Author response: The impact of measles immunization campaigns in India using a nationally representative sample of 27,000 child deaths
Bibliographic record
Abstract
The introduction of the measles vaccine in the 1960s led to large reductions in measles deaths in many countries. Yet, measles remains a major killer of children younger than age five worldwide, particularly among children living in Africa and Asia, where fewer children are immunized. India has been particularly hard hit, with annual child measles deaths exceeding 60,000 in 2005. In the 1990s, India’s national vaccination program made one dose of the measles vaccine part of routine vaccinations through much of the country to help reduce the numbers of measles deaths. However, it was one of the last countries to add a second dose of measles vaccine as recommended by the World Health Organization, which has been shown to prevent infection and death in 90-95% of vaccinated children. In 2008, the Indian government announced it would introduce a second dose of measles vaccine to its routine vaccine schedule for children from 2010 onwards. Prior to the introduction of a second-dose measles vaccine, campaigns were launched to increase immunization rates in regions where few children were being vaccinated. But how many young children’s lives were saved by these campaigns was unknown. Now, Wong et al. show that India’s measles immunization campaigns saved the lives of 41,000 to 56,000 children between 2010 and 2013. This averted between 39-57% of the expected measles deaths nationally during that time period. Wong et al. used data from the Million Death Study, which used household surveys to capture information on the cause of 27,000 child deaths in India between 2005 and 2013, to assess the affects of the state vaccination campaigns on measles deaths. Changes in measles deaths were compared to changes in unrelated child deaths to make sure any differences were related to the vaccination campaigns and not other improvements in children’s health care. Indian states with measles immunization campaigns saw larger decreases in measles deaths among children younger than five than states without such a campaign (27% vs 11%). Similar decreases were not seen in child deaths from other causes. Girls, who are disproportionately affected by measles in India, benefited from larger reductions in deaths than boys in states with immunization campaigns. The success of two-dose measles vaccination campaigns at reducing young children’s deaths in India may help to boost vaccination rates and help combat parents’ hesitance to immunize their children.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.030 | 0.009 |
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".