A cost comparison of electronic and hybrid data collection systems in Ontario during pandemic and seasonal influenza vaccination campaigns
Bibliographic record
Abstract
BACKGROUND: During the pandemic (H1N1) 2009 influenza vaccination campaign, health regions in Canada collected client-level immunization data using fully electronic or hybrid systems, with the latter comprising both electronic and paper-based elements. The objective of our evaluation was to compare projected five-year costs associated with implementing these systems in Ontario public health units (PHUs) during pandemic and seasonal influenza vaccination campaigns. METHODS: Six PHUs provided equipment and staffing costs during the pandemic (H1N1) 2009 influenza vaccination campaign and staffing algorithms for seasonal campaigns. We standardized resources to population sizes 100,000, 500,000 and 1,000,000, assuming equipment lifetime of five years and public health vaccine administration rates of 18% and 2.5% for H1N1 and seasonal campaigns, respectively. Two scenarios were considered: Year 1 pandemic and Year 1 seasonal campaigns, each followed by four regular influenza seasons. Costs were discounted at 5%. RESULTS: Assuming a Year 1 pandemic, the five-year costs per capita for the electronic system decrease as PHU population size increases, becoming increasingly less costly than hybrid systems ($4.33 vs. $4.34 [100,000], $4.17 vs. $4.34 [500,000], $4.12 vs. $4.34 [1,000, 000]). The same trend is observed for the scenario reflecting five seasonal campaigns, with the electronic system being less expensive per capita than the hybrid system for all population sizes ($1.93 vs. $1.95 [100,000], $1.91 vs. $1.94 [500,000], $1.87 vs. $1.94 [1,000, 000]). Sensitivity analyses identified factors related to nurse hours as affecting the direction and magnitude of the results. CONCLUSIONS: Five-year cost projections for electronic systems were comparable or less expensive than for hybrid systems, at all PHU population sizes. An intangible benefit of the electronic system is having data rapidly available for reporting.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".