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Record W4408029621 · doi:10.1016/j.obpill.2025.100171

Management and impact of obesity in Canada: A real-world survey of people with obesity and their physicians

2025· article· en· W4408029621 on OpenAlexaffabout
Jennifer M. Glass, Sophie Carter, Esther Artime, Victoria Higgins, Lewis Harrison, Andrea Leith, David C.W. Lau, Ian Patton, Jennifer L. Kuk

Bibliographic record

VenueObesity Pillars · 2025
Typearticle
Languageen
FieldMedicine
TopicBariatric Surgery and Outcomes
Canadian institutionsYork UniversityCanadian Obesity NetworkUniversity of CalgaryEli Lilly (Canada)
FundersEli Lilly and Company
KeywordsObesityManagement of obesityMedicineEnvironmental healthGerontologyFamily medicineInternal medicineWeight loss

Abstract

fetched live from OpenAlex

Obesity is a chronic relapsing disease associated with multiple complications. This study described real-world demographic/clinical characteristics, including obesity-related complications (ORCs), prescribing rationale, and patient-reported outcome measures (PROMs) for adults living with obesity in Canada accessing treatment. This was a cross-sectional survey of physicians and consulting people with obesity (PwO) in Canada with retrospective data capture in a real-world setting. Canadian data were drawn between July and November 2022 from the multinational Adelphi Real World Obesity Disease Specific Programme™. Consulting PwO were required to be on a weight management program and/or have a current body mass index of ≥30 kg/m 2 . Physicians completed questionnaires for the next 3–5 consecutive PwO seen in their routine clinical practice. A quota was applied for obesity management medication (OMM). PROMs including Work Productivity and Activity Impairment (WPAI) questionnaire were provided voluntarily by PwO. Analyses were descriptive. Overall, 50 physicians (35 general practitioners, 15 endocrinologists) and 199 PwO were analyzed. More than 85 % of PwO had ≥1 ORC. The most common ORCs were hypertension, dyslipidemia, depression, and type 2 diabetes, and one-quarter to one-half of ORCs were not optimally controlled. Approximately two-thirds of the cohort were employed full-time, almost half had private insurance, and almost 70 % were classified as high socio-economic status. Mean number of weight-reduction attempts over the past 3 years was 2.9. Pharmacological treatment for obesity was common among those with ORCs. A general trend towards greater work impairment among people with ORCs than for PwO without ORCs was observed. Among PwO participating in our study, ORCs were common, often uncontrolled, and their presence impacted the likelihood of obesity treatment and possibly impaired work productivity. Medical treatment for obesity was often delayed until ORCs developed, suggesting that preventative healthcare measures are not the norm for PwO in Canada. A large proportion of PwO had high socioeconomic status, suggesting that PwO who access treatment may not be representative of the overall population of PwO in Canada. • Obesity is a chronic, relapsing and burdensome disease that is often not treated appropriately in Canada due to socioeconomic, healthcare system and payer barriers. • >85 % of people with obesity (PwO) in the study had obesity-related complications (ORCs), and obesity management medication (OMM) was prescribed primarily for those with ORCs, suggesting that treatment is mostly initiated after ORCs occur rather than preventatively. • WPAI questionnaire results suggest that there was a general trend towards greater activity/work impairment among PwO who had ORCs than for those without ORCs, although statistical analysis was not conducted. • Effective treatment should be accessible for all PwO in Canada to help reduce obesity and ORCs, but further work is needed to achieve this goal, including educating physicians and improving equitable access to OMM, particularly for the prevention of ORCs.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.046
Threshold uncertainty score0.528

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.009
GPT teacher head0.239
Teacher spread0.230 · 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 teacher head, 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

Citations3
Published2025
Admission routes2
Has abstractyes

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