Higher Composite Social Determinants of Health Scores Are Associated With Worse Survival in Patients With Small Bowel Neuroendocrine Tumors

ABSTRACT

Background

Patients with early‐stage well‐differentiated small bowel neuroendocrine tumors (sbNETs) experience favorable prognosis with timely diagnosis and treatment. However, the impact of social determinants of health (SDH) and care at Minority‐Serving Hospitals (MSHs) on outcomes remains understudied. This study evaluates the combined influence of SDH and hospital type on long‐term survival in sbNETs.

Methods

The 2010–2020 National Cancer Database was queried for adults with G1/G2 sbNETs, Stage I/II disease, who underwent surgical resection. Patients with G3 NETs, Stage III/IV disease, neuroendocrine carcinoma, duodenal primary tumors, or incomplete follow‐up were excluded. A composite SDH score was developed using four sociogeographic factors: (1) low income, (2) low education, (3) rurality, (4) travel distance > 250 miles. Patients were categorized as SDH+ (score 0–1) or SDH– burden (score 2–4). Hospitals in the top decile for Black and Hispanic representation were classified as MSHs. Kaplan‐Meier and Cox regression analyses were performed.

Results

Among 2688 patients (median age 64; 48% female), 57% were SDH–. Five‐, 10‐, and 15‐year survival rates were 79%, 62%, and 49%, respectively. At MSHs, 5‐year survival was 82% for SDH+ versus 63% for SDH–. At non‐MSHs, survival was 82% for SDH+ versus 77% for SDH–. Median survival for SDH– patients was 11 years at MSHs versus 12 at non‐MSHs. In regression analysis, SDH– status was linked to worse survival (HR 1.24, p < 0.001); treatment at non‐MSHs predicted better survival (HR 0.41, p < 0.001). Black patients had higher mortality risk (HR 1.20, p = 0.002); no difference was observed for Hispanic patients.

Conclusion

Higher SDH burden and treatment at MSHs were associated with decreased survival in early‐stage sbNETs. Implementing a composite SDH score in clinical practice may provide a practical tool for risk stratification and guide equity‐focused interventions for patients with sbNETs.

1. Introduction

Small bowel neuroendocrine tumors (sbNETs) are a subset of gastrointestinal neoplasms characterized by indolent growth and favorable long‐term survival, particularly in early‐stage disease [1, 2]. Advances in diagnostic imaging and surgical management have enabled many patients with stage I and II well‐differentiated sbNETs to achieve survival exceeding a decade [2]. Despite these favorable tumor characteristics, disparities in outcomes persist across patient populations, raising concern about the impact of nonclinical factors on prognosis.

Social determinants of health (SDH) encompass economic, educational, and environmental factors that influence a person's health and access to cancer care [3]. In common malignancies, studies have shown that a higher SDH burden is associated with delayed diagnosis, limited access to treatment, and poorer survival. These determinants are increasingly recognized as critical modifiers of cancer outcomes [3, 4, 5], including among patients with neuroendocrine tumors [6, 7]. Although overall survival may be influenced by underlying differences in life expectancy, the survival gap observed among SDH−disadvantaged patients at Minority‐Serving Hospitals (MSHs) suggests system‐level barriers that compound social disadvantage beyond baseline aging effects. Composite SDH indices that incorporate variables, such as income, education level, rurality, and travel burden, may offer an integrated view of the sociogeographic barriers patients face. However, such indices have not been evaluated in the context of sbNETs.

Institutional characteristics, including facility type, have been implicated in disparities in cancer care and outcomes. Studies across cancer types suggest that treatment at certain facilities, particularly those with fewer resources or limited access to subspecialists, may be associated with inferior outcomes [8, 9, 10, 11]. MSHs, which care for a disproportionately high number of racial and ethnic minority patients, often operate under financial strain and may face structural barriers that limit the delivery of high‐quality oncologic care. While the roles of SDH and hospital characteristics have been studied in common cancers, their combined impact remains poorly understood in rare tumors such as sbNETs [12, 13, 14, 15].

With the rising incidence of sbNETs, the influence of SDH and treatment facility type on cancer outcomes warrants increasing attention. This study examines the association of a composite SDH score and treatment at MSHs with survival in patients with early‐stage sbNETs. We hypothesized that both a high SDH burden and care at MSHs would be independently associated with worse survival, despite favorable tumor biology.

2. Methods

2.1. Data Source and Study Design

A retrospective cohort study was conducted using data from the National Cancer Database (NCDB) for the years 2010–2020, a hospital‐based cancer registry jointly managed by the American College of Surgeons Commission on Cancer and the American Cancer Society. The NCDB is a hospital‐based cancer registry jointly managed by the American College of Surgeons Commission on Cancer and the American Cancer Society. The NCDB collects comprehensive cancer data from more than 1500 Commission on Cancer–accredited hospitals across the United States, capturing approximately 70% of new cancer diagnoses annually. Data for this study were obtained from a deidentified 2022 NCDB participant user file. The study was deemed exempt from Institutional Review Board (IRB) approval by the University of Miami Miller School of Medicine.

2.2. Study Population

Eligible patients were adults (age ≥ 18) with histologically confirmed, well‐differentiated (Grade 1 or Grade 2) sbNETs, staged I or II disease according to AJCC criteria. Inclusion required patients to have resectable disease of the primary tumor site. Patients with Grade 3 tumors or neuroendocrine carcinoma, stage III or stage IV disease, or duodenal primary tumors, or incomplete survival or demographic data were excluded from the analysis.

The histologic classification was based on the International Classification of Diseases for Oncology, Third Edition (ICD‐O‐3). Neuroendocrine tumors were identified using the following codes: 8150, 8151, 8152, 8153, 8154, 8155, 8156, 8157, 8158, 8240, 8241, 8242, 8243, 8245, 8249, 8270, 8271, 8272, 8360, 8370, 8581, 8585, 8586, 8690, 8693, 8700, 8711, 8714, and 8806. Neuroendocrine carcinomas were excluded from the analysis and identified by the following codes: 8013, 8041, 8042, 8043, 8044, 8045, 8046, 8244, 8246, 8247, 8248, 8574, and 8980.

2.3. Exposure Variables

A SDH composite score (range 0–4) was developed using four variables: 1) low income, defined as ZIP code–level income in the lowest national quartile; 2) low education, defined as ≥ 29% of the population without a high school diploma; 3) rurality, based on residence in nonmetropolitan areas; and 4) distance to treatment > 250 miles. Patients were stratified into two groups: 1) SDH+ (low burden; score 0–1) SDH− (high burden; score 2‐4). MSHs were defined according to established methodology based on the racial and ethnic composition of the facility's cancer patient population. Additional variables such as insurance status, were considered in multivariable analyses but excluded from the SDH composite due to being derived from individual‐level data. Using NCDB data, hospitals were ranked by the proportion of patients identified as Black or Hispanic. Facilities in the top decile were designated as MSHs. All other facilities were classified as non‐MSHs [15]. Hospital volume was determined from institutional case volumes across all small bowel malignancies and categorized into quartiles and tertiles based on average annual case counts.

2.4. Outcome Measure

The primary endpoint was overall survival (OS), defined as time from diagnosis to death or last known follow‐up.

2.5. Statistical Analysis

Descriptive statistics were used to summarize baseline characteristics. Missing data were handled using mode imputation for categorical variables and mean imputation for continuous variables; all results were based on the imputed data set. Demographic and clinical variables were compared between SDH+ and SDH− groups using Chi‐square or Fisher's exact tests for categorical data and Wilcoxon rank‐sum tests for continuous data. Kaplan‐Meier curves were generated to estimate survival across SDH and hospital strata. The log‐rank test assessed survival differences. Multivariable Cox proportional hazards regression was conducted to evaluate independent associations between SDH score, hospital type, and OS, adjusting for age, sex, and private insurance status. Analyses were conducted using R version 4.4.1 and the gtsummary package for tabulation of results. Significance was determined using α = 0.05, and p < 0.05 were considered statistically significant.

3. Results

A total of 2,688 patients with stage I or II well‐differentiated sbNETs met the inclusion criteria. The median age at diagnosis was 64 years (interquartile range [IQR] 54–73), with 48% of the cohort female. Based on the composite SDH score, 57% of patients were classified as SDH− (score 2–4), reflecting a higher burden of sociogeographic disadvantage.

Baseline characteristics were largely similar between the SDH+ (score 0–1) and SDH− groups in terms of age distribution (median age 63 vs. 64 years, p = 0.13), sex (48% female in both groups, p = 0.7), and tumor stage (stage I: 31% in SDH+ vs. 34% in SDH − , p = 0.2). However, significant differences were observed in private insurance coverage, with SDH− patients less likely to have private insurance compared to SDH+ patients (40% vs. 48%, p < 0.001). SDH− patients were also more frequently treated at low‐volume hospitals (35% vs. 27%, p < 0.001), highlighting potential access disparities. (Table 1 ).

Table 1.

Baseline Characteristics of Patients with Stage I–II sbNETs by SDH Score.

Characteristic	Overall N = 2767	0‐1 N = 1144	2‐4 N = 1623	p‐valuea
Age at Diagnosis	64 (54, 73)	63 (53, 73)	64 (54, 73)	0.14
Age > 65 Years	1315 (48%)	537 (47%)	778 (48%)	0.6
Sex				0.8
Female	1333 (48%)	547 (48%)	786 (48%)
Male	1434 (52%)	597 (52%)	837 (52%)
Private Insurance	1187 (43%)	544 (48%)	643 (40%)	< 0.001
Hospital Distance (mi)				< 0.001
0 to 12.49	1756 (63%)	849 (74%)	907 (56%)
12.5 to 249	979 (35%)	289 (25%)	690 (43%)
250+	32 (1.2%)	< 10	26 (1.6%)
Hospital Volume (Quartiles)				< 0.001
High	622 (22%)	295 (26%)	327 (20%)
Moderate‐High	645 (23%)	288 (25%)	357 (22%)
Moderate‐Low	628 (23%)	249 (22%)	379 (23%)
Low	872 (32%)	312 (27%)	560 (35%)
Minority‐Serving Hospitals	266 (9.6%)	111 (9.7%)	155 (9.6%)	0.9
Pathological Stage				0.2
Stage 1	912 (33%)	360 (31%)	552 (34%)
Stage 2	1855 (67%)	784 (69%)	1071 (66%)
Primary Tumor Resection	2517 (91%)	1039 (91%)	1478 (91%)	0.8
Radiotherapy	< 10	< 10	< 10	0.6
Chemotherapy	28 (1.0%)	12 (1.0%)	16 (1.0%)	0.9
Hormonal Therapy	28 (1.0%)	10 (0.9%)	18 (1.1%)	0.5

Note: Cell counts fewer than 10 were suppressed in accordance with NCDB DUA requirements.

^a Wilcoxon rank sum test; Pearson's Chi‐squared test; Fisher's exact test.

Multivariable Cox regression analysis demonstrated that a high SDH score was significantly associated with increased mortality (hazard ratio [HR] 1.24; 95% confidence interval [CI], 1.14–1.34; p < 0.001). Treatment at non‐MSH hospitals was independently associated with improved survival (HR 0.41; 95% CI, 0.36–0.47; p < 0.001). Additional factors associated with reduced mortality included female sex (HR 0.92; p = 0.038) and having private insurance (HR 0.82; p < 0.001). Increasing age at diagnosis conferred higher risk (HR per year 1.07; p < 0.001), and Black race was associated with a 20% increase in mortality risk compared to White patients (HR 1.20; p = 0.002). Hispanic ethnicity was not significantly associated with survival (HR 0.87; p = 0.2). (Table 2).

Table 2.

Multivariable Cox Proportional Hazards Regression for Overall Survival.

Characteristic	HRa	95% CIa	p‐value
SDH Score (2 Groups)
0–1	—	—
2–4	1.24	1.14, 1.34	< 0.001
Minority‐Serving Hospitals
MSH	—	—
Non‐MSH	0.41	0.36, 0.47	< 0.001
Primary Tumor Resection	0.71	0.63, 0.79	< 0.001
Age at Diagnosis	1.07	1.06, 1.07	< 0.001
Female Sex	0.92	0.85, 1.00	0.038
African American (Ref: White)	1.20	1.07, 1.35	0.002
Hispanic Ethnicity	0.87	0.70, 1.09	0.2
Private Insurance	0.82	0.74, 0.91	< 0.001
Academic/Research Program	0.97	0.88, 1.07	0.5
Hospital Volume (Tertiles)
High	—	—
Moderate	1.03	0.93, 1.14	0.6
Low	1.04	0.93, 1.16	0.5

Note: Hazard ratios and 95% confidence intervals from a Cox model assessing factors associated with overall survival. High SDH score (2–4), treatment at MSHs, increasing age, and Black race were associated with worse outcomes. Protective factors included female sex and private insurance.

^a HR = Hazard Ratio, CI = Confidence Interval.

Overall survival analysis revealed a progressive decline over time, with 5‐year, 10‐year, and 15‐year OS rates of 79%, 62%, and 49%, respectively. (Table 3) The median survival was 13 years. When stratified by hospital type and SDH score, survival differences became evident. Among patients treated at MSHs, those with lower SDH burden (SDH + ) had a 5‐year OS of 82%, while those with higher SDH burden (SDH − ) had a markedly lower 5‐year OS of 63%. (Figure 1A) Median survival for the SDH− group at MSHs was 11 years. In contrast, at non‐MSHs, the effect of SDH burden on survival was less pronounced. Both SDH+ and SDH− patients had relatively higher and closer 5‐year survival rates of 82% and 77%, respectively. (Figures 1B) Median survival for the SDH− group in non‐MSH settings was 12 years, indicating a potential mitigating effect of hospital type on the impact of SDH. Comparison of survival among SDH+ patients treated at MSHs versus non‐MSHs showed minimal difference observed, with overlapping curves and 5‐year survival near 82% in both settings. (Figure 2A) Among SDH− patients, treatment at non‐MSHs was associated with better survival compared to MSHs. Median survival was 12 years at non‐MSHs versus 11 years at MSHs, and 5‐year survival rates were 77% versus 63%. (Figure 2B).

Table 3.

Overall and Stratified Survival Estimates by SDH Score and Hospital Type.

Characteristic	5 Year	10 Year	15 Year	Median survival
Overall	79% (77%, 80%)	62% (59%, 65%)	49% (42%, 56%)	13 (12, —)
Minority‐Serving Hospitals
Minority‐Serving Hospitals
MSH	71% (65%, 78%)	54% (45%, 65%)	51% (40%, 63%)	— (9.2, —)
Non‐MSH	79% (78%, 81%)	62% (59%, 66%)	48% (41%, 57%)	13 (12, —)
Minority‐Serving Hospitals
MSH, SDH 0‐1	82% (75%, 91%)	56% (41%, 78%)	56% (41%, 78%)	— (8.4, —)
MSH, SDH 2‐4	63% (55%, 73%)	53% (42%, 66%)	47% (34%, 65%)	11 (7.6, —)
Non‐MSH, SDH 0‐1	82% (79%, 84%)	64% (59%, 70%)	53% (44%, 64%)	— (10, —)
Non‐MSH, SDH 2‐4	77% (75%, 80%)	60% (57%, 65%)	45% (36%, 57%)	12 (12, —)
Age > 65 Years
No	89% (87%, 91%)	80% (77%, 83%)	70% (62%, 79%)	— (—, —)
Yes	67% (65%, 70%)	42% (37%, 47%)	27% (18%, 39%)	8.5 (7.8, 9.3)
Facility Type
Academic	80% (77%, 83%)	62% (57%, 68%)	46% (32%, 66%)	12 (12, —)
Integrated	75% (71%, 79%)	59% (53%, 66%)	— (—, —)	12 (10, —)
Community	78% (75%, 80%)	59% (54%, 64%)	49% (40%, 58%)	13 (11, —)
Hospital Distance (mi)
0 to 12.49	79% (77%, 82%)	62% (58%, 67%)	50% (42%, 60%)	16 (12, —)
12.5 to 249	77% (74%, 80%)	59% (54%, 65%)	43% (30%, 61%)	12 (12, —)
250+	76% (61%, 95%)	76% (61%, 95%)	— (—, —)	— (—, —)
Pathological Stage
Stage 1	80% (77%, 83%)	69% (64%, 74%)	56% (46%, 70%)	— (12, —)
Stage 2	78% (76%, 80%)	58% (54%, 62%)	44% (36%, 55%)	12 (10, —)
Primary Tumor Resection
No	84% (79%, 89%)	71% (63%, 79%)	— (—, —)	— (12, —)
Yes	78% (76%, 80%)	61% (58%, 64%)	47% (40%, 56%)	13 (12, —)
Chemotherapy
No	79% (77%, 80%)	62% (59%, 65%)	49% (42%, 56%)	13 (12, —)
Yes	58% (41%, 81%)	51% (33%, 77%)	— (—, —)	— (2.9, —)
Hormonal Therapy
No	79% (77%, 80%)	62% (59%, 65%)	49% (43%, 57%)	13 (12, —)
Yes	70% (49%, 100%)	47% (24%, 92%)	— (—, —)	6.1 (4.8, —)

Note: Kaplan‐Meier survival estimates at 5, 10, and 15 years, along with median survival times, are presented for the full cohort and subgroups defined by SDH score and hospital type.

Figure 1.

(A) Kaplan‐Meier Survival Curve Stratified by SDH Score at Minority‐Serving Hospitals (MSHs). This figure depicts overall survival among early‐stage sbNET patients treated at MSHs, stratified by composite SDH score. Patients with low SDH burden (SDH + ; score 0–1) had significantly improved survival compared to those with high SDH burden (SDH − ; score 2–4). Five‐year survival was 82% for SDH+ versus 63% for SDH − , with median survival of 11 years for the latter. (B) Kaplan‐Meier Survival Curve Stratified by SDH Score at Nonminority‐Serving Hospitals (Non‐MSHs). Survival outcomes among patients treated at non‐MSHs stratified by SDH score. Both SDH+ and SDH− patients had higher survival compared to their counterparts at MSHs. Five‐year survival was 82% for SDH+ and 77% for SDH− patients, with a narrower gap in survival curves than observed at MSHs.

Figure 2.

(A) Kaplan‐Meier Survival Curve by Hospital Type in SDH+ Patients. Comparison of survival among SDH+ patients treated at MSHs versus non‐MSHs. Minimal difference was observed, with overlapping curves and 5‐year survival near 82% in both settings. These findings suggest hospital type has limited influence on survival in patients with low SDH burden. (B) Kaplan‐Meier Survival Curve by Hospital Type in SDH− Patients. Among SDH− patients, treatment at non‐MSHs was associated with better survival compared to MSHs. Median survival was 12 years at non‐MSHs versus 11 years at MSHs, and 5‐year survival rates were 77% versus 63%, respectively highlighting a synergistic effect of SDH burden and hospital type on survival outcomes.

4. Discussion

This study highlights the significant influence of SDH and hospital type on long‐term survival in patients with early‐stage well‐differentiated sbNETs. Despite the indolent nature of sbNETs and the expectation for favorable outcomes following complete surgical resection [1, 2], the present study identified substantial disparities in survival based on composite SDH scores and treatment at MSHs. The analysis demonstrated that patients with a higher SDH burden had significantly worse survival, with a 24% increased hazard of death after adjusting for demographic and clinical covariates. This finding aligns with existing literature demonstrating that lower income, lower educational attainment, rurality, and increased travel burden negatively impact cancer care and outcomes across various malignancies [16, 17, 18, 19]. Prior studies evaluating the role of SDH in sbNETs have largely focused on individual variables. For example, DiPeri et al. identified associations between low income, lack of insurance, and residence outside metropolitan areas with worse overall survival using NCDB data [19]. Our study builds upon the prior work by using a composite SDH score to assess the cumulative impact of multiple social disadvantages. This approach provides a broader perspective on how overlapping social factors may contribute to disparities in cancer outcomes.

The role of institutional context was equally notable. Patients treated at non‐MSHs experienced markedly improved survival compared to those treated at MSHs, independent of SDH burden. This may reflect systemic disparities in hospital infrastructure, access to multidisciplinary care, and the availability of specialty services such as surgical oncology and endocrinology [20]. Prior research has shown that MSHs, while critical safety‐net providers, may be under‐resourced and overburdened, leading to delays in diagnosis and suboptimal adherence to clinical guidelines [21, 22]. Importantly, the impact of high SDH burden on survival was more pronounced at MSHs than at non‐MSHs, suggesting that well‐resourced institutions may buffer some of the adverse effects of social disadvantage. Notably, hospital volume was not associated with survival outcomes in this cohort, which may reflect the standardized nature of surgical treatment for early‐stage sbNETs that limits variability by institutional caseload. Alternatively, it may suggest that access to non‐MSHs and subspecialty care (factors not captured by volume metrics) plays a more critical role in influencing survival in rare tumors such as sbNETs.

Racial disparities were also evident in the present study. Black patients with sbNETs had a significantly higher risk of mortality compared to White patients, even after adjusting for SDH and hospital factors. A study by Goksu et al. using the Surveillance, Epidemiology, and End Results data set similarly identified race/ethnicity to be an independent prognostic factor in patients with all non‐pancreatic gastrointestinal NETs [22]. This persistent disparity underscores the influence of structural racism and systemic barriers that extend beyond socioeconomic indicators [23, 24]. Factors such as implicit bias, communication gaps, and differential access to high‐quality care have all been implicated in previous studies of racial disparities in oncology [25, 26]. Conversely, Hispanic ethnicity was not independently associated with worse outcomes in our analysis. This finding is consistent with observations in other cancer populations, where Hispanic patients often demonstrate comparable or even better outcomes despite elevated social risk [26, 27]. However, this relationship is likely multifactorial and may reflect unmeasured variables such as cultural factors, social support networks, or access to care. Importantly, treating Hispanic patients as a single group may obscure meaningful differences among subgroups, and further research is needed to better understand these nuances [26, 27, 28].

The study findings carry implications for clinical care and public health policy. First, routine screening for SDH factors at the point of cancer diagnosis could identify high‐risk patients who may benefit from enhanced navigation services or social support interventions. Our study demonstrated that patients with higher SDH burden had significantly worse survival, with a 24% increased hazard of death, suggesting a clear opportunity for risk stratification and targeted care coordination. Second, policies aimed at strengthening MSHs, through funding, workforce support, and integration into specialty referral networks, could alleviate institutional disparities and improve care delivery for vulnerable populations. Notably, we found that treatment at non‐MSHs was independently associated with improved survival, and that the survival gap between low‐ and high‐SDH patients was markedly greater at MSHs compared to non‐MSHs, highlighting how institutional context can compound or mitigate social disadvantage. Third, the SDH composite score used in this study based on income, education, rurality, and travel distance may serve as a practical tool to identify patients at elevated risk. Although rurality and long travel distance may appear conceptually related, our analysis showed no patient‐level overlap, and each variable captured a distinct dimension of access to care. While the SDH score has not yet been validated for clinical use, such composite indices could inform resource allocation, prioritize referrals, and be incorporated into clinical dashboards to support equitable care planning. Finally, healthcare databases and quality initiatives should continue to incorporate SDH metrics to facilitate real‐time monitoring, institutional benchmarking, and the development of equity‐focused interventions. Similar calls for reform have been suggested to be of benefit in the treatment of other malignancies of the breast, colon, lung, and prostate [29].

The study has several limitations of note. First, the analysis is retrospective in design with reliance on registry data, which may lack granularity on individual‐level socioeconomic variables, tumor burden, and recurrence data. The SDH score, although composite, is based on zip code–level aggregates and may misclassify individual patient exposures. Additionally, while the NCDB captures a large proportion of US cancer cases, it does not include non‐CoC‐accredited hospitals, which may limit generalizability. There is also potential for residual confounding and misclassification bias, particularly in aggregate SDH measures. The observed use of radiotherapy in a small number of early‐stage cases is atypical and may reflect potential miscoding within the NCDB data set. Furthermore, the NCDB does not provide tumor‐specific volume metrics for sbNETs to distinguish them from overall small bowel malignancies, nor does it explicitly define guideline‐concordant care. Despite these limitations, our study represents one of the largest to investigate the dual impact of SDH and hospital type in a rare tumor context. Although our retrospective design limits causal inference, the strength and consistency of the associations suggest a potential mechanistic link between sociogeographic disadvantage, institutional context, and long‐term survival. These findings underscore the importance of looking beyond tumor biology when addressing outcome disparities and highlight the need for a systems‐level approach to improving equity in cancer care.

In conclusion, this study demonstrates that both higher social disadvantage and care at MSH are independently linked to poorer survival in patients with early‐stage well‐differentiated sbNETs. While these tumors are typically indolent with excellent prognosis, our findings suggest that social and institutional factors significantly influence outcomes. The compounded effect of high SDH burden at MSHs points to broader structural inequities that go beyond individual patient characteristics. These results underscore the importance of incorporating social risk screening into cancer care and strengthening support for hospitals that serve vulnerable populations. Addressing these disparities will require a coordinated effort across clinical, institutional, and policy levels.

Consent

The authors have nothing to report.

Conflicts of Interest

The authors declare no conflicts of interest.

IRB Statement

The study did not involve living human subjects or accessing identifiable information or identifiable biospecimen. For this reason, the UM's Human Subject Research Office (HSRO), determined that this study does not require institutional review board (IRB) review, approval, or oversight.

Synopsis

Patients with early‐stage sbNETs who face greater social disadvantage, measured by a composite social determinant of health score, have worse long‐term survival. Outcomes were impacted by treatment at Minority‐Serving Hospitals, where survival was lower compared with non‐ Minority‐Serving Hospitals. These findings highlight the prognostic value of social determinants of health in risk stratification and equity‐focused care.

Alnajar A., Akcin M., Lew J. I., and Vaghaiwalla T. M., “Higher Composite Social Determinants of Health Scores Are Associated With Worse Survival in Patients With Small Bowel Neuroendocrine Tumors,” Journal of Surgical Oncology 133 (2025): 117‐125. 10.1002/jso.70143.

Data Availability Statement

Data for this study were obtained from the National Cancer Database (NCDB). The NCDB is not publicly available; access requires a formal application. The American College of Surgeons and Commission on Cancer have not verified and are not responsible for the analytic methods or conclusions of this study.