Sociodemographic Correlates of High Cardiovascular Health Across Childhood and Adolescence: A Prospective Study Among 2 Cohorts in the ECHO Consortium.

BACKGROUND: This study seeks to characterize cardiovascular health (CVH) from early childhood to late adolescence and identify sociodemographic correlates of high CVH that serve as levers for optimizing CVH across early life. METHODS AND RESULTS: Among 1530 youth aged 3 to 20 years from 2 cohorts in the ECHO (Environmental Influences on Child Health Outcomes) consortium, we first derived CVH scores on the basis of the Life's Essential 8 construct comprising 4 behavioral (nicotine use/exposure, physical activity, sleep, and diet) and 4 health factors (body mass index, blood pressure, non-high-density lipoprotein cholesterol, and fasting glucose) during early childhood (mean age, 3.5 years), middle childhood (8.0 years), early adolescence (13.3 years), and late adolescence (17.8 years). Next, we used generalized regression to estimate the probability of high (versus not high) CVH with respect to sociodemographic characteristics. Overall CVH score was stable across life stages: 81.2±7.6, 83.3±8.0, and 81.7±8.9 of 100 possible points in early childhood, middle childhood, and early adolescence, respectively. Accordingly, during these life stages, most children (63.3%-71.5%) had high CVH (80 to <100). However, CVH declined by late adolescence, with an average score of 75.5±10.2 and 39.4% high CVH. No children had optimal CVH (score=100) at any time. Correlates of high CVH include non-Hispanic White race and ethnicity, maternal college education, and annual household income >$70 000. These associations were driven by behavioral factors. CONCLUSIONS: Although most youth maintained high CVH across childhood, the decline by late adolescence indicates that cardiovascular disease prevention should occur before the early teen years. Disparities in high CVH over time with respect to sociodemographic characteristics were explained by behavioral factors, pointing toward prevention targets.

Using Power Analysis to Choose the Unit of Randomization, Outcome, and Approach for Subgroup Analysis for a Multilevel Randomized Controlled Clinical Trial to Reduce Disparities in Cardiovascular Health.

We give examples of three features in the design of randomized controlled clinical trials which can increase power and thus decrease sample size and costs. We consider an example multilevel trial with several levels of clustering. For a fixed number of independent sampling units, we show that power can vary widely with the choice of the level of randomization. We demonstrate that power and interpretability can improve by testing a multivariate outcome rather than an unweighted composite outcome. Finally, we show that using a pooled analytic approach, which analyzes data for all subgroups in a single model, improves power for testing the intervention effect compared to a stratified analysis, which analyzes data for each subgroup in a separate model. The power results are computed for a proposed prevention research study. The trial plans to randomize adults to either telehealth (intervention) or in-person treatment (control) to reduce cardiovascular risk factors. The trial outcomes will be measures of the Essential Eight, a set of scores for cardiovascular health developed by the American Heart Association which can be combined into a single composite score. The proposed trial is a multilevel study, with outcomes measured on participants, participants treated by the same provider, providers nested within clinics, and clinics nested within hospitals. Investigators suspect that the intervention effect will be greater in rural participants, who live farther from clinics than urban participants. The results use published, exact analytic methods for power calculations with continuous outcomes. We provide example code for power analyses using validated software.

Household food insecurity and associations with hemoglobin A1c and acute diabetes-related complications in youth and young adults with type 1 diabetes: The SEARCH for Diabetes in Youth study.

AIMS: To examine, among youth and young adults (YYA) with type 1 diabetes (T1D), the association of household food insecurity (HFI) with: 1) HbA1c and 2) episodes of diabetic ketoacidosis (DKA) and severe hypoglycemia. METHODS: HFI was assessed using the U.S. Household Food Security Survey Module in SEARCH for Diabetes in Youth participants with T1D between 2016 and 2019. Linear and logistic regression models adjusted for age, diabetes duration, sex, race, ethnicity, clinic site, parent/participant education, household income, health insurance, and diabetes technology use. RESULTS: Of 1830 participants (mean age 20.8 ± 5.0 years, 70.0 % non-Hispanic White), HbA1c was collected for 1060 individuals (mean HbA1c 9.2 % ± 2.0 %). The prevalence of HFI was 16.4 %. In the past 12 months, 18.2 % and 9.9 % reported an episode of DKA or severe hypoglycemia, respectively. Compared to participants who were food secure, HFI was associated with a 0.33 % (95 % CI 0.003, 0.657) higher HbA1c level. Those with HFI had 1.58 (95 % CI 1.13, 2.21) times the adjusted odds of an episode of DKA and 1.53 (95 % CI 0.99, 2.37) times the adjusted odds of an episode of severe hypoglycemia as those without HFI. CONCLUSIONS: HFI is associated with higher HbA1c levels and increased odds of DKA in YYA with T1D.

Properties of the Household Food Security Survey Module Scale in Young Adults with Diabetes.

BACKGROUND: The Household Food Security Survey Module (HFSSM) was not tailored to people with chronic diseases or young adults (YAs). OBJECTIVES: We aim to evaluate whether the 18-item HFSSM meets assumptions underlying the scale among YAs with diabetes. METHODS: Data from 1887 YAs with youth-onset type 1 diabetes or type 2 diabetes were used from the SEARCH for Diabetes in Youth Study, 2016-2019, and on 925 who returned for the SEARCH Food Security Cohort Study, 2018-2021, all of whom had completed the HFSSM. Guttman scaling properties (affirmation of preceding less severe items) and Rasch model properties (probability to answer an item based on difficulty level) were assessed. RESULTS: Items 3 (balanced meals) and 6 (eating less than one should) were affirmed more frequently than expected (nonmonotonic response pattern). At 1.2%-3.5%, item nonresponse was rare among type 1 diabetes but higher among type 2 diabetes (range: 3.1%-10.6%). Items 9 (not eating the whole day) and 3 did not meet the Guttman scaling properties. Rasch modeling revealed that item 3 had the smallest difficulty parameter. INFIT indices suggested that some responses to item 3 did not match the pattern in the rest of the sample. Classifying household food insecurity (HFI) based on items 1 and 2 compared with other 2-item combinations, including item 3, revealed a substantial undercount of HFI ranging from 5% to 8% points. CONCLUSIONS: Use of the HFSSM among YAs with diabetes could potentially result in biased HFI reporting and affect estimates of HFI prevalence in this population.

Maternal Pre-Pregnancy BMI, Breastfeeding, and Child BMI.

OBJECTIVES: Breastfeeding practices may protect against offspring obesity, but this relationship is understudied among women with obesity. We describe the associations between breastfeeding practices and child BMI for age z-score (BMIz), stratified by maternal BMI. METHODS: We analyzed 8134 dyads from 21 cohorts in the Environmental Influences on Child Health Outcomes Program. Dyads with data for maternal pre-pregnancy BMI, infant feeding practices, and ≥1 child BMI assessment between the ages of 2 and 6 years were included. The associations between breastfeeding practices and continuous child BMIz were assessed by using multivariable linear mixed models. RESULTS: Maternal pre-pregnancy BMI category prevalence was underweight: 2.5%, healthy weight: 45.8%, overweight: 26.0%, and obese: 25.6%. Median child ages at the cessation of any breastfeeding and exclusive breastfeeding across the 4 BMI categories were 19, 26, 24, and 17 weeks and 12, 20, 17, and 12 weeks, respectively. Results were in the hypothesized directions for BMI categories. Three months of any breastfeeding was associated with a lower BMIz among children whose mothers were a healthy weight (-0.02 [-0.04 to 0.001], P = .06), overweight (-0.04 [-0.07 to -0.004], P = .03), or obese (-0.04 [-0.07 to -0.006], P = .02). Three months of exclusive breastfeeding was associated with a lower BMIz among children whose mothers were a healthy weight (-0.06 [-0.10 to -0.02], P = .002), overweight (-0.05 [-0.10 to 0.005], P = .07), or obese (-0.08 [-0.12 to -0.03], P = .001). CONCLUSIONS: Human milk exposure, regardless of maternal BMI category, was associated with a lower child BMIz in the Environmental Influences on Child Health Outcomes cohorts, supporting breastfeeding recommendations as a potential strategy for decreasing the risk of offspring obesity.