What are the causes for low birthweight in Japan? A single hospital-based study.
Yoshifumi KasugaSatoru IkenoueMasumi TamagawaMaki OishiToyohide EndoYu SatoMiho IidaYasunori SatoMamoru TanakaDaigo OchiaiPublished in: PloS one (2021)
Low-birthweight (LBW; <2,500 g) babies are at a higher risk of poor educational achievement, disability, and metabolic diseases than normal-birthweight babies in the future. However, reliable data on factors that contribute to LBW have not been considered previously. Therefore, we aimed to examine the distribution of the causes for LBW. A retrospective review of cases involving 4,224 babies whose mothers underwent perinatal care at Keio University Hospital between 2013 and 2019 was conducted. The LBW incidence was 24% (1,028 babies). Of the 1,028 LBW babies, 231 babies were from multiple pregnancies. Of the 797 singleton LBW babies, 518 (65%) were born preterm. Obstetric complications in women with preterm LBW babies included premature rupture of membrane or labor onset (31%), hypertensive disorders of pregnancy (HDP, 64%), fetal growth restriction (24%), non-reassuring fetal status (14%), and placental previa/vasa previa (8%). Of the 279 term LBW babies, 109 (39%) were small for gestational age. Multiple logistic regression analyses revealed the following factors as LBW risk factors in term neonates: low pre-pregnancy maternal weight, inadequate gestational weight gain, birth at 37 gestational weeks, HDP, anemia during pregnancy, female sex, and neonatal congenital anomalies. HDP was an LBW risk factor not only in preterm births but also in term births. Our results suggest that both modifiable and non-modifiable factors are causes for LBW. It may be appropriate to consider a heterogeneous rather than a simple classification of LBW and to evaluate future health risks based on contributing factors.
Keyphrases
- gestational age
- birth weight
- preterm birth
- weight gain
- risk factors
- low birth weight
- body mass index
- healthcare
- pregnant women
- current status
- physical activity
- machine learning
- pregnancy outcomes
- multiple sclerosis
- blood pressure
- weight loss
- emergency department
- palliative care
- chronic kidney disease
- artificial intelligence
- pain management
- drug induced