The reality of diagnosing and treating depression

In Europe, almost one citizen in five is diagnosed with depression. The 12-month prevalence of depression varies between different regions but is approximately 6% across all countries. The first depressive episode typically occurs in a period between adolescence and the mid-40s [Reference Malhi and Mann1]. The European Study of the Epidemiology of Mental Disorders showed that only 36.8% of participants with an affective disorder and 20.6% with an anxiety disorder reached out to health services for help. Of the participants who sought help, 20.7% received no therapy to alleviate their mental health problems [Reference Alonso and Lépine2]. Depressive disorders have a peak prevalence between the ages of 25 to 35. They are referred to as complex disorders because their etiology involves an interaction of genetic and environmental risk factors. Despite the availability of evidence-based treatments, only 45% of patients with depression recover permanently [Reference Liu, Daumit, Dua, Aquila, Charlson and Cuijpers3]; According to the World Health Organization, unipolar depression and other mental disorders on average lead to a significant functional impairment more debilitating than chronic diseases like diabetes [Reference Alonso and Lépine2]. Compared with the general population, people with mental illnesses such as depression are in poorer health and their life expectancy is lower [Reference Liu, Daumit, Dua, Aquila, Charlson and Cuijpers3], showing a need for improvement of diagnostic capabilities, as well as expansion of care for the affected.

The associated reasons are outlined in the first part of this policy paper below and are used as the basis for European Psychiatric Association (EPA) action points which aim to ensure that practitioners will soon be better able to help affected individuals better (Table 1). The second part of the paper presents developments that may revolutionize the treatment of depression in the coming years but are not yet established in clinical care.

Table 1.

Improving clinical care now. The table shows the recommendations by the task force that can be implemented to improve clinical care now

This paper focuses on depressive disorders to pinpoint their unmet health care and research needs.

Diagnosing depression and its comorbidities

To date, health systems and research facilities have failed to adequately meet the needs of patients requiring treatment for depression [Reference Wang, Aguilar-Gaxiola, Alonso, Angermeyer, Borges and Bromet4]. Mental illnesses affect all age groups and social classes, albeit with varying frequency and severity. Moreover, in general practice physical illness that occurs either independently of or in the context of treatment of mental illness is often also inadequately treated [Reference Lee, Delpozo-Banos, Lloyd, Jones, Walters and Owen5]. For example, although cardiovascular diseases have a higher prevalence in patients with psychiatric disorders as well as depression, this group of patients has a below-average detection [Reference Martin and Smith6].

Many subjects with major depressive disorder initially present to their general practitioner (GP) [Reference Engels, König, Magaard, Härter, Hawighorst-Knapstein and Chaudhuri7]. One study found that GPs correctly diagnosed depression in only 47.3% (95% CI, 41.7 to 53.0%) of cases [Reference Mitchell, Vaze and Rao8]. Other psychiatric diseases are also currently underdiagnosed in primary care [Reference Sinnema, Terluin, Volker, Wensing and van Balkom9]. This underdiagnosis leads to increased morbidity and mortality, worse functional outcome, higher suicide rates, and more frequent use of unnecessary diagnostic procedures [Reference Grinshteyn, Wendel, O’Donohue and Maragakis10]. After being diagnosed with depression, a patient should see a psychiatrist, who will determine a therapeutic regimen and work in close cooperation with other medical disciplines. However, if a psychiatrist is not available, seeking assistance from other qualified mental health practitioners can be a suitable alternative.

The importance of the above issues was described in a Lancet commission paper on the global challenges regarding depression [Reference Herrman, Patel, Kieling, Berk, Buchweitz and Cuijpers11] so the present paper will focus on the European perspective.

Treating depression with evidence-based approaches as early as possible

If depression is not treated, only about 8 to 18% of people achieve remission [Reference Mekonen, Ford, Chan, Hides, Connor and Leung12]. Furthermore, stigmatization of mental disorders aggravates psychopathology and impedes symptom improvement across all mental disorders [Reference Morgan, Reavley, Ross, Too and Jorm13, Reference Stangl, Earnshaw, Logie, van Brakel, C Simbayi and Barré14]. Thus, selecting the correct treatment for depressive disorders is an important task for psychiatrists.

The future – methodology

We initiated a task force consisting of nine EPA board members. Through a series of five meetings, the task force engaged in in-depth discussions, carefully considering various aspects of the topic. The discussions were documented and analyzed thematically to capture the key insights and perspectives shared by the task force members [Reference Maxwell28]. During five meetings, the group exchanged ideas about the future of psychiatry until they felt that they had discussed all relevant topics in sufficient detail. This approach aimed to provide a comprehensive understanding of the future of psychiatry based on expert perspectives and collaborative deliberation.

All participants in the study gave oral consent to participate and to the publication of the results. Because of the participants’ professional status, ethics committee approval was not deemed necessary.

Diagnosing and treating depression tomorrow

Currently, mental health care faces limitations in integrating advanced technology due to fragmented care systems and limited resources. Socioeconomic, stigma and cultural disparities further restrict access to care. To overcome these challenges a multi-faceted approach involving policy changes, increased funding, education, and collaboration between various stakeholders as well as continued research is necessary.

Looking towards the future, our vision for mental health care is transformative. We envision a seamless and personalized experience that empowers individuals to take control of their mental well-being (Figure 1). When someone seeks help for depression or other mental health concerns (e.g., from their general practitioner), they will reach a comprehensive and integrated system of care before symptoms become debilitating.

Figure 1.

The figure shows an individual with unipolar depression in 10 years and positively highlights the aspects that have changed for the better.

Advanced algorithms and artificial intelligence will be used to analyze a wide range of patient data, including medical history, lifestyle factors, and environmental influences, to develop a personalized treatment plan. Wearable technology and other connected devices will allow patients to monitor their own progress and receive real-time feedback on their mental health status. Imaging, HRV, and EEG data can be added if the algorithm needs more information to make a prediction about an individual patient. Additional blood can be drawn for metabolomics, proteomics, transcriptomics, and genomics analyses, the results of which can be entered into the system to predict which treatment will be the most effective and have the fewest adverse effects.

Psychotherapy and other evidence-based treatments will be tailored to each patient’s unique needs and preferences. Patients will have access to a variety of digital resources, including apps and online communities, that can help them stay motivated and engaged in their treatment. Lifestyle and nutrition advice is also provided, and follow-up is implemented with wearables. The patient can obtain information about reducing their personal risk of recurrence of depression in the peripartum period if they want to have children. The stigma around mental illness will be greatly reduced, as society becomes more accepting of mental health issues. Schools and workplaces will prioritize mental well-being, with policies and programs that support their employees and prevent burnout.

Also, in the future mental health care providers will stay up-to-date with the latest research and developments in the field, including advances in technology and evidence-based practices. Finally, providers will continue to enhance their collaborative efforts with other healthcare professionals and support systems, such as family members or friends, to ensure even greater levels of comprehensive and coordinated care in the future.

Increased awareness of mental illness in primary care settings

The high rate of psychiatric comorbidity in primary care patients stresses the need to provide psychiatric care in primary and specialty care settings and to increase cooperation between GPs and psychiatrists [Reference Meadows, Harvey, Joubert, Barton and Bedi29]. Algorithms are needed to assist GPs in recognizing depression and initiating treatment in patients who are likely to have a favorable outcome. A consultation with a psychiatrist should be suggested in cases that are likely to be complex, i.e., patients with poor clinical outcomes after treatment of their medical problems and psychiatric disease in primary care and those with a poor prognosis for social and occupational functioning.

Early psychiatric assessment and treatment recommendations by liaison psychiatrists help clarify cases and facilitate access to appropriate interdisciplinary care [Reference Grover and Kate30]. Psychiatric counseling should quickly lead to low-threshold support and adequate treatment. Providing early training and psycho-education to family members can enhance support and lower the risk of escalation [Reference Seierstad, Brekke, Toftemo and Haavet31].

The development of novel (biological) markers

Precision psychiatry can deliver a tailored diagnostic concept and individualized treatment for heterogeneous clinical conditions such as depression by combining genetic, behavioral, cognitive, and biological markers [Reference Bzdok and Meyer-Lindenberg32]. Several trophic factors were found to be altered in patients with depression compared with controls [Reference Clark-Raymond and Halaris33]. The most studied cytokines in psychoneuroimmunology are interleukin (IL)-6, tumor necrosis factor (TNF) alpha and beta, IL-1b, and interferons. The dysregulation of interleukins [Reference Ogłodek34], neurotrophic factors, and hormones among other molecules, has been associated with schizophrenia and other mental diseases. However, despite extensive research, definitive etiological pathways linking the molecules to these disorders have yet to be discovered [Reference Steiner, Bernstein, Schiltz, Müller, Westphal and Drexhage35].

Various genetic loci have been identified that increase the risk of becoming mentally ill. Nevertheless, this abundance of knowledge has not been translated into routine psychiatric care. Integrating findings from research knowledge into clinical care remains an important challenge for the future, in particular, because current normal routine markers do not allow reliable diagnosis of psychiatric diseases [Reference Hochman, Feldman, Weizman, Krivoy, Gur and Barzilay36, Reference Erjavec, Sagud, Perkovic, Strac, Konjevod and Tudor37].

Machine learning supported diagnostics, prognostics, and therapeutics

The increasing use of technology in psychiatric assessments and treatment will lead to decentralization and broader access to therapeutic services. These developments will enable us to progressively destigmatize psychiatric illnesses and will improve psychiatric care in rural areas and developing and emerging countries [Reference Saraceno, van Ommeren, Batniji, Cohen, Gureje and Mahoney38]. Predictive psychiatry, which often relies on machine-learning algorithms, will play an important role as a pillar in the prevention of relapse and re-emergence of an existing psychiatric illness [Reference Chekroud, Bondar, Delgadillo, Doherty, Wasil and Fokkema39, Reference Salazar de Pablo, Studerus, Vaquerizo-Serrano, Irving, Catalan and Oliver40]. While machine-learning algorithms are often employed in predictive psychiatry studies to forecast treatment outcomes, their clinical utility is still lacking. The practical application of this method is hindered by its prohibitive cost, particularly due to the requirement of MRI scans for models with good accuracy scores. As a result, machine learning has not yet achieved the desired level of effectiveness in the field of precision psychiatry [Reference Chekroud, Bondar, Delgadillo, Doherty, Wasil and Fokkema39].

Implementing therapeutic apps for electronic devices into everyday treatment

Medical care through smartphone apps and associated sensors is at the cusp of moving from being a topic of research to being implemented in outpatient care. Some apps already exist, that help patients track their symptoms, review and apply content from psychotherapy and perform cognitive training and also support patients in adhering to their medication [Reference Zielasek, Reinhardt, Schmidt and Gouzoulis-Mayfrank41, Reference Dayer, Heldenbrand, Anderson, Gubbins and Martin42].

Health apps can improve the diagnosis and prevention of diseases, provide patients with psychoeducation in the face of a shortage of therapists and monitor progress during therapy [Reference Zielasek, Reinhardt, Schmidt and Gouzoulis-Mayfrank41, Reference Ruwaard, Lange, Schrieken, Dolan and Emmelkamp43]. Patients may share very sensitive data via apps, which may also be connected to other sensors such as a smartwatch, so strict data protection and data security rules are needed to reduce the risk of data violations [Reference Oh, Seo, Lee and Kim44].

Plasticity-promoting somatotherapy – a new generation of treatment options

Classical psychedelics typically include lysergic acid diethylamide, psilocybin, and N,N-Dimethyltryptamine [Reference De Gregorio, Aguilar-Valles, Preller, Heifets, Hibicke and Mitchell45]. These substances interact with 5-HT2A receptors, which are predominantly located in the caudate, nucleus accumbens, olfactory tubercule, hippocampus, and the pyramidal cells of the neocortex. The anesthetic ketamine is also a hallucinogen in the broader sense, although it affects the glutamatergic system via the N-methyl-D-aspartate receptor [Reference Vollenweider and Kometer46]. Hallucinogens are thought to disrupt engrained thought patterns by counteracting acquired synaptic deficits, neural atrophy, and loss of connectivity in the frontal neocortex through enhanced neuroplasticity, thus allowing ruminations and negative thought spirals to be disrupted [Reference Nutt and Carhart-Harris47]. However, there is conflicting evidence about the persistence of those effects, especially in the case of ketamine [Reference Aleksandrova and Phillips48].

Also, brain stimulation techniques like rTMS and ECT are able to induce plasticity and modulate dysfunctional brain network activity [Reference Huang, Lu, Antal, Classen, Nitsche and Ziemann49]. New accelerated protocols are being developed and implemented in rTMS treatment regimens along with individualized procedures like fMRI-informed neuronavigated or EEG-feedback closed-loop stimulation, to improve response rates [Reference Tervo, Nieminen, Lioumis, Metsomaa, Souza and Sinisalo50].

Comprehensive recovery approach

In addition to the important interventions and supportive technologies described above, treatment should seamlessly incorporate all relevant life areas of the patient with the aim to not only achieve recovery from the current episode but also build up resilience to support better future overall health. Particularly in depression, patients have a higher risk of relapse if they have only a limited social network [Reference Alsarrani, Hunter, Dunne and Garcia51, Reference Santini, Koyanagi, Tyrovolas, Mason and Haro52]. Holistic treatment requires stigma-free contact with the patient’s employer to enable changes in the workplace; training in lifestyle skills (e.g., nutrition and exercise); close involvement of the patient’s social contacts (e.g., partner, family); and consideration of the patient’s cultural background (cultures differ across Europe and patients might be additionally shaped by migration) [Reference Schouler-Ocak, Haasen and Heinz53].

Patients with depression and their relatives and social networks should be increasingly integrated into treatment and research. Further, therapy requires coordination between the treating doctors and the therapists involved.

Studies have provided evidence that besides social participation, diet, and physical activity are beneficial for mental health and are cost-effective [Reference Aguilar-Latorre, Navarro, Oliván-Blázquez, Gervilla, Magallón Botaya and Calafat-Villalonga54]. Furthermore, psychiatrists, GPSs, and therapists should regularly receive super- and intervision to steadily improve the quality of care [Reference Martin, Copley and Tyack55].

Open Science to accelerate progress

Open Science encompasses various aspects of the scientific working process. Its central idea is that everyone should be able to contribute to research [Reference Fecher, Friesike, Bartling and Friesike56]. Consequently, scientific publications should be publicly accessible without charge, and data sets used should be published to ensure better replicability and reproducibility in general [Reference Klein, Ratliff, Vianello, Adams, Bahník and Bernstein57]. Machine learning and genetic data analysis usually require very large data sets. To benefit from synergies, Open Science is an important element because it allows the necessary size of data sets to be achieved more easily and can also provide external validation data sets [Reference Burns, Roncal, Kleissas, Lillaney, Manavalan and Perlman58].

However, Open Science has also been criticized because it increases the number of retractions and articles containing errors, which pollute the quality of journals [Reference Tennant and Lieungh59]. Open-source projects are often run on a voluntary basis so correcting faulty processes takes longer [Reference Senyard and Michlmayr60].

Reasonable data protection laws

As the world becomes increasingly technological, data streams can be analyzed much more easily. However, when personal data are combined with large external data sets, data scientists can derive information about individuals that the person may want to keep confidential or may not even be aware of [Reference Matturdi, Zhou, Li and Lin61]. The provision of new technologies for gigantic data sets and analytical methods for evaluating them has given rise to the vision of deep phenotyping. Phenotyping is a necessary building block for tracing psychiatric diseases back to their biological correlates. However, the further one explores this issue, the more ethical questions arise that need to be addressed [Reference Shen, Silverman, Monette, Kimble, Rauch and Baker62]. Therefore, to maximize benefits while minimizing harm to study participants, in 2018 the European Union introduced the General Data Protection Regulation (GDPR). The GDPR also aims to harmonize regulations across Europe [Reference Chico63]. Although it does not solve all data protection-related issues, the GDPR does provide a framework for harmonizing data protection laws and simplifying the implementation of multicenter studies across the European Union. On the one hand, data regulation should protect participants’ data, but on the other, it should also provide feasible strategies to facilitate data acquisition and processing to allow meaningful scientific research to be performed [Reference Chico63]. We recommend establishing an interdisciplinary task force to specifically work on solving these issues.

Ethical electronic health records

An electronic health record (EHR) is a digital database that not only contains a patient’s personal data but also archives longitudinal data about diseases, diagnostic procedures, treatments, and responses to therapy. Consequently, the most obvious advantage of EHRs is that they provide easy accessibility to medical data for medical professionals in case a patient moves and thus changes doctors [Reference Menachemi and Collum64]. The introduction of EHRs can improve the safety, quality, and efficiency of medical systems over the long term because the improved flow of information means that redundant examinations can be avoided [Reference Gillum65]. A public funding source is needed to create an ethical EHR, ensure consistent standards, develop less error-prone interfaces, and avoid conflicts of interest so that the focus is always on patient well-being and evidence-based medicine [Reference Kush, Helton, Rockhold and David Hardison66].

To develop an EHR that can meet these requirements, computer scientists and doctors who are familiar with the development and requirements of such an application must work together to prevent regulations, especially those that may be rather detrimental to patient welfare and beneficial to other parties [Reference Buijink, Visser and Marshall67]. With the constant progress of scientific knowledge, the contents of an EHR must also be regularly re-evaluated and improved. Consequently, independent specialists must periodically accredit EHRs, including testing them for malfunctions and checking whether decision support systems are still working according to current medical standards and whether the EHRs meet current safety standards. Regular peer review of the medical content and source codes could be used to raise awareness of content that is outdated or malfunctioning [Reference Kruse, Frederick, Jacobson and Monticone68].

In addition to establishing a uniform system that avoids the potential for creating multiple error-prone interfaces, clear and mandatory security standards are also needed. The lack of such an infrastructure is also currently hampering the establishment of a Europe-wide EHR [Reference Halamka and Dentzer69].

Enabling better healthcare research and saving resources

Open Science, reasonable data protection, and a standard EHR will enable better healthcare research by providing large datasets and thus facilitating the evaluation of treatment strategies and the early diagnosis and prevention of diseases. Specifically tailored therapeutic approaches can be developed to offer patients the best treatment, which ultimately translates into the effective implementation of precision medicine [Reference Delgadillo and Lutz70]. Currently, only 9% of patients with unipolar depression being treated in primary care receive adequate treatment. If patients with unipolar depression were diagnosed more reliably and treated more effectively, healthcare costs would decrease. The ratio of means costs is 2.28-fold higher in adults with depression than in non-depressed adults [Reference König, König and Konnopka71]. In addition, studies evaluating excess costs largely do not assess indirect costs, such as those caused by reduced productivity [Reference König, König and Konnopka71].

The US Preventive Services Task Force currently recommends broad screening in all adults, regardless of risk factors, because early treatment improves patient symptomatology [Reference Maurer, Raymond and Davis72].

Furthermore, it is important to acknowledge the critical role of patient engagement in depression treatment. Despite having access to various treatment options, a significant barrier for individuals with depression is the motivation to actively participate in their treatment. Addressing this issue requires a multifaceted approach that considers strategies to improve patient engagement. Psychoeducation, shared decision-making, and tailored interventions are essential for optimizing treatment outcomes [Reference Nolan and O’Connor73].

The potential for reduced healthcare costs with improved diagnosis and treatment of unipolar depression is a valid consideration, further research is needed to comprehensively evaluate the cost-effectiveness of these interventions.