A Multistate Model for Analyzing Transitions Between Body Mass Index Categories During Childhood: The Generation XXI Birth Cohort Study.
Journal
American journal of epidemiology
ISSN: 1476-6256
Titre abrégé: Am J Epidemiol
Pays: United States
ID NLM: 7910653
Informations de publication
Date de publication:
01 02 2019
01 02 2019
Historique:
received:
20
03
2018
accepted:
04
10
2018
pubmed:
13
10
2018
medline:
19
11
2019
entrez:
13
10
2018
Statut:
ppublish
Résumé
Prevalences of overweight and obesity in young children have risen dramatically in the last several decades in most developed countries. Childhood overweight and obesity are known to have immediate and long-term health consequences and are now recognized as important public health concerns. We used a Markov 4-state model with states defined by 4 body mass index (BMI; weight (kg)/height (m)2) categories (underweight (<-2 standard deviations (SDs) of BMI z score), normal weight (-2 ≤ SD ≤ 1), overweight (1 < SD ≤ 2), and obese (>2 SDs of BMI z score)) to study the rates of transition to higher or lower BMI categories among children aged 4-10 years. We also used this model to study the relationships between explanatory variables and their transition rates. The participants consisted of 4,887 children from the Generation XXI Birth Cohort Study (Porto, Portugal; 2005-2017) who underwent anthropometric evaluation at age 4 years and in at least 1 of the subsequent follow-up waves (ages 7 and 10 years). Children who were normal weight were more likely to move to higher BMI categories than to lower categories, whereas overweight children had similar rates of transition to the 2 adjacent categories. We evaluated the associations of maternal age and education, type of delivery, sex, and birth weight with childhood overweight and obesity, but we observed statistically significant results only for sex and maternal education with regard to the progressive transitions.
Identifiants
pubmed: 30312367
pii: 5127084
doi: 10.1093/aje/kwy232
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM