nach oben

Quality of Life Research

Erschienen in:

01.06.2014

Using existing data to identify candidate items for a health state classification system in multiple sclerosis

verfasst von: Ayse Kuspinar, Lois Finch, Simon Pickard, Nancy E. Mayo

Erschienen in: Quality of Life Research | Ausgabe 5/2014

Einloggen, um Zugang zu erhalten

Abstract

Purpose

In multiple sclerosis (MS), the use of preference-based measures is limited to generic measures such as Health Utilities Index Mark 2 and 3, the EQ-5D and the SF-6D. However, the challenge of using such generic preference-based measures in people with MS is that they may not capture all domains of health relevant to the disease. Therefore, the main aim of this paper is to describe the development of a health state classification system for MS patients. The specific objectives are: (1) to identify items best reflecting the domains of quality of life important to people with MS and (2) to provide evidence for the discriminative capacity of the response options by cross-walking onto a visual analog scale of health rating.

Methods

The data come from an epidemiologically sampled population of people with MS diagnosed post-1994. The dataset consisted of 206 items relating to impairments, activity limitations, participation restrictions, health perception and quality of life. Important domains were identified from the responses to the Patient Generated Index, an individualized measure of quality of life. The extent to which the items formed a uni-dimensional, linear construct was estimated using Rasch analysis, and the best item was selected using the threshold map.

Results

The sample was young (mean age 43) and predominantly female (n = 140/189; 74 %). The P-PBMSI classification system consisted of five items, with three response levels per item, producing a total of 243 possible health states. Regression coefficient values consistently decreased between response levels and the linear test for trend were statistically significant for all items. The linear test for trend indicated that for each item the response options provided the same discriminative ability within the magnitude of their capacity. A scoring algorithm was estimated using a simple additive formula. The classification system demonstrated convergent validity against other measures of similar constructs and known-groups validity between different clinical subgroups.

Conclusion

This study produced a health state classifier system based on items impacted upon by MS, and demonstrated the potential to discriminate the health impact of the disease.

Miller, D. M., Moore, S. M., Fox, R. J., Atreja, A., Fu, A. Z., Lee, J. C., et al. (2011). Web-based self-management for patients with multiple sclerosis: A practical, randomized trial. Telemedicine Journal & E-Health, 17, 5–13.CrossRef

Barlow, J., Turner, A., Edwards, R., & Gilchrist, M. (2009). A randomised controlled trial of lay-led self-management for people with multiple sclerosis. Patient Education and Counseling, 77, 81–89.PubMedCrossRef

Bombardier, C. H., Cunniffe, M., Wadhwani, R., Gibbons, L. E., Blake, K. D., & Kraft, G. H. (2008). The efficacy of telephone counseling for health promotion in people with multiple sclerosis: A randomized controlled trial. Archives of Physical Medicine and Rehabilitation, 89, 1849–1856.PubMedCrossRef

McAuley, E., Motl, R. W., Morris, K. S., Hu, L., Doerksen, S. E., Elavsky, S., et al. (2007). Enhancing physical activity adherence and well-being in multiple sclerosis: A randomised controlled trial. Multiple Sclerosis, 13, 652–659.PubMedCrossRef

Grossman, P., Kappos, L., Gensicke, H., D’Souza, M., Mohr, D. C., Penner, I. K., et al. (2010). MS quality of life, depression, and fatigue improve after mindfulness training: A randomized trial. Neurology, 75, 1141–1149.PubMedCentralPubMedCrossRef

Forman, A. C., & Lincoln, N. B. (2010). Evaluation of an adjustment group for people with multiple sclerosis: A pilot randomized controlled trial. Clinical Rehabilitation, 24, 211–221.PubMedCrossRef

Cosio, D., Jin, L., Siddique, J., & Mohr, D. C. (2011). The effect of telephone-administered cognitive-behavioral therapy on quality of life among patients with multiple sclerosis. Annals of Behavioral Medicine, 41, 227–234.PubMedCentralPubMedCrossRef

Kavia, R. B., De, R. D., Constantinescu, C. S., Stott, C. G., & Fowler, C. J. (2010). Randomized controlled trial of Sativex to treat detrusor overactivity in multiple sclerosis. Multiple Sclerosis, 16, 1349–1359.PubMedCrossRef

Moller, F., Poettgen, J., Broemel, F., Neuhaus, A., Daumer, M., & Heesen, C. (2011). HAGIL (Hamburg Vigil Study): A randomized placebo-controlled double-blind study with modafinil for treatment of fatigue in patients with multiple sclerosis. Multiple Sclerosis, 17, 1002–1009.PubMedCrossRef

10.

Freeman, J. A., Thompson, A. J., Fitzpatrick, R., Hutchinson, M., Miltenburger, C., Beckmann, K., et al. (2001). European Study Group on Interferon-beta: Interferon-beta1b in the treatment of secondary progressive MS: Impact on quality of life. Neurology, 57, 1870–1875.PubMedCrossRef

11.

Rudick, R. A., Miller, D., Hass, S., Hutchinson, M., Calabresi, P. A., Confavreux, C., et al. (2007). AFFIRM and SENTINEL investigators: Health-related quality of life in multiple sclerosis: Effects of natalizumab. Annals of Neurology, 62, 335–346.PubMedCrossRef

12.

Fox, R. J., Miller, D. H., Phillips, J. T., Hutchinson, M., Havrdova, E., Kita, M., et al. (2012). Placebo-controlled phase 3 study of oral BG-12 or glatiramer in multiple sclerosis. New England Journal of Medicine, 367, 1087–1097.PubMedCrossRef

13.

Freedman, M. S., Bar-Or, A., Atkins, H. L., Karussis, D., Frassoni, F., Lazarus, H., et al. (2010). The therapeutic potential of mesenchymal stem cell transplantation as a treatment for multiple sclerosis: Consensus report of the International MSCT Study Group. Multiple Sclerosis, 16, 503–510.PubMedCrossRef

14.

Zamboni, P., Menegatti, E., Galeotti, R., Malagoni, A. M., Tacconi, G., Dall’Ara, S., et al. (2009). The value of cerebral Doppler venous haemodynamics in the assessment of multiple sclerosis. Journal of the Neurological Sciences, 282, 21–27.PubMedCrossRef

15.

Al-Omari, M. H., & Rousan, L. A. (2010). Internal jugular vein morphology and hemodynamics in patients with multiple sclerosis. International Angiology, 29, 115–120.PubMed

16.

Baracchini, C., Perini, P., Calabrese, M., Causin, F., Rinaldi, F., & Gallo, P. (2011). No evidence of chronic cerebrospinal venous insufficiency at multiple sclerosis onset. Annals of Neurology, 69, 90–99.PubMedCrossRef

17.

Centonze, D., Floris, R., Stefanini, M., Rossi, S., Fabiano, S., Castelli, M., et al. (2011). Proposed chronic cerebrospinal venous insufficiency criteria do not predict multiple sclerosis risk or severity. Annals of Neurology, 70, 51–58.PubMedCrossRef

18.

Zivadinov, R., Marr, K., Cutter, G., Ramanathan, M., Benedict, R. H., Kennedy, C., et al. (2011). Prevalence, sensitivity, and specificity of chronic cerebrospinal venous insufficiency in MS. Neurology, 77, 138–144.PubMedCrossRef

19.

Feeny, D., Furlong, W., Torrance, G. W., Goldsmith, C. H., Zhu, Z., DePauw, S., et al. (2002). Multiattribute and single-attribute utility functions for the health utilities index mark 3 system. Medical Care, 40, 113–128.PubMedCrossRef

20.

Torrance, G. W. (1986). Measurement of health state utilities for economic appraisal. J Health Economics, 5, 1–30.CrossRef

21.

Torrance, G. W., Boyle, M. H., & Horwood, S. P. (1982). Application of multi-attribute utility theory to measure social preferences for health states. Operations Research, 30, 1043–1069.PubMedCrossRef

22.

Brazier, J., Ratcliffe, J., Salomon, J. A., & Tsuchiya, A. (2007). Measuring and valuing health benefits for economic evaluation. New York: Oxford University Press Inc.

23.

Mavranezouli, I., Brazier, J. E., Young, T. A., & Barkham, M. (2011). Using Rasch analysis to form plausible health states amenable to valuation: The development of CORE-6D from a measure of common mental health problems (CORE-OM). Quality of Life Research, 20, 321–333.PubMedCrossRef

24.

Kind, P. (2005). Values and valuation in the measurement of HRQoL. In P. Fayers & D. Hays (Eds.), Assessing quality of life in clinical trials (pp. 391–404). New York: Oxford University Press Inc.

25.

Feeny, D., Torrance, G. W., & Furlong, W. (1996). Health utilities index. In B. Spilker (Ed.), Quality of life and pharmaeconomics in clinicals trials (pp. 239–252). Philadelphia: Lippincott-Raven Publishers.

26.

Feeny, D. (2005). Preference-based measures: Utility and quality-adjusted life years. In P. Fayers & D. Hays (Eds.), Assessing quality of life in clinical trials (pp. 405–429). New York: Oxford University Press Inc.

27.

Guyatt, G. H., Feeny, D. H., & Patrick, D. L. (1993). Measuring health-related quality of life. Annals of Internal Medicine, 118, 622–629.PubMedCrossRef

28.

Revicki, D. A., & Kaplan, R. M. (1993). Relationship between psychometric and utility-based approaches to the measurement of health-related quality of life. Quality of Life Research, 2, 477–487.PubMedCrossRef

29.

Berzon, R., Mauskopf, J. A., & Simeon, G. P. (1996). Choosing a health profile (descriptive) and/or a patient-preference (utility) measure for a clinical trial. In B. Spilker (Ed.), Quality of life and pharmaeconomics in clinical trials (pp. 375–379). Philadelphia: Lippincott-Raven Publishers.

30.

Feeny, D. H., & Torrance, G. W. (1989). Incorporating utility-based quality-of-life assessment measures in clinical trials. Two examples. Medical Care, 27, S190–S204.PubMedCrossRef

31.

Torrance, G. W. (1987). Utility approach to measuring health-related quality of life. Journal of Chronic Diseases, 40, 593–603.PubMedCrossRef

32.

Kind, P., Lafata, J. E., Matuszewski, K., & Raisch, D. (2009). The use of QALYs in clinical and patient decision-making: Issues and prospects. Value Health, 12(Suppl 1), S27–S30.PubMedCrossRef

33.

Hawthorne, G., & Richardson, J. (2001). Measuring the value of program outcomes: A review of multiattribute utility measures. Expert Review of Pharmacoeconomics & Outcomes Research, 1, 215–228.CrossRef

34.

Guyatt, G. H., Veldhuyzen Van Zanten, S. J., Feeny, D. H., & Patrick, D. L. (1989). Measuring quality of life in clinical trials: A taxonomy and review. Canadian Medical Association Journal, 140, 1441–1448.PubMedCentralPubMed

35.

Kuspinar, A., & Mayo, N. E. (2013). Do generic utility measures capture what is important to the quality of life of people with multiple sclerosis? Health and Quality of Life Outcomes, 11, 71.PubMedCentralPubMedCrossRef

36.

Hemmett, L., Holmes, J., Barnes, M., & Russell, N. (2004). What drives quality of life in multiple sclerosis? QJM, 97, 671–676.PubMedCrossRef

37.

Nicholl, C. R., Lincoln, N. B., Francis, V. M., & Stephan, T. F. (2001). Assessing quality of life in people with multiple sclerosis. Disability and Rehabilitation, 23, 597–603.PubMedCrossRef

38.

Fisk, J. D., Brown, M. G., Sketris, I. S., Metz, L. M., Murray, T. J., & Stadnyk, K. J. (2005). A comparison of health utility measures for the evaluation of multiple sclerosis treatments. Journal of Neurology, Neurosurgery and Psychiatry, 76, 58–63.PubMedCentralPubMedCrossRef

39.

Williams, A. (2005). The EuroQol instrument. In P. Kind, R. Brooks, & R. Rabin (Eds.), EQ-5D concepts and methods: A developmental history (pp. 1–17). Dordrecht: Springers.CrossRef

40.

Poissant, L., Mayo, N. E., Wood-Dauphinee, S., & Clarke, A. E. (2003). The development and preliminary validation of a Preference-Based Stroke Index (PBSI). Health and Quality of Life Outcomes, 1, 43.PubMedCentralPubMedCrossRef

41.

Pickard, A. S., Shaw, J. W., Lin, H. W., Trask, P. C., Aaronson, N., Lee, T. A., et al. (2009). A patient-based utility measure of health for clinical trials of cancer therapy based on the European Organization for the Research and Treatment of Cancer Quality of Life Questionnaire. Value Health, 12, 977–988.PubMedCrossRef

42.

Rowen, D., Brazier, J., Young, T., Gaugris, S., Craig, B. M., King, M. T., et al. (2011). Deriving a preference-based measure for cancer using the EORTC QLQ-C30. Value Health, 14, 721–731.PubMedCrossRef

43.

Melsop, K. A., Boothroyd, D. B., & Hlatky, M. A. (2003). Quality of life and time trade-off utility measures in patients with coronary artery disease. American Heart Journal, 145, 36–41.PubMedCrossRef

44.

McKenna, S. P., Ratcliffe, J., Meads, D. M., & Brazier, J. E. (2008). Development and validation of a preference based measure derived from the Cambridge Pulmonary Hypertension Outcome Review (CAMPHOR) for use in cost utility analyses. Health and Quality of Life Outcomes, 6, 65.PubMedCentralPubMedCrossRef

45.

Revicki, D. A., Leidy, N. K., Brennan-Diemer, F., Sorensen, S., & Togias, A. (1998). Integrating patient preferences into health outcomes assessment: The multiattribute Asthma Symptom Utility Index. Chest, 114, 998–1007.PubMedCrossRef

46.

Revicki, D. A., Leidy, N. K., Brennan-Diemer, F., Thompson, C., & Togias, A. (1998). Development and preliminary validation of the multiattribute Rhinitis Symptom Utility Index. Quality of Life Research, 7, 693–702.PubMedCrossRef

47.

Brazier, J., Czoski-Murray, C., Roberts, J., Brown, M., Symonds, T., & Kelleher, C. (2008). Estimation of a preference-based index from a condition-specific measure: The King’s Health Questionnaire. Medical Decision Making, 28, 113–126.PubMedCrossRef

48.

Torrance, G. W., Keresteci, M. A., Casey, R. W., Rosner, A. J., Ryan, N., & Breton, M. C. (2004). Development and initial validation of a new preference-based disease-specific health-related quality of life instrument for erectile function. Quality of Life Research, 13, 349–359.PubMedCrossRef

49.

Guyatt, G. H., Bombardier, C., & Tugwell, P. X. (1986). Measuring disease-specific quality of life in clinical trials. Canadian Medical Association Journal, 134, 889–895.PubMedCentralPubMed

50.

Brazier, J., & Fitzpatrick, R. (2002). Measures of health-related quality of life in an imperfect world: A comment on Dowie. Health Economics, 11, 17–19.PubMedCrossRef

51.

Patrick, D. L., Burke, L. B., Powers, J. H., Scott, J. A., Rock, E. P., Dawisha, S., et al. (2007). Patient-reported outcomes to support medical product labeling claims: FDA perspective. Value Health, 10(Suppl 2), S125–S137.PubMedCrossRef

52.

Lin, F. J., Longworth, L., Pickard, A. S. (2013). Evaluation of content on EQ-5D as compared to disease-specific utility measures. Quality Life Research, 22(4), 853–874.

53.

Mayo, N. (2008). Setting the agenda for multiple sclerosis rehabilitation research. Multiple Sclerosis, 14, 1154–1156.PubMedCrossRef

54.

Freeman, J. A., Hobart, J. C., Langdon, D. W., & Thompson, A. J. (2000). Clinical appropriateness: A key factor in outcome measure selection: The 36 item short form health survey in multiple sclerosis. Journal of Neurology, Neurosurgery and Psychiatry, 68, 150–156.PubMedCentralPubMedCrossRef

55.

Nortvedt, M. W., Riise, T., Myhr, K. M., & Nyland, H. I. (2000). Performance of the SF-36, SF-12, and RAND-36 summary scales in a multiple sclerosis population. Medical Care, 38, 1022–1028.PubMedCrossRef

56.

Kikuchi, H., Mifune, N., Niino, M., Ohbu, S., Kira, J., Kohriyama, T., et al. (2011). Impact and characteristics of quality of life in Japanese patients with multiple sclerosis. Quality of Life Research, 20, 119–131.PubMedCrossRef

57.

Twiss, J., Doward, L. C., McKenna, S. P., & Eckert, B. (2010). Interpreting scores on multiple sclerosis-specific patient reported outcome measures (the PRIMUS and U-FIS). Health and Quality of Life Outcomes, 8, 117.PubMedCentralPubMedCrossRef

58.

Ruta, D. A., Garratt, A. M., & Russell, I. T. (1999). Patient centred assessment of quality of life for patients with four common conditions. Quality Health Care, 8, 22–29.CrossRef

59.

Sullivan, M. J., Edgley, K., & Dehoux, E. (1990). A survey of multiple sclerosis: I. Perceived cognitive problems and compensatory strategy use. Canadian Journal of Rehabilitation, 4, 99–105.

60.

Marrie, R. A., Miller, D. M., Chelune, G. J., & Cohen, J. A. (2003). Validity and reliability of the MSQLI in cognitively impaired patients with multiple sclerosis. Multiple Sclerosis, 9, 621–626.PubMedCrossRef

61.

Paltamaa, J., West, H., Sarasoja, T., Wikstrom, J., & Malkia, E. (2005). Reliability of physical functioning measures in ambulatory subjects with MS. Physiotherapy Research International, 10, 93–109.PubMedCrossRef

62.

Kurtzke, J. F. (1983). Rating neurologic impairment in multiple sclerosis: An expanded disability status scale (EDSS). Neurology, 33, 1444–1452.PubMedCrossRef

63.

Ruta, D. A., Garratt, A. M., Leng, M., Russell, I. T., & MacDonald, L. M. (1994). A new approach to the measurement of quality of life. The Patient-Generated Index. Medical Care, 32, 1109–1126.PubMedCrossRef

64.

Schumacker, R. E. (1996). Editor’s note. Structural Equation Modeling: A Multidisciplinary Journal, 3, 1–2.CrossRef

65.

Wright, B. D. (1996). Comparing Rasch measurement and factor analysis. Structural Equation Modeling: A Multidisciplinary Journal, 3, 3–24.CrossRef

66.

Chang, C. H. (1996). Finding two dimensions in MMPI-2 depression. Structural Equation Modeling: A Multidisciplinary Journal, 3, 41–49.CrossRef

67.

Young, T., Yang, Y., Brazier, J. E., Tsuchiya, A., & Coyne, K. (2009). The first stage of developing preference-based measures: constructing a health-state classification using Rasch analysis. Quality of Life Research, 18, 253–265.PubMedCrossRef

68.

Andrich, D., Lyne, A., Sheridan, B., & Luo, G. (2004). Rasch unidimensional measurement models (RUMM) 2020. Perth, Western Australia: Rumm Laboratory Pty Ltd.

69.

Pallant, J. F., & Tennant, A. (2007). An introduction to the Rasch measurement model: An example using the Hospital Anxiety and Depression Scale (HADS). British Journal of Clinical Psychology, 46, 1–18.PubMedCrossRef

70.

Gibbons, C. J., Mills, R. J., Thornton, E. W., Ealing, J., Mitchell, J. D., Shaw, P. J., et al. (2011). Rasch analysis of the hospital anxiety and depression scale (HADS) for use in motor neurone disease. Health and Quality of Life Outcomes, 9, 82.PubMedCentralPubMedCrossRef

71.

Bond, T. G., & Fox, C. M. (2007). Applying the Rasch model: Fundamental measurement in the human science. New Jersey: Lawrence Erlbaum Associates Inc.

72.

Allen, M. J., & Yen, W. M. (2002). Introduction to measurement theory. Long Grove: Waveland Press Inc.

73.

Cohen, J. (1992). A power primer. Psychological Bulletin, 112, 155–159.PubMedCrossRef

74.

Broome, H. (2012) The association between cognition, social functioning, physical impairment, and relationship factors in individuals with multiple sclerosis (pp. 1–195). The University of Hull.

75.

Mead, D. E. (2002). Marital distress, co-occurring depression, and marital therapy: A review. Journal of Marital and Family Therapy, 28, 299–314.PubMedCrossRef

76.

Lee, E. K. O., & Oh, H. (2012). Marital satisfaction among adults with disabilities in South Korea. Journal of Disability Studies Policy, 23, 215–224.CrossRef

77.

Mayo, N. E., Hum, S., & Kuspinar, A. (2013). Methods and measures: What’s new for MS? Multiple Sclerosis, 19, 709–713.PubMedCrossRef

78.

Krupp, L. B., & Pollina, D. A. (1996). Mechanisms and management of fatigue in progressive neurological disorders. Current Opinion in Neurology, 9, 456–460.PubMedCrossRef

79.

Fisk, J. D., Pontefract, A., Ritvo, P. G., Archibald, C. J., & Murray, T. J. (1994). The impact of fatigue on patients with multiple sclerosis. Canadian Journal of Neurological Sciences, 21, 9–14.PubMed

80.

Freal, J. E., Kraft, G. H., & Coryell, J. K. (1984). Symptomatic fatigue in multiple sclerosis. Archives of Physical Medicine and Rehabilitation, 65, 135–138.PubMed

81.

Murray, T. J. (1985). Amantadine therapy for fatigue in multiple sclerosis. Canadian Journal of Neurological Sciences, 12, 251–254.PubMed

82.

Deloire, M. S., Bonnet, M. C., Salort, E., Arimone, Y., Boudineau, M., Petry, K. G., et al. (2006). How to detect cognitive dysfunction at early stages of multiple sclerosis? Multiple Sclerosis, 12, 445–452.PubMedCrossRef

83.

Peyser, J. M., Edwards, K. R., Poser, C. M., & Filskov, S. B. (1980). Cognitive function in patients with multiple sclerosis. Archives of Neurology, 37, 577–579.PubMedCrossRef

84.

Brazier, J., Akehurst, R., Brennan, A., Dolan, P., Claxton, K., McCabe, C., et al. (2005). Should patients have a greater role in valuing health states? Applied Health Economics and Health Policy, 4, 201–208.PubMedCrossRef

85.

Ubel, P. A., Loewenstein, G., & Jepson, C. (2003). Whose quality of life? A commentary exploring discrepancies between health state evaluations of patients and the general public. Quality of Life Research, 12, 599–607.PubMedCrossRef

86.

Whitehead, S. J., & Ali, S. (2010). Health outcomes in economic evaluation: The QALY and utilities. British Medical Bulletin, 96, 5–21.PubMedCrossRef

87.

Stallard, J., & Major, R. E. (1995). The influence of orthosis stiffness on paraplegic ambulation and its implications for functional electrical stimulation (FES) walking systems. Prosthetics and Orthotics International, 19, 108–114.PubMed

88.

Brissot, R., Gallien, P., Le Bot, M. P., Beaubras, A., Laisne, D., Beillot, J., et al. (2000). Clinical experience with functional electrical stimulation-assisted gait with Parastep in spinal cord-injured patients. Spine (Phila Pa 1976), 25, 501–508.CrossRef

89.

Thoumie, P., Perrouin-Verbe, B., Le, C. G., Bedoiseau, M., Busnel, M., Cormerais, A., et al. (1995). Restoration of functional gait in paraplegic patients with the RGO-II hybrid orthosis. A multicentre controlled study. I. Clinical evaluation. Paraplegia, 33, 647–653.PubMedCrossRef

90.

Rothman, M., Burke, L., Erickson, P., Leidy, N. K., Patrick, D. L., & Petrie, C. D. (2009). Use of existing patient-reported outcome (PRO) instruments and their modification: The ISPOR good research practices for evaluating and documenting content validity for the use of existing instruments and their modification PRO task force report. Value Health, 12, 1075–1083.PubMedCrossRef

91.

Food, U. S. (2009). Drug administration: Guidance for industry: Patient-reported outcome measures: Use in medical product development to support labeling claims. Federal Register, 74, 65132–65133.

Titel: Using existing data to identify candidate items for a health state classification system in multiple sclerosis
verfasst von: Ayse Kuspinar
Lois Finch
Simon Pickard
Nancy E. Mayo
Publikationsdatum: 01.06.2014
Verlag: Springer International Publishing
Erschienen in: Quality of Life Research / Ausgabe 5/2014
Print ISSN: 0962-9343
Elektronische ISSN: 1573-2649
DOI: https://doi.org/10.1007/s11136-013-0604-5

Springer Medizin

Abstract

Purpose

Methods

Results

Conclusion

Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten

Weitere Artikel der Ausgabe 5/2014

Impact of hypoalphalipoproteinemia on quality of life in Taiwanese women with central obesity

Factors associated with health-related quality of life among pulmonary tuberculosis patients in Manila, the Philippines

Relationship of EQ-5D quality of life with the presence of co-morbidity and extra-articular features in patients with rheumatoid arthritis

Role of global stress in the health-related quality of life of caregivers: evidence from the Survey of the Health of Wisconsin

Fatigue in patients with spinal muscular atrophy type II and congenital myopathies: evaluation of the fatigue severity scale

Shifting responses in quality of life: People living with dialysis