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.

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

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

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.

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Introduction

Coronavirus disease 2019 (COVID‑19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It was first identified in December 2019 in Wuhan, Hubei, China, and has resulted in an ongoing pandemic. As of 12 August 2020, more than 20.2 million cases have been reported across 188 countries and territories, resulting in more than 741,000 deaths. More than 12.5 million people have recovered. Most people infected with the COVID-19 virus will experience mild to moderate respiratory illness and recover without requiring special treatment. Older people, and those with underlying medical problems like cardiovascular disease, diabetes, chronic respiratory disease, and cancer are more likely to develop serious illness.

These numbers are sampled exclusively from Denmark between 11th of March 2020 and 9th of August 2020.

Content

This contains 10 data files:

• Cases_by_age.csv: Current number of confirmed cases for each age group.
• Cases_by_sex.csv: Current number of confirmed cases for men and women.
• Deaths_over_time.csv: The death toll for each day.
• Municipality_test_pos.csv: Number of tested and confirmed cases for each Danish municipality.
• Newly_admitted_over_time.csv: Number of newly hospitalised people for each region per day.
• Region_summary.csv: Number of tested and confirmed cases for each Danish region.
• Rt_cases.csv: Reproduction rate each day. A key measure of how fast the virus is growing.
• Rt_hospitalised.csv: Reproduction rate for hospitalised cases.
• Test_pos_over_time.csv: Number of new positive cases over time and total tested.
• Test_regions.csv: Number of tests done in each Danish region.

Wiki about COVID-19 in Denmark: https://en.wikipedia.org/wiki/COVID-19_pandemic_in_Denmark Dashboard with information on COVID-19 in Denmark: https://experience.arcgis.com/experience/aa41b29149f24e20a4007a0c4e13db1d Currentcase count: https://www.worldometers.info/coronavirus/country/denmark/

PurposeThis study aimed to describe the demographic and clinical characteristics of cancer patients with COVID-19, exploring factors associated with adverse outcomes.Patients and methodsThis retrospective cohort study methodically extracted and curated data from electronic medical records (EMRs) of numerous healthcare institutions on cancer patients diagnosed with a confirmed SARS-CoV-2 infection between May 2020 and August 2021, to identify risk factors linked to extended hospitalization and mortality. The retrieved information encompassed the patients’ demographic and clinical characteristics, including the incidence of prolonged hospitalization, acute complications, and COVID-19-related mortality.ResultsA total of 1446 cancer patients with COVID-19 were identified (mean [Standard deviation] age, 59.2 [14.3] years). Most patients were female (913 [63.1%]), non-white (646 [44.7%]), with non-metastatic (818 [56.6%]) solid tumors (1318 [91.1%]), and undergoing chemotherapy (647 [44.7%]). The rate of extended hospitalization due to COVID-19 was 46% (n = 665), which was significantly impacted by age (p = 0.012), sex (p = 0.003), race and ethnicity (p = 0.049), the presence of two or more comorbidities (p = 0.006), hematologic malignancies (p = 0.013), metastatic disease (p = 0.002), and a performance status ≥ 2 (p = 0.001). The COVID-19-related mortality rate was 18.9% (n = 273), and metastatic disease (

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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.)

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%.

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%.

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)

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%.

This record contains raw data related to article “Incidence and predictors of hepatocellular carcinoma in patients with autoimmune hepatitis"

Abstract

Background and aims: Autoimmune hepatitis (AIH) is a rare chronic liver disease of unknown aetiology; the risk of hepatocellular carcinoma (HCC) remains unclear and risk factors are not well-defined. We aimed to investigate the risk of HCC across a multicentre AIH cohort and to identify predictive factors.

Methods: We performed a retrospective, observational, multicentric study of patients included in the International Autoimmune Hepatitis Group Retrospective Registry. The assessed clinical outcomes were HCC development, liver transplantation, and death. Fine and Gray regression analysis stratified by centre was applied to determine the effects of individual covariates; the cumulative incidence of HCC was estimated using the competing risk method with death as a competing risk.

Results: A total of 1,428 patients diagnosed with AIH from 1980 to 2020 from 22 eligible centres across Europe and Canada were included, with a median follow-up of 11.1 years (interquartile range 5.2-15.9). Two hundred and ninety-three (20.5%) patients had cirrhosis at diagnosis. During follow-up, 24 patients developed HCC (1.7%), an incidence rate of 1.44 cases/1,000 patient-years; the cumulative incidence of HCC increased over time (0.6% at 5 years, 0.9% at 10 years, 2.7% at 20 years, and 6.6% at 30 years of follow-up). Patients who developed cirrhosis during follow-up had a significantly higher incidence of HCC. The cumulative incidence of HCC was 2.6%, 4.6%, 5.6% and 6.6% at 5, 10, 15, and 20 years after the development of cirrhosis, respectively. Obesity (hazard ratio [HR] 2.94, p = 0.04), cirrhosis (HR 3.17, p = 0.01), and AIH/PSC variant syndrome (HR 5.18, p = 0.007) at baseline were independent risk factors for HCC development.

Conclusions: HCC incidence in AIH is low even after cirrhosis development and is associated with risk factors including obesity, cirrhosis, and AIH/PSC variant syndrome.

Impact and implications: The risk of developing hepatocellular carcinoma (HCC) in individuals with autoimmune hepatitis (AIH) seems to be lower than for other aetiologies of chronic liver disease. Yet, solid data for this specific patient group remain elusive, given that most of the existing evidence comes from small, single-centre studies. In our study, we found that HCC incidence in patients with AIH is low even after the onset of cirrhosis. Additionally, factors such as advanced age, obesity, cirrhosis, alcohol consumption, and the presence of the AIH/PSC variant syndrome at the time of AIH diagnosis are linked to a higher risk of HCC. Based on these findings, there seems to be merit in adopting a specialized HCC monitoring programme for patients with AIH based on their individual risk factors.

BackgroundWe aim to evaluate the global, regional, and national burden of Uterine Cancer (UC) from 1990 to 2019.MethodsWe gathered UC data across 204 countries and regions for the period 1990-2019, utilizing the Global Burden of Disease Database (GBD) 2019 public dataset. Joinpoint regression analysis was employed to pinpoint the year of the most significant changes in global trends. To project the UC trajectory from 2020 to 2044, we applied the Nordpred analysis, extrapolating based on the average trend observed in the data. Furthermore, the Bayesian Age-Period-Cohort (BAPC) model with integrated nested Laplace approximations was implemented to confirm the stability of the Nordpred analysis predictions.ResultsGlobally, the age-standardized rate (ASR) of incidence for UC has increased from 1990 to 2019 with an Average Annual Percentage Change (AAPC) of 0.50%. The ASR for death has declined within the same period (AAPC: -0.8%). An increase in the ASR of incidence was observed across all Socio-demographic Index (SDI) regions, particularly in High SDI regions (AAPC: 1.12%), while the ASR for death decreased in all but the Low SDI regions. Over the past 30 years, the highest incidence rate was observed in individuals aged 55-59 (AAPC: 0.76%). Among 204 countries and regions, there was an increase in the ASR of incidence in 165 countries and an increase in the ASR of deaths in 77 countries. Our projections suggest that both the incidence and death rates for UC are likely to continue their decline from 2020 to 2044.ConclusionsUC has significantly impacted global health negatively, with its influence stemming from a range of factors including geographical location, age-related and racial disparities, and SDI.

Bangladesh BD: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70 data was reported at 18.100 % in 2021. This records a decrease from the previous number of 18.900 % for 2020. Bangladesh BD: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70 data is updated yearly, averaging 21.400 % from Dec 2000 (Median) to 2021, with 22 observations. The data reached an all-time high of 24.700 % in 2000 and a record low of 18.100 % in 2021. Bangladesh BD: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Bangladesh – Table BD.World Bank.WDI: Social: Health Statistics. 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).;World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).;Weighted average;This is the Sustainable Development Goal indicator 3.4.1 [https://unstats.un.org/sdgs/metadata/].

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%.

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%.

This dataset, released December 2016, contains statistics for deaths of people aged 0-74 years during the years 2010-2014 based on the following causes: cancer, diabetes, circulatory system diseases, respiratory systems diseases and external causes. The data is by Primary Health Network (PHN) 2017 geographic boundaries based on the 2016 Australian Statistical Geography Standard (ASGS). There are 31 PHNs set up by the Australian Government. Each network is controlled by a board of medical …Show full descriptionThis dataset, released December 2016, contains statistics for deaths of people aged 0-74 years during the years 2010-2014 based on the following causes: cancer, diabetes, circulatory system diseases, respiratory systems diseases and external causes. The data is by Primary Health Network (PHN) 2017 geographic boundaries based on the 2016 Australian Statistical Geography Standard (ASGS). There are 31 PHNs set up by the Australian Government. Each network is controlled by a board of medical professionals and advised by a clinical council and community advisory committee. The boundaries of the PHNs closely align with the Local Hospital Networks where possible. For more information please see the data source notes on the data. Source: Data compiled by PHIDU from deaths data based on the 2010 to 2014 Cause of Death Unit Record Files supplied by the Australian Coordinating Registry and the Victorian Department of Justice, on behalf of the Registries of Births, Deaths and Marriages and the National Coronial Information System. Please note: AURIN has spatially enabled the original data. "*" - Indicates statistically significant, at the 95% confidence level. "**" - Indicates statistically significant, at the 99% confidence level. "~" - Indicates modelled estimates have Relative Root Mean Square Errors (RRMSEs) from 0.25 to 0.50 and should be used with caution. "~~" - Indicates modelled estimates have RRMSEs greater than 0.50 but less than 1 and are considered too unreliable for general use. '?' - Indicates modelled estimates are considered too unreliable. Blank cell - Indicates data was not shown/not applicable/not published/not available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data). Abbreviation Information: "ASR per #" - Indirectly age-standardised rate per specified population. "SDR" - Indirectly age-standardised death ratio. "95% C.I" - upper and lower 95% confidence intervals. Copyright attribution: Torrens University Australia - Public Health Information Development Unit, (2018): ; accessed from AURIN on 12/3/2020. Licence type: Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Australia (CC BY-NC-SA 3.0 AU)

ObjectiveThe prognostic role of age among patients affected by Oral Tongue Squamous Cell Carcinoma (OTSCC) is a topic of debate. Recent cohort studies have found that patients diagnosed at 40 years of age or younger have a better prognosis. The aim of this cohort study was to clarify whether age is an independent prognostic factor and discuss heterogeneity of outcomes by stage and treatments in different age groups.MethodsWe performed a study on 577 consecutive patients affected by primary tongue cancer and treated with surgery and adjuvant therapy according to stage, at European Institute of Oncology, IRCCS. Patients with age at diagnosis below 40 years totaled 109 (19%). Overall survival (OS), disease-free survival (DFS), tongue specific free survival (TSFS) and cause-specific survival (CSS) were compared by age groups. Multivariate Cox proportional hazards models were used to assess the independent role of age.ResultsThe median follow-up time was 5.01 years (range 0–18.68) years with follow-up recorded up to February 2020. After adjustment for all the significant confounding and prognostic factors, age remained independently associated with OS and DSF (respectively, p = 0.002 and p = 0.02). In CSS and TSFS curves, the role of age seems less evident (respectively, p = 0.14 and p = 0.0.37). In the advanced stage sub-group (stages III–IV), age was significantly associated with OS and CSS with almost double increased risk of dying (OS) and dying from tongue cancer (CSS) in elderly compared to younger groups (OS: HR = 2.16 95%, CI: 1.33–3.51, p= 0.001; CSS: HR = 1.76 95%, CI: 1.03–3.01, p = 0.02, respectively). In our study, young patients were more likely to be treated with intensified therapies (glossectomies types III–V and adjuvant radio-chemotherapy). Age was found as a prognostic factor, independently of other significant factors and treatment. Also the T–N tract involved by disease and neutrophil-to-lymphocyte ratio ≥3 were independent prognostic factors.ConclusionsYoung age at diagnosis is associated with a better overall survival. Fewer younger people than older people died from tongue cancer in advanced stages.