In the period 2014-2020, around ** percent of cancer patients surveyed between 2014 and 2020 from all ethnic groups survived a period of at least 5 years after diagnosis. This statistic shows the 5-year relative cancer survival rates in the U.S., by ethnic group, in periods between 1975 and 2020.

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 statistic displays the five-year survival rate in children with diagnosed cancer, by selected locations, time periods, and type of cancer. In Australia, children with leukaemias had a five-year chance of survival of over 80 percent in the measured period 1997-2006. In comparison, Chinese children with leukaemias in Shanghai had a chance of little more than 50 percent to survive five years (measured in the period 2002-2005).

The graph presents prostate cancer relative survival rates in the U.S. from 2001 to 2016, showing 1-year, 5-year, and 10-year relative survival percentages based on age groups. The x-axis represents age groups, while the y-axis indicates survival rates at different time intervals. Survival rates remain high across all age groups, with patients aged 65–69 having the highest 10-year survival rate of 99.5%. In contrast, men aged 80 and older have the lowest survival rates, with 92.1% at 1 year and 82.7% at 10 years. The data highlights that younger patients generally experience better long-term survival outcomes.

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

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.

In the period 2014-2020, approximately ** percent of liver cancer patients in the United States survived a period of at least 5 years after diagnosis. This statistic shows the 5-year relative cancer survival rates in the United States for the period 2014-2020, by type of cancer.

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 ...).

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.

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).

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+)

Update 2 March 2023: Following the merger of NHS Digital and NHS England on 1st February 2023 we are reviewing the future presentation of the NHS Outcomes Framework indicators. As part of this review, the annual publication which was due to be released in March 2023 has been delayed. Further announcements about this dataset will be made on this page in due course. A measure of the number of adults diagnosed with any type of cancer in a year who are still alive five years after diagnosis. This indicator attempts to capture the success of the NHS in preventing people from dying once they have been diagnosed with any type of cancer. As of May 2020, please refer to the data tables published by Public Health England (PHE). This publication is released on an annual basis. A link to the PHE publications, within which the data is held, is available via the resource link below. On the publication page select the ‘Data Tables index of cancer survival 20xx to 20xx’. The data for this indicator is available by applying suitable filters to the dataset contained within the 'Data_Complete’ tab. Legacy unique identifier: P01735

National (excluding Quebec) estimates of five-year net survival for 11 types of cancer by age group at diagnosis. 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.

Between 2020 and 2024, the relative five-year survival rate for lung cancer was **** percent among women, and **** percent among men. Survival rates for lung cancer have significantly increased in Norway since 1984.

Background: Socioeconomic inequality in survival after cancer have been reported in several countries and also in Denmark. Changes in cancer diagnostics and treatment may have changed the gap in survival between affluent and deprived patients and we investigated if the differences in relative survival by income has changed in Danish cancer patients over the past 25 years. Methods: The 1- and 5-year relative survival by income quintile is computed by comparing survival among cancer patients diagnosed 1987–2009 to the survival of a cancer-free matched sample of the background population. The comparison is done within the 15 most common cancers and all cancers combined. The gap in relative survival due to socioeconomic inequality for the period 1987–1991 is compared the period 2005–2009. Results: The relative 5-year survival increased for all 15 cancer sites investigated in the study period. In general, low-income patients diagnosed in 1987–1991 had between 0% and 11% units lower 5-year relative survival compared with high-income patients; however, only four sites (breast, prostate, bladder and head & neck) were statistically different. In patients diagnosed 2005–2009, the gap in 5-year RS was ranging from 2% to 22% units and statistically significantly different for 9 out of 15 sites. The results for 1-year relative survival were similar to the 5-year survival gap. An estimated 22% of all deaths at five years after diagnosis could be avoided had patients in all income groups had same survival as the high-income group. Conclusion: In this nationwide population-based study, we observed that the large improvements in both short- and long-term cancer survival among patients diagnosed 1987–2009. The improvements have been most pronounced for high-income cancer patients, leading to stable or even increasing survival differences between richest and poorest patients. Improving survival among low-income patients would improve survival rates among Danish cancer patients overall and reduce differences in survival when compared to other Western European countries.

Survival estimates for adults diagnosed with cancer, by stage, for years 2012, 2013, 2014 and 2015, England

County Cancer Death Rates

County-level cancer death rates with related variables

By Noah Rippner [source]

About this dataset

This dataset provides comprehensive information on county-level cancer death and incidence rates, as well as various related variables. It includes data on age-adjusted death rates, average deaths per year, recent trends in cancer death rates, recent 5-year trends in death rates, and average annual counts of cancer deaths or incidence. The dataset also includes the federal information processing standards (FIPS) codes for each county.

Additionally, the dataset indicates whether each county met the objective of a targeted death rate of 45.5. The recent trend in cancer deaths or incidence is also captured for analysis purposes.

The purpose of the death.csv file within this dataset is to offer detailed information specifically concerning county-level cancer death rates and related variables. On the other hand, the incd.csv file contains data on county-level cancer incidence rates and additional relevant variables.

To provide more context and understanding about the included data points, there is a separate file named cancer_data_notes.csv. This file serves to provide informative notes and explanations regarding the various aspects of the cancer data used in this dataset.

Please note that this particular description provides an overview for a linear regression walkthrough using this dataset based on Python programming language. It highlights how to source and import the data properly before moving into data preparation steps such as exploratory analysis. The walkthrough further covers model selection and important model diagnostics measures.

It's essential to bear in mind that this example serves as an initial attempt at creating a multivariate Ordinary Least Squares regression model using these datasets from various sources like cancer.gov along with US Census American Community Survey data. This baseline model allows easy comparisons with future iterations intended for improvements or refinements.

Important columns found within this extensively documented Kaggle dataset include County names along with their corresponding FIPS codes—a standardized coding system by Federal Information Processing Standards (FIPS). Moreover,Met Objective of 45.5? (1) column denotes whether a specific county achieved the targeted objective of a death rate of 45.5 or not.

Overall, this dataset aims to offer valuable insights into county-level cancer death and incidence rates across various regions, providing policymakers, researchers, and healthcare professionals with essential information for analysis and decision-making purposes

How to use the dataset

• Familiarize Yourself with the Columns:

  • County: The name of the county.
  • FIPS: The Federal Information Processing Standards code for the county.
  • Met Objective of 45.5? (1): Indicates whether the county met the objective of a death rate of 45.5 (Boolean).
  • Age-Adjusted Death Rate: The age-adjusted death rate for cancer in the county.
  • Average Deaths per Year: The average number of deaths per year due to cancer in the county.
  • Recent Trend (2): The recent trend in cancer death rates/incidence in the county.
  • Recent 5-Year Trend (2) in Death Rates: The recent 5-year trend in cancer death rates/incidence in the county.
  • Average Annual Count: The average annual count of cancer deaths/incidence in the county.

• Determine Counties Meeting Objective: Use this dataset to identify counties that have met or not met an objective death rate threshold of 45.5%. Look for entries where Met Objective of 45.5? (1) is marked as True or False.

• Analyze Age-Adjusted Death Rates: Study and compare age-adjusted death rates across different counties using Age-Adjusted Death Rate values provided as floats.

• Explore Average Deaths per Year: Examine and compare average annual counts and trends regarding deaths caused by cancer, using Average Deaths per Year as a reference point.

• Investigate Recent Trends: Assess recent trends related to cancer deaths or incidence by analyzing data under columns such as Recent Trend, Recent Trend (2), and Recent 5-Year Trend (2) in Death Rates. These columns provide information on how cancer death rates/incidence have changed over time.

• Compare Counties: Utilize this dataset to compare counties based on their cancer death rates and related variables. Identify counties with lower or higher average annual counts, age-adjusted death rates, or recent trends to analyze and understand the factors contributing ...

This presents the latest one- and five-year age-standardised relative survival rates for cancers of the bladder, breast (in women), cervix, colon, lung, oesophagus, prostate and stomach with data for the government office regions (GOR) and strategic health authorities (SHA).

Source agency: Office for National Statistics

Designation: National Statistics

Language: English

Alternative title: Cancer survival in England by Health Authority