描述并评估一种新方法，使用普查中的社区收入（按收入概率加权）来近似分类的个人收入。

Describing and assessing a new method of approximating categorical individual-level income using community-level income from the census (weighting by income probabilities).

机构信息

Center for Community Health Integration, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA.

Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA.

出版信息

Health Serv Res. 2022 Dec;57(6):1348-1360. doi: 10.1111/1475-6773.14026. Epub 2022 Jul 23.

DOI:10.1111/1475-6773.14026

PMID:35832029

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9643096/

Abstract

OBJECTIVE

To assess a new approach (weighting by "income probabilities [IP]") that uses US Census data from the patients' communities to approximate individual-level income, an important but often missing variable in health services research.

DATA SOURCES

Community (census tract level) income data came from the 2017 5-year American Community Survey (ACS). The patient data included those diagnosed with cancer in 2017 in Ohio (n = 65,759). The reference population was the 2017 5-year ACS Public Use Microdata Sample (n = 564,357 generalizing to 11,288,350 Ohioans).

STUDY DESIGN/METHODS: We applied the traditional approach of income approximation using median census tract income along with two IP based approaches to estimate the proportions in the patient data with incomes of 0%-149%, 150%-299%, 300%-499%, and 500%+ of the federal poverty level (FPL) ("class-relevant income grouping") or 0%-138%, 139%-249%, 250%-399%, and 400%+ FPL ("policy-relevant income grouping"). These estimated income distributions were then compared with the known income distributions of the reference population.

DATA COLLECTION/EXTRACTION METHODS: The patient data came from Ohio's cancer registry. The other data were publicly available.

PRINCIPAL FINDINGS

Both IP based approaches consistently outperformed the traditional approach overall and in subgroup analyses, as measured by the weighted average absolute percentage point differences between the proportions of each of the income categories of the reference population and the estimated proportions generated by the income approximation approaches ("average percent difference," or APD). The smallest APD for an IP based method, 0.5%, was seen in non-Hispanic White females in the class-relevant income grouping (compared with 16.5% for the conventional method), while the largest APD, 7.1%, was seen in non-Hispanic Black females in the policy-relevant income grouping (compared with 18.0% for the conventional method).

CONCLUSIONS

Weighting by IP substantially outperformed the conventional approach of estimating the distribution of incomes in patient data.

摘要

目的

评估一种新方法（通过“收入概率[IP]”进行加权），该方法利用患者所在社区的美国人口普查数据来近似个体收入，这是健康服务研究中一个重要但经常缺失的变量。

数据来源

社区（普查区层面）收入数据来自 2017 年的五年期美国社区调查（ACS）。患者数据包括 2017 年在俄亥俄州诊断出患有癌症的患者（n=65759 人）。参考人群是 2017 年五年期 ACS 公共使用微数据样本（n=564357 人，推广至 11288350 名俄亥俄州人）。

研究设计/方法：我们应用了传统的收入近似方法，使用中位数普查区收入以及两种基于 IP 的方法来估计患者数据中收入为联邦贫困线（FPL）的 0%-149%、150%-299%、300%-499%和 500%+的比例（“相关收入分组”）或收入为 0%-138%、139%-249%、250%-399%和 400%+FPL 的比例（“政策相关收入分组”）。然后，将这些估计的收入分布与参考人群的已知收入分布进行比较。

数据收集/提取方法：患者数据来自俄亥俄州的癌症登记处。其他数据是公开的。

主要发现

两种基于 IP 的方法总体上都优于传统方法，在亚组分析中也是如此，这可以通过参考人群的每个收入类别的比例与收入近似方法生成的估计比例之间的加权平均绝对百分点差异来衡量（“平均百分比差异”或 APD）。在相关收入分组中，非西班牙裔白人女性的 IP 基础方法的 APD 最小，为 0.5%（与传统方法的 16.5%相比），而在政策相关收入分组中，非西班牙裔黑人女性的 APD 最大，为 7.1%（与传统方法的 18.0%相比）。