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Developing a predictive model for classifying high-risk groups of post-stroke depressive symptoms based on the health ecology model and Harvard Cancer Index.

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Title: Developing a predictive model for classifying high-risk groups of post-stroke depressive symptoms based on the health ecology model and Harvard Cancer Index.
Authors: Zhuang, Zeming (AUTHOR), Ji, Longfei (AUTHOR), Chen, Yuxi (AUTHOR), Chen, Enlin (AUTHOR), Mo, Fengling (AUTHOR), Zhou, Jiakun (AUTHOR), Zhang, Mingzhe (AUTHOR), Zhang, Lifang (AUTHOR)
Source: Psychology, Health & Medicine. Aug2026, Vol. 31 Issue 7, p1823-1841. 19p.
Subjects: Tumor risk factors, Mental depression risk factors, Risk assessment, Goodness-of-fit tests, Self-evaluation, Prediction models, Research funding, Center for Epidemiologic Studies Depression Scale, Health status indicators, Satisfaction, Cronbach's alpha, Receiver operating characteristic curves, Ecology, Questionnaires, Multiple regression analysis, Health insurance, Interviewing, Statistical sampling, Sex distribution, Smoking, Residential patterns, Disease prevalence, Multivariate analysis, Relative medical risk, Internet, Pain, Sleep, Marital status, Field research, Stroke, Theory, Psychological tests, Data analysis software, Alcohol drinking, Stroke patients, Mental depression, Activities of daily living, Employment, Disease incidence, Social participation, Disease complications, Middle age, Old age
Geographic Terms: China
Abstract: Depressive symptoms affect a significant proportion of stroke survivors, negatively affecting quality of life and functional recovery. This study was guided by the health ecology model (HEM). It aimed to build a predictive model for identifying high-risk individuals with post-stroke depression symptoms (PSDS), providing theoretical support for prevention strategies. Data were extracted from the CHARLS. Depressive symptoms were measured using the CESD-10 scale. Guided by the HEM, influencing factors were identified and stratified. Binary logistic regression was used to analyze determinants of PSDS, while the Harvard Cancer Index was used to assess the risk of depressive symptoms among stroke survivors. A total of 54.13% of participants met CESD-10 criteria for depression. Multivariate analysis identified self-rated health status, activities of daily living, pain, drinking, night sleep time, marital status, life satisfaction, employment, and medical insurance as factors significantly associated with PSDS. Notably, risk stratification via the Harvard Cancer Index revealed a clear ordered trend with PSDS prevalence increasing progressively alongside higher risk categories (χ2 = 41.395, p < 0.001). Significant differences existed between each consecutive risk level (χ2 = 69.132, p < 0.001). PSDS is determined by multiple factors. The Harvard Cancer Index effectively stratifies PSDS risk in stroke survivors with distinct prevalence differences across ordered risk grades. This index provides a practical tool for identifying high-risk individuals and directly supports the development of targeted and efficient intervention strategies. [ABSTRACT FROM AUTHOR]
Copyright of Psychology, Health & Medicine is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Depressive symptoms affect a significant proportion of stroke survivors, negatively affecting quality of life and functional recovery. This study was guided by the health ecology model (HEM). It aimed to build a predictive model for identifying high-risk individuals with post-stroke depression symptoms (PSDS), providing theoretical support for prevention strategies. Data were extracted from the CHARLS. Depressive symptoms were measured using the CESD-10 scale. Guided by the HEM, influencing factors were identified and stratified. Binary logistic regression was used to analyze determinants of PSDS, while the Harvard Cancer Index was used to assess the risk of depressive symptoms among stroke survivors. A total of 54.13% of participants met CESD-10 criteria for depression. Multivariate analysis identified self-rated health status, activities of daily living, pain, drinking, night sleep time, marital status, life satisfaction, employment, and medical insurance as factors significantly associated with PSDS. Notably, risk stratification via the Harvard Cancer Index revealed a clear ordered trend with PSDS prevalence increasing progressively alongside higher risk categories (χ2 = 41.395, p &lt; 0.001). Significant differences existed between each consecutive risk level (χ2 = 69.132, p &lt; 0.001). PSDS is determined by multiple factors. The Harvard Cancer Index effectively stratifies PSDS risk in stroke survivors with distinct prevalence differences across ordered risk grades. This index provides a practical tool for identifying high-risk individuals and directly supports the development of targeted and efficient intervention strategies. [ABSTRACT FROM AUTHOR]
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  Data: &lt;i&gt;Copyright of Psychology, Health &amp; Medicine is the property of Taylor &amp; Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder&#39;s express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.&lt;/i&gt; (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1080/13548506.2025.2601066
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      – Code: eng
        Text: English
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        PageCount: 19
        StartPage: 1823
    Subjects:
      – SubjectFull: Tumor risk factors
        Type: general
      – SubjectFull: Mental depression risk factors
        Type: general
      – SubjectFull: Risk assessment
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      – SubjectFull: Goodness-of-fit tests
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      – SubjectFull: Prediction models
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      – SubjectFull: Research funding
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      – SubjectFull: Center for Epidemiologic Studies Depression Scale
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      – SubjectFull: Health status indicators
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      – SubjectFull: Satisfaction
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      – SubjectFull: Cronbach's alpha
        Type: general
      – SubjectFull: Receiver operating characteristic curves
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      – SubjectFull: Ecology
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      – SubjectFull: Multiple regression analysis
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      – SubjectFull: Disease prevalence
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      – SubjectFull: Marital status
        Type: general
      – SubjectFull: Field research
        Type: general
      – SubjectFull: Stroke
        Type: general
      – SubjectFull: Theory
        Type: general
      – SubjectFull: Psychological tests
        Type: general
      – SubjectFull: Data analysis software
        Type: general
      – SubjectFull: Alcohol drinking
        Type: general
      – SubjectFull: Stroke patients
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      – SubjectFull: Mental depression
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      – SubjectFull: Middle age
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      – SubjectFull: China
        Type: general
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      – TitleFull: Developing a predictive model for classifying high-risk groups of post-stroke depressive symptoms based on the health ecology model and Harvard Cancer Index.
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              Text: Aug2026
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