THE IMPACT OF COST-SHARING CHANGES ON PRESCRIPTION ANTIBIOTIC FILL RATES IN CHILDREN WITH OTITIS MEDIA
Bibliographic record
Abstract
From 1975, when the SPDP was initiated, until June 30, 1987, those with STD benefits paid a flat copayment per prescription, with the balance paid by the Province, directly to the pharmacy. On July 1, 1987 the cost-sharing scheme was changed to a deductible plus coinsurance in a nonassignment format. The assignment format returned on January 1, 1989. Children receiving SAP benefits retained first dollar coverage. \n\nThe objective of the present study was to determine the impact of these changes on prescription antibiotic fill rates for children aged 0 to 14 years, with an otitis media diagnosis in the physician claims data base. Children receiving SAP benefits served as the nonequivalent control group.\n\nThe hypotheses were:\n• the changes to STD benefits would have no impact on prescription antibiotic utilization rates in either group, and\n• stratification of the time series on selected variables would not reveal a statistically significant impact when comparing pre- and post-intervention\nperiods in either population.\n\nThe study was population-based and quasi-experimental, multiple time series by design. Data were gathered by record linkage of three Saskatchewan Health data bases: MCIB, PDSB, and the HIRF. Analysis included descriptive and interrupted time series analyses. \n\nEach population demonstrated a statistically significant decrease (p <0.05) in prescription antibiotic fill rates following the SPDP changes in STD benefits. However, the decrease experienced by the STD population was larger than seen in the SAP population by 2.569 prescriptions per 100 OM episodes.\n\nThe second set of null hypotheses was rejected in all cases except both levels of Parent type in the SAP group, Specialist level of MD type in the SAP group, and the South level of Location in both SAP and STD groups. Interpopulation differences were found on all categories except Location-South. The STD population consistently demonstrated a decrease, while the SAP population demonstrated no changes or increases in fill rates.\n\nOverall the increased cost sharing for the SPDP STD beneficiaries negatively affected the fill rates of a necessary prescribed medication. Those considering increasing the patient cost share of a prescription drug program are cautioned that this may negatively affect the acquisition of necessary medications. This may worsen health outcome, and short-term savings may be offset by long-term expenses. Public program changes should include an evaluation component to facilitate such assessment.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".