Document Type
Article
Publication Date
7-13-2022
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.
Keywords
censuses, data collection/methods, health status disparities, income/statistics and numerical data, residence characteristics/statistics and numerical data, social class
Language
English
Publication Title
Health Services Research
Grant
132678‐RSGI‐19‐213‐01‐CPHPS
Rights
© 2022 The Authors. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/), which permits non-commercial copying and redistribution of the material in any medium or format, provided the original work is not changed in any way and is properly cited.
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Recommended Citation
Kim U, Koroukian SM, Stange KC, Spilsbury JC, Dong W, Rose J. Describing and assessing a new method of approximating categorical individual-level income using community-level income from the census (weighting by income probabilities). Health Serv Res. 2022; 57(6): 1348-1360. doi:10.1111/1475-6773.14026
Manuscript Version
Final Publisher Version