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

Bridging the Immunisation Gap: Socioeconomic and Geographic Drivers of Pediatric Immunisation Disparities between High-performing and Underperforming Indian States | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Bridging the Immunisation Gap: Socioeconomic and Geographic Drivers of Pediatric Immunisation Disparities between High-performing and Underperforming Indian States Yendouname Kandjoni, Samadou Tchakondo, Sangénis Assogba Ayao, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8398905/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Apr, 2026 Read the published version in BMC Pediatrics → Version 1 posted 15 You are reading this latest preprint version Abstract 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. Immunisation socioeconomic determinants geographic drivers India maternal education health disparities Figures Figure 1 Figure 2 INTRODUCTION Childhood's immunization is still one of the most effective public health measures, saving an estimated two to three million lives each year against diseases that are easy to avoid [ 1 , 2 ]. The Expanded Programme on Immunisation (EPI), which the World Health Organisation established in 1974, aimed to make sure that every child received essential vaccines that can save them against common childhood diseases like measles, polio, diphtheria, pertussis, tetanus, and tuberculosis through the Bacillus Calmette-Guérin (BCG) vaccine [ 1 ]. In India, the history of organized vaccination finds its origin back in approximately two centuries. The first documented inoculation took place in Bombay (now Mumbai) in 1802, marking the beginning of a long national engagement with preventive medicine [ 3 ]. After independence, India invested heavily and put a lot of effort in national vaccine production and distribution. The government introduced the Expanded Programme of Immunization in 1978, followed by the Universal Immunization Programme (UIP) in 1985, which remains one of the largest immunization initiatives in the world [ 4 ]. Although national efforts have considerably improved vaccine coverage, large differences persist between states. India’s average full immunization rate was reported at 93.5 percent in 2023-24, yet coverage remains uneven accross states, for instance Tamil Nadu achieved roughly 90 percent, while Nagaland lagged at about 58 percent [ 5 , 6 ]. These differences show that there are still unfair differences in access, infrastructure, and social factors that affect child health outcomes in different areas. A part from vaccine availability, accessibility, affordability, it is globally known that studies consistently shows that socioeconomic and geographic factors play a decisive role in immunization services utilisation. Maternal education, in particular, enhances awareness of preventive care and the ability to navigate health services. In India, maternal education and child immunisation have been shown to be strongly positively correlated by Vikram et al. (2012) [ 7 ], with regional and gender differences also being reported. Sinha et al. (2013) found that children living in rural areas and female children were less likely to be fully immunized [ 8 ]. Maternal education is still a major factor in socioeconomic disparities in vaccination coverage, according to a more recent study by Sinha et al. (2020) [ 9 ]. But even with all these revelations, there hasn't been much comparative research done on why some states for example Tamil Nadu, maintain nearly universal coverage, while others like Nagaland continue to lag behind. It is then important to figure out these inequalities in order to apply effective models to settings with poor performance. Following all of these, our study utilises data from the fifth National Family Health Survey (NFHS-5, 2019-21) to compare the socioeconomic determinants of full immunisation in Tamil Nadu and Nagaland. we intend through this study to effectively inform evidence-based interventions that support an equitable and long-term immunisation coverage throughout India by identifying state-specific predictors and transferable strategies. METHODOLOGY Study Design, Data Source and extraction This study was a cross-sectional analytical design, we used publicly available secondary data from the National Family Health Survey (NFHS-5, 2019–2021). It was conducted by the International Institute for Population Sciences (IIPS) in collaboration with the Ministry of Health and Family Welfare, Government of India, NFHS-5 and provides nationally representative data on key demographic and health indicators [ 10 ]. The analysis used the NFHS-5 children’s recode file obtained from the DHS Program after authorization. From that file we extracted records for children aged 12–23 months and limited the dataset to observations from the two states of interest (Tamil Nadu and Nagaland). For state-specific comparisons we created two separate datasets by applying the state filter to the extracted child records (one dataset for Tamil Nadu, one for Nagaland) and retained a pooled dataset for overall descriptive reporting. Study Population Inclusion: children aged 12–23 months with a vaccination card available, residing in the Indian states of Tamil Nadu and Nagaland. Exclusion: records for children outside the 12–23 months age band and records missing a vaccination card were excluded prior to variable construction. After filtering by state, age and possession of a vaccination card, the final analytic sample comprised 1,739 children (Tamil Nadu n = 1,255; Nagaland n = 484). Variable Definition Outcome Variable The dependent variable, immunization status , was constructed as a binary outcome following WHO and DHS definitions. A child was coded “fully immunized” (1) if vaccination records indicated receipt of one dose of BCG, three doses each of DPT and OPV, and one dose of a measles-containing vaccine. Children missing any of these doses were coded “not fully immunized” (0). Only documented doses from vaccination cards were used; verbal recall data were excluded. Explanatory Variables Independent variables included maternal age, education, household wealth index, place of residence, religion, perceived distance to health facility, birth order, and sex of the child. Maternal age was retained as a continuous variable for regression analyses and also categorized for descriptive purposes. Birth order was collapsed into three categories: first , second , and third or higher order births . Maternal education was grouped as no education , primary , secondary , or higher . All variables were coded and labeled according to DHS standards. The predictors are shown in Table 1 . Table 1 Independent variables and their categories Variable Categories / Coding Maternal education No education, Primary, Secondary, Higher Wealth index Poor, Middle, Rich [ 17 ] Place of residence Urban, Rural Religion Hindu, Christian, Muslim, Others Birth order 1, 2, 3, ≥4 Distance to health facility No problem, Big problem Sex of the child Male, Female Mother’s age Continuous and grouped (18–22, 23–27, 28–32, 33–37, 38–42, 43–47 years) Data weighting and missing data handling We applied sampling weights to ensure representativeness of state-level estimates. The DHS child sampling weight (v005) was divided by 1,000,000 and applied in all descriptive and inferential analyses. The survey design was accounted for using primary sampling units (clusters) and stratification variables provided in the DHS dataset. Missing data on immunization status and explanatory variables were excluded listwise (less than 5% of observations). Data were cleaned and recoded in IBM SPSS Statistics Version 25. Statistical Analysis and software Weighted descriptive statistics summarized the distribution of child immunisation, maternal, and household characteristics. Chi-square (χ²) tests assessed bivariate associations between immunization status and explanatory variables To identify independent predictors, binary logistic regression models were fitted separately for Tamil Nadu and Nagaland, adjusting for all covariates. We expressed the results as Adjusted Odds Ratios (AORs) with 95% Confidence Intervals (CIs) and p-values. We performed model diagnostics the Hosmer-Lemeshow goodness-of-fit test (p > 0.05 indicating acceptable fit) and checks for multicollinearity using the Variance Inflation Factor (VIF < 2). We did not detect any multicollinearity or influential outliers. The regression model was specified as: $$\:\text{logit}\left({p}_{i}\right)={\beta\:}_{0}+\sum\:{\beta\:}_{k}{X}_{ki}$$ where \(\:{p}_{i}\) is the probability of being fully immunized, and \(\:{X}_{ki}\) represents predictor variables. Model fit was assessed using the Hosmer–Lemeshow goodness-of-fit test (p > 0.05 indicating adequate fit), and Variance Inflation Factors (VIF) were checked to confirm absence of multicollinearity (VIF < 2). All analyses were stratified by state to facilitate cross-state comparison of determinants and were conducted in IBM SPSS Statistics 25, and statistical significance was determined at p < 0.05 (two-tailed). Ethical Considerations we used anonymized, publicly available NFHS-5 data and therefore did not require ethical approval. The DHS Program obtained informed consent from all respondents during primary data collection. We have used the data in compliance with the DHS terms and conditions. RESULTS Characteristics of the study population A total of 1,739 children aged 12–23 months were included in the analysis; 1,255 from Tamil Nadu and 484 from Nagaland. The mean age of mothers was 26.8 years in Tamil Nadu and 28.8 years in Nagaland. More than 90% of children in Tamil Nadu resided in rural areas, compared with 68% in Nagaland. Educational attainment differed substantially between the two states, nearly 94% of mothers in Tamil Nadu had at least secondary education, whereas only 72% of mothers in Nagaland did (Fig. 1 ). Full immunization coverage was 93.6% in Tamil Nadu and 72.7% in Nagaland, confirming the substantial interstate disparity identified in the NFHS-5 dataset (Fig. 2 ). Bivariate distribution of full immunization by socioeconomic and demographic characteristics Table 2 presents the distribution of full immunization across socioeconomic and demographic characteristics in Tamil Nadu and Nagaland. Clear contrasts emerge between the two states. In Nagaland, full immunization was lower overall, and disparities were evident across residence, education, and wealth. Only 31.7% of urban and 68.3% of rural children were fully immunized, while non-immunization was concentrated among rural households (84.1%). Mothers with no education (9.1%) or primary education (17.6%) had markedly lower coverage than those with secondary (60.8%) or higher education (12.5%). Similarly, children from poor households (61.9%) were more likely to be incompletely immunized (75.0%) than those from rich households (20.5%). Birth order showed a clear gradient; coverage declined from 43.5% among firstborns to 17.0% among fourth-order or higher births. Differences by religion and distance to health facility were also apparent, with lower coverage among families reporting distance as a major problem (36.6%) or belonging to minority religions. In Tamil Nadu, overall immunization coverage was high and relatively uniform across subgroups. Urban (46.8%) and rural (53.2%) areas showed only minor differences. Coverage remained high even among poorer households (13.9%) and those reporting distance barriers (16.2%). Educational attainment again showed a positive relationship; children of mothers with higher education (44.2%) or secondary education (49.3%) were more often fully immunized than those whose mothers had only primary (4.1%) or no education (2.4%). Unlike Nagaland, the influence of birth order, sex, or religion was minimal (Table 2 ). Table 2 Descriptive statistics of immunization status by socioeconomic and demographic variables, Tamil Nadu and Nagaland (NFHS-5, 2019-21) Variable Category Fully immunized (%) - Nagaland Not Fully immunized (%) - Nagaland Fully immunized (%) -Tamil Nadu Not Fully immunized (%) -Tamil Nadu Type of residence Urban 31.7 15.9 46.8 51.9 Rural 68.3 84.1 53.2 48.1 Mother’s age (years) Mean ± SD 28.0 ± 5.14 29.0 ± 5.69 26.82 ± 4.52 26.11 ± 3.48 Highest educational level No education 9.1 18.8 2.4 1.3 Primary 17.6 10.5 4.1 1.3 Secondary 60.8 62.4 49.3 35.9 Higher 12.5 8.3 44.2 61.5 Wealth index Poor 61.9 75.0 13.9 12.7 Middle 17.6 15.2 27.8 25.3 Rich 20.5 9.8 58.5 62.0 Distance to health facility No problem 63.4 51.5 83.8 83.3 Big problem 36.6 48.5 16.2 16.7 Religion Hindu 5.4 1.5 90.6 97.4 Christian 93.8 96.2 5.3 1.3 Muslim 0.9 2.3 4.0 1.3 Birth order 1 43.5 31.6 49.8 59.0 2 25.9 21.1 41.0 30.8 3 13.6 19.5 8.1 9.0 4 or more 17.0 27.8 1.1 1.2 Sex of child Male 51.7 47.0 53.6 64.6 Female 48.3 53.0 46.4 35.4 Factors associated with immunization coverage in Tamil Nadu and Nagaland Table 3 presents the chi-square test results examining the association between full immunization and selected socioeconomic and demographic factors in Tamil Nadu and Nagaland. In Nagaland, full immunization was significantly associated with maternal education (χ² = 3.983, p = 0.006), household wealth (χ² = 9.702, p = 0.011), place of residence (χ² = 13.011, p = 0.001), distance to the nearest health facility (χ² = 5.550, p = 0.018), religion (χ² = 5.540, p = 0.038), and birth order (χ² = 20.504, p = 0.012). Children of educated mothers, those living in urban areas, and those from wealthier households were more likely to be fully immunized. Respondents who did not perceive distance as a major problem also showed higher coverage, indicating the influence of geographic accessibility. In Tamil Nadu, only maternal education remained significantly associated with full immunization (χ² = 9.764, p = 0.024). Variables such as residence, wealth, religion, distance to health facilities, and birth order showed no significant relationships (p > 0.05), suggesting that Tamil Nadu’s immunization program provides relatively equitable access across socioeconomic groups. Across both states, no statistically significant difference in immunization coverage was found between male and female children (Nagaland: p = 0.353; Tamil Nadu: p = 0.058), indicating near gender parity in vaccination. Overall, these bivariate results highlighted maternal education as the most consistent determinant of full immunization, while structural barriers such as residence and distance remain key challenges in Nagaland. Table 3 Chi-Square (χ²) test of association between immunization status and selected variables, Tamil Nadu and Nagaland Variable χ² (p-value) - Nagaland χ² (p-value) - Tamil Nadu Type of residence 13.011 (p = 0.001) 0.780 (p = 0.377) Mother’s age (years) 3.501 (p = 0.598) 11.463 (p = 0.113) Highest educational level 3.983 (p = 0.006) 9.764 (p = 0.024) Wealth index 9.702 (p = 0.011) 0.431 (p = 0.808) Distance to health facility 5.550 (p = 0.018) 0.014 (p = 0.906) Religion 5.540 (p = 0.038) 5.609 (p = 0.244) Birth order 20.504 (p = 0.012) 3.648 (p = 0.488) Sex of child 0.861 (p = 0.353) 3.665 (p = 0.058) Note χ² = Chi-square test statistic. Predictors of immunization coverage in each state (binary logistic regression) Multivariate logistic regression in this study was used to: Predict the probability of full immunization based on multiple socioeconomic factors that have shown significant association with immunization coverage. Identify the most important predictors of full immunization in each state. Control for the influence of other factors to isolate the effect of individual predictors. The study included a total of 1,739 children aged 12–23 months from Tamil Nadu (n = 1255) and Nagaland (n = 484). The overall full immunization coverage was 93.6% in Tamil Nadu and 72.7% in Nagaland. The multivariable logistic regression results (Table 4 ) further clarified these patterns. After adjustment, maternal education remained a significant predictor of full immunization in both Tamil Nadu (AOR = 1.98; 95% CI: 1.13–3.46; p = 0.017) and Nagaland (AOR = 3.46; 95% CI: 1.16–10.30; p = 0.026). Urban residence also predicted higher odds of full immunization in Nagaland (AOR = 1.89; p = 0.047), while mother’s age was marginally significant in Tamil Nadu (AOR = 1.22; p = 0.037). Other variables, including child’s sex, wealth index, and distance to health facility, lost significance after adjustment, suggesting that their effects are largely mediated by education and residence. Table 4 Summary of binary logistic regression table in both States Variable AOR (95% CI) Tamil Nadu p-value AOR (95% CI) Nagaland p-value Type of residence (Urban vs Rural) 0.77 (0.46–1.28) 0.309 1.89 (1.01–3.54) 0.047 Religion Not significant 0.389 Not significant 0.162 Distance to health facility (Big problem vs No problem) 1.22 (0.64–2.32) 0.546 1.47 (0.93–2.32) 0.096 Highest educational level (Higher vs No education) 1.98 (1.13–3.46) 0.017 3.46 (1.16–10.30) 0.026 Birth order number (≥ 3 vs 1–2) Not significant 0.600 Not significant 0.500 Sex of child (Male vs Female) 0.63 (0.39–1.02) 0.061 1.30 (0.84–2.01) 0.243 Mother’s age (years) 1.22 (1.01–1.46) 0.037 1.01 (0.85–1.19) 0.925 Wealth index (Rich vs Poor) Not significant 0.860 Not significant 0.373 Age group of mothers Not significant 0.078 Not significant 0.789 DISCUSSION This study revealed significant disparities in immunization coverage between Tamil Nadu and Nagaland, emphasizing the role of socioeconomic and demographic factors. Maternal education was identified as the most consistent determinant in both states, with higher education substantially enhancing the likelihood of complete immunisation in Tamil Nadu (AOR = 1.98; p = 0.017) and Nagaland (AOR = 3.46; p = 0.026). These findings align with previous studies such as Vikram et al. (2012) and Goodman et al. (2023), which revealed that educated mothers are more likely to understand the benefits of vaccination and effectively navigate health systems [ 5 ][ 10 ]. Jejaw et al. (2025) reported as well that children of mothers with no or only primary education were significantly more likely to miss vaccinations compared with those of mothers with secondary or higher education [ 12 ]. Therefore, if maternal education is strenghened, particularly in Nagaland, it could play a crucial role in improving vaccine utilisation. Wealth status showed contrasting effects in our study, across states. First of all, in Nagaland, fully vaccination was strongly linked to household wealth (χ² = 9.702; p = 0.011), but in Tamil Nadu, wealth was not a significant factor (p = 0.808). This shows that services are more fairly available in Tamil Nadu. Secondly, children from poor households in Nagaland had lower coverage (61.9%) than those from rich households (20.5% not fully immunized). Finally, Goodman et al. (2023) noted that compared to poor familes, children from wealthier families face fewer financial and logistical barriers to immunization [ 11 ], and similar to this, Rutstein and Johnson (2004) emphasized that economic disparities are magnified in regions with weaker infrastructure [ 13 ]. Targeted outreach and awareness programs for low-income households could help close this gap. Geographic accessibility also emerged as a structural barrier. In Nagaland, respondents who did not see distance as a problem had higher immunization coverage (63.4%) than those who reported it as a major problem (36.6%) (χ² = 5.550; p = 0.018). In Tamil Nadu, this factor was not showing significance (p = 0.906), potentially reflecting the efficiency of its rural outreach system. Chard et al. (2020) and Goodman et al. (2023) found also that limited physical access to health facilities decrease vaccination rates in remote areas [ 1 ][ 11 ]. Tamil Nadu’s robust health infrastructure and mobile immunisation initiatives, according to Lahariya (2014), appear to lessen these obstacles [ 4 ]. If mobile outreach is expanded and reliable service delivery ensured in Nagaland, it could significantly improve child immunization coverage. About Place of residence, we found urban-rural disparities in Nagaland, where the proportion of children fully immunized was higher in urban area (31.7%) than rural (15.9%) (χ² = 13.011; p = 0.001). In Tamil Nadu, no significant difference was observed (p = 0.377), demonstrating the reach of its community-based services. Sinha et al. (2013) also found that immunisation coverage were low in rural areas of India's northeastern states [ 8 ]. Increasing rural outreach and community-level awareness initiatives in Nagaland may help to address these disparities. In Nagaland, birth order was significantly related to coverage (χ² = 20.504; p = 0.012). Firstborn children had the highest coverage (43.5%), while children of third or higher order had the lowest (17.0%). No significant association was found in Tamil Nadu (p = 0.488). This pattern mirrors Sharma et al. (2021) and Mathew et al. (2012), who linked declining vaccination rates with increasing parity due to resource competition and reduced parental attention [ 15 ][ 14 ]. Household-level interventions and reminders for families with multiple children may therefore be effective. Religion was significant in Nagaland (χ² = 5.540; p = 0.038) but not in Tamil Nadu (p = 0.244). Christian families, representing the majority in Nagaland, had relatively higher coverage (93.8% fully immunized) than minority groups. Sinha et al. (2013) observed similar cultural influences in vaccination behavior [ 8 ], while Shrivastwa et al. (2015) found that the association between religion and vaccination varies by regional context [ 16 ]. Context-specific communication strategies that engage community and faith leaders may therefore improve acceptance in culturally diverse settings. Mother’s age was a marginally significant predictor in Tamil Nadu (AOR = 1.22; p = 0.037) but not in Nagaland (p = 0.925), suggesting that slightly older mothers in Tamil Nadu may have better awareness or experience navigating healthcare systems. Sex of the child was not associated with immunization in either state (p > 0.05), reflecting near gender parity, a positive contrast to earlier findings by Sinha et al. (2013) [ 8 ]. Overall, the results indicate that while Tamil Nadu’s strong primary healthcare system and equitable outreach have minimized socioeconomic gradients, Nagaland’s lower coverage stems largely from educational, geographic, and structural disadvantages. Strengthening women’s education, improving rural service accessibility, and tailoring communication to local contexts remain essential for achieving universal immunization coverage in India. Strengths and Limitations Strengths Large, representative dataset from NFHS-5; comprehensive range of variables. The focus on socioeconomic and demographic determinants, such as maternal education, wealth index, and healthcare access, offers a multidimensional perspective. Limitations Cross-sectional nature of NFHS-5 data limits causal inferences; potential underreporting of vaccination status due to recall bias. Further studies should focus on conducting longitudinal studies to assess the long-term impact of maternal education and socioeconomic improvements on immunization rates, particularly in low-performing regions like Nagaland, and eventually Investigate the role of health system infrastructure and human resource availability in influencing immunization coverage. CONCLUSION Our study demonstrates that disparities in child immunisation coverage between Tamil Nadu and Nagaland reflect underlying socioeconomic and structural inequalities. Maternal education emerged as the most consistent predictor of full immunisation across both states, underscoring the importance of female literacy in shaping preventive health behaviors. In Nagaland, lower coverage among rural residents and higher birth-order children indicates that geographic and familial factors interact with education to influence vaccine uptake. Conversely, Tamil Nadu’s near-universal coverage is the proof of the benefits of sustained investment in primary healthcare and effective outreach mechanisms. These findings suggest that equitable immunisation cannot be achieved solely through the availability of vaccines; it necessitates a multisectoral approach. Strengthening women’s education, improving rural healthcare access, and developing targeted follow-up strategies for larger households are essential for closing remaining gaps. Addressing these multidimensional determinants will support India’s progress toward universal immunisation coverage and contribute to reducing preventable childhood illnesses. Declarations Consent for publication All authors consent to the publication of this manuscript in its current form. Competing Interests The authors declare no competing interests. Ethical Approval and consent to participate Ethical approval for this study was not required, as it involved secondary analysis of publicly available, anonymized data from the National Family Health Survey (NFHS-5). The NFHS survey obtained ethical approval from institutional review boards (IRBs) and informed consent from all participants at the time of data collection. Clinical trial number Not applicable. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Author Contribution Y. K. conceptualized the study, designed the methodology, and conducted data analysis and interpretation. S. T., K. S. G., and S. A. A. contributed to data analysis and manuscript drafting. R. T. and K. A. J. provided critical input on data interpretation. Dr. G. J. H. offered methodological guidance and substantive revisions to the manuscript. All authors reviewed and approved the final version for submission. Acknowledgement The authors express their gratitude to the School of Public Health, SRM Institute of Science and Technology (SRM IST), for their guidance and support throughout the study. We extend our appreciation to the National Family Health Survey (NFHS) team for providing access to the publicly available datasets utilized in this research. We also thank our colleagues and mentors for their valuable feedback and encouragement, which greatly enhanced the quality of this work. Data Availability The data analysed for this study are publicly available through the Demographic and Health Surveys (DHS) Program. Specifically, this study utilized the dataset from the National Family Health Survey (NFHS-5, 2019-2021) for Tamil Nadu and Nagaland, which can be accessed from the DHS website (https:/dhsprogram.com/data/available-datasets.cfm)upon approval of a data request. All analyses and findings presented in this article are based on secondary data and comply with ethical research practices. References Chard AN, Gacic-Dobo M, Diallo MS, Sodha SV, Wallace AS. Routine vaccination coverage worldwide, 2019. MMWR Morb Mortal Wkly Rep. 2020;69(45):1706–10. World Health Organization (WHO). Immunization.. 2023. Available from: https://www.who.int/health-topics/vaccines-and-immunization Plotkin SA. History of vaccination. Proc Natl Acad Sci USA. 2014;111(34):12283–7. Lahariya C. A brief history of vaccines and vaccination in India. Indian J Med Res. 2014;139(4):491–511. Ministry of Health and Family Welfare. (Government of India). Update on Immunization of Children. 2024. Ministry of Health and Family Welfare (Government of India). National Family Health Survey (NFHS-5), Phase II, 2019-20. 2021. Vikram K, Vanneman R, Desai S. Linkages between maternal education and childhood immunization in India. Soc Sci Med. 2012;75(2):331–9. Sinha D, Singh AK, Kaur P, Sharma P. Gender and urban–rural disparities in child immunization in India: Insights from DLHS and NFHS. BMC Public Health. 2013;13:487. Sinha RK, Acharya R, Jejawal N, Dwivedi LK. Socioeconomic inequalities in full immunization coverage among children in India: Evidence from a cross-sectional study. BMC Pediatr. 2020;20:295. International Institute for Population Sciences (IIPS) and ICF. National Family Health Survey (NFHS-5), 2019-21: India. Mumbai: IIPS; 2021. Goodman OK, Wagner AL, Riopelle D, Mathew JL, Boulton ML. Vaccination inequities among children 12–23 months in India: An analysis of inter-state differences. Vaccine X. 2023;14:100310. Jejaw M, Tafere TZ, Tiruneh MG, et al. Missed opportunities for vaccination in Sub-Saharan Africa: a multilevel mixed-effects analysis. BMC Public Health. 2025;25(1):62. Rutstein SO, Johnson K. The DHS Wealth Index. DHS Comparative Reports No. 6. Calverton, MD: ORC Macro; 2004. Mathew JL. Inequity in childhood immunization in India: a systematic review. Indian Pediatr. 2012;49(3):203–23. 10.1007/s13312-012-0063-z . Sharma A, Gupta R, Patel S, Kumar S, Bose P. Impact of birth order on immunization: A multilevel analysis in low-income countries. Vaccine. 2021;39(12):1534–541. Shrivastwa N, Gillespie BW, Kolenic GE, Lepkowski JM, Boulton ML. Predictors of vaccination in India for children aged 12–36 months. Am J Prev Med. 2015;49(6 Suppl 4):S435–44. Program DHS. Wealth Index Construction [Internet]. Bethesda (MD): ICF International; [cited 2025 Jan 18]. Available from: https://dhsprogram.com/topics/wealth-index/Wealth-Index-Construction.cfm Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 14 Apr, 2026 Read the published version in BMC Pediatrics → Version 1 posted Editorial decision: Revision requested 12 Mar, 2026 Reviews received at journal 10 Mar, 2026 Reviews received at journal 09 Mar, 2026 Reviews received at journal 06 Mar, 2026 Reviewers agreed at journal 04 Mar, 2026 Reviewers agreed at journal 02 Mar, 2026 Reviewers agreed at journal 02 Mar, 2026 Reviewers agreed at journal 02 Mar, 2026 Reviewers agreed at journal 01 Mar, 2026 Editor invited by journal 05 Feb, 2026 Reviewers agreed at journal 13 Jan, 2026 Reviewers invited by journal 07 Jan, 2026 Editor assigned by journal 22 Dec, 2025 Submission checks completed at journal 22 Dec, 2025 First submitted to journal 18 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Angeline","email":"","orcid":"","institution":"SRM Institute of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Kezia","middleName":"J.","lastName":"Angeline","suffix":""},{"id":564478087,"identity":"7338b2d2-a1ad-4b46-b71c-bb717a0052d4","order_by":6,"name":"Gladius H. Jennifer","email":"","orcid":"","institution":"SRM Institute of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Gladius","middleName":"H.","lastName":"Jennifer","suffix":""}],"badges":[],"createdAt":"2025-12-18 21:23:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8398905/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8398905/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12887-026-06822-6","type":"published","date":"2026-04-14T15:56:52+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":99636396,"identity":"b2d706c8-fc20-4f4c-a51e-591dc2080b3a","added_by":"auto","created_at":"2026-01-06 17:12:30","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":66822,"visible":true,"origin":"","legend":"","description":"","filename":"KANDJONIYendounameManuscriptsv3.1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8398905/v1/3c6c60056bc0370dae3078e7.docx"},{"id":99795153,"identity":"b9c28719-4cde-4e52-a961-5f798b19bf9d","added_by":"auto","created_at":"2026-01-08 13:37:07","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":10052,"visible":true,"origin":"","legend":"","description":"","filename":"34e057fc6c2847cf820654117ec64879.json","url":"https://assets-eu.researchsquare.com/files/rs-8398905/v1/6331b66174edcc140cc6211f.json"},{"id":99794907,"identity":"cbdcd327-5903-4194-b52d-25fcd237c107","added_by":"auto","created_at":"2026-01-08 13:36:38","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":83135,"visible":true,"origin":"","legend":"","description":"","filename":"34e057fc6c2847cf820654117ec648791enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8398905/v1/35bd174aac26ef570d0cbfcc.xml"},{"id":99636398,"identity":"1da45046-24b5-4588-a187-cd5318bee7cc","added_by":"auto","created_at":"2026-01-06 17:12:31","extension":"xml","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":84193,"visible":true,"origin":"","legend":"","description":"","filename":"34e057fc6c2847cf820654117ec648791structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8398905/v1/37c4c88f409f5de3ab0560b7.xml"},{"id":99636400,"identity":"a1cb8159-e202-4e20-a89b-c50cb06ac1d2","added_by":"auto","created_at":"2026-01-06 17:12:31","extension":"html","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":94447,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8398905/v1/c69d131cc9ac43b983bab455.html"},{"id":99636395,"identity":"b45747b1-857c-45b4-8b0f-5fc2684988c3","added_by":"auto","created_at":"2026-01-06 17:12:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":87098,"visible":true,"origin":"","legend":"\u003cp\u003eMother's education level distribution per states\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8398905/v1/a60237e9b8d8f9a434ad18ff.png"},{"id":99636394,"identity":"cb281012-a35c-46db-9d8f-c15448a55c3a","added_by":"auto","created_at":"2026-01-06 17:12:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":73843,"visible":true,"origin":"","legend":"\u003cp\u003eImmunization coverage distribution by State\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8398905/v1/13f8f0fdbb2ffbde6858e323.png"},{"id":107350697,"identity":"0af0bb73-f28c-4be3-b59c-aa39e1b2d119","added_by":"auto","created_at":"2026-04-20 16:00:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":641973,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8398905/v1/9dbf385e-cc96-48cd-9f83-66aedaa07aad.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bridging the Immunisation Gap: Socioeconomic and Geographic Drivers of Pediatric Immunisation Disparities between High-performing and Underperforming Indian States","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eChildhood's immunization is still one of the most effective public health measures, saving an estimated two to three million lives each year against diseases that are easy to avoid [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The Expanded Programme on Immunisation (EPI), which the World Health Organisation established in 1974, aimed to make sure that every child received essential vaccines that can save them against common childhood diseases like measles, polio, diphtheria, pertussis, tetanus, and tuberculosis through the Bacillus Calmette-Gu\u0026eacute;rin (BCG) vaccine [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn India, the history of organized vaccination finds its origin back in approximately two centuries. The first documented inoculation took place in Bombay (now Mumbai) in 1802, marking the beginning of a long national engagement with preventive medicine [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. After independence, India invested heavily and put a lot of effort in national vaccine production and distribution. The government introduced the Expanded Programme of Immunization in 1978, followed by the Universal Immunization Programme (UIP) in 1985, which remains one of the largest immunization initiatives in the world [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough national efforts have considerably improved vaccine coverage, large differences persist between states. India\u0026rsquo;s average full immunization rate was reported at 93.5 percent in 2023-24, yet coverage remains uneven accross states, for instance Tamil Nadu achieved roughly 90 percent, while Nagaland lagged at about 58 percent [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. These differences show that there are still unfair differences in access, infrastructure, and social factors that affect child health outcomes in different areas.\u003c/p\u003e \u003cp\u003eA part from vaccine availability, accessibility, affordability, it is globally known that studies consistently shows that socioeconomic and geographic factors play a decisive role in immunization services utilisation. Maternal education, in particular, enhances awareness of preventive care and the ability to navigate health services. In India, maternal education and child immunisation have been shown to be strongly positively correlated by Vikram et al. (2012) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], with regional and gender differences also being reported. Sinha et al. (2013) found that children living in rural areas and female children were less likely to be fully immunized [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Maternal education is still a major factor in socioeconomic disparities in vaccination coverage, according to a more recent study by Sinha et al. (2020) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBut even with all these revelations, there hasn't been much comparative research done on why some states for example Tamil Nadu, maintain nearly universal coverage, while others like Nagaland continue to lag behind. It is then important to figure out these inequalities in order to apply effective models to settings with poor performance.\u003c/p\u003e \u003cp\u003eFollowing all of these, our study utilises data from the fifth National Family Health Survey (NFHS-5, 2019-21) to compare the socioeconomic determinants of full immunisation in Tamil Nadu and Nagaland. we intend through this study to effectively inform evidence-based interventions that support an equitable and long-term immunisation coverage throughout India by identifying state-specific predictors and transferable strategies.\u003c/p\u003e"},{"header":"METHODOLOGY","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design, Data Source and extraction\u003c/h2\u003e \u003cp\u003eThis study was a cross-sectional analytical design, we used publicly available secondary data from the National Family Health Survey (NFHS-5, 2019\u0026ndash;2021). It was conducted by the International Institute for Population Sciences (IIPS) in collaboration with the Ministry of Health and Family Welfare, Government of India, NFHS-5 and provides nationally representative data on key demographic and health indicators [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe analysis used the NFHS-5 children\u0026rsquo;s recode file obtained from the DHS Program after authorization. From that file we extracted records for children aged 12\u0026ndash;23 months and limited the dataset to observations from the two states of interest (Tamil Nadu and Nagaland). For state-specific comparisons we created two separate datasets by applying the state filter to the extracted child records (one dataset for Tamil Nadu, one for Nagaland) and retained a pooled dataset for overall descriptive reporting.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Population\u003c/h3\u003e\n\u003cp\u003eInclusion: children aged 12\u0026ndash;23 months with a vaccination card available, residing in the Indian states of Tamil Nadu and Nagaland.\u003c/p\u003e \u003cp\u003eExclusion: records for children outside the 12\u0026ndash;23 months age band and records missing a vaccination card were excluded prior to variable construction. After filtering by state, age and possession of a vaccination card, the final analytic sample comprised 1,739 children (Tamil Nadu n\u0026thinsp;=\u0026thinsp;1,255; Nagaland n\u0026thinsp;=\u0026thinsp;484).\u003c/p\u003e\n\u003ch3\u003eVariable Definition\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eOutcome Variable\u003c/h2\u003e \u003cp\u003eThe dependent variable, \u003cem\u003eimmunization status\u003c/em\u003e, was constructed as a binary outcome following WHO and DHS definitions. A child was coded \u0026ldquo;fully immunized\u0026rdquo; (1) if vaccination records indicated receipt of one dose of BCG, three doses each of DPT and OPV, and one dose of a measles-containing vaccine. Children missing any of these doses were coded \u0026ldquo;not fully immunized\u0026rdquo; (0). Only documented doses from vaccination cards were used; verbal recall data were excluded.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eExplanatory Variables\u003c/h3\u003e\n\u003cp\u003eIndependent variables included maternal age, education, household wealth index, place of residence, religion, perceived distance to health facility, birth order, and sex of the child. Maternal age was retained as a continuous variable for regression analyses and also categorized for descriptive purposes. Birth order was collapsed into three categories: \u003cem\u003efirst\u003c/em\u003e, \u003cem\u003esecond\u003c/em\u003e, and \u003cem\u003ethird or higher order births\u003c/em\u003e. Maternal education was grouped as \u003cem\u003eno education\u003c/em\u003e, \u003cem\u003eprimary\u003c/em\u003e, \u003cem\u003esecondary\u003c/em\u003e, or \u003cem\u003ehigher\u003c/em\u003e. All variables were coded and labeled according to DHS standards. The predictors are shown in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIndependent variables and their categories\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategories / Coding\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaternal education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo education, Primary, Secondary, Higher\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWealth index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoor, Middle, Rich [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlace of residence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban, Rural\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReligion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHindu, Christian, Muslim, Others\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBirth order\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1, 2, 3, \u0026ge;4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance to health facility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo problem, Big problem\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex of the child\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale, Female\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMother\u0026rsquo;s age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eContinuous and grouped (18\u0026ndash;22, 23\u0026ndash;27, 28\u0026ndash;32, 33\u0026ndash;37, 38\u0026ndash;42, 43\u0026ndash;47 years)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData weighting and missing data handling\u003c/h2\u003e \u003cp\u003eWe applied sampling weights to ensure representativeness of state-level estimates. The DHS child sampling weight (v005) was divided by 1,000,000 and applied in all descriptive and inferential analyses. The survey design was accounted for using primary sampling units (clusters) and stratification variables provided in the DHS dataset. Missing data on immunization status and explanatory variables were excluded listwise (less than 5% of observations). Data were cleaned and recoded in IBM SPSS Statistics Version 25.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStatistical Analysis and software\u003c/h3\u003e\n\u003cp\u003eWeighted descriptive statistics summarized the distribution of child immunisation, maternal, and household characteristics. Chi-square (χ\u0026sup2;) tests assessed bivariate associations between immunization status and explanatory variables\u003c/p\u003e \u003cp\u003eTo identify independent predictors, binary logistic regression models were fitted separately for Tamil Nadu and Nagaland, adjusting for all covariates. We expressed the results as Adjusted Odds Ratios (AORs) with 95% Confidence Intervals (CIs) and p-values. We performed model diagnostics the Hosmer-Lemeshow goodness-of-fit test (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 indicating acceptable fit) and checks for multicollinearity using the Variance Inflation Factor (VIF\u0026thinsp;\u0026lt;\u0026thinsp;2). We did not detect any multicollinearity or influential outliers.\u003c/p\u003e \u003cp\u003eThe regression model was specified as:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\text{logit}\\left({p}_{i}\\right)={\\beta\\:}_{0}+\\sum\\:{\\beta\\:}_{k}{X}_{ki}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{p}_{i}\\)\u003c/span\u003e\u003c/span\u003eis the probability of being fully immunized, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{ki}\\)\u003c/span\u003e\u003c/span\u003erepresents predictor variables. Model fit was assessed using the Hosmer\u0026ndash;Lemeshow goodness-of-fit test (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 indicating adequate fit), and Variance Inflation Factors (VIF) were checked to confirm absence of multicollinearity (VIF\u0026thinsp;\u0026lt;\u0026thinsp;2).\u003c/p\u003e \u003cp\u003eAll analyses were stratified by state to facilitate cross-state comparison of determinants and were conducted in IBM SPSS Statistics 25, and statistical significance was determined at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (two-tailed).\u003c/p\u003e\n\u003ch3\u003eEthical Considerations\u003c/h3\u003e\n\u003cp\u003ewe used anonymized, publicly available NFHS-5 data and therefore did not require ethical approval. The DHS Program obtained informed consent from all respondents during primary data collection. We have used the data in compliance with the DHS terms and conditions.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of the study population\u003c/h2\u003e \u003cp\u003eA total of 1,739 children aged 12\u0026ndash;23 months were included in the analysis; 1,255 from Tamil Nadu and 484 from Nagaland. The mean age of mothers was 26.8 years in Tamil Nadu and 28.8 years in Nagaland. More than 90% of children in Tamil Nadu resided in rural areas, compared with 68% in Nagaland. Educational attainment differed substantially between the two states, nearly 94% of mothers in Tamil Nadu had at least secondary education, whereas only 72% of mothers in Nagaland did (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFull immunization coverage was 93.6% in Tamil Nadu and 72.7% in Nagaland, confirming the substantial interstate disparity identified in the NFHS-5 dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eBivariate distribution of full immunization by socioeconomic and demographic characteristics\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the distribution of full immunization across socioeconomic and demographic characteristics in Tamil Nadu and Nagaland. Clear contrasts emerge between the two states.\u003c/p\u003e \u003cp\u003eIn Nagaland, full immunization was lower overall, and disparities were evident across residence, education, and wealth. Only 31.7% of urban and 68.3% of rural children were fully immunized, while non-immunization was concentrated among rural households (84.1%). Mothers with no education (9.1%) or primary education (17.6%) had markedly lower coverage than those with secondary (60.8%) or higher education (12.5%). Similarly, children from poor households (61.9%) were more likely to be incompletely immunized (75.0%) than those from rich households (20.5%). Birth order showed a clear gradient; coverage declined from 43.5% among firstborns to 17.0% among fourth-order or higher births. Differences by religion and distance to health facility were also apparent, with lower coverage among families reporting distance as a major problem (36.6%) or belonging to minority religions.\u003c/p\u003e \u003cp\u003eIn Tamil Nadu, overall immunization coverage was high and relatively uniform across subgroups. Urban (46.8%) and rural (53.2%) areas showed only minor differences. Coverage remained high even among poorer households (13.9%) and those reporting distance barriers (16.2%). Educational attainment again showed a positive relationship; children of mothers with higher education (44.2%) or secondary education (49.3%) were more often fully immunized than those whose mothers had only primary (4.1%) or no education (2.4%). Unlike Nagaland, the influence of birth order, sex, or religion was minimal (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics of immunization status by socioeconomic and demographic variables, Tamil Nadu and Nagaland (NFHS-5, 2019-21)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFully immunized (%) - Nagaland\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot Fully immunized (%) - Nagaland\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFully immunized (%) -Tamil Nadu\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNot Fully immunized (%) -Tamil Nadu\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType of residence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e46.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e51.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e84.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e53.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e48.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMother\u0026rsquo;s age (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26.82\u0026thinsp;\u0026plusmn;\u0026thinsp;4.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e26.11\u0026thinsp;\u0026plusmn;\u0026thinsp;3.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHighest educational level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e62.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e49.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e35.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e44.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e61.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWealth index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e75.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e25.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRich\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e58.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e62.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDistance to health facility\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo problem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e51.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e83.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e83.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBig problem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReligion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHindu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e90.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e97.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChristian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e96.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMuslim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBirth order\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e49.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e59.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e41.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e30.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 or more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex of child\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e53.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e64.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e46.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e35.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eFactors associated with immunization coverage in Tamil Nadu and Nagaland\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the chi-square test results examining the association between full immunization and selected socioeconomic and demographic factors in Tamil Nadu and Nagaland.\u003c/p\u003e \u003cp\u003eIn Nagaland, full immunization was significantly associated with maternal education (χ\u0026sup2; = 3.983, p\u0026thinsp;=\u0026thinsp;0.006), household wealth (χ\u0026sup2; = 9.702, p\u0026thinsp;=\u0026thinsp;0.011), place of residence (χ\u0026sup2; = 13.011, p\u0026thinsp;=\u0026thinsp;0.001), distance to the nearest health facility (χ\u0026sup2; = 5.550, p\u0026thinsp;=\u0026thinsp;0.018), religion (χ\u0026sup2; = 5.540, p\u0026thinsp;=\u0026thinsp;0.038), and birth order (χ\u0026sup2; = 20.504, p\u0026thinsp;=\u0026thinsp;0.012). Children of educated mothers, those living in urban areas, and those from wealthier households were more likely to be fully immunized. Respondents who did not perceive distance as a major problem also showed higher coverage, indicating the influence of geographic accessibility.\u003c/p\u003e \u003cp\u003eIn Tamil Nadu, only maternal education remained significantly associated with full immunization (χ\u0026sup2; = 9.764, p\u0026thinsp;=\u0026thinsp;0.024). Variables such as residence, wealth, religion, distance to health facilities, and birth order showed no significant relationships (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), suggesting that Tamil Nadu\u0026rsquo;s immunization program provides relatively equitable access across socioeconomic groups.\u003c/p\u003e \u003cp\u003eAcross both states, no statistically significant difference in immunization coverage was found between male and female children (Nagaland: p\u0026thinsp;=\u0026thinsp;0.353; Tamil Nadu: p\u0026thinsp;=\u0026thinsp;0.058), indicating near gender parity in vaccination.\u003c/p\u003e \u003cp\u003eOverall, these bivariate results highlighted maternal education as the most consistent determinant of full immunization, while structural barriers such as residence and distance remain key challenges in Nagaland.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eChi-Square (χ\u0026sup2;) test of association between immunization status and selected variables, Tamil Nadu and Nagaland\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eχ\u0026sup2; (p-value) - Nagaland\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eχ\u0026sup2; (p-value) - Tamil Nadu\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType of residence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.011 (p\u0026thinsp;=\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.780 (p\u0026thinsp;=\u0026thinsp;0.377)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMother\u0026rsquo;s age (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.501 (p\u0026thinsp;=\u0026thinsp;0.598)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.463 (p\u0026thinsp;=\u0026thinsp;0.113)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHighest educational level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.983 (p\u0026thinsp;=\u0026thinsp;0.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.764 (p\u0026thinsp;=\u0026thinsp;0.024)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWealth index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.702 (p\u0026thinsp;=\u0026thinsp;0.011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.431 (p\u0026thinsp;=\u0026thinsp;0.808)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance to health facility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.550 (p\u0026thinsp;=\u0026thinsp;0.018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.014 (p\u0026thinsp;=\u0026thinsp;0.906)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReligion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.540 (p\u0026thinsp;=\u0026thinsp;0.038)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.609 (p\u0026thinsp;=\u0026thinsp;0.244)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBirth order\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.504 (p\u0026thinsp;=\u0026thinsp;0.012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.648 (p\u0026thinsp;=\u0026thinsp;0.488)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex of child\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.861 (p\u0026thinsp;=\u0026thinsp;0.353)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.665 (p\u0026thinsp;=\u0026thinsp;0.058)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003eχ\u0026sup2; = Chi-square test statistic.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003ePredictors of immunization coverage in each state (binary logistic regression)\u003c/h2\u003e \u003cp\u003eMultivariate logistic regression in this study was used to:\u003c/p\u003e \u003cp\u003e \u003cb\u003ePredict\u003c/b\u003e the probability of full immunization based on multiple socioeconomic factors that have shown significant association with immunization coverage.\u003c/p\u003e \u003cp\u003e \u003cb\u003eIdentify\u003c/b\u003e the most important predictors of full immunization in each state.\u003c/p\u003e \u003cp\u003e \u003cb\u003eControl\u003c/b\u003e for the influence of other factors to isolate the effect of individual predictors.\u003c/p\u003e \u003cp\u003eThe study included a total of 1,739 children aged 12\u0026ndash;23 months from Tamil Nadu (n\u0026thinsp;=\u0026thinsp;1255) and Nagaland (n\u0026thinsp;=\u0026thinsp;484). The overall full immunization coverage was 93.6% in Tamil Nadu and 72.7% in Nagaland.\u003c/p\u003e \u003cp\u003eThe multivariable logistic regression results (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) further clarified these patterns. After adjustment, maternal education remained a significant predictor of full immunization in both Tamil Nadu (AOR\u0026thinsp;=\u0026thinsp;1.98; 95% CI: 1.13\u0026ndash;3.46; p\u0026thinsp;=\u0026thinsp;0.017) and Nagaland (AOR\u0026thinsp;=\u0026thinsp;3.46; 95% CI: 1.16\u0026ndash;10.30; p\u0026thinsp;=\u0026thinsp;0.026). Urban residence also predicted higher odds of full immunization in Nagaland (AOR\u0026thinsp;=\u0026thinsp;1.89; p\u0026thinsp;=\u0026thinsp;0.047), while mother\u0026rsquo;s age was marginally significant in Tamil Nadu (AOR\u0026thinsp;=\u0026thinsp;1.22; p\u0026thinsp;=\u0026thinsp;0.037). Other variables, including child\u0026rsquo;s sex, wealth index, and distance to health facility, lost significance after adjustment, suggesting that their effects are largely mediated by education and residence.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of binary logistic regression table in both States\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAOR (95% CI) Tamil Nadu\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAOR (95% CI) Nagaland\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType of residence\u003c/b\u003e (Urban vs Rural)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.77 (0.46\u0026ndash;1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.89 (1.01\u0026ndash;3.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReligion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot significant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot significant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDistance to health facility\u003c/b\u003e (Big problem vs No problem)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.22 (0.64\u0026ndash;2.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.47 (0.93\u0026ndash;2.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHighest educational level\u003c/b\u003e (Higher vs No education)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.98 (1.13\u0026ndash;3.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.46 (1.16\u0026ndash;10.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBirth order number\u003c/b\u003e (\u0026ge;\u0026thinsp;3 vs 1\u0026ndash;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot significant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot significant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex of child\u003c/b\u003e (Male vs Female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.63 (0.39\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.30 (0.84\u0026ndash;2.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.243\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMother\u0026rsquo;s age (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.22 (1.01\u0026ndash;1.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.01 (0.85\u0026ndash;1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.925\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWealth index\u003c/b\u003e (Rich vs Poor)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot significant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot significant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.373\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge group of mothers\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot significant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot significant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.789\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study revealed significant disparities in immunization coverage between Tamil Nadu and Nagaland, emphasizing the role of socioeconomic and demographic factors. Maternal education was identified as the most consistent determinant in both states, with higher education substantially enhancing the likelihood of complete immunisation in Tamil Nadu (AOR\u0026thinsp;=\u0026thinsp;1.98; p\u0026thinsp;=\u0026thinsp;0.017) and Nagaland (AOR\u0026thinsp;=\u0026thinsp;3.46; p\u0026thinsp;=\u0026thinsp;0.026). These findings align with previous studies such as Vikram et al. (2012) and Goodman et al. (2023), which revealed that educated mothers are more likely to understand the benefits of vaccination and effectively navigate health systems [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e][\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Jejaw et al. (2025) reported as well that children of mothers with no or only primary education were significantly more likely to miss vaccinations compared with those of mothers with secondary or higher education [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Therefore, if maternal education is strenghened, particularly in Nagaland, it could play a crucial role in improving vaccine utilisation.\u003c/p\u003e \u003cp\u003eWealth status showed contrasting effects in our study, across states. First of all, in Nagaland, fully vaccination was strongly linked to household wealth (χ\u0026sup2; = 9.702; p\u0026thinsp;=\u0026thinsp;0.011), but in Tamil Nadu, wealth was not a significant factor (p\u0026thinsp;=\u0026thinsp;0.808). This shows that services are more fairly available in Tamil Nadu. Secondly, children from poor households in Nagaland had lower coverage (61.9%) than those from rich households (20.5% not fully immunized). Finally, Goodman et al. (2023) noted that compared to poor familes, children from wealthier families face fewer financial and logistical barriers to immunization [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], and similar to this, Rutstein and Johnson (2004) emphasized that economic disparities are magnified in regions with weaker infrastructure [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Targeted outreach and awareness programs for low-income households could help close this gap.\u003c/p\u003e \u003cp\u003eGeographic accessibility also emerged as a structural barrier. In Nagaland, respondents who did not see distance as a problem had higher immunization coverage (63.4%) than those who reported it as a major problem (36.6%) (χ\u0026sup2; = 5.550; p\u0026thinsp;=\u0026thinsp;0.018). In Tamil Nadu, this factor was not showing significance (p\u0026thinsp;=\u0026thinsp;0.906), potentially reflecting the efficiency of its rural outreach system. Chard et al. (2020) and Goodman et al. (2023) found also that limited physical access to health facilities decrease vaccination rates in remote areas [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e][\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Tamil Nadu\u0026rsquo;s robust health infrastructure and mobile immunisation initiatives, according to Lahariya (2014), appear to lessen these obstacles [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. If mobile outreach is expanded and reliable service delivery ensured in Nagaland, it could significantly improve child immunization coverage.\u003c/p\u003e \u003cp\u003eAbout Place of residence, we found urban-rural disparities in Nagaland, where the proportion of children fully immunized was higher in urban area (31.7%) than rural (15.9%) (χ\u0026sup2; = 13.011; p\u0026thinsp;=\u0026thinsp;0.001). In Tamil Nadu, no significant difference was observed (p\u0026thinsp;=\u0026thinsp;0.377), demonstrating the reach of its community-based services. Sinha et al. (2013) also found that immunisation coverage were low in rural areas of India's northeastern states [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Increasing rural outreach and community-level awareness initiatives in Nagaland may help to address these disparities.\u003c/p\u003e \u003cp\u003eIn Nagaland, birth order was significantly related to coverage (χ\u0026sup2; = 20.504; p\u0026thinsp;=\u0026thinsp;0.012). Firstborn children had the highest coverage (43.5%), while children of third or higher order had the lowest (17.0%). No significant association was found in Tamil Nadu (p\u0026thinsp;=\u0026thinsp;0.488). This pattern mirrors Sharma et al. (2021) and Mathew et al. (2012), who linked declining vaccination rates with increasing parity due to resource competition and reduced parental attention [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e][\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Household-level interventions and reminders for families with multiple children may therefore be effective.\u003c/p\u003e \u003cp\u003eReligion was significant in Nagaland (χ\u0026sup2; = 5.540; p\u0026thinsp;=\u0026thinsp;0.038) but not in Tamil Nadu (p\u0026thinsp;=\u0026thinsp;0.244). Christian families, representing the majority in Nagaland, had relatively higher coverage (93.8% fully immunized) than minority groups. Sinha et al. (2013) observed similar cultural influences in vaccination behavior [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], while Shrivastwa et al. (2015) found that the association between religion and vaccination varies by regional context [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Context-specific communication strategies that engage community and faith leaders may therefore improve acceptance in culturally diverse settings.\u003c/p\u003e \u003cp\u003eMother\u0026rsquo;s age was a marginally significant predictor in Tamil Nadu (AOR\u0026thinsp;=\u0026thinsp;1.22; p\u0026thinsp;=\u0026thinsp;0.037) but not in Nagaland (p\u0026thinsp;=\u0026thinsp;0.925), suggesting that slightly older mothers in Tamil Nadu may have better awareness or experience navigating healthcare systems. Sex of the child was not associated with immunization in either state (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), reflecting near gender parity, a positive contrast to earlier findings by Sinha et al. (2013) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOverall, the results indicate that while Tamil Nadu\u0026rsquo;s strong primary healthcare system and equitable outreach have minimized socioeconomic gradients, Nagaland\u0026rsquo;s lower coverage stems largely from educational, geographic, and structural disadvantages. Strengthening women\u0026rsquo;s education, improving rural service accessibility, and tailoring communication to local contexts remain essential for achieving universal immunization coverage in India.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and Limitations\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eStrengths\u003c/strong\u003e \u003cp\u003eLarge, representative dataset from NFHS-5; comprehensive range of variables. The focus on socioeconomic and demographic determinants, such as maternal education, wealth index, and healthcare access, offers a multidimensional perspective.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eLimitations\u003c/strong\u003e \u003cp\u003eCross-sectional nature of NFHS-5 data limits causal inferences; potential underreporting of vaccination status due to recall bias.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eFurther studies should focus on conducting longitudinal studies to assess the long-term impact of maternal education and socioeconomic improvements on immunization rates, particularly in low-performing regions like Nagaland, and eventually Investigate the role of health system infrastructure and human resource availability in influencing immunization coverage.\u003c/p\u003e \u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eOur study demonstrates that disparities in child immunisation coverage between Tamil Nadu and Nagaland reflect underlying socioeconomic and structural inequalities. Maternal education emerged as the most consistent predictor of full immunisation across both states, underscoring the importance of female literacy in shaping preventive health behaviors.\u003c/p\u003e \u003cp\u003eIn Nagaland, lower coverage among rural residents and higher birth-order children indicates that geographic and familial factors interact with education to influence vaccine uptake. Conversely, Tamil Nadu\u0026rsquo;s near-universal coverage is the proof of the benefits of sustained investment in primary healthcare and effective outreach mechanisms.\u003c/p\u003e \u003cp\u003eThese findings suggest that equitable immunisation cannot be achieved solely through the availability of vaccines; it necessitates a multisectoral approach. Strengthening women\u0026rsquo;s education, improving rural healthcare access, and developing targeted follow-up strategies for larger households are essential for closing remaining gaps. Addressing these multidimensional determinants will support India\u0026rsquo;s progress toward universal immunisation coverage and contribute to reducing preventable childhood illnesses.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConsent for publication\u003c/h2\u003e \u003cp\u003eAll authors consent to the publication of this manuscript in its current form.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting Interests\u003c/strong\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthical Approval and consent to participate\u003c/strong\u003e \u003cp\u003eEthical approval for this study was not required, as it involved secondary analysis of publicly available, anonymized data from the National Family Health Survey (NFHS-5). The NFHS survey obtained ethical approval from institutional review boards (IRBs) and informed consent from all participants at the time of data collection.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eClinical trial number\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eY. K. conceptualized the study, designed the methodology, and conducted data analysis and interpretation. S. T., K. S. G., and S. A. A. contributed to data analysis and manuscript drafting. R. T. and K. A. J. provided critical input on data interpretation. Dr. G. J. H. offered methodological guidance and substantive revisions to the manuscript. All authors reviewed and approved the final version for submission.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors express their gratitude to the School of Public Health, SRM Institute of Science and Technology (SRM IST), for their guidance and support throughout the study. We extend our appreciation to the National Family Health Survey (NFHS) team for providing access to the publicly available datasets utilized in this research. We also thank our colleagues and mentors for their valuable feedback and encouragement, which greatly enhanced the quality of this work.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data analysed for this study are publicly available through the Demographic and Health Surveys (DHS) Program. Specifically, this study utilized the dataset from the National Family Health Survey (NFHS-5, 2019-2021) for Tamil Nadu and Nagaland, which can be accessed from the DHS website (https:/dhsprogram.com/data/available-datasets.cfm)upon approval of a data request. All analyses and findings presented in this article are based on secondary data and comply with ethical research practices.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChard AN, Gacic-Dobo M, Diallo MS, Sodha SV, Wallace AS. Routine vaccination coverage worldwide, 2019. MMWR Morb Mortal Wkly Rep. 2020;69(45):1706\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization (WHO). Immunization.. 2023. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/health-topics/vaccines-and-immunization\u003c/span\u003e\u003cspan address=\"https://www.who.int/health-topics/vaccines-and-immunization\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePlotkin SA. History of vaccination. Proc Natl Acad Sci USA. 2014;111(34):12283\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLahariya C. A brief history of vaccines and vaccination in India. Indian J Med Res. 2014;139(4):491\u0026ndash;511.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMinistry of Health and Family Welfare. (Government of India). \u003cem\u003eUpdate on Immunization of Children.\u003c/em\u003e 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMinistry of Health and Family Welfare (Government of India). \u003cem\u003eNational Family Health Survey (NFHS-5), Phase II, 2019-20.\u003c/em\u003e 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVikram K, Vanneman R, Desai S. Linkages between maternal education and childhood immunization in India. Soc Sci Med. 2012;75(2):331\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSinha D, Singh AK, Kaur P, Sharma P. Gender and urban\u0026ndash;rural disparities in child immunization in India: Insights from DLHS and NFHS. BMC Public Health. 2013;13:487.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSinha RK, Acharya R, Jejawal N, Dwivedi LK. Socioeconomic inequalities in full immunization coverage among children in India: Evidence from a cross-sectional study. BMC Pediatr. 2020;20:295.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInternational Institute for Population Sciences (IIPS) and ICF. National Family Health Survey (NFHS-5), 2019-21: India. Mumbai: IIPS; 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoodman OK, Wagner AL, Riopelle D, Mathew JL, Boulton ML. Vaccination inequities among children 12\u0026ndash;23 months in India: An analysis of inter-state differences. Vaccine X. 2023;14:100310.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJejaw M, Tafere TZ, Tiruneh MG, et al. Missed opportunities for vaccination in Sub-Saharan Africa: a multilevel mixed-effects analysis. BMC Public Health. 2025;25(1):62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRutstein SO, Johnson K. \u003cem\u003eThe DHS Wealth Index.\u003c/em\u003e DHS Comparative Reports No. 6. Calverton, MD: ORC Macro; 2004.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMathew JL. Inequity in childhood immunization in India: a systematic review. Indian Pediatr. 2012;49(3):203\u0026ndash;23. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s13312-012-0063-z\u003c/span\u003e\u003cspan address=\"10.1007/s13312-012-0063-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharma A, Gupta R, Patel S, Kumar S, Bose P. Impact of birth order on immunization: A multilevel analysis in low-income countries. Vaccine. 2021;39(12):1534\u0026ndash;541.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShrivastwa N, Gillespie BW, Kolenic GE, Lepkowski JM, Boulton ML. Predictors of vaccination in India for children aged 12\u0026ndash;36 months. Am J Prev Med. 2015;49(6 Suppl 4):S435\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eProgram DHS. \u003cem\u003eWealth Index Construction\u003c/em\u003e [Internet]. Bethesda (MD): ICF International; [cited 2025 Jan 18]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dhsprogram.com/topics/wealth-index/Wealth-Index-Construction.cfm\u003c/span\u003e\u003cspan address=\"https://dhsprogram.com/topics/wealth-index/Wealth-Index-Construction.cfm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bped","sideBox":"Learn more about [BMC Pediatrics](http://bmcpediatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bped/default.aspx","title":"BMC Pediatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Immunisation, socioeconomic determinants, geographic drivers, India, maternal education, health disparities","lastPublishedDoi":"10.21203/rs.3.rs-8398905/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8398905/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eImmunising children saves 2\u0026ndash;3\u0026nbsp;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.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo compare the level and socioeconomic and geographic determinants of full immunisation coverage among children aged 12\u0026ndash;23 months in Tamil Nadu and Nagaland using data from the fifth National Family Health Survey (NFHS-5, 2019-21).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA 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.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eImmunization coverage was higher in Tamil Nadu (93.6%) than in Nagaland (72.7%). In Nagaland, full immunization was significantly associated with maternal education (χ\u0026sup2;=3.983, p\u0026thinsp;=\u0026thinsp;0.006), wealth (χ\u0026sup2;=9.702, p\u0026thinsp;=\u0026thinsp;0.011), residence (χ\u0026sup2;=13.011, p\u0026thinsp;=\u0026thinsp;0.001), distance to health facility (χ\u0026sup2;=5.550, p\u0026thinsp;=\u0026thinsp;0.018), religion (χ\u0026sup2;=5.540, p\u0026thinsp;=\u0026thinsp;0.038), and birth order (χ\u0026sup2;=20.504, p\u0026thinsp;=\u0026thinsp;0.012), whereas in Tamil Nadu only maternal education was significant (χ\u0026sup2;=9.764, p\u0026thinsp;=\u0026thinsp;0.024). Multivariable analysis substantiated maternal education as the most significant predictor in both states (Tamil Nadu: AOR\u0026thinsp;=\u0026thinsp;1.98; p\u0026thinsp;=\u0026thinsp;0.017; Nagaland: AOR\u0026thinsp;=\u0026thinsp;3.46; p\u0026thinsp;=\u0026thinsp;0.026). In Nagaland, urban residence also increased the odds of full immunization (AOR\u0026thinsp;=\u0026thinsp;1.89; p\u0026thinsp;=\u0026thinsp;0.047), while in Tamil Nadu, mother\u0026rsquo;s age showed a marginal positive effect (AOR\u0026thinsp;=\u0026thinsp;1.22; p\u0026thinsp;=\u0026thinsp;0.037). After adjustment, other variables were not significant.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe 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.\u003c/p\u003e","manuscriptTitle":"Bridging the Immunisation Gap: Socioeconomic and Geographic Drivers of Pediatric Immunisation Disparities between High-performing and Underperforming Indian States","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-06 17:12:26","doi":"10.21203/rs.3.rs-8398905/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-12T07:31:57+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-11T03:27:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-09T17:09:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-06T06:06:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"7700905433771908718585565219816969523","date":"2026-03-04T11:05:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"274601394754295049171635817102605543475","date":"2026-03-03T03:20:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"332061616500706993404359820785111772264","date":"2026-03-03T02:04:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"12908038707730972133788421825495461256","date":"2026-03-02T14:33:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"89135042203025976864812726317486846564","date":"2026-03-02T03:42:19+00:00","index":"hide","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-05T10:24:57+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"130894633681079210570703003480839660487","date":"2026-01-13T11:37:04+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-07T20:48:06+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-22T07:40:30+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-22T07:39:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pediatrics","date":"2025-12-18T21:18:21+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bped","sideBox":"Learn more about [BMC Pediatrics](http://bmcpediatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bped/default.aspx","title":"BMC Pediatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b2b832b8-579e-41d7-8934-cc3ac34279e1","owner":[],"postedDate":"January 6th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-20T16:00:09+00:00","versionOfRecord":{"articleIdentity":"rs-8398905","link":"https://doi.org/10.1186/s12887-026-06822-6","journal":{"identity":"bmc-pediatrics","isVorOnly":false,"title":"BMC Pediatrics"},"publishedOn":"2026-04-14 15:56:52","publishedOnDateReadable":"April 14th, 2026"},"versionCreatedAt":"2026-01-06 17:12:26","video":"","vorDoi":"10.1186/s12887-026-06822-6","vorDoiUrl":"https://doi.org/10.1186/s12887-026-06822-6","workflowStages":[]},"version":"v1","identity":"rs-8398905","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8398905","identity":"rs-8398905","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}