Vaccination coverage in rural Burkina Faso under the effects of COVID-19: evidence from a panel study in eight districts
Bibliographic record
Abstract
Abstract Background Improving infant immunization completion and promoting equitable vaccination coverage are crucial to reducing global under-5 childhood mortality. Although there have been hypotheses that the impact of the COVID-19 pandemic would decrease the delivery of health services and immunization campaigns in low- and middle-income countries, the available evidence is still inconclusive. We conducted a study in rural Burkina Faso to assess changes in vaccination coverage during the pandemic. A secondary objective was to examine long-term trends in vaccination coverage throughout 2010–2021. Methods Using a quasi-experimental approach, we conducted three rounds of surveys (2019, 2020, 2021) in rural Burkina Faso that we pooled with two previous rounds of demographic and household surveys (2010, 2015) to assess trends in vaccination coverage. The study population comprised infants aged 0–13 months from a sample of 325 households randomly selected in eight districts (n = 736). We assessed vaccination coverage by directly observing the infants’ vaccination booklet. Effects of the pandemic on infant vaccination completion were analyzed using multi-level logistic regression models with random intercepts at the household and district levels. Results A total of 736 child-year observations were included in the analysis. The proportion of children with age-appropriate complete vaccination was 69.76% in 2010, 55.38% in 2015, 50.47% in 2019–2020, and 64.75% in 2021. Analyses assessing changes in age-appropriate full-vaccination coverage before and during the pandemic show a significant increase (OR: 1.8, 95% CI: 1.14–2.85). Our models also confirmed the presence of heterogeneity in full vaccination between health administrative districts. The pandemic could have increased inequities in infant vaccination completion between these districts. The analyses suggest no disruption in age-appropriate full vaccination due to COVID-19. Our findings from our sensitivity analyses to examine trends since 2010 did not show any steady trends. Conclusion Our findings in Burkina Faso do not support the predicted detrimental effects of COVID-19 on the immunization schedule for infants in low- and middle-income countries. Analyses comparing 2019 and 2021 show an improvement in age-appropriate full vaccination. Regardless of achieving and sustaining vaccination coverage levels in Burkina Faso, this should remain a priority for health systems and political agendas.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".