In the UK, around 1 in 4 adults over 65 years suffers from depression. Depression case finding followed by alerting patients and their general practioners (GPs) (screening + GP) is a promising strategy to facilitate depression management, but its cost-effectiveness remains unclear.

To investigate the cost-effectiveness of screening + GP compared with standard of care (SoC) in northern England.

Conducted alongside the CASCADE study, 1020 adults aged 65+ years were recruited. Participants with baseline Geriatric Depression Scale (GDS) ≥5 were allocated to the intervention arm and those >5 to SoC. Resource use and EQ-5D-5L data were collected at baseline and 6 months. Incremental cost-effectiveness ratio was calculated. Non-parametric bootstrapping was performed to capture sampling uncertainty. The results are presented using cost-effectiveness acceptability curves. Sensitivity analyses were conducted to assess the robustness of primary findings. Subgroup analyses were undertaken to examine the cost-effectiveness among participants with more comparable baseline characteristics across treatment groups.

Screening + GP incurred £37 more costs and 0.006 fewer quality-adjusted life years than SoC; the probability of the former being cost-effective was <5% at a £30 000 cost-effectiveness threshold. Sensitivity analyses confirmed the base-case findings. Subgroup analyses indicated that screening + GP was cost-effective when patients with baseline GDS 2–7, 3–6 and 4–5, respectively, were analysed.

Screening + GP was dominated by SoC in northern England. However, subgroup analyses suggested it could be cost-effective if patients with more balanced baseline characteristics were analysed. Economic evaluations alongside randomised controlled trials are warranted to validate these findings.

Recent research highlights the dynamics of suicide risk, resulting in a shift toward real-time methodologies, such as ecological momentary assessment (EMA), to improve suicide risk identification. However, EMA’s reliance on active self-reporting introduces challenges, including participant burden and reduced response rates during crises. This study explores the potential of Screenomics—a passive digital phenotyping method that captures intensive, real-time smartphone screenshots—to detect suicide risk through text-based analysis.

Seventy-nine participants with past-month suicidal ideation or behavior completed daily EMA prompts and provided smartphone data over 28 days, resulting in approximately 7.5 million screenshots. Text from screenshots was analyzed using a validated dictionary encompassing suicide-related and general risk language.

Results indicated significant associations between passive and active suicidal ideation and suicide planning with specific language patterns. Detection of words related to suicidal thoughts and general risk-related words strongly correlated with self-reported suicide risk, with distinct between- and within-person effects highlighting the dynamic nature of suicide risk factors.

This study demonstrates the feasibility of leveraging smartphone text data for real-time suicide risk detection, offering a scalable, low-burden alternative to traditional methods. Findings suggest that dynamic, individualized monitoring via passive data collection could enhance suicide prevention efforts by enabling timely, tailored interventions. Future research should refine language models and explore diverse populations to extend the generalizability of this innovative approach.