Lung Cancer Disparities in the United States: The Role of Smoking, Comorbidities, Socioeconomic Status, and Regional Variation

Lung Cancer Disparities in the United States: The Role of Smoking, Comorbidities, Socioeconomic Status, and Regional Variation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Lung Cancer Disparities in the United States: The Role of Smoking, Comorbidities, Socioeconomic Status, and Regional Variation Bugra Zengin, Mohammad Alqaisieh, Salih Akgun, Canan D. Dirican, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8834204/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Lung cancer disproportionately affects certain racial and regional populations in the United States. This study examines disparities in clinical characteristics, socioeconomic distribution, and hospital outcomes among adults hospitalized with lung cancer. Methods A retrospective analysis was performed using the National Inpatient Sample 2019–2020 (NIS). Patients with a primary lung cancer diagnosis were evaluated across racial groups and U.S. regions. Key variables included smoking status, comorbidity burden, length of stay (LOS), mortality, and total hospital charges. Results White patients demonstrated the highest smoking prevalence, whereas Black patients experienced the longest LOS. Hispanic patients incurred the highest total hospital charges. Regionally, lung cancer admissions were most common in the South, which also showed lower socioeconomic status and reduced screening access. In-hospital mortality was not significantly associated with hospital region, whereas socioeconomic status showed a graded association, with progressively lower mortality risk among patients in higher income quartiles. Conclusions Significant racial and regional disparities exist in lung cancer burden and hospital outcomes. Targeted interventions addressing socioeconomic barriers, screening inequities, and comorbidity management are needed to reduce these disparities. Lung cancer disparities socioeconomic factors comorbidities regional variation Figures Figure 1 Simple Summary Lung cancer remains the leading cause of cancer-related mortality in the United States, but the burden of disease is not shared equally across populations. In this study, we used a large, nationally representative inpatient dataset to evaluate how race, geography, comorbidity burden, and socioeconomic status contribute to disparities in lung cancer hospitalizations, outcomes, and resource use. We found substantial differences in smoking prevalence, disease severity, hospital charges, and length of stay across racial and regional groups. Understanding these patterns can help guide more equitable screening strategies, resource allocation, and public health interventions. 1. Introduction Lung cancer remains the leading cause of cancer-related mortality in the United States, accounting for approximately 125,000 deaths annually. 1 Although lung cancer incidence and mortality have declined over recent decades, largely driven by reductions in smoking and advances in early detection and treatment overall survival remains poor, with a five-year survival rate of approximately 20%. 1–3 Prognosis is influenced by multiple factors, including tumor stage and histology, comorbidity burden, behavioral risk factors such as smoking, and access to timely, guideline-concordant care. 4 – 8 Substantial disparities in lung cancer outcomes have been consistently documented across racial, ethnic, socioeconomic, and geographic groups. 9 Differences in survival by race and ethnicity are not fully explained by biological factors or tumor genomics, as studies evaluating mutation frequency and ancestry-related markers have failed to identify consistent biologic drivers of these disparities. 10 , 11 Instead, a growing body of evidence suggests that inequities in access to care, quality of treatment, and timeliness of diagnosis often shaped by socioeconomic disadvantage play a central role in determining stage at presentation and subsequent outcomes. 12 , 13 Comorbidity burden further complicates this relationship, as chronic conditions disproportionately affecting minority populations may limit treatment options and worsen survival. 8 , 14 Geographic variation also contributes meaningfully to lung cancer burden and outcomes. Southern states consistently demonstrate higher lung cancer incidence and mortality, a pattern associated with socioeconomic disadvantage, lower access to preventive services, and reduced uptake of lung cancer screening. 1 , 12 , 15 In contrast, Western regions often exhibit lower incidence but higher healthcare costs and distinct patterns of resource utilization, reflecting regional differences in healthcare delivery and pricing structures. 16 These geographic and socioeconomic factors intersect with race and ethnicity to shape patterns of disease presentation, hospitalization, and outcomes. Despite extensive literature on lung cancer disparities, most studies have focused on incidence, stage at diagnosis, or long-term survival, with less attention given to disparities in inpatient outcomes and healthcare utilization during hospitalization. 17 Moreover, disparities are often examined in isolation, without accounting for the intersection of race, socioeconomic status, comorbidity burden, and geographic region. To address these gaps, we used a nationally representative inpatient database to evaluate racial, socioeconomic, and regional disparities in lung cancer hospitalizations, focusing on in-hospital mortality, length of stay, and total hospital charges. By examining these intersecting factors, our study aims to provide a more granular understanding of structural inequities shaping inpatient lung cancer care in the United States. 2. Materials and Methods We conducted a cross-sectional observational study using the National Inpatient Sample 2019–2020(NIS), the largest publicly available all-payer inpatient database in the United States. Adults aged ≥ 18 years with a primary diagnosis of lung cancer were identified using ICD-10 codes previously validated for epidemiologic surveillance. We quantified regional admission prevalence by calculating the survey-weighted proportion of hospitalizations attributed to primary lung cancer across the four U.S. Census regions: Northeast, Midwest, South, and West. Patients were further stratified by race and ethnicity (White, Black, Hispanic, Other), with the Other category representing Asian or Pacific Islander, Native American, or individuals classified as Other in the NIS dataset. Primary study outcomes included length of stay (LOS), in-hospital mortality, total hospital charges, smoking prevalence, and distribution across income quartiles. Secondary analyses compared regional patterns of lung cancer admission prevalence and regional smoking prevalence, reflecting known geographic variation in lung cancer risk and healthcare access across the United States. Accordingly, observed disparities reflect differences in inpatient burden of care rather than disease incidence or prevalence. All analyses were performed using STATA/SE 17.0 (StataCorp LLC, College Station, TX). Given the complex survey design of the NIS, we applied survey weights, strata, and cluster variables in accordance with HCUP recommendations. Descriptive statistics were compared using chi-square tests for categorical variables and ANOVA for continuous variables. Multivariable models incorporated survey-weighted procedures to preserve national representativeness. Statistical significance was defined as p < 0.05. Multivariable survey-weighted logistic and linear regression models were constructed to evaluate the independent associations of race with in-hospital mortality, length of stay, and total hospital charges. Race was modeled as a categorical variable with White patients serving as the reference group, and all models adjusted for age, sex, smoking status, comorbidities (COPD, congestive heart failure, diabetes mellitus, chronic kidney disease, obesity), income quartile, and hospital region. This study used publicly available, de-identified data and was therefore exempt from Institutional Review Board approval. 3. Results 3.1 Patient Characteristics by Race A total of 213,435 weighted hospitalizations for primary lung cancer were included in the analysis. The mean age of the cohort was 68.8 years, and 50.5% of patients were female. The racial distribution consisted of approximately 75% White, 12% Black, 5% Hispanic, and 8% classified as Other. Significant racial differences were observed in baseline demographic and clinical characteristics. Patients aged 60 years or older were most common among White patients (84.2%). Smoking prevalence differed significantly across race (p < 0.001), with White patients having the highest proportion of smokers (47.5%), followed by Hispanic patients (41.6%), Black patients (40.6%), and patients categorized as Other race (38.5%). Comorbidity prevalence also varied by race. COPD was most frequent among White patients (39.3%), followed by Black patients (34.3%) and Hispanic patients (26.3%). Congestive heart failure was highest in Black patients (14.4%), compared with Whites (12.0%) and Hispanics (11.0%). Diabetes mellitus was most common among Hispanic patients (31.1%). Obesity was highest in White patients (11.9%) and lowest among patients classified as Other race (6.6%). Chronic kidney disease was most prevalent in Black patients (16.6%). 3.2 Hospital Outcomes by Race Mean LOS differed significantly across races (p < 0.001). Black patients had the longest LOS (6.95 days), followed by Hispanic (6.62), Other (6.27), and White patients (5.83). Regression analysis confirmed race as a predictor of LOS (β = 0.262, p < 0.001). Total hospital charges also varied: Hispanic patients incurred the highest mean charges ( $ 114,594), followed by Other ( $ 102,646), Black ( $ 88,498), and White patients ( $ 87,058). 3.3 Regional Variation Regional analyses demonstrated substantial geographic disparities in lung cancer hospitalizations. The South accounted for the highest proportion of admissions at 39.5%, followed by the Midwest, Northeast, and West. Smoking prevalence also differed significantly across regions (p < 0.001). The Midwest demonstrated the highest proportion of smokers at 47.0%, followed by the Northeast at 48.4% and the West at 46.6%, while the South had the lowest smoking prevalence at 42.9%. Race distributions varied significantly by hospital region (p < 0.001). White patients were most heavily concentrated in the South, representing 38.6% of all White lung cancer hospitalizations, while Black patients were even more concentrated in this region at 53.8%. Hispanic patients showed a different pattern, with the West accounting for 29.1% of all Hispanic lung cancer hospitalizations and the Northeast for 21.9%. Patients categorized as Other race were most represented in the West at 32.4%, followed by the Northeast at 24.6%. Income patterns also demonstrated regional variation. The lowest national income quartile accounted for 28.6% of all lung cancer hospitalizations nationwide but was disproportionately represented in the South, where 40.3% of patients were from the lowest-income areas. The Midwest also showed elevated representation at 27.3%. In contrast, the Northeast and West had lower proportions from the lowest-income quartile at 17.3% and 17.1%, respectively. Patients from the highest income quartile were most concentrated in the Northeast (35.1%) and West (30.8%) and least represented in the South (12.1%). Inpatient mortality did not differ significantly across regions (p = 0.185). 3.4 Socioeconomic Status and Racial Distribution Income quartile demonstrated a strong association with race (p < 0.001). Black patients had the highest concentration in the lowest national income quartile, with 55.1% residing in ZIP codes corresponding to the bottom 25% of median household income. Hispanic patients also showed a high proportion in this quartile at 42.6%. In contrast, White patients were more evenly distributed across income levels, with 24.7% in the lowest quartile and a substantially higher proportion in the highest income quartile. Patients categorized as Other race similarly showed greater representation in the upper income quartiles compared with Black and Hispanic patients. 3.5 Multivariable Analyses of Mortality, Length of Stay, and Hospital Charges In survey-weighted multivariable logistic regression adjusting for sex, age, smoking status, comorbidities (COPD, CHF, diabetes mellitus, chronic kidney disease, obesity), income quartile, and hospital region, race remained independently associated with in-hospital mortality. Each incremental racial category was associated with higher odds of mortality (adjusted OR 1.07, 95% CI 1.02–1.12, p = 0.003). Female sex was associated with significantly lower odds of in-hospital mortality (aOR 0.77, 95% CI 0.71–0.83, p < 0.001). Comorbid COPD (aOR 1.21), CHF (aOR 1.69), and CKD (aOR 1.20) were independently associated with increased mortality risk, while smoking status and obesity were associated with lower odds of in-hospital mortality. Income quartile demonstrated a graded protective association, with patients from higher-income ZIP codes experiencing lower mortality risk compared with those from the lowest-income quartile. In survey-weighted multivariable linear regression, race remained an independent predictor of length of stay (LOS). After full adjustment, higher racial category was associated with longer hospitalization (β = 0.26 days, 95% CI 0.18–0.34, p < 0.001). Female sex was associated with shorter LOS, while COPD, CHF, and CKD were associated with significantly longer hospitalizations. Increasing income quartile was associated with progressively shorter LOS, suggesting a socioeconomic gradient in inpatient utilization. Regional differences persisted, with shorter LOS observed in the Northeast and West compared with the reference region. In adjusted analyses of total hospital charges, race remained significantly associated with increased charges (β = $ 4,458, 95% CI $ 2,933– $ 5,984, p < 0.001), independent of demographic, clinical, socioeconomic, and regional factors. Female sex and smoking status were associated with lower charges, while COPD, CHF, and obesity were associated with significantly higher hospital charges. Patients treated in Western hospitals incurred substantially higher charges, whereas hospitals in the Northeast were associated with lower charges, reflecting persistent regional variation in healthcare pricing. 4. Discussion This nationally representative analysis reveals substantial racial, regional, and socioeconomic disparities among hospitalized lung cancer patients in the United States. 4.1 Racial Variation in Smoking and Comorbidity Burden Prior research has shown that racial disparities in lung cancer outcomes persist even after accounting for smoking exposure, suggesting that differences in comorbidity burden, disease severity, and access to care play important roles. 8 , 10 Black and Hispanic patients have been reported to experience worse clinical outcomes despite similar or lower smoking prevalence compared with White patients. 18 , 19 Consistent with this literature, we found that although White patients had the highest smoking prevalence, Black and Hispanic patients experienced worse inpatient outcomes, including longer lengths of stay and greater healthcare utilization, indicating that smoking behavior alone does not explain observed disparities. Ryan et al. has also described earlier lung cancer onset among racial and ethnic minority populations, attributed to socioeconomic disadvantage, cumulative environmental exposures, delayed screening, and a higher burden of systemic risk factors. 19 In our cohort, Black and Hispanic patients were younger at hospitalization than White patients, a pattern that may contribute to more complex hospital courses. The literature further demonstrates that Black and Hispanic populations bear a disproportionate burden of chronic comorbidities such as congestive heart failure, chronic kidney disease, and diabetes, reflecting long-standing inequities in preventive care. 14 , 20 , 21 We similarly observed higher prevalences of these conditions among Black and Hispanic patients, which likely contribute to prolonged hospitalization and increased resource utilization. Importantly, prior work by Williams et al. suggests that racial disparities in lung cancer outcomes often persist after adjustment for clinical factors. 22 In our fully adjusted models accounting for smoking status, comorbidity burden, socioeconomic status, and geographic region, race remained independently associated with longer length of stay, higher hospital charges, and modestly increased odds of in-hospital mortality, highlighting the role of broader structural and systemic factors in shaping inpatient lung cancer outcomes. 4.2 Resource Utilization Differences Prior studies have shown that racial and ethnic differences in hospital resource utilization reflect not only disease severity but also structural factors such as access to outpatient care, insurance coverage, and regional variation in healthcare pricing. Black patients, in particular, have been reported to experience longer hospitalizations, often attributed to delayed presentation, higher comorbidity burden, and barriers to timely ambulatory care. 16 , 23 Consistent with this literature, we found that Black patients had the longest lengths of stay. This pattern likely reflects a greater burden of chronic comorbidities and more complex inpatient management needs and may also be influenced by limited access to preventive and outpatient care, although such mechanisms cannot be directly assessed in an inpatient-only dataset. Hospital charges demonstrated a different pattern, with Hispanic patients incurring the highest total costs despite shorter average lengths of stay. While Hispanic patients were most frequently hospitalized in the South, they were also disproportionately represented in the West and Northeast, regions known to have substantially higher healthcare costs compared with the South and Midwest. Regional pricing variation alone may therefore account for a meaningful portion of the observed cost differences. 16 Beyond geography, prior studies suggest that differences in diagnostic intensity, procedural utilization, and patterns of care-seeking once hospitalized may contribute to increased inpatient costs among Hispanic patients, even in the absence of prolonged hospitalization. 24 , 25 Socioeconomic constraints, including underinsurance and limited access to timely outpatient evaluation, may further lead to more resource-intensive inpatient care. In adjusted analyses, racial differences in total hospital charges persisted after accounting for length of stay, comorbidity burden, income quartile, and hospital region, indicating that excess costs are not solely driven by longer hospitalization or measurable clinical severity and likely reflect a combination of regional pricing structures and structural barriers to outpatient care. 4.3 Geographic Disparities Prior research has shown that the Southern United States bears a disproportionate burden of lung cancer, driven by socioeconomic disadvantage, limited access to preventive care, and structural barriers to early detection. 26 Some studies have also reported lower documented smoking prevalence in the South, a finding often attributed to underreporting, inconsistent documentation, and reduced engagement in preventive healthcare rather than true differences in exposure. 27 – 29 Consistent with this literature, we found that the South accounted for nearly two-fifths of all lung cancer hospitalizations despite having the lowest recorded smoking prevalence in our cohort. This apparent discrepancy likely reflects incomplete capture of smoking behavior as well as broader gaps in risk awareness and routine healthcare utilization. 30 Limited access to lung cancer screening may further contribute to these patterns. Southern states have among the lowest rates of low-dose CT screening nationally despite high screening eligibility, and reduced screening has been associated with later-stage diagnosis and increased reliance on inpatient care. 4 , 15 , 31 Our findings of higher hospitalization burden in the South align with these observations. Socioeconomic disadvantages appear to amplify regional differences. The South had the highest proportion of patients residing in the lowest income quartile, a factor linked to delayed presentation and increased dependence on hospital-based care. 32 Although inpatient mortality did not differ significantly by region, disparities in stage at diagnosis and access to definitive treatment may influence outcomes beyond hospitalization. 33 Finally, regional variation in resource utilization persisted after adjustment, with hospitals in the West incurring higher total charges and those in the Northeast demonstrating shorter stays and lower costs. These differences likely reflect regional pricing and practice patterns rather than disease biology alone. 4.4 Intersection of Race and Socioeconomic Status Prior studies have consistently demonstrated pronounced socioeconomic gradients across racial and ethnic groups, with Black and Hispanic populations disproportionately residing in lower-income neighborhoods. These structural inequities influence housing stability, educational and employment opportunities, environmental exposures, insurance coverage, and access to preventive healthcare. 34 Lower neighborhood income has been closely linked to reduced primary care access, fewer preventive services, and limited availability of accredited lung cancer screening programs, contributing to delayed diagnosis and more advanced disease at presentation. 35 , 36 Consistent with this literature, we observed that Black patients were most heavily represented in the lowest income quartile, followed by Hispanic patients, whereas White and Other race patients were more frequently concentrated in higher income quartiles. Among Black patients, historical and systemic factors including residential segregation, disproportionate environmental exposures, and long-standing barriers to wealth accumulation likely contribute to reduced engagement in preventive care and delayed evaluation of respiratory symptoms, resulting in higher inpatient utilization. 30 , 34 , 37 Hispanic patients similarly demonstrated a substantial concentration in lower income quartiles, reflecting additional challenges related to immigration status, language barriers, underinsurance, and employment in sectors with limited health benefits. 38 – 40 These barriers may limit access to routine care and screening awareness, leading to more advanced disease or more resource-intensive inpatient evaluations, which may help explain the higher hospital charges observed in this group. Overall, the intersection of race and socioeconomic status plays a central role in shaping lung cancer presentation, screening uptake, and inpatient resource utilization. Our findings reinforce the need for targeted strategies to expand equitable screening access, strengthen culturally informed preventive care, and address upstream socioeconomic determinants that contribute to persistent disparities in lung cancer outcomes. 4.5 Policy and Practice Implications Targeted strategies are needed to reduce disparities in lung cancer outcomes. Expanding access to low dose CT screening, particularly in regions and populations with low uptake, may help facilitate earlier detection. Improving access to primary care and strengthening chronic disease management could reduce disease severity at presentation and decrease reliance on inpatient care. Policies that address structural determinants of health, including insurance coverage, transportation barriers, and regional shortages of specialty services, are also essential. In addition, culturally tailored smoking cessation programs and community-based education efforts may help engage high risk groups and reduce preventable disease burden. 4.6 Strengths and Limitations Strengths include the use of a large, nationally representative database and robust survey-weighted analyses. Limitations include the inability to assess cancer stage, outpatient care patterns, time-to-treatment, or tumor biology, which all influence outcomes. Future research integrating NIS data with cancer registry datasets could provide a more comprehensive picture. The incorporation of survey-weighted multivariable models strengthens the validity of our findings by accounting for confounding demographic, clinical, socioeconomic, and geographic factors. These interpretations are hypotheses generating and should be validated in future studies incorporating cancer stage, longitudinal outpatient data, and individual level measures of screening access and socioeconomic status. 5. Conclusions Lung cancer disparities in the United States are influenced by race, comorbidity burden, socioeconomic status, and geographic region. Interventions aimed at improving access to screening, preventive care, and equitable distribution of healthcare resources are essential for addressing these inequities. Declarations Competing Interests The authors have no relevant financial or non-financial interests to disclose. Ethics Approval This study used publicly available, de-identified administrative data from the HCUP National Inpatient Sample. In accordance with institutional and national research guidelines, ethical approval and individual patient consent were not required . Consent to Participate Not applicable. This research involved analysis of de-identified administrative data and did not involve human subjects directly. Consent to Publish Not applicable. This manuscript does not include any individual patient data, identifiable information, images, or case reports requiring specific publication consent. Funding The authors declare that no funds, grants, or other financial support were received during the preparation of this manuscript. Author Contribution All authors contributed to the study conception and design. Data acquisition, data cleaning, and statistical analysis were performed by Bugra Zengin, MD, Mohammad Alqaisieh, MD, and Salih Akgun, MD. The first draft of the manuscript was written by Bugra Zengin, MD. All authors reviewed and revised the manuscript critically for important intellectual content and approved the final version. Data Availability The datasets analyzed during the current study are publicly available through the Healthcare Cost and Utilization Project (HCUP) National Inpatient Sample (NIS) database, maintained by the Agency for Healthcare Research and Quality (AHRQ). Access to the data requires an HCUP Data Use Agreement and can be obtained at: [https://www.hcup-us.ahrq.gov/nisoverview.jsp](https:/www.hcup-us.ahrq.gov/nisoverview.jsp) References Siegel Mph RL, et al. Cancer statistics, 2023. CA Cancer J Clin. 2023;73:17–48. Islami F, et al. Proportion and number of cancer cases and deaths attributable to potentially modifiable risk factors in the United States. CA Cancer J Clin. 2018;68:31–54. Krist AH, et al. Screening for Lung Cancer: US Preventive Services Task Force Recommendation Statement. JAMA. 2021;325:962–70. Patricia Rivera M, et al. Addressing Disparities in Lung Cancer Screening Eligibility and Healthcare Access. An Official American Thoracic Society Statement. 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Racial Differences in Clinical Characteristics Variable White (%) Black (%) Hispanic (%) Other (%) P-value Smoking 47.45 40.58 41,63 38.46 <0.001 COPD 39.3 34.33 26.31 26.62 <0.001 CHF 12.01 14.42 11.09 9.83 <0.001 Diabetes 20.6 27.89 31.08 26 <0.001 Obesity 11.97 11.51 11.19 6.63 <0.001 CKD 11.74 16.59 12.27 10.11 60 84.21 76.95 79.04 78.99 <0.001 Table 2. Hospital Outcomes by Race Outcome White Black Hispanic Other Mean LOS (days) 5.83 6.95 6.62 6.27 Total Charges ($) 87,058 88,498 114,594 102,646 Table 3. Income Quartile Distribution by Race Income Quartile White (%) Black (%) Hispanic (%) Other (%) P-value Q1 (lowest) 24.49 55.05 38.21 21.15 <0.001 Q2 28.21 21.33 24.34 22.79 Q3 25.65 14.4 22.43 25.81 Q4 (highest) 21.64 9.23 15.02 30.25 Table 4. Regional Variation in Smoking, Mortality, and Income Region Smoking (%) Mortality (%) Q1 Income (%) Q4 Income (%) P-value Northeast 48.4 07.34 17.25 35.06 <0.001 Midwest 47.0 06.41 27.25 14.33 South 42.9 06.27 40.26 12.06 West 46.6 06.63 17.08 30.84 Table 5. Multivariable Survey-Weighted Logistic Regression for In-Hospital Mortality Outcome: In-hospital mortality Reference groups: White race, male sex, income quartile 1 (lowest), hospital region 1 Variable Adjusted OR 95% CI P value Race Black vs White 1.10 1.04–1.17 0.002 Hispanic vs White 1.05 0.96–1.15 0.278 Other vs White 1.03 0.95–1.12 0.462 Female sex 0.77 0.71–0.83 <0.001 COPD 1.21 1.11–1.31 <0.001 Congestive heart failure 1.69 1.52–1.89 <0.001 Diabetes mellitus 0.94 0.85–1.03 0.182 Chronic kidney disease 1.20 1.07–1.35 0.002 Smoking 0.65 0.60–0.71 60 years 1.01 0.91–1.12 0.850 Obesity 0.68 0.59–0.79 <0.001 Income quartile 2 vs 1 0.89 0.81–0.99 0.026 Income quartile 3 vs 1 0.82 0.74–0.91 <0.001 Income quartile 4 vs 1 0.78 0.69–0.88 <0.001 Region 2 vs 1 0.82 0.67–1.00 0.056 Region 3 vs 1 0.76 0.62–0.93 0.008 Region 4 vs 1 0.87 0.70–1.09 0.224 Table Footnote All models were survey-weighted using National Inpatient Sample sampling weights and adjusted for demographic, clinical, socioeconomic, and geographic variables. Race was modeled categorically with White patients as the reference group. Income quartile was defined using ZIP code–level median household income as follows: quartile 1 (0–25th percentile), quartile 2 (26th–50th percentile, including the median), quartile 3 (51st–75th percentile), and quartile 4 (76th–100th percentile). Hospital region was classified according to U.S. Census regions: region 1 (Northeast), region 2 (Midwest), region 3 (South), and region 4 (West). COPD = chronic obstructive pulmonary disease; CHF = congestive heart failure; CI = confidence interval; OR = odds ratio. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8834204","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":601169328,"identity":"d5b37070-1bf9-4e65-9c52-8b05588cbfc8","order_by":0,"name":"Bugra Zengin","email":"","orcid":"","institution":"Hamilton Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Bugra","middleName":"","lastName":"Zengin","suffix":""},{"id":601169329,"identity":"d934c19f-96e0-4a66-a7fa-16787abd8b11","order_by":1,"name":"Mohammad Alqaisieh","email":"","orcid":"","institution":"University of Illinois","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"","lastName":"Alqaisieh","suffix":""},{"id":601169330,"identity":"21c478b8-b3e2-48d9-9021-6f3a064da836","order_by":2,"name":"Salih Akgun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYFAC5obDQNIAiBkfMzAcIEYLI1wLszHRWpihWtikidIi336w8XABg52xfETuserCnDt5DOy9j1/g02JwJrHh8AyGZDPDG3lpt2due1bMwHPczAKvFgagFh4GZhvDGTlmt3m3HU5skEhjM8DrsP6HIC31YC3FRGlhuAG25bCZvESOGTNUC/MDvA67AbLF4LixAc8bY2mQljaeY2x4LZHvTz78maei2nB+e47hZ5CWfvY25g949UDsAqIDUDbQCjYJwlpA1jUg2MTYMgpGwSgYBSMIAADPykqQ9Fc0gQAAAABJRU5ErkJggg==","orcid":"","institution":"JFK University Medical Center","correspondingAuthor":true,"prefix":"","firstName":"Salih","middleName":"","lastName":"Akgun","suffix":""},{"id":601169332,"identity":"2d41e936-040d-4246-8b2b-9ef808c6a1c7","order_by":3,"name":"Canan D. Dirican","email":"","orcid":"","institution":"NYMC at St Mary’s General Hospital, St Clare’s Health","correspondingAuthor":false,"prefix":"","firstName":"Canan","middleName":"D.","lastName":"Dirican","suffix":""},{"id":601169334,"identity":"f437ea7d-7bc2-4014-a0d1-8561d69488c0","order_by":4,"name":"Ahmed Demirci","email":"","orcid":"","institution":"JFK University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Ahmed","middleName":"","lastName":"Demirci","suffix":""},{"id":601169335,"identity":"639d4fde-624b-43a2-9805-1f8ab10addd2","order_by":5,"name":"Mohammad Salameh","email":"","orcid":"","institution":"Hamilton Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"","lastName":"Salameh","suffix":""},{"id":601169336,"identity":"750b5aaa-d350-4144-81f4-519636989f4e","order_by":6,"name":"Jamil Nazzal","email":"","orcid":"","institution":"Hamilton Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Jamil","middleName":"","lastName":"Nazzal","suffix":""},{"id":601169337,"identity":"9c2d719e-dd0c-451e-bba2-fda5ad41292b","order_by":7,"name":"Jasneet Randhawa","email":"","orcid":"","institution":"Hamilton Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Jasneet","middleName":"","lastName":"Randhawa","suffix":""},{"id":601169338,"identity":"95f75792-2b48-481d-96a5-9908e897468d","order_by":8,"name":"Nimrat Bains","email":"","orcid":"","institution":"Hamilton Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Nimrat","middleName":"","lastName":"Bains","suffix":""},{"id":601169339,"identity":"c9bac6bb-a753-455c-a6f8-103f260e3658","order_by":9,"name":"Sanjay Jain","email":"","orcid":"","institution":"Medical University of South Carolina Hollings Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Sanjay","middleName":"","lastName":"Jain","suffix":""}],"badges":[],"createdAt":"2026-02-09 20:38:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8834204/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8834204/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104182408,"identity":"8aa6ea24-f588-4ed9-b2b3-f82941fd84ea","added_by":"auto","created_at":"2026-03-08 17:37:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":128981,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of Clinical or Regional Characteristics\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8834204/v1/09ae59e2c7f5af9cc1a40713.png"},{"id":108806950,"identity":"7b499a45-6656-4569-8542-41902799613e","added_by":"auto","created_at":"2026-05-08 15:29:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":429577,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8834204/v1/33e61512-75ab-4f27-83e4-fdb303fe4d04.pdf"},{"id":104182410,"identity":"010bac8b-7013-4af2-94f5-73d16b6c665d","added_by":"auto","created_at":"2026-03-08 17:37:59","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":412255,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryLungCancerDisparitiesintheUnitedStatesTheRoleofSmokingJComorbiditiesJSocioeconomicStatusJandRegionalVariation.docx","url":"https://assets-eu.researchsquare.com/files/rs-8834204/v1/aec67644687a762762bd7c3f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Lung Cancer Disparities in the United States: The Role of Smoking, Comorbidities, Socioeconomic Status, and Regional Variation","fulltext":[{"header":"Simple Summary","content":"\u003cp\u003eLung cancer remains the leading cause of cancer-related mortality in the United States, but the burden of disease is not shared equally across populations. In this study, we used a large, nationally representative inpatient dataset to evaluate how race, geography, comorbidity burden, and socioeconomic status contribute to disparities in lung cancer hospitalizations, outcomes, and resource use. We found substantial differences in smoking prevalence, disease severity, hospital charges, and length of stay across racial and regional groups. Understanding these patterns can help guide more equitable screening strategies, resource allocation, and public health interventions.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eLung cancer remains the leading cause of cancer-related mortality in the United States, accounting for approximately 125,000 deaths annually.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e Although lung cancer incidence and mortality have declined over recent decades, largely driven by reductions in smoking and advances in early detection and treatment overall survival remains poor, with a five-year survival rate of approximately 20%.\u003csup\u003e1\u0026ndash;3\u003c/sup\u003ePrognosis is influenced by multiple factors, including tumor stage and histology, comorbidity burden, behavioral risk factors such as smoking, and access to timely, guideline-concordant care.\u003csup\u003e\u003cspan additionalcitationids=\"CR5 CR6 CR7\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003eSubstantial disparities in lung cancer outcomes have been consistently documented across racial, ethnic, socioeconomic, and geographic groups.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e Differences in survival by race and ethnicity are not fully explained by biological factors or tumor genomics, as studies evaluating mutation frequency and ancestry-related markers have failed to identify consistent biologic drivers of these disparities.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e Instead, a growing body of evidence suggests that inequities in access to care, quality of treatment, and timeliness of diagnosis often shaped by socioeconomic disadvantage play a central role in determining stage at presentation and subsequent outcomes.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e Comorbidity burden further complicates this relationship, as chronic conditions disproportionately affecting minority populations may limit treatment options and worsen survival.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eGeographic variation also contributes meaningfully to lung cancer burden and outcomes. Southern states consistently demonstrate higher lung cancer incidence and mortality, a pattern associated with socioeconomic disadvantage, lower access to preventive services, and reduced uptake of lung cancer screening.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e In contrast, Western regions often exhibit lower incidence but higher healthcare costs and distinct patterns of resource utilization, reflecting regional differences in healthcare delivery and pricing structures.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e These geographic and socioeconomic factors intersect with race and ethnicity to shape patterns of disease presentation, hospitalization, and outcomes.\u003c/p\u003e \u003cp\u003eDespite extensive literature on lung cancer disparities, most studies have focused on incidence, stage at diagnosis, or long-term survival, with less attention given to disparities in inpatient outcomes and healthcare utilization during hospitalization. \u003csup\u003e17\u003c/sup\u003eMoreover, disparities are often examined in isolation, without accounting for the intersection of race, socioeconomic status, comorbidity burden, and geographic region. To address these gaps, we used a nationally representative inpatient database to evaluate racial, socioeconomic, and regional disparities in lung cancer hospitalizations, focusing on in-hospital mortality, length of stay, and total hospital charges. By examining these intersecting factors, our study aims to provide a more granular understanding of structural inequities shaping inpatient lung cancer care in the United States.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003eWe conducted a cross-sectional observational study using the National Inpatient Sample 2019\u0026ndash;2020(NIS), the largest publicly available all-payer inpatient database in the United States. Adults aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years with a primary diagnosis of lung cancer were identified using ICD-10 codes previously validated for epidemiologic surveillance. We quantified regional admission prevalence by calculating the survey-weighted proportion of hospitalizations attributed to primary lung cancer across the four U.S. Census regions: Northeast, Midwest, South, and West. Patients were further stratified by race and ethnicity (White, Black, Hispanic, Other), with the Other category representing Asian or Pacific Islander, Native American, or individuals classified as Other in the NIS dataset.\u003c/p\u003e \u003cp\u003ePrimary study outcomes included length of stay (LOS), in-hospital mortality, total hospital charges, smoking prevalence, and distribution across income quartiles. Secondary analyses compared regional patterns of lung cancer admission prevalence and regional smoking prevalence, reflecting known geographic variation in lung cancer risk and healthcare access across the United States. Accordingly, observed disparities reflect differences in inpatient burden of care rather than disease incidence or prevalence.\u003c/p\u003e \u003cp\u003eAll analyses were performed using STATA/SE 17.0 (StataCorp LLC, College Station, TX). Given the complex survey design of the NIS, we applied survey weights, strata, and cluster variables in accordance with HCUP recommendations. Descriptive statistics were compared using chi-square tests for categorical variables and ANOVA for continuous variables. Multivariable models incorporated survey-weighted procedures to preserve national representativeness. Statistical significance was defined as p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eMultivariable survey-weighted logistic and linear regression models were constructed to evaluate the independent associations of race with in-hospital mortality, length of stay, and total hospital charges. Race was modeled as a categorical variable with White patients serving as the reference group, and all models adjusted for age, sex, smoking status, comorbidities (COPD, congestive heart failure, diabetes mellitus, chronic kidney disease, obesity), income quartile, and hospital region.\u003c/p\u003e \u003cp\u003eThis study used publicly available, de-identified data and was therefore exempt from Institutional Review Board approval.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Patient Characteristics by Race\u003c/h2\u003e \u003cp\u003eA total of 213,435 weighted hospitalizations for primary lung cancer were included in the analysis. The mean age of the cohort was 68.8 years, and 50.5% of patients were female. The racial distribution consisted of approximately 75% White, 12% Black, 5% Hispanic, and 8% classified as Other.\u003c/p\u003e \u003cp\u003eSignificant racial differences were observed in baseline demographic and clinical characteristics. Patients aged 60 years or older were most common among White patients (84.2%). Smoking prevalence differed significantly across race (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with White patients having the highest proportion of smokers (47.5%), followed by Hispanic patients (41.6%), Black patients (40.6%), and patients categorized as Other race (38.5%).\u003c/p\u003e \u003cp\u003eComorbidity prevalence also varied by race. COPD was most frequent among White patients (39.3%), followed by Black patients (34.3%) and Hispanic patients (26.3%). Congestive heart failure was highest in Black patients (14.4%), compared with Whites (12.0%) and Hispanics (11.0%). Diabetes mellitus was most common among Hispanic patients (31.1%). Obesity was highest in White patients (11.9%) and lowest among patients classified as Other race (6.6%). Chronic kidney disease was most prevalent in Black patients (16.6%).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Hospital Outcomes by Race\u003c/h2\u003e \u003cp\u003eMean LOS differed significantly across races (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Black patients had the longest LOS (6.95 days), followed by Hispanic (6.62), Other (6.27), and White patients (5.83). Regression analysis confirmed race as a predictor of LOS (β\u0026thinsp;=\u0026thinsp;0.262, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eTotal hospital charges also varied: Hispanic patients incurred the highest mean charges (\u003cspan\u003e$\u003c/span\u003e114,594), followed by Other (\u003cspan\u003e$\u003c/span\u003e102,646), Black (\u003cspan\u003e$\u003c/span\u003e88,498), and White patients (\u003cspan\u003e$\u003c/span\u003e87,058).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Regional Variation\u003c/h2\u003e \u003cp\u003eRegional analyses demonstrated substantial geographic disparities in lung cancer hospitalizations. The South accounted for the highest proportion of admissions at 39.5%, followed by the Midwest, Northeast, and West. Smoking prevalence also differed significantly across regions (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The Midwest demonstrated the highest proportion of smokers at 47.0%, followed by the Northeast at 48.4% and the West at 46.6%, while the South had the lowest smoking prevalence at 42.9%.\u003c/p\u003e \u003cp\u003eRace distributions varied significantly by hospital region (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). White patients were most heavily concentrated in the South, representing 38.6% of all White lung cancer hospitalizations, while Black patients were even more concentrated in this region at 53.8%. Hispanic patients showed a different pattern, with the West accounting for 29.1% of all Hispanic lung cancer hospitalizations and the Northeast for 21.9%. Patients categorized as Other race were most represented in the West at 32.4%, followed by the Northeast at 24.6%.\u003c/p\u003e \u003cp\u003eIncome patterns also demonstrated regional variation. The lowest national income quartile accounted for 28.6% of all lung cancer hospitalizations nationwide but was disproportionately represented in the South, where 40.3% of patients were from the lowest-income areas. The Midwest also showed elevated representation at 27.3%. In contrast, the Northeast and West had lower proportions from the lowest-income quartile at 17.3% and 17.1%, respectively. Patients from the highest income quartile were most concentrated in the Northeast (35.1%) and West (30.8%) and least represented in the South (12.1%).\u003c/p\u003e \u003cp\u003eInpatient mortality did not differ significantly across regions (p\u0026thinsp;=\u0026thinsp;0.185).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Socioeconomic Status and Racial Distribution\u003c/h2\u003e \u003cp\u003eIncome quartile demonstrated a strong association with race (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Black patients had the highest concentration in the lowest national income quartile, with 55.1% residing in ZIP codes corresponding to the bottom 25% of median household income. Hispanic patients also showed a high proportion in this quartile at 42.6%. In contrast, White patients were more evenly distributed across income levels, with 24.7% in the lowest quartile and a substantially higher proportion in the highest income quartile. Patients categorized as Other race similarly showed greater representation in the upper income quartiles compared with Black and Hispanic patients.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Multivariable Analyses of Mortality, Length of Stay, and Hospital Charges\u003c/h2\u003e \u003cp\u003eIn survey-weighted multivariable logistic regression adjusting for sex, age, smoking status, comorbidities (COPD, CHF, diabetes mellitus, chronic kidney disease, obesity), income quartile, and hospital region, race remained independently associated with in-hospital mortality. Each incremental racial category was associated with higher odds of mortality (adjusted OR 1.07, 95% CI 1.02\u0026ndash;1.12, p\u0026thinsp;=\u0026thinsp;0.003). Female sex was associated with significantly lower odds of in-hospital mortality (aOR 0.77, 95% CI 0.71\u0026ndash;0.83, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Comorbid COPD (aOR 1.21), CHF (aOR 1.69), and CKD (aOR 1.20) were independently associated with increased mortality risk, while smoking status and obesity were associated with lower odds of in-hospital mortality. Income quartile demonstrated a graded protective association, with patients from higher-income ZIP codes experiencing lower mortality risk compared with those from the lowest-income quartile.\u003c/p\u003e \u003cp\u003eIn survey-weighted multivariable linear regression, race remained an independent predictor of length of stay (LOS). After full adjustment, higher racial category was associated with longer hospitalization (β\u0026thinsp;=\u0026thinsp;0.26 days, 95% CI 0.18\u0026ndash;0.34, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Female sex was associated with shorter LOS, while COPD, CHF, and CKD were associated with significantly longer hospitalizations. Increasing income quartile was associated with progressively shorter LOS, suggesting a socioeconomic gradient in inpatient utilization. Regional differences persisted, with shorter LOS observed in the Northeast and West compared with the reference region.\u003c/p\u003e \u003cp\u003eIn adjusted analyses of total hospital charges, race remained significantly associated with increased charges (β = \u003cspan\u003e$\u003c/span\u003e4,458, 95% CI \u003cspan\u003e$\u003c/span\u003e2,933\u0026ndash;\u003cspan\u003e$\u003c/span\u003e5,984, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), independent of demographic, clinical, socioeconomic, and regional factors. Female sex and smoking status were associated with lower charges, while COPD, CHF, and obesity were associated with significantly higher hospital charges. Patients treated in Western hospitals incurred substantially higher charges, whereas hospitals in the Northeast were associated with lower charges, reflecting persistent regional variation in healthcare pricing.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis nationally representative analysis reveals substantial racial, regional, and socioeconomic disparities among hospitalized lung cancer patients in the United States.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Racial Variation in Smoking and Comorbidity Burden\u003c/h2\u003e \u003cp\u003ePrior research has shown that racial disparities in lung cancer outcomes persist even after accounting for smoking exposure, suggesting that differences in comorbidity burden, disease severity, and access to care play important roles.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e Black and Hispanic patients have been reported to experience worse clinical outcomes despite similar or lower smoking prevalence compared with White patients.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e Consistent with this literature, we found that although White patients had the highest smoking prevalence, Black and Hispanic patients experienced worse inpatient outcomes, including longer lengths of stay and greater healthcare utilization, indicating that smoking behavior alone does not explain observed disparities.\u003c/p\u003e \u003cp\u003eRyan et al. has also described earlier lung cancer onset among racial and ethnic minority populations, attributed to socioeconomic disadvantage, cumulative environmental exposures, delayed screening, and a higher burden of systemic risk factors.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e In our cohort, Black and Hispanic patients were younger at hospitalization than White patients, a pattern that may contribute to more complex hospital courses. The literature further demonstrates that Black and Hispanic populations bear a disproportionate burden of chronic comorbidities such as congestive heart failure, chronic kidney disease, and diabetes, reflecting long-standing inequities in preventive care.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e We similarly observed higher prevalences of these conditions among Black and Hispanic patients, which likely contribute to prolonged hospitalization and increased resource utilization.\u003c/p\u003e \u003cp\u003eImportantly, prior work by Williams et al. suggests that racial disparities in lung cancer outcomes often persist after adjustment for clinical factors.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e In our fully adjusted models accounting for smoking status, comorbidity burden, socioeconomic status, and geographic region, race remained independently associated with longer length of stay, higher hospital charges, and modestly increased odds of in-hospital mortality, highlighting the role of broader structural and systemic factors in shaping inpatient lung cancer outcomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Resource Utilization Differences\u003c/h2\u003e \u003cp\u003ePrior studies have shown that racial and ethnic differences in hospital resource utilization reflect not only disease severity but also structural factors such as access to outpatient care, insurance coverage, and regional variation in healthcare pricing. Black patients, in particular, have been reported to experience longer hospitalizations, often attributed to delayed presentation, higher comorbidity burden, and barriers to timely ambulatory care.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eConsistent with this literature, we found that Black patients had the longest lengths of stay. This pattern likely reflects a greater burden of chronic comorbidities and more complex inpatient management needs and may also be influenced by limited access to preventive and outpatient care, although such mechanisms cannot be directly assessed in an inpatient-only dataset.\u003c/p\u003e \u003cp\u003eHospital charges demonstrated a different pattern, with Hispanic patients incurring the highest total costs despite shorter average lengths of stay. While Hispanic patients were most frequently hospitalized in the South, they were also disproportionately represented in the West and Northeast, regions known to have substantially higher healthcare costs compared with the South and Midwest. Regional pricing variation alone may therefore account for a meaningful portion of the observed cost differences.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eBeyond geography, prior studies suggest that differences in diagnostic intensity, procedural utilization, and patterns of care-seeking once hospitalized may contribute to increased inpatient costs among Hispanic patients, even in the absence of prolonged hospitalization.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e Socioeconomic constraints, including underinsurance and limited access to timely outpatient evaluation, may further lead to more resource-intensive inpatient care.\u003c/p\u003e \u003cp\u003eIn adjusted analyses, racial differences in total hospital charges persisted after accounting for length of stay, comorbidity burden, income quartile, and hospital region, indicating that excess costs are not solely driven by longer hospitalization or measurable clinical severity and likely reflect a combination of regional pricing structures and structural barriers to outpatient care.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Geographic Disparities\u003c/h2\u003e \u003cp\u003ePrior research has shown that the Southern United States bears a disproportionate burden of lung cancer, driven by socioeconomic disadvantage, limited access to preventive care, and structural barriers to early detection.\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e Some studies have also reported lower documented smoking prevalence in the South, a finding often attributed to underreporting, inconsistent documentation, and reduced engagement in preventive healthcare rather than true differences in exposure.\u003csup\u003e\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eConsistent with this literature, we found that the South accounted for nearly two-fifths of all lung cancer hospitalizations despite having the lowest recorded smoking prevalence in our cohort. This apparent discrepancy likely reflects incomplete capture of smoking behavior as well as broader gaps in risk awareness and routine healthcare utilization.\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eLimited access to lung cancer screening may further contribute to these patterns. Southern states have among the lowest rates of low-dose CT screening nationally despite high screening eligibility, and reduced screening has been associated with later-stage diagnosis and increased reliance on inpatient care.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e Our findings of higher hospitalization burden in the South align with these observations.\u003c/p\u003e \u003cp\u003eSocioeconomic disadvantages appear to amplify regional differences. The South had the highest proportion of patients residing in the lowest income quartile, a factor linked to delayed presentation and increased dependence on hospital-based care.\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e Although inpatient mortality did not differ significantly by region, disparities in stage at diagnosis and access to definitive treatment may influence outcomes beyond hospitalization.\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e Finally, regional variation in resource utilization persisted after adjustment, with hospitals in the West incurring higher total charges and those in the Northeast demonstrating shorter stays and lower costs. These differences likely reflect regional pricing and practice patterns rather than disease biology alone.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Intersection of Race and Socioeconomic Status\u003c/h2\u003e \u003cp\u003ePrior studies have consistently demonstrated pronounced socioeconomic gradients across racial and ethnic groups, with Black and Hispanic populations disproportionately residing in lower-income neighborhoods. These structural inequities influence housing stability, educational and employment opportunities, environmental exposures, insurance coverage, and access to preventive healthcare.\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e Lower neighborhood income has been closely linked to reduced primary care access, fewer preventive services, and limited availability of accredited lung cancer screening programs, contributing to delayed diagnosis and more advanced disease at presentation.\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eConsistent with this literature, we observed that Black patients were most heavily represented in the lowest income quartile, followed by Hispanic patients, whereas White and Other race patients were more frequently concentrated in higher income quartiles. Among Black patients, historical and systemic factors including residential segregation, disproportionate environmental exposures, and long-standing barriers to wealth accumulation likely contribute to reduced engagement in preventive care and delayed evaluation of respiratory symptoms, resulting in higher inpatient utilization.\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eHispanic patients similarly demonstrated a substantial concentration in lower income quartiles, reflecting additional challenges related to immigration status, language barriers, underinsurance, and employment in sectors with limited health benefits.\u003csup\u003e\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e These barriers may limit access to routine care and screening awareness, leading to more advanced disease or more resource-intensive inpatient evaluations, which may help explain the higher hospital charges observed in this group.\u003c/p\u003e \u003cp\u003eOverall, the intersection of race and socioeconomic status plays a central role in shaping lung cancer presentation, screening uptake, and inpatient resource utilization. Our findings reinforce the need for targeted strategies to expand equitable screening access, strengthen culturally informed preventive care, and address upstream socioeconomic determinants that contribute to persistent disparities in lung cancer outcomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Policy and Practice Implications\u003c/h2\u003e \u003cp\u003eTargeted strategies are needed to reduce disparities in lung cancer outcomes. Expanding access to low dose CT screening, particularly in regions and populations with low uptake, may help facilitate earlier detection. Improving access to primary care and strengthening chronic disease management could reduce disease severity at presentation and decrease reliance on inpatient care. Policies that address structural determinants of health, including insurance coverage, transportation barriers, and regional shortages of specialty services, are also essential. In addition, culturally tailored smoking cessation programs and community-based education efforts may help engage high risk groups and reduce preventable disease burden.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.6 Strengths and Limitations\u003c/h2\u003e \u003cp\u003eStrengths include the use of a large, nationally representative database and robust survey-weighted analyses. Limitations include the inability to assess cancer stage, outpatient care patterns, time-to-treatment, or tumor biology, which all influence outcomes. Future research integrating NIS data with cancer registry datasets could provide a more comprehensive picture. The incorporation of survey-weighted multivariable models strengthens the validity of our findings by accounting for confounding demographic, clinical, socioeconomic, and geographic factors.\u003c/p\u003e \u003cp\u003eThese interpretations are hypotheses generating and should be validated in future studies incorporating cancer stage, longitudinal outpatient data, and individual level measures of screening access and socioeconomic status.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eLung cancer disparities in the United States are influenced by race, comorbidity burden, socioeconomic status, and geographic region. Interventions aimed at improving access to screening, preventive care, and equitable distribution of healthcare resources are essential for addressing these inequities.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting Interests\u003c/h2\u003e \u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthics Approval\u003c/h2\u003e \u003cp\u003eThis study used publicly available, de-identified administrative data from the HCUP National Inpatient Sample. In accordance with institutional and national research guidelines, ethical approval and individual patient consent were \u003cb\u003enot required\u003c/b\u003e.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to Participate\u003c/strong\u003e \u003cp\u003eNot applicable. This research involved analysis of de-identified administrative data and did not involve human subjects directly.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to Publish\u003c/strong\u003e \u003cp\u003eNot applicable. This manuscript does not include any individual patient data, identifiable information, images, or case reports requiring specific publication consent.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe authors declare that no funds, grants, or other financial support were received during the preparation of this manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed to the study conception and design. Data acquisition, data cleaning, and statistical analysis were performed by Bugra Zengin, MD, Mohammad Alqaisieh, MD, and Salih Akgun, MD. The first draft of the manuscript was written by Bugra Zengin, MD. All authors reviewed and revised the manuscript critically for important intellectual content and approved the final version.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets analyzed during the current study are publicly available through the Healthcare Cost and Utilization Project (HCUP) National Inpatient Sample (NIS) database, maintained by the Agency for Healthcare Research and Quality (AHRQ). Access to the data requires an HCUP Data Use Agreement and can be obtained at: [https://www.hcup-us.ahrq.gov/nisoverview.jsp](https:/www.hcup-us.ahrq.gov/nisoverview.jsp)\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiegel Mph RL, et al. Cancer statistics, 2023. CA Cancer J Clin. 2023;73:17\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIslami F, et al. Proportion and number of cancer cases and deaths attributable to potentially modifiable risk factors in the United States. 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Effect of socioeconomic status on stage at diagnosis of lung cancer in a hospital-based multicenter retrospective clinical epidemiological study in China, 2005\u0026ndash;2014. Cancer Med. 2017;6:2440\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilliams DR, Collins C. Racial residential segregation: a fundamental cause of racial disparities in health. Public Health Rep. 2001;116:404.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDiez Roux AV, Mair C. Neighborhoods and health. Ann N Y Acad Sci. 2010;1186:125\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePruitt SL, Shim MJ, Mullen PD, Vernon SW, Amick BC. Association of Area Socioeconomic Status and Breast, Cervical, and Colorectal Cancer Screening: A Systematic Review. Cancer Epidemiol Biomarkers Prev. 2009;18:2579\u0026ndash;99.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMikati I, Benson AF, Luben TJ, Sacks JD, Richmond-Bryant J. Disparities in Distribution of Particulate Matter Emission Sources by Race and Poverty Status. Am J Public Health. 2018;108:480\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkinlotan M, et al. Cervical Cancer Screening Barriers and Risk Factor Knowledge Among Uninsured Women. J Community Health. 2017;42:770\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePitkin Derose K, Bahney BW, Lurie N, Escarce JJ. Review: immigrants and health care access, quality, and cost. Med Care Res Rev. 2009;66:355\u0026ndash;408.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFlores G. Language Barriers to Health Care in the United States. N Engl J Med. 2006;355:229\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Racial Differences in Clinical Characteristics\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003eVariable\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003eWhite (%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003eBlack (%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003eHispanic (%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003eOther (%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003eP-value\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003eSmoking\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e47.45\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e40.58\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e41,63\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e38.46\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\u0026lt;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003eCOPD\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e39.3\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e34.33\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e26.31\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e26.62\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\u0026lt;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003eCHF\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e12.01\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e14.42\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e11.09\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e9.83\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\u0026lt;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003eDiabetes\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e20.6\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e27.89\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e31.08\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e26\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\u0026lt;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003eObesity\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e11.97\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e11.51\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e11.19\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e6.63\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\u0026lt;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003eCKD\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e11.74\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e16.59\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e12.27\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e10.11\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\u0026lt;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003eAge \u0026gt;60\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e84.21\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e76.95\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e79.04\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e78.99\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\u0026lt;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 2. Hospital Outcomes by Race\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003eOutcome\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003eWhite\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003eBlack\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003eHispanic\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003eOther\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003eMean LOS (days)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e5.83\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e6.95\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e6.62\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e6.27\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003eTotal Charges ($)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e87,058\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e88,498\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e114,594\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e102,646\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 3. Income Quartile Distribution by Race\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003eIncome Quartile\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003eWhite\u003cbr\u003e(%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003eBlack\u003cbr\u003e(%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003eHispanic\u003cbr\u003e(%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003eOther\u003cbr\u003e(%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003eP-value\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003eQ1 (lowest)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e24.49\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e55.05\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e38.21\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e21.15\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\u0026lt;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003eQ2\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e28.21\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e21.33\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e24.34\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e22.79\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003eQ3\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e25.65\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e14.4\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e22.43\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e25.81\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003eQ4 (highest)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e21.64\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e9.23\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e15.02\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e30.25\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 4. Regional Variation in Smoking, Mortality, and Income\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003eRegion\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003eSmoking (%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003eMortality (%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003eQ1 Income (%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003eQ4 Income (%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003eP-value\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003eNortheast\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e48.4\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e07.34\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e17.25\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e35.06\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\u0026lt;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003eMidwest\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e47.0\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e06.41\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e27.25\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e14.33\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003eSouth\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e42.9\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e06.27\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e40.26\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e12.06\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003eWest\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e46.6\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e06.63\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e17.08\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e30.84\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5. Multivariable Survey-Weighted Logistic Regression for In-Hospital Mortality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOutcome:\u003c/strong\u003e In-hospital mortality\u003cbr\u003e\u003cstrong\u003eReference groups:\u003c/strong\u003e White race, male sex, income quartile 1 (lowest), hospital region 1\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cstrong\u003eAdjusted OR\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"null\"\u003e\u003cstrong\u003eRace\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"null\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"null\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"null\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eBlack vs White\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e1.10\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e1.04\u0026ndash;1.17\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e0.002\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eHispanic vs White\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e1.05\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e0.96\u0026ndash;1.15\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e0.278\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eOther vs White\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e1.03\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e0.95\u0026ndash;1.12\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e0.462\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eFemale sex\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e0.77\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e0.71\u0026ndash;0.83\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u0026lt;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eCOPD\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e1.21\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e1.11\u0026ndash;1.31\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u0026lt;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eCongestive heart failure\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e1.69\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e1.52\u0026ndash;1.89\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u0026lt;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eDiabetes mellitus\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e0.94\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e0.85\u0026ndash;1.03\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e0.182\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eChronic kidney disease\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e1.20\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e1.07\u0026ndash;1.35\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e0.002\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eSmoking\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e0.65\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e0.60\u0026ndash;0.71\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u0026lt;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAge \u0026gt;60 years\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e1.01\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e0.91\u0026ndash;1.12\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e0.850\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eObesity\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e0.68\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e0.59\u0026ndash;0.79\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u0026lt;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIncome quartile 2 vs 1\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e0.89\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e0.81\u0026ndash;0.99\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e0.026\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIncome quartile 3 vs 1\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e0.82\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e0.74\u0026ndash;0.91\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u0026lt;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIncome quartile 4 vs 1\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e0.78\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e0.69\u0026ndash;0.88\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u0026lt;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eRegion 2 vs 1\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e0.82\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e0.67\u0026ndash;1.00\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e0.056\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eRegion 3 vs 1\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e0.76\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e0.62\u0026ndash;0.93\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e0.008\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eRegion 4 vs 1\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e0.87\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e0.70\u0026ndash;1.09\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e0.224\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable Footnote\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll models were survey-weighted using National Inpatient Sample sampling weights and adjusted for demographic, clinical, socioeconomic, and geographic variables. Race was modeled categorically with White patients as the reference group. \u003cstrong\u003eIncome quartile\u003c/strong\u003e was defined using ZIP code\u0026ndash;level median household income as follows: quartile 1 (0\u0026ndash;25th percentile), quartile 2 (26th\u0026ndash;50th percentile, including the median), quartile 3 (51st\u0026ndash;75th percentile), and quartile 4 (76th\u0026ndash;100th percentile). \u003cstrong\u003eHospital region\u003c/strong\u003e was classified according to U.S. Census regions: region 1 (Northeast), region 2 (Midwest), region 3 (South), and region 4 (West). COPD = chronic obstructive pulmonary disease; CHF = congestive heart failure; CI = confidence interval; OR = odds ratio.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Lung cancer, disparities, socioeconomic factors, comorbidities, regional variation","lastPublishedDoi":"10.21203/rs.3.rs-8834204/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8834204/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eLung cancer disproportionately affects certain racial and regional populations in the United States. This study examines disparities in clinical characteristics, socioeconomic distribution, and hospital outcomes among adults hospitalized with lung cancer.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA retrospective analysis was performed using the National Inpatient Sample 2019\u0026ndash;2020 (NIS). Patients with a primary lung cancer diagnosis were evaluated across racial groups and U.S. regions. Key variables included smoking status, comorbidity burden, length of stay (LOS), mortality, and total hospital charges.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWhite patients demonstrated the highest smoking prevalence, whereas Black patients experienced the longest LOS. Hispanic patients incurred the highest total hospital charges. Regionally, lung cancer admissions were most common in the South, which also showed lower socioeconomic status and reduced screening access. In-hospital mortality was not significantly associated with hospital region, whereas socioeconomic status showed a graded association, with progressively lower mortality risk among patients in higher income quartiles.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eSignificant racial and regional disparities exist in lung cancer burden and hospital outcomes. Targeted interventions addressing socioeconomic barriers, screening inequities, and comorbidity management are needed to reduce these disparities.\u003c/p\u003e","manuscriptTitle":"Lung Cancer Disparities in the United States: The Role of Smoking, Comorbidities, Socioeconomic Status, and Regional Variation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-08 17:37:54","doi":"10.21203/rs.3.rs-8834204/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"083ca3ef-2582-416b-8146-d61023f8a887","owner":[],"postedDate":"March 8th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-08T14:09:19+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-08T14:26:37+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-08 17:37:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8834204","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8834204","identity":"rs-8834204","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}