Top

Quality of Life Research

Gepubliceerd in:

04-05-2023

Patient-reported mental health and well-being trajectories in oncology patients during radiation therapy: an exploratory retrospective cohort analysis using the Ontario Cancer Registry

Auteurs: Jae-Yung Kwon, Jacek Kopec, Jason M. Sutherland, Leah K. Lambert, Aslam H. Anis, Richard Sawatzky

Gepubliceerd in: Quality of Life Research | Uitgave 10/2023

Abstract

Purpose

Mental health and well-being trajectories are not expected to be homogeneous in diverse clinical populations. This exploratory study aims to identify subgroups of patients with cancer receiving radiation therapy who have different mental health and well-being trajectories, and examine which socio-demographic, physical symptoms, and clinical variables are associated with such trajectories.

Methods

Retrospective analysis of radiation therapy patients diagnosed with cancer in 2017 was conducted using data from the Ontario Cancer Registry (Canada) and linked with administrative health data. Mental health and well-being were measured using items from the Edmonton Symptom Assessment System—revised questionnaire. Patients completed up to 6 repeated measurements. We used latent class growth mixture models to identify heterogeneous mental health trajectories of anxiety, depression, and well-being. Bivariate multinomial logistic regressions were conducted to explore variables associated with the latent classes (subgroups).

Results

The cohort (N = 3416) with a mean age of 64.5 years consisted of 51.7% females. Respiratory cancer was the most common diagnosis (30.4%) with moderate to severe comorbidity burden. Four latent classes with distinct anxiety, depression, and well-being trajectories were identified. Decreasing mental health and well-being trajectories are associated with being female; living in neighborhoods with lower income, greater population density, and higher proportion of foreign-born individuals; and having higher comorbidity burden.

Conclusions

The findings highlight the importance of considering social determinants of mental health and well-being, in addition to symptoms and clinical variables, when providing care for patients undergoing radiation therapy.

vorige artikel Patient-preferred outcomes in patients with vestibular schwannoma: a qualitative content analysis of symptoms, side effects and their impact on health-related quality of life

volgende artikel Cognitive impairment and health-related quality of life amongst older Australians: evidence from a longitudinal investigation

Alleen toegankelijk voor geautoriseerde gebruikers

Niedzwiedz, C. L., Knifton, L., Robb, K. A., Katikireddi, S. V., & Smith, D. J. (2019). Depression and anxiety among people living with and beyond cancer: A growing clinical and research priority. BMC Cancer, 19(1), 943. https://doi.org/10.1186/s12885-019-6181-4CrossRefPubMedPubMedCentral

Chen, A. M., Hsu, S., Felix, C., Garst, J., & Yoshizaki, T. (2018). Effect of psychosocial distress on outcome for head and neck cancer patients undergoing radiation. The Laryngoscope, 128(3), 641–645. https://doi.org/10.1002/lary.26751CrossRefPubMed

Mols, F., Husson, O., Roukema, J.-A., & van de Poll-Franse, L. V. (2013). Depressive symptoms are a risk factor for all-cause mortality: Results from a prospective population-based study among 3080 cancer survivors from the PROFILES registry. Journal of Cancer Survivorship, 7(3), 484–492. https://doi.org/10.1007/s11764-013-0286-6CrossRefPubMed

Mausbach, B. T., Decastro, G., Schwab, R. B., Tiamson-Kassab, M., & Irwin, S. A. (2020). Healthcare use and costs in adult cancer patients with anxiety and depression. Depression and Anxiety, 37(9), 908–915. https://doi.org/10.1002/da.23059CrossRefPubMedPubMedCentral

Mitchell, A. J., Chan, M., Bhatti, H., Halton, M., Grassi, L., Johansen, C., & Meader, N. (2011). Prevalence of depression, anxiety, and adjustment disorder in oncological, haematological, and palliative-care settings: A meta-analysis of 94 interview-based studies. The Lancet Oncology, 12(2), 160–174. https://doi.org/10.1016/S1470-2045(11)70002-XCrossRefPubMed

Rutherford, C., Müller, F., Faiz, N., King, M. T., & White, K. (2020). Patient-reported outcomes and experiences from the perspective of colorectal cancer survivors: Meta-synthesis of qualitative studies. Journal of Patient-Reported Outcomes, 4(1), 27. https://doi.org/10.1186/s41687-020-00195-9CrossRefPubMedPubMedCentral

Dinesh, A. A., Helena Pagani Soares Pinto, S., Brunckhorst, O., Dasgupta, P., & Ahmed, K. (2021). Anxiety, depression and urological cancer outcomes: A systematic review. Urologic Oncology: Seminars and Original Investigations. https://doi.org/10.1016/j.urolonc.2021.08.003CrossRefPubMed

Goyal, N. G., Levine, B. J., Van Zee, K. J., Naftalis, E., & Avis, N. E. (2018). Trajectories of quality of life following breast cancer diagnosis. Breast Cancer Research and Treatment, 169(1), 163–173. https://doi.org/10.1007/s10549-018-4677-2CrossRefPubMedPubMedCentral

Chambers, S. K., Ng, S. K., Baade, P., Aitken, J. F., Hyde, M. K., Wittert, G., Frydenberg, M., & Dunn, J. (2017). Trajectories of quality of life, life satisfaction, and psychological adjustment after prostate cancer. Psycho-Oncology, 26(10), 1576–1585. https://doi.org/10.1002/pon.4342CrossRefPubMedPubMedCentral

10.

Cai, T., Huang, Y., Huang, Q., Xia, H., & Yuan, C. (2021). Symptom trajectories in patients with breast cancer: An integrative review. International Journal of Nursing Sciences, 9(1), 120–128. https://doi.org/10.1016/j.ijnss.2021.12.011CrossRefPubMedPubMedCentral

11.

Sayani, A. (2019). Health equity in national cancer control plans: An analysis of the Ontario cancer plan. International Journal of Health Policy and Management, 8(9), 550–556. https://doi.org/10.15171/ijhpm.2019.40CrossRefPubMedPubMedCentral

12.

Bubis, L. D., Davis, L., Mahar, A., Barbera, L., Li, Q., Moody, L., Karanicolas, P., Sutradhar, R., & Coburn, N. G. (2018). Symptom burden in the first year after cancer diagnosis: An analysis of patient-reported outcomes. Journal of Clinical Oncology: Official Journal of the American Society of Clinical Oncology, 36(11), 1103–1111. https://doi.org/10.1200/JCO.2017.76.0876CrossRefPubMed

13.

Kuluski, K., Peckham, A., Williams, A., & Upshur, R. (2016). What gets in the way of person-centred care for people with multimorbidity? Lessons from Ontario, Canada. Healthcare Quarterly (Toronto, Ontario), 19, 17–23. https://doi.org/10.12927/hcq.2016.24694CrossRef

14.

Cancer Care Ontario. (2018, June 28). Ontario Cancer Registry. Cancer Care Ontario. Retrieved November 9, 2021, from https://www.cancercareontario.ca/en/cancer-care-ontario/programs/data-research/ontario-cancer-registry

15.

Barbera, L., Lee, F., & Sutradhar, R. (2019). Use of patient-reported outcomes in regional cancer centres over time: A retrospective study. Canadian Medical Association Open Access Journal, 7(1), E101–E108. https://doi.org/10.9778/cmajo.20180074CrossRef

16.

Bruera, E., Kuehn, N., Miller, M. J., Selmser, P., & Macmillan, K. (1991). The Edmonton Symptom Assessment System (ESAS): A simple method for the assessment of palliative care patients. Journal of Palliative Care, 7(2), 6–9.CrossRefPubMed

17.

Watanabe, S. M., Nekolaichuk, C., Beaumont, C., Johnson, L., Myers, J., & Strasser, F. (2011). A multicenter study comparing two numerical versions of the Edmonton Symptom Assessment System in palliative care patients. Journal of Pain and Symptom Management, 41(2), 456–468. https://doi.org/10.1016/j.jpainsymman.2010.04.020CrossRefPubMed

18.

Noel, C. W., Forner, D., Chepeha, D. B., Baran, E., Chan, K. K. W., Parmar, A., Husain, Z., Karam, I., Hallet, J., Coburn, N. G., & Eskander, A. (2021). The Edmonton Symptom Assessment System: A narrative review of a standardized symptom assessment tool in head and neck oncology. Oral Oncology, 123, 105595. https://doi.org/10.1016/j.oraloncology.2021.105595CrossRefPubMed

19.

Chang, V. T., Hwang, S. S., & Feuerman, M. (2000). Validation of the Edmonton Symptom Assessment Scale. Cancer, 88(9), 2164–2171. https://doi.org/10.1002/(sici)1097-0142(20000501)88:9%3c2164::aid-cncr24%3e3.0.co;2-5CrossRefPubMed

20.

Saetra, P., Fossum, M., Svensson, E., & Cohen, M. Z. (2016). Evaluation of two instruments of perceived symptom intensity in palliative care patients in an outpatient clinic. Journal of Clinical Nursing, 25(5–6), 799–810. https://doi.org/10.1111/jocn.13100CrossRefPubMed

21.

Hannon, B., Dyck, M., Pope, A., Swami, N., Banerjee, S., Mak, E., Bryson, J., Rodin, G., Ridley, J., Lo, C., Le, L. W., & Zimmermann, C. (2015). Modified Edmonton Symptom Assessment System including constipation and sleep: Validation in outpatients with cancer. Journal of Pain and Symptom Management, 49(5), 945–952. https://doi.org/10.1016/j.jpainsymman.2014.10.013CrossRefPubMed

22.

Davison, S. N., Jhangri, G. S., & Johnson, J. A. (2006). Longitudinal validation of a modified Edmonton Symptom Assessment System (ESAS) in haemodialysis patients. Nephrology, Dialysis, Transplantation: Official Publication of the European Dialysis and Transplant Association - European Renal Association, 21(11), 3189–3195. https://doi.org/10.1093/ndt/gfl380CrossRefPubMed

23.

Vignaroli, E., Pace, E. A., Willey, J., Palmer, J. L., Zhang, T., & Bruera, E. (2006). The Edmonton Symptom Assessment System as a screening tool for depression and anxiety. Journal of Palliative Medicine, 9(2), 296–303. https://doi.org/10.1089/jpm.2006.9.296CrossRefPubMed

24.

Bagha, S. M., Macedo, A., Jacks, L. M., Lo, C., Zimmermann, C., Rodin, G., & Li, M. (2013). The utility of the Edmonton Symptom Assessment System in screening for anxiety and depression. European Journal of Cancer Care, 22(1), 60–69. https://doi.org/10.1111/j.1365-2354.2012.01369.xCrossRefPubMed

25.

Charlson, M. E., Pompei, P., Ales, K. L., & MacKenzie, C. R. (1987). A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation. Journal of Chronic Diseases, 40(5), 373–383. https://doi.org/10.1016/0021-9681(87)90171-8CrossRefPubMed

26.

World Health Organization. (2020). IACR—International Classification of Diseases for Oncology (ICD-O). Retrieved November 9, 2021, from http://www.iacr.com.fr/index.php?option=com_content&view=category&layout=blog&id=100&Itemid=577

27.

R Core Team. (2019). R: A language and environment for statistical computing (3.6.1.). Vienna. Retrieved from http://www.R-project.org

28.

Muthén, L. K., & Muthén, B. O. (2017). Mplus user’s guide (8th ed.). Los Angeles, CA

29.

Kwon, J.-Y., Sawatzky, R., Baumbusch, J., Lauck, S., & Ratner, P. A. (2021). Growth mixture models: A case example of the longitudinal analysis of patient-reported outcomes data captured by a clinical registry. BMC Medical Research Methodology, 21(1), 79. https://doi.org/10.1186/s12874-021-01276-zCrossRefPubMedPubMedCentral

30.

Berlin, K. S., Parra, G. R., & Williams, N. A. (2014). An introduction to latent variable mixture modeling (part 2): Longitudinal latent class growth analysis and growth mixture models. Journal of Pediatric Psychology, 39(2), 188–203. https://doi.org/10.1093/jpepsy/jst085CrossRefPubMed

31.

Burant, C. J. (2016). Latent growth curve models: Tracking changes over time. The International Journal of Aging and Human Development, 82(4), 336–350. https://doi.org/10.1177/0091415016641692CrossRefPubMed

32.

Enders, C. K. (2010). Applied missing data analysis. Guilford Press.

33.

Nylund, K. L., Asparouhov, T., & Muthén, B. O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Structural Equation Modeling: A Multidisciplinary Journal, 14(4), 535–569. https://doi.org/10.1080/10705510701575396CrossRef

34.

Asparouhov, T., & Muthén, B. (2014). Auxiliary variables in mixture modeling: Using the BCH method in Mplus to estimate a distal outcome model and an arbitrary secondary model. Retrieved from https://www.statmodel.com/examples/webnotes/webnote21.pdf

35.

Elsner, K., Naehrig, D., Halkett, G. K. B., & Dhillon, H. M. (2017). Reduced patient anxiety as a result of radiation therapist-led psychosocial support: A systematic review. Journal of Medical Radiation Sciences, 64(3), 220–231. https://doi.org/10.1002/jmrs.208CrossRefPubMedPubMedCentral

36.

Whitaker, K. L., Scott, S. E., & Wardle, J. (2015). Applying symptom appraisal models to understand sociodemographic differences in responses to possible cancer symptoms: A research agenda. British Journal of Cancer, 112(1), S27–S34. https://doi.org/10.1038/bjc.2015.39CrossRefPubMedPubMedCentral

37.

Shi, Q., Smith, T. G., Michonski, J. D., Stein, K. D., Kaw, C., & Cleeland, C. S. (2011). Symptom burden in cancer survivors 1 year after diagnosis: A report from the American Cancer Society’s Studies of Cancer Survivors. Cancer, 117(12), 2779–2790. https://doi.org/10.1002/cncr.26146CrossRefPubMed

38.

Agénor, M. (2020). Future directions for incorporating intersectionality into quantitative population health research. American Journal of Public Health, 110(6), 803–806. https://doi.org/10.2105/AJPH.2020.305610CrossRefPubMedPubMedCentral

39.

Pinto, A. D., Glattstein-Young, G., Mohamed, A., Bloch, G., Leung, F.-H., & Glazier, R. H. (2016). Building a foundation to reduce health inequities: Routine collection of sociodemographic data in primary care. Journal of the American Board of Family Medicine: JABFM, 29(3), 348–355. https://doi.org/10.3122/jabfm.2016.03.150280CrossRefPubMed

40.

Wagenmakers, E.-J., Sarafoglou, A., & Aczel, B. (2022). One statistical analysis must not rule them all. Nature, 605(7910), 423–425. https://doi.org/10.1038/d41586-022-01332-8CrossRefPubMed

Titel: Patient-reported mental health and well-being trajectories in oncology patients during radiation therapy: an exploratory retrospective cohort analysis using the Ontario Cancer Registry
Auteurs: Jae-Yung Kwon
Jacek Kopec
Jason M. Sutherland
Leah K. Lambert
Aslam H. Anis
Richard Sawatzky
Publicatiedatum: 04-05-2023
Uitgeverij: Springer International Publishing
Gepubliceerd in: Quality of Life Research / Uitgave 10/2023
Print ISSN: 0962-9343
Elektronisch ISSN: 1573-2649
DOI: https://doi.org/10.1007/s11136-023-03430-0

Bohn Stafleu van Loghum

Deel dit onderdeel of sectie (kopieer de link)

Abstract

Purpose

Methods

Results

Conclusions

Log in om toegang te krijgen

Andere artikelen Uitgave 10/2023

The use of PROMIS measures in clinical studies in patients with inflammatory arthritis: a systematic review

The Personalized Priority and Progress Questionnaire (PPPQ): A personalized instrument for quality of life and self-management for use in clinical trials and practice

The structure of well-being: a single underlying factor with genetic and environmental influences

Patient-preferred outcomes in patients with vestibular schwannoma: a qualitative content analysis of symptoms, side effects and their impact on health-related quality of life

Classification of pain intensity with the pain beliefs and perceptions inventory (PBPI) and the pain catastrophizing scales (PCS)

Use of the visual analogue scale for health state valuation: a scoping review