Building effective intervention models utilizing big data to prevent the obesity epidemic.

INTRODUCTION: The exposome consists of factors an individual is exposed to across the life course. The exposome is dynamic, meaning the factors are constantly changing, affecting each other and individuals in different ways. Our exposome dataset includes social determinants of health as well as policy, climate, environment, and economic factors that could impact obesity development. The objective was to translate spatial exposure to these factors with the presence of obesity into actionable population-based constructs that could be further explored. METHODS: Our dataset was constructed from a combination of public-use datasets and the Center of Disease Control's Compressed Mortality File. Spatial Statistics using Queens First Order Analysis was performed to identify hot- and cold-spots of obesity prevalence; followed by Graph Analysis, Relational Analysis, and Exploratory Factor Analysis to model the multifactorial spatial connections. RESULTS: Areas of high and low presence of obesity had different factors associated with obesity. Factors associated with obesity in areas of high obesity propensity were: poverty / unemployment; workload, comorbid conditions (diabetes, CVD) and physical activity. Conversely, factors associated in areas where obesity was rare were: smoking, lower education, poorer mental health, lower elevations, and heat. DISCUSSION: The spatial methods described within the paper are scalable to large numbers of variables without issues of multiple comparisons lowering resolution. These types of spatial structural methods provide insights into novel variable associations or factor interactions that can then be studied further at the population or policy levels.

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.

Characteristics of U.S. adults taking prescription antipsychotic medications, National Health and Nutrition Examination Survey 2013-2018.

BACKGROUND: Global 12-month psychosis prevalence is estimated at roughly 0.4%, although prevalence of antipsychotic use in the U.S. is estimated at roughly 1.7%. Antipsychotics are frequently prescribed for off label uses, but have also been shown to carry risk factors for certain comorbid conditions and with other prescription medications. The study aims to describe the socio-demographic and health characteristics of U.S. adults taking prescription antipsychotic medications, and to better understand the association of antipsychotic medications and comorbid chronic diseases. METHODS: The study pools 2013-2018 data from the National Health and Nutrition Examination Survey (NHANES), a nationally representative cross-sectional survey of non-institutionalized U.S. residents (n = 17,691). Survey staff record prescription medications taken within the past 30 days for each respondent, from which typical and atypical antipsychotic medications were identified. RESULTS: Prevalence of antipsychotic use among U.S. adults was 1.6% (n = 320). Over 90% of individuals taking antipsychotics reported having health insurance and a usual place for care, significantly more than their counterparts not taking antipsychotics. Further, those taking antipsychotics reported higher prevalence of comorbid chronic diseases and took an average of 2.3 prescription medications more than individuals not taking antipsychotics. Individuals taking antipsychotics were more likely to sleep 9 or more hours per night, be a current smoker, and have a body mass index greater than 30 kg/m2. CONCLUSIONS: U.S. adults who take antipsychotic medications report more consistent health care access and higher prevalence of comorbid chronic diseases compared to those not taking antipsychotics. The higher comorbidity prevalence and number of total prescriptions highlight the need for careful assessment and monitoring of existing comorbidities and potential drug-drug interactions among adults taking antipsychotics in the U.S.

Current Issues in Chronic Diseases: A Focus on Dementia and Hypertension in Rural West Texans.

Dementia and hypertension are chronic diseases that affect elderly populations worldwide. The prevalence of these diseases increases each year, especially in rural and underserved rural communities like West Texas. The purpose of this study was to find risk factors of dementia and their impact on rural West Texans. Data was provided by the Project FRONTIER for rural West Texas counties. The SPSS software package was used for statistical analysis. Pearson's chi-squared test was also utilized to determine the relationships between the risk factors considering a level of significance (α)  = 0.05. The findings have shown that age group had significant associations with hypertension, cerebral, neurologic disease, Romberg test, and muscle strength for both males and females (p≤0.002). Hypertension was significantly associated with cognitive disorder and diabetes in both males and females (p≤0.011). Age group in females was significantly associated with parkinsonism (p = 0.02), neurological stroke (p = 0.002), reflexes (p = 0.003), and sensory intact (hands/feet) (p = 0.004), respectively, whereas age for males was not significantly associated with those variables (p = 0.29, p = 0.05, p = 0.56, and p = 0.76, respectively). Hypertension in females was significantly associated with cardiovascular disease (p = 0.001) and depression (p = 0.001) but was not found to be significant for males (p = 0.30 and p = 0.09, respectively). Both males and females in Hispanic and non-Hispanic groups were found to be significantly associated with Alzheimer's disease (p = 0.0001 and p = 0.045, respectively). Hispanic and non-Hispanic females were found to be significantly associated with hypertension (p = 0.026). Gender-specific differences in dementia risk factors exist and integrating such variables may guide relevant policymaking to reduce dementia incidence in rural West Texas.

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 (SE = 0.324) and Georgia had the lowest at 57.97 years (SE = 0.313). New Mexico had the longest mean survival time of 189.09 months (SE = 20.414) and Hawaii the shortest at 119.01 (SE = 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.

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

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.

Psychometrics of the "Self-Efficacy Consumption of Fruit and Vegetables Scale" in African American women.

PURPOSE: Assess the psychometric properties of the Self-Efficacy Consumption of Fruit and Vegetable Scale (F/V scale) in African American women. SETTING: Midwestern Health Maintenance Organization. SUBJECTS: 221 African American women age 40-65 with BMI≥30 MEASURES: F/V scale was compared to eating efficacy/availability subscale reported on the WEL and mean micronutrient intake (vitamins A, C, K, folate, potassium, and beta-carotene reported on 3-day food records. RESULTS: F/V scale construct validity and internal consistency were assessed and compared to: 1) the original scale validation in Chinese women, 2) WEL scale, and 3) to micronutrient intake from 3-day food records. Total scale scores differed between African American women (µ=1.87+/-0.87) and Chinese (µ=0.41). In a Chinese population, F/V scale factored into two subscales; the F/V factored into one subscale in African American women. Construct validity was supported with correlation between the F/V scale and the eating efficacy WEL subscale (r2=-0.336, p=0.000). There was not a significant correlation between dietary consumption of micronutrients representative of fruit and vegetable intake and the F/V scale. CONCLUSION: The F/V scale developed for Chinese populations can be reliably used with African American women.

WAIT, WON'T! WANT: BARRIERS TO HEALTH CARE AS PERCEIVED BY MEDICALLY AND SOCIALLY DISENFRANCHISED COMMUNITIES.

OBJECTIVES: We explored barriers to healthcare as perceived by members of medically and socially disenfranchised communities. METHODS: We conducted focus groups with 28 women and 32 men from Northeast Ohio who identified themselves as African-American, Hispanic/Latino, lesbian/gay/bisexual/transgendered, and/or Russian immigrant. RESULTS: Participants described their experiences of waiting, things they won't tolerate, when they won't participate, and what they want from providers. They described behaviors, actions and relationship characteristics that they want from their providers and characteristics that they prefer in health systems. CONCLUSIONS: The themes of Wait, Won't, and Want have healthcare practice and policy implications. Patient-provider interactions are known to be significant determinants of healthcare outcomes and these exploratory findings suggest that they might also affect patient self-management strategies. Future efforts should focus on developing and testing patient-centered strategies that address the themes identified to increase engagement to increase self-management of health.

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.

Exploration of preterm birth rates using the public health exposome database and computational analysis methods.

Recent advances in informatics technology has made it possible to integrate, manipulate, and analyze variables from a wide range of scientific disciplines allowing for the examination of complex social problems such as health disparities. This study used 589 county-level variables to identify and compare geographical variation of high and low preterm birth rates. Data were collected from a number of publically available sources, bringing together natality outcomes with attributes of the natural, built, social, and policy environments. Singleton early premature county birth rate, in counties with population size over 100,000 persons provided the dependent variable. Graph theoretical techniques were used to identify a wide range of predictor variables from various domains, including black proportion, obesity and diabetes, sexually transmitted infection rates, mother's age, income, marriage rates, pollution and temperature among others. Dense subgraphs (paracliques) representing groups of highly correlated variables were resolved into latent factors, which were then used to build a regression model explaining prematurity (R-squared = 76.7%). Two lists of counties with large positive and large negative residuals, indicating unusual prematurity rates given their circumstances, may serve as a starting point for ways to intervene and reduce health disparities for preterm births.

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.

Infant obesity and severe obesity growth patterns in the first two years of life.

Distinguishing an obesity growth pattern that originates during infancy is clinically important. Infancy based obesity prevention interventions may be needed while precursors of later health are forming. Infant obesity and severe obesity growth patterns in the first 2-years are described and distinguished from a normal weight growth pattern. A retrospective chart review was conducted. Body mass index (BMI) growth patterns from birth to 2-years are described for children categorized at 5-years as normal weight (n = 61), overweight (n = 47), obese (n = 41) and severely obese (n = 72) cohorts using WHO reference standards. BMI values were calculated at birth, 1-week; 2-, 4-, 6-, 9-, 12-, 15-, 18-months; and 2- and 5-years. Graphs of the longitudinal Analysis of Variance of Means of BMI values identified the earliest significant divergence of a cohort's average BMI pattern from other cohorts' patterns. ANOVA and Pearson Product Moment correlations were also performed. Statistically significant differences in BMI values and differences in growth patterns between cohorts were evident as early as 2-6 months post-birth. Children who were obese or severely obese at 5-years demonstrated a BMI pattern that differed within the first 2-years of life from that of children who were normal weight at 5-years. The earliest significant correlation between early BMI values and 5-year BMI value was at 4-months post-birth. The study fills an important gap by demonstrating early onset of an infant obesity growth pattern in full-term children who were healthy throughout their first 5 years of life.

Utilising infant growth to predict obesity status at 5 years.

AIM: This study determines if an early life growth pattern in healthy infants can predict obesity at age 5. METHODS: Randomly selected from all healthy children born from 1997 to 2001 in a Midwestern US Health Maintenance Organization; growth patterns from birth to 5 years were described for children who were categorised by obesity classification at 5 years into normal weight (n = 61), overweight (n = 47), obese (n = 41) and morbidly obese (n = 72) cohorts using World Health Organization body mass index (BMI) criteria. A retrospective longitudinal analysis based on weighted least squares was performed on BMI by age (1 week; 2, 4, 6, 9, 12, 15 and 18 months; and 2, 3, 4 and 5 years). Graphs of the longitudinal repeated measures analysis of variance of means allowed identification of the earliest significant divergence of a cohort's average BMI pattern from other cohorts' patterns. RESULTS: Distinctions in growth patterns and BMIs were evident before 1-year post-birth. Children who were obese or morbidly obese at 5 years demonstrated a BMI pattern that differed from children who were normal weight at 5 years. CONCLUSIONS: Identifying obesity development in early life may assist with prevention of later obesity. The results merit future study. An early life BMI growth pattern is clinically important because it permits discrimination of those who do and do not fit a normal weight pattern, guiding individualised interventions in the first year of life while precursors of later health are still forming.

A pilot study: does obesity begin in the first year of life?

AIM: To determine if growth patterns in healthy infants can identify associations with obesity at age 5 years. METHOD: Body mass index growth patterns from birth to 1 year were described for cohorts of children who were classified at 5 years as normal weight (n = 61), overweight (n = 47), obese (n = 41), and morbidly obese (n = 72). A longitudinal analysis of body mass index means based on the age postbirth was conducted and graphed. RESULTS: Distinctions in growth patterns were evident before 1 year postbirth. Children who were normal weight at 5 years demonstrated a growth pattern in the first year that differed from children who were overweight, obese, or morbidly obese at 5 years. CONCLUSIONS: Obesity growth patterns were seen in infancy and are clinically important because identification of infants who do not fit a normal weight pattern can occur and thus guide individualized interventions in the first year postbirth while precursors of later health are still forming.

Which chronic obstructive pulmonary disease patients will be likely to attend consistently a pulmonary rehabilitation program?

INTRODUCTION: Chronic obstructive pulmonary disease (COPD) is the fourth leading cause of death in the United States, and millions of COPD patients are disabled and unable to work. Pulmonary rehabilitation (PR) programs are available to assist with disability, but it is not clear who is likely to consistently participate in them. The purpose of this study was to determine which participants were likely to consistently attend a PR program. METHODS: A retrospective medical record review was used to assess 104 community-dwelling adults with COPD who completed the PR program at a Midwest medical center between 2000 and 2005. SAMPLE: The sample consisted of 32 men and 72 women with a mean age of 59.9 years (±19.10 years), mean predicted one-second forced expiratory volume (FEV(1)) of 46.45% (SD = 20.1), mean percent forced vital capacity (FVC%) of 67.61 (SD = 16.61), mean FEV(1)/FVC% ratio of 51.15% (SD = 18.17), and mean residual volume (RV) of 150.66% (SD = 67.01). RESULTS: Contextual variables of current smoking (beta = -.36), male sex (beta = .19), not having emphysema (beta = -.27), and FVC% (beta = .32) were significant predictors of attendance at (a dose of) PR. The number of selected comorbidities significantly predicted the dose of PR (beta = -.20). CONCLUSION: These findings support the ability to identify factors that predict attendance at a PR program. Nurses can assess patients at risk for lack of consistent PR attendance and implement interventions to improve attendance. Specifically, smoking cessation prior to or as an integral part of PR programs may improve attendance.

Church-based heart health project: health status of urban african americans.

One of the major health disparities in the African-American population is the high incidence of underdiagnosed cardiovascular disease prior to onset of symptoms. Cardiovascular diseases are one of the chief causes of decreased longevity, reduced quality of life, and poor treatment outcomes among African Americans. The Church-Based Heart Health Project, a pilot initiative of Kaiser Permanente (KP) Ohio's Center of Excellence for Health Disparities and Cultural Competency for African American Health, was implemented in 2004 as an innovative and proactive response to confront this cardiovascular health disparity in greater Cleveland's African-American population. The goal of this program was to reduce individual participants' risks for cardiac events (that is, heart attack, heart disease, or cardiac death) by 1) providing individual risk assessment and interpretation and 2) cataloging the generalized health status of urban churchgoing African Americans in greater Cleveland. We describe the cardiovascular risk factors present in a random population of urban churchgoing African Americans participating in sponsored health screenings at their church. A convenience sample of 144 African-American adults participated in this study. Twenty-five percent (37) were men and 75% (107) were women, and participants' mean age was 54.2 years. Ninety percent were not members of KP Ohio. Cardiovascular risk factors measured included body mass index, lipid levels (cholesterol, high-density lipoprotein, low-density lipoprotein, triglycerides), blood pressure, brief health history, Framingham Coronary Heart Disease Prediction Score, and National Heart, Lung, and Blood Institute prediction score for ten-year risk. A large portion of the population was found to have at least one risk factor for coronary heart disease (CHD).