Who is more prone to depression? Analysis of micro-level data of patients with cancer.
| Název práce v češtině: | Kdo je náchylnější k depresím? Analýza dat na mikroúrovni pacientů s rakovinou. |
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| Název v anglickém jazyce: | Who is more prone to depression? Analysis of micro-level data of patients with cancer. |
| Klíčová slova: | Determinanty deprese, pacienti s rakovinou, COVID-19, logistická regrese, multinomická logistická regrese |
| Klíčová slova anglicky: | Depression determinants, cancer patients, COVID-19, logistic regression, multinomial logistic regression |
| Akademický rok vypsání: | 2022/2023 |
| Typ práce: | bakalářská práce |
| Jazyk práce: | angličtina |
| Ústav: | Institut ekonomických studií (23-IES) |
| Vedoucí / školitel: | PhDr. Mgr. Jana Votápková, Ph.D. |
| Řešitel: | skrytý - zadáno vedoucím/školitelem |
| Datum přihlášení: | 19.06.2023 |
| Datum zadání: | 19.06.2023 |
| Datum a čas obhajoby: | 10.06.2024 09:00 |
| Místo konání obhajoby: | Opletalova, O105, místnost č. 105 |
| Datum odevzdání elektronické podoby: | 29.04.2024 |
| Datum proběhlé obhajoby: | 10.06.2024 |
| Oponenti: | Mgr. Petra Landovská |
| Seznam odborné literatury |
| American Psychiatric Association. 2013. Diagnostic and Statistical Manual of Mental Disorders. American Psychiatric Association: 5th ed.
Cohee, A. A., Kroenke, K., Vachon, E., et al. 2020. Predictors of depression outcomes in adults with cancer: A 12month longitudinal study. Journal of Psychosomatic Research: Volume 136. Mejareh, Z. N., Abdollahi, B., Hoseinipalangi, Z., et al. 2021. Global, regional, and national prevalence of depression among cancer patients: A systematic review and meta-analysis. Indian J Psychiatry. Lemon, J., Edelman, S., Kidman, A. D. 2003. Perceptions of the “Mind-Cancer” Relationship Among the Public, Cancer Patients, and Oncologists. Journal of Psychosocial Oncology: Volume 21, Issue 4, Pages 43-58. Linden, W., Vodermaier, A., Mackenzie, R., et al. 2012. Anxiety and depression after cancer diagnosis: prevalence rates by cancer type, gender, and age. J Affect Disord. Pitman, A., Suleman, S., Hyde, N., et al. 2018. Depression and anxiety in patients with cancer. Smith, H. R. 2015. Depression in cancer patients: Pathogenesis, implications and treatment (Review). Oncology Letters. Thames, B. J., Thomason, D. J. 1997. Stress Management for the Health of It. Clemson extension. Walker, J., Hansen, C. H., Martin, P., et al. 2014. Prevalence, associations, and adequacy of treatment of major depression in patients with cancer: a cross-sectional analysis of routinely collected clinical data. Lancet Psychiatry. Young, K., Singh, G. 2018. Biological Mechanisms of Cancer-Induced Depression. Frontiers in Psychiatry: Volume 9. |
| Předběžná náplň práce v anglickém jazyce |
| Research question and motivation
Medical research estimates that 90 % of illness and disease is caused by stress. Cancer is a serious diagnosis and goes hand in hand with a lot of exposure to stress. Additionally, according to Linden et al. (2012), long-term high levels of mental discomfort in people with cancer lead to states of depression, anxiety, or both. The Diagnostic and Statistical Manual of Mental Disorders specifies depression as a condition where affect, sleep disturbances, and similar disorders occur. This supports Cohee’s et al. claim that depression is very common in cancer patients, affecting 8-24 % of all patients within their first year of cancer diagnosis. This incidence is significantly higher than the incidence in the general population (Pitman et al., 2018). Moreover, Smith (2015) revealed that any form of depression increases mortality by 39 %, and that even slight signs of depression increase it by 25 %. Lemon, Edelman and Kidman (2003) found that more than 70 % of oncologists and 85 % of patients believe that good mood slows down the progression of cancer. Thus, people with cancer could greatly benefit from effective therapies that would help them manage depressive symptoms (Young and Singh, 2018), as only 5 % of cancer patients with depression visit a qualified mental health specialist (Walker et al., 2014). However, the meta-analysis conducted by Mejareh et al. (2021) suggests that before any psychotherapy, it is important to first identify the factors potentially contributing to mental disorders in people with cancer, and then to seek an appropriate form of therapy to cure these disorders. In my bachelor thesis, I will focus on adults from the noninstitutionalized population in the United States and will estimate factors that reduce depression in people diagnosed with cancer. Contribution The aim of my thesis is to contribute to the existing literature on cancer patients by finding out what factors decrease the probability of depression in people diagnosed with cancer. The results should suggest what adjustments to clinical practice guidelines should be made to reduce the incidence of depression in people diagnosed with cancer or whom to target with psychotherapies in order to increase the effectiveness of cancer treatment and reduce overall mortality of cancer. Moreover, the results can be applied even for people who are suffering from depression but do not have cancer. If so, it is plausible that some determinants, which are significant for subpopulation of cancer patients, are insignificant for health people. One way or the other, it will be possible to determine which group is more sensitive to depression, and this information could make prevention more effective. Methodology Data from the National Health Interview Survey, a micro-level database on individuals aged 18+ from United States, will be analysed. To study the probability of depression in cancer patients, three alternative dependent variables will be used – how often cancer patients are depressed, whether they take any medication against depression and what is their level of depression. Independent variables include sociodemographic and other variables such as age, socio-demographic conditions, health status, presence of health insurance, type of cancer, household composition, etc… The effect of independent variables on the probability of depression will be tested using multinomial logit model. The results should show which independent variables increase the probability of occurrence of depression, and for these variables there should be a modification of the clinical practice guidelines or individuals with these sociodemographic characteristics should be targeted with psychotherapies. Outline 1. Introduction 2. Literature review 3. Methodology 4. Results 5. Discussion 6. Conclusion |