Evidence map›Paper›PMID 35356650›Full record

SynthesisJournal of global health2022

A systematic review on estimating population attributable fraction for risk factors for small-for-gestational-age births in 81 low- and middle-income countries.

Sabi Gurung, Hannah Hanzi Tong, Emily Bryce, Joanne Katz, Anne Cc Lee, Robert E Black, Neff Walker

Open access · goldAbstract readSystematic Review
In one paragraph

Synthesis in Journal of global health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 4 pooled it
13.7field-weighted citation impact, top 1% of its field
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

23 citing papers in PubMed, 4 syntheses or guidelines pooled it, 37 citations in OpenAlex.

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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

7 authors at 2 institutions in 1 country.

Sabi GurungDepartment of International Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA.
Hannah Hanzi TongDepartment of International Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA.
Emily BryceDepartment of International Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA.
Joanne KatzDepartment of International Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA.
Anne Cc LeeDepartment of Pediatric Newborn Medicine, Global Advancement of Infants and Mothers (AIM), Brigham and Women's Hospital, Boston, Massachusetts, USA.
Robert E BlackDepartment of International Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA.
Neff WalkerDepartment of International Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA.
Johns Hopkins University · USBrigham and Women's Hospital · US

Funding

The Roles of Perinatal Nutrition, Infection and Inflammation in the Neurodevelopment of Bangladeshi InfantsK23HD091390 · NICHD · BRIGHAM AND WOMEN'S HOSPITAL · PI LEE, ANNE SHEE CC · 2017 to 2021
$864k
Prioritization of modifiable risk factors for adverse pregnancy outcomes and neonatal mortality in rural NepalR01HD092411 · NICHD · JOHNS HOPKINS UNIVERSITY · PI KATZ, JOANNE · 2018 to 2020
$822k
NICHD NIH HHS K23 HD091390NICHD NIH HHS R01 HD092411
6 · The paper itself

Abstract

Background: Small for gestational age (SGA) is a public health concern since it is associated with mortality in neonatal and post-neonatal period. Despite the large magnitude of the problem, little is known about the population-attributable risk (PAR) of various risk factors for SGA. This study estimated the relative contribution of risk factors for SGA, as a basis for identifying priority areas for developing and/or implementing interventions to reduce the incidence of SGA births and related mortality and morbidity. Methods: We conducted a literature review on 63 potential risk factors for SGA to quantify the risk relationship and estimate the prevalence of risk factors (RFs). We calculated the population-attributable fraction for each of the identified RF for 81 Countdown countries and calculated regional estimates. Twenty-five RFs were included in the final model while extended model included all the 25 RFs from the final model and two additional RFs. Results: In the final and extended models, the RFs included in each model have a total PAF equal to 63.97% and 69.66%, respectively of SGA across the 81 LMICs. In the extended model, maternal nutritional status has the greatest PAF (28.15%), followed by environmental and other exposures during pregnancy (15.82%), pregnancy history (11.01%), and general health issues or morbidity (10.34%). The RFs included in the final and extended model for Sub-Saharan African (SSA) region have a total PAF of 63.28% and 65.72% of SGA, respectively. In SSA, the top three RF categories in the extended model are nutrition (25.05%), environment and other exposure (13.01%), and general health issues or morbidity (10.72%), while in South-Asia's it was nutrition (30.56%), environment and other exposure (15.27%) and pregnancy history (11.68%). Conclusions: The various types of RFs that play a role in SGA births highlight the importance of a multifaceted approach to tackle SGA. Depending on the types of RFs, intervention should be strategically targeted at either individual or household and/or community or policy level. There is also a need to research the mechanisms by which some of the RFs might hinder fetal growth.

Indexed as

Developing CountriesInfant, Newborn, DiseasesFemaleHumansInfant, NewbornInfant, Small for Gestational AgeMaternal Nutritional Physiological PhenomenaPregnancyRisk Factors

Identifiers

PMID35356650
PMCPMC8942297
OpenAlexW4226372668

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.