Air Pollution Impact on Pregnancy and Early Childhood Development (APiPED) in India: Protocol for a Cohort Study
Bibliographic record
Abstract
BACKGROUND: The impact of air pollution on early childhood development in low-pollution settings is well known. However, comprehensive evidence from high-pollution environments such as the Delhi National Capital Region in India remains limited. OBJECTIVE: This maternal-child cohort study aims to investigate the impact of air pollution on pregnancy outcomes and early childhood development, providing critical insights to inform targeted interventions and public health policy. METHODS: This longitudinal maternal-child cohort study will enroll 2500 pregnant women from rural and urban Delhi National Capital Region and follow their children up to 2 years of age. Maternal data on pregnancy complications, delivery mode, parity, and gestational weight gain will be collected from clinical records and structured questionnaires, while newborn outcomes (gestational age, birth weight, anthropometry, and congenital anomalies) will be assessed from birth records and clinical examination. Early childhood development will be evaluated through standardized anthropometry and the Developmental Assessment Scale for Indian Infants. For a 10% subsample, trimester-specific and postnatal indoor and outdoor air pollution exposure (particulate matter with a diameter of less than 2.5 micrometers and particulate matter with a diameter of less than 10 micrometers) will be monitored using portable air quality monitors, along with household surveys and time-activity diaries. Maternal and infant blood samples will be analyzed for inflammatory, oxidative stress, and cardiovascular biomarkers. Exposure estimates will be integrated into personal exposure models, and associations with health outcomes will be examined using multivariable regression and longitudinal mixed-effects models. RESULTS: As of July 2025, 45% of the planned sample size have been recruited, with baseline data collection completed, and 10% undergoing exposure assessment and sample collection. CONCLUSIONS: This study will provide a comprehensive evaluation of the effects of air pollution on maternal health, pregnancy outcomes, and early childhood development in urban and rural settings in India. This will generate context-specific evidence to support maternal and child health policies, air pollution mitigation strategies, and personal protection measures for pregnant women and infants in polluted environments. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/72683.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.031 | 0.007 |
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".