# Gender disparities in childhood vaccination in India: exploring the role of son preference using NFHS-5 data

## TL;DR

In India, girls from households with a preference for sons are less likely to be fully vaccinated, highlighting gender disparities in healthcare access.

## Contribution

This study uses NFHS-5 data to quantify how son preference and related factors contribute to vaccination gaps among girls.

## Key findings

- Girls in son-preferring households had lower vaccination rates (70.8% vs. 76.9%).
- 84% of the vaccination gap was explained by factors like birth order, ANC visits, and household wealth.
- Unmeasured cultural norms accounted for 16% of the remaining gap.

## Abstract

Every child has the fundamental human right to life-saving healthcare, including vaccination. Yet in India, this right is less accessible to girls in households shaped by son preference, a deep-rooted cultural bias that devalues girl children. While immunization prevents millions of deaths each year, girls are less likely to be fully vaccinated—not due to biological need, but because of gender-based discrimination in the allocation of time and care within the home.

Using nationally representative data from the National Family Health Survey-5 (NFHS-5; 2019–21), we analyzed a sample of 20,899 girls aged12–23 months. Full vaccination (BCG, three doses of DPT, three doses of polio, measles) was the outcome. Maternal son preference was the key exposure. We employed a four-stage generalized linear model (GLM) with a log link to estimate adjusted relative risks, progressively controlling for child, maternal, and household variables. Fairlie decomposition analysis was conducted to quantify the extent to which these characteristics explain the observed vaccination gap between girls and boys.

Girls in son-preferring households had significantly lower vaccination rates (70.8% vs. 76.9%). The GLM showed a consistent negative association between son preference and vaccination across all models; however, it became non-significant (ARR: 0.99, 95% CI: 0.97–1.02) after full adjustment. Fairlie decomposition analysis revealed that 84% of this gap was statistically explained by factors such as birth order (21%), antenatal care (ANC) visits (29%), and household wealth (21%), which were the largest contributors. The remaining 16% was unexplained by unmeasured cultural norms.

Interventions should target girls of higher birth order, those in the poorest households, and those born to mothers with low education. Strategies could include leveraging antenatal care visits to deliver gender-sensitive health messaging and expanding the role of community health workers (CHWs) and Anganwadi teams for door-to-door monitoring. Closing the remaining 16% of the unexplained gap demands confronting cultural norms, ensuring that every child—regardless of birth order or family wealth—has an equal right to protection.