Non response, incomplete and inconsistent responses to self-administered health-related quality of life measures in the general population: patterns, determinants and impact on the validity of estimates — a population-based study in France using the MOS SF-36

Health and Quality of Life Outcomes

Jacques Pouchot³  Etienne Audureau²  Laurent Quinquis¹  Joel Coste² 

[1] Biostatistics and Epidemiology Unit, Assistance Publique-Hôpitaux de Paris, Hôtel Dieu, Paris, France;Research unit APEMAC, EA 4360, Nancy-Université, Université Paris-Descartes, Université Metz Paul Verlaine, Paris, France;Department of Internal Medicine, Assistance Publique-Hôpitaux de Paris, Hôpital Européen Georges Pompidou, Paris, France

关键词: Imputation;    Bias;    Determinants;    Missing forms;    Missing items;    SF-36;    Quality of life;   

Others  :  823874
DOI  :  10.1186/1477-7525-11-44

 received in 2012-07-02, accepted in 2013-03-04,  发布年份 2013

【 摘 要 】

Background

Health-related quality of life (HRQoL) measures are increasingly used in the general population. However, little is known about patterns and determinants of unanswered or unusable questionnaires and their consequences on estimates of HRQoL.

Methods

The 2003 Decennial Health Survey collected socio-demographic and health information, including HRQoL, for 30,782 adults representative of the French population. The pattern, determinants and impact on estimate validity of non, incomplete and inconsistent responses to the SF-36 questionnaire were determined. For this, phi coefficients, polytomous logistic regression models and multiple imputation methods were used.

Results

Only 48% of the subjects eligible for the HRQoL measurement provided a complete and consistent SF-36 questionnaire. Three patterns of non-response and five of partial (incomplete or inconsistent) response were identified, sharing largely similar socio-demographic profiles (higher age, lower educational level and economic status, foreign background, and isolated). The consequences of non and partial responses on HRQoL estimates were large in several groups of subjects although these biases ran in opposite directions and partially neutralized each other.

Conclusions

When measuring HRQoL in the general population, missing and inconsistent data are frequent, especially in elderly, educationally and socio-economically deprived, foreign and isolated groups. Methods for handling missing data are required to correct for potentially the associated and serious selection and non-differential information biases in studies targeting or investigating these groups.

【 授权许可】

   
2013 Coste et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

【 参考文献 】

• [1]Zahran HS, Kobau R, Moriarty DG, Zack MM, Holt J, Donehoo R: Health-related quality of life surveillance–United States, 1993–2002. MMWR Surveill Summ 2005, 54:1-35.
• [2]Daponte-Codina A, Bolivar-Munoz J, Toro-Cardenas S, Ocana-Riola R, Benach-Rovira J, Navarro-Lopez V: Area deprivation and trends in inequalities in self-rated health in Spain, 1987–2001. Scand J Public Health 2008, 36:504-15.
• [3]Ohaeri JU, Awadalla AW, Gado OM: Subjective quality of life in a nationwide sample of Kuwaiti subjects using the short version of the WHO quality of life instrument. Soc Psychiatry Psychiatr Epidemiol 2009, 44:693-701.
• [4]Fayers PM, Curran D, Machin D: Incomplete quality of life data in randomized trials: missing items. Stat Med 1998, 17:679-96.
• [5]Curran D, Molenberghs G, Fayers PM, Machin D: Incomplete quality of life data in randomized trials: missing forms. Stat Med 1998, 17:697-709.
• [6]Etter JF, Perneger TV: Analysis of non-response bias in a mailed health survey. J Clin Epidemiol 1997, 50:1123-8.
• [7]Perneger TV, Burnand B: A simple imputation algorithm reduced missing data in SF-12 health surveys. J Clin Epidemiol 2005, 58:142-149.
• [8]Cheung YB, Daniel R, Ng GY: Response and non-response to a quality-of life question on sexual life: a case study of the simple mean imputation method. Qual Life Res 2006, 15:1493-1501.
• [9]Peyre H, Coste J, Leplege A: Identifying type and determinants of missing items in quality of life questionnaires: Application to the SF-36 French version of the 2003 Decennial Health Survey. Health Qual Life Outcomes 2010, 8:16. BioMed Central Full Text
• [10]Ware JE, Snow KK, Kosinski M, Gandek B: SF-36 Health Survey Manual and Interpretation Guide. Boston: The Health Institute, New England Medical Center; 1993.
• [11]Peyre H, Leplège A, Coste J: Missing data methods for dealing with missing items in quality of life questionnaires. A comparison by simulation of personal mean score, full information maximum likelihood, multiple imputation, and hot deck techniques applied to the SF-36 in the French 2003 decennial health survey. Qual Life Res 2011, 20:287-300.
• [12]Lanoë J, Makdessi-Raynaud Y: L’état de santé en France en 2003: santé perçue, morbidité déclarée et recours aux soins à travers l’enquête décennale santé. Etudes et résultats (DRESS) 2005, 436:1-12.
• [13]Leplege A, Ecosse E, Verdier A, Perneger TV: The French SF-36 Health Survey: translation, cultural adaptation and preliminary psychometric evaluation. J Clin Epidemiol 1998, 51:1013-23.
• [14]Leplege A, Ecosse E, Pouchot J, Coste J, Perneger TV: Le questionnaire MOS SF-36, manuel de l’utilisation et guide d’interprétation des scores. Paris: ESTEM; 2001.
• [15]Ware JE, Gandek B: Overview of the SF-36 Health Survey and the International Quality of Life Assessment (IQOLA). J Clin Epidemiol 1998, 51:903-11.
• [16]Rueff B, Carnac J, Darne B: Detection of alcoholic patients using the systematic cage autoquestionnaire. Presse Med 1989, 18:1654-6.
• [17]Charlson ME, Pompei P, Ales KL, MacKenzie CR: A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 1987, 40:373-83.
• [18]Elixhauser A, Steiner C, Harris DR, Coffey RM: Comorbidity measures for use with administrative data. Med Care 1998, 36:8-27.
• [19]Graham JW: Missing Data Analysis: Making it work in the real world. Annu Rev Psychol 2009, 60:549-576.
• [20]Morris J, Coyle D: Quality of life questionnaires in cancer clinical trials: Imputing missing values. Psychooncology 1994, 3:215-22.
• [21]Tsai SY, Chi LY, Lee C, Chou P: Health-related quality of life as a predictor of mortality among community-dwelling older persons. Eur J Epidemiol 2007, 22:19-26.
• [22]Haywood KL, Garratt AM, Fitzpatrick R: Quality of life in older people: A structured review of generic self-assessed health instruments. Qual Life Res 2005, 14:1651-68.
• [23]Hayes V, Morris J, Wolfe C, Morgan M: The SF-36 health survey questionnaire: Is it suitable for use with older adults ? Age Ageing 1995, 24:120-125.
• [24]Brazier JE, Walters SJ, Nicholl JP, Kohler B: Using the SF-36 and Euroqol on an elderly population. Qual Life Res 1996, 5:195-204.
• [25]Parker SG, Peet SM, Jagger C, Farhan M, Castelden CM: Measuring health status in older patients. The SF-36 in practice. Age Ageing 1998, 27:13-8.
• [26]Parker SG, Bechinger-English D, Jagger C, Spiers N, Lindesay J: Factors affecting completion of the SF-36 in older people. Age Ageing 2006, 35:376-81.
• [27]Vercherin P, Gutknecht C, Guillemin F, Ecochard R, Mennen LI, Mercier M: Missing data mechanisms of the questionnaire SF-36’s items in the SUVIMAX study. Rev Epidemiol Santé Pub 2003, 51:513-25.
• [28]Walters SJ, Munro JF, Brazier JE: Using the SF-36 with older adults: a cross-sectional community-based survey. Age Ageing 2001, 30:337-43.
• [29]Iglesias CP, Birks YF, Torgerson DJ: Improving the measurement of quality of life in older people: the York SF-12. QJM 2001, 94:695-698.
• [30]Launer LJ, Wind AW, Deeg DJH: Nonresponse pattern and bias in a community-based cross-sectional study of cognitive functioning among the elderly. Am J Epidemiol 1994, 139:803-12.
• [31]van den Akker M, Buntinx F, Metsemakers JF, Knottnerus JA: Morbidity in responders and non-responders in a register-based population survey. Fam Pract 1998, 15:261-3.
• [32]Korkeila K, Suominen S, Ahvenainen J, Ojanlatva A, Rautava P, Helenius H, Koskenvuo M: Non-Response and Related Factors in a Nation-Wide Health Survey. Eur J Epidemiol 2001, 17:991-9.
• [33]Jacomb P, Jorm A, Korten A, Christensen H, Henderson AS: Predictors of Refusal to Participate: A Longitudinal Health Survey of the Elderly in Australia. BMC Publ Health 2002, 2:4. BioMed Central Full Text
• [34]Little R, Rubin D: Statistical analysis with missing data. New York: John Wiley and Sons; 1987.
• [35]Criqui MH, Austin M, Barrett-Connor E: The effect of non-response on risk ratios in a cardiovascular disease study. J Chron Dis 1979, 32:633-8.
• [36]Rothman KJ, Greenland S: Precision and validity in epidemiologic studies. In Modern epidemiology. 2nd edition. Edited by Rothman KJ, Greenland S. Philadelphia: Lippincott Williams & Wilkins; 1998.