Evidence map›Paper›PMID 39719546›Full record

ArticleBMC pregnancy and childbirth2024

Trajectory of breastfeeding among Chinese women and risk prediction models based on machine learning: a cohort study.

Yi Liu, Jie Xiang, Ping Yan, Yuanqiong Liu, Peng Chen, Yujia Song, Jianhua Ren

Abstract read
In one paragraph

Article in BMC pregnancy and childbirth, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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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.

Yi LiuDepartment of Obstetric Nursing, West China Second University Hospital, Sichuan University, No. 1416, Chenglonglu Avenue, Chengdu, Sichuan, Jinjiang District, 610066, China.
Jie Xiang *Department of Obstetric Nursing, West China Second University Hospital, Sichuan University, No. 1416, Chenglonglu Avenue, Chengdu, Sichuan, Jinjiang District, 610066, China. huaxixiangjie@126.com.
Ping YanDepartment of Obstetric Nursing, West China Second University Hospital, Sichuan University, No. 1416, Chenglonglu Avenue, Chengdu, Sichuan, Jinjiang District, 610066, China.
Yuanqiong LiuDepartment of Obstetric Nursing, West China Second University Hospital, Sichuan University, No. 1416, Chenglonglu Avenue, Chengdu, Sichuan, Jinjiang District, 610066, China.
Peng ChenSchool of Computer and Software Engineering, Xihua University, Chengdu, Sichuan, China.
Yujia SongSchool of Computer and Software Engineering, Xihua University, Chengdu, Sichuan, China.
Jianhua Ren *Department of Obstetric Nursing, West China Second University Hospital, Sichuan University, No. 1416, Chenglonglu Avenue, Chengdu, Sichuan, Jinjiang District, 610066, China. r.jianhua@connect.polyu.hk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBreastfeeding is the optimal source of nutrition for infants and young children, essential for their healthy growth and development. However, a gap in cohort studies tracking breastfeeding up to six months postpartum may lead caregivers to miss critical intervention opportunities.

methodsThis study conducted a three-wave prospective cohort analysis to examine maternal breastfeeding trajectories within the first six months postpartum and to develop risk prediction models for each period using advanced machine learning algorithms. Conducted at a leading Maternal and Children's hospital in China from October 2021 to June 2022, data were gathered via self-administered surveys and electronic health records.

resultsOf the 3307 women recruited, 3175 completed the surveys, yielding a 96% effective response rate. Breastfeeding(BF) rates were observed at 100%, 96%,93% and 83% at discharge, 42 day, 3 month and 6 month postpartum, respectively. Exclusively breastfeeding(EBF) rates were recorded at 91%, 64%,72% and 58% for the same intervals. Among the five machine learning methods employed, Random Forest (RF) demonstrated superior accuracy in predicting breastfeeding patterns, with classification accuracies of 0.629 and an area under the receiver operating characteristic curve (AUC) of 0.8122 at 42 days, 0.925 and an AUC of 0.9800 at 3 months, and 0.836 and an AUC of 0.9463 at 6 months postpartum, respectively. Key predictive factors for breastfeeding at 42 days postpartum included the newborn's birth weight and the mother's pre-delivery and prenatal weights. Predictors for feeding type at 3 months and 6 months postpartum included early feeding types and the scores from the Breastfeeding Self-Efficacy Scale-short Form (BSES-SF) at 6 months. The predictive model based on follow-up data showed strong performance.

conclusionBreastfeeding rates slightly declined from discharge to 6 months postpartum. The breastfeeding context in this region is comparatively optimistic both within China and internationally. Factors such as newborn's birth weight, gestational age, maternal weight management before and during pregnancy, early support and breastfeeding success, breastfeeding knowledge and self-efficacy are intricately linked to long-term breastfeeding outcomes. This study highlights critical, modifiable risk factors for early breastfeeding stages, providing valuable insights for enhancing breastfeeding intervention programs and informed decision-making.

Indexed as

Breast FeedingMachine LearningAdultChinaCohort StudiesEast Asian PeopleFemaleHumansInfantInfant, NewbornPostpartum PeriodPregnancyProspective StudiesRisk AssessmentSurveys and QuestionnairesYoung AdultBreastfeedingFollow-upMachine-learningPrediction models

Identifiers

PMID39719546
PMCPMC11668035

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LicenceCC BY-NC-ND
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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.