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!

Lung cancer is the deadliest cancer worldwide, accounting for 1.82 million deaths in 2022. The second most deadly form of cancer is colorectum cancer, followed by liver cancer. However, lung cancer is only the sixth leading cause of death worldwide, with heart disease and stroke accounting for the highest share of deaths. Male vs. female cases Given that lung cancer causes the highest number of cancer deaths worldwide, it may be unsurprising to learn that lung cancer is the most common form of new cancer cases among males. However, among females, breast cancer is by far the most common form of new cancer cases. In fact, breast cancer is the most prevalent cancer worldwide, followed by prostate cancer. Prostate cancer is a very close second to lung cancer among the cancers with the highest rates of new cases among men. Male vs. female deaths Lung cancer is by far the deadliest form of cancer among males but is the second deadliest form of cancer among females. Breast cancer, the most prevalent form of cancer among females worldwide, is also the deadliest form of cancer among females. Although prostate cancer is the second most prevalent cancer among men, it is the fifth deadliest cancer. Lung, liver, stomach, colorectum, and oesophagus cancers all have higher deaths rates among males.

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.

From 2018 to 2022, the overall death rate for lung and bronchus cancer in the Kentucky was 61 per 100,000 for males and 43.2 per 100,000 for females. This statistic presents the death rates for lung and bronchus cancer in the United States from 2018 to 2022, by state and gender.

Rate: Number of deaths due to cancer of the trachea, bronchus, and lung per 100,000 Population.

Definition: Number of deaths per 100,000 with malignant neoplasm (cancer) cancer of the trachea, bronchus, and lung as the underlying cause (ICD-10 codes: C33-C34).

Data Sources:

(1) Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File. CDC WONDER On-line Database accessed at http://wonder.cdc.gov/cmf-icd10.html

(2) Death Certificate Database, Office of Vital Statistics and Registry, New Jersey Department of Health

(3) Population Estimates, State Data Center, New Jersey Department of Labor and Workforce Development

** Description**

This dataset contains data about lung cancer Mortality and is a comprehensive collection of patient information, specifically focused on individuals diagnosed with cancer. This dataset contains comprehensive information on 800,000 individuals related to lung cancer diagnosis, treatment, and outcomes. With 16 well-structured columns. This large-scale dataset is designed to aid researchers, data scientists, and healthcare professionals in studying patterns, building predictive models, and enhancing early detection and treatment strategies.

🌍 The Societal Impact of Lung Cancer

Lung cancer is not just a disease — it's a global crisis that steals time, health, and hope from millions of people every year. As the #1 cause of cancer deaths worldwide, it takes more lives annually than breast, colon, and prostate cancer combined.

But behind every statistic is a story:

A parent who never saw their child graduate.

A worker who had to leave their job too soon.

A community that lost a leader, a friend, a neighbor.

Why does this matter? Lung cancer often goes undetected until it's too late. It’s aggressive, silent, and devastating — especially in underserved areas where early detection is rare and treatment options are limited. It doesn’t just affect patients. It affects families, economies, and healthcare systems on a massive scale.

This dataset represents more than numbers. It represents 800,000 real-world stories — people who can help us unlock patterns, train models, and advance life-saving research.

By working with this data, you're not just analyzing a dataset — you're stepping into the fight against one of humanity’s deadliest diseases.

Let’s turn insight into impact. (😊The above descriptions is generated with the help of AI, Just wanted to share this dataset That all. Thank you)

Legacy unique identifier: P00509

Lung Cancer Deaths reports the number, crude rate, and age-adjusted mortality rate (AAMR) of deaths due to lung cancer. Dimensions Year;Measure Type;Variable Full Description Lung cancer forms in tissues of the lung, usually in the cells lining air passages. Deaths with ICD-10 code C34 as the underlying cause of death are recorded as lung cancer deaths. Data are reported annually.

In 2020, approximately ** men and ** women per 100,000 population died from lung cancer in England and Wales. During the provided time interval, there has been a noticeable decrease in the mortality of lung cancer among men, while the rate among women has remained at similar levels since the year 2000.

Dataset Description This dataset contains information on cancer deaths by country, type, and year. It includes data on 18 different types of cancer, including liver cancer, kidney cancer, larynx cancer, breast cancer, thyroid cancer, stomach cancer, bladder cancer, uterine cancer, ovarian cancer, cervical cancer, prostate cancer, pancreatic cancer, esophageal cancer, testicular cancer, nasopharynx cancer, other pharynx cancer, colon and rectum cancer, non-melanoma skin cancer, lip and oral cavity cancer, brain and nervous system cancer, tracheal, bronchus, and lung cancer, gallbladder and biliary tract cancer, malignant skin melanoma, leukemia, Hodgkin lymphoma, multiple myeloma, and other cancers.

Data Fields The dataset includes the following data fields:

• Country: The country where the cancer death occurred.
• Code: The country code for the country where the cancer death occurred.
• Year: The year in which the cancer death occurred.
• Liver cancer: The number of cancer deaths from liver cancer in the country in the year.
• Kidney cancer: The number of cancer deaths from kidney cancer in the country in the year.
• Larynx cancer: The number of cancer deaths from larynx cancer in the country in the year.
• Breast cancer: The number of cancer deaths from breast cancer in the country in the year.
• Thyroid cancer: The number of cancer deaths from thyroid cancer in the country in the year.
• Stomach cancer: The number of cancer deaths from stomach cancer in the country in the year.
• Bladder cancer: The number of cancer deaths from bladder cancer in the country in the year.
• Uterine cancer: The number of cancer deaths from uterine cancer in the country in the year.
• Ovarian cancer: The number of cancer deaths from ovarian cancer in the country in the year.
• Cervical cancer: The number of cancer deaths from cervical cancer in the country in the year.
• Prostate cancer: The number of cancer deaths from prostate cancer in the country in the year.
• Pancreatic cancer: The number of cancer deaths from pancreatic cancer in the country in the year.
• Esophageal cancer: The number of cancer deaths from esophageal cancer in the country in the year.
• Testicular cancer: The number of cancer deaths from testicular cancer in the country in the year.
• Nasopharynx cancer: The number of cancer deaths from nasopharynx cancer in the country in the year.
• Other pharynx cancer: The number of cancer deaths from other pharynx cancer in the country in the year.
• Colon and rectum cancer: The number of cancer deaths from colon and rectum cancer in the country in the year.
• Non-melanoma skin cancer: The number of cancer deaths from non-melanoma skin cancer in the country in the year.
• Lip and oral cavity cancer: The number of cancer deaths from lip and oral cavity cancer in the country in the year.
• Brain and nervous system cancer: The number of cancer deaths from brain and nervous system cancer in the country in the year.
• Tracheal, bronchus, and lung cancer: The number of cancer deaths from tracheal, bronchus, and lung cancer in the country in the year.
• Gallbladder and biliary tract cancer: The number of cancer deaths from gallbladder and biliary tract cancer in the country in the year.
• Malignant skin melanoma: The number of cancer deaths from malignant skin melanoma in the country in the year.
• Leukemia: The number of cancer deaths from leukemia in the country in the year.
• Hodgkin lymphoma: The number of cancer deaths from Hodgkin lymphoma in the country in the year.
• Multiple myeloma: The number of cancer deaths from multiple myeloma in the country in the year.
• Other cancers: The number of cancer deaths from other cancers in the country in the year.

Data Source The data in this dataset was collected from the World Health Organization (WHO). The WHO collects data on cancer deaths from countries around the world.

Usage This dataset can be used to study cancer deaths by country, type, and year. It can also be used to compare cancer death rates between different countries or over time.

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It is estimated that in 2025 there will be a total of 226,650 new cases of lung and bronchus cancer in the United States. The highest number of these cases are estimated to be in the state of Florida. This statistic presents the estimated number of new lung and bronchus cancer cases in the United States in 2025, by state.

This statistic shows the death rate of lung and bronchus cancer in the United States from 1999 to 2023. The maximum rate in the given period was **** per every 100,000 age-adjusted population in 2000. The minimum rate stood at **** in 2023.

Deaths from lung cancer - Directly age-Standardised Rates (DSR) per 100,000 population Source: Office for National Statistics (ONS) Publisher: Information Centre (IC) - Clinical and Health Outcomes Knowledge Base Geographies: Local Authority District (LAD), Government Office Region (GOR), National, Primary Care Trust (PCT), Strategic Health Authority (SHA) Geographic coverage: England Time coverage: 2005-07, 2007 Type of data: Administrative data

Supporting figures and tables. Figure S1, Prevalence of smoking by age in 1950 birth cohort. Summary of shared input data (used by all 5 models) on smoking patterns for the US cohort born in 1950. Prevalence shown is estimated in the absence of lung cancer mortality. Version 1.0 of the Smoking History Generator (SHG) refers to published data through 2000 (Anderson, et al.), and version 1.5 supplies the 1950 birth cohort used for this analysis with data through 2009 and projections past 2009. Figure S2, Other-cause mortality, by smoking quintile, in 1950 birth cohort. These curves show the other-cause (non-lung cancer) mortality for never smokers and for current smokers by smoking quintile (Q, of cigarettes per day) for the male birth cohort of 1950, out to age 99. Former smokers are intermediate to current and never smokers. There is a similar plot for females. These were shared inputs used by all the models. Note that the rates of non-lung cancer mortality represent the US population, not trial (NLST or PLCO) participants. Figure S3, Prevalence of smoking by age in 1950 birth cohort. Output from one model showing smoking prevalence by age (calendar year), in a no screening scenario. Proportions of current/former/never smokers are in the presence of lung cancer mortality as well as all-cause mortality. Figure S4, Prevalence of smoking by age and pack-years in 1950 birth cohort. Output from one model showing smoking prevalence by category of pack-year and age. The proportion of the cohort by age that has accumulated the specified number of pack-years in the presence of lung cancer mortality and other-cause mortality. Figure S5, Incidence, no screening scenario, output from all models. For predictions past observed SEER data (over age 60) there are no observed data, but we used an age-period-cohort model to project past observed years (‘Projected’ red double line in plots below), which shows that the models are most divergent after age 85, when SEER data become most sparse. We cannot strictly compare incidence to that in prior birth cohorts since smoking patterns are dissimilar, and incidence varies by cohort. Figure S6, Mortality, no screening scenario, output from all models. The vertical line at age 90 indicates age at which all event counts (screens, deaths and deaths averted, and life years gained) were truncated for the analyses reported here. Although the models ranked programs similarly, there was variability in the total numbers of predicted lung cancer cases, deaths, and therefore lung cancer deaths prevented. The differences in rates in the no screening scenario in large part explains the predicted differences between models. The four models (E, F, S, and U) which use two-stage or multi-stage clonal expansion models have more similarly shaped curves than the fifth model (M), which does not use a clonal expansion component (see Table S1 in File S1). Figure S7, Results from all models analogous to Figure 1 in article. Figure S8, Results from all models analogous to Figure 2 in article. Figure S9, Secondary results with reduced operative candidacy with age. The dashed line denotes the efficiency frontier in the main analysis. Table S1, Additional Detail on Models. Table S2, Complete List of 120 Consensus Efficient Scenarios. Table S3, Comparison of Consensus Efficient Scenarios Identified Using Life-years Saved or Lung Cancer Deaths Avoided as Measure of Benefit. (DOCX)

Information about the rates of cancer deaths in each state is reported. The data shows the total rate as well as rates based on sex, age, and race. Rates are also shown for three specific kinds of cancer: breast cancer, colorectal cancer, and lung cancer.

Original Data

Column Description

More than half of males in China are current smokers and evidence from western countries tells us that an unprecedented number of smoking-attributable deaths will occur as the Chinese population ages. We used the China Lung Cancer Policy Model (LCPM) to simulate effects of computed tomography (CT)-based lung cancer screening in China, comparing the impact of a screening guideline published in 2015 by a Chinese expert group to a version developed for the United States by the U.S. Centers for Medicare & Medicaid Services (CMS). The China LCPM, built using an existing lung cancer microsimulation model, can project population outcomes associated with interventions for smoking-related diseases. After calibrating the model to published Chinese smoking prevalence and lung cancer mortality rates, we simulated screening from 2016 to 2050 based on eligibility criteria from the CMS and Chinese guidelines, which differ by age to begin and end screening, pack-years smoked, and years since quitting. Outcomes included number of screens, mortality reduction, and life-years saved for each strategy. We projected that in the absence of screening, 14.98 million lung cancer deaths would occur between 2016 and 2050. Screening with the CMS guideline would prevent 0.72 million deaths and 5.8 million life-years lost, resulting in 6.58% and 1.97% mortality reduction in males and females, respectively. Screening with the Chinese guideline would prevent 0.74 million deaths and 6.6 million life-years lost, resulting in 6.30% and 2.79% mortality reduction in males and females, respectively. Through 2050, 1.43 billion screens would be required using the Chinese screening strategy, compared to 988 million screens using the CMS guideline. In conclusion, CT-based lung cancer screening implemented in 2016 and based on the Chinese screening guideline would prevent about 20,000 (2.9%) more lung cancer deaths through 2050, but would require about 445 million (44.7%) more screens than the CMS guideline.

For this indicator, the average number of lung cancer deaths recorded as underlying or contributing cause of death in the period year T to year T-4 is reported. Data is available according to gender breakdown.

Mortality from lung cancer, directly age-standardised rate, persons, under 75 years, 2004-08 (pooled) per 100,000 European Standard population by Local Authority by local deprivation quintile. Local deprivation quintiles are calculated by ranking small areas (Lower Super Output Areas (LSOAs)) within each Local Authority based on their Index of Multiple Deprivation 2007 (IMD 2007) deprivation score, and then grouping the LSOAs in each Local Authority into five groups (quintiles) with approximately equal numbers of LSOAs in each. The upper local deprivation quintile (Quintile 1) corresponds with the 20% most deprived small areas within that Local Authority. The mortality rates have been directly age-standardised using the European Standard Population in order to make allowances for differences in the age structure of populations. There are inequalities in health. For example, people living in more deprived areas tend to have shorter life expectancy, and higher prevalence and mortality rates of most cancers. Lung cancer accounts for 7% of all deaths among men and in England every year and 4% of deaths among women every year. This amounts to 24% of all cancer deaths among men in England and 18% of all cancer deaths among women in England1. Reducing inequalities in premature mortality from all cancers is a national priority, as set out in the Department of Health’s Vital Signs Operating Framework 2008/09-2010/111. This indicator has been produced in order to quantify inequalities in lung cancer mortality by deprivation. This indicator has been discontinued and so there will be no further updates. Legacy unique identifier: P01406

Lung Cancer Deaths reports the number, crude rate, and age-adjusted mortality rate (AAMR) of deaths due to lung cancer.

Number and rate of new cancer cases diagnosed annually from 1992 to the most recent diagnosis year available. Included are all invasive cancers and in situ bladder cancer with cases defined using the Surveillance, Epidemiology and End Results (SEER) Groups for Primary Site based on the World Health Organization International Classification of Diseases for Oncology, Third Edition (ICD-O-3). Random rounding of case counts to the nearest multiple of 5 is used to prevent inappropriate disclosure of health-related information.