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The role of mental health and addiction among high-cost patients: a population-based study

2017· article· en· W6921201069 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2017
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsAddictionMental healthIndirect costsTotal costComorbidityHealth carePsychosisCost driver

Abstract

fetched live from OpenAlex

Aims: Previous work found that, among high-cost patients, those with a majority of mental health and addiction (MHA)-related costs (>50%) incur over 30% more costs than other high-cost patients. However, this work did not examine other high-cost patients in depth or whether they had any MHA-related costs. The objective of this analysis was to examine the role of MHA-related care among other high-cost patients. Methods: Using administrative healthcare data from Ontario, Canada, this study selected all patients in the 90th percentile of the cost distribution in 2012. It focused primarily on two groups based on the percentage of MHA-related costs relative to total costs: (1) high-cost patients with some MHA-related costs (0% > and <50%) and (2) high-cost patients with no MHA-related costs (0%). We examined socio-demographic and clinical characteristics, utilization and costs for both groups, and modeled patient-level costs using appropriate regression techniques. We also compared these groups with high-cost patients with a majority of MHA-related costs (>50%). Results: High-cost patients with some MHA-related costs incurred over 40% more costs than those without ($27,883 vs $19,702). Patients with some MHA-related costs were older, lived in poorer neighborhoods, and had higher levels of comorbidity compared to those without. After controlling for relevant variables, having any type of MHA-related utilization increased costs by $2,698. Having a diagnosis of psychosis had a large impact on costs. Limitations: This study did not examine children and adolescents. We were only able to account for 91% of all costs incurred by the public third-party payer; addiction-related costs from community-based agencies were not available. Conclusions: High-cost patients with MHA incur higher costs compared to those without. When considering interventions aimed at high-cost patients, policy-makers should consider their complex nature, specifically both their physical and MHA-related comorbidities.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.114
Threshold uncertainty score0.226

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.020
GPT teacher head0.289
Teacher spread0.268 · 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 designObservational
Domainnot available
GenreEmpirical

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
Published2017
Admission routes1
Has abstractyes

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