Impact of COVID-19 on routine immunisation in South-East Asia and Western Pacific: Disruptions and solutions
Bibliographic record
Abstract
BACKGROUND: Data on COVID-19-induced disruption to routine vaccinations in the South-East Asia and Western Pacific regions (SEAR/WPR) have been sparse. This study aimed to quantify the impact of COVID-19 on routine vaccinations by country, antigen, and sector (public or private), up to 1 June 2020, and to identify the reasons for disruption and possible solutions. METHODS: Sanofi Pasteur teams from 19 countries in SEAR/WPR completed a structured questionnaire reporting on COVID-19 disruptions for 13-19 routinely delivered antigens per country, based on sales data, government reports, and regular physician interactions. Data were analysed descriptively, disruption causes ranked, and solutions evaluated using a modified public health best practices framework. FINDINGS: 95% (18/19) of countries reported vaccination disruption. When stratified by country, a median of 91% (interquartile range 77-94) of antigens were impacted. Infancy and school-entry age vaccinations were most impacted. Both public and private sector healthcare providers experienced disruptions. Vaccination rates had not recovered for 39% of impacted antigens by 1 June 2020. Fear of infection, movement/travel restrictions, and limited healthcare access were the highest-ranked reasons for disruption. Highest-scoring solutions were separating vaccination groups from unwell patients, non-traditional vaccination venues, virtual engagement, and social media campaigns. Many of these solutions were under-utilised. INTERPRETATION: COVID-19-induced disruption of routine vaccination was more widespread than previously reported. Adaptable solutions were identified which could be implemented in SEAR/WPR and elsewhere. Governments and private providers need to act urgently to improve coverage rates and plan for future waves of the pandemic, to avoid a resurgence of vaccine-preventable diseases. FUNDING: Sanofi Pasteur.
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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".