2025-07-25 ペンシルベニア州立大学(PennState)
<関連情報>
- https://www.psu.edu/news/eberly-college-science/story/predicting-vaccination-levels-without-accurate-or-timely-vaccination
- https://www.sciencedirect.com/science/article/pii/S0264410X25005742
日常的な監視データと調査データを用いた地方レベルでのワクチン接種率の推定予測 Prediction of subnational-level vaccination coverage estimates using routine surveillance data and survey data
Deepit Bhatia, Natasha Crowcroft, Sébastien Antoni, M. Carolina Danovaro-Holliday, Anindya Sekhar Bose, Anna Minta, Balcha Masresha, Matthew J. Ferrari
Vaccine Available online: 27 May 2025
DOI:https://doi.org/10.1016/j.vaccine.2025.127277
Highlights
- Inequities persist in vaccination coverage against measles.
- Identifying areas of low coverage is crucial in preventing future outbreaks.
- Existing measures have a trade-off between accuracy and timeliness.
- We estimate subnational coverage based on characteristics of suspected cases.
- Estimates are well correlated with survey-based measures and can be generated yearly.
Abstract
Background
Measles vaccination has significantly reduced the global burden of the disease, but disparities in vaccination coverage persist. Accurate and timely estimates of subnational vaccination coverage are crucial for identifying high-risk areas and guiding targeted interventions. However, existing methods face limitations related to accuracy, timeliness, and spatial resolution. We explored the use of routinely collected case-based surveillance data to predict measles vaccination coverage at the subnational level.
Methods
The study used aggregated case data from 18 countries in the WHO African region, obtained from the WHO measles surveillance database. Three surveillance-based indicators were derived: mean age of suspected measles cases, proportion of vaccinated suspected cases, and proportion of IgM-negative suspected cases. These indicators were used to build a beta regression model with measles vaccination coverage from the Demographic and Health Surveys (DHS) as the gold standard. We compared out-of-sample predictions created using this model to withheld DHS estimates using Pearson’s rho.
Findings
We found that each of the three surveillance-based indicators were more strongly correlated with DHS-based survey coverage than administrative estimates. Out-of-sample predictions achieved high correlation with DHS-based coverage, with a rho of 0.74.
Interpretation
The findings suggest that routinely collected measles surveillance data can effectively predict subnational measles vaccination coverage. The approach addresses limitations of existing methods by providing yearly estimates that are more accurate than administrative data and more readily available than surveys. This enables timely identification of low-coverage areas and facilitates targeted interventions.
