Abstract
BACKGROUND
Bipolar disorders (BDs) are severe mental conditions affecting between 1% and 3% of the population (Bauer & Pfennig, 2005; McGrath et al., 2023). If not promptly detected and treated, BDs tend to become debilitating conditions whose prevalence and incidence have increased over the past decades (Lai et al., 2024). BDs have been associated with significant disability, high rates of morbidity and mortality (Biazus et al., 2023; Chan et al., 2023; Carvalho et al., 2020; Vos et al., 2015), burdening health costs worldwide (Global Burden of Disease Collaborative Network, 2023) – in the UK, the annual health and social care costs of a person living with BDs are estimated to be around £14,938 (Simon et al., 2021).
Contributing factors include (i) an early age of onset, with a peak at age 19.5, and 32% of cases originating before the age of 25 (Solmi et al., 2022), (ii) a hugely delayed detection/diagnosis averaging to 10 years (Bipolar UK, 2024), and (iii) delayed interventions (Jauhar et al., 2019). Early interventions and particularly preventive approaches have the potential to tackle most of these problems and improve outcomes of BDs. However, the first rate-limiting step towards effective prevention is the ability to detect most individuals who are at-risk of developing BDs (Martini et al., 2021).
Preventive approaches for BDs follow the advancements consolidated in the field of psychosis prevention (Fusar-Poli et al., 2021). The first window of opportunity is to detect those who have subtle signs and symptoms of BD, akin to the identification of young people at clinical high-risk for psychosis (CHR-P) (for an overview of early detection strategies for CHR-P see Andreou et al., 2023). Consolidating the emerging clinical high-risk detection approaches for BDs (Cerqueira et al., 2024; Ratheesh and Bechdolf, 2024; Faedda et al., 2019) would, in turn, advance preventive (indicated prevention) strategies for these conditions.
A core advancement of knowledge in this field is represented by the introduction of detection instruments that can be used in clinical practice to identify individuals at clinical high-risk for BDs (Martini et al., 2024). However, detecting clinical high-risk states for BDs is complicated by their inherent episodic nature, long time duration before full-blown disorders, complex presentation and criteria definition (Martini et al., 2024; Salazar de Pablo et al., 2023).
To address this gap, the Semi-structured Interview for Bipolar At-Risk States (SIBARS) was developed a few years ago, building on the early clinical principles of the bipolar at-risk (BAR) (Bechdolf et al., 2010) and revised bipolar at-risk states (BARS) (Fusar-Poli et al., 2018) criteria. A preliminary version of the SIBARS has shown promising prognostic accuracy (Harrell’s C = 0.742) in a small clinical sample (Fusar-Poli et al., 2018). However, the other psychometric properties of the SIBARS are still untested.
The current study tackles these limitations to advance psychometric knowledge in this field. We administered the SIBARS to young participants recruited from the general population and assessed the core psychometric properties of this instrument in terms of dimensionality, reliability (internal and inter-rater reliability), and validity (convergent, divergent and concurrent criterion validity).
Bipolar disorders (BDs) are severe mental conditions affecting between 1% and 3% of the population (Bauer & Pfennig, 2005; McGrath et al., 2023). If not promptly detected and treated, BDs tend to become debilitating conditions whose prevalence and incidence have increased over the past decades (Lai et al., 2024). BDs have been associated with significant disability, high rates of morbidity and mortality (Biazus et al., 2023; Chan et al., 2023; Carvalho et al., 2020; Vos et al., 2015), burdening health costs worldwide (Global Burden of Disease Collaborative Network, 2023) – in the UK, the annual health and social care costs of a person living with BDs are estimated to be around £14,938 (Simon et al., 2021).
Contributing factors include (i) an early age of onset, with a peak at age 19.5, and 32% of cases originating before the age of 25 (Solmi et al., 2022), (ii) a hugely delayed detection/diagnosis averaging to 10 years (Bipolar UK, 2024), and (iii) delayed interventions (Jauhar et al., 2019). Early interventions and particularly preventive approaches have the potential to tackle most of these problems and improve outcomes of BDs. However, the first rate-limiting step towards effective prevention is the ability to detect most individuals who are at-risk of developing BDs (Martini et al., 2021).
Preventive approaches for BDs follow the advancements consolidated in the field of psychosis prevention (Fusar-Poli et al., 2021). The first window of opportunity is to detect those who have subtle signs and symptoms of BD, akin to the identification of young people at clinical high-risk for psychosis (CHR-P) (for an overview of early detection strategies for CHR-P see Andreou et al., 2023). Consolidating the emerging clinical high-risk detection approaches for BDs (Cerqueira et al., 2024; Ratheesh and Bechdolf, 2024; Faedda et al., 2019) would, in turn, advance preventive (indicated prevention) strategies for these conditions.
A core advancement of knowledge in this field is represented by the introduction of detection instruments that can be used in clinical practice to identify individuals at clinical high-risk for BDs (Martini et al., 2024). However, detecting clinical high-risk states for BDs is complicated by their inherent episodic nature, long time duration before full-blown disorders, complex presentation and criteria definition (Martini et al., 2024; Salazar de Pablo et al., 2023).
To address this gap, the Semi-structured Interview for Bipolar At-Risk States (SIBARS) was developed a few years ago, building on the early clinical principles of the bipolar at-risk (BAR) (Bechdolf et al., 2010) and revised bipolar at-risk states (BARS) (Fusar-Poli et al., 2018) criteria. A preliminary version of the SIBARS has shown promising prognostic accuracy (Harrell’s C = 0.742) in a small clinical sample (Fusar-Poli et al., 2018). However, the other psychometric properties of the SIBARS are still untested.
The current study tackles these limitations to advance psychometric knowledge in this field. We administered the SIBARS to young participants recruited from the general population and assessed the core psychometric properties of this instrument in terms of dimensionality, reliability (internal and inter-rater reliability), and validity (convergent, divergent and concurrent criterion validity).
| Original language | English |
|---|---|
| Journal | Journal of affective disorders |
| Publication status | Accepted/In press - 26 May 2025 |