Bridging the Immunisation Gap: Socioeconomic and Geographic Drivers of Pediatric Immunisation Disparities between High-performing and Underperforming Indian States

Background Immunising children saves 2–3 million lives every year, making it one of the most effective public health interventions. India's childhood immunisation rates are steadily improving, but there are still big differences between states. Tamil Nadu consistently has one of the highest rates of child immunisation, while Nagaland is still below the national average. If we figure out the socioeconomic factors which contribute to this difference, we can guide context-specific strategies to improve coverage in underperforming areas. Objective To compare the level and socioeconomic and geographic determinants of full immunisation coverage among children aged 12–23 months in Tamil Nadu and Nagaland using data from the fifth National Family Health Survey (NFHS-5, 2019-21). Methods A survey weighted cross-sectional analysis was performed on 1,739 children (Tamil Nadu: 1,255; Nagaland: 484). To be fully immunised, a child had to get BCG, three doses of DPT, three doses of OPV, and two doses of MCV. Weighted bivariate chi-square tests and multivariable logistic regression models were used to assess associations between full immunization and socioeconomic, demographic, and geographic characteristics. Results Immunization coverage was higher in Tamil Nadu (93.6%) than in Nagaland (72.7%). In Nagaland, full immunization was significantly associated with maternal education (χ²=3.983, p = 0.006), wealth (χ²=9.702, p = 0.011), residence (χ²=13.011, p = 0.001), distance to health facility (χ²=5.550, p = 0.018), religion (χ²=5.540, p = 0.038), and birth order (χ²=20.504, p = 0.012), whereas in Tamil Nadu only maternal education was significant (χ²=9.764, p = 0.024). Multivariable analysis substantiated maternal education as the most significant predictor in both states (Tamil Nadu: AOR = 1.98; p = 0.017; Nagaland: AOR = 3.46; p = 0.026). In Nagaland, urban residence also increased the odds of full immunization (AOR = 1.89; p = 0.047), while in Tamil Nadu, mother’s age showed a marginal positive effect (AOR = 1.22; p = 0.037). After adjustment, other variables were not significant. Conclusion The main factors for the differences in immunisation coverage between states are gaps in maternal education and access to healthcare. To increase vaccination coverage in states which are low-performing such as Nagaland, it is crutial to enhance women's education, extend outreach to rural populations, and address socioeconomic barriers.