Abstract
Objective
To assess the burden of illness of people with fibromyalgia (FM) and their spouses compared with selected match populations in Denmark.
Methods
Population-based, cohort case-control study using data from Danish registries from 1994 to 2021. Individuals with an FM diagnosis were identified from the National Patient Register (2008–2019) and randomly matched to a 1:4 general population comparator. Spouses or persons co-living with subjects with FM at the time of diagnosis were compared with matched comparator spouses. Healthcare and societal costs, socioeconomic status and occurrence of comorbidities were evaluated for subjects with FM, spouses and controls.
Results
9712 subjects with FM (94.9% females, mean age 50 years) and 5946 spouses were included. At year of diagnosis, subjects with FM had significantly more comorbidities compared with controls, including significantly more comorbid rheumatic disorders. The highest risk at the time of FM diagnosis was a comorbid diagnosis of ankylosing spondylitis (OR 7.0, 95% CI 4.9 to 10.0). Significantly more comorbidities were also observed in spouses. Subjects with FM and spouses had higher healthcare and public transfer costs and lower income from employment at all timepoints. Loss of income from employment in subjects with FM occurred years before establishment of the FM diagnosis. The employment rate after diagnosis was 22%. 10 years after the FM diagnosis, 50% received disability pension as compared with 11% of matched controls. The observed net average increased societal cost for subjects with FM amounted to €27 193 per patient-year after diagnosis.
Conclusion
FM has major health and socioeconomic consequences for patients, their partners and society and call for improved healthcare strategies matching patients’ needs.
Keywords: fibromyalgia, economics, health services research
WHAT IS ALREADY KNOWN ON THIS TOPIC
Studies conducted in the USA and Europe highlight problems with healthcare pathways and a heavier economic burden due to increased healthcare use and societal costs in patients with fibromyalgia.
Most studies, however, are conducted over a decade ago, are based on small and highly selected patient samples, and few have had access to high-quality register data.
No studies have evaluated health inequities and social costs in spouses of patients with fibromyalgia.
WHAT THIS STUDY ADDS
Our burden-of-illness study showed that compared with age, sex, spouse and geographically matched controls, individuals with fibromyalgia, as well as their spouses, had significantly higher rates of comorbidity, increased contact with all sectors of the Danish healthcare system, lower income, higher unemployment rates and higher risk for disability pension years before and after diagnosis.
Loss of income from employment was observed to occur several years before the fibromyalgia diagnosis was established.
HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY
The results support health inequity and increased societal costs in patients with fibromyalgia and their partners.
Attention should be focused on improvement in care provision and development of healthcare strategies and care pathways aiming at early diagnosis and provision of interventions matching individual patient needs.
Introduction
Fibromyalgia (FM) is a prevalent chronic pain disorder reported to affect approximately 2% of the general population worldwide,1 and up to 6% of the general European population.2 The typical patient is female in her 40s or 50s presenting with widespread pain and tenderness, for which no apparent tissue-based pathology can be identified. Other defining symptoms include sleep disturbance, fatigue and cognitive impairment, as well as symptoms related to internal organ systems.3 FM is strongly associated with disability affecting daily life activities,4 incapacity for normal employment and poor social participation5–7 and has been shown to also influence the physical health, mental well-being and healthcare use of spouses of people with FM.8
Although common, management of FM continues to represent a clinical challenge. Confirming a diagnosis of FM is often difficult and prolonged and excessive healthcare utilisation with numerous clinical visits, investigations and specialist consultations prior to obtaining a diagnosis have been documented by several studies.9 10 This delay is unfortunate as studies have shown that a diagnostic label provides the patient with meaning and value for symptoms, which facilitate management and reduce healthcare utilisation, with cost reduction further augmented by early diagnosis.10–12 Additional research indicates that a diagnosis of FM fails to provide definitive answers or bestow validity and significance to the illness experience of patients unless followed by an immediate and targeted intervention.13 Despite this, research persistently illuminates issues across countries in service provision and access for individuals with FM. These include challenges facing general practitioners seeking involvement of secondary care services for people with FM, lack of multidisciplinary, specialised services to support those affected and low levels of patient satisfaction.9 14–19
Cost-of-illness studies are necessary to inform the public and the healthcare system as well as the policy makers of the socioeconomic impact of health disorders and how to best organise service provision. Previously published studies have evaluated the economic burden of FM finding a heavier economic burden of FM compared with other disorders or healthy subjects.20–24 However, these cost-of-illness studies are not easily comparable to a Danish setting for several reasons such as the perspective of the cost analysis, the healthcare system varying per country and the cost components considered. Most studies are based on small and highly selected patient samples, and few have had access to high-quality register data for both patients and spouses. Thus, the overall objectives of the Danish FM cost-of-illness study were to assess the burden of illness of individuals with FM and their spouses compared with selected match populations in Denmark.
The study assesses the following objectives:
The incidence and prevalence of comorbidities of individuals with FM and their spouses.
Costs related to healthcare utilisation in primary and secondary care, medication usage and municipalities homecare for both individuals with FM and their spouses.
The societal burden of individuals with FM and their spouses, including the ability to work, social welfare and impact on productivity loss.
Methods
Study design and participants
The study was a population-based cohort case-control study using data from various comprehensive national Danish registries from January 1994 through December 2021. The study was conducted in accordance with the Strengthening the Reporting of Observational studies in Epidemiology statement and according to a prespecified protocol (see online supplemental file A).
rmdopen-2023-003904supp001.pdf (272.9KB, pdf)
In Denmark, health and demographic information on all citizens is updated annually in a series of national registries, with a high degree of completeness and made available from Statistics Denmark. Individual linkage between the registries is possible by using the unique 10-digit personal identification number, assigned to all Danish residents at birth. The Danish National Patient Register (NPR) includes information on all hospital contacts, the most important variables being date and time for hospital arrival and departure, outpatient contact, diagnoses and treatment. Reporting of data on each single healthcare contact, excluding primary care visits, is mandatory. Data from the NPR were used to identify the study population in this study. We identified all patients in the NPR with a hospital contact with a primary or secondary FM diagnosis according to the classification code provided in the 10th version of the International Classification of Diseases (ie, ICD-10: M79.7). Patients with FM were included the first time they had an FM diagnosis hospital contact in the period 2008–2019. The FM diagnosis was first present in 2008, so there was no exclusion period (washout period) to ensure that patients were naïve FM incidences at the inclusion time. The patients with FM were randomly matched to a 1:4 general population comparator matched by the specific year of age, sex, spouse and municipality at index year (year of FM diagnosis) using SAS SURVEYSELECT procedures. Patients were followed each year before and after their index date (FM diagnosis) and compared with their 1:4 control sharing the same index date. In the spouse analyses, we included all persons who in the index year were spouse or co-living with a patient with FM or a control. In the spouse analyses the spouses of patients with FM were compared with the control spouses.
Study variables and outcome definitions
Identification of comorbidity was based on ICD-10 diagnoses registered in the NPR in connection with a hospital contact (both primary and secondary hospital diagnoses were included) in the period from 1 January 1994 until 31 December 2021. So, in all patients with FM the burden of various comorbidities was followed from year 14 before the index date and 2 years after index date (index year and year 1 after index). The comorbidity was grouped according to the 21 WHO chapters in ICD-1025 and reported as the accumulated comorbidity for each WHO chapter. Comorbidity chapter 13; diseases of the musculoskeletal system and connective tissue was divided in more details and reported separately in the comorbidity analysis.
Information on socioeconomic status was obtained from nationwide registries on employment, educational level, income and pensions from the period 2002 to 2020. Employment status was categorised as employed, unemployed, sick pay (public funded), education, disability pension, early retirement, child under 18 years and age pension. Employment status analysis and analysis of income loss was performed for both all FM patients and in analyses only including FM patients aged 18 to 64 years. Average income per patient-year with FM, spouses and comparators was differentiated into income deriving from employment, social security and unemployment benefit, sick pay, disability pension, early retirement, age pension, other public transfer, other pensions and total income. Information on healthcare costs was obtained from the NPR that includes information on all hospital contacts, the National Health Insurance Service Registry that includes information on all primary sector contacts (general practitioners, specialists, etc) and the Danish National Prescription Registry that contains information about the total sales of prescribed medicinal products in Denmark since 1994. The register does not contain information about drugs handed out at hospitals. Yearly healthcare costs per patient-year for the period 2002–2020 for patients with FM, spouses and comparators were calculated using information on frequency and costs of hospital contacts (somatic and psychiatric inpatient and outpatient costs), primary healthcare sector costs and prescription medication costs divided into pain medication costs and costs of other medications.
Patient and public involvement
The study was initiated by the Danish Fibromyalgia and Pain Association and representatives from this patient association were involved in the conception and design of the study, interpretation of data and drafting of the manuscript ensuring the patient perspective. The Danish Fibromyalgia Association will participate in dissemination of study findings as part of the partnership strategy, including use of the study findings to inform the public and the healthcare system as well as the policy makers.
Statistical analysis
Statistical significance was defined as p<0.05.
Statistical analysis was performed using SAS V.9.4 TS Level 1M5 (SAS, Cary, North Carolina, USA).
Descriptive statistics were used to characterise the population where continuous variables were summarised as mean and SD. Categorical variables were summarised as the total number of events and frequency (%). Categorical variables were compared via χ2 tests.
A conditional logistic regression model (nested case-control design) was used to estimate the differences in comorbidities. ORs were presented with their corresponding 95% CIs.
Cost and income were calculated as mean cost and income per patient year and a two-step one generalised linear model with a gamma distribution and a log link function were used to test for significant differences between cases and controls.26 Mean cost and income per patient-year were presented with p values.
A multinomial logistic regression model was used to estimate the OR for being unemployed versus employed (reference) for cases aged 18–64 years versus their controls adjusting for education level at index time. Unemployed status in the model was divided into being unemployed, on sick pay, disability pension, early retirement or under education all with employed as reference. ORs were presented with their corresponding 95% CIs.
Results
A total of 9712 individuals were registered with a first-time FM diagnosis hospital contact (ie, ICD-10: M 79.7) in the period 2008–2019 and included in the study population. Table 1 summarises baseline sociodemographic characteristics, including age distribution, marital status, educational level and employment status at index year for the FM diagnosis in study subjects with an FM diagnosis, their spouses and matched controls. A pronounced sex and geographical inequality were present; 94.9% of the identified FM cases were females and most of the diagnosis registered in the Capital Region, fewest in the Region of Northern Jutland. Significantly lower educational level and proportion of employed were observed in study subjects with an FM diagnosis and their spouses relative to matched controls.
Table 1.
Sociodemographic characteristics in the study population at index year for fibromyalgia diagnosis
| Fibromyalgia | Control | P value | Spouse | Control | P value | |
| Number (n) | 9712 | 38 841 | 5946 | 23 715 | ||
| Sex (n, %) | Matched | Matched | ||||
| Female | 9212 (94.9) | 332 (5.6) | 1358 (5.7) | |||
| Male | 500 (5.1) | 5614 (94.4) | 22 357 (94.3) | |||
| Age (mean, SD) | 50 (13) | 50 (13) | Matched | 51 (13) | 51 (13) | Matched |
| Age group (n, %) | Matched | |||||
| <30 years | 603 (6.2) | 2412 (6.2) | 231 (3.9) | 866 (3.7) | ||
| 30–39 years | 1545 (15.9) | 6180 (15.9) | 861 (14.5) | 3423 (14.4) | ||
| 40–49 years | 2697 (27.8) | 10 788 (27.8) | 1560 (26.2) | 6367 (26.8) | ||
| 50–59 years | 2832 (29.2) | 11 328 (29.2) | 1777 (29.9) | 6869 (29.0) | ||
| 60–69 years | 1265 (13.0) | 5060 (13.0) | 998 (16.8) | 4170 (17.6) | ||
| 70–79 years | 588 (6.1) | 2352 (6.1) | 428 (7.2) | 1671 (7.0) | ||
| 80+ years | 182 (1.9) | 721 (1.9) | 91 (1.5) | 349 (1.5) | ||
| Region (n, %) | Matched | Matched | ||||
| Capital | 4039 (41.6) | 16 152 (41.6) | 2129 (35.8) | 8475 (35.7) | ||
| Zealand | 2573 (26.5) | 10 289 (26.5) | 1676 (28.2) | 6687 (28.2) | ||
| Southern Denmark | 1313 (13.5) | 5252 (13.5) | 885 (14.9) | 3535 (14.9) | ||
| Central Jutland | 1310 (13.5) | 5240 (13.5) | 930 (15.6) | 3716 (15.7) | ||
| Northern Jutland | 477 (4.9) | 1908 (4.9) | 326 (5.5) | 1302 (5.5) | ||
| Marital status (n, %) | Matched | Matched | ||||
| Married/Cohabiting | 5949 (61.3) | 23 789 (61.2) | ||||
| Living alone | 3763 (38.7) | 15 052 (38.8) | ||||
| Education (n, %) | 0.0000 | 0.0000 | ||||
| Primary | 3539 (36.4) | 8826 (22.7) | 1612 (27.1) | 4604 (19.4) | ||
| Secondary | 505 (5.2) | 2241 (5.8) | 287 (4.8) | 1198 (5.1) | ||
| Vocational | 3548 (36.5) | 13 376 (34.4) | 2775 (46.7) | 10 200 (43.0) | ||
| College short | 307 (3.2) | 1752 (4.5) | 297 (5.0) | 1489 (6.3) | ||
| College medium | 1396 (14.4) | 8494 (21.9) | 527 (8.9) | 3310 (14.0) | ||
| College long | 226 (2.3) | 3476 (8.9) | 314 (5.3) | 2508 (10.6) | ||
| Unknown | 191 (2.0) | 676 (1.7) | 134 (2.3) | 406 (1.7) | ||
| Employment status (n, %) B*C* | 0.0000 | 0.0000 | ||||
| Employed | 2479 (25.5) | 25 162 (64.8) | 4135 (69.5) | 17 946 (75.7) | ||
| Education | 459 (4.7) | 2235 (5.8) | 131 (2.2) | 629 (2.7) | ||
| Unemployed | 2772 (28.5) | 2641 (6.8) | 329 (5.5) | 767 (3.2) | ||
| Sick pay (public) | 605 (6.2) | 561 (1.4) | 72 (1.2) | 182 (0.8) | ||
| Disability pension | 2086 (21.5) | 2827 (7.3) | 373 (6.3) | 764 (3.2) | ||
| Early retirement | 73 (0.8) | 712 (1.8) | 119 (2.0) | 476 82.0) | ||
| Child under 18 years | 23 (0.2) | 92 (0.2) | ||||
| Age pension | 1215 (12.5) | 4609 (11.9) | 787 (13.2) | 2949 (12.4) |
B*: employment status at index year (year for fibromyalgia diagnosis) based on a variable (SOCIO13) from Statistic Denmark for the labour market affiliation status. The variable is based on information about the most important source of income or employment for the person in the year.
C*: <5 observations unknown employment status are not reported.
Comorbidities
ORs for comorbidities split according to the WHO ICD-10 chapters (figure 1A,B) showed that at the time of diagnosis, study subjects with an FM diagnosis, as well as their spouses, had significantly more comorbidities distributed across most of the WHO chapters. In subjects with FM, the highest risk in the index year was comorbid endocrine disorder (OR 3.0, 95% CI 2.9 to 3.2), nervous system disorder (OR 3.4, 95% CI 3.2 to 3.5), digestive tract disorder (OR 3.4, 95% CI 3.3 to 3.6) and mental disorder (OR 3.5, 95% CI 3.3 to 3.7) (online supplemental table S1). Notably, subjects with an FM diagnosis had an increased risk of also receiving other rheumatic diagnoses. As illustrated in table 2, the risk of being diagnosed with another rheumatic disorder was significantly increased >10 years prior to the FM diagnosis; the highest risk being a diagnosis of psoriatic arthropathy (OR 4.0, 95% CI 1.9 to 8.6). At the time of the FM diagnosis, the risk of a comorbid diagnosis of ankylosing spondylitis was the highest (OR 7.0, 95% CI 4.9 to 10.0).
Figure 1.
(A and B) Forest plot illustrating ORs for comorbidities grouped according to the WHO disease chapters in 10th version of the International Classification of Diseases in study subjects with fibromyalgia (A) and fibromyalgia spouses (B). The comorbidity reported is the accumulated comorbidity for each WHO disease chapter followed from year 14 before the fibromyalgia index date.
Table 2.
ORs fibromyalgia versus control for comorbidity before, after and at the time of fibromyalgia diagnosis grouped according to the WHO chapter 13 in ICD-10, diseases of the musculoskeletal system and connective tissue
| WHO chapter 13—diseases of the musculoskeletal system and connective tissue (M00-M99) | ||||||
| Year | Seropositive rheumatoid arthritis (M05) | Other rheumatoid arthritis (M06) | Other psoriatic arthropathies (M07.3) | Ankylosing spondylitis (M45.9) | Other inflammatory spondylopathies (M46) | All rest musculoskeletal system and connective tissue |
| OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | |
| −14 | 1.5 (0.9 to 2.7) | 2.6 (1.6 to 4.3) | 1.8 (0.5 to 5.8) | (B*) | (B*) | 3.8 (3.5 to 4.1) |
| −13 | 1.8 (1.1 to 2.9) | 2.6 (1.7 to 4.0) | 4.0 (1.9 to 8.6) | 2.2 (0.9 to 5.4) | 3.3 (1.0 to 10.9) | 3.7 (3.5 to 4.0) |
| −12 | 1.7 (1.1 to 2.6) | 3.2 (2.3 to 4.7) | 3.8 (1.9 to 7.5) | 2.9 (1.3 to 6.4) | 3.1 (1.2 to 8.4) | 3.7 (3.5 to 3.9) |
| −11 | 1.7 (1.1 to 2.5) | 3.2 (2.3 to 4.5) | 4.4 (2.5 to 7.8) | 3.3 (1.7 to 6.6) | 3.6 (1.5 to 8.6) | 3.7 (3.5 to 3.9) |
| −10 | 2.1 (1.5 to 3.0) | 3.2 (2.3 to 4.4) | 4.2 (2.5 to 6.8) | 3.0 (1.6 to 5.5) | 3.0 (1.4 to 6.3) | 3.7 (3.5 to 3.9) |
| −9 | 2.2 (1.6 to 3.0) | 3.7 (2.8 to 4.9) | 4.2 (2.7 to 6.6) | 3.1 (1.7 to 5.5) | 3.0 (2.1 to 6.9) | 3.8 (3.6 to 4.0) |
| −8 | 2.1 (1.6 to 2.9) | 3.7 (2.9 to 4.9) | 4.1 (2.7 to 6.2) | 3.6 (2.1 to 6.2) | 3.8 (2.2 to 6.7) | 3.8 (3.7 to 4.0) |
| −7 | 2.2 (1.6 to 2.9) | 3.7 (2.9 to 4.8) | 4.3 (3.0 to 6.3) | 4.4 (2.7 to 7.3) | 3.8 (2.7 to 7.7) | 4.0 (3.8 to 4.2) |
| −6 | 2.2 (1.7 to 2.8) | 3.7 (2.9 to 4.7) | 4.5 (3.2 to 6.4) | 4.7 (2.9 to 7.6) | 4.6 (3.2 to 7.9) | 4.2 (4.0 to 4.4) |
| −5 | 2.3 (1.8 to 3.0) | 3.6 (2.9 to 4.4) | 4.7 (3.4 to 6.5) | 4.9 (3.1 to 7.8) | 5.1 (3.6 to 8.3) | 4.4 (4.2 to 4.6) |
| −4 | 2.3 (1.8 to 2.9) | 3.6 (2.9 to 4.4) | 4.7 (3.5 to 6.4) | 5.4 (3.5 to 8.4) | 5.5 (3.9 to 8.7) | 4.8 (4.6 to 5.1) |
| −3 | 2.3 (1.9 to 2.9) | 4.0 (3.3 to 4.8) | 4.8 (3.6 to 6.3) | 6.1 (4.0 to 9.2) | 5.9 (4.1 to 8.6) | 5.2 (5.0 to 5.5) |
| −2 | 2.5 (2.0 to 3.1) | 4.3 (3.6 to 5.1) | 4.9 (3.7 to 6.3) | 6.3 (4.2 to 9.4) | 5.9 (4.2 to 8.2) | 5.9 (5.6 to 6.2) |
| −1 | 2.5 (2.0 to 3.0) | 4.5 (3.8 to 5.3) | 5.1 (4.0 to 6.5) | 6.5 (4.4 to 9.6) | 5.6 (4.1 to 7.7) | 6.7 (6.3 to 7.0) |
| Index | 2.5 (2.1 to 3.1) | 5.6 (4.7 to 6.5) | 6.4 (5.1 to 8.1) | 7.0 (4.9 to 10.0) | 6.2 (4.6 to 8.2) | (B*) |
| 1 | 2.6 (2.2 to 3.2) | 5.4 (4.6 to 6.3) | 6.1 (4.8 to 7.6) | 7.2 (5.0 to 10.3) | 5.6 (4.3 to 7.4) | (B*) |
Conditional logistic regression.
B*: Conditional logistic regression model did not converge.
Significant OR marked bold.
ICD-10, 10th version of the International Classification of Diseases.
rmdopen-2023-003904supp002.pdf (48.3KB, pdf)
Health costs: primary healthcare sector, somatic hospital inpatient and outpatient contacts and medication
The average annual health costs per person per year by cost categories for individuals with FM in Denmark before and after diagnosis compared with matched controls are presented in table 3. As illustrated, the average annual direct health costs in subjects with FM increased from €3910 prior to diagnosis to €6038 around the time of diagnosis, reflecting an increased utilisation of healthcare resources in both primary and secondary care associated with obtaining the diagnosis. These greater costs diminished after the time of diagnosis but remained higher in subjects with FM at all timepoints (figure 2). Municipality homecare costs were significantly higher among subjects with FM (table 3) and subjects with FM also had greater medical expenses, especially non-pain medications, both before and after diagnosis (figure 2).
Table 3.
Income and costs per patient-year in patients with fibromyalgia versus controls before and after diagnosis
| Before | After (A*) | |||||
| FM | Control | P value | FM | Control | P value | |
| € | € | € | € | |||
| Health cost | ||||||
| Inpatient somatic | 943 | 563 | 0 | 1789 | 842 | 0 |
| Outpatient somatic | 1413 | 765 | 0 | 2466 | 1151 | 0 |
| Psychiatric inpatients and outpatients | 266 | 189 | 0 | 229 | 191 | 0 |
| Primary healthcare sector | 678 | 358 | 0 | 780 | 404 | 0 |
| Prescription pain medication | 114 | 21 | 0 | 144 | 25 | 0 |
| Prescription other medication | 496 | 268 | 0 | 630 | 284 | 0 |
| Direct total health costs | 3910 | 2164 | 0 | 6038 | 2897 | 0 |
| Homecare costs (B*) | ||||||
| Homecare—practical help | 86 | 31 | 0 | 144 | 48 | 0 |
| Homecare—care | 175 | 139 | 0 | 372 | 222 | 0 |
| Homecare costs total | 261 | 170 | 0 | 516 | 270 | 0 |
| Income | ||||||
| Income from employment | 17 852 | 33 079 | 0 | 9431 | 33 483 | 0 |
| Public transfers | ||||||
| Unemployment | ||||||
| Unemployment benefit | 1121 | 950 | 0 | 250 | 657 | 0 |
| Social security | 3794 | 1208 | 0 | 6712 | 1323 | 0 |
| Sick pay | 1709 | 689 | 0 | 942 | 443 | 0 |
| Pensions | ||||||
| Disability pension | 3772 | 1432 | 0 | 6989 | 1959 | 0 |
| Early retirement | 208 | 461 | 0 | 213 | 552 | 0 |
| Age pension | 1068 | 1004 | 0 | 2771 | 2561 | 0 |
| Other public transfer payments | ||||||
| Students grants | 298 | 396 | 0 | 191 | 209 | 0 |
| Housing benefits | 797 | 414 | 0 | 964 | 495 | 0 |
| Child benefits | 1687 | 1487 | 0 | 1005 | 1027 | 0 |
| Green check | 195 | 163 | 0 | 167 | 132 | 0 |
| Total public transfers | 14 649 | 8204 | 0 | 20 204 | 9358 | 0 |
| Total income and public transfers | 32 501 | 41 283 | 0 | 29 635 | 42 841 | 0 |
| € | € | € | € | |||
| Net cost per patient | ||||||
| Direct total health costs | 3910 | 2164 | 6038 | 2897 | ||
| Indirect costs, foregone earnings (C*) | 15 227 | 24 052 | ||||
| Sum of direct and indirect costs | 19 137 | 2164 | 30 090 | 2897 | ||
| Net costs (case costs-control costs) | 16 973 | 27 193 | ||||
A*: the index cost and index year income is included in the after period.
B*: homecare data first available from 2008.
C*: foregone earnings=income from employment control−income from employment case.
FM, fibromyalgia.
Figure 2.
Illustrates healthcare and medication costs in study subjects with fibromyalgia compared with matched controls.
Similar findings were observed in spouses of subjects with FM, who also had increased use of healthcare resources and prescription medications at all timepoints compared with matched controls (figure 3).
Figure 3.
Illustrates healthcare and medication costs in spouses of study subjects with fibromyalgia compared with matched controls.
Indirect costs: social costs, employment rate and income
Figure 4 shows income from employment and total public transfer per patient-year for subjects with FM and their spouses aged 18–64 years, and respective matched controls. The average yearly income from employment was found to be significantly lower in subjects diagnosed with FM at all timepoints; from 17 years before diagnosis until 10 years after. A lower income from employment was also observed in spouses of subjects with FM. As illustrated by figures 4 and 5, the average income from employment in subjects with FM aged 18–64 years started to decline >5 years before establishment of the FM diagnosis, where approximately 48% were employed and working, and reached a minimum in the first years after the diagnosis where approximately 22% were employed and working and 33% receiving disability pension. At 10 years from diagnosis, 50% of the subjects with FM were receiving disability pension compared with 11% of the controls. Full compensation by public transfer payment was first obtained around the time of diagnosis and at a significantly lower yearly total income level as compared with matched controls; €29 635 vs €42 841, p<0.0000 (table 3).
Figure 4.
Income from employment and total public transfer per patient-year for study subjects with fibromyalgia and spouses of study subjects with fibromyalgia aged 18–64 years, and their respective matched controls.
Figure 5.
Illustrates employment status in study subjects with fibromyalgia aged 18–64 years 15 years before and 10 years after the fibromyalgia index year.
The average yearly costs and income before and after date of diagnosis for subjects with FM and matched controls are summarised in table 3, showing a net average increased societal cost of €27 193 per patient-year for subjects with FM after diagnosis.
Discussion
The study showed that compared with age, sex, spouse and geographically matched controls, individuals with FM, as well as their spouses, had significantly higher rates of comorbidity, increased contact with all sectors of the Danish healthcare system, lower income, higher unemployment rates and higher risk for disability pension years before and after diagnosis, supporting that FM is associated with significant health inequities and societal costs.
The diversity of symptoms associated with FM and lack of specific biological markers to consolidate the diagnosis probably contributes to the lengthy and expensive processes patients undergo to get a diagnosis.10 27 28 Also, an area of contention among health professionals still seems to be the benefits or harms of the diagnostic label FM.9 28 Continued challenges diagnosing FM and an increased use of healthcare resources and repeated diagnostic investigations in numerous specialties in patients with potentially unrecognised FM are supported by a recent Danish study demonstrating more hospital contacts, performance of more surgical procedures and more specialist consultations in patients with clinical FM features at 7-year follow-up.29 Under-recognition and underdiagnosis of FM, especially in males, has also been demonstrated among patients referred for specialised pain rehabilitation in tertiary care settings in Denmark.30 FM as a standalone diagnosis is however rare, which was also corroborated by our study findings showing a significant increased frequency of comorbid diagnoses in the FM study population, in particular comorbid diagnoses of endocrine disorder, nervous system disorder, digestive tract disorder and mental disorder. The concept of FM and FM-like features as a frequent comorbidity and complicating pain problem in inflammatory and degenerative rheumatic disorders is also increasingly backed by research.31 Chronic widespread non-arthritic pain, the key clinical feature in FM, is for example reported to be present in more than one-third of patients with psoriatic arthritis and to be associated with poorer disease and treatment outcomes.32 In the current study, the risk of being diagnosed with another rheumatic disorder was significantly increased >10 years prior to the FM diagnosis; the highest risk being a diagnosis of psoriatic arthropathy. Whether FM was a missed pre-existing condition, a missed differential diagnosis, or occurred later as a complication generated by the specific rheumatic disorder cannot be inferred from the current study.
Studies support that individuals with chronic pain in general use significantly more public funded health services.33 In line, several studies have evaluated the annual cost of FM in the USA and Europe and reported that patients with FM are high consumers of healthcare services and that there are substantial direct and indirect healthcare costs associated with the pain disorder.20–24 Differences across countries in drivers of costs related to FM have been observed, including significantly higher direct costs in the USA compared with France and Germany. Indirect costs represented a significant proportion of the total costs, particularly in Europe.23 The estimates in our study also demonstrated significant contributions from indirect costs, accounting for more than three-quarters of the total net costs, of which public transfer and loss of income from employment were the major contributors. There is currently no cure for FM and management of the disorder is aimed at symptom reduction and maintaining functional ability.34 Work disability and loss of employment is a particular concern in this patient population where symptom severity and overall symptom burden have been found to predict loss of employment and early disability retirement.6 7 In line, our cost-of-illness estimates showed that FM is associated with a substantial loss of income from employment even years before establishment of the diagnosis, a low employment rate of about 22% in the years after diagnosis and that 50% of the subjects with FM were receiving disability pension 10 years from diagnosis compared with 11% of the matched controls. Even where employment was maintained, the income from employment in individuals with FM was significantly lower than that of controls at all timepoints. However, this finding was not adjusted for educational level, which was found to be lower in both individuals with FM and their partners when compared with matched controls. A non-negligible loss in household productivity was also indicated based on significant higher homecare costs in subjects with FM compared with matched controls. Although we cannot make any assumption about causal relationships, it could be speculated that a considerable diagnostic delay and inadequate pain management during a prolonged period of repeated investigations in search for an explanation of the pain leads to a poorer outcome prognosis in this patient population. An early and tailored intervention would potentially assist patients to adjust to pain and adopt active self-management strategies preventing pain-related disability, including work disability and loss of employment. It could also be valuable to conduct subgroup analyses, for example, of patients with multiple healthcare contacts, those with specific comorbidities and those retained in work to estimate the costs of illness burden in these subsets.
Our study also aimed to evaluate the excess healthcare and societal costs in partners of patients with FM. The cost analyses showed that compared with their matched controls spouses of patients with FM had significantly higher rates of comorbidities, incurred higher healthcare and medication costs and had lower income from employment. Similar findings have been reported in a smaller study examining the physical health, healthcare use and psychological well-being of spouses of people with FM8 and in a larger Danish study looking at welfare consequences for people with epilepsy and their partners.35 There may be various explanations for the higher costs and negative social outcomes in partners of people with FM, an area that is poorly explored by research. Family strain is a significant component of the chronic stress profile associated with chronic pain and is reported to adversely affect the health and social outcomes of both patients and caregivers.36 37 FM could potentially disrupt family dynamics due to increased need for care leading to major role shifts within the family, unemployment leading to financial hardship and dissolution of social relationships and support networks. These factors could subsequently have a detrimental impact on the physical health, psychological well-being and healthcare utilisation of partners. In a comparative study exploring the impact of FM on marital relationships, spouses of women with FM reported lower marital satisfaction than control husbands, and social support was found to mediate the relation between role strains and marital satisfaction. The study concluded that husbands are significantly impacted by their wives’ condition and suggested that interventions should focus more attention on partners, and possibly targeting social support.38
A key strength of this study is that the study was a population-based, case-control study using various comprehensive national Danish health and social registries with a wide coverage and high degree of data validity. Some limitations need to be considered. First, as the aim of the study was to identify the total burden of an FM diagnosis, we included all cases in the NPR with a first hospital diagnosis of FM but did not consider the criteria for any other verification of the diagnosis. Also, the majority of identified FM cases were females. Second, all physicians working in healthcare units in Denmark are obliged to report data, including ICD-coded diagnosis, on all inpatient and specialist outpatient visits. Selection of study participants with FM in this study was based on ICD codes rendering a risk of selection bias towards more severe cases while missing patients with more mild or potentially non-recognised FM who are managed entirely in the primary healthcare sector. Third, as in most cost-of-illness analyses it is difficult to isolate the cost of the disease in question from that of other comorbid conditions. Thus, the cost estimated cannot be conclusively attributed to FM but should be considered as the yearly excess direct health costs and the productivity costs for a person diagnosed with FM relatively to a comparable person without the diagnosis. Finally, subjective data describing important factors impacting healthcare utilisation, such as self-reports of symptom severity and interference, were not captured by the data sources.
In conclusion, the study found that even years before diagnosis, FM is associated with significantly more comorbidities, higher health-related and social transfer costs and lower levels of employment and income compared with age, sex, spouse and geographically matched controls. Loss of income from employment was observed to commence several years before the FM diagnosis was established. FM was also associated with a significant socioeconomic impact on the spouses of those with FM. Studies like this substantiate the need for a patient-focused improvement in care provision and development of healthcare strategies and care pathways aiming at early diagnosis and provision of interventions matching individual patient needs.
Footnotes
Contributors: KA: contributed to study conception and design, literature search, interpretation of data, figures, drafting the manuscript and approving the final version. KA takes responsibility for all coauthors and the integrity of the work as a whole. RI: contributed to study conception and design, data collection, the analysis and interpretation of data, figures, revising the manuscript and approving the final version. RI had access to data throughout the process and knowledge of roles and responsibilities of each author. PHD: contributed to interpretation of data, revising the manuscript and approving the final version. PHD had access to data throughout the process and knowledge of roles and responsibilities of each author. JO: contributed to study conception and design, interpretation of data, revising the manuscript and approving the final version. JO had access to data throughout the process and knowledge of roles and responsibilities of each author. KL: contributed to study conception and design, interpretation of data, revising the manuscript and approving the final version. KL had access to data throughout the process and knowledge of roles and responsibilities of each author. JK: contributed to study conception and design, data collection, the analysis and interpretation of data, revising the manuscript and approving the final version. JK had access to data throughout the process and knowledge of roles and responsibilities of each author. LEK: contributed to study conception and design, interpretation of data, drafting and revising the manuscript and approving the final version. LEK had access to data throughout the process and knowledge of roles and responsibilities of each author. KA and LEK take full responsibility as guarantors, had final responsibility for the decision to submit the publication and take responsibility for all coauthors and the integrity of the work as a whole.
Funding: The research was funded by the Danish Fibromyalgia and Pain Association and the OAK Foundation. Representatives from the Danish Fibromyalgia and Pain Associations were involved in the conception and design of the study, interpretation of data and drafting of the manuscript ensuring the patient perspective, but had no role in the data collection and data analyses. KA and LEK had full access to all the data in the study, had final responsibility for the decision to submit the publication and take responsibility for all coauthors and the integrity of the work as a whole.
Competing interests: None declared.
Patient and public involvement statement: Patients and/or the public were involved in the design, or conduct, or reporting, or dissemination plans of this research. Refer to the 'Methods' section for further details.
Provenance and peer review: Not commissioned; externally peer reviewed.
Author note: Transparency statement: KA and LEK affirm that the manuscript is honest, accurate and in accordance with the prespecified protocol that can be accessed in the online supplemental material.
Supplemental material: This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.
Data availability statement
Data are available on reasonable request. Unidentified and additional data making the basis for this work can be requested after proper communication with KA and LEK and according to Danish national legislation.
Ethics statements
Patient consent for publication
Not applicable.
Ethics approval
According to Danish legislation, no ethical approval or informed consent was required as the study involved only linkage of registry data. The records held in The National Patient Registry (NPR) and Civil Registration System Statistics Denmark (CPR) are not publicly available, and permission to get data was granted by written permission in accordance with the Data Protection Act § 10, stk. 3 (approval nr. 2012-54-0271).
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Associated Data
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Supplementary Materials
rmdopen-2023-003904supp001.pdf (272.9KB, pdf)
rmdopen-2023-003904supp002.pdf (48.3KB, pdf)
Data Availability Statement
Data are available on reasonable request. Unidentified and additional data making the basis for this work can be requested after proper communication with KA and LEK and according to Danish national legislation.