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Antipsychotic Drug Dispensations in Older Adults, Including Continuation After a Fall-Related Hospitalization: Identifying Adherence to Screening Tool of Older Persons’ Potentially Inappropriate Prescriptions Criteria Using the Nova Scotia Seniors' Pharmacare Program and Canadian Institute for Health's Discharge Databases

2018· article· en· W2888828785 on OpenAlexaffabout
Shanna Trenaman, Barbara Hill-Taylor, Kara Matheson, David M. Gardner, Ingrid Sketris

Bibliographic record

VenueCurrent Therapeutic Research · 2018
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsNova Scotia Health AuthorityDalhousie University
Fundersnot available
KeywordsMedicineMedical prescriptionAntipsychoticNova scotiaRisperidoneOddsLogistic regressionCohortPsychiatryFamily medicineSchizophrenia (object-oriented programming)Internal medicine

Abstract

fetched live from OpenAlex

PURPOSE: Despite well-established concerns regarding adverse drug effects, antipsychotics are frequently prescribed for older adults. Our first objective was to identify trends in antipsychotic dispensations to older Nova Scotians. STOPP (Screening Tool of Older Persons' Potentially Inappropriate Prescriptions) criteria identify antipsychotic use in those with a history of falls as potentially inappropriate. Our second objective was to identify trends, predictors, and adherence with this STOPP criteria by identifying continued antipsychotic dispensations following a fall-related hospitalization. METHODS: A descriptive cross-sectional cohort study of Nova Scotia Seniors' Pharmacare Program (NSSPP) beneficiaries ≥ 66 years with at least one antipsychotic dispensation annually from April 1, 2009 to March 31, 2014 was completed. As well, unique beneficiaries with at least one antipsychotic dispensation in the four-year period between April 1, 2009 and March 31, 2013 were linked to fall-related hospitalizations recorded in the Canadian Institute for Health Information Discharge Abstract Database. The relationship of age, sex, fiscal year, days supply and length-of-stay were studied to identify predictors of continued antipsychotic dispensation post-discharge. Descriptive statistics and multivariate logistic analysis were performed. Odds ratios for the association of risk factors and adherence to STOPP criteria were calculated. FINDINGS: We identified that in each year observed, there were 6% of eligible NSSPP beneficiaries that received at least one antipsychotic dispensation. Approximately 70% of antipsychotic dispensations were for second generation agents, primarily quetiapine and risperidone. Of the unique beneficiaries with at least one antipsychotic dispensation in the four-year period between April 1, 2009 and March 31, 2013 who survived a fall-related hospitalization over 75% were dispensed an antipsychotic in the 100 days following hospital discharge. Logistic regression showed no statistically significant association between potentially inappropriate therapy and potential predictors in multivariate analysis. IMPLICATIONS: In each year from 2009 to 2014, 6% of Nova Scotia Seniors' Pharmacare beneficiaries were dispensed at least one antipsychotic prescription. Over 75% of the older adults who received an antipsychotic dispensation in the 100 days prior to a fall-related hospitalization, continued the drug class after discharge. This demonstrates that despite the recommendations of quality indicators such as the STOPP criteria, antipsychotics are continued in individuals at a high risk of falling. Future investigations are needed to inform health team, system, and policy interventions to improve concordance with this antipsychotic specific STOPP criterion when appropriate.

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)0.005
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 score0.358
Threshold uncertainty score0.720

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
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 head0.199
GPT teacher head0.482
Teacher spread0.283 · 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

Citations8
Published2018
Admission routes2
Has abstractyes

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