Trends in the quality of antenatal care in India: Patterns of change across 36 states and union territories, 1999–2021
Bibliographic record
Abstract
Background: Antenatal care (ANC) quality is important to maternal and neonatal mortality. However, trends in the quality of ANC received by pregnant women in India have been understudied. This paper seeks to fill this gap by examining the long-term patterns nationwide and the state-specific prevalence of inadequate ANC quality received by pregnant women in India. Methods: We utilised data from four National Family Health Surveys (NFHS) conducted in 1999 (NFHS-2), 2006 (NFHS-3), 2016 (NFHS-4), and 2021 (NFHS-5) across India's 36 states/union territories (UTs). The sample includes mothers who had given birth within three years (NFHS-2) and five years (NFHS-3, NHFS-4, and NFHS-5) before each survey. We define inadequate ANC quality as not completing seven essential ANC services (weight measurement, blood pressure measurement, urine sampling, blood sampling, provision of iron supplements, provision of tetanus vaccination, and ultrasound scans) during pregnancy. We calculated the standardised absolute change to quantify the change in the share of women receiving inadequate quality ANC nationally and by each state/UT. Additionally, we estimated the population headcount of mothers who received inadequate-quality ANC in 2021 and identified the socioeconomic correlates associated with inadequate ANC quality. Results: The prevalence of inadequate ANC quality substantially declined between 1999-2021, from 84.8% (95% confidence interval (CI) = 84.1-85.5) to 28.8% (95% CI = 28.5-29.2). However, between-state inequality in ANC quality has increased over this time. We identified a weak correlation between prevalence and population headcounts in 2021. Socioeconomically disadvantaged groups exhibited a higher prevalence of inadequate quality of ANC than less disadvantaged groups. Conclusions: The proportion of pregnant women receiving inadequate ANC quality has decreased over time throughout India. However, multi-faceted efforts at national and state levels are necessary to enhance the effectiveness of existing policies. Additionally, innovative and targeted approaches are required to ensure the timely and equitable provision of high-quality ANC.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".