Evidence map›Paper›PMID 40656242›Full record

ReviewCureus2025

Outcome Comparability and SWOC (Strength-Weakness-Opportunity-Challenges) Analysis of the National Family Health Survey (NFHS) 1 to 5 for Demographic, Maternal and Child Health, and Behavioral Determinants.

Lalima Gupta, Raghvendra Gumashta, Rajendra Mahor, Girjesh Gupta, Maharshi N Ojha, Jyotsna Gumashta

Abstract readReview
In one paragraph

Review in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Lalima GuptaCommunity Medicine, People's College of Medical Sciences and Research Centre, Bhopal, IND.
Raghvendra GumashtaCommunity Medicine, People's College of Medical Sciences and Research Centre, Bhopal, IND.
Rajendra MahorCommunity Medicine, Index Medical College, Hospital and Research Centre, Indore, IND.
Girjesh GuptaCommunity Medicine, People's College of Medical Sciences and Research Centre, Bhopal, IND.
Maharshi N OjhaCommunity Medicine, People's College of Medical Sciences and Research Centre, Bhopal, IND.
Jyotsna GumashtaPhysiology, All India Institute of Medical Sciences, Nagpur, Nagpur, IND.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The effective development of health programs requires the practical application of evidence-based strategies, equitable financial allocation, and the integration of operational research into public health actions. These elements are essential for attaining measurable improvements in healthcare systems and population well-being. By aligning research-driven approaches with policy implementation, we can accelerate progress toward achieving Sustainable Development Goal 3 (SDG-3) targets. This study aimed to engage in an in-depth, evidence-based quantitative and qualitative analysis of National Family Health Survey (NFHS) datasets, as well as a category and sub-category-wise comparability assessment of health indicators. Each round of the NFHS outcomes was analyzed for its application towards programmatic development, implementation, and evaluation. Studies analyzed were representative of each of the studied categories, i.e., demography, maternal health, child health, and behavioral pattern. Literature published under the subheads demography (159), maternal health (286), child health (318), and behavioral pattern (94) and indexed on PubMed Central were analyzed for inferential patterned study. This was followed by the outcome comparability and Strength-Weakness-Opportunity-Challenges (SWOC) analysis of trends, emphasis, and applicability. This trend analysis was quantitatively and qualitatively analyzed. There was a deviation in sub-criterion identification over various rounds, posing challenges to the use of NFHS data with multiple, varied, and rich resources available for healthcare and support. The lowest expected decline per round compared to their previous rounds in stunting, wasting, and unmet need for family planning was -2.5%, -4.3%, and 1%, respectively. In contrast, the lowest increase per round for female sterilization and antenatal care (ANC) was +1.3% and +1%, respectively. Strengths include the quality of data, sub-categorization over NFHS rounds, use of new tools for assessments, and elimination of some parameters. Weakness was illustrated through unsynchronized conduct of rounds, viz., 6, 7, 10, and 5 years' time gap observed in NFHS-1, 2, 3, 4, and 5, respectively. Opportunity of using data for national development initiatives and fulfilling Sustainable Development Goal-3 can be availed to benefit the healthcare-deprived masses. Threats include intrinsic limitations of data collection, processes of outcome measures for addressing upgradation needs, especially of Reproductive, Maternal, Neonatal, Child Health + Adolescents (RMNCH+A), National Programme for Prevention and Control of Non-Communicable Disease (NPNCD), and national nutrition programs. The policy development, programmatic design, intervention implementation, effectiveness assessment, and National Health Program evaluation need stronger, consecutive, and appropriate evidence datasets for public health action. The felt need of public health intervention can be augmented significantly by using NFHS datasets over time while ensuring adaptable, flexible, and resource-friendly mechanisms of action. Triangulating NFHS datasets, programmatic needs, and financial allocations will hence help create user-friendly public health networks.

Indexed as

child healthdemographymaternal healthnfhstrend analysis

Identifiers

PMID40656242
PMCPMC12254923

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LicenceCC BY
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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.