Rural-urban differentials in child body mass index over time

BACKGROUND

The body mass index is a simple index based on weight and height that can be used to screen children and adults for potential weight problems. The objective of this study was to investigate urban-rural variations in child BMI and its distribution from 2006 to 2016 in four low and middle-income countries.

METHODS

This study used data from the Young Lives prospective cohort study conducted in Ethiopia, India, Peru, and Vietnam to assess the BMI change for children aged 5 to 15 between 2006 and 2016. We adopted a mixed-effect model to analyze the data.

RESULTS

The study revealed substantial changes and rises in BMI in Vietnam, Peru, India, and Ethiopia between 2006 and 2016. Peru had the highest BMI changes in both urban-rural areas. A low BMI was observed in Ethiopia and India. Urban-rural differences had a significant role in determining BMI variation. In urban Ethiopia, the mean BMI increased from 14.56 kg/m2 to 17.52 kg/m2, and in rural areas, it increased from 14.57 kg/m2 to 16.67 kg/m2. Similarly, in urban Vietnam, the BMI increased from 16 kg/m2 to 20.3 kg/m2, and in rural areas, it increased from 14.69 kg/m2 to 18.93 kg/m2.

CONCLUSIONS

The findings showed an increase in BMI changes in Ethiopia, India, Peru, and Vietnam from 2006 to 2016. Urban-rural differences have a significant contribution to determining BMI variation.

Data for modeling the height and diameter of Pinus merkusii and P. michoacana in Zambia

Data on tree height and diameter of non-native Pinus merkusii (Jung. & de Vriese) and Pinus michoacana (Martinez) collected from Zambia's non-native forest plantations is presented here. A total of 1542 and 1883 pairs of height and diameter datasets for developing and testing the models are presented, respectively. During a five-year interval forest inventories, data collected supported the development of allometric relationships between height and diameter for the country's two species. Datasets are intended to be reused for various purposes to enhance the understanding of the tree height and diameter relationships of new and existing planted non-native species in Zambia. For detailed examples of the application of this data, see the article, "Stand characteristics and climate modulate height to diameter relationship in Pinus merkusii and P. michoacana in Zambia" Ng'andwe et al., 2021.

Bayesian latent-class mixed-effect hybrid models for dyadic longitudinal data with non-ignorable dropouts

The analysis of longitudinal dyadic data is challenging due to the complicated correlations within and between dyads, as well as possibly non-ignorable dropouts. Based on a mixed-effects hybrid model, we propose an approach to analyze longitudinal dyadic data with non-ignorable dropouts. We factorize the joint distribution of the measurement and dropout processes into three components: the marginal distribution of random effects, the conditional distribution of the dropout process given the random effects, and the conditional distribution of the measurement process given the random effects and missing data patterns. We model the conditional dropout process using a discrete survival model, and the conditional measurement process using a latent-class pattern-mixture model. These models account for the dyadic interdependence using the "actor" and "partner" effects and dyad-specific random effects. We use the latent-dropout-class approach to address the problem of a large number of missing data patterns caused by the dyadic data structure. We evaluate the performance of the proposed method using a simulation study, and apply our method to a longitudinal dyadic data set that arose from a prostate cancer trial.