A novel method for estimating distributions of body mass index.
A novel method for estimating distributions of body mass index.
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DOI：
10.1186/s12963-016-0076-2
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发表时间：
2016
影响因子：
3.3
通讯作者：
Murray CJ
中科院分区：
文献类型：
--
作者：
Ng M;Liu P;Thomson B;Murray CJ
了解身体质量指数（BMI）的分布趋势是监测全球超重和肥胖流行病的一个关键方面。传统的人群健康指标往往只关注估计和报告平均BMI以及超重和肥胖的患病率，而不能完全表征BMI的分布。在这项研究中，我们提出了一种新的方法，允许估计整个分布。所提出的方法利用优化算法，L-BFGS-B，从三个常用的人口健康统计数据：平均BMI，超重的患病率，肥胖的患病率，来获得BMI的分布。我们进行了一系列的模拟，以检查该方法的属性，准确性和鲁棒性。然后，我们通过将其应用于2011-2012年美国国家健康与营养调查（NHANES）来说明该方法的实际应用。我们的方法在各种模拟场景中的表现令人满意，从而产生与真实分布密切相关的经验（估计）分布。应用该方法的NHANES数据也显示了高度的一致性之间的经验和真实的分布。在存在大量异常值的情况下，该方法在捕获极值方面不太令人满意。然而，它在估计集中趋势和五分位数方面仍然准确。所提出的方法提供了一种工具，可以有效地估计BMI的整个分布。跟踪BMI分布的能力将提高我们捕捉超重和肥胖严重程度变化的能力，并使我们能够更好地监测这一流行病。
Understanding trends in the distribution of body mass index (BMI) is a critical aspect of monitoring the global overweight and obesity epidemic. Conventional population health metrics often only focus on estimating and reporting the mean BMI and the prevalence of overweight and obesity, which do not fully characterize the distribution of BMI. In this study, we propose a novel method which allows for the estimation of the entire distribution. The proposed method utilizes the optimization algorithm, L-BFGS-B, to derive the distribution of BMI from three commonly available population health statistics: mean BMI, prevalence of overweight, and prevalence of obesity. We conducted a series of simulations to examine the properties, accuracy, and robustness of the method. We then illustrated the practical application of the method by applying it to the 2011–2012 US National Health and Nutrition Examination Survey (NHANES). Our method performed satisfactorily across various simulation scenarios yielding empirical (estimated) distributions which aligned closely with the true distributions. Application of the method to the NHANES data also showed a high level of consistency between the empirical and true distributions. In situations where there were considerable outliers, the method was less satisfactory at capturing the extreme values. Nevertheless, it remained accurate at estimating the central tendency and quintiles. The proposed method offers a tool that can efficiently estimate the entire distribution of BMI. The ability to track the distributions of BMI will improve our capacity to capture changes in the severity of overweight and obesity and enable us to better monitor the epidemic.