The Effectiveness of Pfizer-BioNTech and Oxford-AstraZeneca Vaccines to Prevent Severe COVID-19 in Costa Rica: Nationwide, Ecological Study of Hospitalization Prevalence
Bibliographic record
Abstract
BACKGROUND: The Costa Rican COVID-19 vaccination program has used Pfizer-BioNTech and Oxford-AstraZeneca vaccines. Real-world estimates of the effectiveness of these vaccines to prevent hospitalizations range from 90%-98% for two doses and from 70%-91% for a single dose. Almost all of these estimates predate the Delta variant. OBJECTIVE: The aim of this study is to estimate the dose-dependent effectiveness of COVID-19 vaccines to prevent severe illness in real-world conditions in Costa Rica, after the Delta variant became dominant. METHODS: This observational study is a secondary analysis of hospitalization prevalence. The sample is all 3.67 million adult residents living in Costa Rica by mid-2021. The study is based on public aggregated data of 5978 COVID-19-related hospital records from September 14, 2021, to October 20, 2021, and 6.1 million vaccination doses administered to determine hospitalization prevalence by dose-specific vaccination status. The intervention retrospectively evaluated is vaccination with Pfizer-BioNTech (78%) and Oxford-AstraZeneca (22%). The main outcome studied is being hospitalized. RESULTS: Vaccine effectiveness against hospitalization (VEH) was estimated as 93.4% (95% CI 93.0-93.9) for complete vaccination and 76.7% (95% CI 75.0-78.3) for single-dose vaccination among adults of all ages. VEH was lower and more uncertain among older adults aged ≥58 years: 92% (95% CI 91%-93%) for those who had received full vaccination and 64% (95% CI 58%-69%) for those who had received partial vaccination. Single-dose VEH declined over time during the study period, especially in the older age group. Estimates were sensitive to possible errors in the population count used to determine the residual number of unvaccinated people when vaccine coverage is high. CONCLUSIONS: The Costa Rican COVID-19 vaccination program that administered Pfizer-BioNTech and Oxford-AstraZeneca vaccines seems to be highly effective at preventing COVID-19-related hospitalization after the Delta variant became dominant. Even a single dose seems to provide some degree of protection, which is good news for people whose second dose of the Pfizer-BioNTech vaccine was postponed several weeks to more rapidly increase the number of people vaccinated with a first dose. Timely monitoring of vaccine effectiveness is important to detect eventual failures and motivate the public to get vaccinated by providing information regarding the effectiveness of the vaccines.
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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) | 0.000 | 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 | 0.000 | 0.001 |
| 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 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".