Causes of Death among Cancer Patients: Emerging Trends in the 21st Century
Wang, J.; Liu, J.; Liu, Z.; Zhou, Z.; Ousmane, D.; Liu, L.; Peng, L.; Yuan, X.
Show abstract
Most studies examining the causes of cancer-related deaths have primarily focused on specific cancer types, often neglecting the evolving spectrum of death causes among cancer patients in the 21st century. This study, utilizing data from the National Cancer Institutes Surveillance, Epidemiology, and End Results (SEER) Program, analyzed the causes of death in patients diagnosed with 36 types of cancer between 2000 and 2021. By categorizing these causes into deaths from index cancers, non-index cancers, and non-cancer causes, this study provides a comprehensive analysis of cause-of-death patterns and emerging trends, stratified by year of death, age at diagnosis, and survival duration. The findings reveal that while relative mortality rates from index cancers remain elevated for brain, pancreatic, and gallbladder cancers, significant declines were observed for lung, liver, nasopharyngeal, and esophageal cancers, as well as multiple myeloma cancers, reflecting advancements in cancer treatment. Besides, relative mortality rates from non-index cancers surpassed those from index cancers in oral cavity, oropharyngeal, vaginal, and small intestine cancers, indicating a potential benefit from enhanced surveillance and early detection of non-index cancers in these patient populations. Importantly, non-cancer-related causes of death, such as heart disease, chronic liver disease and cirrhosis, also emerged as prominent contributors to mortality among cancer patients. The results of this study offer critical and current data to inform public health policy, optimize healthcare resource allocation, and facilitate international collaboration in cancer research and control. Meanwhile, this study is of great reference value for developing countries to formulate medium- and long-term public health policies.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Sodium-glucose cotransporter 2 inhibitors versus dipeptidyl peptidase 4 inhibitors on new-onset overall cancer in type 2 diabetes mellitus: a population-based study 94%
- COVID-19 Outcomes in Patients with Cancer: Findings from the University of California Health System Database 93%
- Circulating serum miRNAs predict response to platinum chemotherapy in high-grade serous ovarian cancer 92%
Similar papers in this journal
- Cumulative COVID-19 incidence, mortality, and prognosis in cancer survivors: a population-based study in Reggio Emilia, Northern Italy 95%
- Cancer and the risk of COVID-19 diagnosis, hospitalisation, and death: a population-based multi-state cohort study including 4,618,377 adults in Catalonia, Spain 94%
- Age-related differences in cancer relative survival in the US: a SEER-18 analysis 94%
Similar papers in this journal
- Underlying reasons for post-mortem diagnosed lung cancer cases – A robust retrospective comparative study from Hungary (HULC study) 93%
- Distinct metastatic organotropism shapes prognosis in lung adenocarcinoma with brain metastasis 92%
- BNIP3 upregulation characterizes cancer cell subpopulation with increased fitness and proliferation 91%
Similar papers in this journal
- Pan-cancer analyses of the associations between 109 pre-existing conditions and cancer treatment patterns across 19 adult cancers 95%
- Area-based socioeconomic inequalities in cancer mortality in Germany – widening, narrowing or reversing inequalities between 2003 and 2019? 93%
- Genomic alterations and abnormal expression of APE2 in multiple cancers 92%
Similar papers in this journal
- Molecular Heterogeneity in Early-Onset Colorectal Cancer: Pathway-Specific Insights in High-Risk Populations 93%
- NDRG1 expression is an independent prognostic factor in inflammatory breast cancer 93%
- Ethnicity-Specific Molecular Alterations in MAPK and JAK/STAT Pathways in Early-Onset Colorectal Cancer 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.