A multifactorial obesity model developed from nationwide public health exposome data and modern computational analyses

Summary Statement of the problem Obesity is both multifactorial and multimodal, making it difficult to identify, unravel and distinguish causative and contributing factors. The lack of a clear model of aetiology hampers the design and evaluation of interventions to prevent and reduce obesity. Methods Using modern graph-theoretical algorithms, we are able to coalesce and analyse thousands of inter-dependent variables and interpret their putative relationships to obesity. Our modelling is different from traditional approaches; we make no a priori assumptions about the population, and model instead based on the actual characteristics of a population. Paracliques, noise-resistant collections of highly-correlated variables, are differentially distilled from data taken over counties associated with low versus high obesity rates. Factor analysis is then applied and a model is developed. Results and conclusions Latent variables concentrated around social deprivation, community infrastructure and climate, and especially heat stress were connected to obesity. Infrastructure, environment and community organisation differed in counties with low versus high obesity rates. Clear connections of community infrastructure with obesity in our results lead us to conclude that community level interventions are critical. This effort suggests that it might be useful to study and plan interventions around community organisation and structure, rather than just the individual, to combat the nation’s obesity epidemic

Social Determinants and the Classification of Disease: Descriptive Epidemiology of Selected Socially Mediated Disease Constellations

Background Most major diseases have important social determinants. In this context, classification of disease based on etiologic or anatomic criteria may be neither mutually exclusive nor optimal. Methods and Findings Units of analysis comprised large metropolitan central and fringe metropolitan counties with reliable mortality rates – (n = 416). Participants included infants and adults ages 25 to 64 years with selected causes of death (1999 to 2006). Exposures included that residential segregation and race-specific social deprivation variables. Main outcome measures were obtained via principal components analyses with an orthogonal rotation to identify a common factor. To discern whether the common factor was socially mediated, negative binomial multiple regression models were developed for which the dependent variable was the common factor. Results showed that infant deaths, mortality from assault, and malignant neoplasm of the trachea, bronchus and lung formed a common factor for race-gender groups (black/white and men/women). Regression analyses showed statistically significant, positive associations between low socio-economic status for all race-gender groups and this common factor. Conclusions Between 1999 and 2006, deaths classified as “assault” and “lung cancer”, as well as “infant mortality” formed a socially mediated factor detectable in population but not individual data. Despite limitations related to death certificate data, the results contribute important information to the formulation of several hypotheses: (a) disease classifications based on anatomic or etiologic criteria fail to account for social determinants; (b) social forces produce demographically and possibly geographically distinct population-based disease constellations; and (c) the individual components of population-based disease constellations (e.g., lung cancer) are phenotypically comparable from one population to another but genotypically different, in part, because of socially mediated epigenetic variations. Additional research may produce new taxonomies that unify social determinants with anatomic and/or etiologic determinants. This may lead to improved medical management of individuals and populations

USING BIG DATA TO UNDERSTAND CVD RISK AT THE POPULATION LEVEL

Disclosure: This study is funded by NIH Award # 1362134. Therapeutic Area: ASCVD/CVD Risk Factors Background: In 2019, cardiovascular disease (CVD) led to over 240 deaths and 5081 years of disability adjusted life years (DALY) per 100,000 individuals globally. Previous studies have shown that along with individual factors (genetics, individual habits), factors at the populational-level, such as pollution, ambient temperature, business density, green spaces, and grocery store proximity, can also impact an individual's cardiovascular health. Intervention at the individual level is available, but personalized healthcare is costly and inaccessible to many. Thus, greater understanding of the modifiable CVD risk factors at the population-level is necessary for the development of broad-scale interventions. Prior studies have used Big Data from Electronic Health Records to improve cardiovascular care for individuals with CVD, but few have analyzed Big Data related to CVD risk at the population-level. This study analyzed the risk factors associated with CVD mortality in places with low CVD mortality (cold-spots) using Big Data to understand the underlying factors related to CVD at the spatial geographic context. Methods: Data was obtained from the 2017-2018 HRSA Area Health Resource File and CDC WONDER compressed mortality file (National Center for Health Statistics. Compressed Mortality File) and from the National Association for Public Health Statistics and Information Systems (NAPHSIS). Getis-Ord Gi* statistic and Pearson product moment correlation were performed followed by Graph Analysis and Factor Analysis to elucidate the multifactorial connections within geographic CVD, develop cold spots, and determine potentially modifiable risk factors of CVD. Results: Sixteen cold-spot multiple variable paracliques were determined with 104 variables associated with CVD mortality. Ten latent constructs were generated, encompassing factors such as population density, age group, gender, house ownership; smoking prevalence; obesity and physical inactivity; higher wages; business density and grocery store proximity; fine particulate matter; poverty. Conclusion: CVD mortality increases with high population and business density, long term smoking, empty calorie consumption, high heat indexes, particulate matter pollution, lower household incomes, and Black homeownership. Findings from this study can be used to develop passive population-based interventions that have a broad impact on city and building planning to reduce CVD risk at the community level

Predicting Childhood Obesity Based on Single and Multiple Well-Child Visit Data Using Machine Learning Classifiers

Childhood obesity is a public health concern in the United States. Consequences of childhood obesity include metabolic disease and heart, lung, kidney, and other health-related comorbidities. Therefore, the early determination of obesity risk is needed and predicting the trend of a child’s body mass index (BMI) at an early age is crucial. Early identification of obesity can lead to early prevention. Multiple methods have been tested and evaluated to assess obesity trends in children. Available growth charts help determine a child’s current obesity level but do not predict future obesity risk. The present methods of predicting obesity include regression analysis and machine learning-based classifications and risk factor (threshold)-based categorizations based on specific criteria. All the present techniques, especially current machine learning-based methods, require longitudinal data and information on a large number of variables related to a child’s growth (e.g., socioeconomic, family-related factors) in order to predict future obesity-risk. In this paper, we propose three different techniques for three different scenarios to predict childhood obesity based on machine learning approaches and apply them to real data. Our proposed methods predict obesity for children at five years of age using the following three data sets: (1) a single well-child visit, (2) multiple well-child visits under the age of two, and (3) multiple random well-child visits under the age of five. Our models are especially important for situations where only the current patient information is available rather than having multiple data points from regular spaced well-child visits. Our models predict obesity using basic information such as birth BMI, gestational age, BMI measures from well-child visits, and gender. Our models can predict a child’s obesity category (normal, overweight, or obese) at five years of age with an accuracy of 89%, 77%, and 89%, for the three application scenarios, respectively. Therefore, our proposed models can assist healthcare professionals by acting as a decision support tool to aid in predicting childhood obesity early in order to reduce obesity-related complications, and in turn, improve healthcare

Does Survival Vary for Breast Cancer Patients in the United States? A Study from Six Randomly Selected States

Background . Breast cancer is the most common cancer in women. Disparities in some characteristics of breast cancer patients and their survival data for six randomly selected states in the US were examined. Materials and Methods . A probability random sampling method was used to select the records of 2,000 patients from each of six randomly selected states. Demographic and disease characteristics were extracted from the Surveillance Epidemiology and End Results (SEER) database. To evaluate relationships between variables, we employed a Cox Proportional Regression to compare survival times in the different states. Results . Iowa had the highest mean age of diagnosis at 64.14 years ( S E = 0.324 ) and Georgia had the lowest at 57.97 years ( S E = 0.313 ). New Mexico had the longest mean survival time of 189.09 months ( S E = 20.414 ) and Hawaii the shortest at 119.01 ( S E = 5.394 ) months, a 70.08-month difference (5.84 years). Analysis of stage of diagnosis showed that the highest survival times for Whites and American Indians/Alaska Natives were for stage I cancers. The highest survival times for Blacks varied. Stage IV cancer consistently showed the lowest survival times. Conclusions . Differences in breast cancer characteristics across states highlight the need to understand differences between the states that result in variances in breast cancer survival

Use of Six Sigma for Eliminating Missed Opportunities for Prevention Services

Delivery of primary care preventative services can be significantly increased utilizing Six Sigma methods. Missed preventative service opportunities were compared in the study clinic with the community clinic in the same practice. The study clinic had 100% preventative services, compared with only 16.3% in the community clinic. Preventative services can be enhanced to Six Sigma quality when the nurse executive and medical staff agree on a single standard of nursing care executed via standing orders

Race-gender specific geographic distribution of the Common Factor.

<p>USA. 1999–2006.</p

Factor loading and alpha-reliability from principal components analysis.

<p>Factor loading and alpha-reliability from principal components analysis.</p