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Record W4386090058 · doi:10.3389/frhs.2023.1267580

Editorial: Mental health economics and public mental health policy: mental health services costs, quality and its impact on reducing the burden of mental illness

2023· editorial· en· W4386090058 on OpenAlexaboutno aff
Denise Razzouk

Bibliographic record

VenueFrontiers in Health Services · 2023
Typeeditorial
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthMental illnessPsychiatryPublic healthQuality (philosophy)Health economicsMedicineEnvironmental healthPsychologyBusinessNursing

Abstract

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Mental Health Economics has a crucial role to guide decision-makers and planners in evaluating the implementation and the costs and benefits of health policies, services and interventions. The majority of the literature available is usually focused on the costs of intervention and services regardless of the importance of measuring the quality of mental healthcare after the implementation of new therapeutics and services. (1) Cost-effectiveness studies are important tools for comparing costs and benefits among therapeutical interventions but are hardly applicable to all contexts and their results vary according to multiple factors not always measurable. For instance, the implementation of a cost-effective public health policy might result in an opposite outcome than is expected if its implementation isn't adequately evaluated and monitored.In this research topic, we have four manuscripts discussing different aspects related to national investments in mental healthcare in low and middle-income countries, costs of mental health services in the real-world scenario, the implementation challenges and guidelines of new digital health technologies in mental health services and the critical issues of evaluating mental health services costs without mental health outcomes data.Chisholm et al. (2) estimated the return on investment and the cost-benefit ratios of mental health investments in seven Asian and African countries demonstrating the remarkable economic and social return to society of scaling up a modest amount of investment in mental health interventions considering different contexts, GDP and countries characteristics. Of note, the intrinsic value of mental health was taken into account, which is a crucial point when policymakers only base their decisions on narrow perspectives used in the costeffectiveness and clinical trial studies dismissing the societal and economic burden of mental illness. In this study, the economic burden was up to 1% of countries' GDP while the need for scaling-up investment would be less than 0.14% of countries' GDP and with a benefit-cost ratio for depression up to 30:1.Oliveira et al. (3) analysed an administrative database of mental healthcare expenditures and explored the characteristics of patients with chronic psychosis associated with these costs across one decade in Canada. As expected, hospitalisation was the main part of the total costs, but the interesting finding is that hospitalisations increased over time, especially in the presence of other medical comorbidities (>5), highlighting the importance of integrating and monitoring physical and mental care to optimise costs and outcomes. Of note, the distribution of mental health expenditures varied over time with a remarkable increase in acute medical hospitalisations, outpatient visits, medication and home care. These findings shed light on the need of measuring costs in a comprehensive approach because costs do not necessarily decrease over time with the decrease in psychiatric hospitalisations.The evaluation of mental health services quality and health policy investments depends on the quality of data. Of note, outcomes in mental health should be measured in a comprehensive approach considering not only symptom improvement but social, occupational and functional recovery. However, administrative health systems rarely contain relevant or accurate data regarding mental health outcomes. Mental health outcomes and costs vary over time, context and type of service and disorder. For this reason, a longitudinal analysis is crucial to assess the value of investments, services costs, planning health policies and improving mental health services.Mark, TL(4) described the challenges and gaps in evaluating and planning mental health services using data from USA's mental health system database. The need for a linkage between databases regarding outcomes, costs and services delivered remains a crucial challenge to evaluate the quality of mental health services. The administrative databases have several limitations in terms of evaluating costs and outcomes. In this manuscript, Mark purposed some measures for mental health information system improvement, focusing on outcomes data.Iorfino et al. (5) discussed the potential positive impacts of new digital health technologies for mental healthcare quality and efficiency targeting young people using dynamic simulation modelling and describing a framework on how it was implemented in health services research in Australia. The authors described some measures needed to guide the implementation of such tools in the real world. Of note, the implementation of new health technologies requires the participation of health services, funders, managers and also patients. Monitoring the implementation process of such tools is crucial for further evaluations of mental health services' efficiency, cost-effectiveness and quality of care.The social and economic impact of mental health on society has been growing over the decades and several measures have been purposed to minimise the burden of mental illness. A myriad of solutions including preventive measures, medication, psychological therapies, social therapies and the advent of new health technologies promise better outcomes and social and economic returns. However, there are, at least, three major bottlenecks for promoting mental health and enhancing mental health system efficiency: the lack of effective investment; poor quality of mental health system data and a paucity of rigour evaluations for the implementation and for the evaluation of implemented services and policy.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.412
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0010.001
Science and technology studies0.0030.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.405
Teacher spread0.384 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEditorial

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2023
Admission routes1
Has abstractyes

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