structural equation modeling; Mediation analysis; quality of life
Abstract :
[en] Purpose: Quality of life (QOL) of a patient is usually computed as the (weighted) sum of items and analysed by means of multiple regressions to evaluate its relationships with various measured factors. The aim of the present study was to compare results derived under classical statistical method with those obtained under more appropriate statistical techniques for QOL.
Methods: Analyses were applied to data from 4155 subjects participated in 2012 in a community based sample study in the French speaking part of Belgium and which completed a web-based questionnaire on their weight-related experience. Confirmatory factor analysis (CFA) and structural equation modeling (SEM) were carried out to derive QOL and to test direct/indirect effects of body mass index (BMI), age, body image discrepancy (BID), latent socio-economic (SOCIO) and latent subjective-norm (SN).
Results: No major differences were found under both SEM and the product of coefficients approach using SAS PROCESS macro developed by Hayes. Significant direct and indirect effects on physical and psychological dimensions of QOL were found for age, BMI and SOCIO while significant direct effects were found for BID and SN (p < 0.0001). Factor loadings were found to be significantly different according to gender (p < 0.0001).
Conclusions: BID and SN are partially mediators on the relationships between BMI and QOL. The study also confirms the role of SOCIO on the (un)observable variables included in the model. However, the large sample size provided significant tests with small effect size and couldn’t highlight pertinent differences between both methods.
Disciplines :
Public health, health care sciences & services
Author, co-author :
Dardenne, Nadia ; Université de Liège - ULiège > Département des sciences de la santé publique > Biostatistique
Pétré, Benoît ; Université de Liège - ULiège > Département des sciences de la santé publique > Education thérap. du patient au serv. des soins int.
Husson, Eddy ; Université de Liège - ULiège > Département des sciences de la santé publique > Santé publique : aspects spécifiques
Guillaume, Michèle ; Université de Liège - ULiège > Département des sciences de la santé publique > Epidémiologie nutritionnelle
Donneau, Anne-Françoise ; Université de Liège - ULiège > Département des sciences de la santé publique > Biostatistique
Language :
English
Title :
Assessing quality of life in an obesity observational study: A Structural Equation Modeling Approach
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Bibliography
Aghili, R., Ridderstråle, M., Kia, M., Ebrahim Valojerdi, A., Malek, M., Farshchi, A., & Khamseh, M. E. (2017). The challenge of living with diabetes in women and younger adults: a structural equation model. Primary Care Diabetes, 11(5), 467–473. 10.1016/j.pcd.2017.05.001. DOI: 10.1016/j.pcd.2017.05.001
Annette Alstadsæter, E. A., Alstadsæter, A., & Feiring, E. (2014). Does healthcare moderate the impact of socioeconomic status on Selfrated Health? Journal of Clinical Research & Bioethics, 05(01). https://doi.org/10.4172/2155-9627.1000169.
Arpey, N. C., Gaglioti, A. H., & Rosenbaum, M. E. (2017). How socioeconomic status affects patient perceptions of health care: a qualitative study. Journal of Primary Care & Community Health, 8(3), 169–175. 10.1177/2150131917697439. DOI: 10.1177/2150131917697439
Ashing-Giwa, K. T., & Lim, J.-W. (2008). Predicting health-related quality of life: testing the contextual model using structural equation modeling. Applied Research in Quality of Life, 3(3), 215–230. 10.1007/s11482-009-9057-y. DOI: 10.1007/s11482-009-9057-y
Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182. http://www.ncbi.nlm.nih.gov/pubmed/3806354. Accessed 14 Nov 2017. DOI: 10.1037/0022-3514.51.6.1173
Barrett, P. (2007). Structural equation modelling: adjudging model fit. Personality and Individual Differences, 42(5), 815–824. 10.1016/J.PAID.2006.09.018. DOI: 10.1016/J.PAID.2006.09.018
Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238–246. http://www.ncbi.nlm.nih.gov/pubmed/2320703. Accessed 15 Nov 2017. DOI: 10.1037/0033-2909.107.2.238
Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88(3), 588–606. 10.1037/0033-2909.88.3.588. DOI: 10.1037/0033-2909.88.3.588
Blumenthal, S. J., & Kagen, J. (2002). The effects of socioeconomic status on health in rural and urban America. JAMA, 287(1), 109. 10.1001/jama.287.1.109-JMS0102-3-1. DOI: 10.1001/jama.287.1.109-JMS0102-3-1
Boehmer, S., & Luszczynska, A. (2006). Two kinds of items in quality of life instruments: ‘indicator and causal variables’ in the EORTC QLQ-C30. Quality of Life Research, 15(1), 131–141. 10.1007/s11136-005-8290-6. DOI: 10.1007/s11136-005-8290-6
Bofah, E. A., & Hannula, M. S. (2017). Home resources as a measure of socio-economic status in Ghana. Large-Scale Assessments in Education, 5(1), 1. 10.1186/s40536-017-0039-5. DOI: 10.1186/s40536-017-0039-5
Browne, M. W., & Cudeck, R. (1992). Alternative ways of assessing model fit. Sociological Methods & Research, 21(2), 230–258. 10.1177/0049124192021002005. DOI: 10.1177/0049124192021002005
Cha, K. M., Chung, Y. K., Lim, K. Y., Noh, J. S., Chun, M., Hyun, S. Y., Kang, D. R., Oh, M. J., & Kim, N. H. (2017). Depression and insomnia as mediators of the relationship between distress and quality of life in cancer patients. Journal of Affective Disorders, 217, 260–265. 10.1016/j.jad.2017.04.020. DOI: 10.1016/j.jad.2017.04.020
Costa, D. S. J. (2015). Reflective, causal, and composite indicators of quality of life: a conceptual or an empirical distinction? Quality of Life Research, 24(9), 2057–2065. 10.1007/s11136-015-0954-2. DOI: 10.1007/s11136-015-0954-2
Danner, D., Hagemann, D., & Fiedler, K. (2015). Mediation analysis with structural equation models: combining theory, design, and statistics. European Journal of Social Psychology, 45(4), 460–481. 10.1002/ejsp.2106. DOI: 10.1002/ejsp.2106
Donneau, A. F., Mauer, M., Coens, C., Bottomley, A., & Albert, A. (2014). Longitudinal quality of life data: a comparison of continuous and ordinal approaches. Quality of Life Research, 23(10), 2873–2881. 10.1007/s11136-014-0730-8. DOI: 10.1007/s11136-014-0730-8
Dragan, A., & Akhtar-Danesh, N. (2007). Relation between body mass index and depression: a structural equation modeling approach. BMC Medical Research Methodology, 7(1), 17. 10.1186/1471-2288-7-17. DOI: 10.1186/1471-2288-7-17
Fitzpatrick, R., Fletcher, A., Gore, S., Jones, D., Spiegelhalter, D., & Cox, D. (1992). Quality of life measures in health care. I: applications and issues in assessment. BMJ (Clinical research ed.), 305(6861), 1074–1077. http://www.ncbi.nlm.nih.gov/pubmed/1467690. Accessed 5 Dec 2017. DOI: 10.1136/bmj.305.6861.1074
Hayes, A. F. (1986). PROCESS: A Versatile Computational Tool for Observed Variable Mediation, Moderation, and Conditional Process Modeling 1. http://www.afhayes.com/. Accessed 23 March 2018.
Hayes, A. F., Montoya, A. K., & Rockwood, N. J. (2017). The analysis of mechanisms and their contingencies: PROCESS versus structural equation modeling. Australasian Marketing Journal; AMJ, 25(1), 76–81. 10.1016/j.ausmj.2017.02.001. DOI: 10.1016/j.ausmj.2017.02.001
Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. 10.1080/10705519909540118. DOI: 10.1080/10705519909540118
Iacobucci, D., Saldanha, N., & Deng, X. (2007). A meditation on mediation: evidence that structural equations models perform better than regressions. Journal of Consumer Psychology, 17(2), 139–153. 10.1016/S1057-7408(07)70020-7. DOI: 10.1016/S1057-7408(07)70020-7
Kline, R. B. (2011). Principles and practice of structural equation modeling. New York: Guilford Press.
Krieger, N., Williams, D. R., & Moss, N. E. (1997). Measuring social class in US public health research: concepts, methodologies, and guidelines. Annual Review of Public Health, 18(1), 341–378. 10.1146/annurev.publhealth.18.1.341. DOI: 10.1146/annurev.publhealth.18.1.341
Litwin, H., & Sapir, E. V. (2009). Perceived income adequacy among older adults in 12 countries: findings from the survey of health, ageing, and retirement in Europe. The Gerontologist, 49(3), 397–406. 10.1093/geront/gnp036. DOI: 10.1093/geront/gnp036
MacKinnon, D. P., Fairchild, A. J., & Fritz, M. S. (2007). Mediation analysis. Annual Review of Psychology, 58, 593–614. 10.1146/annurev.psych.58.110405.085542. DOI: 10.1146/annurev.psych.58.110405.085542
Marmot, M. (2013). Report on social determinants of health and the health divide in the WHO European region. World Health Organization., 234. http://www.euro.who.int/__data/assets/pdf_file/0004/251878/Review-of-social-determinants-and-the-health-divide-in-the-WHO-European-Region-FINAL-REPORT.pdf. Accessed 14 Nov 2017.
Martinez, S. A., Beebe, L. A., Thompson, D. M., Wagener, T. L., Terrell, D. R., & Campbell, J. E. (2018). A structural equation modeling approach to understanding pathways that connect socioeconomic status and smoking. PLoS One, 13(2), e0192451. 10.1371/journal.pone.0192451. DOI: 10.1371/journal.pone.0192451
Meuleners, L. B., Lee, A. H., Binns, C. W., & Lower, A. (2003). Quality of life for adolescents: Assessing measurement properties using structural equation modelling. Quality of Life Research: an International Journal of Quality of Life Aspects of Treatment, Care and Rehabilitation, 12(3), 283–290. http://www.ncbi.nlm.nih.gov/pubmed/12769141. Accessed 17 Nov 2017. DOI: 10.1023/A:1023221913292
Moon, J. R., Cho, Y. A., Huh, J., Kang, I.-S., & Kim, D.-K. (2016). Structural equation modeling of the quality of life for patients with marfan syndrome. Health and Quality of Life Outcomes, 14(1), 83. 10.1186/s12955-016-0488-5. DOI: 10.1186/s12955-016-0488-5
Niyonsenga, T., Trepka, M. J., Lieb, S., & Maddox, L. M. (2013). Measuring socioeconomic inequality in the incidence of AIDS: rural-urban considerations. AIDS and Behavior, 17(2), 700–709. 10.1007/s10461-012-0236-8. DOI: 10.1007/s10461-012-0236-8
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory. New York: McGraw-Hill.
Øvrum, A. (2011). Socioeconomic status and lifestyle choices: evidence from latent class analysis. Health Economics, 20(8), 971–984. 10.1002/hec.1662. DOI: 10.1002/hec.1662
Pétré, B., Donneau, A.-F., Crutze, C., Husson, E., Scheen, A., & Guillaume, M. (2015). Obese subjects involvement in a population-based survey: the use of information and communication technologies (ICT) to avoid stigmatization. Quality of Life Research, 24(5), 1131–1135. 10.1007/s11136-014-0800-y. DOI: 10.1007/s11136-014-0800-y
Pétré, B., Scheen, A., Ziegler, O., Donneau, A.-F., Dardenne, N., Husson, E., Albert, A., & Guillaume, M. (2016). Body image discrepancy and subjective norm as mediators and moderators of the relationship between body mass index and quality of life. Patient Preference and Adherence, 10, 2261–2270. 10.2147/PPA.S112639. DOI: 10.2147/PPA.S112639
Post, M. W. M. (2014). Definitions of quality of life: what has happened and how to move on. Topics in Spinal Cord Injury Rehabilitation, 20(3), 167–180. 10.1310/sci2003-167. DOI: 10.1310/sci2003-167
Ribeiro, M. R. C., da Silva, A. A. M., de Britto e Alves, M. T. S. S., Batista, R. F. L., Ribeiro, C. C. C., Schraiber, L. B., et al. (2017). Effects of socioeconomic status and social support on violence against pregnant women: a structural equation modeling analysis. PLoS One, 12(1), e0170469. 10.1371/journal.pone.0170469. DOI: 10.1371/journal.pone.0170469
Rosseel, Y. (2012). Lavaan: an R package for structural equation modeling. Journal of Statistical Software, 48(2), 1–36. 10.18637/jss.v048.i02. DOI: 10.18637/jss.v048.i02
Rosseel, Y. (2014). Structural equation modeling with lavaan. Using R for personality research, 1–127. https://personality-project.org/r/tutorials/summerschool.14/rosseel_sem_intro.pdf. Accessed 14 Nov 2017.
Rosseel, Y. (2017). The lavaan tutorial. http://lavaan.ugent.be/tutorial/tutorial.pdf. Accessed 15 Nov 2017.
Sartipi, M., Nedjat, S., Mansournia, M. A., Baigi, V., & Fotouhi, A. (2016). Assets as a socioeconomic status index: categorical principal components analysis vs. latent class analysis. Archives of Iranian Medicine, 19(11), 791–796 doi:0161911/AIM.009.
Scheen, A., Bourguignon, J. P., Hubermont, G., Ziegler, O., Böhme, P., Collin, J. F., Romain, M. L., Lair, M., de Beaufort, C., Michel, G., & Guillaume, M. (2010). Éducation thérapeutique et préventive face au diabète et à l’obésité à risque chez l’adulte et l’adolescent: le projet Interreg IV EDUDORA2. Diabetes & Metabolism, 36, A84. 10.1016/S1262-3636(10)70341-4. DOI: 10.1016/S1262-3636(10)70341-4
Sideridis, G., Simos, P., Papanicolaou, A., & Fletcher, J. (2014). Using structural equation modeling to assess functional connectivity in the brain: power and sample size considerations. Educational and Psychological Measurement, 74(5), 733–758. 10.1177/0013164414525397. DOI: 10.1177/0013164414525397
Steen, J., Loeys, T., Moerkerke, B., & Vansteelandt, S. (2017). Medflex: an R package for flexible mediation analysis using natural effect models. Journal of Statistical Software, 76(11), 1–46. 10.18637/jss.v076.i11. DOI: 10.18637/jss.v076.i11
Stephenson, M. T., Holbert, R. L., & Zimmerman, R. S. (2006). On the use of structural equation modeling in health communication research. Health Communication, 20(2), 159–167. 10.1207/s15327027hc2002_7. DOI: 10.1207/s15327027hc2002_7
Stevens, J. (2009). Applied multivariate statistics for the social sciences. In J. P. Stevens (Ed.), Version details - Trove (5th ed.). New York: Routledge, c2009. http://trove.nla.gov.au/work/17113022?q&sort=holdings+desc&_=1510743660016&versionId=44804159+218188866. Accessed 15 Nov 2017.
Stunkard, A. J., Sørensen, T., & Schulsinger, F. (1983). Use of the Danish adoption register for the study of obesity and thinness. Research Publications - Association for Research in Nervous and Mental Disease, 60, 115–120. http://www.ncbi.nlm.nih.gov/pubmed/6823524. Accessed 14 Nov 2017.
VanderWeele, T. J. (2016). Mediation analysis: a practitioner’s guide. Annual Review of Public Health, 37(1), 17–32. 10.1146/annurev-publhealth-032315-021402. DOI: 10.1146/annurev-publhealth-032315-021402
Verdugo, M. A., Schalock, R. L., Keith, K. D., & Stancliffe, R. J. (2005). Quality of life and its measurement: important principles and guidelines. Journal of Intellectual Disability Research, 49(10), 707–717. 10.1111/j.1365-2788.2005.00739.x. DOI: 10.1111/j.1365-2788.2005.00739.x
Wagstaff, A., & Watanabe, N. (2003). What difference does the choice of SES make in health inequality measurement? Health Economics, 12(10), 885–890. 10.1002/hec.805. DOI: 10.1002/hec.805
Wolf, E. J., Harrington, K. M., Clark, S. L., & Miller, M. W. (2013). Sample size requirements for structural equation models: An evaluation of power, Bias, and solution propriety. Educational and Psychological Measurement, 76(6), 913–934. 10.1177/0013164413495237. DOI: 10.1177/0013164413495237
Yang Hansen, K., & Munck, I. (2012). Exploring the measurement profiles of socioeconomic background indicators and their differences in reading achievement: A two-level latent class analysis. IERI Monograph Series, 5. http://www.ierinstitute.org/fileadmin/Documents/IERI_Monograph/IERI_Monograph_Volume_05_Chapter_3.pdf. Accessed 27 Feb 2018.
Yanuar, F., Ibrahim, K., & Jemain, A. A. (2010). On the application of structural equation modeling for the construction of a health index. Environmental Health and Preventive Medicine, 15(5), 285–291. 10.1007/s12199-010-0140-7. DOI: 10.1007/s12199-010-0140-7
Ziegler, O., Filipecki, J., Girod, I., & Guillemin, F. (2005). Development and validation of a French obesity-specific quality of life questionnaire: Quality of life, obesity and dietetics (QOLOD) rating scale. Diabetes & Metabolism, 31(3 Pt 1), 273–283. http://www.ncbi.nlm.nih.gov/pubmed/16142018. Accessed 14 Nov 2017. DOI: 10.1016/S1262-3636(07)70194-5
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