Change in BMI Distribution over a 24-Year Period and Associated Socioeconomic Gradients: A Quantile Regression Analysis

Bayesian modeling of quantiles of body mass index among under-five children in Ethiopia

BACKGROUND

Body Mass Index (BMI) is a measurement of nutritional status, which is a vital pre-condition for good health. The prevalence of childhood malnutrition and the potential long-term health risks associated with obesity in Ethiopia have recently increased globally. The main objective of this study was to investigate the factors associated with the quantiles of under-five children's BMI in Ethiopia.

METHODS

Data on 5,323 children, aged between 0-59 months from March 21, 2019, to June 28, 2019, were obtained from the Ethiopian Mini Demographic Health Survey (EMDHS, 2019), based on the standards set by the World Health Organization. The study used a Bayesian quantile regression model to investigate the association of factors with the quantiles of under-five children's body mass index. Markov Chain Monte Carlo (MCMC) with Gibbs sampling was used to estimate the country-specific marginal posterior distribution estimates of model parameters, using the Brq R package.

RESULTS

Out of a total of 5323 children included in this study, 5.09% were underweight (less than 12.92 BMI), 10.05% were overweight (BMI: 17.06 - 18.27), and 5.02% were obese (greater than or equal to 18.27 BMI) children's. The result of the Bayesian quantile regression model, including marginal posterior credible intervals (CIs), showed that for the prediction of the 0.05 quantile of BMI, the current age of children [ β = -0.007, 95% CI :(-0.01, -0.004)], the region Afar [ β = - 0.32, 95% CI: (-0.57, -0.08)] and Somalia[ β = -0.72, 95% CI: (-0.96, -0.49)] were negatively associated with body mass index while maternal age [ β = 0.01, 95% CI: (0.005, 0.02)], mothers primary education [ β = 0.19, 95% CI: (0.08, 0.29)], secondary and above [ β = 0.44, 95% CI: (0.29, 0.58)], and family follows protestant [ β = 0.22, 95% CI: (0.07, 0.37)] were positively associated with body mass index. In the prediction of the 0.95 (or 0.85?) quantile of BMI, in the upper quantile, still breastfeeding [ β = -0.25, 95% CI: (-0.41, -0.10)], being female [ β = -0.13, 95% CI: (-0.23, -0.03)] were negatively related while wealth index [ β = 0.436, 95% CI: (0.25, 0.62)] was positively associated with under-five children's BMI.

CONCLUSIONS

In conclusion, the research findings indicate that the percentage of lower and higher BMI for under-five children in Ethiopia is high. Factors such as the current age of children, sex of children, maternal age, religion of the family, region and wealth index were found to have a significant impact on the BMI of under-five children both at lower and upper quantile levels. Thus, these findings highlight the need for administrators and policymakers to devise and implement strategies aimed at enhancing the normal or healthy weight status among under-five children in Ethiopia.

Factors affecting Hemoglobin A1c in the longitudinal study of the Iranian population using mixed quantile regression

Diabetes, a major non-communicable disease, presents challenges for healthcare systems worldwide. Traditional regression models focus on mean effects, but factors can impact the entire distribution of responses over time. Linear mixed quantile regression models (LQMMs) address this issue. A study involving 2791 diabetic patients in Iran explored the relationship between Hemoglobin A1c (HbA1c) levels and factors such as age, sex, body mass index (BMI), disease duration, cholesterol, triglycerides, ischemic heart disease, and treatments (insulin, oral anti-diabetic drugs, and combination). LQMM analysis examined the association between HbA1c and the explanatory variables. Associations between cholesterol, triglycerides, ischemic heart disease (IHD), insulin, oral anti-diabetic drugs (OADs), a combination of OADs and insulin, and HbA1c levels exhibited varying degrees of correlation across all quantiles (p < 0.05), demonstrating a positive effect. While BMI did not display significant effects in the lower quantiles (p > 0.05), it was found to be significant in the higher quantiles (p < 0.05). The impact of disease duration differed between the low and high quantiles (specifically at the quantiles of 5, 50, and 75; p < 0.05). Age was discovered to have an association with HbA1c in the higher quantiles (specifically at the quantiles of 50, 75, and 95; p < 0.05). The findings reveal important associations and shed light on how these relationships may vary across different quantiles and over time. These insights can serve as guidance for devising effective strategies to manage and monitor HbA1c levels.

Determinants of the population health distribution: an illustration examining body mass index

Most epidemiological studies examine how risk factors relate to average difference in outcomes (linear regression) or odds of a binary outcome (logistic regression); they do not explicitly examine whether risk factors are associated differentially across the distribution of the health outcome investigated. This paper documents a phenomenon found repeatedly in the minority of epidemiological studies which do this (via quantile regression): associations between a range of established risk factors and body mass index (BMI) are progressively stronger in the upper ends of the BMI distribution. In this paper, we document this finding and provide illustrative evidence of it in the 1958 British birth cohort study. Associations of low childhood socio-economic position, high maternal weight, low childhood general cognition and adult physical inactivity with higher BMI are larger at the upper end of the BMI distribution, on both absolute and relative scales. For example, effect estimates for socio-economic position and childhood cognition were around three times larger at the 90th compared with 10th quantile, while effect estimates for physical inactivity were increasingly larger from the 50th to 90th quantiles, yet null at lower quantiles. We provide potential explanations for these findings and discuss implications. Risk factors may have larger causal effects among those in worse health, and these effects may not be discovered when health is only examined in average terms. In such scenarios, population-based approaches to intervention may have larger benefits than anticipated when assuming equivalent benefit across the population. Further research is needed to understand why effect estimates differ across the BMI outcome distribution and to investigate whether differential effects exist for other physical and mental health outcomes.

The differential effect of maternal dietary patterns on quantiles of Birthweight

BACKGROUND

Maternal dietary habits during pregnancy are considered essential for development and growth of the fetus as well as maternal health. It has an effect on the birthweight of infants. However, little is known about the effect of dietary patterns on birthweight in urban South Africa. This study aimed to investigate differential effect of dietary patterns of pregnant women on quantiles of birthweight.

METHODS

Data for the study were obtained from a Mother and Child in the Environment birth cohort study in Durban South Africa. Quantile regression was used to investigate the effect of maternal dietary patterns on quantiles of birthweight. Data collection was conducted during the period of 2013 to 2017 in Durban South Africa. Using factor analysis, eight dietary groups were identified from 687 pregnant women in the cohort. Quantile regression analysis was employed to identify the differential effects of the seven dietary groups and demographic factors on the birthweight.

RESULTS

The quantile regression estimates at the 50th quantile and the ordinary regression estimates painted the same picture about the conditional mean effect of covariates on the birthweight. But unlike the quantile regression the ordinary regression fails to give insights about the covariates effect disparities at the low and/or upper birthweight quantiles. All the dietary groups show a significant differential effect at different birthweight quantiles. For instance, increased frequency of protein rich foods intake was associated with reduction in birthweight at lower and upper quantiles; increased frequency of junk foods intake has a slight increase in birthweight at the lower quantiles but significantly higher increase at the 95th quantile (p < 0.001); increase in consuming vegetable rich foods, reduced birthweight at 95th quantile (p < 0.001). The results further showed that employment (p = 0.006) and family size (p = 0.002) had differential effects across different birthweight quantiles.

CONCLUSIONS

Both maternal undernutrition and overnutrition of protein rich foods, junk foods, snack and energy foods and vegetable rich foods have shown a substantial varying effects on those infants with birthweights in the lower and upper birthweight quantiles.

Inequalities in participation and time spent in moderate-to-vigorous physical activity: a pooled analysis of the cross-sectional health surveys for England 2008, 2012, and 2016

Background

Evidence is unclear on whether inequalities in average levels of moderate-to-vigorous physical activity (MVPA) reflect differences in participation, differences in the amount of time spent active, or both. Using self-reported data from 24,882 adults (Health Survey for England 2008, 2012, 2016), we examined gender-specific inequalities in these separate aspects for total and domain-specific MVPA.

Methods

Hurdle models accommodate continuous data with excess zeros and positive skewness. Such models were used to assess differences between income groups in three aspects: (1) the probability of doing any MVPA, (2) the average hours/week spent in MVPA, and (3) the average hours/week spent in MVPA conditional on participation (MVPA-active). Inequalities were summarised on the absolute scale using average marginal effects (AMEs) after confounder adjustment.

Results

Inequalities were robust to adjustment in each aspect for total MVPA and for sports/exercise. Differences between adults in high-income versus low-income households in sports/exercise MVPA were 2.2 h/week among men (95% confidence interval (CI): 1.6, 2.8) and 1.7 h/week among women (95% CI: 1.3, 2.1); differences in sports/exercise MVPA-active were 1.3 h/week (95% CI: 0.4, 2.1) and 1.0 h/week (95% CI: 0.5, 1.6) for men and women, respectively. Heterogeneity in associations was evident for the other domains. For example, adults in high-income versus low-income households were more likely to do any walking (men: 13.0% (95% CI: 10.3, 15.8%); women: 10.2% (95% CI: 7.6, 12.8%)). Among all adults (including those who did no walking), the average hours/week spent walking showed no difference by income. Among those who did any walking, adults in high-income versus low-income households walked on average 1 h/week less (men: − 0.9 h/week (95% CI: − 1.7, − 0.2); women: − 1.0 h/week (95% CI: − 1.7, − 0.2)).

Conclusions

Participation and the amount of time that adults spend in MVPA typically favours those in high-income households. Monitoring inequalities in MVPA requires assessing different aspects of the distribution within each domain. Reducing inequalities in sports/exercise requires policy actions and interventions to move adults in low-income households from inactivity to activity, and to enable those already active to do more. Measures to promote walking should focus efforts on reducing the sizeable income gap in the propensity to do any walking.