Systems modelling and simulation to guide targeted investments to reduce youth suicide and mental health problems in a low-middle-income country

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This study aimed to develop a dynamic simulation model that could be used to examine the potential effectiveness of alternative interventions for reducing youth mental health problems and suicidal behavior in Bogotá, Colombia.​ Methods A system dynamics model was designed using a participatory approach involving three workshops conducted in 2021 and 2022. These workshops engaged 78 stakeholders from various health and social sectors to map key mental health outcomes and influential factors affecting them. A model was subsequently developed, tested, and presented to the participants for interactive feedback, guided by a moderator. Simulation analyses were conducted to compare projected mental health outcomes for a range of intervention scenarios with projections for a reference scenario corresponding to business-as-usual. Results A total of 6,670 suicide attempts and 347 suicides are projected among 7 − 17-year-olds from January 1, 2023, to early 2031 under the business-as-usual scenario. Mental health issues among 12-17-year-olds are projected to increase from 18·9% (2023) to 27·8% (2031), and substance use issues from 2·29% to 2·49% over the same period. School-based suicide prevention and gatekeeper training are the most effective strategies, reducing total numbers of suicide attempts and suicides by more than 20% (i.e., compared to business-as-usual). However, discontinuous funding significantly hinders these effective suicide prevention efforts. Conclusion Systems modeling is an important tool for understanding where best strategic financial and political investments lie for improving youth mental health in resource constrained settings. Figures Figure 1 Figure 2 Figure 3 Figure 4 Background There is a wide recognition of the global burden and disability related to common mental health and substance misuse problems (1) . Reducing suicidality in young people [YP] is a significant public health concern worldwide. (1) This issue is particularly alarming in low and middle-income countries [LMICs], where 77% of all suicides occur (1) . Mental health (MH) problems and suicide are considered multifactorial issues influenced by a complex network of interacting individual, political, social, cultural, economic, and environmental factors (2) .The complex nature of these interactions means that effective interventions and policies have to consider the social determinants of health, as well as contextual and governmental factors such as the accessibility, affordability and quality of care, mental health literacy and stigma (3) . Despite the importance of suicide as a public health concern and the increased awareness on intervening effectively to reduce the burden of MH conditions, emerging evidence suggests that current interventions are not yielding substantial impacts (4, 5) , as trends are continuously on the rise (6–8) . These problems represent a significant challenge for LMICs, which are in a constant struggle to increase their mental capital through rapid implementation of interventions that are effective in reducing MH problems at a population level while also making the best use of limited resources (9) . To guide policy development, international agencies and academic institutions have issued a strong recommendation for governments to adopt systematic evidence-informed decision-making (EIDM) strategies (10, 11) . These approaches consider critical factors, including social determinants, resource availability, political commitment, and the regional social context, as essential components for achieving successful outcomes. Furthermore, EIDM places significant emphasis on the necessity of equipping stakeholders with comprehensive evidence, while also considering public opinion, effectiveness, sustainability, affordability, and acceptability as pivotal factors when crafting policies. Given the complexities inherent in reconciling conflicting evidence related to various interventions and their applicability in diverse populations (10, 12) , EIDM serves as a reliable framework to provide guidance and assurance in the decision-making process. Effective decision-making in public policy necessitates transparent, reliable governance that can adeptly respond to changing circumstances while ensuring equity across the population. (13) In the context of Low- and Middle-Income Countries (LMICs) like Colombia, substantial documented deficiencies in decision-making processes have led to the characterization of those processes as arbitrary, occasionally contradictory, and often unrealistic. Decision-making in these instances is often influenced by short-term needs or anecdotal evidence and frequently aligns with personal interests, ultimately undermining the overall quality and impact of implemented programs and policies (12, 13) (14) . This subjective decision-making approach not only carries the potential for negative impacts on population health outcomes, resulting in unintended harms, but also leads to the unnecessary waste of resources. Addressing these issues is vital for enhancing the effectiveness and sustainability of public policies in Colombia and other LMICs. (1, 10, 12, 15, 16) As effective decision making becomes a more challenging task that requires consideration of multiple interacting determinants in a changing world, systems modelling, and simulation emerges as a valuable tool in tackling these decision-making challenges. It provides a means of capturing the dynamics of a set of interconnected variables over time, as well as forecasting the effects of different interventions directly affecting one or more of those variables (4) .This approach provides the opportunity to thoroughly map and quantify the complex causal mechanisms that drive mental health and suicide outcomes (4) . As a quantitative method within the realm of complex systems science, it can capture population and demographic changes, fluctuations in economic and social drivers, workforce dynamics, and, most notably, the potentially non-additive effects resulting from combinations of interventions (4) . This tool allows informed decisions about the most effective allocation of limited resources (4) . Used widely in other fields, including infectious disease epidemiology, its application in global mental health could prove especially useful in LMICs such as Colombia, countering the short-term trial and error decisions usually adopted in these countries whilst also addressing the need for local and contextual interventions leading to effective change. Bogotá, the capital city of Colombia, is experiencing significant growth in its population. With an expanding urban demographic, the city faces heightened risks of mental disorders, exacerbated by increased contextual complexities. These complexities encompass social, family, educational, and vocational aspects, creating a growing burden on mental health particularly affecting young people (17) (18) (For more information about Colombia see Text Box 1). The aim of this project is to use a participatory approach to develop a system dynamics model capturing this complexity to be used as a decision-support tool for strategic planning and investments in youth mental health and suicide prevention. This is an international collaborative endeavour between CSART (Computer Simulation and Advanced Research Technologies, international), The University of Sydney (Australia), Swiss Tropical and Public Health Institute (Switzerland) and Pontificia Universidad Javeriana (Colombia), in partnership with a broad range of in-country stakeholders. Text Box 1: Colombian Context Population Colombia has 48 million inhabitants, with approximately 46% of its population (around 22 million people) under the age of 25 (as reported by the National Administrative Department of Statistics, or DANE, in 2018 (17) and 2023 (19) ) Colombia’s capital city, has around 8M inhabitants with almost a 1:1 female to male ratio (20) , almost a third (8) of Bogota’s population is under 25 years of age and most of the population does not identify with any ethnic group (17) . Bogota reported the highest numbers of Venezuelan migrants in the country (21) and faces significant levels of unemployment, homelessness, violence, drug trafficking, criminality, homicide, and internal displacement related to Colombia’s armed conflict (7, 22–24) . Gross Domestic Product According to the World Bank, Colombia is classified as an upper middle-income country, with a Gross Domestic Product of 314·46 billion USD in 2021. Despite its economic standing, Colombia ranks as the second most economically unequal country in the region. Corruption According to the Corruption Perception Index from 2021, the country scored 39 points over 100 (where 0 is corruption perception and 100 no corruption perception among citizens). Indicating a high perception which has not changed since 2012 (25) . Mental health issues One of Colombia's most pressing challenges is the exposure of its citizens to violence, a consequence of one of the world's longest-lasting internal conflicts. The latest National Mental Health Survey (NMHS) from the Ministry of Health and Social Protection (MSPS) in 2015 revealed that 12·2% of adolescents and 9·6% of adults grapple with mental health problems. Lifetime prevalence of mental health disorders stands at 7·2% in the adolescent group and 9·1% in adults. In other countries, lifetime prevalence of any psychiatric disorder range from 12·0 to 47·4% in adults (26) . In relation to substance use, the substance uses national study done in 2019 showed that in the 12 to 17 age group, the last-month prevalence of binge drinking was 12·1%, and out of these, 16·4% had some harmful or risky alcohol use. Additionally, 2·5% of all adolescents (12–17 years old) who were surveyed reported using illegal substances in the previous year (27) In the 18–24 age group, 8·90% are at risk of or already misusing alcohol, and 7·47% have used illegal drugs in the last year. Last-year prevalence of drug use range from 2·6% in low LMICs to 9·3% in high income countries (28) . For alcohol abuse, last-year prevalence range from 1·2% in low LMICs to 1·4% in both upper-middle- and high-income countries (28) . In terms of suicide, there has been an alarming upward trend over the past decade. In 2021, the reported suicide rate was 5·7 per 100,000, with rates of 6·5 per 100,000 for young people aged 15–17 and 9·2 per 100,000 for those aged 18–19, with 72·3% of cases occurring in urban areas. Recent WHO (29) estimates show that suicide incidence ranges from 2·0 (Jordan) to 87·5 (Lesotho). General System of Social Security in Health (SGSSS) established by Law 100 of 1993 The principal goal of the SGSSS is to transition from isolated interventions to providing comprehensive care and prevention programs (30) . The country has a strong mental health legal framework which is designed to ensure the right to mental health is fully exercised, and advocates for the improvement of well-being, with an emphasis on prevention, promotion, treatment, and rehabilitation across various settings, from the community level to specialized and complex care (31–33) . Public Policy In 2016, mental health became a significant focus of public policy with the issuance of document 3863 by the National Economic and Social Policy Council (34) , as well as through the Ten-year Public Health Plan (2022–2031), which prioritizes mental health and coexistence (34) Methods Model development A system dynamics model was developed through a participatory approach with three workshops which were conducted in 2021 and early 2022. A total of 78 stakeholders participated in the workshops, 57 in the first workshop, 42 in the second workshop, and 54 in the last workshop. Participants represented the education sector, health department, insurers, child protection, non-governmental organizations, primary care, health providers, policy makers, special interest groups, academics, and individuals with lived experience (youth) and/or their caregivers. The aim of these workshops was to map the key pathways and the principal social and economic factors contributing to mental health problems (such as employment, education, family violence, etc.) and routes through the MH care system. This involved identifying system barriers, incentives, disincentives, bottlenecks, and hidden pathways to create a valid model of the mental health system for young individuals in Bogotá. During the first workshop, discussions related to key MH outcomes, and influential factors impacting these outcomes were mapped, including aspects such as care pathways, healthcare systems, and support services. Additionally, interventions related to mental health and suicide prevention were discussed for potential inclusion in the model. The objective of the second workshop was to present a preliminary version of the model and gather feedback and recommendations on its various components and key model assumptions. Overall, recommendations were provided for the model, along with specific critiques regarding individual components. The directionality of causal relationships between certain model components received significant attention and the identification of missing or alternative data that could improve the model. Afterwards, the interventions were discussed to identify each intervention's specific components, their direct effects within the model, and any unintended consequences. The third workshop presented a draft of the model including how stakeholder input was incorporated and how data and evidence were selected to inform model development. A brief demonstration of the model and its key preliminary insights took place, after which participants could interact with it under the guidance of a moderator. Participants provided feedback and recommendations on the overall user interface, data sources informing the model, and specific suggestions regarding components. Model structure, calibration, and outputs The system dynamics model used for the simulation analyses presented here consists of a set of interconnected sub-models, or sectors, that includes: 1) a population sector, capturing changes in population size and structure resulting from births, migration, aging, and mortality; 2) a sector modelling changes in the prevalence of mental health problems; 3) a developmental vulnerability sector, capturing exposure to adverse experiences in childhood that increase the risk of mental health problems in adolescence and adulthood (parental mental health and substance use problems, poverty, family violence, internal displacement); 4) an education sector, modelling secondary and post-secondary education and vocational training enrolment and completion rates; 5) an unemployment and poverty sector, capturing economic participation, unemployment, and informal employment and poverty; 6) a substance use sector, capturing changes in the prevalence of substance use problems; 7) a sector modelling exposure to family violence and violent crime; 8) an internal displacement sector, capturing the socioeconomic and mental health effects of forced displacement resulting from conflict-related violence; 9) a health services sector, modelling access to mental health services (community-based and hospital) and treatment-dependent recovery; and 10) a suicidal behavior sector, capturing initial and repeat suicide attempts and suicide deaths. Detailed descriptions of all model sectors are provided in the Supplementary Material. Parameter estimates and other numerical inputs were derived from published research or publicly available data (where this was possible) or were estimated via constrained optimization (see below; Table S2). Model construction and analysis were performed with Stella Architect version 2.1.5 (isee systems, Lebanon, NH, USA; see www.iseesystems ). Parameter values that could not be derived directly from available data or published research were estimated via constrained optimization, implemented in Stella Architect version 2.1.5, using historical time series data for a wide range of sociodemographic and health-related outcomes (see Supplementary Material; Fig. 1 ). Powell’s method was used to obtain the set of (optimal) parameter values minimizing the mean of the absolute differences between the observed time series values and the corresponding model outputs, where each difference was expressed as a percentage of the observed value (i.e., the mean absolute percent error was used as the objective function for the optimization analysis; (35) . Primary model outputs include total (cumulative) numbers of suicide attempts and suicide deaths for children and adolescents aged 7 − 17 years, numbers of child and adolescent suicide attempts and suicides per year, and the proportions of children and adolescents aged 12 − 17 years with mental health problems and substance use problems. All outputs are calculated every 0.0625 years (about 3 and a half weeks) over a period of 21 years, starting from 1 January 2010, so that the impacts of health services and community interventions were modelled from the time of implementation (1 January 2023) to the start of 2031 (we ran simulations from the start of 2010 to permit comparisons of model outputs with past system behaviour; see Supplementary Material). Policy testing and sensitivity analyses We modelled the effects on child and adolescent mental health and suicidal behaviour of six health services and community interventions prioritised by stakeholders in the participatory modelling workshops: school-based suicide prevention programs, anti-bullying programs, gatekeeper suicide prevention training, increased access to suicide helplines, general practitioner mental health training, and community connectedness programs. Details of all interventions are provided in the Supplementary Material (see Table S1 ). A total of 10 alternative intervention scenarios were compared with a baseline (business as usual) scenario in which existing policies and programs remain in place and current growth in community-based mental health services capacity is maintained until the end of the simulation (see Fig. 2 ). Sensitivity analyses were performed to assess the impact of uncertainty in estimates of the direct intervention effects on the simulation results. Latin hypercube sampling was employed to generate 200 sets of values for all model parameters determining the direct effects of the interventions on mental health, suicidal behaviour, substance use, and engagement with mental health services from a relatively broad (joint) distribution of values (see Table 1 ). Differences in the projected total number of suicide attempts, suicide mortality, and the prevalence of mental health and substance use problems between the baseline and intervention scenarios were calculated for each set of parameter vales and summarized using simple descriptive statistics (means, medians, and 95% intervals). Table 1 Parameters included in the sensitivity analyses. Definitions and data sources for all parameters are provided in the Supplementary Material (see Table S1 ). m and s are the location and log-scale parameters, respectively, for parameters specified using a lognormal distribution; a and b are the ‘prior sample sizes’ for parameters specified using a Beta distribution (see, e.g., Appendix A of Gelman et al., 2014 (36) ). Parameter Distribution m s a b Mean SD Anti-bullying programs effect on disorder onset Lognormal -0·3731 0·05 - - 0·6895 0·0345 Community connectedness programs effect on disorder onset Lognormal -0·2403 0·05 - - 0·7874 0·0394 Community connectedness programs effect on substance abuse Lognormal -0·3933 0·05 - - 0·6757 0·0338 Gatekeeper training programs effect on suicidal behaviour Lognormal -0·2169 0·05 - - 0·8060 0·0403 Maximum proportion of General Practitioners completing training Beta - - 499·8 1999·2 0·2000 0·0100 Maximum suicide helpline caller increase Beta - - 499·8 1999·2 0·2000 0·0100 MH services provision rate ratio primary care Lognormal -0·6116 0·05 - - 0·5431 0·0272 Per service recovery rate ratio primary care Lognormal -0·7720 0·05 - - 0·4627 0·0231 School-based MH programs effect on services engagement Lognormal 0·5921 0.05 - - 1·8100 0·0905 School-based MH programs effect on substance abuse Lognormal -0·2370 0·05 - - 0·7900 0·0395 School-based MH programs effect on suicidal behaviour Lognormal -0·4712 0·05 - - 0·6250 0·0313 Suicide helplines effect on suicidal behaviour Lognormal -0·1972 0·05 - - 0·8221 0·0411 Results 1. Suicide attempts and suicide mortality Modelled baseline numbers of suicide attempts and suicides per year for 7 − 17-year-olds are presented in Fig. 1 . A total of 6670 suicide attempts and 347 suicides are projected from 1 January 2023 (the default starting date for all interventions) to the beginning of 2031 (i.e., assuming a status quo), with the suicide attempt rate increasing from 772 to 898 suicide attempts per year (67·6 to 84·0 suicide attempts per 10 5 population per year) and suicide mortality increasing from 40 to 47 suicides per year (3·51 to 4·36 suicides per 10 5 population per year) over this period (see Fig. 1 ). School-based suicide prevention programs and gatekeeper suicide prevention training are substantially more effective than the remaining interventions in preventing suicidal behaviour (where each intervention is implemented alone; Fig. 2 ), reducing the total number of suicide attempts by 28·0% (95% interval, 23·8 − 31·9%) and 22·6% (95% interval, 13·6 − 30·7%), respectively, and total suicide mortality by 27·7% (95% interval, 23·5 − 31·5%) and 22·5% (95% interval, 13·4 − 30·5%), respectively. Note that all intervals reported in this paper are derived from the distributions of model outputs calculated in the sensitivity analyses; they provide a measure of the impact of uncertainty in the intervention effect estimates but should not be interpreted as confidence intervals. Anti-bullying programs and increased access to suicide helpline services reduce total numbers of suicide attempts and suicides by 3·8 and 3·5% respectively, while the effectiveness of community connectedness programs and general practitioner mental health training on suicidal behaviour is more limited (see Fig. 2 ). Nearly half of all suicide attempts and suicides projected under the baseline scenario are prevented when school-based suicide prevention programs are combined with gatekeeper suicide prevention training, anti-bullying programs, and increased access to suicide helplines (Fig. 2 ). 2. Mental health and substance use problems Under the baseline scenario, the prevalence of mental health problems among 12 − 17-year-olds is projected to increase from 18·9% at the beginning of 2023 to 27·8% at the start of 2031, while the proportion of 12 − 17-year-olds with a substance use problem is projected to increase from 2·29% to 2·49% (i.e., over the same period; see Fig. 3 ). Programs that act to directly increase community connectedness reduce the projected prevalence of mental health problems and substance use problems in 2031 by 2·12 percentage points (95% interval, 1·38 − 2·75 percentage points) and 0·78 percentage points (95% interval, 0·62 − 0·92 percentage points), respectively. More substantial reductions are achieved when community connectedness programs are combined with school-based suicide prevention programs (which are assumed to cover both substance use prevention and suicide prevention) and anti-bullying programs; under this scenario, the prevalence of mental health problems increases to 21·6% in 2031 (a 6·2 percentage point reduction relative to the baseline projection), while the prevalence of substance use problems declines to 1·39% (a 1·1 percentage point reduction; see Fig. 3 ). The most effective combination of four interventions for preventing suicidal behaviour (school-based suicide prevention programs + gatekeeper suicide prevention training + increased access to suicide helplines + anti-bullying programs; scenario g in Fig. 2 ) also reduces the prevalence of mental health problems and substance use problems in 2031 by 4·55 percentage points (95% interval, 3·55 − 5·53 percentage points) and 0·50 percentage points (95% interval, 0·35 − 0·64 percentage points), respectively (see Fig. 3 ). 3. Discontinuous program funding Discontinuous (or cyclical) funding restricts the projected impacts of effective suicide prevention interventions considerably (see Fig. 4 ). Percentage reductions in total numbers of suicide attempts and suicides for 7 − 17-year-olds achieved with a combination school-based suicide prevention programs, gatekeeper suicide prevention training, increased access to suicide helplines, and anti-bullying programs fall from 46·7% (95% interval, 40·2 − 52·3%) and 46·2% (95% interval, 39·8 − 51·8%), respectively, to 34·2% ( 37 ) and 34·0% ( 37 ), respectively, when interrupted funding results in all four interventions ending and recommencing midway through 2026 and again in mid-2030 (Fig. 2 ). Discussion System dynamics involves integrating all available evidence, including administrative data, previous research, expert opinions, and lived experiences, into a logically consistent, interactive 'what-if' tool. This tool serves to enhance the understanding of potential impacts associated with proposed interventions before adoption, and it is particularly relevant when resources are limited. (38) . This process involves interdisciplinary and collaborative work with stakeholders to understand mechanisms, proposed interventions and possible outcomes within complex health systems (39) (40) . In Colombia, a nation with a history marked by persistent challenges, there are opportunities for improvement in areas such as civic participation, transparency, effective policy implementation, and robust monitoring mechanisms. Addressing issues related to resource allocation and enhancing governance practices will contribute to fostering positive change, thereby employing system dynamics becomes indispensable for navigating and addressing these intricate issues (41) . The Colombian governments (Presidential, mayorship and regional government) are elected for a four-year term with no option for re-election.This essentially means that all public and political plans should ideally be designed, performed, and measured in a four-year timeframe and be sufficiently transparent so that the next government can seamlessly continue their execution (42) . However, it has been documented that with the change of governments the continuity of implementation is often jeopardized by political will, public opinion, and availability of resources, which become significant drivers in the new decision-making process (43) posing a risk to achieving significant public health outcomes. As such the incorporation of system dynamics offers a structured approach to assess and predict the potential impacts of policy changes, facilitating a more informed and sustainable decision-making process in Colombia's dynamic political landscape. The objective of this project was to create a decision support tool based on national and regional data and best research evidence to examine the potential effectiveness of alternative interventions for reducing youth mental health problems and suicidal behavior. Out of the intervention scenarios analyzed, school-based suicide prevention programs and gatekeeper suicide prevention training were the most effective intervention in reducing suicide attempts long term. The findings of our study are consistent with international literature. A recent meta-analysis analyzed the effect of suicide prevention programs for school-aged youth demonstrating small effects on suicidal behavior (g = 0·17, 95% CI [0·07 − 0·26], p < ·01) and psychological distress (g = 0.16, 95% CI [0.10–0.23], p < .01), but larger effects on suicide awareness and helping seeking (44) . In the same line, European anti-bullying programs were reported to be moderately cost effective when implemented for 6 years or longer, with an estimated reduction in bullying of 20% and an associated increase in quality adjusted life years (QALYS) (45) . Furthermore, anti-bullying strategies have been shown to impact significantly on mental wellness (46) . The locally customized decision support tool provided through this action research has not only contributed to making a stronger case for investment in school-based suicide prevention programs and anti-bullying campaigns, but it has also provided this case in the context of its estimated impact over the next decade in a LMIC context compared to a suite of other commonly advocated strategies such as mental health training for primary care providers, such as general practitioners (GPs). At a community level, our findings are consistent with evidence suggesting that gatekeeper suicide prevention programs improve knowledge of and attitudes toward suicide (47–49) , and can reduce suicide deaths and non-lethal suicide attempts (50, 51) . Culturally tailored gatekeeper training is also effective for specific groups; for example, research conducted with Indigenous communities in North America and other studies in military personnel have demonstrated positive results (52, 53) . Gatekeeping strategies can help to address mental health care gaps in more disadvantaged countries. For instance, hairdressers in Togo, Africa were trained in MH counselling, due to the steep cost of therapy, and so far, this initiative has been well-received by the community (54) . Colombia has witnessed similar informal initiatives, including the “Puente de la Variante surveillance team” in Ibague city, which was established as a volunteer community vigilance group in response to a high number of suicides involving a local bridge. Mr. Victor Guerrero, a restaurant owner located close to the bridge, has prevented more than 300 deaths. Due to his efforts the initiative received private and public support in 2022 (55) . The impact of gatekeeper suicide prevention programs extends beyond individual awareness, shaping community perspectives on suicide; as such, community advocacy is needed to extend the delivery of this type of training to community settings where young people interact, as well as in suicide hotspots. While previous evidence from our research group showed that improving social connectedness is one of the most successful interventions (4) , in this study, its impact was limited for suicide prevention but significant for reducing mental health and substance use problems (16) . Colombian intrinsic factors like forced displacement, trauma (including transgenerational trauma), unemployment, migration challenges, domestic violence, homelessness, financial insecurity, and lack of green and blue spaces can further alter the social fabric (3) . As such, culturally and contextually appropriate social connectedness programs are needed to support positive environments and coexistence. Considering the wide diversity of contexts in the country, these strategies might need appropriate evidence to support effective implementation and replication among populations with similar characteristics, instead of scaling-up one size fits all programs. One of the most important findings in our study was the insight related to combining interventions which can have a greater than additive (synergistic) impact in our social context. Systems modelling offers a virtual space to examine the best mix, focus, timing, extent, frequency, and intensity of investments before they are implemented in the real world, saving time, resources, and potentially the lives of young people. Simulation, particularly in resource-constrained scenarios, has proved feasible and advantageous for evidence-based decision-making in LMIC contexts (38) . Limitations While developing our model, a substantial challenge surfaced in the lack of unification among national information databases. Identifying 180 files (e.g., worksheets, dashboards, reports) from disconnected databases presented a significant hurdle, requiring a meticulous review and integration of these disparate databases into a cohesive framework. Lack of consensus among information-generating entities is apparent, as for instance, commonly used age breakdowns present limitations for studying the 10–24 age group in many official sources (56) . Regarding data in Colombia, the existence of multiple information sources, duplicities, and lack of interoperability have been highlighted previously (56) . However, despite this fragmentation and the difficulty of identifying and collating available data, the quantity and quality of data available was sufficient for model development. For some indicators which might not have been publicly available, data donations from participants served to fill gaps, showing the complementary nature and value of participatory processes for systems modelling (57) . While better data is the cornerstone of decision-making and public policy (58) , it is important to note that limited data availability, use of estimates, and other data deficiencies do not necessarily constitute a barrier to undertaking systems modelling projects in LMICs. Previous findings showcase that, in spite of uncertain baseline projections and even dramatically different projected impacts on outcomes of interest, best strategies remain highly consistent across alternative baseline scenarios (38) . For LMICs, there is hence a possibility of simultaneously developing improved decision-support tools and improving information systems, rather than doing so sequentially. In Colombia, comprehending the impact of implementation, especially of public initiatives; establishing an accurate feedback mechanism capable of monitoring the quality of the registered information; and researching the associations between mental health problems and determinants such as poverty and migration remain important shortfalls to be addressed. In addition, the collaborative nature of our approach enhances stakeholder familiarity with the software, ensuring user-friendliness and facilitating real-world impact exploration through feedback (57) . Nevertheless, the methodology's complexity extends development timelines, and training can pose challenges, given its applicability across diverse knowledge fields (57) . Moreover, the broader acceptability of these emerging decision support tools (beyond the stakeholders involved in model development) in the socio-economic context of a LMIC community remains uncertain. Conclusion Suicidality emerges as a pressing global health concern, significantly impacting societies and leaving enduring imprints on families and communities worldwide. The evidence underscores the imperative of prioritizing the well-being of our youth, especially those from diverse ethnic backgrounds. Amidst diverse and challenging circumstances, advanced decision support tools offer indispensable insights, informing decision-making and shaping evidence-based programs with the potential for a lasting positive impact on individuals' lives. This relevance extends notably to addressing challenges encountered in LMICs, where factors such as budgetary constraints, limited human capital, intersectoral disconnection, and insufficient monitoring and evaluation processes contribute to the fragmentation and overall suboptimal quality and effectiveness of public health policies (57) . In the specific context of Colombia, where decision-making processes face challenges and concerns related to continuity, sustainability, and transparency, the routine application of participatory system dynamics modeling emerges as a strategic solution. This significance is particularly pronounced in a socio-political landscape marked by efforts to ensure project continuity across successive administrations (57) . Modeling stands out as a valuable tool, providing transparency and facilitating informed decision-making to guide future policy endeavors. Underscoring the value for effective decision-making in resource-constrained settings, this research not only emphasizes the relevance of systems modeling but also demonstrates its feasibility in developing sophisticated decision support tools, even in contexts where a lack of sufficient data is often assumed. Abbreviations LMIC Low – Middle income countries YP Young people MH Mental Health EIDM evidence-informed decision-making CSART Computer Simulation and Advanced Research Technologies NMHS National Mental Health Survey MSPS Ministry of Health and Social Protection QALYS quality adjusted life years GPs general practitioners Declarations Ethics approval and consent to participate The study was developed following the international ethical consideration, all the information was recollected after participants signed an informed consent and all measures were taken to ensure confidentiality and security of participants' data. The project was approved by the Human Research Ethics Committee of the Pontificia Universidad Javeriana (Bogotá, Colombia) and the Hospital Universitario San Ignacio, protocol number FM-CIE-0103-21. Consent for publication Not applicable Availability of data and materials The authors had unrestricted access to all study data and bear responsibility for upholding data integrity and ensuring the accuracy of data analysis. You can find supplementary results on the national census data that is available on the DANE website. As for the data related to suicides and mental health care, they are publicly available if they are requested from the Colombian Ministry of Health under confidentiality agreements. To obtain the consolidated data within the model, it is possible to do so through a direct request to the main author of this model, who will provide a platform usage guide. Competing interests LOP, AS, MSN, DS, ANM, SC, AMH, JMUR, CGR declare no competing interests. IBH is the Co-Director, Health and Policy at the Brain and Mind Centre (BMC) University of Sydney.The BMC operates an early-intervention youth services at Camperdown under contract to headspace. He is the Chief Scientific Advisor to, and a 3.2% equity shareholder in, InnoWell Pty Ltd which aims to transform mental health services through the use of innovative technologies. JO is both Head of Systems Modelling, Simulation & Data Science, and Co-Director of the Mental Wealth Initiative at the University of Sydney's Brain and Mind Centre. She is also Managing Director of Computer Simulation & Advanced Research Technologies (CSART) and acts as Advisor to the OECD Neuroscience-inspired Policy Initiative and the Brain Capital Alliance. Funding The study's sponsors did not participate in the study's design, data collection, data analysis, data interpretation, or report writing. The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This research was carried in its totality thanks to a grant from Foundation Botnar (REG-19-026). Authors Contributions Manuscript concept and drafting: L.O.P, A.S, M.S.N, D.S. and J.O.; Model development: A.S.; Data analysis: A.S Critical revision of manuscript for important intellectual content: S.C, A.M.M, A.N.M J.M.U.R, C.G.R, and I.B.H. All authors have read and approved the final version of the manuscript. Acknowledgments The authors would like to thank all young people, supportive others, health professionals and stakeholders who participated in this study. In addition, the authors wish to thank Naifer Alexandra Morales, as research assistant of this study. To our medical research assistant Laura C. Gallego-Sanchez for her contributions drafting this manuscript. The authors would also like to thank the people with lived experiences for their participation in the knowledge translation team. References World Health Organization. Suicide 2023. [Online] Available from: https://www.who.int/news-room/fact-sheets/detail/suicide . Accessed: November 2023. Kendler KS. 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Un paso adelante y medio atrás: el cambio del Estado en Colombia desde la perspectiva de la administración pública (1957–2017). Bogotá: Departamento nacional administrativo de Función Pública; 2018. p. 1–93. Brann KL, Baker D, Smith-Millman MK, Watt SJ, DiOrio C. A meta-analysis of suicide prevention programs for school-aged youth. Children and Youth Services Review. 2021;121:105826. Persson M, Wennberg L, Beckman L, Salmivalli C, Svensson M. The Cost-Effectiveness of the Kiva Antibullying Program: Results from a Decision-Analytic Model. Prev Sci. 2018;19(6):728–37. Fraguas D, Díaz-Caneja CM, Ayora M, Durán-Cutilla M, Abregú-Crespo R, Ezquiaga-Bravo I, et al. Assessment of School Anti-Bullying Interventions: A Meta-analysis of Randomized Clinical Trials. JAMA Pediatr. 2021;175(1):44–55. Reis C, Cornell D. An evaluation of suicide gatekeeper training for school counselors and teachers. Professional School Counseling. 2008;11(6):386–94. Tompkins TL, Witt J, Abraibesh N. Does a gatekeeper suicide prevention program work in a school setting? Evaluating training outcome and moderators of effectiveness. Suicide Life Threat Behav. 2010;40(5):506–15. Wyman PA, Brown CH, Inman J, Cross W, Schmeelk-Cone K, Guo J, et al. Randomized trial of a gatekeeper program for suicide prevention: 1-year impact on secondary school staff. J Consult Clin Psychol. 2008;76(1):104–15. Breet E, Matooane M, Tomlinson M, Bantjes J. Systematic review and narrative synthesis of suicide prevention in high-schools and universities: a research agenda for evidence-based practice. BMC Public Health. 2021;21(1). Althaus D, Hegerl U, Niklewski G, Schmidtke A. The alliance against depression: 2-year evaluation of a community-based intervention to reduce suicidality. Psychological Medicine. 2006;36(9):1225–33. Nasir BF, Hides L, Kisely S, Ranmuthugal G, Nicholson GC, Black E, et al. The need for a culturally-tailored gatekeeper training intervention program in preventing suicide among Indigenous peoples: a systematic review. BMC Psychiatry. 2016;16(1). Burnette C, Ramchand R, Ayer L. Gatekeeper Training for Suicide Prevention: A Theoretical Model and Review of the Empirical Literature. Rand Health Q. 2015;5(1):16. Peltier E. Need Therapy? In West Africa, Hairdressers Can Help: New York Times. 2023. Available from: https://www.nytimes.com/2023/11/26/world/africa/hair-salon-mental-health-services.html Accessed: December 2023. Gonzales, F. Quieren desalojar al "Ángel del puente de la variante" en Ibagué. Blu Radio; 2014. Available from: https://www.bluradio.com/nacion/quieren-desalojar-al-angel-del-puente-de-la-variante-en-ibague-rg10 Accessed: November 2023. Bernal-Acevedo O, Forero-Camacho JC. Sistemas de información en el sector salud en Colombia. Revista Gerencia y Políticas de Salud. 2011;10(21):85–100. Occhipinti J-A, Rose D, Skinner A, Rock D, Song YJC, Prodan A, et al. Sound Decision Making in Uncertain Times: Can Systems Modelling Be Useful for Informing Policy and Planning for Suicide Prevention? International Journal of Environmental Research and Public Health [Internet]. 2022; 19(3). United Nations System Chief Board For Coordination. Data and statistics. 2018. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterialglobalhealth.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 19 Jun, 2024 Reviews received at journal 13 Jun, 2024 Reviews received at journal 09 Jun, 2024 Reviewers agreed at journal 03 Jun, 2024 Reviewers agreed at journal 02 Jun, 2024 Reviewers invited by journal 02 Jun, 2024 Editor invited by journal 16 May, 2024 Editor assigned by journal 13 May, 2024 Submission checks completed at journal 13 May, 2024 First submitted to journal 10 May, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4402240","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":304123333,"identity":"279a085e-d69f-41b8-b84f-9c241b346d7d","order_by":0,"name":"Laura Ospina-Pinillos","email":"data:image/png;base64,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","orcid":"","institution":"Pontificia Universidad Javeriana, Hospital Universitario San Ignacio","correspondingAuthor":true,"prefix":"","firstName":"Laura","middleName":"","lastName":"Ospina-Pinillos","suffix":""},{"id":304123334,"identity":"52a0d849-e8a1-48fe-b680-7807dc78f97c","order_by":1,"name":"Adam Skinner","email":"","orcid":"","institution":"The University of Sydney","correspondingAuthor":false,"prefix":"","firstName":"Adam","middleName":"","lastName":"Skinner","suffix":""},{"id":304123335,"identity":"cb815b23-c429-41c8-a133-5d7a18ba8ca3","order_by":2,"name":"Mónica Natalí Sánchez-Nítola","email":"","orcid":"","institution":"Pontificia Universidad Javeriana, Hospital Universitario San Ignacio","correspondingAuthor":false,"prefix":"","firstName":"Mónica","middleName":"Natalí","lastName":"Sánchez-Nítola","suffix":""},{"id":304123336,"identity":"546d5f9e-6123-4e04-96de-8c03055d4f01","order_by":3,"name":"Débora L. Shambo-Rodríguez","email":"","orcid":"","institution":"Pontificia Universidad Javeriana, Hospital Universitario San Ignacio","correspondingAuthor":false,"prefix":"","firstName":"Débora","middleName":"L.","lastName":"Shambo-Rodríguez","suffix":""},{"id":304123337,"identity":"3cf80abf-c606-4265-bd30-73a948ea40c0","order_by":4,"name":"Alvaro Andrés Navarro-Mancilla","email":"","orcid":"","institution":"Pontificia Universidad Javeriana, Hospital Universitario San Ignacio","correspondingAuthor":false,"prefix":"","firstName":"Alvaro","middleName":"Andrés","lastName":"Navarro-Mancilla","suffix":""},{"id":304123338,"identity":"c155d22f-7d80-475c-8f1e-99d9d322e8ee","order_by":5,"name":"Salvador Camacho","email":"","orcid":"","institution":"Swiss Tropical and Public Health Institute","correspondingAuthor":false,"prefix":"","firstName":"Salvador","middleName":"","lastName":"Camacho","suffix":""},{"id":304123339,"identity":"5cfab6e8-7c72-4076-8a06-5e46b865f727","order_by":6,"name":"Adriane Martin","email":"","orcid":"","institution":"Swiss Tropical and Public Health Institute","correspondingAuthor":false,"prefix":"","firstName":"Adriane","middleName":"","lastName":"Martin","suffix":""},{"id":304123340,"identity":"70d69c86-554d-4ca4-b127-4c937dd6efd1","order_by":7,"name":"Jose Miguel Uribe Restrepo","email":"","orcid":"","institution":"Pontificia Universidad Javeriana, Hospital Universitario San Ignacio","correspondingAuthor":false,"prefix":"","firstName":"Jose","middleName":"Miguel Uribe","lastName":"Restrepo","suffix":""},{"id":304123341,"identity":"fca01a52-094c-4e0b-a742-a41fed0cd4cf","order_by":8,"name":"Carlos Gomez-Restrepo","email":"","orcid":"","institution":"Pontificia Universidad Javeriana, Hospital Universitario San Ignacio","correspondingAuthor":false,"prefix":"","firstName":"Carlos","middleName":"","lastName":"Gomez-Restrepo","suffix":""},{"id":304123342,"identity":"30c4ae8c-e813-49e2-a0c5-e05fb75fa5fb","order_by":9,"name":"Ian B Hickie","email":"","orcid":"","institution":"The University of Sydney","correspondingAuthor":false,"prefix":"","firstName":"Ian","middleName":"B","lastName":"Hickie","suffix":""},{"id":304123344,"identity":"6ae8f8ab-ae89-428b-a5a8-4d722bb6042a","order_by":10,"name":"Jo-an Occhipinti","email":"","orcid":"","institution":"The University of Sydney","correspondingAuthor":false,"prefix":"","firstName":"Jo-an","middleName":"","lastName":"Occhipinti","suffix":""}],"badges":[],"createdAt":"2024-05-10 18:28:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4402240/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4402240/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57298873,"identity":"e119b603-2b13-44ff-90a8-62933e367eed","added_by":"auto","created_at":"2024-05-28 20:45:42","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":282820,"visible":true,"origin":"","legend":"\u003cp\u003eProjected numbers of suicide attempts and suicides per year for children and adolescents (7−17 years) in Bogotá, Colombia under the baseline scenario. Data for suicide attempts and suicides are from the Suicidal Behaviour Epidemiological Surveillance Subsystem (SISVECOS) and the National Institute of Legal Medicine and Forensic Sciences (INMLCF), respectively.\u003c/p\u003e","description":"","filename":"floatimage1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4402240/v1/79e564ad3c87d409cb99c5f9.jpg"},{"id":57298872,"identity":"30870d03-c2ac-4fdf-bd8d-881e04debd9b","added_by":"auto","created_at":"2024-05-28 20:45:42","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1002950,"visible":true,"origin":"","legend":"\u003cp\u003eDifferences in total (cumulative) numbers of suicide attempts and suicide mortality between the baseline and intervention scenarios for children and adolescents (7−17 years) in Bogotá, Colombia over the period 2023−2031. Mean numbers of prevented suicide attempts and suicides (reported in the second column from the left) and mean percentage reductions and 95% intervals (in the rightmost column) were derived from the distributions of projected outcomes calculated in the sensitivity analyses. Note that the 95% intervals provide a measure of the impact of uncertainty in the assumed intervention effects but should not be interpreted as confidence intervals. The plot on the right shows the mean percentage reductions (closed circles) and 95% and 50% intervals (light and heavy lines, respectively).\u003c/p\u003e","description":"","filename":"floatimage2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4402240/v1/63f1da539e90893505bcea81.jpg"},{"id":57298877,"identity":"94c4e32f-6600-4081-9fec-87b71d33546f","added_by":"auto","created_at":"2024-05-28 20:45:42","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":353580,"visible":true,"origin":"","legend":"\u003cp\u003ePrevalence of child and adolescent mental health problems and substance use problems (12−17 years) in Bogotá, Colombia under the baseline scenario and for selected intervention scenarios: f (red), community connectedness programs; a + b + f (blue), school-based suicide prevention programs + anti-bullying programs + community connectedness programs; a + b + c + d (yellow), school-based suicide prevention programs + anti-bullying programs + gatekeeper suicide prevention training + suicide helplines. Solid lines correspond to median values derived from the sensitivity analyses. Pointwise 95% and 50% intervals are indicated with light and dark shading, respectively.\u003c/p\u003e","description":"","filename":"floatimage3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4402240/v1/ded54f98cdc31f2675aafac9.jpg"},{"id":57298876,"identity":"b6871609-50d5-450e-a94d-532d87d47f76","added_by":"auto","created_at":"2024-05-28 20:45:42","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":374595,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of cyclical program funding on reductions in suicidal behaviour among children and adolescents (7−17 years) in Bogotá, Colombia. Continuous funding (blue): school-based suicide prevention programs + anti-bullying programs + gatekeeper suicide prevention training + suicide helplines (intervention scenario g in Fig. 2), continuously funded from 1 January 2023 until the end of the simulation. Cyclical funding (red): school-based suicide prevention programs + anti-bullying programs + gatekeeper suicide prevention training + suicide helplines, commencing in 2023 (as in the continuous funding simulation), but with discontinuous funding, so that all four interventions end and restart midway through 2026 and 2030 (we assumed a four-year funding cycle aligned with, although not necessarily attributable to, presidential elections). Median values derived from the sensitivity analyses are plotted as solid lines. Pointwise 95% and 50% intervals are indicated with light and dark shading, respectively.\u003c/p\u003e","description":"","filename":"floatimage4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4402240/v1/26400e3bea7832767b14624e.jpg"},{"id":57299101,"identity":"4f6edc42-c229-48ac-957c-8efa14e6e916","added_by":"auto","created_at":"2024-05-28 20:53:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2640468,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4402240/v1/45cd3834-be53-4b38-88dd-a3038efd8b97.pdf"},{"id":57298874,"identity":"7b23aadf-c4d7-424e-a60a-235ad8acf695","added_by":"auto","created_at":"2024-05-28 20:45:42","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":4889262,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterialglobalhealth.docx","url":"https://assets-eu.researchsquare.com/files/rs-4402240/v1/81d22132546892df3a570cff.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Systems modelling and simulation to guide targeted investments to reduce youth suicide and mental health problems in a low-middle-income country","fulltext":[{"header":"Background","content":"\u003cp\u003eThere is a wide recognition of the global burden and disability related to common mental health and substance misuse problems \u003csup\u003e(1)\u003c/sup\u003e. Reducing suicidality in young people [YP] is a significant public health concern worldwide.\u003csup\u003e(1)\u003c/sup\u003e This issue is particularly alarming in low and middle-income countries [LMICs], where 77% of all suicides occur\u003csup\u003e(1)\u003c/sup\u003e. Mental health (MH) problems and suicide are considered multifactorial issues influenced by a complex network of interacting individual, political, social, cultural, economic, and environmental factors \u003csup\u003e(2)\u003c/sup\u003e .The complex nature of these interactions means that effective interventions and policies have to consider the social determinants of health, as well as contextual and governmental factors such as the accessibility, affordability and quality of care, mental health literacy and stigma \u003csup\u003e(3)\u003c/sup\u003e. Despite the importance of suicide as a public health concern and the increased awareness on intervening effectively to reduce the burden of MH conditions, emerging evidence suggests that current interventions are not yielding substantial impacts\u003csup\u003e(4, 5)\u003c/sup\u003e, as trends are continuously on the rise \u003csup\u003e(6\u0026ndash;8)\u003c/sup\u003e. These problems represent a significant challenge for LMICs, which are in a constant struggle to increase their mental capital through rapid implementation of interventions that are effective in reducing MH problems at a population level while also making the best use of limited resources\u003csup\u003e(9)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo guide policy development, international agencies and academic institutions have issued a strong recommendation for governments to adopt systematic evidence-informed decision-making (EIDM) strategies \u003csup\u003e(10, 11)\u003c/sup\u003e. These approaches consider critical factors, including social determinants, resource availability, political commitment, and the regional social context, as essential components for achieving successful outcomes. Furthermore, EIDM places significant emphasis on the necessity of equipping stakeholders with comprehensive evidence, while also considering public opinion, effectiveness, sustainability, affordability, and acceptability as pivotal factors when crafting policies. Given the complexities inherent in reconciling conflicting evidence related to various interventions and their applicability in diverse populations \u003csup\u003e(10, 12)\u003c/sup\u003e, EIDM serves as a reliable framework to provide guidance and assurance in the decision-making process.\u003c/p\u003e \u003cp\u003eEffective decision-making in public policy necessitates transparent, reliable governance that can adeptly respond to changing circumstances while ensuring equity across the population. \u003csup\u003e(13)\u003c/sup\u003e In the context of Low- and Middle-Income Countries (LMICs) like Colombia, substantial documented deficiencies in decision-making processes have led to the characterization of those processes as arbitrary, occasionally contradictory, and often unrealistic. Decision-making in these instances is often influenced by short-term needs or anecdotal evidence and frequently aligns with personal interests, ultimately undermining the overall quality and impact of implemented programs and policies \u003csup\u003e(12, 13) (14)\u003c/sup\u003e. This subjective decision-making approach not only carries the potential for negative impacts on population health outcomes, resulting in unintended harms, but also leads to the unnecessary waste of resources. Addressing these issues is vital for enhancing the effectiveness and sustainability of public policies in Colombia and other LMICs. \u003csup\u003e(1, 10, 12, 15, 16)\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eAs effective decision making becomes a more challenging task that requires consideration of multiple interacting determinants in a changing world, systems modelling, and simulation emerges as a valuable tool in tackling these decision-making challenges. It provides a means of capturing the dynamics of a set of interconnected variables over time, as well as forecasting the effects of different interventions directly affecting one or more of those variables \u003csup\u003e(4)\u003c/sup\u003e.This approach provides the opportunity to thoroughly map and quantify the complex causal mechanisms that drive mental health and suicide outcomes \u003csup\u003e(4)\u003c/sup\u003e. As a quantitative method within the realm of complex systems science, it can capture population and demographic changes, fluctuations in economic and social drivers, workforce dynamics, and, most notably, the potentially non-additive effects resulting from combinations of interventions \u003csup\u003e(4)\u003c/sup\u003e. This tool allows informed decisions about the most effective allocation of limited resources \u003csup\u003e(4)\u003c/sup\u003e. Used widely in other fields, including infectious disease epidemiology, its application in global mental health could prove especially useful in LMICs such as Colombia, countering the short-term trial and error decisions usually adopted in these countries whilst also addressing the need for local and contextual interventions leading to effective change.\u003c/p\u003e \u003cp\u003eBogot\u0026aacute;, the capital city of Colombia, is experiencing significant growth in its population. With an expanding urban demographic, the city faces heightened risks of mental disorders, exacerbated by increased contextual complexities. These complexities encompass social, family, educational, and vocational aspects, creating a growing burden on mental health particularly affecting young people \u003csup\u003e(17) (18)\u003c/sup\u003e (For more information about Colombia see Text Box 1). The aim of this project is to use a participatory approach to develop a system dynamics model capturing this complexity to be used as a decision-support tool for strategic planning and investments in youth mental health and suicide prevention. This is an international collaborative endeavour between CSART (Computer Simulation and Advanced Research Technologies, international), The University of Sydney (Australia), Swiss Tropical and Public Health Institute (Switzerland) and Pontificia Universidad Javeriana (Colombia), in partnership with a broad range of in-country stakeholders.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eText Box 1: Colombian Context\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePopulation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eColombia has 48\u0026nbsp;million inhabitants, with approximately 46% of its population (around 22\u0026nbsp;million people) under the age of 25 (as reported by the National Administrative Department of Statistics, or DANE, in 2018 \u003csup\u003e(17)\u003c/sup\u003e and 2023\u003csup\u003e(19)\u003c/sup\u003e) Colombia\u0026rsquo;s capital city, has around 8M inhabitants with almost a 1:1 female to male ratio \u003csup\u003e(20)\u003c/sup\u003e, almost a third \u003csup\u003e(8)\u003c/sup\u003e of Bogota\u0026rsquo;s population is under 25 years of age and most of the population does not identify with any ethnic group \u003csup\u003e(17)\u003c/sup\u003e. Bogota reported the highest numbers of Venezuelan migrants in the country\u003csup\u003e(21)\u003c/sup\u003eand faces significant levels of unemployment, homelessness, violence, drug trafficking, criminality, homicide, and internal displacement related to Colombia\u0026rsquo;s armed conflict \u003csup\u003e(7, 22\u0026ndash;24)\u003c/sup\u003e.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGross Domestic Product\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAccording to the World Bank, Colombia is classified as an upper middle-income country, with a Gross Domestic Product of 314\u0026middot;46\u0026nbsp;billion USD in 2021. Despite its economic standing, Colombia ranks as the second most economically unequal country in the region.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCorruption\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAccording to the Corruption Perception Index from 2021, the country scored 39 points over 100 (where 0 is corruption perception and 100 no corruption perception among citizens). Indicating a high perception which has not changed since 2012 \u003csup\u003e(25)\u003c/sup\u003e.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMental health issues\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOne of Colombia's most pressing challenges is the exposure of its citizens to violence, a consequence of one of the world's longest-lasting internal conflicts. The latest National Mental Health Survey (NMHS) from the Ministry of Health and Social Protection (MSPS) in 2015 revealed that 12\u0026middot;2% of adolescents and 9\u0026middot;6% of adults grapple with mental health problems. Lifetime prevalence of mental health disorders stands at 7\u0026middot;2% in the adolescent group and 9\u0026middot;1% in adults. In other countries, lifetime prevalence of any psychiatric disorder range from 12\u0026middot;0 to 47\u0026middot;4% in adults \u003csup\u003e(26)\u003c/sup\u003e. In relation to substance use, the substance uses national study done in 2019 showed that in the 12 to 17 age group, the last-month prevalence of binge drinking was 12\u0026middot;1%, and out of these, 16\u0026middot;4% had some harmful or risky alcohol use. Additionally, 2\u0026middot;5% of all adolescents (12\u0026ndash;17 years old) who were surveyed reported using illegal substances in the previous year \u003csup\u003e(27)\u003c/sup\u003eIn the 18\u0026ndash;24 age group, 8\u0026middot;90% are at risk of or already misusing alcohol, and 7\u0026middot;47% have used illegal drugs in the last year. Last-year prevalence of drug use range from 2\u0026middot;6% in low LMICs to 9\u0026middot;3% in high income countries \u003csup\u003e(28)\u003c/sup\u003e. For alcohol abuse, last-year prevalence range from 1\u0026middot;2% in low LMICs to 1\u0026middot;4% in both upper-middle- and high-income countries \u003csup\u003e(28)\u003c/sup\u003e. In terms of suicide, there has been an alarming upward trend over the past decade. In 2021, the reported suicide rate was 5\u0026middot;7 per 100,000, with rates of 6\u0026middot;5 per 100,000 for young people aged 15\u0026ndash;17 and 9\u0026middot;2 per 100,000 for those aged 18\u0026ndash;19, with 72\u0026middot;3% of cases occurring in urban areas. Recent WHO \u003csup\u003e(29)\u003c/sup\u003e estimates show that suicide incidence ranges from 2\u0026middot;0 (Jordan) to 87\u0026middot;5 (Lesotho).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGeneral System of Social Security in Health (SGSSS) established by Law 100 of 1993\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe principal goal of the SGSSS is to transition from isolated interventions to providing comprehensive care and prevention programs \u003csup\u003e(30)\u003c/sup\u003e. The country has a strong mental health legal framework which is designed to ensure the right to mental health is fully exercised, and advocates for the improvement of well-being, with an emphasis on prevention, promotion, treatment, and rehabilitation across various settings, from the community level to specialized and complex care \u003csup\u003e(31\u0026ndash;33)\u003c/sup\u003e.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePublic Policy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIn 2016, mental health became a significant focus of public policy with the issuance of document 3863 by the National Economic and Social Policy Council \u003csup\u003e(34)\u003c/sup\u003e, as well as through the Ten-year Public Health Plan (2022\u0026ndash;2031), which prioritizes mental health and coexistence \u003csup\u003e(34)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e "},{"header":"Methods","content":"\u003ch2\u003eModel development\u003c/h2\u003e\u003cp\u003e A system dynamics model was developed through a participatory approach with three workshops which were conducted in 2021 and early 2022. A total of 78 stakeholders participated in the workshops, 57 in the first workshop, 42 in the second workshop, and 54 in the last workshop. Participants represented the education sector, health department, insurers, child protection, non-governmental organizations, primary care, health providers, policy makers, special interest groups, academics, and individuals with lived experience (youth) and/or their caregivers.\u003c/p\u003e \u003cp\u003eThe aim of these workshops was to map the key pathways and the principal social and economic factors contributing to mental health problems (such as employment, education, family violence, etc.) and routes through the MH care system. This involved identifying system barriers, incentives, disincentives, bottlenecks, and hidden pathways to create a valid model of the mental health system for young individuals in Bogot\u0026aacute;. During the first workshop, discussions related to key MH outcomes, and influential factors impacting these outcomes were mapped, including aspects such as care pathways, healthcare systems, and support services. Additionally, interventions related to mental health and suicide prevention were discussed for potential inclusion in the model.\u003c/p\u003e \u003cp\u003eThe objective of the second workshop was to present a preliminary version of the model and gather feedback and recommendations on its various components and key model assumptions. Overall, recommendations were provided for the model, along with specific critiques regarding individual components. The directionality of causal relationships between certain model components received significant attention and the identification of missing or alternative data that could improve the model. Afterwards, the interventions were discussed to identify each intervention's specific components, their direct effects within the model, and any unintended consequences. The third workshop presented a draft of the model including how stakeholder input was incorporated and how data and evidence were selected to inform model development. A brief demonstration of the model and its key preliminary insights took place, after which participants could interact with it under the guidance of a moderator. Participants provided feedback and recommendations on the overall user interface, data sources informing the model, and specific suggestions regarding components.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eModel structure, calibration, and outputs\u003c/h2\u003e \u003cp\u003eThe system dynamics model used for the simulation analyses presented here consists of a set of interconnected sub-models, or sectors, that includes: 1) a population sector, capturing changes in population size and structure resulting from births, migration, aging, and mortality; 2) a sector modelling changes in the prevalence of mental health problems; 3) a developmental vulnerability sector, capturing exposure to adverse experiences in childhood that increase the risk of mental health problems in adolescence and adulthood (parental mental health and substance use problems, poverty, family violence, internal displacement); 4) an education sector, modelling secondary and post-secondary education and vocational training enrolment and completion rates; 5) an unemployment and poverty sector, capturing economic participation, unemployment, and informal employment and poverty; 6) a substance use sector, capturing changes in the prevalence of substance use problems; 7) a sector modelling exposure to family violence and violent crime; 8) an internal displacement sector, capturing the socioeconomic and mental health effects of forced displacement resulting from conflict-related violence; 9) a health services sector, modelling access to mental health services (community-based and hospital) and treatment-dependent recovery; and 10) a suicidal behavior sector, capturing initial and repeat suicide attempts and suicide deaths. Detailed descriptions of all model sectors are provided in the Supplementary Material. Parameter estimates and other numerical inputs were derived from published research or publicly available data (where this was possible) or were estimated via constrained optimization (see below; Table S2). Model construction and analysis were performed with Stella Architect version 2.1.5 (isee systems, Lebanon, NH, USA; see \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.iseesystems\" target=\"_blank\"\u003ewww.iseesystems\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.iseesystems\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eParameter values that could not be derived directly from available data or published research were estimated via constrained optimization, implemented in Stella Architect version 2.1.5, using historical time series data for a wide range of sociodemographic and health-related outcomes (see Supplementary Material; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Powell\u0026rsquo;s method was used to obtain the set of (optimal) parameter values minimizing the mean of the absolute differences between the observed time series values and the corresponding model outputs, where each difference was expressed as a percentage of the observed value (i.e., the mean absolute percent error was used as the objective function for the optimization analysis; \u003csup\u003e(35)\u003c/sup\u003e. Primary model outputs include total (cumulative) numbers of suicide attempts and suicide deaths for children and adolescents aged 7\u0026thinsp;\u0026minus;\u0026thinsp;17 years, numbers of child and adolescent suicide attempts and suicides per year, and the proportions of children and adolescents aged 12\u0026thinsp;\u0026minus;\u0026thinsp;17 years with mental health problems and substance use problems. All outputs are calculated every 0.0625 years (about 3 and a half weeks) over a period of 21 years, starting from 1 January 2010, so that the impacts of health services and community interventions were modelled from the time of implementation (1 January 2023) to the start of 2031 (we ran simulations from the start of 2010 to permit comparisons of model outputs with past system behaviour; see Supplementary Material).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003ePolicy testing and sensitivity analyses\u003c/h2\u003e \u003cp\u003eWe modelled the effects on child and adolescent mental health and suicidal behaviour of six health services and community interventions prioritised by stakeholders in the participatory modelling workshops: school-based suicide prevention programs, anti-bullying programs, gatekeeper suicide prevention training, increased access to suicide helplines, general practitioner mental health training, and community connectedness programs. Details of all interventions are provided in the Supplementary Material (see Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). A total of 10 alternative intervention scenarios were compared with a baseline (business as usual) scenario in which existing policies and programs remain in place and current growth in community-based mental health services capacity is maintained until the end of the simulation (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSensitivity analyses were performed to assess the impact of uncertainty in estimates of the direct intervention effects on the simulation results. Latin hypercube sampling was employed to generate 200 sets of values for all model parameters determining the direct effects of the interventions on mental health, suicidal behaviour, substance use, and engagement with mental health services from a relatively broad (joint) distribution of values (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Differences in the projected total number of suicide attempts, suicide mortality, and the prevalence of mental health and substance use problems between the baseline and intervention scenarios were calculated for each set of parameter vales and summarized using simple descriptive statistics (means, medians, and 95% intervals).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParameters included in the sensitivity analyses. Definitions and data sources for all parameters are provided in the Supplementary Material (see Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). \u003cem\u003em\u003c/em\u003e and \u003cem\u003es\u003c/em\u003e are the location and log-scale parameters, respectively, for parameters specified using a lognormal distribution; \u003cem\u003ea\u003c/em\u003e and \u003cem\u003eb\u003c/em\u003e are the \u0026lsquo;prior sample sizes\u0026rsquo; for parameters specified using a Beta distribution (see, e.g., Appendix A of Gelman et al., 2014\u003csup\u003e(36)\u003c/sup\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDistribution\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003em\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003es\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ea\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eb\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-bullying programs effect on disorder onset\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLognormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0\u0026middot;3731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026middot;05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u0026middot;6895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u0026middot;0345\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommunity connectedness programs effect on disorder onset\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLognormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0\u0026middot;2403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026middot;05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u0026middot;7874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u0026middot;0394\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommunity connectedness programs effect on substance abuse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLognormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0\u0026middot;3933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026middot;05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u0026middot;6757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u0026middot;0338\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGatekeeper training programs effect on suicidal behaviour\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLognormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0\u0026middot;2169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026middot;05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u0026middot;8060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u0026middot;0403\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum proportion of General Practitioners completing training\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e499\u0026middot;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1999\u0026middot;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u0026middot;2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u0026middot;0100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum suicide helpline caller increase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e499\u0026middot;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1999\u0026middot;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u0026middot;2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u0026middot;0100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMH services provision rate ratio primary care\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLognormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0\u0026middot;6116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026middot;05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u0026middot;5431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u0026middot;0272\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePer service recovery rate ratio primary care\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLognormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0\u0026middot;7720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026middot;05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u0026middot;4627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u0026middot;0231\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSchool-based MH programs effect on services engagement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLognormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026middot;5921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u0026middot;8100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u0026middot;0905\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSchool-based MH programs effect on substance abuse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLognormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0\u0026middot;2370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026middot;05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u0026middot;7900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u0026middot;0395\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSchool-based MH programs effect on suicidal behaviour\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLognormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0\u0026middot;4712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026middot;05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u0026middot;6250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u0026middot;0313\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSuicide helplines effect on suicidal behaviour\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLognormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0\u0026middot;1972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026middot;05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u0026middot;8221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u0026middot;0411\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e1. Suicide attempts and suicide mortality\u003c/h2\u003e \u003cp\u003eModelled baseline numbers of suicide attempts and suicides per year for 7\u0026thinsp;\u0026minus;\u0026thinsp;17-year-olds are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. A total of 6670 suicide attempts and 347 suicides are projected from 1 January 2023 (the default starting date for all interventions) to the beginning of 2031 (i.e., assuming a status quo), with the suicide attempt rate increasing from 772 to 898 suicide attempts per year (67\u0026middot;6 to 84\u0026middot;0 suicide attempts per 10\u003csup\u003e5\u003c/sup\u003e population per year) and suicide mortality increasing from 40 to 47 suicides per year (3\u0026middot;51 to 4\u0026middot;36 suicides per 10\u003csup\u003e5\u003c/sup\u003e population per year) over this period (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). School-based suicide prevention programs and gatekeeper suicide prevention training are substantially more effective than the remaining interventions in preventing suicidal behaviour (where each intervention is implemented alone; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), reducing the total number of suicide attempts by 28\u0026middot;0% (95% interval, 23\u0026middot;8\u0026thinsp;\u0026minus;\u0026thinsp;31\u0026middot;9%) and 22\u0026middot;6% (95% interval, 13\u0026middot;6\u0026thinsp;\u0026minus;\u0026thinsp;30\u0026middot;7%), respectively, and total suicide mortality by 27\u0026middot;7% (95% interval, 23\u0026middot;5\u0026thinsp;\u0026minus;\u0026thinsp;31\u0026middot;5%) and 22\u0026middot;5% (95% interval, 13\u0026middot;4\u0026thinsp;\u0026minus;\u0026thinsp;30\u0026middot;5%), respectively. Note that all intervals reported in this paper are derived from the distributions of model outputs calculated in the sensitivity analyses; they provide a measure of the impact of uncertainty in the intervention effect estimates but should not be interpreted as confidence intervals. Anti-bullying programs and increased access to suicide helpline services reduce total numbers of suicide attempts and suicides by 3\u0026middot;8 and 3\u0026middot;5% respectively, while the effectiveness of community connectedness programs and general practitioner mental health training on suicidal behaviour is more limited (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Nearly half of all suicide attempts and suicides projected under the baseline scenario are prevented when school-based suicide prevention programs are combined with gatekeeper suicide prevention training, anti-bullying programs, and increased access to suicide helplines (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e2. Mental health and substance use problems\u003c/h3\u003e\n\u003cp\u003eUnder the baseline scenario, the prevalence of mental health problems among 12\u0026thinsp;\u0026minus;\u0026thinsp;17-year-olds is projected to increase from 18\u0026middot;9% at the beginning of 2023 to 27\u0026middot;8% at the start of 2031, while the proportion of 12\u0026thinsp;\u0026minus;\u0026thinsp;17-year-olds with a substance use problem is projected to increase from 2\u0026middot;29% to 2\u0026middot;49% (i.e., over the same period; see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Programs that act to directly increase community connectedness reduce the projected prevalence of mental health problems and substance use problems in 2031 by 2\u0026middot;12 percentage points (95% interval, 1\u0026middot;38\u0026thinsp;\u0026minus;\u0026thinsp;2\u0026middot;75 percentage points) and 0\u0026middot;78 percentage points (95% interval, 0\u0026middot;62\u0026thinsp;\u0026minus;\u0026thinsp;0\u0026middot;92 percentage points), respectively. More substantial reductions are achieved when community connectedness programs are combined with school-based suicide prevention programs (which are assumed to cover both substance use prevention and suicide prevention) and anti-bullying programs; under this scenario, the prevalence of mental health problems increases to 21\u0026middot;6% in 2031 (a 6\u0026middot;2 percentage point reduction relative to the baseline projection), while the prevalence of substance use problems declines to 1\u0026middot;39% (a 1\u0026middot;1 percentage point reduction; see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The most effective combination of four interventions for preventing suicidal behaviour (school-based suicide prevention programs\u0026thinsp;+\u0026thinsp;gatekeeper suicide prevention training\u0026thinsp;+\u0026thinsp;increased access to suicide helplines\u0026thinsp;+\u0026thinsp;anti-bullying programs; scenario g in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) also reduces the prevalence of mental health problems and substance use problems in 2031 by 4\u0026middot;55 percentage points (95% interval, 3\u0026middot;55\u0026thinsp;\u0026minus;\u0026thinsp;5\u0026middot;53 percentage points) and 0\u0026middot;50 percentage points (95% interval, 0\u0026middot;35\u0026thinsp;\u0026minus;\u0026thinsp;0\u0026middot;64 percentage points), respectively (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3. Discontinuous program funding\u003c/h2\u003e \u003cp\u003eDiscontinuous (or cyclical) funding restricts the projected impacts of effective suicide prevention interventions considerably (see Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Percentage reductions in total numbers of suicide attempts and suicides for 7\u0026thinsp;\u0026minus;\u0026thinsp;17-year-olds achieved with a combination school-based suicide prevention programs, gatekeeper suicide prevention training, increased access to suicide helplines, and anti-bullying programs fall from 46\u0026middot;7% (95% interval, 40\u0026middot;2\u0026thinsp;\u0026minus;\u0026thinsp;52\u0026middot;3%) and 46\u0026middot;2% (95% interval, 39\u0026middot;8\u0026thinsp;\u0026minus;\u0026thinsp;51\u0026middot;8%), respectively, to 34\u0026middot;2% (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e) and 34\u0026middot;0% (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e), respectively, when interrupted funding results in all four interventions ending and recommencing midway through 2026 and again in mid-2030 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eSystem dynamics involves integrating all available evidence, including administrative data, previous research, expert opinions, and lived experiences, into a logically consistent, interactive 'what-if' tool. This tool serves to enhance the understanding of potential impacts associated with proposed interventions before adoption, and it is particularly relevant when resources are limited. \u003csup\u003e(38)\u003c/sup\u003e. This process involves interdisciplinary and collaborative work with stakeholders to understand mechanisms, proposed interventions and possible outcomes within complex health systems \u003csup\u003e(39) (40)\u003c/sup\u003e. In Colombia, a nation with a history marked by persistent challenges, there are opportunities for improvement in areas such as civic participation, transparency, effective policy implementation, and robust monitoring mechanisms. Addressing issues related to resource allocation and enhancing governance practices will contribute to fostering positive change, thereby employing system dynamics becomes indispensable for navigating and addressing these intricate issues \u003csup\u003e(41)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe Colombian governments (Presidential, mayorship and regional government) are elected for a four-year term with no option for re-election.This essentially means that all public and political plans should ideally be designed, performed, and measured in a four-year timeframe and be sufficiently transparent so that the next government can seamlessly continue their execution \u003csup\u003e(42)\u003c/sup\u003e. However, it has been documented that with the change of governments the continuity of implementation is often jeopardized by political will, public opinion, and availability of resources, which become significant drivers in the new decision-making process \u003csup\u003e(43)\u003c/sup\u003e posing a risk to achieving significant public health outcomes. As such the incorporation of system dynamics offers a structured approach to assess and predict the potential impacts of policy changes, facilitating a more informed and sustainable decision-making process in Colombia's dynamic political landscape.\u003c/p\u003e \u003cp\u003eThe objective of this project was to create a decision support tool based on national and regional data and best research evidence to examine the potential effectiveness of alternative interventions for reducing youth mental health problems and suicidal behavior. Out of the intervention scenarios analyzed, school-based suicide prevention programs and gatekeeper suicide prevention training were the most effective intervention in reducing suicide attempts long term. The findings of our study are consistent with international literature. A recent meta-analysis analyzed the effect of suicide prevention programs for school-aged youth demonstrating small effects on suicidal behavior (g\u0026thinsp;=\u0026thinsp;0\u0026middot;17, 95% CI [0\u0026middot;07\u0026thinsp;\u0026minus;\u0026thinsp;0\u0026middot;26], p \u0026lt; \u0026middot;01) and psychological distress (g\u0026thinsp;=\u0026thinsp;0.16, 95% CI [0.10\u0026ndash;0.23], p\u0026thinsp;\u0026lt;\u0026thinsp;.01), but larger effects on suicide awareness and helping seeking \u003csup\u003e(44)\u003c/sup\u003e. In the same line, European anti-bullying programs were reported to be moderately cost effective when implemented for 6 years or longer, with an estimated reduction in bullying of 20% and an associated increase in quality adjusted life years (QALYS) \u003csup\u003e(45)\u003c/sup\u003e. Furthermore, anti-bullying strategies have been shown to impact significantly on mental wellness\u003csup\u003e(46)\u003c/sup\u003e. The locally customized decision support tool provided through this action research has not only contributed to making a stronger case for investment in school-based suicide prevention programs and anti-bullying campaigns, but it has also provided this case in the context of its estimated impact over the next decade in a LMIC context compared to a suite of other commonly advocated strategies such as mental health training for primary care providers, such as general practitioners (GPs).\u003c/p\u003e \u003cp\u003eAt a community level, our findings are consistent with evidence suggesting that gatekeeper suicide prevention programs improve knowledge of and attitudes toward suicide\u003csup\u003e(47\u0026ndash;49)\u003c/sup\u003e, and can reduce suicide deaths and non-lethal suicide attempts \u003csup\u003e(50, 51)\u003c/sup\u003e. Culturally tailored gatekeeper training is also effective for specific groups; for example, research conducted with Indigenous communities in North America and other studies in military personnel have demonstrated positive results\u003csup\u003e(52, 53)\u003c/sup\u003e. Gatekeeping strategies can help to address mental health care gaps in more disadvantaged countries. For instance, hairdressers in Togo, Africa were trained in MH counselling, due to the steep cost of therapy, and so far, this initiative has been well-received by the community \u003csup\u003e(54)\u003c/sup\u003e. Colombia has witnessed similar informal initiatives, including the \u0026ldquo;Puente de la Variante surveillance team\u0026rdquo; in Ibague city, which was established as a volunteer community vigilance group in response to a high number of suicides involving a local bridge. Mr. Victor Guerrero, a restaurant owner located close to the bridge, has prevented more than 300 deaths. Due to his efforts the initiative received private and public support in 2022 \u003csup\u003e(55)\u003c/sup\u003e. The impact of gatekeeper suicide prevention programs extends beyond individual awareness, shaping community perspectives on suicide; as such, community advocacy is needed to extend the delivery of this type of training to community settings where young people interact, as well as in suicide hotspots.\u003c/p\u003e \u003cp\u003eWhile previous evidence from our research group showed that improving social connectedness is one of the most successful interventions\u003csup\u003e(4)\u003c/sup\u003e, in this study, its impact was limited for suicide prevention but significant for reducing mental health and substance use problems\u003csup\u003e(16)\u003c/sup\u003e. Colombian intrinsic factors like forced displacement, trauma (including transgenerational trauma), unemployment, migration challenges, domestic violence, homelessness, financial insecurity, and lack of green and blue spaces can further alter the social fabric \u003csup\u003e(3)\u003c/sup\u003e. As such, culturally and contextually appropriate social connectedness programs are needed to support positive environments and coexistence. Considering the wide diversity of contexts in the country, these strategies might need appropriate evidence to support effective implementation and replication among populations with similar characteristics, instead of scaling-up one size fits all programs. One of the most important findings in our study was the insight related to combining interventions which can have a greater than additive (synergistic) impact in our social context. Systems modelling offers a virtual space to examine the best mix, focus, timing, extent, frequency, and intensity of investments before they are implemented in the real world, saving time, resources, and potentially the lives of young people. Simulation, particularly in resource-constrained scenarios, has proved feasible and advantageous for evidence-based decision-making in LMIC contexts\u003csup\u003e(38)\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eWhile developing our model, a substantial challenge surfaced in the lack of unification among national information databases. Identifying 180 files (e.g., worksheets, dashboards, reports) from disconnected databases presented a significant hurdle, requiring a meticulous review and integration of these disparate databases into a cohesive framework. Lack of consensus among information-generating entities is apparent, as for instance, commonly used age breakdowns present limitations for studying the 10\u0026ndash;24 age group in many official sources\u003csup\u003e(56)\u003c/sup\u003e. Regarding data in Colombia, the existence of multiple information sources, duplicities, and lack of interoperability have been highlighted previously \u003csup\u003e(56)\u003c/sup\u003e. However, despite this fragmentation and the difficulty of identifying and collating available data, the quantity and quality of data available was sufficient for model development. For some indicators which might not have been publicly available, data donations from participants served to fill gaps, showing the complementary nature and value of participatory processes for systems modelling \u003csup\u003e(57)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWhile better data is the cornerstone of decision-making and public policy \u003csup\u003e(58)\u003c/sup\u003e, it is important to note that limited data availability, use of estimates, and other data deficiencies do not necessarily constitute a barrier to undertaking systems modelling projects in LMICs. Previous findings showcase that, in spite of uncertain baseline projections and even dramatically different projected impacts on outcomes of interest, best strategies remain highly consistent across alternative baseline scenarios \u003csup\u003e(38)\u003c/sup\u003e. For LMICs, there is hence a possibility of simultaneously developing improved decision-support tools and improving information systems, rather than doing so sequentially. In Colombia, comprehending the impact of implementation, especially of public initiatives; establishing an accurate feedback mechanism capable of monitoring the quality of the registered information; and researching the associations between mental health problems and determinants such as poverty and migration remain important shortfalls to be addressed.\u003c/p\u003e \u003cp\u003eIn addition, the collaborative nature of our approach enhances stakeholder familiarity with the software, ensuring user-friendliness and facilitating real-world impact exploration through feedback \u003csup\u003e(57)\u003c/sup\u003e. Nevertheless, the methodology's complexity extends development timelines, and training can pose challenges, given its applicability across diverse knowledge fields \u003csup\u003e(57)\u003c/sup\u003e. Moreover, the broader acceptability of these emerging decision support tools (beyond the stakeholders involved in model development) in the socio-economic context of a LMIC community remains uncertain.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eSuicidality emerges as a pressing global health concern, significantly impacting societies and leaving enduring imprints on families and communities worldwide. The evidence underscores the imperative of prioritizing the well-being of our youth, especially those from diverse ethnic backgrounds. Amidst diverse and challenging circumstances, advanced decision support tools offer indispensable insights, informing decision-making and shaping evidence-based programs with the potential for a lasting positive impact on individuals' lives. This relevance extends notably to addressing challenges encountered in LMICs, where factors such as budgetary constraints, limited human capital, intersectoral disconnection, and insufficient monitoring and evaluation processes contribute to the fragmentation and overall suboptimal quality and effectiveness of public health policies \u003csup\u003e(57)\u003c/sup\u003e. In the specific context of Colombia, where decision-making processes face challenges and concerns related to continuity, sustainability, and transparency, the routine application of participatory system dynamics modeling emerges as a strategic solution. This significance is particularly pronounced in a socio-political landscape marked by efforts to ensure project continuity across successive administrations \u003csup\u003e(57)\u003c/sup\u003e. Modeling stands out as a valuable tool, providing transparency and facilitating informed decision-making to guide future policy endeavors. Underscoring the value for effective decision-making in resource-constrained settings, this research not only emphasizes the relevance of systems modeling but also demonstrates its feasibility in developing sophisticated decision support tools, even in contexts where a lack of sufficient data is often assumed.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLMIC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLow \u0026ndash; Middle income countries\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eYP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eYoung people\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMental Health\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEIDM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eevidence-informed decision-making\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCSART\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eComputer Simulation and Advanced Research Technologies\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNMHS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Mental Health Survey\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMSPS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMinistry of Health and Social Protection\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eQALYS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003equality adjusted life years\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGPs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003egeneral practitioners\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThe study was developed following the international ethical consideration, all the information was recollected after participants signed an informed consent and all measures were taken to ensure confidentiality and security of participants\u0026apos; data. The project was approved by the Human Research Ethics Committee of the Pontificia Universidad Javeriana (Bogot\u0026aacute;, Colombia) and the Hospital Universitario San Ignacio, protocol number FM-CIE-0103-21.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors had unrestricted access to all study data and bear responsibility for upholding data integrity and ensuring the accuracy of data analysis. You can find supplementary results on the national census data that is available on the DANE website. As for the data related to suicides and mental health care, they are publicly available if they are requested from the Colombian Ministry of Health under confidentiality agreements. To obtain the consolidated data within the model, it is possible to do so through a direct request to the main author of this model, who will provide a platform usage guide.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eLOP, AS, MSN, DS, ANM, SC, AMH, JMUR, CGR declare no competing interests. IBH is the Co-Director, Health and Policy at the Brain and Mind Centre (BMC) University of Sydney.The BMC operates an early-intervention youth services at Camperdown under contract to headspace. He is the Chief Scientific Advisor to, and a 3.2% equity shareholder in, InnoWell Pty Ltd which aims to transform mental health services through the use of innovative technologies. JO is both Head of Systems Modelling, Simulation \u0026amp; Data Science, and Co-Director of the Mental Wealth Initiative at the University of Sydney\u0026apos;s Brain and Mind Centre. She is also Managing Director of Computer Simulation \u0026amp; Advanced Research Technologies (CSART) and acts as Advisor to the OECD Neuroscience-inspired Policy Initiative and the Brain Capital Alliance.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThe study\u0026apos;s sponsors did not participate in the study\u0026apos;s design, data collection, data analysis, data interpretation, or report writing. The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This research was carried in its totality thanks to a grant from Foundation Botnar (REG-19-026).\u003c/p\u003e\n\u003cp\u003eAuthors Contributions\u003c/p\u003e\n\u003cp\u003eManuscript concept and drafting: L.O.P, A.S, M.S.N, D.S. and J.O.; Model development: A.S.; Data analysis: A.S Critical revision of manuscript for important intellectual content: S.C, A.M.M, A.N.M J.M.U.R, C.G.R, \u0026nbsp;and I.B.H. All authors have read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank all young people, supportive others, health professionals and stakeholders who participated in this study. In addition, the authors wish to thank Naifer Alexandra Morales, as research assistant of this study. To our medical research assistant Laura C. Gallego-Sanchez for her contributions drafting this manuscript.\u003c/p\u003e\n\u003cp\u003eThe authors would also like to thank the people with lived experiences for their participation in the knowledge translation team.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. Suicide 2023. 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Data and statistics. 2018.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-global-and-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [BMC Global and Public Health](https://bmcglobalpublichealth.biomedcentral.com/)","snPcode":"44263","submissionUrl":"https://submission.springernature.com/new-submission/44263/3","title":"BMC Global and Public Health","twitterHandle":"@BMC_GPH","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4402240/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4402240/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eDespite suicide's public health significance and global mental health awareness, current suicide prevention efforts show limited impact, posing a challenge for low and middle Income countries (LMICS). This study aimed to develop a dynamic simulation model that could be used to examine the potential effectiveness of alternative interventions for reducing youth mental health problems and suicidal behavior in Bogot\u0026aacute;, Colombia.​\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA system dynamics model was designed using a participatory approach involving three workshops conducted in 2021 and 2022. These workshops engaged 78 stakeholders from various health and social sectors to map key mental health outcomes and influential factors affecting them. A model was subsequently developed, tested, and presented to the participants for interactive feedback, guided by a moderator. Simulation analyses were conducted to compare projected mental health outcomes for a range of intervention scenarios with projections for a reference scenario corresponding to business-as-usual.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 6,670 suicide attempts and 347 suicides are projected among 7\u0026thinsp;\u0026minus;\u0026thinsp;17-year-olds from January 1, 2023, to early 2031 under the business-as-usual scenario. Mental health issues among 12-17-year-olds are projected to increase from 18\u0026middot;9% (2023) to 27\u0026middot;8% (2031), and substance use issues from 2\u0026middot;29% to 2\u0026middot;49% over the same period. School-based suicide prevention and gatekeeper training are the most effective strategies, reducing total numbers of suicide attempts and suicides by more than 20% (i.e., compared to business-as-usual). However, discontinuous funding significantly hinders these effective suicide prevention efforts.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eSystems modeling is an important tool for understanding where best strategic financial and political investments lie for improving youth mental health in resource constrained settings.\u003c/p\u003e","manuscriptTitle":"Systems modelling and simulation to guide targeted investments to reduce youth suicide and mental health problems in a low-middle-income country","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-28 20:45:37","doi":"10.21203/rs.3.rs-4402240/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-06-20T03:47:45+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-13T08:25:39+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-09T21:36:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"315518636119797387848399171749134766497","date":"2024-06-03T09:24:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"54154300075537578441924901392189988359","date":"2024-06-02T19:24:57+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-02T17:10:24+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-05-16T06:42:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-13T09:02:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-13T08:59:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Global and Public Health","date":"2024-05-10T18:18:50+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-global-and-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [BMC Global and Public Health](https://bmcglobalpublichealth.biomedcentral.com/)","snPcode":"44263","submissionUrl":"https://submission.springernature.com/new-submission/44263/3","title":"BMC Global and Public Health","twitterHandle":"@BMC_GPH","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e641cc03-c41e-48e1-be94-a83c4b645f31","owner":[],"postedDate":"May 28th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-09-26T04:08:33+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-28 20:45:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4402240","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4402240","identity":"rs-4402240","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}