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Record W4320485624 · doi:10.2196/44308

Health Care Resource Utilization in Adults Living With Type 1 Diabetes Mellitus in the South African Public Health Sector: Protocol for a 1-Year Retrospective Analysis With a 5-, 10-, and 25-Year Projection

2023· article· en· W4320485624 on OpenAlexvenueno aff
Sindeep Bhana, Poobalan Naidoo, S Pillay, Ebrahim Variava, Kiolan Naidoo, Neeresh Rohitlall, Sekhuthe Lauren, Bruno Pauly

Bibliographic record

VenueJMIR Research Protocols · 2023
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePublic healthHealth careProtocol (science)GerontologyType 2 Diabetes MellitusFamily medicineDiabetes mellitusEnvironmental healthAlternative medicineNursingEconomic growth

Abstract

fetched live from OpenAlex

Background Type 1 diabetes mellitus (T1DM) is less common than type 2 diabetes mellitus but is increasing in frequency in South Africa. It tends to affect younger individuals, and upon diagnosis, exogenous insulin is essential for survival. In South Africa, the health care system is divided into private and public health care systems. The private system is well resourced, whereas the public sector, which treats more than 80% of the population, has minimal resources. There are currently no studies in South Africa, and Africa at large, that have evaluated the immediate and long-term costs of managing people living with T1DM in the public sector. Objective The primary objective was to quantify the cost of health care resource utilization over a 12-month period in patients with controlled and uncontrolled T1DM in the public health care sector. In addition, we will project costs for 5, 10, and 25 years and determine if there are cost differences in managing subsets of patients who achieve glycemic control (hemoglobin A1c [HbA1c] <7%) and those who do not. Methods The study was performed in accordance with Good Epidemiological Practice. Ethical clearance and institutional permissions were acquired. Clinical data were collected from 2 tertiary hospitals in South Africa. Patients with T1DM, who provided written informed consent, and who satisfied the inclusion criteria were enrolled in the study. Data collection included demographic and clinical characteristics, acute and chronic complications, hospital admissions, and so on. We plan to perform a cost-effectiveness analysis to quantify the costs of health care utilization in the preceding 12 months. In addition, we will estimate projected costs over the next 10 years, assuming that study participants maintain their current HbA1c level. The cost-effectiveness analysis will be modeled using the IQVIA CORE Diabetes Model. The primary outcome measures are incremental quality-adjusted life years, incremental costs, incremental cost-effectiveness ratios, and incremental life years. Results Ethical clearance and institutional approval were obtained (reference number 200407). Enrollment began on February 9, 2021, and was completed on August 24, 2021, with 224 participants. A database lock was performed on October 29, 2021. The statistical analysis and clinical study report were completed in January 2022. Conclusions At present, there are no data assessing the short- and long-term costs of managing patients with T1DM in the South African public sector. It is hoped that the findings of this study will help policy makers optimally use limited resources to reduce morbidity and mortality in people living with T1DM. International Registered Report Identifier (IRRID) RR1-10.2196/44308

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)0.016
metaresearch head score (Gemma)0.013
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: Protocol · Consensus signal: Protocol
Teacher disagreement score0.016
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.013
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0080.009
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0160.003

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 head0.186
GPT teacher head0.486
Teacher spread0.299 · 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
GenreProtocol

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

Citations1
Published2023
Admission routes1
Has abstractyes

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