Risk Factors and COVID-19 Survival Among Cancer Survivors in New Jersey: A Population-based study

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Paddock, Hao Liu, Shengguo Li, Adana A. M. Llanos, Susan German, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7483142/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: People diagnosed with cancer are more susceptible to COVID-19-related mortality. Other than race, age, and sex, less is known about other factors associated with a higher risk of death after COVID-19 diagnosis among cancer survivors. Objective: The goal was to examine factors associated with survival time in cancer survivors following a COVID-19 diagnosis, focusing on sociodemographic and clinical factors in a population-based cohort of New Jersey residents. Methods : Cancer cases identified using the New Jersey State Cancer Registry (NJSCR) were linked with COVID-19 cases from Communicable Disease Reporting and Surveillance System (CDRSS) diagnosed in 2020-21, creating an analytic dataset 63,330 people. Area Deprivation Index incorporated environmental and structural factors related to neighborhood disadvantage. Competing risk regression analyses were used to identify factors associated with survival. Results: People diagnosed with cancer were more likely to die from COVID-19 than die from cancer within the first five months following their COVID-19 diagnosis. Cumulative incidence rate of cancer-specific deaths surpassed that of COVID-19-specific deaths ( P <0.001), after three years. After adjusting for other covariates, females had a lower risk of dying from COVID-19 than males (HR=0.63, 95% CI= 0.58-0.65, P<0.001). Relative to whites, Blacks were 47% more likely (HR=1.47, 95% CI= 1.35-1.60, P<0.001) to die from COVID-19. Older age, API race, Hispanic ethnicity, those living in more socioeconomically disadvantaged areas of New Jersey, diagnosed with hematological cancers, or diagnosed later stage also had a significantly higher risk of dying from COVID-19 ( P <0.001). Conclusions . This study of cancer survivors illustrates that males, Black, API, or Hispanic individuals, as well as those living in more socioeconomically disadvantaged areas of New Jersey, diagnosed with a hematologic cancer or at late-stage tumor had increased risks of COVID-19-related death. Targeting more aggressive interventions to this vulnerable cancer population (e.g., access to vaccines or early treatments) may reduce morbidity and mortality. Cancer survivors COVID social vulnerability social determinants Figures Figure 1 Figure 2 Figure 3 Introduction COVID-19 continues to pose a threat to cancer survivors as it remains one of the top respiratory illnesses in 2025, causing approximately 10-20 deaths per week in New Jersey. [1] As of August 2025, there have been more than 2.5 million deaths from COVID-19 in the US, with New Jersey ranking 21 st . [2] Cancer is noted as an underlying condition for 10.2% of lab-confirmed COVID-19-associated deaths [3] indicating the importance of identifying risk factors specific to cancer survivors. Cancer survivors are particularly susceptible to COVID-19 due to a combination of disease-related and treatment-induced immunosuppression. Several studies have documented shorter survival following SARS-CoV-2 infection and higher COVID-19-related mortality among cancer survivors (13% to 40.5%) [4-11] or a 2 to 7-fold increase in mortality due to COVID-19 [12, 13] . Worse survival among cancer patients diagnosed with and treated for COVID-19 is associated with older age, having more comorbid conditions, having a history of tobacco use, being male, and being diagnosed with certain types of cancer (lung, solid tumors, hematological cancers, late stage cancer, multiple tumors, or a more recent diagnosis. [13-17] Furthermore, the emergence of the COVID-19 pandemic highlighted health inequities in the US that may be affecting COVID-19 health-related outcomes. [14,15,18,19] Fu et al found that Black cancer survivors experienced significantly more severe COVID-19 outcomes compared with White cancer survivors, after adjustment for demographic and clinical risk factors. [18] Similarly, non-Hispanic Black and non-Hispanic Asian/Pacific Islander race, and Hispanic ethnicity independently increased the risk of COVID-19 death in New York. [14, 18, 19] Other data suggests that residence in less densely populated areas of the US was also associated with better outcomes. [15] Previous studies evaluating mortality among US cancer patients diagnosed with COVID-19 have been conducted at a single institution, registry, or health system. [18, 20-23] One large multi-center study included 4749 patients across 83 centers in the US. [15] Large studies have been conducted by two states that have linked state cancer registry data with hospital medical records [14, 24, 25] These studies have consistently indicated that COVID-19 infection after a cancer diagnosis is associated with an increase in all-cause mortality. [25] However, these studies didn’t address neighborhood specific factors that may influence COVID-19 health outcomes in cancer survivors. The goal of the study was to examine sociodemographic and clinical factors associated with COVID-19 and cancer-related survival time following a COVID-19 diagnosis in a statewide cohort of cancer survivors in New Jersey. New Jersey is uniquely positioned to study this population because of access to our robust cancer and COVID-19 registries, diverse population, high incidence rates, and being at the epicenter of the COVID-19 outbreak. This data linkage and survival analysis allowed us to examine patterns and characteristics in New Jersey cancer survivors diagnosed with COVID-19. METHODS Study Design To investigate covid-related outcomes in cancer survivors, we performed a data linkage between two New Jersey population based datasets – the New Jersey State Cancer Registry (NJSCR) and the Communicable Disease Reporting and Surveillance System (CDRSS). Then, a competing risks survival analysis was conducted including all NJ residents diagnosed with cancer from 1979 through 2022, alive in January 2020 (start of COVID-19 pandemic), and diagnosed with PCR-confirmed COVID-19 in 2020 and/or 2021. Institutional Review Board approval was obtained from the Rutgers Institutional Review Board (NJSCR) and Rowan University Institutional Review Board (CDRSS), conforming with the standards of United States Federal Policy for the Protection of Human Subjects. Data were obtained following data release policies of the New Jersey Department of Health and data use agreements were executed among all involved. Data Sources and Linkage Incident cancer cases were obtained from the New Jersey State Cancer Registry (NJSCR), which is a high-quality population-based cancer incidence registry covering a diverse population of 9.3 million people residing in New Jersey. Since 1978, NJ regulations have required health care facilities, physicians, dentists, and clinical laboratories to report cancer cases to the NJSCR when initially diagnosed, when the patient is first admitted or treated, and/or when diagnosed with subsequent primary cancers. [26] Cancer data from NJSCR consistently meets the highest standards, including 98% or higher completeness, ≤ 2% missing age, sex, or county, and ≤ 3% missing race. Vital status is updated regularly using state death certificates and the National Death Index (NDI). NJSCR data for the linkage consisted of demographic and clinical variables for people diagnosed with cancer from 1979 through 2021 and were alive at the start of 2020 (n=744,929). PCR-confirmed COVID-19 cases were obtained from the New Jersey Department of Health (NJDOH), Communicable Disease Reporting and Surveillance System (CDRSS), considered the most complete accounting of COVID-19 cases. The CDRSS is an electronic, web-enabled system where public health partners statewide can instantly report and track communicable disease incidence. Public Law 116-136, § 18115(a), the Coronavirus Aid, Relief, and Economic Security (CARES) Act, required “every laboratory that performed or analyzed a test intended to detect SARS-CoV-2 or to diagnose a possible case of COVID-19” to report the results, positive and negative, from each such test to public health authorities. Positive laboratory reports are recorded into CDRSS either by electronic laboratory reporting transmissions or manual data entry at clinical laboratories and acute care hospitals. Follow-up information was added by trained users in various public health roles. [27] CDRSS data for the linkage consisted of PCR-confirmed COVID-19 cases diagnosed among New Jersey residents during 2020-2021 (n=1,526,213). Cancer survivors were linked to the Area Deprivation Index (ADI) by census block group. The ADI is based on a measure originally created by the Health Resources and Services Administration (HRSA) adapted and validated to the Census Block Group neighborhood level. [28-31] 29-32 The ADI allows for rankings of neighborhoods by socioeconomic disadvantage in a region of interest. [30] It is a composite measure comprised of 17 U.S. Census indicators in the domains of education, employment, occupation, income, housing, and poverty at the census block group level based on 2017-2021 American Community Survey data. State-level decile rankings for New Jersey were used, constructed by ranking the ADI scores from low to high for New Jersey and grouping the block groups into bins corresponding to each 10% range of the ADI. Rank 1 is the lowest ADI (least disadvantaged) and rank 10 is the highest ADI (most disadvantaged). Probabilistic linkages between cancer cases (NJSCR) and COVID-19 cases (CDRSS) were performed using Match*Pro (Version 2.3) linkage software. The linkage used first name, last name, date of birth, address, phone number, sex, and date of death as matching variables. To reduce the amount of resource-intensive manual review, automatic Match*Pro classification rules were created after thorough review of data patterns in the results. These rules automatically assigned match status to pairs that met pre-defined criteria, thereby reducing manual review volume, maximizing the number of true matches, and minimizing false matches. Accurint® was used extensively to validate match status of a subset of pairs captured by the rules and for adjudication of uncertain pairs. NJSCR staff conducted extensive quality control procedures and created an analytic dataset that included date of COVID-19 onset, date and cause of death, demographics, tumor characteristics, and area deprivation indices. Risk Factors of Interest There were eight independent variables of interest for the analysis: age at COVID-19 diagnosis, time from cancer diagnosis to COVID diagnosis, sex (male, female), race (White, Black, Asian or Pacific Islander (API), other, unknown), ethnicity (Hispanic vs not), cancer type [blood/hematologic cancers (Lymphoma, Myeloma, Leukemia) or solid tumors], cancer stage (in situ, localized, regional, distant), and Area Deprivation Index. Statistical Analyses The focus of this analysis was on mortality in cancer survivors diagnosed with COVID-19. The endpoints of interest were death from COVID-19 and death from cancer, with the latter treated as competing risks. Deaths that were not due to cancer or COVID-19, such as accidents or cardiovascular-related deaths, were treated as censoring events in the analysis of competing events. Data from patients who were alive at their follow-up are included in the analysis and considered as an independent censoring event for observing COVID-specific death and cancer-specific death. Follow-up time was calculated in months starting from the date of COVID-19 diagnosis to the date of death or censoring (whichever occurred first). Cumulative incidence rates of death due to COVID-19 or cancer were estimated and compared between cancer survivors who were diagnosed with COVID-19 using Gray’s method. [32] The associations between the covariates and times to COVID-specific death and cancer-specific death were analyzed using the sub-distribution hazard ratio regression model based on Fine and Gray’s method. [33] Risk factors of interest included race, ethnicity, stage of disease at diagnosis, national and state ADI ranks, time between cancer diagnosis and covid diagnosis, cancer type and age at covid diagnosis. Since subjects with cancer who died before COVID-19 diagnosis weren’t included in the analysis, we adjusted the competing risk regression model using the left truncation method by including the time from cancer diagnosis to COVID diagnosis as a covariate. All analyses were performed in R, where the competing risk analyses were done using the package “cmprsk.” Results Characteristics of the Sample A total of 63,341 cancer survivors were identified who were diagnosed with in situ or malignant cancer and also had a COVID-19 diagnosis between February 21, 2020 and January 1, 2022. Due to small numbers unknown cancer type (n = 1) and other sex were excluded (n = 10). The final analytic cohort included a total of 63,330 cancer survivors (Fig. 1 ). Of these, 4,625 died from COVID-19, and 3,004 died from cancer-related causes. The maximum follow-up time was 34 months among those who died from COVID-19 and 40 months among those who died from cancer. Table 1 shows the summary statistics of risk factors stratified by study participant status at the time of analysis (censored, died from COVID, or died from cancer-related causes). More than half of the cancer survivors who had COVID-19 were female (55%), while more than half of those who died of COVID-19 were male (58%). Those who died of COVID-19 had a median age of 81 years at the time of their COVID-19 diagnosis, while those who died of cancer had a median age of 71 at the time of their COVID-19 diagnosis. Table 1 Characteristics of Cancer Survivors Overall and by Cause of Death Characteristic Overall N = 63,330 1 COVID death N = 4,625 1 Cancer death N = 3,004 1 Censored N = 55,701 1 Sex Male 28,523 (45%) 2,702 (58%) 1,432 (48%) 24,389 (44%) Female 34,807 (55%) 1,923 (42%) 1,572 (52%) 31,312 (56%) Race .. White 51,707 (82%) 3,702 (80%) 2,416 (80%) 45,589 (82%) Black 8,339 (13%) 752 (16%) 462 (15%) 7,125 (13%) API 2,044 (3.2%) 141 (3.0%) 101 (3.4%) 1,802 (3.2%) Other 707 (1.1%) 28 (0.6%) 21 (0.7%) 658 (1.2%) Unknown 533 (0.8%) 2 (< 0.1%) 4 (0.1%) 527 (0.9%) Ethnicity Non-Hispanic 53,533 (85%) 4,011 (87%) 2,537 (84%) 46,985 (84%) Hispanic 9,797 (15%) 614 (13%) 467 (16%) 8,716 (16%) Cancer Stage In situ 8,237 (13%) 460 (9.9%) 132 (4.4%) 7,645 (14%) Localized 33,456 (53%) 2,415 (52%) 916 (30%) 30,125 (54%) Regional 10,829 (17%) 682 (15%) 705 (23%) 9,442 (17%) Distant 7,318 (12%) 751 (16%) 1,072 (36%) 5,495 (9.9%) Unknown/Unstaged 3,490 (5.5%) 317 (6.9%) 179 (6.0%) 2,994 (5.4%) Cancer type Solid 57,217 (90%) 4,080 (88%) 2,706 (90%) 50,431 (91%) Blood/Hematopoietic 6,113 (9.7%) 545 (12%) 298 (9.9%) 5,270 (9.5%) Age, COVID-19 dx 66 (56, 77) 81 (72, 88) 71 (62, 80) 65 (55, 75) Cancer dx - COVID dx(yrs) 8 (3, 14) 10 (4, 17) 3 (1, 9) 8 (3, 14) Area Deprivation Index 6.00 (3.00, 8.00) 6.00 (3.00, 8.00) 6.00 (4.00, 9.00) 6.00 (3.00, 8.00) 1 n (%) for categorical variables; median (IQR) for continuous variables Dx = Diagnosis Cumulative Incidence Rates for COVID-specific and Cancer-specific Deaths The cumulative rates as a function of months since COVID diagnosis are shown in Fig. 2 for both COVID-specific and cancer-specific deaths. Cancer survivors were more likely to die from COVID-19 than cancer within the first five months following their COVID-19 diagnosis. The estimated cumulative incidence rate of COVID-19-specific death plateaued at approximately five months, whereas the cumulative incidence rate of cancer-specific death continued to rise over time. Approximately three years after a COVID-19 diagnosis, the cumulative incidence rate of cancer-specific death surpassed that of COVID-19-specific death. Table 2 presents the short-term and long-term cumulative incidence rates of COVID-19-specific and cancer-specific death at 2 weeks, 1 month, and 36 months after a COVID diagnosis. At 2 weeks, 3.7% of people with cancer died from COVID-19, compared to 0.30% from cancer. At one month, 5.8% died from COVID-19, whereas 0.55% died from cancer. By 36 months, 7.7% died from COVID-19, whereas 8.8% died from cancer. Table 2 Cumulative Incidence of Death at 0.5, 1.0, and 36 months after COVID-19 diagnosis in Cancer Survivors Death 0.5 months 1 month 36 months COVID-19 3.7% (3.5%, 3.8%) 5.8% (5.6%, 6.0%) 7.7% (7.4%, 7.9%) Cancer 0.30% (0.26%, 0.34%) 0.55% (0.49%, 0.61%) 8.8% (8.1%, 9.5%) Figure 3 shows the cumulative incidence rates as a function of months since COVID-19 diagnosis for COVID-19-specific and cancer-specific death, separately for the important baseline characteristics, including sex, race, ethnicity, and cancer type. The short-term and long-term specific cumulative incidence rates at 2 weeks, 1 month, and 36 months are reported for COVID-specific death in Table A and cancer-specific death in Table B in the Supplemental material. Males were significantly more likely to die from either COVID-19 ( P < 0.001) or cancer ( P = 0.001) than females although the gap between sexes was greater for covid deaths. Black cancer survivors were more likely to die from COVID-19 than those identified as White, API, and other race ( P < 0.001, P = 0.003, P < 0.001, respectively). Hispanic cancer survivors appeared to be more likely to die from COVID-19 and death occurred more rapidly than in their non-Hispanic counterparts ( P < 0.001).Of note is the plateau seen around 5 months post covid-diagnosis that is consistent across covariates and strata. Competing Risk Regression Analysis of Risk Factors for COVID-specific Death Results of the univariate regression analysis are presented in Table 3 . Among the demographic variables analyzed, female sex and Hispanic ethnicity were identified as protective factors against COVID-19 mortality in cancer patients, while Black race, compared to White, was recognized as a risk factor (HR = 1.271, 95% CI = 1.175–1.375, P < 0.001). Survivors of hematologic cancers had a higher risk of dying from COVID-19 compared to those with solid cancers (HR = 1.259, 95% CI = 1.152–1.376, P < 0.001). Other factors increasing the risk for dying from COVID-19 were residents of areas with greater socioeconomic disadvantage (HR = 1.032, 95% CI = 1.021–1.042, P < 0.001), older survivors (HR = 1.08, 95% CI = 1.077–1.083, P < 0.001) and those further from their cancer diagnosis (HR = 1.023, 95% CI = 1.020–1.026, P < 0.001). Table 3. Univariate Competing Risks Regression Model for COVID-19-specific Death Risk factors HR 1 95% CI 1 p-value Sex Male — — Female 0.572 0.539, 0.606 < 0.001 Race White — — Black 1.271 1.175, 1.375 < 0.001 API 0.972 0.822, 1.150 0.740 Other 0.572 0.394, 0.828 0.003 Unknown 0.059 0.015, 0.234 < 0.001 Ethnicity Non-Hispanic — — Hispanic 0.842 0.774, 0.917 < 0.001 Stage In situ — — Localized 1.293 1.171, 1.429 < 0.001 Regional 1.117 0.992, 1.257 0.067 Distant 1.870 1.665, 2.100 < 0.001 Unknown or Unstaged 1.675 1.452, 1.932 < 0.001 Cancer type Solid — — Blood/Hematopoietic 1.259 1.152, 1.376 < 0.001 Area Deprivation Index 1.032 1.021, 1.042 < 0.001 Cancer dx to COVID-19 dx, years 1.023 1.020, 1.026 < 0.001 Age at COVID-19 dx 1.080 1.077, 1.083 < 0.001 1 HR = Hazard Ratio, CI = Confidence Interval, Dx = diagnosis All variables were entered into the multivariate regression model, results are shown in Table 4 . After adjusting for other covariates, females continued to have a lower risk of dying from COVID-19 than males (HR = 0.63, 95% CI = 0.58–0.65, P < 0.001). Relative to whites, Blacks were 47% more likely (HR = 1.47, 95% CI = 1.35–1.60, P < 0.001) and API cancer survivors were 44% (HR = 1.44, 95% CI = 1.22–1.71, P < 0.001) significantly higher risk of COVID-19-specific death in the adjusted model. Hispanic cancer survivors did not hold their protective effect in the multivariate model, and when adjusted for other variables had a significantly higher risk of dying from COVID-19 compared to non-Hispanics (HR = 1.20, 95% CI = 1.10–1.32, P < 0.001). Patients with distant stage cancer continued to have a significantly higher risk of dying from COVID-19 compared to patients with in-situ cancers (HR = 1.74, 95% CI = 1.52–1.99, P < 0.001) while regional stage cancer had an increased risk in the adjusted model (HR = 1.22, 95% CI = 1.09–1.38, P < 0.001). Survivors of hematologic cancers continued to have a higher risk of dying from COVID-19 compared to those with solid cancers (HR = 1.17, 95% CI = 1.05–1.31, P = 0.006), although lost significance. Individuals residing in more socioeconomically disadvantaged areas (HR = 1.02, 95% CI = 1.01–1.03, P < 0.001) and those who were older at COVID-19 diagnosis (HR = 1.09, 95% CI = 1.08–1.09, P < 0.001) had higher risks of dying from COVID-19. Time between cancer diagnosis and covid diagnosis did not have meaningful results in the multivariate model. Table 4 Multivariate Competing Risks Regression Model on Covid-specific Death Risk factors HR 1 95% CI 1 p-value Sex Male — — Female 0.62 0.58, 0.65 < 0.001 Race White — — Black 1.47 1.35, 1.60 < 0.001 API 1.44 1.22, 1.71 < 0.001 Other 0.79 0.54, 1.15 0.2 Unknown 0.09 0.02, 0.36 < 0.001 Ethnicity Non-Hispanic — — Hispanic 1.20 1.10, 1.32 < 0.001 Stage In situ — — Localized 1.08 0.98, 1.20 0.12 Regional 1.22 1.09, 1.38 < 0.001 Distant 1.74 1.52, 1.99 < 0.001 Unknown/Unstaged 1.27 1.10, 1.47 0.001 Cancer type Solid — — Blood/Hematopoietic 1.17 1.05, 1.31 0.006 Area Deprivation Index 1.02 1.01, 1.03 < 0.001 Cancer Dx to COVID Dx, years 1.00 0.99, 1.00 0.003 Age at COVID Dx 1.09 1.08, 1.09 < 0.001 1 HR = Hazard Ratio, CI = Confidence Interval, Dx = diagnosis Discussion In this large population-based study of cancer survivors diagnosed with COVID-19 followed three years after a COVID-19 diagnosis, we found that older cancer survivors, those identified as male, Black, API, or Hispanic, as well as those living in more socioeconomically disadvantaged areas of New Jersey, diagnosed with hematologic cancers or late-stage tumors had a significantly higher risk of dying from COVID-19. These findings are consistent with existing studies in the US and Europe suggesting that older age, [ 4 , 6 , 16 , 18 , 20 , 21 , 34 ] male sex, [ 16 , 18 , 35 ] hematologic cancer diagnosis, [ 13 , 18 , 21 , 36 ] and later-stage disease [ 4 , 36 ] are risk factors of COVID-19-related death among cancer survivors. Our study extends prior literature by showing that greater neighborhood socioeconomic disadvantage was associated with higher risk for COVID-19-related death, and consistent with general population studies, we illustrate that Black cancer survivors have a higher probability of dying from COVID-19. Variations within sociodemographic cancer survivor subgroups, such as Hispanic and Black populations in New Jersey, are associated with further inequities in COVID-19-related outcomes. Not only did Black individuals have the lowest survival from COVID-19 over the entire study period, but they also experienced the shortest survival times after a diagnosis of COVID-19 compared to individuals of other races. These findings demonstrate that cancer survivors identified as Black experience a disproportionate burden from COVID-19-related death. Increasing awareness and education about the importance of preventing COVID-19 infection would benefit Black cancer survivors. Previous studies found that male sex is a risk factor for COVID-19 death and our results extend that female sex demonstrates a significant protective factor for COVID-19 death. COVID-19 mortality differences between male and female sex have been studied [ 37 ] and results vary by state. Comorbidities such as diabetes[ 38 ] or cardiovascular disease [ 39 ] are more prevalent in New Jersey males than females and may further increase the risk of COVID-19 death. Females are also known to exhibit slightly higher rates of cancer screenings [ 40 ] and tend to have a healthier lifestyle [ 41 ] lending to the protective effect. We found that the cumulative incidence COVID-19 death increased dramatically from 0–5 months post COVID-19 diagnosis and then stabilized through the 40th month of observation. This plateau was seen in both sexes, all races/ethnicities, and cancer types. Vaccinations, herd immunity, improved primary and secondary prevention, increased awareness, and effective treatments could potentially be attributable to this pattern. Patients with hematologic cancers are especially vulnerable to COVID-19 likely due to the more intensive and immunosuppressive therapies for these patients versus solid tumor patients, [ 16 ] and our study confirmed the poorer outcomes compared to those diagnosed with solid tumors (HR = 1.17, p = 0.006) when adjusted for covariates. While COVID-19 mortality has decreased over time, as the virus evolves some therapies may lose their clinical efficacy against new variants or new variants may be more severe. Therefore, COVID-19 risk will remain a major challenge for patients with hematologic malignancies. It will be important to manage risk factors for this population to prevent severe COVID-19. Neighborhood or area-level socioeconomic disadvantage is increasingly recognized as a social driver of health outcomes and is characterized by multiple factors. [ 42 ] Our study confirmed that neighborhood socioeconomic disadvantage was independently associated with inferior survival from COVID-19 (HR = 1.02, p < 0.001) in cancer survivors, with a 2% increase in the risk with a one-unit increase in the area deprivation rank. Neighborhood socioeconomic status may be used as an indicator of access to resources and could impact the time from onset of COVID-19 symptoms to diagnosis and subsequent receipt of care. Several key factors may contribute to survival inequities among cancer survivors living in socioeconomically disadvantaged communities. People may be disproportionately represented in essential jobs—including health care, maintenance, delivery, and public transportation—that were excluded from shelter-in-place mandates. They also tend to rely more heavily on public transportation and are more likely to live in multigenerational households. These living arrangements are especially common in crowded and socioeconomically disadvantaged settings, where effective physical distancing is more difficult. As a result, the risk of COVID-19 infection increases, particularly for elderly family members with chronic illnesses, including cancer. Additionally, people facing socioeconomic challenges may be unable to take time off work, which can lead to delays in receiving care or interruptions in cancer treatment and/or COVID-19 therapy. [ 19 ],[ 43 ] These barriers can significantly increase the risk of poorer survival outcomes. Strengths and Limitations Robust data sources are one of the strengths of this project. Both cancer and COVID-19 cases are reportable by law and extensive efforts have been undertaken to ensure a complete and reliable dataset for analysis. Despite this strength, it is likely that COVID-19 diagnoses are underestimated because health systems were severely compromised during the pandemic and staffing was decreased, thereby affecting the completeness of COVID-19 data collection. While labs were required to report incidence immediately, additional hospitalization information was not as complete. Our ability to reliably collect COVID-19 incidence has also changed over time as testing requirements loosened or individuals used home testing kits, which are not reported to the NJDOH. To reduce this effect, we limited our analysis to the first two years of the pandemic when testing was required for most New Jerseyans. There are notable data points not included in this analysis due to availability at the time. Future research will benefit from the addition of information about multiple COVID-19 infections and COVID-19 vaccine receipt. Several cancer centers have created care registries to rapidly collect and disseminate real-time data to help clinicians make evidence-based decisions as they encounter individuals affected by COVID-19 (ASCO, ASH, CCC19 [ 44 ]). While it is important to understand New Jersey-relevant data to effect informed decisions about patient care, there are broader implications. New Jersey has a large, diverse population with a high cancer incidence. Findings from this study can be implemented at other cancer centers nationwide to develop interventions that target specific demographic and malignancy groups for greater impact on COVID-19-related mortality. In conclusion, this study of cancer survivors illustrates that males, Black, API, and Hispanic individuals, as well as those living in more socioeconomically disadvantaged areas of New Jersey, diagnosed with a hematologic cancer or at late-stage tumor had increased risks of COVID-19-related death. Targeting more aggressive interventions to this vulnerable cancer population (e.g., access to vaccines or early treatments) may reduce morbidity and mortality. Cancer centers and other facilities treating cancer survivors may consider targeted screening questions to guide prevention strategies, with particular attention to men, Black, API, and Hispanic survivors, as well as those residing in underserved, economically strained neighborhoods. These measures could include recommendations and clinic reminders regarding staying up to date with COVID-19 vaccinations, donning masks in public settings, COVID-19 testing at symptom onset, as well as increased access to early treatment options. Additionally, healthcare providers can play a vital role in educating high-risk cancer survivors about COVID-19 prevention, early detection, and treatment. Using these data to prevent death among vulnerable groups at high risk for hospitalization and death is an urgent priority to mitigate persistent outcomes inequities. Abbreviations Abbreviation Meaning ADI Area Deprivation Index API Asian or Pacific Islander ASCO American Society of Clinical Oncology ASH American Society of Hematology CCC19 The COVID-19 and Cancer Consortium CDRSS Communicable Disease Reporting and Surveillance System CI Confidence Interval Dx Diagnosis ELR Electronic Laboratory Reporting HRSA Health Resources and Services Administration HR Hazard Ratio NJSCR New Jersey State Cancer Registry PCR Polymerase chain reaction US United States Declarations Conflicts of Interest None Declared Clinical Trial Number: not applicable Human Ethics: This study has been approved by the Rutgers Institutional Review Board [NJSCR] Protocol # Pro2020001473 and Rowan University Institutional Review Board [NJDOH] # PRO-2023-468. Consent to Participate declarations: not applicable Consent to Publish declaration: not applicable Author Contribution Development of Specific Aims: LP, HL, AE, AL, SMData Collection, Linkage, and Preparation: SL, SG, SF, JP, JSData Analysis, Tables, Figures, Interpretation: SG, HL, SL, LP, SMMain manuscript writing: LP, SM, HL, SG, SLAll authors reviewed and provided input for manuscript draft. Acknowledgement We thank the staff at the New Jersey Department of Health who collected cancer and COVID-19 data, especially those who worked overtime during the pandemic to improve data collection. The New Jersey State Cancer Registry is funded by the National Cancer Institute’s Surveillance, Epidemiology and End Results (SEER) Program (#75N91021D00009), Centers for Disease Control and Prevention’s National Program of Cancer Registries (#NU58DP007117) with additional support from the State of New Jersey, New Jersey Department of Health, and the Rutgers Cancer Institute of New Jersey. Data Availability Data from the New Jersey State Cancer Registry and the Communicable Disease Reporting and Surveillance System cannot be shared openly to protect privacy. References New Jersey Department of Health. Respiratory Illness Surveillance Report, Week Ending May 31, 2025 (MMWR 22). In; 2025. Centers for Disease Control and Prevention. United States COVID-19 Deaths, Emergency Department (ED) Visits, and Test Positivity by Geographic Area . https://covid.cdc.gov/covid-data-tracker/#maps_deaths-total. New Jersey Covid-19 Dashboard . https://www.nj.gov/health/cd/topics/covid2019_dashboard.shtml. Grivas P, Khaki AR, Wise-Draper TM , et al. Association of clinical factors and recent anticancer therapy with COVID-19 severity among patients with cancer: a report from the COVID-19 and Cancer Consortium. Ann Oncol 2021;32(6):787-800. Garassino MC, Whisenant JG, Huang L-C , et al. COVID-19 in patients with thoracic malignancies (TERAVOLT): first results of an international, registry-based, cohort study. The Lancet. Oncology 2020;21:914 - 922. Lee LYW, Cazier J-B, Starkey T , et al. COVID-19 prevalence and mortality in patients with cancer and the effect of primary tumour subtype and patient demographics: a prospective cohort study. The Lancet Oncology 2020;21(10):1309-1316. Wang Q, Berger NA, Xu R. Analyses of Risk, Racial Disparity, and Outcomes Among US Patients With Cancer and COVID-19 Infection. JAMA Oncol 2021;7(2):220-227. de Azambuja E, Brandão M, Wildiers H , et al. Impact of solid cancer on in-hospital mortality overall and among different subgroups of patients with COVID-19: a nationwide, population-based analysis. ESMO Open 2020;5(5):e000947. Sharafeldin N, Bates B, Song Q , et al. Outcomes of COVID-19 in Patients With Cancer: Report From the National COVID Cohort Collaborative (N3C). J Clin Oncol 2021;39(20):2232-2246. Albiges L, Foulon S, Bayle A , et al. Determinants of the outcomes of patients with cancer infected with SARS-CoV-2: results from the Gustave Roussy cohort. Nature Cancer 2020;1(10):965-975. Lièvre A, Turpin A, Ray-Coquard I , et al. Risk factors for Coronavirus Disease 2019 (COVID-19) severity and mortality among solid cancer patients and impact of the disease on anticancer treatment: A French nationwide cohort study (GCO-002 CACOVID-19). Eur J Cancer 2020;141:62-81. Liang W, Guan W, Chen R , et al. Cancer patients in SARS-CoV-2 infection: a nationwide analysis in China. Lancet Oncol 2020;21(3):335-337. Lee AJX, Purshouse K. COVID-19 and cancer registries: learning from the first peak of the SARS-CoV-2 pandemic. Br J Cancer 2021;124(11):1777-1784. Zhang X, Gates Kuliszewski M, Kahn AR , et al. Early COVID-19 Hospitalizations Among New York State Residents with a History of Invasive Cancer. J Registry Manag 2022;49(4):114-125. Hawley JE, Sun T, Chism DD , et al. Assessment of Regional Variability in COVID-19 Outcomes Among Patients With Cancer in the United States. JAMA Netw Open 2022;5(1):e2142046. Henley SJ. COVID-19 and other underlying causes of cancer deaths—United States, January 2018–July 2022. MMWR. Morbidity and Mortality Weekly Report 2022;71. Lara OD, O'Cearbhaill RE, Smith MJ , et al. COVID-19 outcomes of patients with gynecologic cancer in New York City. Cancer 2020;126(19):4294-4303. Fu J, Reid SA, French B , et al. Racial Disparities in COVID-19 Outcomes Among Black and White Patients With Cancer. JAMA Netw Open 2022;5(3):e224304. Llanos AAM, Ashrafi A, Ghosh N , et al. Evaluation of Inequities in Cancer Treatment Delay or Discontinuation Following SARS-CoV-2 Infection. JAMA Netw Open 2023;6(1):e2251165. Miyashita H, Mikami T, Chopra N , et al. Do patients with cancer have a poorer prognosis of COVID-19? An experience in New York City. Ann Oncol 2020;31(8):1088-1089. Kuderer NM, Choueiri TK, Shah DP , et al. Clinical impact of COVID-19 on patients with cancer (CCC19): a cohort study. The Lancet 2020;395(10241):1907-1918. Kwon DH, Cadena J, Nguyen S , et al. COVID-19 outcomes in patients with cancer: Findings from the University of California health system database. Cancer Med 2022;11(11):2204-2215. Borno HT, Kim MO, Hong JC , et al. COVID-19 Outcomes Among Patients With Cancer: Observations From the University of California Cancer Consortium COVID-19 Project Outcomes Registry. Oncologist 2022;27(5):398-406. Milgrom ZZ, Milgrom DP, Han Y , et al. Breast Cancer Screening, Diagnosis, and Surgery during the Pre- and Peri-pandemic: Experience of Patients in a Statewide Health Information Exchange. Ann Surg Oncol 2023;30(5):2883-2894. Valvi N, Patel H, Bakoyannis G , et al. COVID-19 Diagnosis and Risk of Death Among Adults With Cancer in Indiana: Retrospective Cohort Study. JMIR Cancer 2022;8(4):e35310. State of New Jersey Department of Health. NJ State Cancer Registry . https://www.state.nj.us/health/ces/reporting-entities/njscr/. State of New Jersey Department of Health. Communicable Disease Reporting and Surveillance System . https://www.nj.gov/health/cd/reporting/cdrss/. Singh GK. Area deprivation and widening inequalities in US mortality, 1969-1998. Am J Public Health 2003;93(7):1137-43. Kind AJH, Buckingham WR. Making Neighborhood-Disadvantage Metrics Accessible - The Neighborhood Atlas. N Engl J Med 2018;378(26):2456-2458. Kind Amy JH, Buckingham William R. Making Neighborhood-Disadvantage Metrics Accessible — The Neighborhood Atlas. New England Journal of Medicine 2018;378(26):2456-2458. University of Wisconsin School of Medicine and Public Health. Area Deprivation Index V4. In: Health. UoWSoMaP, (ed); 2024. Gray RJ. A Class of K-Sample Tests for Comparing the Cumulative Incidence of a Competing Risk. The Annals of Statistics 1988;16(3):1141-1154. Fine JP, Gray RJ. A Proportional Hazards Model for the Subdistribution of a Competing Risk. Journal of the American Statistical Association 1999;94(446):496-509. Robilotti EV, Babady NE, Mead PA , et al. Determinants of COVID-19 disease severity in patients with cancer. Nat Med 2020;26(8):1218-1223. Zhang H, Han H, He T , et al. Clinical Characteristics and Outcomes of COVID-19-Infected Cancer Patients: A Systematic Review and Meta-Analysis. J Natl Cancer Inst 2021;113(4):371-380. Dai M, Liu D, Liu M , et al. Patients with Cancer Appear More Vulnerable to SARS-CoV-2: A Multicenter Study during the COVID-19 Outbreak. Cancer Discov 2020;10(6):783-791. Danielsen AC, Lee KMN, Boulicault M , et al. Sex disparities in COVID-19 outcomes in the United States: Quantifying and contextualizing variation. Social Science & Medicine 2022;294:114716. New Jersey State Health Assessment Data. Diabetes (Diagnosed) Prevalence by Sex and Age Group, New Jersey, 2021-2023 . https://www-doh.nj.gov/doh-shad/indicator/view/DiabetesPrevalence.AgeGroup.html. New Jersey State Health Assessment Data. Cardiovascular Disease - High Cholesterol by Sex, New Jersey, 2015, 2017, 2021 . https://www-doh.nj.gov/doh-shad/indicator/view/CardiovascularDiseaseHC.Sex.html. National Cancer Institute N, DHHS, . Cancer Trends Progress Report. In. Bethesda, MD; 2025. Wang Y, Cao P, Liu F , et al. Gender Differences in Unhealthy Lifestyle Behaviors among Adults with Diabetes in the United States between 1999 and 2018. Int J Environ Res Public Health 2022;19(24). Lusk JB, Hoffman MN, Clark AG , et al. Neighborhood Socioeconomic Disadvantage, Healthcare Access, and Outcomes of Hospitalizations for Common Pulmonary Conditions: A National Study of Medicare Beneficiaries. Ann Am Thorac Soc 2023;20(10):1416-1424. Castro AD, Mayr FB, Talisa VB , et al. Variation in Clinical Treatment and Outcomes by Race Among US Veterans Hospitalized With COVID-19. JAMA Netw Open 2022;5(10):e2238507. Rubinstein EB, Miller WL, Hudson SV , et al. Cancer Survivorship Care in Advanced Primary Care Practices: A Qualitative Study of Challenges and Opportunities. JAMA Internal Medicine 2017;177(12):1726-1732. Additional Declarations No competing interests reported. Supplementary Files 2025.0814cancersurvivorscovidsurvivalpaperFINALsupplementarymaterials.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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-7483142","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":527324057,"identity":"5b7a69c3-fda7-44e1-9d5f-958cdb0fdfcb","order_by":0,"name":"Lisa E. 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2","display":"","copyAsset":false,"role":"figure","size":171098,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCumulative Incidence Rate of Death due to Covid-19 and Death Due to Cancer in Cancer Survivors\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7483142/v1/c03a33cb4beef2c479dd6cb9.jpeg"},{"id":93335650,"identity":"00642d62-5458-4cbb-8b28-1fcdf0a99cc7","added_by":"auto","created_at":"2025-10-12 14:00:09","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":806692,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCumulative Incidence of Death by Sex, Race, Ethnicity, and Cancer Type\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7483142/v1/779c56095fd496cb76a45ade.jpeg"},{"id":98383664,"identity":"048d621b-aa1f-4fd5-9b15-579db53e1bad","added_by":"auto","created_at":"2025-12-17 08:10:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2601220,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7483142/v1/e88edb2a-1ec4-457c-abdf-a20f61188d4f.pdf"},{"id":93335639,"identity":"87f50899-b4da-4f76-b9c0-643550f6f0fc","added_by":"auto","created_at":"2025-10-12 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[1] As of August 2025, there have been more than 2.5 million deaths from COVID-19 in the US, with New Jersey ranking 21\u003csup\u003est\u003c/sup\u003e. [2] \u0026nbsp;Cancer is noted as an underlying condition for 10.2% of lab-confirmed COVID-19-associated deaths [3] \u0026nbsp;indicating the importance of identifying risk factors specific to cancer survivors. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; Cancer survivors are particularly susceptible to COVID-19 due to a combination of disease-related and treatment-induced immunosuppression. \u0026nbsp;Several studies have documented shorter survival following SARS-CoV-2 infection and higher COVID-19-related mortality among cancer survivors (13% to 40.5%) [4-11] or a 2 to 7-fold increase in mortality due to COVID-19 [12, 13] . Worse survival among cancer patients diagnosed with and treated for COVID-19 is associated with older age, having more comorbid conditions, having a history of tobacco use, being male, and being diagnosed with certain types of cancer (lung, solid tumors, hematological cancers, late stage cancer, multiple tumors, or a more recent diagnosis. [13-17] Furthermore, the emergence of the COVID-19 pandemic highlighted health inequities in the US that may be affecting COVID-19 health-related outcomes. [14,15,18,19] Fu et al found that Black cancer survivors experienced significantly more severe COVID-19 outcomes compared with White cancer survivors, after adjustment for demographic and clinical risk factors. [18] \u0026nbsp;Similarly, non-Hispanic Black and non-Hispanic Asian/Pacific Islander race, and Hispanic ethnicity independently increased the risk of COVID-19 death in New York. [14, 18, 19] Other data suggests that residence in less densely populated areas of the US was also associated with better outcomes. [15]\u003c/p\u003e\n\u003cp\u003ePrevious studies evaluating mortality among US cancer patients diagnosed with COVID-19 have been conducted at a single institution, registry, or health system. [18, 20-23] \u0026nbsp;One large multi-center study included 4749 patients across 83 centers in the US. [15] Large studies have been conducted by two states that have linked state cancer registry data with hospital medical records [14, 24, 25] These studies have consistently indicated that COVID-19 infection after a cancer diagnosis is associated with an increase in all-cause mortality. [25] \u0026nbsp;However, these studies didn\u0026rsquo;t address neighborhood specific factors that may influence COVID-19 health outcomes in cancer survivors. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe goal of the study was to examine sociodemographic and clinical factors associated with COVID-19 and cancer-related survival time following a COVID-19 diagnosis in a statewide cohort of cancer survivors in New Jersey. \u0026nbsp;New Jersey is uniquely positioned to study this population because of access to our robust cancer and COVID-19 registries, diverse population, high incidence rates, and being at the epicenter of the COVID-19 outbreak. \u0026nbsp;This data linkage and survival analysis allowed us to examine patterns and characteristics in New Jersey cancer survivors diagnosed with COVID-19. \u0026nbsp;\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e\u003cstrong\u003eStudy Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate covid-related outcomes in cancer survivors, we performed a data linkage between two New Jersey population based datasets \u0026ndash; the New Jersey State Cancer Registry (NJSCR) and the Communicable Disease Reporting and Surveillance System (CDRSS). \u0026nbsp;Then, a competing risks survival analysis was conducted including all NJ residents diagnosed with cancer from 1979 through 2022, alive in January 2020 (start of COVID-19 pandemic), and diagnosed with PCR-confirmed COVID-19 in 2020 and/or 2021. \u0026nbsp; Institutional Review Board approval was obtained from the Rutgers Institutional Review Board (NJSCR) and Rowan University Institutional Review Board (CDRSS), conforming with the standards of United States Federal Policy for the Protection of Human Subjects. \u0026nbsp; Data were obtained following data release policies of the New Jersey Department of Health and data use agreements were executed among all involved. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Sources and Linkage\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIncident cancer cases were obtained from the New Jersey State Cancer Registry (NJSCR), which is a high-quality population-based cancer incidence registry covering a diverse population of 9.3 million people residing in New Jersey. \u0026nbsp;Since 1978, NJ regulations have required health care facilities, physicians, dentists, and clinical laboratories to report cancer cases to the NJSCR when initially diagnosed, when the patient is first admitted or treated, and/or when diagnosed with subsequent primary cancers. [26] Cancer data from NJSCR consistently meets the highest standards, including 98% or higher completeness, \u0026le; 2% missing age, sex, or county, and \u0026le; 3% missing race. Vital status is updated regularly using state death certificates and the National Death Index (NDI). \u0026nbsp;NJSCR data for the linkage consisted of demographic and clinical variables for people diagnosed with cancer from 1979 through 2021 and were alive at the start of 2020 (n=744,929). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePCR-confirmed COVID-19 cases were obtained from the New Jersey Department of Health (NJDOH), Communicable Disease Reporting and Surveillance System (CDRSS), considered the most complete accounting of COVID-19 cases. \u0026nbsp;The CDRSS is an electronic, web-enabled system where public health partners statewide can instantly report and track communicable disease incidence. Public Law 116-136, \u0026sect; 18115(a), the Coronavirus Aid, Relief, and Economic Security (CARES) Act, required \u0026ldquo;every laboratory that performed or analyzed a test intended to detect SARS-CoV-2 or to diagnose a possible case of COVID-19\u0026rdquo; to report the results, positive and negative, from each such test to public health authorities. \u0026nbsp;Positive laboratory reports are recorded into CDRSS either by electronic laboratory reporting transmissions or manual data entry at clinical laboratories and acute care hospitals. Follow-up information was added by trained users in various public health roles. [27]\u0026nbsp; CDRSS data for the linkage consisted of PCR-confirmed COVID-19 cases diagnosed among New Jersey residents during 2020-2021 (n=1,526,213). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCancer survivors were linked to the Area Deprivation Index (ADI) by census block group. \u0026nbsp; The ADI is based on a measure originally created by the Health Resources and Services Administration (HRSA) adapted and validated to the Census Block Group neighborhood level. [28-31] 29-32 The ADI allows for rankings of neighborhoods by socioeconomic disadvantage in a region of interest. [30] It is a composite measure comprised of 17 U.S. Census indicators in the domains of education, employment, occupation, income, housing, and poverty at the census block group level based on 2017-2021 American Community Survey data. \u0026nbsp;State-level decile rankings for New Jersey were used, constructed by ranking the ADI scores from low to high for New Jersey and grouping the block groups into bins corresponding to each 10% range of the ADI. \u0026nbsp;Rank 1 is the lowest ADI (least disadvantaged) and rank 10 is the highest ADI (most disadvantaged).\u003c/p\u003e\n\u003cp\u003eProbabilistic linkages between cancer cases (NJSCR) and COVID-19 cases (CDRSS) were performed using Match*Pro (Version 2.3) linkage software. The linkage used first name, last name, date of birth, address, phone number, sex, and date of death as matching variables. To reduce the amount of resource-intensive manual review, automatic Match*Pro classification rules were created after thorough review of data patterns in the results. These rules automatically assigned match status to pairs that met pre-defined criteria, thereby reducing manual review volume, maximizing the number of true matches, and minimizing false matches. Accurint\u0026reg; was used extensively to validate match status of a subset of pairs captured by the rules and for adjudication of uncertain pairs.\u003c/p\u003e\n\u003cp\u003eNJSCR staff conducted extensive quality control procedures and created an analytic dataset that included date of COVID-19 onset, date and cause of death, demographics, tumor characteristics, and area deprivation indices. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRisk Factors of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere were eight independent variables of interest for the analysis: age at COVID-19 diagnosis, time from cancer diagnosis to COVID diagnosis, sex (male, female), race (White, Black, Asian or Pacific Islander (API), other, unknown), ethnicity (Hispanic vs not), cancer type [blood/hematologic cancers (Lymphoma, Myeloma, Leukemia) or solid tumors], cancer stage (in situ, localized, regional, distant), and Area Deprivation Index.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe focus of this analysis was on mortality in cancer survivors diagnosed with COVID-19. \u0026nbsp; The endpoints of interest were death from COVID-19 and death from cancer, with the latter treated as competing risks. Deaths that were not due to cancer or COVID-19, such as accidents or cardiovascular-related deaths, were treated as censoring events in the analysis of competing events. Data from patients who were alive at their follow-up are included in the analysis and considered as an independent censoring event for observing COVID-specific death and cancer-specific death. \u0026nbsp;Follow-up time was calculated in months starting from the date of COVID-19 diagnosis to the date of death or censoring (whichever occurred first).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCumulative incidence rates of death due to COVID-19 or cancer were estimated and compared between cancer survivors who were diagnosed with COVID-19 using Gray\u0026rsquo;s method. [32] The associations between the covariates and times to COVID-specific death and cancer-specific death were analyzed using the sub-distribution hazard ratio regression model based on Fine and Gray\u0026rsquo;s method. [33] Risk factors of interest included race, ethnicity, stage of disease at diagnosis, national and state ADI ranks, time between cancer diagnosis and covid diagnosis, cancer type and age at covid diagnosis. \u0026nbsp;Since subjects with cancer who died before COVID-19 diagnosis weren\u0026rsquo;t included in the analysis, we adjusted the competing risk regression model using the left truncation method by including the time from cancer diagnosis to COVID diagnosis as a covariate. All analyses were performed in R, where the competing risk analyses were done using the package \u0026ldquo;cmprsk.\u0026rdquo;\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eCharacteristics of the Sample\u003c/h2\u003e\u003cp\u003eA total of 63,341 cancer survivors were identified who were diagnosed with in situ or malignant cancer and also had a COVID-19 diagnosis between February 21, 2020 and January 1, 2022. Due to small numbers unknown cancer type (n\u0026thinsp;=\u0026thinsp;1) and other sex were excluded (n\u0026thinsp;=\u0026thinsp;10). The final analytic cohort included a total of 63,330 cancer survivors (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Of these, 4,625 died from COVID-19, and 3,004 died from cancer-related causes. The maximum follow-up time was 34 months among those who died from COVID-19 and 40 months among those who died from cancer.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the summary statistics of risk factors stratified by study participant status at the time of analysis (censored, died from COVID, or died from cancer-related causes). More than half of the cancer survivors who had COVID-19 were female (55%), while more than half of those who died of COVID-19 were male (58%). Those who died of COVID-19 had a median age of 81 years at the time of their COVID-19 diagnosis, while those who died of cancer had a median age of 71 at the time of their COVID-19 diagnosis.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eCharacteristics of Cancer Survivors Overall and by Cause of Death\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverall \u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;63,330\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCOVID death \u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;4,625\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCancer death \u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;3,004\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCensored \u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;55,701\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28,523 (45%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,702 (58%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,432 (48%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e24,389 (44%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34,807 (55%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,923 (42%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,572 (52%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e31,312 (56%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRace\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e..\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e51,707 (82%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3,702 (80%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2,416 (80%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e45,589 (82%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlack\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8,339 (13%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e752 (16%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e462 (15%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7,125 (13%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAPI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,044 (3.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e141 (3.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e101 (3.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1,802 (3.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e707 (1.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28 (0.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21 (0.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e658 (1.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e533 (0.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (\u0026lt;\u0026thinsp;0.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (0.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e527 (0.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEthnicity\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Hispanic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53,533 (85%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4,011 (87%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2,537 (84%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e46,985 (84%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHispanic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9,797 (15%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e614 (13%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e467 (16%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8,716 (16%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCancer Stage\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIn situ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8,237 (13%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e460 (9.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e132 (4.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7,645 (14%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLocalized\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33,456 (53%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,415 (52%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e916 (30%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e30,125 (54%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegional\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10,829 (17%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e682 (15%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e705 (23%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9,442 (17%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7,318 (12%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e751 (16%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,072 (36%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5,495 (9.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnknown/Unstaged\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3,490 (5.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e317 (6.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e179 (6.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2,994 (5.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCancer type\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSolid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e57,217 (90%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4,080 (88%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2,706 (90%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e50,431 (91%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlood/Hematopoietic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6,113 (9.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e545 (12%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e298 (9.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5,270 (9.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge, COVID-19 dx\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e66 (56, 77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e81 (72, 88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e71 (62, 80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e65 (55, 75)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCancer dx - COVID dx(yrs)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8 (3, 14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (4, 17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (1, 9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8 (3, 14)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eArea Deprivation Index\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.00 (3.00, 8.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.00 (3.00, 8.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.00 (4.00, 9.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.00 (3.00, 8.00)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003csup\u003e1\u003c/sup\u003en (%) for categorical variables; median (IQR) for continuous variables\u003c/p\u003e\u003cp\u003eDx\u0026thinsp;=\u0026thinsp;Diagnosis\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eCumulative Incidence Rates for COVID-specific and Cancer-specific Deaths\u003c/h3\u003e\n\u003cp\u003eThe cumulative rates as a function of months since COVID diagnosis are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e for both COVID-specific and cancer-specific deaths. Cancer survivors were more likely to die from COVID-19 than cancer within the first five months following their COVID-19 diagnosis. The estimated cumulative incidence rate of COVID-19-specific death plateaued at approximately five months, whereas the cumulative incidence rate of cancer-specific death continued to rise over time. Approximately three years after a COVID-19 diagnosis, the cumulative incidence rate of cancer-specific death surpassed that of COVID-19-specific death.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the short-term and long-term cumulative incidence rates of COVID-19-specific and cancer-specific death at 2 weeks, 1 month, and 36 months after a COVID diagnosis. At 2 weeks, 3.7% of people with cancer died from COVID-19, compared to 0.30% from cancer. At one month, 5.8% died from COVID-19, whereas 0.55% died from cancer. By 36 months, 7.7% died from COVID-19, whereas 8.8% died from cancer.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eCumulative Incidence of Death at 0.5, 1.0, and 36 months after COVID-19 diagnosis in Cancer Survivors\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDeath\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.5 months\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 month\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36 months\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCOVID-19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.7% (3.5%, 3.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.8% (5.6%, 6.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.7% (7.4%, 7.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.30% (0.26%, 0.34%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.55% (0.49%, 0.61%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.8% (8.1%, 9.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the cumulative incidence rates as a function of months since COVID-19 diagnosis for COVID-19-specific and cancer-specific death, separately for the important baseline characteristics, including sex, race, ethnicity, and cancer type. The short-term and long-term specific cumulative incidence rates at 2 weeks, 1 month, and 36 months are reported for COVID-specific death in \u003cb\u003eTable A\u003c/b\u003e and cancer-specific death in \u003cb\u003eTable B\u003c/b\u003e in the Supplemental material. Males were significantly more likely to die from either COVID-19 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) or cancer (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) than females although the gap between sexes was greater for covid deaths. Black cancer survivors were more likely to die from COVID-19 than those identified as White, API, and other race (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, respectively). Hispanic cancer survivors appeared to be more likely to die from COVID-19 and death occurred more rapidly than in their non-Hispanic counterparts (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).Of note is the plateau seen around 5 months post covid-diagnosis that is consistent across covariates and strata.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eCompeting Risk Regression Analysis of Risk Factors for COVID-specific Death\u003c/h2\u003e\u003cp\u003eResults of the univariate regression analysis are presented in \u003cb\u003eTable\u0026nbsp;3\u003c/b\u003e. Among the demographic variables analyzed, female sex and Hispanic ethnicity were identified as protective factors against COVID-19 mortality in cancer patients, while Black race, compared to White, was recognized as a risk factor (HR\u0026thinsp;=\u0026thinsp;1.271, 95% CI\u0026thinsp;=\u0026thinsp;1.175\u0026ndash;1.375, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Survivors of hematologic cancers had a higher risk of dying from COVID-19 compared to those with solid cancers (HR\u0026thinsp;=\u0026thinsp;1.259, 95% CI\u0026thinsp;=\u0026thinsp;1.152\u0026ndash;1.376, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Other factors increasing the risk for dying from COVID-19 were residents of areas with greater socioeconomic disadvantage (HR\u0026thinsp;=\u0026thinsp;1.032, 95% CI\u0026thinsp;=\u0026thinsp;1.021\u0026ndash;1.042, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), older survivors (HR\u0026thinsp;=\u0026thinsp;1.08, 95% CI\u0026thinsp;=\u0026thinsp;1.077\u0026ndash;1.083, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and those further from their cancer diagnosis (HR\u0026thinsp;=\u0026thinsp;1.023, 95% CI\u0026thinsp;=\u0026thinsp;1.020\u0026ndash;1.026, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"1\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eTable\u0026nbsp;3. Univariate Competing Risks Regression Model for COVID-19-specific Death\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRisk factors\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHR\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95% CI\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.572\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.539, 0.606\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRace\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlack\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.271\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.175, 1.375\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAPI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.972\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.822, 1.150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.740\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.572\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.394, 0.828\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.015, 0.234\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEthnicity\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Hispanic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHispanic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.842\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.774, 0.917\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eStage\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIn situ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLocalized\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.293\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.171, 1.429\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegional\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.117\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.992, 1.257\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.067\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.870\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.665, 2.100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnknown or Unstaged\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.675\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.452, 1.932\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCancer type\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSolid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlood/Hematopoietic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.259\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.152, 1.376\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eArea Deprivation Index\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.021, 1.042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCancer dx to COVID-19 dx, years\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.020, 1.026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge at COVID-19 dx\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.077, 1.083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eHR = Hazard Ratio, CI\u0026thinsp;=\u0026thinsp;Confidence Interval, Dx\u0026thinsp;=\u0026thinsp;diagnosis\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAll variables were entered into the multivariate regression model, results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e. After adjusting for other covariates, females continued to have a lower risk of dying from COVID-19 than males (HR\u0026thinsp;=\u0026thinsp;0.63, 95% CI\u0026thinsp;=\u0026thinsp;0.58\u0026ndash;0.65, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Relative to whites, Blacks were 47% more likely (HR\u0026thinsp;=\u0026thinsp;1.47, 95% CI\u0026thinsp;=\u0026thinsp;1.35\u0026ndash;1.60, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and API cancer survivors were 44% (HR\u0026thinsp;=\u0026thinsp;1.44, 95% CI\u0026thinsp;=\u0026thinsp;1.22\u0026ndash;1.71, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) significantly higher risk of COVID-19-specific death in the adjusted model. Hispanic cancer survivors did not hold their protective effect in the multivariate model, and when adjusted for other variables had a significantly higher risk of dying from COVID-19 compared to non-Hispanics (HR\u0026thinsp;=\u0026thinsp;1.20, 95% CI\u0026thinsp;=\u0026thinsp;1.10\u0026ndash;1.32, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Patients with distant stage cancer continued to have a significantly higher risk of dying from COVID-19 compared to patients with in-situ cancers (HR\u0026thinsp;=\u0026thinsp;1.74, 95% CI\u0026thinsp;=\u0026thinsp;1.52\u0026ndash;1.99, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) while regional stage cancer had an increased risk in the adjusted model (HR\u0026thinsp;=\u0026thinsp;1.22, 95% CI\u0026thinsp;=\u0026thinsp;1.09\u0026ndash;1.38, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Survivors of hematologic cancers continued to have a higher risk of dying from COVID-19 compared to those with solid cancers (HR\u0026thinsp;=\u0026thinsp;1.17, 95% CI\u0026thinsp;=\u0026thinsp;1.05\u0026ndash;1.31, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006), although lost significance. Individuals residing in more socioeconomically disadvantaged areas (HR\u0026thinsp;=\u0026thinsp;1.02, 95% CI\u0026thinsp;=\u0026thinsp;1.01\u0026ndash;1.03, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and those who were older at COVID-19 diagnosis (HR\u0026thinsp;=\u0026thinsp;1.09, 95% CI\u0026thinsp;=\u0026thinsp;1.08\u0026ndash;1.09, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) had higher risks of dying from COVID-19. Time between cancer diagnosis and covid diagnosis did not have meaningful results in the multivariate model.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eMultivariate Competing Risks Regression Model on Covid-specific Death\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRisk factors\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHR\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95% CI\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.58, 0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRace\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlack\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.35, 1.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAPI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.22, 1.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.54, 1.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.02, 0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEthnicity\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Hispanic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHispanic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.10, 1.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eStage\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIn situ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLocalized\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.98, 1.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegional\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.09, 1.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.52, 1.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnknown/Unstaged\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.10, 1.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCancer type\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSolid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlood/Hematopoietic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.05, 1.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eArea Deprivation Index\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.01, 1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCancer Dx to COVID Dx, years\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.99, 1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge at COVID Dx\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.08, 1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eHR = Hazard Ratio, CI\u0026thinsp;=\u0026thinsp;Confidence Interval, Dx\u0026thinsp;=\u0026thinsp;diagnosis\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this large population-based study of cancer survivors diagnosed with COVID-19 followed three years after a COVID-19 diagnosis, we found that older cancer survivors, those identified as male, Black, API, or Hispanic, as well as those living in more socioeconomically disadvantaged areas of New Jersey, diagnosed with hematologic cancers or late-stage tumors had a significantly higher risk of dying from COVID-19. These findings are consistent with existing studies in the US and Europe suggesting that older age, [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] male sex, [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] hematologic cancer diagnosis, [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] and later-stage disease [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] are risk factors of COVID-19-related death among cancer survivors. Our study extends prior literature by showing that greater neighborhood socioeconomic disadvantage was associated with higher risk for COVID-19-related death, and consistent with general population studies, we illustrate that Black cancer survivors have a higher probability of dying from COVID-19.\u003c/p\u003e\u003cp\u003eVariations within sociodemographic cancer survivor subgroups, such as Hispanic and Black populations in New Jersey, are associated with further inequities in COVID-19-related outcomes. Not only did Black individuals have the lowest survival from COVID-19 over the entire study period, but they also experienced the shortest survival times after a diagnosis of COVID-19 compared to individuals of other races. These findings demonstrate that cancer survivors identified as Black experience a disproportionate burden from COVID-19-related death. Increasing awareness and education about the importance of preventing COVID-19 infection would benefit Black cancer survivors.\u003c/p\u003e\u003cp\u003ePrevious studies found that male sex is a risk factor for COVID-19 death and our results extend that female sex demonstrates a significant protective factor for COVID-19 death. COVID-19 mortality differences between male and female sex have been studied [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] and results vary by state. Comorbidities such as diabetes[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] or cardiovascular disease [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] are more prevalent in New Jersey males than females and may further increase the risk of COVID-19 death. Females are also known to exhibit slightly higher rates of cancer screenings [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] and tend to have a healthier lifestyle [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] lending to the protective effect.\u003c/p\u003e\u003cp\u003eWe found that the cumulative incidence COVID-19 death increased dramatically from 0\u0026ndash;5 months post COVID-19 diagnosis and then stabilized through the 40th month of observation. This plateau was seen in both sexes, all races/ethnicities, and cancer types. Vaccinations, herd immunity, improved primary and secondary prevention, increased awareness, and effective treatments could potentially be attributable to this pattern.\u003c/p\u003e\u003cp\u003ePatients with hematologic cancers are especially vulnerable to COVID-19 likely due to the more intensive and immunosuppressive therapies for these patients versus solid tumor patients, [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] and our study confirmed the poorer outcomes compared to those diagnosed with solid tumors (HR\u0026thinsp;=\u0026thinsp;1.17, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006) when adjusted for covariates. While COVID-19 mortality has decreased over time, as the virus evolves some therapies may lose their clinical efficacy against new variants or new variants may be more severe. Therefore, COVID-19 risk will remain a major challenge for patients with hematologic malignancies. It will be important to manage risk factors for this population to prevent severe COVID-19.\u003c/p\u003e\u003cp\u003eNeighborhood or area-level socioeconomic disadvantage is increasingly recognized as a social driver of health outcomes and is characterized by multiple factors. [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] Our study confirmed that neighborhood socioeconomic disadvantage was independently associated with inferior survival from COVID-19 (HR\u0026thinsp;=\u0026thinsp;1.02, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in cancer survivors, with a 2% increase in the risk with a one-unit increase in the area deprivation rank. Neighborhood socioeconomic status may be used as an indicator of access to resources and could impact the time from onset of COVID-19 symptoms to diagnosis and subsequent receipt of care. Several key factors may contribute to survival inequities among cancer survivors living in socioeconomically disadvantaged communities. People may be disproportionately represented in essential jobs\u0026mdash;including health care, maintenance, delivery, and public transportation\u0026mdash;that were excluded from shelter-in-place mandates. They also tend to rely more heavily on public transportation and are more likely to live in multigenerational households. These living arrangements are especially common in crowded and socioeconomically disadvantaged settings, where effective physical distancing is more difficult. As a result, the risk of COVID-19 infection increases, particularly for elderly family members with chronic illnesses, including cancer. Additionally, people facing socioeconomic challenges may be unable to take time off work, which can lead to delays in receiving care or interruptions in cancer treatment and/or COVID-19 therapy. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e],[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] These barriers can significantly increase the risk of poorer survival outcomes.\u003c/p\u003e\n\u003ch3\u003eStrengths and Limitations\u003c/h3\u003e\n\u003cp\u003eRobust data sources are one of the strengths of this project. Both cancer and COVID-19 cases are reportable by law and extensive efforts have been undertaken to ensure a complete and reliable dataset for analysis. Despite this strength, it is likely that COVID-19 diagnoses are underestimated because health systems were severely compromised during the pandemic and staffing was decreased, thereby affecting the completeness of COVID-19 data collection. While labs were required to report incidence immediately, additional hospitalization information was not as complete. Our ability to reliably collect COVID-19 incidence has also changed over time as testing requirements loosened or individuals used home testing kits, which are not reported to the NJDOH. To reduce this effect, we limited our analysis to the first two years of the pandemic when testing was required for most New Jerseyans. There are notable data points not included in this analysis due to availability at the time. Future research will benefit from the addition of information about multiple COVID-19 infections and COVID-19 vaccine receipt.\u003c/p\u003e\u003cp\u003eSeveral cancer centers have created care registries to rapidly collect and disseminate real-time data to help clinicians make evidence-based decisions as they encounter individuals affected by COVID-19 (ASCO, ASH, CCC19 [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]). While it is important to understand New Jersey-relevant data to effect informed decisions about patient care, there are broader implications. New Jersey has a large, diverse population with a high cancer incidence. Findings from this study can be implemented at other cancer centers nationwide to develop interventions that target specific demographic and malignancy groups for greater impact on COVID-19-related mortality.\u003c/p\u003e\u003cp\u003eIn conclusion, this study of cancer survivors illustrates that males, Black, API, and Hispanic individuals, as well as those living in more socioeconomically disadvantaged areas of New Jersey, diagnosed with a hematologic cancer or at late-stage tumor had increased risks of COVID-19-related death. Targeting more aggressive interventions to this vulnerable cancer population (e.g., access to vaccines or early treatments) may reduce morbidity and mortality. Cancer centers and other facilities treating cancer survivors may consider targeted screening questions to guide prevention strategies, with particular attention to men, Black, API, and Hispanic survivors, as well as those residing in underserved, economically strained neighborhoods. These measures could include recommendations and clinic reminders regarding staying up to date with COVID-19 vaccinations, donning masks in public settings, COVID-19 testing at symptom onset, as well as increased access to early treatment options. Additionally, healthcare providers can play a vital role in educating high-risk cancer survivors about COVID-19 prevention, early detection, and treatment. Using these data to prevent death among vulnerable groups at high risk for hospitalization and death is an urgent priority to mitigate persistent outcomes inequities.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"449\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eAbbreviation\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 352px;\"\u003e\n \u003cp\u003eMeaning\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eADI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 352px;\"\u003e\n \u003cp\u003eArea Deprivation Index\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eAPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 352px;\"\u003e\n \u003cp\u003eAsian or Pacific Islander\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eASCO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 352px;\"\u003e\n \u003cp\u003eAmerican Society of Clinical Oncology\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eASH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 352px;\"\u003e\n \u003cp\u003eAmerican Society of Hematology\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eCCC19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 352px;\"\u003e\n \u003cp\u003eThe COVID-19 and Cancer Consortium\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eCDRSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 352px;\"\u003e\n \u003cp\u003eCommunicable Disease Reporting and Surveillance System\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 352px;\"\u003e\n \u003cp\u003eConfidence Interval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eDx\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 352px;\"\u003e\n \u003cp\u003eDiagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eELR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 352px;\"\u003e\n \u003cp\u003eElectronic Laboratory Reporting\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eHRSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 352px;\"\u003e\n \u003cp\u003eHealth Resources and Services Administration\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 352px;\"\u003e\n \u003cp\u003eHazard Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eNJSCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 352px;\"\u003e\n \u003cp\u003eNew Jersey State Cancer Registry\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003ePCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 352px;\"\u003e\n \u003cp\u003ePolymerase chain reaction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eUS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 352px;\"\u003e\n \u003cp\u003eUnited States\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eConflicts of Interest\u003c/h2\u003e\u003cp\u003eNone Declared\u003c/p\u003e\u003c/p\u003e\u003cp\u003eClinical Trial Number: not applicable\u003c/p\u003e\u003cp\u003e Human Ethics: This study has been approved by the Rutgers Institutional Review Board [NJSCR] Protocol # Pro2020001473 and Rowan University Institutional Review Board [NJDOH] # PRO-2023-468.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003cp\u003edeclarations: not applicable\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent to Publish\u003c/strong\u003e\u003cp\u003edeclaration: not applicable\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eDevelopment of Specific Aims: LP, HL, AE, AL, SMData Collection, Linkage, and Preparation: SL, SG, SF, JP, JSData Analysis, Tables, Figures, Interpretation: SG, HL, SL, LP, SMMain manuscript writing: LP, SM, HL, SG, SLAll authors reviewed and provided input for manuscript draft.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank the staff at the New Jersey Department of Health who collected cancer and COVID-19 data, especially those who worked overtime during the pandemic to improve data collection. The New Jersey State Cancer Registry is funded by the National Cancer Institute\u0026rsquo;s Surveillance, Epidemiology and End Results (SEER) Program (#75N91021D00009), Centers for Disease Control and Prevention\u0026rsquo;s National Program of Cancer Registries (#NU58DP007117) with additional support from the State of New Jersey, New Jersey Department of Health, and the Rutgers Cancer Institute of New Jersey.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData from the New Jersey State Cancer Registry and the Communicable Disease Reporting and Surveillance System cannot be shared openly to protect privacy.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eNew Jersey Department of Health. Respiratory Illness Surveillance Report, Week Ending May 31, 2025 (MMWR 22). 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Area deprivation and widening inequalities in US mortality, 1969-1998. Am J Public Health 2003;93(7):1137-43.\u003c/li\u003e\n\u003cli\u003eKind AJH, Buckingham WR. Making Neighborhood-Disadvantage Metrics Accessible - The Neighborhood Atlas. N Engl J Med 2018;378(26):2456-2458.\u003c/li\u003e\n\u003cli\u003eKind Amy JH, Buckingham William R. Making Neighborhood-Disadvantage Metrics Accessible \u0026mdash; The Neighborhood Atlas. New England Journal of Medicine 2018;378(26):2456-2458.\u003c/li\u003e\n\u003cli\u003eUniversity of Wisconsin School of Medicine and Public Health. Area Deprivation Index V4. In: Health. UoWSoMaP, (ed); 2024.\u003c/li\u003e\n\u003cli\u003eGray RJ. A Class of K-Sample Tests for Comparing the Cumulative Incidence of a Competing Risk. The Annals of Statistics 1988;16(3):1141-1154.\u003c/li\u003e\n\u003cli\u003eFine JP, Gray RJ. A Proportional Hazards Model for the Subdistribution of a Competing Risk. Journal of the American Statistical Association 1999;94(446):496-509.\u003c/li\u003e\n\u003cli\u003eRobilotti EV, Babady NE, Mead PA\u003cem\u003e, et al.\u003c/em\u003e Determinants of COVID-19 disease severity in patients with cancer. Nat Med 2020;26(8):1218-1223.\u003c/li\u003e\n\u003cli\u003eZhang H, Han H, He T\u003cem\u003e, et al.\u003c/em\u003e Clinical Characteristics and Outcomes of COVID-19-Infected Cancer Patients: A Systematic Review and Meta-Analysis. J Natl Cancer Inst 2021;113(4):371-380.\u003c/li\u003e\n\u003cli\u003eDai M, Liu D, Liu M\u003cem\u003e, et al.\u003c/em\u003e Patients with Cancer Appear More Vulnerable to SARS-CoV-2: A Multicenter Study during the COVID-19 Outbreak. Cancer Discov 2020;10(6):783-791.\u003c/li\u003e\n\u003cli\u003eDanielsen AC, Lee KMN, Boulicault M\u003cem\u003e, et al.\u003c/em\u003e Sex disparities in COVID-19 outcomes in the United States: Quantifying and contextualizing variation. Social Science \u0026amp; Medicine 2022;294:114716.\u003c/li\u003e\n\u003cli\u003eNew Jersey State Health Assessment Data. \u003cem\u003eDiabetes (Diagnosed) Prevalence by Sex and Age Group, New Jersey, 2021-2023\u003c/em\u003e. https://www-doh.nj.gov/doh-shad/indicator/view/DiabetesPrevalence.AgeGroup.html.\u003c/li\u003e\n\u003cli\u003eNew Jersey State Health Assessment Data. \u003cem\u003eCardiovascular Disease - High Cholesterol by Sex, New Jersey, 2015, 2017, 2021\u003c/em\u003e. https://www-doh.nj.gov/doh-shad/indicator/view/CardiovascularDiseaseHC.Sex.html.\u003c/li\u003e\n\u003cli\u003eNational Cancer Institute N, DHHS, . Cancer Trends Progress Report. In. Bethesda, MD; 2025.\u003c/li\u003e\n\u003cli\u003eWang Y, Cao P, Liu F\u003cem\u003e, et al.\u003c/em\u003e Gender Differences in Unhealthy Lifestyle Behaviors among Adults with Diabetes in the United States between 1999 and 2018. Int J Environ Res Public Health 2022;19(24).\u003c/li\u003e\n\u003cli\u003eLusk JB, Hoffman MN, Clark AG\u003cem\u003e, et al.\u003c/em\u003e Neighborhood Socioeconomic Disadvantage, Healthcare Access, and Outcomes of Hospitalizations for Common Pulmonary Conditions: A National Study of Medicare Beneficiaries. Ann Am Thorac Soc 2023;20(10):1416-1424.\u003c/li\u003e\n\u003cli\u003eCastro AD, Mayr FB, Talisa VB\u003cem\u003e, et al.\u003c/em\u003e Variation in Clinical Treatment and Outcomes by Race Among US Veterans Hospitalized With COVID-19. JAMA Netw Open 2022;5(10):e2238507.\u003c/li\u003e\n\u003cli\u003eRubinstein EB, Miller WL, Hudson SV\u003cem\u003e, et al.\u003c/em\u003e Cancer Survivorship Care in Advanced Primary Care Practices: A Qualitative Study of Challenges and Opportunities. JAMA Internal Medicine 2017;177(12):1726-1732.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cancer survivors, COVID, social vulnerability, social determinants","lastPublishedDoi":"10.21203/rs.3.rs-7483142/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7483142/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003ePeople diagnosed with cancer are more susceptible to COVID-19-related mortality. Other than race, age, and sex, less is known about other factors associated with a higher risk of death after COVID-19 diagnosis among cancer survivors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eThe goal was to examine factors associated with survival time in cancer survivors following a COVID-19 diagnosis, focusing on sociodemographic and clinical factors in a population-based cohort of New Jersey residents.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: Cancer cases identified using the New Jersey State Cancer Registry (NJSCR) were linked with COVID-19 cases from Communicable Disease Reporting and Surveillance System (CDRSS) diagnosed in 2020-21, creating an analytic dataset 63,330 people. Area Deprivation Index incorporated environmental and structural factors related to neighborhood disadvantage. Competing risk regression analyses were used to identify factors associated with survival.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003ePeople diagnosed with cancer were more likely to die from COVID-19 than die from cancer within the first five months following their COVID-19 diagnosis. Cumulative incidence rate of cancer-specific deaths surpassed that of COVID-19-specific deaths (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001), after three years. After adjusting for other covariates, females had a lower risk of dying from COVID-19 than males (HR=0.63, 95% CI= 0.58-0.65, P\u0026lt;0.001). Relative to whites, Blacks were 47% more likely (HR=1.47, 95% CI= 1.35-1.60, P\u0026lt;0.001) to die from COVID-19. Older age, API race, Hispanic ethnicity, those living in more socioeconomically disadvantaged areas of New Jersey, diagnosed with hematological cancers, or diagnosed later stage also had a significantly higher risk of dying from COVID-19 (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e. This study of cancer survivors illustrates that males, Black, API, or Hispanic individuals, as well as those living in more socioeconomically disadvantaged areas of New Jersey, diagnosed with a hematologic cancer or at late-stage tumor had increased risks of COVID-19-related death. Targeting more aggressive interventions to this vulnerable cancer population (e.g., access to vaccines or early treatments) may reduce morbidity and mortality.\u003c/p\u003e","manuscriptTitle":"Risk Factors and COVID-19 Survival Among Cancer Survivors in New Jersey: A Population-based study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-12 14:00:04","doi":"10.21203/rs.3.rs-7483142/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5e76174e-09e4-41d1-8f66-5d1b9bab3314","owner":[],"postedDate":"October 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-17T08:09:34+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-12 14:00:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7483142","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7483142","identity":"rs-7483142","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}