Evidence map›Paper›PMID 37208642›Full record

ArticleBMC pregnancy and childbirth2023

The dose-response association between LEAP 1000 and birthweight - no clear mechanisms: a structural equation modeling approach.

Sarah Quinones, Shao Lin, Lili Tian, Pauline Mendola, Jacob Novignon, Clement Adamba, Tia Palermo

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC pregnancy and childbirth, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.3field-weighted citation impact, top 45% 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

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. 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

7 authors at 5 institutions in 2 countries.

Sarah QuinonesDepartment of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, State University of New York, Buffalo, NY, 14214, USA. squinone@buffalo.edu.
Shao LinDepartment of Environmental Health Sciences, One University Place, 212D University at Albany, State University of New York, Rensselaer, NY, 12144, USA.
Lili TianDepartment of Biostatistics, State University of New York, 717 Kimball Tower University at Buffalo, Buffalo, NY, 14214, USA.
Pauline MendolaDepartment of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, State University of New York, Buffalo, NY, 14214, USA.
Jacob NovignonDepartment of Economics, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana.
Clement AdambaInstitute of Statistical, Social and Economic Research, University of Ghana-Legon, P.O. Box LG 74, Legon-Accra, Ghana.
Tia PalermoDepartment of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, State University of New York, Buffalo, NY, 14214, USA.
University at Buffalo, State University of New York · USAlbany State University · USBuffalo State University · USKwame Nkrumah University of Science and Technology · GHUniversity of Ghana · GH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBirthweight is an important indicator of maternal and fetal health globally. The multifactorial origins of birthweight suggest holistic programs that target biological and social risk factors have great potential to improve birthweight. In this study, we examine the dose-response association of exposure to an unconditional cash transfer program before delivery with birthweight and explore the potential mediators of the association.

methodsData for this study come from the Livelihood Empowerment Against Poverty (LEAP) 1000 impact evaluation conducted between 2015 and 2017 among a panel sample of 2,331 pregnant and lactating women living in rural households of Northern Ghana. The LEAP 1000 program provided bi-monthly cash transfers and premium fee waivers to enroll in the National Health Insurance Scheme (NHIS). We used adjusted and unadjusted linear and logistic regression models to estimate the associations of months of LEAP 1000 exposure before delivery with birthweight and low birthweight, respectively. We used covariate-adjusted structural equation models (SEM) to examine mediation of the LEAP 1000 dose-response association with birthweight by household food insecurity and maternal-level (agency, NHIS enrollment, and antenatal care) factors.

resultsOur study included a sample of 1,439 infants with complete information on birthweight and date of birth. Nine percent of infants (N = 129) were exposed to LEAP 1000 before delivery. A 1-month increase in exposure to LEAP 1000 before delivery was associated with a 9-gram increase in birthweight and 7% reduced odds of low birthweight, on average, in adjusted models. We found no mediation effect by household food insecurity, NHIS enrollment, women's agency, or antenatal care visits.

conclusionsLEAP 1000 cash transfer exposure before delivery was positively associated with birthweight, though we did not find any mediation by household- or maternal-level factors. The results of our mediation analyses may serve to inform program operations and improve targeting and programming to optimize health and well-being among this population.

trial registrationThe evaluation is registered in the International Initiative for Impact Evaluation's (3ie) Registry for International Development Impact Evaluations (RIDIESTUDY- ID-55942496d53af) and in the Pan African Clinical Trial Registry (PACTR202110669615387).

Indexed as

LactationPovertyBirth WeightFemaleHumansInfantInfant, Low Birth WeightInfant, NewbornLatent Class AnalysisPregnancyAntenatal careBirthweightCash transfersEmpowermentFood insecurityGhanaLEAP 1000SEM

Identifiers

PMID37208642
PMCPMC10197393
OpenAlexW4377139633

What OpenQuestion holds

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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.