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.

This release summarises the survival of adults diagnosed with cancer in England between 2015 and 2019 and followed to 2020, and children diagnosed with cancer in England between 2002 and 2019 and followed to 2020. Adult cancer survival estimates are presented by age, deprivation, gender, stage at diagnosis and geography. Update 8th June 2022: We have now published an additional ODS data file ‘Cancer Survival in England Back Series, cancers diagnosed from 2006 to 2018: Adults’. This publication presents a back series of 1- to 5-year net survival for adults (15 to 99 years) diagnosed with cancer between 2006 to 2018 (5-year rolling cohorts). This back series was completed due to a change in methodology in the most recent publication (Cancers diagnosed between 2015 and 2019) to allow for comparison of net survival estimates over time.

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.

The five-year survival rate after being diagnosed with cancer in Russia has followed an upward trend over the period under observation. In 2023, almost 59 percent of cancer patients continued to be registered with an oncological establishment for at least five years after getting their diagnosis. Lip cancer had the highest five-year survival rate, at over 75 percent in that year.

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

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

Source: Cancer Registry Database, HPA. Note: 1. The observed survival rate indicates the proportion of patients who were diagnosed with cancer at a given time and are alive at a certain point in time after diagnosis. All deaths are considered in this calculation regardless of cause, which reflects total mortality in the group of patients, not just that attributable solely to cancer. 2. Cumulative relative survival (%), which expresses the probability of cancer survival after adjustment for competing causes of death, was estimated as the ratio of observed to expected survival. Observed survival was based on deaths from all causes. Expected survival was based on cases only to the age-, year- and sex-specific mortalities observed in the general population (comparable group).

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).

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 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

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...

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.

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

ObjectiveAccessibility to quality healthcare, histopathology of tumor, tumor stage and geographical location influence survival rates. Comprehending the bases of these differences in cervical cancer survival rate, as well as the variables linked to poor prognosis, is critical to improving survival. We aimed to perform the first thorough meta-analysis and systematic review of cervical cancer survival times in Africa based on race, histopathology, geographical location and age.Methods and materialsMajor electronic databases were searched for articles published about cervical cancer survival rate in Africa. The eligible studies involved studies which reported 1-year, 3-year or 5-year overall survival (OS), disease-free survival (DFS) and/or locoregional recurrence (LRR) rate of cervical cancer patients living in Africa. Two reviewers independently chose the studies and evaluated the quality of the selected publications, in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA-P). We used random effects analysis to pooled the survival rate across studies and heterogeneity was explored via sub-group and meta-regression analyses. A leave-one-out sensitivity analysis was undertaken, as well as the reporting bias assessment. Our findings were reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA-P).ResultsA total of 16,122 women with cervical cancer were covered in the 45 articles (59 studies), with research sample sizes ranging from 22 to 1,059 (median = 187.5). The five-year overall survival (OS) rate was 40.9% (95% CI: 35.5–46.5%). The five-year OS rate ranged from 3.9% (95% CI: 1.9–8.0%) in Malawi to as high as 76.1% (95% CI: 66.3–83.7%) in Ghana. The five-year disease-free survival rate was 66.2% (95% CI: 44.2–82.8%) while the five-year locoregional rate survival was 57.0% (95% CI: 41.4–88.7%).ConclusionTo enhance cervical cancer survival, geographical and racial group health promotion measures, as well as prospective genetic investigations, are critically required.

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 ...

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+)

As of 2017, the five year survival rate after being diagnosed with cancer in Sweden was approximately ** percent. The five-year survival rate is the share of people who are alive five years after their diagnosis. In the provided time interval, cancer survival rates has increased in Sweden.

BackgroundPrevious studies focused more on the short-term risk of cardiovascular (CV) death due to traumatic psychological stress after a cancer diagnosis and the acute cardiotoxicity of anticancer treatments than on the long-term risk of CV death.MethodsTime trends in the proportions of CV death (PCV), cancer death (PCA), and other causes in deaths from all causes were used to show preliminary relationships among the three causes of death in 4,806,064 patients with cancer from the Surveillance, Epidemiology, and End Results (SEER) program. Competing mortality risk curves were used to investigate when the cumulative CV mortality rate (CMRCV) began to outweigh the cumulative cancer mortality rate (CMRCA) for patients with cancer who survived for more than 10 years. Multivariable competing risk models were further used to investigate the potential factors associated with CV death.ResultsFor patients with cancer at all sites, the PCV increased from 22.8% in the 5th year after diagnosis to 31.0% in the 10th year and 35.7% in the 20th year, while the PCA decreased from 57.7% in the 5th year after diagnosis to 41.2 and 29.9% in the 10th year and 20th year, respectively. The PCV outweighed the PCA (34.6% vs. 34.1%) since the 15th year for patients with cancer at all sites, as early as the 9th year for patients with colorectal cancer (37.5% vs. 33.2%) and as late as the 22nd year for patients with breast cancer (33.5% vs. 30.6%). The CMRCV outweighed the CMRCA since the 25th year from diagnosis. Multivariate competing risk models showed that an increased risk of CV death was independently associated with older age at diagnosis [hazard ratio and 95% confidence intervals [HR (95%CI)] of 43.39 (21.33, 88.28) for ≥ 80 vs. ≤ 30 years] and local metastasis [1.07 (1.04, 1.10)] and a decreased risk among women [0.82 (0.76, 0.88)], surgery [0.90 (0.87, 0.94)], and chemotherapy [0.85 (0.81, 0.90)] among patients with cancer who survived for more than 10 years. Further analyses of patients with cancer who survived for more than 20 years and sensitivity analyses by cancer at all sites showed similar results.ConclusionCV death gradually outweighs cancer death as survival time increases for most patients with cancer. Both the cardio-oncologist and cardio-oncology care should be involved to reduce CV deaths in long-term cancer survivors.

IntroductionLung cancer ranks the leading cause of cancer-related death worldwide. This retrospective cohort study was designed to determine time-dependent death hazards of diverse causes and conditional survival of lung cancer.MethodsWe collected 816,436 lung cancer cases during 2000-2015 in the SEER database, after exclusion, 612,100 cases were enrolled for data analyses. Cancer-specific survival, overall survival and dynamic death hazard were assessed in this study. Additionally, based on the FDA approval time of Nivolumab in 2015, we evaluated the effect of immunotherapy on metastatic patients’ survival by comparing cases in 2016-2018 (immunotherapy era, n=7135) and those in 2013-2016 (non-immunotherapy era, n=42061).ResultsOf the 612,100 patients, 285,705 were women, the mean (SD) age was 68.3 (11.0) years old. 252,558 patients were characterized as lung adenocarcinoma, 133,302 cases were lung squamous cell carcinoma, and only 78,700 cases were small cell lung carcinomas. TNM stage was I in 140,518 cases, II in 38,225 cases, III in 159,095 cases, and IV in 274,262 patients. 164,394 cases underwent surgical intervention. The 5-y overall survival and cancer-specific survival were 54.2% and 73.8%, respectively. The 5-y conditional survival rate of cancer-specific survival is improved in a time-dependent pattern, while conditional overall survival tends to be steady after 5-y follow-up. Except from age, hazard disparities of other risk factors (such as stage and surgery) diminished over time according to the conditional survival curves. After 8 years since diagnosis, mortality hazard from other causes became higher than that from lung cancer. This critical time point was earlier in elder patients while was postponed in patients with advanced stages. Moreover, both cancer-specific survival and overall survival of metastatic patients in immunotherapy era were significantly better than those in non-immunotherapy era (P

Lung cancer is the number one cancer-related cause of death in Sweden and worldwide. In most countries, five-year survival estimates vary between 10% and 20% with evidence of improved survival over time. Over the last decades, the management of lung cancer has changed including the introduction of national guidelines, new diagnostic procedures and treatments. This study aimed to investigate temporal trends in lung cancer survival both overall and in subgroups defined by established prognostic factors (i.e., sex, stage, histopathology and smoking history). We estimated one-, two-, and five-year relative survival, and excess mortality, in patients diagnosed with squamous cell carcinoma or adenocarcinoma of the lung between 1995 and 2016 in Sweden. We used population-based information available in a national lung cancer research database (LCBaSe) generated by cross-linkage between the Swedish National Lung Cancer Register and several Swedish health and sociodemographic registers. We included 36,935 patients diagnosed with squamous cell carcinoma or adenocarcinoma of the lung between 1995 and 2016. The overall one-, two- and five-year survival estimates increased between 1995 and 2016, from 38% to 53%, 21% to 37%, and 14% to 24%, respectively. Over the study period, we also found improved survival in subgroups, for example in patients with stages III-IV disease, patients with adenocarcinoma, and never-smokers. The excess mortality decreased over the study period, both overall and in all subgroups. Lung cancer survival increased over time in the overall lung cancer population. Of special note was evidence of improved survival in patients with stage IV disease. Our results corroborate a previously observed global trend of improved survival in patients with lung cancer.