Skip to content
Frontiers
Search

Original Research

Frontiers in Cancer Control and Society ·

Volume 4 - 2026 | https://doi.org/10.3389/fcacs.2026.1752674

Trends in cancer-and respiratory failure-related mortality in the US adult population: an analysis of the CDC WONDER database from 1999 to 2023

  • Chengchi Xia1
  • Tianshu Rong1
  • Baoqing Wang1*corresponding author
  1. Department of Oncology, The Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
Article metrics0 viewsView details
PDFCiteMetrics

Abstract

This study aimed to retrospectively analyze secular trends in respiratory failure-associated mortality (AAMR) and its demographic and geographic variations among US adult cancer patients from 1999 to 2023. This study conducted a comprehensive retrospective analysis of respiratory failure-associated mortality data from 1999 to 2023 among US adult cancer patients and assessed secular trends (AAPC/APC) as well as variations across geographic, urban/rural, and racial/ethnic subgroups. During the study period, the overall age-adjusted mortality rate (AAMR) demonstrated a statistically significant long-term decline (AAPC: −0.60, 95% CI: −0.87 to −0.33, P < 0.05). However, this trend was punctuated by a synchronized and significant surge in AAMR across all subgroups between 2018 and 2021, with Annual Percentage Changes (APCs) of 5.02 (95% CI: 3.52–6.55) for males and 5.56 (95% CI: 3.29–7.89) for females (P < 0.05). Persistent health inequities were identified: non-Hispanic Black individuals (AAMR: 2023: 34.56, 95% CI: 33.86–35.26) and residents in non-metropolitan areas (AAMR: 2023: 30.52, 95% CI: 29.99–31.05) exhibited markedly higher mortality risks compared to other racial and geographic cohorts (P < 0.05). The collective reversal in respiratory failure-associated mortality after 2018, likely driven by cumulative treatment toxicities and pandemic-related disruptions, highlights a critical shift in cancer survivorship risks. Persistent racial and urban-rural inequities underscore systemic barriers to specialized care. To mitigate this rising burden, it is essential to institutionalize interdisciplinary oncology-pulmonary collaboration and implement targeted screening for high-risk populations.

Keywords

1 Introduction

Cancer continues to represent a premier global health challenge, consistently ranking among the leading causes of morbidity and mortality worldwide (1, 2). As therapeutic landscapes evolve and survival rates improve, the clinical focus has increasingly shifted from oncological management to the mitigation of complex, multi-system complications that compromise long-term outcomes. Within this context, Respiratory Failure (RF) has emerged as a decisive driver of unplanned hospitalizations and intensive care utilization (3, 4), with cancer patients facing a significantly higher risk of respiratory-related death compared to the general hospitalized population.

Pathophysiologically, the interface between malignancy and RF is characterized by a distinct causal hierarchy, where the underlying cancer and its corresponding therapeutic regimens function as the primary risk factors. RF can be precipitated through direct neoplastic mechanisms—such as tumor invasion of the lung parenchyma, metastatic infiltration, or compressive airway obstruction—and via indirect systemic pathways, including paraneoplastic syndromes, cancer-associated cachexia, and generalized frailty (4–6). In parallel, the escalating utilization of intensive oncological modalities—encompassing localized radiotherapy, cytotoxic chemotherapy, and novel targeted agents—frequently contributes to treatment-related lung injury (TILI) (7–9). Within this framework, therapy-induced pulmonary toxicity is recognized as a potential biological pathway that can progress into acute conditions such as acute respiratory distress syndrome (ARDS) or interstitial pneumonia (10). Consequently, while the malignancy remains the fundamental underlying cause of death, RF frequently represents the immediate clinical event that complicates the patient's prognosis and precipitates mortality.

Despite extensive documentation of acute RF outcomes in critical care settings (11), a significant gap persists in the medical literature regarding long-term, population-level mortality trends. Most existing research focuses on short-term ICU survival, whereas the longitudinal shifts in RF-related deaths among the broader cancer population—particularly those influenced by shifting socio-demographic factors—remain under-explored. Clinical trials often report isolated toxicity rates but frequently fail to capture the real-world longitudinal trends influenced by shifting treatment paradigms and social determinants of health.

The impetus for this investigation arises from the critical necessity of integrating these clinical observations of treatment-related toxicity with macro-level national epidemiological surveillance. As advancements in oncological care significantly extend patient survivorship, the prolonged exposure to diverse pneumotoxic regimens has the potential to reshape contemporary mortality patterns. Bridging the analytical gap between specific adverse events and broad secular trends is therefore essential for a more comprehensive understanding of evolving risks. By utilizing over two decades of comprehensive national data (1999–2023), this study seeks to identify pivotal temporal shifts and elucidate the persistent socio-demographic inequities across racial and geographic strata. Our objective is to establish a diagnostic foundation for evolving interdisciplinary management models and provide evidence-based insights for more robust prognostic frameworks. Ultimately, this research offers a strategic roadmap to mitigate the disproportionate health burden and address the disparities identified within this vulnerable population.

2 Methods

2.1 Data extraction

Data Source and Study Population Mortality data were obtained from the United States Multiple Cause of Death (MCD) database, maintained by the Centers for Disease Control and Prevention (CDC) and accessed via the CDC WONDER (Wide-ranging Online Data for Epidemiologic Research) online tool (12). The study analyzed records from 1999 to 2023. To focus the analysis on adult populations where the clinical intersection of malignancies and respiratory complications is most prevalent, data extraction was restricted to the population aged 25 years and older. Case Identification and Classification We identified relevant mortality records using the International Classification of Diseases, Tenth Revision (ICD-10) codes (13). The study focused on cases where the underlying cause of death was documented as the co-occurrence of the following two conditions: Malignant Neoplasms (Cancer): ICD-10 codes C00–C97. Respiratory Failure: Including acute respiratory failure (J96.0), chronic respiratory failure (J96.1), and unspecified respiratory failure (J96.9).

Data Stratification and Statistical Basis To account for demographic shifts and facilitate trend analysis, raw mortality data were stratified by sex, race, geographic region (Northeast, Midwest, South, and West), and 10-year age cohorts (e.g., 25–34, 35–44,…, 85+ years). These stratified data served as the foundational metrics for calculating Age-Adjusted Mortality Rates (AAMR) and conducting subsequent temporal trend analyses, ensuring that changes in the US population age structure over the study period were statistically controlled.

2.2 Statistical analysis

We analyzed sex, ethnoracial, age, urbanization, and census-related patterns by computing both crude and age-adjusted mortality rates (AAMR) per 100,000 individuals. AAMR was age-standardized to the 2000 US population using direct standardization, a widely recognized technique that facilitates the comparison of mortality rates by adjusting for age-related differences in the population (14). Temporal trends in AAMR were evaluated by fitting log-linear regression models using Joinpoint Regression Program (Version 5.4.0, National Cancer Institute). Joinpoint regression was employed to detect inflection points in the AAMR trends from 1999 to 2023, following established methodological guidelines. Given that this study spans 25 years, the Joinpoint regression software was configured to identify up to four inflection points where significant changes in trend occurred (15, 16). The Grid Search method, coupled with a permutation test and a parametric method, was used to estimate the annual percent change (APC) and its corresponding 95% confidence intervals (CIs). Furthermore, the average annual percent change (AAPC) was utilized to quantify the average rate of change over the entire 25-year study period. To evaluate the cumulative geographic shift in mortality burden, we calculated the total percent change, defined as the relative difference in AAMR between the baseline year (1999) and the final year (2023).

To project future mortality trajectories, Autoregressive Integrated Moving Average (ARIMA) models were constructed based on the 1999–2023 AAMR time series. The optimal ARIMA (p, d, q) parameters were identified by minimizing the Bayesian Information Criterion (BIC) (17), and model stationarity was verified using the Augmented Dickey-Fuller (ADF) test. Based on the best-fit model, AAMR values were projected through 2046 with corresponding 95% CIs. These projections are presented as exploratory heuristic scenarios to highlight potential future mortality burdens under the assumption of prevailing trends. All statistical analyses were performed using two-sided tests, and a trend change or difference was considered statistically significant when the P-value was less than 0.05.

3 Results

During our study period (1999–2023), the total number of deaths associated with respiratory failure among adult cancer patients in the United States experienced a substantial increase, rising from 36,812 cases in 1999 to 79,968 cases in 2023, representing a remarkable surge of 117.23%.

The AAMR for the overall population correspondingly increased from 20.78 (95% CI: 20.57–21.00) in 1999 to 28.42 (95% CI: 28.22–28.62) in 2023. Notably, despite the higher absolute number of deaths and the terminal AAMR level compared to the start of the period, the long-term trend analysis of the AAMR across the entire study period revealed a statistically significant average annual decline (AAPC: −0.60%; 95% CI: −0.87% to −0.33%). This indicates that the rate of mortality change, on average, exhibited a net downward trend between 1999 and 2023 (Table 1).

Table 1. Deaths, AAMR, and APC related to respiratory failure in adult cancer patients in the United States, 1999–2023.

MeasureDeathsAAMR
Deaths_1999Deaths_2023percent.changeAAMR_1999AAMR_2023AAPC (95% CI)
Both36,81279,968117.2320.78 (20.57–21.00)28.42 (28.22–28.62)−0.60 (−0.87 to −0.33*)
Sex
Female16,95936,810117.0516.69 (16.44–16.94)24.27 (24.02–24.52)−0.50 (−0.90 to −0.11*)
Male19,85343,158117.3927.16 (26.77–27.54)34.04 (33.71–34.36)−0.89 (−1.10 to −0.67*)
Census region
Northeast7,79315,622100.4621.07 (20.60–21.53)30.29 (29.80–30.77)−1.68 (−1.95 to −1.41*)
Midwest6,85715,999133.3216.37 (15.98–16.76)26.99 (26.57–27.42)−0.51 (−0.91 to −0.10*)
South12,85029,432129.0420.46 (20.11–20.81)27.44 (27.12–27.76)−0.06 (−0.47 to 0.35)
West9,31218,915103.1226.23 (25.70–26.77)29.90 (29.47–30.33)−0.35 (−0.77 to 0.07)
Race
Hispanic1,6236,731314.7317.32 (16.43–18.22)22.07 (21.52–22.62)−0.82 (−1.31 to −0.33*)
NH Black3,9529,867149.6725.40 (24.60–26.20)34.56 (33.86–35.26)−1.12 (−1.66 to −0.58*)
NH White30,17959,30996.5220.51 (20.28–20.74)29.13 (28.89–29.37)−0.37 (−0.61 to −0.13*)
NH Other9643,868301.2418.77 (17.52–20.02)19.48 (18.86–20.10)2.26 (−1.03 to 5.65)
Urbanization#
Metropolitan30,55657,31987.5921.20 (20.97–21.44)25.25 (25.04–25.46)−1.15 (−1.42 to −0.88*)
Nonmetropolitan6,25613,475115.3918.99 (18.52–19.46)30.52 (29.99–31.05)−0.18 (−0.42 to 0.07)
Age group##
25–34 years274549100.360.68 (0.68–0.68)1.21 (1.21–1.21)2.65 (2.05–3.26*)
35–44 years1,0971,61447.132.43 (2.43–2.43)3.64 (3.64–3.64)1.79 (0.97–2.62*)
45–54 years2,9204,23044.867.98 (7.98–7.98)10.45 (10.45–10.45)1.33 (0.90–1.77*)
55–64 years5,81613,529132.6224.46 (24.46–24.46)32.32 (32.32–32.32)1.24 (0.77–1.71*)
65–74 years10,57824,562132.2057.43 (57.43–57.43)70.81 (70.81–70.81)1.04 (0.74–1.34*)
75–84 years11,45124,025109.8193.67 (93.67–93.67)130.80 (130.80–130.80)1.47 (0.99–1.95*)
85+ years4,67611,459145.06112.57 (112.57–112.57)184.97 (184.97–184.97)2.27 (1.83–2.71*)

*Indicates statistically significant AAPC.

#Metropolitan AAMR_2023 uses 2020 data instead.

##Age group AAMR calculations use crude rates.

3.1 Trends in mortality within subgroups of adult cancer patients dying with respiratory failure

3.1.1 Age and sex distribution and long-term trends

Our analysis of mortality data associated with respiratory failure among adult cancer patients in the US from 1999 to 2023 revealed a pronounced concentration in the elderly population for both the age distribution and the AAMR (Figure 1). In both 1999 and 2023, the burden of this multiple-cause mortality surged sharply with increasing age, with the AAMR in the 85-years-and-over age group consistently holding the highest position.

**(A)** Age-group trends in respiratory failure-related deaths among US adults with cancer, 1999 and 2023. **(B)** Sex differences in respiratory failure-related deaths among US adults with cancer, 1999 and 2023.
Figure 1. (A) Age-group trends in respiratory failure-related deaths among US adults with cancer, 1999 and 2023. (B) Sex differences in respiratory failure-related deaths among US adults with cancer, 1999 and 2023.

Despite fluctuations in the overall AAMR, the absolute number of respiratory failure-related deaths in cancer patients increased across all age groups, concentrating particularly in the older cohorts of 75–84 and 85 years and over. Conversely, the total number of deaths and the AAMR for younger age groups (e.g., 25–34, 35–44 years) remained at extremely low levels (Supplementary Figure S1).

Throughout the entire study period, the AAMR for males was consistently and significantly higher than that for females (2023: 34.0 per 100,000 for males vs. 24.3 per 100,000 for females; Supplementary Figure S2). The trend in male mortality exhibited a four-phase pattern. The initial phase (1999–2014) showed a significant decline (APC: −2.15%; 95% CI: −2.21% to −2.09%), followed by a stabilization between 2014 and 2018 (APC: −0.65%; 95% CI: −1.37% to 0.07%). Mortality then surged significantly during the 2018–2021 period (APC: 5.02%; 95% CI: 3.52%−6.55%), before stabilizing again in the final study period (2021–2023; APC: −0.73%; 95% CI: −2.07% to 0.63%).

The female mortality trend followed a similar pattern. Mortality declined continuously and significantly from 1999 to 2015 (APC: −1.74%; 95% CI: −1.83% to −1.65%). Subsequently, the trend stabilized between 2015 and 2018 (APC: −0.39%; 95% CI: −2.65% to 1.92%). Following this, the female AAMR also increased significantly during the 2018–2021 period (APC: 5.56%; 95% CI: 3.29%−7.89%), before returning to stability at the end of the study period (2021–2023; APC: 0.48%; 95% CI: −1.54% to 2.53%).

The 2023 mortality pyramid distribution for respiratory failure-related deaths in adult cancer patients (Figure 1B) clearly demonstrated age dependence, with both the total number of deaths and the AAMR rising markedly with age and peaking in the 85-years-and-over age group. In terms of absolute death counts, females in the 85-years-and-over group (13,256 cases) slightly outnumbered males (12,966 cases), reflecting the demographic advantage of the elderly female population base. However, a significant sex disparity was observed in the AAMR distribution: males consistently exhibited higher AAMRs than females across all older age groups. Specifically, the AAMR for males aged 85 and over (99.8 per 100,000) was markedly higher than that for females (85.1 per 100,000); similarly, in the 75–84 age group, the male AAMR (70.6 per 100,000) exceeded that of females (60.2 per 100,000). This further confirms that, after age structure adjustment, males bear a higher risk burden of mortality from the comorbidity of cancer and respiratory failure (Table 1).

3.1.2 Race

Significant disparities were observed in the AAMR among different racial and ethnic groups in the United States. Non-Hispanic Black individuals had the highest AAMR (34.56 per 100,000), followed by Non-Hispanic White individuals (29.13 per 100,000). In contrast, Non-Hispanic Other (19.48 per 100,000) and Hispanic (22.07 per 100,000) populations recorded the lowest overall mortality rates.

The AAMR for the Non-Hispanic Black population experienced a substantial decline between 1999 and 2018 (APC: −2.31%; 95% CI: −2.43% to −2.19%). This trend subsequently reversed, showing a significant surge from 2018 to 2021 (APC: 5.75%; 95% CI: 1.69%−9.97%), followed by a stabilization after 2021.

The Non-Hispanic White population experienced an initial decline (1999–2014 APC: −1.70%; 95% CI: −1.94% to −1.45%), followed by a period of stabilization from 2014 to 2018. Similar to the Black population, this group then saw a significant spike in AAMR between 2018 and 2021 (APC: 5.87%; 95% CI: 4.29%−7.48%), also stabilizing after 2021.

The Hispanic population's AAMR showed a continuous and significant decline from 1999 to 2018 (1999–2009 APC: −2.01%; 2009–2018 APC: −1.15%). Consistent with the other groups, the AAMR increased significantly from 2018 to 2021 (APC: 4.57%; 95% CI: 1.11%−8.16%), followed by a return to stability.

The Non-Hispanic Other population's AAMR significantly decreased from 1999 to 2018 (APC: −4.38%; 95% CI: −5.12% to −3.64%). However, between 2018 and 2021, this group experienced an exceptionally dramatic surge in AAMR (APC: 61.33%; 95% CI: 23.19%−111.28%), followed by a slight reversal (Figure 2, Table 1).

Trends in respiratory failure-related deaths among adult cancer patients by race in the United States, 1999–2023.
Figure 2. Trends in respiratory failure-related deaths among adult cancer patients by race in the United States, 1999–2023.

3.1.3 Geographic disparities

State-level stratified analysis revealed notable geographic disparities in the AAMR for respiratory failure among cancer patients, with an extreme range spanning from a low of 15.6 per 100,000 in Maine to a high of 58.4 per 100,000 in Mississippi. This stark difference signifies that the states with the highest mortality rates (e.g., Mississippi, Arkansas, South Dakota, Maryland, and Kansas, comprising the top 10th percentile, AAMR38.6) face a risk nearly 3.7 times greater than those in the lowest states (e.g., Maine, Hawaii, Rhode Island, Delaware, and Oklahoma, comprising the bottom 10th percentile, AAMR17.9; Supplementary Table S1, Figure 3).

Trends in respiratory failure-related deaths among adult cancer patients in the United States, by state, 1999 to 2023. **(A)** Deaths: Total number of fatalities in each state in 2023; **(B)** AAMR: Age-adjusted mortality rate per 100,000 population in 2023; **(C)** Percent Change: Total percent change in the absolute number of deaths from 1999 to 2023, representing the cumulative relative increase or decrease in the death toll over the 25-year period; **(D)** AAPC: Average Annual Percentage Change in AAMR for each state from 1999 to 2023.
Figure 3. Trends in respiratory failure-related deaths among adult cancer patients in the United States, by state, 1999 to 2023. (A) Deaths: Total number of fatalities in each state in 2023; (B) AAMR: Age-adjusted mortality rate per 100,000 population in 2023; (C) Percent Change: Total percent change in the absolute number of deaths from 1999 to 2023, representing the cumulative relative increase or decrease in the death toll over the 25-year period; (D) AAPC: Average Annual Percentage Change in AAMR for each state from 1999 to 2023.

Furthermore, the cumulative increase in the absolute death toll exhibited even more pronounced variations across the United States from 1999 to 2023 (Figure 3C). While some states showed relative stability, others experienced a dramatic escalation in the number of deaths, with the total percent change exceeding 500% in certain regions. This total percent change reflects the combined impact of increasing cancer-related respiratory complications, population growth, and shifting demographic structures over the 25-year study period.

This geographic heterogeneity in mortality is also evident at the regional level (Supplementary Figure S3). Based on 2023 aggregate data, the Northeast region recorded the highest overall mortality, leading with an AAMR of 30.3 per 100,000, closely followed by the West (29.9 per 100,000). In contrast, the South (27.4 per 100,000) and Midwest (27.0 per 100,000) regions exhibited relatively lower mortality rates.

However, within all geographic stratifications, the urban-rural disparity is key to understanding this trend (Supplementary Figure S4). The AAMR in non-metropolitan areas (30.5 per 100,000) was significantly higher than that in metropolitan areas (25.2 per 100,000). More notably, the divergence in these trends is becoming entrenched: metropolitan areas experienced a significant declining trend in AAMR from 1999 to 2023 (AAPC: −1.15%; 95% CI: −1.42% to −0.88%). This contrasts sharply with non-metropolitan areas, where the AAMR exhibited near stagnation, maintaining a relatively stable long-term trend (AAPC: −0.18%; 95% CI: −0.42% to 0.07%).

3.2 Mortality projections: significant growth and sex divergence by 2046

Based on the predictions generated by the ARIMA model, the AAMR associated with respiratory failure in adult cancer patients is projected to exhibit a significant upward trend over the next two decades, with the persistence of sex-based differences (Figure 4, Supplementary Table S2). Overall, the AAMR for the total population is anticipated to increase substantially by 2046. Males are projected to have the highest AAMR (44.9 per 100,000), while females are projected to have the lowest (38.8 per 100,000), though the absolute gap between them is expected to narrow. Specifically, the male population is forecasted to maintain the highest mortality rate, with the AAMR projected to increase from 34.0 in 2023 to 44.9 in 2046, an increase of approximately 32.0%. The female population, despite starting from a lower AAMR base, is projected to experience the largest percentage growth; their AAMR is expected to surge from 24.3 in 2023 to 38.8 in 2046, a projected increase of 59.7%. For the overall population (both sexes), the AAMR is projected to rise from 28.4 in 2023 to 42.4 in 2046, representing an increase of approximately 49.2%.

Predicted mortality associated with respiratory failure among US adult cancer patients.
Figure 4. Predicted mortality associated with respiratory failure among US adult cancer patients.

Importantly, despite these high projected AAMR values, the forecasting model indicates considerable uncertainty. The 95% CI for all populations by 2046 are exceptionally wide; for instance, the 95% CI for males spans from 14.9 to 75.0 per 100,000. This underscores that, while these projections serve as heuristic scenarios for a potential escalation in mortality burden, they are subject to substantial volatility in the absence of effective clinical or policy interventions.

4 Discussion

4.1 Temporal shifts and the confounding impact of public health crises

This study provides a comprehensive retrospective analysis of respiratory failure (RF)-associated mortality trends among US adult cancer patients from 1999 to 2023. A pivotal finding is the collective reversal of mortality trajectories observed around 2018, a shift that warrants a nuanced evaluation of both acute external shocks and long-term clinical evolutions. While the temporal alignment suggests that the COVID-19 pandemic acted as a significant confounding factor (18, 19), the observed surge likely reflects a synergistic convergence of this public health crisis with fundamental shifts in oncological treatment and diagnostic practices. From a pathophysiological perspective, the immunosuppressed status and treatment-related pulmonary vulnerabilities of cancer patients likely lowered the physiological threshold for SARS-CoV-2 to progress into acute respiratory failure (20, 21). In parallel, the pandemic precipitated systemic disruptions, where the reallocation of intensive care resources and the deferral of routine oncological screenings may have led to more advanced disease stages and increased terminal complications. Notably, the impact of the pandemic served to accentuate the persistent and profound geographic, urban/rural, and racial/ethnic inequities identified in our analysis. The marked surge in RF-associated AAMR between 2019 and 2021 was disproportionately concentrated in non-metropolitan areas and among minoritized populations, suggesting that the acute systemic shocks of COVID-19 exacerbated long-standing structural health disparities to intensify the mortality burden in these vulnerable subgroups.

4.2 Synergistic drivers: therapeutic evolution and diagnostic refinement

Beyond the immediate impact of the pandemic, the overall upward trajectory in RF-associated mortality is attributable to several major clinical factors. One primary driver is the “survival paradox” arising from the evolution of oncological treatment modalities. As advancements in precision medicine, targeted therapies, and immune checkpoint inhibitors (ICIs) have substantially prolonged the survival of cancer patients (22), this extended lifespan inherently increases the duration of exposure to potentially pneumotoxic interventions. Cumulative pulmonary toxicity from modern therapies can lead to irreversible parenchymal lung injury and fibrosis (23–25), ultimately progressing to acute or chronic respiratory failure. This is particularly evident in the sustained rise of AAMRs among younger survivors and may explain the significant predicted percentage increase in AAMR among females (26).

Furthermore, these clinical trends are mirrored by significant shifts in diagnostic criteria and medical documentation. The rise in reported RF-associated deaths may be linked to the widespread adoption of the Berlin Definition for ARDS (27), which improved clinical recognition and diagnostic sensitivity. The transition to Electronic Health Record (EHR) systems and more rigorous “oncology assessments” has facilitated more comprehensive documentation of comorbidities and organ dysfunction on death certificates (28). Consequently, what may appear as an increase in mortality risk is, in part, a reflection of improved diagnostic precision and the heightened clinical awareness of respiratory complications in the aging oncological population.

4.3 Demographic heterogeneity and socio-epidemiological disparities

These complex mortality patterns are accompanied by salient demographic and socio-epidemiological disparities. The burden of RF mortality showed a clear concentration in the elderly, with the 85-years-and-over cohort consistently exhibiting the highest rates, while sex differences persisted as male AAMRs remained significantly higher than those for females. Racial disparities were equally pronounced, as the AAMR for the Non-Hispanic Black population far exceeded that of other groups (29–31). This inequity is rooted in several mechanisms: primarily, the higher prevalence of shared cardiopulmonary risk factors—such as smoking, obesity, and COPD—among Non-Hispanic Black individuals, which amplifies treatment-related pulmonary toxicity. Biologically, it is hypothesized that African Americans may exhibit a heightened susceptibility to higher peripheral resistance and arterial stiffness (13, 32, 33), potentially leading to left ventricular hypertrophy or dysfunction and reducing their tolerance for cancer treatment-related complications. Combined with persistent socioeconomic inequities and variations in access to healthcare resources, these factors entrench a higher AAMR burden in the Non-Hispanic Black population (34, 35).

4.4 Geographic inequities and the urban-rural healthcare gap

At the regional level, the significant geographical inequities identified—with the Northeast and West regions exhibiting the highest AAMRs—further refine our understanding of the urban-rural divide. This regional concentration suggests a paradox where regions with some of the nation's premier medical hubs also experience profound internal disparities. The entrenched mortality gap between non-metropolitan and metropolitan areas is driven by a multi-layered convergence of environmental, behavioral, and structural factors. Primarily, the slower decline in smoking-related mortality and the higher prevalence of untreated chronic obstructive pulmonary disease (COPD) in US rural areas provide a baseline of pulmonary vulnerability among cancer patients (36–38).

This vulnerability is exacerbated by a “cascading failure” in healthcare access within rural regions. The chronic scarcity of primary care physicians leads to delayed oncological screenings and late-stage diagnoses (39), while the lack of specialized respiratory support in resource-constrained rural hospitals prevents early intervention for acute respiratory distress. Consequently, cancer patients in non-metropolitan areas are more likely to present with advanced respiratory complications and have fewer opportunities to benefit from the advanced lung-protective strategies and non-invasive ventilation techniques available in urban tertiary centers. By solidifying the higher RF mortality burden in non-metropolitan areas, these systemic barriers transform geographic location into a critical determinant of survival, highlighting that the “healthcare gap” is as much about the quality of local infrastructure as it is about physical distance.

4.5 Prognostic modeling and clinical implications

Looking forward, based on our ARIMA model, the AAMR associated with respiratory failure is projected to maintain an upward trajectory through 2046. However, the interpretation of these long-term trends must account for the inherent stochasticity and potential for future volatility. Notably, the exceptionally wide 95% Confidence Intervals (CI)—exemplified by the male cohort spanning from 14.9 to 75.0 per 100,000—reflect the nuanced limitations of extrapolating current patterns amidst a complex and evolving clinical landscape. Consequently, rather than serving as definitive forecasting, these projections are better framed as heuristic scenarios that underscore the potential escalation of mortality burden in the absence of adaptive interventions.

From this proactive perspective, our findings emphasize the value of fostering interdisciplinary collaboration between oncology and respiratory medicine (40), such as through pilot awareness programs to bridge specialty gaps and facilitate early risk identification (41). The conceptual utility of dedicated respiratory oncology or survivorship clinics warrants further exploration to institutionalize proactive care frameworks to mitigate cumulative pulmonary toxicities. Finally, the projected escalation of AAMR among women—while subject to the aforementioned model volatility—underscores a critical need to evaluate whether current clinical frameworks sufficiently account for gender-specific physiological responses (42).

5 Limitation

The conclusions of this study should be interpreted with caution. First, the reliance on ICD-10 codes may introduce diagnostic non-specificity; respiratory failure is frequently used as a terminal coding diagnosis for end-stage patients, potentially reflecting the physiological dying process rather than a specific oncological complication. Second, the absence of granular clinical data—such as cancer staging, respiratory failure severity, and specific treatment regimens—precludes a definitive causal assessment of pneumotoxic therapies. Third, unmeasured confounders like individual-level socioeconomic status and screening intensity may influence the observed mortality patterns. Finally, while the ARIMA model projections indicate potential long-term trends, they are subject to the inherent stochasticity and model volatility discussed previously. Future research using detailed clinical registries is necessary to more precisely isolate the impact of targeted interventions on patient outcomes.

6 Conclusion

This study demonstrates a pivotal reversal in RF-associated mortality among US adult cancer patients, with AAMRs surging collectively after 2018 following a period of sustained decline. This burden is disproportionately concentrated among males, Non-Hispanic Black individuals, and residents of non-metropolitan areas. The findings underscore an urgent need to transition from siloed care to integrated clinical models. Specifically, enhancing interdisciplinary collaboration between oncology and pulmonology, alongside the establishment of dedicated cardio-pulmonary oncology clinics, is essential to mitigate the rising mortality and address persistent sociodemographic inequities in this high-risk population.

Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fcacs.2026.1752674/full#supplementary-material

Supplementary material Supplementary material at the source

Supplementary material Supplementary material at the source

Supplementary material Supplementary material at the source

Supplementary material Supplementary material at the source

Supplementary material Supplementary material at the source

Supplementary material Supplementary material at the source

Footnotes

Edited by: Prithwish De, Ontario Health, Canada

Reviewed by: Adnan Bhat, University of Florida, United States

Hafsa Shahid, MetroHealth Medical Center, United States

Statements

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.

Funding

The author(s) declared that financial support was not received for this work and/or its publication.

Data availability

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

CX: Conceptualization, Software, Methodology, Writing – original draft, Visualization. TR: Software, Writing – original draft, Validation, Visualization. BW: Supervision, Funding acquisition, Writing – review & editing, Resources.

Acknowledgements

We sincerely thank the developers and maintainers of the easy CDC package, which greatly simplifies programmatic access to and data processing of the CDC WONDER database.

References

  1. 1. Mathur P, Sathishkumar K, Chaturvedi M, Das P, Sudarshan KL, Santhappan S. Cancer statistics, 2020: report from National Cancer Registry Programme, India. JCO Glob Oncol. (2020) 6: 1063 – 75. doi: doi:10.1200/GO.20.00122
  2. 2. Schwartz SM. Epidemiology of cancer. Clin Chem. (2024) 70: 140 – 9. doi: doi:10.1093/clinchem/hvad202
  3. 3. Lyons PG, McEvoy CA, Hayes-Lattin B. Sepsis and acute respiratory failure in patients with cancer: how can we improve care and outcomes even further? Curr Opin Crit Care. (2023) 29: 472 – 83. doi: doi:10.1097/MCC.0000000000001078
  4. 4. Heybati K, Deng J, Bhandarkar A, Zhou F, Zamanian C, Arya N. Outcomes of acute respiratory failure in patients with cancer in the United States. Mayo Clin Proc. (2024) 99: 578 – 92. doi: doi:10.1016/j.mayocp.2023.07.024
  5. 5. Pingleton SK. Acute respiratory failure in chronic obstructive pulmonary disease. Curr Opin Pulm Med. (1996) 2: 111 – 5. doi: doi:10.1097/00063198-199603000-00006
  6. 6. Acute respiratory failure in the patient with cancer: diagnostic and management strategies. Crit Care Clin. (2010) 26: 21 – 40. doi: doi:10.1016/j.ccc.2009.10.001
  7. 7. Lee MY, Yoon SY, Kim KH, Lee N, Kim HY, Hwang JH. Pulmonary toxicities of molecular targeted antineoplastic agents: a single-center 10-year experience. Korean J Intern Med. (2021) 36: 689 – 98. doi: doi:10.3904/kjim.2020.295
  8. 8. Long K, Suresh K. Pulmonary toxicity of systemic lung cancer therapy. Respirol Carlton Vic. (2020) 25 Suppl 2: 72 – 9. doi: doi:10.1111/resp.13915
  9. 9. de Rouw N, Piet B, Derijks HJ, van den Heuvel MM, Ter Heine R. Mechanisms, management and prevention of pemetrexed-related toxicity. Drug Saf. (2021) 44: 1271 – 81. doi: doi:10.1007/s40264-021-01135-2
  10. 10. Ghanbar MI, Suresh K. Pulmonary toxicity of immune checkpoint immunotherapy. J Clin Invest. (2024) 134: e170503. doi: doi:10.1172/JCI170503
  11. 11. Khan BA, Perkins AJ, Khan SH, Unverzagt FW, Lasiter S, Gao S. Mobile critical care recovery program for survivors of acute respiratory failure: a randomized clinical trial. JAMA Netw Open. (2024) 7: e2353158. doi: doi:10.1001/jamanetworkopen.2023.53158
  12. 12. CDC Wonder. (n.d). Available online at: (Accessed February 18, 2026).
  13. 13. Brownlow BN, Williams DP, Kapuku G, Vasey MW, Anderson NB, Koenig J. Ethnic differences in resting total peripheral resistance: a systematic review and meta-analysis. Psychosom Med. (2020) 82: 548 – 60. doi: doi:10.1097/PSY.0000000000000820
  14. 14. Anderson RN, Rosenberg HM. Age standardization of death rates: implementation of the year 2000 standard. Natl Vital Stat Rep. (1998) 47:1–16, 20.
  15. 15. Li HZ, Du LB. Application of joinpoint regression model in cancer epidemiological time trend analysis. Zhonghua Yu Fang Yi Xue Za Zhi. (2020) 54: 908 – 12. doi: doi:10.3760/cma.j.cn112150-20200616-00889
  16. 16. Clegg LX, Hankey BF, Tiwari R, Feuer EJ, Edwards BK. Estimating average annual per cent change in trend analysis. Stat Med. (2009) 28: 3670 – 82. doi: doi:10.1002/sim.3733
  17. 17. Lu ZH, Chow SM, Loken E. A comparison of Bayesian and frequentist model selection methods for factor analysis models. Psychol Methods. (2017) 22: 361. doi: doi:10.1037/met0000145
  18. 18. Zamani Rarani F, Zamani Rarani M, Hamblin MR, Rashidi B, Hashemian SM, Mirzaei H. Comprehensive overview of COVID-19-related respiratory failure: focus on cellular interactions. Cell Mol Biol Lett. (2022) 27: 63. doi: doi:10.1186/s11658-022-00363-3
  19. 19. Wilcox SR. Management of respiratory failure due to COVID-19. BMJ. (2020) 369: m1786. doi: doi:10.1136/bmj.m1786
  20. 20. Delorey TM, Ziegler CGK, Heimberg G, Normand R, Yang Y, Segerstolpe Å. COVID-19 tissue atlases reveal SARS-CoV-2 pathology and cellular targets. Nature. (2021) 595: 107 – 13. doi: doi:10.1038/s41586-021-03570-8
  21. 21. Liao YT, Shen HC, Huang JR, Sun CY, Ko HJ, Chang CJ. Clinical characteristics and outcomes among critically ill patients with cancer and COVID-19-related acute respiratory failure. BMC Pulm Med. (2024) 24: 34. doi: doi:10.1186/s12890-024-02850-z
  22. 22. Withrow DR, Nicholson BD, Morris EJA, Wong ML, Pilleron S. Age-related differences in cancer relative survival in the United States: a SEER-18 analysis. Int J Cancer. (2023) 152: 2283 – 91. doi: doi:10.1002/ijc.34463
  23. 23. McDonald S, Rubin P, Phillips TL, Marks LB. Injury to the lung from cancer therapy: clinical syndromes, measurable endpoints, and potential scoring systems. Int J Radiat Oncol Biol Phys. (1995) 31: 1187 – 203. doi: doi:10.1016/0360-3016(94)00429-O
  24. 24. Morgan GW, Pharm B, Breit SN. Radiation and the lung: a reevaluation of the mechanisms mediating pulmonary injury. Int J Radiat Oncol Biol Phys. (1995) 31: 361 – 9. doi: doi:10.1016/0360-3016(94)00477-3
  25. 25. Malaviya R, Kipen HM, Businaro R, Laskin JD, Laskin DL. Pulmonary toxicants and fibrosis: innate and adaptive immune mechanisms. Toxicol Appl Pharmacol. (2020) 409: 115272. doi: doi:10.1016/j.taap.2020.115272
  26. 26. Dong M, Cioffi G, Wang J, Waite KA, Ostrom QT, Kruchko C. Sex differences in cancer incidence and survival: a pan-cancer analysis. Cancer Epidemiol Biomark Prev. (2020) 29: 1389 – 97. doi: doi:10.1158/1055-9965.EPI-20-0036
  27. 27. ARDS Definition Task Force, Ranieri, VM, Rubenfeld GD, Thompson BT, Ferguson ND, Caldwell E. Acute respiratory distress syndrome: the Berlin definition. JAMA. (2012) 307: 2526 – 33. doi: doi:10.1001/jama.2012.5669
  28. 28. Conderino S, Bendik S, Richards TB, Pulgarin C, Chan PY, Townsend J. The use of electronic health records to inform cancer surveillance efforts: a scoping review and test of indicators for public health surveillance of cancer prevention and control. BMC Med Inform Decis Mak. (2022) 22: 91. doi: doi:10.1186/s12911-022-01831-8
  29. 29. Ford CN, Leet RW, Kipling LM, Rhee MK, Jackson SL, Wilson PWF. Racial differences in performance of HbA1c for the classification of diabetes and prediabetes among US adults of non-Hispanic black and white race. Diabet Med. (2019) 36: 1234 – 42. doi: doi:10.1111/dme.13979
  30. 30. Tran HN, Siu S, Iribarren C, Udaltsova N, Klatsky AL. Ethnicity and risk of hospitalization for asthma and chronic obstructive pulmonary disease. Ann Epidemiol. (2011) 21: 615 – 22. doi: doi:10.1016/j.annepidem.2010.10.015
  31. 31. Forno E, Ortega VE, Celedón JC. Asthma and chronic obstructive pulmonary disease. Clin Chest Med. (2023) 44: 519 – 30. doi: doi:10.1016/j.ccm.2023.03.008
  32. 32. Morris AA, Patel RS, Binongo JN, Poole J, Al Mheid I, Ahmed Y. Racial differences in arterial stiffness and microcirculatory function between Black and White Americans. J Am Heart Assoc. (2013) 2: e002154. doi: doi:10.1161/JAHA.112.002154
  33. 33. Hardy ST, Holliday KM, Chakladar S, Engeda JC, Allen NB, Heiss G. Heterogeneity in blood pressure transitions over the life course: age-specific emergence of racial/ethnic and sex disparities in the United States. JAMA Cardiol. (2017) 2: 653 – 61. doi: doi:10.1001/jamacardio.2017.0652
  34. 34. Dickman SL, Himmelstein DU, Woolhandler S. Inequality and the health-care system in the USA. Lancet Lond Engl. (2017) 389: 1431 – 41. doi: doi:10.1016/S0140-6736(17)30398-7
  35. 35. Ahad A, Fatima E, Sultan W, Nasar MH, Jamil A, Shakoor M. Patterns in mortality associated with heart failure and lung cancer among older adults in the United States: an analysis of 20 years. Int J Cardiol Cardiovasc Risk Prev. (2024) 24: 200353. doi: doi:10.1016/j.ijcrp.2024.200353
  36. 36. Hendi AS, Ho JY. Smoking and the widening inequality in life expectancy between metropolitan and nonmetropolitan areas of the United States. Front Public Health. (2022) 10: 942842. doi: doi:10.3389/fpubh.2022.942842
  37. 37. Picazo F, Duan KI, Hee Wai T, Hayes S, Leonhard AG, Fonseca GA. Rural residence associated with receipt of recommended postdischarge chronic obstructive pulmonary disease care among a cohort of U.S. veterans. Ann Am Thorac Soc. (2025) 22: 515 – 22. doi: doi:10.1513/AnnalsATS.202405-493OC
  38. 38. Carlson SA, Wheaton AG, Watson KB, Liu Y, Croft JB, Greenlund KJ. Geographic differences in sex-specific chronic obstructive pulmonary disease mortality rate trends among adults aged ≥25 years - United States, 1999-2019. MMWR Morb Mortal Wkly Rep. (2022) 71: 613 – 8. doi: doi:10.15585/mmwr.mm7118a1
  39. 39. Jensen L, Monnat SM, Green JJ, Hunter LM, Sliwinski MJ. Rural population health and aging: toward a multilevel and multidimensional research agenda for the 2020s. Am J Public Health. (2020) 110: 1328 – 31. doi: doi:10.2105/AJPH.2020.305782
  40. 40. Traylor AM, Schweissing E, Bonaventura CD, Salas E. Training transdisciplinary health promotion teams: opportunities and challenges from team science. Am J Health Promot. (2021) 35: 740 – 4. doi: doi:10.1177/08901171211007955d
  41. 41. Hui D, Hoge G, Bruera E. Models of supportive care in oncology. Curr Opin Oncol. (2021) 33: 259 – 66. doi: doi:10.1097/CCO.0000000000000733
  42. 42. Klein SL, Flanagan KL. Sex differences in immune responses. Nat Rev Immunol. (2016) 16: 626 – 38. doi: doi:10.1038/nri.2016.90

Citation

Xia, C., et al. (2026). Trends in cancer-and respiratory failure-related mortality in the US adult population: an analysis of the CDC WONDER database from 1999 to 2023. Frontiers in Cancer Control and Society, Volume 4. https://doi.org/
Received
Accepted
Published
Volume
4 - 2026

Copyright

Copyright © 2026 Xia, Rong and Wang.

License: https://creativecommons.org/licenses/by/4.0/

*Correspondence: Baoqing Wang

Article metrics

Views
0
Downloads
0
Citations
0
Shares
0

Related articles

Original ResearchAdvanced stage at diagnosis and aggressive histology dominate prostate cancer presentation in Kenya: a registry analysisCharles Waihenya, Daniel Kinyuru Ojuka, Camille Ragin, Charnita Zeigler-Johnson, Regina Wachuka Mbugua, S. M. ThumbiOriginal ResearchChildhood cancer incidence, survival and mortality patterns in Argentina, 2000–2022: from the Argentine Oncopediatric Registry (ROHA)Florencia Moreno, Maria Agustina Chaplin, Mariana Nana, María Graciela AbriataOriginal ResearchFactors associated with breast self-examination practice among female medical students in selected universities in Borama, SomalilandNafisa Abdihamid Mohamed, Khadar Mowlid Abdi, Vitalis Okoth Odero, Hodo Abdikarim