The Journal of surgical research · 2026
Concordance Between Area Deprivation Index and Self-Reported Income in Breast Cancer Survivors.
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Bozzuto Laura M, Schumacher Jessica R, Tan Angelina D, Hanlon Bret M, Neuman Heather B
Аннотация
INTRODUCTION: Self-reported income is the standard to identify low-income subjects; however, collecting this information can be onerous and subject to high levels of missing data. Area deprivation index (ADI) is a composite measure of neighborhood socioeconomic disadvantage using zip code. The objective of this study was to compare the performance of ADI to self-reported income in identifying low-income individuals with breast cancer in urban and rural settings.
METHODS: This was a posthoc secondary analysis of women with breast cancer enrolled from 2019 to 2023 at 10 clinic sites within the National Cancer Institute Community Oncology Research program. Data were obtained from participants who self-reported income and had valid zip codes. The primary outcome was concordance between ADI and self-reported income.
RESULTS: Among 491 patients, 190 (38.7%) reported low-income and 114 (23.2%) were identified as disadvantaged by ADI. There was fair agreement between self-reported income and ADI (k-value 0.26; 95% confidence interval, 0.17, 0.34). ADI was concordant with self-reported income for approximately two-thirds of patients. However, sensitivity in identifying low-income patients was only 37.9%. ADI sensitivity for identifying low-income patients was higher in rural than in urban settings (55% versus 33%), though agreement remained only slight (k = 0.20; 95% confidence interval, -0.005, 0.40).
CONCLUSIONS: ADI can be a useful tool for researchers to identify low-income patients, particularly when collection of patient-level data is not feasible or missing, but limitations in sensitivity and accuracy exist.
METHODS: This was a posthoc secondary analysis of women with breast cancer enrolled from 2019 to 2023 at 10 clinic sites within the National Cancer Institute Community Oncology Research program. Data were obtained from participants who self-reported income and had valid zip codes. The primary outcome was concordance between ADI and self-reported income.
RESULTS: Among 491 patients, 190 (38.7%) reported low-income and 114 (23.2%) were identified as disadvantaged by ADI. There was fair agreement between self-reported income and ADI (k-value 0.26; 95% confidence interval, 0.17, 0.34). ADI was concordant with self-reported income for approximately two-thirds of patients. However, sensitivity in identifying low-income patients was only 37.9%. ADI sensitivity for identifying low-income patients was higher in rural than in urban settings (55% versus 33%), though agreement remained only slight (k = 0.20; 95% confidence interval, -0.005, 0.40).
CONCLUSIONS: ADI can be a useful tool for researchers to identify low-income patients, particularly when collection of patient-level data is not feasible or missing, but limitations in sensitivity and accuracy exist.