Double Burden of Malnutrition among Chinese Women of Reproductive Age and Their Social Determinants.
Jingqi SongJi ZhangWafaie FawziYangmu HuangPublished in: Nutrients (2020)
This study aimed to examine the impact of a wide range of demographic, socioeconomic, and community factors on the double burden of malnutrition among women of reproductive age using longitudinal data. We used data about 11,348 women of reproductive age who participated in the China Health and Nutrition Survey (CHNS), a longitudinal survey, between 1989 and 2015. Nutritional outcomes were categorized into four groups, namely underweight, normal weight, overweight, and obesity, with normal weight as reference. A multinomial logit model was fitted due to geographic clustering and repeated observations of individuals. The prevalence of underweight decreased over time from 1991 but has tended to rise again since 2004, while the prevalence of overweight/obesity continued to rise between 1991 and 2015. Improved individual factors, socioeconomic status, and community urbanization reduced the risk of underweight but elevated the risk of overweight and obesity. The medium levels, rather than the highest levels, of household income and community urbanization are associated with a higher risk of overweight and obesity. The notable increase in underweight prevalence is a cause for concern to be addressed along with efforts to curb the rising tide of overweight. In order to enhance the nutritional status of women of reproductive age, it is essential to improving the community environment, levels of education, and living environment from a wider context. Long-term and targeted plans are urgently needed for nutrition improvements among the different populations.
Keyphrases
- mental health
- healthcare
- physical activity
- polycystic ovary syndrome
- weight loss
- weight gain
- risk factors
- pregnancy outcomes
- cervical cancer screening
- body mass index
- insulin resistance
- type diabetes
- breast cancer risk
- public health
- adipose tissue
- electronic health record
- big data
- cancer therapy
- machine learning
- metabolic syndrome
- drug delivery
- health information
- health insurance
- human health
- high fat diet induced