Antibiotic Expenditure by Public Healthcare Institutions in Shandong Province in China, 2012–2016
Bibliographic record
Abstract
Background: Overuse of antibiotics may not only increase the burden of antimicrobial resistance, but also accelerate the growth of healthcare expenditures. China has issued a series of policies to improve antibiotic use and reduce the cost of medicine in healthcare institutions. This study aimed to evaluate the trends of antibiotic expenditures in public healthcare institutions in Shandong from 2012 to 2016 and to assess antibiotic expenditures by drug class and level of healthcare institutions. Methods: We collected data from the centralized bidding procurement (CBP) system in Shandong province between 2012 and 2016. Governmental health facilities including secondary and tertiary hospitals, and urban and rural primary healthcare centres (PHCs) procured antibiotics via this system. Antibiotics were classified according to the Anatomical Therapeutic Chemical (ATC) classification system. Antibiotic expenditures were assessed using total annual expenditures (US dollars) and expenditure per person per year (US dollars). Results: The overall antibiotic expenditures were $717 million in 2016, a 56% increase compared to 2012. Parenteral antibiotics accounted for 84% of the total antibiotic expenditures in 2016. The majority of the antibiotics were procured by secondary and tertiary hospitals (33% and 44%, respectively). The expenditures continuously increased in secondary hospitals, tertiary hospitals and urban PHCs from 2012 to 2016, while antibiotic procurement decreased in urban PHCs since 2015. The third-generation cephalosporins (J01DD) were among the top five ATC classes of antibiotics in all the healthcare institutions. Fluoroquinolones (J01MA) were commonly procured by tertiary hospitals, rural and urban PHCs. The expenditures on carbapenems (J01DH) raised sharply in tertiary hospitals. Conclusions: The overall antibiotic expenditures kept increasing in the public healthcare institutions in Shandong. The trends of increasing expenditures began to decline in 2016, which may be associated with the antibiotic stewardship initiatives. The expenditures for expensive and critical important classes of antibiotics increased, therefore it is of importance to develop policies on improving the rational use of antibiotics.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".