Evidence map›Paper›PMID 39353710›Full record

SynthesisBMJ paediatrics open2024

Feasibility and diagnostic accuracy of neonatal anthropometric measurements in identifying low birthweight and preterm infants in Africa: a systematic review and meta-analysis.

Fitsum Weldegebriel Belay, Rekiku Fikre, Akalewold Alemayehu, Andrew Clarke, Sarah Williams, Hannah Richards, Yohannes Chanyalew Kassa, Fanuel Belayneh Bekele

Abstract readMeta-AnalysisSystematic Review
In one paragraph

Synthesis in BMJ paediatrics open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

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

  1. Nutrition assessment of hospitalized very preterm infants: Best practices for accurate anthropometry in neonatal intensive care unit settings.Nutrition in clinical practice : official publication of the American Society for Parenteral and Enteral Nutrition · 2025
    Guideline
  2. Article
  3. Article
  4. Article
  5. 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

8 authors.

Fitsum Weldegebriel BelayDepartment of Pediatrics and Child Health, Hawassa University College of Medicine and Health Sciences, Hawassa, Ethiopia fitsedememmj@gmail.com.ORCID http://orcid.org/0000-0002-9671-7422
Rekiku FikreHawassa University College of Medicine and Health Sciences, Hawassa, Ethiopia.ORCID http://orcid.org/0000-0003-1324-1631
Akalewold AlemayehuHawassa University College of Medicine and Health Sciences, Hawassa, Ethiopia.ORCID http://orcid.org/0000-0002-6448-6092
Andrew ClarkeLancaster University, Lancaster, UK.ORCID http://orcid.org/0000-0003-3127-5309
Sarah WilliamsSave the Children, London, UK.
Hannah RichardsSave the Children International, Copenhagen, UK.
Yohannes Chanyalew KassaSave the Children International, Addis Ababa, Ethiopia.ORCID http://orcid.org/0009-0009-9644-0839
Fanuel Belayneh BekeleHawassa University College of Medicine and Health Sciences, Hawassa, Ethiopia.ORCID http://orcid.org/0000-0001-7021-0773

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundComplications of prematurity are the leading cause of under-5 mortality globally and 80% of newborn deaths are of low birth weight (LBW) babies. Early identification of LBW and preterm infants is crucial to initiate timely interventions.

objectiveTo evaluate the feasibility and diagnostic accuracy of alternative neonatal anthropometric measurements in identifying LBW and preterm infants in Africa.

methodsIn this systematic review and meta-analysis, we evaluated the diagnostic performance of infant foot length, mid-upper arm circumference (MUAC), head and chest circumferences against birth weight and gestational age. Pooled correlation between the index and the reference methods was estimated. Multiple anthropometric thresholds were considered in estimating the pooled sensitivity, specificity and area under receiver operating characteristic curve (AUC).

results21 studies from 8 African countries met the inclusion criteria. Correlation coefficients with birth weight were 0.79 (95% CI 0.70 to 0.85) for chest circumference, 0.71 (95% CI 0.62 to 0.78) for MUAC and 0.66 (95% CI 0.59 to 0.73) for foot length. Foot length measured by rigid ruler showed a higher correlation than tape measurement. Chest circumference with 28.8 cm cut-off detects LBW babies with AUC value of 0.92 (95% CI 0.71 to 0.97). Foot length identified preterm infants, with 82% sensitivity, 89% specificity and AUC of 0.91 (95% CI 0.69 to 0.98) at a 7.2 cm optimal cut-off point. MUAC had an AUC of 0.83 (95% CI 0.47 to 0.95) for preterm detection. In identifying LBW babies, foot length and MUAC have AUC values of 0.89 (95% CI 0.70 to 0.96) and 0.91 (95% CI 0.73 to 0.97) at 7.3 cm and 9.8 cm optimal cut-off points, respectively. Foot length and MUAC are relatively simple and minimise the risk of exposing infants to cold.

conclusionNewborn foot length, MUAC, head and chest circumferences have comparable diagnostic accuracy in identifying LBW and preterm babies. Using foot length and MUAC in low-resource settings are the most feasible proxy measures for screening where weighing scales are not available. PROSPERO REGISTRATION NUMBER: CRD42023454497.

Indexed as

AnthropometryInfant, Low Birth WeightInfant, PrematureAfricaBirth WeightFeasibility StudiesGestational AgeHumansInfant, NewbornNeonatal ScreeningSensitivity and SpecificityDeveloping CountriesHealth services researchInfantLow and Middle Income CountriesNeonatology

Identifiers

PMID39353710
PMCPMC11448207

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.