Developing National Information Systems to Monitor COVID-19 Vaccination: A Global Observational Study
Bibliographic record
Abstract
Background: Strong information systems are essential for safe and effective immunization programs. The COVID-19 vaccine rollout presented all immunization information systems (IIS) with challenging demands-requiring in-depth vaccine implementation data at all health system levels in real time. The system development approaches taken by countries were heterogeneous, with some countries opting to adapt existing systems and others implementing new ones. Objective: Using data reported by Member States to the World Health Organization (WHO), we aim to develop a global understanding of (1) the types of IIS used to monitor COVID-19 vaccination implemented in 2021 and (2) the approaches taken by countries to develop these systems. Methods: We conducted a descriptive analysis of data reported through a supplemental questionnaire of the WHO/United Nations Children's Emergency Fund (UNICEF) Joint Reporting Form on Immunization, collecting data for 2021 on (1) the use of and developmental approaches taken for 7 IIS functions (appointments, aggregate reporting, individual-level reporting, reminders, home-based records, safety surveillance, and stock management), and (2) modifications needed for digital health frameworks to permit COVID-19 vaccination monitoring. Results: In total, 188 of 194 WHO Member States responded to the supplemental questionnaire, with 155 reporting on the IIS-related questions. Among those reporting, for each of the 7 IIS functions explored, greater than 85% of responding countries reported that the system was in place for COVID-19 vaccines. Among responding countries, "aggregate reporting system" was the system most frequently reported as being in place (n=116, 98.3%), while "reminder system" was the least (n=77, 89%). Among the countries reporting using a system, whether an existing system was adapted for COVID-19 vaccines or a new one was developed varied by system. Additionally, two-thirds (n=127, 67.6%) of countries reported establishing at least one new system, ranging from 72% (n=42) in high-income countries (HICs) to 62% (n=16) in low-income countries. Concurrently, 55.3% (n=104) of countries reported adapting at least one system already in place for COVID-19 vaccines, with 62% (n=36) of HICs reporting this compared to about 53% for other income groups. Of those reporting developing new systems, for each of the systems explored, more than 85% of countries reported that they intended to keep new systems specific to COVID-19 vaccines. Further, 147 of the 188 (78.2%) Member States responding to the supplemental questionnaire responded to the digital health frameworks question. Lastly, 31% (n=46) of responding countries reported needing to adapt them for COVID-19 vaccination systems. HICs had a higher percentage. Conclusions: Nearly all countries have adapted existing or developed new IIS to monitor COVID-19 vaccination. The approaches varied, notably by income group. Reflection is needed on how to sustain the investments made in IIS during the pandemic. Continued support for IIS is critical, given their essential role in program monitoring and performance.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".