Brand name or generic? What are the health professionals prescribed for treating diabetes? A longitudinal analysis of the National Health Insurance reimbursement database
Bibliographic record
Abstract
OBJECTIVE: This study aimed to explore whether physicians prescribe more brand-name oral hypoglycemic agents (OHA) for diabetic patients with medical training background (MP) than for general patients (GP). RESEARCH DESIGN AND METHODS: A longitudinal analysis of 1,000,000 National Health Insurance cohorts of 1998-2008 was conducted. Univariate and multivariate models were performed to assess the associations of the outcome (the ratio of brand-name/generic odds in the MP group to that in the GP group) and the covariates, including patient medical training background, characteristics of patient, prescriber, and medical settings, and market competition. A generalized estimating equation method was used to control the dependency of longitudinal data. RESULTS: A total of 46,850 diabetic patients were prescribed with 2,703,149 OHA prescriptions during the study period. Compared with GP, MP had 1.37 times greater odds of being prescribed with brand-name instead of generic OHA, among whom pharmacists and physicians had the highest odds ratios of 2.78 (95%CI, 1.05-7.36) and 1.68 (95%CI, 0.99-2.85), respectively. Patients' diabetes severity, prescribers' level of experience, medical settings that were publicly owned, had a higher accreditation level, and were located in a higher urbanized area, lower market competition, and earlier dates of prescription were positively associated with brand-name prescription. Among all medical sub-specialties, cardiologists were more likely to prescribe brand-name OHA. CONCLUSIONS: This study is the first to demonstrate how a patients' medical training background, in addition to the characteristics of patients, prescribers, and medical settings, and market competition might influence physicians' prescribing choice of brand-name or generic OHA.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".