The Relationship among Antibiotic Consumption, Socioeconomic Factors and Climatic Conditions
Bibliographic record
Abstract
BACKGROUND: Antibiotic consumption in human populations is one of the factors responsible for the emergence of resistant organisms. It is important to track population-based data on an ongoing basis, and to explore the determinants of regional variation in antibiotic consumption. METHODS: Population-level data were obtained on all outpatient oral antibiotic prescriptions dispensed within British Columbia (BC) between 1996 and 2007. Prescriptions were expressed as the defined daily dose per 1000 inhabitants. Geographical information systems mapping was used to display the spatial variations of antibiotic consumption in BC. The relationships among antibiotic consumption, socioeconomic factors and climatic conditions were explored using Pearson's correlation and regression modelling. RESULTS: Overall antibiotic consumption was highest in the northern regions of BC. Higher rates of consumption were associated with a greater proportion of the Aboriginal population, lower levels of education and individuals younger than 15 years of age. An inverse correlation was found between some classes of antibiotics and the following factors: individuals older than 65 years of age, mortality rate, doctor-to-population ratio, household size and higher July temperatures. The adjusted regression analyses indicated that higher antibiotic consumption was associated with a higher proportion of Aboriginals and household income. CONCLUSION: Different rates of antibiotic consumption exist within BC. The use of antibiotics is correlated with several socioeconomic factors and climatic conditions. It may be useful to consider these factors when designing policies to address antibiotic consumption in the community.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".