MétaCan
Menu
Back to cohort

Sociodemographic Factors and Utilization of Pediatric Oncology Satellite Clinics in Ontario, Canada

2024· article· en· W4405802318 on OpenAlexaffabout
Maria Chiu, A. Ait Ali, Felicia Ga-Yin Leung, Chaoran Dong, Petros Pechlivanoglou, David M. Hodgson, Paul Gibson

Bibliographic record

VenueJAMA Network Open · 2024
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsHealth Sciences CentreMcMaster UniversityMcMaster Children's HospitalPrincess Margaret Cancer CentreInstitute for Clinical Evaluative SciencesHamilton Health SciencesUniversity Health NetworkPediatric Oncology GroupUniversity of TorontoSickKids FoundationHospital for Sick Children
Fundersnot available
KeywordsMedicineAttendanceOddsPopulationCancer registryDemographyFamily medicineLogistic regressionEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

Importance: Pediatric cancer care services in high-income nations are mainly centralized in metropolitan cities. To allow treatments closer to home, patients across Ontario, Canada, a geographically large province, are offered decentralized care via satellite clinics; however, it is unclear whether the utilization of these pediatric oncology satellite clinics differs by area-level sociodemographic factors. Objective: To examine whether sociodemographic factors, such as area-level income and rurality, are independently associated with the odds of satellite clinic visit and the hazards of time to first visit among pediatric oncology patients receiving cancer treatment. Design, Setting, and Participants: This is a retrospective population-based cohort study of patients aged 0 to 17 years with cancer living in a satellite catchment area in Ontario, Canada (from 2015 to 2022, with follow-up through 2023). The Pediatric Oncology Group of Ontario Networked Information System cancer registry (containing age, sex, diagnosis type, and year information) was linked to satellite, OpenStreetMap, and postal-code databases to ascertain rural or urban dwelling, neighborhood-based income, and driving time saved. Exposures: Age, sex, rural or urban dwelling, and neighborhood-level income quintiles. Main Outcomes and Measures: The primary outcomes were the odds of satellite clinic attendance within 1 year of diagnosis and the hazards of first clinic visit after starting systemic therapy, derived from multivariable logistic and Cox proportional hazards models, respectively. Results: Among the 1280 eligible patients (median [IQR] age, 7.0 [3.0-13.0] years; 753 male [58.8%]), 844 (65.9%) visited a satellite clinic within 1 year of diagnosis with a median (IQR) of 39 (14-67) days to first visit. Driving time saved (>60 minutes) was the factor most associated with satellite use, followed by diagnostic type (with patients with central nervous system tumors least likely to visit). Rural (vs urban) patients had significantly lower odds of satellite visit within a year of diagnosis (odds ratio, 0.48; 95% CI, 0.31-0.74; P = .001) and lower instantaneous likelihood of visiting after start of treatment (hazard ratio, 0.65; 95% CI, 0.53-0.81; P < .001). Living in a lower-income (vs middle-income) area was also associated with significantly lower utilization (odds ratio, 0.53; 95% CI, 0.35-0.80; P = .009; hazard ratio, 0.73; 95% CI, 0.60-0.89; P = .002). Conclusions and Relevance: This population-based study of pediatric oncology patients found that satellite clinics, despite being designed to reduce transportation and financial burdens, were disproportionately underutilized by patients living in rural and lower-income areas. Monitoring area-level social determinants of health can help inform interventions to improve timely and equitable access to childhood cancer care closer to home.

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)0.001
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 score0.081
Threshold uncertainty score0.582

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.069
GPT teacher head0.354
Teacher spread0.285 · 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
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueJAMA Network OpenSame topicChildhood Cancer Survivors' Quality of LifeFrench-language works237,207