Determinants of public institutional births in India: An analysis using the National Family Health Survey (NFHS-5) factsheet data
Bibliographic record
Abstract
A BSTRACT Background: Institutional births ensure deliveries happen under the supervision of skilled healthcare personnel in an enabling environment. For countries like India, with high neonatal and maternal mortalities, achieving 100% coverage of institutional births is a top policy priority. In this respect, public health institutions have a key role, given that they remain the preferred choice by most of the population, owing to the existing barriers to healthcare access. While research in this domain has focused on private health institutions, there are limited studies, especially in the Indian context, that look at the enablers of institutional births in public health facilities. In this study, we look to identify the significant predictors of institutional birth in public health facilities in India. Method: We rely on the National Family Health Survey (NFHS-5) factsheet data for analysis. Our dependent variable (DV) in this study is the % of institutional births in public health facilities. We first use Welch’s t -test to determine if there is any significant difference between urban and rural areas in terms of the DV. We then use multiple linear regression and partial F-test to identify the best-fit model that predicts the variation in the DV. We generate two models in this study and use Akaike’s Information Criterion (AIC) and adjusted R 2 values to identify the best-fit model. Results: We find no significant difference between urban and rural areas ( P = 0.02, α =0.05) regarding the mean % of institutional births in public health facilities. The best-fit model is an interaction model with a moderate effect size (Adjusted R2 = 0.35) and an AIC of 179.93, lower than the competitive model (AIC = 183.56). We find household health insurance (β = -0.29) and homebirth conducted under the supervision of skilled healthcare personnel (β = -0.56) to be significant predictors of institutional births in public facilities in India. Additionally, we observe low body mass index (BMI) and obesity to have a synergistic impact on the DV. Our findings show that the interaction between low BMI and obesity has a strong negative influence (β = -0.61) on institutional births in public health facilities in India. Conclusion: Providing households with health insurance coverage may not improve the utilisation of public health facilities for deliveries in India, where other barriers to public healthcare access exist. Therefore, it is important to look at interventions that minimise the existing barriers to access. While the ultimate objective from a policy perspective should be achieving 100% coverage of institutional births in the long run, a short-term strategy makes sense in the Indian context, especially to manage the complications arising during births outside an institutional setting.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".