Muhammad Sarim Azad Khan1
Asma Chaudhary2†
Mirha Imran Khan3
Ibrahiem Azeem Ajaz4
Arham Khalid5
Aroosha Waheed5
Mohammed Hammad Jaber Amin6*†- 1Department of Medicine, Combined Military Hospital Lahore Medical College, Lahore, Pakistan
- 2Department of Medicine, Fazaia Medical College, Islamabad, Pakistan
- 3Department of Medicine, Combined Military Hospital Institute of Medical Science, Multan, Pakistan
- 4Department of Medicine, Rashid Latif Medical College, Lahore, Pakistan
- 5Department of Medicine, Rawalpindi Medical University, Rawalpindi, Pakistan
- 6Department of Medicine, Alzaiem Alazhari University, Khartoum, Sudan
Purpose: Anemia and malignancy concurrently contribute to reduced quality of life, treatment intolerance, and increased morbidity and mortality, disproportionately affecting older adults, socioeconomically disadvantaged groups, and minority populations. Despite its clinical importance, national patterns and population-level disparities in the mortality burden associated with this comorbidity remain poorly characterized. This study aims to examine mortality trends associated with concurrent anemia and malignancy in the United States using CDC WONDER (Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research) database, with a focus on demographic and geographic disparities to inform targeted interventions.
Methods: Mortality data from CDC WONDER were analyzed for individuals aged 65 years and older using ICD-10 codes C00–C97 for malignancies and D50–D64 for anemia. Age-adjusted mortality rates (AAMRs) and annual percent changes (APCs) were calculated by year, age, sex, race/ethnicity, cancer type, and geographic region. Subgroup analyses focused on common anemia-associated cancers, including colorectal, gastric, lung, ovarian, female genital, breast, prostate, and hematologic malignancies, based on national prevalence.
Results: Between 1999 and 2020, there were 264,331 anemia and malignancy -related deaths among U.S. adults aged ≥65. The overall AAMR remained relatively stable moving from 31.5 in 1999 to 31.1 in 2020. A significant decline occurred through 2017 (APC: –0.86), followed by a sharp increase from 2017 to 2020 (APC: 4.63). Males had higher AAMRs than females (28.8 vs. 22.6). Black individuals had the highest mortality (42.4). The Midwest had the highest regional AAMR (30.6), while the South had the lowest (27.6). States in the top 90th percentile for AAMR included North Dakota, Maryland, Rhode Island, West Virginia, and DC. Nonmetropolitan areas had higher AAMRs than metropolitan ones (32.9 vs. 27.8). Colorectal cancer showed the largest decline (AAPC: -2.03) followed by gastric (AAPC: -1.96) and prostate cancer (AAPC: -1.40), while breast, lung, gynecological, and hematological cancers remained stable.
Conclusion: Among adults aged ≥65, anemia and malignancy-related mortality declined until 2017 but increased sharply through 2020, with highest rates in males, Black individuals, and nonmetropolitan/Midwestern residents. Declines were seen in colorectal, gastric, and prostate cancers, while other cancers remained stable, highlighting the need for targeted interventions to reduce disparities.
Highlights
● Concurrent anemia and malignancy related mortality declined from 1999–2017 but rose significantly from 2017–2020.
● NH Black individuals had the highest AAMR
● Males and nonmetropolitan residents showed highest mortality rates.
● The Midwest and certain states (WV, MD, ND) had the highest mortality burdens.
● Colorectal cancer demonstrated the steepest decline [AAPC: -2.03] followed by gastric cancer [AAPC: -1.96] and prostate cancer [AAPC: -1.40]
● Lung cancer exhibited an initial increase from 1999 to 2012, a period of stability from 2012 to 2015 and a subsequent sharp rise from 2015 to 2020.
1 Introduction
Cancer remains a major public health challenge in the United States, with over 1.6 million new cases annually (1). Despite advances in detection and treatment, it is projected to surpass cardiovascular disease as the leading cause of death (2). Anemia, the third leading global cause of years lived with disability (YLD) (3), contributed to 5,928 deaths in 2023, with a mortality rate of 1.8 per 100,000 population (4). The etiology of cancer-related anemia (CRA) is multifactorial, involving bone marrow infiltration, cytokine-induced iron sequestration, shortened red cell survival, chronic blood loss, oxidative stress and organ dysfunction (5, 6). Both severe anemia and CRA often necessitates repeated blood transfusions, which are life-saving in the acute setting but can lead to long-term complications such as iron overload, organ toxicity, alloimmunization, and transfusion reactions that complicate future management (7, 8). Over time, these sequelae contributes to poorer clinical outcomes and increased mortality among affected patients (9). Beyond its clinical toll, anemia in the setting of malignancy imposes substantial health and economic burdens (10).
Nephrotoxic agents such as cisplatin reduce erythropoietin production, while newer treatments, including antibody–drug conjugates, tyrosine kinase inhibitors, PARP inhibitors, and immunotherapies, can impair erythropoiesis, promote inflammatory pathways and worsen existing anemia, further complicating management (11–13). The European Cancer Anemia Survey (ECAS) reported that nearly 40% of chemotherapy patients had hemoglobin levels below 10 g/dL, underscoring the high prevalence and ongoing management challenges associated with anemia in oncology (12).
Anemia prevalence varies widely across cancer types, affecting approximately 40–50% of patients with prostate cancer, 36% with hematologic malignancies, 30% with breast cancer (BC), 23% with lung cancer, and 25% with colorectal cancer (CRC), with rates increasing with disease progression (14, 15). Although the prognostic significance of anemia remains debated, evidence consistently associates it with poorer functional status, reduced treatment tolerance, and increased mortality across several malignancies (16). Socioeconomic and demographic disparities further exacerbate these outcomes, with this comorbidity disproportionately affecting socioeconomically deprived and rural populations, as well as Black, Hispanic, and American Indian/Alaska Native individuals, despite lower cancer incidence rates (17, 18). In older adults (>65 years), anemia is an established risk factor for hospitalization, postoperative complications, and higher all-cause mortality (19, 20).
However, most existing research has focused on clinical outcomes within specific cancer subtypes or treatment settings (21–23), leaving substantial uncertainty regarding national patterns and population-level disparities in mortality associated with anemia and malignancy, especially in older adults. A comprehensive evaluation across demographic and geographic subgroups is crucial to better quantify the public health burden and inform equitable cancer care strategies. To address this gap, the present study utilizes the CDC WONDER (Centers for Disease Control and Prevention Wide-Ranging OnLine Data for Epidemiologic Research) database to assess national trends in concurrent anemia and malignancy mortality in the United States. The findings aim to offer an epidemiologic foundation for evidence-based interventions and policy initiatives to reduce disparities in cancer outcomes.
2 Methodology
2.1 Study setting and population
The CDC WONDER database was utilized to extract death certificate data spanning from 1999 to 2020, covering all 50 U.S. states and the District of Columbia. The dataset was accessed on October 3rd 2025 (24). Our analysis utilized the ‘Multiple Cause of Death’ Public Use dataset, including all cases in which anemia and malignant neoplasms were recorded as either the underlying or a contributing cause of death (ie, listed anywhere on the death certificate). Malignant neoplasms were identified using ICD-10 codes C00–C97, while anemia was classified using codes D50–D53 (Nutritional anemias), D55-D59 (Hemolytic anemias), and D60-D64 (Aplastic and other anemias). Subgroup analyses utilized ICD-10 codes to identify specific malignancy types listed in the Multiple Cause of Death fields. Anemia (D50–D64) was a mandatory contributing cause for inclusion in all subtype-specific mortality calculations. To capture the comprehensive mortality burden involving these organ systems, our coding definitions included both primary neoplasms and where relevant, secondary (metastatic) involvement. Specifically, we defined the subgroups as: CRC (C18–C20, C21.8, C78.5); Gastric Cancer (C16); Lung Cancer (C34, C78.0); BC (C50); Prostate Cancer (C61); Gynecologic Cancers (C51–C58, C79.6); and Hematologic Malignancies (C81–C96). According to the latest national statistics from the United States, these cancer types were chosen to reflect the most prevalent cancers causing anemia. These same ICD-10 codes have been validated and employed in previous studies to categorize patients with malignancies and anemia (22, 25). Under this inclusive “Multiple Cause” framework, a single death certificate listing multiple distinct malignancies (e.g., primary BC with secondary lung involvement) contributed to the mortality rates of both respective subgroups, reflecting the complex comorbidity profile of the decedent. Hematologic malignancies were aggregated into a composite category encompassing lymphomas, multiple myeloma, and all acute/chronic leukemias (C81–C96) to assess the collective burden of hematopoeitic neoplasms. In compliance with CDC WONDER data confidentiality policies, mortality counts between one and nine were suppressed. Consequently, specific demographic or geographic stratifications yielding suppressed counts were deemed unreliable and excluded from the final analysis to ensure statistical robustness. This study follows STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines (26).
2.2 Data abstraction
For this study, data were extracted on multiple mortality-related variables, including gender, year of death, state of residence, place of death, age distribution (≥65), urban–rural status, and regional categorization. Demographic factors comprised age and race/ethnicity, with race/ethnicity classified as non-Hispanic (NH) White, NH Black or African American, Hispanic or Latino, NH American Indian or Alaska Native, and NH Asian or Pacific Islander. These race/ethnicity categories have previously been used within analyses from the CDC WONDER database and rely on reported data on death certificates. Place of death was grouped into medical facilities (outpatient, emergency department, inpatient, death on arrival, or unspecified), home, hospice, or nursing home/long-term care settings. Urban–rural status was assigned using the 2013 National Center for Health Statistics classification, defining counties as urban (large metropolitan ≥1 million; medium/small metropolitan 50,000–999,999) or rural (<50,000) (27). Geographic regions followed U.S. Census Bureau definitions: Northeast, Midwest, South, and West (28).
2.3 Data analysis
To analyze national trends in anemia and malignancy-related mortality, crude rates (CMR) and age-adjusted mortality rates (AAMRs) per 100,000 population were calculated for the years 1999 to 2020, along with their corresponding 95% confidence intervals (CIs). CMRs were determined by dividing the number of concurrent anemia and malignant neoplasms-related deaths by the corresponding United States population of that year. AAMRs were calculated by standardizing the anemia and malignant neoplasm-related deaths to the 2000 United States population as previously described (29). These rates were stratified by sex, race/ethnicity, state, urban-rural classification, census region, metropolitan status, and year. Annual percent changes (APCs) and 95% CIs in AAMRs were estimated using the Joinpoint Regression Program (Version 5.3.0, National Cancer Institute) to assess trends and detect significant inflection points over time (30). This approach detects significant temporal shifts in AAMRs by applying log-linear regression models to periods of change. APCs and their 95% CIs were derived for each segment between joinpoints using the Monte Carlo permutation test to identify the optimal number of inflection points, conducting 4,499 permutations. A maximum of four joinpoints was permitted. Trends were classified as increasing or decreasing when the slope of mortality change significantly differed from zero. A two-tailed t-test was used to determine the statistical significance of APCs, with a P-value less than 0.05 considered statistically significant. Models were fit using the grid-search method assuming constant variance and uncorrelated errors. Population denominators were not smoothed. For stratified analyses (e.g., by sex or race), identical algorithm settings were applied, but models were fit independently without ensuring parallel trends or identical joinpoint locations, allowing the software to detect distinct temporal patterns for each demographic group.
3 Results
Between 1999 and 2020, there were 264,331 anemia and malignancy related deaths among U.S. adults aged 65 and older (Table 1). These deaths occurred across various settings, with the majority taking place in medical facilities (38.19%), followed by deaths at home (27.88%), in nursing homes or long-term care facilities (25.49%), hospice settings (4.37%), and other locations (3.88%) (Supplementary Table 1).
Table 1. Anemia and malignancy–related deaths, stratified by sex and race in older adults in the United States, 1999 to 2020.
3.1 Annual trends in AAMR for anemia and malignancy
Over the 22-year study period, the overall AAMR for concurrent anemia and malignancy among older adults remained relatively stable, moving from 31.53 per 100,000 population in 1999 to 31.05 in 2020. Joinpoint analysis revealed a statistically significant downward trend from 1999 through 2017 (APC: –0.86; 95% CI: –1.04 to –0.68), followed by a sharp increase from 2017 through 2020 (APC: 4.63; 95% CI: 2.11 to 7.21).(AAPC: -0.09; 95% CI: -0.45 to 0.26, p value>0.05). (Figures 1, 2, Tables 2, 3).
Figure 1. Overall and sex stratified anemia and malignancy related age-adjusted mortality rates per 100,000 among older adults aged ≥65 years in the United States, 1999–2020.*Indicates that the annual percent change (APC) is significantly different from zero at α = 0.05. AAMR, age-adjusted mortality rate; APC, annual percent change; CI, confidence interval.
Figure 2. Joinpoint model of anemia and malignancy-related AAMR per 100,000; overall and stratified by sex, 1999-2020 (*indicates the APC is statistically significant).
Table 2. Annual percent change (APC) of anemia and malignancy related age-adjusted mortality rates per 100,000 in older adults the United States, 1999 to 2020.
Table 3. Overall and sex‐stratified anemia and malignancy related age-adjusted mortality rates per 100,000 in older adults in the United States, 1999 to 2020.
3.2 Anemia and malignancy-related AAMR stratified by gender
Throughout the study period, men consistently had higher overall AAMRs than women (AAMR: 28.78 [95% CI: 28.67–28.89] for men vs. 22.61 [95% CI: 22.48–22.74] for women). Among men, AAMR declined over time (AAPC: –0.43; 95% CI: –0.82 to –0.04), while it remained stable for women (AAPC: –0.06; 95% CI: –0.43 to 0.32; p = 0.76).
For men, AAMR fell markedly from 44.29 in 1999 to 35.88 in 2017 (APC: –1.22; 95% CI: –1.41 to –1.02), then rose significantly to 40.99 in 2020 (APC: 4.42; 95% CI: 1.67 to 7.24). For women, the AAMR decreased from 24.37 in 1999 to 21.57 in 2017 (APC: –0.81; 95% CI: –1.00 to –0.63), followed by a significant increase to 24.47 in 2020 (APC: 4.59; 95% CI: 1.90 to 7.34) (Figures 1, 2, Tables 2, 3).
3.3 Anemia and malignancy-related AAMR stratified by race/ethnicity
The highest number of anemia and malignancy- related deaths occurred among NH White individuals (208,784), followed by NH Black (32,187), Hispanic (14,602), NH Asian or Pacific Islander (7,089), and NH American Indian or Alaska Native populations (1,167). Overall AAMRs were highest among NH Black or African Americans, followed by NH Whites, NH American Indian or Alaska Natives, Hispanic or Latinos, and NH Asian or Pacific Islanders (NH Black or African American: 42.39, 95% CI: 41.93 to 42.86; NH White: 27.88, 95% CI: 27.76 to 28.00;NH American Indian or Alaska Native: 27.53, 95% CI: 25.91 to 29.15; Hispanic or Latino: 24.25, 95% CI: 23.85 to 24.64; NH Asian or Pacific Islander: 22.16, 95% CI: 21.64 to 22.68). From 1999 to 2020, AAMRs remained relatively stable among NH American Indian or Alaska Native (AAPC: -0.46; 95% CI: -1.64 to 0.73; p-value = 0.43), Hispanic or Latino populations (AAPC: -0.25; 95% CI: -0.56 to 0.06; p-value = 0.10) and NH Whites (AAPC: 0.24; 95% CI: 0.10 to 0.58; p-value=0.16). A significant downward trend was observed in NH Asian or Pacific Islanders (APC: -1.51; 95% CI: -2.03 to -0.97; p-value = 0.00001). Among NH Black or African Americans, AAMR decreased from 1999 through 2018 (APC: -2.49; 95% CI: -2.74 to -2.25; p-value <0.000001), followed by a sharp rise from 2018 to 2020 (APC: 8.04; 95% CI: 0.08 to 16.63; p-value = 0.047). A similar pattern was observed among NH White populations, with a significant decline from 1999 through 2017 (APC −0.59; 95% CI −0.76 to −0.42; p < 0.001), followed by a significant increase through 2020 (APC 5.39; 95% CI 2.92 to 7.93; p < 0.001) (Figures 3, 4, Tables 2, 4).
Figure 3. Race stratified anemia and malignancy related age-adjusted mortality rates per 100,000 among older adults aged ≥65 years in the United States, 1999–2020.* Indicates that the annual percent change (APC) is significantly different from zero at α = 0.05. AAMR, age-adjusted mortality rate; APC, annual percent change; CI, confidence interval.
Figure 4. Joinpoint model of anemia and malignancy-related AAMR per 100,000; stratified by race, 1999-2020 (*indicates the APC is statistically significant).
Table 4. Anemia and malignancy– related age-adjusted mortality rates per 100,000, Stratified by race in older adults in the United States, 1999 to 2020.
3.4 Anemia and malignancy-related AAMR Stratified by geographical regions
Geographic disparities in AAMR were substantial, with state-level rates ranging from 14.33 (95% CI: 13.43 to 15.24) in Nevada to 46.49 (95% CI: 43.73 to 49.25) in North Dakota. States within the top 90th percentile for AAMRs included Maryland, North Dakota, Rhode Island, West Virginia, and the District of Columbia, each recording nearly 1.5 times the rates of those in the bottom 10th percentile, which included Louisiana, Arizona, Georgia, Nevada, and Utah. (Supplementary Table 2) These regional trends are visually represented on the WONDER Map using quantile classification (Figure 5).
Figure 5. States stratified anemia and malignancy related age-adjusted mortality rates per 100,000 among older adults aged ≥65 years in the United States, 1999–2020.
Regionally, the highest mortality rates over the study period were observed in the Midwest (AAMR: 30.64; 95% CI: 30.40 to 30.88), followed by the West (AAMR: 28.62; 95% CI: 28.38 to 28.85), Northeast (AAMR: 28.46; 95% CI: 28.22 to 28.71), and South (AAMR: 27.64; 95% CI: 27.46 to 27.82). (Figure 6, Table 2, Supplementary Table 3). During the study period, the Northeast was the only region to exhibit a consistent downward trend in AAMR (AAPC: -0.95; 95% CI: -1.22 to -0.68). In contrast, both the Midwest and South experienced a steady decline in AAMR from 1999 to 2016 (APC: Midwest -1.49; 95% CI: -1.69 to -1.28; South -0.66; 95% CI: -0.89 to -0.43), followed by a significant increase through 2020 (APC: Midwest 4.18; 95% CI: 2.35 to 6.04; South 4.70; 95% CI: 2.88 to 6.55). In the West, AAMR declined from 1999 to 2018 (APC: -0.48; 95% CI: -0.80 to -0.16), after which it remained stable through 2020 (APC: 5.18; 95% CI: -3.94 to 15.17). Overall, AAPC was -0.43 (95% CI: -0.78 to -0.08) for the Midwest, 0.34 (95% CI: -0.01 to 0.70; p>0.05) for the South, and 0.05 (95% CI: -0.80 to 0.90; p>0.05) for the West.
Figure 6. Census region stratified anemia and malignancy related age-adjusted mortality rates per 100,000 among older adults aged ≥65 years in the United States, 1999–2020.
Non-metropolitan areas consistently experienced higher AAMRs than metropolitan areas (Non-metropolitan: 32.89; 95% CI: 32.61 to 33.16; Metropolitan: 27.81; 95% CI: 27.69 to 27.93). Between 1999 and 2017, metropolitan AAMRs declined steadily (APC: -0.88; 95% CI: -1.09 to -0.66), followed by a notable rise from 2017 to 2020 (APC: 4.44; 95% CI: 1.49 to 7.46). For non-metropolitan areas, AAMRs also experienced a downward trend from 1999 to 2016 (APC: -0.82; 95% CI: -1.09 to -0.55), before increasing through 2020 (APC: 4.06; 95% CI: 1.79 to 6.39). (Figure 7, Table 5). During the study period, AAPC for metropolitan areas was -0.13 (95% CI: -0.55 to 0.28; p>0.05) and 0.09 (95% CI: -0.35 to 0.53; p>0.05) for non-metropolitan areas.
Figure 7. Urbanization stratified anemia and malignancy related age-adjusted mortality rates per 100,000 among older adults aged ≥65 years in the United States, 1999–2020.* Indicates that the annual percentchange (APC) is significantly different from zero at α = 0.05. AAMR, age-adjusted mortality rate; APC, annual percent change; CI, confidence interval.
Table 5. Anemia and malignancy– related age-adjusted mortality rates per 100,000, stratified by urban-rural classification in older adults in the United States, 1999 to 2020.
3.5 Subgroup analysis by cancer type
From 1999 to 2020, various malignancies exhibited a significant downward trend, albeit with some variability among them. CRC demonstrated the steepest decline (AAPC: -2.03; 95%CI: -2.45 to −1.60, p-value <0.05); followed by gastric cancer (AAPC: -1.96; 95% CI: -2.66 to -1.26) and prostate cancer (AAPC: 1.40; 95%CI: -2.02 to -0.77). BC, lung, gynecological, and hematological cancers showed a non-significant change over the study period. Lung cancer: [AAPC: 0.96: 95% CI: -0.20 to 2.13 (p-value=0.1)]; BC: [AAPC: 0.12; 95% CI: -0.73 to 0.99 (p value= 0.7)]; Gynecological cancer: [AAPC: 0.55; 95% CI: -0.20 to 1.30 (p value= 0.15)]; Hematological cancer: [AAPC: 0.02; 95% CI: -0.59 to 0.64 (p value= 0.943)].
CRC demonstrated a significant decline in AAMR from 1999 to 2016 (APC: –2.84; 95% CI: –3.09 to –2.58), followed by a plateau through 2020 (APC: 1.48; 95% CI: –0.73 to 3.74; P = 0.176). Gynecologic cancers also showed a steady decline from 1999 to 2016 (APC: –0.92; 95% CI: –1.41 to –0.42), but this was followed by a marked increase from 2016 to 2020 (APC: 7.01; 95% CI: 3.19 to 10.96). Prostate cancer mortality declined from 1999 to 2017 (APC: –2.54; 95% CI: –2.84 to –2.25), after which a significant rise was observed from 2017 to 2020 (APC: 5.76; 95% CI: 1.21 to 10.52). Similarly, BC mortality decreased steadily from 1999 to 2017 (APC: –0.89; 95% CI: –1.33 to –0.46), but reversed to a significant increase from 2017 to 2020 (APC: 6.46; 95% CI: 0.36 to 12.94). Gastric cancer showed a significant decline from 1999 to 2011 (APC: –3.02; 95% CI: –3.86 to –2.17), followed by stability until 2020 (APC: –0.53; 95% CI: –1.89 to 0.84; P = 0.42). Lung cancer exhibited a distinct pattern, with an initial steady increase from 1999 to 2012 (APC: 1.27; 95% CI: 0.78 to 1.76), a period of stability from 2012 to 2015 (APC: –4.18; 95% CI: –11.62 to 3.90; P = 0.28), and a subsequent sharp rise from 2015 to 2020 (APC: 3.35; 95% CI: 1.57 to 5.16). Hematological cancers declined steadily from 1999 to 2006 (APC: –1.51; 95% CI: –2.42 to –0.60), remained stable from 2006 to 2018 (APC: –0.06; 95% CI: –0.50 to 0.38; P = 0.77), and then reversed to a significant increase in mortality from 2018 to 2020 (APC: 6.11; 95% CI: 0.26 to 12.30) (Figures 8, 9).
Figure 8. Joinpoint model of anemia and malignancy-related AAMR per 100,000; stratified by cancer type, 1999-2020 (*indicates the APC is statistically significant).
Figure 9. Anemia and malignancy related age-adjusted mortality rates per 100,000 among older adults aged ≥65 years in the United States, stratified by cancer type, 1999–2020.* Indicates that the annual percent change (APC) is significantly different from zero at α = 0.05. AAMR, age-adjusted mortality rate; APC, annual percent change; CI, confidence interval.
4 Discussion
In this 22-year CDC mortality analysis, several key patterns emerged. AAMRs declined from 1999 to 2017, but rose sharply thereafter through 2020 with consistently higher rates among males than females. Racial disparities were evident, as NH Black or African American individuals had the highest AAMRs, while Asian or Pacific Islanders had the lowest. Geographic variation was also marked: states in the top 90th percentile (e.g., Maryland, North Dakota, Rhode Island) recorded nearly 1.5-fold higher AAMRs than those in the lowest decile (e.g., Louisiana, Arizona, Georgia, Nevada, Utah). The Midwest and non-metropolitan regions bore the greatest burden. By cancer type, CRC and gastric cancers showed the most pronounced and sustained mortality declines, whereas BC, gynecologic, prostate, lung, and hematologic cancers demonstrated initial decreases followed by concerning increases in recent years (Figure 10).
Figure 10. Central illustration summarizing key trends in anemia and malignancy-related age-adjusted mortality rates per 100,000 among older adults aged ≥65 years in the United States, 1999–2020. AAMR, age-adjusted mortality rate; NH, non-Hispanic.
Mortality associated with concurrent anemia and malignancy is reported to be highest among individuals over 75 years of age, partly due to reduced physiological tolerance to anemia (31). Moreover, increased eryptosis, programmed erythrocyte death has been observed in the elderly, leading to greater red blood cell loss and worsening anemia (32). The initial decline in anemia and malignancy-related mortality from 1999 to 2017 may likely reflect advances in early cancer detection, the introduction of more effective systemic therapies, and improvements in supportive care, including erythropoiesis-stimulating agents (ESAs), iron supplementation, and optimized transfusion practices guided by ASCO/ASH recommendations introduced in 2010 (33). These interventions might have potentially helped reduce anemia severity and improve patient outcomes.
The post-2017 resurgence in mortality may likely reflect a convergence of therapeutic shifts, administrative changes, rising frailty and multimorbidity, and the acute shock of the COVID-19 pandemic. The U.S. transition from ICD-9-CM to ICD-10-CM in 2015 increased diagnostic specificity, and as clinicians adapted, more granular electronic health record (EHR) problem lists led to more frequent documentation of comorbidities such as anemia, subsequently appearing more often on death certificates and contributing to an administrative rise in mortality (34, 35). This may also have been compounded by the long-term “lag effect” of restrictive ESA policies; following the 2011 FDA Risk Evaluation and Mitigation Strategy (REMS), ESA use declined sharply (33, 36), leaving many older, frail adults with undertreated chronic anemia and potentially poorer long-term outcomes. Concurrently, the therapeutic landscape shifted with the widespread adoption of immune checkpoint inhibitors (ICIs), which, despite improving survival, introduced immune-related hematologic toxicities, including hemolytic anemia and cytopenias, potentially increasing the complexity of clinical management in older adults (37). The rising mortality trend may also reflect the growing multimorbidity burden among aging cancer patients. National data show that the prevalence of multiple chronic conditions among older U.S. adults increased substantially, independent of cancer status (38). As oncology care increasingly involves adults over 80 years with significant cardiovascular, renal, and metabolic comorbidities, anemia becomes closely intertwined with broader frailty trajectories (39). Even mild anemia has been associated with functional decline and cardiac stress, features termed “anemia-associated frailty” (31, 39). Thus, the late-period mortality rise may align with an older, more comorbid population living longer with cancer but accumulating greater physiological vulnerability (40).
The sharp 2019–2020 mortality escalation likely reflects interacting biological and administrative factors. While this dataset cannot confirm specific biomarkers, literature suggests that biologically, older cancer patients may have faced a “double hit”: beyond the direct mortality of viral infection, the COVID-19 “cytokine storm” elevates interleukin-6 (IL-6) and hepcidin, sequestering iron and blocking erythropoiesis. This mechanism, described in prior studies, potentially exacerbates pre-existing anemia in cancer patients and might precipitate lethal hematologic decompensation in already compromised marrow (41–43). This synergy may have been further aggravated by systemic delays in blood transfusions and supportive care (44). Administratively, the rise might have been amplified by 2020 National Center for Health Statistics (NCHS) guidance, explicitly encouraging certifiers to detail the “chain of events” and include all “significant conditions contributing to death” in Part II of the death certificate to fully capture the pandemic’s risk profile (45). This increased documentation of “background” comorbidities like anemia suggests the 2020 peak may represent a composite of true excess mortality and a surveillance artifact.
Our study demonstrated that men consistently exhibited higher AAMRs than women, aligning with reports of higher anemia prevalence and cancer incidence among male oncology cohorts, which may partly explain their increased mortality (46). This disparity may also reflect men’s lower physiological tolerance to anemia and greater susceptibility to hypoxia at comparable hemoglobin levels (47). Furthermore, males generally experience more aggressive cancer progression and poorer survival outcomes, influenced by a combination of biological, behavioral, and treatment-related factors (48).In contrast, females appear to benefit from several protective mechanisms, including estrogen-mediated immune enhancement, more effective tumor surveillance, genetic advantages from dual X chromosomes, and greater engagement in health care and treatment adherence (49–51). For instance, “escape from X-inactivation tumor suppressor” (EXITS) genes—such as MAGEC3, KDM6A, KDM5C, DDX3X, CNKSR2, and ATRX- provide females with biallelic tumor-suppressive protection, whereas males carry only a single copy. Many of these genes also regulate p53 activity, a mechanism cited in literature that may contribute to the higher prevalence of p53 mutations seen in male cancers (52).
Higher mortality among NH Black populations likely reflects structural racism and social determinants of health (SDOH) interacting with biological risks. Beyond treatment differences (53, 54), cumulative social stress (“weathering”) may accelerate biological aging, increasing vulnerability to cancer and anemia (55). Structural barriers, including lower screening, delayed hematologic referrals, and reduced supportive care (56, 57), as well as residence in environmental justice zones with high pollutant exposure (58) cultural mistrust and underrepresentation in research (59) further exacerbate disparities. In contrast, lower mortality in Asian American populations may relate to distinct cancer epidemiology and genetic profiles, such as higher prevalence of EGFR mutations responsive to targeted therapies (60), alongside socioeconomic factors.
Pronounced regional and urban–rural disparities were evident, with nonmetropolitan populations consistently experiencing higher mortality. These differences likely reflect structural workforce shortages limiting access to specialized oncology care, transportation barriers, compounded by travel distance and limited public transit infrastructure that reduce adherence to complex treatment regimens and supportive services (61, 62). National data further indicate higher mortality across multiple conditions in rural areas, reflecting broader disparities in healthcare access, screening, and treatment, including higher mastectomy rates and lower radiation use (63, 64). Regionally, the Northeast showed sustained mortality declines that may likely be due to better healthcare access, early detection, and improved anemia management (65). In contrast, the Midwest and South experienced post-2016 increases, which may have been influenced by socioeconomic inequities, healthcare disruptions, delayed diagnoses, higher comorbidity burdens, and limited oncology resources (66). The West exhibited modest declines with a late plateau, occurring alongside regional variations in cancer subtypes and supportive care practices. Overall, these findings highlight persistent geographic inequities and underscore the need for equity-focused strategies to improve early detection, expand access to care, and optimize outcomes nationwide.
In our subgroup analysis, we observed heterogeneous cancer-specific trends. CRC, gastric, and prostate cancers demonstrated significant overall declines in mortality, with CRC showing the steepest reduction until 2016, followed by gastric and prostate cancer. Notably, lung cancer showed the most dynamic trajectory, alternating between periods of rise, decline, and resurgence. These findings highlight the complexity of concurrent anemia and cancer related mortality and underscore the need to contextualize each trend within the framework of evolving screening practices, therapeutic advances, and comorbid burden. The decline in CRC, gastric, and prostate cancer mortality temporally coincides with public health and clinical interventions. CRC screening, formally recommended by the American Cancer Society (ACS) from 2001 onward and supported by Medicare coverage, led to widespread adoption of colonoscopy, fecal immunochemical testing (FIT), and sigmoidoscopy at defined intervals for individuals aged 50 years and older. Population-level evidence shows screening reduces CRC mortality by over 50% through early detection and polyp removal (67–69). Our findings suggest that the mortality decline observed in the 2010s may have been associated with these structured screening practices. Mortality plateauing after 2016 likely reflects screening saturation and rising incidence in younger adults (70).
Gastric cancer mortality has declined due to multiple public health and clinical advances, including reduced Helicobacter pylori prevalence, improved food preservation, lower smoking and salt intake, and enhanced sanitation, collectively decreasing both incidence and death rates (71, 72). Prostate cancer mortality also fell between 1999 and 2017, that may have largely been driven by prostate-specific antigen (PSA) screening introduced in the late 1980s, which facilitated earlier detection and stage migration (67, 73). However, after 2017, AAMR rose, potentially reflecting the 2012 United States Preventive Services Task Force (USPSTF) Grade D recommendation against routine PSA screening, which may have reduced early diagnosis and increased metastatic presentations at detection (74). Similarly, BC mortality declined from 1999 to 2017, potentially aided by widespread mammography screening and advances in systemic therapies, with the reporting a 44% reduction in deaths since 1989 (75). Yet, in this cohort with concurrent anemia, mortality increased after 2017. A 2025 study indicated that 85% of BC diagnosed in 2017 were stage I or II, typically low-risk yet these now account for a substantial proportion of deaths, suggesting that factors beyond tumor stage, including treatment delays, comorbidities, or barriers to optimal care, may contribute (76). Gynecological cancers generally declined from 1999 to 2016, which may have driven by effective cervical cancer screening through Pap smears and HPV testing (68), as well as HPV vaccination beginning in 2008 (77). Cancers lacking routine screening, such as ovarian and some other gynecological cancers, have experienced rising mortality since 2017. For example, uterine cancer deaths increased by 1.5% per year from 2013 to 2022, with similar upward trends observed in ovarian cancer.
Hematological cancers exhibited a biphasic mortality pattern: a steep decline from 1999 to 2006, a plateau from 2006 to 2018, and an increase after 2018. The early decline maybe temporally associated with the introduction of rituximab in 1997, which revolutionized B-cell malignancy treatment (78). The subsequent plateau likely reflects limits of initial therapeutic advances and the emergence of therapy-resistant disease subtypes. The recent rise in mortality may be partly attributable to the COVID-19 pandemic, as patients with hematologic malignancies are particularly vulnerable to severe SARS-CoV-2 infection (42). Lung cancer exhibited a dynamic, biphasic mortality pattern in anemic patients. Mortality rose from 1999 to 2012, largely reflecting the cumulative effects of historical smoking behaviors, as tobacco use remains the leading risk factor for lung cancer (79, 80). From 2012, mortality stabilized, coinciding with the 2013 USPSTF recommendation for annual low-dose CT screening in high-risk adults, although slow uptake, limited measurable impact (80). A subsequent sharp rise from 2015 to 2020 coincided with the COVID-19 pandemic, which disproportionately increased mortality among lung cancer patients (43). This increase in mortality is also attributable to smoking, increases with advancing age and is influenced by birth cohort effects, indicating the long-term impact of smoking behaviors (81).
4.1 Limitations
This study has several limitations. Reliance on death certificates and ICD-10 coding may have led to potential misclassification or underreporting of anemia and malignancy as causes of death (82). Anemia is often viewed as a clinical sign rather than a distinct cause of death; therefore, it is likely only recorded when severe or directly contributing to the terminal event, suggesting our rates are conservative estimates. Variation in coding practices across states and over time, such as differences between coroner and medical examiner systems may influence observed geographic trends (83). As an ecological study relying on aggregate data, we were unable to perform multivariate adjustments for key patient-level confounders. The absence of data on socioeconomic status, specific insurance type (e.g., Medicare Advantage vs. Fee-for-Service), healthcare access, facility-level oncologic capacity, and lifestyle introduces potential residual confounding. Consequently, the disparities reported herein represent unadjusted associations and may be influenced by these unmeasured effect modifiers. The CDC WONDER database also lacks detailed clinical information, such as hemoglobin levels, cancer stage, treatment regimens, and specific comorbidity indices, which precludes causal inference regarding the impact of specific interventions.Temporal changes in diagnostic criteria, coding practices, and reporting accuracy may also have influenced observed trends. While we noted that estimates for smaller groups like NH American Indian or Alaska Natives may be unstable due to sample sizes, we must also acknowledge racial misclassification as a distinct source of bias. Previous studies indicate that AI/AN and Asian/Pacific Islander individuals are sometimes misclassified as White or “Other” on death records, which may lead to an underestimation of mortality rates in these specific populations distinct from sample size issues (84). Reliance on death certificate data captures the co-occurrence of anemia and malignancy codes but cannot definitively establish clinical etiology. Consequently, this study identifies ‘anemia in the setting of malignancy’ and may include cases of incidental or multifactorial anemia not strictly caused by the tumor or its treatment.while we discuss potential biological mechanisms such as inflammatory cytokine activity, this epidemiological dataset does not contain biomarker data; therefore, these associations remain hypothesis-generating. Nevertheless, despite these limitations, the use of a large, nationally representative dataset spanning over two decades provides valuable insights into long-term population trends, enabling the identification of meaningful disparities in anemia and malignancy related mortality that warrant targeted public health and clinical intervention.
5 Conclusion
Between 1999 and 2020, anemia and malignancy-related mortality among U.S. adults aged ≥65 exhibited an initial decline followed by a significant resurgence in recent years. These trends were associated with persistent demographic and geographic disparities, with the highest rates observed among males, NH black individuals, and residents of nonmetropolitan areas. While CRC and gastric cancers showed sustained declines, the recent reversals in BC, prostate, and lung cancer mortality likely reflect a complex interplay of administrative coding shifts, an aging multimorbid population, and therapeutic changes. These findings underscore the importance of targeted public health strategies to address the structural and regional inequities characterizing the burden of anemia in older cancer patients.
Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.
Author contributions
MK: Data curation, Visualization, Methodology, Investigation, Writing – review & editing, Writing – original draft. AC: Visualization, Conceptualization, Methodology, Writing – review & editing, Data curation, Investigation, Writing – original draft, Supervision. MI: Data curation, Investigation, Writing – review & editing, Writing – original draft. IA: Writing – original draft, Writing – review & editing. AK: Writing – original draft, Writing – review & editing. AW: Writing – review & editing. MH: Supervision, Writing – review & editing, Project administration, Methodology.
Funding
The author(s) declared that financial support was not received for this work and/or its publication.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that generative AI was not used in the creation of this manuscript.
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Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fonc.2026.1722891/full#supplementary-material
Abbreviations
AAMR, Age-Adjusted Mortality Rate; APC, Annual Percent Change; AAPC, Average Annual Percentage Change; AML, Acute Myeloid Leukemia; CDC WONDER, Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research; CI, Confidence Intervals; CMR, Crude Mortality Rates; CRA, Cancer Related Anemia; CIA, Chemotherapy-Induced Anemia; EGFR, Epidermal Growth Factor Receptor; ECAS, European Cancer Anemia Survey; ICD, International Statistical Classification of Diseases and Related Health Problems.; NH, non-Hispanic; STROBE, Strengthening the Reporting of Observational Studies in Epidemiology.
References
1. National Cancer Institute. Cancer Stat Facts: Common Cancer Sites. Bethesda, MD: National Cancer Institute (2025). Available online at: https://seer.cancer.gov/statfacts/html/common.html.
2. Weir HK, Anderson RN, Coleman King SM, Hong Y, Moller B, Leadbetter S, et al. Heart disease and cancer deaths—trends and projections in the United States, 1969–2020. Prev Chronic Dis. (2016) 13:E157. doi: 10.5888/pcd13.160211
3. GBD 2021 Anaemia Collaborators. Prevalence, years lived with disability, and trends in anaemia burden by severity and cause, 1990-2021: findings from the Global Burden of Disease Study 2021. Lancet Haematol. (2023) 10:e713–34. doi: 10.1016/S2352-3026(23)00160-6
4. Centers for Disease Control and Prevention. Anemia or iron deficiency. Atlanta, GA: FastStats (2025). Available online at: https://www.cdc.gov/nchs/fastats/anemia.htm.
5. Schwartz RN. Anemia in patients with cancer: incidence, causes, impact, management, and use of treatment guidelines and protocols. Am J Health Syst Pharm. (2007) 64:S5–S30. doi: 10.2146/ajhp060601
6. Abdel-Razeq H and Hashem H. Recent update in the pathogenesis and treatment of chemotherapy and cancer induced anemia. Crit Rev Oncol Hematol. (2020) 145:102837. doi: 10.1016/j.critrevonc.2019.102837
7. Fung EB, Harmatz P, Milet M, Olivieri N, Martin M, Sechaud R, et al. Morbidity and mortality in chronically transfused subjects with thalassemia and sickle cell disease: a report from the multi-center study of iron overload. Am J Hematol. (2007) 82:255–65. doi: 10.1002/ajh.20809
8. Rasel M, Hashmi MF, and Mahboobi SK. Transfusion iron overload. In: StatPearls. StatPearls Publishing, Treasure Island, FL (2024). Available online at: https://www.ncbi.nlm.nih.gov/books/NBK562146/.
9. Han SV, Park M, Kwon YM, Kim J, Lee J, Choi S, et al. Mild anemia and risk for all-cause, cardiovascular, and cancer deaths in apparently healthy elderly Koreans. Korean J Fam Med. (2019) 40:151–8. doi: 10.4082/kjfm.17.0089
10. Hunt JM. Reversing productivity losses from iron deficiency: the economic case. J Nutr. (2002) 132:794S–801S. doi: 10.1093/jn/132.4.794S
11. Schrijvers D, De Samblanx H, Roila F, and Guidelines Working Group ESMO. Erythropoiesis-stimulating agents in the treatment of anaemia in cancer patients: ESMO Clinical Practice Guidelines for use. Ann Oncol. (2010) 21:v244–7. doi: 10.1093/annonc/mdq202
12. Ludwig H, Van Belle S, Barrett-Lee P, Birgegård G, Bokemeyer C, Gascón P, et al. The European Cancer Anaemia Survey (ECAS): a large, multinational, prospective survey defining the prevalence, incidence, and treatment of anaemia in cancer patients. Eur J Cancer. (2004) 40:2293–306. doi: 10.1016/j.ejca.2004.06.019
13. Gilreath JA and Rodgers GM. How I treat cancer-associated anemia. Blood. (2020) 136:801–13. doi: 10.1182/blood.2019004017
14. Merlini L, Cartenì G, Iacobelli S, Porta C, Pedrazzoli P, Marchetti P, et al. Anemia prevalence and treatment practice in patients with non-myeloid tumors receiving chemotherapy. Cancer Manag Res. (2013) 5:205–14. doi: 10.2147/CMAR.S45236
15. Almehmadi M, Salih M, Elmissbah TE, Alghamdi N, Alzahrani M, Alsulimani A, et al. Prevalence of anemia among Saudi patients with solid cancers at diagnosis in King Faisal Hospital, Taif Province, Kingdom of Saudi Arabia. PLoS One. (2021) 16:e0246202. doi: 10.1371/journal.pone.0246202
16. Calabrich A and Katz A. Management of anemia in cancer patients. Future Oncol. (2011) 7:507–17. doi: 10.2217/fon.11.24
17. Islami F, Baeker Bispo J, Lee H, Miller KD, DeSantis CE, Goding Sauer A, et al. American Cancer Society’s report on the status of cancer disparities in the United States, 2023. CA Cancer J Clin. (2024) 74:136–66. doi: 10.3322/caac.21812
18. Cardenas-Turanzas M, Cesta MA, Wakefield C, Price KJ, Wang H, Nates JL, et al. Factors associated with anemia in patients with cancer admitted to an intensive care unit. J Crit Care. (2010) 25:112–9. doi: 10.1016/j.jcrc.2009.02.004
19. Baron DM, Hochrieser H, Posch M, Metnitz B, Rhodes A, Moreno RP, et al. Preoperative anaemia is associated with poor clinical outcome in non-cardiac surgery patients. Br J Anaesth. (2014) 113:416–23. doi: 10.1093/bja/aeu098
20. Culleton BF, Manns BJ, Zhang J, Tonelli M, Klarenbach S, and Hemmelgarn BR. Impact of anemia on hospitalization and mortality in older adults. Blood. (2006) 107:3841–6. doi: 10.1182/blood-2005-10-4308
21. Tuin A, Diehl T, Billion T, Mirza M, Tauseef A, and Abdul Jabbar AB. Trends in hemolytic anemia-related mortality in the United States from 1999-2022. Front Hematol. (2025) 4:1560766. doi: 10.3389/frhem.2025.1560766
22. Zuin M, Ferrucci L, and Zuliani G. Iron deficiency anemia-related mortality trends in US older subjects, 1999 to 2019. Aging Clin Exp Res. (2025) 37:99. doi: 10.1007/s40520-025-02982-0
23. Dai D, Han S, Li L, Zhang J, Zhao X, Chen J, et al. Anemia is associated with poor outcomes of metastatic castration-resistant prostate cancer: a systematic review and meta-analysis. Am J Transl Res. (2018) 10:3877–86.
24. Multiple Cause of Death Data on CDC WONDER. Available online at: https://wonder.cdc.gov/mcd.html (Accessed October 3, 2025).
25. Harding MC, Sloan CD, Merrill RM, Harding JC, Thacker EL, Wu P, et al. Transitions from heart disease to cancer as the leading cause of death in US states, 1999–2016. Prev Chronic Dis. (2019) 15:E158. doi: 10.5888/pcd15.180151
26. von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP, et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. J Clin Epidemiol. (2008) 61:344–9. doi: 10.1016/j.jclinepi.2007.11.008
27. Aggarwal R, Chiu N, Loccoh EC, Kazi DS, Yeh RW, and Wadhera RK. Rural-urban disparities: diabetes, hypertension, heart disease, and stroke mortality among black and white adults, 1999-2018. J Am Coll Cardiol. (2021) 77:1480–1. doi: 10.1016/j.jacc.2021.01.032
28. Ingram DD and Franco SJ. NCHS urban-rural classification scheme for counties. US department of health and human services. Cent Dis Control Prevent. (2013) 2:1–73.
30. Joinpoint Regression Program. Available online at: https://surveillance.cancer.gov/joinpoint/ (Accessed October 4 2025).
31. Simon GI, Craswell A, Thom O, Chew MS, Anstey CM, and Fung YL. Impacts of aging on anemia tolerance, transfusion thresholds, and patient blood management. Transfus Med Rev. (2019) 33:154–61. doi: 10.1016/j.tmrv.2019.03.001
32. Lupescu A, Bissinger R, Goebel T, Salker MS, Alzoubi K, Lang F, et al. Enhanced suicidal erythrocyte death contributing to anemia in the elderly. Cell Physiol Biochem. (2015) 36:773–83. doi: 10.1159/000430137
33. Bohlius J, Bohlke K, Castelli R, Engert A, Langer C, Lyman GH, et al. Management of cancer-associated anemia with erythropoiesis-stimulating agents: ASCO/ASH clinical practice guideline update. J Clin Oncol. (2019) 37:1336–51. doi: 10.1200/JCO.18.02142
34. Centers for Medicare & Medicaid Services (CMS), National Center for Health Statistics (NCHS). ICD-10-CM Official Guidelines for Coding and Reporting Fiscal Year 2017 (October 1, 2016 – September 30, 2017). Baltimore (MD: CMS (2016). Available online at: https://www.cms.gov/medicare/coding/icd10/downloads/2017-icd-10-cm-guidelines.pdf (Accessed October 3, 2025).
35. Rezaeiahari M, Brown CC, and Ali MM. Impact of the transition from ICD-9-CM to ICD-10-CM on the rates of severe maternal morbidity in arkansas: an analysis of claims data. Womens Health Rep (New Rochelle). (2022) 3:458–64. doi: 10.1089/whr.2021.0092
36. Debeljak N, Solár P, and Sytkowski AJ. Erythropoietin and cancer: the unintended consequences of anemia correction. Front Immunol. (2014) 5:563. doi: 10.3389/fimmu.2014.00563
37. Davis EJ, Salem JE, Young A, Green JR, Ferrell PB, Ancell KK, et al. Hematologic complications of immune checkpoint inhibitors. Oncologist. (2019) 24:584–8. doi: 10.1634/theoncologist.2018-0574
38. Sun M, Wang L, Wang X, Tong L, Jin L, and Li B. Trends and all-cause mortality associated with multimorbidity of non-communicable diseases among adults in the United States, 1999-2018: a retrospective cohort study. Epidemiol Health. (2023) 45:e2023023. doi: 10.4178/epih.e2023023
39. Röhrig G. Anemia in the frail, elderly patient. Clinical interventions in aging. 11. (2016) 11:319–26. doi: 10.2147/CIA.S90727
40. Michalak SS, Rupa-Matysek J, and Gil L. Comorbidities, repeated hospitalizations, and age ≥ 80 years as indicators of anemia development in the older population. Ann Hematol. (2018) 97:1337–47. doi: 10.1007/s00277-018-3321-x
41. Szabo R, Petrisor C, Bodolea C, Simon R, Maries I, Tranca S, et al. Hyperferritinemia, low circulating iron and elevated hepcidin may negatively impact outcome in COVID-19 patients: a pilot study. Antioxidants. (2022) 11:1364. doi: 10.3390/antiox11071364
42. Acar IH, Guner SI, Ak MA, Gocer M, Ozturk E, Atalay F, et al. Impact of COVID-19 on outcomes of patients with hematologic Malignancies: A multicenter, retrospective study. Mediterr J Hematol Infect Dis. (2022) 14:e2022074. doi: 10.4084/MJHID.2022.074
43. Wu M, Liu S, Wang C, Wu Y, and Liu J. Risk factors for mortality among lung cancer patients with covid-19 infection: A systematic review and meta-analysis. PLoS One. (2023) 18:e0291178. doi: 10.1371/journal.pone.0291178
44. Riera R, Bagattini ÂM, Pacheco RL, Pachito DV, Roitberg F, and Ilbawi A. Delays and disruptions in cancer health care due to COVID-19 pandemic: systematic review. JCO Glob Oncol. (2021) 7:311–23. doi: 10.1200/GO.20.00639
45. Centers for Disease Control and Prevention, National Center for Health Statistics, National Vital Statistics System. Vital Statistics Reporting Guidance No. 03: Guidance for Certifying Deaths Due to Coronavirus Disease 2019 (COVID-19) (2023). Available online at: https://www.cdc.gov/nchs/data/nvss/vsrg/vsrg03508.pdf (Accessed October 3, 2025).
46. Čiburienė E, Čelutkienė J, Aidietienė S, Ščerbickaitė G, and Lyon AR. The prevalence of iron deficiency and anemia and their impact on survival in patients at a cardio-oncology clinic. Cardiooncology. (2020) 6:29. doi: 10.1186/s40959-020-00086-4
47. Visagie M, Pearson KR, Purvis TE, Gehrie EA, Resar LMS, and Frank SM. Greater anemia tolerance among hospitalized females compared to males. Transfusion. (2019) 59:2551–8. doi: 10.1111/trf.15338
48. Dong M, Cioffi G, Wang J, Waite KA, Ostrom QT, Kruchko C, et al. Sex differences in cancer incidence and survival: a pan-cancer analysis. Cancer Epidemiol Biomarkers Prev. (2020) 29:1389–97. doi: 10.1158/1055-9965.EPI-20-0036
49. Ma J, Yao Y, Tian Y, Chen K, and Liu B. Advances in sex disparities for cancer immunotherapy: unveiling the dilemma of Yin and Yang. Biol Sex Differ. (2022) 13:58. doi: 10.1186/s13293-022-00469-5
50. Guiriguet C, Pera G, Castells A, Toran P, Grau J, Rivero I, et al. Impact of comorbid conditions on participation in an organised colorectal cancer screening programme: a cross-sectional study. BMC Cancer. (2017) 17:524. doi: 10.1186/s12885-017-3516-x
51. Thompson AE, Anisimowicz Y, Miedema B, Hogg W, Wodchis WP, and Aubrey-Bassler K. The influence of gender and other patient characteristics on health care-seeking behaviour: a QUALICOPC study. BMC Fam Pract. (2016) 17:38. doi: 10.1186/s12875-016-0440-0
52. Rubin JB. The spectrum of sex differences in cancer. Trends Cancer. (2022) 8:303–15. doi: 10.1016/j.trecan.2022.01.013
53. Sekeres MA, Peterson B, Dodge RK, Mayer RJ, Moore JO, Lee EJ, et al. Differences in prognostic factors and outcomes in African Americans and whites with acute myeloid leukemia. Blood. (2004) 103:4036–42. doi: 10.1182/blood-2003-09-3118
54. Waxman AJ, Mink PJ, Devesa SS, Anderson WF, Weiss BM, Kristinsson SY, et al. Racial disparities in incidence and outcome in multiple myeloma: a population-based study. Blood. (2010) 116:5501–6. doi: 10.1182/blood-2010-07-298760
55. Williams DR, Lawrence JA, and Davis BA. Racism and health: evidence and needed research. Annu Rev Public Health. (2019) 40:105–25. doi: 10.1146/annurev-publhealth-040218-043750
56. The Lancet Oncology. Racial disparities in cancer care: can we close the gap? Lancet Oncol. (2021) 22:1643. doi: 10.1016/S1470-2045(21)00669-0
57. Huang H, Wei T, Huang Y, Zhang A, Zhang H, Zhang Z, et al. Association between social determinants of health and survival among the US cancer survivors population. BMC Med. (2024) 22:343. doi: 10.1186/s12916-024-03563-0
58. Schwalb ME, Visvanathan K, Connor AE, George CM, Rule AM, Guallar E, et al. Hazardous environmental pollutants and cancer disparities: a systematic review on the consideration of race and ethnicity in environmental epidemiology research. Curr Environ Health Rep. (2025) 12:44. doi: 10.1007/s40572-025-00507-5
59. Aldrighetti CM, Niemierko A, Van Allen E, Willers H, and Kamran SC. Racial and ethnic disparities among participants in precision oncology clinical studies. JAMA Netw Open. (2021) 4:e2133205. doi: 10.1001/jamanetworkopen.2021.33205
60. Trinh-Shevrin C, Sacks R, Ahn J, and Yi SS. Opportunities and challenges in precision medicine: improving cancer prevention and treatment for asian americans. J Racial Ethn Health Disparities. (2018) 5:1–6. doi: 10.1007/s40615-016-0334-9
61. Unger JM, Moseley A, Symington B, Chavez-MacGregor M, Ramsey SD, and Hershman DL. Geographic distribution and survival outcomes for rural patients with cancer treated in clinical trials. JAMA Netw Open. (2018) 1:e181235. doi: 10.1001/jamanetworkopen.2018.1235
62. Charlton M, Schlichting J, Chioreso C, Ward M, and Vikas P. Challenges of rural cancer care in the United States. Oncol (Williston Park). (2015) 29:633–40.
63. Blake KD, Moss JL, Gaysynsky A, Srinivasan S, and Croyle RT. Making the case for investment in rural cancer control: an analysis of rural cancer incidence, mortality, and funding trends. Cancer Epidemiol Biomarkers Prev. (2017) 26:992–7. doi: 10.1158/1055-9965.EPI-17-0092
64. Moy E, Garcia MC, Bastian B, Rossen LM, Ingram DD, Faul M, et al. Leading causes of death in nonmetropolitan and metropolitan areas—United States, 1999–2014. MMWR Surveill Summ. (2017) 66:1–8. doi: 10.15585/mmwr.ss6601a1
65. Rees JR, Weiss JE, Gunn CM, Klement RJ, Bickell NA, Smith AW, et al. Cancer epidemiology in the Northeastern United States (2013–2017). Cancer Res Commun. (2023) 3:1538–50. doi: 10.1158/2767-9764.CRC-23-0152
66. Glisan A, Nielsen E, Billion T, Johnson S, Patel R, Lee J, et al. Regional trends in colorectal cancer mortality in people aged 45–84 years in the US, 1999–2022. J Gastrointest Oncol. (2024) 15:2533–42. doi: 10.21037/jgo-24-624
67. American Cancer Society. History of ACS Recommendations for the Early Detection of Cancer in People Without Symptoms. Available online at: https://www.cancer.org/health-care-professionals/american-cancer-society-prevention-early-detection-guidelines/overview/chronological-history-of-acs-recommendations.html (Accessed 2025 October 07).
68. Gross CP, Andersen MS, Krumholz HM, McAvay GJ, Proctor D, and Tinetti ME. Relation between medicare screening reimbursement and stage at diagnosis for older patients with colon cancer. JAMA. (2006) 296:2815–22. doi: 10.1001/jama.296.23.2815
69. Zhang J, Chen G, Li Z, Zhang P, Li X, Gan D, et al. Colonoscopic screening is associated with reduced Colorectal Cancer incidence and mortality: a systematic review and meta-analysis. J Cancer. (2020) 11:5953–70. doi: 10.7150/jca.46661
70. American Cancer Society National Colorectal Cancer Roundtable. Data and progress (2025). Available online at: https://nccrt.org/our-impact/data-and-progress/ (Accessed October 07, 2025).
71. Chen YC, Malfertheiner P, Yu HT, Kuo CL, Chang YY, Meng FT, et al. Global prevalence of helicobacter pylori infection and incidence of gastric cancer between 1980 and 2022. Gastroenterology. (2024) 166:605–19. doi: 10.1053/j.gastro.2023.12.022
72. Wong MCS, Huang J, Chan PSF, Choi P, Lao XQ, Tse S, et al. Global incidence and mortality of gastric cancer, 1980–2018. JAMA Netw Open. (2021) 4:e2118457. doi: 10.1001/jamanetworkopen.2021.18457
73. Hilton WM, Padalecki SS, Ankerst DP, Leach RJ, and Thompson IM. Temporal changes in the clinical approach to diagnosing prostate cancer. J Natl Cancer Inst Monogr. (2012) 2012:162–8. doi: 10.1093/jncimonographs/lgs038
74. Burgess L, Aldrighetti CM, Ghosh A, Niemierko A, Chino F, Huynh MJ, et al. Association of the USPSTF grade D recommendation against prostate-specific antigen screening with prostate cancer–specific mortality. JAMA Netw Open. (2022) 5:e2211869. doi: 10.1001/jamanetworkopen.2022.11869
75. Giaquinto AN, Sung H, Newman LA, Miller KD, Wagle NS, Jemal A, et al. Breast cancer statistics, 2024. CA Cancer J Clin. (2024) 74:477–95. doi: 10.3322/caac.21863
76. American Society of Clinical Oncology. Breast cancer mortality by clinical stage: low-risk cancers cause most breast cancer deaths each year (2025). Available online at: https://dailynews.ascopubs.org/do/breast-cancer-mortality-clinical-stage-low-risk-cancers-cause-most-breast-cancer-deaths (Accessed October 8, 2025).
77. Soucheray S. HPV vaccines linked to drop in cervical cancer deaths. Minneapolis, MN: CIDRAP (2024). Available online at: https://www.cidrap.umn.edu/human-papillomavirus-hpv/hpv-vaccines-linked-drop-cervical-cancer-deaths.
78. Papageorgiou SG, Thomopoulos TP, Liaskas A, and Vassilakopoulos TP. Monoclonal antibodies in the treatment of diffuse large B-cell lymphoma: moving beyond rituximab. Cancers. (2022) 14:1917. doi: 10.3390/cancers14081917
79. Centers for Disease Control and Prevention (CDC). Tobacco use--United States, 1900-1999. MMWR Morb Mortal Wkly Rep. (1999) 48:986–93.
80. Jani CT, Singh H, Abdallah N, Mouchati C, Arora S, Kareff S, et al. Trends in lung cancer incidence and mortality (1990-2019) in the United States: A comprehensive analysis of gender and state-level disparities. JCO Glob Oncol. (2023) 9):e2300255. doi: 10.1200/GO.23.00255
81. Liu X, Yu Y, Wang M, Mubarik S, Wang F, Wang Y, et al. The mortality of lung cancer attributable to smoking among adults in China and the United States during 1990–2017. Cancer Commun. (2020) 40:611–9. doi: 10.1002/cac2.12099
82. McGivern L, Shulman L, Carney JK, Shapiro S, and Bundock E. Death certification errors and the effect on mortality statistics. Public Health Rep. (2017) 132:669–75. doi: 10.1177/0033354917736514
83. National Research Council. Strengthening forensic science in the United States: a path forward. Washington (DC: National Academies Press (2009).
Keywords: age-adjusted mortality rate, anemia, cancer, CDC WONDER, epidemiology, mortality trends
Citation: Khan MSA, Chaudhary A, Khan MI, Ajaz IA, Khalid A, Waheed A and Amin MHJ (2026) Regional, demographic and temporal trends in anemia and malignant cancer-related mortality in U.S. older adults: a nationwide CDC WONDER analysis (1999–2020). Front. Oncol. 16:1722891. doi: 10.3389/fonc.2026.1722891
Received: 11 October 2025; Accepted: 02 January 2026; Revised: 13 December 2025;
Published: 21 January 2026.
Edited by:
Hussain Alizadeh, University of Pécs, HungaryReviewed by:
Jasmine D. Parnell-Peake, Lincoln University, United StatesSamuel Agegnew Wondm, Debre Tabor University, Ethiopia
Copyright © 2026 Khan, Chaudhary, Khan, Ajaz, Khalid, Waheed and Amin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Mohammed Hammad Jaber Amin, bW9oYW1tZXNqYWJlcjEyM0BnbWFpbC5jb20=
†ORCID: Asma Chaudhary, orcid.org/0009-0005-8617-9797
Mohammed Hammad Jaber Amin, orcid.org/0000-0002-1366-2375