Cargando…

Cluster analysis of maternal socioeconomic and non-modifiable factors with birth & placental outcomes

BACKGROUND: Maternal social disadvantage adversely affects both maternal and offspring health outcomes. This study aimed to explore clustering of socioeconomic and non-modifiable factors in mothers and their association with birth and placental outcomes, which are under-researched in this context. M...

Descripción completa

Detalles Bibliográficos
Autores principales: Teo, S, Murrin, C, Mehegan, J, Douglass, A, Segurado, R, Kelleher, C, Phillips, C, Hébert, J
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10596486/
http://dx.doi.org/10.1093/eurpub/ckad160.829
Descripción
Sumario:BACKGROUND: Maternal social disadvantage adversely affects both maternal and offspring health outcomes. This study aimed to explore clustering of socioeconomic and non-modifiable factors in mothers and their association with birth and placental outcomes, which are under-researched in this context. METHODS: This analysis of the Lifeways Cross-Generational Cohort includes 250 mother-child pairs. Self-completed general health and food frequency questionnaires in early pregnancy provided information on diet, lifestyle, socioeconomic status (SES) and non-modifiable factors. Parity, untrimmed placental weight (PW) and birthweight (BW) were abstracted from hospital records, and the BW:PW ratio was generated. TwoStep cluster analysis clustered mothers based on age, parity, marital status, net household income, private healthcare insurance, homeowner status, and tertiary education. Differences between clusters were assessed using one-way ANOVA and chi-square tests. RESULTS: A three-cluster solution was found to be optimal with a modest average silhouette coefficient of 0.3. Clusters were classified as “Married Homeowners” (n = 140, 37.1%), “Highest Income” (n = 58, 23.2%) and “Renters” (n = 52, 20.8%) based on predictor importance. Renters were younger, more likely to smoke, have a medical card (an indicator of social disadvantage) and have poorer dietary quality and dietary inflammatory status (as indicated by lower Healthy Eating Index (HEI-2015) and higher energy-adjusted Dietary Inflammatory Index (E-DII) scores) compared to other clusters (all p < 0.01). Offspring of Renters were more likely to have lower BW (-259.26g, p < 0.01) and shorter birth length (-1.31cm, p < 0.01) and head circumference (-0.59cm, p = 0.02) compared to offspring of Married Homeowners. CONCLUSIONS: Results suggest associations of SES (particularly housing status) with birth, but not placental, outcomes and less favourable lifestyle factors, which may have multi-behavioural and multi-sectoral policy implications. KEY MESSAGES: • Associations between low SES, adverse lifestyle behaviours and birth outcomes highlight the importance of a multi-sectoral public health strategy. • Placental outcomes should be examined in further studies.