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  4. Investigation of time-dependent risk of mental disorders after infertility diagnosis, through survival analysis and data mining
 
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Investigation of time-dependent risk of mental disorders after infertility diagnosis, through survival analysis and data mining

Other Title
a nationwide cohort study
Journal
European Journal of Contraception and Reproductive Health Care
Date Issued
2018-05-04
Author(s)
MING-HONG HSIEH  
DOI
10.1080/13625187.2018.1450972
URI
https://www.scopus.com/pages/publications/85047958820
https://scholars.csmu.edu.tw/handle/123456789/23665
Abstract
Objectives: Infertile patients are vulnerable to mental disorders. However, a time-dependent model predicting the onset of mental disorders specific to infertile patients is lacking. This study examined the risk factors for the development of mental disorders in infertile patients and measured the duration until the occurrence of mental disorders after a diagnosis of infertility. Methods: A total of 13,317 infertile patients in the 2002–2013 Taiwan National Health Insurance Research Database were observed. The 11 independent variables included in the hypothesised model, together with the dates of infertility and mental disorder diagnoses, were analysed using Cox proportional hazards. Data-mining methods using C5.0 and Apriori supplemented the statistical analyses. Results: The total prevalence rate of mental disorders among infertile patients in Taiwan was 12.41%, including anxiety (4.66%), depression (1.81%) and other mental disorders (5.94%). The average time interval for onset of mental illness identified using survival analysis was 1.67 years. Income, occupation, treatment method, co-morbidity, region and hospital level and ownership were significant predictors of development of mental illness (all p <.05). Conclusions: The four categories of factors associated with time-dependent onset were demographics, health, health care provider and geographical characteristics. Certain patient characteristics may predict a higher likelihood of onset of a specific mental disorder. Clinical practitioners may use the findings to identify high-risk patients and make timely health interventions.
Type
journal article
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