Establishing a Multicenter Active Adverse Events Following Immunization Sentinel Surveillance Network Across 22 Tertiary Care Hospitals in India: Protocol for a Prospective Observational Study
Bibliographic record
Abstract
BACKGROUND: The rapid evolution of immunization programs in low- and middle-income countries (LMICs) has necessitated an augmentation of capacity for postlicensure vaccine safety monitoring. OBJECTIVE: This study describes the protocol for establishing a Multicenter Active Adverse Events Following Immunization Surveillance System (MAASS) network in India, which conducted prospective observational surveillance for 12 adverse pediatric outcomes between November 1, 2017, and March 20, 2020. METHODS: A multistage site selection process was implemented, beginning with an initial screening survey followed by in-person visits to assess the suitability of potential tertiary care hospitals for inclusion in the network. We adopted a decentralized, collaborative approach to develop the study protocol, standardize case definitions, establish data collection procedures, and create a common data model for monitoring and analysis. Outcomes selected for surveillance included acute disseminated encephalomyelitis, anaphylaxis, aseptic meningitis, dengue, Guillain-Barré syndrome, Kawasaki disease, malaria, seizure, sepsis, thrombocytopenia, intussusception, and urinary tract infections. We screened all children aged 1-24 months who were hospitalized for more than 24 hours at participating sites to identify suspected or confirmed cases of these outcomes using a structured checklist. Written informed consent was obtained from the parent or legally authorized representative for inclusion in the study. Demographic, socioeconomic, and vaccine exposure information was collected for all included participants. Additional clinical information was gathered to assess the level of diagnostic certainty according to standardized case definitions. The study progressed through 3 distinct phases: network establishment (January-November 2017), active surveillance (November 2017-March 2020), and database analysis (April 2020-March 2024). The dissemination process is currently underway. RESULTS: A geographically representative data network was established across 15 public and 7 private tertiary care hospitals in 17 states and 1 union territory in India. During the study period, we screened 90,147 age-eligible admissions and confirmed 8362 cases with study outcomes. Using multiple analytic study designs, we generated a database of outcomes and exposures to investigate associations between vaccine-event pairs of interest. CONCLUSIONS: The MAASS network is unprecedented in its scope and scale among LMICs. While the study is specific to India, the lessons learned in establishing and implementing the network offer valuable insights for developing active surveillance systems and strengthening capacity for benefit-risk evaluations of vaccines in resource-constrained settings. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/64050.
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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 | 0.092 | 0.058 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 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".