In 2019, it was estimated that around 44 percent of all cancer deaths among adults aged 30 years or above in the United States could be attributed to potentially modifiable risk factors. At that time, cigarette smoking attributed to around 28.5 percent of all cancer deaths. This statistic shows the proportion of cancer deaths in the United States attributable to select risk factors in 2019.

Decrease the cancer death rate from 185.7 per 100,000 in 2013 to 180.3 per 100,000 by 2019.

This data provides high-level data on historical registrations (or cases) and deaths, including information about the cancer types and breakdowns by gender variables.

In Germany, the number of deaths caused by liver cancer between 2019 and 2023 were consistently higher for men, reaching around *** thousand death cases in 2023. Conversely, women reported around *** thousand deaths in the same period. This statistic depicts the number of liver cancer deaths in Germany between 2019 and 2023, by gender.

In 2019, it was estimated that all deaths from cancer in the cervix uteri and Kaposi sarcoma among women in the United States aged 30 years and older could be attributed to potentially modifiable risk factors. This statistic shows the proportion of cancer deaths among women in the United States attributable to modifiable risk factors in 2019.

In 2019, it was estimated that around **** percent of all cancer deaths among men and **** percent among women aged 30 years or above in the United States could be attributed to potentially modifiable risk factors. At that time, cigarette smoking attributed to around **** percent of cancer deaths among men and **** percent of deaths among women. This statistic shows the proportion of cancer deaths in the United States attributable to select risk factors in 2019, by gender.

Cancer diagnoses and age-standardised incidence rates for all types of cancer by age and sex including breast, prostate, lung and colorectal cancer.

Causes of death from major cancers in Yunlin County in the 108th year (female mortality causes)

BackgroundUnderstanding the effects of demographic drivers on lung cancer mortality trends is critical for lung cancer control. We have examined the drivers of lung cancer mortality at the global, regional, and national levels.MethodsData on lung cancer death and mortality were extracted from the Global Burden of Disease (GBD) 2019. Estimated annual percentage change (EAPC) in the age-standardized mortality rate (ASMR) for lung cancer and all-cause mortality were calculated to measure temporal trends in lung cancer from 1990 to 2019. Decomposition analysis was used to analyze the contributions of epidemiological and demographic drivers to lung cancer mortality.ResultsDespite a non-significant decrease in ASMR [EAPC = −0.31, 95% confidence interval (CI): −1.1 to 0.49], the number of deaths from lung cancer increased by 91.8% [95% uncertainty interval (UI): 74.5–109.0%] between 1990 and 2019. This increase was due to the changes in the number of deaths attributable to population aging (59.6%), population growth (56.7%), and non-GBD risks (3.49%) compared with 1990 data. Conversely, the number of lung cancer deaths due to GBD risks decreased by 19.8%, mainly due to tobacco (−12.66%), occupational risks (−3.52%), and air pollution (−3.47%). More lung cancer deaths (1.83%) were observed in most regions, which were due to high fasting plasma glucose levels. The temporal trend of lung cancer ASMR and the patterns of demographic drivers varied by region and gender. Significant associations were observed between the contributions of population growth, GBD risks and non-GBD risks (negative), population aging (positive), and ASMR in 1990, the sociodemographic index (SDI), and the human development index (HDI) in 2019.ConclusionPopulation aging and population growth increased global lung cancer deaths from 1990 to 2019, despite a decrease in age-specific lung cancer death rates due to GBD risks in most regions. A tailored strategy is needed to reduce the increasing burden of lung cancer due to outpacing demographic drivers of epidemiological change globally and in most regions, taking into account region- or gender-specific risk patterns.

The Get Data Out programme from the National Disease Registration Service publishes detailed statistics about small groups of cancer patients in a way that ensures patient anonymity is maintained. The Get Data Out programme currently covers 15 cancer sites. This data release is a corrected re-release of detailed statistics for 2013-2019 treatment data. The correction means that surgery counts are no longer slightly underreported. There are some small changes in group sizes of usually no more than 2%, although this is larger for non-melanoma skin cancers. The 15 cancer sites now covered by Get Data Out are: ‘Bladder, Urethra, Renal Pelvis and Ureter’, ‘Bone cancer’, ‘Brain, meningeal and other primary CNS tumours’, ‘Eye cancer’, ‘Head and neck’, ‘Kaposi sarcoma’, ‘Kidney’, ‘Oesophageal and Stomach’, ‘Ovary, fallopian tube and primary peritoneal carcinomas’, ‘Pancreas’, ‘Prostate’, ‘Sarcoma’, ‘Skin tumours’, ‘Soft tissue and peripheral nerve cancer’, ‘Testicular tumours including post-pubertal teratomas’. Anonymisation standards are designed into the data by aggregation at the outset. Patients diagnosed with a certain type of tumour are divided into many smaller groups, each of which contains approximately 100 patients with the same characteristics. These groups are aimed to be clinically meaningful and differ across cancer sites. For each group of patients, Get Data Out routinely publish statistics about incidence, routes to diagnosis, treatments and survival. All releases and documentation are available on the Get Data Out main technical page. Before using the data, we recommend that you read the guide for first time users. The data is available in an open format for anyone to access and use. We hope that by releasing anonymous detailed data like this we can help researchers, the public and patients themselves discover more about cancer. If you have feedback or any other queries about Get Data Out, please email us at NDRSenquires@nhs.net and mention 'Get Data Out' in your email.

Analysis of ‘Cancer Deaths’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/43e665f9-3550-4036-bb9a-7c6cb4f46524 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

Decrease the cancer death rate from 185.7 per 100,000 in 2013 to 180.3 per 100,000 by 2019.

--- Original source retains full ownership of the source dataset ---

In Germany, the number of deaths caused by cancer remained relatively stable between 2019 and 2023. Men reported approximately ** to ** thousand more deaths than women for each of the analyzed years. This statistic depicts the number of deaths from cancer in Germany between 2019 and 2023, by gender.

Age-standardised rate of mortality from oral cancer (ICD-10 codes C00-C14) in persons of all ages and sexes per 100,000 population.RationaleOver the last decade in the UK (between 2003-2005 and 2012-2014), oral cancer mortality rates have increased by 20% for males and 19% for females1Five year survival rates are 56%. Most oral cancers are triggered by tobacco and alcohol, which together account for 75% of cases2. Cigarette smoking is associated with an increased risk of the more common forms of oral cancer. The risk among cigarette smokers is estimated to be 10 times that for non-smokers. More intense use of tobacco increases the risk, while ceasing to smoke for 10 years or more reduces it to almost the same as that of non-smokers3. Oral cancer mortality rates can be used in conjunction with registration data to inform service planning as well as comparing survival rates across areas of England to assess the impact of public health prevention policies such as smoking cessation.References:(1) Cancer Research Campaign. Cancer Statistics: Oral – UK. London: CRC, 2000.(2) Blot WJ, McLaughlin JK, Winn DM et al. Smoking and drinking in relation to oral and pharyngeal cancer. Cancer Res 1988; 48: 3282-7. (3) La Vecchia C, Tavani A, Franceschi S et al. Epidemiology and prevention of oral cancer. Oral Oncology 1997; 33: 302-12.Definition of numeratorAll cancer mortality for lip, oral cavity and pharynx (ICD-10 C00-C14) in the respective calendar years aggregated into quinary age bands (0-4, 5-9,…, 85-89, 90+). This does not include secondary cancers or recurrences. Data are reported according to the calendar year in which the cancer was diagnosed.Counts of deaths for years up to and including 2019 have been adjusted where needed to take account of the MUSE ICD-10 coding change introduced in 2020. Detailed guidance on the MUSE implementation is available at: https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/articles/causeofdeathcodinginmortalitystatisticssoftwarechanges/january2020Counts of deaths for years up to and including 2013 have been double adjusted by applying comparability ratios from both the IRIS coding change and the MUSE coding change where needed to take account of both the MUSE ICD-10 coding change and the IRIS ICD-10 coding change introduced in 2014. The detailed guidance on the IRIS implementation is available at: https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/bulletins/impactoftheimplementationofirissoftwareforicd10causeofdeathcodingonmortalitystatisticsenglandandwales/2014-08-08Counts of deaths for years up to and including 2010 have been triple adjusted by applying comparability ratios from the 2011 coding change, the IRIS coding change and the MUSE coding change where needed to take account of the MUSE ICD-10 coding change, the IRIS ICD-10 coding change and the ICD-10 coding change introduced in 2011. The detailed guidance on the 2011 implementation is available at https://webarchive.nationalarchives.gov.uk/ukgwa/20160108084125/http://www.ons.gov.uk/ons/guide-method/classifications/international-standard-classifications/icd-10-for-mortality/comparability-ratios/index.htmlDefinition of denominatorPopulation-years (aggregated populations for the three years) for people of all ages, aggregated into quinary age bands (0-4, 5-9, …, 85-89, 90+)

In 2019, it was estimated that around 90 percent of tracheal cancer deaths among people aged 30 years and older in the United States could be attributable to cigarette smoking. This statistic shows the proportion of cancer deaths in the United States attributable to cigarette smoking in 2019.

This publication reports on newly diagnosed cancers registered in England in addition to cancer deaths registered in England during 2020. It includes this summary report showing key findings, spreadsheet tables with more detailed estimates, and a methodology document.

ObjectiveLung cancer is responsible for millions of deaths yearly, and its burden is severe worldwide. This study aimed to investigate the burden of lung cancer in the population of Wuhan based on the surveillance data from 2010 to 2019.MethodsData of this study was obtained from the Mortality Register System established by the Wuhan Center for Disease Control and Prevention. The study systematically analyzed the burden of lung cancer deaths in the population of Wuhan and its 13 administrative regions from 2010 to 2019 via the Joinpoint regression models, Age-Period-Cohort (APC) models, and decomposition analysis.ResultsThis study found the upward and downward trends in the age-standardized mortality rates (ASMRs) and age-standardized years of life lost rates (ASYLLRs) of lung cancer from 2010 to 2019. In Joinpoint regression models, the corresponding estimated annual percentage change (EAPC) were 1.00% and -1.90%, 0.60%, and -3.00%, respectively. In APC models, lung cancer mortality tended to increase with age for both sexes in Wuhan, peaking at the 85-89 age group; The period effects for different populations have started to gradually decline in recent years. In addition, the cohort effects indicated that the risk of lung cancer death was highest among those born in the 1950s-1955s, at 1.08 (males) and 1.01 (females). Among all administrative districts in Wuhan, the ASMR of lung cancer in the Xinzhou District has remained the highest over the study period. In decomposition analysis, both population aging (P

Lithuania was down by 0.2% of Male Deaths from Cancer in 2019, compared to a year earlier.

One-year and five-year net survival for adults (15-99) in England diagnosed with one of 29 common cancers, by age and sex.

BackgroundThe burden of breast cancer has been increasing globally. The epidemiology burden and trends need to be updated. This study aimed to update the burden and trends of breast cancer incidences, deaths, and disability-adjusted life-years (DALYs) from 1990 to 2019, using the Global Burden of Disease 2019 study.MethodsThe data of incidences, deaths, DALYs, and age-standardized rates were extracted. Estimated annual percentage changes were used to quantify the trends of age-standardized rates. Besides, the population attributable fractions of the risk factors of breast cancer were also estimated.ResultsGlobally, the incidences of breast cancer increased to 2,002,354 in 2019. High social-development index (SDI) quintiles had the highest incidence cases with a declining trend in age-standardized incidence rate. In 2019, the global deaths and DALYs of breast cancer increased to 700,660 and 20,625,313, respectively. From 1990 to 2019, the age-standardized mortality rates and age-standardized DALY rates declined globally, especially in high and high-middle SDI quintiles. Besides, the trends varied from different regions and countries. The proportion of the patients in the 70+ years age group increased globally. Deaths of breast cancer attributable to high fasting plasma glucose and high body mass index increased globally, and high fasting plasma glucose was the greatest contributor to the global breast cancer deaths.ConclusionThe burden of breast cancer in higher SDI quintiles had gone down while the burden was still on the rise in lower SDI quintiles. It is necessary to appeal to the public to decrease the exposure of the risk factors.