Prediction of subnational-level vaccination coverage estimates using routine surveillance data and survey data

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...

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Vydané v:Vaccine Ročník 60; s. 127277
Hlavní autori: Bhatia, Deepit, Crowcroft, Natasha, Antoni, Sébastien, Danovaro-Holliday, M. Carolina, Bose, Anindya Sekhar, Minta, Anna, Masresha, Balcha, Ferrari, Matthew J.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Netherlands Elsevier Ltd 11.07.2025
Elsevier Limited
Elsevier Science
Predmet:
Accuracy
Adolescent
Africa - epidemiology
Age groups
Allergy and Immunology
Beta regression
Child
Child, Preschool
Dengue fever
DHS
Epidemics
Epidemiological Monitoring
Estimates
Female
Humans
Immunization
Immunoglobulin M
Indicators
Infant
Infections
Male
Measles
Measles - epidemiology
Measles - prevention & control
Measles Vaccine - administration & dosage
monitoring
Out-of-sample prediction
Polls & surveys
Population Surveillance
prediction
Predictions
regression analysis
Regression models
Rubella
Spatial discrimination
Spatial resolution
Surveillance
Surveys
Vaccination
Vaccination - statistics & numerical data
Vaccination Coverage - statistics & numerical data
Vaccination coverage estimation
Vaccines
World Health Organization
Africa
Vaccination coverage estimation
Measles
Out-of-sample prediction
DHS
Beta regression
ISSN:0264-410X, 1873-2518, 1873-2518
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Popis
Shrnutí: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. 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. 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. 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. •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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0264-410X
1873-2518
1873-2518
DOI:10.1016/j.vaccine.2025.127277