Infertility is a significant global public health issue, exerting profound personal, social, and economic consequences on individuals and families. The societal and familial pressures associated with infertility contribute substantially to psychological distress, leading to high rates of depression and anxiety among affected individuals. Studies indicate that the prevalence of depression and anxiety among infertile women reaches 44.32% and 54.24% in low- and middle-income countries, while in high-income nations, these rates drop to 28.03% and 25.05%, respectively. For infertile men, depression prevalence stands at approximately 18.30%, with Chinese studies reporting 20.8% for depression, 7.8% for anxiety, and 15.4% for both combined. Among individuals undergoing assisted reproductive technology (ART) treatments, depression and anxiety affect both male and female partners, with rates varying between 7.9%–9.4% for depression and 8.7%–13.5% for anxiety. Notably, severe depression has been linked to reduced pregnancy success rates, while anxiety in men has been associated with lower total motile sperm counts in in-vitro fertilization (IVF) procedures.
Given the widespread psychological impact of infertility, it is critical to understand the diverse characteristics and mental health needs of affected individuals. Developing effective interventions necessitates recognizing that infertility patients form a heterogeneous group, rather than treating them as a singular entity. While previous research has sought to categorize infertility patients, most studies have relied on single classification methods, which may introduce subjectivity and compromise the accuracy of findings. Employing multiple classification techniques, such as K-means clustering and Latent Class Analysis (LCA), could enhance the reliability and stability of patient profiling. While K-means clustering identifies patient clusters based on arbitrary distance measures, LCA assigns individuals to probabilistic latent classes. Despite the successful application of both methods in psychological research, no prior study has utilized them to examine depression and anxiety profiles among infertility patients. The present study aims to address this gap by employing K-means clustering to define distinct psychological profiles and verifying their stability using LCA.
Beyond classification, depression and anxiety encompass multiple symptom dimensions that are often assessed using aggregate scores. However, such approaches risk overlooking the unique significance of specific symptoms. Network analysis presents an alternative, allowing researchers to examine interactions between symptoms and identify central symptoms—those most interconnected within the network. These central symptoms may play a crucial role in the onset and persistence of psychiatric disorders, making them potential targets for more effective interventions. By leveraging network analysis, this study seeks to move beyond generalized symptom scores and pinpoint the most influential symptoms within each depression and anxiety profile.
This study, therefore, has two primary objectives: (1) to classify infertility patients into distinct depression and anxiety profiles using K-means clustering and validate these profiles through LCA, and (2) to employ network analysis to identify the central symptoms driving psychological distress within each profile. Based on existing research, the study hypothesizes that (a) infertility patients exhibit diverse depression and anxiety profiles rather than forming a homogeneous group, and (b) the most central symptoms within each profile differ, indicating varying psychological mechanisms. By elucidating these patterns, the findings aim to inform tailored mental health interventions, optimizing psychological support for individuals experiencing infertility-related distress.
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