A quantile regression approach to re-investigate the relationship between sleep duration and body mass index in Taiwan

Analyzing the Influence of Wine and Beer Drinking, Smoking, and Leisure Time Screen Viewing Activity on Body Weight: A Cross-Sectional Study in Germany

The increasing global prevalence of overweight and obesity highlights an urgent need to explore modifiable obesogenic factors. This study investigated the impact of lifestyle factors, such as beer and wine drinking, cigarette smoking, and leisure time screen viewing activities, on body weight and the development of obesity. Individual level data were selected from a random sample of 3471 German adults using a two-stage disproportionate random sampling procedure. The empirical analysis employed a two-stage equations system and combined the endogenous treatment effects model with the quantile regression technique. Our estimations showed that the decisions to smoke and consume wine and beer were positively interrelated, especially in women. Frequent beer/wine drinkers of normal weight were found to have a lower BMI in the male subsample. Quantile regression estimates indicated a significant influence of smoking on BMI in both genders, with smokers’ BMI following an upward trend, especially in the upper quantiles of the distribution. Leisure time screen activity was found to have a major impact on females’ BMI. Prolonged television viewing and regular computer gaming had a strong relationship with weight increase in overweight women, whereas internet surfing was inversely correlated with the BMI of normal weight and slightly overweight female participants. Nutrition and health policies should direct individuals toward alternative recreational activities in order to substitute screen usage and reduce sedentary time. This study also raised doubts about the general belief that smokers have a lower body weight. As unhealthy behaviors usually co-occur or cluster together, obesity prevention interventions might also contribute to a decrease in smoking.

Body mass index of women in Bangladesh: comparing Multiple Linear Regression and Quantile Regression

This study explored the association between socio-demographic factors and the body mass index (BMI) of women of reproductive age (15–49 years) in Bangladesh. Data from the 2014 Bangladesh Demographic and Health Survey (BDHS-14) were analysed using Multiple Linear Regression (MLR) and Quantile Regression (QR) analyses. The study sample comprised 15,636 non-pregnant women aged 15–49. The mean BMI of the women was 22.35±4.12 kg/m2. Over half (56.75%) had a BMI in the normal range (18<BMI<25 kg/m2), and 18.50%, 20.00% and 4.75% were underweight (BMI≤18 kg/m2), overweight (25≤BMI<30 kg/m2) and obese (BMI≥30 kg/m2), respectively. The results of the MLR found that age, wealth index, urban/rural place of residence, geographical division, womenʼs educational status, husbandʼs educational status, womenʼs working status and total number of children ever born were significantly (p<0.001) associated with respondents’ mean BMI. The QR results showed different associations between socio-demographic factors and mean BMI, as well as a different conditional distribution of mean BMI. Overall, the results indicated that women with uneducated husbands, with little or no education and from less-affluent households from rural areas tended to be more underweight compared with women in other groups. The inter-relationship between the study womenʼs mean BMI and associated socio-demographic factors was assessed using QR analysis to identify the most vulnerable cohorts of women in Bangladesh.

Socioeconomic differences in body mass index in Spain: An intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy

Many studies have demonstrated the existence of simple, unidimensional socioeconomic gradients in body mass index (BMI). However, in the present paper we move beyond such traditional analyses by simultaneously considering multiple demographic and socioeconomic dimensions. Using the Spanish National Health Survey 2011–2012, we apply intersectionality theory and multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) to analyze 14,190 adults nested within 108 intersectional strata defined by combining categories of gender, age, income, educational achievement and living situation. We develop two multilevel models to obtain information on stratum-specific BMI averages and the degree of clustering of BMI within strata expressed by the intra-class correlation coefficient (ICC). The first model is a simple variance components analysis that provides a detailed mapping of the BMI disparities in the population and measures the accuracy of stratum membership to predict individual BMI. The second model includes the variables used to define the intersectional strata as a way to identify stratum-specific interactions. The first model suggests moderate but meaningful clustering of individual BMI within the intersectional strata (ICC = 12.4%). Compared with the population average (BMI = 26.07 Kg/m2), the stratum of cohabiting 18-35-year-old females with medium income and high education presents the lowest BMI (-3.7 Kg/m2), while cohabiting 36-64-year-old females with low income and low education show the highest BMI (+2.6 Kg/m2). In the second model, the ICC falls to 1.9%, suggesting the existence of only very small stratum specific interaction effects. We confirm the existence of a socioeconomic gradient in BMI. Compared with traditional analyses, the intersectional MAIHDA approach provides a better mapping of socioeconomic and demographic inequalities in BMI. Because of the moderate clustering, public health policies aiming to reduce BMI in Spain should not solely focus on the intersectional strata with the highest BMI, but should also consider whole population polices.

Time trends and factors in body mass index and obesity among children in China: 1997-2011

BACKGROUND

Research on the shift in children's body mass index (BMI) distribution is limited and conditional mean models used in the previous research have limitations in capturing cross-distribution variations in effects. The objectives are to analyze the shift in Chinese children's BMI distribution and to test the associations between BMI distribution and other factors.

METHODS

We analyzed data collected from children 7 to 17 years old from the China Health and Nutrition Survey (CHNS) conducted in 1997, 2000, 2004, 2006, 2009 and 2011, from 2814 participants with 6799 observations. Longitudinal quantile regression (QR) was used to explore the effect of several factors on BMI trends in 2015.

RESULTS

The BMI curves shift to the right in boys and girls, with the distributions becoming wider, indicating a higher proportion of children have become overweight. The 5th, 15th, 50th, 85th and 95th BMI percentile curves all shifted upward from 1997 to 2011, and the higher percentiles had greater increases. The prevalence of overweight and obesity increased in boys and girls between 1997 and 2011, from 6.5 to 15.5% in boys and from 4.6 to 10.4% in girls. Energy intake and parents' BMI levels had a positive association with children's BMI. Per capita income was positively associated with changes in BMI only at the upper percentiles of the BMI distributions in boys. Increased physical activity (PA) was associated with decreased BMI in girls.

CONCLUSIONS

Children in China are becoming increasingly overweight. Energy intake, parental BMI, PA and early menarche age in girls are associated with elevated BMI in children.

Unconditional quantile regressions to determine the social gradient of obesity in Spain 1993-2014

Background

There is a well-documented social gradient in obesity in most developed countries. Many previous studies have conventionally categorised individuals according to their body mass index (BMI), focusing on those above a certain threshold and thus ignoring a large amount of the BMI distribution. Others have used linear BMI models, relying on mean effects that may mask substantial heterogeneity in the effects of socioeconomic variables across the population.

Method

In this study, we measure the social gradient of the BMI distribution of the adult population in Spain over the past two decades (1993–2014), using unconditional quantile regressions. We use three socioeconomic variables (education, income and social class) and evaluate differences in the corresponding effects on different percentiles of the log-transformed BMI distribution. Quantile regression methods have the advantage of estimating the socioeconomic effect across the whole BMI distribution allowing for this potential heterogeneity.

Results

The results showed a large and increasing social gradient in obesity in Spain, especially among females. There is, however, a large degree of heterogeneity in the socioeconomic effect across the BMI distribution, with patterns that vary according to the socioeconomic indicator under study. While the income and educational gradient is greater at the end of the BMI distribution, the main impact of social class is around the median BMI values. A steeper social gradient is observed with respect to educational level rather than household income or social class.

Conclusion

The findings of this study emphasise the heterogeneous nature of the relationship between social factors and obesity across the BMI distribution as a whole. Quantile regression methods might provide a more suitable framework for exploring the complex socioeconomic gradient of obesity.

Electronic supplementary material

The online version of this article (doi:10.1186/s12939-016-0454-1) contains supplementary material, which is available to authorized users.

Statistical methods for body mass index: A selective review

Obesity rates have been increasing over recent decades, causing significant concern among policy makers. Excess body fat, commonly measured by body mass index, is a major risk factor for several common disorders including diabetes and cardiovascular disease, placing a substantial burden on health care systems. To guide effective public health action, we need to understand the complex system of intercorrelated influences on body mass index. This paper, based on all eligible articles searched from Global health, Medline and Web of Science databases, reviews both classical and modern statistical methods for body mass index analysis. We give a description of each of these methods, exploring the classification, links and differences between them and the reasons for choosing one over the others in different settings. We aim to provide a key resource and statistical library for researchers in public health and medicine to deal with obesity and body mass index data analysis.

Stochastic variability in stress, sleep duration, and sleep quality across the distribution of body mass index: insights from quantile regression

BACKGROUND

Obesity has become a problem in the USA and identifying modifiable factors at the individual level may help to address this public health concern. A burgeoning literature has suggested that sleep and stress may be associated with obesity; however, little is know about whether these two factors moderate each other and even less is known about whether their impacts on obesity differ by gender.

PURPOSE

This study investigates whether sleep and stress are associated with body mass index (BMI) respectively, explores whether the combination of stress and sleep is also related to BMI, and demonstrates how these associations vary across the distribution of BMI values.

METHODS

We analyze the data from 3,318 men and 6,689 women in the Philadelphia area using quantile regression (QR) to evaluate the relationships between sleep, stress, and obesity by gender.

RESULTS

Our substantive findings include: (1) high and/or extreme stress were related to roughly an increase of 1.2 in BMI after accounting for other covariates; (2) the pathways linking sleep and BMI differed by gender, with BMI for men increasing by 0.77-1 units with reduced sleep duration and BMI for women declining by 0.12 unit with 1 unit increase in sleep quality; (3) stress- and sleep-related variables were confounded, but there was little evidence for moderation between these two; (4) the QR results demonstrate that the association between high and/or extreme stress to BMI varied stochastically across the distribution of BMI values, with an upward trend, suggesting that stress played a more important role among adults with higher BMI (i.e., BMI > 26 for both genders); and (5) the QR plots of sleep-related variables show similar patterns, with stronger effects on BMI at the upper end of BMI distribution.

CONCLUSIONS

Our findings suggested that sleep and stress were two seemingly independent predictors for BMI and their relationships with BMI were not constant across the BMI distribution.

Sleep duration versus sleep insufficiency as predictors of cardiometabolic health outcomes

OBJECTIVE

The objective of the present study was to investigate the relationship between sleep insufficiency and sleep duration, particularly regarding negative cardiometabolic health outcomes already considered to be affected by reduced sleep time.

METHODS

A total of N=30,934 participants from the 2009 Behavioural Risk Factor Surveillance System (BRFSS) answered questions about their sleep duration as well as subjective feelings of sleep insufficiency. Outcomes included body mass index (BMI), obesity (BMI ≥ 30kgm(-2)) and history of hypertension, diabetes, hypercholesterolaemia, heart attack and stroke. Linear and logistic regression models examined whether cardiometabolic outcomes were associated with (1) sleep duration alone, (2) sleep insufficiency alone and (3) the combined effect of sleep duration and sleep insufficiency.

RESULTS

Results indicated that, when examined alone, sleep duration <5h (versus 7h) was related to BMI (B=2.716, p<0.01), obesity (B=2.080, p<0.000001), diabetes (B=3.162, p<0.000001), hypertension (B=2.703, p<0.000001), hypercholesterolaemia (B=1.922, p<0.00001), heart attack (B=4.704, p<0.000001) and stroke (B=4.558, p<0.000001), and sleep insufficiency (days per week, continuous) was related to BMI (B=0.181, p<0.01), obesity (B=1.061, p<0.000001) and hypercholesterolaemia (B=1.025, p<0.01). All of these relationships remained significant after adjustment for covariates, except for diabetes and sleep duration. Also, after adjustment, a significant relationship between insufficient sleep and hypertension emerged (B=1.039, p<0.001). When evaluated together, after adjustment for covariates, significant relationships remained between sleep duration <5h (versus 7h) and BMI (B=1.266, p<0.05), obesity (B=1.389, p<0.05), hypertension (B=1.555, p<0.01), heart attack (B=2.513, p<0.01) and stroke (B=1.807, p<0.05). It should be noted that relationships between sleep duration >9h (versus 7h) were seen for heart attack (B=1.863, p<0.001) and stroke (B=1.816, p<0.01). In these models, sleep insufficiency was associated with hypercholesterolaemia (B=1.031, p<0.01) and hypertension (B=1.027, p<0.05).

CONCLUSIONS

These analyses show that both sleep duration and insufficiency are related to cardiometabolic health outcomes, and that when evaluated together, both variables demonstrate unique effects.