Claudia Nau

Attention deficit disorder, stimulant use, and childhood body mass index trajectory.

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.

Antibiotic use and childhood body mass index trajectory

Background/Objectives:Antibiotics 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/Methods:We 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.Results:Among 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.Conclusions:We 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.

Community socioeconomic deprivation and obesity trajectories in children using electronic health records

Longitudinal studies of the role of community context in childhood obesity are lacking. The objective of this study was to examine associations of community socio economic deprivation (CSD) with trajectories of change in body mass index (BMI) in childhood and adolescence.

A New Method for Determining Why Length of Life is More Unequal in Some Populations Than in Others

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.

Why the racial gap in life expectancy is declining in the United States

BACKGROUND Blacks have lower life expectancy than whites in the United States. That disparity could be due to racial differences in the causes of death, with blacks being more likely to die of causes that affect the young, or it could be due to differences in the average ages of blacks and whites who die of the same cause. Prior studies fail to distinguish these two possibilities. OBJECTIVE In this study we determine how much of the 2000–10 reduction in the racial gap in life expectancy resulted from narrowing differences in the cause-specific mean age at death for blacks and whites, as opposed to changing cause-specific probabilities for blacks and whites. METHOD We introduce a method for separating the difference-in-probabilities and difference-inage components of group disparities in life expectancy. RESULTS Based on the new method, we find that 60% of the decline in the racial gap in life expectancy from 2000 to 2010 was attributable to reduction in the age component, largely because of declining differences in the age at which blacks and whites die of chronic diseases. CONCLUSION Our findings shed light on the sources of the declining racial gap in life expectancy in the United States, and help to identify where advances need to be made to achieve the goal of eliminating racial disparities in life expectancy.

Hispanic-White Differences in Lifespan Variability in the United States

This study is the first to investigate whether and, if so, why Hispanics and non-Hispanic whites in the United States differ in the variability of their lifespans. Although Hispanics enjoy higher life expectancy than whites, very little is known about how lifespan variability—and thus uncertainty about length of life—differs by race/ethnicity. We use 2010 U.S. National Vital Statistics System data to calculate lifespan variance at ages 10+ for Hispanics and whites, and then decompose the Hispanic-white variance difference into cause-specific spread, allocation, and timing effects. In addition to their higher life expectancy relative to whites, Hispanics also exhibit 7 % lower lifespan variability, with a larger gap among women than men. Differences in cause-specific incidence (allocation effects) explain nearly two-thirds of Hispanics’ lower lifespan variability, mainly because of the higher mortality from suicide, accidental poisoning, and lung cancer among whites. Most of the remaining Hispanic-white variance difference is due to greater age dispersion (spread effects) in mortality from heart disease and residual causes among whites than Hispanics. Thus, the Hispanic paradox—that a socioeconomically disadvantaged population (Hispanics) enjoys a mortality advantage over a socioeconomically advantaged population (whites)—pertains to lifespan variability as well as to life expectancy. Efforts to reduce U.S. lifespan variability and simultaneously increase life expectancy, especially for whites, should target premature, young adult causes of death—in particular, suicide, accidental poisoning, and homicide. We conclude by discussing how the analysis of Hispanic-white differences in lifespan variability contributes to our understanding of the Hispanic paradox.

Exploring the forest instead of the trees: An innovative method for defining obesogenic and obesoprotective environments

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 obesogenic 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.

Why Lifespans Are More Variable Among Blacks Than Among Whites in the United States

Lifespans are both shorter and more variable for blacks than for whites in the United States. Because their lifespans are more variable, there is greater inequality in length of life—and thus greater uncertainty about the future—among blacks. This study is the first to decompose the black-white difference in lifespan variability in America. Are lifespans more variable for blacks because they are more likely to die of causes that disproportionately strike the young and middle-aged, or because age at death varies more for blacks than for whites among those who succumb to the same cause? We find that it is primarily the latter. For almost all causes of death, age at death is more variable for blacks than it is for whites, especially among women. Although some youthful causes of death, such as homicide and HIV/AIDS, contribute to the black-white disparity in variance, those contributions are largely offset by the higher rates of suicide and drug poisoning deaths for whites. As a result, differences in the causes of death for blacks and whites account, on net, for only about one-eighth of the difference in lifespan variance.

Using a computational model to quantify the potential impact of changing the placement of healthy beverages in stores as an intervention to "Nudge" adolescent behavior choice

BackgroundProduct placement influences consumer choices in retail stores. While sugar sweetened beverage (SSB) manufacturers expend considerable effort and resources to determine how product placement may increase SSB purchases, the information is proprietary and not available to the public health and research community. This study aims to quantify the effect of non-SSB product placement in corner stores on adolescent beverage purchasing behavior. Corner stores are small privately owned retail stores that are important beverage providers in low-income neighborhoods – where adolescents have higher rates of obesity.MethodsUsing data from a community-based survey in Baltimore and parameters from the marketing literature, we developed a decision-analytic model to simulate and quantify how placement of healthy beverage (placement in beverage cooler closest to entrance, distance from back of the store, and vertical placement within each cooler) affects the probability of adolescents purchasing non-SSBs.ResultsIn our simulation, non-SSB purchases were 2.8 times higher when placed in the “optimal location” – on the second or third shelves of the front cooler – compared to the worst location on the bottom shelf of the cooler farthest from the entrance. Based on our model results and survey data, we project that moving non-SSBs from the worst to the optional location would result in approximately 5.2 million more non-SSBs purchased by Baltimore adolescents annually.ConclusionsOur study is the first to quantify the potential impact of changing placement of beverages in corner stores. Our findings suggest that this could be a low-cost, yet impactful strategy to nudge this population—highly susceptible to obesity—towards healthier beverage decisions.

Healthy versus Unhealthy Suppliers in Food Desert Neighborhoods: A Network Analysis of Corner Stores’ Food Supplier Networks

Background: Products in corner stores may be affected by the network of suppliers from which storeowners procure food and beverages. To date, this supplier network has not been well characterized. Methods: Using network analysis, we examined the connections between corner stores (n = 24) in food deserts of Baltimore City (MD, USA) and their food/beverage suppliers (n = 42), to determine how different store and supplier characteristics correlated. Results: Food and beverage suppliers fell into two categories: Those providing primarily healthy foods/beverages (n = 15) in the healthy supplier network (HSN) and those providing primarily unhealthy food/beverages (n = 41) in the unhealthy supplier network (UHSN). Corner store connections to suppliers in the UHSN were nearly two times greater (t = 5.23, p < 0.001), and key suppliers in the UHSN core were more diverse, compared to the HSN. The UHSN was significantly more cohesive and densely connected, with corner stores sharing a greater number of the same unhealthy suppliers, compared to HSN, which was less cohesive and sparsely connected (t = 5.82; p < 0.001). Compared to African Americans, Asian and Hispanic corner storeowners had on average −1.53 (p < 0.001) fewer connections to suppliers in the HSN (p < 0.001). Conclusions: Our findings indicate clear differences between corner stores’ HSN and UHSN. Addressing ethnic/cultural differences of storeowners may also be important to consider.