1249Trends and disparities in adult body mass index across 47 prefectures in Japan, 1975–2018. (2nd September 2021)

• Record Type: Journal Article
• Authors: Ikeda, Nayu; Nakaya, Tomoki; Bennett, James; Ezzati, Majid; Nishi, Nobuo
• Is Part Of: International journal of epidemiology. Volume 50(2021)Supplement 1
• Publication Date: 2021-09-02
• Subjects: Epidemiology -- Periodicals; 614.4
• DOI: 10.1093/ije/dyab168.308
• Languages: English
• ISSNs: 0300-5771

Abstract

Abstract: Background: Consistent and comparable health information across prefectures is necessary for monitoring progress in improving population health and reducing geographic health disparities in Japan. We aimed to examine long-term trends and variations in adult body mass index (BMI) by prefecture. Methods: We obtained a total sample of 233, 988 males and 261, 086 females aged 20–79 years with anthropometric measurements from National Health and Nutrition Surveys conducted annually during 1975–2018. We applied a spatio-temporal Bayesian hierarchical model to estimate the annual time series of prefectural mean BMI by 20-year age group and sex. Results: Prefectural mean BMI consistently increased in males regardless of age but decreased in younger females. It increased in older females until starting to decrease in the early 2000s. Mean BMI was noticeably higher in Okinawa, the southernmost prefecture, than in other prefectures from 1975 in males and from the 1990s in females. The interquartile range of mean BMI across prefectures was consistently higher in females than in males. It decreased over time to plateau during the 2000s in both sexes. Conclusions: Mean BMI increased in males across prefectures and there is a need for stimulating a further reduction in geographic disparities particularly for females. Okinawa once ranked first in longevity but has shown relatively poor performance in recent years, which may be partly attributable to the distinctive trends in adult BMI. Key messages: Bayesian hierarchical modelling is useful for reconstructing long-term spatio-temporal trends of mean BMI by integrating small-size survey samples.