Background In The Netherlands, it is unknown whether the number of youth suicide-related emergency department visits has changed over time. Also, insight is needed in the hospital costs for managing these patients, as a first step toward the economic evaluation of suicide prevention measures. Aims This study examines (a) changes in emergency department-recorded suicide attempts, suicidal ideation and non-suicidal self-injury in youth, including repeat emergency department visits; and (b) related hospital costs for these patients, from a health insurer perspective. Method In this cross-sectional study, data from various sources was combined to identify all youth aged ≤27 years visiting a Dutch inner-city emergency department between 2016 and 2023 for a suicide attempt, suicidal ideation or non-suicidal self-injury. Hospital records were reviewed manually to determine inclusion. Ambiguities were discussed within an expert panel and descriptive analyses, Poisson regression and logistic regression analyses were performed. For a subset of 30 patients, invoiced costs were determined. Results The number of suicide attempts increased by approximately 5% annually, peaking in 2022 (n = 172); there were significantly more female patients (71%), and the median age was 21 years. Cases of suicidal ideation showed a similar trend, whereas the number of recorded non-suicidal self-injuries reduced. A total of 28.5% of all patients (n = 281) had one or multiple repeat visits for the above reasons. Median suicide attempt-related costs per case were €930, range €385-€33 473. Conclusions Since 2016, an increasing number of youth visited the emergency department of a Dutch hospital after a suicide attempt, but this increase does not seem to continue after 2022. Hospital-invoiced costs differ substantially between patients.

Background: Waiting times in child and adolescent mental health services limit timely access to care for youth and are widely recognized as a complex systems problem, yet their underlying drivers remain insufficiently understood. In the Netherlands, these services face significant challenges due to increasing waiting times. Objective: To identify professionals’ perceptions of the root causes of waiting times in child and adolescent mental health services and to relate these to relevant policy developments. Method: This qualitative study used root-cause analysis based on semi-structured interviews with professionals across the system. A targeted review of national and regional policy changes was conducted to contextualize the findings. Results: We identified three main perceived causes: high demand, staff shortages, and poor throughput. High demand is perceived to be driven by factors such as a broad definition of youth care in Dutch law and increased mental health awareness among younger generations. Staff shortages are perceived to be associated with a poor sector image, a tight labor market, and insufficient training opportunities for care staff. Poor throughput is perceived to stem from inefficiencies in collaboration between care providers, the education sector, and municipalities, as well as procurement challenges. These causes were described as interrelated. Policy developments appeared to primarily target demand, with less emphasis on workforce capacity and service throughput. Conclusions: Waiting times in child and adolescent mental health services are perceived to arise from interacting causes rather than a single factor, suggesting that coordinated approaches addressing demand, workforce, and service organization are required.

Introduction There is a large and increasing shortage of nursing staff. To alleviate this problem, healthcare systems should prioritize healthcare interventions that improve nurse retention over healthcare interventions that reduce it or leave it unchanged. One way to do so is to evaluate interventions on their anticipated impact on nurse intention-to-stay, which is an important precursor of retention. An overview of available instruments to quantify nurse intention-to-stay is lacking, resulting in researchers re-inventing the wheel. This review aims to fill this gap. Methods A systematic literature search was performed in the databases Medline ALL via Ovid, Embase.com, Web of Science Core Collection, CINAHL Plus, PsycINFO, the Cochrane Central Register of Controlled Trials via Wiley, and Google Scholar (200 highest-ranked references only). The search string consisted of terms and associated synonyms for 1) nursing staff, 2) personnel intent to stay/leave, and 3) surveys. Articles were included when there was a quantitative method mentioned for measuring the intention of nurses to stay or quit nursing and/or their job/position/organization. Information was extracted on the year of publication, study design, study population, number of participants, instrument used for measuring intention-to-stay, and whether the instrument was focused on leaving the job, organization, or profession. In addition, we checked whether the instrument was used to evaluate the (expected or realized) impact of an intervention and if an association was determined between intention-to-stay (measured through the instrument) and retention. The protocol was not registered. Results 967 articles fulfilled our inclusion criteria, most of which were published in recent years. A total of 485 instruments were found. Nine regularly used instruments were identified, differing in their respective popularity over time, their size, the population for which they were developed and the strength of their link to actual retention. Notably, compared with the large body of literature on nurse intention-to-stay generally, the number of studies specifically measuring the impact of an intervention on nurse intention-to-stay is limited (n=20). Most of these intervention studies focused on changes in nurse training/mentorship or mental health support. Discussion & conclusion Many different instruments exist to measure nurses' intention-to-stay. To add to our identified instruments, a comparative study is needed to identify which instrument offers the strongest predictive value for nurse retention. The absence of studies specifically evaluating the impact of interventions on nurses' intention-to-stay creates a critical gap in understanding how health interventions influence retention. Funding Dutch Research Council, 406.XS.04.151.

Objectives: Data needed for economic evaluations in healthcare are often subject to privacy regulations and confidentiality, limiting accessibility. This poses challenges for conducting, reviewing, and validating health economic evaluations. The use of “synthetic data” may solve this problem. Methods: An economic evaluation compared “shamectomy” with “usual care” for the prevention of a fictitious disease called shame. A data set (Dorg) was created, consisting of 1000 patients in the base case. Next, synthetic data (Dsyn) were created from Dorg. Dorg and Dsyn were used, separately, to inform a model-based economic evaluation, and the similarity of the results was assessed for various scenarios: different sizes of Dorg, order of synthetization, method of synthetization, number of synthesized data sets, and missing data. Results: With standard settings, incremental cost-effectiveness ratio (ICER)-results for shamectomy were €25 848/quality-adjusted life-year in Dorg and on average €25 857 in 500 Dsyns, 95% CI (€16 776; €60 021). In the base case, 15% of the generated Dsyns resulted in an ICER leading to a positive reimbursement decision, as opposed to a negative decision when using Dorg. With smaller Dorg data sets (n = 50 and n = 500), ICER ranges increased to 95% CI (negative; €151 542) and 95% CI (negative; €669 717), respectively. Conclusions: Outcomes and conclusions of economic analyses based on synthetic data may deviate from those obtained by using the original data. For data sets < 1000 patients, which are common, deviations may be substantial and lead to suboptimal policy decisions. Based on our results, we propose a stepwise approach to using synthetic data for model-based health economic evaluations, using a large number of synthetic data sets (ie, >100) with the same size as the original data.

Objectives: Health economic (HE) models are often considered as “black boxes” because they are not publicly available and lack transparency, which prevents independent scrutiny of HE models. Additionally, validation efforts and validation status of HE models are not systematically reported. Methods to validate HE models in absence of their full underlying code are therefore urgently needed to improve health policy making. This study aimed to develop and test a generic dashboard to systematically explore the workings of HE models and validate their model parameters and outcomes. Methods: The Probabilistic Analysis Check dashBOARD (PACBOARD) was developed using insights from literature, health economists, and a data scientist. Functionalities of PACBOARD are (1) exploring and validating model parameters and outcomes using standardized validation tests and interactive plots, (2) visualizing and investigating the relationship between model parameters and outcomes using metamodeling, and (3) predicting HE outcomes using the fitted metamodel. To test PACBOARD, 2 mock HE models were developed, and errors were introduced in these models, eg, negative costs inputs, utility values exceeding 1. PACBOARD metamodeling predictions of incremental net monetary benefit were validated against the original model's outcomes. Results: PACBOARD automatically identified all errors introduced in the erroneous HE models. Metamodel predictions were accurate compared with the original model outcomes. Conclusions: PACBOARD is a unique dashboard aiming at improving the feasibility and transparency of validation efforts of HE models. PACBOARD allows users to explore the working of HE models using metamodeling based on HE models’ parameters and outcomes.