Competition Among Pharmacies as a Determinant of Drug Expenditures
Bibliographic record
Abstract
This study explored and compared the associations between pharmacy competition and drug expenditures by individuals with influenza. This study used a dataset consisting of 6 694 534 individuals who purchased drugs for influenza at pharmacies from 2015 to 2019 in China. Patients' annual average influenza-specific drug expenditures per visit at pharmacies was the outcome variable of interest. Pharmacy competition was measured using the Herfindahl-Hirschman index. A 3-way fixed-effects model combined with a lagged identification strategy was constructed to estimate the association between pharmacy competition and drug expenditures. When the radius of the market was set to 1, 5, and 10 km, for each 10% increase in the degree of total competition in the market, an individual's annual average influenza-specific drug expenditures per visit fell by 0.65%, 2.21%, and 5.20%, respectively. With a more detailed understanding of the underlying mechanism through which pharmacy competition affects the behaviors of health care providers, competition can be considered as a potential tool to assist decision makers in the design of policies to curtail the growth in drug expenditures.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".