Disease Burden – Prevalence
Globally, depressive disorders substantially contribute to a degree of overall disease burden. Thus, prioritizing depression treatment and prevention represents a significant healthcare priority. In terms of its prevalence, depression frequently occurs in adolescents, with conservative estimates that approximately 12% of a 12- to 17-year-old population experience symptoms of major depressive episodes (Clayborne et al., 2019). Adolescence is a critical developmental period characterized by building and understanding healthy relationships, exploring personal interests, developing essential skills for life and the workplace, and eventually transitioning to further education or the labor force. Depression experienced during this period can disrupt these processes, ultimately affecting an individual’s long-term socioeconomic standing and peer-to-peer, familial, and romantic relationships.
Disease Burden – Comorbidities
Furthermore, experiencing depression in early life can be associated with various poor outcomes in addition to depression’s possible recurrence. For instance, there is an increased risk of type 2 diabetes – 5% cumulative incidence – and obesity – 12% incidence in confirmed depression cases (Clayborne et al., 2018). Another example is an increased risk of other mental health disorders, including substance use disorders and anxiety, with a p-value lesser than 0.5 (Clayborne et al., 2018). Apart from that, there is evidence of links between early-life depression and many poor psychosocial outcomes, such as lower perceived social support, lower educational performance, and unemployment.
Disease Burden – Costs
Costs due to adolescent depression span from direct medical costs related to the use of medication and inpatient and outpatient care to indirect costs related to work absence, early retirement, and premature death. Direct medical costs caused by adolescent depressive disorders are substantial – 3.10 million USD annually per single age group – differing between the clinical depression subtypes (Ssegonja et al., 2020). In turn, indirect societal costs accrue from increased education needs, criminality, and increased social welfare dependence (p<0.05) due to poor performance in the labor market (Ssegonja et al., 2020). In this context, indirect costs of depression more significantly contribute to depression-related costs than direct costs.
Barriers and Challenges
Contemporary adolescent depression research faces many barriers and challenges. The mechanisms of how different interventions work are still not broadly understood (Cuijpers et al., 2020). Over the past decades, approximately 500 randomized trials have studied the antidepressant effects, whereas over 600 trials have focused on the effects of psychotherapies for depression (Cuijpers et al., 2020). However, only 20% of drug trials and less than 30% of therapy trials display low bias risk, making the outcomes uncertain (Cuijpers et al., 2020). Consequently, there is a barrier represented by a lack of clarity regarding depression boundaries and heterogeneity on the one hand and a scarcity of reliable research on the other.
Spontaneous Recovery & Placebo
Another notable challenge is the high rates of spontaneous recovery and placebo effects. In a meta-analysis including 177 studies and 44 240 patients, 54% responded to antidepressants, 54% to psychotherapies, and 38% responded to a placebo (Cuijpers, 2018). In this context, patients with depression who did not receive care showed comparable results. These findings pose a challenge that a substantial part of patients treated with psychotherapy or medication might have recovered with placebo or without treatment. In other words, for most patients who respond to treatment, the time investment in psychotherapy and the potential adverse effects of medications might not be necessary to get better.
Nonresponse and Relapse Rates
In contrast to the response to drug or placebo, a large group of patients is difficult to treat or do not respond to treatment. Although patients may respond to another drug after failing to respond to an initially prescribed drug, the chance of a successful response is almost halved with every new treatment (Cuijpers, 2018). One estimate suggests that approximately 30% of patients with depressive disorders have a chronic course with limited response to treatment (Cuijpers, 2018). Another challenge is that treatment effects are probably overestimated because of the high relapse rates, estimated at 50% (Cuijpers, 2018). Thus, there are concerns about long-term effectiveness and biases regarding publications and sponsorships.
Solutions to Current Challenges
The treatment challenges are partly addressed with preventive interventions. They include approaches such as cognitive behavioral therapy, interpersonal therapy, and coaching. These interventions are usually delivered in schools, healthcare centers, and other community settings by teachers, clinicians, and other trained personnel. In particular, interventions focused on cognitive behavioral therapy (CBT) have demonstrated significant value. It showed 0.14 statistically significant evidence of successful prevention and 95% cost-efficiency compared to non-treatment (Ssegonja et al., 2020). These interventions aim to equip adolescents with skills to be able to recognize certain life stressors (triggers or thoughts), develop alternative thinking patterns and ultimately employ appropriate behavioral responses to them.
Universal Collaboration
Another solution to the challenges posed by depression is to draw attention to the matter, promoting collaboration at different societal levels. An example of such a promotion is the Healthy People organization, which took responsibility for mapping the desired outcomes of healthcare field development. In terms of adolescent depression, it issued several objectives, such as MHMD‑06 increasing the number of treated adolescent depression cases and EMC‑D04 focusing on anxiety and depression treatment among children and adolescents (Healthy People 2030, n.d.). The former aims to increase the current rate of 43.3% to 46.4% cases, whereas the latter is yet to be developed. The desired effect is achieved through the federal support and scholarly attention these objectives receive.
Mental Health Benefits Legislation
In the meantime, the Community Preventive Services Task Force (CPSTF) promotes mental health benefits legislation to improve financial protection and increase the proper use of health services by people with mental health conditions. Particularly, CPSTF recommends legislation that helps ensure no greater restrictions for mental health coverage than physical health coverage (Healthy People 2030, n.d.). In addition, CPSTF found evidence supporting that mental health benefits legislation is frequently associated with increased diagnoses of mental health conditions, increased care accessibility, reduced suicide rates, and prevalence of poor mental health.
Quality Improvement
Using clinical registries is crucial to the systematic measurement of clinical outcomes in achieving better patient value. A clinical or patient registry is an organized system using observational studies to collect clinical data as structures, processes, and outcome measures to evaluate specific outcomes for a population characterized by a certain condition, disease, or exposure (Kampstra et al., 2018). The goal of measuring outcomes includes guiding clinical decision-making, benchmarking, monitoring, initiating improvement interventions, public accountability, and scientific research. Structurally measuring outcomes and using them to determine possible improvements contributes to achieving higher service value for patients.
Collaborative Care Model
For example, one study applied a web-based disease registry to track patients with symptoms of depression to support treatment management in primary care. The Breakthrough Collaborative Model (BCM) was used to create a cycle of structured conference sessions during which depression outcomes were studied and interpreted, and variations in work processes were discussed. Moreover, the model was used as a tool to facilitate insights and improvement efforts into data. Additionally, evidence-based depression management training was provided to primary care providers. As a result, the majority of patients experienced relief in depression symptoms (p<0.01) (Kampstra et al., 2018). Thus, the experiment led to a meaningful improvement in depression management.
Predictive Machine Learning
However, traditional clinical risk assessment tools sometimes do not demonstrate enough accuracy to identify high-risk patients. This is why machine learning approaches have been applied to EHR data to predict suicide risk in adult and adolescent populations (Su et al., 2020). A combination of patient demographic characteristics, procedures, diagnoses, laboratory tests, and medications was used to construct machine-learning models to predict the risk of suicide attempts – a five to 10-fold improvement – among patients receiving care in a children’s medical center (Su et al., 2020). The results observed from the proposed models constitute greater accuracy in suicide attempt prediction compared to the base rate.
Future Implication – Challenges
A thorough analysis of topics associated with adolescent depression provides a lot of information for future implications in nursing care. The fact that the particular treatment’s success can vary depending on each individual case prevents overgeneralization by providers. Additionally, the issue of statistical significance sets a milestone for researchers, emphasizing future research requirements. In the meantime, the possibility of successful treatment without the use of antidepressants and psychotherapies provides an opportunity to avoid adverse medication effects and potential time loss. Finally, high relapse rates foster critical thinking in healthcare professionals regarding the medication and possible bias around it.
Future Implication – Solutions
The acknowledged solutions and practices in terms of depression treatment add significance to evidence-based practice. For example, the success rate of depression prevention in comparison to depression treatment motivates providers to pay increased attention to regular screenings. In turn, the collaboration initiatives and their ability to provide aid, both professional and informational, ensure a qualitative delivery of services and safeguard less-experienced practitioners. Lastly, governmental support covers the areas where local and individual efforts might not be enough to achieve desired targets. Consequently, it unties the hands of professionals, making their services accessible to a more significant number of people in need.
Future Implication – Improvement
The tendency for performance measurement and quality improvement increases the quality of life not only for patients but for practitioners as well. The use of clinical records allows for increased data transparency and accessibility to healthcare professionals, contributing to the speed and accuracy of disease diagnosis and treatment. In this context, the practices, such as BCM, have a diversified purpose of technology implementation, quality improvement, and interdisciplinary collaboration promotion. Meanwhile, the ability to further implement technological advances such as machine learning can save a great amount of time and effort for practitioners and simultaneously improve service quality.
References
Clayborne, Z. M., Varin, M., & Colman, I. (2019). Systematic review and meta-analysis: adolescent depression and long-term psychosocial outcomes.Journal of the American Academy of Child & Adolescent Psychiatry, 58(1), 72-79. Web.
Cuijpers, P. (2018). The challenges of improving treatments for depression. Jama, 320(24), 2529-2530. Web.
Cuijpers, P., Stringaris, A., & Wolpert, M. (2020). Treatment outcomes for depression: challenges and opportunities.The Lancet Psychiatry, 7(11), 925-927. Web.
Healthy People 2030. (n.d.). Adolescents. Web.
Ssegonja, R., Sampaio, F., Alaie, I., Philipson, A., Hagberg, L., Murray, K., & Feldman, I. (2020). Cost-effectiveness of an indicated preventive intervention for depression in adolescents: A model to support decision making. Journal of Affective Disorders, 277, 789-799.
Kampstra, N. A., Zipfel, N., van der Nat, P. B., Westert, G. P., van der Wees, P. J., & Groenewoud, A. S. (2018). Health outcomes measurement and organizational readiness support quality improvement: a systematic review. BMC health services research, 18(1), 1-14.
Su, C., Aseltine, R., Doshi, R., Chen, K., Rogers, S. C., & Wang, F. (2020). Machine learning for suicide risk prediction in children and adolescents with electronic health records. Translational psychiatry, 10(1), 1-10.