Risk of Excess and Inadequate Gestational Weight Gain among Hispanic Women: Effects of Immigration Generational Status.
Sajeevika S DaundasekaraDaniel P O'ConnorJodi Berger CardosoTracey A LedouxDaphne C HernandezPublished in: International journal of environmental research and public health (2020)
There is a dearth of information on the risk of inadequate and excess gestational weight gain (GWG) among different generations of Hispanic women in the United States. Therefore, the objective of this study was to understand the relationship of GWG and immigration across three generations of Hispanic women. The study was conducted using data from National Longitudinal Survey of Youth 1979 (NLSY79). The study sample included 580 (unweighted count) women (148 first-generation, 117 second-generation, and 315 third-/higher-generation). Sociodemographic and immigration data were extracted from the main NLSY79 survey, and pregnancy data were extracted from the child/young adult survey following the biological children born to women in NLSY79. Covariate adjusted weighted logistic regression models were conducted to assess the risk of inadequate and excess GWG among the groups. Average total GWG was 14.98 kg, 23% had inadequate GWG, and 50% had excess GWG. After controlling for the covariates, there was no difference in the risk of inadequate GWG between the three generations. First-generation women (OR = 0.47, p = 0.039) and third-/higher-generation women (OR = 0.39, p = 0.004) had significantly lower risk of excess GWG compared to second-generation women. It is important to recognize the generational status of Hispanic women as a risk factor for excess GWG.
Keyphrases
- weight gain
- polycystic ovary syndrome
- pregnancy outcomes
- body mass index
- breast cancer risk
- birth weight
- cervical cancer screening
- mental health
- insulin resistance
- african american
- cross sectional
- computed tomography
- magnetic resonance imaging
- magnetic resonance
- physical activity
- adipose tissue
- big data
- weight loss
- machine learning
- social media
- electronic health record
- preterm birth
- artificial intelligence
- deep learning
- gestational age
- preterm infants
- contrast enhanced