Racial differences in healthcare expenditures for prevalent multimorbidity combinations in the USA: a cross-sectional study.

BACKGROUND: We aimed to model total charges for the most prevalent multimorbidity combinations in the USA and assess model accuracy across Asian/Pacific Islander, African American, Biracial, Caucasian, Hispanic, and Native American populations. METHODS: We used Cerner HealthFacts data from 2016 to 2017 to model the cost of previously identified prevalent multimorbidity combinations among 38 major diagnostic categories for cohorts stratified by age (45-64 and 65 +). Examples of prevalent multimorbidity combinations include lipedema with hypertension or hypertension with diabetes. We applied generalized linear models (GLM) with gamma distribution and log link function to total charges for all cohorts and assessed model accuracy using residual analysis. In addition to 38 major diagnostic categories, our adjusted model incorporated demographic, BMI, hospital, and census division information. RESULTS: The mean ages were 55 (45-64 cohort, N = 333,094) and 75 (65 + cohort, N = 327,260), respectively. We found actual total charges to be highest for African Americans (means $78,544 [45-64], $176,274 [65 +]) and lowest for Hispanics (means $29,597 [45-64], $66,911 [65 +]). African American race was strongly predictive of higher costs (p < 0.05 [45-64]; p < 0.05 [65 +]). Each total charge model had a good fit. With African American as the index race, only Asian/Pacific Islander and Biracial were non-significant in the 45-64 cohort and Biracial in the 65 + cohort. Mean residuals were lowest for Hispanics in both cohorts, highest in African Americans for the 45-64 cohort, and highest in Caucasians for the 65 + cohort. Model accuracy varied substantially by race when multimorbidity grouping was considered. For example, costs were markedly overestimated for 65 + Caucasians with multimorbidity combinations that included heart disease (e.g., hypertension + heart disease and lipidemia + hypertension + heart disease). Additionally, model residuals varied by age/obesity status. For instance, model estimates for Hispanic patients were highly underestimated for most multimorbidity combinations in the 65 + with obesity cohort compared with other age/obesity status groupings. CONCLUSIONS: Our finding demonstrates the need for more robust models to ensure the healthcare system can better serve all populations. Future cost modeling efforts will likely benefit from factoring in multimorbidity type stratified by race/ethnicity and age/obesity status.

The path between breakfast eating habit, sleep duration and physical activity on obesity status: An epidemiological study in schoolchildren.

Background: Although the literature suggests that skipping breakfast, insufficient sleep, and reduced physical activity are associated with childhood obesity their co-influence and their in-between interactions on weight status have rarely been studied. Aim: To examine the co-influence of breakfast eating habits, sleep duration, and physical activity on the weight status of children 10-12 years old from several schools of Greece. Methods: A cross-sectional study was conducted on 1688 students in Greece, during 2014-2016. Children's Body Mass Index (BMI) was calculated and classified according to the International Obesity Task Force (IOTF) classification. Logistic regression models and path analysis were used. Results: Overweight/obesity prevalence was higher in boys (32.5% vs. 20.4%; p < 0.001). Average sleep duration decreased the odds of overweight/obesity [OR (95% CI): 0.86 (0.76, 0.97)] independently of the frequency of breakfast habit. Interaction between sleep duration with breakfast habit (p = 0.002) and physical activity (p < 0.001) was observed. Path analysis showed a negative association of BMI with sleep duration (standardized beta = -0.095, p < 0.001). A third-order interaction between breakfast habit, sleep duration, and physical activity revealed that daily breakfast eating along with adequate sleep and moderate/adequate physical activity levels, decreased the odds of over-weight/obesity by 55% [OR 0.45, 95% CI (0.27, 0.72)]. Conclusion: Although sleep duration is inversely associated with weight status independently of breakfast habit, the co-influence of adequate sleep duration with frequent breakfast eating and moderate/adequate physical activity seems to be a profoundly higher associated as a result of synergy against childhood obesity.

Comorbid depression and obesity, and its transition on the risk of functional disability among middle-aged and older Chinese: a cohort study.

BACKGROUND: Evidence has indicated that depression and obesity were associated with functional disability, independently. However, little is known about the detrimental impact of comorbid depression and obesity, as well as its transition on functional disability. This study investigated the association of baseline depression-obesity status and its dynamic change with incident functional disability among middle-aged and older Chinese. METHODS: This cohort study included 5507 participants aged ≥45 years from the 2011 and 2015 waves of China Health and Retirement Longitudinal Study. Depression was defined with a score ≥ 10 using the 10-item Centre for Epidemiologic Studies Depression Scale. Obesity was defined as body mass index ≥28 kg/m2. Participants were cross-classified by depression and obesity status at baseline, and its change during follow-up. Logistic regression models were constructed to evaluate the association of baseline depression-obesity status and its transition with incident functional disability defined by the Katz index of activities of daily living scale. RESULTS: Over four-year follow-up, 510 (9.3%) participants developed functional disability. Individuals with baseline comorbid depression and obesity had the highest risk of functional disability (OR = 2.84, 95% CI: 1.95-4.15) than non-depressive participants without obesity, or those with depression or obesity alone. When investigating the dynamic changes of depression-obesity status on functional disability incidence, those with stable comorbidity throughout two surveys had the greatest risk of functional disability (OR = 4.06, 95% CI: 2.11-7.80). Progression of depression-obesity status was associated with increased risk of functional disability, while regression from baseline to follow-up was linked to attenuated risk estimates. CONCLUSIONS: Among middle-aged and older Chinese adults, the risk of functional disability was exaggerated with comorbid depression and obesity. Our data further suggest that transitions of depression and obesity over time are associated with the risk of developing functional disability.

Association of angiotensin-converting enzyme gene polymorphism with pulse pressure and its interaction with obesity status in Heilongjiang province.

Angiotensin I converting enzyme (ACE) gene is one of the most-studied candidate genes related to essential hypertension (EH). Pulse pressure (PP) may reflect vascular stiffness, especially in patients with EH, and has been used to predict EH. Previous evidence has indicated that obesity is a traditional risk factor of hypertension. The aim of the present study was to investigate the interaction between the obesity status and ACE gene polymorphisms on the development of high level of PP. A total of 1980 adults (1024 hypertensive and 956 normotensive) were included in this study and genotyped for ACE gene polymorphisms. The results showed that rs4343 and rs4351 in ACE gene were risk factors of high level of pulse pressure (p < 0.05). We also detected positive interactions between the two SNPs and obesity status in the pathway of high level PP.

Dietary habits among 177,091 Greek schoolchildren by age, sex, weight status, region, and living area. A cross-sectional study.

BACKGROUND: This study aimed to investigate the dietary habits from early childhood to adolescence among participants from all regions of the country and living areas (rural/urban) and assess potential associations between dietary habits and obesity in both sexes. METHODS: Population data were derived from a cross-sectional health survey on a representative sample of 177,091 children aged 6-18 years. Dietary habits were considered via a self-completed questionnaire (Mediterranean diet quality index for children and adolescents [KIDMED]). Trained investigators assessed the anthropometric data. RESULTS: KIDMED scores were 6.7 ± 2.4 and 6.8 ± 2.3 for boys and girls, respectively, whereas a percentage of almost 10% of the total study population had insufficient dietary habits. Dietary habits peaked around age 11 years and then gradually worsened until the end of adolescence in both sexes, with an annual trend equal to -0.28 ± 0.02 (p < 0.001) for boys and -0.31 ± 0.03 (p < 0.001) for girls. Schoolchildren who are overweight/obese presented higher percentages in all unhealthy dietary habits (e.g., skipping breakfast, going often to a fast food restaurant, and consuming a lot of sweets) than children with a normal weight (all p-values < 0.001). Dietary habits did not noteworthy differ by area of living, that is, urban versus rural, of participants in both sexes. In addition, no noticeable differences in the values of the KIDMED index were found among all regions of Greece, with the highest values in Attica and Crete in both sexes. CONCLUSIONS: Because Greek schoolchildren do not fully adopt the traditional cardio-protective Mediterranean diet, it could be helpful to provide certain recommendations, especially for adolescents, to decrease the risk for future adverse health consequences.

Income-related inequality in obesity and its determinants in Spain: What happens beyond the obesity threshold?

This paper computes and decomposes income-related inequalities in three metrics of obesity, namely, status, depth and severity, for Spain, a European country characterized by a universal health care system with very high and rising obesity prevalence rates. Furthermore, this paper investigates the main determinants of the reduction in obesity inequalities observed over time among the female Spanish population. To compute these inequality indexes, we use cross-sectional and individual-level data gathered from the Spanish National Health Survey. We document income-related inequalities in obesity, that are more pronounced in depth and severity and are to the detriment of poor women in Spain. University education is the most important determinant for all three inequality indexes. We further report that inequalities in obesity tend to decline over time for women, which is explained mainly by a substantial decrease in the degree of inequality in secondary education and a large decrease in the income elasticity of obesity.

Gender- and Obesity-Specific Association of Co-Exposure to Personal Care Product and Plasticizing Chemicals and Short Sleep Duration among Adults: Evidence from the National Health and Nutrition Examination Survey 2011-2016.

There is limited evidence about the gender- and obesity-specific effects of personal care product and plasticizing chemicals (PCPPCs) on short sleep duration in adults. We evaluated the gender- and obesity-specific association of co-exposure to PCPPCs and short sleep duration among adults aged 20-60 years using the National Health and Nutrition Examination Survey (NHANES) 2011-2016, a secondary data source from the United States. Seventeen PCPPCs, including five phenols, two parabens, and ten phthalates, were detected, and sleep duration was assessed among 3012 adults. Logistic regression, weighted quantile sum (WQS) regression, and Bayesian kernel machine regression (BKMR) were employed. We found that bisphenol A (BPA), mono (caboxy-isooctyl) phthalate (MCOP), and mono (3-carboxypropyl) phthalate (MCPP) were consistently positively associated with short sleep duration in both females and males regardless of obesity status, except for BPA with general obesity. In particular, mono benzyl phthalate (MBzP) revealed a positive association in females, mono (2-ethyl-5-oxohexyl) phthalate (MEOHP) revealed a positive association in males, and MiBP revealed a positive association in abdominal obesity. Similar associations were observed in the mixture. Our study highlights that PCPPCs are independently associated with an increasing risk of short sleep duration in adults both individually and as a mixture; however, gender- and obesity-specific differences may have little effect on certain individual PCPPCs on short sleep duration.

Keeping obesity status is a risk factor of hypertension onset: evidence from a community-based longitudinal cohort study in North China.

Objective: The purpose of our study was to investigate the association of obesity status change with hypertension onset based on a community-based longitudinal cohort study in North China. Methods: This longitudinal study included 3,581 individuals free of hypertension at baseline in the first survey (2011-2012). All participants were followed up (2018-2019). According to the criteria, a total of 2,618 individuals were collected for analysis. We used adjusted Cox regression models and Kaplan-Meier survival analysis to estimate the association between obesity status change and hypertension onset. Additionally, we applied the forest plot to visualize the subgroup analysis including age, gender, and the differences in some variables between baseline and follow-up. Finally, we conducted a sensitivity analysis to examine the stability of our results. Results: Over nearly 7 years of follow-up, a total of 811 (31%) developed hypertension. The new hypertension incidence was mostly observed in those who were obese all the time (P for trend < 0.01). In the fully adjusted Cox regression model, being obese all the time increased the risk of hypertension by 30.10% [HR 4.01 (95% CI 2.20-7.32)]. The Kaplan-Meier survival analysis revealed the change in obesity status as an important feature to predict the occurrence of hypertension. Sensitivity analysis shows a consistent trend between the change in obesity status and hypertension onset in all populations. Subgroup analysis showed that age above 60 years was an important risk factor for hypertension onset, that men were more likely than women to develop hypertension, and that weight control was beneficial in avoiding future hypertension in women. There were statistically significant differences in ΔBMI, ΔSBP, ΔDBP, and ΔbaPWV between the four groups, and all variables, except baPWV changes, increased the risk of future hypertension. Conclusion: Our study shows that obese status was notably associated with a significant risk of hypertension onset among the Chinese community-based cohort.

Predicting Obesity in Adults Using Machine Learning Techniques: An Analysis of Indonesian Basic Health Research 2018.

Obesity is strongly associated with multiple risk factors. It is significantly contributing to an increased risk of chronic disease morbidity and mortality worldwide. There are various challenges to better understand the association between risk factors and the occurrence of obesity. The traditional regression approach limits analysis to a small number of predictors and imposes assumptions of independence and linearity. Machine Learning (ML) methods are an alternative that provide information with a unique approach to the application stage of data analysis on obesity. This study aims to assess the ability of ML methods, namely Logistic Regression, Classification and Regression Trees (CART), and Naïve Bayes to identify the presence of obesity using publicly available health data, using a novel approach with sophisticated ML methods to predict obesity as an attempt to go beyond traditional prediction models, and to compare the performance of three different methods. Meanwhile, the main objective of this study is to establish a set of risk factors for obesity in adults among the available study variables. Furthermore, we address data imbalance using Synthetic Minority Oversampling Technique (SMOTE) to predict obesity status based on risk factors available in the dataset. This study indicates that the Logistic Regression method shows the highest performance. Nevertheless, kappa coefficients show only moderate concordance between predicted and measured obesity. Location, marital status, age groups, education, sweet drinks, fatty/oily foods, grilled foods, preserved foods, seasoning powders, soft/carbonated drinks, alcoholic drinks, mental emotional disorders, diagnosed hypertension, physical activity, smoking, and fruit and vegetables consumptions are significant in predicting obesity status in adults. Identifying these risk factors could inform health authorities in designing or modifying existing policies for better controlling chronic diseases especially in relation to risk factors associated with obesity. Moreover, applying ML methods on publicly available health data, such as Indonesian Basic Health Research (RISKESDAS) is a promising strategy to fill the gap for a more robust understanding of the associations of multiple risk factors in predicting health outcomes.

Understanding the Interrelationship between Education, Income, and Obesity among Adults in Saudi Arabia.

OBJECTIVE: To examine the association of sociodemographic variables with the odds of being obese among adults in Saudi Arabia, and to examine whether or not the association between the educational level and the odds of being obese among adults in Saudi Arabia is modified by the income level. METHODS: A total of 3,925 participants were recruited for this cross--sectional study. Sociodemographic and anthropometric data were collected using standardized procedures. Unadjusted and adjusted logistic regression models were examined, with a dichotomous obesity status variable as the outcome. Furthermore, an interaction term for income level with educational level was tested and appeared significant. Thus, additional regression models were run in order to examine the association between educational level and obesity status separately among the low- and higher-income groups. RESULTS: Compared to participants with a college degree or higher, illiterate participants and those with an elementary education had higher odds of obesity (OR: 2.76, 95% CI: 1.81-4.22, and OR: 2.68, 95% CI: 1.89-3.82, respectively). However, participants with a low income had lower odds than participants who had a higher income (OR: 0.84, 95% CI: 0.70-0.99). Examining the association between educational level and obesity while stratifying by income revealed that a negative association between education and obesity exists among both income groups. However, the magnitude of the ORs was higher among participants with higher income, suggesting a stronger association between education and obesity among wealthier individuals. CONCLUSION: Individuals in the highest income bracket with lower levels of education may have greater odds of obesity. Targeting them in intervention programs is warranted.

Comparative assessment of anthropometric and bioimpedence methods for determining adiposity.

BACKGROUND: Obesity is a risk factor for different chronic conditions. Over the years, obesity has become a pandemic and it is therefore important that effective diagnostic tools are developed. Obesity is a measure of adiposity and it has become increasingly evident that anthropometric measures such as body mass index (BMI) used to estimate adiposity are inadequate. This study therefore examined the ability of different anthropometric measurements to diagnose obesity within a cross-section of Ghanaian women. METHODS: We obtained anthropometric measurements and used that to generate derived measures of adiposity such as body adiposity index (BAI) and conicity index. Furthermore we also measured adiposity using a bioimpedance analyser. Associations between these measurements and percentage body fat (%BF) were drawn in order to determine the suitability of the various measures to predict obesity. The prevalence of obesity was determined using both %BF and BMI. RESULTS: BMI, Waist and hip circumference and visceral fat (VF) were positively correlated with % BF whereas skeletal muscle mass was negatively correlated. Prevalence of obesity was 16% and 31.6% using BMI and %BF respectively. Receiver operating characteristic (ROC) analysis showed that these differences in prevalence was due to BMI based misclassification of persons who have obesity as overweight. Similar, shortfalls were observed for the other anthropometric measurements using ROC. CONCLUSIONS: No single measure investigated could adequately predict obesity as an accumulation of fat using current established cut-off points within our study population. Large scale epidemiological studies are therefore needed to define appropriate population based cut-off points if anthropometric measurements are to be employed in diagnosing obesity within a particular population.

The Impact of Random Individual Differences in Weight Change on the Measurable Objectives of Lifestyle Weight Management Services.

Obese adults and children can be referred to lifestyle weight management services (LWMS) in which physical activity and/or dietary advice are delivered. Service providers quantify the 'weight' change between an initial measurement and follow-up measurement(s), which could be 12-24 months later. A control group is usually absent. The aim of this article is to scrutinise the various LWMS objectives for this weight change that are recommended by UK authorities. UK guidelines recommend that an adult LWMS should (A) reduce the sample mean body mass of all enrolled adults by at least 3% and/or (B) reduce the body mass of at least 30% of adults by at least 5%. We highlight the potential for objective B to be met even if no LWMS is implemented, especially over the recommended follow-up periods of 12-24 months. This is due to unavoidable random within-participant fluctuations in weight over such periods of time. A ≥1 kg reduction in mean body mass is also to be expected, even without any LWMS. Therefore, we suggest that objectives A and B are too liberal. Obesity status in children is indicated by the body mass index (BMI) z-score. Nevertheless, another UK recommendation is for an LWMS to "maintain" or "reduce" the BMI z-score of 80% of the enrolled children. Besides there being no stated thresholds for "maintain" and "reduce", it is inconceivable to deem an LWMS successful even if 80% of children do not alter their obesity status and even if the remaining 20% of children actually increase their obesity status. Here, we think the BMI z-score has been confused with the body mass z-score. In conclusion, measurable objectives of UK-based LWMS need to be clarified, and possibly altered, to account for typical amounts of random variability in individual weight measurements over the service time period.