Search

The article presents a meta-data analysis exploring the interrelationship between maternal body mass index, gestational weight gain, and the risk of being overweight or obese throughout childhood, using data taken from Europe, North America, and Australia.. Topics explored include US Institute of Medicine,, individual participant data, Maternal Obesity and Childhood Outcomes; odds ratio, population attributable risk fraction, standard deviation score, and World Health Organization

To address the current obesity and inactivity epidemics, public health researchers have attempted to identify spatial factors that influence physical inactivity and obesity. Technologic and methodologic developments have led to a revolutionary ability to examine dynamic, high-resolution measures of temporally matched location and behavior data through GPS, accelerometry, and GIS. These advances allow the investigation of spatial energetics, high-spatiotemporal resolution data on location and time-matched energetics, to examine how environmental characteristics, space, and time are linked to activity-related health behaviors with far more robust and detailed data than in previous work. Although the transdisciplinary field of spatial energetics demonstrates promise to provide novel insights on how individuals and populations interact with their environment, there remain significant conceptual, technical, analytical, and ethical challenges stemming from the complex data streams that spatial energetics research generates. First, it is essential to better understand what spatial energetics data represent, the relevant spatial context of analysis for these data, and if spatial energetics can establish causality for development of spatially relevant interventions. Second, there are significant technical problems for analysis of voluminous and complex data that may require development of spatially aware scalable computational infrastructures. Third, the field must come to agreement on appropriate statistical methodologies to account for multiple observations per person. Finally, these challenges must be considered within the context of maintaining participant privacy and security. This article describes gaps in current practice and understanding and suggests solutions to move this promising area of research forward. [ABSTRACT FROM AUTHOR]

In previous articles in the American Journal of Epidemiology (Am J Epidemiol. 2013;177 (5):431-442) and American Journal of Public Health (Am J Public Health. 2013;103(10): 1895-1901), Masters et al. reported age-specific hazard ratios for the contrasts in mortality rates between obesity categories. They corrected the observed hazard ratios for selection bias caused by what they postulated was the nonrepresentativeness of the participants in the National Health Interview Study that increased with age, obesity, and ill health. However, it is possible that their regression approach to remove the alleged bias has not produced, and in general cannot produce, sensible hazard ratio estimates. First, we must consider how many nonparticipants there might have been in each category of obesity and of age at entry andhowmuch higher the mortality rates would have to be in nonparticipants than in participants in these same categories. What plausible set of numerical values would convert the ("biased") decreasing-with-age hazard ratios seen in the data into the ("unbiased") increasing-with-age ratios that they computed? Can these values be encapsulated in (and can sensible values be recovered from) one additional internal variable in a regression model? Second, one must examine the age pattern of the hazard ratios that have been adjusted for selection. Without the correction, the hazard ratios are attenuated with increasing age. With it, the hazard ratios at older ages are considerably higher, but those at younger ages are well below one. Third, one must test whether the regression approach suggested by Masters et al. would correct the nonrepresentativeness that increased with age and ill health that I introduced into real and hypothetical data sets. I found that the approach did not recover the hazard ratio patterns present in the unselected data sets: the corrections overshot the target at older ages and undershot it at lower ages. [ABSTRACT FROM AUTHOR]

Reviews are provided for the articles "The Impact of Obesity on U.S. Mortality Levels: The Importance of Age and Cohort Factors in Population Estimates" by R.K. Masters, E.N. Reither, D.A. Powers, Y.C. Yang, A.E. Burger, and B.G. Link which appears in a 2013 issue of this journal and "Obesity and U.S. Mortality Risk Over the Adult Life Course" by Masters, Powers,and Link which appears in a 2013 issue of the "American Journal of Epidemiology."