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Factors Affecting Salt Reduction Measure Adoption among Chinese Residents

Author

Listed:
  • Zeying Huang

    (Institute of Food and Nutrition Development, Ministry of Agriculture and Rural Affairs, Beijing 100081, China)

  • Di Zeng

    (Centre for Global Food and Resources, The University of Adelaide, Adelaide, SA 5005, Australia)

Abstract

China has the highest mortality rate caused by diseases and conditions associated with its high-salt diet. Since 2016, China has initiated a national salt reduction campaign that aims at promoting the usage of salt information on food labels and salt-restriction spoons and reducing condiment and pickled food intake. However, factors affecting individuals’ decisions to adopt these salt reduction measures remain largely unknown. By comparing the performances of logistic regression, stepwise logistic regression, lasso logistic regression and adaptive lasso logistic regression, this study aims to fill this gap by analyzing the adoption behaviour of 1610 individuals from a nationally representative online survey. It was found that the practices were far from adopted and only 26.40%, 22.98%, 33.54% and 37.20% reported the adoption of labelled salt information, salt-restriction spoons, reduced condiment use in home cooking and reduced pickled food intake, respectively. Knowledge on salt, the perceived benefits of salt reduction, participation in nutrition education and training programs on sodium reduction were positively associated with using salt information labels. Adoption of the other measures was largely explained by people’s awareness of hypertension risks and taste preferences. It is therefore recommended that policy interventions should enhance Chinese individuals’ knowledge of salt, raise the awareness of the benefits associated with a low-salt diet and the risks associated with consuming excessive salt and reshape their taste choices.

Suggested Citation

  • Zeying Huang & Di Zeng, 2021. "Factors Affecting Salt Reduction Measure Adoption among Chinese Residents," IJERPH, MDPI, vol. 18(2), pages 1-15, January.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:2:p:445-:d:476696
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    References listed on IDEAS

    as
    1. Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
    2. Juan Chen & Ye Tian & Yixing Liao & Shuaishuai Yang & Zhuoting Li & Chao He & Dahong Tu & Xinying Sun, 2013. "Salt-Restriction-Spoon Improved the Salt Intake among Residents in China," PLOS ONE, Public Library of Science, vol. 8(11), pages 1-9, November.
    3. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
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    Keywords

    salt; reduction; diet; adoption; label; China;
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