Number and rate of new cancer cases diagnosed annually from 1992 to the most recent diagnosis year available. Included are all invasive cancers and in situ bladder cancer with cases defined using the Surveillance, Epidemiology and End Results (SEER) Groups for Primary Site based on the World Health Organization International Classification of Diseases for Oncology, Third Edition (ICD-O-3). Random rounding of case counts to the nearest multiple of 5 is used to prevent inappropriate disclosure of health-related information.

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+)

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.

Note: DPH is updating and streamlining the COVID-19 cases, deaths, and testing data. As of 6/27/2022, the data will be published in four tables instead of twelve. The COVID-19 Cases, Deaths, and Tests by Day dataset contains cases and test data by date of sample submission. The death data are by date of death. This dataset is updated daily and contains information back to the beginning of the pandemic. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Cases-Deaths-and-Tests-by-Day/g9vi-2ahj. The COVID-19 State Metrics dataset contains over 93 columns of data. This dataset is updated daily and currently contains information starting June 21, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-State-Level-Data/qmgw-5kp6 . The COVID-19 County Metrics dataset contains 25 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-County-Level-Data/ujiq-dy22 . The COVID-19 Town Metrics dataset contains 16 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Town-Level-Data/icxw-cada . To protect confidentiality, if a town has fewer than 5 cases or positive NAAT tests over the past 7 days, those data will be suppressed. COVID-19 cases and associated deaths that have been reported among Connecticut residents, broken down by race and ethnicity. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. Deaths reported to the either the Office of the Chief Medical Examiner (OCME) or Department of Public Health (DPH) are included in the COVID-19 update. The following data show the number of COVID-19 cases and associated deaths per 100,000 population by race and ethnicity. Crude rates represent the total cases or deaths per 100,000 people. Age-adjusted rates consider the age of the person at diagnosis or death when estimating the rate and use a standardized population to provide a fair comparison between population groups with different age distributions. Age-adjustment is important in Connecticut as the median age of among the non-Hispanic white population is 47 years, whereas it is 34 years among non-Hispanic blacks, and 29 years among Hispanics. Because most non-Hispanic white residents who died were over 75 years of age, the age-adjusted rates are lower than the unadjusted rates. In contrast, Hispanic residents who died tend to be younger than 75 years of age which results in higher age-adjusted rates. The population data used to calculate rates is based on the CT DPH population statistics for 2019, which is available online here: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Population/Population-Statistics. Prior to 5/10/2021, the population estimates from 2018 were used. Rates are standardized to the 2000 US Millions Standard population (data available here: https://seer.cancer.gov/stdpopulations/). Standardization was done using 19 age groups (0, 1-4, 5-9, 10-14, ..., 80-84, 85 years and older). More information about direct standardization for age adjustment is available here: https://www.cdc.gov/nchs/data/statnt/statnt06rv.pdf Categories are mutually exclusive. The category “multiracial” includes people who answered ‘yes’ to more than one race category. Counts may not add up to total case counts as data on race and ethnicity may be missing. Age adjusted rates calculated only for groups with more than 20 deaths. Abbreviation: NH=Non-Hispanic. Data on Connecticut deaths were obtained from the Connecticut Deaths Registry maintained by the DPH Office of Vital Records. Cause of death was determined by a death certifier (e.g., physician, APRN, medical

Update 2 March 2023: Following the merger of NHS Digital and NHS England on 1st February 2023 we are reviewing the future presentation of the NHS Outcomes Framework indicators. As part of this review, the annual publication which was due to be released in March 2023 has been delayed. Further announcements about this dataset will be made on this page in due course. Directly standardised mortality rate from cancer for people aged under 75, per 100,000 population. To ensure that the NHS is held to account for doing all that it can to prevent deaths from cancer in people under 75. Some different patterns have been observed in the 2020 mortality data which are likely to have been impacted by the coronavirus (COVID-19) pandemic. Statistics from this period should also be interpreted with care. Legacy unique identifier: P01733

According to the WHO, breast cancer is the most commonly occurring cancer worldwide. In 2020 alone, there were 2.3 million new breast cancer diagnoses and 685,000 deaths. Yet breast cancer mortality in high-income countries has dropped by 40% since the 1980s when health authorities implemented regular mammography screening in age groups considered at risk. Early detection and treatment are critical to reducing cancer fatalities, and your machine learning skills could help streamline the process radiologists use to evaluate screening mammograms. Currently, early detection of breast cancer requires the expertise of highly-trained human observers, making screening mammography programs expensive to conduct. RSNA collected screening mammograms and supporting information from two sites, totaling just under 20,000 imaging studies.

According to the World Cancer Report 2020 published by the World Health Organization's Institute for Research on Cancer (IARC), there will be 19.29 million new cancer cases and 9.96 million deaths globally in 2020, of which 4.569 million new cases and 3.003 million deaths will occur in China, accounting for 23.7% and 30.2% of the global new cases and deaths, respectively. Among them, China had 4.569 million new cancer cases and 3.003 million deaths, accounting for 23.7% and 30.2% of the global new cases and deaths respectively. China has become the largest country in the world in terms of new cancer cases and deaths.Nasopharyngeal cancer is a kind of malignant tumor with a very high clinical incidence rate, and it is at the top of the list of malignant tumors in otorhinolaryngology. Due to the deep and hidden nasopharyngeal part, the complex relationship with the surrounding area, and the differences in clinical manifestations, early diagnosis is very difficult, and it is very easy to miss the optimal time of treatment due to missed or misdiagnosis. Due to the unique anatomical location and tumor biological behavior of nasopharyngeal cancer, simultaneous radiotherapy has been the main treatment for nasopharyngeal cancer, followed by radiotherapy, chemotherapy, targeted therapy, surgery, and traditional Chinese medicine.Early tumor diagnosis refers to the use of rapid and easy methods to screen out a very small number of tumor high-risk groups from a large number of target populations that appear healthy and have not yet developed symptoms, which can detect tumors early and reduce the risk of morbidity, especially for cancer types with high morbidity and mortality rates and a long developmental cycle, such as lung, gastric, and colorectal cancers. From a global perspective, China's cancer incidence and mortality rates are at a high level, and there are multiple reasons for this phenomenon - medical technology needs to be improved, the quality of the living environment is poor, the routine of life is irregular, and living habits are poor. Compared with chronic diseases such as cardiovascular disease and diabetes, tumor is a "fatal disease" that requires early diagnosis and treatment, and the earlier the diagnosis, the greater the hope of cure. To integrate the data resources and results of early diagnosis of nasopharyngeal cancer and to promote related research, a literature review and information extraction analysis were carried out, and a biomarker-based early diagnosis database of nasopharyngeal cancer was constructed to assist the early diagnosis of nasopharyngeal cancer. The database covers the types of biomarkers, name, specificity, sensitivity, AUC, cell lines used, sample type, sample size, references, and their links. The database contains many types of biomarkers and is a powerful tool for early screening and diagnosis of nasopharyngeal cancer.

Cancer patients suffer from worse coronavirus disease-2019 (COVID-19) outcomes. Whether active oncologic treatment is an additional risk factor in this population remains unclear. Therefore, here we have conducted a systematic review and meta-analysis to summarize the existing evidence for the effect of active oncologic treatment on COVID-19 outcomes. Systematic search of databases (PubMed, Embase) was conducted for studies published from inception to July 1, 2020, with a subsequent search update conducted on 10 October 2020. In addition, abstracts and presentations from major conference proceedings (ASCO, ESMO, AACR) as well as pre-print databases (medxriv, bioxriv) were searched. Retrospective and prospective studies reporting clinical outcomes in cancer patients with laboratory confirmation or clinical diagnosis of COVID-19 and details of active or recent oncologic treatment were selected. Random-effects model was applied throughout meta-analyses. Summary outcome measure was the pooled odds ratio (OR) of death for active cancer therapy versus no active cancer therapy for each of the following modalities: recent surgery, chemotherapy, targeted therapy, immunotherapy, or chemoimmunotherapy. Sixteen retrospective and prospective studies (3558 patients) were included in the meta-analysis. Active chemotherapy was associated with higher risk of death compared to no active chemotherapy (OR, 1.60, 95% CI, 1.14–2.23). No significant association with risk of death was identified for active targeted therapy, immunotherapy, chemoimmunotherapy, or recent surgery. Meta-analysis of multivariate adjusted OR of death for active chemotherapy was consistently associated with higher risk of death compared to no active chemotherapy (OR, 1.42, 95% CI, 1.01–2.01). Active chemotherapy appears to be associated with higher risk of death in cancer patients with COVID-19. Further research is necessary to characterize the complex interactions between active cancer treatment and COVID-19.

Time series data for the statistic Mortality from CVD, cancer, diabetes or CRD between exact ages 30 and 70 (%) and country Bahamas, The. Indicator Definition:Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS).The indicator "Mortality from CVD, cancer, diabetes or CRD between exact ages 30 and 70 (%)" stands at 20.40 as of 12/31/2021, the highest value since 12/31/2017. Regarding the One-Year-Change of the series, the current value constitutes an increase of 2.00 percent compared to the value the year prior.The 1 year change in percent is 2.00.The 3 year change in percent is 0.4926.The 5 year change in percent is -0.4878.The 10 year change in percent is -4.23.The Serie's long term average value is 21.22. It's latest available value, on 12/31/2021, is 3.88 percent lower, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/2020, to it's latest available value, on 12/31/2021, is +2.00%.The Serie's change in percent from it's maximum value, on 12/31/2000, to it's latest available value, on 12/31/2021, is -10.53%.

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Demographic Summary of Available Imaging

Characteristic	Value (N = 26254)
Age (years)	Mean ± SD: 61.4± 5 Median (IQR): 60 (57-65) Range: 43-75
Sex	Male: 15512 (59%) Female: 10742 (41%)
Race	White: 23969 (91.3%) Black: 1135 (4.3%) Asian: 547 (2.1%) American Indian/Alaska Native: 88 (0.3%) Native Hawaiian/Other Pacific Islander: 87 (0.3%) Unknown: 428 (1.6%)
Ethnicity	Not Available

Background: The aggressive and heterogeneous nature of lung cancer has thwarted efforts to reduce mortality from this cancer through the use of screening. The advent of low-dose helical computed tomography (CT) altered the landscape of lung-cancer screening, with studies indicating that low-dose CT detects many tumors at early stages. The National Lung Screening Trial (NLST) was conducted to determine whether screening with low-dose CT could reduce mortality from lung cancer.

Methods: From August 2002 through April 2004, we enrolled 53,454 persons at high risk for lung cancer at 33 U.S. medical centers. Participants were randomly assigned to undergo three annual screenings with either low-dose CT (26,722 participants) or single-view posteroanterior chest radiography (26,732). Data were collected on cases of lung cancer and deaths from lung cancer that occurred through December 31, 2009. This dataset includes the low-dose CT scans from 26,254 of these subjects, as well as digitized histopathology images from 451 subjects.

Results: The rate of adherence to screening was more than 90%. The rate of positive screening tests was 24.2% with low-dose CT and 6.9% with radiography over all three rounds. A total of 96.4% of the positive screening results in the low-dose CT group and 94.5% in the radiography group were false positive results. The incidence of lung cancer was 645 cases per 100,000 person-years (1060 cancers) in the low-dose CT group, as compared with 572 cases per 100,000 person-years (941 cancers) in the radiography group (rate ratio, 1.13; 95% confidence interval [CI], 1.03 to 1.23). There were 247 deaths from lung cancer per 100,000 person-years in the low-dose CT group and 309 deaths per 100,000 person-years in the radiography group, representing a relative reduction in mortality from lung cancer with low-dose CT screening of 20.0% (95% CI, 6.8 to 26.7; P=0.004). The rate of death from any cause was reduced in the low-dose CT group, as compared with the radiography group, by 6.7% (95% CI, 1.2 to 13.6; P=0.02).

Conclusions: Screening with the use of low-dose CT reduces mortality from lung cancer. (Funded by the National Cancer Institute; National Lung Screening Trial ClinicalTrials.gov number, NCT00047385).

Data Availability: A summary of the National Lung Screening Trial and its available datasets are provided on the Cancer Data Access System (CDAS). CDAS is maintained by Information Management System (IMS), contracted by the National Cancer Institute (NCI) as keepers and statistical analyzers of the NLST trial data. The full clinical data set from NLST is available through CDAS. Users of TCIA can download without restriction a publicly distributable subset of that clinical data, along with the CT and Histopathology images collected during the trial. (These previously were restricted.)

This dataset presents the footprint of male cancer mortality statistics in Australia for all cancers combined and the 11 top cancer groupings (bladder, colorectal, head and neck, kidney, leukaemia, …Show full descriptionThis dataset presents the footprint of male cancer mortality statistics in Australia for all cancers combined and the 11 top cancer groupings (bladder, colorectal, head and neck, kidney, leukaemia, lung, lymphoma, melanoma of the skin, pancreas, prostate and stomach) and their respective ICD-10 codes. The data spans the years 2009-2013 and is aggregated to Statistical Area Level 4 (SA4) from the 2011 Australian Statistical Geography Standard (ASGS). Mortality data refer to the number of deaths due to cancer in a given time period. Cancer deaths data are sourced from the Australian Institute of Health and Welfare (AIHW) 2013 National Mortality Database (NMD). For further information about this dataset, please visit: Australian Institute of Health and Welfare - Cancer Incidence and Mortality Across Regions (CIMAR) books. Australian Institute of Health and Welfare - 2013 National Mortality Database. Please note: AURIN has spatially enabled the original data. Due to changes in geographic classifications over time, long-term trends are not available. Values assigned to "n.p." in the original data have been removed from the data. The Australian and jurisdictional totals include people who could not be assigned a SA4. The number of people who could not be assigned a SA4 is less than 1% of the total. The Australian total also includes residents of Other Territories (Cocos (Keeling) Islands, Christmas Island and Jervis Bay Territory). Cause of Death Unit Record File data are provided to the AIHW by the Registries of Births, Deaths and Marriages and the National Coronial Information System (managed by the Victorian Department of Justice) and include cause of death coded by the Australian Bureau of Statistics (ABS). The data are maintained by the AIHW in the NMD. Year refers to year of occurrence of death for years up to and including 2012, and year of registration of death for 2013. Deaths registered in 2011 and earlier are based on the final version of cause of death data; deaths registered in 2012 and 2013 are based on revised and preliminary versions, respectively and are subject to further revision by the ABS. Cause of death information are based on underlying cause of death and are classified according to the International Classification of Diseases and Related Health Problems (ICD). Deaths registered in 1997 onwards are classified according to the 10th revision (ICD-10). Colorectal deaths presented are underestimates. For further information, refer to "Complexities in the measurement of bowel cancer in Australia" in Causes of Death, Australia (ABS cat. no. 3303.0). Copyright attribution: Government of the Commonwealth of Australia - Australian Institute of Health and Welfare, (2016): ; accessed from AURIN on 12/3/2020. Licence type: Creative Commons Attribution 3.0 Australia (CC BY 3.0 AU)

This dataset presents the footprint of cancer mortality statistics in Australia for all cancers combined and the 6 top cancer groupings (colorectal, leukaemia, lung, lymphoma, melanoma of the skin …Show full descriptionThis dataset presents the footprint of cancer mortality statistics in Australia for all cancers combined and the 6 top cancer groupings (colorectal, leukaemia, lung, lymphoma, melanoma of the skin and pancreas) and their respective ICD-10 codes. The data spans the years 2006-2010 and is aggregated to 2015 Department of Health Primary Health Network (PHN) areas, based on the 2011 Australian Statistical Geography Standard (ASGS). Mortality data refer to the number of deaths due to cancer in a given time period. Cancer deaths data are sourced from the Australian Institute of Health and Welfare (AIHW) 2013 National Mortality Database (NMD). For further information about this dataset, please visit: Australian Institute of Health and Welfare - Cancer Incidence and Mortality Across Regions (CIMAR) books. Australian Institute of Health and Welfare - 2013 National Mortality Database. Please note: AURIN has spatially enabled the original data using the Department of Health - PHN Areas. Due to changes in geographic classifications over time, long-term trends are not available. Values assigned to "n.p." in the original data have been removed from the data. The Australian and jurisdictional totals include people who could not be assigned a PHN. The number of people who could not be assigned a PHN is less than 1% of the total. The Australian total also includes residents of Other Territories (Cocos (Keeling) Islands, Christmas Island and Jervis Bay Territory). Cause of Death Unit Record File data are provided to the AIHW by the Registries of Births, Deaths and Marriages and the National Coronial Information System (managed by the Victorian Department of Justice) and include cause of death coded by the Australian Bureau of Statistics (ABS). The data are maintained by the AIHW in the NMD. Year refers to year of occurrence of death for years up to and including 2012, and year of registration of death for 2013. Deaths registered in 2011 and earlier are based on the final version of cause of death data; deaths registered in 2012 and 2013 are based on revised and preliminary versions, respectively and are subject to further revision by the ABS. Cause of death information are based on underlying cause of death and are classified according to the International Classification of Diseases and Related Health Problems (ICD). Deaths registered in 1997 onwards are classified according to the 10th revision (ICD-10). Colorectal deaths presented are underestimates. For further information, refer to "Complexities in the measurement of bowel cancer in Australia" in Causes of Death, Australia (ABS cat. no. 3303.0). Copyright attribution: Government of the Commonwealth of Australia - Australian Institute of Health and Welfare, (2016): ; accessed from AURIN on 12/3/2020. Licence type: Creative Commons Attribution 3.0 Australia (CC BY 3.0 AU)

Time series data for the statistic Mortality from CVD, cancer, diabetes or CRD between exact ages 30 and 70 (%) and country Netherlands. Indicator Definition:Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS).The indicator "Mortality from CVD, cancer, diabetes or CRD between exact ages 30 and 70 (%)" stands at 9.90 as of 12/31/2021. Regarding the One-Year-Change of the series, the current value constitutes an increase of 3.13 percent compared to the value the year prior.The 1 year change in percent is 3.13.The 3 year change in percent is -3.88.The 5 year change in percent is -10.81.The 10 year change in percent is -18.18.The Serie's long term average value is 12.55. It's latest available value, on 12/31/2021, is 21.09 percent lower, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/2020, to it's latest available value, on 12/31/2021, is +3.13%.The Serie's change in percent from it's maximum value, on 12/31/2000, to it's latest available value, on 12/31/2021, is -39.63%.

Cancer is a global health problem and one of the leading causes of human death. According to the data released by the International Agency for Research on Cancer (IARC) in 2022, there were 19.3 million new cancer cases and nearly 10 million cancer deaths worldwide in 2020. At the same time, with rising morbidity and mortality, cancer has become the leading cause of death and a major public health problem for the Chinese population. China ranked first in the world in the number of new cancer cases and deaths in 2020. Camptothecin (CPT) , which has extensive antitumor activity, is a natural pentacyclic monoterpene alkaloid isolated from Camptotheca acuminata by Wall and Wani in 1966. In the 1970s, CPT was clinically approved to treat stomach cancer, bladder cancer, and certain types of leukemia. Camptothecin, as a natural drug candidate parent nucleus, has developed so far, and a large number of derivatives have been derived. The CDAD database integrates the latest laboratory data on the inhibition of cancer cells by camptothecin derivatives, as well as the anti-cancer data of camptothecin derivatives in the previously published literature. Each entry contains detailed information about the camptothecin derivatives, such as SMILE, molecular weight, IUPAC designation, median inhibition concentration (IC50), duration of action, target and related literature and patents, etc. This data will contribute to the further development of camptothecin derivatives and promote the anticancer research of camptothecin derivatives.

Time series data for the statistic Mortality from CVD, cancer, diabetes or CRD between exact ages 30 and 70 (%) and country Algeria. Indicator Definition:Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS).The indicator "Mortality from CVD, cancer, diabetes or CRD between exact ages 30 and 70 (%)" stands at 13.30 as of 12/31/2021. Regarding the One-Year-Change of the series, the current value constitutes an increase of 4.72 percent compared to the value the year prior.The 1 year change in percent is 4.72.The 3 year change in percent is -5.00.The 5 year change in percent is -5.67.The 10 year change in percent is -11.92.The Serie's long term average value is 15.40. It's latest available value, on 12/31/2021, is 13.66 percent lower, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/2020, to it's latest available value, on 12/31/2021, is +4.72%.The Serie's change in percent from it's maximum value, on 12/31/2000, to it's latest available value, on 12/31/2021, is -26.11%.

Time series data for the statistic Mortality from CVD, cancer, diabetes or CRD between exact ages 30 and 70 (%) and country Tonga. Indicator Definition:Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS).The indicator "Mortality from CVD, cancer, diabetes or CRD between exact ages 30 and 70 (%)" stands at 26.90 as of 12/31/2021, the highest value since 12/31/2014. Regarding the One-Year-Change of the series, the current value constitutes an increase of 4.67 percent compared to the value the year prior.The 1 year change in percent is 4.67.The 3 year change in percent is 2.67.The 5 year change in percent is 1.89.The 10 year change in percent is -1.10.The Serie's long term average value is 27.20. It's latest available value, on 12/31/2021, is 1.10 percent lower, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/2020, to it's latest available value, on 12/31/2021, is +4.67%.The Serie's change in percent from it's maximum value, on 12/31/2000, to it's latest available value, on 12/31/2021, is -7.24%.

Many human cancers develop as a result of exposure to risk factors related to the environment and ways of life. The aim of this study was to estimate attributable fractions of 25 types of cancers resulting from exposure to modifiable risk factors in Brazil. The prevalence of exposure to selected risk factors among adults was obtained from population-based surveys conducted from 2000 to 2008. Risk estimates were based on data drawn from meta-analyses or large, high quality studies. Population-attributable fractions (PAF) for a combination of risk factors, as well as the number of preventable deaths and cancer cases, were calculated for 2020. The known preventable risk factors studied will account for 34% of cancer cases among men and 35% among women in 2020, and for 46% and 39% deaths, respectively. The highest attributable fractions were estimated for tobacco smoking, infections, low consumption of fruits and vegetables, excess weight, reproductive factors, and physical inactivity. This is the first study to systematically estimate the fraction of cancer attributable to potentially modifiable risk factors in Brazil. Strategies for primary prevention of tobacco smoking and control of infection and the promotion of a healthy diet and physical activity should be the main priorities in policies for cancer prevention in the country.

This dataset presents the footprint of cancer mortality statistics in Australia for all cancers combined and the 6 top cancer groupings (colorectal, leukaemia, lung, lymphoma, melanoma of the skin …Show full descriptionThis dataset presents the footprint of cancer mortality statistics in Australia for all cancers combined and the 6 top cancer groupings (colorectal, leukaemia, lung, lymphoma, melanoma of the skin and pancreas) and their respective ICD-10 codes. The data spans the years 2009-2013 and is aggregated to Statistical Area Level 4 (SA4) from the 2011 Australian Statistical Geography Standard (ASGS). Mortality data refer to the number of deaths due to cancer in a given time period. Cancer deaths data are sourced from the Australian Institute of Health and Welfare (AIHW) 2013 National Mortality Database (NMD). For further information about this dataset, please visit: Australian Institute of Health and Welfare - Cancer Incidence and Mortality Across Regions (CIMAR) books. Australian Institute of Health and Welfare - 2013 National Mortality Database. Please note: AURIN has spatially enabled the original data. Due to changes in geographic classifications over time, long-term trends are not available. Values assigned to "n.p." in the original data have been removed from the data. The Australian and jurisdictional totals include people who could not be assigned a SA4. The number of people who could not be assigned a SA4 is less than 1% of the total. The Australian total also includes residents of Other Territories (Cocos (Keeling) Islands, Christmas Island and Jervis Bay Territory). Cause of Death Unit Record File data are provided to the AIHW by the Registries of Births, Deaths and Marriages and the National Coronial Information System (managed by the Victorian Department of Justice) and include cause of death coded by the Australian Bureau of Statistics (ABS). The data are maintained by the AIHW in the NMD. Year refers to year of occurrence of death for years up to and including 2012, and year of registration of death for 2013. Deaths registered in 2011 and earlier are based on the final version of cause of death data; deaths registered in 2012 and 2013 are based on revised and preliminary versions, respectively and are subject to further revision by the ABS. Cause of death information are based on underlying cause of death and are classified according to the International Classification of Diseases and Related Health Problems (ICD). Deaths registered in 1997 onwards are classified according to the 10th revision (ICD-10). Colorectal deaths presented are underestimates. For further information, refer to "Complexities in the measurement of bowel cancer in Australia" in Causes of Death, Australia (ABS cat. no. 3303.0). Copyright attribution: Government of the Commonwealth of Australia - Australian Institute of Health and Welfare, (2016): ; accessed from AURIN on 12/3/2020. Licence type: Creative Commons Attribution 3.0 Australia (CC BY 3.0 AU)

BackgroundThe appropriate time to discontinue chemotherapy at the end of life has been widely discussed. In contrast, few studies have investigated the patterns of endocrine treatment near death. In this study, we aimed to investigate the end-of-life endocrine treatment patterns of older women with metastatic breast cancer and explore characteristics associated with treatment.MethodsA retrospective cohort study of all older women (age ≥65 years) with hormone receptor-positive breast cancer who died in Sweden, 2016 − 2020. We used routinely collected administrative and health data with national coverage. Treatment initiation was defined as dispensing during the last three months of life with a nine-month washout period, while continuation and discontinuation were assessed by previous use during the same period. We used log-binomial models to explore factors associated with the continuation and initiation of endocrine treatments.ResultsWe included 3098 deceased older women with hormone receptor-positive breast cancer (median age 78). Overall, endocrine treatment was continued by 39% and initiated by 5% and of women during their last three months of life, while 31% discontinued and 24% did not use endocrine treatment during their last year of life. Endocrine treatment continuation was more likely among older and less educated women, and among women who had multi-dose drug dispensing, chemotherapy, and CDK4/6 use. Only treatment-related factors were associated with treatment initiation.ConclusionMore than a third of women with metastatic breast cancer continue endocrine treatments potentially past the point of benefit, whereas late initiation is less frequent. Further research is warranted to determine whether our results reflect overtreatment at the end of life once patients’ preferences and survival prognosis are considered.

Time series data for the statistic Mortality from CVD, cancer, diabetes or CRD between exact ages 30 and 70 (%) and country Mongolia. Indicator Definition:Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS).The indicator "Mortality from CVD, cancer, diabetes or CRD between exact ages 30 and 70 (%)" stands at 26.30 as of 12/31/2021. Regarding the One-Year-Change of the series, the current value constitutes an increase of 7.35 percent compared to the value the year prior.The 1 year change in percent is 7.35.The 3 year change in percent is -9.93.The 5 year change in percent is -13.49.The 10 year change in percent is -23.77.The Serie's long term average value is 34.00. It's latest available value, on 12/31/2021, is 22.66 percent lower, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/2020, to it's latest available value, on 12/31/2021, is +7.35%.The Serie's change in percent from it's maximum value, on 12/31/2001, to it's latest available value, on 12/31/2021, is -34.58%.