A Low-Cost, Integrated Immunization, Health, and Nutrition Intervention in Conflict Settings in Pakistan—The Impact on Zero-Dose Children and Polio Coverage
Bibliographic record
Abstract
Pakistan is one of two countries globally still endemic for poliovirus. While increasing immunization coverage is a concern, providing equitable access to care is also a priority, especially for conflict-affected populations. Recognizing these challenges, Naunehal, an integrated model of maternal, newborn, and child health (MNCH), immunization, and nutrition services delivered through community mobilization, mobile outreach, and private-sector engagement was implemented in conflict-affected union councils (UCs) with high poliovirus transmission, including Kharotabad 1(Quetta, Balochistan) and Bakhmal Ahmedzai (Lakki Marwat, Khyber Pakhtunkhwa). A quasi-experimental pre–post-design was used to assess the impact of the interventions implemented between April 2021 and April 2022, with a baseline and an endline survey. For each of the intervention UCs, a separate, matched-control UC was identified. At endline, the proportion of fully immunized children increased significantly from 27.5% to 51.0% in intervention UCs with a difference-in-difference (DiD) estimate of 13.6%. The proportion of zero-dose children and non-recipients of routine immunization (NR-RI) children decreased from 31.6% to 0.9% and from 31.9% to 3.4%, respectively, with a significant decrease in the latter group. Scaling up and assessing the adoption and feasibility of integrated interventions to improve immunization coverage can inform policymakers of the viability of such services in such contexts.
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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 | 0.001 | 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".