Introduction This Hungarian cross-sectional study examined patterns and differences in suicide risk factors across various suicidality groups, including individuals with single or multiple suicide attempts, as well as gender-specific variations. Additionally, it explored these risk factors within a biopsychosocial framework to offer a comprehensive understanding of their interconnected effects. Materials and methods A total of 300 psychiatric inpatients were recruited from Péterfy Sandor Hospital in Budapest, Hungary, including 146 individuals (48.67%) with a history of suicide attempts and 154 (51.33%) without such a history. Participants ranged in age from 18 to 85 years, with a mean age of 37.98 years (SD = 12.80 for suicide attempters, 13.72 for non-attempters). The overall sample comprised 83 males (27.7%) and 217 females (72.3%). Logistic regression analysis was conducted to assess the influence of demographic characteristics, life history variables, and psychiatric diagnoses on suicide risk, aiming to identify significant predictors of suicide attempts within a biopsychosocial framework. Results Depression was the most prevalent psychiatric diagnosis in the sample. Significant predictors of suicide attempts included family history of suicide (OR = 2.283, p = 0.015), prescription drug misuse (OR = 1.900, p = 0.047), and nicotine dependence (OR = 1.869, p = 0.035). In repeated suicide attempts, bipolar disorder (OR = 5.761, p = 0.006), borderline personality disorder (OR = 5.132, p = 0.003), depression (OR = 4.064, p = 0.004), and job loss (OR = 4.348, p = 0.031) emerged as the strongest predictors. Among men, job loss (OR = 4.074, p = 0.014) was a prominent risk factor, while among women, having two or more children (OR = 2.740, p = 0.036) and a family history of suicide (OR = 2.459, p = 0.028) significantly increased suicide risk. Relationship conflict was also associated with higher risk in women (OR = 0.382, p = 0.035). Conclusions Our research supports the notion that suicide risk factors interact with one another, and in certain cases, their effects may be synergistic—mutually reinforcing—rather than antagonistic. Similarly, protective factors also appear to amplify each other’s impact, suggesting a cumulative and interactive model of both risk and resilience.
Szeifert et al. (Wed,) studied this question.