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Dynamic prediction of lung cancer suicide risk based on meteorological factors and clinical characteristics：A landmarking analysis approach

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  • Zhou, Yuying
  • Lao, Jiahui
  • Cao, Yiting
  • Wang, Qianqian
  • Wang, Qin
  • Tang, Fang

Abstract

Suicide is a severe public health issue globally. Accurately identifying high-risk lung cancer patients for suicidal behavior and taking timely intervention measures has become a focus of current research. This study intended to construct dynamic prediction models for identifying suicide risk among lung cancer patients. Patients were sourced from the Surveillance, Epidemiology, and End Results database, while meteorological data was acquired from the Centers for Disease Control and Prevention. This cohort comprised 455, 708 eligible lung cancer patients from January 1979 to December 2011. A Cox proportional hazard regression model based on landmarking approach was employed to explore the impact of meteorological factors and clinical characteristics on suicide among lung cancer patients, and to build dynamic prediction models for the suicide risk of these patients. Additionally, subgroup analyses were conducted by age and sex. The model's performance was evaluated using the C-index, Brier score, area under curve (AUC) and calibration plot. During the study period, there were 666 deaths by suicide among lung cancer patients. Multivariable Cox results from the dynamic prediction model indicated that age, marital status, race, sex, primary site, stage, monthly average daily sunlight, and monthly average temperature were significant predictors of suicide. The dynamic prediction model demonstrated well consistency and discrimination capabilities. Subgroup analyses revealed that the association of monthly average daily sunlight and monthly average temperature with suicide remained significant among female and younger lung cancer patients. The dynamic prediction model can effectively incorporate covariates with time-varying to predict lung cancer patients' suicide death. The results of this study have significant implications for assessing lung cancer individuals' suicide risk.

Suggested Citation

  • Zhou, Yuying & Lao, Jiahui & Cao, Yiting & Wang, Qianqian & Wang, Qin & Tang, Fang, 2024. "Dynamic prediction of lung cancer suicide risk based on meteorological factors and clinical characteristics：A landmarking analysis approach," Social Science & Medicine, Elsevier, vol. 357(C).
    • Handle: RePEc:eee:socmed:v:357:y:2024:i:c:s0277953624006543
    DOI: 10.1016/j.socscimed.2024.117201
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    References listed on IDEAS

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    1. Seo-Eun Cho & Zong Woo Geem & Kyoung-Sae Na, 2021. "Development of a Suicide Prediction Model for the Elderly Using Health Screening Data," IJERPH, MDPI, vol. 18(19), pages 1-10, September.
    2. Nicholas G. Zaorsky & Ying Zhang & Leonard Tuanquin & Shirley M. Bluethmann & Henry S. Park & Vernon M. Chinchilli, 2019. "Suicide among cancer patients," Nature Communications, Nature, vol. 10(1), pages 1-7, December.
    3. Hans C. Van Houwelingen, 2007. "Dynamic Prediction by Landmarking in Event History Analysis," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 34(1), pages 70-85, March.
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