Population based cancer incidence rates were abstracted from National Cancer Institute, State Cancer Profiles for all available counties in the United States for which data were available. This is a national county-level database of cancer data that are collected by state public health surveillance systems. All-site cancer is defined as any type of cancer that is captured in the state registry data, though non-melanoma skin cancer is not included. All-site age-adjusted cancer incidence rates were abstracted separately for males and females. County-level annual age-adjusted all-site cancer incidence rates for years 2006–2010 were available for 2687 of 3142 (85.5%) counties in the U.S. Counties for which there are fewer than 16 reported cases in a specific area-sex-race category are suppressed to ensure confidentiality and stability of rate estimates; this accounted for 14 counties in our study. Two states, Kansas and Virginia, do not provide data because of state legislation and regulations which prohibit the release of county level data to outside entities. Data from Michigan does not include cases diagnosed in other states because data exchange agreements prohibit the release of data to third parties. Finally, state data is not available for three states, Minnesota, Ohio, and Washington. The age-adjusted average annual incidence rate for all counties was 453.7 per 100,000 persons. We selected 2006–2010 as it is subsequent in time to the EQI exposure data which was constructed to represent the years 2000–2005. We also gathered data for the three leading causes of cancer for males (lung, prostate, and colorectal) and females (lung, breast, and colorectal). The EQI was used as an exposure metric as an indicator of cumulative environmental exposures at the county-level representing the period 2000 to 2005. A complete description of the datasets used in the EQI are provided in Lobdell et al. and methods used for index construction are described by Messer et al. The EQI was developed for the period 2000– 2005 because it was the time period for which the most recent data were available when index construction was initiated. The EQI includes variables representing each of the environmental domains. The air _domain includes 87 variables representing criteria and hazardous air pollutants. The water _domain includes 80 variables representing overall water quality, general water contamination, recreational water quality, drinking water quality, atmospheric deposition, drought, and chemical contamination. The land _domain includes 26 variables representing agriculture, pesticides, contaminants, facilities, and radon. The built _domain includes 14 variables representing roads, highway/road safety, public transit behavior, business environment, and subsidized housing environment. The sociodemographic environment includes 12 variables representing socioeconomics and crime. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Human health data are not available publicly. EQI data are available at: https://edg.epa.gov/data/Public/ORD/NHEERL/EQI. Format: Data are stored as csv files. This dataset is associated with the following publication: Jagai, J., L. Messer, K. Rappazzo , C. Gray, S. Grabich , and D. Lobdell. County-level environmental quality and associations with cancer incidence#. Cancer. John Wiley & Sons Incorporated, New York, NY, USA, 123(15): 2901-2908, (2017).

Analysis of ‘🎗️ Cancer Rates by U.S. State’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/cancer-rates-by-u-s-statee on 13 February 2022.

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

About this dataset

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

This dataset was created by Adam Helsinger and contains around 100 samples along with Range, Rate, technical information and other features such as: - Range - Rate - and more.

How to use this dataset

• Analyze Range in relation to Rate
• Study the influence of Range on Rate
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Adam Helsinger

Start A New Notebook!

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

The number of new cases, age-standardized rates and average age at diagnosis of cancers 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). Cancer incidence rates are age-standardized using the direct method and the final 2011 Canadian postcensal population structure. Random rounding of case counts to the nearest multiple of 5 is used to prevent inappropriate disclosure of health-related information.

Dataset Card for Lung Cancer

  Dataset Summary

The effectiveness of cancer prediction system helps the people to know their cancer risk with low cost and it also helps the people to take the appropriate decision based on their cancer risk status. The data is collected from the website online lung cancer prediction system .

  Supported Tasks and Leaderboards

[More Information Needed]

  Languages

[More Information Needed]

  Dataset Structure… See the full description on the dataset page: https://huggingface.co/datasets/virtual10/lungs_cancer.

Rapid Cancer Registration Data (RCRD) provides a quick, indicative source of cancer data. It is provided to support the planning and provision of cancer services. The data is based on a rapid processing of cancer registration data sources, in particular on Cancer Outcomes and Services Dataset (COSD) information. In comparison, National Cancer Registration Data (NCRD) relies on additional data sources, enhanced follow-up with trusts and expert processing by cancer registration officers. The Rapid Cancer Registration Data (RCRD) may be useful for service improvement projects including healthcare planning and prioritisation. However, it is poorly suited for epidemiological research due to limitations in the data quality and completeness.

This dataset contains data about lung cancer Mortality. This database is a comprehensive collection of patient information, specifically focused on individuals diagnosed with cancer. It is designed to facilitate the analysis of various factors that may influence cancer prognosis and treatment outcomes. The database includes a range of demographic, medical, and treatment-related variables, capturing essential details about each patient's condition and history.

Key components of the database include:

Demographic Information: Basic details about the patients such as age, gender, and country of residence. This helps in understanding the distribution of cancer cases across different populations and regions.

Medical History: Information about each patient’s medical background, including family history of cancer, smoking status, Body Mass Index (BMI), cholesterol levels, and the presence of other health conditions such as hypertension, asthma, cirrhosis, and other cancers. This section is crucial for identifying potential risk factors and comorbidities.

Cancer Diagnosis: Detailed data about the cancer diagnosis itself, including the date of diagnosis and the stage of cancer at the time of diagnosis. This helps in tracking the progression and severity of the disease.

Treatment Details: Information regarding the type of treatment each patient received, the end date of the treatment, and the outcome (whether the patient survived or not). This is essential for evaluating the effectiveness of different treatment approaches.

The structure of the database allows for in-depth analysis and research, making it possible to identify patterns, correlations, and potential causal relationships between various factors and cancer outcomes. It is a valuable resource for medical researchers, epidemiologists, and healthcare providers aiming to improve cancer treatment and patient care.

id: A unique identifier for each patient in the dataset. age: The age of the patient at the time of diagnosis. gender: The gender of the patient (e.g., male, female). country: The country or region where the patient resides. diagnosis_date: The date on which the patient was diagnosed with lung cancer. cancer_stage: The stage of lung cancer at the time of diagnosis (e.g., Stage I, Stage II, Stage III, Stage IV). family_history: Indicates whether there is a family history of cancer (e.g., yes, no). smoking_status: The smoking status of the patient (e.g., current smoker, former smoker, never smoked, passive smoker). bmi: The Body Mass Index of the patient at the time of diagnosis. cholesterol_level: The cholesterol level of the patient (value). hypertension: Indicates whether the patient has hypertension (high blood pressure) (e.g., yes, no). asthma: Indicates whether the patient has asthma (e.g., yes, no). cirrhosis: Indicates whether the patient has cirrhosis of the liver (e.g., yes, no). other_cancer: Indicates whether the patient has had any other type of cancer in addition to the primary diagnosis (e.g., yes, no). treatment_type: The type of treatment the patient received (e.g., surgery, chemotherapy, radiation, combined). end_treatment_date: The date on which the patient completed their cancer treatment or died. survived: Indicates whether the patient survived (e.g., yes, no).

This dataset contains artificially generated data with as close a representation of reality as possible. This data is free to use without any licence required.

Good luck Gakusei!

The Synthetic Oral Cancer Prediction Dataset is designed for educational and research purposes to analyse factors associated with oral cancer risk, progression, and treatment outcomes. The dataset includes anonymised, synthetic data on various clinical, lifestyle, and demographic factors for individuals diagnosed with oral cancer.

Dataset Features

• ID: Unique identifier for each participant.
• Country: Country of residence of the participant.
• Age: Age of the participant (in years).
• Gender: Gender of the participant (Male/Female).
• Tobacco Use: History of tobacco use (Yes/No).
• Alcohol Consumption: History of alcohol consumption (Yes/No).
• HPV Infection: Presence of human papillomavirus infection (Yes/No).
• Betel Quid Use: History of Betel quid use (Yes/No).
• Chronic Sun Exposure: History of chronic sun exposure (Yes/No).
• Poor Oral Hygiene: Poor oral hygiene habits (Yes/No).
• Diet (Fruits & Vegetables Intake): Frequency of consuming fruits and vegetables (Yes/No).
• Family History of Cancer: Family history of cancer (Yes/No).
• Compromised Immune System: Whether the participant has a compromised immune system (Yes/No).
• Oral Lesions: Presence of oral lesions (Yes/No).
• Unexplained Bleeding: Presence of unexplained bleeding (Yes/No).
• Difficulty Swallowing: Difficulty in swallowing (Yes/No).
• White or Red Patches in Mouth: Presence of white or red patches in the mouth (Yes/No).
• Tumor Size (cm): Size of the tumor in centimeters.
• Cancer Stage: Stage of the oral cancer (1-4).
• Treatment Type: Type of treatment received (e.g., Surgery, Radiation, Chemotherapy).
• Survival Rate (5-Year, %): 5-year survival rate in percentage.
• Cost of Treatment (USD): Total cost of treatment in USD.
• Economic Burden (Lost Workdays per Year): Economic burden due to lost workdays each year.
• Early Diagnosis: Whether early diagnosis was made (Yes/No).
• Oral Cancer (Diagnosis): Diagnosis of oral cancer (Yes/No).

Distribution

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Usage

This dataset can be used for the following applications:

• Cancer Research: Investigate the relationship between various lifestyle, clinical, and demographic factors with oral cancer risk and progression.
• Predictive Modeling: Build machine learning models to predict cancer diagnosis, survival rate, or treatment outcomes based on participant data.
• Healthcare and Public Health: Study the impact of lifestyle factors (e.g., tobacco, alcohol, diet) on the development and progression of oral cancer.
• Educational Purposes: Provide a dataset for students and researchers in oncology, medical data science, and public health fields to analyze cancer risk factors and treatment outcomes.

Coverage

This synthetic dataset is fully anonymized and complies with data privacy standards. It includes a wide array of factors that support diverse research and analysis in the oncology and public health domains.

License

CC0 (Public Domain)

Who Can Use It

• Cancer Researchers: To explore correlations between lifestyle factors, clinical features, and treatment outcomes in oral cancer.
• Oncologists and Healthcare Providers: To analyze the effectiveness of different treatments and factors that affect prognosis and survival.
• Public Health Professionals: To study the broader societal and economic impacts of oral cancer and develop preventive measures.
• Data Scientists and Machine Learning Practitioners: To develop predictive models for diagnosing oral cancer and improving treatment planning.
• Educators and Students: As a resource for studying cancer risk analysis, healthcare data science, and public health analytics.

Analysis of ‘Breast Cancer Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yasserh/breast-cancer-dataset on 28 January 2022.

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

Description:

Breast cancer is the most common cancer amongst women in the world. It accounts for 25% of all cancer cases, and affected over 2.1 Million people in 2015 alone. It starts when cells in the breast begin to grow out of control. These cells usually form tumors that can be seen via X-ray or felt as lumps in the breast area.

The key challenges against it’s detection is how to classify tumors into malignant (cancerous) or benign(non cancerous). We ask you to complete the analysis of classifying these tumors using machine learning (with SVMs) and the Breast Cancer Wisconsin (Diagnostic) Dataset.

Acknowledgements:

This dataset has been referred from Kaggle.

Objective:

• Understand the Dataset & cleanup (if required).
• Build classification models to predict whether the cancer type is Malignant or Benign.
• Also fine-tune the hyperparameters & compare the evaluation metrics of various classification algorithms.

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

Cervical cancer is one of the leading causes of cancer-related deaths among women worldwide. Early detection and accurate prediction of cervical cancer can significantly improve the chances of successful treatment and save lives. This dataset help to develop a predictive model using machine learning techniques to identify individuals at high risk of cervical cancer, allowing for timely intervention and medical care.

Age standardized rate of cancer incidence, by selected sites of cancer and sex, three-year average, census metropolitan areas.

Pancreatic cancer is an extremely deadly type of cancer. Once diagnosed, the five-year survival rate is less than 10%. However, if pancreatic cancer is caught early, the odds of surviving are much better. Unfortunately, many cases of pancreatic cancer show no symptoms until the cancer has spread throughout the body. A diagnostic test to identify people with pancreatic cancer could be enormously helpful.

About Dataset 📌 Overview This dataset has been carefully synthesized to support research in lung cancer survival prediction, enabling the development of models that estimate:

Whether a patient is likely to survive at least one year post-diagnosis (Binary Classification). The probability of survival based on clinical and lifestyle factors (Regression Analysis). The dataset is designed for machine learning and deep learning applications in medical AI, oncology research, and predictive healthcare.

📜 Dataset Generation Process The dataset was generated using a combination of real-world epidemiological insights, medical literature, and statistical modeling. The feature distributions and relationships have been carefully modeled to reflect real-world clinical scenarios, ensuring biomedical validity.

📖 Medical References & Sources The dataset structure is based on well-established lung cancer risk factors and survival indicators documented in leading medical research and clinical guidelines:

World Health Organization (WHO) Reports on lung cancer epidemiology. National Cancer Institute (NCI) & American Cancer Society (ACS) guidelines on lung cancer risk factors and treatment outcomes. The IASLC Lung Cancer Staging Project (8th Edition): Standard reference for lung cancer staging. Harrison’s Principles of Internal Medicine (20th Edition): Provides an in-depth review of lung cancer diagnosis and treatment. Lung Cancer: Principles and Practice (2022, Oxford University Press): Clinical insights into lung cancer detection, treatment, and survival factors. 🔬 Features of the Dataset Each record in the dataset represents an individual’s clinical condition, lifestyle risk factors, and survival outcome. The dataset includes the following features:

1️⃣ Patient Demographics Age → A key risk factor for lung cancer progression and survival. Gender → Male and female lung cancer survival rates can differ. Residence → Urban vs. Rural (impact of environmental factors). 2️⃣ Risk Factors & Lifestyle Indicators These factors have been linked to lung cancer risk in epidemiological studies:

Smoking Status → (Current Smoker, Former Smoker, Never Smoked). Air Pollution Exposure → (Low, Moderate, High). Biomass Fuel Use → (Yes/No) – Associated with household air pollution. Factory Exposure → (Yes/No) – Industrial exposure increases lung cancer risk. Family History → (Yes/No) – Genetic predisposition to lung cancer. Diet Habit → (Vegetarian, Non-Vegetarian, Mixed) – Nutritional impact on cancer progression. 3️⃣ Symptoms (Primary Predictors) These are key clinical indicators associated with lung cancer detection and severity:

Hemoptysis (Coughing Blood) Chest Pain Fatigue & Weakness Chronic Cough Unexplained Weight Loss 4️⃣ Tumor Characteristics & Clinical Features Tumor Size (mm) → The size of the detected tumor. Histology Type → (Adenocarcinoma, Squamous Cell Carcinoma, Small Cell Carcinoma). Cancer Stage → (Stage I to Stage IV). 5️⃣ Treatment & Healthcare Facility Treatment Received → (Surgery, Chemotherapy, Radiation, Targeted Therapy). Hospital Type → (Private, Government, Medical College). 6️⃣ Target Variables (Predicted Outcomes) Survival (Binary) → 1 (Yes) if the patient survives at least 1 year, 0 (No) otherwise. Survival Probability (%) (Can be derived) → Estimated probability of survival within one year. ⚡ Why This Dataset is Valuable? ✅ Balanced Data Distribution Designed to ensure a representative distribution of lung cancer survival cases. Prevents model bias and improves generalization in predictive models. ✅ Medically-Inspired Feature Engineering Features are derived from real-world lung cancer risk factors, validated through medical literature. Incorporates both lifestyle and clinical indicators to enhance predictive accuracy.(no real person data is used,just have made an biomedical environment) ✅ Diverse Risk Factors Considered Smoking, air pollution, and genetic history as primary lung cancer contributors. Symptom severity and tumor histology influence survival rates. ✅ Scalability & ML Suitability Ideal for classification and regression tasks in machine learning. Can be used with deep learning (TensorFlow, PyTorch), ML models (XGBoost, Random Forest, SVM), and explainable AI techniques like SHAP and LIME. 📂 Dataset Usage & Applications This dataset is highly useful for multiple healthcare AI applications, including:

🩺 Predictive Analytics → Early detection of high-risk lung cancer patients. 🤖 Healthcare Chatbots → AI-powered risk assessment tools.

Death rate has been age-adjusted by the 2000 U.S. standard population. Single-year data are only available for Los Angeles County overall, Service Planning Areas, Supervisorial Districts, City of Los Angeles overall, and City of Los Angeles Council Districts.Lung cancer is a leading cause of cancer-related death in the US. People who smoke have the greatest risk of lung cancer, though lung cancer can also occur in people who have never smoked. Most cases are due to long-term tobacco smoking or exposure to secondhand tobacco smoke. Cities and communities can take an active role in curbing tobacco use and reducing lung cancer by adopting policies to regulate tobacco retail; reducing exposure to secondhand smoke in outdoor public spaces, such as parks, restaurants, or in multi-unit housing; and improving access to tobacco cessation programs and other preventive services.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

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.

Background

Sarcomas are uncommon cancers that can affect any part of the body. There are many different types of sarcoma and subtypes can be grouped into soft tissue or bone sarcomas. About 15 people are diagnosed every day in the UK. 3 in every 200 people with cancer in the UK have sarcoma.

A highly granular dataset with a confirmed sarcoma event including hospital presentation, serial physiology, demography, treatment prescribed and administered, prescribed and administered drugs. The infographic includes data from 27/12/2004 to 31/12/2021 but data is available from the past 10 years+.

PIONEER geography: The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix.

EHR. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & an expanded 250 ITU bed capacity during COVID. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.

Scope: All hospitalised patients from 2004 onwards, curated to focus on Sarcoma. Longitudinal & individually linked, so that the preceding & subsequent health journey can be mapped & healthcare utilisation prior to & after admission understood. The dataset includes highly granular patient demographics & co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to acute care process (timings, staff grades, specialty review, wards and triage). Along with presenting complaints, outpatients admissions, microbiology results, referrals, procedures, therapies, all physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations and others), and all blood results (urea, albumin, platelets, white blood cells and others). Includes all prescribed & administered treatments and all outcomes. Linked images are also available (radiographs, CT scans, MRI).

Available supplementary data: Matched controls; ambulance, OMOP data, synthetic data.

Available supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.

Mortality Rates for Lake County, Illinois. Explanation of field attributes: Average Age of Death – The average age at which a people in the given zip code die. Cancer Deaths – Cancer deaths refers to individuals who have died of cancer as the underlying cause. This is a rate per 100,000. Heart Disease Related Deaths – Heart Disease Related Deaths refers to individuals who have died of heart disease as the underlying cause. This is a rate per 100,000. COPD Related Deaths – COPD Related Deaths refers to individuals who have died of chronic obstructive pulmonary disease (COPD) as the underlying cause. This is a rate per 100,000.

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

All individuals diagnosed with cancer from 2000 to 2007 were identified in the Cancer Register of Southern Sweden, but only individuals who were also identified in the Population Register of Scania were included in this cohort. Age- and gender-matched controls were identified in the Population Register of Scania. The controls were reconciled with the cancer registry in southern Sweden so that they had no prior diagnosis of cancer and with the Population Register of Scania that they were alive at time of diagnosis to the matched case. Also spouses to cancer patients were used as controls.

For each individual, healthcare costs were monitored related to the date of diagnosis. Costs for outpatient care, inpatient care, number of days in hospital and medications were included. Costs were also calculated for the controls.

Other information available about the individuals in the cohort are age, sex, domicile, type of tumor and medication.

Purpose:

To study the health cost per individual in relation to mortality and comorbidity.

Dataset includes the study controls (individuals matched by age and sex ) Also spouses to cancer patients were included in the control group.

The Cancer Mapping data consists of counts of newly diagnosed cancer among New York State residents and is in response to legislation regarding "Cancer incidence and environmental facility maps" signed into law in 2010 (Public Health Law §2401-B). The law specifies the publication of maps showing cancer counts for small geographic areas along with certain facilities regulated by the State Department of Environmental Conservation. The official web site is called Environmental Facilities and Cancer Mapping.

The dataset is ONLY for the cancer-related data fields on the Environmental Facilities and Cancer Mapping web site. This dataset includes observed counts for 23 separate anatomical sites at the level of census block group. Block groups are small geographic areas typically averaging 1,000 to 1,500 people. To protect confidentiality, each area contains a minimum of 6 total cancers among males and 6 total cancers among females.

For more information, check out http://www.health.ny.gov/statistics/cancer/registry/about.htm .