Evidence map›Paper›PMID 38937489›Full record

ArticleScientific reports2024

Contributing factors for reduction in maternal mortality ratio in India.

Himanshu Tolani, Sutapa Bandyopadhyay Neogi, Anuj Kumar Pandey, Pijush Kanti Khan, Sidharth Sekhar Mishra

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Himanshu TolaniInternational Institute of Health Management Research, New Delhi, India.ORCID 0000-0003-4922-6206
Sutapa Bandyopadhyay NeogiInternational Institute of Health Management Research, New Delhi, India. sutapa@iihmrdelhi.edu.in.ORCID 0000-0002-2173-966X
Anuj Kumar PandeyInternational Institute of Health Management Research, New Delhi, India.ORCID 0000-0002-3525-4823
Pijush Kanti KhanInternational Institute of Health Management Research, New Delhi, India.ORCID 0000-0003-2118-3945
Sidharth Sekhar MishraInternational Institute of Health Management Research, New Delhi, India.ORCID 0000-0002-9711-5503

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Maternal mortality ratio (MMR) estimates have been studied over time for understanding its variation across the country. However, it is never sufficient without accounting for presence of variability across in terms of space, time, maternal and system level factors. The study endeavours to estimate and quantify the effect of exposures encompassing all maternal health indicators and system level indicators along with space-time effects influencing MMR in India. Using the most recent level of possible -factors of MMR, maternal health indicators from the National Family Health Survey (NFHS: 2019-21) and system level indicators from government reports a heatmap compared the relative performance of all 19 SRS states. Facet plots with a regression line was utilised for studying patterns of MMR for different states in one frame. Using Bayesian Spatio-temporal random effects, evidence for different MMR patterns and quantification of spatial risks among individual states was produced using estimates of MMR from SRS reports (2014-2020). India has witnessed a decline in MMR, and for the majority of the states, this drop is linear. Few states exhibit cyclical trend such as increasing trends for Haryana and West Bengal which was evident from the two analytical models i.e., facet plots and Bayesian spatio- temporal model. Period of major transition in MMR levels which was common to all states is identified as 2009-2013. Bihar and Assam have estimated posterior probabilities for spatial risk that are relatively greater than other SRS states and are classified as hot spots. More than the individual level factors, health system factors account for a greater reduction in MMR. For more robust findings district level reliable estimates are required. As evident from our study the two most strong health system influencers for reducing MMR in India are Institutional delivery and Skilled birth attendance.

Indexed as

Bayes TheoremMaternal MortalityAdultFemaleHumansIndiaMaternal HealthPregnancyBayesianHeatmapMaternal mortality ratioNFHSSpatio-temporalSRSSustainable development goals

Identifiers

PMID38937489
PMCPMC11211505

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.