Medical costs attributable to overweight and obesity in Japanese individuals

Misuzu Fujita, Yasunori Sato, Kengo Nagashima, Sho Takahashi, Akira Hata

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Objective: We aimed to reveal the association between body mass index (BMI) and medical costs in the current Japanese population, and to estimate the population attributable fraction (PAF) of medical costs due to overweight and obesity. Methods: A generalized linear mixed model with log link function and gamma distribution was used to evaluate the association between BMI and medical costs in 34,537 beneficiaries of the National Health Insurance aged 40–69 years in Chiba City. Medical cost data were obtained from insurance claims submitted between April 2012 and March 2016. PAFs due to overweight (BMI ≥25.0 and <30.0 kg/m 2 ) and obesity (BMI ≥30.0 kg/m 2 ) were calculated. Results: Overweight and obesity were significant predictors of excessive medical costs in all age and sex groups. PAF due to overweight and obesity was estimated to be 9.62% (95% confidence interval, 8.52–10.73%). Additionally, PAFs in 40–59-year-old individuals (12.76% in men and 11.63% in women) were greater than those in 60–69-year-old subjects (6.55% in men and 7.80% in women) for both sexes. Conclusions: In the Japanese population, overweight and obesity are an excessive financial burden with an estimated PAF of 9.62% of total medical costs.

Original languageEnglish
Pages (from-to)479-484
Number of pages6
JournalObesity Research and Clinical Practice
Volume12
Issue number5
DOIs
Publication statusPublished - 2018 Sept 1
Externally publishedYes

Keywords

  • Health insurance claims
  • Medical costs
  • Obesity
  • Overweight
  • Population attributable fraction

ASJC Scopus subject areas

  • Endocrinology, Diabetes and Metabolism
  • Nutrition and Dietetics

Fingerprint

Dive into the research topics of 'Medical costs attributable to overweight and obesity in Japanese individuals'. Together they form a unique fingerprint.

Cite this