What is it about?

Childhood vaccinations are important to protect kids from diseases and improve public health. However, figuring out how many children are actually getting vaccinated is tricky due to various challenges with the data. One problem in surveys like the National Family Health Survey is that when a child's vaccination record is missing, the information comes from what the mother remembers, which may not always be accurate. This study looks at how much estimates of childhood vaccination rates rely on mothers' memory—whether they can correctly recall which vaccines their child has received. Using advanced statistical methods (called spatial Bayesian models), the study estimates vaccination rates in different parts of India for the years 2015 and 2020, while considering different levels of accuracy in maternal recall.

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Why is it important?

By using advanced statistical tools (Bayesian models), we explored how well a mother's memory can predict whether a child missed a vaccine dose, showing that this information is important for more than just tracking missed vaccinations. Our analysis identified regions with consistently low vaccination rates (called "cold-spots"), which can help target efforts to improve immunization in those areas and protect people from preventable diseases. The study compares vaccine coverage maps with and without maternal recall data to see where previous estimates may have been too high due to missing health records. By identifying regions with low vaccination coverage and tracking changes over time, the study helps show the pros and cons of relying on maternal recall for vaccination data. This information can help make better decisions on where and how to focus vaccination efforts in India and similar countries. The study highlights that it's important to know which regions need more focus, as there are large differences in vaccination rates across different areas. We also looked at factors like income, parents' education, and cultural beliefs, analyzing them in very small areas (1x1 km) to understand their impact on vaccination coverage. The study was innovative because it created detailed local maps of vaccine coverage, which revealed differences that broader studies missed. By analyzing vaccination patterns over time and in specific locations using spGLM and INLA models, we gained valuable insights into how healthcare is delivered in India. Going forward, continuous monitoring and strategies based on this evidence are needed to address inequalities, reduce dropout rates, and reach full immunization coverage, ensuring children’s health is protected.

Perspectives

We found that a mother's memory plays a big role in improving vaccination estimates, especially in areas where health records are missing. However, its impact differs from state to state. This shows that the local context, like the development level and healthcare infrastructure, is important when interpreting results. Between 2016 and 2021, we saw an overall improvement in the first dose of the measles vaccine (MCV1) across India, with fewer areas showing zero coverage. This suggests progress in vaccination efforts and fewer gaps in coverage. However, we also found that children missing follow-up doses is a separate issue that needs specific attention to achieve full vaccination coverage.

Ritika Singh
Indian Institute of Technology Delhi

Read the Original

This page is a summary of: Quantifying the role of maternal recall in estimates of routine immunisation rates in India: a large-scale sub-national Bayesian modelling study, July 2024, ACM (Association for Computing Machinery),
DOI: 10.1145/3674829.3675062.
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