Estimate of the direct and indirect annual cost of bacterial conjunctivitis in the United States
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to estimate both the direct and indirect annual costs of treating bacterial conjunctivitis (BC) in the United States. This was a cost of illness study performed from a U.S. healthcare payer perspective. METHODS: A comprehensive review of the medical literature was supplemented by data on the annual incidence of BC which was obtained from an analysis of the National Ambulatory Medical Care Survey (NAMCS) database for the year 2005. Cost estimates for medical visits and laboratory or diagnostic tests were derived from published Medicare CPT fee codes. The cost of prescription drugs was obtained from standard reference sources. Indirect costs were calculated as those due to lost productivity. Due to the acute nature of BC, no cost discounting was performed. All costs are expressed in 2007 U.S. dollars. RESULTS: The number of BC cases in the U.S. for 2005 was estimated at approximately 4 million yielding an estimated annual incidence rate of 135 per 10,000. Base-case analysis estimated the total direct and indirect cost of treating patients with BC in the United States at $ 589 million. One- way sensitivity analysis, assuming either a 20% variation in the annual incidence of BC or treatment costs, generated a cost range of $ 469 million to $ 705 million. Two-way sensitivity analysis, assuming a 20% variation in both the annual incidence of BC and treatment costs occurring simultaneously, resulted in an estimated cost range of $ 377 million to $ 857 million. CONCLUSION: The economic burden posed by BC is significant. The findings may prove useful to decision makers regarding the allocation of healthcare resources necessary to address the economic burden of BC in the United States.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".