Emerging reports suggest that obese patients who are hospitalized with COVID-19 may have worse outcomes; whether this association extends to those who are not hospitalized is unclear. This study examines the association between obesity and death 21 days after diagnosis of COVID-19 among patients who receive care in an integrated health care system, accounting for obesity-related comorbidities and sociodemographic factors.

WHAT'S KNOWN ON THIS SUBJECT: Childhood attention-deficit/ hyperactivity disorder has been associated with both childhood and adult obesity, whereas treatment with stimulants has been associated with delayed child growth. No longitudinal studies with details about dates of diagnosis, treatment, and duration of stimulant use have been published. WHAT THIS STUDY ADDS:Using electronic health record data, this was the first study to evaluate the independent associations of attention-deficit/hyperactivity disorder diagnosis, stimulant treatment, age at first stimulant use, and duration of stimulant use on longitudinal BMI trajectories throughout childhood and adolescence.abstract BACKGROUND: Childhood attention-deficit/hyperactivity disorder (ADHD) has been associated with childhood and adult obesity, and stimulant use with delayed childhood growth, but the independent influences are unclear. No longitudinal studies have examined associations of ADHD diagnosis and stimulant use on BMI trajectories throughout childhood and adolescence. METHODS:We used longitudinal electronic health record data from the Geisinger Health System on 163 820 children ages 3 to 18 years in Pennsylvania. Random effects linear regression models were used to model BMI trajectories with increasing age in relation to ADHD diagnosis, age at first stimulant use, and stimulant use duration, while controlling for confounding variables.RESULTS: Mean (SD) age at first BMI was 8.9 (5.0) years, and children provided a mean (SD) of 3.2 (2.4) annual BMI measurements. On average, BMI trajectories showed a curvilinear relation with age. There were consistent associations of unmedicated ADHD with higher BMIs during childhood compared with those without ADHD or stimulants. Younger age at first stimulant use and longer duration of stimulant use were each associated with slower BMI growth earlier in childhood but a more rapid rebound to higher BMIs in late adolescence. CONCLUSIONS:The study provides the first longitudinal evidence that ADHD during childhood not treated with stimulants was associated with higher childhood BMIs. In contrast, ADHD treated with stimulants was associated with slower early BMI growth but a rebound later in adolescence to levels above children without a history of ADHD or stimulant use. The findings have important clinical and neurobiological implications. Pediatrics 2014;133:668-676

Background/ObjectivesAntibiotics are commonly prescribed for children. Use of antibiotics early in life has been linked to weight gain but there are no large-scale, population-based, longitudinal studies of the full age range among mainly healthy children.Subjects/MethodsWe used electronic health record data on 163,820 children aged 3-18 years and mixed effects linear regression to model associations of antibiotic orders with growth curve trajectories of annual body mass index (BMI) controlling for confounders. Models evaluated three kinds of antibiotic associations – reversible (time-varying indicator for an order in year before each BMI), persistent (time-varying cumulative orders up to BMIj), and progressive (cumulative orders up to prior BMI [BMIj-1]) – and whether these varied by age.ResultsAmong 142,824 children under care in the prior year, a reversible association was observed and this short-term BMI gain was modified by age (p < 0.001); effect size peaked in mid-teen years. A persistent association was observed and this association was stronger with increasing age (p < 0.001). The addition of the progressive association among children with at least three BMIs (n = 79,752) revealed that higher cumulative orders were associated with progressive weight gain; this did not vary by age. Among children with an antibiotic order in the prior year and at least seven lifetime orders, antibiotics (all classes combined) were associated with an average weight gain of approximately 1.4 kg at age 15 years. When antibiotic classes were evaluated separately, the largest weight gain at 15 years was associated with macrolide use.ConclusionsWe found evidence of reversible, persistent, and progressive effects of antibiotic use on BMI trajectories, with different effects by age, among mainly healthy children. The results suggest that antibiotic use may influence weight gain throughout childhood and not just during the earliest years as has been the primary focus of most prior studies.

Objective Longitudinal studies of the role of community context in childhood obesity are lacking. The objective of this study was to examine associations of community socioeconomic deprivation (CSD) on trajectories of change in body mass index (BMI) in childhood and adolescence. Methods Data come from electronic health records on 163,473 children aged 3-18 residing in 1288 communities in Pennsylvania whose weight and height were measured longitudinally. CSD at the year of birth was measured using 6 census variables and modeled in quartiles. Trajectories of BMI within CSD quartiles were estimated using random effects growth-curve models accounting for differences by age, sex, race/ethnicity as well ascorrecting for non-constant residual variance across age groups. Results CSD was associated with higher BMI at average age (10.7 years) and with more rapid growth of BMI over time. Children born in communities with greater CSD had steeper increases of BMI at younger ages. Those born into the poorest communities displayed sustained accelerated BMI growth. CSD remained associated with BMI trajectories after adjustment for a measure of household socioeconomic deprivation. Conclusions Higher CSD may be associated with more obesogenic growth trajectories in early life. Findings suggest that individual-level interventions that ignore the effect of community context on obesity related behaviors may be less efficient.

Background Asthma causes numerous hospital encounters annually, including emergency department visits and hospitalizations. To improve patient outcomes and reduce the number of these encounters, predictive models are widely used to prospectively pinpoint high-risk patients with asthma for preventive care via care management. However, previous models do not have adequate accuracy to achieve this goal well. Adopting the modeling guideline for checking extensive candidate features, we recently constructed a machine learning model on Intermountain Healthcare data to predict asthma-related hospital encounters in patients with asthma. Although this model is more accurate than the previous models, whether our modeling guideline is generalizable to other health care systems remains unknown. Objective This study aims to assess the generalizability of our modeling guideline to Kaiser Permanente Southern California (KPSC). Methods The patient cohort included a random sample of 70.00% (397,858/568,369) of patients with asthma who were enrolled in a KPSC health plan for any duration between 2015 and 2018. We produced a machine learning model via a secondary analysis of 987,506 KPSC data instances from 2012 to 2017 and by checking 337 candidate features to project asthma-related hospital encounters in the following 12-month period in patients with asthma. Results Our model reached an area under the receiver operating characteristic curve of 0.820. When the cutoff point for binary classification was placed at the top 10.00% (20,474/204,744) of patients with asthma having the largest predicted risk, our model achieved an accuracy of 90.08% (184,435/204,744), a sensitivity of 51.90% (2259/4353), and a specificity of 90.91% (182,176/200,391). Conclusions Our modeling guideline exhibited acceptable generalizability to KPSC and resulted in a model that is more accurate than those formerly built by others. After further enhancement, our model could be used to guide asthma care management. International Registered Report Identifier (IRRID) RR2-10.2196/resprot.5039

The current study examines how poverty and education in both the family and school contexts influence adolescent weight. Prior research has produced an incomplete and often counterintuitive picture. We develop a framework to better understand how income and education operate alone and in conjunction with each other across families and schools. We test it by analyzing data from Wave 1 of the U.S.-based National Longitudinal Study of Adolescent Health (N= 16,133 in 132 schools) collected in 1994–1995. Using hierarchical logistic regression models and parallel indicators of family- and school-level poverty and educational resources, we find that at the family-level, parent’s education, but not poverty status, is associated with adolescent overweight. At the school-level, the concentration of poverty within a school, but not the average level of parent’s education, is associated with adolescent overweight. Further, increases in school poverty diminish the effectiveness of adolescents’ own parents’ education for protecting against the risks of overweight. The findings make a significant contribution by moving beyond the investigation of a single socioeconomic resource or social context. The findings push us to more fully consider when, where, and why money and education matter independently and jointly across health-related contexts.

Why is there greater variability in individual longevity in some populations than in others? We propose a decomposition method designed to address that question by quantifying the effects of population differences in the spread, allocation, and timing of the principal causes of death. Applying the method to the United States and Sweden, we find that spread effects account for about two-thirds of the greater variance in age at death among American adults, meaning that two-thirds of the U.S.-Sweden difference would persist if the two countries differed only with respect to within-cause variance among adults. The remainder of the difference is due largely to allocation effects, with the greater incidence of homicides and fatal traffic accidents alone accounting for more than one-fourth of the greater variance in age at death among adults in the United States.

Past research has assessed the association of single community characteristics with obesity, ignoring the spatial co-occurrence of multiple community-level risk factors. We used conditional random forests (CRF), a non-parametric machine learning approach to identify the combination of community features that are most important for the prediction of obesegenic and obesoprotective environments for children. After examining 44 community characteristics, we identified 13 features of the social, food, and physical activity environment that in combination correctly classified 67% of communities as obesoprotective or obesogenic using mean BMI-z as a surrogate. Social environment characteristics emerged as most important classifiers and might provide leverage for intervention. CRF allows consideration of the neighborhood as a system of risk factors.