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复杂样本调查中不同群体间健康差异的图形展示及相关统计量度

Graphical displays and related statistical measures of health disparities between groups in complex sample surveys.

作者信息

Ramos Mark Louie, Graubard Barry, Gastwirth Joseph

机构信息

Department of Health and Human Services, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.

Department of Health Policy and Administration, The Pennsylvania State University, University Park, PA, USA.

出版信息

J R Stat Soc Ser A Stat Soc. 2025 May 22. doi: 10.1093/jrsssa/qnaf044.

DOI:10.1093/jrsssa/qnaf044
PMID:40809906
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12341090/
Abstract

Different methods for describing health disparities in the distributions of continuous measured health-related variables among groups provide more insight into the nature and impact of the disparities than comparing measures of central tendency. Transformations of the Lorenz curve and analogues of the Gini index used in the analysis of income inequality are adapted to provide graphical and analytical measures of health disparities. Akin to the classical Peters-Belson regression method for partitioning a disparity into a component explained by group differences in a set of covariates and an unexplained component, a new modified Lorenz curve is proposed. The estimation of these curves/measures is adapted for data obtained from surveys with complex sample weighted designs. The statistical properties of sample weighted estimators of the proposed measures and their bootstrap variances are explored through simulation studies. Applications are demonstrated using BMI and blood lead levels among race/ethnic groups of adult females and children, respectively, from the 2013-2018 and 1988-1994 US National Health and Nutrition Examination Surveys. Another application examines disparities in distance to nearest acute care hospital among census blocks in the US state of New York grouped by their level of urbanicity using US census data and the American Hospital Association survey.

摘要

与比较集中趋势指标相比,描述各组间连续测量的健康相关变量分布中健康差异的不同方法能更深入地洞察差异的本质和影响。用于分析收入不平等的洛伦兹曲线变换和基尼指数类似物经调整后,可用于提供健康差异的图形和分析指标。类似于将差异分解为由一组协变量中的组间差异解释的部分和未解释部分的经典彼得斯 - 贝尔森回归方法,提出了一种新的修正洛伦兹曲线。这些曲线/指标的估计适用于从具有复杂样本加权设计的调查中获得的数据。通过模拟研究探索了所提指标的样本加权估计量及其自助法方差的统计特性。分别使用2013 - 2018年美国国家健康与营养检查调查中成年女性种族/族裔群体的体重指数(BMI)和1988 - 1994年儿童种族/族裔群体的血铅水平进行了应用演示。另一项应用使用美国人口普查数据和美国医院协会调查,研究了美国纽约州按城市化水平分组的普查街区到最近急诊医院的距离差异。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a1c7/12341090/ff633a661aff/nihms-2098237-f0009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a1c7/12341090/6224d35a5682/nihms-2098237-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a1c7/12341090/c03e6103d316/nihms-2098237-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a1c7/12341090/75a577ffb93a/nihms-2098237-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a1c7/12341090/100ad5156835/nihms-2098237-f0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a1c7/12341090/eccf11d9097f/nihms-2098237-f0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a1c7/12341090/25c7fe33766f/nihms-2098237-f0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a1c7/12341090/0cfed4c976d0/nihms-2098237-f0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a1c7/12341090/ee6531c0388c/nihms-2098237-f0008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a1c7/12341090/ff633a661aff/nihms-2098237-f0009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a1c7/12341090/6224d35a5682/nihms-2098237-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a1c7/12341090/c03e6103d316/nihms-2098237-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a1c7/12341090/75a577ffb93a/nihms-2098237-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a1c7/12341090/100ad5156835/nihms-2098237-f0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a1c7/12341090/eccf11d9097f/nihms-2098237-f0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a1c7/12341090/25c7fe33766f/nihms-2098237-f0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a1c7/12341090/0cfed4c976d0/nihms-2098237-f0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a1c7/12341090/ee6531c0388c/nihms-2098237-f0008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a1c7/12341090/ff633a661aff/nihms-2098237-f0009.jpg

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