An Interrupted Time Series Analysis of the Impact of the COVID-19 Pandemic on Routine Vaccination Uptake in Kenya
Bibliographic record
Abstract
A strategic priority of the World Health Organization's Immunization Agenda 2030 is to increase vaccination coverage and equity through reaching "zero-dose" children. Through an ecological study, we sought to quantify the impact of the COVID-19 pandemic on the coverage of the pentavalent and the measles/rubella vaccines in Kenya, without implying causality. The monthly number of doses from January 2017 to August 2022 were obtained from the Kenya Health Information System for the pentavalent and the measles/rubella vaccines. Immediate (step) and long-term (ramp) changes following interruptions occurring during the period from March 2020 to December 2020 were assessed through an interrupted time series analysis using an autoregressive integrated moving average (ARIMA) model, accounting for seasonality. In December 2020, there was an immediate decrease of 8337, 12,212, and 20,848 in the number of doses for the first, second, and third dose of the pentavalent vaccine, respectively (statistically significant for the third dose only). This corresponded to a percentage relative difference of -21.6, -20.1, and -24.5, respectively, for three doses of pentavalent vaccines, while for measles/rubella vaccine it was -27.3 and -33.6, respectively, for the first and second dose. COVID-19 resulted in interruptions affecting routine immunization, but recovery occurred within four months.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".