Sentences with phrase «child covariates»

BMI z - score at T2 was not significantly predicted by T1 maternal feeding practices (R 2 Change =.01, p =.857), or by T1 child eating behaviours (R 2 Change =.01, p =.707) after controlling for maternal and child covariates, and T1 BMIz.

After controlling for maternal and child covariates, we found that maternal influenza infection anytime during pregnancy was not associated with increased ASD risk (adjusted HR [AHR], 1.04 [95 % CI, 0.68 - 1.58]-RRB-.

Not exact matches

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 status.

Table 3 summarizes the effect of covariate adjustment on estimated relationships between breastfeeding duration and child cognitive outcomes.

Covariates included mother's age, education, smoking during pregnancy, and participation in the federal nutritional support program for Women, Infants, and Children; and infant's gender, race, birth weight, congenital malformation reported at birth, live birth order, and single or multiple birth.

Compared with women who breastfed their first child for ≥ 12 months, women who did not breastfeed were more likely to develop hypertension (hazard ratio (HR) = 1.27, 95 % confidence interval (CI): 1.18, 1.36), adjusting for family history and lifestyle covariates.

Covariates included the child's sex, calendar conception year (categorical variable), gestational age, maternal prepregnancy body mass index (BMI, calculated as weight in kilograms divided by height in meters squared)(BMI < 18.5 = underweight; 18.5 ≤ BMI < 25 = normal weight; 25 ≤ BMI < 30 = overweight; BMI ≥ 30 = obese), maternal age at delivery (younger than 20, 20 to 24, 25 to 29, 30 to 34, and ≥ 35 years), maternal education at delivery (≤ high school graduate, some college education, college graduate, postgraduate, or unknown), maternal race / ethnicity (Asian, black, white, or other), and gestational diabetes (yes / no).

The general pattern showing large jurisdictional differences after controlling for the covariates is consistent across each of the five developmental domains with children in Queensland and the ACT showing higher vulnerability compared to children living in the other jurisdictions.

In contrast to this, children living in the ACT show increased odds across all five developmental domains when controlling for covariates.

With these covariates in the final model, behavior problems were independently associated with concurrent child overweight (adjusted odds ratio: 2.95; 95 % confidence interval: 1.34 — 6.49).

Bullying behavior has been shown to vary with the child's race, age, and sex, 7,8,32 as has the amount and type of television viewing.35, 36 The association between bullying and socioeconomic status, including parental income and education, has not been explicitly explored, but socioeconomic status has been shown to be strongly associated with externalizing behavior generally.28 Socioeconomic status is also known to influence both television viewing and parenting style.22, 36 Model covariates therefore included the child's sex; race (Hispanic, African American, or non - Hispanic / non — African American); the child's age when the bullying question was asked in 2000; and the parents» income and educational levels.

Possible maternal, child and school covariates were tested in a univariate model and those that were significant (α < 0.05) were tested for significance in multivariate models.

Values are ORs from logistic regression: ORs represent the unit change in the covariates (eg, at age 15 mo, mothers of non-Hispanic white children have 0.68 times lower odds of intrusiveness than do mothers of children who are not non-Hispanic white].

Because maternal trait anxiety was highly correlated with a multitude of the study's dependent variables (eg, mother's depression and child outcomes), it also was used as a covariate in subsequent data analyses.

After adjustment for the covariates and for the 3 paternal parenting dimensions, the odds of a child being in a heavier BMI category decreased by 26 % (95 % CI: 15 % — 35 %) for each 1 - point increase in paternal control score (P <.001).

Child interest in food at T2 was not significantly predicted by the model combining potential covariates and prior child interest in food (steChild interest in food at T2 was not significantly predicted by the model combining potential covariates and prior child interest in food (stechild interest in food (step 1).

Associations Between Children's Consumption of Food Groups and Presence of Television at Meals, Controlling for Covariates and Sociodemographic Factors †

After adjustment for the covariates, strong evidence was also found for an association between paternal parenting style and child BMI category (P =.002).

For analyses of white matter volume, children's age and pubertal status were also included as covariates.

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 / d).

Covariates in the models were the child's sex and race - ethnicity, family ITNR, and maternal education, depressive symptoms, and figure rating.

Covariates were those identified in previous analyses of this sample to independently contribute to child BMI status.16 Parent - reported child variables were gender (male or female), number of siblings in the household (0, 1, 2, or ≥ 3), and language other than English spoken at home by the child (yes or no).

After controlling for these covariates, genotype, group, and the child's relationship with his or her primary support were all significant predictors of children's depression scores.

After adjustment for the covariates and for each other, there was no evidence for an association among any of the 3 maternal parenting dimensions and child BMI status (all P values were ≥.69).

We used multiple imputation with the method of chained equations to account for missing maternal data for children with a mother in the household.24 In addition to the mother's BMI status (missing for 1085 [22 %] of the children), imputation was conducted for 4 maternal covariates with few (< 1 %) missing cases (education [n = 13], warmth [n = 47], control [n = 49], and irritability [n = 48]-RRB-.

Similarly, after adjustment for the covariates, there was no evidence for an association between maternal parenting style and child BMI status (P =.85).

It has been shown that inferences resulting from this analysis are virtually identical no matter which of these outcome measures is used.30 In addition to the covariates previously noted, the regression analysis was repeated to include annual household income, mother's treatment setting (primary vs psychiatric outpatient care), and treatment status of child during the 3 - month follow - up period in order to investigate the further potential confounding effects of these variables.

This finding persisted even when controlling for obesity at age 3 years, several postulated intermediates (including child bottle - feeding and television viewing), and ostensible confounders such as maternal depression, maternal smoking during pregnancy, child birth weight, and other relevant covariates.

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).

In the first GEE analysis, none of the covariates (e.g., age, sex, ancestral proportion scores) were significant predictors of children's depression scores.

We used an imputed variable for household income provided by the FFCWS given the degree of missing data (∼ 10 %).12 Supplementary models included birth weight, maternal report of the child's health status, and the number of siblings as covariates.

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.

For all analyses examining hippocampus or amygdala volumes, children's total cortical brain volume (total white + total cortical gray) was included as a covariate to assess specificity.

In addition, 172 children were missing data on other covariates, leaving 3116 children for the current analyses.

At step 1, we controlled for the respective T1 eating behaviour, and for potential covariates of maternal age, BMI and education, and child age and gender (1 = male; 2 = female).

After adjustment for the covariates and for all of the maternal and paternal parenting dimensions, higher paternal control score was strongly associated with decreased odds of the child being in a heavier BMI category (OR: 0.75; 95 % CI: 0.65 — 0.86; P <.001).

Covariates capturing parent characteristics included: (1) marital status using a dichotomised indicator of whether they were married / cohabiting or not; (2) employment status categorised into a binary indicator distinguishing whether the parent was in paid employment or not; and (3) presences of siblings whose youngest child was younger than 16.

Where program effects were moderated by the child's sex in a coherent way, we have noted this in the presentation of the findings, in which case the model includes SES as a covariate rather than a classification factor and includes all interactions among treatment, marital status, and sex.

Even after controlling for parental monitoring of nonmedia - related behaviors and other covariates, children were at lower risk of smoking and drinking if their parents prohibited them from watching R - rated movies.

In addition, changes in friend smoking have been found to mediate the movie smoking effect on behavior; therefore, friend smoking was rejected as a covariate.37, 38 More specifically, by being strict regarding R - rated movie viewing, parents decrease the risk of their children having a smoking sibling because that sibling presumably has comparable restrictions.

Hypothesis 3 (unadjusted associations are attenuated by adjusting for child, family and community characteristics): Adjusting for covariates almost always attenuated associations among water and toilet access and test scores.

Baseline covariates included in regression models were site of enrollment (hospital or office), age of child at interview, and characteristics of the mother (age, education, race / ethnicity, employment), father (employment), family (marital status / father in household, number of siblings, owned home, income), and infant (low birth weight, source of payment for care).

Controlling for endogenous covariates (including school quality) thus has the net result of denying the possibility that there are multiple pathways by which the neighborhood may influence developmental outcomes among children (22).

Child age was included as a covariate given that it was associated with cortisol levels in preliminary analyses.

Thus, varying levels of child or adult responsibility for the drawings did not confound other effects tested in this study and did not need to be included as a covariate in further analyses.

Others have also consistently reported that breastfed children score slightly higher than those bottlefed on the Bayley Scales of Infant Development or later tests of IQ, such as the McCarthy Scales, after controlling for standard covariates including socioeconomic status (SES), maternal age and education, maternal smoking and drinking, 16, 17 and in one study maternal psychological state.18 Longitudinal studies indicate that these differences persist to 5 years and into school age.

bMultiple linear regression adjusted for other covariates (age, number of children, education, equivalent household income IADL disability, number of chronic disease, economic activity, and number of social activities).

To assess the influence of widowhood within each sex, we considered the possible covariates of depressive symptoms identified by the results of previous population - based studies, including age, education, income, experience with chronic diseases, disability, number of children, and social participation.

aMultinomial logistic regression adjusted for other covariates (age, education, equivalent household income IADL disability, number of chronic disease, number of children, economic activity, and number of social activities).

In the second step, we calculated a two - factorial MANCOVA using FHalc and FHaspd as independent factors and number of children living in the household as a covariate.