly over time in both populations. Conclusion: The incidence and prevalence of fibromyalgia are higher in the MS population than the general population. Fibromyalgia should be considered in the management of pain in persons with MS. & 2012 Elsevier B.V. All rights reserved.
Keywords: Multiple sclerosis Fibromyalgia Cohort studies Validation Prevalence Incidence
1. Introduction Fibromyalgia is a condition of unknown etiology that is characterized by persistent, wide-ranging pain involving multiple tender points (Branco et al., 2010; Clauw et al., 2011; Reisine et al., 2004; Wolfe et al., 1995). Familial aggregation of fibromyalgia and genetic association studies suggest that genetic factors play a role (Arnold et al., 2004; Light et al., 2012), while environmental factors such as psychosocial stressors and physical trauma have been proposed as precipitating factors. Fibromyalgia is estimated to affect approximately 2–5% of the general population, though it is much more prevalent among women, is associated with mood disorders, cognitive complaints, fatigue and sleep disturbance, and adversely
n Correspondence to: Health Sciences Center, GF-533, 820 Sherbrook Street, Winnipeg, MB, Canada R3A 1R9. Tel.: þ204 787 4951; fax: þ 204 787 1486. E-mail address: [email protected] (R.A. Marrie).
2211-0348/$ - see front matter & 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.msard.2012.06.001
impacts quality of life (QOL) (Branco et al., 2010; Clauw et al., 2011; Fuller-Thomson et al., 2011; Reisine et al., 2004; Wolfe et al., 1995). Recent studies have described the prevalence of fibromyalgia in the general population (Branco et al., 2010; Perrot et al., 2011), but incidence data are limited (Weir et al., 2006). Fibromyalgia shares many features with multiple sclerosis (MS) including an etiology secondary to both genetic and environmental factors, predominance among women, associations with depression and anxiety, and adverse effects on QOL (Pugliatti et al., 2002; Siegert and Abernethy, 2005; Weir et al., 2006). MS is a chronic, inflammatory, demyelinating disease of the central nervous system (CNS) estimated to affect more than 2.5 million persons worldwide (Beck et al., 2005; Dean, 1994). Comorbid conditions are increasingly recognized to be common and to impact clinical outcomes in MS (Marrie et al., 2009, 2010a,b); however, data on the epidemiology of fibromyalgia among persons with MS are limited. Among 8822 respondents from The North American Research Committee on Multiple
R.A. Marrie et al. / Multiple Sclerosis and Related Disorders 1 (2012) 162–167
Sclerosis (NARCOMS) Registry, a self-report registry for persons with MS, nearly five percent (428) reported a diagnosis of fibromyalgia (Marrie et al., 2008), and those who did, reported significantly lower physical and mental QOL, as measured by the Short Form-12 (Marrie et al., 2011). Because of the suggestion that comorbid fibromyalgia may add to the already substantial physical, psychosocial and economic impact of MS, we aimed to develop and validate administrative case definitions for fibromyalgia that could be used to estimate the incidence and prevalence of fibromyalgia in the MS and general populations.
2. Materials and methods 2.1. Study setting We conducted this study in Manitoba, a central Canadian province with a population of approximately 1.2 million (Health Information Management Branch, 2008). Data sources included provincial administrative (health) data and medical records data. 2.2. Administrative data With the approval of the Manitoba Health Information Privacy Committee we obtained access to anonymized administrative data from Manitoba Health (MH), a provincial government department which delivers health care services to 98% of the population (Health Information Management Branch, 2008). Since 1984, all provincial residents have been assigned a unique personal health identification number (PHIN) which is attached to all hospital, physician and prescription claims submitted to MH. Hospital discharge abstracts (claims) include the dates of admission and discharge and up to 16 discharge diagnoses using International Classification of Disease (ICD)-10-CA codes. Before 2004, discharge diagnoses were captured using five-digit ICD-9CM codes. Physician claims include the dates of service, and the three-digit ICD-9-CM code for one physician-assigned diagnosis. A population registry is maintained by MH, and is updated when an individual moves into or out of Manitoba, changes marital or family status, or dies. 2.3. Study populations We first developed and validated an administrative case definition for MS, as detailed elsewhere (Marrie et al., 2010a,b). This definition was then used to identify all persons with MS in Manitoba. After excluding other persons with a ICD-9/10 diagnostic code for any demyelinating disease, we identified a non-MS (control) cohort from the general population, individually matched on sex, year of birth and region of residence (postal code) to the MS cohort; we identified up to 5 matches for each case (Marrie et al., 2010a,b). As reported previously, 430 persons with definite MS who had consented to linkage of their medical records and administrative data, comprised our validation cohort (Marrie et al., 2012). A trained abstractor used a standardized data collection form to capture comorbidities from the medical records of these participants. Fibromyalgia was considered to be present if it was recorded as a diagnosis in the medical record; insufficient data were available to permit verification of diagnoses according to the American College of Rheumatology criteria (Wolfe et al., 1990). All data linkage was performed via scrambled PHIN, using anonymized versions of the administrative databases in order to protect participants’ confidentiality.
163
2.4. Development and validation of administrative case definitions We selected ICD-9/10 codes for fibromyalgia (729.1, M79.7). As noted above, hospital claims prior to 2004 used 4 digit ICD-9 codes, following which ICD-10 codes were used. Physician claims are coded using only 3 digits making the code used 729 (other disorders of soft tissue). We did not use prescription claims because we could not identify any medications specific to the treatment of fibromyalgia. From these hospital discharge and physician billing data, we developed several administrative case definitions, varying the number of physician and hospital claims required and the number of years used, to classify a person as having fibromyalgia. In the validation cohort, we compared the classification of fibromyalgia cases according to the administrative case definitions to medical records review by estimating sensitivity (Se), specificity (Sp), and positive predictive value (PPV) and negative predictive value (NPV). We also calculated a k statistic for the agreement between administrative and medical records data. We interpreted agreement using k as follows: slight (0–0.20), fair (0.21–0.40), moderate (0.41–0.60), substantial (0.61–0.80), and almost perfect (0.81–1.0) (Landis and Koch, 1977). 2.5. Prevalence and incidence We applied the case definitions which maximized kappa to the MS and general population cohorts. Once a person met the case definition, he or she was defined as affected in all subsequent years while resident in Manitoba (National Diabetes Surveillance System, 2003). We determined the point prevalence on October 1 each year using the mid-year population figures from the MH population registry for denominators in the calculations. To estimate incidence we required a conservative 5 year run-in period preceding the first fibromyalgia claim to ensure that cases were truly incident. For comparability with other studies, we used the direct method to age-standardize the results to the 2001 Canadian population (Rothman and Greenland, 1998), and calculated 95% confidence intervals (CI) assuming a Poisson distribution. In a matched cohort design a matched analysis is not needed (Rothman and Greenland, 1998), and adjustment is not needed to control for confounding due to the matching variables if followup time is the same in both cohorts. However, if follow-up time is not the same due to differential survival for example, as in our study, then adjustment is needed. Using negative binomial regression to account for overdispersion we calculated prevalence ratios (PR) and incidence rate ratios (IRR) and 95% CI between the MS and control groups, adjusting for sex, age, and year. Cell sizesr5 were suppressed. Ethics approval was obtained from the University of Manitoba Health Research Ethics Board. Written informed consent was obtained from all participants in the validation cohort (according to the Declaration of Helsinki). Statistical analyses used SAS 9.2 (SAS Institute Inc., Cary NC).
3. Results 3.1. Participants The study population included 4192 persons with MS (71.7% women) and 20,940 persons from the general population. As reported previously, most of the 430 persons in the validation cohort were white (391, 91.6%), and women (331, 77.0%) (Marrie et al., 2012). They had a mean (SD) age at symptom onset of 33.2 (11.1) years. Based on medical records review the frequency of fibromyalgia was 2.4%, and based on self-report was 3.5%.
164
R.A. Marrie et al. / Multiple Sclerosis and Related Disorders 1 (2012) 162–167
Table 1 Prevalence (Prev) of fibromyalgia per 100 population in 2006 in Manitoba with 95% confidence interval (CI) according to age and sex. Agea (years)
Multiple sclerosis population
General population
Females
20–44 45–59 Z60 Age-adjusted
Females
Males
Prev
95% CI
Prev
95% CI
Prev
95% CI
Prev
95% CI
5.77 9.67 10.1 7.78
4.34, 8.00, 7.99, 6.66,
3.11 7.05 3.88 4.32
1.48, 4.87, 2.30, 2.93,
2.25 4.68 5.03 3.52
1.83, 4.14, 4.34, 3.17,
1.68 1.74 2.36 1.85
1.06, 1.24, 1.76, 1.46,
7.68 11.7 12.8 8.91
6.53 10.2 6.55 5.72
2.77 5.29 5.83 3.86
2.68 2.44 3.16 2.25
Age groups collapsed because cell sizes o 5 suppressed.
9 8
MS Women GP Women
7
MS Men
6
GP Men
5 4 3 2 1
04
02
20
00
20
98
20
19
96
94
19
92
19
90
19
88
19
19
19
19
86
0
84
Age-standardized prevalence per 100 population
a
Males
As the Poisson model suggested an interaction between age group and population (po0.0001), we stratified the regression analysis by age group. Among persons aged 20–44 years, the prevalence of fibromyalgia was 2-fold higher in the MS population than the general population (PR 2.56; 95% CI: 2.25–2.91). The same was true for persons aged 45–59 years (PR 2.28; 95% CI: 2.05–2.54). Although the prevalence of fibromyalgia was still higher in the MS population among persons aged 60 years and older, this difference was less pronounced (PR 1.72; 95% CI: 1.56–1.90). Over the interval 1996–2005, prevalence of fibromyalgia rose 9% per year (PR 1.09; 95% CI: 1.08–1.10) after adjusting for age group, sex, population and the interaction between age group and population (Fig. 1). There was no interaction between population and year.
Year
3.4. Incidence Fig. 1. Age-standardized prevalence of fibromyalgia in the multiple sclerosis (MS) and general populations (GP) per 100 population according to year and sex.
Agreement between self-report and medical records for fibromyalgia was ‘‘substantial’’ with an estimated k ¼0.78 (95% CI: 0.59–0.97). 3.2. Case definitions Several administrative case definitions (labeled A to AD) were tested for fibromyalgia (Supplemental Table 1). Definitions that used only one year of data were relatively insensitive, with a maximum sensitivity of 40% when only one hospital or physical claims was required. They were, however, highly specific with the lowest specificity being 95.5% for the most sensitive definition of one hospital or physician claim. The maximum sensitivity of 80% was seen with multiple case definitions including those using 2, 4 and 5 years of data. Agreement between the administrative case definitions and medical records varied from slight to moderate (k ¼0.09–0.48). The highest level of agreement was reached for definitions ‘R’ ( Z5 hospital or physician claims in 3 years, k ¼0.48) and ‘X’ ( Z5 hospital or physician claims in 4 years, k ¼0.46). Both definitions had moderate sensitivity (60%) and high specificity (97–98%). 3.3. Prevalence To estimate the prevalence of fibromyalgia in the MS and general populations we applied definition ‘R’. In 2005, the ageadjusted prevalence of fibromyalgia was 3.04% (95% CI: 2.77– 3.32) in the general population and 6.82% (95% CI: 5.91–7.72) in the MS population. The prevalence of fibromyalgia was higher in women than men in both populations (Table 1) and adjusting for age group, year and population, the prevalence of fibromyalgia was two-fold higher among women (PR 2.16; 95% CI: 2.01–2.32).
Again applying definition ‘R’, the average annual incidence of fibromyalgia per 100,000 persons per year after onset of MS was 123.7, while it was 91.4 in the general population (Table 2). After adjustment for age, sex and year, the incidence of fibromyalgia was 44% higher in the MS than the general population (IRR 1.44; 95% CI: 1.01–2.07). In both populations, the incidence of fibromyalgia was two-fold higher in women than men (IRR 2.10; 95% CI: 1.44–3.06). As compared to persons aged 20–44 years, the incidence in persons aged 45–60 years was two-fold higher (IRR 2.06; 95% CI: 1.37–3.10), and the incidence in persons aged 60 years and older was four-fold higher (IRR 4.24; 95% CI: 1.88– 9.59). Over the interval from 1989 to 2005, the incidence of fibromyalgia increased slightly in both populations (IRR 1.08; 95% CI: 1.05–1.12) (Fig. 2). Among incident cases, the mean age of onset of fibromyalgia in the MS population was 53.1 (13.4) years, and in the general population was 52.6 (13.2) years (p¼ 0.80).
4. Discussion Fibromyalgia causes widespread pain, generally accompanied by fatigue and non-restorative sleep (Clauw et al., 2011). In the general population, fibromyalgia adversely impacts quality of life and employment, and confers a substantial economic burden (Reisine et al., 2004). In MS, comorbidities causing pain, such as migraine and fibromyalgia, have independent negative effects on QOL (Marrie et al., 2011; Villani et al., 2011). In the NARCOMS population, individuals with MS who self-report health information, fibromyalgia most impacted physical QOL among the 14 comorbidities evaluated (Marrie et al., 2011). Pain affects 45–79% of individuals with MS (Stenager et al., 1991; Svendsen et al., 2003). To better understand the potential impact of pain on this already debilitating disease, we must improve our understanding of the occurrence of fibromyalgia in MS.
R.A. Marrie et al. / Multiple Sclerosis and Related Disorders 1 (2012) 162–167
165
Table 2 Average annual incidence (Inc) of fibromyalgia per 100,000 persons per year in the multiple sclerosis and general populations. Age (years)
Females
Multiple sclerosis 20–44 45–59 Z60 Age-adjusted
Inc
95% CI
n
Inc
95% CI
a
65.2, 82.6, 156.2, 103.7,
a
60
98.2 131.1 244.8 133.6
17
58.4 161.8 69.8 98.1
21.9, 87.0, 22.5, 61.0,
155.7 300.7 216.6 157.9
74 108 58 240
64.4 160.8 144.6 108.0
51.3, 80.9 133.17, 194.2 111.8, 187.0 95.2, 122.6
13 15 14 42
38.8 49.4 62.4 48.6
22.5, 29.8, 37.0, 36.0,
66.8 81.9 105.4 65.8
a
147.7 208.0 383.8 172.0
a a
n
Inc
95% CI
27 28 22 77
89.2 140.6 182.5 123.7
61.2, 97.1, 120.2, 98.9,
130.0 203.6 277.1 154.7
87 123 72 282
58.6 126.1 115.1 91.4
47.5, 105.7, 91.4, 81.3,
72.3 150.5 145.0 102.7
Suppressed due to small cell sizes.
Age-standardized incidence per 100,000 persons per year
a
Both
n
a
General population 20–44 45–59 Z60 Age-adjusted
Males
700 MS
600
GP
500 400 300 200 100 0
1989
1991
1993
1995
1997 Year
1999
2001
2003
Fig. 2. Age-standardized incidence of fibromyalgia in the multiple sclerosis (MS) and general populations (GP) per 100,000 persons per year.
Developing our case definition for fibromyalgia, which required at least five hospital or physician claims in three years, proved challenging. Physician claims in Manitoba use only three digit ICD-9-CM codes and at this level the code for fibromyalgia (729.1) becomes 729, that is, other disorders of soft tissues. Thus it encompasses rheumatism unspecified, neuralgia, neuritis and radiculitis unspecified, panniculitis unspecified, fasciitis unspecified, pain in limb, residual foreign body in soft tissue, nontraumatic compartment syndrome, other musculoskeletal symptoms referable to limbs, other and unspecified disorders of soft tissue, in addition to the desired myalgia and myositis not otherwise specified. This made it critical to require multiple diagnostic claims, and to validate the definition against medical records review. Nonetheless, we were able to identify highly specific definitions that agreed ‘moderately’ with medical records, and prevalence in the general population was consistent with expectations (Perrot et al., 2011; Wolfe et al., 1995). However, given the nonspecific ICD-9 code that was used, this definition may not perform as well in non-MS populations who might have recurrent uses of other codes encompassed by 729; we recommend further evaluation of this definition in other populations. In jurisdictions where 4-digit codes are available comparison of the incidence and prevalence of fibromyalgia for case definitions using 729 versus 729.1 would be valuable. In jurisdictions which use ICD-10 codes for hospital and physician claims the specific code for fibromyalgia (M79.7) may also facilitate the development of superior case definitions. The availability of an administrative case definition provides another potential method for identifying fibromyalgia, along with self-report and records review (Horton et al., 2010).
Until recently, limited data described the incidence and prevalence of fibromyalgia in the general population. Our findings of a general population prevalence of 3%, with a higher prevalence in women (3.5%) than men (1.9%) are consistent with those reported elsewhere. For example, based on a self-administered questionnaire and physical examination, 2% of persons in Wichita (United States) had fibromyalgia (Wolfe et al., 1995). Based on a telephone-administered questionnaire 2.9–4.7% of the general population in five European countries have fibromyalgia (Branco et al., 2010). We found the average annual incidence in the general population was 91.4 per 100,000 persons per year, and that disease incidence increased slightly over more than 15 years. An American study using administrative claims data with one ICD-9 code of 729.1 reported age-adjusted incidence rates per 1000 person-years of 11.28 cases for women and 6.88 cases for men (Weir et al., 2006), consistent with our findings. A Norwegian study reported an average annual incidence of 583 cases/100,000 females aged 20–49 years (Forseth et al., 1997). Neither study examined temporal trends in incidence. In North America, 4.8% of NARCOMS participants self-reported physician-diagnosed fibromyalgia with a mean age of diagnosis of 43.0 years (Marrie et al., 2008). For the first time, we used a population-based design to estimate the incidence and prevalence of fibromyalgia in the MS population. In 2005, the prevalence was 6.8% while the average annual incidence was 123.7 per 100,000 persons per year. Both the incidence and prevalence of fibromyalgia were higher in the MS than the general population. Several possible explanations exist for this finding. First, the presence of one chronic condition may increase the chances of another condition being diagnosed due to increased health services use. Second, MS and fibromyalgia could share common etiologic factors. Third, fibromyalgia is believed to be of neurogenic origin, due to abnormal processing of painful stimuli in the CNS (Clauw et al., 2011), sometimes described as a central sensitivity syndrome (Yunus, 2012), raising the possibility that fibromyalgia could be a consequence of MS in some individuals. Fibromyalgia occurs more often in persons with other postulated central sensitivity syndromes such as irritable bowel syndrome (Yunus, 2012), and in persons with other immune-mediated diseases such as rheumatoid arthritis (Weir et al., 2006; Yunus, 2012). Further examination of the overlap of these conditions with fibromyalgia may improve our understanding of the mechanisms of pain in fibromyalgia and ultimately result in improved treatment. Due to practical and ethical issues we could not review the medical records of all providers caring for our validation cohort, thus we potentially underestimated the prevalence of fibromyalgia based on medical records review. The validation cohort
166
R.A. Marrie et al. / Multiple Sclerosis and Related Disorders 1 (2012) 162–167
included only 10 persons with fibromyalgia, contributing to wide confidence intervals regarding the sensitivity of our case definition and limiting us to examining incidence and prevalence using only three age groups. Agreement between the case definition and medical records was only moderate; however, our findings regarding the prevalence of fibromyalgia in the general population were consistent with the literature. Despite these issues, this study had several strengths. The administrative case definition was validated in a cohort with similar characteristics to the population in which it was ultimately applied, the design was novel and population-based.
5. Conclusions Fibromyalgia affected 3% of the general population, and nearly twice as many persons in the MS population (over one in twenty in MS patients). Moreover, the incidence of fibromyalgia was 44% higher in the MS population than in the general population though the incidence rose only slightly in both populations. While these findings need to be replicated in other jurisdictions, further evaluation of the mechanisms underlying comorbid fibromyalgia and MS is warranted; particularly given the potential cumulative effect of these conditions. Recognition of the occurrence of fibromyalgia in MS is also important to optimize management of both conditions.
Disclosures Ruth Ann Marrie receives research funding from: Canadian Institutes of Health Research, Public Health Agency of Canada, Manitoba Health Research Council, Health Sciences Centre Foundation, Multiple Sclerosis Society of Canada, Multiple Sclerosis Scientific Foundation, Rx & D Health Research Foundation, and has conducted clinical trials funded by Bayer Inc. and SanofiAventis. Nancy Yu receives research support from the Canadian International Development Agency, the Multiple Sclerosis Society of Canada, CIHR, and Manitoba Health and Healthy Living. Stella Leung reports no disclosures. Lawrence Elliott receives research support from the Canadian Institutes of Health Research, Health Sciences Centre Foundation, Public Health Agency of Canada, and the Multiple Sclerosis Society of Canada. Sharon Warren receives research funding from the CIHR, the Canadian Health Services Research Foundation, Alberta Health Services and SSHRC. Christina Wolfson receives research funding from the Multiple Sclerosis Society of Canada, Canadian Institutes of Health Research, Canada Foundation for Innovation, and Public Health Agency of Canada. Helen Tremlett currently receives funding from the Multiple Sclerosis Society of Canada [Don Paty Career Development Award]; US National MS Society [#RG 4202-A-2 (PI)]; Canadian Institutes of Health Research [MOP: #190898 (PI) and MOP93646 (PI)]; Michael Smith Foundation for Health Research and is the Canada Research Chair for Neuroepidemiology and Multiple Sclerosis. Other funding is as follows: she has received speaker honoraria and/or travel expenses to attend conferences from: the Consortium of MS Centers, US National MS Society, Swiss Multiple Sclerosis Society, the University of British Columbia Multiple Sclerosis Research Program, Teva Pharmaceuticals and Bayer Pharmaceutical (honoraria declined) and ECTRIMS (2011), UK MS Trust and the Chesapeake Health Education Program, US Veterans Affairs (2012, honorarium declined). Unless otherwise
stated, all speaker honoraria are either donated to an MS charity or to an unrestricted grant for use by her research group. John Fisk is the Director of the endMS Atlantic Regional Research and Training Center which is funded by the Multiple Sclerosis Society of Canada. He receives research funding from the Canadian Institutes of Health Research (CIHR) and in the past has received grants, honoraria and consultation fees from AstraZeneca, Bayer, Biogen-Idec Canada, Heron Evidence Development Limited, Hoffmann-La Roche, MAPI Research Trust, Novartis, Sanofi-Aventis, Serono Canada, and Quality Metric Incorporated. James Blanchard receives research support from the Multiple Sclerosis Society of Canada, CIHR, Bill & Melinda Gates Foundation, Canadian International Development Agency and the United States Agency for International Development.
Acknowledgment/funding source This study was supported (in part) by Don Paty Career Development and Operating Grants from the Multiple Sclerosis Society of Canada, Manitoba Health Research Council, Canadian Institutes of Health Research, Public Health Agency of Canada, and Rx & D Health Research Foundation. The funding source(s) had neither a role in the study design, collection, analysis or interpretation of the data, nor in the decision to submit the article for publication. The results and conclusions presented are those of the authors. No official endorsement by Manitoba Health is intended or should be inferred. We thank John Hanly, M.D., for his helpful comments on an earlier version of this manuscript. CIHR Team in the Epidemiology and Impact of Comorbidity on Multiple Sclerosis (by site): University of Manitoba (James Blanchard, M.D., Ph.D.; Patricia Caetano, Ph.D.; Lawrence Elliott, M.D., M.Sc.; Stella Leung, M.Sc.; Ruth Ann Marrie, M.D., Ph.D.; Bo Nancy Yu, M.D., Ph.D.) Dalhousie University (Virender Bhan, M.B.B.S.; John D. Fisk, Ph.D.), University of Alberta (Joanne Profetto-McGrath, Ph.D.; Sharon Warren, Ph.D.; Larry Svenson, B.Sc.); McGill University (Christina Wolfson, Ph.D.); University of British Columbia (Helen Tremlett, Ph.D.); University of Calgary (Scott Patten, M.D.).
Appendix A. Supplementary materials Supplementary data associated with this article can be found in the online version at doi:10.1016/j.msard.2012.06.001.
References Arnold LM, Hudson JI, Hess EV, Ware AE, Fritz DA, Auchenbach MB, et al. Family study of fibromyalgia. Arthritis & Rheumatism 2004;50:944–52. Beck CA, Metz LM, Svenson LW, Patten SB. Regional variation of multiple sclerosis prevalence in Canada. Multiple Sclerosis 2005;11:516–9. Branco JC, Bannwarth B, Failde I, Abello Carbonell J, Blotman F, Spaeth M, et al. Prevalence of fibromyalgia: a survey in five European countries. Seminars in Arthritis and Rheumatism 2010;39:448–53. Clauw DJ, Arnold LM, McCarberg BH. The science of fibromyalgia. Mayo Clinic Proceedings 2011;86:907–11. Dean G. How many people in the world have multiple sclerosis. Neuroepidemiology 1994;13:1–7. Forseth K, Gran J, Husby G. A population study of the incidence of fibromyalgia among women aged 26–55 yr. Rheumatology 1997;36:1318–23. Fuller-Thomson E, Nimigon-Young J, Brennenstuhl S. Individuals with fibromyalgia and depression: findings from a nationally representative Canadian survey. Rheumatology International 2011 [January 8, Epub ahead of print]. Health Information Management Branch. Population report. Winnipeg, Manitoba: Manitoba Health and Healthy Living; 2008. Horton M, Rudick RA, Hara-Cleaver C, Marrie RA. Validation of a self-report comorbidity questionnaire for multiple sclerosis. Neuroepidemiology 2010;35:83–90. Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics 1977;33:159–74.
R.A. Marrie et al. / Multiple Sclerosis and Related Disorders 1 (2012) 162–167
Light K, White A, Tadler S, Iacob E, Light A. Genetics and gene expression involving stress and distress pathways in fibromyalgia with and without comorbid chronic fatigue syndrome. Pain Research and Treatment 2012:427869. Marrie R, Yu B, Leung S, Elliott L, Caetano P, Warren S, et al. Rising prevalence of vascular comorbidities in MS: validation of administrative definitions for diabetes, hypertension, hyperlipidemia. Multiple Sclerosis Journal 2012, http://dx.doi.org/10.1177/1352458512437814 [Epub ahead of print. February 10]. Marrie RA, Horwitz R, Cutter G, Tyry T. Cumulative impact of comorbidity on quality of life in MS. Acta Neurologica Scandinavica 2011, http://dx.doi.org/ 10.1111/j.1600-0404.2011.01526.x. Marrie RA, Horwitz R, Cutter G, Tyry T, Campagnolo D, Vollmer T. Comorbidity, socioeconomic status, and multiple sclerosis. Multiple Sclerosis 2008;14:1091–8. Marrie RA, Horwitz RI, Cutter G, Tyry T, Campagnolo D, Vollmer T. Comorbidity delays diagnosis and increases disability at diagnosis in MS. Neurology 2009;72:117–24. Marrie RA, Rudick R, Horwitz R, Cutter G, Tyry T, Campagnolo D, et al. Vascular comorbidity is associated with more rapid disability progression in multiple sclerosis. Neurology 2010a;74:1041–7. Marrie RA, Yu N, Blanchard JF, Leung S, Elliott L. The rising prevalence and changing age distribution of multiple sclerosis in Manitoba. Neurology 2010;74:465–71. National Diabetes Surveillance System. Responding to the challenge of diabetes in Canada. Ottawa: Health Canada; 2003. Perrot S, Vicaut E, Servant D, Ravaud P. Prevalence of fibromyalgia in France: a multi-step study research combining national screening and clinical confirmation: the DEFI study (determination of epidemiology of fibromyalgia). BMC Musculoskeletal Disorders 2011;12:224. Pugliatti M, Sotgiu S, Rosati G. The worldwide prevalence of multiple sclerosis. Clinical Neurology and Neurosurgery 2002;104:182–91.
167
Reisine S, Fifield J, Walsh S, Dauser D. Employment and quality of life outcomes among women with fibromyalgia compared to healthy controls. Women & Health 2004;39:1–20. Rothman KJ, Greenland S, editors. Modern epidemiology. Philadelphia, PA: Lippincott Williams & Wilkins; 1998. Siegert RJ, Abernethy DA. Depression in multiple sclerosis: a review. Journal of Neurology, Neurosurgery and Psychiatry 2005;76:469–75. Stenager E, Knudsen L, Jensen K. Acute and chronic pain syndromes in multiple sclerosis. Acta Neurologica Scandinavica 1991;84:197–200. Svendsen KB, Jensen TS, Overvad K, Hansen HJ, Koch-Henriksen N, Bach FW. Pain in patients with multiple sclerosis. A population-based study. Archives of Neurology 2003;60:1089–94. Villani V, Prosperini L, Pozzilli C, Salvetti M, Sette G. Quality of life of multiple sclerosis patients with comorbid migraine. Neurological Sciences 2011;32:149–51. Weir PT, Harlan GA, Nkoy FL, Jones SS, Hegmann KT, Gren LH, et al. The incidence of fibromyalgia and its associated comorbidities: a population-based retrospective cohort study based on International Classification of Diseases, 9th Revision codes. Journal of Clinical Rheumatology 2006;12:124–8. Wolfe F, Ross K, Anderson J, Russell J, Hebert L. The prevalence and characteristics of fibromyalgia in the general population. Arthritis & Rheumatism 1995;38: 19–28. Wolfe F, Smythe H, Yunus M, Bennett R, Bombardier C, Goldenberg D, et al. The American College of Rheumatology 1990 criteria for the classification of fibromyalgia. Report of the Multicenter Criteria Committee. Arthritis & Rheumatism 1990;33:160–72. Yunus M. The prevalence of fibromyalgia in other chronic pain conditions. Pain Research and Treatment 2012;584573 [8 p.].