Identifying overnutrition risk groups in Bangladeshi married women based on sociodemographic factors: A classification and regression tree model. (August 2022)

• Record Type: Journal Article
• Authors: Khan, Jahidur Rahman; Faisal, Abu Saleh Mosa; Das, Sukanta; Awan, Nabil
• Is Part Of: Obesity medicine. Volume 33(2022)
• Publication Date: 2022-08
• Subjects: Overweight and obesity -- Women -- Risk groups -- Tree model; Obesity -- Periodicals; Obesity; Obesity; Periodicals; Periodicals; 616.398005
• DOI: 10.1016/j.obmed.2022.100425
• Languages: English
• ISSNs: 2451-8476

Abstract

Abstract: Aims: Overweight and obesity among women are major health issues in Bangladesh. To develop preventive strategies to deal with this epidemic, identifying women who are at risk for overweight and obesity is critical. This study aims to identify married women at high risk for overweight and obesity in Bangladesh. Methods: The study has used a sample of 14207 married women aged 15–49 years with a body mass index (BMI) ≥18.5 kg/m 2 from the 2017-18 Bangladesh Demographic and Health Survey. The overweight and obese status of women based on BMI is the primary outcome variable, and a range of sociodemographic variables are included as predictors. The Classification and Regression Tree (CART) is used to identify important predictors and high-risk groups. Results: The CART models show that household wealth status is the most crucial predictor of overweight and obesity in married women. Women aged 26 to 49 years who live in affluent households and do not work or breastfeed are at the high-risk group. Conclusions: This study suggests that the formulation of strategies to prevent overweight and obesity in married women in Bangladesh should be tailored for different social classes and age groups. To reduce the burden, preventive interventions may target the high-risk group. Highlights: Several sociodemographic factors predict married women's overweight and obesity. Particular groups based on these factors are most prone to overweight and obesity. These may help design and implement targeted interventions.