Analysis of the Universal Immunization Programme and introduction of a rotavirus vaccine in India with IndiaSim
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: India has the highest under-five death toll globally, approximately 20% of which is attributed to vaccine-preventable diseases. India's Universal Immunization Programme (UIP) is working both to increase immunization coverage and to introduce new vaccines. Here, we analyze the disease and financial burden alleviated across India's population (by wealth quintile, rural or urban area, and state) through increasing vaccination rates and introducing a rotavirus vaccine. METHODS: We use IndiaSim, a simulated agent-based model (ABM) of the Indian population (including socio-economic characteristics and immunization status) and the health system to model three interventions. In the first intervention, a rotavirus vaccine is introduced at the current DPT3 immunization coverage level in India. In the second intervention, coverage of three doses of rotavirus and DPT and one dose of the measles vaccine are increased to 90% randomly across the population. In the third, we evaluate an increase in immunization coverage to 90% through targeted increases in rural and urban regions (across all states) that are below that level at baseline. For each intervention, we evaluate the disease and financial burden alleviated, costs incurred, and the cost per disability-adjusted life-year (DALY) averted. RESULTS: Baseline immunization coverage is low and has a large variance across population segments and regions. Targeting specific regions can approximately equate the rural and urban immunization rates. Introducing a rotavirus vaccine at the current DPT3 level (intervention one) averts 34.7 (95% uncertainty range [UR], 31.7-37.7) deaths and $215,569 (95% UR, $207,846-$223,292) out-of-pocket (OOP) expenditure per 100,000 under-five children. Increasing all immunization rates to 90% (intervention two) averts an additional 22.1 (95% UR, 18.6-25.7) deaths and $45,914 (95% UR, $37,909-$53,920) OOP expenditure. Scaling up immunization by targeting regions with low coverage (intervention three) averts a slightly higher number of deaths and OOP expenditure. The reduced burden of rotavirus diarrhea is the primary driver of the estimated health and economic benefits in all intervention scenarios. All three interventions are cost saving. CONCLUSION: Improving immunization coverage and the introduction of a rotavirus vaccine significantly alleviates disease and financial burden in Indian households. Population subgroups or regions with low existing immunization coverage benefit the most from the intervention. Increasing coverage by targeting those subgroups alleviates the burden more than simply increasing coverage in the population at large.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 source (direct Gemma or distilled Codex), 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".