In the period 2013 to 2017, over ** percent of those aged between 15 and 44 years who were diagnosed with liver cancer in England survived for at least *** year after being diagnosed, while ** percent survived for five years. Over the period provided, the older age groups have a lower survival rate than the younger age groups.

In the years 2016 to 2020, over ** percent of patients diagnosed with melanoma of the skin in England aged between 15 and 44 years of age would survive for at least one year, while patients this age had a five-year survival rate of nearly ** percent. The survival rates for melanoma of the skin did generally fall if the patient was older when diagnosed.

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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How to use the dataset

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 | |:-------------------------------------------|:-------------------------------------------------------------------...

This bulletin presents the latest one- and five-year age-standardised net survival estimates for adults (aged 15-99 years) diagnosed in England with one of the 21 most common cancers. These cancers comprise over 90% of all newly diagnosed cancers. Source agency: Office for National Statistics Designation: National Statistics Language: English Alternative title: Cancer survival rates

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+)

This table contains 600 series, with data for years 1997 - 1997 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (15 items: Canada; Prince Edward Island; Newfoundland and Labrador; Nova Scotia ...), Sex (3 items: Both sexes; Females; Males ...), Selected sites of cancer (ICD-9) (4 items: Colorectal cancer; Prostate cancer; Lung cancer; Female breast cancer ...), Characteristics (5 items: Relative survival rate for cancer; High 95% confidence interval; relative survival rate for cancer; Number of cases; Low 95% confidence interval; relative survival rate for cancer ...).

This release summarises the survival of adults diagnosed with cancer in England between 2016 and 2020 and followed to 2021, and children diagnosed with cancer in England between 2002 and 2020 and followed to 2021. Adult cancer survival estimates are presented by age, deprivation, gender, stage at diagnosis, and geography.

BackgroundOral cancer leads to a considerable use of health care resources. Wide resection of the tumor and reconstruction with a pedicle flap/ free flap is widely used. This study was conducted to investigate if young age at the time of diagnosis of oral cancer requiring this treatment confers a worse prognosis. MethodsA total of 2339 patients who underwent resections for oral cancer from 2004 to 2005 were identified from The Taiwan National Health Insurance Research Database. Survival analysis, Cox proportional regression model, propensity scores, and sensitivity test were used to evaluate the association between 5-year survival rates and age. ResultsIn the Cox proportional regression model, the older age group (>65 years) had the worst survival rate (hazard ratio [HR], 1.80; 95% confidence interval [CI], 1.45-2.22; P<0.001). When analyzed using the propensity scores, the adjusted 5-year survival rates were also poorer for oral cancer patients with older age (>65 years), compared to those with younger age (<45 years) (P<0.001). In sensitivity test, the adjusted hazard ratio remained no statistically elevated in the younger age group (<45 years). ConclusionsFor those oral cancer patients who underwent wide excision and reconstruction, young age did not confer a worse prognosis using a Cox proportional regression model, propensity scores or sensitivity test. Young oral cancer patients may be treated using general guidelines and do not require more aggressive treatment.

Legacy unique identifier: P00624

This statistic displays the age-standardized mortality rate of all cancer among males in Canada between 1988 and 2020, with forecasts from 2021 to 2023. In 1990, the mortality rate for all cancer reached ***** per 100,000 population among males.

Cancer mortality rates in Canada

The mortality rate due to cancer in Canada has steadily declined over the last decades and the trend is expected to continue. The rate of male deaths per 100,000 population had fallen to around *** deaths per 100,000 population in 2020. Cancer mortality rate in females is estimated to reach around *** deaths per 100,000 population in 2023. There is also some variance in mortality rates among genders based on the type of cancer. The mortality rate for lung cancer among men is about ** deaths per 100,000 and ** deaths per 100,000 in women. Men are generally found to have a higher frequency of overall cancer diagnoses than women, including most types of cancers.

The five-year survival rate for most men also tended to be lower than for women. Based on cancer sites, it has been hypothesized that differences in genders such as tobacco smoking, viral infections, hormones, and metal toxicity may be one of the major causes of discrepancies in mortality rates. In 2023, there will be an estimated ****** cancer cases among Canadians between ** and ** years of age. About ** percent of new cancer cases were located in Europe and ** percent of cases located in North America in 2020.

The United States Cancer Statistics (USCS) online databases in WONDER provide cancer incidence and mortality data for the United States for the years since 1999, by year, state and metropolitan areas (MSA), age group, race, ethnicity, sex, childhood cancer classifications and cancer site. Report case counts, deaths, crude and age-adjusted incidence and death rates, and 95% confidence intervals for rates. The USCS data are the official federal statistics on cancer incidence from registries having high-quality data and cancer mortality statistics for 50 states and the District of Columbia. USCS are produced by the Centers for Disease Control and Prevention (CDC) and the National Cancer Institute (NCI), in collaboration with the North American Association of Central Cancer Registries (NAACCR). Mortality data are provided by the Centers for Disease Control and Prevention (CDC), National Center for Health Statistics (NCHS), National Vital Statistics System (NVSS).

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

This data package contains information on cancer its type, its occurrence by age, type and site. It also provides detailed data on adult and childhood cancer survival rates and deaths caused by breast cancer in females.

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!

This is historical data. The update frequency has been set to "Static Data" and is here for historic value. Updated on 8/14/2024 Cancer Mortality Rate - This indicator shows the age-adjusted mortality rate from cancer (per 100,000 population). Maryland’s age adjusted cancer mortality rate is higher than the US cancer mortality rate. Cancer impacts people across all population groups, however wide racial disparities exist. Link to Data Details

National (excluding Quebec) estimates of five-year net survival for 31 types of cancer. Net survival refers to the survival probability that would be observed in the hypothetical situation where the cancer of interest is the only possible cause of death. Predicted survival provides a more up-to-date estimate of survival by exclusively using the survival experienced by cancer cases during a recent period.

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...

Statistics on observed and relative survival of cancer patients (25 cancers plus for all cancers combined) in Scotland, at 1, 3, 5 and 10 years, by age and sex. Source agency: ISD Scotland (part of NHS National Services Scotland) Designation: National Statistics Language: English Alternative title: Cancer Survival

National estimates of five-year net survival for 12 types of cancer by age group at diagnosis. The age distributions of cases of these cancers are skewed toward older ages. Net survival refers to the survival probability that would be observed in the hypothetical situation where the cancer of interest is the only possible cause of death.

IntroductionPediatric cancer survival is increasing over time in European countries. However, there are survival differences in survival between Eastern and Western European member countries. The available mortality data based on the Romanian National Statistics Institute reports to Eurostat place Romania among the European countries with the highest child cancer mortality rates. The current study aims to investigate pediatric cancer survival and mortality outcomes in Romania, using the Romanian national-level cancer registry data. The Registry results add to the literature to illustrate the profile of pediatric cancers in Eastern Europe.MethodsThe study included 4,144 cancer patients aged 0–19 years, whose data were collected in the Romanian National Pediatric Oncology and Hematology Registry. These data comprise all the new cases diagnosed in Romanian pediatric cancer facilities from January 1, 2010, to December 31, 2019. Survival probabilities were examined according to patient characteristics, such as tumor type, demography, geography and place of residence. The Chi-square test (Fisher Exact Test) was used to compare patients' personal and clinical characteristics by rural/urban designation. The Cox proportional hazards regression model was used to estimate the hazard ratios and 95% confidence intervals by rural/urban designation, economic development region, and selected cancer subtypes, according to the International Classification of Childhood Cancer, 3rd edition. The mean follow-up time was 6.09 ± 3.84 years. To calculate the 5-year survival rates, the study period ended on December 31, 2017, and the sample size was restricted to 3,308. A predictive model using multivariable logistic regression was used to assess the age group and rural-urban survival probabilities as well as survival probabilities for major cancer subtypes.ResultsThe 5-year overall survival probability for the 0–14 and 15–19 age groups was 73% (95% CI: 71, 75) and 69% (65%, 72%) respectively. Categorized further by smaller age groups for the 0–14 age group, the survival rates were 75% (0–4 years), 73% (5–9 years) and 69% (10–14 years). Hodgkin lymphoma (92%), nephroblastoma and other nonepithelial renal tumors (89 %), and lymphoid leukemias (80%) had the highest survival rates among all the seven major cancer subtypes in the 0–14 years population. The worst survival was observed for CNS tumors (62%), rhabdomyosarcoma (62%), neuroblastoma (67%), and bone tumors (52%). As compared to pediatric cancer patients residing in urban areas, significantly more rural patients died from cancer (32.6% vs. 22.4%, p < 0.0001).Discussion/conclusionThis is Romania's first pediatric cancer survival study based on well-validated national cancer registry data. The Romanian Pediatric Cancer Registry continues to shed light on the profile of pediatric cancers in Romania. Overall survival rates in Romania were lower than survival rates reported from the EU-15 countries. Rural patients had lower survival than urban patients. Future studies should investigate the relationship between patients' clinical and socioeconomic characteristics and survival outcomes. Further research is also needed to investigate recurrence and secondary malignancies among this population.