Varicella healthcare resource utilization in middle income countries: a pooled analysis of the multi-country MARVEL study in Latin America & Europe
Bibliographic record
Abstract
Varicella is a mild and self-limited illness in children, but can result in significant healthcare resource utilization (HCRU). To quantify/contrast varicella-associated HCRU in five middle-income countries (Hungary, Poland, Argentina, Mexico, and Peru) where universal varicella vaccination was unimplemented, charts were retrospectively reviewed among 1–14 year-olds. Data were obtained on management of primary varicella between 2009–2016, including outpatient/inpatient visits, allied healthcare contacts, tests/procedures, and medications. These results are contrasted across countries, and a regression model is fit to extrapolated country-level costs as a function of gross domestic product (GDP). A total of 401 outpatients and 386 inpatients were included. Significant differences between countries were observed in the number of skin lesions among outpatients, ranging from 5.3% to 25.4% of patients with ≥250 lesions. Among inpatients, results were less variable. Average ambulatory medical visits ranged from 1.1 to 2.2. Average hospital stay ranged from 3.6 to 6.8 days. Use of tests/procedures was infrequent in outpatients, except in Argentina (13.3%); among inpatients, a test/procedure was ordered for 81.3% of patients, without regional variation. Prescription medications were administered in 44.4% of outpatients (range 9.3%–80.0%), and in 86% of inpatients (range 70.4%–94.9%). Total estimated spending on varicella treatment in the absence of vaccination was predicted from income levels (GDP) with an exponential function (R2 = 0.89). This study demonstrates that substantial HCRU is associated with varicella resulting in significant public health burden that could be alleviated through the use of varicella vaccination. Differences observed between countries possibly reflect treatment guidelines, healthcare resource availabilities and physician practices.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".