Racial and Socioeconomic Disparities in Survival Among Women with Advanced-stage Ovarian Cancer Who Received Systemic Therapy

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Washington, Shama D. Karanth, Meghann Wheeler, Livingstone Aduse-Poku, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3225591/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Oct, 2023 Read the published version in Cancer Causes & Control → Version 1 posted 7 You are reading this latest preprint version Abstract Purpose The purpose of this study was to assess the association between race/ethnicity and all-cause mortality among women with advanced-stage ovarian cancer who received systemic therapy. Methods We analyzed data from the National Cancer Database on women diagnosed with advanced-stage ovarian cancer from 2004 to 2015 who received systemic therapy. Race/ethnicity was categorized as Non-Hispanic (NH) White, NH-Black, Hispanic, NH-Asian/Pacific Islander, and Other. Income and education were combined to form a composite measure of socioeconomic status (SES) and categorized into low-, mid-, and high-SES. Multivariable Cox proportional hazards models were used to assess whether race/ethnicity was associated with the risk of death. Models were adjusted for age, SES, comorbidity level, and receipt of surgery. Results The study population comprised 53,367 women (52.4% ages ≥ 65 years, 82% NH-White, 8.7% NH-Black, 5.7% Hispanic, and 2.7% NH-Asian/Pacific Islander) in the analysis. After adjusting for covariates, the NH-Black race was associated with a higher risk of death versus NH-White race (aHR: 1.12; 95% CI: 1.07,1.18), while Hispanic race was associated with a lower risk of death compared to NH-White women (aHR: 0.87; 95% CI: 0.80, 0.95). Furthermore, NH-Black women versus NH-White women had an increased risk of mortality among those with low-SES characteristics (aHR:1.12; 95% CI:1.03–1.22) and mid-SES groups (aHR: 1.13; 95% CI:1.05–1.21). Conclusions Among women with advanced-stage ovarian cancer who received systemic therapy, NH-Black women experienced poorer survival compared to NH-White women. Future studies should be directed to identify drivers of ovarian cancer disparities, particularly racial differences in treatment response and surveillance. Gynecologic cancers Systemic therapy Cancer disparities Socioeconomic Status Figures Figure 1 Figure 2 BACKGROUND Ovarian cancer is the fifth leading cause of cancer-related deaths in women in the United States and is estimated to account for 13,270 deaths in 2023 [ 1 ]. Efficient screening techniques or simple diagnostic tests for ovarian cancer are currently lacking [ 2 , 3 ]. Thus, most women with ovarian cancer are diagnosed at advanced stages, leading to poor 5-year survival rates: 49% for White women and 41% for Black women [ 1 , 4 ]. Given these dismal survival rates, new treatment regimes, including the use of primary systemic therapy, are emerging [ 5 , 6 ]. Guideline-adherent treatment for advanced-stage ovarian cancer usually involves cytoreductive surgery followed by adjuvant chemotherapy and sometimes radiotherapy [ 5 , 6 ]. However, many women with advanced ovarian cancer have a high risk of surgical complications and recurrence, and primary systemic therapy has been used for this select group of women [ 7 , 8 ]. For instance, neoadjuvant chemotherapy, such as carboplatin and paclitaxel, is recommended if there is a low likelihood of achieving optimal primary cytoreductive surgery for women diagnosed with stage III or IV ovarian cancer [ 7 ]. Immunotherapy, such as checkpoint inhibitors, has also shown promise for the treatment of recurrent ovarian cancer in recent randomized control trials [ 8 , 9 ]. Despite the widespread use of systemic therapy, there are racial disparities in survival of advanced-stage ovarian cancer. Several studies have found that Black women experience poorer survival of advanced-stage ovarian cancer compared to their White counterparts [ 10 , 11 ]. Socioeconomic and access-to-healthcare factors further widen the racial disparity in ovarian cancer [ 12 – 14 ]. Although the inadequate receipt of guideline-recommended treatment has been well documented for its contribution to the racial disparities in ovarian cancer survival, [ 13 , 15 – 17 ] the factors that contribute to these disparities among women who have received equivalent treatment are not well understood [ 18 ]. Therefore, there is a critical need to evaluate disparities in advanced-stage ovarian cancer survival among women who have received systemic therapy in the United States (U.S.). In this study, we utilized data from the National Cancer Database (NCDB) to evaluate the association between race/ethnicity and risk of all-cause death among women with advanced-stage ovarian cancer who have equal utilization of systemic therapy. We also investigated whether demographic characteristics, comorbidity level, and receipt of surgery modified those associations. METHODS Data Source The data for this study were obtained from the 2016 NCDB Participant User Files (PUF); the NCDB is a joint program of the American College of Surgeons Commission on Cancer and the American Cancer Society [ 19 ]. The NCDB includes over 70% of all patients with newly diagnosed cancers in the United States annually [ 19 ]. The data are abstracted by certified tumor registrars as part of the National Cancer Registrars Association [ 20 ]. The data includes patient demographics, tumor characteristics, clinical and pathological TNM stage classification, treatments, and survival data. This analysis was considered exempt by the University of Florida Institutional Review Board as the data used in this study were obtained from a de-identified NCDB database. Study Cohort The study cohort included women that met the following criteria: (1) stage III-IV ovarian cancer (International Classification of Diseases for Oncology, Third Edition topography code- C56.9), (2) women diagnosed between 2004 and 2015, (3) received systemic therapy (defined as chemotherapy, immunotherapy, and/or hormone therapy by the NCDB), and (4) had no missing values for sociodemographic characteristics, treatment receipt, tumor characteristics, and time elapsed between the date of diagnosis and the date of last contact or death. Exposure, Outcome, and Covariates The main exposure for this data analysis was race/ethnicity, which was derived by combining the variable for race with the variable for Spanish origin. The data were categorized into the following groups: non-Hispanic White (NH-White), non-Hispanic Black (NH-Black), Hispanic, non-Hispanic Asian/Pacific Islander (NH-Asian/PI), and Other. Hereafter, we exclude the NH prefix when referencing racial groups. Our outcome of interest was all-cause mortality, with the survival time measured (in months) from the date of diagnosis to death or last contact (whichever occurred first). The sociodemographic covariates included in this study were age at diagnosis (< 65 and ≥ 65 years), area-level educational level as the percentage of individuals in the patient’s zip code without a high school degree categorized into quartiles based on all United States (US) zip codes (21% or more, 13%-20.9%, 7%-12.9%, and < 7%), and median household income was estimated by zip code of the patient recorded at the time of diagnosis and categorized as quartiles (< $ 38,000, 2: $ 38,000 - $ 47,999, $ 48,000- $ 62,999, and ≥ $ 63,000) [ 21 ]. The quartile classifications for area-level educational level and median household income were combined to form a composite score for SES groups: low (2–3), mid (4–7), and high (8) (Supplementary Table 1) [ 22 ]. Healthcare access factors included primary insurance (no insurance, private insurance/managed care, and government insurance) and cancer treatment facility type (academic and non-academic). Treatment receipt was defined as receipt of chemotherapy (yes vs. no), receipt of immunotherapy (yes vs. no), receipt of hormone therapy (yes vs. no), receipt of surgery (yes vs. no), and receipt of radiation therapy (yes vs. no). Charlson-Deyo comorbidity index (CCI) score was categorized as no comorbidities and one or more comorbidities. Additional covariates included histology (serous carcinoma, mucinous carcinoma, endometrioid carcinoma, and other) and tumor grade (low, intermediate, high, and unknown) [ 23 ]. Covariates were selected based on a priori knowledge regarding their associations with the exposure and outcome of interest. Statistical Analysis The distributions of study covariates were summarized in the overall sample and by race/ethnicity. Kaplan-Meier curves were used to visualize the probability of survival by race/ethnicity overall and by each SES group, and the differences in survival probabilities were examined by the log-rank test. Number of deaths and mortality rates (reported as the number of deaths per 10 person-years) were summarized for the overall study population and according to race/ethnicity and further by stratified age, CCI, SES, and surgery receipt. The association between racial/ethnic groups (reference: White) and risk of all-cause mortality was examined in three multivariable Cox proportional hazards models. The first model was adjusted for age and SES; the second model was additionally adjusted for primary insurance and cancer facility type; and the final model was additionally adjusted for CCI, treatment receipt, histology, and grade. In the final model, all covariates (age, SES, facility type, primary insurance, radiation receipt, surgery receipt, cancer histology, grade, and CCI) violated the proportional hazard assumptions. Therefore, we created and controlled for the interaction terms between the covariates and follow-up time, and no violation was observed afterward. Subgroup analyses were conducted by age at diagnosis, CCI, and SES. The interaction term between race/ethnicity and age at diagnosis, CCI, or SES was added into the multivariable Cox proportional hazards models. The significance of interaction tests was determined by the Wald test. All analyses were performed using SAS 9.4 (Cary, NC) and R studio V 4.1.1. All statistical tests were 2-sided, and a p-value < 0.05 indicated statistical significance. RESULTS The final study sample included 53,367 women diagnosed with advanced-stage ovarian cancer and had received systemic therapy (Supplementary Fig. 1) . Table 1 presents the demographic and clinical characteristics of women diagnosed with advanced-stage (III-IV) primary ovarian cancer from 2004–2015. Approximately 50% of the women were ≥ 65 years old at the time of diagnosis, the majority did not have any comorbidities (78.0%), and most were White (82.0%), followed by Black (8.7%), Hispanic (5.7%), Asian/Pacific Islander (2.7%), and Other (0.9%). Most women had government insurance (55.8%) and lived in regions classified as the mid-SES group (61.1%). White women (22.3%) made up the largest proportion of women living in regions classified as the high-SES group. Women who identified as Black, Hispanic, Asian/PI, or Other were more likely to be younger (< 65 years) at diagnosis and more likely to be treated at academic facilities. Additionally, Black women were less likely to receive surgery (64.0% Black vs 73.9% White) and more likely to have rarer histological types of ovarian cancer (40.2% Black vs 34.8% White). The median time from diagnosis to last follow-up or death was 21.7 months for White women and 19.5 months for Black women ( Table 1 ) . Table 1 Sociodemographic and Cancer Characteristics for Advanced-Stage (III-IV) Ovarian Cancer Patients Who Received Systemic Therapy Overall (%) Race n (%) N = 53367 NH-White N = 43760 (82.0) NH-Black N = 4634 (8.7) Hispanic N = 3062 (5.7) NH-Asian/PI N = 1449 (2.7) Other N = 462 (0.9) Age < 65 25411 (47.6) 19855 (45.4) 2536 (54.7) 1831 (59.8) 911 (62.9) 278 (60.2) 65+ 27956 (52.4) 23905 (54.6) 2098 (45.3) 1231 (40.2) 538 (37.1) 184 (39.8) Charlson-Deyo Comorbidity score 0 41621 (78.0) 34528 (78.9) 3198 (69.0) 2364 (77.2) 1185 (81.8) 346 (74.9) 1 11746 (22.0) 9232 (21.1) 1436 (31.0) 698 (22.8) 264 (18.2) 116 (25.1) Primary insurance Not insured 1977 (3.7) 1182 (2.7) 278 (6.0) 378 (12.3) 109 (7.5) 30 (6.5) Private 21608 (40.5) 17942 (41.0) 1682 (36.3) 1081 (35.3) 726 (50.1) 177 (38.3) Government 29782 (55.8) 24636 (56.3) 2674 (57.7) 1603 (52.4) 614 (42.4) 255 (55.2) SES Composite Groups Low SES 9962 (18.7) 6301 (14.4) 2153 (46.5) 1183 (38.6) 191 (13.2) 134 (29.0) Mid SES 32588 (61.1) 27694 (63.3) 2166 (46.7) 1606 (52.5) 873 (60.3) 249 (53.9) High SES 10817 (20.3) 9765 (22.3) 315 (6.8) 273 (8.9) 385 (26.6) 79 (17.1) Cancer facility type Academic 21916 (41.1) 17135 (39.2) 2273 (49.1) 1516 (49.5) 753 (52.0) 239 (51.7) Not academic 31451 (58.9) 26625 (60.8) 2361 (51.0) 1546 (50.5) 696 (48.0) 223 (48.3) Chemotherapy Yes 53171 (99.6) 43591 (99.6) 4620 (99.7) 3057 (99.8) 1446 (99.8) 457 (98.9) No 196 (0.4) 169 (0.4) 14 (0.3) 5 (0.2) 3 (0.2) 5 (1.1) Immunotherapy Yes 804 (1.5) 671 (1.5) 64 (1.4) 48 (1.6) 18 (1.2) 3 (0.7) No 52563 (98.5) 43089 (98.5) 4570 (98.6) 3014 (98.4) 1431 (98.8) 459 (99.4) Hormone therapy Yes 834 (1.6) 677 (1.6) 72 (1.6) 51 (1.7) 23 (1.6) 11 (2.4) No 52533 (98.4) 43083 (98.5) 4562 (98.5) 3011 (98.3) 1426 (98.4) 451 (97.6) Surgery Receipt Yes 39042 (73.2) 32323 (73.9) 2966 (64.0) 2261 (73.8) 1147 (79.2) 345 (74.7) No 14325 (26.8) 11437 (26.1) 1668 (36.0) 801 (26.2) 302 (20.8) 117 (25.3) Radiation Receipt Yes 682 (1.3) 547 (1.3) 70 (1.5) 40 (1.3) 23 (1.6) 2 (0.4) No 52685 (98.7) 43213 (98.8) 4564 (98.5) 3022 (98.7) 1426 (98.4) 460 (99.6) Grade Low 956 (1.8) 792 (1.8) 74 (1.6) 57 (1.9) 27 (1.9) 6 (1.3) Intermediate 3327 (6.2) 2781 (6.4) 263 (5.7) 175 (5.7) 87 (6.0) 21 (4.6) High 29594 (55.5) 24424 (55.8) 2346 (50.6) 1681 (54.9) 856 (59.1) 287 (62.1) Unknown 19490 (36.5) 15763 (36.0) 1951 (42.1) 1149 (37.5) 479 (33.1) 148 (32.0) Histology Serous 32359 (60.6) 26859 (61.4) 2561 (55.3) 1799 (58.8) 862 (59.5) 278 (60.2) Mucinous 849 (1.6) 637 (1.5) 114 (2.5) 69 (2.3) 22 (1.5) 7 (1.5) Endometrioid 1308 (2.5) 1054 (2.4) 98 (2.1) 100 (3.3) 46 (3.2) 10 (2.2) Other 18851 (35.3) 15210 (34.8) 1861 (40.2) 1094 (35.7) 519 (35.8) 167 (36.2) Stage 3 30278 (56.7) 25179 (57.5) 2346 (50.6) 1669 (54.5) 822 (56.7) 262 (56.7) 4 23089 (43.3) 18581 (42.5) 2288 (49.4) 1393 (45.5) 627 (43.3) 200 (43.3) Column percentages were reported in the table. Other race includes Hawaiian, Micronesian, Chamorran, Guamanian, Polynesian, Tahitian, Samoan, Tongan, Melanesian, Fiji Islander, and New Guinean. Abbreviations: NH, Non-Hispanic; PI, Pacific Islander; AI, American Indian; AN, Alaskan Native; NOS, Not Otherwise Specified. SES, Socioeconomic status (education and income). Kaplan-Meier curves by race/ethnicity (Figure 1) indicated the lowest survival probabilities among Black women (log-rank P <0.0001). Furthermore, Kaplan-Meier curves, stratified by SES, show lower survival probabilities among Black women from both low- and mid-SES groups but not the high-SES group (Figure 2). The death rate for Black women with ovarian cancer was 3.06 deaths/10 person-years: 1.19 times the death rate for White women (Table 2) . In the Cox proportional hazards regression models assessing all-cause mortality, Black women had a higher risk of death compared to White women across all three adjusted models [fully adjusted hazard ratio (aHR): 1.12; 95% CI: 1.07,1.18)], while Hispanic women had a lower risk of death compared to White women (aHR: 0.87; 95% CI: 0.80,0.95) (Table 2). Tests of interaction for age, and CCI were non-significant across all the fully adjusted models. The tests of interaction for surgery receipt were not significant for the first model ( P -interaction=0.07) neither for the fully adjusted model ( P -interaction=0.20). However, in fully adjusted models stratified by age, a significant association persisted among Black women (<65 years, aHR: 1.13; 95% CI: 1.05-1.22; ≥65 years aHR: 1.09; 95% CI: 1.02-1.17), while Hispanic women continued to have a lower risk of death relative to White women (<65 years, aHR: 0.87; 95% CI: 0.77-0.99; ≥65 years aHR: 0.87; 95% CI: 0.78-0.98), (Table 2). In fully adjusted Cox proportional hazards models stratified by SES groups (Table 2) , Black women experienced increased risk of mortality compared to White women in the low-SES group (aHR:1.12; 95% CI:1.03-1.22) and mid-SES group (aHR: 1.13; 95% CI:1.05-1.21); while Hispanic women had lower risk of mortality compared to White women in the both the low- and mid-SES groups. Tests of interaction for SES and race/ethnicity were non-significant across all models. Table 2. Cox Proportional Hazards Models of All-cause Death, Stratified by Race, Ethnicity, Age, Comorbidity Score, and Socioeconomic status. HR and 95% CI Race/Ethnicity n/N Person-years Death Rate/10 person-years Model 1 * Model 2† Model 3‡ Overall NH-White NH-Black Hispanic NH-Asian/PI Other 30248/43760 3295/4634 1771/3062 776/1449 279/462 117930 10759 8515 4049 1260 2.57 (2.54, 2.59) 3.06 (2.96, 3.17) 2.08 (1.99, 2.18) 1.92 (1.79, 2.06) 2.21 (1.97, 2.49) REF 1.29 (1.22, 1.35) 0.98 (0.90, 1.06) 1.04 (0.92, 1.17) 1.33 (1.09, 1.62) REF 1.30 (1.24, 1.37) 0.99 (0.91, 1.07) 1.08 (0.95, 1.20) 1.40 (1.15, 1.70) REF 1.12 (1.07. 1.18) 0.87 (0.80, 0.95) 0.94 (0.83, 1.07) 1.11 (0.91, 1.37) Age<65 NH-White NH-Black Hispanic NH-Asian/PI Other 12445/19855 1684/2536 961/1,831 450/911 154/278 61132 6556 5565 2702 853 2.04 (2.00, 2.07) 2.57 (2.45, 2.69) 1.73 (1.62, 1.84) 1.67 (1.52, 1.83) 1.81 (1.54, 2.11) REF 1.31 (1.22, 1.41) 0.98 (0.86, 1.10) 1.09 (0.91, 1.31) 1.37 (1.02, 1.84) REF 1.27 (1.18, 1.36) 0.94 (0.83, 1.06) 1.11 (0.93, 1.33) 1.40 (1.04, 1.88) REF 1.13 (1.05, 1.22) 0.87 (0.77, 0.99) 1.02 (0.84, 1.22) 1.20 (0.88, 1.63) Age≥65 NH-White NH-Black Hispanic NH-Asian/PI Other 17803/23905 1611/2098 810/1231 326/538 125/184 56798 4203 2950 1348 407 3.13 (3.09, 3.18) 3.83 (3.65, 4.02) 2.75 (2.56, 2.94) 2.42 (2.17, 2.69) 3.07 (2.57, 3.65) REF 1.23 (1.15, 1.31) 0.94 (0.84, 1.05) 0.90 (0.76, 1.07) 1.21 (0.92, 1.60) P-interaction=0.45 REF 1.27 (1.18, 1.35) 0.97 (0.87, 1.09) 0.95 (0.80, 1.13) 1.27 (0.96, 1.67) P-interaction= 0.42 REF 1.09 (1.02, 1.17) 0.87 (0.78, 0.98) 0.85 (0.71, 1.01) 1.05 (0.79, 1.38) P-interaction=0.56 Comorbidity=0 NH-White NH-Black Hispanic NH-Asian/PI Other 23384/34528 2226/3198 1323/2364 627/1185 198/346 96227 7798 6844 3366 1013 2.43 (2.40, 2.46) 2.86 (2.74, 2.98) 1.93 (1.83, 2.04) 1.86 (1.72, 2.01) 1.96 (1.70, 2.24) REF 1.27 (1.20, 1.35) 0.96 (0.87, 1.06) 1.03 (0.89, 1.19) 1.23 (0.97, 1.56) REF 1.28 (1.20, 1.35) 0.96 (0.87, 1.06) 1.08 (0.93, 1.25) 1.29 (1.02, 1.64) REF 1.13 (1.07, 1.20) 0.87 (0.79, 0.96) 0.95 (0.82, 1.11) 1.06 (0.83, 1.36) Comorbidity ≥1 NH-White NH-Black Hispanic NH-Asian/PI Other 6864/9232 1069/1436 448/698 149/264 81/116 21703 2961 1671 683 247 3.16 (3.09, 3.24) 3.61 (3.40, 3.83) 2.68 (2.44, 2.94) 2.18 (1.85, 2.55) 3.28 (2.62, 4.06) REF 1.17 (1.07, 1.27) 0.91 (0.79, 1.07) 0.82 (0.64, 1.05) 1.27 (0.89, 1.81) P-interaction=0.21 REF 1.19 (1.09, 1.30) 0.93 (0.80, 1.08) 0.87 (0.69, 1.11) 1.34 (0.94, 1.92) P-interaction=0.25 REF 1.08 (0.99, 1.18) 0.86 (0.74, 1.00) 0.85 (0.66, 1.084) 1.23 (0.85, 1.76) P-interaction=0.25 Low SES NH-White NH-Black Hispanic NH-Asian/PI Other 4580/6301 1593/2153 702/1,183 101/191 91/134 16071 4756 3260 548 341 2.85 (2.77, 2.93) 3.35 (3.19, 3.52) 2.15 (2.00, 2.32) 1.84 (1.51, 2.23) 2.70 (2.16, 3.26) REF 1.32 (1.21, 1.43) 0.95 (0.82, 1.10) 0.91 (0.70, 1.20) 1.42 (1.00, 2.02) REF 1.32 (1.22, 1.44) 0.96 (0.83, 1.11) 0.94 (0.71, 1.23) 1.48 (1.04, 2.10) REF 1.12 (1.03, 1.22) 0.83 (0.71, 0.96) 0.81 (0.62, 1.08) 1.10 (0.76, 1.60) Mid SES NH-White NH-Black Hispanic NH-Asian/PI Other 19287/27694 1504/2166 914/1606 484/873 149/249 74165 5132 4452 2373 701 2.60 (2.56, 2.64) 2.93 (2.79, 3.08) 2.05 (1.92, 2.19) 2.04 (1.86, 2.23) 2.13 (1.80, 2.49) REF 1.28 (1.19, 1.37) 0.98 (0.88, 1.10) 1.04 (0.89, 1.22) 1.25 (0.95, 1.64) REF 1.29 (1.21, 1.38) 0.98 (0.88, 1.10) 1.08 (0.92, 1.27) 1.32 (1.01, 1.73) REF 1.13 (1.05, 1.21) 0.88 (0.79, 0.99) 0.94 (0.79, 1.10) 1.08 (0.81, 1.42) High SES NH-White NH-Black Hispanic NH-Asian/PI Other 6381/9765 198/315 155/273 191/385 39/79 27694 870 802 1128 218 2.30 (2.25, 2.36) 2.28 (1.98, 2.61) 1.93 (1.65, 2.26) 1.69 (1.47, 1.95) 1.79 (1.29, 2.42) REF 1.13 (0.96, 1.34) 1.04 (0.82, 1.31) 1.04 (0.77, 1.39) 1.28 (0.76, 2.16) P-interaction= 0.25 REF 1.13 (0.96, 1.33) 1.02 (0.81, 1.30) 1.07 (0.80, 1.44) 1.30 (0.77, 2.19) P-interaction= 0.21 REF 1.07 (0.90, 1.26) 0.97 (0.77, 1.22) 1.05 (0.78, 1.41) 1.18 (0.71, 1.97) P-interaction= 0.61 Bold value indicates P<0.05. *Adjusted for age (not included in models stratified by age) and socioeconomic status. †In addition, adjusted for insurance, and academic facility type. ‡In addition, adjusted for radiation receipt, surgery receipt, cancer histology, grade, and Charlson/Deyo comorbidity index score (not included in models stratified by Charlson/Deyo score). CI indicates confidence interval; HR, hazard ratio; n, number of deaths; N, total number of individuals. Death rate calculated as number of deaths divided by person-years (reported as cases/10-person years). Discussion In the present analysis of women diagnosed with advanced-stage ovarian cancer who received systemic therapy, we observed that the risk of all-cause mortality among Black women was 12% higher than among White women. The increased risk of all-cause mortality observed in Black women spans all age groups and encompasses the low and mid SES categories, further emphasizing the disparity between Black and White women. Conversely, Hispanic women had a lower overall risk of all-cause mortality, across all models, compared to White women. However, there was no evidence of effect modification between race/ethnicity, age, CCI, surgery, and SES groups. These results emphasize the need to address racial disparities in the survival of advanced-stage ovarian cancer, as racial disparities persist even among women who have all received systemic treatment. Our results are consistent with prior research suggesting that racial disparities in ovarian cancer survival persist, even when all women received systemic treatment [18, 24, 25]. A cohort study among members of Kaiser Permanente Northern California found that Black women experienced poor survival rates compared to White women, even when both groups had equal access to care and received the same systemic therapy (specifically adjuvant first-line therapy of Carboplatin and paclitaxel) [18]. The study also reported that Black women were more likely to have rare histological types of ovarian cancer, similar to the findings of our study (grouped in the “Other” category). However, even among more rare histological types, such as malignant ovarian germ cell tumors, poorer survival was still found among Black women compared to their White counterparts, despite having similar adjuvant treatment patterns (including surgery, radiation, and chemotherapy) [24]. Another study using NCDB data reported that Black women were more likely to receive neoadjuvant chemotherapy before surgery, as opposed to primary surgery plus adjuvant chemotherapy, due to extensive tumor burden or prediction of poor surgical performance, yet they still experienced lower survival compared to their White counterparts [26, 27]. Our study’s findings support this conclusion, as we found that the disparity between Black and White persisted even among women who only received primary surgery. However, our study adds to the existing literature as we found that the racial disparity in advanced-stage ovarian cancer persists despite receipt of chemotherapy, hormonal therapy, and/or immunotherapy. Our study also found that Black women in the low- and mid-SES groups, but not the high SES groups, exhibited poorer survival rates compared to all other racial/ethnic groups. However, Hispanic women within the same SES groups demonstrated better survival rates compared to White women. Similarly, Park et al. found that Black women had poorer survival rates while Hispanic women had better 5-year survival rates across histological types compared to White women [28]. This study, along with other studies on racial disparities in ovarian cancer, is consistent with the ‘Hispanic Paradox’ phenomenon. Despite facing socioeconomic barriers to health, Hispanic individuals have similar or better survival than their White counterparts [29, 30]. Additional studies are needed to evaluate the ethnic differences that might contribute to survival outcomes among women with ovarian cancer [30, 31]. Several clinical differences may also play a significant role in the Black-White disparity in ovarian cancer survival. Our study found that the Black-White disparity persisted even among women who did not report any comorbidities. This finding indicates that healthcare disparities, such as access to and utilization of healthcare resources, as well as other clinical factors, may be at play [32, 33]. For instance, a study from the Ovarian Cancer in Women of African Ancestry consortium found that nulliparity, body mass index, and postmenopausal hormone therapy duration were all independent mediators in the racial disparity in ovarian cancer survival [32]. Biological differences may also contribute to the racial disparity in ovarian cancer as the highest levels of mutations in micro-RNA (miRNA) genes, which can lead to dysregulation of miRNA processing or degradation, have been found among African American women [34]. Additionally, Black women with ovarian cancer are more likely to have a high expression of immune cells, which is associated with a favorable response to immunotherapy, [35-37] yet they are less likely to be enrolled in clinical trials involving immunotherapy [38]. Therefore, there are most likely clinical and biological factors that remain unaddressed, contributing to the racial disparities in the survival of advanced-stage ovarian cancer, regardless of treatment receipt. Our study has several strengths. First, it included a large sample size from the NCDB, which undergoes strict quality control measures to ensure high-quality standardized data [19]. Additionally, we were able to adjust for many potential confounders, including demographic, comorbidities, treatment, and tumor characteristics. Furthermore, we were able to make a composite measure of SES to evaluate its role in survival disparities. We comprehensively evaluated the effect modification by age, SES, CCI, and receipt of surgery and mortality. However, our study had limitations that need to be considered. First, the NCDB only records first-course treatments, meaning that patients who receive systemic therapy after their primary treatment were not able to be included in our study [19]. Second, NCDB reports systemic therapy as chemotherapy, hormone therapy, immunotherapy, or hematologic transplant and endocrine procedures; however, we did not examine hematologic transplant and endocrine procedures nor does the definition specify targeted therapy. Lastly, we were not able to evaluate ovarian cancer-specific death, as the database only records vital status and not the specific causes of death. Despite these limitations, our study allowed us to evaluate disparities in race/ethnicity and SES among women with advanced-stage ovarian cancer who have received systemic therapy while maintaining sufficient power. Conclusions This U.S. clinical registry study evaluated the racial/ethnic and socioeconomic disparities in survival among women with advanced-stage ovarian cancer who have received systemic treatment. Our findings revealed that Black women, compared to White women, experienced poor survival rates despite receiving systemic therapy. This Black-White disparity remains prevalent across all age groups, with and in low- and mid-SES backgrounds, aligning with existing literature. Therefore, we urge the development and implementation of multitargeted interventions and policies that address these disparities and strive to reduce and/or eliminate the racial disparity in overall survival among women with advanced-stage ovarian cancer. Declarations Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Competing interests The authors have no relevant financial or non-financial interests to disclose. Authors' contributions CW and SK performed the data analysis. TA designed the study and critically revised the manuscript. CW and SK drafted the manuscript. All authors reviewed the manuscript. The author(s) read and approved the final manuscript. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. References Siegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. 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Boffa DJ, Rosen JE, Mallin K, Loomis A, Gay G, Palis B, et al. Using the National Cancer Database for Outcomes Research: A Review. JAMA Oncol. 2017;3(12):1722-8. doi: 10.1001/jamaoncol.2016.6905. CTR Exam. 2020. Available at: https://www.ncra-usa.org/CTR/Certification-Exam . Surgeons ACo. National Cancer Database [2016 Patient User Files]. 2023. Salem ME, Puccini A, Trufan SJ, Sha W, Kadakia KC, Hartley ML, et al. Impact of Sociodemographic Disparities and Insurance Status on Survival of Patients with Early-Onset Colorectal Cancer. Oncologist. 2021;26(10):e1730-e41. doi: 10.1002/onco.13908. Huffman DL, Jayakrishnan TT, Shankar K, Peterson CE, Wegner RE. Disparities in ovarian cancer treatment and overall survival according to race: An update. Gynecol Oncol. 2021;162(3):674-8. doi: 10.1016/j.ygyno.2021.07.011. Hinchcliff E, Rauh-Hain JA, Clemmer JT, Diver E, Hall T, Stall J, et al. Racial disparities in survival in malignant germ cell tumors of the ovary. Gynecol Oncol. 2016;140(3):463-9. doi: 10.1016/j.ygyno.2016.01.006. Montes de Oca MK, Chen Q, Howell E, Wilson LE, Meernik C, Previs RA, et al. Healthcare Access Dimensions and Ovarian Cancer Survival: SEER-Medicare Analysis of the ORCHiD Study. JNCI Cancer Spectr. 2023. doi: 10.1093/jncics/pkad011. Amin SA, Collin LJ, Setoguchi S, Satagopan JM, Buckley de Meritens A, Bandera EV. Neoadjuvant Chemotherapy in Ovarian Cancer: Are There Racial Disparities in Use and Survival? Cancer Epidemiol Biomarkers Prev. 2023;32(2):175-82. doi: 10.1158/1055-9965.EPI-22-0758. Škof E, Merlo S, Pilko G, Kobal B. The role of neoadjuvant chemotherapy in patients with advanced (stage IIIC) epithelial ovarian cancer. Radiol Oncol. 2016;50(3):341-6. doi: 10.1515/raon-2016-0034. Park HK, Ruterbusch JJ, Cote ML. Recent Trends in Ovarian Cancer Incidence and Relative Survival in the United States by Race/Ethnicity and Histologic Subtypes. Cancer Epidemiol Biomarkers Prev. 2017;26(10):1511-8. doi: 10.1158/1055-9965.EPI-17-0290. The Hispanic paradox. Lancet. 2015;385(9981):1918. doi: 10.1016/S0140-6736(15)60945-X. Franzini L, Ribble JC, Keddie AM. Understanding the Hispanic paradox. Ethn Dis. 2001;11(3):496-518. Mullins MA, Ruterbusch JJ, Clarke P, Uppal S, Wallner LP, Cote ML. Trends and racial disparities in aggressive end-of-life care for a national sample of women with ovarian cancer. Cancer. 2021;127(13):2229-37. doi: 10.1002/cncr.33488. Harris HR, Guertin KA, Camacho TF, Johnson CE, Wu AH, Moorman PG, et al. Racial disparities in epithelial ovarian cancer survival: An examination of contributing factors in the Ovarian Cancer in Women of African Ancestry consortium. Int J Cancer. 2022;151(8):1228-39. doi: 10.1002/ijc.34141. Sakhuja S, Yun H, Pisu M, Akinyemiju T. Availability of healthcare resources and epithelial ovarian cancer stage of diagnosis and mortality among Blacks and Whites. J Ovarian Res. 2017;10(1):57. doi: 10.1186/s13048-017-0352-1. Asare A, Yao H, Lara OD, Wang Y, Zhang L, Sood AK. Race-associated molecular changes in gynecologic malignancies. Cancer Res Commun. 2022;2(2):99-109. doi: 10.1158/2767-9764.crc-21-0018. Mills AM, Peres LC, Meiss A, Ring KL, Modesitt SC, Abbott SE, et al. Targetable Immune Regulatory Molecule Expression in High-Grade Serous Ovarian Carcinomas in African American Women: A Study of PD-L1 and IDO in 112 Cases From the African American Cancer Epidemiology Study (AACES). Int J Gynecol Pathol. 2019;38(2):157-70. doi: 10.1097/PGP.0000000000000494. Wilson C, Soupir AC, Thapa R, Creed J, Nguyen J, Segura CM, et al. Tumor immune cell clustering and its association with survival in African American women with ovarian cancer. PLoS Comput Biol. 2022;18(3):e1009900. doi: 10.1371/journal.pcbi.1009900. Peres LC, Colin-Leitzinger C, Sinha S, Marks JR, Conejo-Garcia JR, Alberg AJ, et al. Racial Differences in the Tumor Immune Landscape and Survival of Women with High-Grade Serous Ovarian Carcinoma. Cancer Epidemiol Biomarkers Prev. 2022;31(5):1006-16. doi: 10.1158/1055-9965.EPI-21-1334. Wagar MK, Mojdehbakhsh RP, Godecker A, Rice LW, Barroilhet L. Racial and ethnic enrollment disparities in clinical trials of poly(ADP-ribose) polymerase inhibitors for gynecologic cancers. Gynecol Oncol. 2022;165(1):49-52. doi: 10.1016/j.ygyno.2022.01.032. Additional Declarations No competing interests reported. Supplementary Files SupplementaryInformationCancerCausesandControl.docx Cite Share Download PDF Status: Published Journal Publication published 24 Oct, 2023 Read the published version in Cancer Causes & Control → Version 1 posted Editorial decision: Major revision 06 Sep, 2023 Reviews received at journal 29 Aug, 2023 Reviewers agreed at journal 09 Aug, 2023 Reviewers invited by journal 09 Aug, 2023 Submission checks completed at journal 02 Aug, 2023 Editor assigned by journal 02 Aug, 2023 First submitted to journal 01 Aug, 2023 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-3225591","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":223462043,"identity":"5c3accc6-6e8c-46e3-bc17-6ca2fd014d96","order_by":0,"name":"Caretia J. 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Akinyemiju","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYJCCAwwMCTxgFpCSYzjABmIy41TOg67FmCgtILVwgcQGQlrs2bsTDzDUpMmYSyQf3fBA5nB63420xAcMFdaJDbhs4Tm74QDDsRweyxlpaTcSeA7nzryRdtiA4Uw6bi0SuUAtbBU8BjdyzMBaNtxIb5NgbDtMQMs/kJb8byAt6QY30tt/MP4joIWxLQdkCxtIS4LBjbRjDIwNeLScAfolsS+Nx+DMM5DD0g1nnnmWLJFwLN0Ylxb29t7NHz58S7Y3OJ787ObPHmt5vuNphh8+1FjL4tICBgkgQgBIMvYgixAE/AeAxA+ilI6CUTAKRsEIAwAukGPyCOpO1AAAAABJRU5ErkJggg==","orcid":"","institution":"Duke University School of Medicine","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Tomi","middleName":"F.","lastName":"Akinyemiju","suffix":""}],"badges":[],"createdAt":"2023-08-01 19:14:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3225591/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3225591/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10552-023-01810-y","type":"published","date":"2023-10-24T15:01:37+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":41244535,"identity":"08699151-1628-428b-bf33-bda2bfdde606","added_by":"auto","created_at":"2023-08-08 14:26:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":31345,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan Meir curve of Patients with Advanced-Stage Ovarian Cancer with Systemic Therapy Receipt by Race/Ethnicity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAbbreviations: NH non-Hispanic. PI Pacific Islander.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3225591/v1/2fca462641d74bfc32c5a9f4.png"},{"id":41244534,"identity":"8878bcc7-b1a5-42da-88fa-09b5130ad8f5","added_by":"auto","created_at":"2023-08-08 14:26:23","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":90436,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan Meir curves of Patients with Advanced-Stage Ovarian Cancer with Systemic Therapy by Socioeconomic Categories\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea. Low SES Group\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKaplan Meier curves for survival probability by race among the low socioeconomic group.\u003c/p\u003e\n\u003cp\u003eAbbreviations: NH indicates non-Hispanic.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb. Mid SES Group\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKaplan Meier curves for survival probability by race among the middle socioeconomic group.\u003c/p\u003e\n\u003cp\u003eNH indicates non-Hispanic.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ec. High SES Group\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKaplan Meier curves for survival probability by race among the high socioeconomic group. NH indicates non-Hispanic.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3225591/v1/83550221bd0e9d0b8222e455.png"},{"id":45453738,"identity":"f692aa43-80c9-4adf-8f68-18874e45671a","added_by":"auto","created_at":"2023-10-30 15:05:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":829341,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3225591/v1/a34699bc-eec2-43b2-bb0c-f3e5af73fb1b.pdf"},{"id":41244536,"identity":"8f38ed3b-2375-496f-9fa1-cdd7d9263652","added_by":"auto","created_at":"2023-08-08 14:26:23","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":39838,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformationCancerCausesandControl.docx","url":"https://assets-eu.researchsquare.com/files/rs-3225591/v1/10d8a075d92c89cc5c90586c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Racial and Socioeconomic Disparities in Survival Among Women with Advanced-stage Ovarian Cancer Who Received Systemic Therapy","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eOvarian cancer is the fifth leading cause of cancer-related deaths in women in the United States and is estimated to account for 13,270 deaths in 2023 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Efficient screening techniques or simple diagnostic tests for ovarian cancer are currently lacking [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Thus, most women with ovarian cancer are diagnosed at advanced stages, leading to poor 5-year survival rates: 49% for White women and 41% for Black women [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Given these dismal survival rates, new treatment regimes, including the use of primary systemic therapy, are emerging [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGuideline-adherent treatment for advanced-stage ovarian cancer usually involves cytoreductive surgery followed by adjuvant chemotherapy and sometimes radiotherapy [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, many women with advanced ovarian cancer have a high risk of surgical complications and recurrence, and primary systemic therapy has been used for this select group of women [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. For instance, neoadjuvant chemotherapy, such as carboplatin and paclitaxel, is recommended if there is a low likelihood of achieving optimal primary cytoreductive surgery for women diagnosed with stage III or IV ovarian cancer [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Immunotherapy, such as checkpoint inhibitors, has also shown promise for the treatment of recurrent ovarian cancer in recent randomized control trials [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite the widespread use of systemic therapy, there are racial disparities in survival of advanced-stage ovarian cancer. Several studies have found that Black women experience poorer survival of advanced-stage ovarian cancer compared to their White counterparts [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Socioeconomic and access-to-healthcare factors further widen the racial disparity in ovarian cancer [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Although the inadequate receipt of guideline-recommended treatment has been well documented for its contribution to the racial disparities in ovarian cancer survival, [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] the factors that contribute to these disparities among women who have received equivalent treatment are not well understood [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Therefore, there is a critical need to evaluate disparities in advanced-stage ovarian cancer survival among women who have received systemic therapy in the United States (U.S.).\u003c/p\u003e \u003cp\u003eIn this study, we utilized data from the National Cancer Database (NCDB) to evaluate the association between race/ethnicity and risk of all-cause death among women with advanced-stage ovarian cancer who have equal utilization of systemic therapy. We also investigated whether demographic characteristics, comorbidity level, and receipt of surgery modified those associations.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Source\u003c/h2\u003e \u003cp\u003eThe data for this study were obtained from the 2016 NCDB Participant User Files (PUF); the NCDB is a joint program of the American College of Surgeons Commission on Cancer and the American Cancer Society [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The NCDB includes over 70% of all patients with newly diagnosed cancers in the United States annually [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The data are abstracted by certified tumor registrars as part of the National Cancer Registrars Association [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The data includes patient demographics, tumor characteristics, clinical and pathological TNM stage classification, treatments, and survival data. This analysis was considered exempt by the University of Florida Institutional Review Board as the data used in this study were obtained from a de-identified NCDB database.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy Cohort\u003c/h2\u003e \u003cp\u003eThe study cohort included women that met the following criteria: (1) stage III-IV ovarian cancer (International Classification of Diseases for Oncology, Third Edition topography code- C56.9), (2) women diagnosed between 2004 and 2015, (3) received systemic therapy (defined as chemotherapy, immunotherapy, and/or hormone therapy by the NCDB), and (4) had no missing values for sociodemographic characteristics, treatment receipt, tumor characteristics, and time elapsed between the date of diagnosis and the date of last contact or death.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eExposure, Outcome, and Covariates\u003c/h2\u003e \u003cp\u003eThe main exposure for this data analysis was race/ethnicity, which was derived by combining the variable for race with the variable for Spanish origin. The data were categorized into the following groups: non-Hispanic White (NH-White), non-Hispanic Black (NH-Black), Hispanic, non-Hispanic Asian/Pacific Islander (NH-Asian/PI), and Other. Hereafter, we exclude the NH prefix when referencing racial groups. Our outcome of interest was all-cause mortality, with the survival time measured (in months) from the date of diagnosis to death or last contact (whichever occurred first). The sociodemographic covariates included in this study were age at diagnosis (\u0026lt;\u0026thinsp;65 and \u0026ge;\u0026thinsp;65 years), area-level educational level as the percentage of individuals in the patient\u0026rsquo;s zip code without a high school degree categorized into quartiles based on all United States (US) zip codes (21% or more, 13%-20.9%, 7%-12.9%, and \u0026lt;\u0026thinsp;7%), and median household income was estimated by zip code of the patient recorded at the time of diagnosis and categorized as quartiles (\u0026lt; \u003cspan\u003e$\u003c/span\u003e38,000, 2: \u003cspan\u003e$\u003c/span\u003e38,000 -\u003cspan\u003e$\u003c/span\u003e47,999, \u003cspan\u003e$\u003c/span\u003e48,000-\u003cspan\u003e$\u003c/span\u003e62,999, and \u0026ge; \u003cspan\u003e$\u003c/span\u003e63,000) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The quartile classifications for area-level educational level and median household income were combined to form a composite score for SES groups: low (2\u0026ndash;3), mid (4\u0026ndash;7), and high (8) \u003cb\u003e(Supplementary Table\u0026nbsp;1)\u003c/b\u003e [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Healthcare access factors included primary insurance (no insurance, private insurance/managed care, and government insurance) and cancer treatment facility type (academic and non-academic). Treatment receipt was defined as receipt of chemotherapy (yes vs. no), receipt of immunotherapy (yes vs. no), receipt of hormone therapy (yes vs. no), receipt of surgery (yes vs. no), and receipt of radiation therapy (yes vs. no). Charlson-Deyo comorbidity index (CCI) score was categorized as no comorbidities and one or more comorbidities. Additional covariates included histology (serous carcinoma, mucinous carcinoma, endometrioid carcinoma, and other) and tumor grade (low, intermediate, high, and unknown) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Covariates were selected based on \u003cem\u003ea priori\u003c/em\u003e knowledge regarding their associations with the exposure and outcome of interest.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eThe distributions of study covariates were summarized in the overall sample and by race/ethnicity. Kaplan-Meier curves were used to visualize the probability of survival by race/ethnicity overall and by each SES group, and the differences in survival probabilities were examined by the log-rank test. Number of deaths and mortality rates (reported as the number of deaths per 10 person-years) were summarized for the overall study population and according to race/ethnicity and further by stratified age, CCI, SES, and surgery receipt. The association between racial/ethnic groups (reference: White) and risk of all-cause mortality was examined in three multivariable Cox proportional hazards models. The first model was adjusted for age and SES; the second model was additionally adjusted for primary insurance and cancer facility type; and the final model was additionally adjusted for CCI, treatment receipt, histology, and grade. In the final model, all covariates (age, SES, facility type, primary insurance, radiation receipt, surgery receipt, cancer histology, grade, and CCI) violated the proportional hazard assumptions. Therefore, we created and controlled for the interaction terms between the covariates and follow-up time, and no violation was observed afterward.\u003c/p\u003e \u003cp\u003eSubgroup analyses were conducted by age at diagnosis, CCI, and SES. The interaction term between race/ethnicity and age at diagnosis, CCI, or SES was added into the multivariable Cox proportional hazards models. The significance of interaction tests was determined by the Wald test. All analyses were performed using SAS 9.4 (Cary, NC) and R studio V 4.1.1. All statistical tests were 2-sided, and a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicated statistical significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eThe final study sample included 53,367 women diagnosed with advanced-stage ovarian cancer and had received systemic therapy \u003cstrong\u003e(Supplementary Fig.\u0026nbsp;1)\u003c/strong\u003e. Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e presents the demographic and clinical characteristics of women diagnosed with advanced-stage (III-IV) primary ovarian cancer from 2004\u0026ndash;2015. Approximately 50% of the women were \u0026ge;\u0026thinsp;65 years old at the time of diagnosis, the majority did not have any comorbidities (78.0%), and most were White (82.0%), followed by Black (8.7%), Hispanic (5.7%), Asian/Pacific Islander (2.7%), and Other (0.9%). Most women had government insurance (55.8%) and lived in regions classified as the mid-SES group (61.1%). White women (22.3%) made up the largest proportion of women living in regions classified as the high-SES group. Women who identified as Black, Hispanic, Asian/PI, or Other were more likely to be younger (\u0026lt;\u0026thinsp;65 years) at diagnosis and more likely to be treated at academic facilities. Additionally, Black women were less likely to receive surgery (64.0% Black vs 73.9% White) and more likely to have rarer histological types of ovarian cancer (40.2% Black vs 34.8% White). The median time from diagnosis to last follow-up or death was 21.7 months for White women and 19.5 months for Black women \u003cstrong\u003e(\u003c/strong\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cstrong\u003e)\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSociodemographic and Cancer Characteristics for Advanced-Stage (III-IV) Ovarian Cancer Patients Who Received Systemic Therapy\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eOverall (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eRace n (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;53367\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNH-White\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;43760\u003c/p\u003e\n \u003cp\u003e(82.0)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNH-Black\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;4634\u003c/p\u003e\n \u003cp\u003e(8.7)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHispanic\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;3062\u003c/p\u003e\n \u003cp\u003e(5.7)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNH-Asian/PI\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;1449\u003c/p\u003e\n \u003cp\u003e(2.7)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;462\u003c/p\u003e\n \u003cp\u003e(0.9)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e25411 (47.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e19855 (45.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2536 (54.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1831 (59.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e911 (62.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e278 (60.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e27956 (52.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e23905 (54.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2098 (45.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1231 (40.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e538 (37.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e184 (39.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"11\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharlson-Deyo Comorbidity score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e41621 (78.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e34528 (78.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3198 (69.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2364 (77.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1185 (81.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e346 (74.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e11746 (22.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e9232 (21.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1436 (31.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e698 (22.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e264 (18.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e116 (25.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"11\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrimary insurance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot insured\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1977 (3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1182 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e278 (6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e378 (12.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e109 (7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 (6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrivate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e21608 (40.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e17942 (41.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1682 (36.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1081 (35.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e726 (50.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e177 (38.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGovernment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e29782 (55.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e24636 (56.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2674 (57.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1603 (52.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e614 (42.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e255 (55.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"11\"\u003e\n \u003cp\u003e\u003cstrong\u003eSES Composite Groups\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow SES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e9962 (18.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e6301 (14.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2153 (46.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1183 (38.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e191 (13.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e134 (29.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMid SES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e32588 (61.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e27694 (63.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2166 (46.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1606 (52.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e873 (60.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e249 (53.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh SES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e10817 (20.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e9765 (22.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e315 (6.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e273 (8.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e385 (26.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79 (17.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"11\"\u003e\n \u003cp\u003e\u003cstrong\u003eCancer facility type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAcademic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e21916 (41.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e17135 (39.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2273 (49.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1516 (49.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e753 (52.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e239 (51.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot academic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e31451 (58.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e26625 (60.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2361 (51.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1546 (50.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e696 (48.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e223 (48.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eChemotherapy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e53171 (99.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e43591 (99.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4620 (99.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3057 (99.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1446 (99.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e457 (98.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e196 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e169 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e5 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"11\"\u003e\n \u003cp\u003e\u003cstrong\u003eImmunotherapy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e804 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e671 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64 (1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e48 (1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e18 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e52563 (98.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e43089 (98.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4570 (98.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3014 (98.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1431 (98.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e459 (99.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"11\"\u003e\n \u003cp\u003e\u003cstrong\u003eHormone therapy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e834 (1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e677 (1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72 (1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e51 (1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e23 (1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e52533 (98.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e43083 (98.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4562 (98.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3011 (98.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1426 (98.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e451 (97.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"11\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurgery Receipt\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e39042 (73.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e32323 (73.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2966 (64.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2261 (73.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1147 (79.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e345 (74.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e14325 (26.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e11437 (26.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1668 (36.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e801 (26.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e302 (20.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e117 (25.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"11\"\u003e\n \u003cp\u003e\u003cstrong\u003eRadiation Receipt\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e682 (1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e547 (1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e40 (1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e23 (1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e52685 (98.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e43213 (98.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4564 (98.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3022 (98.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1426 (98.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e460 (99.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrade\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e956 (1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e792 (1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74 (1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e57 (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e27 (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntermediate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3327 (6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2781 (6.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e263 (5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e175 (5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e87 (6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21 (4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e29594 (55.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e24424 (55.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2346 (50.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1681 (54.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e856 (59.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e287 (62.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e19490 (36.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e15763 (36.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1951 (42.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1149 (37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e479 (33.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e148 (32.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHistology\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSerous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e32359 (60.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e26859 (61.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2561 (55.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1799 (58.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e862 (59.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e278 (60.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMucinous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e849 (1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e637 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e114 (2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e69 (2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e22 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEndometrioid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1308 (2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1054 (2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98 (2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e100 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e46 (3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e18851 (35.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e15210 (34.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1861 (40.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1094 (35.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e519 (35.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e167 (36.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eStage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e30278 (56.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e25179 (57.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2346 (50.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1669 (54.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e822 (56.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e262 (56.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e23089 (43.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e18581 (42.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2288 (49.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1393 (45.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e627 (43.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200 (43.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eColumn percentages were reported in the table.\u003c/p\u003e\n\u003cp\u003eOther race includes Hawaiian, Micronesian, Chamorran, Guamanian, Polynesian, Tahitian, Samoan, Tongan, Melanesian, Fiji Islander, and New Guinean.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAbbreviations: NH, Non-Hispanic; PI, Pacific Islander; AI, American Indian; AN, Alaskan Native; NOS, Not Otherwise Specified. SES, Socioeconomic status (education and income).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eKaplan-Meier curves by race/ethnicity \u003cstrong\u003e(Figure 1)\u003c/strong\u003e indicated the lowest survival probabilities among Black women (log-rank\u0026nbsp;\u003cem\u003eP\u003c/em\u003e\u0026lt;0.0001).\u003c/p\u003e\n\u003cp\u003eFurthermore, Kaplan-Meier curves, stratified by SES, show lower survival probabilities among Black women from both low- and mid-SES groups but not the high-SES group \u003cstrong\u003e(Figure 2).\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The death rate for Black women with ovarian cancer was 3.06 deaths/10 person-years: 1.19 times the death rate for White women \u003cstrong\u003e(Table 2)\u003c/strong\u003e. In\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ethe Cox proportional hazards regression models assessing all-cause mortality, Black women had a higher risk of death compared to White women across all three adjusted models [fully adjusted hazard ratio (aHR): 1.12; 95% CI: 1.07,1.18)], while Hispanic women had a lower risk of death compared to White women (aHR: 0.87; 95% CI: 0.80,0.95) \u003cstrong\u003e(Table 2).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTests of interaction for age, and CCI were non-significant across all the fully adjusted models. The tests of interaction for surgery receipt were not significant for the first model (\u003cem\u003eP\u003c/em\u003e-interaction=0.07) neither for the fully adjusted model (\u003cem\u003eP\u003c/em\u003e-interaction=0.20). However, in fully adjusted models stratified by age, a significant association persisted among Black women (\u0026lt;65 years, aHR: 1.13; 95% CI: 1.05-1.22;\u0026nbsp;\u0026ge;65 years aHR: 1.09;\u0026nbsp;95% CI: 1.02-1.17), while Hispanic women continued to have a lower risk of death relative to White women (\u0026lt;65 years, aHR: 0.87; 95% CI: 0.77-0.99;\u0026nbsp;\u0026ge;65 years aHR: 0.87;\u0026nbsp;95% CI: 0.78-0.98), \u003cstrong\u003e(Table 2).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;In fully adjusted Cox proportional hazards models stratified by SES groups \u003cstrong\u003e(Table 2)\u003c/strong\u003e, Black women experienced increased risk of mortality compared to White women in the low-SES group (aHR:1.12; 95% CI:1.03-1.22)\u0026nbsp;and mid-SES group (aHR:\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e1.13; 95% CI:1.05-1.21); while Hispanic women had lower risk of mortality compared to White women in the both the low- and mid-SES groups. Tests of interaction for SES and race/ethnicity were non-significant across all models.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Cox Proportional Hazards Models of All-cause Death, Stratified by Race, Ethnicity, Age, Comorbidity Score, and Socioeconomic status.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"762\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.385826771653543%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.598425196850394%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.811023622047244%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.960629921259843%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.748031496062993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.748031496062993%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR and 95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.960629921259843%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.7874015748031497%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.385826771653543%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace/Ethnicity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.598425196850394%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003en/N\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.811023622047244%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePerson-years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.960629921259843%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDeath Rate/10 person-years\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.748031496062993%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1 *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.748031496062993%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u0026dagger;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.960629921259843%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 3\u0026Dagger;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.7874015748031497%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.385826771653543%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.598425196850394%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.811023622047244%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.960629921259843%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.748031496062993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.748031496062993%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.960629921259843%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.7874015748031497%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.385826771653543%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eNH-White\u003c/p\u003e\n \u003cp\u003eNH-Black\u003c/p\u003e\n \u003cp\u003eHispanic\u003c/p\u003e\n \u003cp\u003eNH-Asian/PI\u003c/p\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.598425196850394%\" valign=\"top\"\u003e\n \u003cp\u003e30248/43760\u003c/p\u003e\n \u003cp\u003e3295/4634\u003c/p\u003e\n \u003cp\u003e1771/3062\u003c/p\u003e\n \u003cp\u003e776/1449\u003c/p\u003e\n \u003cp\u003e279/462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.811023622047244%\" valign=\"top\"\u003e\n \u003cp\u003e117930\u003c/p\u003e\n \u003cp\u003e10759\u003c/p\u003e\n \u003cp\u003e8515\u003c/p\u003e\n \u003cp\u003e4049\u003c/p\u003e\n \u003cp\u003e1260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.960629921259843%\" valign=\"top\"\u003e\n \u003cp\u003e2.57 (2.54, 2.59)\u003c/p\u003e\n \u003cp\u003e3.06 (2.96, 3.17)\u003c/p\u003e\n \u003cp\u003e2.08 (1.99, 2.18)\u003c/p\u003e\n \u003cp\u003e1.92 (1.79, 2.06)\u003c/p\u003e\n \u003cp\u003e2.21 (1.97, 2.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.748031496062993%\" valign=\"top\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1.29 (1.22, 1.35)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.98 (0.90, 1.06)\u003c/p\u003e\n \u003cp\u003e1.04 (0.92, 1.17)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1.33 (1.09, 1.62)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.748031496062993%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1.30 (1.24, 1.37)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.99 (0.91, 1.07)\u003c/p\u003e\n \u003cp\u003e1.08 (0.95, 1.20)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1.40 (1.15, 1.70)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.960629921259843%\" valign=\"top\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1.12 (1.07. 1.18)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.87 (0.80, 0.95)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.94 (0.83, 1.07)\u003c/p\u003e\n \u003cp\u003e1.11 (0.91, 1.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.7874015748031497%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.385826771653543%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u0026lt;65\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.598425196850394%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.811023622047244%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.960629921259843%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.748031496062993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.748031496062993%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.960629921259843%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.7874015748031497%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.385826771653543%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eNH-White\u003c/p\u003e\n \u003cp\u003eNH-Black\u003c/p\u003e\n \u003cp\u003eHispanic\u003c/p\u003e\n \u003cp\u003eNH-Asian/PI\u003c/p\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.598425196850394%\" valign=\"top\"\u003e\n \u003cp\u003e12445/19855\u003c/p\u003e\n \u003cp\u003e1684/2536\u003c/p\u003e\n \u003cp\u003e961/1,831\u003c/p\u003e\n \u003cp\u003e450/911\u003c/p\u003e\n \u003cp\u003e154/278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.811023622047244%\" valign=\"top\"\u003e\n \u003cp\u003e61132\u003c/p\u003e\n \u003cp\u003e6556\u003c/p\u003e\n \u003cp\u003e5565\u003c/p\u003e\n \u003cp\u003e2702\u003c/p\u003e\n \u003cp\u003e853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.960629921259843%\" valign=\"top\"\u003e\n \u003cp\u003e2.04 (2.00, 2.07)\u003c/p\u003e\n \u003cp\u003e2.57 (2.45, 2.69)\u003c/p\u003e\n \u003cp\u003e1.73 (1.62, 1.84)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.67 (1.52, 1.83)\u003c/p\u003e\n \u003cp\u003e1.81 (1.54, 2.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.748031496062993%\" valign=\"top\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1.31 (1.22, 1.41)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.98 (0.86, 1.10)\u003c/p\u003e\n \u003cp\u003e1.09 (0.91, 1.31)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1.37 (1.02, 1.84)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.748031496062993%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1.27 (1.18, 1.36)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.94 (0.83, 1.06)\u003c/p\u003e\n \u003cp\u003e1.11 (0.93, 1.33)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1.40 (1.04, 1.88)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.960629921259843%\" valign=\"top\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1.13 (1.05, 1.22)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.87 (0.77, 0.99)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e1.02 (0.84, 1.22)\u003c/p\u003e\n \u003cp\u003e1.20 (0.88, 1.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.7874015748031497%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.385826771653543%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u0026ge;65\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.598425196850394%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.811023622047244%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.960629921259843%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.748031496062993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.748031496062993%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.960629921259843%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.7874015748031497%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.385826771653543%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eNH-White\u003c/p\u003e\n \u003cp\u003eNH-Black\u003c/p\u003e\n \u003cp\u003eHispanic\u003c/p\u003e\n \u003cp\u003eNH-Asian/PI\u003c/p\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.598425196850394%\" valign=\"top\"\u003e\n \u003cp\u003e17803/23905\u003c/p\u003e\n \u003cp\u003e1611/2098\u003c/p\u003e\n \u003cp\u003e810/1231\u003c/p\u003e\n \u003cp\u003e326/538\u003c/p\u003e\n \u003cp\u003e125/184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.811023622047244%\" valign=\"top\"\u003e\n \u003cp\u003e56798\u003c/p\u003e\n \u003cp\u003e4203\u003c/p\u003e\n \u003cp\u003e2950\u003c/p\u003e\n \u003cp\u003e1348\u003c/p\u003e\n \u003cp\u003e407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.960629921259843%\" valign=\"top\"\u003e\n \u003cp\u003e3.13 (3.09, 3.18)\u003c/p\u003e\n \u003cp\u003e3.83 (3.65, 4.02)\u003c/p\u003e\n \u003cp\u003e2.75 (2.56, 2.94)\u003c/p\u003e\n \u003cp\u003e2.42 (2.17, 2.69)\u003c/p\u003e\n \u003cp\u003e3.07 (2.57, 3.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.748031496062993%\" valign=\"top\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1.23 (1.15, 1.31)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.94 (0.84, 1.05)\u003c/p\u003e\n \u003cp\u003e0.90 (0.76, 1.07)\u003c/p\u003e\n \u003cp\u003e1.21 (0.92, 1.60)\u003c/p\u003e\n \u003cp\u003eP-interaction=0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.748031496062993%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1.27 (1.18, 1.35)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.97 (0.87, 1.09)\u003c/p\u003e\n \u003cp\u003e0.95 (0.80, 1.13)\u003c/p\u003e\n \u003cp\u003e1.27 (0.96, 1.67)\u003c/p\u003e\n \u003cp\u003eP-interaction= 0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.960629921259843%\" valign=\"top\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1.09 (1.02, 1.17)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.87 (0.78, 0.98)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.85 (0.71, 1.01)\u003c/p\u003e\n \u003cp\u003e1.05 (0.79, 1.38)\u003c/p\u003e\n \u003cp\u003eP-interaction=0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.7874015748031497%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"99.21259842519684%\" colspan=\"9\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eComorbidity=0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.7874015748031497%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.385826771653543%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eNH-White\u003c/p\u003e\n \u003cp\u003eNH-Black\u003c/p\u003e\n \u003cp\u003eHispanic\u003c/p\u003e\n \u003cp\u003eNH-Asian/PI\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.598425196850394%\" valign=\"top\"\u003e\n \u003cp\u003e23384/34528\u003c/p\u003e\n \u003cp\u003e2226/3198\u003c/p\u003e\n \u003cp\u003e1323/2364\u003c/p\u003e\n \u003cp\u003e627/1185\u003c/p\u003e\n \u003cp\u003e198/346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.811023622047244%\" valign=\"top\"\u003e\n \u003cp\u003e96227\u003c/p\u003e\n \u003cp\u003e7798\u003c/p\u003e\n \u003cp\u003e6844\u003c/p\u003e\n \u003cp\u003e3366\u003c/p\u003e\n \u003cp\u003e1013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.960629921259843%\" valign=\"top\"\u003e\n \u003cp\u003e2.43 (2.40, 2.46)\u003c/p\u003e\n \u003cp\u003e2.86 (2.74, 2.98)\u003c/p\u003e\n \u003cp\u003e1.93 (1.83, 2.04)\u003c/p\u003e\n \u003cp\u003e1.86 (1.72, 2.01)\u003c/p\u003e\n \u003cp\u003e1.96 (1.70, 2.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.748031496062993%\" valign=\"top\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1.27 (1.20, 1.35)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.96 (0.87, 1.06)\u003c/p\u003e\n \u003cp\u003e1.03 (0.89, 1.19)\u003c/p\u003e\n \u003cp\u003e1.23 (0.97, 1.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.748031496062993%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1.28 (1.20, 1.35)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.96 (0.87, 1.06)\u003c/p\u003e\n \u003cp\u003e1.08 (0.93, 1.25)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1.29 (1.02, 1.64)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.960629921259843%\" valign=\"top\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1.13 (1.07, 1.20)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.87 (0.79, 0.96)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.95 (0.82, 1.11)\u003c/p\u003e\n \u003cp\u003e1.06 (0.83, 1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.7874015748031497%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"99.21259842519684%\" colspan=\"9\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eComorbidity \u0026ge;1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.7874015748031497%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.385826771653543%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eNH-White\u003c/p\u003e\n \u003cp\u003eNH-Black\u003c/p\u003e\n \u003cp\u003eHispanic\u003c/p\u003e\n \u003cp\u003eNH-Asian/PI\u003c/p\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.598425196850394%\" valign=\"top\"\u003e\n \u003cp\u003e6864/9232\u003c/p\u003e\n \u003cp\u003e1069/1436\u003c/p\u003e\n \u003cp\u003e448/698\u003c/p\u003e\n \u003cp\u003e149/264\u003c/p\u003e\n \u003cp\u003e81/116\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.811023622047244%\" valign=\"top\"\u003e\n \u003cp\u003e21703\u003c/p\u003e\n \u003cp\u003e2961\u003c/p\u003e\n \u003cp\u003e1671\u003c/p\u003e\n \u003cp\u003e683\u003c/p\u003e\n \u003cp\u003e247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.960629921259843%\" valign=\"top\"\u003e\n \u003cp\u003e3.16 (3.09, 3.24)\u003c/p\u003e\n \u003cp\u003e3.61 (3.40, 3.83)\u003c/p\u003e\n \u003cp\u003e2.68 (2.44, 2.94)\u003c/p\u003e\n \u003cp\u003e2.18 (1.85, 2.55)\u003c/p\u003e\n \u003cp\u003e3.28 (2.62, 4.06)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.748031496062993%\" valign=\"top\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1.17 (1.07, 1.27)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.91 (0.79, 1.07)\u003c/p\u003e\n \u003cp\u003e0.82 (0.64, 1.05)\u003c/p\u003e\n \u003cp\u003e1.27 (0.89, 1.81)\u003c/p\u003e\n \u003cp\u003eP-interaction=0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.748031496062993%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1.19 (1.09, 1.30)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.93 (0.80, 1.08)\u003c/p\u003e\n \u003cp\u003e0.87 (0.69, 1.11)\u003c/p\u003e\n \u003cp\u003e1.34 (0.94, 1.92)\u003c/p\u003e\n \u003cp\u003eP-interaction=0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.960629921259843%\" valign=\"top\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003cp\u003e1.08 (0.99, 1.18)\u003c/p\u003e\n \u003cp\u003e0.86 (0.74, 1.00)\u003c/p\u003e\n \u003cp\u003e0.85 (0.66, 1.084)\u003c/p\u003e\n \u003cp\u003e1.23 (0.85, 1.76)\u003c/p\u003e\n \u003cp\u003eP-interaction=0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.7874015748031497%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.385826771653543%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow SES\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.598425196850394%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.811023622047244%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.960629921259843%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.748031496062993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.748031496062993%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.960629921259843%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.7874015748031497%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.385826771653543%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eNH-White\u003c/p\u003e\n \u003cp\u003eNH-Black\u003c/p\u003e\n \u003cp\u003eHispanic\u003c/p\u003e\n \u003cp\u003eNH-Asian/PI\u003c/p\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.598425196850394%\" valign=\"top\"\u003e\n \u003cp\u003e4580/6301\u003c/p\u003e\n \u003cp\u003e1593/2153\u003c/p\u003e\n \u003cp\u003e702/1,183\u003c/p\u003e\n \u003cp\u003e101/191\u003c/p\u003e\n \u003cp\u003e91/134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.811023622047244%\" valign=\"top\"\u003e\n \u003cp\u003e16071\u003c/p\u003e\n \u003cp\u003e4756\u003c/p\u003e\n \u003cp\u003e3260\u003c/p\u003e\n \u003cp\u003e548\u003c/p\u003e\n \u003cp\u003e341\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.960629921259843%\" valign=\"top\"\u003e\n \u003cp\u003e2.85 (2.77, 2.93)\u003c/p\u003e\n \u003cp\u003e3.35 (3.19, 3.52)\u003c/p\u003e\n \u003cp\u003e2.15 (2.00, 2.32)\u003c/p\u003e\n \u003cp\u003e1.84 (1.51, 2.23)\u003c/p\u003e\n \u003cp\u003e2.70 (2.16, 3.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.748031496062993%\" valign=\"top\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1.32 (1.21, 1.43)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.95 (0.82, 1.10)\u003c/p\u003e\n \u003cp\u003e0.91 (0.70, 1.20)\u003c/p\u003e\n \u003cp\u003e1.42 (1.00, 2.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.748031496062993%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1.32 (1.22, 1.44)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.96 (0.83, 1.11)\u003c/p\u003e\n \u003cp\u003e0.94 (0.71, 1.23)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1.48 (1.04, 2.10)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.748031496062993%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1.12 (1.03, 1.22)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.83 (0.71, 0.96)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.81 (0.62, 1.08)\u003c/p\u003e\n \u003cp\u003e1.10 (0.76, 1.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.385826771653543%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMid SES\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.598425196850394%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.811023622047244%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.960629921259843%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.748031496062993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.58005249343832%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.916010498687665%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.385826771653543%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eNH-White\u003c/p\u003e\n \u003cp\u003eNH-Black\u003c/p\u003e\n \u003cp\u003eHispanic\u003c/p\u003e\n \u003cp\u003eNH-Asian/PI\u003c/p\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.598425196850394%\" valign=\"top\"\u003e\n \u003cp\u003e19287/27694\u003c/p\u003e\n \u003cp\u003e1504/2166\u003c/p\u003e\n \u003cp\u003e914/1606\u003c/p\u003e\n \u003cp\u003e484/873\u003c/p\u003e\n \u003cp\u003e149/249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.811023622047244%\" valign=\"top\"\u003e\n \u003cp\u003e74165\u003c/p\u003e\n \u003cp\u003e5132\u003c/p\u003e\n \u003cp\u003e4452\u003c/p\u003e\n \u003cp\u003e2373\u003c/p\u003e\n \u003cp\u003e701\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.960629921259843%\" valign=\"top\"\u003e\n \u003cp\u003e2.60 (2.56, 2.64)\u003c/p\u003e\n \u003cp\u003e2.93 (2.79, 3.08)\u003c/p\u003e\n \u003cp\u003e2.05 (1.92, 2.19)\u003c/p\u003e\n \u003cp\u003e2.04 (1.86, 2.23)\u003c/p\u003e\n \u003cp\u003e2.13 (1.80, 2.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.748031496062993%\" valign=\"top\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1.28 (1.19, 1.37)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.98 (0.88, 1.10)\u003c/p\u003e\n \u003cp\u003e1.04 (0.89, 1.22)\u003c/p\u003e\n \u003cp\u003e1.25 (0.95, 1.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.748031496062993%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1.29 (1.21, 1.38)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.98 (0.88, 1.10)\u003c/p\u003e\n \u003cp\u003e1.08 (0.92, 1.27)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1.32 (1.01, 1.73)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.748031496062993%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1.13 (1.05, 1.21)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.88 (0.79, 0.99)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.94 (0.79, 1.10)\u003c/p\u003e\n \u003cp\u003e1.08 (0.81, 1.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.385826771653543%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh SES\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.598425196850394%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.811023622047244%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.960629921259843%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.748031496062993%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.748031496062993%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.748031496062993%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.385826771653543%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eNH-White\u003c/p\u003e\n \u003cp\u003eNH-Black\u003c/p\u003e\n \u003cp\u003eHispanic\u003c/p\u003e\n \u003cp\u003eNH-Asian/PI\u003c/p\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.598425196850394%\" valign=\"top\"\u003e\n \u003cp\u003e6381/9765\u003c/p\u003e\n \u003cp\u003e198/315\u003c/p\u003e\n \u003cp\u003e155/273\u003c/p\u003e\n \u003cp\u003e191/385\u003c/p\u003e\n \u003cp\u003e39/79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.811023622047244%\" valign=\"top\"\u003e\n \u003cp\u003e27694\u003c/p\u003e\n \u003cp\u003e870\u003c/p\u003e\n \u003cp\u003e802\u003c/p\u003e\n \u003cp\u003e1128\u003c/p\u003e\n \u003cp\u003e218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.960629921259843%\" valign=\"top\"\u003e\n \u003cp\u003e2.30 (2.25, 2.36)\u003c/p\u003e\n \u003cp\u003e2.28 (1.98, 2.61)\u003c/p\u003e\n \u003cp\u003e1.93 (1.65, 2.26)\u003c/p\u003e\n \u003cp\u003e1.69 (1.47, 1.95)\u003c/p\u003e\n \u003cp\u003e1.79 (1.29, 2.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.748031496062993%\" valign=\"top\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003cp\u003e1.13 (0.96, 1.34)\u003c/p\u003e\n \u003cp\u003e1.04 (0.82, 1.31)\u003c/p\u003e\n \u003cp\u003e1.04 (0.77, 1.39)\u003c/p\u003e\n \u003cp\u003e1.28 (0.76, 2.16)\u003c/p\u003e\n \u003cp\u003eP-interaction= 0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.748031496062993%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003cp\u003e1.13 (0.96, 1.33)\u003c/p\u003e\n \u003cp\u003e1.02 (0.81, 1.30)\u003c/p\u003e\n \u003cp\u003e1.07 (0.80, 1.44)\u003c/p\u003e\n \u003cp\u003e1.30 (0.77, 2.19)\u003c/p\u003e\n \u003cp\u003eP-interaction= 0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.748031496062993%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003cp\u003e1.07 (0.90, 1.26)\u003c/p\u003e\n \u003cp\u003e0.97 (0.77, 1.22)\u003c/p\u003e\n \u003cp\u003e1.05 (0.78, 1.41)\u003c/p\u003e\n \u003cp\u003e1.18 (0.71, 1.97)\u003c/p\u003e\n \u003cp\u003eP-interaction= 0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eBold value indicates P\u0026lt;0.05.\u003c/p\u003e\n\u003cp\u003e*Adjusted for age (not included in models stratified by age) and socioeconomic status.\u003c/p\u003e\n\u003cp\u003e\u0026dagger;In addition, adjusted for insurance, and academic facility type.\u003c/p\u003e\n\u003cp\u003e\u0026Dagger;In addition, adjusted for radiation receipt, surgery receipt, cancer histology, grade, and Charlson/Deyo comorbidity index score (not included in models stratified by Charlson/Deyo score).\u003c/p\u003e\n\u003cp\u003eCI indicates confidence interval; HR, hazard ratio; n, number of deaths; N, total number of individuals.\u003c/p\u003e\n\u003cp\u003eDeath rate calculated as number of deaths divided by person-years (reported as cases/10-person years).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the present analysis of women diagnosed with advanced-stage ovarian cancer who received systemic therapy, we observed that the risk of all-cause mortality among Black women was 12% higher than among White women. The increased risk of all-cause mortality observed in Black women spans all age groups and encompasses the low and mid SES categories, further emphasizing the disparity between Black and White women. Conversely, Hispanic women had a lower overall risk of all-cause mortality, across all models, compared to White women. However, there was no evidence of effect modification between race/ethnicity, age, CCI, surgery, and SES groups. These results emphasize the need to\u0026nbsp;address racial disparities in the survival of advanced-stage ovarian cancer, as racial disparities persist even among women who have all received systemic treatment.\u003c/p\u003e\n\u003cp\u003eOur results are consistent with prior research suggesting that racial disparities in ovarian cancer survival persist, even when all women received systemic treatment [18, 24, 25]. A cohort study among members of Kaiser Permanente Northern California found that Black women experienced poor survival rates compared to White women, even when both groups had equal access to care and received the same systemic therapy (specifically adjuvant first-line therapy of Carboplatin and paclitaxel) [18]. The study also reported that Black women were more likely to have rare histological types of ovarian cancer, similar to the findings of our study (grouped in the \u0026ldquo;Other\u0026rdquo; category). \u0026nbsp;However, even among more rare histological types, such as malignant ovarian germ cell tumors, poorer survival was still found among Black women compared to their White counterparts, despite having similar adjuvant treatment patterns (including surgery, radiation, and chemotherapy) [24]. Another study using NCDB data reported that Black women were more likely to receive neoadjuvant chemotherapy before surgery, as opposed to primary surgery plus adjuvant chemotherapy, due to extensive tumor burden or prediction of poor surgical performance, yet they still experienced lower survival compared to their White counterparts [26, 27].\u003csup\u003e\u0026nbsp;\u003c/sup\u003eOur study\u0026rsquo;s findings support this conclusion, as we found that the disparity between Black and White persisted even among women who only received primary surgery. However, our study adds to the existing literature as we found that the racial disparity in advanced-stage ovarian cancer persists despite receipt of chemotherapy, hormonal therapy, and/or immunotherapy. Our study also found that Black women in the low- and mid-SES groups, but not the high SES groups, exhibited poorer survival rates compared to all other racial/ethnic groups. However, Hispanic women within the same SES groups demonstrated better survival rates compared to White women. Similarly, Park et al. found that Black women had poorer survival rates while Hispanic women had better 5-year survival rates across histological types compared to White women\u0026nbsp;[28]. This study, along with other studies on racial disparities in ovarian cancer, is consistent with the \u0026lsquo;Hispanic Paradox\u0026rsquo; phenomenon. Despite facing socioeconomic barriers to health, Hispanic individuals have similar or better survival than their White counterparts\u0026nbsp;[29, 30]. Additional studies are needed to evaluate the ethnic differences that might contribute to survival outcomes among women with ovarian cancer\u0026nbsp;[30, 31].\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eSeveral clinical differences may also play a significant role in the Black-White disparity in ovarian cancer survival. Our study found that the Black-White disparity persisted even among women who did not report any comorbidities. This finding indicates that healthcare disparities, such as access to and utilization of healthcare resources, as well as other clinical factors, may be at play [32, 33]. For instance, a study from the Ovarian Cancer in Women of African Ancestry consortium found that nulliparity, body mass index, and postmenopausal hormone therapy duration were all independent mediators in the racial disparity in ovarian cancer survival [32]. Biological differences may also contribute to the racial disparity in ovarian cancer as the highest levels of mutations in micro-RNA (miRNA) genes, which can lead to dysregulation of miRNA processing or degradation, have been found among African American women [34]. Additionally, Black women with ovarian cancer are more likely to have a high expression of immune cells, which is associated with a favorable response to immunotherapy, [35-37] yet they are less likely to be enrolled in clinical trials involving immunotherapy [38]. Therefore, there are most likely clinical and biological factors that remain unaddressed, contributing to the racial disparities in the survival of advanced-stage ovarian cancer, regardless of treatment receipt. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur study has several strengths. First, it included a large sample size from the NCDB, which undergoes strict quality control measures to ensure high-quality standardized data [19]. Additionally, we were able to adjust for many potential confounders, including demographic, comorbidities, treatment, and tumor characteristics. Furthermore, we were able to make a composite measure of SES to evaluate its role in survival disparities. We comprehensively evaluated the effect modification by age, SES, CCI, and receipt of surgery and mortality. However, our study had limitations that need to be considered. First, the NCDB only records first-course treatments, meaning that patients who receive systemic therapy after their primary treatment were not able to be included in our study [19]. Second, NCDB reports systemic therapy as chemotherapy, hormone therapy, immunotherapy, or hematologic transplant and endocrine procedures; however, we did not examine hematologic transplant and endocrine procedures nor does the definition specify targeted therapy. Lastly, we were not able to evaluate ovarian cancer-specific death, as the database only records vital status and not the specific causes of death. Despite these limitations, our study allowed us to evaluate disparities in race/ethnicity and SES among women with advanced-stage ovarian cancer who have received systemic therapy while maintaining sufficient power.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis U.S. clinical registry study evaluated the racial/ethnic and socioeconomic disparities in survival among women with advanced-stage ovarian cancer who have received systemic treatment. Our findings revealed that Black women, compared to White women, experienced poor survival rates despite receiving systemic therapy. This Black-White disparity remains prevalent across all age groups, with and in low- and mid-SES backgrounds, aligning with existing literature. Therefore, we urge the development and implementation of multitargeted interventions and policies that address these disparities and strive to reduce and/or eliminate the racial disparity in overall survival among women with advanced-stage ovarian cancer.\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCW and SK performed the data analysis. TA designed the study and critically revised the manuscript. CW and SK drafted the manuscript. All authors reviewed the manuscript. The author(s) read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSiegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. CA Cancer J Clin. 2023;73(1):17-48. doi: 10.3322/caac.21763.\u003c/li\u003e\n\u003cli\u003eReid F, Bhatla N, Oza AM, Blank SV, Cohen R, Adams T, et al. The World Ovarian Cancer Coalition Every Woman Study: identifying challenges and opportunities to improve survival and quality of life. Int J Gynecol Cancer. 2021;31(2):238-44. doi: 10.1136/ijgc-2019-000983. \u003c/li\u003e\n\u003cli\u003eJochum F, De Rozario T, Lecointre L, Faller E, Boisrame T, Dabi Y, et al. 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Oncologist. 2021;26(10):e1730-e41. doi: 10.1002/onco.13908.\u003c/li\u003e\n\u003cli\u003eHuffman DL, Jayakrishnan TT, Shankar K, Peterson CE, Wegner RE. Disparities in ovarian cancer treatment and overall survival according to race: An update. Gynecol Oncol. 2021;162(3):674-8. doi: 10.1016/j.ygyno.2021.07.011.\u003c/li\u003e\n\u003cli\u003eHinchcliff E, Rauh-Hain JA, Clemmer JT, Diver E, Hall T, Stall J, et al. Racial disparities in survival in malignant germ cell tumors of the ovary. Gynecol Oncol. 2016;140(3):463-9. doi: 10.1016/j.ygyno.2016.01.006.\u003c/li\u003e\n\u003cli\u003eMontes de Oca MK, Chen Q, Howell E, Wilson LE, Meernik C, Previs RA, et al. Healthcare Access Dimensions and Ovarian Cancer Survival: SEER-Medicare Analysis of the ORCHiD Study. JNCI Cancer Spectr. 2023. doi: 10.1093/jncics/pkad011.\u003c/li\u003e\n\u003cli\u003eAmin SA, Collin LJ, Setoguchi S, Satagopan JM, Buckley de Meritens A, Bandera EV. 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Ethn Dis. 2001;11(3):496-518.\u003c/li\u003e\n\u003cli\u003eMullins MA, Ruterbusch JJ, Clarke P, Uppal S, Wallner LP, Cote ML. Trends and racial disparities in aggressive end-of-life care for a national sample of women with ovarian cancer. Cancer. 2021;127(13):2229-37. doi: 10.1002/cncr.33488.\u003c/li\u003e\n\u003cli\u003eHarris HR, Guertin KA, Camacho TF, Johnson CE, Wu AH, Moorman PG, et al. Racial disparities in epithelial ovarian cancer survival: An examination of contributing factors in the Ovarian Cancer in Women of African Ancestry consortium. Int J Cancer. 2022;151(8):1228-39. doi: 10.1002/ijc.34141.\u003c/li\u003e\n\u003cli\u003eSakhuja S, Yun H, Pisu M, Akinyemiju T. Availability of healthcare resources and epithelial ovarian cancer stage of diagnosis and mortality among Blacks and Whites. J Ovarian Res. 2017;10(1):57. doi: 10.1186/s13048-017-0352-1.\u003c/li\u003e\n\u003cli\u003eAsare A, Yao H, Lara OD, Wang Y, Zhang L, Sood AK. Race-associated molecular changes in gynecologic malignancies. Cancer Res Commun. 2022;2(2):99-109. doi: 10.1158/2767-9764.crc-21-0018.\u003c/li\u003e\n\u003cli\u003eMills AM, Peres LC, Meiss A, Ring KL, Modesitt SC, Abbott SE, et al. Targetable Immune Regulatory Molecule Expression in High-Grade Serous Ovarian Carcinomas in African American Women: A Study of PD-L1 and IDO in 112 Cases From the African American Cancer Epidemiology Study (AACES). Int J Gynecol Pathol. 2019;38(2):157-70. doi: 10.1097/PGP.0000000000000494.\u003c/li\u003e\n\u003cli\u003eWilson C, Soupir AC, Thapa R, Creed J, Nguyen J, Segura CM, et al. Tumor immune cell clustering and its association with survival in African American women with ovarian cancer. PLoS Comput Biol. 2022;18(3):e1009900. doi: 10.1371/journal.pcbi.1009900.\u003c/li\u003e\n\u003cli\u003ePeres LC, Colin-Leitzinger C, Sinha S, Marks JR, Conejo-Garcia JR, Alberg AJ, et al. Racial Differences in the Tumor Immune Landscape and Survival of Women with High-Grade Serous Ovarian Carcinoma. Cancer Epidemiol Biomarkers Prev. 2022;31(5):1006-16. doi: 10.1158/1055-9965.EPI-21-1334.\u003c/li\u003e\n\u003cli\u003eWagar MK, Mojdehbakhsh RP, Godecker A, Rice LW, Barroilhet L. Racial and ethnic enrollment disparities in clinical trials of poly(ADP-ribose) polymerase inhibitors for gynecologic cancers. Gynecol Oncol. 2022;165(1):49-52. doi: 10.1016/j.ygyno.2022.01.032.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cancer-causes-and-control","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"caco","sideBox":"Learn more about [Cancer Causes \u0026 Control](https://www.springer.com/journal/10552)","snPcode":"10552","submissionUrl":"https://submission.nature.com/new-submission/10552/3","title":"Cancer Causes \u0026 Control","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Gynecologic cancers, Systemic therapy, Cancer disparities, Socioeconomic Status","lastPublishedDoi":"10.21203/rs.3.rs-3225591/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3225591/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eThe purpose of this study was to assess the association between race/ethnicity and all-cause mortality among women with advanced-stage ovarian cancer who received systemic therapy.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe analyzed data from the National Cancer Database on women diagnosed with advanced-stage ovarian cancer from 2004 to 2015 who received systemic therapy. Race/ethnicity was categorized as Non-Hispanic (NH) White, NH-Black, Hispanic, NH-Asian/Pacific Islander, and Other. Income and education were combined to form a composite measure of socioeconomic status (SES) and categorized into low-, mid-, and high-SES. Multivariable Cox proportional hazards models were used to assess whether race/ethnicity was associated with the risk of death. Models were adjusted for age, SES, comorbidity level, and receipt of surgery.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe study population comprised 53,367 women (52.4% ages\u0026thinsp;\u0026ge;\u0026thinsp;65 years, 82% NH-White, 8.7% NH-Black, 5.7% Hispanic, and 2.7% NH-Asian/Pacific Islander) in the analysis. After adjusting for covariates, the NH-Black race was associated with a higher risk of death versus NH-White race (aHR: 1.12; 95% CI: 1.07,1.18), while Hispanic race was associated with a lower risk of death compared to NH-White women (aHR: 0.87; 95% CI: 0.80, 0.95). Furthermore, NH-Black women versus NH-White women had an increased risk of mortality among those with low-SES characteristics (aHR:1.12; 95% CI:1.03\u0026ndash;1.22) and mid-SES groups (aHR: 1.13; 95% CI:1.05\u0026ndash;1.21).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eAmong women with advanced-stage ovarian cancer who received systemic therapy, NH-Black women experienced poorer survival compared to NH-White women. Future studies should be directed to identify drivers of ovarian cancer disparities, particularly racial differences in treatment response and surveillance.\u003c/p\u003e","manuscriptTitle":"Racial and Socioeconomic Disparities in Survival Among Women with Advanced-stage Ovarian Cancer Who Received Systemic Therapy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-08-08 14:26:18","doi":"10.21203/rs.3.rs-3225591/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2023-09-06T18:38:50+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-08-30T00:44:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"2b330293-4665-4e6c-8c18-8b024a6f540f","date":"2023-08-09T18:13:05+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-08-09T11:43:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-08-02T09:31:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-08-02T09:31:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cancer Causes \u0026 Control","date":"2023-08-01T19:02:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cancer-causes-and-control","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"caco","sideBox":"Learn more about [Cancer Causes \u0026 Control](https://www.springer.com/journal/10552)","snPcode":"10552","submissionUrl":"https://submission.nature.com/new-submission/10552/3","title":"Cancer Causes \u0026 Control","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"7eb8f89d-3058-484c-b429-d3c34172cd21","owner":[],"postedDate":"August 8th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-10-30T15:03:16+00:00","versionOfRecord":{"articleIdentity":"rs-3225591","link":"https://doi.org/10.1007/s10552-023-01810-y","journal":{"identity":"cancer-causes-and-control","isVorOnly":false,"title":"Cancer Causes \u0026 Control"},"publishedOn":"2023-10-24 15:01:37","publishedOnDateReadable":"October 24th, 2023"},"versionCreatedAt":"2023-08-08 14:26:18","video":"","vorDoi":"10.1007/s10552-023-01810-y","vorDoiUrl":"https://doi.org/10.1007/s10552-023-01810-y","workflowStages":[]},"version":"v1","identity":"rs-3225591","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3225591","identity":"rs-3225591","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}