Shuyang Yao1, Ruyue Zhang1, Laura M Thornton2, Christine M Peat2, Baiyu Qi2, Shufa Du3, Huijun Wang4, Bing Zhang4, Cynthia M Bulik1,2,3. 1. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. 2. Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA. 3. Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA. 4. National Institute for Nutrition and Health, Chinese Center for Diseases Control and Prevention, Beijing, PR China.
Abstract
OBJECTIVE: We describe the prevalence and sociodemographic factors associated with screen-detected disordered eating and related traits in a population-based sample of women in China. We also explored prevalence trends over time. METHOD: A total of 4,218 females aged 12-50 were sampled from 15 provinces as part of the China Health and Nutrition Survey (CHNS) in 2015. The SCOFF questionnaire screened for disordered eating and the selected questions from the Eating Disorders Examination-Questionnaire measured dietary restraint, shape concerns, and weight concerns. Body mass index (BMI) was measured and sociodemographic factors captured urban/rural residence, age, ethnicity, income, education, marital status, and occupational status. We calculated the prevalence of screen-detected disordered eating and related traits broadly and across several dimensions and compared prevalence estimates to 2009 and 2011 reports. RESULTS: We detected 296 individuals who screened positive for disordered eating on the SCOFF (prevalence = 7.04%). Positive screens were associated with urban residence (p = .002) and higher education levels (p < .001). Scores on restraint, shape concerns, and weight concerns were all higher for individuals in urban versus village locations (all p's < .001), and with higher BMI (p < .001) for shape and weight concerns. The prevalence of screen-detected disordered eating increased numerically across 2009, 2011, and 2015. DISCUSSION: The prevalence of screen-detected disordered eating in mainland China was comparable to other populations worldwide obtained from a recent meta-analysis. The distribution of disordered eating and related traits varied by several sociodemographic factors, which include age, BMI, urban/rural residence, education, and income, suggesting important directions for case detection and intervention in China.
OBJECTIVE: We describe the prevalence and sociodemographic factors associated with screen-detected disordered eating and related traits in a population-based sample of women in China. We also explored prevalence trends over time. METHOD: A total of 4,218 females aged 12-50 were sampled from 15 provinces as part of the China Health and Nutrition Survey (CHNS) in 2015. The SCOFF questionnaire screened for disordered eating and the selected questions from the Eating Disorders Examination-Questionnaire measured dietary restraint, shape concerns, and weight concerns. Body mass index (BMI) was measured and sociodemographic factors captured urban/rural residence, age, ethnicity, income, education, marital status, and occupational status. We calculated the prevalence of screen-detected disordered eating and related traits broadly and across several dimensions and compared prevalence estimates to 2009 and 2011 reports. RESULTS: We detected 296 individuals who screened positive for disordered eating on the SCOFF (prevalence = 7.04%). Positive screens were associated with urban residence (p = .002) and higher education levels (p < .001). Scores on restraint, shape concerns, and weight concerns were all higher for individuals in urban versus village locations (all p's < .001), and with higher BMI (p < .001) for shape and weight concerns. The prevalence of screen-detected disordered eating increased numerically across 2009, 2011, and 2015. DISCUSSION: The prevalence of screen-detected disordered eating in mainland China was comparable to other populations worldwide obtained from a recent meta-analysis. The distribution of disordered eating and related traits varied by several sociodemographic factors, which include age, BMI, urban/rural residence, education, and income, suggesting important directions for case detection and intervention in China.
Authors: Sau Fong Leung; Ka Li Lee; Sze Man Lee; Sik Chi Leung; Wing Sze Hung; Wai Leng Lee; Yuen Yee Leung; Man Wai Li; Tak Kin Tse; Hoi Kei Wong; Yuen Ni Wong Journal: Int J Nurs Stud Date: 2008-10-21 Impact factor: 5.837
Authors: Sook Ning Chua; Ellen E Fitzsimmons-Craft; S Bryn Austin; Denise E Wilfley; C Barr Taylor Journal: Int J Eat Disord Date: 2022-04-02 Impact factor: 5.791