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Socioeconomic burden of schizophrenia: a targeted literature review of types of costs and associated drivers across 10 countries

2022· article· en· W6901996825 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2022
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsnot available
Fundersnot available
KeywordsIndirect costsTotal costEconomic costCost driverStakeholderCost–benefit analysisSocioeconomic statusCost databaseCost estimate

Abstract

fetched live from OpenAlex

Schizophrenia has the highest median societal cost per patient of all mental disorders. This review summarizes the different costs/cost drivers (cost components) associated with schizophrenia in 10 countries, including all cost types and stakeholder perspectives, and highlights aspects of disease associated with greatest costs. Targeted literature review based on a search of published research from 2006 to 2021 in the United States (US), United Kingdom (UK), France, Germany, Italy, Spain, Canada, Japan, Brazil, and China. Sixty-four published articles (primary studies and literature reviews) were included. Comprehensive data were available on costs in schizophrenia overall, with very limited data for individual countries except the US. Most data is related to direct and not indirect costs, with extremely scarce data for several key cost components (adverse events, suicide, long-term care). Total schizophrenia-related per person per year (PPPY) costs were $2,004–94,229, with considerable variability among countries. Indirect costs were the main cost driver (50–90% of all costs), ranging from $1,852 to $62,431 PPPY. However, indirect costs are not collected systematically or incorporated in health technology assessments. Total schizophrenia-related PPPY direct costs were $4,394–31,798, with inpatient cost as the main cost driver (∼20–99% of direct costs). Intangible costs were not reported. Despite limited evidence, total schizophrenia-related costs were higher in patients with than without negative symptoms, largely due to increased costs of medication and medical visits. As this was not a systematic review, prioritization of studies may have resulted in exclusion of potentially relevant data. All costs were converted to USD but not corrected for inflation or subjected to a gross domestic product deflator. Direct costs are most commonly reported in schizophrenia. The substantial underreporting of indirect and intangible costs undervalues the true economic burden of schizophrenia from a payer, patient, and societal perspective. The true costs of diseases such as schizophrenia extend far beyond the obvious direct costs of hospital visits, outpatient appointments and medications to include indirect costs such as loss of productivity among patients and caregivers due to unemployment, early retirement and premature death. This review of literature published between 2006 and 2021 reveals that the indirect costs of schizophrenia actually account for between 50% and 90% of all costs, but are often not taken into account in healthcare planning. In addition, intangible costs, including the pain, suffering, stress, and anxiety experienced by patients and caregivers due to schizophrenia have not been reported in the literature. Costs were also higher for patients with negative symptoms of schizophrenia (where patients appear withdrawn and lacking in emotion, with few social relationships) compared with those with positive symptoms (including delusions or hallucinations). This is largely due to the greater costs for medications and medical visits among patients with negative symptoms. In summary, this review demonstrates that the true cost of schizophrenia, including direct, indirect, and intangible costs, is likely to be substantially higher than the values for the cost of disease currently reported.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.024
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.005
Bibliometrics0.0240.025
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.014
GPT teacher head0.290
Teacher spread0.276 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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