Infant and child mortality in Afghanistan: A scoping review
Bibliographic record
Abstract
Background and Aims: Since 1990, global child and infant mortality rates have typically stabilized or decreased due to improved healthcare, vaccination rollouts, and international funding. However, Afghanistan continues to face the highest child and infant mortality rates globally, with 43 deaths per 1000 live births. This study aims to examine the factors contributing to this high mortality rate and propose interventions to address the issue. Methods: A comprehensive literature search was conducted using databases such as Google Scholar and PubMed, focusing on articles published in English within the last 10 years (2013-2023). The search terms included "Child mortality," "Infant mortality," "SIDS," "COVID-19," and "Afghanistan." Original studies, systematic reviews, case studies, and reports meeting the inclusion criteria were selected for analysis. Additional sources from organizations such as UNICEF, the World Bank Group, WHO, and EMRO were also reviewed. Results: The study findings reveal significant challenges contributing to Afghanistan's high infant and child mortality rates. These challenges include birth defects, preterm birth, malnutrition, sudden infant death syndrome (SIDS), traumatic injuries, fatal infections, infanticide, and abuse. The ongoing conflict, insecurity, and humanitarian crises further exacerbate the situation, leading to increased child casualties. Despite efforts by international agencies like UNICEF to provide vaccines and maternal education, the infant mortality rate remains high. Conclusion: In conclusion, Afghanistan's child and infant mortality rates are of significant concern, and it is imperative that action be taken to reduce the incidence of child and infant mortality rates.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.018 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".