Trends and Disparities in Cardiovascular Disease Burden Among Cancer Deaths in the U.S. (1999–2020): A CDC WONDER Disproportionality Analysis

Trends and Disparities in Cardiovascular Disease Burden Among Cancer Deaths in the U.S. (1999–2020): A CDC WONDER Disproportionality Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Trends and Disparities in Cardiovascular Disease Burden Among Cancer Deaths in the U.S. (1999–2020): A CDC WONDER Disproportionality Analysis Faizan Ahmed, Tehmasp Rehman Mirza, Fenilkumar Kotadiya, Yusra Junaid, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7776019/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 Cardiovascular disease (CVD) and cancer are the leading causes of mortality in the U.S., with significant overlap in risk factors and outcomes. This study examines the burden of CVD in cancer-related deaths using the Reporting Odds Ratio (ROR) to identify disparities by sex, race, region, and urbanization from 1999 to 2020. Methods A disproportionality analysis was conducted to calculate ROR of CVD burden in cancer patients compared to non-cancer population across age groups 15–85 years. A ROR greater than 1 indicated a higher burden of CVD in cancer patients. Average Annual Percentage Changes (AAPCs) were calculated to evaluate trends, with p-values determining significance. Results The highest ROR across age groups was in the 15–24 group at 1.83, declining to 0.34 in ages 55–64, and rising to 0.56 in ages 85+. The yearly trends showed ROR increased in all groups, with the highest increase in 55–64 group (AAPC: 1.69). Males exhibited higher RORs than females, with the steepest increase in middle-aged females (AAPC: 2.59). Young Hispanics had the highest ROR among racial groups (15–24: 2.558), while the Midwest showed the highest regional ROR (15–24: 2.311). Urban areas had higher RORs than rural areas, with medical facilities reporting the highest RORs. Conclusion This study highlights significant disparities in CVD burden among cancer patients, with younger individuals, males, Hispanics, and urban residents at higher risk. The trends underscore the need for targeted cardio-oncological interventions, to mitigate the increasing burden of CVD and cancer. Addressing systemic disparities in healthcare access and delivery is critical to improving outcomes. Cardiovascular Oncology Mortality Odds Ratio Burden Epidemiology United States Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Cardiovascular disease (CVD) and cancer constitute the two most common primary causes of the mortality burden in the US. In 2020, heart disease was recorded as the number one cause of death (690,882 deaths) followed by cancer marking 598,932 deaths. This marks a substantial increase in deaths by a solid 4.8% since 2012 1 . Although CVD and cancer independently are fatal, when the lines intersect, the risk further increases by two to six times. CVD has become the most common cause of mortality for people with different types of cancer 2 . Depending upon the month since diagnosis and the prognosis of the cancer involved, If the risk of fatality from CVD is > 20%, then it results in a < 30% risk of fatality from cancer. It is common knowledge that both diseases share several risk factors like DM, HTN, age, gender, tobacco smoking, obesity and western diet etc and hence the strategic reduction of modifiable risk factors, screening, interventions and implementation of the ideal health metrics proposed by the AHA have resulted in better outcomes for both, cardiovascular health and decreased cardiovascular events related to cancer-therapy. Better healthcare has resulted in an increased life expectancy of 1.1 years from CVD and 0·4 years from cancer 3 . Despite the remarkable advances in the field of cardio-oncology, due to discriminatory policies, reduced access to high-quality care in all geographical regions and long-standing wealth inequalities, there still lies a burden due to racial, gender, social and geographical disparities 4 . According to the cancer stats in 2023, AIAN people reported the highest mortality rate of cancer irrespective of gender, followed closely by Black people 5 . The mortality rates between males and females showed a narrow sex-gap. In another study, Patel SR et al highlighted that the risk of CVD related mortality in cancer patients increased 1.16% in comparison with the general population, but when categorized by ethnicity, the mortality ratio was 1.76, 2.28, 3.68, 2.65, and 1.84 for Whites, Blacks, AIAN, Asians/Pacific Islanders, and Hispanic/Latinx, respectively 6 . Although several studies have been done on the proposed interaction between CVD and cancer, yet the burden it collectively pertains needs to be explored more. The present study highlights how the intersection between the two most significant global burdens is proportional with the racial, gender and place of death. Methods Study Setting and Population: Disproportionality Analysis is a statistical method commonly used in pharmacovigilance to understand the relation between specific drugs and reported adverse effects. It explores the disparity between expected and reported values. Applying a similar reasoning, we explored the contrast in burden of CVD in cancer and non-cancer deaths. In this study, death certificate data were retrieved from the CDC WONDER (Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research) database and examined for the years 1999 to 2020. The dataset includes cause of death information from death certificates across the 50 states and the District of Columbia. The data was identified using ICD-10 codes: CVD (I00-I99) and malignant cancer (C00-C97). Data were extracted using the Multiple Cause-of-Death Public Use files, which record all causes listed on death certificates, whether underlying or contributing. The study population was divided into four key variables: A (records with both CVD and Cancer), B (records with Cancer), C (records with CVD excluding patients in the group A), and D (all mortality records excluding patients in group B). Additionally, the pre-selected age range (15+) was stratified into 10-year groups: [Young Adults: 15–24, 25–34; Middle-aged Adults: 35–44, 45–54, 55–64; Older Adults: 65–74, 75–84, and 85+]. For each age group, the CVD burden in cancer and non-cancer mortality was evaluated, and the ratio was calculated to understand the Reporting Odds Ratio (ROR) of CVD. This study was exempt from local institutional review board approval because it used a de-identified government-issued public use dataset and adhered to the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines for reporting. Data Calculation: The core analytical measure in this study is the Reporting Odds Ratio (ROR), a comparative metric that assesses the relative frequency or burden of a specific outcome across two distinct populations or conditions. It is calculated by dividing the incidence/prevalence/deaths of the outcome in one group by the incidence/prevalence/deaths in another, providing insight into how much more or less likely the outcome is to occur in one group compared to another. In this study, ROR was used to compare the burden of cardiovascular disease (CVD) between cancer-related and non-cancer-related mortalities. A ROR greater than 1 indicated a higher burden of CVD in cancer mortality. ROR = Burden of CVD in Cancer Mortality / Burden of CVD in Non-Cancer Mortality For clarity, the full stepwise derivation of the ROR calculations is described in the Supplementary Material . In addition to calculating the Reporting Odds Ratio (ROR), we also determined the corresponding confidence intervals (CIs) and standard errors (SEs) to ensure the statistical robustness of our findings. To quantify the uncertainty around the ROR estimates, we calculated the SE of the ROR using the formula for the SE of a ratio, which accounts for the variability in both the numerator and the denominator. The CIs for the ROR were then derived using the SE, applying the normal distribution to construct a 95% confidence interval. These calculations allowed us to assess the precision of the ROR estimates and determine whether observed differences were statistically significant, providing a comprehensive understanding of the disparities or trends being analyzed. Data Abstraction: Each age group was stratified by sex, race, urbanization, and location of death, including medical facilities, home, hospice, and nursing home/long-term care facility. Race/ethnicity was classified as Non-Hispanic (NH) White, NH Black or African American, Hispanic or Latino, NH American Indian or Alaskan Native, and NH Asian or Pacific Islander. This information relies on reported data on death certificates and has been validated in previous analyses of the WONDER database. 8 The National Center for Health Statistics Urban-Rural Classification Scheme was used to assess the population by urban (large metropolitan areas with populations ≥ 1 million, medium/small metropolitan areas with populations between 50,000-999,999) and rural (populations < 50,000) counties, according to the 2013 U.S. Census classification. Regions were categorized into Northeast, Midwest, South, and West, following the U.S. Census Bureau definitions. Statistical Analysis: To examine and quantify trends within the individual cohorts, ROR values with their standard errors from 1999 to 2020 were input into the Join Point Regression Program (Joinpoint V 5.2.0.0, National Cancer Institute), which calculated the annual percent change (APC) and average annual percent change (AAPC) with 95% confidence intervals (CIs). This method identifies significant changes in ROR over time by fitting log-linear regression models where temporal variation occurred. APCs and AAPCs were considered increasing or decreasing if the slope describing the change in mortality was significantly different from zero, as determined by 2-tailed t-tests. A p-value of < 0.05 was considered statistically significant. Results Overall: The overall CVD burden in cancer mortality [A/B] indicates a rising trend across Age Groups (AGs) with the highest in the oldest AG [15–24: 0.181, 55–64: 0.218, 85+: 0.394]. Similarly, CVD burden in non-cancer deaths also rose progressively across AGs albeit showcasing a steeper rise [15–24: 0.099, 55–64: 0.649, 85+: 0.701]. Consequently, this disparity in increase led to the ROR in older AGs to be lower despite having a greater burden of CVD in cancer-related deaths [15–24: 1.828 (1.778–1.879), 55–64: 0.336 (0.335–0.337), 85+: 0.562 (0.561–0.564)] (Supplementary Table 1, Supplementary Fig. 1 , Fig. 1 ) . Additionally, middle-aged AGs have the lowest value forming a distinctive reverse J-shaped curve. Trend Analyses from 1999–2020 displayed a rise in AAPCs across all age groups with the highest increase in ROR in the 55–64 AG (1.69 AAPC, 95% CI: 1.52–1.83, p < 0.01) (Supplementary Table 2) . This trend can be attributed to the rising AAPC of CVD burden in cancer mortality coupled with the declining AAPC of CVD burden in non-cancer deaths [A/B (55–64): 0.96, 95% CI: 0.85 to 1.07, p < 0.01; C/D (55–64): -0.67, 95% CI: -0.72 to -0.63, p < 0.01] (Supplementary Table 2) . Sex Sex-based analysis showed males had a consistently higher ROR across most AGs over the study period ( Fig. 2 ) . Highest recorded ROR was in the male population [Male (15–24): 2.12 (2.047–2.196); Female (15–24): 1.306 (1.249–1.364)]. The lowest ROR was recorded in the Female population in the middle AGs (55–64: 0.327 (0.326–0.329)), however, the ROR of male population was comparable [55–64: 0.345 (0.343–0.348)] (Supplementary Table 3, Supplementary Fig. 2) . The upward trend to older AGs was higher for males [Male (85+): 2.12 (2.047–2.196); Female (85+): 1.306 (1.249–1.364)]. Trend based analyses revealed all AGs displayed positive AAPC with the steepest recorded in middle-aged females [Female (55–64): 2.59, 95% CI: 1.96 to 3.27, p < 0.01; Male (55–64): 2.02, 95% CI: 1.87 to 2.18, p < 0.01] (Supplementary Table 2) . Regional: In regional stratification, Northeast and West census regions generally had a higher ROR than the other regions in all AGs with the disparity less evident in older AGs ( Fig. 3 ) . Overall ROR was higher in younger age groups, with the highest recorded in the Western region [West (15–24): 2.311 (2.196–2.433); Northeast (15–24): 2.255 (2.122–2.396); South (15–24): 1.607 (1.533–1.684); Midwest (15–24): 1.258 (1.171–1.351)]. The lowest ROR was recorded in the Midwest region [45–54: 0.245 (0.242–0.248)] (Supplementary Table 4, Supplementary Fig. 3) . Trend based analyses revealed the steepest positive AAPC was recorded in the Midwest [Midwest (35–44): 2.59, 95% CI: 1.96 to 3.27, p < 0.01; South (55–64): 2.02, 95% CI: 1.87 to 2.18, p < 0.01; West (55–64): 1.92, 95% CI: 1.62 to 2.13, p < 0.01; Northeast (75–84): 0.69, 95% CI: 0.46 to 0.88, p < 0.01]. Furthermore, only 15–24 AG had a negative trend in all regions, except the South, with the greatest fall in the Midwest cohort [Midwest: -2.52, 95% CI: -3.73 to -1.1, p < 0.01; Northeast: -1.36, 95% CI: -2.52 to -0.4, p < 0.01; West: -0.24, 95% CI: -1.61 to 0.59, p = 0.51; South: 0.73, 95% CI: 0.13 to 1.4, p < 0.01] (Supplementary Table 2) . Race In racial stratification, Hispanics generally had a higher ROR than the other races in all AGs with the difference less evident in older AGs ( Fig. 4 ) . Overall ROR was higher in younger age groups, with the highest recorded in the Hispanics [Hispanics (15–24): 2.558 (2.418–2.706); Non-Hispanic (NH) Asians (15–24): 1.859 (1.646–2.098); NH Blacks (15–24): 1.763 (1.654–1.88); NH American Indians (15–24): 1.763 (1.277–2.434); NH Whites (15–24): 1.626 (1.564–1.69)] (Supplementary Table R) . Middle AGs recorded the lowest RORs, particularly in NH Whites [45–54: 0.32 (0.318–0.322)]. The increase of ROR in the older AGs had the greatest effect on NH Asians [85+: 0.58 (0.57–0.59)] (Supplementary Table 5, Supplementary Fig. 4) . Trend based analyses revealed negative AAPCs dominantly in the younger AGs for NH Asians (15–24: -2.76, 95% CI: -4.26 to -0.92, p < 0.01) and Hispanics (15–24: -0.63, 95% CI: -1.37 to 0.2, p = 0.13). Moreover, majority of the AGs stratified by race had a positive AAPC with the steepest seen in NH American Indian [NH American Indian (65–74): 2.20, 95% CI: 1.75 to 2.69, p < 0.01; NH White (45–54): 1.66, 95% CI: 1.54 to 1.79, p < 0.01; Hispanic (55–64): 1.92, 95% CI: 1.62 to 2.13, p < 0.01; NH Black (55–64): 1.20, 95% CI: 1.01 to 1.38, p < 0.01; NH Asian (55–64): 0.76, 95% CI: 0.06 to 1.23, p = 0.04] (Supplementary Table 2) . Urban: In urban stratification, Metropolitan and Nonmetropolitan cohorts showcased similar RORs in all age groups, especially the middle-aged and older AGs ( Fig. 5 ) . Overall ROR was higher in younger age groups with the highest recorded in Metropolitan areas [Metro (15–24): 1.905 (1.85–1.963); Non-Metro (15–24): 1.425 (1.32–1.538)]. The lowest ROR was recorded in Nonmetropolitan areas [Non-Metro (45–54): 0.319 (0.315–0.323); Metro (45–54): 0.342 (0.34–0.344)] (Supplementary Table 3, Supplementary Fig. 5) . Across the study period, there was a positive change in ROR for all AGs. The steepest AAPC was recorded in the Non-Metropolitan areas [Non-Metro (45–54): 2.02, 95% CI: 1.69 to 2.31, p < 0.01; Metro (55–64): 1.64, 95% CI: 1.47 to 1.78, p < 0.01] (Supplementary Table 2) . Place Of Death Medical facilities had a higher ROR compared to other locations consistently in all AGs ( Fig. 6 ) . The highest ROR for each location was generally seen in younger AGs (Medical Facility (15–24): 1.447 ; Decadents Home (15–24): 1.071 ; Hospice (15–24): 0.383 ; Nursing Home (85+): 0.540 ). Other/Unknown locations had a notable ROR in the younger AGs (15–24: 5.32 ), alluding to possible limitations (Supplementary Table 6) . Discussion When stratifying the ROR by 10-year age groups, the peak is in the 15–24 age cohort, followed by a decline in middle-aged groups (45–54, 55–64) and a rise in older age cohorts. In the period between 1999 and 2020, the overall ROR has increased in all age groups, a cause for concern. Of note, only the 15–24 age cohort has ROR > 1, indicating that the cardiovascular disease (CVD) burden in cancer-related mortality is higher than in mortality from other causes 7 . As the main causes of death in young individuals are accidents, homicide, and suicide, the high ROR suggests that cancer and its treatment can be a significant contributor to CVD-related mortality in this age group. This highlights the need for more cardio-oncological interventions in young cancer patients. For the older age groups, ROR < 1 suggests the number of deaths from either CVD or cancer alone is higher compared to deaths in which both occur 7 , 8 . Perhaps it is so because individuals are dying from one condition before each develops the other. Due to the limitation in the data, competing risk analysis could not be carried out. However, the increasing trend in the ROR value for the higher age groups shows greater susceptibility to both diseases together. Rather than interpreting ROR, age group comparison is more meaningful 9 . Gender variations also displayed the reverse J-shaped curve. The most significant variations are seen in younger age groups (15–24, 25–34), with males having significantly higher RORs than females. Even though the male ROR is higher in all age groups, the gap diminishes with increasing age 10 . Since cardiovascular risk is greatest in the immediate period following cancer diagnosis and persists for years, younger men should be a priority for cardiovascular screening. Increasing time has seen ROR increase in males and females in all age groups except 15–24, with an increase in males but not females. This again highlights the requirement for intervention in younger males 11 . When ROR is compared by race, Hispanic patients have the greatest ROR, especially in young and middle age. Other races have relatively similar RORs. Studies prove that cancer and CVD have similar risk factors such as obesity, high cholesterol, diabetes, hypertension, smoking, and lack of physical activity, which are disproportionately higher among Hispanic populations 12 , 13 . The Hispanic/Latinx community also has high healthcare disparities with a higher number of uninsured people (30.1% compared to 11.1% in non-Hispanic Whites), which are responsible for declining outcomes and decreased access to treatment. ROR has been rising over the years in all ages of Hispanic patients except in the 15–25 group, which has a slight decline, further emphasizing the need for more cardio-oncological interventions among this under-served group 14 . For other races, while their overall RORs are not as elevated as in Hispanics, time trends reveal rising RORs in all age groups of the African American group 15 , 16 . The White group has rising RORs in all age groups except 15–24. Asian and American Indian groups have mixed trends 17 . Overall ROR has been increasing in all age groups over the years, with the greatest ROR in the Northeastern states, followed by the West, South, and Midwest. This suggests an increased burden of cancer-related CVD in the Northeast and West, with greater emphasis on the need for increased cardio-oncology interventions in these states 18 . Though a study by the Oxford University Press suggested cancer and cardiovascular mortality gains in the highly populated Atlantic and Pacific coastal regions, it does not always imply that there is less CVD burden in cancer mortality. Interestingly, in the 15–25 years age group, ROR decreased in the Northeast, Midwest, and West from 1999 to 2020 but increased in the South. Urban-rural trends follow the reverse J-shaped curve with the highest ROR in young patients, decreasing in the middle-aged, and increasing in the old. Metropolitan sites have higher RORs than non-metropolitan sites, especially in the 15–24 age group. Although CDC data show higher individual cancer and CVD mortality in rural sites, our study indicates a higher ROR in metropolitan sites, which reflects a higher co-existence of cancer and CVD in young urban populations 8 . More research is required to investigate this trend, but meanwhile, more cardio-oncology clinics in metropolitan sites could be the solution to the increasing burden. Finally, stratification by place of death documents the greatest ROR in the "other/unknown" category, followed by medical facilities, with lower RORs for hospice and home. This pattern may be due to the influence of cardiotoxic oncologic therapy worsening CVD in the hospital setting. All in all, these findings highlight the need for selective cardio-oncological interventions, particularly among young patients, men, Hispanic patients, urban residents, and practice in a hospital environment. Limitations: The CDC WONDER database may have biases because of underreporting, incorrect classification, and jurisdictional conflicts. The inability to exclude specific codes may cause the CVD burden in non-cancer fatalities to be slightly inflated, and the use of ICD-10 codes may also lead to inaccuracies 19 . Further restricting our comprehension of long-term health implications, comorbidities, and treatment outcomes is the fact that our research only looks at mortality data. Socioeconomic variables including insurance coverage and healthcare access were not investigated, despite racial and regional discrepancies being looked at 18 . Though useful, the classification of age groups may miss important transition periods that call for focused interventions. Conclusion This study highlights significant disparities in CVD burden among cancer patients, with younger individuals, males, Hispanics, and urban residents at higher risk. The highest RORs were observed in younger age groups (15–24), particularly among males and Hispanics, emphasizing the need for early cardio-oncological interventions. Regional variations revealed greater CVD burden in the Northeast and West, while urban areas showed higher RORs than rural settings. Rising ROR trends across all age groups underscore the growing intersection of cancer and CVD. Addressing systemic healthcare disparities and implementing targeted interventions for high-risk populations are critical to improving outcomes. Future research should explore treatment-specific risks and socioeconomic influences to refine care strategies. Declarations Prevention Wide-ranging Online Data for Epidemiologic Research (CDC WONDER) database. The authors obtained access in accordance with the CDC’s data use guidelines. Funding Statement: This research did not receive any grants from funding agencies in the public, commercial or not-for-profit sectors. Conflict of Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. All authors have reviewed and approved the final manuscript. Ethics Approval Statement: This study used de-identified, publicly available mortality data from the CDC WONDER database. As no human subjects or identifiable data were involved, institutional review board approval was not required. Patient Consent Statement: Not applicable. Informed consent was waived because the study used de-identified, publicly available data. Permission to Reproduce Material from Other Sources: Not applicable. No third-party material was reproduced. Clinical Trial Registration: Not applicable. References Weir HK, Anderson RN, King SMC, et al. Heart Disease and Cancer Deaths — Trends and Projections in the United States, 1969–2020. 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Eliminating disparities in cardiovascular health: Six strategic imperatives and a framework for action. Circulation . 2005;111(10):1332-1336. doi:10.1161/01.CIR.0000158134.24860.91/ASSET/04D4AECB-8C8C-49BA-8768-460A8A730A93/ASSETS/GRAPHIC/22FF1.JPEG Pearson-Stuttard J, Guzman-Castillo M, Penalvo JL, et al. Modeling future cardiovascular disease mortality in the United States. Circulation . 2016;133(10):967-978. doi:10.1161/CIRCULATIONAHA.115.019904/-/DC1 Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterialcvdburdencancerdeaths.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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7776019","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":542178028,"identity":"ce746cdb-a43d-45da-845f-c7b5c242fb2e","order_by":0,"name":"Faizan Ahmed","email":"","orcid":"","institution":"Jersey Shore University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Faizan","middleName":"","lastName":"Ahmed","suffix":""},{"id":542178029,"identity":"e26bf1a3-544f-4afc-862a-65db715eb758","order_by":1,"name":"Tehmasp Rehman Mirza","email":"","orcid":"","institution":"Shalamar Medical and Dental 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States\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7776019/v1/361906ca64171c30b1be3bf0.png"},{"id":95663199,"identity":"a8f84c4a-333c-4331-a4c0-2da90cf361ca","added_by":"auto","created_at":"2025-11-11 16:38:30","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":46650,"visible":true,"origin":"","legend":"\u003cp\u003eCVD Burden in Malignant Neoplasm-Related Reporting Odds Ratio Stratified by Region and Age Group in the Unites States\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7776019/v1/3afe06d97c1780d77360b8c6.png"},{"id":95663158,"identity":"45da7782-9ff0-42c9-8d8c-45fd8d867e01","added_by":"auto","created_at":"2025-11-11 16:38:26","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":30471,"visible":true,"origin":"","legend":"\u003cp\u003eCVD Burden in Malignant Neoplasm-Related Reporting Odds Ratio Stratified by Race and Age Group in the Unites States\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7776019/v1/ce81ad33e7b7a7a9733cfa78.png"},{"id":95663225,"identity":"1663ce0a-b913-4c9c-9088-101ca236bfa8","added_by":"auto","created_at":"2025-11-11 16:38:33","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":36736,"visible":true,"origin":"","legend":"\u003cp\u003eCVD Burden in Malignant Neoplasm-Related Reporting Odds Ratio Stratified by Urbanization and Age Group in the Unites States\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7776019/v1/b7bad84403defdccb14ab64f.png"},{"id":95663083,"identity":"ff494296-1788-461e-b225-f8ebfa1bef16","added_by":"auto","created_at":"2025-11-11 16:38:22","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":29406,"visible":true,"origin":"","legend":"\u003cp\u003eCVD Burden in Malignant Neoplasm-Related Reporting Odds Ratio Stratified by Place of Death and Age Group in the Unites States\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7776019/v1/e220f132bd8038c221c5c0e5.png"},{"id":96249728,"identity":"25ae1125-66e1-4891-a0d9-d0922164c0c1","added_by":"auto","created_at":"2025-11-19 07:36:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":987101,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7776019/v1/e0a664de-8239-4f73-8d33-0b22d0165309.pdf"},{"id":95797126,"identity":"474424cd-d669-4371-8a85-1befd7d38765","added_by":"auto","created_at":"2025-11-13 08:01:11","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":2482936,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterialcvdburdencancerdeaths.docx","url":"https://assets-eu.researchsquare.com/files/rs-7776019/v1/ec838859bd2de6cc55d2fcdb.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Trends and Disparities in Cardiovascular Disease Burden Among Cancer Deaths in the U.S. (1999–2020): A CDC WONDER Disproportionality Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCardiovascular disease (CVD) and cancer constitute the two most common primary causes of the mortality burden in the US. In 2020, heart disease was recorded as the number one cause of death (690,882 deaths) followed by cancer marking 598,932 deaths. This marks a substantial increase in deaths by a solid 4.8% since 2012\u003csup\u003e1\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAlthough CVD and cancer independently are fatal, when the lines intersect, the risk further increases by two to six times. CVD has become the most common cause of mortality for people with different types of cancer \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Depending upon the month since diagnosis and the prognosis of the cancer involved, If the risk of fatality from CVD is \u0026gt;\u0026thinsp;20%, then it results in a\u0026thinsp;\u0026lt;\u0026thinsp;30% risk of fatality from cancer.\u003c/p\u003e\u003cp\u003eIt is common knowledge that both diseases share several risk factors like DM, HTN, age, gender, tobacco smoking, obesity and western diet etc and hence the strategic reduction of modifiable risk factors, screening, interventions and implementation of the ideal health metrics proposed by the AHA have resulted in better outcomes for both, cardiovascular health and decreased cardiovascular events related to cancer-therapy. Better healthcare has resulted in an increased life expectancy of 1.1 years from CVD and 0\u0026middot;4 years from cancer \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eDespite the remarkable advances in the field of cardio-oncology, due to discriminatory policies, reduced access to high-quality care in all geographical regions and long-standing wealth inequalities, there still lies a burden due to racial, gender, social and geographical disparities \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAccording to the cancer stats in 2023, AIAN people reported the highest mortality rate of cancer irrespective of gender, followed closely by Black people \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. The mortality rates between males and females showed a narrow sex-gap. In another study, Patel SR et al highlighted that the risk of CVD related mortality in cancer patients increased 1.16% in comparison with the general population, but when categorized by ethnicity, the mortality ratio was 1.76, 2.28, 3.68, 2.65, and 1.84 for Whites, Blacks, AIAN, Asians/Pacific Islanders, and Hispanic/Latinx, respectively \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAlthough several studies have been done on the proposed interaction between CVD and cancer, yet the burden it collectively pertains needs to be explored more. The present study highlights how the intersection between the two most significant global burdens is proportional with the racial, gender and place of death.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003eStudy Setting and Population:\u003c/h2\u003e\u003cp\u003eDisproportionality Analysis is a statistical method commonly used in pharmacovigilance to understand the relation between specific drugs and reported adverse effects. It explores the disparity between expected and reported values. Applying a similar reasoning, we explored the contrast in burden of CVD in cancer and non-cancer deaths. In this study, death certificate data were retrieved from the CDC WONDER (Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research) database and examined for the years 1999 to 2020. The dataset includes cause of death information from death certificates across the 50 states and the District of Columbia. The data was identified using ICD-10 codes: CVD (I00-I99) and malignant cancer (C00-C97). Data were extracted using the Multiple Cause-of-Death Public Use files, which record all causes listed on death certificates, whether underlying or contributing. The study population was divided into four key variables: A (records with both CVD and Cancer), B (records with Cancer), C (records with CVD excluding patients in the group A), and D (all mortality records excluding patients in group B). Additionally, the pre-selected age range (15+) was stratified into 10-year groups: [Young Adults: 15\u0026ndash;24, 25\u0026ndash;34; Middle-aged Adults: 35\u0026ndash;44, 45\u0026ndash;54, 55\u0026ndash;64; Older Adults: 65\u0026ndash;74, 75\u0026ndash;84, and 85+]. For each age group, the CVD burden in cancer and non-cancer mortality was evaluated, and the ratio was calculated to understand the Reporting Odds Ratio (ROR) of CVD. This study was exempt from local institutional review board approval because it used a de-identified government-issued public use dataset and adhered to the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines for reporting.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData Calculation:\u003c/h3\u003e\n\u003cp\u003eThe core analytical measure in this study is the Reporting Odds Ratio (ROR), a comparative metric that assesses the relative frequency or burden of a specific outcome across two distinct populations or conditions. It is calculated by dividing the incidence/prevalence/deaths of the outcome in one group by the incidence/prevalence/deaths in another, providing insight into how much more or less likely the outcome is to occur in one group compared to another. In this study, ROR was used to compare the burden of cardiovascular disease (CVD) between cancer-related and non-cancer-related mortalities. A ROR greater than 1 indicated a higher burden of CVD in cancer mortality.\u003c/p\u003e\u003cp\u003eROR\u0026thinsp;=\u0026thinsp;Burden of CVD in Cancer Mortality / Burden of CVD in Non-Cancer Mortality\u003c/p\u003e\u003cp\u003eFor clarity, the full stepwise derivation of the ROR calculations is described in the \u003cb\u003eSupplementary Material\u003c/b\u003e. In addition to calculating the Reporting Odds Ratio (ROR), we also determined the corresponding confidence intervals (CIs) and standard errors (SEs) to ensure the statistical robustness of our findings. To quantify the uncertainty around the ROR estimates, we calculated the SE of the ROR using the formula for the SE of a ratio, which accounts for the variability in both the numerator and the denominator. The CIs for the ROR were then derived using the SE, applying the normal distribution to construct a 95% confidence interval. These calculations allowed us to assess the precision of the ROR estimates and determine whether observed differences were statistically significant, providing a comprehensive understanding of the disparities or trends being analyzed.\u003c/p\u003e\n\u003ch3\u003eData Abstraction:\u003c/h3\u003e\n\u003cp\u003eEach age group was stratified by sex, race, urbanization, and location of death, including medical facilities, home, hospice, and nursing home/long-term care facility. Race/ethnicity was classified as Non-Hispanic (NH) White, NH Black or African American, Hispanic or Latino, NH American Indian or Alaskan Native, and NH Asian or Pacific Islander. This information relies on reported data on death certificates and has been validated in previous analyses of the WONDER database.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e The National Center for Health Statistics Urban-Rural Classification Scheme was used to assess the population by urban (large metropolitan areas with populations\u0026thinsp;\u0026ge;\u0026thinsp;1\u0026nbsp;million, medium/small metropolitan areas with populations between 50,000-999,999) and rural (populations\u0026thinsp;\u0026lt;\u0026thinsp;50,000) counties, according to the 2013 U.S. Census classification. Regions were categorized into Northeast, Midwest, South, and West, following the U.S. Census Bureau definitions.\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis:\u003c/h2\u003e\u003cp\u003eTo examine and quantify trends within the individual cohorts, ROR values with their standard errors from 1999 to 2020 were input into the Join Point Regression Program (Joinpoint V 5.2.0.0, National Cancer Institute), which calculated the annual percent change (APC) and average annual percent change (AAPC) with 95% confidence intervals (CIs). This method identifies significant changes in ROR over time by fitting log-linear regression models where temporal variation occurred. APCs and AAPCs were considered increasing or decreasing if the slope describing the change in mortality was significantly different from zero, as determined by 2-tailed t-tests. A p-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eOverall:\u003c/h2\u003e\u003cp\u003eThe overall CVD burden in cancer mortality [A/B] indicates a rising trend across Age Groups (AGs) with the highest in the oldest AG [15\u0026ndash;24: 0.181, 55\u0026ndash;64: 0.218, 85+: 0.394]. Similarly, CVD burden in non-cancer deaths also rose progressively across AGs albeit showcasing a steeper rise [15\u0026ndash;24: 0.099, 55\u0026ndash;64: 0.649, 85+: 0.701]. Consequently, this disparity in increase led to the ROR in older AGs to be lower despite having a greater burden of CVD in cancer-related deaths [15\u0026ndash;24: 1.828 (1.778\u0026ndash;1.879), 55\u0026ndash;64: 0.336 (0.335\u0026ndash;0.337), 85+: 0.562 (0.561\u0026ndash;0.564)] \u003cb\u003e(Supplementary Table\u0026nbsp;1, Supplementary Fig.\u0026nbsp;1\u003c/b\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Additionally, middle-aged AGs have the lowest value forming a distinctive reverse J-shaped curve. Trend Analyses from 1999\u0026ndash;2020 displayed a rise in AAPCs across all age groups with the highest increase in ROR in the 55\u0026ndash;64 AG (1.69 AAPC, 95% CI: 1.52\u0026ndash;1.83, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) \u003cb\u003e(Supplementary Table\u0026nbsp;2)\u003c/b\u003e. This trend can be attributed to the rising AAPC of CVD burden in cancer mortality coupled with the declining AAPC of CVD burden in non-cancer deaths [A/B (55\u0026ndash;64): 0.96, 95% CI: 0.85 to 1.07, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; C/D (55\u0026ndash;64): -0.67, 95% CI: -0.72 to -0.63, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01] \u003cb\u003e(Supplementary Table\u0026nbsp;2)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSex\u003c/h3\u003e\n\u003cp\u003eSex-based analysis showed males had a consistently higher ROR across most AGs over the study period \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Highest recorded ROR was in the male population [Male (15\u0026ndash;24): 2.12 (2.047\u0026ndash;2.196); Female (15\u0026ndash;24): 1.306 (1.249\u0026ndash;1.364)]. The lowest ROR was recorded in the Female population in the middle AGs (55\u0026ndash;64: 0.327 (0.326\u0026ndash;0.329)), however, the ROR of male population was comparable [55\u0026ndash;64: 0.345 (0.343\u0026ndash;0.348)] \u003cb\u003e(Supplementary Table\u0026nbsp;3, Supplementary Fig.\u0026nbsp;2)\u003c/b\u003e. The upward trend to older AGs was higher for males [Male (85+): 2.12 (2.047\u0026ndash;2.196); Female (85+): 1.306 (1.249\u0026ndash;1.364)]. Trend based analyses revealed all AGs displayed positive AAPC with the steepest recorded in middle-aged females [Female (55\u0026ndash;64): 2.59, 95% CI: 1.96 to 3.27, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; Male (55\u0026ndash;64): 2.02, 95% CI: 1.87 to 2.18, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01] \u003cb\u003e(Supplementary Table\u0026nbsp;2)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eRegional:\u003c/h3\u003e\n\u003cp\u003eIn regional stratification, Northeast and West census regions generally had a higher ROR than the other regions in all AGs with the disparity less evident in older AGs \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Overall ROR was higher in younger age groups, with the highest recorded in the Western region [West (15\u0026ndash;24): 2.311 (2.196\u0026ndash;2.433); Northeast (15\u0026ndash;24): 2.255 (2.122\u0026ndash;2.396); South (15\u0026ndash;24): 1.607 (1.533\u0026ndash;1.684); Midwest (15\u0026ndash;24): 1.258 (1.171\u0026ndash;1.351)]. The lowest ROR was recorded in the Midwest region [45\u0026ndash;54: 0.245 (0.242\u0026ndash;0.248)] \u003cb\u003e(Supplementary Table\u0026nbsp;4, Supplementary Fig.\u0026nbsp;3)\u003c/b\u003e. Trend based analyses revealed the steepest positive AAPC was recorded in the Midwest [Midwest (35\u0026ndash;44): 2.59, 95% CI: 1.96 to 3.27, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; South (55\u0026ndash;64): 2.02, 95% CI: 1.87 to 2.18, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; West (55\u0026ndash;64): 1.92, 95% CI: 1.62 to 2.13, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; Northeast (75\u0026ndash;84): 0.69, 95% CI: 0.46 to 0.88, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01]. Furthermore, only 15\u0026ndash;24 AG had a negative trend in all regions, except the South, with the greatest fall in the Midwest cohort [Midwest: -2.52, 95% CI: -3.73 to -1.1, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; Northeast: -1.36, 95% CI: -2.52 to -0.4, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; West: -0.24, 95% CI: -1.61 to 0.59, p\u0026thinsp;=\u0026thinsp;0.51; South: 0.73, 95% CI: 0.13 to 1.4, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01] \u003cb\u003e(Supplementary Table\u0026nbsp;2)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eRace\u003c/h2\u003e\u003cp\u003eIn racial stratification, Hispanics generally had a higher ROR than the other races in all AGs with the difference less evident in older AGs \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Overall ROR was higher in younger age groups, with the highest recorded in the Hispanics [Hispanics (15\u0026ndash;24): 2.558 (2.418\u0026ndash;2.706); Non-Hispanic (NH) Asians (15\u0026ndash;24): 1.859 (1.646\u0026ndash;2.098); NH Blacks (15\u0026ndash;24): 1.763 (1.654\u0026ndash;1.88); NH American Indians (15\u0026ndash;24): 1.763 (1.277\u0026ndash;2.434); NH Whites (15\u0026ndash;24): 1.626 (1.564\u0026ndash;1.69)] \u003cb\u003e(Supplementary Table R)\u003c/b\u003e. Middle AGs recorded the lowest RORs, particularly in NH Whites [45\u0026ndash;54: 0.32 (0.318\u0026ndash;0.322)]. The increase of ROR in the older AGs had the greatest effect on NH Asians [85+: 0.58 (0.57\u0026ndash;0.59)] \u003cb\u003e(Supplementary Table\u0026nbsp;5, Supplementary Fig.\u0026nbsp;4)\u003c/b\u003e. Trend based analyses revealed negative AAPCs dominantly in the younger AGs for NH Asians (15\u0026ndash;24: -2.76, 95% CI: -4.26 to -0.92, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and Hispanics (15\u0026ndash;24: -0.63, 95% CI: -1.37 to 0.2, p\u0026thinsp;=\u0026thinsp;0.13). Moreover, majority of the AGs stratified by race had a positive AAPC with the steepest seen in NH American Indian [NH American Indian (65\u0026ndash;74): 2.20, 95% CI: 1.75 to 2.69, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; NH White (45\u0026ndash;54): 1.66, 95% CI: 1.54 to 1.79, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; Hispanic (55\u0026ndash;64): 1.92, 95% CI: 1.62 to 2.13, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; NH Black (55\u0026ndash;64): 1.20, 95% CI: 1.01 to 1.38, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; NH Asian (55\u0026ndash;64): 0.76, 95% CI: 0.06 to 1.23, p\u0026thinsp;=\u0026thinsp;0.04] \u003cb\u003e(Supplementary Table\u0026nbsp;2)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eUrban:\u003c/h2\u003e\u003cp\u003eIn urban stratification, Metropolitan and Nonmetropolitan cohorts showcased similar RORs in all age groups, especially the middle-aged and older AGs \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Overall ROR was higher in younger age groups with the highest recorded in Metropolitan areas [Metro (15\u0026ndash;24): 1.905 (1.85\u0026ndash;1.963); Non-Metro (15\u0026ndash;24): 1.425 (1.32\u0026ndash;1.538)]. The lowest ROR was recorded in Nonmetropolitan areas [Non-Metro (45\u0026ndash;54): 0.319 (0.315\u0026ndash;0.323); Metro (45\u0026ndash;54): 0.342 (0.34\u0026ndash;0.344)] \u003cb\u003e(Supplementary Table\u0026nbsp;3, Supplementary Fig.\u0026nbsp;5)\u003c/b\u003e. Across the study period, there was a positive change in ROR for all AGs. The steepest AAPC was recorded in the Non-Metropolitan areas [Non-Metro (45\u0026ndash;54): 2.02, 95% CI: 1.69 to 2.31, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; Metro (55\u0026ndash;64): 1.64, 95% CI: 1.47 to 1.78, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01] \u003cb\u003e(Supplementary Table\u0026nbsp;2)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003ePlace Of Death\u003c/h2\u003e\u003cp\u003eMedical facilities had a higher ROR compared to other locations consistently in all AGs \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. The highest ROR for each location was generally seen in younger AGs (Medical Facility (15\u0026ndash;24): \u003cb\u003e1.447\u003c/b\u003e; Decadents Home (15\u0026ndash;24): \u003cb\u003e1.071\u003c/b\u003e; Hospice (15\u0026ndash;24): \u003cb\u003e0.383\u003c/b\u003e; Nursing Home (85+): \u003cb\u003e0.540\u003c/b\u003e). Other/Unknown locations had a notable ROR in the younger AGs (15\u0026ndash;24: \u003cb\u003e5.32\u003c/b\u003e), alluding to possible limitations \u003cb\u003e(Supplementary Table\u0026nbsp;6)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWhen stratifying the ROR by 10-year age groups, the peak is in the 15\u0026ndash;24 age cohort, followed by a decline in middle-aged groups (45\u0026ndash;54, 55\u0026ndash;64) and a rise in older age cohorts. In the period between 1999 and 2020, the overall ROR has increased in all age groups, a cause for concern. Of note, only the 15\u0026ndash;24 age cohort has ROR\u0026thinsp;\u0026gt;\u0026thinsp;1, indicating that the cardiovascular disease (CVD) burden in cancer-related mortality is higher than in mortality from other causes \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. As the main causes of death in young individuals are accidents, homicide, and suicide, the high ROR suggests that cancer and its treatment can be a significant contributor to CVD-related mortality in this age group. This highlights the need for more cardio-oncological interventions in young cancer patients. For the older age groups, ROR\u0026thinsp;\u0026lt;\u0026thinsp;1 suggests the number of deaths from either CVD or cancer alone is higher compared to deaths in which both occur \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Perhaps it is so because individuals are dying from one condition before each develops the other. Due to the limitation in the data, competing risk analysis could not be carried out. However, the increasing trend in the ROR value for the higher age groups shows greater susceptibility to both diseases together. Rather than interpreting ROR, age group comparison is more meaningful \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eGender variations also displayed the reverse J-shaped curve. The most significant variations are seen in younger age groups (15\u0026ndash;24, 25\u0026ndash;34), with males having significantly higher RORs than females. Even though the male ROR is higher in all age groups, the gap diminishes with increasing age \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Since cardiovascular risk is greatest in the immediate period following cancer diagnosis and persists for years, younger men should be a priority for cardiovascular screening. Increasing time has seen ROR increase in males and females in all age groups except 15\u0026ndash;24, with an increase in males but not females. This again highlights the requirement for intervention in younger males \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eWhen ROR is compared by race, Hispanic patients have the greatest ROR, especially in young and middle age. Other races have relatively similar RORs. Studies prove that cancer and CVD have similar risk factors such as obesity, high cholesterol, diabetes, hypertension, smoking, and lack of physical activity, which are disproportionately higher among Hispanic populations \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. The Hispanic/Latinx community also has high healthcare disparities with a higher number of uninsured people (30.1% compared to 11.1% in non-Hispanic Whites), which are responsible for declining outcomes and decreased access to treatment. ROR has been rising over the years in all ages of Hispanic patients except in the 15\u0026ndash;25 group, which has a slight decline, further emphasizing the need for more cardio-oncological interventions among this under-served group \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eFor other races, while their overall RORs are not as elevated as in Hispanics, time trends reveal rising RORs in all age groups of the African American group \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. The White group has rising RORs in all age groups except 15\u0026ndash;24. Asian and American Indian groups have mixed trends \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eOverall ROR has been increasing in all age groups over the years, with the greatest ROR in the Northeastern states, followed by the West, South, and Midwest. This suggests an increased burden of cancer-related CVD in the Northeast and West, with greater emphasis on the need for increased cardio-oncology interventions in these states \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Though a study by the Oxford University Press suggested cancer and cardiovascular mortality gains in the highly populated Atlantic and Pacific coastal regions, it does not always imply that there is less CVD burden in cancer mortality. Interestingly, in the 15\u0026ndash;25 years age group, ROR decreased in the Northeast, Midwest, and West from 1999 to 2020 but increased in the South.\u003c/p\u003e\u003cp\u003eUrban-rural trends follow the reverse J-shaped curve with the highest ROR in young patients, decreasing in the middle-aged, and increasing in the old. Metropolitan sites have higher RORs than non-metropolitan sites, especially in the 15\u0026ndash;24 age group. Although CDC data show higher individual cancer and CVD mortality in rural sites, our study indicates a higher ROR in metropolitan sites, which reflects a higher co-existence of cancer and CVD in young urban populations \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. More research is required to investigate this trend, but meanwhile, more cardio-oncology clinics in metropolitan sites could be the solution to the increasing burden.\u003c/p\u003e\u003cp\u003eFinally, stratification by place of death documents the greatest ROR in the \"other/unknown\" category, followed by medical facilities, with lower RORs for hospice and home. This pattern may be due to the influence of cardiotoxic oncologic therapy worsening CVD in the hospital setting.\u003c/p\u003e\u003cp\u003eAll in all, these findings highlight the need for selective cardio-oncological interventions, particularly among young patients, men, Hispanic patients, urban residents, and practice in a hospital environment.\u003c/p\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eLimitations:\u003c/h2\u003e\u003cp\u003eThe CDC WONDER database may have biases because of underreporting, incorrect classification, and jurisdictional conflicts. The inability to exclude specific codes may cause the CVD burden in non-cancer fatalities to be slightly inflated, and the use of ICD-10 codes may also lead to inaccuracies \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Further restricting our comprehension of long-term health implications, comorbidities, and treatment outcomes is the fact that our research only looks at mortality data. Socioeconomic variables including insurance coverage and healthcare access were not investigated, despite racial and regional discrepancies being looked at \u003csup\u003e18\u003c/sup\u003e. Though useful, the classification of age groups may miss important transition periods that call for focused interventions.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study highlights significant disparities in CVD burden among cancer patients, with younger individuals, males, Hispanics, and urban residents at higher risk. The highest RORs were observed in younger age groups (15\u0026ndash;24), particularly among males and Hispanics, emphasizing the need for early cardio-oncological interventions. Regional variations revealed greater CVD burden in the Northeast and West, while urban areas showed higher RORs than rural settings. Rising ROR trends across all age groups underscore the growing intersection of cancer and CVD. Addressing systemic healthcare disparities and implementing targeted interventions for high-risk populations are critical to improving outcomes. Future research should explore treatment-specific risks and socioeconomic influences to refine care strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003ePrevention Wide-ranging Online Data for Epidemiologic Research (CDC WONDER) database. The authors obtained access in accordance with the CDC\u0026rsquo;s data use guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Statement:\u0026nbsp;\u003c/strong\u003eThis research did not receive any grants from funding agencies in the public, commercial or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interests:\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u0026nbsp;All authors have reviewed and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval Statement:\u0026nbsp;\u003c/strong\u003eThis study used de-identified, publicly available mortality data from the CDC WONDER database. As no human subjects or identifiable data were involved, institutional review board approval was not required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient Consent Statement:\u0026nbsp;\u003c/strong\u003eNot applicable.\u0026nbsp;Informed consent was waived because the study used de-identified, publicly available data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePermission to Reproduce Material from Other Sources:\u0026nbsp;\u003c/strong\u003eNot applicable. No third-party material was reproduced.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Registration:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWeir HK, Anderson RN, King SMC, et al. Heart Disease and Cancer Deaths \u0026mdash; Trends and Projections in the United States, 1969\u0026ndash;2020. \u003cem\u003ePrev Chronic Dis\u003c/em\u003e. 2019;13. doi:10.5888/PCD13.160211\u003c/li\u003e\n\u003cli\u003eStoltzfus KC, Zhang Y, Sturgeon K, et al. Fatal heart disease among cancer patients. \u003cem\u003eNature Communications 2020 11:1\u003c/em\u003e. 2020;11(1):1-8. doi:10.1038/s41467-020-15639-5\u003c/li\u003e\n\u003cli\u003eAnderson C, Nichols HB, Deal AM, Park YMM, Sandler DP. Changes in cardiovascular disease risk and risk factors among women with and without breast cancer. \u003cem\u003eCancer\u003c/em\u003e. 2018;124(23):4512-4519. doi:10.1002/CNCR.31775\u003c/li\u003e\n\u003cli\u003eStrongman H, Gadd S, Matthews A, et al. Medium and long-term risks of specific cardiovascular diseases in survivors of 20 adult cancers: a population-based cohort study using multiple linked UK electronic health records databases. \u003cem\u003eThe Lancet\u003c/em\u003e. 2019;394(10203):1041-1054. doi:10.1016/S0140-6736(19)31674-5\u003c/li\u003e\n\u003cli\u003eAtlantis E, Shi Z, Penninx BJWH, Wittert GA, Taylor A, Almeida OP. Chronic medical conditions mediate the association between depression and cardiovascular disease mortality. \u003cem\u003eSoc Psychiatry Psychiatr Epidemiol\u003c/em\u003e. 2012;47(4):615-625. doi:10.1007/S00127-011-0365-9/METRICS\u003c/li\u003e\n\u003cli\u003eGernaat SAM, Boer JMA, van den Bongard DHJ, et al. The risk of cardiovascular disease following breast cancer by Framingham risk score. \u003cem\u003eBreast Cancer Res Treat\u003c/em\u003e. 2018;170(1):119-127. doi:10.1007/S10549-018-4723-0/TABLES/3\u003c/li\u003e\n\u003cli\u003eShin J, Ko H, Choi YH, Choi I, Song YM. Risk of comorbid cardiovascular disease in Korean long-term cancer survivors. \u003cem\u003eEur J Cancer Care (Engl)\u003c/em\u003e. 2019;28(6):e13151. doi:10.1111/ECC.13151\u003c/li\u003e\n\u003cli\u003ePinheiro LC, Reshetnyak E, Safford MM, Nanus D, Kern LM. Healthcare fragmentation and cardiovascular risk control among older cancer survivors in the Reasons for Geographic And Racial Differences in Stroke (REGARDS) study. \u003cem\u003eJournal of Cancer Survivorship 2020 15:2\u003c/em\u003e. 2020;15(2):325-332. doi:10.1007/S11764-020-00933-4\u003c/li\u003e\n\u003cli\u003eWang FM, Reiter\u0026ndash;Brennan C, Dardari Z, et al. Association between coronary artery calcium and cardiovascular disease as a supporting cause in cancer: The CAC consortium. \u003cem\u003eAm J Prev Cardiol\u003c/em\u003e. 2020;4:100119. doi:10.1016/J.AJPC.2020.100119\u003c/li\u003e\n\u003cli\u003eBalla S, Gomez SE, Rodriguez F. Disparities in Cardiovascular Care and Outcomes for Women From Racial/Ethnic Minority Backgrounds. \u003cem\u003eCurrent Treatment Options in Cardiovascular Medicine 2020 22:12\u003c/em\u003e. 2020;22(12):1-17. doi:10.1007/S11936-020-00869-Z\u003c/li\u003e\n\u003cli\u003eChin MH, Walters AE, Cook SC, Huang ES. Interventions to Reduce Racial and Ethnic Disparities in Health Care. \u003cem\u003eMedical Care Research and Review\u003c/em\u003e. 2007;64(5 SUPPL.). doi:10.1177/1077558707305413\u003c/li\u003e\n\u003cli\u003eFishkin T, Wang A, Frishman WH, Aronow WS. Healthcare Disparities in Cardiovascular Medicine. \u003cem\u003eCardiol Rev\u003c/em\u003e. 2024;32(4):328-333. doi:10.1097/CRD.0000000000000507\u003c/li\u003e\n\u003cli\u003eKhalid Y, Fradley M, Dasu N, Dasu K, Shah A, Levine A. Gender disparity in cardiovascular mortality following radiation therapy for Hodgkin\u0026rsquo;s lymphoma: A systematic review. \u003cem\u003eCardio-Oncology\u003c/em\u003e. 2020;6(1):1-8. doi:10.1186/S40959-020-00067-7/FIGURES/4\u003c/li\u003e\n\u003cli\u003eLv Y, Cao X, Yu K, et al. Gender differences in all-cause and cardiovascular mortality among US adults: from NHANES 2005\u0026ndash;2018. \u003cem\u003eFront Cardiovasc Med\u003c/em\u003e. 2024;11:1283132. doi:10.3389/FCVM.2024.1283132/BIBTEX\u003c/li\u003e\n\u003cli\u003eQato DM, Lindau ST, Conti RM, Schumm LP, Alexander GC. Racial and ethnic disparities in cardiovascular medication use among older adults in the United States. \u003cem\u003ePharmacoepidemiol Drug Saf\u003c/em\u003e. 2010;19(8):834-842. doi:10.1002/PDS.1974\u003c/li\u003e\n\u003cli\u003eWong MS, Hoggatt KJ, Steers WN, et al. Racial/Ethnic Disparities in Mortality Across the Veterans Health Administration. \u003cem\u003eHealth Equity\u003c/em\u003e. 2019;3(1):99-108. doi:10.1089/HEQ.2018.0086/ASSET/IMAGES/LARGE/FIGURE1.JPEG\u003c/li\u003e\n\u003cli\u003eMcclelland RL, Jorgensen NW, Post WS, Szklo M, Kronmal RA. Methods for estimation of disparities in medication use in an observational cohort study: results from the Multi-Ethnic Study of Atherosclerosis. \u003cem\u003ePharmacoepidemiol Drug Saf\u003c/em\u003e. 2013;22(5):533-541. doi:10.1002/PDS.3406\u003c/li\u003e\n\u003cli\u003eMensah GA. Eliminating disparities in cardiovascular health: Six strategic imperatives and a framework for action. \u003cem\u003eCirculation\u003c/em\u003e. 2005;111(10):1332-1336. doi:10.1161/01.CIR.0000158134.24860.91/ASSET/04D4AECB-8C8C-49BA-8768-460A8A730A93/ASSETS/GRAPHIC/22FF1.JPEG\u003c/li\u003e\n\u003cli\u003ePearson-Stuttard J, Guzman-Castillo M, Penalvo JL, et al. Modeling future cardiovascular disease mortality in the United States. \u003cem\u003eCirculation\u003c/em\u003e. 2016;133(10):967-978. doi:10.1161/CIRCULATIONAHA.115.019904/-/DC1\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":"Cardiovascular, Oncology, Mortality, Odds Ratio, Burden, Epidemiology, United States","lastPublishedDoi":"10.21203/rs.3.rs-7776019/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7776019/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eCardiovascular disease (CVD) and cancer are the leading causes of mortality in the U.S., with significant overlap in risk factors and outcomes. This study examines the burden of CVD in cancer-related deaths using the Reporting Odds Ratio (ROR) to identify disparities by sex, race, region, and urbanization from 1999 to 2020.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA disproportionality analysis was conducted to calculate ROR of CVD burden in cancer patients compared to non-cancer population across age groups 15\u0026ndash;85 years. A ROR greater than 1 indicated a higher burden of CVD in cancer patients. Average Annual Percentage Changes (AAPCs) were calculated to evaluate trends, with p-values determining significance.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe highest ROR across age groups was in the 15\u0026ndash;24 group at 1.83, declining to 0.34 in ages 55\u0026ndash;64, and rising to 0.56 in ages 85+. The yearly trends showed ROR increased in all groups, with the highest increase in 55\u0026ndash;64 group (AAPC: 1.69). Males exhibited higher RORs than females, with the steepest increase in middle-aged females (AAPC: 2.59). Young Hispanics had the highest ROR among racial groups (15\u0026ndash;24: 2.558), while the Midwest showed the highest regional ROR (15\u0026ndash;24: 2.311). Urban areas had higher RORs than rural areas, with medical facilities reporting the highest RORs.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis study highlights significant disparities in CVD burden among cancer patients, with younger individuals, males, Hispanics, and urban residents at higher risk. The trends underscore the need for targeted cardio-oncological interventions, to mitigate the increasing burden of CVD and cancer. Addressing systemic disparities in healthcare access and delivery is critical to improving outcomes.\u003c/p\u003e","manuscriptTitle":"Trends and Disparities in Cardiovascular Disease Burden Among Cancer Deaths in the U.S. (1999–2020): A CDC WONDER Disproportionality Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-11 16:28:21","doi":"10.21203/rs.3.rs-7776019/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":"9b79bcf6-4a24-441d-b71f-f3fdc7672d2b","owner":[],"postedDate":"November 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-17T12:54:03+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-11 16:28:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7776019","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7776019","identity":"rs-7776019","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}