Research on the Influencing Factors of Depression based on Logistic Regression

Authors

  • Xiangyi Li

DOI:

https://doi.org/10.61173/wwtzsz53

Keywords:

Data analysis, depression, logistic regression models

Abstract

The purpose of this study was to analyze the influencing factors of depression through logistic regression model, and to provide doctors with recommendations for the prevention and treatment of depression based on the analysis results. Depression is a global mental health problem that affects the lives of more than 300 million people, and its incidence continues to rise. The causes of depression are complex and involve the interplay of biological, psychological, social, and environmental factors. Studies have shown that many factors such as genetics, neuroendocrine, immune system, and social environment can influence the onset and progression of depression. To better prevent and treat depression, researchers have proposed a variety of methodological pathways, such as linear mixed-effects (LME) models, and interventions by improving physical health and promoting physical activity. In addition, understanding the symptoms and effects of depression and how to provide help is critical for public education and early intervention. By delving into the multifactorial nature of depression, this paper can better understand its causes and provide new ideas and approaches for prevention and treatment.

References

[1] Syvälahti E.Biological aspects of depression. Acta Psychiatrica Scandinavica Supplementum, 1994.

[2] Shadrina M, Bondarenko E A, Slominsky P A. Genetics factors in major depression disease. Frontiers in psychiatry, 2018, 9: 334.

[3] Saveanu R V, Nemeroff C B. Etiology of depression: genetic and environmental factors. Psychiatric clinics, 2012, 35(1): 51- 71.

[4] Fang S, Wang X Q, et al. Survey of Chinese persons managing depressive symptoms: help-seeking behaviours and their influencing factors. Comprehensive psychiatry, 2019, 95: 152127.

[5] Couch Y, et al. Low-dose lipopolysaccharide (LPS) inhibits aggressive and augments depressive behaviours in a chronic mild stress model in mice. Journal of neuroinflammation, 2016, 13: 1-17.

[6] Chang Yunqi, Xiao Shujuan, Dong Fang, Zhang Zhichen. Classification of depression and related factors in older adults based on potential profile analysis. Chinese Mental Health Journal, 2020, 34(5).

[7] Beck A T. Cognitive models of depression. Clinical advances in cognitive psychotherapy: Theory and application, 2002, 14(1): 29-61.

[8] Schulz D. Depression development: From lifestyle changes to motivational deficits. Behavioural brain research, 2020, 395: 112845.

[9] Antoniuk S, Bijata M, Ponimaskin E, Wlodarczyk J. Chronic unpredictable mild stress for modeling depression in rodents: Meta-analysis of model reliability. Neuroscience & Biobehavioral Reviews, 2019, 99: 101-116.

[10] Goodman S H, Gotlib I H. Risk for psychopathology in the children of depressed mothers: a developmental model for understanding mechanisms of transmission. Psychological review, 1999, 106(3): 458.

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Published

2024-12-31