Open Access

The impact of arthritis and joint pain on individual healthcare expenditures: findings from the Medical Expenditure Panel Survey (MEPS), 2011

  • Edith M. Williams1, 2,
  • Rebekah J. Walker2, 3, 4,
  • Trevor Faith1 and
  • Leonard E. Egede2, 3, 4Email author
Arthritis Research & Therapy201719:38

DOI: 10.1186/s13075-017-1230-3

Received: 22 November 2016

Accepted: 12 January 2017

Published: 28 February 2017

Abstract

Background

Joint pain, including back pain, and arthritis are common conditions in the United States, affecting more than 100 million individuals and costing upwards of $200 billion each year. Although activity limitations associated with these disorders impose a substantial economic burden, this relationship has not been explored in a large U.S. cohort.

Methods

In this study, we used the Medical Expenditures Panel Survey to investigate whether functional limitations explain the difference in medical expenditures between patients with arthritis and joint pain and those without. We used sequential explanatory linear models to investigate this relationship and accounted for various covariates.

Results

Unadjusted mean expenditures were $10,587 for those with joint pain or arthritis, compared with $3813 for those without. In a fully adjusted model accounting also for functional limitations, those with joint pain or arthritis paid $1638 more than those without, a statistically significant difference.

Conclusions

The growing economic and public health burden of arthritis and joint pain, as well as the corresponding complications of functional, activity, and sensory limitations, calls for an interdisciplinary approach and heightened awareness among providers to identify strategies that meet the needs of high-risk patients in order to prevent and delay disease progression.

Keywords

Joint pain Arthritis Healthcare cost Medical expenditures MEPS Functional limitations

Background

Joint pain (including back pain) and arthritis (osteoarthritis, rheumatoid arthritis) are some of the most common and costly conditions in the United States, affecting more than 100 million individuals and costing more than $200 billion per year [1]. Compared with the general population, those with diseases characterized by arthritis/rheumatism and chronic joint pain experience high rates of functional limitation [17]. Functional limitations include those related to limitations in mobility, self-care, daily activities, and pain [1, 3]. Researchers have observed significant impacts to the individual patient as a result of functional limitations, as well as a quantifiable burden to society. One example is the impact of functional limitation on mental health. Turner and Turner [8] examined the relationships among physical disability, mental health, and unemployment. They found that the three factors were closely related and suggested that this combination could lead to a unique pattern of disadvantage for these individuals. They observed that those with disabilities were five times more likely to be involuntarily unemployed and that rates of depression were higher for all individuals with physical limitations, but the trend was more pronounced for those without a job [8]. These unique and compounding factors may significantly increase both healthcare use and associated costs.

Annual direct costs have been estimated on the basis of reported ambulatory care visits, hospitalizations, diagnostic tests, medications, assistive devices, nonallopathic treatments, travel to visits, and paid household help [9]. In other studies focused on specific disorders (i.e., gout, ankylosing spondylitis), researchers have explored costs of illness to the patient and found that functional limitations contribute to such costs and in some cases are the most important predictor of total costs in patients [10, 11].

Indirect costs have been estimated from missed work days or, for retirees and homemakers, the number of days of activity limitation, as well as employment, household income, and receipt of supplemental security income for disability [9, 10, 12]. One investigation revealed that employers lost 28.2 million workdays annually ($4.95 billion in lost income) due to functional limitation caused by chronic diseases, including arthritis [3]. Additionally, 5.3% of the entire U.S. working population experiences work limitations attributable to arthritis [1]. The monetary cost of these work-specific limitations is not yet known, but the prevalence at which these limitations occur may have a significant impact on workplace productivity, especially in physically demanding fields.

Although activity limitations associated with musculoskeletal disorders impose a substantial economic burden on American society, this relationship has not been explored in a large U.S. cohort. As a result, in this study, we used the Medical Expenditures Panel Survey (MEPS) to investigate whether functional limitations explain the difference in medical expenditures between patients with any type or degree of arthritis and/or joint pain and those without.

Methods

Sample population

Using the MEPS consolidated file for 2011, we analyzed data from 35,313 adults (ages 18 years and older). Of this sample, 3793 adults self-reported arthritis or joint pain of some type. MEPS is an ongoing national household survey for the civilian noninstitutionalized U.S. population, with oversampling for blacks and Hispanics [13]. The data are collected through in-person interviews and include detailed information on demographic characteristics, self-reported health conditions, health status, use of medical services, charges and sources of payment, access to care, satisfaction with care, health insurance coverage, income, and employment for each person in the household [13]. The complex survey design provides weights to account for sampling and stratification, and it allows generalization to the U.S. population [14, 15]. Use of the complex survey design enables the 35,313 adults in the sample population to represent 311,125,758 individuals living in the United States in 2011. In addition, it allows generalization from the 3793 adults with self-reported arthritis and/or joint pain in the MEPS sample to represent the 150,378,648 population of adults in the United States with arthritis or joint pain.

Dependent variable

The dependent variable was total healthcare expenditures, defined as the sum of direct payments for office-based medical, hospital inpatient (including zero night stays) and outpatient, emergency department, pharmacy, dental, home health, and other medical care. Total healthcare expenditures represent annual expenditures for the year of reporting (2011) for each individual. Expenditures represent direct payments, not charges, for care provided during 2011 and are collected through both self-report via the household component and through provider reporting via the medical provider component of the survey. The medical provider component is used to verify information collected at the household level, as well as to collect information not known by the household [13].

Independent variable

The primary independent variable was any self-reported diagnosis with arthritis or joint pain. Joint pain and arthritis are used here as overarching terms; however, they include any diagnosis of this nature, including various rheumatic conditions as well as back pain. We were also interested in the independent variable “any limitation,” which summarizes whether a person has any limitations in instrumental activities of daily living, activities of daily living, function (walking), activity, or sensory (any visual or hearing impairment). The presence of any limitation was defined as a positive response to any of these components.

Covariates

Additional covariates were included on the basis of the Anderson model for healthcare use, and we categorized them as predisposing, enabling, and need factors [16]. Predisposing factors included age, race/ethnicity, gender, region, and metropolitan statistical area (MSA). Age was categorized into four groups of 18–34 years, 35–44 years, 45–64 years, and 65+ years. Race/ethnicity was grouped into four categories of non-Hispanic white (NHW), non-Hispanic black, non-Hispanic other (including both Asian and other categories), and Hispanic. Gender was dichotomized. Region was categorized as Northeast, Midwest, South, and West. MSA was dichotomized as MSA (urban) vs. non-MSA (rural).

Enabling factors included household income, employment status, education, insurance, and marital status. Household income was categorized into four groups: <$25,000, $25,000–$49,999, $50,000–$74,999, and > $75,000. Employment was dichotomized. Education was categorized into four groups as less than high school, high school, college, and graduate school. Insurance was categorized into three categories of private, public, and uninsured. Marital status was categorized into three groups of never married, separated/divorced/widowed, and married.

Need factors included health status, body mass index (BMI), and comorbidities. Comorbidities included chronic bronchitis, asthma, cancer, coronary heart disease, myocardial infarction, other heart disease, angina, diabetes, emphysema, high blood pressure, high cholesterol, depression, and stroke. Health status was self-reported into five categories of excellent, very good, good, fair, and poor. BMI was categorized into four groups of underweight, normal, overweight, and obese. Binary indicators of comorbidities were based on a positive response to a question, “Have you ever been diagnosed with ___?,” for each diagnosis.

Statistical analysis

We used sequential explanatory linear models to investigate whether functional limitations explain the difference in medical expenditures between patients with arthritis and joint pain and those without. For each model, we ran a generalized linear model (GLM) using gamma distribution with total expenditures as the dependent variable and reporting joint pain or arthritis as the main independent variable. We then added variables in blocks according to the Anderson model for healthcare use categories of predisposing variables, enabling variables, and need variables. Finally, we ran a final fully adjusted model and added any functional limitation. After each GLM, we determined the marginal effects using the margins command in Stata 14.0 statistical software (StataCorp, College Station, TX, USA). In order to generalize our study findings to the U.S. population, the complex sampling design of the MEPS dataset was taken into account by using sampling weight, variance estimation stratum, and primary sampling unit in all regression models and sample demographic estimates.

Results

Table 1 shows the characteristics for U.S. adults with and without arthritis or joint pain. Of the 3793 adults who self-reported any arthritis and/or joint pain, 63% reported any limitations, compared with 18% of individuals not reporting arthritis or joint pain. Unadjusted mean expenditures were $10,587 for those reporting joint pain or arthritis, compared with $3813 for those without. Most (87.7%) of U.S. adults reporting arthritis or joint pain are older than 45 years of age, and they are predominantly NHW (75.6%) and female (63.1%). The majority are not employed (58.1%) and have private insurance (63.1%). The most common comorbidities are high blood pressure (63.3%) and high cholesterol (55.8%), and the majority are overweight (32.2%) or obese (42.6%).
Table 1

Sample characteristics of U.S. adults with self-reported arthritis or joint pain (sample = 3791; population = 311,125,758)

 

U.S. adults reporting arthritis or joint pain

U.S. adults in sample with no reported arthritis or joint pain

Limitations, %

 Any limitations

62.86

18.18

 IADL

9.16

1.80

 ADL

5.68

1.03

 Walk limitations

39.01

6.12

Mean total expenditure ± SD

$10,587 ± $504

$3813 ± $119

Predisposing factors, %

 Age, years

  18–24

0.77

15.36

  25–44

11.54

39.19

  45–64

45.85

32.72

  65+

41.85

12.72

 Race

  Non-Hispanic white

75.64

64.76

  Non-Hispanic black

11.66

11.42

  Non-Hispanic other

4.77

7.58

  Hispanic

7.93

16.24

 Sex

  Male

36.86

50.49

  Female

63.14

49.51

 Region

  Northeast

18.29

18.28

  Midwest

23.24

21.07

  South

38.48

36.75

  West

19.99

23.91

 MSA

  Rural

19.17

14.72

  Urban

80.83

85.28

Enabling factors, %

 Income

   < $25,000

48.59

42.88

  $25,000–$49,999

29.33

29.80

  $50,000-$74,999

12.38

14.70

   > $75,000

9.70

12.62

 Employment

  Not employed

58.05

28.73

  Employed

41.95

71.27

 Education

  Less than high school

17.34

14.66

  High school

31.44

27.70

  College

40.87

45.59

  Graduate school

10.35

12.05

 Insurance

  Any private

63.09

68.99

  Public only

30.46

14.67

  No insurance

6.45

16.35

 Marital status

  Never married

10.48

31.04

  Separated/divorced/widowed

35.30

16.61

  Married

54.22

52.34

Need factors, %

 Asthma

15.21

7.80

 Diabetes

20.11

7.19

 Emphysema

7.12

1.21

 High blood pressure

63.28

26.05

 High cholesterol

55.75

25.01

 Stroke

9.89

2.14

 Depression

17.32

6.98

 CVD

32.32

9.91

 BMI

  Underweight

1.45

1.96

  Normal

23.80

37.05

  Overweight

32.17

34.29

  Obese

42.58

26.70

Abbreviations: ADL Activities of daily living, BMI Body mass index, CVD Cardiovascular disease, IADL Instrumental activities of daily living, MSA Metropolitan statistical area

As shown in Table 2, adults with self-reported arthritis or joint pain pay, on average, $6773 more in medical expenditures than those not reporting arthritis or joint pain. Adjustment decreased the marginal difference to $4427 when accounting for predisposing factors; $3980 when accounting for predisposing and enabling; and $2764 when accounting for predisposing, enabling, and need factors. Finally, in a fully adjusted model accounting also for functional limitations, those reporting joint pain or arthritis paid $1638 more than those without. Those with functional limitations regardless of comorbidity paid $3308 more than those without functional limitations, which was a higher marginal impact than any other comorbidity included in the model.
Table 2

Sequential explanatory model for marginal total healthcare expenditures in those with self-reported joint pain and/or arthritis

Variable

Model 1

Model 2

Model 3

Model 4

Model 5

No reported joint pain and/or arthritis (reference)

Self-reported joint pain and/or arthritis

$6773*

$4427*

$3980*

$2764*

$1638*

Predisposing factors

 Age, years

  18–24 (reference)

 

  25–44

 

$1571*

$1873*

$2381*

$2372*

  45–64

 

$3633*

$3717*

$3224*

$3038*

  65+

 

$6150*

$3510*

$2087*

$1607*

 Race

  Non-Hispanic white (reference)

 

  Non-Hispanic black

 

−$883**

−$1053**

−$1325**

−$1287*

  Non-Hispanic other

 

−$543

−$448

−$741

−$136

  Hispanic

 

−$2196*

−$1960*

−$1845*

−$1683*

 Sex

  Male (reference)

 

  Female

 

$1053*

$929*

$1272*

$1383*

 Region

  Northeast (reference)

 

  Midwest

 

−$149

$78

−$101

−$168.78

  South

 

−$514

−$69

−$458

−$325.85

  West

 

$272

$489

$610

$746.14

 MSA

  Rural (reference)

 

  Urban

 

$797**

$1124*

$1216*

$1241*

Enabling factors

     

 Income

   < $25,000 (reference)

  

  $25,000–$49,999

  

−$414

−$293

−$44

  $50,000–$74,999

  

$3

$444

$817

   > $75,000

  

$364

$1487***

$1761**

 Employment

  Not employed (reference)

  

  Employed

  

−$3239

−$2654*

−$2018*

 Education

  Less than high school (reference)

  

  High school

  

$849***

$1566**

$1634*

  College

  

$709

$1242*

$1305*

  Graduate school

  

$1515**

$2493*

$2457*

 Insurance

  Any private (reference)

  

  Public only

  

$1526***

$1639 ***

$947

  No insurance

  

−$3615*

−$3875*

−$3943*

 Marital status

  Married (reference)

  

  Separated/divorced/widowed

  

$63

$34

−$131

  Never married

  

−$55

$79

−$172

Need factors****

     

 Asthma

   

$2428*

$2001*

 Diabetes

   

$3001*

$2669*

 Emphysema

   

$2476***

$1559***

 High blood pressure

   

$938*

$876*

 High cholesterol

   

$1149*

$1194*

 Stroke

   

$2683*

$1727***

 Depression

   

$3182*

$1939*

 CVD

   

$2814*

$2375*

 BMI

  Underweight (reference)

   

  Normal

   

$726

$1200

  Overweight

   

$388

$732

  Obese

   

$534

$824

Risk factor of interest****

 Any limitations

    

$3308*

 IADL

    

$2488***

 ADL

    

$2284***

 Walk limitations

    

$253

Abbreviations: ADL Activities of daily living, BMI Body mass index, CVD Cardiovascular disease, IADL Instrumental activities of daily living, MSA Metropolitan statistical area

Sequential linear models were used, with each column representing a separate generalized linear model using gamma distribution

* p < 0.001

** p < 0.01

*** p < 0.05

****Reference groups compared with those without need factor or risk factor

Discussion

Our analyses showed that individuals self-reporting any diagnosis of arthritis and/or joint pain had substantially higher mean total healthcare expenditures ($6773) than individuals without either. In addition, whereas accounting for predisposing, enabling, and need variables lowered this marginal difference in expenditures, it did not remove the significance. Adjusting for functional limitations further decreased the difference in healthcare expenditures to $1638, but it did not remove significance. The marginal decrease resulting from adding functional limitations into the model was similar in size to the marginal decrease resulting from adding all other comorbidities.

This study emphasizes the need for aggressive measures and strategies for the prevention, early recognition, and treatment of functional limitations in populations with arthritis and joint pain because they have significant impact on differences in medical expenditures. In an investigation of risk factors for work disability, Allaire et al. [17] found that two of the most common indicators of work cessation were severity of disease condition and physical limitations in arthritis and other rheumatic conditions. Work cessation is especially important to populations with chronic conditions, often manifested in two primary concerns. First, loss of employment means loss of income, and for a population that experiences disproportionately high healthcare costs, losing the primary source of income could lead many individuals to financial ruin. Second, according to the Henry J. Kaiser Family Foundation, 49% of the U.S. adult population obtain health insurance through their employers [18]. Thus, many individuals with arthritis or joint pain who cease working because of functional limitations will lose their health insurance and have to pay significant out-of-pocket expenses or be forced to purchase private insurance, which may be more expensive and/or provide less coverage.

Our findings are consistent with existing literature that estimates annual costs associated with some rheumatic conditions to be $10,000–$50,000 more per patient than those for the general population [1922]. Major cost drivers include inpatient hospitalizations [23], poor physical and mental health, and low education and employment levels. Significant functional and emotional challenges resulting from symptoms, side effects, and complications may also include anxiety, depression, mood disorders, and decreased health-related quality of life [2427], which further increase service use costs. In another study, Ozaras et al. [28] observed that in patients with ankylosing spondylitis and psoriatic arthritis, disease activity (self-reported pain and symptoms) was associated with greater physical limitation. Given the direct relationships observed between disease activity, comorbidities, and limitations [17, 2831], the provision of optimal care to individuals with arthritis and joint pain is critical. This ultimately results in control of disease and slows the progression of related complications such as functional, activity, and sensory limitations.

Though the present study had a number of strengths, including the generalizability of results to the U.S. population, it has some limitations. First, diagnoses were based on self-report, which may differ from physician diagnosis. Second, though we attempted to include factors that could affect the relationship based on a theoretical framework, there are additional factors not in the dataset, including social determinants such as social support and emotional distress. Additionally, self-reports are subject to recall bias, which can introduce random error into the data. Finally, the data are cross-sectional in nature, which precludes commentary on causation or direction of the association.

Conclusions

Our study shows that self-reported arthritis and/or joint pain of any type is associated with higher total healthcare expenditures, and though accounting for functional limitations decreases this difference, the difference remains significant. The growing economic and public health burden of arthritis and joint pain [17, 32, 33], as well as the corresponding complications of functional, activity, and sensory limitations, calls for an interdisciplinary approach and heightened awareness among providers to identify strategies that meet the needs of high-risk patients in order to prevent and delay disease progression. This study provides an estimate for potential savings from future interventions geared toward reduction of any limitations in patients with arthritis and joint pain. Future researchers may benefit from this analysis and expand upon it by examining specifically where these increased costs are coming from, such as whether the increased cost is due to a greater number of visits to health professionals, in-hospital treatments, medications, or other causes. Enumerating the breakdown of these expenditures would allow future development of solutions to target specific areas with disproportionately high costs.

Abbreviations

ADL: 

Activities of daily living

BMI: 

Body mass index

CVD: 

Cardiovascular disease

GLM: 

Generalized linear model

IADL: 

Instrumental activities of daily living

MEPS: 

Medical Expenditures Panel Survey

MSA: 

Metropolitan statistical area

NHW: 

Non-Hispanic white

Declarations

Funding

This study was supported by the National Institute of Diabetes and Digestive and Kidney Diseases (grant K24DK093699 [to principal investigator LEE]).

Availability of data and materials

The datasets generated and/or analyzed during the current study are available in the Agency for Healthcare Research and Quality repository (http://meps.ahrq.gov/mepsweb/data_stats/download_data_files.jsp and http://meps.ahrq.gov/data_stats/download_data/pufs/h36/h36u11doc.shtml).

Authors’ contributions

LEE was involved in conception of the study and statistical design, participated in data analysis, and oversaw manuscript development. RJW carried out the data analysis, drafted the Methods and Results sections of the manuscript, and revised the manuscript. EW conceived of and designed the study, drafted the manuscript, and helped to revise the manuscript. TF participated in drafting and helping to revise the manuscript. All authors revised the article critically for important intellectual content and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

In this analysis, we used secondary data from the Medical Expenditures Panel Survey. As such, all ethics approvals and consent to participate were waived by the institutional review board at the Medical University of South Carolina, Charleston, SC, USA.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Public Health Sciences, Medical University of South Carolina
(2)
Center for Health Disparities Research, Medical University of South Carolina
(3)
Division of General Internal Medicine and Geriatrics, Department of Medicine, Medical University of South Carolina
(4)
Health Equity and Rural Outreach Innovation Center (HEROIC), Ralph H. Johnson Veterans Affairs Medical Center

References

  1. Ma VY, Chan L, Carruthers KJ. Incidence, prevalence, costs, and impact on disability of common conditions requiring rehabilitation in the United States: stroke, spinal cord injury, traumatic brain injury, multiple sclerosis, osteoarthritis, rheumatoid arthritis, limb loss, and back pain. Arch Phys Med Rehabil. 2014;95(5):986–95.View ArticlePubMedPubMed CentralGoogle Scholar
  2. Spaetgens B, Tran-Duy A, Wijnands JM, van der Linden S, Boonen A. Health and utilities in patients with gout under the care of a rheumatologist. Arthritis Care Res (Hoboken). 2015;67(8):1128–36.View ArticleGoogle Scholar
  3. Vuong TD, Wei F, Beverly CJ. Absenteeism due to functional limitations caused by seven common chronic diseases in US workers. J Occup Environ Med. 2015;57(7):779–84.View ArticlePubMedPubMed CentralGoogle Scholar
  4. Theis KA, Murphy L, Hootman JM, Wilkie R. Social participation restriction among US adults with arthritis: a population-based study using the International Classification of Functioning, Disability and Health. Arthritis Care Res (Hoboken). 2013;65(7):1059–69.View ArticleGoogle Scholar
  5. Durcan L, Wilson F, Conway R, Cunnane G, O’Shea FD. Increased body mass index in ankylosing spondylitis is associated with greater burden of symptoms and poor perceptions of the benefits of exercise. J Rheumatol. 2012;39(12):2310–4.View ArticlePubMedGoogle Scholar
  6. Iqbal I, Dasgupta B, Taylor P, Heron L, Pilling C. Elicitation of health state utilities associated with differing durations of morning stiffness in rheumatoid arthritis. J Med Econ. 2012;15(6):1192–200.View ArticlePubMedGoogle Scholar
  7. Nuñez DE, Keller C, Ananian CD. A review of the efficacy of the self-management model on health outcomes in community-residing older adults with arthritis. Worldviews Evid Based Nurs. 2009;6(3):130–48.View ArticlePubMedGoogle Scholar
  8. Turner J, Turner R. Physical disability, unemployment, and mental health. Rehabil Psych. 2004;49(3):241–9.View ArticleGoogle Scholar
  9. Ward MM. Functional disability predicts total costs in patients with ankylosing spondylitis. Arthritis Rheum. 2002;46(1):223–31.View ArticlePubMedGoogle Scholar
  10. Dall TM, Gallo P, Koenig L, Gu Q, Ruiz D. Modeling the indirect economic implications of musculoskeletal disorders and treatment. Cost Eff Resour Alloc. 2013;11(1):5.View ArticlePubMedPubMed CentralGoogle Scholar
  11. Spaetgens B, Wijnands JM, van Durme C, van der Linden S, Boonen A. Cost of illness and determinants of costs among patients with gout. J Rheumatol. 2015;42(2):335–44.View ArticlePubMedGoogle Scholar
  12. Wolfe F, Michaud K, Choi HK, Williams R. Household income and earnings losses among 6,396 persons with rheumatoid arthritis. J Rheumatol. 2005;32(10):1875–83.PubMedGoogle Scholar
  13. Agency for Healthcare Research and Quality (AHRQ). MEPS HC-147: 2011 full year consolidated data file. Rockville: AHRQ; 2013. http://meps.ahrq.gov/mepsweb/data_stats/download_data/pufs/h147/h147doc.pdf. Accessed 5 Apr 2014.Google Scholar
  14. Agency for Healthcare Research and Quality (AHRQ). MEPS HC-036: 1996–2011 pooled linkage variance estimation file. Rockville: AHRQ; 2013. http://meps.ahrq.gov/data_stats/download_data/pufs/h36/h36u11doc.shtml. Accessed 18 Aug 2014.Google Scholar
  15. Agency for Healthcare Research and Quality (AHRQb). Medical Expenditure Panel Survey. 2011 Full year consolidated data file 2013c, Available from http://meps.ahrq.gov/mepsweb/data_stats/download_data_files.jsp.
  16. Aday LA, Andersen R. A framework for the study of access to medical care. Health Serv Res. 1974;9(3):208–20.PubMedPubMed CentralGoogle Scholar
  17. Allaire SJ, AlHeresh R, Keysor JJ. Risk factors for work disability associated with arthritis and other rheumatic conditions. Work. 2013;45(4):499–503.PubMedGoogle Scholar
  18. Henry J, Kaiser Family Foundation (KFF). Health insurance coverage of the total population. Menlo Park: KFF; 2015.Google Scholar
  19. Kan HJ, Song X, Johnson BH, Bechtel B, O’Sullivan D, Molta CT. Healthcare utilization and costs of systemic lupus erythematosus in Medicaid. Biomed Res Int. 2013;2013:808391.PubMedGoogle Scholar
  20. Panopalis P, Yazdany J, Gillis JZ, Julian L, Trupin L, Hersh AO, et al. Health care costs and costs associated with changes in work productivity among persons with systemic lupus erythematosus. Arthritis Rheum. 2008;59(12):1788–95.View ArticlePubMedPubMed CentralGoogle Scholar
  21. Panopalis P, Clarke AE, Yelin E. The economic burden of systemic lupus erythematosus. Best Pract Res Clin Rheumatol. 2012;26(5):695–704.View ArticlePubMedGoogle Scholar
  22. Birnbaum H, Pike C, Kaufman R, Marynchenko M, Kidolezi Y, Cifaldi M. Societal cost of rheumatoid arthritis patients in the US. Curr Med Res Opin. 2010;26(1):77–90.View ArticlePubMedGoogle Scholar
  23. Garris C, Jhingran P, Bass D, Engel-Nitz N, Riedel A, Dennis G. Healthcare utilization and cost of systemic lupus erythematosus in a US managed care health plan. J Med Econ. 2013;16(5):667–77.View ArticlePubMedGoogle Scholar
  24. Jolly M. How does quality of life of patients with systemic lupus erythematosus compare with that of other common chronic illnesses? J Rheumatol. 2005;32(9):1706–8.PubMedGoogle Scholar
  25. Da Costa D, Dobkin PL, Fitzcharles MA, Fortin PR, Beaulieu A, Zummer M, et al. Determinants of health status in fibromyalgia: a comparative study with systemic lupus erythematosus. J Rheumatol. 2000;27(2):365–72.PubMedGoogle Scholar
  26. Greco C, Rudy T, Manzi S. Adaptation to chronic pain in systemic lupus erythematosus: applicability of the multidimensional pain inventory. Pain Med. 2003;4(1):39–50.View ArticlePubMedGoogle Scholar
  27. Cornwell C, Schmitt M. Perceived health status, self-esteem and body image in women with rheumatoid arthritis or systemic lupus erythematosus. Res Nurs Health. 1990;13(2):99–107.View ArticlePubMedGoogle Scholar
  28. Ozaras N, Havan N, Poyraz E, Rezvani A, Aydin T. Functional limitations due to foot involvement in spondyloarthritis. J Phys Ther Sci. 2016;28(7):2005–8.View ArticlePubMedPubMed CentralGoogle Scholar
  29. Omariba DWR. Gender differences in functional limitations among Canadians with arthritis: the role of disease duration and comorbidity. Health Rep. 2011;22(4):7–14.PubMedGoogle Scholar
  30. Marques WV, Cruz VA, Rego J, Silva NA. The impact of comorbidities on the physical function in patients with rheumatoid arthritis. Rev Bras Reumatol Engl Ed. 2016;56(1):14–21.View ArticlePubMedGoogle Scholar
  31. Shih VC, Song J, Chang RW, Dunlop DD. Racial differences in activities of daily living limitation onset in older adults with arthritis: a national cohort study. Arch Phys Med Rehab. 2005;86(8):1521–6.View ArticleGoogle Scholar
  32. Drenkard C, Bao G, Dennis G, Kan HJ, Jhingran PM, Molta CT, et al. Burden of systemic lupus erythematosus on employment and work productivity: data from a large cohort in the southeastern United States. Arthritis Care Res (Hoboken). 2014;66(6):878–87.View ArticleGoogle Scholar
  33. Looper KJ, Mustafa SS, Zelkowitz P, Purden M, Baron M, McGill Early Arthritis Research Group. Work instability and financial loss in early inflammatory arthritis. Int J Rheum Dis. 2012;15(6):546–53.View ArticlePubMedGoogle Scholar

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