Association Between an Intersectionality-Based Risk Score and Cancer Mortality: A Retrospective Cohort Study

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Shour, Bret Friday, Laura Palombi, Catherine Benziger, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7546882/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background Cancer mortality disparities persist, and existing prognostic tools have not captured overlapping vulnerabilities, limiting their ability to characterize disparities in mortality and rising digestive cancer incidence among young adults. Our study aims to develop and determine whether a new Intersectionality Score is associated with all-cause cancer mortality, compare its prognostic performance with the Charlson Index and TNM staging, and assess digestive cancer incidence among young adults across intersectionality risk groups. Methods A retrospective cohort study of 5,793 adults with cancer treated at Essentia Health in the upper Midwest (2014–2024) used electronic health records to examine outcomes among patients who initiated chemotherapy or radiation. The outcome was all-cause mortality at last follow-up. The secondary outcome was digestive cancer incidence among young adults (18–49 years). The exposure was the Intersectionality Score, a 12-point index of overlapping social, clinical variables analyzed as continuous and categorical variables. Descriptive statistics, DeLong’s test, and linear, logistic, and Poisson regression models were performed using Stata/SE 18.5, with two-sided P ≤ .05. Results Among 5,793 cancer patients (mean [SD] age, 66.9 [12.4] years; 38.6% female), 50.4% were deceased at last follow-up, and 16.7% had a diagnosis of digestive cancer. Intersectionality Score demonstrated improved prognostic discrimination (AUC 0.630) in cancer mortality prediction compared to CCI (0.605) and TNM staging (0.554) and was significantly associated with mortality: moderate-risk patients had higher odds of death (OR, 1.71; 95% CI, 1.37–2.14) than low-risk patients. Among young adults, digestive cancer incidence was higher in 2017–2019 (IRR, 1.47; 95% CI, 1.31–1.64) and 2020–2022 (IRR, 1.44; 95% CI, 1.29–1.61) versus 2014–2016. Moderate-risk patients had a higher incidence in 2020–2022 (IRR, 1.19) and 2023–2024 (IRR, 1.32). Conclusion The intersectionality risk score was associated with all-cause mortality and demonstrated improved prognostic discrimination over the Charlson Index and TNM staging. Digestive cancer incidence increased among young adults classified as moderate risk. Our cumulative intersectionality score may improve mortality risk stratification and help detect shifting patterns in early-onset digestive cancers among patients with overlapping vulnerabilities, with potential relevance for rural and resource-constrained settings globally. Figures Figure 1 Figure 2 Introduction Cancer mortality remains a leading public health concern in the United States, with persistent disparities across socioeconomic status and geography, despite advances in early detection and treatment [ 1 – 3 ]. In 2024, over 600,000 cancer deaths were recorded, and incidence is rising for several malignancies, including digestive/colorectal cancer among younger adults, a trend that disproportionately affects marginalized populations [ 1 , 3 , 4 ]. Traditional prognostic tools such as the Charlson Comorbidity Index (CCI) and Tumor–Node–Metastasis (TNM) staging provide important clinical insights but often fail to capture the broader social and structural forces associated with adverse cancer outcomes, underscoring the limitations of models that overlook contextual barriers to care [ 5 – 9 ]. Notably, no current index is statistically designed to stratify risk among cancer patients experiencing compounding disadvantages, nor to identify patterns such as the increasing incidence of digestive cancers in younger adults. To address this gap, we developed an Intersectionality Score, a point-based index derived from Electronic Health Record (EHR) data, and evaluated its prognostic performance alongside CCI and TNM staging in a large rural oncology cohort in the United States. Intersectionality theory, which examines how overlapping social categories interact to produce unique experiences of discrimination and privilege shaped by broader power structures, provides a compelling framework to address these shortcomings [ 10 , 11 ]. While widely applied in qualitative health equity research, intersectionality theory has yet to be systematically operationalized in quantitative oncology studies in a way that integrates both clinical and social determinants of health. Existing indices often rely on neighborhood-level or single-domain measures that inadequately reflect the compound disadvantage faced by many patients [ 12 , 14 ]. Emerging evidence highlights the independent impact of public insurance, long travel distances, comorbidity burden, and appointment nonadherence on mortality, especially in rural populations [ 11 , 15 – 17 ]. Prior work in our rural health system demonstrated that missed (including oncology) appointments are frequently associated with these barriers, signaling structural vulnerability that traditional clinical indices may miss [ 11 , 18 ]. Building on this foundation, we developed and internally evaluated the Shour Intersectionality Score (SIS), a 12-point cumulative index derived from EHR data that captures overlapping demographic and care delivery vulnerabilities. This measure integrates both social context and cancer-specific clinical complexity, supporting calls for patient-level prognostic tools [ 6 , 8 , 13 ]. We examined its association with all-cause mortality among oncology patients and compared its prognostic performance to the CCI and TNM staging. To explore broader epidemiologic patterns, we also examined whether digestive cancer incidence among young adults (aged 18–49 years) changed over time by intersectionality risk group. We hypothesized that patients with higher scores would have significantly higher odds of death, and that this SIS would offer stronger predictive value than the CCI and TNM staging models. The SIS was developed using an Intersectionality theory [ 6 , 8 , 20 – 22 ]. While several short-term mortality models, such as the Prognostic Score for Hospitalized Cancer Patients (PROMISE), Gustave Roussy Immune Score (GRIm), and C-Reactive Protein - Triglyceride–Glucose Index (CTI), have demonstrated utility in acute inpatient oncology settings, these biomarker-based tools require lab values not routinely available across ambulatory care, limiting their applicability for population-level risk stratification [ 23 , 24 ]. Hence, the SIS index was operationalized using routinely collected EHR data to create a practical, scalable tool for patient stratification aimed at improving the precision and equity of cancer care delivery. Methods Following Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines [ 14 ], we conducted a retrospective cohort study of adult patients diagnosed with cancer and initiating treatment within Essentia Health (Service Area 10), a large rural health system serving the upper Midwest, between January 1, 2014, and December 31, 2024. Data were extracted from the Essentia Health Epic EHR system. The source population was 1,160,176 unique patients with at least one scheduled visit. From this population, we identified 5,793 adults (≥ 18 years) diagnosed with cancer who initiated chemotherapy or radiation therapy and had at least one recent oncology visit within the study period. Eligible patients had a cancer diagnosis and received active treatment. Patients were excluded if they were under 18 years of age, received care outside the specified timeframe, or were treated exclusively with palliative or supportive care. This study was reviewed and approved by the Essentia Health Institutional Review Board (IRB #EH25918-EHIR-1.0; FWA #00000635) under an expedited procedure. The Essentia Health IRB waived the requirement for informed consent because only de-identified electronic health record data were used and the research posed no more than minimal risk, consistent with 45 CFR 46.116(f). Study Measures The study outcome was all-cause mortality, defined as vital status (deceased vs alive) at last recorded follow-up. The main exposure was the SIS, analyzed both as a continuous and as a 3-level categorical variable. The SIS was constructed as a cumulative index to capture overlapping individual-level vulnerabilities across structural, sociodemographic, and clinical domains. Twelve dichotomous risk components were defined based on theory and empirical literature 6–10 linking them to barriers in cancer care delivery. Patients were assigned one point for each criterion met: age 75 years or older; female sex; non-White race/ethnicity (including Hispanic, Black, American Indian/Alaska Native, Asian/Pacific Islander, and Other); public insurance status (Medicaid or other government coverage); travel distance to clinic greater than 20 miles; high comorbidity burden (CCI ≥ 5); advanced stage at diagnosis (Stage III–IV); higher-risk cancer type (hematologic, ill-defined/secondary, or respiratory malignancies); high no-show burden (≥ 3 missed appointments); missed initial consultation; infusion-based appointment type; and low overall care engagement (1–27 total completed visits). The total SIS was computed as the unweighted sum of these 12 binary indicators, resulting in a range from 0 (no identified risks) to 12 (maximum cumulative risk). Higher scores reflect greater intersectional vulnerability due to the accumulation of multiple social, geographic, and clinical risk factors. For descriptive and inferential analyses, the score was further categorized into three ordinal levels: Low Intersectional Risk (scores 0–3), Moderate Intersectional Risk (scores 4–7), and High Intersectional Risk (scores 8–12). Statistical Analysis First, descriptive statistics were used to summarize patient characteristics, and univariate logistic regression models estimated Odds Ratios (ORs) and 95% Confidence Intervals (CIs) for the association between patient characteristics and mortality. Second, to evaluate prognostic discrimination, we calculated the Area Under the Receiver Operating Characteristic (AUC) curve for the SIS, CCI, and TNM stage at diagnosis, and compared AUCs using DeLong’s test. Third, linear regression models assessed variation in the continuous SIS by patient characteristics to evaluate internal validity. Fourth, multivariable logistic regression estimated the association between SIS category and mortality, adjusting for treatment era, chemotherapy, radiation therapy, department of care, presence of metastasis, and cancer progression. Variables used to construct the SIS were excluded from model adjustment to avoid overadjustment and multicollinearity, as the composite score functions as an internally adjusted measure. All regression and ROC analyses were conducted using the complete-case sample (N = 1,856); missing data were addressed using listwise deletion. Finally, given the rising incidence of colorectal cancers among younger adults, a trend that disproportionately affects marginalized populations 1,3,4 , we evaluated whether digestive cancer incidence among young adults (aged 18–49 years) changed over time across SIS categories. We restricted the sample to patients aged 18–49 years with incident digestive cancers (colorectal, esophageal/biliary, pancreatic, or other digestive tumors), yielding 98 cases. We then constructed a count dataset collapsed by treatment era (2014–2016, 2017–2019, 2020–2022, 2023–2024) and SIS Categories. We used Poisson regression with interaction terms to assess the statistical significance of trends in cancer incidence over time and by risk group. Incidence Risk Ratios (IRRs) and 95% CIs were estimated, using the 2014–2016 era and low-risk group as references. To visually depict trends, we generated a line graph of incident case counts over time by risk group. All analyses were performed using Stata/SE version 18.5, with statistical significance defined as two-sided P ≤ .05. Results Among 5,793 patients with cancer ( Table 1 ), the mean (SD) age was 66.9 (12.4) years; 8.3% were aged 18–49, 28.1% were 50–64, 36.8% were 65–74, and 26.8% were 75 or older. Overall, 38.6% of patients were female. Most patients were non-Hispanic White (94.7%), with 8.1% insured by government or Medicaid and 43.0% living more than 20 miles from the clinic. Patients were distributed across four treatment eras: 2014–2016 (19.5%), 2017–2019 (29.5%), 2020–2022 (30.9%), and 2023–2024 (20.1%). Nearly all patients-initiated chemotherapy (90.3%), while 22.0% received radiation therapy. However, 34.0% missed at least one appointment, and 7.5% missed their initial consultation. Advanced-stage disease (Stage III–IV) was observed in 54.0% of cases, and 50.4% of the sample had died at last follow-up. Digestive cancer accounted for 16.7% of the total cohort. The Charlson Comorbidity Index was ≥5 for 36.1% of patients. The mean (SD) SIS was 3.71 (1.49), with 44.4% classified as low risk (score 0–3), 55.0% as moderate (4–7), and 0.6% as high risk (8–12). In bivariate analyses, multiple factors (age, treatment era, cancer type, late-stage disease, metastasis, and Charlson score) were significantly associated with mortality, all p < .001. Compared to the early treatment era (2014–2016), mortality odds were lower in 2020–2022 (OR = 0.53; 95% CI, 0.45–0.62) and 2023–2024 (OR = 0.22; 95% CI, 0.18–0.26), both p < .001. Compared to patients with a low Intersectionality Score, those with moderate (OR = 2.15; 95% CI, 1.77–2.63; p < .001) or high scores (OR = 2.69; 95% CI, 1.21–5.98; p = .016) had significantly greater odds of death. Table 1. Patient Characteristics and Associations with Mortality among Patients with Cancer (N = 5,793) Characteristic Patients, No. (%) Living Status OR 95% CI p-value Age at treatment initiation, mean (SD) [range], y 66.89 (12.38) [18–99] Age categories 18–49 482 (8.3) Ref. 50–64 1630 (28.1) 2.48 1.99–3.10 < .001 65–74 2129 (36.8) 2.46 1.98–3.06 < .001 75+ 1,552 (26.8) 4.33 3.46–5.41 <0.001 Sex Male 3557 (61.4) Ref. Female 2,236 (38.6) 0.94 0.84–1.04 0.225 Treatment Era 2014–2016 (Early) 1127 (19.5) Ref. 2017–2019 (Mid) 1708 (29.5) 0.91 0.78–1.06 0.232 2020–2022 (COVID-era) 1792 (30.9) 0.53 0.45–0.62 <.001 2023–2024 (Recent) 1166 (20.1) 0.22 0.18–0.26 <.001 Race and ethnicity Non-Hispanic White 5488 (94.7) Ref. Non-White 305 (5.3) 0.86 0.68–1.08 0.205 Insurance status Government/ Medicaid 468 (8.1) Ref. Medicare 1683 (29.1) 1.78 1.45–2.19 <0.001 Private/Other 3642 (62.9) 1.22 1.01–1.48 0.044 Distance categories 0–5 miles 1362 (23.5) Ref. 5–10 miles 1332 (23.0) 1.38 1.18–1.60 20 miles 2492 (43.0) 1.04 0.91–1.19 0.535 Department/Clinic Medical Oncology/Hematology 1201 (20.7) Ref. Radiation Oncology 681 (11.8) 0.74 0.61–0.89 0.002 Radiology 58 (1.0 0.72 0.42–1.23 0.228 Infusion/Procedural 3853 (66.5) 1.38 1.21–1.57 <0.001 Appointment type Telehealth visits 838 (14.5) Ref. General Appointment 1274 (22.0) 2.73 2.27–3.28 <0.001 Infusion 3681 (63.5) 2.66 2.27–3.13 <0.001 Completed visit categories Low (1–27) 3850 (66.5) Ref. Moderate (28–50) 1258 (21.7) 0.78 0.68–0.88 <0.001 High (51+) 685 (11.8) 1.38 1.17–1.63 <0.001 Chemotherapy sessions No (0) 562 (9.7) Ref. Yes (1+ 5231 (90.3) 1.76 1.47–2.10 <0.001 Radiation Therapy session No (0) 4520 (78.0) Ref. Yes (1+) 1273 (22.0) 0.62 0.55–0.70 <0.001 Any no-show No (0) 3826 (66.0) Ref. 1–2 (Low) 1766 (34.9) 1.62 1.44–1.81 <0.001 ≥3 (High) 201 (3.50) 1.51 1.14–2.01 0.005 Missed initial consultation No 5358 (92.5) Ref. Yes 435 (7.5) 1.31 1.07–1.59 0.008 Cancer type Hematologic (Leukemias, Lymphomas, Plasma Cell/Myeloma) 1151 (19.9) Ref. Ill-defined/Secondary (Unknown, Metastatic, Secondary) 582 (10.1) 3.91 3.27–4.66 <0.001 Respiratory (Lung/Respiratory) 1053 (18.2) 2.56 2.14–3.05 <0.001 Digestive (Colorectal, Esophagus/Bile Duct, Pancreas, Other Digestive) 966 (16.7) 1.14 0.97–1.34 0.104 Genitourinary (Male/Female Genital, Urinary Tract, Kidney/Renal, Uterine) 1485 (25.6) 1.74 1.42–2.14 <0.001 Other (Head & Neck, CNS/Eye/Brain, Skin/Melanoma, Soft Tissue/Bone, Endocrine/Thyroid) 556 (9.6) 4.81 3.87–5.98 <0.001 Cancer stage at diagnosis* Early (Stage 0–II) 682 (36.8) Ref. Late (III-IV) 1002 (54.0) 2.00 1.63–2.45 <0.001 Unknown/Unspecified 172 (9.3) 1.11 0.77–1.59 0.576 Stage advancement* No 1842 (31.8) Ref. Yes 14 (0.2) 2.07 0.72–5.99 0.179 Metastasis development* No 1480 (25.6) Ref. Yes 376 (6.5) 4.27 3.36–5.44 <0.001 Charlson Comorbidity score categories 0 (None) 905 (15.6) Ref. 1–2 (Low) 1617 (27.9) 0.93 0.79–1.09 0.376 3–4 (Moderate) 1181 (20.4) 1.40 1.18–1.67 <0.001 5+ (High) 2090 (36.1) 2.28 1.95–2.68 <0.001 Intersectionality Score, mean (SD) [range] 3.71 (1.49) [0–10] Categorical Intersectionality Risk Levels Low Intersectional Risk (Score 0–3) 2573 (44.4) Ref. Moderate Intersectional Risk (Score 4–7) 3185 (55.0) 2.15 1.77–2.63 <.001 High Intersectional Risk (Score 8–12) 35 (0.6) 2.69 1.21–5.98 .016 Mortality/Vital status Alive 2872 (49.6) Deceased 2921 (50.4) Abbreviations: SD=Standard Deviation; OR = Odds Ratio; CI = Confidence Interval; p-values <0.05. Note: Percentages may not sum to 100 due to rounding *Values for initial cancer stage, stage advancement, and metastasis development may not add up to the sample size (N = 5,793) because of missing data (n = 3,937; 67.96%). - Intersectionality Score Notes: Score range: 0 = lowest cumulative risk to 12 = highest cumulative risk - All values were derived from univariate logistic regression models. Living status was modeled across full sample (N = 5,793). Reference categories used for each variable; only non-reference categories shown. - Mortality/Vital status Ors were left intentionally blank, as it serves as the outcome Figure 1 displays comparative ROC curves for the SIS, CCI, and TNM staging. The Intersectionality Score demonstrated the highest area under the curve (AUC = 0.630; 95% CI, 0.605–0.655), followed by the Charlson Index (AUC = 0.605; 95% CI, 0.578–0.631) and TNM stage at diagnosis (AUC = 0.554; 95% CI, 0.531–0.577). Differences in discriminatory ability were statistically significant (DeLong test χ² = 22.63; P < .001). In linear regression models ( Table 2 ), the Intersectionality Score was significantly higher among patients treated during the COVID-era (2020–2022) (β = 0.16; 95% CI, 0.05–0.27; P = .005) and the recent era (2023–2024) (β = 0.25; 95% CI, 0.13–0.38; P < .001) compared with those treated in 2014–2016. Deceased patients had significantly higher scores than those alive (β = 0.44; 95% CI, 0.36–0.52; P < .001). Receipt of chemotherapy was positively associated with higher scores (β = 0.86; 95% CI, 0.74–0.99; P < .001), while receipt of radiation was inversely associated (β = –0.73; 95% CI, –0.82 to –0.64; P < .001). Compared with patients treated in medical oncology, those seen in infusion or procedural departments had higher scores (β = 0.62; 95% CI, 0.52–0.71; P < .001), whereas those treated in radiation oncology had lower scores (β = –0.45; 95% CI, –0.59 to –0.32; P < .001). Presence of metastasis was associated with a markedly higher score (β = 1.08; 95% CI, 0.90–1.26; P < .001), whereas cancer progression was not statistically significant (β = –0.09; 95% CI, –0.98 to 0.79; P = 0.835). In multivariable logistic regression adjusting for treatment era, chemotherapy, radiation, department, metastasis, and advanced stage patients with moderate intersectionality risk had significantly higher odds of mortality compared with those at low risk (OR = 1.71; 95% CI, 1.37–2.14; P < .001), while high-risk patients showed a non-significant association (OR = 1.82; 95% CI, 0.76–4.37; P = .177). Table 2. Associations between Intersectionality Score and Mortality Among Oncology Patients Characteristic SIS Association β (95% CI) p-value Mortality Association OR (95% CI) p-value Treatment Era (Ref = 2014–2016) 2017–2019 (Mid) 0.04 (–0.07 to 0.15) 0.450 – – 2020–2022 (COVID) 0.16 (0.05 to 0.27) 0.005 – – 2023–2024 (Recent) 0.25 (0.13 to 0.38) <.001 – – Mortality Status (Ref = Alive) 0.44 (0.36 to 0.52) <.001 – – Chemotherapy (Yes vs No) 0.86 (0.74 to 0.99) <.001 – – Radiation (Yes vs No) –0.73 (–0.82 to –0.64) <.001 – – Department (Ref = Medical Oncology) Radiation Oncology –0.45 (–0.59 to –0.32) <.001 – – Radiology 0.21 (–0.16 to 0.59) 0.267 – – Infusion/Procedural 0.62 (0.52 to 0.71) <.001 – – Metastasis Present (Yes vs No) 1.08 (0.90 to 1.26) <.001 – – Advanced Stage (Yes vs No) –0.09 (–0.98 to 0.79) 0.835 – – Intersectionality Risk Category (Ref = Low) – – – – Moderate vs Low – – 1.71 (1.37 to 2.14) <.001 High vs Low – – 1.82 (0.76 to 4.37) 0.177 Notes: - Intersectionality risk associations (left columns) reflect separate linear regression models using the Intersectionality Score (range: 0–12) as the continuous dependent variable. - Mortality associations (right columns; N = 1,856) reflect a multivariable logistic regression using mortality status (1 = deceased) as the outcome, adjusted for treatment era, chemotherapy, radiation therapy, department category, metastasis, and advanced stage. - Variables used to construct the Intersectionality score were excluded from both models to avoid over-adjustment, circularity, and multicollinearity. For digestive cancer among young adults (age 18–49) ( Table 3 ), incidence rates were significantly higher during 2017–2019 (IRR, 1.47; 95% CI, 1.31–1.64; P < .001) and 2020–2022 (IRR, 1.44; 95% CI, 1.29–1.61; P < .001) compared with 2014–2016. A modest decline was observed in 2023–2024 (IRR, 0.88; 95% CI, 0.78–1.00; P = .048). High intersectionality risk was independently associated with significantly lower incidence (IRR, 0.007; 95% CI, 0.003–0.020; P < .001), likely reflecting very low case counts in that group. Significant interaction effects were observed for moderate-risk patients in 2020–2022 (IRR, 1.19; 95% CI, 1.02–1.38; P = .026) and 2023–2024 (IRR, 1.32; 95% CI, 1.12–1.56; P = .001), indicating a pattern of increase in incidence over time. Table 3. Treatment Era, Intersectionality Risk, and Digestive Cancer* Incidence Among Young Adults Variable IRR 95% CI P Value Treatment Era 2017–2019 vs 2014–2016 1.47 1.31–1.64 <.001 2020–2022 vs 2014–2016 1.44 1.29–1.61 <.001 2023–2024 vs 2014–2016 0.88 0.78–1.00 .048 Intersectionality Risk Category Moderate Risk vs Low Risk 1.09 0.97–1.23 .144 High Risk vs Low Risk 0.007 0.003–0.020 <.001 Interaction: Treatment Era × Risk Group 2017–2019 × Moderate Risk 1.06 0.91–1.24 .435 2017–2019 × High Risk 1.54 0.47–5.01 .478 2020–2022 × Moderate Risk 1.19 1.02–1.38 .026 2020–2022 × High Risk 2.43 0.79–7.41 .120 2023–2024 × Moderate Risk 1.32 1.12–1.56 .001 2023–2024 × High Risk 2.27 0.68–7.57 .184 Note: All models adjusted for interaction terms; Incidence Rate Ratios (IRR) reflect relative changes in incident case counts. The baseline group is low-risk patients during the 2014–2016 era. * Digestive cancers include colorectal, esophagus/bile duct, pancreas, and other digestive Figure 2 illustrates these trends visually, showing that the incidence of digestive cancer cases increased among low- and moderate-risk patients from 2014 to 2016 through 2020 to 2022, with a subsequent decline in low-risk incidence and a continued elevation among moderate-risk patients in 2023 to 2024. Case counts among high-risk patients remained consistently low across all treatment eras. Discussion Our study contributes new evidence by developing and operationalizing a cumulative intersectionality-based risk score that captures both clinical and social vulnerability, offering an intersectional approach to characterizing cancer outcomes and uncovering emerging disparities in early-onset digestive cancers. Our additive score aligns with public health traditions of cumulative vulnerability indices [ 6 , 7 ]. Specifically, we included 12 components - spanning demographic, socioeconomic, clinical, and access-related factors identified in prior literature as associated with disparities in cancer care delivery [ 11 – 13 ]. By summing these domains into an unweighted score, we aimed to reflect the cumulative burden of multiple, overlapping barriers faced by oncology patients in real-world settings to help with stratification and analytic modeling of intersectional disadvantage at the patient level. Overall, our findings support the hypothesis that SIS is associated with mortality among oncology patients, demonstrating prognostic value and internal validity as a composite measure of social and clinical vulnerability, with three key findings. First, we observed a significant temporal decline in all-cause mortality, with patients initiating treatment during 2020–2022 and 2023–2024 experiencing substantially lower odds of death (OR, 0.53 and 0.22, respectively) compared with those treated in 2014–2016. These patterns align with national trends and emerging evidence on rural cancer disparities and evolving care delivery models [ 1 , 20 ]. Second, the Intersectionality Score showed significantly better prognostic accuracy for all-cause mortality than the Charlson Index and TNM stage (AUC = 0.630 vs. 0.605 and 0.554; P < .001), with adjusted models indicating 71% higher odds of death for moderate-risk patients compared to low-risk, while the high-risk category showed a non-significant increase. The cumulative burden of social and clinical risk factors enables meaningful stratification of mortality risk, and the additive structure of the Intersectionality Score supports pragmatic implementation, consistent with public health approaches to structural disadvantage [ 6 – 8 ]. Unlike traditional tools that overlook access-related barriers, the Intersectionality Score integrates multidimensional factors across sociodemographic (e.g., public insurance, race, age), clinical (advanced stage, high-risk tumor types), and care delivery domains (distance to clinic, missed consultations, no-shows) [ 10 – 13 ]. Notably, several variables within the score, such as missed consultations and long travel distances, have previously been linked to care disruptions and early mortality, particularly in rural or underserved populations [ 15 – 18 ]. The Intersectionality Score builds on this literature by unifying these predictors into a single risk metric that may aid in identifying patients who could benefit from additional resources, such as transportation support, social work referral, or intensive follow-up. Third, digestive cancer incidence among young adults rose significantly during the 2017–2019 and 2020–2022 treatment eras, with a disproportionate increase observed among those in the moderate SIS risk group. Although incidence declined modestly in 2023–2024, rates remained elevated for moderate-risk patients, while high-risk patients consistently showed markedly lower-case counts. Previous global and U.S.-based studies support our findings of rising digestive cancer incidence among younger adults, particularly among subgroups with underlying vulnerabilities [ 21 , 22 ]. A multicountry analysis [ 21 ] showed that early-onset colorectal cancer (ages 25–49) increased significantly in 27 countries, often more rapidly than in older adults, echoing our observed trends in low- and moderate-risk groups from 2014 to 2022. Similarly, a steep annual increase in incidence among U.S. adults aged 20–49 was reported [ 22 ], which highlights racial and socioeconomic disparities, mirroring our finding that moderate-risk patients experienced a disproportionate rise in incidence during later treatment eras. Our findings should be interpreted in the context of the following limitations. First, the analysis was conducted within a single rural health system with a predominantly non-Hispanic White population (94.7%), which limits external validity to more racially diverse or urban settings. Second, although the SIS represents an internally adjusted index composed of 12 theory-informed components, we applied equal weighting across domains, an approach that assumes uniform risk contribution and oversimplifies interactions among predictors [ 4 , 6 ]. Future work should explore weighted or machine learning–based alternatives to optimize score calibration. Third, complete-case analysis was used to maintain analytic consistency, but this may have biased results if patients with missing data were systematically different. Fourth, the outcome (vital status) was assessed cross-sectionally at last follow-up; time-to-event models may yield additional insights into survival dynamics over time. Fifth, although ROC analyses established comparative prognostic accuracy, additional validation in other settings and calibration assessments are needed before clinical deployment. Finally, although we did not compare the SIS to biomarker-based indices (PROMISE, GRIm, or CTI) due to data constraints, these tools serve complementary purposes focused on short-term mortality prediction in hospitalized patients, and future external validation should assess comparative and additive value across care settings [ 23 , 24 ]. While generalizability is limited by the study setting, the score itself is constructed from routinely available EHR variables, which enhances its adaptability to other health systems. SIS may be especially useful in safety-net or rural settings where structural disadvantage is both prevalent and undermeasured by traditional oncology risk tools. Replication across other diseases/conditions and diverse populations will be essential to confirm its transportability. Conclusions Our Intersectionality Score was significantly associated with all-cause mortality and showed higher discriminative ability than both the Charlson Index and TNM staging. Patients with moderate intersectionality risk had notably higher odds of death. We also observed rising digestive cancer incidence among younger adults, with moderate-risk patients experiencing a disproportionate increase between 2017 and 2022. These results support further examination of cumulative risk measures in oncology care, especially in rural settings where structural barriers are often underrecognized. Embedding such tools in clinical workflows could support earlier, more holistic identification of high-risk patients. Our findings suggest that this cumulative intersectionality score may improve risk stratification and help detect shifting patterns in early-onset digestive cancers among vulnerable patients, with potential relevance for rural and resource-constrained settings globally. Abbreviations AUC Area Under the Curve CCI Charlson Comorbidity Index CI Confidence Interval CNS Central Nervous System CTI C-Reactive Protein–Triglyceride–Glucose Index EHR Electronic Health Record EIRH Essentia Institute of Rural Health GRIm Gustave Roussy Immune Score ICD International Classification of Diseases IRB Institutional Review Board IRR Incidence Rate Ratio OR Odds Ratio PROMISE Prognostic Score for Hospitalized Cancer Patients Ref. Reference Category ROC Receiver Operating Characteristic SD Standard Deviation SIS Shour Intersectionality Score STROBE Strengthening the Reporting of Observational Studies in Epidemiology TNM Tumor, Node, Metastasis Declarations Ethics approval and consent to participate This study was reviewed and approved by the Essentia Health Institutional Review Board (IRB #EH25918-EHIR-1.0; FWA #00000635) under an expedited procedure. The Essentia Health IRB waived the requirement for informed consent because only de-identified electronic health record data were used and the research posed no more than minimal risk, consistent with 45 CFR 46.116(f). All study procedures were conducted in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Declaration of Helsinki and its later amendments. Consent for publication Not applicable. Availability of data and materials De-identified aggregate data and the analytic code used in this study are available from the corresponding author on reasonable request, contingent on IRB review and a data use agreement with Essentia Health. The underlying EHR-derived dataset contains protected health information and cannot be shared publicly. All materials provided will exclude direct identifiers and adhere to HIPAA and institutional policies. Competing interests The authors declare that they have no competing interests. Funding Funding for this study was provided by the Essentia Institute of Rural Health (EIRH-25-2105, Scientist Discretionary Fund) awarded to the Principal Investigator, Abdul R. Shour, MS, PhD, for the period of April 2025 to March 2026. The funder had no role in the design, conduct, analysis, or reporting of the study. Authors' contributions ARS had full access to all the data in the study and takes responsibility for the integrity and accuracy of the data analysis. ARS and AO conceptualized and designed the study. ARS acquired, analyzed, and interpreted the data. ARS and RA drafted the manuscript. ARS, BF, LP, CB, RA, AO critically revised the manuscript for important intellectual content. ARS and RA conducted the statistical analysis. ARS obtained funding and provided administrative, technical, and material support. AO provided supervision. All authors read and approved the final manuscript. Acknowledgements We gratefully acknowledge Katherine Dean, Executive Director of the Essentia Institute for Rural Health (EIRH), for her leadership and administrative support. We also thank Dr. Stephen Waring, Principal Research Scientist, for his early contributions to the study’s proposal development. Additional thanks go to the EIRH research informatics and administrative team for their essential support: Michelle Sikkink (Research and Evaluation Specialist), Anthony Castillo and Catharine A. Karow (Research Informatics Analysts II), Nancy Dold (Grants Manager), and Theresa Ekblad (Technology and Informatics Supervisor). Authors' information ARS is a Research Scientist II at the Essentia Institute of Rural Health and serves as Co-Chair of the Cancer Scientific Interest Group within the Health Care Systems Research Network. His work focuses on cancer epidemiology, and he leads multiple funded studies examining cancer outcomes and health disparities in rural populations. RA is an assistant professor and epidemiologist at the Medical College of Wisconsin, with expertise in global health and social determinants of health. BF is the Medical Director of Oncology Research at the Essentia Health Cancer Center and a practicing hematologist-oncologist with research interests in rural cancer care delivery. LP is a Research Scientist III at the Essentia Institute of Rural Health and adjunct professor at the University of Minnesota, with research focused on rural health equity, behavioral health, and pharmacy practice. CB is the Director of Research at the Essentia Health Heart and Vascular Center and adjunct faculty at the University of Minnesota. Her work centers on cardiovascular and other chronic disease prevention and clinical trials. AO is a clinical professor and oncology service line director at Marshfield Clinic Health System. He serves as principal investigator for multiple NIH- and NCI-funded cancer research initiatives in community settings. References Siegel RL, Giaquinto AN, Jemal A. Cancer statistics, 2024. CA: a cancer journal for clinicians. 2024 Jan 1;74(1). Patel MI, Lopez AM, Blackstock W, Reeder-Hayes K, Moushey EA, Phillips J, Tap W. Cancer disparities and health equity: a policy statement from the American Society of Clinical Oncology. Journal of Clinical Oncology. 2020 Oct 10;38(29):3439-48. American Cancer Society. Cancer Facts & Figures 2024 [Internet]. Atlanta: American Cancer Society; 2024 [cited 2025 Jul 12]. Available from: https://www.cancer.org/research/cancer-facts-statistics/all-cancer-facts-figures/2024-cancer-facts-figures.html Saka AH, Giaquinto AN, McCullough LE, Tossas KY, Star J, Jemal A, Siegel RL. Cancer statistics for African American and Black people, 2025. CA: a cancer journal for clinicians. 2025 Apr;75(2):111-40. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. Journal of chronic diseases. 1987 Jan 1;40(5):373-83. Bauer GR. Incorporating intersectionality theory into population health research methodology: challenges and the potential to advance health equity. Social science & medicine. 2014 Jun 1;110:10-7. Bowleg L. The problem with the phrase women and minorities: intersectionality—an important theoretical framework for public health. American journal of public health. 2012 Jul;102(7):1267-73. Kapilashrami A, Hankivsky O. Intersectionality and why it matters to global health. The Lancet. 2018 Jun 30;391(10140):2589-91. Evans CR, Williams DR, Onnela JP, Subramanian SV. A multilevel approach to modeling health inequalities at the intersection of multiple social identities. Social science & medicine. 2018 Apr 1;203:64-73. Crenshaw K. Demarginalizing the intersection of race and sex: A black feminist critique of antidiscrimination doctrine, feminist theory and antiracist politics. Droit et société. 2021;108:465. Shour A, Onitilo AA. Distance matters: investigating no-shows in a large rural provider network. Clinical Medicine & Research. 2024 Jan 31;21(4):177-91. Flanagan BE, Hallisey EJ, Adams E, Lavery A. Measuring community vulnerability to natural and anthropogenic hazards: the Centers for Disease Control and Prevention’s Social Vulnerability Index. Journal of environmental health. 2018 Jun;80(10):34. Kind AJ, Buckingham WR. Making neighborhood-disadvantage metrics accessible—the neighborhood atlas. The New England journal of medicine. 2018 Jun 28;378(26):2456. Von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP, Strobe Initiative. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: guidelines for reporting observational studies. International journal of surgery. 2014 Dec 1;12(12):1495-9. Yabroff KR, Reeder-Hayes K, Zhao J, Halpern MT, Lopez AM, Bernal-Mizrachi L, Collier AB, Neuner J, Phillips J, Blackstock W, Patel M. Health insurance coverage disruptions and cancer care and outcomes: systematic review of published research. JNCI: Journal of the National Cancer Institute. 2020 Jul 1;112(7):671-87. Reddy KP, Berkowitz CL, Jarrell K, Berger R, Hulse S, Elmore LC, Fishman R, Mateo AM, Sataloff DM, Tchou JC, Zhang JQ. The Effect of Rurality on Time to Surgery and Overall Survival among Women with Breast Cancer. Annals of Surgery. 2025:10-97. Kumsa FA, Fowke JH, Hashtarkhani S, White BM, Shrubsole MJ, Shaban-Nejad A. The association between neighborhood obesogenic factors and prostate cancer risk and mortality: the Southern Community Cohort Study. ArXiv. 2024 May 28:arXiv-2405. Shour AR, Jones GL, Anguzu R, Doi SA, Onitilo AA. Development of an evidence-based model for predicting patient, provider, and appointment factors that influence no-shows in a rural healthcare system. BMC Health Services Research. 2023 Sep 14;23(1):989. Kim DW, Lee S, Kwon S, Nam W, Cha IH, Kim HJ. Deep learning-based survival prediction of oral cancer patients. Scientific reports. 2019 May 6;9(1):6994. Kenzik KM, Davis ES, Franks JA, Bhatia S. Estimating the impact of rurality in disparities in cancer mortality. JCO oncology practice. 2024 Jul;20(7):993-1002. Siegel RL, Torre LA, Soerjomataram I, Hayes RB, Bray F, Weber TK, Jemal A. Global patterns and trends in colorectal cancer incidence in young adults. Gut. 2019 Dec 1;68(12):2179-85. Abualkhair WH, Zhou M, Ahnen D, Yu Q, Wu XC, Karlitz JJ. Trends in incidence of early-onset colorectal cancer in the United States among those approaching screening age. JAMA network open. 2020 Jan 3;3(1):e1920407-. Uyar GC, Mirallas O, Başkurt K, Martin-Cullell B, Yeşilbaş E, Recuero-Borau J, Kaya S, Garcés VN, Yücel SE, Cano KS, Gómez-Puerto D. Prediction of 90-day mortality among cancer patients with unplanned hospitalisation: a retrospective validation study of three prognostic scores. The Lancet Regional Health–Europe. 2025 Jul 1;54. Parikh RB, Manz C, Chivers C, Regli SH, Braun J, Draugelis ME, Schuchter LM, Shulman LN, Navathe AS, Patel MS, O’Connor NR. Machine learning approaches to predict 6-month mortality among patients with cancer. JAMA network open. 2019 Oct 2;2(10):e1915997-. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 15 Oct, 2025 Reviewers agreed at journal 15 Oct, 2025 Reviewers agreed at journal 12 Oct, 2025 Reviewers invited by journal 06 Oct, 2025 Editor assigned by journal 01 Oct, 2025 Editor invited by journal 11 Sep, 2025 Submission checks completed at journal 09 Sep, 2025 First submitted to journal 09 Sep, 2025 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7546882","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":530824982,"identity":"1ef08079-b59e-4997-8c30-bfbe47014179","order_by":0,"name":"Abdul R. 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12:25:43","extension":"html","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":139551,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7546882/v1/d31e46844f6577030f05d0fe.html"},{"id":93774688,"identity":"fbf919fc-bd32-4e81-a87f-e01d7cdd9c3b","added_by":"auto","created_at":"2025-10-17 12:25:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":104577,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eComparative ROC Curves for Mortality Predictions in Cancer Patients: \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eComparative ROC curves show discriminatory ability of the Intersectionality Score (AUC = 0.630), Charlson Comorbidity Index (AUC = 0.605), and TNM staging (AUC = 0.554) for mortality prediction in oncology patients. The Intersectionality Score demonstrated significantly higher discrimination than both the Charlson Index and TNM staging (P \u0026lt; .001 for both comparisons, DeLong test).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7546882/v1/db6bee74f130d2008d503a55.png"},{"id":93774696,"identity":"23978851-a607-4797-a882-0d81a55025db","added_by":"auto","created_at":"2025-10-17 12:25:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":70745,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eDigestive Cancer Incidence Among Young Adults (18–49 Years) by Intersectionality Risk, 2014–2024: \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eIncident digestive cancer case counts increased among low- and moderate-risk patients from 2014–2016 through 2020–2022, with a subsequent decline in low-risk incidence and continued elevation among moderate-risk patients in 2023–2024. Case counts among high-risk patients remained consistently low across all treatment eras. Statistically significant increases were observed for moderate-risk patients in 2020–2022 (IRR, 1.19; P = .026) and 2023–2024 (IRR, 1.32; P = .001) compared with 2014–2016, based on Poisson regression with interaction terms (\u003c/em\u003e\u003cu\u003e\u003cem\u003eTable 3\u003c/em\u003e\u003c/u\u003e\u003cem\u003e).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7546882/v1/86812db9227556d9f421ec13.png"},{"id":93777540,"identity":"82dcd2f5-04ab-4787-a6e8-28d896c30ee5","added_by":"auto","created_at":"2025-10-17 12:41:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1625034,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7546882/v1/3ba1d012-788b-423d-9626-fc56a3782f93.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association Between an Intersectionality-Based Risk Score and Cancer Mortality: A Retrospective Cohort Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCancer mortality remains a leading public health concern in the United States, with persistent disparities across socioeconomic status and geography, despite advances in early detection and treatment [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In 2024, over 600,000 cancer deaths were recorded, and incidence is rising for several malignancies, including digestive/colorectal cancer among younger adults, a trend that disproportionately affects marginalized populations [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Traditional prognostic tools such as the Charlson Comorbidity Index (CCI) and Tumor\u0026ndash;Node\u0026ndash;Metastasis (TNM) staging provide important clinical insights but often fail to capture the broader social and structural forces associated with adverse cancer outcomes, underscoring the limitations of models that overlook contextual barriers to care [\u003cspan additionalcitationids=\"CR6 CR7 CR8\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Notably, no current index is statistically designed to stratify risk among cancer patients experiencing compounding disadvantages, nor to identify patterns such as the increasing incidence of digestive cancers in younger adults. To address this gap, we developed an Intersectionality Score, a point-based index derived from Electronic Health Record (EHR) data, and evaluated its prognostic performance alongside CCI and TNM staging in a large rural oncology cohort in the United States.\u003c/p\u003e\u003cp\u003eIntersectionality theory, which examines how overlapping social categories interact to produce unique experiences of discrimination and privilege shaped by broader power structures, provides a compelling framework to address these shortcomings [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. While widely applied in qualitative health equity research, intersectionality theory has yet to be systematically operationalized in quantitative oncology studies in a way that integrates both clinical and social determinants of health. Existing indices often rely on neighborhood-level or single-domain measures that inadequately reflect the compound disadvantage faced by many patients [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Emerging evidence highlights the independent impact of public insurance, long travel distances, comorbidity burden, and appointment nonadherence on mortality, especially in rural populations [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Prior work in our rural health system demonstrated that missed (including oncology) appointments are frequently associated with these barriers, signaling structural vulnerability that traditional clinical indices may miss [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e Building on this foundation, we developed and internally evaluated the Shour Intersectionality Score (SIS), a 12-point cumulative index derived from EHR data that captures overlapping demographic and care delivery vulnerabilities. This measure integrates both social context and cancer-specific clinical complexity, supporting calls for patient-level prognostic tools [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. We examined its association with all-cause mortality among oncology patients and compared its prognostic performance to the CCI and TNM staging. To explore broader epidemiologic patterns, we also examined whether digestive cancer incidence among young adults (aged 18\u0026ndash;49 years) changed over time by intersectionality risk group. We hypothesized that patients with higher scores would have significantly higher odds of death, and that this SIS would offer stronger predictive value than the CCI and TNM staging models. The SIS was developed using an Intersectionality theory [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. While several short-term mortality models, such as the Prognostic Score for Hospitalized Cancer Patients (PROMISE), Gustave Roussy Immune Score (GRIm), and C-Reactive Protein - Triglyceride\u0026ndash;Glucose Index (CTI), have demonstrated utility in acute inpatient oncology settings, these biomarker-based tools require lab values not routinely available across ambulatory care, limiting their applicability for population-level risk stratification [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Hence, the SIS index was operationalized using routinely collected EHR data to create a practical, scalable tool for patient stratification aimed at improving the precision and equity of cancer care delivery.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eFollowing Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], we conducted a retrospective cohort study of adult patients diagnosed with cancer and initiating treatment within Essentia Health (Service Area 10), a large rural health system serving the upper Midwest, between January 1, 2014, and December 31, 2024. Data were extracted from the Essentia Health Epic EHR system. The source population was 1,160,176 unique patients with at least one scheduled visit. From this population, we identified 5,793 adults (\u0026ge;\u0026thinsp;18 years) diagnosed with cancer who initiated chemotherapy or radiation therapy and had at least one recent oncology visit within the study period. Eligible patients had a cancer diagnosis and received active treatment. Patients were excluded if they were under 18 years of age, received care outside the specified timeframe, or were treated exclusively with palliative or supportive care. This study was reviewed and approved by the Essentia Health Institutional Review Board (IRB #EH25918-EHIR-1.0; FWA #00000635) under an expedited procedure. The Essentia Health IRB waived the requirement for informed consent because only de-identified electronic health record data were used and the research posed no more than minimal risk, consistent with 45 CFR 46.116(f).\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Measures\u003c/h2\u003e\u003cp\u003eThe study outcome was all-cause mortality, defined as vital status (deceased vs alive) at last recorded follow-up. The main exposure was the SIS, analyzed both as a continuous and as a 3-level categorical variable. The SIS was constructed as a cumulative index to capture overlapping individual-level vulnerabilities across structural, sociodemographic, and clinical domains. Twelve dichotomous risk components were defined based on theory and empirical literature\u003csup\u003e6\u0026ndash;10\u003c/sup\u003e linking them to barriers in cancer care delivery. Patients were assigned one point for each criterion met: age 75 years or older; female sex; non-White race/ethnicity (including Hispanic, Black, American Indian/Alaska Native, Asian/Pacific Islander, and Other); public insurance status (Medicaid or other government coverage); travel distance to clinic greater than 20 miles; high comorbidity burden (CCI\u0026thinsp;\u0026ge;\u0026thinsp;5); advanced stage at diagnosis (Stage III\u0026ndash;IV); higher-risk cancer type (hematologic, ill-defined/secondary, or respiratory malignancies); high no-show burden (\u0026ge;\u0026thinsp;3 missed appointments); missed initial consultation; infusion-based appointment type; and low overall care engagement (1\u0026ndash;27 total completed visits). The total SIS was computed as the unweighted sum of these 12 binary indicators, resulting in a range from 0 (no identified risks) to 12 (maximum cumulative risk). Higher scores reflect greater intersectional vulnerability due to the accumulation of multiple social, geographic, and clinical risk factors. For descriptive and inferential analyses, the score was further categorized into three ordinal levels: \u003cem\u003eLow Intersectional Risk\u003c/em\u003e (scores 0\u0026ndash;3), \u003cem\u003eModerate Intersectional Risk\u003c/em\u003e (scores 4\u0026ndash;7), and \u003cem\u003eHigh Intersectional Risk\u003c/em\u003e (scores 8\u0026ndash;12).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eFirst, descriptive statistics were used to summarize patient characteristics, and univariate logistic regression models estimated Odds Ratios (ORs) and 95% Confidence Intervals (CIs) for the association between patient characteristics and mortality. Second, to evaluate prognostic discrimination, we calculated the Area Under the Receiver Operating Characteristic (AUC) curve for the SIS, CCI, and TNM stage at diagnosis, and compared AUCs using DeLong\u0026rsquo;s test. Third, linear regression models assessed variation in the continuous SIS by patient characteristics to evaluate internal validity. Fourth, multivariable logistic regression estimated the association between SIS category and mortality, adjusting for treatment era, chemotherapy, radiation therapy, department of care, presence of metastasis, and cancer progression. Variables used to construct the SIS were excluded from model adjustment to avoid overadjustment and multicollinearity, as the composite score functions as an internally adjusted measure. All regression and ROC analyses were conducted using the complete-case sample (N\u0026thinsp;=\u0026thinsp;1,856); missing data were addressed using listwise deletion. Finally, given the rising incidence of colorectal cancers among younger adults, a trend that disproportionately affects marginalized populations\u003csup\u003e1,3,4\u003c/sup\u003e, we evaluated whether digestive cancer incidence among young adults (aged 18\u0026ndash;49 years) changed over time across SIS categories. We restricted the sample to patients aged 18\u0026ndash;49 years with incident digestive cancers (colorectal, esophageal/biliary, pancreatic, or other digestive tumors), yielding 98 cases. We then constructed a count dataset collapsed by treatment era (2014\u0026ndash;2016, 2017\u0026ndash;2019, 2020\u0026ndash;2022, 2023\u0026ndash;2024) and SIS Categories. We used Poisson regression with interaction terms to assess the statistical significance of trends in cancer incidence over time and by risk group. Incidence Risk Ratios (IRRs) and 95% CIs were estimated, using the 2014\u0026ndash;2016 era and low-risk group as references. To visually depict trends, we generated a line graph of incident case counts over time by risk group. All analyses were performed using Stata/SE version 18.5, with statistical significance defined as two-sided P\u0026thinsp;\u0026le;\u0026thinsp;.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eAmong 5,793 patients with cancer (\u003cstrong\u003eTable 1\u003c/strong\u003e), the mean (SD) age was 66.9 (12.4) years; 8.3% were aged 18\u0026ndash;49, 28.1% were 50\u0026ndash;64, 36.8% were 65\u0026ndash;74, and 26.8% were 75 or older. Overall, 38.6% of patients were female. Most patients were non-Hispanic White (94.7%), with 8.1% insured by government or Medicaid and 43.0% living more than 20 miles from the clinic. Patients were distributed across four treatment eras: 2014\u0026ndash;2016 (19.5%), 2017\u0026ndash;2019 (29.5%), 2020\u0026ndash;2022 (30.9%), and 2023\u0026ndash;2024 (20.1%). Nearly all patients-initiated chemotherapy (90.3%), while 22.0% received radiation therapy. However, 34.0% missed at least one appointment, and 7.5% missed their initial consultation. Advanced-stage disease (Stage III\u0026ndash;IV) was observed in 54.0% of cases, and 50.4% of the sample had died at last follow-up. Digestive cancer accounted for 16.7% of the total cohort. The Charlson Comorbidity Index was \u0026ge;5 for 36.1% of patients. The mean (SD) SIS was 3.71 (1.49), with 44.4% classified as low risk (score 0\u0026ndash;3), 55.0% as moderate (4\u0026ndash;7), and 0.6% as high risk (8\u0026ndash;12).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn bivariate analyses, multiple factors (age, treatment era, cancer type, late-stage disease, metastasis, and Charlson score) were significantly associated with mortality, all p \u0026lt; .001. Compared to the early treatment era (2014\u0026ndash;2016), mortality odds were lower in 2020\u0026ndash;2022 (OR = 0.53; 95% CI, 0.45\u0026ndash;0.62) and 2023\u0026ndash;2024 (OR = 0.22; 95% CI, 0.18\u0026ndash;0.26), both p \u0026lt; .001. Compared to patients with a low Intersectionality Score, those with moderate (OR = 2.15; 95% CI, 1.77\u0026ndash;2.63; p \u0026lt; .001) or high scores (OR = 2.69; 95% CI, 1.21\u0026ndash;5.98; p = .016) had significantly greater odds of death.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Patient Characteristics and Associations with Mortality among Patients with Cancer (N = 5,793)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"96%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePatients, No. (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLiving Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge at treatment initiation, mean (SD) [range], y\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e66.89 (12.38) [18\u0026ndash;99]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Age categories\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e18\u0026ndash;49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e482 (8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e50\u0026ndash;64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e1630 (28.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e2.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1.99\u0026ndash;3.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e65\u0026ndash;74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e2129 (36.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e2.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1.98\u0026ndash;3.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e75+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e1,552 (26.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e4.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e3.46\u0026ndash;5.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e3557 (61.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e2,236 (38.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.84\u0026ndash;1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.225\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTreatment Era\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e2014\u0026ndash;2016 (Early)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e1127 (19.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e2017\u0026ndash;2019 (Mid)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e1708 (29.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.78\u0026ndash;1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.232\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e2020\u0026ndash;2022 (COVID-era)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e1792 (30.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.45\u0026ndash;0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e2023\u0026ndash;2024 (Recent)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e1166 (20.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.18\u0026ndash;0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace and ethnicity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e5488 (94.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eNon-White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e305 (5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.68\u0026ndash;1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.205\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInsurance status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eGovernment/ Medicaid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e468 (8.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eMedicare\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e1683 (29.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e1.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1.45\u0026ndash;2.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003ePrivate/Other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e3642 (62.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1.01\u0026ndash;1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDistance categories\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0\u0026ndash;5 miles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e1362 (23.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e5\u0026ndash;10 miles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e1332 (23.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1.18\u0026ndash;1.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e10\u0026ndash;20 miles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e607 (10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1.08\u0026ndash;1.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026gt;20 miles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e2492 (43.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.91\u0026ndash;1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.535\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDepartment/Clinic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eMedical Oncology/Hematology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e1201 (20.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eRadiation Oncology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e681 (11.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.61\u0026ndash;0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eRadiology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e58 (1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.42\u0026ndash;1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.228\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eInfusion/Procedural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e3853 (66.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1.21\u0026ndash;1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAppointment type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eTelehealth visits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e838 (14.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eGeneral Appointment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e1274 (22.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e2.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e2.27\u0026ndash;3.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eInfusion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e3681 (63.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e2.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e2.27\u0026ndash;3.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCompleted visit categories\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eLow (1\u0026ndash;27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e3850 (66.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eModerate (28\u0026ndash;50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e1258 (21.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.68\u0026ndash;0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eHigh (51+)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e685 (11.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1.17\u0026ndash;1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChemotherapy sessions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eNo (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e562 (9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eYes (1+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e5231 (90.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e1.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1.47\u0026ndash;2.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRadiation Therapy session\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eNo (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e4520 (78.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eYes (1+)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e1273 (22.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.55\u0026ndash;0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAny no-show\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eNo (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e3826 (66.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e1\u0026ndash;2 (Low)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e1766 (34.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e1.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1.44\u0026ndash;1.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026ge;3 (High)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e201 (3.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1.14\u0026ndash;2.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMissed initial consultation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e5358 (92.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e435 (7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1.07\u0026ndash;1.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCancer type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eHematologic (Leukemias, Lymphomas, Plasma Cell/Myeloma)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e1151 (19.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eIll-defined/Secondary (Unknown, Metastatic, Secondary)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e582 (10.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e3.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e3.27\u0026ndash;4.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eRespiratory (Lung/Respiratory)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e1053 (18.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e2.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e2.14\u0026ndash;3.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eDigestive (Colorectal, Esophagus/Bile Duct, Pancreas, Other Digestive)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e966 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.97\u0026ndash;1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eGenitourinary (Male/Female Genital, Urinary Tract, Kidney/Renal, Uterine)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e1485 (25.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e1.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1.42\u0026ndash;2.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eOther (Head \u0026amp; Neck, CNS/Eye/Brain, Skin/Melanoma, Soft Tissue/Bone, Endocrine/Thyroid)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e556 (9.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e4.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e3.87\u0026ndash;5.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCancer stage at diagnosis*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eEarly (Stage 0\u0026ndash;II)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e682 (36.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eLate (III-IV)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e1002 (54.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1.63\u0026ndash;2.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eUnknown/Unspecified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e172 (9.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.77\u0026ndash;1.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.576\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStage advancement*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e1842 (31.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e14 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e2.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.72\u0026ndash;5.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.179\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMetastasis development*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e1480 (25.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e376 (6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e4.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e3.36\u0026ndash;5.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharlson Comorbidity score categories\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0 (None)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e905 (15.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e1\u0026ndash;2 (Low)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e1617 (27.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.79\u0026ndash;1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.376\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e3\u0026ndash;4 (Moderate)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e1181 (20.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1.18\u0026ndash;1.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e5+ (High)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e2090 (36.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e2.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1.95\u0026ndash;2.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntersectionality Score, mean (SD) [range]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e3.71 (1.49) [0\u0026ndash;10]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u003cstrong\u003eCategorical Intersectionality Risk Levels\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eLow Intersectional Risk (Score 0\u0026ndash;3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e2573 (44.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eModerate Intersectional Risk (Score 4\u0026ndash;7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e3185 (55.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e2.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1.77\u0026ndash;2.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eHigh Intersectional Risk (Score 8\u0026ndash;12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e35 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e2.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1.21\u0026ndash;5.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMortality/Vital status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eAlive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e2872 (49.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eDeceased\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e2921 (50.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cem\u003eAbbreviations: SD=Standard Deviation; OR = Odds Ratio; CI = Confidence Interval; p-values \u0026lt;0.05.\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eNote: Percentages may not sum to 100 due to rounding\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e*Values for initial cancer stage, stage advancement, and metastasis development may not add up to the sample size (N = 5,793) because of missing data (n = 3,937; 67.96%).\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e- Intersectionality Score Notes: \u003cem\u003eScore range: 0 = lowest cumulative risk to 12 = highest cumulative risk\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e- All values were derived from univariate logistic regression models. Living status was modeled across full sample (N = 5,793). Reference categories used for each variable; only non-reference categories shown.\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e- Mortality/Vital status Ors were left intentionally blank, as it serves as the outcome\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 1\u003c/strong\u003e displays comparative ROC curves for the SIS, CCI, and TNM staging. The Intersectionality Score demonstrated the highest area under the curve (AUC = 0.630; 95% CI, 0.605\u0026ndash;0.655), followed by the Charlson Index (AUC = 0.605; 95% CI, 0.578\u0026ndash;0.631) and TNM stage at diagnosis (AUC = 0.554; 95% CI, 0.531\u0026ndash;0.577). Differences in discriminatory ability were statistically significant (DeLong test \u0026chi;\u0026sup2; = 22.63; P \u0026lt; .001).\u003c/p\u003e\n\u003cp\u003eIn linear regression models (\u003cstrong\u003eTable 2\u003c/strong\u003e), the Intersectionality Score was significantly higher among patients treated during the COVID-era (2020\u0026ndash;2022) (\u0026beta; = 0.16; 95% CI, 0.05\u0026ndash;0.27; P = .005) and the recent era (2023\u0026ndash;2024) (\u0026beta; = 0.25; 95% CI, 0.13\u0026ndash;0.38; P \u0026lt; .001) compared with those treated in 2014\u0026ndash;2016. Deceased patients had significantly higher scores than those alive (\u0026beta; = 0.44; 95% CI, 0.36\u0026ndash;0.52; P \u0026lt; .001). Receipt of chemotherapy was positively associated with higher scores (\u0026beta; = 0.86; 95% CI, 0.74\u0026ndash;0.99; P \u0026lt; .001), while receipt of radiation was inversely associated (\u0026beta; = \u0026ndash;0.73; 95% CI, \u0026ndash;0.82 to \u0026ndash;0.64; P \u0026lt; .001). Compared with patients treated in medical oncology, those seen in infusion or procedural departments had higher scores (\u0026beta; = 0.62; 95% CI, 0.52\u0026ndash;0.71; P \u0026lt; .001), whereas those treated in radiation oncology had lower scores (\u0026beta; = \u0026ndash;0.45; 95% CI, \u0026ndash;0.59 to \u0026ndash;0.32; P \u0026lt; .001). Presence of metastasis was associated with a markedly higher score (\u0026beta; = 1.08; 95% CI, 0.90\u0026ndash;1.26; P \u0026lt; .001), whereas cancer progression was not statistically significant (\u0026beta; = \u0026ndash;0.09; 95% CI, \u0026ndash;0.98 to 0.79; P = 0.835).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn multivariable logistic regression adjusting for treatment era, chemotherapy, radiation, department, metastasis, and advanced stage patients with moderate intersectionality risk had significantly higher odds of mortality compared with those at low risk (OR = 1.71; 95% CI, 1.37\u0026ndash;2.14; P \u0026lt; .001), while high-risk patients showed a non-significant association (OR = 1.82; 95% CI, 0.76\u0026ndash;4.37; P = .177).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Associations between Intersectionality Score and Mortality Among Oncology Patients\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"637\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSIS Association\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta; (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMortality Association\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTreatment Era (Ref = 2014\u0026ndash;2016)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;2017\u0026ndash;2019 (Mid)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.04 (\u0026ndash;0.07 to 0.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;2020\u0026ndash;2022 (COVID)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.16 (0.05 to 0.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;2023\u0026ndash;2024 (Recent)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.25 (0.13 to 0.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMortality Status (Ref = Alive)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.44 (0.36 to 0.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eChemotherapy (Yes vs No)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.86 (0.74 to 0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRadiation (Yes vs No)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;0.73 (\u0026ndash;0.82 to \u0026ndash;0.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDepartment (Ref = Medical Oncology)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;Radiation Oncology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;0.45 (\u0026ndash;0.59 to \u0026ndash;0.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;Radiology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.21 (\u0026ndash;0.16 to 0.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;Infusion/Procedural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.62 (0.52 to 0.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMetastasis Present (Yes vs No)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.08 (0.90 to 1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdvanced Stage (Yes vs No)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;0.09 (\u0026ndash;0.98 to 0.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntersectionality Risk Category (Ref = Low)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;Moderate vs Low\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.71 (1.37 to 2.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;High vs Low\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.82 (0.76 to 4.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 637px;\"\u003e\n \u003cp\u003e\u003cem\u003eNotes:\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e- Intersectionality risk associations (left columns) reflect separate linear regression models using the Intersectionality Score (range: 0\u0026ndash;12) as the continuous dependent variable.\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e- Mortality associations (right columns; N = 1,856) reflect a multivariable logistic regression using mortality status (1 = deceased) as the outcome, adjusted for treatment era, chemotherapy, radiation therapy, department category, metastasis, and advanced stage.\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e- Variables used to construct the Intersectionality score were excluded from both models to avoid over-adjustment, circularity, and multicollinearity.\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFor digestive cancer among young adults (age 18\u0026ndash;49) (\u003cstrong\u003eTable 3\u003c/strong\u003e), incidence rates were significantly higher during 2017\u0026ndash;2019 (IRR, 1.47; 95% CI, 1.31\u0026ndash;1.64; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) and 2020\u0026ndash;2022 (IRR, 1.44; 95% CI, 1.29\u0026ndash;1.61; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) compared with 2014\u0026ndash;2016. A modest decline was observed in 2023\u0026ndash;2024 (IRR, 0.88; 95% CI, 0.78\u0026ndash;1.00; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.048). High intersectionality risk was independently associated with significantly lower incidence (IRR, 0.007; 95% CI, 0.003\u0026ndash;0.020; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), likely reflecting very low case counts in that group. Significant interaction effects were observed for moderate-risk patients in 2020\u0026ndash;2022 (IRR, 1.19; 95% CI, 1.02\u0026ndash;1.38; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.026) and 2023\u0026ndash;2024 (IRR, 1.32; 95% CI, 1.12\u0026ndash;1.56; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.001), indicating a pattern of increase in incidence over time.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Treatment Era, Intersectionality Risk, and Digestive Cancer* Incidence Among Young Adults\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIRR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eP Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTreatment Era\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2017\u0026ndash;2019 vs 2014\u0026ndash;2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.31\u0026ndash;1.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2020\u0026ndash;2022 vs 2014\u0026ndash;2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.29\u0026ndash;1.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2023\u0026ndash;2024 vs 2014\u0026ndash;2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.78\u0026ndash;1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.048\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntersectionality Risk Category\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eModerate Risk vs Low Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.97\u0026ndash;1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.144\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHigh Risk vs Low Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.003\u0026ndash;0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eInteraction: Treatment Era \u0026times; Risk Group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2017\u0026ndash;2019 \u0026times; Moderate Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.91\u0026ndash;1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.435\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2017\u0026ndash;2019 \u0026times; High Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.47\u0026ndash;5.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.478\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2020\u0026ndash;2022 \u0026times; Moderate Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.02\u0026ndash;1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2020\u0026ndash;2022 \u0026times; High Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.79\u0026ndash;7.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.120\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2023\u0026ndash;2024 \u0026times; Moderate Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.12\u0026ndash;1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2023\u0026ndash;2024 \u0026times; High Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.68\u0026ndash;7.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.184\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 522px;\"\u003e\n \u003cp\u003e\u003cem\u003eNote: All models adjusted for interaction terms; Incidence Rate Ratios (IRR) reflect relative changes in incident case counts. The baseline group is low-risk patients during the 2014\u0026ndash;2016 era.\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e*\u003cem\u003eDigestive cancers include colorectal, esophagus/bile duct, pancreas, and other digestive\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 2\u003c/strong\u003e illustrates these trends visually, showing that the incidence of digestive cancer cases increased among low- and moderate-risk patients from 2014 to 2016 through 2020 to 2022, with a subsequent decline in low-risk incidence and a continued elevation among moderate-risk patients in 2023 to 2024. Case counts among high-risk patients remained consistently low across all treatment eras.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study contributes new evidence by developing and operationalizing a cumulative intersectionality-based risk score that captures both clinical and social vulnerability, offering an intersectional approach to characterizing cancer outcomes and uncovering emerging disparities in early-onset digestive cancers. Our additive score aligns with public health traditions of cumulative vulnerability indices [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Specifically, we included 12 components - spanning demographic, socioeconomic, clinical, and access-related factors identified in prior literature as associated with disparities in cancer care delivery [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. By summing these domains into an unweighted score, we aimed to reflect the cumulative burden of multiple, overlapping barriers faced by oncology patients in real-world settings to help with stratification and analytic modeling of intersectional disadvantage at the patient level. Overall, our findings support the hypothesis that SIS is associated with mortality among oncology patients, demonstrating prognostic value and internal validity as a composite measure of social and clinical vulnerability, with three key findings.\u003c/p\u003e\u003cp\u003eFirst, we observed a significant temporal decline in all-cause mortality, with patients initiating treatment during 2020\u0026ndash;2022 and 2023\u0026ndash;2024 experiencing substantially lower odds of death (OR, 0.53 and 0.22, respectively) compared with those treated in 2014\u0026ndash;2016. These patterns align with national trends and emerging evidence on rural cancer disparities and evolving care delivery models [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSecond, the Intersectionality Score showed significantly better prognostic accuracy for all-cause mortality than the Charlson Index and TNM stage (AUC\u0026thinsp;=\u0026thinsp;0.630 vs. 0.605 and 0.554; P\u0026thinsp;\u0026lt;\u0026thinsp;.001), with adjusted models indicating 71% higher odds of death for moderate-risk patients compared to low-risk, while the high-risk category showed a non-significant increase. The cumulative burden of social and clinical risk factors enables meaningful stratification of mortality risk, and the additive structure of the Intersectionality Score supports pragmatic implementation, consistent with public health approaches to structural disadvantage [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Unlike traditional tools that overlook access-related barriers, the Intersectionality Score integrates multidimensional factors across sociodemographic (e.g., public insurance, race, age), clinical (advanced stage, high-risk tumor types), and care delivery domains (distance to clinic, missed consultations, no-shows) [\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Notably, several variables within the score, such as missed consultations and long travel distances, have previously been linked to care disruptions and early mortality, particularly in rural or underserved populations [\u003cspan additionalcitationids=\"CR16 CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The Intersectionality Score builds on this literature by unifying these predictors into a single risk metric that may aid in identifying patients who could benefit from additional resources, such as transportation support, social work referral, or intensive follow-up.\u003c/p\u003e\u003cp\u003eThird, digestive cancer incidence among young adults rose significantly during the 2017\u0026ndash;2019 and 2020\u0026ndash;2022 treatment eras, with a disproportionate increase observed among those in the moderate SIS risk group. Although incidence declined modestly in 2023\u0026ndash;2024, rates remained elevated for moderate-risk patients, while high-risk patients consistently showed markedly lower-case counts. Previous global and U.S.-based studies support our findings of rising digestive cancer incidence among younger adults, particularly among subgroups with underlying vulnerabilities [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. A multicountry analysis [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] showed that early-onset colorectal cancer (ages 25\u0026ndash;49) increased significantly in 27 countries, often more rapidly than in older adults, echoing our observed trends in low- and moderate-risk groups from 2014 to 2022. Similarly, a steep annual increase in incidence among U.S. adults aged 20\u0026ndash;49 was reported [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], which highlights racial and socioeconomic disparities, mirroring our finding that moderate-risk patients experienced a disproportionate rise in incidence during later treatment eras.\u003c/p\u003e\u003cp\u003eOur findings should be interpreted in the context of the following limitations. First, the analysis was conducted within a single rural health system with a predominantly non-Hispanic White population (94.7%), which limits external validity to more racially diverse or urban settings. Second, although the SIS represents an internally adjusted index composed of 12 theory-informed components, we applied equal weighting across domains, an approach that assumes uniform risk contribution and oversimplifies interactions among predictors [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Future work should explore weighted or machine learning\u0026ndash;based alternatives to optimize score calibration. Third, complete-case analysis was used to maintain analytic consistency, but this may have biased results if patients with missing data were systematically different. Fourth, the outcome (vital status) was assessed cross-sectionally at last follow-up; time-to-event models may yield additional insights into survival dynamics over time. Fifth, although ROC analyses established comparative prognostic accuracy, additional validation in other settings and calibration assessments are needed before clinical deployment. Finally, although we did not compare the SIS to biomarker-based indices (PROMISE, GRIm, or CTI) due to data constraints, these tools serve complementary purposes focused on short-term mortality prediction in hospitalized patients, and future external validation should assess comparative and additive value across care settings [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWhile generalizability is limited by the study setting, the score itself is constructed from routinely available EHR variables, which enhances its adaptability to other health systems. SIS may be especially useful in safety-net or rural settings where structural disadvantage is both prevalent and undermeasured by traditional oncology risk tools. Replication across other diseases/conditions and diverse populations will be essential to confirm its transportability.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur Intersectionality Score was significantly associated with all-cause mortality and showed higher discriminative ability than both the Charlson Index and TNM staging. Patients with moderate intersectionality risk had notably higher odds of death. We also observed rising digestive cancer incidence among younger adults, with moderate-risk patients experiencing a disproportionate increase between 2017 and 2022. These results support further examination of cumulative risk measures in oncology care, especially in rural settings where structural barriers are often underrecognized. Embedding such tools in clinical workflows could support earlier, more holistic identification of high-risk patients. Our findings suggest that this cumulative intersectionality score may improve risk stratification and help detect shifting patterns in early-onset digestive cancers among vulnerable patients, with potential relevance for rural and resource-constrained settings globally.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eArea Under the Curve\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCCI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCharlson Comorbidity Index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eConfidence Interval\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCNS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCentral Nervous System\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCTI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eC-Reactive Protein\u0026ndash;Triglyceride\u0026ndash;Glucose Index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eEHR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eElectronic Health Record\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eEIRH\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eEssentia Institute of Rural Health\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGRIm\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGustave Roussy Immune Score\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eICD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInternational Classification of Diseases\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIRB\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInstitutional Review Board\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIRR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eIncidence Rate Ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eOdds Ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePROMISE\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePrognostic Score for Hospitalized Cancer Patients\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRef.\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eReference Category\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eReceiver Operating Characteristic\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eStandard Deviation\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSIS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eShour Intersectionality Score\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSTROBE\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eStrengthening the Reporting of Observational Studies in Epidemiology\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTNM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTumor, Node, Metastasis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was reviewed and approved by the Essentia Health Institutional Review Board (IRB #EH25918-EHIR-1.0; FWA #00000635) under an expedited procedure. The Essentia Health IRB waived the requirement for informed consent because only de-identified electronic health record data were used and the research posed no more than minimal risk, consistent with 45 CFR 46.116(f). All study procedures were conducted in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Declaration of Helsinki and its later amendments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDe-identified aggregate data and the analytic code used in this study are available from the corresponding author on reasonable request, contingent on IRB review and a data use agreement with Essentia Health. The underlying EHR-derived dataset contains protected health information and cannot be shared publicly. All materials provided will exclude direct identifiers and adhere to HIPAA and institutional policies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunding for this study was provided by the Essentia Institute of Rural Health (EIRH-25-2105, Scientist Discretionary Fund) awarded to the Principal Investigator, Abdul R. Shour, MS, PhD, for the period of April 2025 to March 2026. The funder had no role in the design, conduct, analysis, or reporting of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eARS\u003c/strong\u003e had full access to all the data in the study and takes responsibility for the integrity and accuracy of the data analysis.\u003cbr\u003e\u003cstrong\u003eARS\u003c/strong\u003e and\u0026nbsp;\u003cstrong\u003eAO\u003c/strong\u003e conceptualized and designed the study.\u003cbr\u003e\u003cstrong\u003eARS\u003c/strong\u003e acquired, analyzed, and interpreted the data.\u003cbr\u003e\u003cstrong\u003eARS\u003c/strong\u003e and\u0026nbsp;\u003cstrong\u003eRA\u003c/strong\u003e drafted the manuscript.\u003cbr\u003e\u003cstrong\u003eARS, BF, LP, CB, RA, AO\u003c/strong\u003e critically revised the manuscript for important intellectual content.\u003cbr\u003e\u003cstrong\u003eARS\u003c/strong\u003e and\u0026nbsp;\u003cstrong\u003eRA\u003c/strong\u003e conducted the statistical analysis.\u003cbr\u003e\u003cstrong\u003eARS\u003c/strong\u003e obtained funding and provided administrative, technical, and material support.\u003cbr\u003e\u003cstrong\u003eAO\u003c/strong\u003e provided supervision.\u003cbr\u003e\u0026nbsp;All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe gratefully acknowledge Katherine Dean, Executive Director of the Essentia Institute for Rural Health (EIRH), for her leadership and administrative support. We also thank Dr. Stephen Waring, Principal Research Scientist, for his early contributions to the study\u0026rsquo;s proposal development. Additional thanks go to the EIRH research informatics and administrative team for their essential support: Michelle Sikkink (Research and Evaluation Specialist), Anthony Castillo and Catharine A. Karow (Research Informatics Analysts II), Nancy Dold (Grants Manager), and Theresa Ekblad (Technology and Informatics Supervisor).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eARS\u003c/strong\u003e is a Research Scientist II at the Essentia Institute of Rural Health and serves as Co-Chair of the Cancer Scientific Interest Group within the Health Care Systems Research Network. His work focuses on cancer epidemiology, and he leads multiple funded studies examining cancer outcomes and health disparities in rural populations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRA\u003c/strong\u003e is an assistant professor and epidemiologist at the Medical College of Wisconsin, with expertise in global health and social determinants of health.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBF\u003c/strong\u003e is the Medical Director of Oncology Research at the Essentia Health Cancer Center and a practicing hematologist-oncologist with research interests in rural cancer care delivery.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLP\u003c/strong\u003e is a Research Scientist III at the Essentia Institute of Rural Health and adjunct professor at the University of Minnesota, with research focused on rural health equity, behavioral health, and pharmacy practice.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCB\u003c/strong\u003e is the Director of Research at the Essentia Health Heart and Vascular Center and adjunct faculty at the University of Minnesota. Her work centers on cardiovascular and other chronic disease prevention and clinical trials.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAO\u003c/strong\u003e is a clinical professor and oncology service line director at Marshfield Clinic Health System. He serves as principal investigator for multiple NIH- and NCI-funded cancer research initiatives in community settings.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eSiegel RL, Giaquinto AN, Jemal A. Cancer statistics, 2024. CA: a cancer journal for clinicians. 2024 Jan 1;74(1).\u003c/li\u003e\n \u003cli\u003ePatel MI, Lopez AM, Blackstock W, Reeder-Hayes K, Moushey EA, Phillips J, Tap W. Cancer disparities and health equity: a policy statement from the American Society of Clinical Oncology. Journal of Clinical Oncology. 2020 Oct 10;38(29):3439-48.\u003c/li\u003e\n \u003cli\u003eAmerican Cancer Society. \u003cem\u003eCancer Facts \u0026amp; Figures 2024\u003c/em\u003e [Internet]. Atlanta: American Cancer Society; 2024 [cited 2025 Jul 12]. Available from: https://www.cancer.org/research/cancer-facts-statistics/all-cancer-facts-figures/2024-cancer-facts-figures.html\u003c/li\u003e\n \u003cli\u003eSaka AH, Giaquinto AN, McCullough LE, Tossas KY, Star J, Jemal A, Siegel RL. Cancer statistics for African American and Black people, 2025. CA: a cancer journal for clinicians. 2025 Apr;75(2):111-40.\u003c/li\u003e\n \u003cli\u003eCharlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. Journal of chronic diseases. 1987 Jan 1;40(5):373-83.\u003c/li\u003e\n \u003cli\u003eBauer GR. Incorporating intersectionality theory into population health research methodology: challenges and the potential to advance health equity. Social science \u0026amp; medicine. 2014 Jun 1;110:10-7.\u003c/li\u003e\n \u003cli\u003eBowleg L. The problem with the phrase women and minorities: intersectionality\u0026mdash;an important theoretical framework for public health. American journal of public health. 2012 Jul;102(7):1267-73.\u003c/li\u003e\n \u003cli\u003eKapilashrami A, Hankivsky O. Intersectionality and why it matters to global health. The Lancet. 2018 Jun 30;391(10140):2589-91.\u003c/li\u003e\n \u003cli\u003eEvans CR, Williams DR, Onnela JP, Subramanian SV. A multilevel approach to modeling health inequalities at the intersection of multiple social identities. Social science \u0026amp; medicine. 2018 Apr 1;203:64-73.\u003c/li\u003e\n \u003cli\u003eCrenshaw K. Demarginalizing the intersection of race and sex: A black feminist critique of antidiscrimination doctrine, feminist theory and antiracist politics. Droit et soci\u0026eacute;t\u0026eacute;. 2021;108:465.\u003c/li\u003e\n \u003cli\u003eShour A, Onitilo AA. Distance matters: investigating no-shows in a large rural provider network. Clinical Medicine \u0026amp; Research. 2024 Jan 31;21(4):177-91.\u003c/li\u003e\n \u003cli\u003eFlanagan BE, Hallisey EJ, Adams E, Lavery A. Measuring community vulnerability to natural and anthropogenic hazards: the Centers for Disease Control and Prevention\u0026rsquo;s Social Vulnerability Index. Journal of environmental health. 2018 Jun;80(10):34.\u003c/li\u003e\n \u003cli\u003eKind AJ, Buckingham WR. Making neighborhood-disadvantage metrics accessible\u0026mdash;the neighborhood atlas. The New England journal of medicine. 2018 Jun 28;378(26):2456.\u003c/li\u003e\n \u003cli\u003eVon Elm E, Altman DG, Egger M, Pocock SJ, G\u0026oslash;tzsche PC, Vandenbroucke JP, Strobe Initiative. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: guidelines for reporting observational studies. International journal of surgery. 2014 Dec 1;12(12):1495-9.\u003c/li\u003e\n \u003cli\u003eYabroff KR, Reeder-Hayes K, Zhao J, Halpern MT, Lopez AM, Bernal-Mizrachi L, Collier AB, Neuner J, Phillips J, Blackstock W, Patel M. Health insurance coverage disruptions and cancer care and outcomes: systematic review of published research. JNCI: Journal of the National Cancer Institute. 2020 Jul 1;112(7):671-87.\u003c/li\u003e\n \u003cli\u003eReddy KP, Berkowitz CL, Jarrell K, Berger R, Hulse S, Elmore LC, Fishman R, Mateo AM, Sataloff DM, Tchou JC, Zhang JQ. The Effect of Rurality on Time to Surgery and Overall Survival among Women with Breast Cancer. Annals of Surgery. 2025:10-97.\u003c/li\u003e\n \u003cli\u003eKumsa FA, Fowke JH, Hashtarkhani S, White BM, Shrubsole MJ, Shaban-Nejad A. The association between neighborhood obesogenic factors and prostate cancer risk and mortality: the Southern Community Cohort Study. ArXiv. 2024 May 28:arXiv-2405.\u003c/li\u003e\n \u003cli\u003eShour AR, Jones GL, Anguzu R, Doi SA, Onitilo AA. Development of an evidence-based model for predicting patient, provider, and appointment factors that influence no-shows in a rural healthcare system. BMC Health Services Research. 2023 Sep 14;23(1):989.\u003c/li\u003e\n \u003cli\u003eKim DW, Lee S, Kwon S, Nam W, Cha IH, Kim HJ. Deep learning-based survival prediction of oral cancer patients. Scientific reports. 2019 May 6;9(1):6994.\u003c/li\u003e\n \u003cli\u003eKenzik KM, Davis ES, Franks JA, Bhatia S. Estimating the impact of rurality in disparities in cancer mortality. JCO oncology practice. 2024 Jul;20(7):993-1002.\u003c/li\u003e\n \u003cli\u003eSiegel RL, Torre LA, Soerjomataram I, Hayes RB, Bray F, Weber TK, Jemal A. Global patterns and trends in colorectal cancer incidence in young adults. Gut. 2019 Dec 1;68(12):2179-85.\u003c/li\u003e\n \u003cli\u003eAbualkhair WH, Zhou M, Ahnen D, Yu Q, Wu XC, Karlitz JJ. Trends in incidence of early-onset colorectal cancer in the United States among those approaching screening age. JAMA network open. 2020 Jan 3;3(1):e1920407-.\u003c/li\u003e\n \u003cli\u003eUyar GC, Mirallas O, Başkurt K, Martin-Cullell B, Yeşilbaş E, Recuero-Borau J, Kaya S, Garc\u0026eacute;s VN, Y\u0026uuml;cel SE, Cano KS, G\u0026oacute;mez-Puerto D. Prediction of 90-day mortality among cancer patients with unplanned hospitalisation: a retrospective validation study of three prognostic scores. The Lancet Regional Health\u0026ndash;Europe. 2025 Jul 1;54.\u003c/li\u003e\n \u003cli\u003eParikh RB, Manz C, Chivers C, Regli SH, Braun J, Draugelis ME, Schuchter LM, Shulman LN, Navathe AS, Patel MS, O\u0026rsquo;Connor NR. Machine learning approaches to predict 6-month mortality among patients with cancer. JAMA network open. 2019 Oct 2;2(10):e1915997-.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7546882/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7546882/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eCancer mortality disparities persist, and existing prognostic tools have not captured overlapping vulnerabilities, limiting their ability to characterize disparities in mortality and rising digestive cancer incidence among young adults. Our study aims to develop and determine whether a new Intersectionality Score is associated with all-cause cancer mortality, compare its prognostic performance with the Charlson Index and TNM staging, and assess digestive cancer incidence among young adults across intersectionality risk groups.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA retrospective cohort study of 5,793 adults with cancer treated at Essentia Health in the upper Midwest (2014\u0026ndash;2024) used electronic health records to examine outcomes among patients who initiated chemotherapy or radiation. The outcome was all-cause mortality at last follow-up. The secondary outcome was digestive cancer incidence among young adults (18\u0026ndash;49 years). The exposure was the Intersectionality Score, a 12-point index of overlapping social, clinical variables analyzed as continuous and categorical variables. Descriptive statistics, DeLong\u0026rsquo;s test, and linear, logistic, and Poisson regression models were performed using Stata/SE 18.5, with two-sided P\u0026thinsp;\u0026le;\u0026thinsp;.05.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAmong 5,793 cancer patients (mean [SD] age, 66.9 [12.4] years; 38.6% female), 50.4% were deceased at last follow-up, and 16.7% had a diagnosis of digestive cancer. Intersectionality Score demonstrated improved prognostic discrimination (AUC 0.630) in cancer mortality prediction compared to CCI (0.605) and TNM staging (0.554) and was significantly associated with mortality: moderate-risk patients had higher odds of death (OR, 1.71; 95% CI, 1.37\u0026ndash;2.14) than low-risk patients. Among young adults, digestive cancer incidence was higher in 2017\u0026ndash;2019 (IRR, 1.47; 95% CI, 1.31\u0026ndash;1.64) and 2020\u0026ndash;2022 (IRR, 1.44; 95% CI, 1.29\u0026ndash;1.61) versus 2014\u0026ndash;2016. Moderate-risk patients had a higher incidence in 2020\u0026ndash;2022 (IRR, 1.19) and 2023\u0026ndash;2024 (IRR, 1.32).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThe intersectionality risk score was associated with all-cause mortality and demonstrated improved prognostic discrimination over the Charlson Index and TNM staging. Digestive cancer incidence increased among young adults classified as moderate risk. Our cumulative intersectionality score may improve mortality risk stratification and help detect shifting patterns in early-onset digestive cancers among patients with overlapping vulnerabilities, with potential relevance for rural and resource-constrained settings globally.\u003c/p\u003e","manuscriptTitle":"Association Between an Intersectionality-Based Risk Score and Cancer Mortality: A Retrospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-17 12:25:38","doi":"10.21203/rs.3.rs-7546882/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"89683089788331465178896205693341216084","date":"2025-10-15T17:02:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"114224030846614875337143744996816581732","date":"2025-10-15T16:36:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"50247534905834980548172643548239136883","date":"2025-10-12T18:01:58+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-06T08:47:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-01T08:59:02+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-11T06:01:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-09T14:45:24+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2025-09-09T14:42:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"91123a26-3106-495a-a6a7-d518515f9026","owner":[],"postedDate":"October 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-17T12:25:39+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-17 12:25:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7546882","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7546882","identity":"rs-7546882","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}