Real-World Effectiveness of COVID-19 Vaccines (ChAdOx-1s, CoronaVac, BBIBP-CorV, mRNA-1273, and BNT162b2) in Jakarta: Protocol for Test-Negative Design of Health Care Data
Bibliographic record
Abstract
BACKGROUND: ChAdOx-1s, CoronaVac, BBIBP-CorV, mRNA-1273, and BNT162b2 are the five common COVID-19 vaccines used in Jakarta. Randomized controlled trials have provided robust evidence of the safety and efficacy profile of these vaccines, but their real-world vaccine effectiveness against symptomatic COVID-19 and deaths in communities with social inequalities and health care constraints remains unclear. OBJECTIVE: This study aims to evaluate the real-world effectiveness of these COVID-19 vaccines during the waves associated with the Delta and Omicron variants by analyzing existing electronic health care sources. METHODS: A population-based study with a test-negative case-control design will be used to evaluate COVID-19 vaccine effectiveness in Jakarta, focusing on the Delta and Omicron waves. It includes adults 18 years and older who underwent reverse transcription polymerase chain reaction testing for symptomatic COVID-19, classifying them as cases or controls based on their test results. The analysis will consider multiple COVID-19 vaccines introduced during these periods, with participants categorized by vaccination status. Several potential confounders will be assessed, including demographic factors and comorbidities. Data will be linked from various health datasets, and statistical analyses will be performed to determine vaccine effectiveness and potential waning immunity over time. After data linkage, patients' identities will be encrypted. RESULTS: The research, funded from 2022 to 2024, involved proposal preparation and ethical review in 2023 and enrollment from early 2024 to July 2024, resulting in about 4 million linked data points. Data analysis is ongoing, with initial results expected for publication in early 2025. CONCLUSIONS: This study will be the first to evaluate the effectiveness of different types of COVID-19 vaccines (inactivated, viral-vector, and mRNA) used in Jakarta during the pandemic, providing valuable scientific evidence to inform future vaccination strategies in the country. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/56519.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.087 | 0.117 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".