Individual and combined effects of marital status, household income, and residence on cancer management and survival in primary bone cancer: a SEER-based retrospective cohort study

Introduction

Primary bone cancers are rare and highly heterogeneous malignancies that account for a very small fraction of all newly diagnosed cancers (1,2). In 2025, an estimated 3,770 new cases of bone and joint cancers and approximately 2,190 related deaths are expected to occur in the United States (3). Socioeconomic status (SES) is a multidimensional construct encompassing several aspects of an individual’s social and economic position, including financial resources, educational attainment, occupational status, social support networks, and residential environment (4,5). According to the Fundamental Cause Theory, SES serves as a “fundamental cause” of health disparities by shaping individuals’ access to flexible resources that can be used to avoid disease risks and reduce mortality (6,7). Extensive evidence has demonstrated that SES indicators such as marital status, household income, and residence are significantly associated with cancer diagnosis, treatment, and prognosis (8-12). In primary bone cancer, the influence of SES may be particularly important because management often requires timely diagnosis, complex multimodal treatment strategies, and access to specialized high-volume centers, making patients highly dependent on healthcare accessibility and resource availability (13,14). Therefore, disparities in SES may have a substantial impact on the management of primary bone cancer. However, owing to the rarity of primary bone cancers, research on SES-related disparities in this population remains extremely limited. Moreover, existing studies have predominantly focused on single SES indicators, isolated oncologic outcomes, or specific histologic subtypes, with limited efforts to comprehensively evaluate how multiple SES factors jointly influence diverse clinical endpoints across the continuum of primary bone cancer care (15-18). These uncertainties highlight an important knowledge gap regarding the relative contribution of socioeconomic disadvantage across the continuum of primary bone cancer care.

Investigating SES disparities in the management of primary bone cancer may help elucidate the underlying mechanisms through which socioeconomic inequalities influence diagnosis, treatment, and prognosis, thereby informing future research on health disparities and guiding targeted policy interventions. Therefore, in this study, we utilized data from the Surveillance, Epidemiology, and End Results (SEER) database to systematically examine three key and available SES indicators, including marital status, household income, and residence, and developed an integrated SES risk score to comprehensively evaluate the association between socioeconomic factors, composite SES risk, and multiple cancer-related outcomes. We present this article in accordance with the STROBE reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-1-0330/rc).

Methods

Patient selection and variable classification

This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. Data for this study were obtained from the SEER database, a population-based cancer registry that covers approximately half of the U.S. population (19). In this study, we utilized data from the SEER-17 registries, which provide detailed demographic, clinical, and treatment information (20). Patients with primary bone cancer were identified according to the International Classification of Diseases for Oncology, Third Edition (ICD-O-3) codes (C40 and C41). Patients aged 20 years or older who were diagnosed between 2000 and 2022 were included. Patients were excluded if they had unknown information regarding race, marital status, median household income, or residence; if the SEER stage at diagnosis was unknown; or if data on cancer-specific survival (CSS) or overall survival (OS) were missing. The detailed selection process for the study population is shown in Figure 1. SEER employs standardized data collection procedures and undergoes rigorous quality control. Survival outcomes are ascertained through linkages with the National Center for Health Statistics and the Social Security Administration, ensuring high completeness and accuracy of vital status information (21).

Figure 1 Patient screening flowchart. SEER, Surveillance, Epidemiology, and End Results; USD, United States dollar.

SES indicators included marital status, median household income, and residence, which were selected based on prior evidence demonstrating their important roles as key determinants of access to care, treatment patterns, and survival across multiple malignancies (8-12). Marital status was categorized as married and unmarried, with the latter including single, divorced, separated, widowed, and unmarried/domestic partner (22). Median household income was obtained from the SEER database and is inflation-adjusted to 2023 United States dollar (USD) at the county level. Based on the approximate national median household income in 2023, it was categorized into <80,000 and ≥80,000 USD (23). Residence was classified as urban or rural according to the SEER Rural-Urban Continuum Codes (RUCC), with codes 1–3 defined as urban and 4–5 as rural (24).

Statistical analysis

The associations of marital status, median household income, and residence with tumor-related outcomes, including the interval from diagnosis to treatment, stage at diagnosis, receipt of surgery, and survival (CSS and OS), were systematically evaluated. Given that marital status, income, and residence have been recognized as important indicators of SES and have been shown to influence oncologic outcomes in various cancers, we further constructed an overall SES index based on these three factors (11,12). Being unmarried, having a median household income of <80,000 USD, and living in a rural area were considered adverse socioeconomic factors. Patients with 0, 1, and 2–3 adverse factors were categorized as having low, moderate, and high SES risk, respectively.

The time from diagnosis to treatment was expressed as the mean ± standard deviation (SD), and comparisons between groups were conducted using the Welch two-sample t-test. Stage at diagnosis was treated as an ordered categorical variable (distant > regional > localized), and its associations with marital status, median household income, residence, and SES risk were evaluated using ordinal logistic regression models. Specifically, this analysis aimed to examine whether differences in socioeconomic factors were associated with the likelihood of being diagnosed at a more advanced cancer stage. For analyses of marital status, median household income, and residence, the multivariable model was adjusted for year of diagnosis, age, sex, race, marital status, median household income, residence, histology, and grade, excluding the variable itself to avoid multicollinearity. For analyses of SES risk, adjustments were made for year of diagnosis, age, sex, histology, and grade.

The associations of marital status, median household income, residence, and SES risk with the likelihood of receiving surgical treatment were examined using logistic regression models. For analyses of marital status, median household income, and residence, the multivariable model was adjusted for year of diagnosis, age, sex, race, marital status, median household income, residence, histology, grade, and stage, excluding the variable of interest itself. For analyses of SES risk, adjustments were made for year of diagnosis, age, sex, race, histology, grade, and stage.

Associations between marital status, median household income, residence, SES risk, and survival outcomes (CSS and OS) were first visualized using Kaplan-Meier survival curves. Subsequently, multivariable Cox proportional hazards regression models were used to adjust for covariates. For analyses of marital status, median household income, and residence, the multivariable model was adjusted for year of diagnosis, age, sex, race, marital status, median household income, residence, histology, grade, stage, surgery, radiotherapy, chemotherapy, and time from diagnosis to treatment, excluding the variable itself. For analyses of SES risk, adjustments were made for year of diagnosis, age, sex, race, grade, stage, local surgery, radiotherapy, chemotherapy, and time from diagnosis to treatment. To account for potential competing risks in CSS, additional analyses were performed using Fine-Gray subdistribution hazard models, with non-cancer death treated as a competing event.

In addition, formal pairwise interaction analyses were conducted to evaluate potential multiplicative interactions among marital status, median household income, and residence. Interaction terms were incorporated into multivariable regression models for each clinical endpoint, and likelihood ratio tests were used to compare models with and without interaction terms. All statistical analyses were performed using R software (version 4.3.2; R Foundation for Statistical Computing, Vienna, Austria). Two-sided P values <0.05 were considered statistically significant.

Results

Patient baseline characteristics

As shown in Table 1, a total of 8,301 patients with primary bone cancer diagnosed between 2000 and 2022 were included in the analysis. The mean age at diagnosis was 51±19 years, and 55.8% of patients were male. The majority of patients were White (82.6%), followed by Black (9.0%) and other races (8.4%). Regarding socioeconomic characteristics, 56.1% of patients were married, 55.0% resided in areas with a median household income ≥80,000 USD, and 90.2% lived in urban regions. Based on these factors, 31.1%, 42.7%, and 26.2% of patients were classified as having low, moderate, and high SES risk, respectively. Histologically, osteosarcoma accounted for 23.6% of cases, Ewing sarcoma for 6.0%, and other histologic types for 70.5%. Tumor grade was Grade I–II in 35.2%, Grade III–IV in 31.9%, and unknown in 32.9% of patients. At diagnosis, 46.1% of tumors were localized, 36.6% were regional, and 17.3% were distant. Most patients (85.1%) underwent primary surgery, while 33.7% and 28.7% received chemotherapy and radiotherapy, respectively. The mean interval from diagnosis to treatment initiation was 36±50 days, and the mean follow-up duration was 65±61 months. Baseline characteristics stratified by SES risk are presented in Table S1.

Interval from diagnosis to treatment

As shown in Table 2, the mean time from diagnosis to treatment varied according to patients’ socioeconomic factors: patients who were married had a shorter interval to surgery compared with unmarried patients (35±45 vs. 38±55 days, P=0.003); patients with a median household income ≥80,000 USD also had a shorter interval to treatment compared with those with an income <80,000 USD (34±49 vs. 38±51 days, P<0.001); and urban patients had a shorter mean interval to treatment compared with rural patients (34±50 vs. 38±53 days, P=0.04).

As shown in Table S1, when considering overall SES risk, there was a significant trend toward longer intervals with increasing SES disadvantage. Patients with low, moderate, and high SES risk had mean times to treatment of 34±45, 37±51, and 39±54 days, respectively, with a statistically significant difference among groups (P<0.001).

Stage at diagnosis

As shown in Table 3, after adjustment for potential confounders, unmarried patients had a higher likelihood of being diagnosed at a more advanced stage compared with married patients [odds ratio (OR) =1.10, 95% confidence interval (CI): 1.01–1.20, P=0.03]. By contrast, no significant associations were observed between median household income (<80,000 vs. ≥80,000 USD, OR =0.99, 95% CI: 0.91–1.09, P=0.91) or residence (rural vs. urban, OR =1.04, 95% CI: 0.90–1.20, P=0.63) and cancer stage in the multivariable model. Regarding overall SES risk, there was a clear trend toward a more advanced stage at diagnosis with increasing socioeconomic disadvantage. Patients with moderate SES risk had higher odds of being diagnosed at a more advanced stage (OR =1.11, 95% CI: 1.02–1.21, P=0.01), and patients with high SES risk had the highest odds (OR =1.15, 95% CI: 1.04–1.26, P=0.003) compared with those with low SES risk.

Receipt of surgery

As shown in Table 4, after adjusting for potential confounders, unmarried patients were significantly less likely to undergo primary surgery compared with married patients (OR =0.71, 95% CI: 0.62–0.83, P<0.001). Similarly, patients with a median household income <80,000 USD had lower odds of receiving surgery than those with higher income (OR =0.84, 95% CI: 0.72–0.97, P=0.02), and patients living in rural areas were also less likely to undergo surgery compared with urban residents (OR =0.80, 95% CI: 0.63–0.98, P=0.03).

Overall SES risk showed a graded association with surgical treatment, with higher risk corresponding to lower likelihood of surgery: patients with moderate SES risk had lower odds of receiving surgery compared with those with low SES risk (OR =0.77, 95% CI: 0.65–0.92, P=0.003), and patients with high SES risk had the lowest odds (OR =0.58, 95% CI: 0.48–0.70, P<0.001).

Survival outcomes

As shown in Figure 2, OS was significantly worse in unmarried patients compared with married [hazard ratio (HR) =1.08, 95% CI: 1.01–1.18, P=0.04, Figure 2A]. Patients with a median household income <80,000 USD also had poorer OS than those with higher income (HR =1.12, 95% CI: 1.05–1.20, P<0.001, Figure 2B). Rural residence was associated with decreased OS compared with urban residence (HR =1.13, 95% CI: 1.02–1.26, P=0.02, Figure 2C). When SES factors were combined into a composite risk score, patients with moderate and high SES risk exhibited a stepwise decrease in OS (moderate vs. low: HR =1.09, 95% CI: 1.00–1.17, P=0.048; high vs. low: HR =1.18, 95% CI: 1.08–1.29, P<0.001, Figure 2D). As shown in Figure 3, CSS was poorer among unmarried patients than among married patients (HR =1.09, 95% CI: 1.02–1.19, P=0.02, Figure 3A). Lower household income (HR =1.14, 95% CI: 1.05–1.26, P=0.002, Figure 3B) and rural residence (HR =1.14, 95% CI: 1.01–1.30, P=0.04, Figure 3C) were also associated with worse CSS. Similarly, increasing SES risk corresponded to progressively poorer CSS (moderate vs. low: HR =1.10, 95% CI: 1.01–1.20, P=0.04; high vs. low: HR =1.19, 95% CI: 1.07–1.32, P=0.001, Figure 3D).

Figure 2 Kaplan-Meier curves for overall survival according to (A) marital status, (B) household income, (C) residence, and (D) composite socioeconomic status. CI, confidence interval; HR, hazard ratio; USD, United States dollar.

Figure 3 Kaplan-Meier curves for cancer-specific survival according to (A) marital status, (B) household income, (C) residence, and (D) composite socioeconomic status. CI, confidence interval; HR, hazard ratio; USD, United States dollar.

As shown in Table 5, after adjusting for potential confounders, adverse socioeconomic factors were independently associated with poorer survival. Unmarried patients had significantly worse OS (HR =1.23, 95% CI: 1.14–1.32, P<0.001) and CSS (HR =1.09, 95% CI: 1.00–1.19, P=0.04) compared with married patients. Patients with median household income <80,000 USD had decreased OS (HR =1.15, 95% CI: 1.07–1.24, P<0.001) and CSS (HR =1.15, 95% CI: 1.06–1.26, P=0.001). Rural residence was independently associated with worse OS (HR =1.14, 95% CI: 1.03–1.27, P=0.004) and CSS (HR =1.15, 95% CI: 1.02–1.31, P=0.03). Regarding overall SES risk, there was a graded relationship between higher risk and worse survival outcomes. Patients with moderate SES risk had poorer survival compared with those with low SES risk (OS: HR =1.20, 95% CI: 1.08–1.31, P<0.001; CSS: HR =1.17, 95% CI 1.06–1.29, P=0.002), whereas those with high SES risk experienced the worst survival (OS: HR =1.32, 95% CI: 1.20–1.45, P<0.001; CSS: HR =1.24, 95% CI: 1.11–1.38, P<0.001). In addition, competing risk analyses using Fine-Gray subdistribution hazard models yielded consistent results, with moderate and high SES risk remaining significantly associated with increased cancer-specific mortality (Figure S1).

Interaction analyses

Pairwise interaction analyses among marital status, household income, and residence were performed across all clinical endpoints (Table S2). No statistically significant interactions were observed for stage at diagnosis, receipt of surgery, or OS. However, borderline interactions between household income and residence were consistently observed for stage at diagnosis (P=0.08), receipt of surgery (P=0.09), and CSS (P=0.07).

Discussion

SES has long been recognized as a major determinant of cancer outcomes (8-10). It influences not only cancer incidence and stage at diagnosis but also access to care and survival. Recent evidence indicates that individuals with lower SES are more likely to present with advanced disease, experience treatment delays, receive fewer definitive or advanced therapies, and have inferior survival (8-10). These disparities likely result from differences in healthcare accessibility, health literacy, comorbidity burden, social support, and environmental exposures (25-27). Marital status, household income, and residence are core SES indicators that have been extensively studied across multiple cancers (11,12). Married patients often benefit from stronger social support, timely care-seeking, and better adherence to treatment (11,22). Higher income facilitates access to specialized care and novel therapies, whereas urban residence generally ensures greater medical resources and service availability (12,28-30). Together, these dimensions interact to shape disparities in cancer management and prognosis.

In terms of primary bone cancer, research on SES disparities remains very limited (15-18). Most previous studies focused on a single SES dimension or outcome, often restricted to specific histologic types. For example, Lin et al. reported that low income and rural residence predicted worse outcomes in chondrosarcoma, while Hu et al. found that insurance status was associated with stage at diagnosis and that racial differences existed in osteosarcoma treatment (15,16). In the present study, we comprehensively evaluated three SES indicators, including marital status, income, and residence, and developed a composite SES risk score to assess their combined influence on interval between diagnosis and treatment, stage at diagnosis, treatment, and survival among patients with primary bone cancer. All three factors independently affected both OS and CSS, though likely through different mechanisms. Marital status was associated with interval between diagnosis and treatment, stage at diagnosis, likelihood of receiving treatment, and diagnostic-to-treatment interval, suggesting that marriage has a comprehensive impact on cancer management. Lower income and rural residence were linked to longer treatment delays and lower treatment access but not to stage at diagnosis, implying that financial and geographic barriers may primarily hinder care after diagnosis. Importantly, the composite SES risk score revealed a graded association. Higher SES risk correlated with longer treatment delays, more advanced stage, lower treatment receipt, and worse OS and CSS.

From a health systems perspective, this graded pattern suggests that socioeconomic disadvantage does not act in isolation but accumulates across multiple dimensions, leading to progressively reduced access to timely and effective cancer care. In particular, the observed stepwise decrease in surgical treatment with increasing SES risk highlights potential structural barriers within healthcare delivery, such as disparities in referral pathways, access to specialized surgical centers, and resource allocation. These findings underscore the need for system-level interventions that prioritize high-risk populations, including improving regional care networks, enhancing access to specialized oncology services, and reducing financial and geographic barriers to treatment (31,32). From a clinical and healthcare delivery perspective, these findings have important implications for the development of patient navigation systems. Patient navigation programs, which aim to guide patients through complex diagnostic and treatment pathways, have been shown to improve timely access to care, treatment adherence, and outcomes, particularly among socioeconomically disadvantaged populations (33,34). In the context of primary bone cancer, where care often requires multidisciplinary coordination and referral to high-volume specialized centers, patients with low income, limited social support, or rural residence may face substantial barriers in navigating the healthcare system (13,14). Our findings suggest that such patients represent a high-risk group who may benefit most from structured navigation support, including early referral coordination, financial counseling, and follow-up management.

In summary, this study provides a systematic assessment of SES influences on the diagnosis, treatment, and survival of primary bone cancers. Its strengths include a large population-based design, integration of multiple SES indicators into a composite score, and simultaneous analysis of diverse clinical outcomes.

However, several limitations should be acknowledged. The retrospective nature of SEER data limits causal inference. SES measures were largely categorical or area-based and may not fully reflect individual-level differences. In particular, median household income was derived at the county level and used as a proxy for individual SES, which may introduce ecological bias and contribute to residual confounding. Unmeasured confounders such as comorbidities, insurance status, and institutional variation may also contribute to residual bias. Despite these limitations, our findings underscore the substantial and cumulative impact of socioeconomic factors on primary bone cancer care and outcomes, highlighting the need for targeted policy and clinical interventions to reduce SES-related disparities and promote equity in cancer management.

Conclusions

Marital status, median household income, and residential location were all closely associated with survival outcomes in patients with primary bone cancer. However, different socioeconomic factors affected distinct dimensions of cancer management. Furthermore, socioeconomic disadvantages exerted an additive effect, with higher composite SES risk scores associated with longer diagnostic-to-treatment delays, more advanced disease stage, lower likelihood of receiving surgery, and poorer survival, demonstrating a graded association. These findings highlight the need for targeted clinical and policy interventions, such as the implementation of patient navigation strategies and structural improvements in healthcare delivery, to reduce socioeconomic disparities and promote equitable access to cancer care.

Acknowledgments

We would like to express our gratitude to the SEER (Surveillance, Epidemiology, and End Results) program, which provided the comprehensive data used in this study. The SEER database is an invaluable resource for cancer research, and its contribution to advancing our understanding of cancer outcomes is greatly appreciated.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-1-0330/rc

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-1-0330/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-1-0330/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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