Users can access data about cancer statistics in the United States including but not limited to searches by type of cancer and race, sex, ethnicity, age at diagnosis, and age at death. Background Surveillance Epidemiology and End Results (SEER) database’s mission is to provide information on cancer statistics to help reduce the burden of disease in the U.S. population. The SEER database is a project to the National Cancer Institute. The SEER database collects information on incidence, prevalence, and survival from specific geographic areas representing 28 percent of the United States population. User functionality Users can access a variety of reso urces. Cancer Stat Fact Sheets allow users to look at summaries of statistics by major cancer type. Cancer Statistic Reviews are available from 1975-2008 in table format. Users are also able to build their own tables and graphs using Fast Stats. The Cancer Query system provides more flexibility and a larger set of cancer statistics than F ast Stats but requires more input from the user. State Cancer Profiles include dynamic maps and graphs enabling the investigation of cancer trends at the county, state, and national levels. SEER research data files and SEER*Stat software are available to download through your Internet connection (SEER*Stat’s client-server mode) or via discs shipped directly to you. A signed data agreement form is required to access the SEER data Data Notes Data is available in different formats depending on which type of data is accessed. Some data is available in table, PDF, and html formats. Detailed information about the data is available under “Data Documentation and Variable Recodes”.

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+)

Users can access data about cancer statistics, specifically incidence and mortality worldwide for the 27 major types of cancer. Background Cancer Mondial is maintained by the Section of Cancer Information (CIN) of International Agency for Research on Cancer by the World Health Organization. Users can access CIN databases including GLOBOCAN, CI5(Cancer Incidence in Five Continents), WHO, ACCIS(Automated Childhood Cancer Information System), ECO (European Cancer Observatory), NORDCAN and Survcan. User functionality Users can access a variety of databases. CIN Databases: GLOBOCAN provides acces s to the most recent estimates (for 2008) of the incidence of 27 major cancers and mortality from 27 major cancers worldwide. CI5 (Cancer Incidence in Five Continents) provides access to detailed information on the incidence of cancer recorded by cancer registries (regional or national) worldwide. WHO presents long time series of selected cancer mortality recorded in selected countries of the world. Collaborative projects: ACCIS (Automated Childhood Cancer Information System) provides access to data on cancer incidence and survival of children collected by European cancer registries. ECO (European Cancer Observatory) provides access to the estimates (for 2008) of the incidence of, and mortality f rom 25 major cancers in the countries of the European Union (EU-27). NORDCAN presents up-to-date long time series of cancer incidence, mortality, prevalence and survival from 40 cancers recorded by the Nordic countries. SurvCan presents cancer survival data from cancer registries in low and middle income regions of the world. Data Notes Data is available in different formats depending on which type of data is accessed. Some data is available in table, PDF, and html formats. Detailed information about the data is available.

List of Top Disciplines of Cancer Research Statistics and Treatment sorted by citations.

BackgroundThe 5-year survival rate of cancer patients is the most commonly used statistic to reflect improvements in the war against cancer. This idea, however, was refuted based on an analysis showing that changes in 5-year survival over time bear no relationship with changes in cancer mortality.MethodsHere we show that progress in the fight against cancer can be evaluated by analyzing the association between 5-year survival rates and mortality rates normalized by the incidence (mortality over incidence, MOI). Changes in mortality rates are caused by improved clinical management as well as changing incidence rates, and since the latter can mask the effects of the former, it can also mask the correlation between survival and mortality rates. However, MOI is a more robust quantity and reflects improvements in cancer outcomes by overcoming the masking effect of changing incidence rates. Using population-based statistics for the US and the European Nordic countries, we determined the association of changes in 5-year survival rates and MOI.ResultsWe observed a strong correlation between changes in 5-year survival rates of cancer patients and changes in the MOI for all the countries tested. This finding demonstrates that there is no reason to assume that the improvements in 5-year survival rates are artificial. We obtained consistent results when examining the subset of cancer types whose incidence did not increase, suggesting that over-diagnosis does not obscure the results.ConclusionsWe have demonstrated, via the negative correlation between changes in 5-year survival rates and changes in MOI, that increases in 5-year survival rates reflect real improvements over time made in the clinical management of cancer. Furthermore, we found that increases in 5-year survival rates are not predominantly artificial byproducts of lead-time bias, as implied in the literature. The survival measure alone can therefore be used for a rough approximation of the amount of progress in the clinical management of cancer, but should ideally be used with other measures.

In the following maps, the U.S. states are divided into groups based on the rates at which people developed or died from cancer in 2013, the most recent year for which incidence data are available.

The rates are the numbers out of 100,000 people who developed or died from cancer each year.

Incidence Rates by State The number of people who get cancer is called cancer incidence. In the United States, the rate of getting cancer varies from state to state.

*Rates are per 100,000 and are age-adjusted to the 2000 U.S. standard population.

‡Rates are not shown if the state did not meet USCS publication criteria or if the state did not submit data to CDC.

†Source: U.S. Cancer Statistics Working Group. United States Cancer Statistics: 1999–2013 Incidence and Mortality Web-based Report. Atlanta (GA): Department of Health and Human Services, Centers for Disease Control and Prevention, and National Cancer Institute; 2016. Available at: http://www.cdc.gov/uscs.

Death Rates by State Rates of dying from cancer also vary from state to state.

*Rates are per 100,000 and are age-adjusted to the 2000 U.S. standard population.

†Source: U.S. Cancer Statistics Working Group. United States Cancer Statistics: 1999–2013 Incidence and Mortality Web-based Report. Atlanta (GA): Department of Health and Human Services, Centers for Disease Control and Prevention, and National Cancer Institute; 2016. Available at: http://www.cdc.gov/uscs.

Source: https://www.cdc.gov/cancer/dcpc/data/state.htm

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 19 cancer sites currently covered by Get Data Out are: ‘Bladder, urethra, renal pelvis and ureter’, ‘Bone’, ‘Brain’, ‘Eye’, ‘Blood cancer (haematological neoplasms)’, ‘Blood cancer (haematological neoplasm) transformations’, ‘Head and neck’, ‘Kaposi sarcoma’, ‘Kidney’, ‘Liver and biliary tract’, ‘Lung, mesothelioma, and other thoracic', Oesophagus and stomach’, ‘Ovary’, ‘Pancreas’, ‘Prostate’, ‘Sarcoma’, ‘Skin tumours’, ‘Soft tissue’, ‘Testes’. 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. This release covers the addition of the diagnosis year 2022 for treatment, plus a refresh of the 2013-2021 treatment data. This is also a first release of a new 'Visualisations' tab on our dashboard which will allow the user to explore the GDO data in graphical and tabular form. Users will now be able to select a single GDO group using drop down menus and display figures of incidence, demographic, treatment, routes to diagnosis, and survival statistics by diagnosis year. Finally, this is a small update to the 2013-2022 incidence data to include more age standardised rates (ASRs) for gender specific groups (genital skin groups for example which previously did not have an ASR published). All releases and documentation are available on the Get Data Out dashboard. Before using the data, we recommend that you read the 'Introduction', 'FAQs' and 'Known limitations' tabs. 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.

Financial overview and grant giving statistics of Cancer Research and Biostatistics

Exploring County-Level Correlations in Cancer Rates and Trends

A Multivariate Ordinary Least Squares Regression Model

By Noah Rippner [source]

About this dataset

This dataset offers a unique opportunity to examine the pattern and trends of county-level cancer rates in the United States at the individual county level. Using data from cancer.gov and the US Census American Community Survey, this dataset allows us to gain insight into how age-adjusted death rate, average deaths per year, and recent trends vary between counties – along with other key metrics like average annual counts, met objectives of 45.5?, recent trends (2) in death rates, etc., captured within our deep multi-dimensional dataset. We are able to build linear regression models based on our data to determine correlations between variables that can help us better understand cancers prevalence levels across different counties over time - making it easier to target health initiatives and resources accurately when necessary or desired

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How to use the dataset

This kaggle dataset provides county-level datasets from the US Census American Community Survey and cancer.gov for exploring correlations between county-level cancer rates, trends, and mortality statistics. This dataset contains records from all U.S counties concerning the age-adjusted death rate, average deaths per year, recent trend (2) in death rates, average annual count of cases detected within 5 years, and whether or not an objective of 45.5 (1) was met in the county associated with each row in the table.

To use this dataset to its fullest potential you need to understand how to perform simple descriptive analytics which includes calculating summary statistics such as mean, median or other numerical values; summarizing categorical variables using frequency tables; creating data visualizations such as charts and histograms; applying linear regression or other machine learning techniques such as support vector machines (SVMs), random forests or neural networks etc.; differentiating between supervised vs unsupervised learning techniques etc.; reviewing diagnostics tests to evaluate your models; interpreting your findings; hypothesizing possible reasons and patterns discovered during exploration made through data visualizations ; Communicating and conveying results found via effective presentation slides/documents etc.. Having this understanding will enable you apply different methods of analysis on this data set accurately ad effectively.

Once these concepts are understood you are ready start exploring this data set by first importing it into your visualization software either tableau public/ desktop version/Qlikview / SAS Analytical suite/Python notebooks for building predictive models by loading specified packages based on usage like Scikit Learn if Python is used among others depending on what tool is used . Secondly a brief description of the entire table's column structure has been provided above . Statistical operations can be carried out with simple queries after proper knowledge of basic SQL commands is attained just like queries using sub sets can also be performed with good command over selecting columns while specifying conditions applicable along with sorting operations being done based on specific attributes as required leading up towards writing python codes needed when parsing specific portion of data desired grouping / aggregating different categories before performing any kind of predictions / models can also activated create post joining few tables possible , when ever necessary once again varying across tools being used Thereby diving deep into analyzing available features determined randomly thus creating correlation matrices figures showing distribution relationships using correlation & covariance matrixes , thus making evaluations deducing informative facts since revealing trends identified through corresponding scatter plots from a given metric gathered from appropriate fields!

Research Ideas

• Building a predictive cancer incidence model based on county-level demographic data to identify high-risk areas and target public health interventions.
• Analyzing correlations between age-adjusted death rate, average annual count, and recent trends in order to develop more effective policy initiatives for cancer prevention and healthcare access.
• Utilizing the dataset to construct a machine learning algorithm that can predict county-level mortality rates based on socio-economic factors such as poverty levels and educational attainment rates

Acknowledgements

If you use this dataset i...

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.

Financial overview and grant giving statistics of American Association for Cancer Research

List of Top Schools of Cancer Research Statistics and Treatment sorted by citations.

*It is noted that multiple studies reported on more than a single cancer site and the total does not therefore equal 34.1A study reported increased risk for males but no increase for females.2A study reported increased risk for total population, but no increase when examined by gender.3A study reported increased risk for males but no increase for females.

Research dataset and analysis for Cancer Treatment including statistics, forecasts, and market insights

In 2022, prostate cancer was the most common type of cancer among newly diagnosed patients in Peru, with approximately ***** new cases reported. Breast cancer ranked second, with close to ***** new cases. As of 2023, Hospital Nacional Edgardo Rebagliati Martins was the hospital with the most oncology equipment for cancer treatment in Peru.

Context

This data comes from aggregation of the tables available on the NIH's National Cancer Institutes State Cancer Profiles, specifically with their incidence tables.

The objective of the State Cancer Profiles Web site is to provide a system to characterize the cancer burden in a standardized manner in order to motivate action, integrate surveillance into cancer control planning, characterize areas and demographic groups, and expose health disparities. The focus is on cancer sites for which there are evidence based control interventions. Interactive graphics and maps provide visual support for deciding where to focus cancer control efforts.

Content

This data has cancer Incidence rates broken down by US County and includes data aggregated from 2012-2016. It has both incidence rates per 100k as well as yearly totals averaged over that period

Potential Future Work

This data is summarized across other potentially illuminating fields. The State Cancer Profiles can be further broken down by cancer area, race/ethnicity, sex, age, and stage. If more fidelity on the data would be helpful please add it to the discussion section and I can work on adding it!

Data Use Restrictions

Read Carefully Before Using

By using these data, you signify your agreement to comply with the following statutorily based requirements.

The Public Health Service Act (42 U.S.C. 242m(d)) provides that the data collected by the National Center for Health Statistics (NCHS) may be used only for the purpose for which they were obtained; any effort to determine the identity of any reported cases, or to use the information for any purpose other than for statistical reporting and analysis, is against the law. The National Program of Cancer Registries (NPCR), Centers for Disease Control and Prevention (CDC), has obtained an assurance of confidentiality pursuant to Section 308(d) of the Public Health Service Act, 42 U.S.C. 242m(d). This assurance provides that identifiable or potentially identifiable data collected by the NPCR may be used only for the purpose for which they were obtained unless the person or establishment from which they were obtained has consented to such use. Any effort to determine the identity of any reported cases, or to use the information for any purpose other than statistical reporting and analysis, is a violation of the assurance.

Therefore users will: - Use the data for statistical reporting and analysis only. - Make no attempt to learn the identity of any person or establishment included in these data. - Make no disclosure or other use of the identity of any person or establishment discovered inadvertently, and advise the appropriate contact for the data provider. In addition to immediately notifying "Contact Us" of the potential disclosure, - For mortality data, notify the Confidentiality Officer at the National Center for Health Statistics (Alvan O. Zarate, Ph.D.), 3311 Toledo Road, Rm 7116, Hyattsville, MD 20782, Phone: 301-458-4601, Fax: 301-458-4021) - For incidence data notify both the Federal agency that provided the data and notify the relevant state or metropolitan area cancer registryExternal Web Site Policy, of any such discovery. - For CDC's National Program of Cancer Registries (NPCR) areas, notify the Associate Director for Science, Office of Science Policy and Technology Transfer, CDC, Mailstop D-50, 1600 Clifton Road, N.E., Atlanta, Georgia, 30333, Phone: 404-639-7240) - For NCI's Surveillance, Epidemiology, and End Results (SEER) Program registry areas, notify the Branch Chief of the Cancer Statistics Branch of the Surveillance Research Program, Division of Cancer Control and Population Sciences, NCI, BG 9609 MSC 9760, 9609 Medical Center Drive, Bethesda, MD 20892-9760, Phone: 301-496-8510, Fax: 301-496-9949.

SIR Standardized incidence ratio; CI confidence interval.

Number of cases , age standardised (per 100 000) cancer incidence rates and number of person-years of observation for White & Indian children in Leicester, and for children in Mumbai & Ahmedabad, India. (All rates are standardised to the age distribution of the Segi standard population).

Data support a paper of this title:

A Geotemporospatial and Causal Inference Epidemiological Exploration of Substance and Cannabinoid Exposure as Drivers of Rising US Pediatric Cancer Rates

Data represent a compilation of various data inputs from numerous sources including the National Cancer Institute SEER*Stat National Program of Cancer Registries and Surveillance, Epidemiology, and End Results SEER*Stat Database: NPCR and SEER Incidence – U.S. Cancer Statistics Public Use Research Database, 2019 submission (2001-2017), United States Department of Health and Human Services, Centers for Disease Control and Prevention and National Cancer Institute. Released June 2020. Available at www.cdc.gov/cancer/public-use program; the National survey of Drug Use and Health conducted by the Substance Abuse and Mental Health Services Administration; and the US Census bureau.

Data also include inverse probability weights for cannabis exposure.

Data also include their geospatial linkage network constructed for all US states which makes Alaska and Hawaii spatially connected to the contiguous USA.

Data also include the R script used to conduct and prepare the analysis.

Explore the dynamic Anal and Colorectal Cancer market trends, CAGR, drivers, and growth forecasts from 2025-2033. Discover key insights into treatments like surgery, radiation therapy, chemotherapy, targeted therapy, and immunotherapy.