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Record W4294202095 · doi:10.1192/j.eurpsy.2022.326

Health resource utilization, costs, and community treatment order status before and after the initiation of second-generation long acting-injectable antipsychotics in patients with schizophrenia in Alberta, Canada

2022· article· en· W4294202095 on OpenAlexaffabout
Pierre Chue, K.O. Wong, S. Klarenbach, Kyana C. Martins, S. Dursun, M. Snaterse, L. Richer

Bibliographic record

VenueEuropean Psychiatry · 2022
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineConfidence intervalSchizophrenia (object-oriented programming)Observational studyDefined daily doseRetrospective cohort studyAntipsychoticHealth careEmergency medicinePediatricsPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

Introduction Long-acting injectable (LAI) antipsychotics and community treatment orders (CTOs) are used in patients with schizophrenia to improve treatment effectiveness through adherence. Objectives Understanding healthcare resource utilization (HRU) and associated costs, and medication adherence in patients with schizophrenia overall and by CTO status before and after second generation antipsychotic (SGA) LAI initiation may guide strategies to optimize health. Methods A retrospective observational single-arm study using administrative data from Alberta was performed. Adults with schizophrenia who initiated SGA-LAI (index date) were included. Medication possession ratio (MPR) was determined; paired t-tests were used to examine differences in HRU and costs ($CDN) between the 2-year pre-index period and 2-year post-index period. Stratified analysis by presence or absence of an active CTO during the pre-post periods was performed. Results Among 1,211 patients who initiated SGA-LAIs, MPR was greater post-index (0.84) compared with pre-index (0.45; 95% confidence interval [CI] 0.36, 0.41). All-cause and mental health-related HRU and costs were lower post-index versus pre-index (p<0.001); total all-cause HRU costs were $33,788 lower post- versus pre-index ($40,343 [standard deviation, SD $68,887] versus $74,131 [SD $75,941], 95% CI [-$38,993, -$28,583]), and total mental health-related HRU costs were $34,198 lower post- versus pre-index ($34,205 [SD $63,428] CDN versus $68,403 [SD $72,088] CDN, 95%CI [-$39,098, -$29,297]). Forty-three percent had ≥1 active CTO during the study period; HRU and costs varied according to CTO status. Conclusions SGA-LAIs are associated with improved adherence, and lower HRU and costs however the latter vary according to CTO status. Disclosure The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this abstract: SD and MS have no competing interest to declare. LR, SK, KW, and KM are members of the Real-World Evidence

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)0.002
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 score0.031
Threshold uncertainty score0.225

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.004
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.254
Teacher spread0.239 · 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
Published2022
Admission routes2
Has abstractyes

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