Validity of geographic-level social determinant of health metrics in pancreatic neuroendocrine tumors

• Published in: Endocrine oncology Vol. 5; no. 1; p. e250029
• Main Authors: Gillis, Andrea, Herring, Brendon, Guenter, Rachael, Chen, Weisheng, Chen, Dai, Chen, Herbert, Rose, John Bart, Manne, Upender, Bhatia, Smita
• Format: Journal Article
• Language: English
• Published: England Bioscientifica Ltd 01.05.2025; Bioscientifica
• Subjects: environmental exposure; health outcome disparities; pancreatic neuroendocrine tumors; social determinants of health; pancreatic neuroendocrine tumors; social determinants of health; health outcome disparities; environmental exposure
• ISSN: 2634-4793; 2634-4793
• DOI: 10.1530/EO-25-0029

Various social determinants of health (SDOH) metrics, also known as area-based social measures, are utilized to evaluate access to cancer care and to explain disparities in outcomes. Little prior work has compared the validity of these various geographic metrics. We reviewed all patients surgically treated for PNETs (2006–2022) at a single comprehensive cancer center. We collected patient demographics including self-reported race (White or Black), billing addresses, tumor characteristics and area-based social measures. We then compared between- and within-race differences to understand accuracy across different geographic levels. One hundred seventy-nine patients were included; 49 (27%) Black, a median age of 60.3 years and 86 (48%) females. At the block group/census tract level, compared to White patients, Black patients lived in neighborhoods with lower educational attainment, lower income, higher rates of uninsurance, higher overall social vulnerability index (SVI), and higher area deprivation index (ADI) (all P < 0.05). These differences, however, were masked when examining county-level area-based social measures. Compared to census block group/tract-level data, for White patients, zip code-level metrics underestimated income and overestimated uninsurance level ( P < 0.05). County-level metrics underestimated White patients’ income and education level but overestimated poverty, uninsurance rate and SVI (all P < 0.05). For Black patients, zip code-level metrics overestimated poverty and uninsurance rates ( P < 0.05); the only inaccurate county-level metric was overestimation of SVI ( P < 0.001). Black patients with PNETs experience more vulnerable area-based social measures, a disparity which may be hidden when analyzing large geographic metrics.