Abstract
An ever-growing body of empirical evidence has demonstrated the relationship between depression and cancer. The objective of this study was to examine whether depression trajectories predict mortality risk above and beyond demographics and other general health-related factors. Participants (n = 2,345) were a part of the Health and Retirement Study. The sample consisted of patients who were assessed once before their cancer diagnosis and thrice after. Depressive symptoms and general health-related factors were based on self-reports. Mortality risk was determined based on whether the patient was alive or not at respective time points. Latent Growth Mixture Modeling was performed to map trajectories of depression, assess differences in trajectories based on demographics and general health-related factors, and predict mortality risk. Four trajectories of depression symptoms emerged: resilient (69.7%), emerging (13.5%), recovery (9.5%), and chronic (7.2%). Overall, females, fewer years of education, higher functional impairment at baseline, and high mortality risk characterized the emerging, recovery, and chronic trajectories. In comparison to the resilient trajectory, mortality risk was highest for the emerging trajectory and accounted for more than half of the deaths recorded for the participants in emerging trajectory. Mortality risk was also significantly elevated, although to a lesser degree, for the recovery and chronic trajectories. The data highlights clinically relevant information about the depression-cancer association that can have useful implications towards cancer treatment, recovery, and public health.
Highlights
Cancer and depression are highly comorbid.
This prospective study examines depression trajectories in cancer patients.
Chronic, emerging, and recovery trajectories significantly predicted mortality risk.
Resilience was the modal response.
Demographic differences and functional impairment distinguished trajectories.
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Notes
Data at Wave 1 (W1) were excluded because allowable responses to the questions were different at W1 as compared to all other waves; each depression item at W1 was rated on a four-point scale ranging from 1 to 4, as opposed to all other waves where depression items were binary with “yes” (1 = yes) or “no” (0 = no) responses from participants, indicating presence or absence of specific depressive symptoms.
This variable was based on the response status of the participant during the time of data collection. Respondents are identified by code 1, non-Respondents by codes 0, 4–7 and 9. Known alive and presumed alive are both treated as indication that the Respondent is living, coded as 1. Non-response code 4 means that the Respondent is alive so far as the researchers that collected the data are aware of, but did not respond. A code of 5 means that the Respondent died between the last interview and the current one, and 6 means that the Respondent had died before a previous wave. A code of 9 means that we don’t know if the individual is alive or not. For the purposes of the current investigation, 1 and 4 were coded as “alive” and the remained were coded as “not alive”.
In all conditional models, the inclusion of covariates nested within the LGMM did not alter the proportions of individual in each trajectory or the shape of the trajectory pattern to a large extent.
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The data used in this publication were collected by the Institute for Social Research at the University of Michigan. The RAND Center for the Study of Aging created the RAND Health and Retirement Study (HRS) data products making it publicly accessible. HRS has been funded and supported by the National Institute on Aging (U01-AG009740) and the Social Security Administration (SSA). The original investigators and funding agency are not responsible for the analyses or interpretations presented here.
Research involving human participants and/or Animals/informed consentSince this study used a publicly available dataset, it was exempt from IRB approval. This study uses data from the Health and Retirement Study (HRS; Bugliari et al., 2020) conducted by the Institute for Social Research at the University of Michigan and approved by the IRB at the University of Michigan.
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Conflicts of interestMy research team, Mr. Shuquan Chen, Dr. George A. Bonanno, and I declare that we have no conflicts of interest regarding this article’s authorship, research, and publication, or any financial disclosures. Teachers College, Columbia University. is the primary affiliation of all authors. Additionally, I am currently affiliated with Weill Cornell Medicine.
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Sanghvi, D.E., Chen, M. & Bonanno, G.A. Prospective trajectories of depression predict mortality in cancer patients. J Behav Med (2024). https://doi.org/10.1007/s10865-024-00485-3
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DOI: https://doi.org/10.1007/s10865-024-00485-3