结合直线和基于地图的距离来研究接近健康食品与疾病之间的联系。

IF 1.8 4区 医学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Sarah C Lotspeich, Ashley E Mullan, Lucy D'Agostino McGowan, Staci A Hepler
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引用次数: 0

摘要

健康的食物对于健康的生活是必不可少的,但是对于一些人来说,获得健康的食物可能比其他人更具挑战性。粮食获取方面的这种差异可能导致福祉方面的差异,在获取健康食品方面面临更多挑战的社区(即获取机会低的社区),疾病的发病率可能不成比例。确定低获取、高风险社区以实施有针对性的干预措施是一项公共卫生优先事项,但目前量化食物获取的方法依赖于距离测量,这些测量要么计算简单(如最短直线路线的长度),要么计算准确(如基于地图的最短驾驶路线的长度),但并非两者兼有。我们提出了一种多重输入方法来结合这些距离测量,使研究人员能够利用一种方法的计算便利性和另一种方法的准确性。该方法结合了所有社区的直线距离和一个子集的基于地图的距离,提供了与所有社区使用基于地图的距离的“黄金标准”模型相比较的估计,并提高了仅对子集使用基于地图的距离的“完整案例”模型的效率。通过采用测量误差框架,可以利用直线距离的信息来计算没有基于地图的距离的任何邻域的信息占位符(即impute)。通过对北卡罗来纳州皮埃蒙特三合会地区的模拟和数据,我们量化并比较了两种健康结果(糖尿病和肥胖)与社区水平获取健康食品之间的关系。估算程序还可以预测一个地区食物获取的整体情况,而不需要对所有社区进行基于地图的测量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Combining Straight-Line and Map-Based Distances to Investigate the Connection Between Proximity to Healthy Foods and Disease.

Healthy foods are essential for a healthy life, but accessing healthy food can be more challenging for some people than others. This disparity in food access may lead to disparities in well-being, potentially with disproportionate rates of diseases in communities that face more challenges in accessing healthy food (i.e., low-access communities). Identifying low-access, high-risk communities for targeted interventions is a public health priority, but current methods to quantify food access rely on distance measures that are either computationally simple (like the length of the shortest straight-line route) or accurate (like the length of the shortest map-based driving route), but not both. We propose a multiple imputation approach to combine these distance measures, allowing researchers to harness the computational ease of one with the accuracy of the other. The approach incorporates straight-line distances for all neighborhoods and map-based distances for just a subset, offering comparable estimates to the "gold standard" model using map-based distances for all neighborhoods and improved efficiency over the "complete case" model using map-based distances for just the subset. Through the adoption of a measurement error framework, information from the straight-line distances can be leveraged to compute informative placeholders (i.e., impute) for any neighborhoods without map-based distances. Using simulations and data for the Piedmont Triad region of North Carolina, we quantify and compare the associations between two health outcomes (diabetes and obesity) and neighborhood-level access to healthy foods. The imputation procedure also makes it possible to predict the full landscape of food access in an area without requiring map-based measurements for all neighborhoods.

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来源期刊
Statistics in Medicine
Statistics in Medicine 医学-公共卫生、环境卫生与职业卫生
CiteScore
3.40
自引率
10.00%
发文量
334
审稿时长
2-4 weeks
期刊介绍: The journal aims to influence practice in medicine and its associated sciences through the publication of papers on statistical and other quantitative methods. Papers will explain new methods and demonstrate their application, preferably through a substantive, real, motivating example or a comprehensive evaluation based on an illustrative example. Alternatively, papers will report on case-studies where creative use or technical generalizations of established methodology is directed towards a substantive application. Reviews of, and tutorials on, general topics relevant to the application of statistics to medicine will also be published. The main criteria for publication are appropriateness of the statistical methods to a particular medical problem and clarity of exposition. Papers with primarily mathematical content will be excluded. The journal aims to enhance communication between statisticians, clinicians and medical researchers.
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