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Record W6891627445 · doi:10.48336/bsze-6193

Investigating the association between prior history of asthma and later diagnosis of COPD: an analysis of British Columbia administrative health database in Canada

2023· article· en· W6891627445 on OpenAlexaffabout

Bibliographic record

VenueMemorial University Research Repository (Memorial University) · 2023
Typearticle
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsAsthmaCOPDObservational studyPopulationCohortCohort studyMedical historyEpidemiologyAsthma medication

Abstract

fetched live from OpenAlex

Adult asthma patients are at an increased likelihood of being diagnosed with chronic obstructive pulmonary diseases (COPD) later in life. The main aim of this dissertation is to examine the factors associated with asthma patients that lead to COPD diagnosis later in life. The study was motivated by the dearth of research explaining the complex relationship between prior history of asthma and a later diagnosis of COPD. Similarly, there is a lack of academic literature and clinical research that examines the association between sub-optimal medication adherence (MA) in asthma patients and their subsequent risk of COPD. Additionally, there exists no gold standard with a clinical or pharmacological rationale for measuring optimal MA in asthma patients using pharmacy-based databases. In examining these critical research areas, meta-analysis and a retrospective observational cohort design were employed. Four linked databases obtained from the Population Data BC, spanning from January 1, 1998, to December 31, 2018, were used for data analysis. The meta-analysis showed that patients with a previous history of asthma were 7.87 times more likely to develop COPD in the future than were non-asthmatics. In addition, an analysis of the Population Data BC found that the following risk factors predicted COPD in asthma patients: “being an older adult (40 years and older)”, “being male and obese”, “a history of tobacco use”, “comorbidity burden”, “length of hospital stay”, “asthma severity levels”, “asthma exacerbations”, and overuse of Short-Acting Beta-2 Agonist (SABA). Also, the study identified medication possession ratio (MPR) and proportion of days covered (PDC) as the most commonly used methods for measuring medication adherence with higher sensitivity. The study identified an adherence threshold of at least 0.80 as optimal in categorizing adherent and non-adherent adult asthma patients. Further, patients who achieved a sub-optimal level of MA were at a significantly increased risk for developing COPD over time after adjusting for relevant confounders. Levels of asthma severity modified the MA effect. Additionally, overuse of short-acting beta-2 agonist (SABA) was significantly associated with an increased risk of COPD after adjusting for relevant confounders and covariates. This study’s findings provide important insights into the lifestyle and behavioural risk factors associated with COPD risk. Healthcare providers and policymakers should highlight the need for smoking cessation programs, weight management, and medication compliance interventions, particularly in difficult-to-control adult asthma patients who are at an elevated risk of developing COPD.

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)0.003
metaresearch head score (Gemma)0.015
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 score0.027
Threshold uncertainty score0.193

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0050.021
Science and technology studies0.0030.001
Scholarly communication0.0030.001
Open science0.0030.001
Research integrity0.0010.002
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 head0.042
GPT teacher head0.283
Teacher spread0.241 · 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
Published2023
Admission routes2
Has abstractyes

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