An Equity Analysis of Zero-Dose Children in India Using the National Family Health Survey Data: Status, Challenges, and Next Steps
Bibliographic record
Abstract
Background While immunization programs across the world have made considerable progress, children and communities continue to be beyond the reach of healthcare services. Globally, they are now referred to as zero-dose (ZD) children (those who have not received a single dose of diphtheria, pertussis, and tetanus-containing vaccine). Pre-COVID-19 pandemic analyses suggest that nearly 50% of vaccine-preventable deaths occur among ZD children. Two-thirds of these children live in extremely poor households suffering from multiple deprivations including lack of access to reproductive health services, water, and sanitation. Hence, ZD children have now been prioritized as a key cohort for identification and integration with the health systems as we build back from the pandemic. Methodology Extracting data from the last two National Family Health Survey (NFHS) rounds (NFHS 4, 2015-2016 and NFHS 5, 2019-2021), this study aims to ascertain the status of ZD children aged 12-23 months in India, the challenges, and the necessary action agenda going forward. Data were analyzed for equity determinants such as gender, place of residence, religion, birth order, caste, and mother's schooling. Key determinants included the change in ZD prevalence at the national, state, and district levels; variations across equity parameters and states with maximum improvements; and disparity across these indicators. A correlation analysis was also conducted to understand the nature of the association between ZD prevalence and critical maternal and child health indicators. Results The overall ZD prevalence between the two rounds was reduced by 4.1% (10.5-6.4%). A total of 26 states in the country reported a ZD prevalence of <10% in NFHS 5 compared to 18 in NFHS 4. In total, 324 districts reported a ZD prevalence of <5%, and 145 districts reported a prevalence of >10%. The equity parameters reflected a slow-footed reduction among ZD for girl children, across urban geographies, firstborn children, mothers with 12 or more years of schooling, and children in families with the highest wealth quintiles. A negative correlation accentuated between the two NFHS rounds was established between first-trimester registration, four or more antenatal visits, institutional deliveries, and ZD prevalence. Conclusions The findings point toward sustained improvement across key equity parameters, however, challenges do exist. Moreover, the impact of the pandemic on immunization programs across the globe and in India is bound to halt and reverse the progress and potentiate further inequities. It is thus imperative that continued and augmented efforts are continued to identify, integrate, and immunize ZD children, families, and communities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".