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Prevalence and predictive risk factors of self-stigmatization in patients with rare diseases: Based on health ecology model

Author

Listed:
  • Haixu Liang
  • Jiaqi Zheng
  • Yongkang Sun

Abstract

Background and aims: Given the cause of self-stigmatization of patients with rare diseases is complicated, and the self-stigmatization can be prevented by managing the related risk factors. This study aimed to report the prevalence as well as influencing factors associated with self-stigmatization in patients with rare diseases. Methods: From January to April 2022, the respondent-driven sampling (RDS) method was used to select patients with rare diseases through the Chinese Organization for Rare Disorders as the subjects. Based on the theoretical model of health ecology, logistic regression analysis was used to explore the association between self-stigmatization level and sample characteristics of patients with rare diseases. Results: A total of 530 patients were included, 50.2% of whom were male, and most of them are under 45 years old (86.5%). The prevalence of self-stigmatization in patients with rare diseases was 85.7%. Logistic regression analysis indicated that age (OR: 0.624, 95% CI: [0.399, 0.976]), mental health status (OR: 0.184, 95% CI: [0.076, 0.445]), family relations (OR: 0.180, 95% CI: [0.074, 0.434]), full time work (OR: 2.835, 95% CI: [1.024, 7.849]) and medical insurance (OR: 0.296, 95% CI: [0.105, 0.835]) were risk factors for self-stigmatization of patients with rare diseases. Conclusions: Chinese patients with rare diseases have a high level of self-stigmatization, and the potential risk factors are multi-level and multi-dimensional.

Suggested Citation

  • Haixu Liang & Jiaqi Zheng & Yongkang Sun, 2023. "Prevalence and predictive risk factors of self-stigmatization in patients with rare diseases: Based on health ecology model," International Journal of Social Psychiatry, , vol. 69(6), pages 1541-1545, September.
  • Handle: RePEc:sae:socpsy:v:69:y:2023:i:6:p:1541-1545
    DOI: 10.1177/00207640231168023
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