You are here

  • Home
  • Archive
  • Volume 67, Issue 9
  • Gout is associated with more comorbidities, poorer health-related quality of life and higher healthcare utilisation in US veterans

Article Text

Article menu

Download PDFPDF

Extended report
Gout is associated with more comorbidities, poorer health-related quality of life and higher healthcare utilisation in US veterans
  1. J A Singh1,2,3,
  2. V Strand4
  1. 1
    Rheumatology Section, Medicine Service, VA Medical Center, Minneapolis, Minnesota, USA
  2. 2
    Center for Chronic Disease Outcomes Research at the VA Medical Center, Minneapolis, Minnesota, USA
  3. 3
    Division of Rheumatology, Department of Medicine, University of Minnesota, Minneapolis, Minnesota, USA
  4. 4
    Division of Immunology/Rheumatology, Stanford University, Palo Alto, California, USA
  1. Dr J A Singh, Minneapolis VA Medical Center, Rheumatology (111R), One Veteran’s Drive, Minneapolis, MN 55417, USA; Jasvinder.Singh{at}va.gov

Abstract

Objective: To better understand the impact of gout on functional status, health-related quality of life (HRQoL), mortality and healthcare utilisation in US veterans.

Methods: All veterans seen in Veterans Integrated Service Network-13 from 1 October 1996 to 31 March 1998 received mailed surveys asking about demographic characteristics; performance of activities of daily living and HRQoL by Short Form-36 (SF-36) for Veterans. Administrative data included demographics; inpatient/outpatient healthcare utilisation; ICD-9 codes for gout, medical comorbidities and arthritis excluding gout—“arthritic comorbidity” and 1-year mortality. Multivariable estimates compared results between veterans with/without gout using least means squared.

Results: Subjects with gout were significantly older, retired, not married, current non-smokers, with more comorbidities. Multivariable-adjusted bodily pain was somewhat worse (49.7 vs 47.1, p<0.01) and mental health (66.7 vs 68.6, p<0.01) domain scores somewhat better in patients with gout, both differences significant but not clinically meaningful (less than threshold of 5–10 points); other SF-36 domain and summary scores and functional limitations were similar. Medical or arthritic comorbidities predicted clinically/statistically lower adjusted scores in all SF-36 domains and physical domains (physical component summary), respectively. Patients with gout had significantly more annual primary care visits (3.5 vs 2.7, p<0.001) and admissions to hospital (18.3% vs 15.1%, p<0.01), fewer mental health visits (10.1% vs 13.7%, p<0.01) and similar mortality (2.6% vs 2.2%, p = 0.23).

Conclusions: Gout is independently associated with higher medical and arthritic comorbidity, primary care and inpatient utilisation. Poorer HRQoL, functional limitation and higher mortality noted in univariate analyses in patients with gout were attributable to higher comorbidity and sociodemographic characteristics.

https://doi.org/10.1136/ard.2007.081604

Statistics from Altmetric.com

    Request Permissions

    If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

    Arthritis is the fifth most prevalent condition for users of the Veterans Affairs (VA) healthcare system, affecting 16%.1 Gout is the commonest inflammatory arthritis in men1–4 with an increasing prevalence, partly due to increased obesity, diuretic use and longevity.5 6 In 2002, gout accounted for 1.4 million outpatient visits in the USA, indicating it to be a significant public health problem.7 Despite being a chronic arthritic condition, effective and affordable treatments are available for gout that may have an impact and improve outcomes.

    Current evidence indicates that gout is underdiagnosed and undertreated—care is suboptimal.8–10 Ascertaining the impact of gout on subjects’ functional status, health-related quality of life (HRQoL) and associated healthcare burden may improve care patterns through an increased awareness of disease outcomes. Only two small studies11 12 and an abstract13 have evaluated functional limitation patterns in patients with gout, indicating that 10–42% of patients reported limitations in activities11 12 and 4.6 more days of absence from work/year.13 Most studies of the effect of gout on health status were performed more than two decades ago14 15 and with the exception of one recent publication16 reported the frequency of recurrent acute gouty arthritis. There has been limited recognition that health status/HRQoL in gout may be related not only to recurrent flares/chronic joint disease, but more importantly to associated medical comorbidities, including hypertension, cardiac and renal disease.17 18 More effective control of gout is cost effective,19 and it is likely that better management of associated comorbidities will significantly improve patients’ lives.

    Much debate in recent decades has focused on whether hyperuricaemia is an independent risk factor for cardiovascular events and cardiac or all-cause mortality.20 In a recent analysis of the Multiple Risk Factor Intervention Trial (MRFIT), an independent association was found between gout and acute myocardial infarction, but the risk of mortality secondary to acute myocardial infarction was not increased in patients with gout.21 Higher mortality was noted in one treatment arm (4/507 vs 0/253) in a recent randomised trial comparing anti-hyperuricaemic drugs in patients with gout.22 To our knowledge, no population-based studies (prospective or retrospective) have examined whether all-cause mortality is increased in gout cohorts, and whether this association is independent of differences in age, gender and comorbidity between populations with and without gout.

    This project was designed to ascertain functional status, HRQoL, healthcare utilisation and mortality patterns in veterans with and without gout. The veteran population was selected for several reasons: (a) it offers a large sample size for a disease predominant in men—in 2003 the Department of Veterans Affairs (VA) served approximately 4.9 million subjects with a budget of $25 billion23; (b) other arthritic conditions are very common in this population1 and (c) state of the art validated VA databases capture demographics, diagnoses, healthcare utilisation and mortality.24 25 Objectives were to (a) compare patient-reported functional status, HRQoL, healthcare utilisation patterns and mortality in veterans with and without gout; (b) determine if these were independently associated with a diagnosis of gout and (c) determine whether and in what aspects medical or other arthritic (non-gout) comorbidities affect HRQoL in veterans with gout.

    METHODS

    We identified a cohort of veterans who used Upper Midwest Veterans Integrated Service Network (VISN-13: Minnesota, North Dakota and South Dakota, and selected counties in Iowa, Nebraska, Wisconsin and Wyoming) for healthcare services between 1 October1996 and 31 March 1998 and had a valid mailing addresses (n = 70 334). We then conducted a cross-sectional mailed survey in August 1998 querying demographics (education level, race/ethnicity), performance of six activities of daily living (ADLs) including bathing, dressing, eating, walking, transferring and using the toilet26 and HRQoL using the Short Form-36 for veterans (SF-36V) (Details previously published as VISN-13 Vet-QOL Study27 28.) Non-responders received the same survey 10 weeks later.

    SF-36 is a generic measure of HRQoL that is valid, reliable and responsive to change in patients with osteoarthritis, rheumatoid and psoriatic arthritis.29–32 SF-36V is a modified version, validated in veterans,33 where role physical and role emotional domains were changed from dichotomous to five-choice ordinal scales to avoid floor and ceiling effects; it is similar to SF-36, version 2.0.34 We calculated eight SF-36V subscales (range from 0 (worst) to 100 (best)), and two summary scores, the physical and mental component summary scores (PCS and MCS; normative distributions with mean of 50 and standard deviations of 10 for the US population) in a standard fashion.35 36 Survey data were supplemented by demographics, International Classification of Diseases (ICD)-9 codes and healthcare utilisation data from the national VA databases.

    Predictor variables

    The main predictor of functional status, HRQoL, healthcare utilisation and mortality was presence or absence of ICD-9 diagnosis of gout. The multivariable analyses adjusted for the following covariates and potential confounders:

    • Sociodemographic: (a) age (in years), employment status (employed, unemployed, retired, unknown), marital status (married, not married; database-derived); (b) gender (male, female), race (white, non-white), education level (less than 8th grade, some high school, high school graduate, college and beyond; survey-derived).

    • Comorbidity: (a) Current smoking status: current smoker versus non-smoker (survey-derived); (b) presence or absence of ICD-9 diagnoses of five common medical comorbidities in veterans1: asthma/chronic obstructive pulmonary disease, diabetes, depression, hypertension, heart disease (database-derived); (c) presence or absence of arthritic comorbidities in nine categories: rheumatoid arthritis, osteoarthritis, spondyloarthropathies, crystal-associated arthritis except gout, unspecified arthritis, infectious arthritis, arthritis associated with haemachromatosis, arthritis associated with endocrine or metabolic diseases, spondylosis (database-derived).

    Database-derived variables were available for most of the sample (∼70 000) and survey-derived variables for ∼35 000–40 000 responders to the questionnaire. Medical and arthritic (non-gout) comorbidities were categorised into three categories (none, one or two or more), as only 7% and 1%, respectively, had three or more comorbidities.

    Study outcomes

    Outcomes of this current study included: (a) percentage of subjects with limitations in each ADL and number of ADLs with limitations for each subject; (b) SF-36V domain, PCS and MCS scores as continuous variables; (c) healthcare utilisation 1 year after the survey: outpatient primary care, medical subspecialty, surgery clinic visits as continuous variables and inpatient and mental health visits as categorical variables (none vs one or more), owing to a right skewed distribution and (d) mortality 1 year after the survey. Health care utilisation and mortality data were obtained for 1 year after the index survey date and ICD-9 codes before the survey, from inpatient and outpatient VA datasets. These datasets are reliable for demographics and valid for most common,25 and specific diagnoses.37 38 In a random sample of patients with an ICD-9 code for gout in the Minneapolis VA database, we found that this diagnosis was supported by medical chart documentation in 78%.39

    Definitions of minimal clinically important differences were based on changes of 5–10 points in SF-36 domain and 2.5–5 points in summary component scores in published randomised controlled trials; consistently similar in various rheumatic conditions.40–43 Although considered to represent the minimum improvement “perceptible” to patients when enrolled in therapeutic trials, minimal clinically important differences offer a reasonable “benchmark” for comparisons across patient and disease populations.

    Statistical analyses

    Continuous and categorical variables between subjects with and without gout were compared between survey responders and non-responders using the Student t test and χ2 testing. We used multiple logistic regression models to compare the proportion of patients with and without gout with: limitation of each ADL; any hospitalisation, any mental health clinic visit or mortality 1 year after the survey. Multiple linear regression analyses modelled the number of primary care, specialty and surgical care visits over the year after the survey and the SF-36V domain and summary scores as outcomes. These regression model-estimated probabilities were calculated using average values of the additional covariates (as described above). All analyses were performed using SPSS, version 11.5 and SAS, version 9.0. Owing to multiple comparisons performed, p values <0.01 were considered significant.

    RESULTS

    Demographic and comorbidity characteristics

    Overall 58% responded to the survey (40 508 of the 70 334 eligible). Of the 70 334 (demographic and utilisation variables were available for most), 1581 (2.2%) had an ICD-9 diagnosis for gout; of the 40 508 survey respondents (HRQoL and functional limitation data were available for this group only), 1090 (2.7%) had an ICD-9 diagnosis for gout. Veterans with gout were significantly older, male, not married, retired and current non-smokers (table 1).

    View this table:
    Table 1 Demographic and clinical characteristics of subjects with and without gout

    Non-responders to the survey were significantly younger (56.3 vs 64.5 years), less likely to be married (47% vs 64.9%), less likely to be retired (26.7% vs 43.9%) and had smaller number of outpatient visits (eight vs nine visits/year) but had similar inpatient utilisation rates (13.1% with hospitalisation vs 13%), as compared with responders.

    The higher prevalence of unadjusted medical and arthritic comorbidity in patients with gout as described in table 1 persisted after adjustment for age, sex and race (1.4 vs 0.8 and 0.5 vs 0.2, respectively; p<0.001 for both).

    Functional limitations and decrements in HRQoL in patients with gout

    The absolute unadjusted differences of 9% and 6.5% limitation in transferring and walking between patients with and patients without gout were not significant after multivariable adjustment (fig 1A). Veterans with gout reported significant, marginally higher unadjusted mean number of limitations in ADLs than those without (1.7 vs 1.6, p = 0.016); no longer significant after multivariable analyses (1.5 vs 1.6, p = 0.88).

    Figure 1
    Figure 1 Unadjusted and adjusted* functional limitation as assessed by limitation of each activity of daily living (A) and health-related quality of life assessed by Short Form-36 for veterans (SF-36V) subscale and summary scale scores (B-C).(A–C) SF36-V subscale scores range from 0 to 100. p Values (below or next to each variable in the graphs) are for test of differences in means between those with and without gout for unadjusted and adjusted analyses, respectively; NS, not significant. Error bars/whiskers represent upper and lower 99% confidence intervals. *Multivariable model adjusted for the following variables: age, employment status, marital status, gender, race, education level, current smoking status, medical comorbidity and arthritis comorbidity (other than gout). BP, bodily pain; GH, general health; MCS, mental component summary; MH, mental health; PCS, physical component summary; PF, physical functioning; RE, role emotional; RP, role physical; SF, social functioning; VT, vitality.

    In unadjusted analyses, subjects with gout reported statistically significant and clinically meaningful lower physical HRQoL: decrements of 5.8–7 points in physical functioning, bodily pain, role physical domains and three points in PCS scores (fig 1B). After multivariable adjustment, none of these domain and PCS score differences were statistically or clinically significant except that bodily pain remained slightly lower in those with gout by 2.6 points, but this did not meet the clinically meaningful difference of 5–10 points. Statistically significant differences (that were small and not clinically meaningful) were noted in mental/emotional and general health HRQoL domain scores in unadjusted analyses; not evident after multivariable adjustment (fig 1C). MCS domain scores were not statistically (or clinically) different in unadjusted or adjusted analyses.

    Effect of medical versus arthritic comorbidity on HRQoL in patients with gout

    After multivariable adjustment, compared with those without gout, patients with gout with one or more medical comorbidities reported significantly lower scores in all SF-36V domains except bodily pain and vitality (7.1–11 point decrements) and PCS and MCS scores (3.1–4.2 point decrements), all of which are clinically meaningful (fig 2A). Those with one or more arthritic comorbidities reported statistically significant and clinically meaningful lower scores in physical HRQoL domains—that is, physical functioning, role physical, bodily pain domains (7.3–10 point decrements) and PCS (5.8 point decrement) (fig 2B). General health, vitality and social functioning domain scores were also statistically significantly and clinically meaningfully lower, by a smaller magnitude (4.5–6.3 point decrements), but no significant differences were noted in role emotional and mental health domain and MCS scores.

    Figure 2
    Figure 2 Effect of comorbidity on health-related quality of life in patients with gout: multivariable-adjusted* association of medical comorbidity (A) or arthritic comorbidity† (B) with Short Form-36 for veterans (SF-36V) subscale and summary scale scores in patients with gout. p Values (next to each variable in the graphs) are for test of differences in means between the groups—that is, those with no comorbidity versus one or more medical comorbidities (A) and those with no arthritic comorbidities versus one or more arthritic comorbidities (B), respectively; NS, not significant. Error bars/whiskers represent upper and lower 99% confidence intervals. *Multivariable models adjusted for age, employment status, marital status, gender, race, education level, current smoking status for both analyses; medical comorbidity (for analyses shown in (A)) and arthritis comorbidity (for analyses shown in (B)). †Arthritic conditions other than gout. BP, bodily pain; GH, general health; MCS, mental component summary; MH, mental health; PCS, physical component summary; PF, physical functioning; RE, role emotional; RP, role physical; SF, social functioning; VT, vitality. Line diagrams represent normative data in US populations matched for age and gender.

    Healthcare utilisation and mortality in subjects with and without gout

    Multivariable adjustments attenuated unadjusted differences in specialty and surgical care visits (1.66 vs 1.52, p = 0.35 and 2.07 vs 1.89, p = 0.06, respectively, 9.5% higher in patients with gout), but primary care visits and inpatient admissions remained 31% and 21% higher in those with gout (p<0.001) (table 2). Multivariable-adjusted visits to mental healthcare clinics were lower in patients with gout versus patients without gout (10.1% vs 13.7%, 26% lower; p<0.01). Details of the regression models are given in Appendix 1. Unadjusted 1-year mortality was higher in veterans with gout than in those without (5.8% vs 3.9%, 50% higher; p<0.01); after multivariable adjustments, it was similar in both groups (2.6 vs 2.2%, 18% higher, respectively; p>0.05).

    View this table:
    Table 2 Health care utilisation and mortality in patients with and without gout

    DISCUSSION

    The two most important findings in this study included (a) significantly more primary care outpatient clinic and inpatient healthcare use by veterans with gout; largely attributable to gout and partially to comorbidities and sociodemographic characteristics; and (b) poorer functional ability and HRQoL, increased specialty medicine and surgical clinic use and higher mortality, primarily attributable to sociodemographic and (medical and arthritic) comorbidities. Since increased primary care and inpatient utilisation appear to be related to gout, further studies should examine which factor/factors are most predictive: disease severity; patient (knowledge about the disease and its treatment, medication compliance); doctor (knowledge, management of arthritic symptoms); treatment (adverse effects) and system factors (quality of care for gout). Gaps in doctors’ and patients’ knowledge44 and suboptimal care for gout have been well described,8 10 therefore future studies should explore whether this is associated with increased health services utilisation or poorer outcomes, or both. A better understanding of why patients use more health services will not only allow us to reduce gout-related healthcare utilisation but also enable us to improve treatment and patient outcomes.

    To our knowledge, this is the first population-based assessment of HRQoL, utilisation and functional ability in a large cohort with gout. A recently published UK study reported lower physical and overall HRQoL in patients with gout than in patients without gout in univariate analyses, but only lower physical HRQoL after multivariable adjustment.16 Our study confirms and extends these HRQoL findings to a much larger population-based US cohort using SF-36V as the HRQoL measure, but differs in important aspects from the previous study in that (a) no differences were noted in physical HRQoL after adjustment for sociodemographics and comorbidity as opposed to the earlier study; (b) response was higher, 58% versus 23%; (c) the study sample was larger, 1500 versus 137 patients with gout; (d) this was a population-based survey of 70 000 adults in a three states of the USA compared with patients >30 years from two general practices in the UK; (e) different cohorts were studied, 98% men with mean age of 68 years versus 81% men with mean age of 64 years; (f) comorbidity assessment was made by ICD-9 codes rather than by self-reporting and (g) different HRQoL assessments were made using SF-36 compared with WHO-QoL-Bref.

    Another significant finding in our study is the 50% higher unadjusted 1-year mortality rate in veterans with gout, largely explained by higher comorbidity and sociodemographic differences. This finding emphasises that gout is a chronic disease with significant associated comorbidities (heart disease, diabetes, hypertension, etc), and that mortality is attributable to these comorbid conditions. This important observation in a population-based study in addition to the recent report of similar myocardial infarction-related mortality in gout versus non-gout from the MRFIT data,21 indicates that increased mortality in patients with gout may be related to other comorbidities and sociodemographics.

    That a greater proportion of patients with gout reported limitations in transferring and walking in unadjusted analyses is consistent with predominantly monarticular and/or lower extremity involvement in this disease. However, we were surprised to find that in multivariable analyses, these ADL limitations were attributable to differences in age, sociodemographics and comorbidities, implying that variables other than gout conferred difficulty in walking/transferring. Such limitations in younger employed men with gout may have an impact on work productivity and social functioning, as reported in two recent abstracts.13 45

    Compared with normative US data matched for age and gender, domain scores (except role emotional, mental health and MCS) were 15–25 points lower across all gout populations—with and without medical and/or arthritic comorbidities; again reflecting the broad impact of this disease upon HRQoL, another significant finding. SF-36V bodily pain domain scores in patients with gout showed larger decrements with arthritic than with medical comorbidities (9 vs 2.7 point decrement), indicating that the focus of care in subjects with gout and more medical comorbidities should be to improve both physical and mental/emotional HRQoL, whereas it should be to improve physical function and pain management in those with gout and other arthritic conditions.

    This study has several limitations: results may not be generalisable to the general US population owing to non-response bias and male predominance, or to other VA networks owing to regional variation. However, the prevalence of gout in this cohort of 2.2% is similar to the 2.7% in a National probability sample.46 The analyses were unable to control for use of drugs and non-VA health services in the sample—both may have had an impact on outcomes. Although data were collected for most common comorbidities, information about renal failure, obesity, metabolic syndrome and alcohol use were not specifically queried owing to limited resources; these may be more common in patients with gout and may be responsible for residual confounding in the adjusted analyses. However, adjustment for hypertension, diabetes and cardiac disease should have partially accounted for these factors. Ascertainment error in diagnosis of gout may be present as reported in a recent study in a Health Maintenance Organisation population47 and our observation of 22% inaccuracy of ICD-9 codes for gout,39 which probably biased our study towards null and may have masked some differences. Cross-sectional measurement of HRQoL is likely to have underestimated the effect of acute versus intercritical gout on HRQoL, which can be estimated in a cohort study.

    This population-based cohort with a large sample size has numerous strengths. Healthcare utilisation, functional limitations and HRQoL were examined and analyses controlled for clinical and demographic differences including socioeconomic status. Utilisation patterns for veterans reported here may provide meaningful information for policy makers; many veterans eligible for Medicare use dual services.48

    In summary, this study documents higher primary care utilisation and inpatient utilisation by veterans with gout, which can only partially be explained by differences in comorbidities and sociodemographic characteristics. Higher mortality, poorer HRQoL and functional limitation in patients with gout appears not to be due to gout itself, but to comorbidity and sociodemographic differences, suggesting that gout is a marker for higher medical comorbidity load, which indicates poor prognosis and higher all-cause mortality. Therefore a diagnosis of gout can be used to identify a high-risk group. Medical and non-gout arthritic comorbidity has a different impact on HRQoL in patients with gout. These findings suggest that more intensive management of comorbidities and of arthritis associated with gout will probably improve outcomes in patients with gout.

    Acknowledgments

    We thank David Nelson and Alisha Baines of the Center for Chronic Diseases Outcomes and Research, a centre for excellence at the Minneapolis VA Medical Center, for their help in statistical analyses and production of graphs.

    Appendix 1

    View this table:
    Table A1 Multivariable regression models for primary care, specialty medicine, surgery and mental health stops with coefficients and odds ratios, as applicable?

    REFERENCES

    1. ↵
      1. Yu W,
      2. Ravelo A,
      3. Wagner T,
      4. Phibbs C,
      5. Bhandari A,
      6. Chen S,
      7. et al
      . Prevalence and costs of chronic conditions in the VA health care system. Med Care 2003;60:146–67S.
      OpenUrlCrossRef
    2. ↵
      1. Gabriel SE,
      2. Crowson CS,
      3. O’Fallon WM
      . The epidemiology of rheumatoid arthritis in Rochester, Minnesota, 1955–1985. Arthritis Rheum 1999;42:415–20.
      OpenUrlCrossRefPubMedWeb of Science
      1. Symmons D,
      2. Turner G,
      3. Webb R,
      4. Asten P,
      5. Barrett E,
      6. Lunt M,
      7. et al
      . The prevalence of rheumatoid arthritis in the United Kingdom: new estimates for a new century. Rheumatology (Oxford) 2002;41:793–800.
      OpenUrlAbstract/FREE Full Text
    4. ↵
      1. Saraux A,
      2. Guedes C,
      3. Allain J,
      4. Devauchelle V,
      5. Valls I,
      6. Lamour A,
      7. et al
      . Prevalence of rheumatoid arthritis and spondyloarthropathy in Brittany, France. Societe de Rhumatologie de l’Ouest. J Rheumatol 1999;26:2622–7.
      OpenUrlPubMedWeb of Science
    5. ↵
      1. Lawrence RC,
      2. Helmick CG,
      3. Arnett FC,
      4. Deyo RA,
      5. Felson DT,
      6. Giannini EH,
      7. et al
      . Estimates of the prevalence of arthritis and selected musculoskeletal disorders in the United States. Arthritis Rheum 1998;41:778–99.
      OpenUrlCrossRefPubMedWeb of Science
    6. ↵
      1. Lawrence RC,
      2. Hochberg MC,
      3. Kelsey JL,
      4. McDuffie FC,
      5. Medsger TA Jr,
      6. Felts WR,
      7. et al
      . Estimates of the prevalence of selected arthritic and musculoskeletal diseases in the United States. J Rheumatol 1989;16:427–41.
      OpenUrlPubMedWeb of Science
    7. ↵
      1. Krishnan E,
      2. Griffith C,
      3. Kwoh K
      . Burden of illness from gout in ambulatory care in the United States. Arthritis Rheum 2005;52(Suppl):S656.
      OpenUrl
    8. ↵
      1. Mikuls TR,
      2. Farrar JT,
      3. Bilker WB,
      4. Fernandes S,
      5. Saag KG
      . Suboptimal physician adherence to quality indicators for the management of gout and asymptomatic hyperuricaemia: results from the UK General Practice Research Database (GPRD). Rheumatology (Oxford) 2005;44:1038–42.
      OpenUrlAbstract/FREE Full Text
      1. Mikuls TR,
      2. Farrar JT,
      3. Bilker WB,
      4. Fernandes S,
      5. Schumacher HR Jr,
      6. Saag KG
      . Gout epidemiology: results from the UK General Practice Research Database, 1990–1999. Ann Rheum Dis 2005;64:267–72.
      OpenUrlAbstract/FREE Full Text
    10. ↵
      1. Singh JA,
      2. Hodges JS,
      3. Toscano JP,
      4. Asch SM
      . Quality of care for gout in the US needs improvement. Arthritis Rheum 2007;57:822–9.
      OpenUrlCrossRefPubMedWeb of Science
    11. ↵
      1. Brown GM,
      2. Dare CM,
      3. Smith PR,
      4. Meyers OL
      . Important problems identified by patients with chronic arthritis. S Afr Med J 1987;72:126–8.
      OpenUrlPubMedWeb of Science
    12. ↵
      1. Alvarez-Nemegyei J,
      2. Cen-Piste JC,
      3. Medina-Escobedo M,
      4. Villanueva-Jorge S
      . Factors associated with musculoskeletal disability and chronic renal failure in clinically diagnosed primary gout. J Rheumatol 2005;32:1923–7.
      OpenUrlAbstract/FREE Full Text
    13. ↵
      1. Kleinman N,
      2. Patel P,
      3. Brook R,
      4. Melkonian A,
      5. Smeeding J,
      6. Joseph-Ridge N
      . Lost days and productivity impact of gout [abstract]. Arthritis Rheum 2005;52(Suppl):S657.
      OpenUrl
    14. ↵
      1. Yu T
      . The efficacy of colchicine prophylaxis in articular gout—a reappraisal after 20 years. Semin Arthritis Rheum 1982;12:256–64.
      OpenUrlCrossRefPubMedWeb of Science
    15. ↵
      1. Yu TF,
      2. Gutman AB
      . Efficacy of colchicine prophylaxis in gout. Prevention of recurrent gouty arthritis over a mean period of five years in 208 gouty subjects. Ann Intern Med 1961;55:179–92.
      OpenUrlCrossRefPubMedWeb of Science
    16. ↵
      1. Roddy E,
      2. Zhang W,
      3. Doherty M
      . Is gout associated with reduced quality of life? A case-control study. Rheumatology (Oxford) 2007;46:1441–4.
      OpenUrlAbstract/FREE Full Text
    17. ↵
      1. Emmerson BT
      . The management of gout. N Engl J Med 1996;334:445–51.
      OpenUrlCrossRefPubMedWeb of Science
    18. ↵
      1. Rott KT,
      2. Agudelo CA
      . Gout. JAMA 2003;289:2857–60.
      OpenUrlCrossRefPubMedWeb of Science
    19. ↵
      1. Ferraz MB,
      2. O’Brien B
      . A cost effectiveness analysis of urate lowering drugs in nontophaceous recurrent gouty arthritis. J Rheumatol 1995;22:908–14.
      OpenUrlPubMedWeb of Science
    20. ↵
      1. Baker JF,
      2. Krishnan E,
      3. Chen L,
      4. Schumacher HR
      . Serum uric acid and cardiovascular disease: recent developments, and where do they leave us? Am J Med 2005;118:816–26.
      OpenUrlCrossRefPubMedWeb of Science
    21. ↵
      1. Krishnan E,
      2. Baker JF,
      3. Furst DE,
      4. Schumacher HR
      . Gout and the risk of acute myocardial infarction. Arthritis Rheum 2006;54:2688–96.
      OpenUrlCrossRefPubMedWeb of Science
    22. ↵
      1. Becker MA,
      2. Schumacher HR Jr,
      3. Wortmann RL,
      4. MacDonald PA,
      5. Eustace D,
      6. Palo WA,
      7. et al
      . Febuxostat compared with allopurinol in patients with hyperuricemia and gout. N Engl J Med 2005;353:2450–61.
      OpenUrlCrossRefPubMedWeb of Science
    23. ↵
      1. Perlin JB,
      2. Kolodner RM,
      3. Roswell RH
      . The Veterans Health Administration: quality, value, accountability, and information as transforming strategies for patient-centered care. Am J Manag Care 2004;10(Pt 2):828–36.
      OpenUrlPubMedWeb of Science
    24. ↵
      1. Cowper DC,
      2. Hynes DM,
      3. Kubal JD,
      4. Murphy PA
      . Using administrative databases for outcomes research: select examples from VA Health Services Research and Development. J Med Syst 1999;23:249–59.
      OpenUrlCrossRefPubMed
    25. ↵
      1. Kashner TM
      . Agreement between administrative files and written medical records: a case of the Department of Veterans Affairs. Med Care 1998;36:1324–36.
      OpenUrlCrossRefPubMedWeb of Science
    26. ↵
      1. Katz S,
      2. Ford AB,
      3. Moskowitz RW,
      4. Jackson BA,
      5. Jaffe MW
      . Studies of illness in the aged. The index of ADL: a standard measure of biological and psychological function. JAMA 1963;185:914–9.
      OpenUrlCrossRefPubMedWeb of Science
    27. ↵
      1. Singh JA,
      2. Borowsky SJ,
      3. Nugent S,
      4. Murdoch M,
      5. Zhao Y,
      6. Nelson DB,
      7. et al
      . Health-related quality of life, functional impairment, and healthcare utilization by veterans: veterans’ quality of life study. J Am Geriatr Soc 2005;53:108–13.
      OpenUrlCrossRefPubMedWeb of Science
    28. ↵
      1. Singh JA,
      2. Nelson DB,
      3. Fink HA,
      4. Nichol KL
      . Health-related quality of life predicts future health care utilization and mortality in veterans with self-reported physician-diagnosed arthritis: the Veterans Arthritis Quality of Life study. Semin Arthritis Rheum 2005;34:755–65.
      OpenUrlCrossRefPubMedWeb of Science
    29. ↵
      1. Kosinski M,
      2. Keller SD,
      3. Ware JE Jr,
      4. Hatoum HT,
      5. Kong SX
      . The SF-36 Health Survey as a generic outcome measure in clinical trials of patients with osteoarthritis and rheumatoid arthritis: relative validity of scales in relation to clinical measures of arthritis severity. Med Care 1999;37(Suppl):MS23–39.
      OpenUrlCrossRefPubMedWeb of Science
      1. Kvien TK,
      2. Kaasa S,
      3. Smedstad LM
      . Performance of the Norwegian SF-36 Health Survey in patients with rheumatoid arthritis. II. A comparison of the SF-36 with disease-specific measures. J Clin Epidemiol 1998;51:1077–86.
      OpenUrlCrossRefPubMedWeb of Science
      1. Tuttleman M,
      2. Pillemer SR,
      3. Tilley BC,
      4. Fowler SE,
      5. Buckley LM,
      6. Alarcon GS,
      7. et al
      . A cross sectional assessment of health status instruments in patients with rheumatoid arthritis participating in a clinical trial. Minocycline in Rheumatoid Arthritis Trial Group. J Rheumatol 1997;24:1910–5.
      OpenUrlPubMedWeb of Science
    32. ↵
      1. Kantz ME,
      2. Harris WJ,
      3. Levitsky K,
      4. Ware JE Jr,
      5. Davies AR
      . Methods for assessing condition-specific and generic functional status outcomes after total knee replacement. Med Care 1992;30(Suppl):MS240–52.
      OpenUrlCrossRefPubMedWeb of Science
    33. ↵
      1. Ren XS,
      2. Kazis L,
      3. Lee A,
      4. Miller DR,
      5. Clark JA,
      6. Skinner K,
      7. et al
      . Comparing generic and disease-specific measures of physical and role functioning: results from the Veterans Health Study. Med Care 1998;36:155–66.
      OpenUrlCrossRefPubMedWeb of Science
    34. ↵
      1. Ware JE,
      2. Kosinski M,
      3. Dewey J
      . How to score version two of the SF-36 Health Survey. 3rd ed. Lincoln, Rhode Island, USA: QualityMetric Inc, 2000.
    35. ↵
      1. Kazis L,
      2. Skinner K,
      3. Rogers W,
      4. Lee A,
      5. Ren X,
      6. Miller D,
      7. et al
      . Health status in VA patients: results from the Veterans Health Study. Am J Med Qual 1999;14:28–38.
      OpenUrlAbstract/FREE Full Text
    36. ↵
      1. Kazis LE,
      2. Miller DR,
      3. Clark J,
      4. Skinner K,
      5. Lee A,
      6. Rogers W,
      7. et al
      . Health-related quality of life in patients served by the Department of Veterans Affairs: results from the Veterans Health Study. Arch Intern Med 1998;158:626–32.
      OpenUrlCrossRefPubMedWeb of Science
    37. ↵
      1. Szeto HC,
      2. Coleman RK,
      3. Gholami P,
      4. Hoffman BB,
      5. Goldstein MK
      . Accuracy of computerized outpatient diagnoses in a Veterans Affairs general medicine clinic. Am J Manag Care 2002;8:37–43.
      OpenUrlPubMedWeb of Science
    38. ↵
      1. Petersen LA,
      2. Wright S,
      3. Normand SL,
      4. Daley J
      . Positive predictive value of the diagnosis of acute myocardial infarction in an administrative database. J Gen Intern Med 1999;14:555–8.
      OpenUrlCrossRefPubMedWeb of Science
    39. ↵
      1. Singh JA,
      2. Hodges JS,
      3. Toscano JP,
      4. Asch SM
      . Quality of care for gout in the US needs improvement. Arthritis Rheum 2007;57:822–9.
      OpenUrlCrossRefPubMedWeb of Science
    40. ↵
      1. Khanna D,
      2. Furst DE,
      3. Clements PJ,
      4. Park GS,
      5. Hays RD,
      6. Yoon J,
      7. et al
      . Responsiveness of the SF-36 and the Health Assessment Questionnaire Disability Index in a systemic sclerosis clinical trial. J Rheumatol 2005;32:832–40.
      OpenUrlAbstract/FREE Full Text
      1. Strand V
      . Longer term benefits of treating rheumatoid arthritis: assessment of radiographic damage and physical function in clinical trials. Clin Exp Rheumatol 2004;22(Suppl 35):S57–64.
      OpenUrlPubMedWeb of Science
      1. Strand V,
      2. Crawford B
      . Improvement in health-related quality of life in patients with SLE following sustained reductions in anti-dsDNA antibodies. Expert Review of Pharmacoeconomics and Outcomes Research 2005;5:317–26.
      OpenUrlCrossRef
    43. ↵
      1. Strand V,
      2. Kelman A
      . Outcome measures in osteoarthritis: randomized controlled trials. Curr Rheumatol Rep 2004;6:20–30.
      OpenUrlPubMed
    44. ↵
      1. Neogi T,
      2. Hunter DJ,
      3. Chaisson CE,
      4. Allensworth-Davies D,
      5. Zhang Y
      . Frequency and predictors of inappropriate management of recurrent gout attacks in a longitudinal study. J Rheumatol 2006;33:104–9.
      OpenUrlPubMedWeb of Science
    45. ↵
      1. Osterhaus JT,
      2. Patel P,
      3. Williams AP,
      4. Bakst A,
      5. Joseph-Ridge N
      . Patient reported outcomes assoicated with gout: baseline results from two clinical trials [abstract]. Arthritis Rheum 2005;52(Suppl):S 401–2.
      OpenUrl
    46. ↵
      1. Kramer HM,
      2. Curhan G
      . The association between gout and nephrolithiasis: the National Health and Nutrition Examination Survey III, 1988–1994. Am J Kidney Dis 2002;40:37–42.
      OpenUrlCrossRefPubMedWeb of Science
    47. ↵
      1. Harrold LR,
      2. Saag KG,
      3. Yood RA,
      4. Mikuls TR,
      5. Andrade SE,
      6. Fouayzi H,
      7. et al
      . Validity of gout diagnoses in administrative data. Arthritis Rheum 2007;57:103–8.
      OpenUrlCrossRefPubMedWeb of Science
    48. ↵
      1. Fleming C,
      2. Fisher ES,
      3. Chang CH,
      4. Bubolz TA,
      5. Malenka DJ
      . Studying outcomes and hospital utilization in the elderly. The advantages of a merged data base for Medicare and Veterans Affairs hospitals. Med Care 1992;30:377–91.
      OpenUrlCrossRefPubMedWeb of Science
    View Abstract

    Footnotes

    • Funding: Supported by a VA Scholar Grant from the Center for Epidemiological and Clinical Research, Minneapolis VA Medical Center, Minneapolis, Minnesota, USA

    • Competing interests: None.

    • Ethics approval: Ethics committee approval obtained.

    • The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs.