Who is getting screened? Estimating uninsured and underinsured women utilizing NBCCEDP screening program in a high insurance state.

Authors

null

David Haynes

University of Minnesota, Minneapolis, MN

David Haynes , Maria Borrero , Dame Idossa , Lauren Roach , Mckenna Haas

Organizations

University of Minnesota, Minneapolis, MN

Research Funding

U.S. National Institutes of Health
U.S. National Institutes of Health

Background: The National Breast and Cervical Cancer Early Detection Program's (NBCCEDP) mission is to improve access to mammography and other health services for underserved women. Research has shown that this national program has improved access to mammography services. However, there is limited research describing the total number of individuals eligible to utilize these services. Howard et. al., described women eligible for NBCCEDP services at the state level. Hughes et. al. have improved upon this with the introduction of geographic small-area estimation techniques that allow us to estimate individuals below the census tract level. Our work builds upon Hughes et. al., by accounting for variations in uninsured and insurance status. Methods: We used a geospatial small area estimation technique, spatially adaptive filters, to calculate standardized incidence ratios describing the utilization rate of NBCCEDP services in Minnesota. The American Community Survey (2010-2014) data describing the insurance status was integrated into the spatially adaptive filters to account for the likelihood that an individual is uninsured. Results: We tested three models that integrate insurance status by age, sex, and race/ethnicity. Our composite model, which adjusts for all three insurance statuses, improves our estimates by reducing estimation error by 95%. We estimate that 49,914 uninsured and underinsured women are eligible to receive services within the state of Minnesota. In addition to an improved state estimate, we also provide estimates at county level to better understand the uptake of these services. Conclusions: This analysis addresses a significant gap in the literature by including secondary datasets that allow for a more accurate prediction of both the underlying population eligible to receive services and calculation of utilization rates to address programmatic goals. Spatially adaptive filters are not restricted to a specific administrative unit and insurance status provides a better screening utilization estimate.

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Abstract Details

Meeting

2023 ASCO Annual Meeting

Session Type

Publication Only

Session Title

Publication Only: Health Services Research and Quality Improvement

Track

Quality Care/Health Services Research

Sub Track

Access to Care

Citation

J Clin Oncol 41, 2023 (suppl 16; abstr e18617)

DOI

10.1200/JCO.2023.41.16_suppl.e18617

Abstract #

e18617

Abstract Disclosures

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