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

As of November 2019, around 63 percent of people aged 16 years and older worldwide believed that tobacco use increases a person's risk of getting cancer. This statistic illustrates the percentage of people worldwide who thought the following increase a person's risk of getting cancer as of 2019.

Cancer survival statistics are typically expressed as the proportion of patients alive at some point subsequent to the diagnosis of their cancer. Statistics compare the survival of patients diagnosed with cancer with the survival of people in the general population who are the same age, race, and sex and who have not been diagnosed with cancer.

Cancer was responsible for around *** deaths per 100,000 population in the United States in 2023. The death rate for cancer has steadily decreased since the 1990’s, but cancer still remains the second leading cause of death in the United States. The deadliest type of cancer for both men and women is cancer of the lung and bronchus which will account for an estimated ****** deaths among men alone in 2025. Probability of surviving Survival rates for cancer vary significantly depending on the type of cancer. The cancers with the highest rates of survival include cancers of the thyroid, prostate, and testis, with five-year survival rates as high as ** percent for thyroid cancer. The cancers with the lowest five-year survival rates include cancers of the pancreas, liver, and esophagus. Risk factors It is difficult to determine why one person develops cancer while another does not, but certain risk factors have been shown to increase a person’s chance of developing cancer. For example, cigarette smoking has been proven to increase the risk of developing various cancers. In fact, around ** percent of cancers of the lung, bronchus and trachea among adults aged 30 years and older can be attributed to cigarette smoking. Other modifiable risk factors for cancer include being obese, drinking alcohol, and sun exposure.

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...

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.

Cervical Cancer Risk Factors for Biopsy: This Dataset is Obtained from UCI Repository and kindly acknowledged! This file contains a List of Risk Factors for Cervical Cancer leading to a Biopsy Examination! About 11,000 new cases of invasive cervical cancer are diagnosed each year in the U.S. However, the number of new cervical cancer cases has been declining steadily over the past decades. Although it is the most preventable type of cancer, each year cervical cancer kills about 4,000 women in the U.S. and about 300,000 women worldwide. In the United States, cervical cancer mortality rates plunged by 74% from 1955 - 1992 thanks to increased screening and early detection with the Pap test. AGE Fifty percent of cervical cancer diagnoses occur in women ages 35 - 54, and about 20% occur in women over 65 years of age. The median age of diagnosis is 48 years. About 15% of women develop cervical cancer between the ages of 20 - 30. Cervical cancer is extremely rare in women younger than age 20. However, many young women become infected with multiple types of human papilloma virus, which then can increase their risk of getting cervical cancer in the future. Young women with early abnormal changes who do not have regular examinations are at high risk for localized cancer by the time they are age 40, and for invasive cancer by age 50. SOCIOECONOMIC AND ETHNIC FACTORS Although the rate of cervical cancer has declined among both Caucasian and African-American women over the past decades, it remains much more prevalent in African-Americans -- whose death rates are twice as high as Caucasian women. Hispanic American women have more than twice the risk of invasive cervical cancer as Caucasian women, also due to a lower rate of screening. These differences, however, are almost certainly due to social and economic differences. Numerous studies report that high poverty levels are linked with low screening rates. In addition, lack of health insurance, limited transportation, and language difficulties hinder a poor woman’s access to screening services. HIGH SEXUAL ACTIVITY Human papilloma virus (HPV) is the main risk factor for cervical cancer. In adults, the most important risk factor for HPV is sexual activity with an infected person. Women most at risk for cervical cancer are those with a history of multiple sexual partners, sexual intercourse at age 17 years or younger, or both. A woman who has never been sexually active has a very low risk for developing cervical cancer. Sexual activity with multiple partners increases the likelihood of many other sexually transmitted infections (chlamydia, gonorrhea, syphilis).Studies have found an association between chlamydia and cervical cancer risk, including the possibility that chlamydia may prolong HPV infection. FAMILY HISTORY Women have a higher risk of cervical cancer if they have a first-degree relative (mother, sister) who has had cervical cancer. USE OF ORAL CONTRACEPTIVES Studies have reported a strong association between cervical cancer and long-term use of oral contraception (OC). Women who take birth control pills for more than 5 - 10 years appear to have a much higher risk HPV infection (up to four times higher) than those who do not use OCs. (Women taking OCs for fewer than 5 years do not have a significantly higher risk.) The reasons for this risk from OC use are not entirely clear. Women who use OCs may be less likely to use a diaphragm, condoms, or other methods that offer some protection against sexual transmitted diseases, including HPV. Some research also suggests that the hormones in OCs might help the virus enter the genetic material of cervical cells. HAVING MANY CHILDREN Studies indicate that having many children increases the risk for developing cervical cancer, particularly in women infected with HPV. SMOKING Smoking is associated with a higher risk for precancerous changes (dysplasia) in the cervix and for progression to invasive cervical cancer, especially for women infected with HPV. IMMUNOSUPPRESSION Women with weak immune systems, (such as those with HIV / AIDS), are more susceptible to acquiring HPV. Immunocompromised patients are also at higher risk for having cervical precancer develop rapidly into invasive cancer. DIETHYLSTILBESTROL (DES) From 1938 - 1971, diethylstilbestrol (DES), an estrogen-related drug, was widely prescribed to pregnant women to help prevent miscarriages. The daughters of these women face a higher risk for cervical cancer. DES is no longer prsecribed.

Cancer Mortality & Incidence Rates: (Country LVL)

Investigating Cancer Trends over time

By Data Exercises [source]

About this dataset

This dataset is a comprehensive collection of data from county-level cancer mortality and incidence rates in the United States between 2000-2014. This data provides an unprecedented level of detail into cancer cases, deaths, and trends at a local level. The included columns include County, FIPS, age-adjusted death rate, average death rate per year, recent trend (2) in death rates, recent 5-year trend (2) in death rates and average annual count for each county. This dataset can be used to provide deep insight into the patterns and effects of cancer on communities as well as help inform policy decisions related to mitigating risk factors or increasing preventive measures such as screenings. With this comprehensive set of records from across the United States over 15 years, you will be able to make informed decisions regarding individual patient care or policy development within your own community!

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This dataset provides comprehensive US county-level cancer mortality and incidence rates from 2000 to 2014. It includes the mortality and incidence rate for each county, as well as whether the county met the objective of 45.5 deaths per 100,000 people. It also provides information on recent trends in death rates and average annual counts of cases over the five year period studied.

This dataset can be extremely useful to researchers looking to study trends in cancer death rates across counties. By using this data, researchers will be able to gain valuable insight into how different counties are performing in terms of providing treatment and prevention services for cancer patients and whether preventative measures and healthcare access are having an effect on reducing cancer mortality rates over time. This data can also be used to inform policy makers about counties needing more target prevention efforts or additional resources for providing better healthcare access within at risk communities.

When using this dataset, it is important to pay close attention to any qualitative columns such as “Recent Trend” or “Recent 5-Year Trend (2)” that may provide insights into long term changes that may not be readily apparent when using quantitative variables such as age-adjusted death rate or average deaths per year over shorter periods of time like one year or five years respectively. Additionally, when studying differences between different counties it is important to take note of any standard FIPS code differences that may indicate that data was collected by a different source with a difference methodology than what was used in other areas studied

Research Ideas

• Using this dataset, we can identify patterns in cancer mortality and incidence rates that are statistically significant to create treatment regimens or preventive measures specifically targeting those areas.
• This data can be useful for policymakers to target areas with elevated cancer mortality and incidence rates so they can allocate financial resources to these areas more efficiently.
• This dataset can be used to investigate which factors (such as pollution levels, access to medical care, genetic make up) may have an influence on the cancer mortality and incidence rates in different US counties

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: death .csv | Column name | Description | |:-------------------------------------------|:-------------------------------------------------------------------...

Lung Cancer Dataset

Introduction:

Lung cancer remains one of the most prevalent and deadly forms of cancer worldwide, posing significant challenges for early detection and effective treatment. To contribute to the global effort in understanding and combating this disease, we are excited to introduce our comprehensive Lung Cancer Dataset, now available on Kaggle.

Scientific Overview:

This dataset is an invaluable asset in the realm of Health Care, providing a structured foundation for the development of cancer detection models. This dataset exemplifies the variety of symptoms of Lung Cancer. Each category within the dataset—'GENDER', 'AGE', 'SMOKING', 'YELLOW_FINGERS', 'ANXIETY', 'PEER_PRESSURE', 'CHRONIC_DISEASE', 'FATIGUE', 'ALLERGY', 'WHEEZING', 'ALCOHOL_CONSUMING', 'COUGHING', 'SHORTNESS_OF_BREATH', 'SWALLOWING_DIFFICULTY', 'CHEST_PAIN'—has been carefully curated to encompass a diverse range of symptoms, ensuring that the resulting models are versatile and accurate. This scientific approach not only enhances the dataset's diversity to record symptoms of lung cancer but also contributes to the broader field of AI-driven health technologies, pushing the boundaries of what health care assistants can achieve.

Dataset Composition

The Lung Cancer Dataset includes a diverse array of symptoms essential for comprehensive analysis and model development. The primary categories of data are as follows:

1. Patient Demographics

Age: Provides the age at diagnosis, enabling analysis of age-related incidence and outcomes. Gender: Includes information on patient gender, facilitating gender-based studies. Smoking Status: Categorized as current smoker, former smoker, or non-smoker, this data is critical for evaluating the impact of smoking on lung cancer risk and progression.

2. Medical History

Comorbidities: Details additional health issues such as chronic obstructive pulmonary disease (COPD), which are relevant for treatment planning and prognosis.

3. Clinical Data

Vital Signs: Records of blood pressure, heart rate, respiratory rate, and other vital signs at diagnosis and during treatment.

Implementation Guide for the Mental Health Dataset:

Data Integration

Dataset Acquisition: Obtain the Lung Cancer Dataset. Data Exploration: Familiarize yourself with the structure and contents of the dataset, including symptoms and conclusions related to different conditions.

Preprocessing

Data Cleaning: Remove any irrelevant or redundant entries, and ensure consistency in formatting across the dataset. Tokenization: Break down the symptoms and conclusions into tokens or individual words to facilitate analysis and model training. Normalization: Standardize the text data by converting it to lowercase and removing punctuation or special characters as needed.

Model Training

Choose a Framework: Select a suitable machine learning or natural language processing framework such as TensorFlow, PyTorch, or spaCy. Model Selection: Decide on the type of model to use, such as recurrent neural networks (RNNs), transformers, or sequence-to-sequence models, based on the complexity of the dataset and the desired level of accuracy. Training Process: Train the chosen model using the preprocessed dataset, adjusting hyperparameters as necessary to optimize performance. Evaluation: Assess the performance of the trained model using appropriate metrics such as accuracy, precision, recall, and F1-score.

Deployment

Integration: Integrate the trained model into a chatbot or virtual assistant application using programming languages like Python or JavaScript. User Interface Design: Design an intuitive user interface that allows users to interact with the chatbot and receive responses related to Lung Cancer. Testing: Conduct thorough testing of the deployed chatbot to ensure functionality, accuracy, and responsiveness in providing relevant result. Feedback Mechanism: Implement a feedback mechanism to gather user feedback and improve the chatbot's performance over time.

Continuous Improvement

Monitoring: Continuously monitor the chatbot's performance and user interactions to identify areas for improvement. Data Updates: Periodically update the dataset with new symptoms to ensure accuracy. Model Refinement: Fine-tune the model based on user feedback and additional training data to enhance the chatbot's effectiveness and accuracy in detecting lung cancer. By following this implementation guide, developers can effectively leverage the Lung Cancer Dataset to build and deploy AI-driven chatbots and virtual assistants that offer accurate predictions to users worldwide.

Potential Applications

The extensive nature of the Lung Cancer Dataset supports a wide range of scientific and clinical applications:

Machine Learning Models: Facilitates the development of predictive algorithms for early detection, prognosis, and personalized t...

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+)

SUMMARYThis analysis, designed and executed by Ribble Rivers Trust, identifies areas across England with the greatest levels of cancer (in persons of all ages). Please read the below information to gain a full understanding of what the data shows and how it should be interpreted.ANALYSIS METHODOLOGYThe analysis was carried out using Quality and Outcomes Framework (QOF) data, derived from NHS Digital, relating to cancer (in persons of all ages).This information was recorded at the GP practice level. However, GP catchment areas are not mutually exclusive: they overlap, with some areas covered by 30+ GP practices. Therefore, to increase the clarity and usability of the data, the GP-level statistics were converted into statistics based on Middle Layer Super Output Area (MSOA) census boundaries.The percentage of each MSOA’s population (all ages) with cancer was estimated. This was achieved by calculating a weighted average based on:The percentage of the MSOA area that was covered by each GP practice’s catchment areaOf the GPs that covered part of that MSOA: the percentage of registered patients that have that illness The estimated percentage of each MSOA’s population with cancer was then combined with Office for National Statistics Mid-Year Population Estimates (2019) data for MSOAs, to estimate the number of people in each MSOA with cancer, within the relevant age range.Each MSOA was assigned a relative score between 1 and 0 (1 = worst, 0 = best) based on:A) the PERCENTAGE of the population within that MSOA who are estimated to have cancerB) the NUMBER of people within that MSOA who are estimated to have cancerAn average of scores A & B was taken, and converted to a relative score between 1 and 0 (1= worst, 0 = best). The closer to 1 the score, the greater both the number and percentage of the population in the MSOA that are estimated to have cancer, compared to other MSOAs. In other words, those are areas where it’s estimated a large number of people suffer from cancer, and where those people make up a large percentage of the population, indicating there is a real issue with cancer within the population and the investment of resources to address that issue could have the greatest benefits.LIMITATIONS1. GP data for the financial year 1st April 2018 – 31st March 2019 was used in preference to data for the financial year 1st April 2019 – 31st March 2020, as the onset of the COVID19 pandemic during the latter year could have affected the reporting of medical statistics by GPs. However, for 53 GPs (out of 7670) that did not submit data in 2018/19, data from 2019/20 was used instead. Note also that some GPs (997 out of 7670) did not submit data in either year. This dataset should be viewed in conjunction with the ‘Health and wellbeing statistics (GP-level, England): Missing data and potential outliers’ dataset, to determine areas where data from 2019/20 was used, where one or more GPs did not submit data in either year, or where there were large discrepancies between the 2018/19 and 2019/20 data (differences in statistics that were > mean +/- 1 St.Dev.), which suggests erroneous data in one of those years (it was not feasible for this study to investigate this further), and thus where data should be interpreted with caution. Note also that there are some rural areas (with little or no population) that do not officially fall into any GP catchment area (although this will not affect the results of this analysis if there are no people living in those areas).2. Although all of the obesity/inactivity-related illnesses listed can be caused or exacerbated by inactivity and obesity, it was not possible to distinguish from the data the cause of the illnesses in patients: obesity and inactivity are highly unlikely to be the cause of all cases of each illness. By combining the data with data relating to levels of obesity and inactivity in adults and children (see the ‘Levels of obesity, inactivity and associated illnesses: Summary (England)’ dataset), we can identify where obesity/inactivity could be a contributing factor, and where interventions to reduce obesity and increase activity could be most beneficial for the health of the local population.3. It was not feasible to incorporate ultra-fine-scale geographic distribution of populations that are registered with each GP practice or who live within each MSOA. Populations might be concentrated in certain areas of a GP practice’s catchment area or MSOA and relatively sparse in other areas. Therefore, the dataset should be used to identify general areas where there are high levels of cancer, rather than interpreting the boundaries between areas as ‘hard’ boundaries that mark definite divisions between areas with differing levels of cancer.TO BE VIEWED IN COMBINATION WITH:This dataset should be viewed alongside the following datasets, which highlight areas of missing data and potential outliers in the data:Health and wellbeing statistics (GP-level, England): Missing data and potential outliersLevels of obesity, inactivity and associated illnesses (England): Missing dataDOWNLOADING THIS DATATo access this data on your desktop GIS, download the ‘Levels of obesity, inactivity and associated illnesses: Summary (England)’ dataset.DATA SOURCESThis dataset was produced using:Quality and Outcomes Framework data: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital.GP Catchment Outlines. Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital. Data was cleaned by Ribble Rivers Trust before use.MSOA boundaries: © Office for National Statistics licensed under the Open Government Licence v3.0. Contains OS data © Crown copyright and database right 2021.Population data: Mid-2019 (June 30) Population Estimates for Middle Layer Super Output Areas in England and Wales. © Office for National Statistics licensed under the Open Government Licence v3.0. © Crown Copyright 2020.COPYRIGHT NOTICEThe reproduction of this data must be accompanied by the following statement:© Ribble Rivers Trust 2021. Analysis carried out using data that is: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital; © Office for National Statistics licensed under the Open Government Licence v3.0. Contains OS data © Crown copyright and database right 2021. © Crown Copyright 2020.CaBA HEALTH & WELLBEING EVIDENCE BASEThis dataset forms part of the wider CaBA Health and Wellbeing Evidence Base.

The rate of liver cancer diagnoses in the United States increases with age. As of 2022, those aged 80 to 84 years had the highest rates of liver cancer. Risk factors for liver cancer include smoking, drinking alcohol, being overweight or obese, and having diabetes. Who is most likely to get liver cancer? Liver cancer in the United States is much more common among men than women. In 2022, there were 12 new liver cancer diagnoses among men per 100,000 population, compared to just five new diagnoses per 100,000 women. Concerning race and ethnicity, non-Hispanic American Indians and Alaska Natives and Hispanics have the highest rates of new liver cancer diagnoses. The five-year survival rate for liver cancer in the United States is around 22 percent; however, this rate is much higher among non-Hispanic Asian and Pacific Islanders than other races and ethnicities. Non-Hispanic Asian and Pacific Islanders have a 33 percent chance of surviving the next five years after a liver cancer diagnosis. Deaths from liver cancer In 2023, there were an estimated 29,911 deaths in the United States due to liver cancer. However, the death rate for liver cancer has remained stable over the past few years. In 2023, the death rate for liver cancer was 6.6 deaths per 100,000 population. It is estimated that in 2025, there will be over 19,000 liver and intrahepatic bile duct cancer deaths among men in the United States and 10,800 such deaths among women.

According to Cognitive Market Research, the global Cancer Diagnosis market size was USD 109614.5 million in 2024. It will expand at a compound annual growth rate (CAGR) of 6.50% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 43845.80 million in 2024 and will grow at a compound annual growth rate (CAGR) of 4.7% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 32884.35 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 25211.34 million in 2024 and will grow at a compound annual growth rate (CAGR) of 8.5% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD 5480.73 million in 2024 and will grow at a compound annual growth rate (CAGR) of 5.9% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 2192.29 million in 2024 and will grow at a compound annual growth rate (CAGR) of 6.2% from 2024 to 2031.
The consumables category is the fastest growing segment of the Cancer Diagnosis industry

Market Dynamics of Cancer Diagnosis Market

Key Drivers for Cancer Diagnosis Market

Increasing Rate of Cancer Diagnostics to Boost Market Growth

The rising global incidence of cancer, which affects millions of people a year, is a primary driver of the need for diagnostic testing. Numerous factors contribute to this tendency, such as the aging population, which increases the risk of developing some cancers in older adults. Changes in lifestyle, including poor eating habits, inactivity, and increased use of alcohol and tobacco, have also contributed to an increase in cancer incidence. Environmental factors, such as exposure to chemicals and hazardous compounds, exacerbate the problem and increase the risk of developing cancer. Therefore, as early detection and diagnosis are becoming more and more important to patients and healthcare professionals, effective cancer diagnostics are essential. The market for cancer diagnostics is expanding as a result of the increased emphasis on prompt and precise cancer detection, which highlights the value of novel diagnostic procedures. For Instance, in 2023, the Pan American Health Organization (PAHO) projects that there will be 20 million new cases and 10 million deaths, and by 2040, nearly 30 million cases will be reported annually.

Innovations in Diagnostic Technologies to Drive Market Growth

The market for cancer diagnostics is expanding as a result of advancements in diagnostic technologies that have greatly improved the precision and effectiveness of cancer detection. For example, non-invasive cancer biomarker identification in physiological fluids is made possible by liquid biopsies, which offer vital insights into tumor dynamics and therapy response. In a similar vein, molecular diagnostics has transformed the detection of particular genetic abnormalities and changes linked to different types of cancer, allowing for more individualized treatment strategies. High-resolution images of tumors are provided by advanced imaging methods like MRI and PET scans, which help with accurate staging and localization. Better patient outcomes result from these technical developments because they increase overall diagnosis accuracy and enable early intervention. The ongoing development of these cutting-edge diagnostic instruments is propelling market expansion and revolutionizing cancer treatment.

Restraint Factor for the Cancer Diagnosis Market

The High Price of Cutting-Edge Diagnostic Technology Will Limit Market Growth

The market for cancer diagnostics is severely hampered by the high price of sophisticated diagnostic tools. Advanced diagnostic instruments, such as molecular tests and imaging technologies, are frequently expensive, which limits healthcare facilities' access to them, especially in settings with limited resources. These institutions' capacity to provide thorough cancer screening and diagnostic services is restricted by this financial barrier, which eventually affects patient outcomes. These financial difficulties are further exacerbated by the costs associated with the development, research, and regulatory approval of new diagnostic instruments. Companies have to spend a lot of money to comply with...

The dataset contains 2 .csv files

This file contains various demographic and health-related data for different regions. Here's a brief description of each column:

File 1st

avganncount: Average number of cancer cases diagnosed annually.

avgdeathsperyear: Average number of deaths due to cancer per year.

target_deathrate: Target death rate due to cancer.

incidencerate: Incidence rate of cancer.

medincome: Median income in the region.

popest2015: Estimated population in 2015.

povertypercent: Percentage of population below the poverty line.

studypercap: Per capita number of cancer-related clinical trials conducted.

binnedinc: Binned median income.

medianage: Median age in the region.

pctprivatecoveragealone: Percentage of population covered by private health insurance alone.

pctempprivcoverage: Percentage of population covered by employee-provided private health insurance.

pctpubliccoverage: Percentage of population covered by public health insurance.

pctpubliccoveragealone: Percentage of population covered by public health insurance only.

pctwhite: Percentage of White population.

pctblack: Percentage of Black population.

pctasian: Percentage of Asian population.

pctotherrace: Percentage of population belonging to other races.

pctmarriedhouseholds: Percentage of married households. birthrate: Birth rate in the region.

File 2nd

This file contains demographic information about different regions, including details about household size and geographical location. Here's a description of each column:

statefips: The FIPS code representing the state.

countyfips: The FIPS code representing the county or census area within the state.

avghouseholdsize: The average household size in the region.

geography: The geographical location, typically represented as the county or census area name followed by the state name.

Each row in the file represents a specific region, providing details about household size and geographical location. This information can be used for various demographic analyses and studies.

This release summarises the diagnoses in 2019 registered by NDRS covering all registerable neoplasms (all cancers, all in situ tumours, some benign tumours and all tumours that have uncertain or unknown behaviours)

BackgroundBetter information on lung cancer occurrence in lifelong nonsmokers is needed to understand gender and racial disparities and to examine how factors other than active smoking influence risk in different time periods and geographic regions. Methods and FindingsWe pooled information on lung cancer incidence and/or death rates among self-reported never-smokers from 13 large cohort studies, representing over 630,000 and 1.8 million persons for incidence and mortality, respectively. We also abstracted population-based data for women from 22 cancer registries and ten countries in time periods and geographic regions where few women smoked. Our main findings were: (1) Men had higher death rates from lung cancer than women in all age and racial groups studied; (2) male and female incidence rates were similar when standardized across all ages 40+ y, albeit with some variation by age; (3) African Americans and Asians living in Korea and Japan (but not in the US) had higher death rates from lung cancer than individuals of European descent; (4) no temporal trends were seen when comparing incidence and death rates among US women age 40–69 y during the 1930s to contemporary populations where few women smoke, or in temporal comparisons of never-smokers in two large American Cancer Society cohorts from 1959 to 2004; and (5) lung cancer incidence rates were higher and more variable among women in East Asia than in other geographic areas with low female smoking. ConclusionsThese comprehensive analyses support claims that the death rate from lung cancer among never-smokers is higher in men than in women, and in African Americans and Asians residing in Asia than in individuals of European descent, but contradict assertions that risk is increasing or that women have a higher incidence rate than men. Further research is needed on the high and variable lung cancer rates among women in Pacific Rim countries.

Some racial and ethnic categories are suppressed for privacy and to avoid misleading estimates when the relative standard error exceeds 30% or the unweighted sample size is less than 50 respondents.

Data Source: Centers for Disease Control and Prevention (CDC). Behavioral Risk Factor Surveillance System Survey (BRFSS) Data

Why This Matters

Breast cancer is the most commonly diagnosed cancer in women and people assigned female at birth (AFAB) and the second leading cause of cancer death in the U.S. Breast cancer screenings can save lives by helping to detect breast cancer in its early stages when treatment is more effective.

While non-Hispanic white women and AFAB individuals are more likely to be diagnosed with breast cancer than their counterparts of other races and ethnicities, non-Hispanic Black women and AFAB individuals die from breast cancer at a significantly higher rate than their counterparts races and ethnicities.

Later-stage diagnoses and prolonged treatment duration partly explain these disparities in mortality rate. Structural barriers to quality health care, insurance, education, affordable housing, and sustainable income that disproportionately affect communities of color also drive racial inequities in breast cancer screenings and mortality.

The District Response

Project Women Into Staying Healthy (WISH) provides free breast and cervical cancer screenings to uninsured or underinsured women and AFAB adults aged 21 to 64. Patient navigation, transportation assistance, and cancer education are also provided.

DC Health’s Cancer and Chronic Disease Prevention Bureau works with healthcare providers to improve the use of preventative health services and provide breast cancer screening services.

DC Health maintains the District of Columbia Cancer Registry (DCCR) to track cancer incidences, examine environmental substances that cause cancer, and identify differences in cancer incidences by age, gender, race, and geographical location.

Nature Reviews Cancer Acceptance Rate - ResearchHelpDesk - Nature Reviews Cancer aims to be the premier source of reviews and commentaries for the scientific communities we serve. We strive to publish articles that are authoritative, accessible and enhanced with clearly understandable figures, tables and other display items. We want to provide unparalleled service to authors, referees and readers, and we work hard to maximize the usefulness and impact of each article. The journal publishes Research Highlights, Comments, Reviews and Perspectives relevant to cancer researchers, with our broad scope ensuring that the articles we publish reach the widest possible audience. Aims & Scope The ultimate aim of cancer research is to eliminate this common and devastating disease from the human population. To develop more effective prevention methods we need to understand what triggers tumorigenesis. To diagnose precancerous lesions and early-stage cancers quickly and accurately we need to detect the earliest molecular changes leading to each type of cancer. To determine a patient's prognosis we need to appreciate which molecular changes affect tumour growth rate and metastasis. And to tailor therapies to individual tumours we need to understand the fundamental differences, not only between a cancer cell and a 'normal' cell, but also between one cancer cell and another. All of these goals depend on a combination of basic and applied research. Nature Reviews Cancer aims to be a gateway from which cancer researchers — from those investigating the molecular basis of cancer to those involved in translational research — access the information that they need to further the ability to diagnose, treat and ultimately prevent cancer. Aims & Scope The ultimate aim of cancer research is to eliminate this common and devastating disease from the human population. To develop more effective prevention methods we need to understand what triggers tumorigenesis. To diagnose precancerous lesions and early-stage cancers quickly and accurately we need to detect the earliest molecular changes leading to each type of cancer. To determine a patient's prognosis we need to appreciate which molecular changes affect tumour growth rate and metastasis. And to tailor therapies to individual tumours we need to understand the fundamental differences, not only between a cancer cell and a 'normal' cell, but also between one cancer cell and another. All of these goals depend on a combination of basic and applied research. Nature Reviews Cancer aims to be a gateway from which cancer researchers — from those investigating the molecular basis of cancer to those involved in translational research — access the information that they need to further the ability to diagnose, treat and ultimately prevent cancer. Subjects Covered: Genomic instability: chromosomal and microsatellite instabilities, defects in DNA repair pathways. Growth promoting signals: dysregulation of growth factor signalling pathways and cell cycle progression, proto-oncogenes and their activation. Growth inhibitory signals: dysregulation of quiescence and differentiation, tumour suppressors and their inactivation. Cancer stem cells. Cell death: evading programmed cell death, including avoidance of immune surveillance systems. Metabolism: pathways of nutrient acquisition and metabolism in tumour cells and cells of the tumour microenvironment, effects of systemic metabolism on cancer initiation and progression. Tumour microenvironment: immune and stromal cells, tumour vasculature, extracellular matrix components, cell–cell communication. Tumour evolution and heterogeneity. Metastasis: tumour cell dissemination, dormancy and growth in new microenvironments. Carcinogenesis and cancer prevention: epidemiology, genetic and environmental triggers, gene–environment interactions, strategies for reducing risk. Cancer diagnosis and prognosis: molecular markers, diagnostic imaging, detecting minimal residual disease. New approaches to cancer therapy: rational drug design, gene therapy, immunotherapy, combination therapies, combating drug resistance, targeting therapies to the individual. Experimental systems and techniques: cell culture systems, animal and patient-derived models, genomic, epigenomic, proteomic and metabolomic approaches to studying cancer. Cancer-associated disease: cancer pain, cachexia, symptoms associated with treatment, psychosocial aspects of cancer. Ethical, legal and social issues surrounding cancer research: trial design, genetic screening, research policy, advocacy. Conventional approaches to cancer diagnosis and treatment: how do they perform, what are their drawbacks and how might they be improved in the future?

June 2020: There has been a change to the construction of this indicator. Please see the 'Notes and definitions' tab and the Specification for further information. This revised indicator is being published as an experimental statistic. Experimental statistics are official statistics which are published in order to involve users and stakeholders in their development and as a means to build in quality at an early stage. The percentage of new cases of cancer which were diagnosed at stage 1 or 2 for the specific cancer sites, morphologies and behaviour: Oesophagus; Stomach; Colon; Rectum; Pancreas; Lung; Melanoma of skin; Breast; Cervix; Uterus; Ovary; Prostate; Testis; Kidney; Bladder; Hodgkin lymphoma; Thyroid; Larynx; Oropharynx; Oral cavity; Non-Hodgkin lymphoma. This indicator relates to a subset of the cancers covered by CCG indicator 1.17 Record of stage of cancer at diagnosis. Legacy unique identifier: P01817