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Global Inequities in Diabetes Technology and Insulin Access and Glycemic Outcomes

2025· article· en· W4413758376 on OpenAlexaff
Alzbeta Šantová, Martin de Bock, Stefanie Lanzinger, Ellen B. Goldbloom, Nataša Bratina, Consuelo Barcala, Doha Alhomaidah, Arun K. Pande, Pravesh Kumar Guness, Iveta Dzīvīte-Krišāne, Catarina Limbert, Zdenĕk Šumnı́k

Bibliographic record

VenueJAMA Network Open · 2025
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Research
Canadian institutionsChildren's Hospital of Eastern OntarioUniversity of Ottawa
Fundersnot available
KeywordsGlycemicDiabetes mellitusMedicineInsulinInternal medicineEndocrinology

Abstract

fetched live from OpenAlex

Importance: Advanced diabetes technologies such as continuous glucose monitoring (CGM), continuous subcutaneous insulin infusion (insulin pumps [CSII]), and glucometers alongside insulin access represent the criterion standard for managing type 1 diabetes (T1D) in children. Global disparities in their access and reimbursement may be associated with glycemic outcomes. Objective: To describe how accessibility and reimbursement of advanced diabetes technologies and insulin are associated with glycated hemoglobin (HbA1c) levels in centers participating in the SWEET initiative, an international pediatric diabetes registry. Design, Setting, and Participants: This global multicenter cross-sectional study collected data from 81 centers in 56 countries. Web-based questionnaires were distributed to representatives of all 121 pediatric diabetes centers participating in the SWEET initiative from March 1 to May 31, 2024, and used to map accessibility of and reimbursement for CGM, CSII, glucometers, and insulin. Reimbursement data were compared with HbA1c levels using the SWEET Study dataset. Participants included 42 349 children with T1D. Exposures: Responses were categorized into 4 groups based on the extent of reimbursement for diabetes technologies and insulin. Main Outcomes and Measures: Mean HbA1c levels across centers calculated from measurements current as of December 31, 2023, analyzed by categories of accessibility of and reimbursement for diabetes technologies and insulin. Results: Data collected from 81 of 121 SWEET centers (67%) across 56 countries included HbA1c levels from 42 349 children with T1D (22 021 male [52%]; mean [SD] age, 14.3 [4.4] years; mean [SD] diabetes duration, 6.0 [4.2] years). Universal access with complete reimbursement for all technologies and insulin was reported by 32 centers from 19 countries, while 8 countries reported no reimbursement for any technologies or insulin. Centers with full reimbursement for CSII, CGM, glucometers, and insulin showed mean HbA1c levels of 7.62% (95% CI, 7.59%-7.64%) to 7.75% (95% CI, 7.73%-7.77%) compared with 9.65% (95% CI, 9.55%-9.71%) to 10.49% (95% CI, 10.40%-10.58%) in centers with no reimbursement and/or no availability (P < .001 for all items). Conclusions and Relevance: This cross-sectional study found that HbA1c levels were associated with the accessibility of modern diabetes technologies and insulin. Efforts to ensure universal accessibility are required to reduce global inequities and glycemic outcomes for children with T1D.

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)0.004
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 score0.013
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.021
GPT teacher head0.357
Teacher spread0.336 · 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

Citations11
Published2025
Admission routes1
Has abstractyes

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