Search Results

Methods: We analyzed two nationally representative prospective cohorts of children in the 1979 National Longitudinal Survey of Youth, ages 2 through 8 at baseline and followed for 6 years, from 1988 to 1994 (cohort 1, n=2672) and 1994 to 2000 (cohort 2, n=1991). The main exposure variable was obesity (defined as never obese, became obese, always obese, and became nonobese). The main outcomes were standardized scores on four cognitive assessments. Univariate regression analyses of test scores on obesity were performed. Fixed-effects regression models, controlling for measured and unmeasured time-invariant confounders, were additionally adjusted for time-variant confounders to analyze the impact of change in obesity status on change in test scores.

Results: Unadjusted analyses revealed a significant association between obesity and Peabody Individual Achievement Test (PIAT) scores. In cohort 1, always obese children had lower PIAT math scores than never obese children (β=–7.48; p<0.05). Always obese boys had lower PIAT math scores than those who were never obese (β=–16.45; p<0.01). In cohort 2, PIAT math scores were lower in the became obese category than the never obese category (β=–4.10; p<0.05). Always obese girls had lower PIAT reading scores than those who were never obese (β=−11.28; p<0.01). Fixed-effects models additionally adjusted for Home Observation Measurement of the Environment, Short Form score and height percentile showed no significant relationship between obesity and test scores in either cohort.

DESIGN/METHODS: We used the National Longitudinal Survey of Youth (NLSY), identifying children 5-14 years of NLSY mothers in 1988 and 1994. We initially determined presence of obesity (body mass index (BMI)>85th percentile) and asthma in the child in 1988 and 1994, as well as dichotomized age (5-9 vs. 10-14 years) and maternal BMI (>85% or not). Multiple logistic regression was used to determine the relationship between obesity and asthma over time, controlling for the child's age, gender, race/ethnicity, and maternal BMI and chronic health status.

RESULTS: In 1988, the presence of obesity was not associated with the presence of asthma (p=0.37). In 1994, the presence of obesity was significantly positively associated with the presence of asthma (odds ratio [OR] 1.88; 95% CI 1.29, 2.72; p<0.001). While this relationship in 1994 was found in both age groups and genders, it was more pronounced among children in the older age group (OR 1.66; 95% CI 1.16, 2.38; p<0.01) and less pronounced in females (OR 0.55; 95% CI 0.38, 0.80; p<0.01). Race/ethnicity was not significantly related to the presence of asthma.

CONCLUSIONS: While recent studies suggest that obesity and asthma are positively related, this study implies that this relationship is relatively new. This suggests the possibility of an evolving environmental or biological risk factor common to both obesity and asthma.

DESIGN/METHODS: We used the National Longitudinal Survey of Youth (NLSY), identifying children 5-14 years of NLSY mothers in 1988 and 1994. We initially determined presence of obesity (body mass index (BMI)>85th percentile) and asthma in the child in 1988 and 1994, as well as dichotomized age (5-9 vs. 10-14 years) and maternal BMI (>85% or not). Multiple logistic regression was used to determine the relationship between obesity and asthma over time, controlling for the child's age, gender, race/ethnicity, and maternal BMI and chronic health status.

RESULTS: In 1988, the presence of obesity was not associated with the presence of asthma (p=0.37). In 1994, the presence of obesity was significantly positively associated with the presence of asthma (odds ratio [OR] 1.88; 95% CI 1.29, 2.72; p<0.001). While this relationship in 1994 was found in both age groups and genders, it was more pronounced among children in the older age group (OR 1.66; 95% CI 1.16, 2.38; p<0.01) and less pronounced in females (OR 0.55; 95% CI 0.38, 0.80; p<0.01). Race/ethnicity was not significantly related to the presence of asthma.

CONCLUSIONS: While recent studies suggest that obesity and asthma are positively related, this study implies that this relationship is relatively new. This suggests the possibility of an evolving environmental or biological risk factor common to both obesity and asthma.

Objective. To determine whether youth with greater exposure to television viewing exhibit higher rates of smoking initiation.

Methods. We used the National Longitudinal Survey of Youth, Child Cohort to examine longitudinally the association of television viewing in 1990 among youth ages 10 to 15 years with smoking initiation from 1990-1992. Television viewing was based on the average of youth and parent reports. We used multiple logistic regression, taking into account sampling weights, and controlled for ethnicity; maternal education, IQ, and work; household structure; number of children; household poverty; child gender; and child aptitude test scores.

Results. Among these youth, smoking increased from 4.8% in 1990 to 12.3% in 1992. Controlling for baseline characteristics, youth who watched 5 or more hours of TV per day were 5.99 times more likely to initiate smoking behaviors (95% confidence interval: 1.39-25.71) than those youth who watched <2 hours. Similarly, youth who watched >4 to 5 hours per day were 5.24 times more likely to initiate smoking than youth who watched <2 hours (95% confidence interval: 1.19-23.10).

Conclusions. Television viewing is associated in a dose-response relationship with the initiation of youth smoking. Television viewing should be included in adolescent risk behavior research. Interventions to reduce television viewing may also reduce youth smoking initiation.

Objective: To evaluate change in prevalence of obesity and other chronic conditions in US children, including incidence, remission, and prevalence.

Design, Setting, and Participants: Prospective study using the National Longitudinal Survey of Youth–Child Cohort (1988-2006) of 3 nationally representative cohorts of children. Children were aged 2 through 8 years at the beginning of each study period, and cohorts were followed up for 6 years, from 1988 to 1994 (cohort 1, n=2337), 1994 to 2000 (cohort 2, n=1759), and 2000 to 2006 (n=905).

Main Outcome Measures: Parent report of a child having a health condition that limited activities or schooling or required medicine, special equipment, or specialized health services and that lasted at least 12 months. Obesity was defined as a body mass index at or above the 95th percentile for age. Chronic conditions were grouped into 4 categories: obesity, asthma, other physical conditions, and behavior/learning problems.

Results: The end-study prevalence of any chronic health condition was 12.8% (95% confidence interval [CI], 11.2%-14.5%) for cohort 1 in 1994, 25.1% (95% CI, 22.7%-27.6%) for cohort 2 in 2000, and 26.6% (95% CI, 23.5%-29.9%) for cohort 3 in2006. There was substantial turnover in chronic conditions: 7.4% (95% CI, 6.5%-8.3%) of participants in all cohorts had a chronic condition at the beginning of the study that persisted to the end, 9.3% (95% CI, 8.3%-10.3%) reported conditions at the beginning that resolved within 6 years, and 13.4% (95% CI, 12.3%-14.6%) had new conditions that arose during the 6-year study period. The prevalence of having a chronic condition during any part of the 6-year study period was highest for cohort 3 (51.5%; 95% CI, 47.3%-55.0%), and there were higher rates among male (adjusted odds ratio [AOR], 1.24; 95% CI, 1.07-1.42), Hispanic (AOR, 1.36; 95% CI, 1.11-1.67), and black (AOR, 1.60; 95% CI, 1.35-1.90) youth.

Conclusions: Prevalence of chronic conditions among children and youth increased from 1988 to 2006. However, presence of these conditions was dynamic over each 6-year cohort.

METHODS. We adopted a counterfactual approach to estimate weight gains in excess of normal growth and the implicit "energy gap" the daily imbalance between energy intake and expenditure. On the basis of Centers for Disease Control and Prevention growth charts, we constructed weight, height, and BMI percentile distributions for cohorts 2 to 4 and 5 to 7 years of age in the 1988–1994 National Health and Nutrition Examination Survey (N = 5000). Under the counterfactual "normal-growth-only" scenario, we assumed that these percentile distributions remained the same as the cohort aged 10 years. Under this assumption, we projected the weight and height distributions for this cohort at 12 to 14 and 15 to 17 years of age on the basis of their baseline weight-for-age and stature-for-age percentiles. We compared these distributions with those for corresponding age groups in the 1999–2002 National Health and Nutrition Examination Survey (N = 3091) ~10 years after the 1988–1994 National Health and Nutrition Examination Survey. We calculated differences between the counterfactual and observed weight distributions and translated this difference into the estimated average energy gap, adjusting for increased total energy expenditure attributable to weight gain. In addition, we estimated the average excess weight accumulated among overweight adolescents in the 1999–2002 National Health and Nutrition Examination Survey, validating our counterfactual assumptions by analyzing longitudinal data from the National Longitudinal Survey of Youth and Bogalusa Heart Study.

RESULTS. Compared with the counterfactual scenario, boys and girls who were aged 2 to 7 in the 1988–1994 National Health and Nutrition Examination Survey gained, on average, an excess of 0.43 kg/year over the 10-year period. Assuming that 3500 kcal leads to an average of 1-lb weight gain as fat, our results suggest that a reduction in the energy gap of 110–165 kcal/day could have prevented this increase. Among overweight adolescents aged 12 to 17 in 1999–2002, results indicate an average energy imbalance ranging from 678 to 1017 kcal/day because of an excess of 26.5 kg accumulated over 10 years.

CONCLUSIONS. Quantifying the energy imbalance responsible for recent changes in weight distribution among children can provide salient targets for population intervention. Consistent behavioral changes averaging 110 to 165 kcal/day may be sufficient to counterbalance the energy gap. Changes in excess dietary intake (eg, eliminating one sugar-sweetened beverage at 150 kcal per can) may be easier to attain than increases in physical activity levels (eg, a 30-kg boy replacing sitting for 1.9 hours with 1.9 hours walking for an extra 150 kcal). Youth at higher levels of weight gain will likely need changes in multiple behaviors and environments to close the energy gap.