![]() Objectives-This report presents trends in mean weight, height, waist circumference, and body mass index (BMI) among adults in the United States from 1999-2000 through 2015-2016. We recommend the development of innovative interventions using SDOH risk and protective pathways as guide to address the current epidemic of childhood overweight and obesity. SDOH represent markers of overweight or obesity in children. Parental attainment of college education, health insurance coverage, female gender, and language spoken in home other than Spanish were protective against overweight or obesity.Ĭonclusions and global health implications: Overweight was more frequent in younger children, children of single parents, and children who lived in a neighborhood with no amenities. The likelihood of obesity was elevated among non-Hispanic Black and Hispanic children (PR = 1.53 95% CI = 1.01-2.31) and (PR = 1.50 95% CI = 1.18-1.90) respectively. Survey log-binomial regression models were built to generate prevalence ratio (PR) estimates to capture the associations between SDOH and overweight or obesity.Ībout 30.6 million children were surveyed of which 9.5 million (31.0%) were either overweight or obese. Based on the literature and pathway plausibility, we examined several SDOH variables as predictors of childhood overweight or obesity in the US. Overweight was defined as Body Mass Index (BMI) ≥ 85th to<95th, while obesity was defined as BMI ≥95th percentile for age and sex. We utilized the National Survey of Children's Health (NSCH) 2016-17 dataset for this analysis. We explore the influence of selected social determinants of health (SDOH) on overweight and obesity among U.S. Through extensive series of experiments, we show new and complementary findings regarding the predictors of future dangerous weight gains.Ĭhildhood obesity is one of the foremost threats to population health in the United States (U.S.) leading to the emergence of co-morbidities and increased healthcare cost. Using our method, we also extensively compare the differences and inequities in patterns across 22 strata determined by the individual's gender, age, race, insurance type, neighborhood type, and income level. In our obesity problem, X refers to the extracted features from very large and multi-site EHR data, and Y indicates significant weight gains. Specifically, we extend an established subgroup-discovery method to generate the desired rules of type X -> Y and show how top features can be extracted from the X side, functioning as the best predictors of Y. In this work, we use a rule discovery method to study this problem, by presenting an approach that offers genuine interpretability and concurrently optimizes the accuracy(being correct often) and support (applying to many samples) of the identified patterns. Overweight and obesity remain a major global public health concern and identifying the individualized patterns that increase the risk of future weight gains has a crucial role in preventing obesity and numerous sub-sequent diseases associated with obesity.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |