In the United States, the average person has a 1 in 6 chance of dying from heart disease and a 1 in 7 chance of dying from cancer. In comparison, the odds of dying from a dog attack are 1 in 43,882. Sadly, the odds of dying from an opioid overdose in the U.S. are 1 in 55, making death from an opioid overdose more likely than dying from a motor vehicle accident. Opioid overdose death rates have increased insignificantly in the U.S. over the past decade. Leading causes of death in the United States Given the high lifetime odds of dying from heart disease or cancer, it is unsurprising that heart disease and cancer are the leading causes of death in the United States. Together, heart disease and cancer account for around 40 percent of all deaths. Other leading causes of death include accidents, stroke, Alzheimer’s disease, and diabetes. However, in 2020 and 2021, COVID-19 was the third leading cause of death in the United States and remained the fourth leading cause of death in 2022, with around 44.5 deaths per 100,000 population. Heart disease in the U.S. Although heart disease is the leading cause of death in the United States, death rates due to heart disease have decreased steadily over the last two decades. In 2019, there were around 162 deaths due to heart disease per 100,000 population. Coronary heart disease is the most common form of heart disease in the United States. Common risk factors for heart disease include high blood pressure, high cholesterol, smoking, excessive drinking, and being overweight or obese. The states with the highest rates of death from heart disease are Oklahoma, Mississippi, and Alabama.

Cancer was responsible for around 142 deaths per 100,000 population in the United States in 2022. The death rate for cancer has steadily decreased since the 1990’s, but cancer still remains the second leading cause of death in the United States. The deadliest type of cancer for both men and women is cancer of the lung and bronchus which will account for an estimated 65,790 deaths among men alone in 2024. Probability of surviving Survival rates for cancer vary significantly depending on the type of cancer. The cancers with the highest rates of survival include cancers of the thyroid, prostate, and testis, with five-year survival rates as high as 99 percent for thyroid cancer. The cancers with the lowest five-year survival rates include cancers of the pancreas, liver, and esophagus. Risk factors It is difficult to determine why one person develops cancer while another does not, but certain risk factors have been shown to increase a person’s chance of developing cancer. For example, cigarette smoking has been proven to increase the risk of developing various cancers. In fact, around 81 percent of cancers of the lung, bronchus and trachea among adults aged 30 years and older can be attributed to cigarette smoking. A recent poll indicated that many U.S. adults believed smoking cigarettes and using other tobacco products increased a person’s risk of developing cancer, but a much smaller percentage believed the same for proven risk factors such as obesity and drinking alcohol.

As of 2018, the risk for men dying from cancer before the age of 75 years in Egypt was slightly higher than for women with 13.1 percent compared to 9.6 percent, respectively. In that year, the overall risk for the general population in Egypt to die from cancer before the age of 75 years was at 11.3 percent.

This dataset presents median age, risk of new cancer cases and deaths, and potential years of life lost (YPLL) before age 70 by language region, cancer site, gender and period, since 1991.

As of 2020, there was a lifetime probability of 0.4 percent that a male in Canada would die from melanoma during his lifetime. This statistic displays the lifetime probability of dying from cancer among males in Canada by cancer type, as of 2020.

BackgroundEarly-onset colorectal cancer (EOCRC) has an alarmingly increasing trend and arouses increasing attention. Causes of death in EOCRC population remain unclear.MethodsData of EOCRC patients (1975–2018) were extracted from the Surveillance, Epidemiology, and End Results database. Distribution of death was calculated, and death risk of each cause was compared with the general population by calculating standard mortality ratios (SMRs) at different follow-up time. Univariate and multivariate Cox regression models were utilized to identify independent prognostic factors for overall survival (OS).ResultsThe study included 36,013 patients, among whom 9,998 (27.7%) patients died of colorectal cancer (CRC) and 6,305 (17.5%) patients died of non-CRC causes. CRC death accounted for a high proportion of 74.8%–90.7% death cases within 10 years, while non-CRC death (especially cardiocerebrovascular disease death) was the major cause of death after 10 years. Non-cancer death had the highest SMR in EOCRC population within the first year after cancer diagnosis. Kidney disease [SMR = 2.10; 95% confidence interval (CI), 1.65–2.64] and infection (SMR = 1.92; 95% CI, 1.48–2.46) were two high-risk causes of death. Age at diagnosis, race, sex, year of diagnosis, grade, SEER stage, and surgery were independent prognostic factors for OS.ConclusionMost of EOCRC patients died of CRC within 10-year follow-up, while most of patients died of non-CRC causes after 10 years. Within the first year after cancer diagnosis, patients had high non-CRC death risk compared to the general population. Our findings help to guide risk monitoring and management for US EOCRC patients.

Background: Accurately recognising that a person may be dying is central for improving their experience of care. Yet recognising dying is difficult and predicting dying frequently inaccurate. Methods: Serial urine samples from patients (n=112) with lung cancer were analysed using high resolution untargeted mass spectrometry. ANOVA and volcano plot analysis demonstrated metabolites that changed in the last weeks of life. Further analysis identified potential biological pathways affected. Cox lasso logistic regression was engaged to develop a multivariable model predicting the probability of survival within the last 30 days of life. Results: In total 124 metabolites changed. ANOVA analysis identified 93 metabolites and volcano plot analysis 85 metabolites. 53 metabolites changed using both approaches. Pathways altered in the last weeks included those associated with decreased oral intake, muscle loss, decreased RNA and protein synthesis, mitochondrial dysfunction, disrupted β-oxidation and one carbon metabolism. Epinephrine and cortisol increased in the last 2 weeks and week respectively. A model predicting time to death using 7 metabolites had excellent accuracy (AUC= 0.86 at day 30, 0.88 at day 20 and 0.85 at day 10) and enabled classification of patients at low, medium and high risk of dying on a Kaplan-Meier survival curve. Conclusions: Metabolomic analysis identified metabolites and their associated pathways that change in the last weeks and days of life in patients with lung cancer. Prognostic tests based on the metabolites identified have the potential to change clinical practice and improve the care of dying patients.

Objective: Most sepsis studies have looked at the general population. The aim of this study is to report on the characteristics, treatment and hospital mortality of patients with cancer diagnosed with sepsis or septic shock. Setting: A single-centre retrospective study at a tertiary care centre looking at patients with cancer who presented to our tertiary hospital with sepsis, septic shock or bacteraemia between 2010 and 2015. Participants: 176 patients with cancer were compared with 176 cancer-free controls. Primary and secondary outcomes: The primary outcome of this study was the in hospital mortality in both cohorts. Secondary outcomes included patient demographics, emergency department (ED) vital signs and parameters of resuscitation along with laboratory work. Results: A total of 352 patients were analysed. The mean age at presentation for the cancer group was 65.39±15.04 years, whereas the mean age for the control group was 74.68±14.04 years (p<0.001). In the cancer cohort the respiratory system was the most common site of infection (37.5%) followed by the urinary system (26.7%), while in the cancer-free arm, the urinary system was the most common site of infection (40.9%). intravenous fluid replacement for the first 24 hours was higher in the cancer cohort. ED, intensive care unit and general practice unit length of stay were comparable in both the groups. 95 (54%) patients with cancer died compared with 75 (42.6%) in the cancer-free group. The 28-day hospital mortality in the cancer cohort was 87 (49.4%) vs 46 (26.1%) in the cancer-free cohort (p=0.009). Patients with cancer had a 2.320 (CI 95% 1.225 to 4.395, p=0.010) odds of dying compared with patients without cancer in the setting of sepsis. Conclusions: This is the first study looking at an in-depth analysis of sepsis in the specific oncology population. Despite aggressive care, patients with cancer have higher hospital mortality than their cancer-free counterparts while adjusting for all other variables.

BackgroundOver the last decades, the number of patients diagnosed with thyroid carcinoma has been increasing, highlighting the importance of comprehensively evaluating causes of death among these patients. This study aimed to comprehensively characterize the risk of death and causes of death in patients with thyroid carcinoma.MethodsA total of 183,641 patients diagnosed with an index thyroid tumor were identified from the Surveillance, Epidemiology, and End Result database (1975–2016). Standardized mortality rates (SMRs) for non-cancer deaths were calculated to evaluate mortality risk and to compare mortality risks with the cancer-free US population. Cumulative mortality rates were calculated to explore the factors associated with higher risk of deaths.ResultsThere were 22,386 deaths recorded during follow-up, of which only 31.0% were due to thyroid cancer and 46.4% due to non-cancer causes. Non-cancer mortality risk among patients with thyroid cancer was nearly 1.6-fold (SMR=1.59) that of the general population. Cardiovascular diseases were the leading cause of non-cancer deaths, accounting for 21.3% of all deaths in thyroid cancer patients. Non-cancer causes were the dominant cause of death in thyroid cancer survivors as of the third year post-diagnosis. We found that males with thyroid cancer had a higher risk of all-cause mortality compared with females. The risk of suicide was highest in the first post-diagnostic year (5 years: SMR=8.27).ConclusionNon-cancer comorbidities have become the major risks of death in patients with thyroid tumor in the US, as opposed to death from the tumor itself. Clinicians and researchers should be aware of these risk trends in order to conduct timely intervention strategies.

In 2020, there was a lifetime probability of 0.2 percent that a female in Canada would die from melanoma during her lifetime. This statistic depicts the lifetime probability of dying from cancer among females in Canada by cancer type, as of 2020.

Death rate has been age-adjusted by the 2000 U.S. standard population. Single-year data are only available for Los Angeles County overall, Service Planning Areas, Supervisorial Districts, City of Los Angeles overall, and City of Los Angeles Council Districts.Lung cancer is a leading cause of cancer-related death in the US. People who smoke have the greatest risk of lung cancer, though lung cancer can also occur in people who have never smoked. Most cases are due to long-term tobacco smoking or exposure to secondhand tobacco smoke. Cities and communities can take an active role in curbing tobacco use and reducing lung cancer by adopting policies to regulate tobacco retail; reducing exposure to secondhand smoke in outdoor public spaces, such as parks, restaurants, or in multi-unit housing; and improving access to tobacco cessation programs and other preventive services.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

Editor’s Choice

• Global Breast Cancer Market size is expected to be worth around USD 49.2 Bn by 2032 from USD 19.8 Bn in 2022, growing at a CAGR of 9.8% during the forecast period from 2022 to 2032.
• Breast cancer is the most common cancer among women worldwide. In 2020, there were about 2.3 million new cases of breast cancer diagnosed globally.
• Breast cancer is the leading cause of cancer-related deaths in women. In 2020, it was responsible for approximately 685,000 deaths worldwide.
• The survival rate of breast cancer has improved over the years. In the United States, the overall five-year survival rate of breast cancer is around 90%.
• The American Cancer Society recommends annual mammograms starting at age 40 for women at average risk.
• Although rare, breast cancer also occurs in men. Less than 1% of breast cancer cases are diagnosed in males.

(Source: WHO, American Cancer Society)

Summary information about the participants used in the study.

In 2022, Turkey had the highest cumulative risk factor for respiratory cancer deaths before the age of 75 by far in the Middle East and North Africa, at 4.4 percent. This was nearly twice the rate of Gaza and the West Bank, which had the second highest risk factor at 2.3 percent.

High-risk human papillomavirus (hrHPV) infection is established as the major cause of invasive cervical cancer (ICC). However, whether hrHPV status in the tumor is associated with subsequent prognosis of ICC is controversial. We aim to evaluate the association between tumor hrHPV status and ICC prognosis using national registers and comprehensive human papillomavirus (HPV) genotyping.

In this nationwide population-based cohort study, we identified all ICC diagnosed in Sweden during the years 2002–2011 (4,254 confirmed cases), requested all archival formalin-fixed paraffin-embedded blocks, and performed HPV genotyping. Twenty out of 25 pathology biobanks agreed to the study, yielding a total of 2,845 confirmed cases with valid HPV results. Cases were prospectively followed up from date of cancer diagnosis to 31 December 2015, migration from Sweden, or death, whichever occurred first. The main exposure was tumor hrHPV status classified as hrHPV-positive and hrHPV-negative. The primary outcome was all-cause mortality by 31 December 2015. Five-year relative survival ratios (RSRs) were calculated, and excess hazard ratios (EHRs) with 95% confidence intervals (CIs) were estimated using Poisson regression, adjusting for education, time since cancer diagnosis, and clinical factors including age at cancer diagnosis and International Federation of Gynecology and Obstetrics (FIGO) stage.

Of the 2,845 included cases, hrHPV was detected in 2,293 (80.6%), and we observed 1,131 (39.8%) deaths during an average of 6.2 years follow-up. The majority of ICC cases were diagnosed at age 30–59 years (57.5%) and classified as stage IB (40.7%). hrHPV positivity was significantly associated with screen-detected tumors, young age, high education level, and early stage at diagnosis (p < 0.001). The 5-year RSR compared to the general female population was 0.74 (95% CI 0.72–0.76) for hrHPV-positive cases and 0.54 (95% CI 0.50–0.59) for hrHPV-negative cases, yielding a crude EHR of 0.45 (95% CI 0.38–0.52) and an adjusted EHR of 0.61 (95% CI 0.52–0.71). Risk of all-cause mortality as measured by EHR was consistently and statistically significantly lower for cases with hrHPV-positive tumors for each age group above 29 years and each FIGO stage above IA. The difference in prognosis by hrHPV status was highly robust, regardless of the clinical, histological, and educational characteristics of the cases. The main limitation was that, except for education, we were not able to adjust for lifestyle factors or other unmeasured confounders.

In conclusion, women with hrHPV-positive cervical tumors had a substantially better prognosis than women with hrHPV-negative tumors. hrHPV appears to be a biomarker for better prognosis in cervical cancer independent of age, FIGO stage, and histological type, extending information from already established prognostic factors. The underlying biological mechanisms relating lack of detectable tumor hrHPV to considerably worse prognosis are not known and should be further investigated.

Purpose:

To compile a comprehensive survival and HPV genotyping data and provide a large-scale population-based evaluation of the association between tumor high risk HPV status and prognosis of invasive cervical cancer.

This dataset (ccHPV_RelativeSurvival.dta) comprises 2845 invasive cervical cancer (ICC) cases diagnosed in Sweden during the years 2002-2011, and had valid human papillomavirus (HPV) results assessed from the formalin-fixed, paraffin-embedded (FFPE) blocks.

In order to control the risk of incidental disclosure of personal information, the data available here has been anonymized in the following manner: • The date of diagnosis has been moved to 2008-07-01 for all subjects. • Follow-up time has been censored at five years after diagnosis. • Age at diagnosis and follow-up time after diagnosis have been microaggregated in groups of five subjects (using function microaggregation in R package sdcMicro 2.5.9, available from https://cran.r-project.org/package=sdcMicro)

Analysis of the anonymized data replicates the results presented in main part of the study (Figures 2 & 3, Tables 1-3) with only minor numerical differences, with the following exceptions: • In Figure 2, relative survival can only be calculated up to five years after diagnosis. • In Table 1, the number of person years and the mean follow-up time differ considerably due to censoring; the distribution of subjects between age groups varies somewhat due to microaggregation. • In Figure 3, the excess hazard ratios for age groups 30-44 and 45-59 in Panel A shift noticeably, but without affecting the overall message (comparable reduced risk across all age strata).

The dataset includes 12 variables, eight of which are necessary for the analysis (core variables) and four of which are included for administrative purposes and convenience of coding the analysis (extra variables). Core variables: • dx_date: Date of diagnosis • age: Age (in years) at diagnosis • x_stage_group: International Federation of Gynecology and Obstetrics (FIGO) stage of tumor, IA; IB; II and III+ • edu_cat: Education (categorical, three levels): 1=low (less than high school); 2=middle (high school); 3=high (university exam and above); 99=missing • exit_new: End of follow-up (date) • censor_new: Censoring status: 1=death; 2=censored due to migration, loss of follow-up or end of study • final_type: Histological type of tumor: SCC=squamous cell carcinoma; AC=adenocarcinoma. • hr_hpv: High-risk HPV status of tumor (main exposure, binary): 0=hrHPV negative; 1= hrHPV positive

Extra variables: • entry: Entry date (copy of diagnosis date) • sex: Gender (all female, for linking to standard population mortality file): 2=female. • dx_year: Year of diagnosis (for linking to standard population mortality file)

Rank, number of deaths, percentage of deaths, and age-specific mortality rates for the leading causes of death, by age group and sex, 2000 to most recent year.

BackgroundWith approval of anti-PD-1/PD-L1, metastatic non-small cell lung cancer (NSCLC) has entered the era of immunotherapy. Since immune-related adverse events (irAEs) occur commonly in patients receiving anti-PD-1/PD-L1, the landscape of death causes may have changed in metastatic NSCLC. We aim to compare patterns of death causes in metastatic NSCLC between the pre-immunotherapy and immunotherapy era to identify the consequent landscape transition of death causes.MethodsIn this cohort study, 298,48patients with metastatic NSCLC diagnosed between 2000 and 2018 were identified from the Surveillance, Epidemiology, and End Results Program. Unsupervised clustering with Bayesian inference method was performed for all patients’ death causes, which separated them into two death patterns: the pre-immunotherapy era group and the immunotherapy era group. Relative risk (RR) of each death cause between two groups was estimated using Poisson regression. Reduced death risk as survival time was calculated with locally weighted scatterplot smooth (Lowess) regression.ResultsTwo patterns of death causes were identified by unsupervised clustering for all patients. Thus, we separated them into two groups, the immunotherapy era (2015-2017, N=40,172) and the pre-immunotherapy era (2000-2011, N=166,321), in consideration of obscure availability to immunotherapy for patients diagnosed in 2012-2014, when the follow-up cutoff was set as three years. Although all-cause death risk had reduced (29.2%, 13.7% and 27.8% for death risks of lung cancer, non-cancer and other cancers), non-cancer deaths in the immunotherapy era (N=2,100, 5.2%; RR=1.155, 95%CI: 1.101-1.211, P

Results of logistic regression analysis: All cases and controls (2,585 cases and 9,362 controls).

As of 2020, the general risk for women dying from cancer before the age of 75 years in the United Arab Emirates was slightly higher than for men with 8.2 percent compared to 4.3 percent, respectively. In that year, the overall risk for the general population in the UAE to die from cancer before the age of 75 years was 5.5 percent.

Background and Aims: Staging inadequately predicts metastatic risk in colon cancer patients. We used a gene expression profile derived from invasive murine colon cancer cells that were highly metastatic in an immunocompetent mouse model to identify colon cancer patients at risk for recurrence in a phase I, exploratory biomarker study. Methods: 55 colorectal cancer patients from Vanderbilt Medical Center (VMC) were used as the training dataset and 177 patients from the Moffitt Cancer Center were used as the independent dataset. The metastasis-associated gene expression profile developed from the mouse model was refined using comparative functional genomics in the VMC gene expression profiles to identify a 34-gene classifier associated with high risk of metastasis and death from colon cancer. A recurrence score derived from the biologically based classifier was tested in the Moffitt dataset. Results: A high score was significantly associated with increased risk of metastasis and death from colon cancer across all pathological stages and specifically in stage II and stage III patients. The recurrence score was shown to independently predict risk of cancer recurrence and death in both univariate and multivariate models. For example, among stage III patients, a high score translated to increased relative risk for cancer recurrence (hazard ratio = 4.7 (95% CI=1.566-14.05)). Furthermore, the recurrence score identified stage III patients whose five-year recurrence-free survival was >88% and for whom adjuvant chemotherapy did not provide improved survival. Conclusion: Our biologically based gene expression profile yielded a potentially useful classifier to predict cancer recurrence and death independently of conventional measures in colon cancer patients. Experiment Overall Design: Gene expression array differences between highly invasive mouse colon cancer cells and non-invasive colon cancer cells were used to develop a metastasis gene expression profile. It was refined using gene expression data from 55 patient (VMC) samples and trained using 177 patient (Moffitt) samples.