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Record W4405729863 · doi:10.1136/bmjopen-2023-078287

Relationship between pharmacotherapy for major depressive disorder and healthcare utilisation in British Columbia, Canada: a retrospective population-based cohort

2024· article· en· W4405729863 on OpenAlexafffundabout
Rohit Vijh, Zeina Waheed, Sandra Peterson, Mary Bunka, Louisa Edwards, Shahzad Ghanbarian, Gavin Wong, Sonya Cressman, Linda Riches, Jehannine Austin, Stirling Bryan, Alison M. Hoens, Kimberlyn McGrail

Bibliographic record

VenueBMJ Open · 2024
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsSimon Fraser UniversityVancouver Coastal HealthUniversity of British Columbia
FundersGenome British ColumbiaMichael Smith Health Research BCGenome Canada
KeywordsMedicinePharmacotherapyMajor depressive disorderPopulationMedical prescriptionPolypharmacyHealth careOdds ratioCohortCohort studyRetrospective cohort studyPsychiatryGerontologyDemographyFamily medicineInternal medicineEnvironmental healthMood

Abstract

fetched live from OpenAlex

OBJECTIVES: To describe the population that meets the criteria for major depressive disorder (MDD) in British Columbia (BC), compare patterns of healthcare utilisation between those with MDD who are and are not prescribed pharmacotherapy, and assess these relationships in models that control for potential confounding variables. DESIGN: We used a population cross-sectional study design among a cohort of individuals living with MDD and examined the relationship between pharmacotherapy and healthcare utilisation between 2019 and 2020 using linked billing and administrative data. SETTING: This study identified individuals with MDD using a validated case definition of International Classification of Diseases (ICD) codes in BC, Canada. PARTICIPANTS: The final study cohort included 549 029 adult participants who met the MDD case definition. EXPLANATORY VARIABLE: Explanatory variable was the use of prescription antidepressant medication during the study period, based on BC PharmaNet data. COVARIATES: Covariates include sociodemographic characteristics (age, sex, urban/rural residence, neighbourhood income quintile and comorbidities). PRIMARY OUTCOME MEASURE: Primary outcome measure was healthcare utilisation (outpatient physician visits, emergency department (ED) visits and hospitalisations). RESULTS: We stratified our analysis based on whether study participants were classified as 'recently incident' or 'actively prevalent'. The odds ratio (OR) for health service utilisation between the pharmacotherapy group and the non-pharmacotherapy group for individuals who were recently incident was 8.14 (95% CI 7.40, 8.95) for outpatient physician visits, 1.04 (95% CI 1.02, 1.07) for ED visits and 1.05 (95% CI 1.00, 1.10) for hospitalisations, after adjusting for comorbidities and other sociodemographic variables in our regression analyses, whereas for the actively prevalent group the ORs were 7.57 (7.27, 8.49), 0.91 (0.89, 0.92) and 1.00 (0.98, 1.02), respectively. CONCLUSION: This study examined the association of pharmacotherapy on healthcare utilisation for adults with MDD in BC. The study revealed higher outpatient physician visits for the pharmacotherapy group and no major association for inpatient visits. For ED visits, recently incident individuals on pharmacotherapy had slightly higher odds of having an ED visit, whereas individuals who were actively prevalent and on pharmacotherapy had a slight decrease in odds. This may suggest a protective effect of pharmacotherapy against a utilisation of resource-intensive healthcare services.

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 score0.018
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.004
Science and technology studies0.0020.001
Scholarly communication0.0010.000
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 head0.081
GPT teacher head0.451
Teacher spread0.370 · 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
Published2024
Admission routes3
Has abstractyes

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