Daniel Z. Q. Gan

Background: Mental disorders are a leading cause of disease burden in 10–24-year-olds. In Australia, fragmentation within and across health, mental health and human services sectors contributes to gaps in service provision and increased disease burden. Comprehensive methodologies are needed to identify service gaps and inefficiencies to shape policy, optimise resource allocation and improve outcomes for young people. Aims: To describe the design and methodology of a population-based data linkage study involving evaluation of health, mental health and human service use among young people in Victoria and identify subgroups experiencing unmet mental health needs. Method: The primary cohort comprises young people aged 12–25 years in the period between January 2018 and December 2025. For comparative purposes, children aged 5–11 years and adults aged 26–53 years are also included. We describe data sources, linkage methodology and planned analyses. Traditional statistical methods alongside contemporary machine learning and natural language processing techniques will be used. Results: The study will provide a comprehensive examination of the clinical characteristics of young people, their service use behaviour and typical pathways through care that cannot be gleaned from examining datasets in isolation. Ongoing consultation and engagement with young people and key stakeholders will inform interpretation, dissemination and translation of findings. Conclusions: By integrating data across multiple service sectors, this study will generate novel evidence regarding pathways through care and unmet mental health needs among Victorian young people. Findings will inform mental health service reform and support improvements in healthcare delivery, service integration and outcomes for young people.

Background:Interest in developing, evaluating and funding digital mental health (DMH) services is growing. However, integrating DMH into mainstream mental health services remains challenging, and evidence on effective integration strategies remains unclear.Objectives:This review aimed to synthesise the existing literature on global efforts to integrate DMH services into existing mental health systems and identify key facilitators and barriers to successful integration.Methods:PRISMA-ScR Checklist guided this review. A systematic search of PubMed, PsycINFO, CINAHL and Google Scholar, and hand search of the reference lists of included studies were conducted. Search terms were related to mental health, digital health, integration and frameworks. Studies providing empirical evidence on effectively integrating DMH into any mental health system and published between 2015 and 2025 were included. Data were extracted using a predefined table and synthesised through thematic analysis.Results:From 1,002 records identified, 19 studies met the inclusion criteria. Most studies were conducted in high-income countries and published between 2021 and 2025. Integration was most frequently examined in primary care (37%) and at the national or policy level (21%). Digital platforms and mobile applications were the most common DMH modalities. Key barriers and facilitators included governance and regulation, funding and reimbursement, service design and implementation, stakeholder engagement, workforce training and awareness, infrastructure and interoperability, data security and privacy, and long-term sustainability. Conclusion:Effective DMH integration requires coordinated system-level action. Clear governance, sustainable financing, workforce development, interoperable infrastructure, and robust data governance are essential to support sustainable and equitable integration.

Introduction: Mental disorders are a leading cause of disease burden in 10-24-year-olds. In Australia, fragmentation within and across sectors (health, mental health and human services) results in gaps in service provision and increases this disease burden. There is an urgent need for reform; however, we need comprehensive and robust methodologies to identify gaps and inefficiencies in service provision to shape policy, ensure efficient allocation of resources and support those most in need. Here we describe the protocol for a population-based data linkage study involving evaluation of health, mental health, and human service use among Victorian young people with the goal of identifying subgroups experiencing unmet mental health needs. Methods and analysis: Our primary cohort includes youth aged 12-25 years in the period January 2018 to November 2023. For comparative purposes, we also include data from children aged 5-11 and adults 26-53 for this timeframe. We outline data sources and linkage methodology. Traditional statistical methods coupled with contemporary machine learning and natural language processing techniques will be used to identify subgroups with unmet mental health needs.Study outcomes: Ongoing consultation and engagement with young people and key stakeholders will inform interpretation, dissemination and translation of findings. We will provide a comprehensive examination of the clinical characteristics of young people, their service use behaviours, and typical pathways through care, that cannot be gleaned from examining datasets in isolation. Findings will inform ongoing mental health service reform that will lead to improvements in healthcare delivery and outcomes for Victorian youth.

Data linkage is a powerful tool for understanding the multifaceted needs and priorities of mental health care from the perspective of users and providers. Its potential remains underutilised in Australian settings – the Productivity Commission Inquiry into Mental Health in 2020 highlighted a significant gap: routinely collected administrative data are seldom leveraged in mental health research and service evaluation. In this manuscript, we provide insights into how data linkage has been used in mental health research, the type of questions that can be addressed, the steps involved in conducting data linkage research and the benefits and limitations of the use of this methodology. We propose crucial recommendations for advancing this field including: enhancing education for stakeholders (including the public, data custodians, ethics committees and policy makers); fostering stronger collaborative relationships with individuals with lived experiences throughout the research journey; improving infrastructure and resources for data linkage activities and linking data across sectors to address complex meaningful research questions. Data linkage is not just a method but a critical strategy to transform mental health research and service evaluation, ensuring more informed, effective and holistic mental health care.

Background and Aims: Since the onset of the COVID‐19 pandemic, a significant rise in mental ill health has been observed globally in young people, particularly those in their final years of secondary school. Students' negative experiences coincide with a critical transitional period which can disrupt milestones in social and educational development. This study aimed to use innovative population‐level data to map the impact of the pandemic on students entering higher education.Methods: Pre‐pandemic (2019/2020) and pandemic (2020/2021) tertiary education application data were obtained from the Victorian Tertiary Admissions Centre. Prevalence of applications for special consideration related to mental ill health were compared between cohorts across various geographical areas and applicant demographic subgroups. Relative risk regression models were used to understand the role of different risk factors.Results: Rates of mental health‐related special consideration applications increased by 38% among all applications (pre‐pandemic: 7.8%, n = 56 916; pandemic: 10.8%, n = 58 260). Highest increases were observed among students in areas with both extended and close‐quarter lockdown experiences, and areas impacted by 2019/2020 black summer bushfires. The increases were higher among Year 12 students and students with other special consideration needs (e.g., physical condition, learning disability). Slightly higher increases were observed in areas with higher socio‐economic status, which may potentially be related to inequality in mental health service access.Conclusion: As consequences of mental health difficulties and academic disruption in youth can be long lasting, it is critical to establish a mental health support framework both in and outside of higher education to facilitate young people's recovery from the pandemic.

AimsWe critically reexplored a recent claim that smoking cessation success rates were higher among people who used e-cigarettes. In particular, we investigated the consistency of this effect among different age groups. In addition, we explored the relationships between vaping, smoking cessation and youth mental health. Methods Cross-sectional analyses of data from the 2013, 2016, and 2019 Australian National Drug Strategy Household Surveys were undertaken. A successful smoking cessation attempt was defined as having abstained from smoking for at least one month over the past year. The Kessler Psychological Distress Scale (K10) was used to measure psychological distress. Multivariable logistic regression analyses were conducted to examine interactions between age and use of e-cigarettes on smoking cessation and youth mental health, adjusting for demographic factors. Results The previous observation that a higher percentage of people quit smoking using e-cigarettes (than without e-cigarettes) was only observed in adults, but not among youth aged 14-25. Moreover, among those that quit smoking using e-cigarettes, a large majority were currently vaping. Youths had the highest rates of mental ill-health and the highest rates of ever-vaping. Discussion & Conclusions Vaping rates are rising rapidly, especially among youths. Given the physical health effects of vaping and its association with higher mental health, we propose a precautionary approach when assessing the complex relationships between vaping and smoking cessation. E-cigarettes as an aid for smoking cessation should only be used when medically prescribed, under close supervision, and only when all other safer smoking cessation aids have been exhausted.

Aims: The specific and multifaceted service needs of young people have driven the development of youth-specific integrated primary mental health care models, such as the internationally pioneering headspace services in Australia. Although these services were designed for early intervention, they often need to cater for young people with severe conditions and complex needs, creating challenges in service planning and resource allocation. There is, however, a lack of understanding and consensus on the definition of complexity in such clinical settings. Methods: This retrospective study involved analysis of headspace’s clinical minimum data set from young people accessing services in Australia between July 1, 2018, and June 30, 2019. Based on consultations with experts, complexity factors were mapped from a range of demographic information, symptom severity, diagnoses, illness stage, primary presenting issues, and service engagement patterns. Consensus clustering was used to identify complexity subgroups based on identified factors. Multinomial logistic regression was then used to evaluate whether these complexity subgroups were associated with other risk factors.Results: A total of 81,622 episodes of care from 76,021 young people across 113 services were analysed. Around 20% of young people clustered into a "high complexity" group, presenting with a variety of complexity factors, including severe disorders, a trauma history, and psychosocial impairments. Two moderate complexity groups were identified representing "distress complexity" and "psychosocial complexity" (about 20% each). Compared with the "distress complexity" group, young people in the “psychosocial complexity" group presented with a higher proportion of education, employment, and housing issues in addition to psychological distress, and had lower levels of service engagement. The distribution of complexity profiles also varied across different headspace services.Conclusions: The proposed data-driven complexity model offers valuable insights for clinical planning and resource allocation. The identified groups highlight the importance of adopting a holistic and multidisciplinary approach to address the diverse factors contributing to clinical complexity. The large number of young people presenting with moderate-to-high complexity to headspace early intervention services emphasises the need for systemic change in youth mental health care to ensure the availability of appropriate and timely support for all young people.

Background: Hospitals are insufficiently resourced to appropriately support young people who present with suicidal crises. Digital mental health innovations have the potential to provide cost-effective models of care to address this service gap and improve care experiences for young people. However, little is currently known about whether digital innovations are feasible to integrate into complex hospital settings or how they should be introduced for sustainability. Objective: This qualitative study explored the potential benefits, barriers, and collective action required for integrating digital therapeutics for the management of suicidal distress in youth into routine hospital practice. Addressing these knowledge gaps is a critical first step in designing digital innovations and implementation strategies that enable uptake and integration. Methods: We conducted a series of semistructured interviews with young people who had presented to an Australian hospital for a suicide crisis in the previous 12 months and hospital staff who interacted with these young people. Participants were recruited from the community nationally via social media advertisements on the web. Interviews were conducted individually, and participants were reimbursed for their time. Using the Normalization Process Theory framework, we developed an interview guide to clarify the processes and conditions that influence whether and how an innovation becomes part of routine practice in complex health systems. Results: Analysis of 29 interviews (n=17, 59% young people and n=12, 41% hospital staff) yielded 4 themes that were mapped onto 3 Normalization Process Theory constructs related to coherence building, cognitive participation, and collective action. Overall, digital innovations were seen as a beneficial complement to but not a substitute for in-person clinical services. The timing of delivery was important, with the agreement that digital therapeutics could be provided to patients while they were waiting to be assessed or shortly before discharge. Staff training to increase digital literacy was considered key to implementation, but there were mixed views on the level of staff assistance needed to support young people in engaging with digital innovations. Improving access to technological devices and internet connectivity, increasing staff motivation to facilitate the use of the digital therapeutic, and allowing patients autonomy over the use of the digital therapeutic were identified as other factors critical to integration. Conclusions: Integrating digital innovations into current models of patient care for young people presenting to hospital in acute suicide crises is challenging because of several existing resource, logistical, and technical barriers. Scoping the appropriateness of new innovations with relevant key stakeholders as early as possible in the development process should be prioritized as the best opportunity to preemptively identify and address barriers to implementation.

Aims: Since the onset of the COVID-19 pandemic, a significant rise in mental ill-health has been observed globally in young people, particularly among those in their final years of secondary school. Students’ negative experiences coincide with a critical transitional period which can disrupt milestones in social and educational development. This study aimed to use innovative population-level data to map the impact of the pandemic on students entering higher education. Methods: Pre-pandemic (2019/2020) and pandemic (2020/2021) tertiary education application data were obtained from the Victorian Tertiary Admissions Centre. Prevalence of applications for special consideration related to mental ill-health were compared between cohorts across various geographical areas and applicant demographic subgroups. Relative risk regression models were used to understand the role of different risk factors. Results: The rate of mental health-related special consideration applications increased by 38% among all applications (pre-pandemic: 7.8%, n=56916; pandemic: 10.8%, n=58260). The highest increases were observed among students in areas with both extended and close-quarter lockdown experiences, and areas impacted by 2019/2020 black summer bushfires. The increases were higher among year 12 students and students with other special consideration needs (e.g., physical condition, learning disability). Slightly higher increases were observed in areas with higher socioeconomic status, which may potentially be related to inequality in mental health service access. Conclusion: As the consequences of mental health difficulties and academic disruption in youth can be long-lasting, it is critical to establish a mental health support framework both in and outside of higher education to facilitate young people’s recovery from the pandemic.