Sentences with phrase «maternal age modeled»

The following covariates were considered in this analysis: household size modeled as a categorical variable (categories), marital status (categories), race and ethnicity (categories), maternal age modeled as a categorical variable (categories), parity (categories), education (categories), employment status (categories), maternal occupation (categories), and postnatal WIC participation.

Not exact matches

All models were adjusted for potential confounders, including maternal education, ethnicity, smoking, gestational age, birth weight, siblings, and day care attendance.

We used multivariable logistic - regression models to adjust for potential confounders, including maternal race or ethnic group (non-Hispanic white vs. other), parity (nulliparous vs. multiparous), insurance status (public or none vs. other), extent of prenatal care (≥ 5 visits vs. < 5 visits), advanced maternal age (≥ 35 years vs. < 35 years), maternal education (> 12 years vs. ≤ 12 years), history or no history of cesarean delivery, and a composite marker of conditions that confer increased medical risk.

To assess the robustness of the results of our regression analysis, we performed covariate adjustment with derived propensity scores to calculate the absolute risk difference (details are provided in the Supplementary Appendix, available with the full text of this article at NEJM.org).14, 15 To calculate the adjusted absolute risk difference, we used predictive margins and G - computation (i.e., regression - model — based outcome prediction in both exposure settings: planned in - hospital and planned out - of - hospital birth).16, 17 Finally, we conducted post hoc analyses to assess associations between planned out - of - hospital birth and outcomes (cesarean delivery and a composite of perinatal morbidity and mortality), which were stratified according to parity, maternal age, maternal education, and risk level.

All the models were adjusted for maternal race or ethnic group, parity, insurance status (for cesarean delivery), extent of prenatal care, maternal age and education, history of cesarean delivery, and a composite of maternal conditions associated with an increased medical risk (chronic hypertension, gestational hypertension, preeclampsia, eclampsia, prepregnancy diabetes, or gestational diabetes).

Of note, our models may underestimate the true maternal costs of suboptimal breastfeeding; we modeled the effects of lactation on only five maternal health conditions despite data linking lactation with other maternal health outcomes.46 In addition, women in our model could not develop type 2 diabetes mellitus, hypertension, or MI before age 35 years, although these conditions are becoming increasingly prevalent among young adults.47 Although some studies have found an association between lactation and rates of postmenopausal diabetes22, 23 and cardiovascular disease, 10 we conservatively limited the duration of lactation's effect on both diabetes and MI.

We used multiple regression to estimate the differences in total cost between the settings for birth and to adjust for potential confounders, including maternal age, parity, ethnicity, understanding of English, marital status, BMI, index of multiple deprivation score, parity, and gestational age at birth, which could each be associated with planned place of birth and with adverse outcomes.12 For the generalised linear model on costs, we selected a γ distribution and identity link function in preference to alternative distributional forms and link functions on the basis of its low Akaike's information criterion (AIC) statistic.

Other maternal variables tested in the model included maternal age, ethnic group, socioeconomic status, parity, prepregnancy weight and height, CES - D score, and use of tobacco.

Once the final additive model was built, interaction terms were tested, involving intended place of birth and: pregnancy risk factors, year, parity, maternal age and time of birth.

When logistic models were stratified by the presence or absence of hypertensive disease, only maternal age older than 34 years (odds ratio [OR], 1.4; 95 % confidence interval [CI], 1.0 - 2.0), pregnancy - associated plasma protein - A of the 95th percentile or less (OR, 1.9; 95 % CI, 1.2 - 3.1), and alpha fetoprotein of the 95th percentile or greater (OR, 2.3; 95 % CI, 1.4 - 3.8) remained statistically significantly associated for abruption.In this large, population - based cohort study, abnormal maternal aneuploidy serum analyte levels were associated with placental abruption, regardless of the presence of hypertensive disease.

Model 1 adjusted for covariates in model 0 plus gestational age and birth weight z score.18 Model 2 adjusted for covariates in model 1 plus child race / ethnicity and maternal age, parity, smoking status, depression at 6 months» post partum, and employment and child care at age 6 months, as well as primary language, annual household income, and parental educational level and marital stModel 1 adjusted for covariates in model 0 plus gestational age and birth weight z score.18 Model 2 adjusted for covariates in model 1 plus child race / ethnicity and maternal age, parity, smoking status, depression at 6 months» post partum, and employment and child care at age 6 months, as well as primary language, annual household income, and parental educational level and marital stmodel 0 plus gestational age and birth weight z score.18 Model 2 adjusted for covariates in model 1 plus child race / ethnicity and maternal age, parity, smoking status, depression at 6 months» post partum, and employment and child care at age 6 months, as well as primary language, annual household income, and parental educational level and marital stModel 2 adjusted for covariates in model 1 plus child race / ethnicity and maternal age, parity, smoking status, depression at 6 months» post partum, and employment and child care at age 6 months, as well as primary language, annual household income, and parental educational level and marital stmodel 1 plus child race / ethnicity and maternal age, parity, smoking status, depression at 6 months» post partum, and employment and child care at age 6 months, as well as primary language, annual household income, and parental educational level and marital status.

Matching variables (maternal race / ethnicity, infant age at last sleep, birth year, and region) were included in all the models.

Models were developed using the following possible predictors of breastfeeding duration: maternal race, maternal education, paternal education, maternal age, socioeconomic status, 22 marital status, parity, mode of delivery, previous breastfeeding experience, timing of feeding method selection, problems with pregnancy / labor / delivery, breastfeeding goal (weeks), family preference for breastfeeding, paternal preference for breastfeeding, having friends who breastfed, randomization group, 16 plans to return to work, infant's 5 - minute Apgar score, and infant's age in minutes when first breastfed (first successful latch and feeding).

To facilitate presentation of the final model, dichotomous variables were constructed for these factors (ie, goal ≤ 26 weeks or > 26 weeks and maternal age ≤ 30 years or > 30 years).

Among the maternal anthropometric (dimension 2) variables, only greater BMI was associated with delayed OL, and this relation remained significant in a model adjusted for maternal age.

In a multivariate model adjusted for prenatal feeding intentions, independent risk factors for delayed OL were maternal age ≥ 30 y, body mass index in the overweight or obese range, birth weight > 3600 g, absence of nipple discomfort between 0 — 3 d postpartum, and infant failing to «breastfeed well» ≥ 2 times in the first 24 h. Postpartum edema was significant in an alternate model excluding body mass index (P < 0.05).

However, in a model adjusted for all 3 of these characteristics, only maternal age (≥ 30 y) remained a significant risk factor (P < 0.05).

Despite collinearity between maternal age, BMI, and infant birth weight, all 3 variables were independently associated with delayed OL in a multivariate model.

The addition of the Infant Feeding Intentions score to the model strengthened the association with maternal age and BMI, with little effect on the other variables.

Among newborn characteristics (dimension 4), higher birth weight and lower 1 - min Apgar score were associated with delayed OL; birth weight > 3600 g remained a significant risk factor in a model adjusted for maternal age and BMI.

Modeling was used in the evaluation of initiation, duration, maternal age, income, household composition, employment, marital status, postpartum depression, preterm birth, smoking, belief that «breast is best,» family history of breastfeeding, and in - hospital formula introduction.

These models allowed us to evaluate the association between individual task score and program outcome after adjustment for important confounders (breed, maternal parity, sex of puppy, and age at return).

We built a generalized estimating equation (GEE) general linear model (GLM) with outcome as the dependent variable; time in the nursing box, licking / grooming per puppy, vertical nursing per puppy, and ventral nursing per puppy were entered as predictors with breed, maternal parity, sex of puppy, and age at return entered as covariates.

21, 36, 37), breed, maternal parity (1 — 5), sex of puppy (1/0, male vs. female), litter size (2 — 10 puppies), and age in months (14 — 17) when the dog returned for training were included as covariates in all models.

The effect of maternal care and age of separation (from the mother) on TC was also evaluated using a generalized linear model with a binomial distribution.

The Patient Protection and Affordable Care Act allocated $ 1.5 billion annually for the Maternal, Infant, and Early Childhood Home Visiting Program (MIECHV) to fund states in implementing home visiting program models for families with children from birth to age 5 as well as pregnant women.

An interaction term between maternal education and age was included in the model in order to estimate PDs and PRs at age 3, 5, 7 and 11 years.

Population average models were used to account for the longitudinal study design and correlation of repeated measurements, and an interaction term between maternal education (our socioeconomic measure) and age was included in order to examine whether differences in health inequalities by age were statistically significant.

Finally, we examined the association between sociodemographic variables (child age, sex, race / ethnicity, maternal obesity, maternal education, poverty) and prevalence of having a chronic condition during any part of the 6 - year study period in multivariate logistic regression models that included all participants.

We tested the role of maternal depression at 36 months (as measured by the continuous CIDI - SF scale) as a mediator of the relation between both chronic maternal IPV and maternal IPV prior to 36 months and obesity risk at age 60 months in separate models using the Preacher and Hayes bootstrapping method.49 We found evidence for simple mediation of maternal IPV prior to 36 months and chronic maternal IPV by maternal depression.

Models adjusted for child's age, sex, race / ethnicity, maternal education, economic hardship, tobacco exposure, and low birth weight.

A covariate was included in the multivariate analyses if theoretical or empirical evidence supported its role as a risk factor for obesity, if it was a significant predictor of obesity in univariate regression models, or if including it in the full multivariate model led to a 5 % or greater change in the OR.48 Model 1 includes maternal IPV exposure, race / ethnicity (black, white, Hispanic, other / unknown), child sex (male, female), maternal age (20 - 25, 26 - 28, 29 - 33, 34 - 50 years), maternal education (less than high school, high school graduation, beyond high school), maternal nativity (US born, yes or no), child age in months, relationship with father (yes or no), maternal smoking during pregnancy (yes or no), maternal depression (as measured by a CIDI - SF cutoff score ≥ 0.5), maternal BMI (normal / underweight, overweight, obese), low birth weight (< 2500 g, ≥ 2500 g), whether the child takes a bottle to bed at age 3 years (yes or no), and average hours of child television viewing per day at age 3 years (< 2 h / d, ≥ 2 h model led to a 5 % or greater change in the OR.48 Model 1 includes maternal IPV exposure, race / ethnicity (black, white, Hispanic, other / unknown), child sex (male, female), maternal age (20 - 25, 26 - 28, 29 - 33, 34 - 50 years), maternal education (less than high school, high school graduation, beyond high school), maternal nativity (US born, yes or no), child age in months, relationship with father (yes or no), maternal smoking during pregnancy (yes or no), maternal depression (as measured by a CIDI - SF cutoff score ≥ 0.5), maternal BMI (normal / underweight, overweight, obese), low birth weight (< 2500 g, ≥ 2500 g), whether the child takes a bottle to bed at age 3 years (yes or no), and average hours of child television viewing per day at age 3 years (< 2 h / d, ≥ 2 h Model 1 includes maternal IPV exposure, race / ethnicity (black, white, Hispanic, other / unknown), child sex (male, female), maternal age (20 - 25, 26 - 28, 29 - 33, 34 - 50 years), maternal education (less than high school, high school graduation, beyond high school), maternal nativity (US born, yes or no), child age in months, relationship with father (yes or no), maternal smoking during pregnancy (yes or no), maternal depression (as measured by a CIDI - SF cutoff score ≥ 0.5), maternal BMI (normal / underweight, overweight, obese), low birth weight (< 2500 g, ≥ 2500 g), whether the child takes a bottle to bed at age 3 years (yes or no), and average hours of child television viewing per day at age 3 years (< 2 h / d, ≥ 2 h / d).

Adjusted for the variables in model 2 plus child's sex, birth weight and height, gestational age, maternal smoking during pregnancy, maternal education, maternal age, prepregnancy height and body mass index, breastfeeding, maternal smoking at child's age 4 years, number of siblings at child's age 4 years, and child's consumption of sweetened beverages, sweets, and meat at age 4 years.

Then, we examined whether maternal problems in reciprocal social behavior directly or indirectly influenced infantile aggression at 18 months of age by including maternal PDS into the model (see bottom of Figure 1).

As demonstrated in Table 2, children whose mothers reported chronic IPV were 80 % more likely to be obese at age 5 years than those with no maternal IPV in the model 1 analysis adjusted for all covariates (OR = 1.80; 95 % confidence interval [CI], 1.24 - 2.61).

Univariate generalized linear models were used to determine the estimated marginal means of the PedsQL scales and subscales adjusting for the child's age, sex, maternal education, and disadvantage index as covariates.

In these models, estimates were adjusted for the child's age, sex, and race - ethnicity; family ITNR; and maternal education, depressive symptoms, and figure rating.

It was interesting to note that in our sample, only 25 % of the variance in child BMI at time 2 could be explained by the combined model of T1 child BMI, maternal BMI, age and education, and child age and gender.

Maternal age at delivery, ethnicity, smoking during pregnancy, parity, paternal diabetes status at follow - up, family social class, sex, offspring physical activity, and offspring smoking habits were not found to be confounders and had no effect on offspring risk of type 2 diabetes / pre-diabetes when entered in multiple logistic regression models.

Logistic regression models were used for controlling eight confounding variables such as maternal age, maternal education, employment status, parity, maternal BMI, hypertension, diabetes and medically assisted conception.

Maternal reports of acceptance interacted with age to predict adherence (β = −.24; p <.05), however, the F - value for the overall model was marginally significant in this case (p =.08) so this result should be treated with caution.

The present study addressed these issues by using person - oriented (latent growth mixture) methods to model heterogeneity in maternal - reported internalizing symptoms from age 2 to 11 years (N = 1,364).

The following variables were included in all 6 multivariate models: maternal race / ethnicity, education, age, income, and nativity.

Note: 1Maternal reports of partner's alcohol consumption; 2Univariable multinomial logistic regression models; 3Multinomial logistic regression models adjusted for maternal age at delivery, parity, Social economic position, maternal education, maternal smoking during first trimester in pregnancy, housing tenure, income, and maternal depressive symptoms at 32 weeks gestation; CL: childhood limited, AO: adolescent onset, EOP: early onset persistent, the Low conduct problems class was used as the reference group.

Maternal age was initially examined as a covariate, but it did not provide predictive value and was strongly correlated with SES (r =.60, p <.001), so it was dropped from the final model for power and parsimony.

1Maternal reports of partner's alcohol consumption; Model 1 adjusted for maternal age at delivery, parity, social economic position, maternal education, maternal smoking during first trimester in pregnancy, housing tenure, income, and maternal depressive symptoms at 32 weeks gestation; Model 2 further adjusted for maternal alcohol use at 18 weeks gestation.

1Maternal reports of partner's alcohol consumption; 2Univariable linear regression models; 3Models adjusted for maternal age at delivery, parity, social economic position, maternal education, maternal smoking during first trimester in pregnancy, housing tenure, income, and maternal depressive symptoms at 32 weeks gestation.

Longitudinal models were applied to assess the relationships between maternal pre-pregnancy BMI and affective problems from age 5 through 17.

We then examined this model with youth - reported antisocial behaviors (ASB) and maternal depressive symptoms when the boys were older, ages 10 to 15.

In the logistic regression models, no infant, maternal or family factors from the original Infant Sleep Study (conducted when the children were aged 6 — 12 months) predicted the presence of sleep problems at the age of 3 to 4 years.