2022
DOI: 10.1007/s12020-022-03201-w
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Global Burden of osteoarthritis associated with high body mass index in 204 countries and territories, 1990–2019: findings from the Global Burden of Disease Study 2019

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Cited by 17 publications
(9 citation statements)
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“…Based on Joinpoint regression and age-period-cohort models, this study systematically estimated the change of the YLDs for knee OA attributable to high BMI in China and globally from 1990 to 2019. On the basis of the latest literature (3,46,47) available for knee OA, the disease burden associated with high BMI in China was quantified, and the present study, found that YLDs for knee OA attributable to high BMI has substantially increased over the past 30 years. The ASDR reached 25.6 (95% UI: 7.5, 61.6) per 100,000 in 2019, which increased by 137.0% compared with 1990.…”
Section: Discussionmentioning
confidence: 60%
“…Based on Joinpoint regression and age-period-cohort models, this study systematically estimated the change of the YLDs for knee OA attributable to high BMI in China and globally from 1990 to 2019. On the basis of the latest literature (3,46,47) available for knee OA, the disease burden associated with high BMI in China was quantified, and the present study, found that YLDs for knee OA attributable to high BMI has substantially increased over the past 30 years. The ASDR reached 25.6 (95% UI: 7.5, 61.6) per 100,000 in 2019, which increased by 137.0% compared with 1990.…”
Section: Discussionmentioning
confidence: 60%
“…In this study, OA burden was more highly correlated with high BMI in countries with higher SDI than countries with lower SDI. Due to relatively poor economic development, access to adequate food remains a problem in many countries with lower SDI, and people are more likely to engage in physical activities that require high energy expenditure [1, 9]. In contrast, higher-income people consume more fat, salt, and processed foods and have higher rates of obesity [9].…”
Section: Discussionmentioning
confidence: 99%
“…It was determined by fitting a regression line to the natural logarithm of the rates (y = α + βx + ϵ), where y represents the natural logarithm of the rate and x represents the calendar year. The EAPC calculation involved multiplying 100 by (exp[β]−1), and its 95% UI was obtained using linear regression modeling ( 15 ). All statistical analyses were performed using R software (version 4.2.1), and a P-value less than 0.05 was considered statistically significant.…”
Section: Methodsmentioning
confidence: 99%