Impact of Socioeconomic Status on Patient-Reported Outcomes (PROs) in Non-Small Cell Lung Cancer (NSCLC)

Impact of Socioeconomic Status on Patient-Reported Outcomes (PROs) in Non-Small Cell Lung Cancer (NSCLC) | 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 Impact of Socioeconomic Status on Patient-Reported Outcomes (PROs) in Non-Small Cell Lung Cancer (NSCLC) Bindu Potugari, Jaya Gupta, Julia Bachler, Anqi wang, Eric Adjei Boakye, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8743761/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 Purpose Patient-reported outcomes (PROs) are a powerful method to assess a patient's well-being and have shown to improve patient’s quality of life and survival in lung cancer. Socioeconomic status (SES) influences patients’ ability to receive timely, high-quality care. The association between area-level SES and PROs among patients diagnosed with Non-Small Cell Lung Cancer (NSCLC) was examined. Methods We conducted a retrospective study of patients (n = 491) diagnosed with NSCLC between September 2021 and December 2023 who completed an NIH Patient-Reported Outcomes Measurement Information System (PROMIS®) questionnaire within 180 days of their initial oncology office visit. The main exposure of interest was socioeconomic disadvantage assessed via state-level Area Deprivation Index (ADI) categorized into (0 to < 5 or ≥ 5 to 10). Outcomes were four PROMIS domains - physical function, fatigue, pain interference, and depression. Four multivariable linear regression models (one for each PROMIS domain) were used to estimate associations between ADI and PROMIS T-scores adjusting for sociodemographic and clinical factors. Results Approximately 49% of patients resided in neighborhoods with ADI score ≥ 5. The mean T-score for depression was 51.5 (SD = 9.1), fatigue was 55.2 (SD = 9.5), pain interference was 56.9 (SD = 10.3), and physical function was 38.2 (SD = 9.4). Patients living in more socioeconomically disadvantaged areas (state ADI ≥ 5) were associated with significantly higher depression scores [β 2.68 (95% CI0.47, 4.89) p = 0.018] and fatigue scores [β 3.06 (95% CI0.25, 5.87) p = 0.033]. Pain interference and physical function were not statistically associated with state ADI. Conclusions Area-level socioeconomic status is independently associated with higher levels of patient-reported depression and fatigue among patients with NSCLC. These findings need further evaluation with prospective studies and underscore the importance of considering socioeconomic factors in assessing patient-reported outcomes and developing targeted interventions to address disparities in healthcare access and outcomes among lung cancer patients. Patient reported outcome (PRO) Patient reported outcome measures (PROMs) Lung cancer Socioeconomic status Area deprivation index (ADI) PROMIS Non-Small Cell Lung Cancer (NSCLC) Introduction Lung cancer remains the leading cause of cancer-related deaths in the United States and worldwide and is the second most common type of cancer in the U.S., with 226,650 estimated new cases of lung and bronchus cancer reported in 2025. 1 Non-Small Cell Lung Cancer (NSCLC) accounts for 85% of all lung cancers. Historically, NSCLC has been associated with a poor prognosis, however, the advent of targeted therapies and immunotherapy has led to meaningful improvements in long-term survival. 1 , 3 Despite the advances in screenings and therapies, the majority of patients with NSCLC present with advanced disease and have high symptom burden. 2 These symptoms range from physical to psychological, including anxiety, fatigue, shortness of breath, weight loss, etc. 2,4 Moderate to severe symptoms at baseline and worsening symptom burden during the first cycle of chemotherapy were independent predictors of poorer overall survival among patients diagnosed with advanced NSCLC. 5 Discrepancies have been found between patient reporting and clinician estimates in assessing the frequency and intensity of cancer-related symptoms in multiple studies, especially for symptoms such as fatigue and decreased appetite. 4 , 6 In response to the discordance between patient and clinician assessment, patient-reported outcome (PRO) monitoring during cancer treatment has emerged as a powerful method to assess patients’ symptoms and well-being directly. 7 Patient-reported outcome measures (PROMs) are validated tools that collect health-related perspectives directly from the patient. PRO monitoring using PROMs during treatment has been shown to improve symptom control, quality of life, and survival. 7 , 8 , 9 , 10 The World Health Organization defines social determinants of health as the conditions in which people are born, grow, live, work, and age, and their access to power, money, and resources. 11 Socioeconomic status influences multiple social determinants of health such as housing, transportation and access to care which collectively affect patients’ ability to receive timely, high-quality care. 47 Socioeconomic status (SES) is a well-recognized determinant of cancer outcomes, influencing access to cancer care, stage at diagnosis, treatment delivery, and survival in lung cancer. 13 , 14 Increasingly, SES is also recognized as a critical driver of symptom burden and health-related quality of life. 12 , 14 Patients with lower SES are more likely to experience financial toxicity, psychosocial distress, limited access to cancer care services, and higher comorbidity burden—all factors that may exacerbate cancer-related symptoms. 12 , 15 Neighborhood-level measures of socioeconomic deprivation offer a pragmatic approach to capturing social disadvantage beyond individual-level variables, which are often unavailable or incomplete in clinical datasets. 16 The Area Deprivation Index (ADI) is a validated composite measure incorporating income, education, employment, and housing quality at the neighborhood level and has been associated with cancer outcomes, healthcare utilization, and mortality. 14 , 17 The relationship between neighborhood-level SES and PROs in NSCLC has not been well characterized, particularly using standardized, validated PROMs embedded in routine clinical care. In this study, we evaluated the association between area-level socioeconomic deprivation, as measured by ADI, and multiple PRO domains—including depression, fatigue, pain interference, and physical function—among patients with NSCLC receiving care at a large, integrated cancer center. Methods Study setting This was a cross-sectional, retrospective study conducted at an academic vertically integrated health system in the Midwest, United States. The health system serves a large urban and suburban community with a diverse patient population. The health system implemented a system-wide PROs monitoring program in 2021 for all outpatient oncology visits. 18 PROMs have been fully integrated into the health system’s electronic health record (EHR) using widely available Epic© tools. All adult patients with a diagnosis of cancer are offered PROM instruments to complete on the patient portal, MyChart, prior to their visit. For patients that do not have MyChart access or have not completed their questionnaires prior to their visit, a tablet is provided to complete questionnaires at the time of their visit with an oncology provider. The National Institute of Health’s Patient-Reported Outcomes Measurement Information System (PROMIS®) was used and was selected due to its high level of precision, computer-adaptive testing (CAT) capabilities, and existing validation within cancer patients. CAT is an algorithm-driven branching logic that allows for accurate measurement of each domain utilizing the minimum number of questions. This decreases time to completion and responder fatigue. 19 An integrated care management model was established for patients with severe scores with reflex referrals to a social worker for depression and a nurse for pain and poor physical function in real time. Patients who were diagnosed with NSCLC and having their PROMs within 180 days of their first office visit at the cancer center were included in this study. This retrospective study was conducted in accordance with declaration of Helsinki and approved by the Henry Ford Health System Institutional Review Board, which waived the requirement for informed consent due to the use of existing, de-identified data. Measures Outcome The outcome variables were four PROMIS instruments: physical function (PROMIS® CAT v2.0 – Physical Function) 20 , pain interference (PROMIS® CAT v1.1 – Pain Interference) 21 , fatigue (PROMIS® CAT v1.0 – Fatigue) 24 , and depression (PROMIS® CAT v1.0 – Depression). 22 , 23 , The PROMIS® measures use T scores (with a mean of 50 and SD of 10) for all domains to account for the fact that patients do not receive the same questions due to CAT. 19 A higher score indicates worse symptoms in the fatigue, pain interference, and depression domains, while a lower score indicates worse function in the physical function domain. 19 – 24 PROMIS® scores are interpreted based on the clinical cut points of “within normal limits”, “mild”, “moderate”, or “severe”. For fatigue, pain interference, and depression, a score of 70 was severe. For physical function, a score of > 45 was normal, 40–45 was mild, 30–40 was moderate, and < 30 was severe. 17 PROMs scores were used as continuous variables instead of categorical variables in this study. Exposure The main exposure variable in the study was area-level socioeconomic disadvantage assessed with Area Deprivation Index (ADI). Individual’s primary residence at the time of diagnosis was geocoded using zip code and census tract was used to calculate state ADI. State ADI was selected over national ADI because 98% of patients seen at the medical center reside in the state the institution is located. State ADI had a score of 1–10, with 1 being the least disadvantaged and 10 being the most disadvantaged. 14 , 17 State ADI scores were categorized into two groups (0 to < 5 or ≥ 5 to 10) for analysis. Covariates Covariates included in the study were age at diagnosis (continuous variable measured in years), self-reported sex (female, male), self-reported race (White, Other [including all other races including African Americans, combined due to small cell counts]), self-reported marital status (married/significant other, divorced/separated/widowed, single), stage of presentation at cancer diagnosis (early stage [stage 1 and 2], late stage [stage 3 and 4]), and Charlson Co-Morbidity Index (0, 1–2, ≥ 3). The Charlson Co-Morbidity Index (CCI) is a predictor of long-term mortality and has been validated for use in cancer patients. 25 Statistical Analysis Descriptive statistics were used to characterize patients included in the cohort. Categorical data were summarized as counts and percentage while continuous measures were summarized using mean and standard deviation. Four multivariable linear regression models (one for each PROMIS domain outcome variable) were used to estimate associations between ADI and the outcome variables adjusting for age at diagnosis, sex, race, marital status, cancer stage and CCI. Each Complete-case analysis was employed for each PROMIS domain to address missing data. Duplicate completed PROMs instruments from the same patients were filtered by using the first contact date. Statistical significance was pre-specified at p < 0.05. All analyses were performed using R statistical software version 4.3.2 (R Project for Statistical Computing). Results A total of 491 patients diagnosed with NSCLC between September 2021 and December 2023 who completed PROMs within 180 days of initial office visit with the cancer provider were included in the study (Table 1 ). The mean age of the cohort was 68.5 (SD = 9.4) years. Approximately half of the patients were females (n = 257; 50.3%) and were married/had significant other (n = 243; 49.5%). Most of the patients identified as White (n = 376; 76.6%) and had late-stage NSCLC at the time of diagnosis (n = 315; 64.2%). A substantial proportion of the patients had a high comorbidity burden (61.8% had CCI ≥ 3). Close to half of the patients (n = 187; 48.6%) resided in neighborhoods with ADI score ≥ 5, corresponding to higher area-level disadvantage and lower socioeconomic status. For all patients, the mean depression T-score was 51.5 (SD = 9.1), mean fatigue T-score was 55.2 (SD = 9.5), mean pain interference T-score was 56.9 (SD = 10.3), and mean physical function T-score was 38.2 (SD = 9.4). PROMIS T scores noted by socioeconomic status ADI = 5, the mean depression T-score was 50.3(SD = 9.1) and 52.6(SD = 9.2), mean fatigue T-score was 54.1(SD = 9.8) and 56.3(SD = 9.4), mean pain interference T-score was 55.3(SD = 10.3) and 58.7(SD = 10.3), and mean physical function T-score was 38.8(SD = 9.8) and 37.5(SD = 8.9) respectively (Table 2 ). Table 1 Characteristics of patients diagnosed with non-small cell lung cancer included in the study (n = 491). Variable N (%) or Mean (SD) ADI <5 198 (51.4%) ≥5 187 (48.6%) Age At Diagnosis (years) 68.5 (9.4) Sex Female 247 (50.3%) Male 244 (49.7%) Race Non-White** 114 (23.2%) White 376 (76.6%) Marital Status Divorced/Separated/Widowed 147 (29.9%) Married 243 (49.5%) Single 97 (19.8%) Stage*** Early Stage 173 (35.2%) Late Stage 315 (64.2%) CCI* 0 66 (14.5%) 1–2 108 (23.7%) ≥3 281 (61.8%) PROMIS Depression T score 51.5 (9.1) PROMIS Fatigue T score 55.2 (9.5) PROMIS Pain Interference T score 56.9 (10.3) PROMIS Physical Function T score 38.2 (9.3) *CCI- Charlston Comorbidity Index; ** Non-whites included 17.3% of African Americans. *** Early stage included stage 1–2, late stage included stage 3–4. Table 2 PROMIS T scores by ADI PROMIS (Domains) (T-scores) Mean (SD) ADI < 5 ADI ≥ 5 Depression 50.3 (9.1) 52.6 (9.2) Fatigue 54.1 (9.8) 56.3 (9.4) Pain Interference 55.3(10.3) 58.7(10.3) Physical function 38.8 (9.8) 37.5 (8.9) In multivariable linear regression analyses adjusting for age, sex, race, marital status, cancer stage at diagnosis, and comorbidity burden, higher neighborhood socioeconomic deprivation (ADI ≥ 5) was independently associated with worse patient-reported outcomes in select domains (Table 3 ) . Patients living in more socioeconomically disadvantaged areas (ADI ≥ 5) were associated with a 2.68-point higher depression T scores [β 2.68 (95% CI0.47, 4.89) p = 0.018] and a 3.06-point higher fatigue scores [β 3.06 (95% CI0.25, 5.87) p = 0.033] compared to those living in areas with low ADI. Higher scores of pain interference were also seen in patients with ADI ≥ 5 [β 3.01 (95% CI-0.11, 6.13)], however, it is not statistically significant. No significant association was identified between ADI and physical function domain [β − 1.30(95% CI − 4.11,1.50)]. Table 3 Multivariable analysis of sociodemographic characteristics in relation to PROMIS domain T scores. Beta Estimate (95% CI) Depression Pain interference Fatigue Physical function ADI < 5 - - - - ≥ 5 2.68 (0.47, 4.89) p = 0.018 3.01 (-0.11, 6.13) p = 0.059 3.06 (0.25, 5.87) p = 0.033 -1.3 (-4.11, 1.5) p = 0.361 Age < 65 - - - - ≥ 65 -0.36 (-2.63, 1.92) p = 0.758 1.28 (-1.82, 4.38) p = 0.416 2.4 (-0.41, 5.2) p = 0.093 -2.5 (-5.27, 0.27) p = 0.076 Sex Female - - - - Male -2.29 (-4.37, -0.2) p = 0.032 0.69 (-2.22, 3.6) p = 0.642 -1.39 (-4.05, 1.27) p = 0.304 1.46 (-1.17, 4.09) p = 0.274 Race Non-white - - - - White 2.75 (0.16, 5.34) p = 0.037 -0.9 (-4.19, 2.39) p = 0.59 3.32 (0.34, 6.29) p = 0.029 -0.91 (-3.83, 2.01) p = 0.542 Marital status Divorced/Separated/Widowed - - - - Married 1.05 (-1.36, 3.45) p = 0.393 2.24 (-1.29, 5.77) p = 0.213 2.54 (-0.7, 5.78) p = 0.124 -1.96 (-5.16, 1.23) p = 0.227 Single 0.01 (-3.1, 3.13) p = 0.993 0.62 (-3.61, 4.86) p = 0.772 1.29 (-2.58, 5.16) p = 0.513 -0.87 (-4.67, 2.92) p = 0.651 Cancer stage Early stage - - - - Late stage -0.29 (-2.45, 1.87) p = 0.792 -0.26 (-3.19, 2.68) p = 0.864 0.02 (-2.64, 2.67) p = 0.989 -1.64 (-4.26, 0.98) p = 0.218 CCI 0 0.91 (-2.19, 4) p = 0.564 1.93 (-2.76, 6.61) p = 0.418 2.17 (-2.1, 6.44) p = 0.317 0.91 (-3.28, 5.1) p = 0.668 1–2 -0.15 (-2.57, 2.28) p = 0.905 -1.73 (-5.22, 1.76) p = 0.33 -1.39 (-4.45, 1.67) p = 0.371 2.47 (-0.61, 5.55) p = 0.115 ≥ 3 - - - - Male sex was associated with significantly lower depression scores compared with female sex [β–2.29 (95% CI − 4.37, − 0.20); p = 0.032], whereas sex was not significantly associated with pain interference, fatigue, or physical function. White race was independently associated with higher depression [β 2.75(95% CI 0.16, 5.34) p = 0.037] and higher fatigue scores [β 3.32(95% CI 0.34, 6.29) p = 0.029] compared with non-White race. Race was not significantly associated with pain interference or physical function. Age at diagnosis, marital status, cancer stage, and comorbidity burden were not independently associated with any of the PROMIS domains. Discussion In this retrospective study, we evaluated the association between area-level socioeconomic status assessed via state ADI and patient-reported outcomes among patients with NSCLC. Using validated PROMIS instruments embedded in routine clinical care, we demonstrate that area-level socioeconomic deprivation is independently associated with worse depression and fatigue among patients with NSCLC, even after adjustment for demographic and clinical factors. Specifically, we found that higher socioeconomic deprivation as measured by ADI was independently associated with worse depression and fatigue. In contrast, no significant association was found between pain interference and physical function, and ADI. Unlike prior studies that rely on individual-level socioeconomic indicators or symptom measures in clinical studies, our findings highlight the importance of structural, area-level disadvantage in shaping the lived experience of patients with lung cancer. These findings underscore the importance of area-level socioeconomic status in shaping symptom burden in lung cancer patients beyond disease specific factors. Our findings are consistent with the current literature demonstrating that socioeconomic disadvantage is associated with adverse health outcomes, including patient reported psychological distress and symptom burden 33 , 34 . Patients with lung cancer were disproportionately affected by low educational attainment, lower income, lower occupational status/unemployment, low health literacy, and adverse health behaviors, all of which may contribute to increased vulnerability to symptom burden and poor quality of life. 26 , 27 , 28 In a large multi-cohort study evaluating the association of socioeconomic status (measured by ADI, educational attainment, or occupational grade) and health conditions in adulthood showed that low socioeconomic status was associated with increased lung cancer risk even after adjustment for lifestyle factors. 29 Depression emerged as a key outcome associated with higher socioeconomic deprivation. Depression is highly prevalent in lung cancer patients with the reported rates ranging from approximately 20–38% in early stage to over 70% in stage 4 patients. 30 , 31 , 32 In a prospective nationwide cohort study, there was increased risk of depressive symptoms in participants living in higher disadvantage neighborhoods over time, independent of individual risk factors. 33 In a study by Tjong etal., NSCLC patients from neighborhoods with lowest income quintile reported higher moderate to severe scores in depression and tiredness compared to higher income quintile similar to our study. 34 However, in a recent retrospective study evaluating socioeconomic status by composite SDOH score (using person level income and education indicator scores) and psychosocial distress (by NCCN distress thermometer) in newly diagnosed lung cancer patients at diagnosis showed no statistically significant association, which is in contrary to our study. 35 However, the authors noted that only 26% of the patients completed distress screening at diagnosis, which may introduce bias in the findings due to the low response. Another study by Rosenzweig et al., evaluating association of ADI and PROs among all cancer patients who presented at stage 4 reported no statistically significant association between ADI and depression on multivariate analysis, however, showed patients with worse ADI had worse depression scores. 36 Addressing depressive symptoms among patients with lung cancer is very important as it impacts other health outcomes. For instance, depression in lung cancer patients was associated with low median overall survival (6.8 months) and poor treatment adherence (58%) compared to patients without depression (14 months and 42% low adherence). 37 Potential mechanisms underlying the association of ADI and depression include chronic psychosocial stress, limited access to mental health services, financial burden, and reduced social support, all of which may be more prevalent in socioeconomically disadvantaged environments. Fatigue emerged as another key outcome independently associated with higher socioeconomic deprivation. The most prevalent reported moderate to severe PRO symptom in NSCLC after diagnosis was tiredness (84% in stage 4 NSCLC and 47% in stage 1–3 lung cancer). 30 , 34 Fatigue is known to cluster with psychological distress, and high levels of depression are associated with higher levels of fatigue. 38 Patients with low socioeconomic status may face greater barriers to addressing modifiable contributors to fatigue, such as poor sleep quality, perceived psychological stress, and limited access to supportive services. 39 The observed association between ADI and depression/fatigue highlights the importance of incorporating social determinants of health into PRO assessment and need future prospective studies to validate our findings and to evaluate for management strategies. In contrast, socioeconomic deprivation was not significantly associated with physical function and pain interference. However, when looking at all cancer patients, pain and physical function domains were significantly associated with median income and unemployment status. 40 These findings warrant careful interpretation. Depression, pain, fatigue, and physical function are interrelated symptom domains. Advanced stage lung cancer patients with greater fatigue reported greater psychological distress and significant functional impairment. 41 , 42 Patients with severe pain often had severe fatigue and emotional distress and worse physical function. 43 Larger studies with repeated PROs over time may assist in further clarification whether socioeconomic deprivation influences the evolution of pain and functional impairment during cancer treatment. Sex and race were independently associated with patient-reported outcomes of depression and fatigue. Male patients reported significantly lower depression scores compared with female patients, consistent with prior studies 31 , demonstrating higher prevalence and reporting of depressive symptoms among women with cancer. Biological differences in stress reactivity and inflammation 43 , as well as sex-based differences in symptom reporting and help-seeking behaviors 32 , may contribute to these findings. White patients reported significantly higher levels of depression and fatigue compared to non-White patients. Traeger et.al., reported higher prevalence of depression in black men, however, noticed white women were at greater risk than black women and white men after adjusted analysis. 45 This finding should be interpreted cautiously and does not imply lower symptom burden among non-White patients. It may reflect differences in symptom expression, cultural norms around emotional reporting, coping strategies or access to supportive care services, or implicit bias in care delivery. 46 In the study by Traeger et al., black women were more likely to meet social work and pastoral care than any other race for depression. The observed disparities—particularly by sex, race, and area deprivation—suggest that one-size-fits-all approaches may fail to address the unique needs of vulnerable subgroups. Future studies with larger and more diverse cohorts are needed to disentangle the complex interplay between race, socioeconomic status, and patient-reported symptoms. Several limitations warrant consideration. The cross-sectional and retrospective study design precludes causal inference, and limits assessment of temporal relationships between PROs and socioeconomic deprivation. PROs were assessed early in the care trajectory around the time of diagnosis. Longitudinal monitoring of impact of SES and PROs with the treatment initiation and disease burden could not be evaluated. Missing PRO data may have introduced selection bias if patients with higher or lower symptom burden were less likely to complete assessments. SES measured at the neighborhood level, while pragmatic and scalable, may not fully capture individual socioeconomic circumstances. Finally, this was a single-center study, which may limit generalizability, although the diverse patient population strengthens the relevance of the findings. Despite the limitations, this study has several important clinical implications. First, our findings support the importance of assessment of social determinants of health into routine oncology practice to help identify patients at higher risk for psychological distress and fatigue. Second, pairing socioeconomic screening with systematic PRO assessment may enable earlier identification of unmet social needs of access to care, transportation needs, housing insecurity etc., and may facilitate targeted interventions early community social worker referral along with psychosocial support, financial navigation, or early referral to supportive oncology services. Conclusion We found that socioeconomic deprivation is independently associated with higher levels of patient-reported depression and fatigue among patients with NSCLC. These findings emphasize the critical role of social determinants of health in shaping symptom burden and quality of life. This highlights the importance of screening for social determinants of health along with PROs for patients with NSCLC and assists in developing equitable, patient-centered cancer care. It also provides a foundation for future interventions aimed at reducing disparities in supportive oncology outcomes. Future prospective studies with longitudinal PRO assessment and SDOH screening are needed to validate the association and it’s causality among patients with NSCLC. Declarations Funding declaration: No Funding received for the study. Author Contribution B.P, J.G and E.B wrote the main manuscript.E B and A.W assisted with analysisAll authors reviewed the manuscript. References Siegel RL, Kratzer TB, Giaquinto AN, Sung H, Jemal A. Cancer statistics, 2025. CA Cancer J Clin. 2025;75(1):10–45. doi: 10.3322/caac.21871 Temel JS, Greer JA, Muzikansky A, et al. Early Palliative Care for Patients with Metastatic Non-Small-Cell Lung Cancer. New England Journal of Medicine . 2010;363(8):733–742. Bar J, Urban D, Amit U, et al. Long-Term Survival of Patients with Metastatic Non-Small Cell Lung Cancer over Five Decades. Journal of Oncology . 13 January 2021 2021;2021doi: 10.1155/2021/7836264 Iyer S, Roughley A, Rider A, Taylor-Stokes G. 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Psychotherapy and Psychosomatics . 2022;91(1):8–35. doi: 10.1159/000521288 Sandström N, Johansson M, Jekunen A, Andersén H. Socioeconomic status and lifestyle patterns in the most common cancer types-community-based research. BMC Public Health. 2023;23(1):1722. Published 2023 Sep 5. doi: 10.1186/s12889-023-16677-6 Hovanec J, Siemiatycki J, Conway DI, et al. Lung cancer and socioeconomic status in a pooled analysis of case-control studies. PLoS One. 2018;13(2):e0192999. Published 2018 Feb 20. doi: 10.1371/journal.pone.0192999 Hovanec J, Kendzia B, Olsson A, et al. Socioeconomic Status, Smoking, and Lung Cancer: Mediation and Bias Analysis in the SYNERGY Study. Epidemiology. 2025;36(2):245–252. doi: 10.1097/EDE.0000000000001807 Kivimäki M, Batty GD, Pentti J, et al. Association between socioeconomic status and the development of mental and physical health conditions in adulthood: a multi-cohort study. Lancet Public Health. 2020;5(3):e140-e149. doi: 10.1016/S2468-2667(19)30248-8 Hirpara DH, Gupta V, Davis LE, et al. Severe symptoms persist for Up to one year after diagnosis of stage I-III lung cancer: An analysis of province-wide patient reported outcomes. Lung Cancer. 2020;142:80–89. doi: 10.1016/j.lungcan.2020.02.014 Sullivan DR, Forsberg CW, Ganzini L, et al. Depression symptom trends and health domains among lung cancer patients in the CanCORS study. Lung Cancer. 2016;100:102–109. doi: 10.1016/j.lungcan.2016.08.008 Tan VS, Tjong MC, Chan WC, et al. A population-based analysis of the management of symptoms of depression among patients with stage IV non-small cell lung cancer (NSCLC) in Ontario, Canada. Support Care Cancer. 2024;32(6):381. Published 2024 May 24. doi: 10.1007/s00520-024-08584-2 Elovainio M, Vahtera J, Pentti J, et al. The Contribution of Neighborhood Socioeconomic Disadvantage to Depressive Symptoms Over the Course of Adult Life: A 32-Year Prospective Cohort Study. Am J Epidemiol. 2020;189(7):679–689. doi: 10.1093/aje/kwaa026 Tjong MC, Doherty M, Tan H, et al. Province-Wide Analysis of Patient-Reported Outcomes for Stage IV Non-Small Cell Lung Cancer. Oncologist. 2021;26(10):e1800-e1811. doi: 10.1002/onco.13890 Emidio OM, Cutrona SL, Person SD, Mazor KM, Frisard C, Lemon SC. Association of neighborhood-level social determinants of health with psychosocial distress in patients newly diagnosed with lung cancer. Cancer Rep (Hoboken). 2022;5(11):e1734. doi: 10.1002/cnr2.1734 Rosenzweig MQ, Althouse AD, Sabik L, et al. The Association Between Area Deprivation Index and Patient-Reported Outcomes in Patients with Advanced Cancer. Health Equity . 2021;5(1):8–16. doi: 10.1089/heq.2020.0037 . Arrieta O, Angulo LP, Núñez-Valencia C, et al. Association of depression and anxiety on quality of life, treatment adherence, and prognosis in patients with advanced non-small cell lung cancer. Ann Surg Oncol. 2013;20(6):1941–1948. doi: 10.1245/s10434-012-2793-5 Hopwood P, Stephens RJ. Depression in patients with lung cancer: prevalence and risk factors derived from quality-of-life data. J Clin Oncol. 2000;18(4):893–903. doi: 10.1200/JCO.2000.18.4.893 Tsai W, Kim JHJ, Yeung NCY, Lu Q. Socioeconomic Status, Stress, and Cancer-related Fatigue among Chinese American Breast Cancer Survivors: The Mediating Roles of Sleep. Asian Am J Psychol . 2024;15(3):213–222. doi: 10.1037/aap0000330 Hutchings H, Behinaein P, Enofe N, et al. Association of Social Determinants with Patient-Reported Outcomes in Patients with Cancer. Cancers . 2024;16(5)doi: 10.3390/cancers16051015 Hung R, Krebs P, Coups EJ, et al. Fatigue and functional impairment in early-stage non-small cell lung cancer survivors. J Pain Symptom Manage. 2011;41(2):426–435. doi: 10.1016/j.jpainsymman.2010.05.017 Brown DJ, McMillan DC, Milroy R. The correlation between fatigue, physical function, the systemic inflammatory response, and psychological distress in patients with advanced lung cancer. Cancer. 2005;103(2):377–382. doi: 10.1002/cncr.20777 Morrison EJ, Novotny PJ, Sloan JA, et al. Emotional Problems, Quality of Life, and Symptom Burden in Patients With Lung Cancer. Clin Lung Cancer. 2017;18(5):497–503. doi: 10.1016/j.cllc.2017.02.008 Slavich GM, Sacher J. Stress, sex hormones, inflammation, and major depressive disorder: Extending Social Signal Transduction Theory of Depression to account for sex differences in mood disorders. Psychopharmacology (Berl). 2019;236(10):3063–3079. doi: 10.1007/s00213-019-05326-9 Traeger L, Cannon S, Keating NL, et al. Race by sex differences in depression symptoms and psychosocial service use among non-Hispanic black and white patients with lung cancer. J Clin Oncol. 2014;32(2):107–113. doi: 10.1200/JCO.2012.46.6466 Traeger L, Cannon S, Pirl WF, Park ER. Depression and Undertreatment of Depression: Potential Risks and Outcomes in Black Patients with Lung Cancer. Journal of Psychosocial Oncology . 2013;31(2):123–135. doi: 10.1080/07347332.2012.761320 Vrtikapa K, Hoque Urmy F, Hoque F. Social Determinants of Health: The Impact of This Overlooked Vital Sign. J Brown Hosp Med . 2025;4(3):138072. Published 2025 Jul 1. doi: 10.56305/001c.138072 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-8743761","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":611560766,"identity":"245b011d-0405-40ed-ba15-28b2f47fe980","order_by":0,"name":"Bindu Potugari","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYBACAyjNwy/B+IxoLYwNQFpGcgazGWlabAxuEKvFXLr5+GOeX3Y8xreb2R7+YLDJl3cgoMVyzrHEZt6+ZB6zO4fZjXkY0iw3HiDksBs5hs28Pcw8Zjfyj0kzMBw2MGwgTks9j/GMZDbJH0Rr4flxmMdAIplNggeoRZ6ADrBfZs5tOM4jcSOZTZrHIM3AgJAWYIgd+PDmT7U9P9hhFTYG8oQcxiABxIxtcHcC0QFitDD8QRIgbMsoGAWjYBSMNAAAFM88ThrTTncAAAAASUVORK5CYII=","orcid":"","institution":"Michigan State University","correspondingAuthor":true,"prefix":"","firstName":"Bindu","middleName":"","lastName":"Potugari","suffix":""},{"id":611560767,"identity":"76948c6b-f3a3-4ea8-a2f0-a430563b523b","order_by":1,"name":"Jaya Gupta","email":"","orcid":"","institution":"Henry Ford Health System","correspondingAuthor":false,"prefix":"","firstName":"Jaya","middleName":"","lastName":"Gupta","suffix":""},{"id":611560768,"identity":"e98d3ebd-3208-4b48-a067-10b7cbdee857","order_by":2,"name":"Julia Bachler","email":"","orcid":"","institution":"Henry Ford Health System","correspondingAuthor":false,"prefix":"","firstName":"Julia","middleName":"","lastName":"Bachler","suffix":""},{"id":611560769,"identity":"20d3c0ab-2eff-4cb1-a302-9bf690be4df1","order_by":3,"name":"Anqi wang","email":"","orcid":"","institution":"Henry Ford Health System","correspondingAuthor":false,"prefix":"","firstName":"Anqi","middleName":"","lastName":"wang","suffix":""},{"id":611560770,"identity":"c881a4da-f14c-4966-87d8-ca934f7d8f14","order_by":4,"name":"Eric Adjei Boakye","email":"","orcid":"","institution":"Henry Ford Health System","correspondingAuthor":false,"prefix":"","firstName":"Eric","middleName":"Adjei","lastName":"Boakye","suffix":""},{"id":611560771,"identity":"e26364f3-975d-4687-8d77-8d654a30a755","order_by":5,"name":"Samantha Tam","email":"","orcid":"","institution":"Henry Ford Health System","correspondingAuthor":false,"prefix":"","firstName":"Samantha","middleName":"","lastName":"Tam","suffix":""},{"id":611560772,"identity":"4c142ebb-67ee-4bcd-a133-e079864f86fa","order_by":6,"name":"Theresa Zatirka","email":"","orcid":"","institution":"Henry Ford Health System","correspondingAuthor":false,"prefix":"","firstName":"Theresa","middleName":"","lastName":"Zatirka","suffix":""},{"id":611560773,"identity":"0f5ed7f8-da7b-4f23-9709-edf856aee703","order_by":7,"name":"Benjamin Movsas","email":"","orcid":"","institution":"Henry Ford Health System","correspondingAuthor":false,"prefix":"","firstName":"Benjamin","middleName":"","lastName":"Movsas","suffix":""},{"id":611560774,"identity":"91602baa-8a00-4279-8fed-4db1c10f1b63","order_by":8,"name":"Shirish Gadgeel","email":"","orcid":"","institution":"Henry Ford Health System","correspondingAuthor":false,"prefix":"","firstName":"Shirish","middleName":"","lastName":"Gadgeel","suffix":""}],"badges":[],"createdAt":"2026-01-30 17:25:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8743761/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8743761/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109242781,"identity":"382a5ebb-4f29-4f4a-b80d-6245d2ec1e06","added_by":"auto","created_at":"2026-05-14 07:11:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":293249,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8743761/v1/f23856bc-3416-4bcc-9c91-631b1866d057.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of Socioeconomic Status on Patient-Reported Outcomes (PROs) in Non-Small Cell Lung Cancer (NSCLC)","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLung cancer remains the leading cause of cancer-related deaths in the United States and worldwide and is the second most common type of cancer in the U.S., with 226,650 estimated new cases of lung and bronchus cancer reported in 2025.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e Non-Small Cell Lung Cancer (NSCLC) accounts for 85% of all lung cancers. Historically, NSCLC has been associated with a poor prognosis, however, the advent of targeted therapies and immunotherapy has led to meaningful improvements in long-term survival.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e Despite the advances in screenings and therapies, the majority of patients with NSCLC present with advanced disease and have high symptom burden.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e These symptoms range from physical to psychological, including anxiety, fatigue, shortness of breath, weight loss, etc. \u003csup\u003e2,4\u003c/sup\u003e Moderate to severe symptoms at baseline and worsening symptom burden during the first cycle of chemotherapy were independent predictors of poorer overall survival among patients diagnosed with advanced NSCLC.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eDiscrepancies have been found between patient reporting and clinician estimates in assessing the frequency and intensity of cancer-related symptoms in multiple studies, especially for symptoms such as fatigue and decreased appetite.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e In response to the discordance between patient and clinician assessment, patient-reported outcome (PRO) monitoring during cancer treatment has emerged as a powerful method to assess patients\u0026rsquo; symptoms and well-being directly.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e Patient-reported outcome measures (PROMs) are validated tools that collect health-related perspectives directly from the patient. PRO monitoring using PROMs during treatment has been shown to improve symptom control, quality of life, and survival.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe World Health Organization defines social determinants of health as the conditions in which people are born, grow, live, work, and age, and their access to power, money, and resources.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e Socioeconomic status influences multiple social determinants of health such as housing, transportation and access to care which collectively affect patients\u0026rsquo; ability to receive timely, high-quality care.\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e Socioeconomic status (SES) is a well-recognized determinant of cancer outcomes, influencing access to cancer care, stage at diagnosis, treatment delivery, and survival in lung cancer.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e Increasingly, SES is also recognized as a critical driver of symptom burden and health-related quality of life.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e Patients with lower SES are more likely to experience financial toxicity, psychosocial distress, limited access to cancer care services, and higher comorbidity burden\u0026mdash;all factors that may exacerbate cancer-related symptoms.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003eNeighborhood-level measures of socioeconomic deprivation offer a pragmatic approach to capturing social disadvantage beyond individual-level variables, which are often unavailable or incomplete in clinical datasets.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e The Area Deprivation Index (ADI) is a validated composite measure incorporating income, education, employment, and housing quality at the neighborhood level and has been associated with cancer outcomes, healthcare utilization, and mortality.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe relationship between neighborhood-level SES and PROs in NSCLC has not been well characterized, particularly using standardized, validated PROMs embedded in routine clinical care. In this study, we evaluated the association between area-level socioeconomic deprivation, as measured by ADI, and multiple PRO domains\u0026mdash;including depression, fatigue, pain interference, and physical function\u0026mdash;among patients with NSCLC receiving care at a large, integrated cancer center.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy setting\u003c/h2\u003e \u003cp\u003eThis was a cross-sectional, retrospective study conducted at an academic vertically integrated health system in the Midwest, United States. The health system serves a large urban and suburban community with a diverse patient population. The health system implemented a system-wide PROs monitoring program in 2021 for all outpatient oncology visits.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e PROMs have been fully integrated into the health system\u0026rsquo;s electronic health record (EHR) using widely available Epic\u0026copy; tools. All adult patients with a diagnosis of cancer are offered PROM instruments to complete on the patient portal, MyChart, prior to their visit. For patients that do not have MyChart access or have not completed their questionnaires prior to their visit, a tablet is provided to complete questionnaires at the time of their visit with an oncology provider. The National Institute of Health\u0026rsquo;s Patient-Reported Outcomes Measurement Information System (PROMIS\u0026reg;) was used and was selected due to its high level of precision, computer-adaptive testing (CAT) capabilities, and existing validation within cancer patients. CAT is an algorithm-driven branching logic that allows for accurate measurement of each domain utilizing the minimum number of questions. This decreases time to completion and responder fatigue.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e An integrated care management model was established for patients with severe scores with reflex referrals to a social worker for depression and a nurse for pain and poor physical function in real time. Patients who were diagnosed with NSCLC and having their PROMs within 180 days of their first office visit at the cancer center were included in this study. This retrospective study was conducted in accordance with declaration of Helsinki and approved by the Henry Ford Health System Institutional Review Board, which waived the requirement for informed consent due to the use of existing, de-identified data.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eOutcome\u003c/h2\u003e \u003cp\u003eThe outcome variables were four PROMIS instruments: physical function (PROMIS\u0026reg; CAT v2.0 \u0026ndash; Physical Function)\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, pain interference (PROMIS\u0026reg; CAT v1.1 \u0026ndash; Pain Interference)\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, fatigue (PROMIS\u0026reg; CAT v1.0 \u0026ndash; Fatigue)\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, and depression (PROMIS\u0026reg; CAT v1.0 \u0026ndash; Depression).\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003c/sup\u003e The PROMIS\u0026reg; measures use T scores (with a mean of 50 and SD of 10) for all domains to account for the fact that patients do not receive the same questions due to CAT.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e A higher score indicates worse symptoms in the fatigue, pain interference, and depression domains, while a lower score indicates worse function in the physical function domain.\u003csup\u003e\u003cspan additionalcitationids=\"CR20 CR21 CR22 CR23\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e PROMIS\u0026reg; scores are interpreted based on the clinical cut points of \u0026ldquo;within normal limits\u0026rdquo;, \u0026ldquo;mild\u0026rdquo;, \u0026ldquo;moderate\u0026rdquo;, or \u0026ldquo;severe\u0026rdquo;. For fatigue, pain interference, and depression, a score of \u0026lt;\u0026thinsp;55 was normal, 55\u0026ndash;60 was mild, 60\u0026ndash;70 was moderate, and \u0026gt;\u0026thinsp;70 was severe. For physical function, a score of \u0026gt;\u0026thinsp;45 was normal, 40\u0026ndash;45 was mild, 30\u0026ndash;40 was moderate, and \u0026lt;\u0026thinsp;30 was severe.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e PROMs scores were used as continuous variables instead of categorical variables in this study.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eExposure\u003c/h3\u003e\n\u003cp\u003eThe main exposure variable in the study was area-level socioeconomic disadvantage assessed with Area Deprivation Index (ADI). Individual\u0026rsquo;s primary residence at the time of diagnosis was geocoded using zip code and census tract was used to calculate state ADI. State ADI was selected over national ADI because 98% of patients seen at the medical center reside in the state the institution is located. State ADI had a score of 1\u0026ndash;10, with 1 being the least disadvantaged and 10 being the most disadvantaged.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e State ADI scores were categorized into two groups (0 to \u0026lt;\u0026thinsp;5 or \u0026ge;\u0026thinsp;5 to 10) for analysis.\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eCovariates included in the study were age at diagnosis (continuous variable measured in years), self-reported sex (female, male), self-reported race (White, Other [including all other races including African Americans, combined due to small cell counts]), self-reported marital status (married/significant other, divorced/separated/widowed, single), stage of presentation at cancer diagnosis (early stage [stage 1 and 2], late stage [stage 3 and 4]), and Charlson Co-Morbidity Index (0, 1\u0026ndash;2, \u0026ge;\u0026thinsp;3). The Charlson Co-Morbidity Index (CCI) is a predictor of long-term mortality and has been validated for use in cancer patients.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics were used to characterize patients included in the cohort. Categorical data were summarized as counts and percentage while continuous measures were summarized using mean and standard deviation. Four multivariable linear regression models (one for each PROMIS domain outcome variable) were used to estimate associations between ADI and the outcome variables adjusting for age at diagnosis, sex, race, marital status, cancer stage and CCI. Each Complete-case analysis was employed for each PROMIS domain to address missing data. Duplicate completed PROMs instruments from the same patients were filtered by using the first contact date. Statistical significance was pre-specified at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. All analyses were performed using R statistical software version 4.3.2 (R Project for Statistical Computing).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 491 patients diagnosed with NSCLC between September 2021 and December 2023 who completed PROMs within 180 days of initial office visit with the cancer provider were included in the study (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The mean age of the cohort was 68.5 (SD\u0026thinsp;=\u0026thinsp;9.4) years. Approximately half of the patients were females (n\u0026thinsp;=\u0026thinsp;257; 50.3%) and were married/had significant other (n\u0026thinsp;=\u0026thinsp;243; 49.5%). Most of the patients identified as White (n\u0026thinsp;=\u0026thinsp;376; 76.6%) and had late-stage NSCLC at the time of diagnosis (n\u0026thinsp;=\u0026thinsp;315; 64.2%). A substantial proportion of the patients had a high comorbidity burden (61.8% had CCI\u0026thinsp;\u0026ge;\u0026thinsp;3). Close to half of the patients (n\u0026thinsp;=\u0026thinsp;187; 48.6%) resided in neighborhoods with ADI score\u0026thinsp;\u0026ge;\u0026thinsp;5, corresponding to higher area-level disadvantage and lower socioeconomic status. For all patients, the mean depression T-score was 51.5 (SD\u0026thinsp;=\u0026thinsp;9.1), mean fatigue T-score was 55.2 (SD\u0026thinsp;=\u0026thinsp;9.5), mean pain interference T-score was 56.9 (SD\u0026thinsp;=\u0026thinsp;10.3), and mean physical function T-score was 38.2 (SD\u0026thinsp;=\u0026thinsp;9.4). PROMIS T scores noted by socioeconomic status ADI\u0026thinsp;\u0026lt;\u0026thinsp;5 and ADI\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;5, the mean depression T-score was 50.3(SD\u0026thinsp;=\u0026thinsp;9.1) and 52.6(SD\u0026thinsp;=\u0026thinsp;9.2), mean fatigue T-score was 54.1(SD\u0026thinsp;=\u0026thinsp;9.8) and 56.3(SD\u0026thinsp;=\u0026thinsp;9.4), mean pain interference T-score was 55.3(SD\u0026thinsp;=\u0026thinsp;10.3) and 58.7(SD\u0026thinsp;=\u0026thinsp;10.3), and mean physical function T-score was 38.8(SD\u0026thinsp;=\u0026thinsp;9.8) and 37.5(SD\u0026thinsp;=\u0026thinsp;8.9) respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of patients diagnosed with non-small cell lung cancer included in the study (n\u0026thinsp;=\u0026thinsp;491).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN (%) or Mean (SD)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eADI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e198 (51.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e187 (48.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge At Diagnosis (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e68.5 (9.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e247 (50.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e244 (49.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-White**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e114 (23.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e376 (76.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced/Separated/Widowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e147 (29.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e243 (49.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e97 (19.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStage***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEarly Stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e173 (35.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLate Stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e315 (64.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCCI*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66 (14.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e108 (23.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e281 (61.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePROMIS Depression T score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51.5 (9.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePROMIS Fatigue T score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55.2 (9.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePROMIS Pain Interference T score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56.9 (10.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePROMIS Physical Function T score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38.2 (9.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003e*CCI- Charlston Comorbidity Index; ** Non-whites included 17.3% of African Americans. *** Early stage included stage 1\u0026ndash;2, late stage included stage 3\u0026ndash;4.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePROMIS T scores by ADI\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePROMIS (Domains)\u003c/p\u003e \u003cp\u003e(T-scores) Mean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eADI\u0026thinsp;\u0026lt;\u0026thinsp;5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eADI\u0026thinsp;\u0026ge;\u0026thinsp;5\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50.3 (9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52.6 (9.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFatigue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54.1 (9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56.3 (9.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePain Interference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55.3(10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58.7(10.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical function\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38.8 (9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37.5 (8.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn multivariable linear regression analyses adjusting for age, sex, race, marital status, cancer stage at diagnosis, and comorbidity burden, higher neighborhood socioeconomic deprivation (ADI\u0026thinsp;\u0026ge;\u0026thinsp;5) was independently associated with worse patient-reported outcomes in select domains (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Patients living in more socioeconomically disadvantaged areas (ADI\u0026thinsp;\u0026ge;\u0026thinsp;5) were associated with a 2.68-point higher depression T scores [β 2.68 (95% CI0.47, 4.89) p\u0026thinsp;=\u0026thinsp;0.018] and a 3.06-point higher fatigue scores [β 3.06 (95% CI0.25, 5.87) p\u0026thinsp;=\u0026thinsp;0.033] compared to those living in areas with low ADI. Higher scores of pain interference were also seen in patients with ADI\u0026thinsp;\u0026ge;\u0026thinsp;5 [β 3.01 (95% CI-0.11, 6.13)], however, it is not statistically significant. No significant association was identified between ADI and physical function domain [β \u0026minus;\u0026thinsp;1.30(95% CI \u0026minus;\u0026thinsp;4.11,1.50)].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariable analysis of sociodemographic characteristics in relation to PROMIS domain T scores.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eBeta Estimate (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eDepression\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePain interference\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eFatigue\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ePhysical function\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eADI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.68 (0.47, 4.89)\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.018\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.01 (-0.11, 6.13)\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.06 (0.25, 5.87)\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.033\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.3 (-4.11, 1.5)\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.361\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.36 (-2.63, 1.92)\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.28 (-1.82, 4.38)\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.4 (-0.41, 5.2)\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.5 (-5.27, 0.27)\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.076\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.29 (-4.37, -0.2)\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.032\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.69 (-2.22, 3.6)\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.39 (-4.05, 1.27)\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.46 (-1.17, 4.09)\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.274\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-white\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.75 (0.16, 5.34)\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.037\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.9 (-4.19, 2.39)\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.32 (0.34, 6.29)\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.029\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.91 (-3.83, 2.01)\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.542\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDivorced/Separated/Widowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.05 (-1.36, 3.45)\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.24 (-1.29, 5.77)\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.54 (-0.7, 5.78)\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.96 (-5.16, 1.23)\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.227\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01 (-3.1, 3.13)\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.62 (-3.61, 4.86)\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.29 (-2.58, 5.16)\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.87 (-4.67, 2.92)\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.651\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEarly stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLate stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.29 (-2.45, 1.87)\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.26 (-3.19, 2.68)\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02 (-2.64, 2.67)\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.64 (-4.26, 0.98)\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.218\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.91 (-2.19, 4)\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.93 (-2.76, 6.61)\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.17 (-2.1, 6.44)\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.91 (-3.28, 5.1)\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.668\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.15 (-2.57, 2.28)\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.73 (-5.22, 1.76)\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.39 (-4.45, 1.67)\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.47 (-0.61, 5.55)\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.115\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMale sex was associated with significantly lower depression scores compared with female sex [β\u0026ndash;2.29 (95% CI \u0026minus;\u0026thinsp;4.37, \u0026minus;\u0026thinsp;0.20); p\u0026thinsp;=\u0026thinsp;0.032], whereas sex was not significantly associated with pain interference, fatigue, or physical function. White race was independently associated with higher depression [β 2.75(95% CI 0.16, 5.34) p\u0026thinsp;=\u0026thinsp;0.037] and higher fatigue scores [β 3.32(95% CI 0.34, 6.29) p\u0026thinsp;=\u0026thinsp;0.029] compared with non-White race. Race was not significantly associated with pain interference or physical function. Age at diagnosis, marital status, cancer stage, and comorbidity burden were not independently associated with any of the PROMIS domains.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this retrospective study, we evaluated the association between area-level socioeconomic status assessed via state ADI and patient-reported outcomes among patients with NSCLC. Using validated PROMIS instruments embedded in routine clinical care, we demonstrate that area-level socioeconomic deprivation is independently associated with worse depression and fatigue among patients with NSCLC, even after adjustment for demographic and clinical factors. Specifically, we found that higher socioeconomic deprivation as measured by ADI was independently associated with worse depression and fatigue. In contrast, no significant association was found between pain interference and physical function, and ADI. Unlike prior studies that rely on individual-level socioeconomic indicators or symptom measures in clinical studies, our findings highlight the importance of structural, area-level disadvantage in shaping the lived experience of patients with lung cancer. These findings underscore the importance of area-level socioeconomic status in shaping symptom burden in lung cancer patients beyond disease specific factors.\u003c/p\u003e \u003cp\u003eOur findings are consistent with the current literature demonstrating that socioeconomic disadvantage is associated with adverse health outcomes, including patient reported psychological distress and symptom burden\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Patients with lung cancer were disproportionately affected by low educational attainment, lower income, lower occupational status/unemployment, low health literacy, and adverse health behaviors, all of which may contribute to increased vulnerability to symptom burden and poor quality of life.\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e In a large multi-cohort study evaluating the association of socioeconomic status (measured by ADI, educational attainment, or occupational grade) and health conditions in adulthood showed that low socioeconomic status was associated with increased lung cancer risk even after adjustment for lifestyle factors.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eDepression emerged as a key outcome associated with higher socioeconomic deprivation. Depression is highly prevalent in lung cancer patients with the reported rates ranging from approximately 20\u0026ndash;38% in early stage to over 70% in stage 4 patients.\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e In a prospective nationwide cohort study, there was increased risk of depressive symptoms in participants living in higher disadvantage neighborhoods over time, independent of individual risk factors.\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e In a study by Tjong etal., NSCLC patients from neighborhoods with lowest income quintile reported higher moderate to severe scores in depression and tiredness compared to higher income quintile similar to our study.\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e However, in a recent retrospective study evaluating socioeconomic status by composite SDOH score (using person level income and education indicator scores) and psychosocial distress (by NCCN distress thermometer) in newly diagnosed lung cancer patients at diagnosis showed no statistically significant association, which is in contrary to our study.\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e However, the authors noted that only 26% of the patients completed distress screening at diagnosis, which may introduce bias in the findings due to the low response. Another study by Rosenzweig et al., evaluating association of ADI and PROs among all cancer patients who presented at stage 4 reported no statistically significant association between ADI and depression on multivariate analysis, however, showed patients with worse ADI had worse depression scores.\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e Addressing depressive symptoms among patients with lung cancer is very important as it impacts other health outcomes. For instance, depression in lung cancer patients was associated with low median overall survival (6.8 months) and poor treatment adherence (58%) compared to patients without depression (14 months and 42% low adherence).\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e Potential mechanisms underlying the association of ADI and depression include chronic psychosocial stress, limited access to mental health services, financial burden, and reduced social support, all of which may be more prevalent in socioeconomically disadvantaged environments.\u003c/p\u003e \u003cp\u003eFatigue emerged as another key outcome independently associated with higher socioeconomic deprivation. The most prevalent reported moderate to severe PRO symptom in NSCLC after diagnosis was tiredness (84% in stage 4 NSCLC and 47% in stage 1\u0026ndash;3 lung cancer).\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e Fatigue is known to cluster with psychological distress, and high levels of depression are associated with higher levels of fatigue.\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e Patients with low socioeconomic status may face greater barriers to addressing modifiable contributors to fatigue, such as poor sleep quality, perceived psychological stress, and limited access to supportive services.\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e The observed association between ADI and depression/fatigue highlights the importance of incorporating social determinants of health into PRO assessment and need future prospective studies to validate our findings and to evaluate for management strategies.\u003c/p\u003e \u003cp\u003eIn contrast, socioeconomic deprivation was not significantly associated with physical function and pain interference. However, when looking at all cancer patients, pain and physical function domains were significantly associated with median income and unemployment status.\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e These findings warrant careful interpretation. Depression, pain, fatigue, and physical function are interrelated symptom domains. Advanced stage lung cancer patients with greater fatigue reported greater psychological distress and significant functional impairment.\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e Patients with severe pain often had severe fatigue and emotional distress and worse physical function.\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e Larger studies with repeated PROs over time may assist in further clarification whether socioeconomic deprivation influences the evolution of pain and functional impairment during cancer treatment.\u003c/p\u003e \u003cp\u003eSex and race were independently associated with patient-reported outcomes of depression and fatigue. Male patients reported significantly lower depression scores compared with female patients, consistent with prior studies\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, demonstrating higher prevalence and reporting of depressive symptoms among women with cancer. Biological differences in stress reactivity and inflammation\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e, as well as sex-based differences in symptom reporting and help-seeking behaviors\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, may contribute to these findings. White patients reported significantly higher levels of depression and fatigue compared to non-White patients. Traeger et.al., reported higher prevalence of depression in black men, however, noticed white women were at greater risk than black women and white men after adjusted analysis.\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e This finding should be interpreted cautiously and does not imply lower symptom burden among non-White patients. It may reflect differences in symptom expression, cultural norms around emotional reporting, coping strategies or access to supportive care services, or implicit bias in care delivery.\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e In the study by Traeger et al., black women were more likely to meet social work and pastoral care than any other race for depression. The observed disparities\u0026mdash;particularly by sex, race, and area deprivation\u0026mdash;suggest that one-size-fits-all approaches may fail to address the unique needs of vulnerable subgroups. Future studies with larger and more diverse cohorts are needed to disentangle the complex interplay between race, socioeconomic status, and patient-reported symptoms.\u003c/p\u003e \u003cp\u003eSeveral limitations warrant consideration. The cross-sectional and retrospective study design precludes causal inference, and limits assessment of temporal relationships between PROs and socioeconomic deprivation. PROs were assessed early in the care trajectory around the time of diagnosis. Longitudinal monitoring of impact of SES and PROs with the treatment initiation and disease burden could not be evaluated. Missing PRO data may have introduced selection bias if patients with higher or lower symptom burden were less likely to complete assessments. SES measured at the neighborhood level, while pragmatic and scalable, may not fully capture individual socioeconomic circumstances. Finally, this was a single-center study, which may limit generalizability, although the diverse patient population strengthens the relevance of the findings.\u003c/p\u003e \u003cp\u003eDespite the limitations, this study has several important clinical implications. First, our findings support the importance of assessment of social determinants of health into routine oncology practice to help identify patients at higher risk for psychological distress and fatigue. Second, pairing socioeconomic screening with systematic PRO assessment may enable earlier identification of unmet social needs of access to care, transportation needs, housing insecurity etc., and may facilitate targeted interventions early community social worker referral along with psychosocial support, financial navigation, or early referral to supportive oncology services.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe found that socioeconomic deprivation is independently associated with higher levels of patient-reported depression and fatigue among patients with NSCLC. These findings emphasize the critical role of social determinants of health in shaping symptom burden and quality of life. This highlights the importance of screening for social determinants of health along with PROs for patients with NSCLC and assists in developing equitable, patient-centered cancer care. It also provides a foundation for future interventions aimed at reducing disparities in supportive oncology outcomes. Future prospective studies with longitudinal PRO assessment and SDOH screening are needed to validate the association and it\u0026rsquo;s causality among patients with NSCLC.\u003c/p\u003e "},{"header":"Declarations","content":"\u003ch2\u003eFunding declaration:\u003c/h2\u003e\n\u003cp\u003eNo Funding received for the study.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eB.P, J.G and E.B wrote the main manuscript.E B and A.W assisted with analysisAll authors reviewed the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiegel RL, Kratzer TB, Giaquinto AN, Sung H, Jemal A. Cancer statistics, 2025. 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Published 2025 Jul 1. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.56305/001c.138072\u003c/span\u003e\u003cspan address=\"10.56305/001c.138072\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\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":"Patient reported outcome (PRO), Patient reported outcome measures (PROMs), Lung cancer, Socioeconomic status, Area deprivation index (ADI), PROMIS, Non-Small Cell Lung Cancer (NSCLC)","lastPublishedDoi":"10.21203/rs.3.rs-8743761/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8743761/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003ePatient-reported outcomes (PROs) are a powerful method to assess a patient's well-being and have shown to improve patient\u0026rsquo;s quality of life and survival in lung cancer. Socioeconomic status (SES) influences patients\u0026rsquo; ability to receive timely, high-quality care. The association between area-level SES and PROs among patients diagnosed with Non-Small Cell Lung Cancer (NSCLC) was examined.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a retrospective study of patients (n\u0026thinsp;=\u0026thinsp;491) diagnosed with NSCLC between September 2021 and December 2023 who completed an NIH Patient-Reported Outcomes Measurement Information System (PROMIS\u0026reg;) questionnaire within 180 days of their initial oncology office visit. The main exposure of interest was socioeconomic disadvantage assessed via state-level Area Deprivation Index (ADI) categorized into (0 to \u0026lt;\u0026thinsp;5 or \u0026ge;\u0026thinsp;5 to 10). Outcomes were four PROMIS domains - physical function, fatigue, pain interference, and depression. Four multivariable linear regression models (one for each PROMIS domain) were used to estimate associations between ADI and PROMIS T-scores adjusting for sociodemographic and clinical factors.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eApproximately 49% of patients resided in neighborhoods with ADI score\u0026thinsp;\u0026ge;\u0026thinsp;5. The mean T-score for depression was 51.5 (SD\u0026thinsp;=\u0026thinsp;9.1), fatigue was 55.2 (SD\u0026thinsp;=\u0026thinsp;9.5), pain interference was 56.9 (SD\u0026thinsp;=\u0026thinsp;10.3), and physical function was 38.2 (SD\u0026thinsp;=\u0026thinsp;9.4). Patients living in more socioeconomically disadvantaged areas (state ADI\u0026thinsp;\u0026ge;\u0026thinsp;5) were associated with significantly higher depression scores [β 2.68 (95% CI0.47, 4.89) p\u0026thinsp;=\u0026thinsp;0.018] and fatigue scores [β 3.06 (95% CI0.25, 5.87) p\u0026thinsp;=\u0026thinsp;0.033]. Pain interference and physical function were not statistically associated with state ADI.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eArea-level socioeconomic status is independently associated with higher levels of patient-reported depression and fatigue among patients with NSCLC. These findings need further evaluation with prospective studies and underscore the importance of considering socioeconomic factors in assessing patient-reported outcomes and developing targeted interventions to address disparities in healthcare access and outcomes among lung cancer patients.\u003c/p\u003e","manuscriptTitle":"Impact of Socioeconomic Status on Patient-Reported Outcomes (PROs) in Non-Small Cell Lung Cancer (NSCLC)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-26 11:00:25","doi":"10.21203/rs.3.rs-8743761/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":"281f0cc1-9f47-47b6-9650-429b83528896","owner":[],"postedDate":"March 26th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-14T07:06:18+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-14T07:10:09+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-26 11:00:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8743761","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8743761","identity":"rs-8743761","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}