This dataset contains real-world information about colorectal cancer cases from different countries. It includes patient demographics, lifestyle risks, medical history, cancer stage, treatment types, survival chances, and healthcare costs. The dataset follows global trends in colorectal cancer incidence, mortality, and prevention.

Use this dataset to build models for cancer prediction, survival analysis, healthcare cost estimation, and disease risk factors.

Dataset Structure Each row represents an individual case, and the columns include:

Patient_ID (Unique identifier) Country (Based on incidence distribution) Age (Following colorectal cancer age trends) Gender (M/F, considering men have 30-40% higher risk) Cancer_Stage (Localized, Regional, Metastatic) Tumor_Size_mm (Randomized within medical limits) Family_History (Yes/No) Smoking_History (Yes/No) Alcohol_Consumption (Yes/No) Obesity_BMI (Normal/Overweight/Obese) Diet_Risk (Low/Moderate/High) Physical_Activity (Low/Moderate/High) Diabetes (Yes/No) Inflammatory_Bowel_Disease (Yes/No) Genetic_Mutation (Yes/No) Screening_History (Regular/Irregular/Never) Early_Detection (Yes/No) Treatment_Type (Surgery/Chemotherapy/Radiotherapy/Combination) Survival_5_years (Yes/No) Mortality (Yes/No) Healthcare_Costs (Country-dependent, $25K-$100K+) Incidence_Rate_per_100K (Country-level prevalence) Mortality_Rate_per_100K (Country-level mortality) Urban_or_Rural (Urban/Rural) Economic_Classification (Developed/Developing) Healthcare_Access (Low/Moderate/High) Insurance_Status (Insured/Uninsured) Survival_Prediction (Yes/No, based on factors)

This dataset provides a detailed and structured overview of oral cancer cases worldwide. It includes key risk factors, symptoms, cancer staging, survival rates, treatment approaches, and economic burden to facilitate research and prediction modeling. The dataset is based on real-world oral cancer statistics, aligning with global health reports and studies.

Key Highlights: Covers high-incidence regions (India, Pakistan, Sri Lanka, Taiwan) and emerging trends in Western nations. Includes tobacco, alcohol, HPV infection, betel quid use, and dietary factors as primary risk factors. Captures economic burden (treatment costs, workdays lost) to assess the financial impact of oral cancer. Provides cancer staging, survival rates, and early diagnosis indicators for better treatment predictions. This dataset is valuable for medical professionals, researchers, data scientists, and policymakers aiming to develop early detection models, assess regional disparities, and improve cancer prevention strategies.

Columns Overview ID – Unique identifier Country – Country name Age – Age of the individual Gender – Male/Female Tobacco Use – Yes/No Alcohol Consumption – Yes/No HPV Infection – Yes/No Betel Quid Use – Yes/No Chronic Sun Exposure – Yes/No Poor Oral Hygiene – Yes/No Diet (Fruits & Vegetables Intake) – Low/Moderate/High Family History of Cancer – Yes/No Compromised Immune System – Yes/No Oral Lesions – Yes/No Unexplained Bleeding – Yes/No Difficulty Swallowing – Yes/No White or Red Patches in Mouth – Yes/No Tumor Size (cm) – Numerical value Cancer Stage – 0 (No Cancer), 1, 2, 3, 4 Treatment Type – Surgery/Radiation/Chemotherapy/Targeted Therapy/No Treatment Survival Rate (5-Year, %) Cost of Treatment (USD) Economic Burden (Lost Workdays per Year) Early Diagnosis (Yes/No) Oral Cancer (Diagnosis) – Yes/No (Target Variable)

Source: https://ourworldindata.org/cancer

The dataset titled "Cancer Types Causing Death," sourced from Our World in Data, provides a comprehensive overview of global cancer mortality trends. According to the dataset, lung cancer leads as the most fatal cancer worldwide, with approximately 1.8 million deaths in 2022, accounting for 18.7% of all cancer-related fatalities . Following lung cancer, colorectal cancer ranks second, causing about 900,000 deaths (9.3%), while liver cancer and breast cancer account for 760,000 (7.8%) and 670,000 (6.9%) deaths, respectively. Stomach cancer also remains a significant cause of death, with 660,000 fatalities (6.8%) .

The dataset highlights that lung cancer's prevalence is closely linked to tobacco use, particularly in regions like Asia. In contrast, breast cancer predominantly affects women, while colorectal cancer impacts both genders equally. Notably, the dataset indicates a decline in age-standardized death rates for certain cancers, such as stomach cancer, due to improved hygiene, sanitation, and antibiotic treatments targeting Helicobacter pylori infections . Our World in Data

Additionally, the dataset underscores the global disparity in cancer mortality, with approximately 70% of cancer deaths occurring in low- and middle-income countries . This disparity is attributed to factors like limited access to early detection, treatment, and preventive measures. The dataset serves as a valuable resource for understanding the global burden of cancer and the need for targeted public health interventions. World Health Organization

Lung Cancer Diagnostic Tests Market Outlook

The lung cancer diagnostic tests market size was valued at USD 2.5 billion in 2023 and is projected to reach USD 6.1 billion by 2032, growing at a Compound Annual Growth Rate (CAGR) of 10.5% during the forecast period. This substantial growth can be attributed to the rising prevalence of lung cancer globally, advancements in diagnostic technologies, and increasing awareness regarding early detection and treatment of lung cancer. The growing aging population and the high incidence of smoking, which is a leading cause of lung cancer, further propel the demand for diagnostic tests.

The increasing prevalence of lung cancer is one of the primary drivers of market growth. Lung cancer remains the leading cause of cancer-related deaths worldwide, necessitating the development of more accurate and early diagnostic methods. With advancements in medical technology, such as molecular diagnostics and non-invasive imaging techniques, the accuracy and efficiency of lung cancer diagnosis have significantly improved. These innovations not only enhance the detection rate but also facilitate personalized treatment plans, thereby improving patient outcomes.

Furthermore, government initiatives and funding for cancer research play a crucial role in market expansion. Many countries are investing heavily in cancer research, leading to the development of new diagnostic tools and techniques. For instance, organizations such as the National Cancer Institute (NCI) in the United States provide substantial grants for lung cancer research, fostering innovations in diagnostics. In addition, public awareness campaigns and screening programs conducted by healthcare organizations and governments encourage early diagnosis, which is vital for successful treatment and survival rates.

The integration of artificial intelligence (AI) and machine learning in diagnostic tools is another significant factor contributing to market growth. AI algorithms can analyze medical images with high precision, aiding radiologists in identifying lung cancer at earlier stages. Moreover, AI-driven software can evaluate large datasets from genetic and molecular tests, providing insights into the most effective treatment options based on individual patient profiles. This technological advancement not only enhances the accuracy of diagnostics but also reduces the time required for analysis, thereby increasing the efficiency of healthcare services.

The EGFR Mutation Test is a pivotal advancement in the realm of lung cancer diagnostics, offering a more personalized approach to treatment. This test specifically identifies mutations in the Epidermal Growth Factor Receptor (EGFR) gene, which are often present in non-small cell lung cancer (NSCLC) patients. By detecting these mutations, healthcare providers can tailor therapies that target the specific genetic alterations, thereby improving treatment efficacy and patient outcomes. The growing adoption of EGFR Mutation Tests underscores the shift towards precision medicine, where treatments are increasingly customized based on individual genetic profiles. This approach not only enhances the effectiveness of therapies but also minimizes adverse effects, as treatments are more accurately aligned with the patient's unique genetic makeup.

Regionally, North America holds the largest share of the lung cancer diagnostic tests market, followed by Europe and Asia Pacific. The dominance of North America can be attributed to the presence of advanced healthcare infrastructure, high healthcare expenditure, and a robust research landscape. The Asia Pacific region, however, is expected to witness the highest growth rate during the forecast period, driven by increasing healthcare investments, growing awareness about lung cancer, and rising incidences of the disease in countries like China and India. The growing middle-class population and improving healthcare access in these countries further support market growth.

Test Type Analysis

The lung cancer diagnostic tests market is segmented by test type into imaging tests, sputum cytology, tissue biopsy, molecular tests, and others. Imaging tests are one of the most commonly used diagnostic methods for lung cancer detection. Techniques such as X-rays, CT scans, and PET scans provide detailed visuals of the lungs, helping in identifying abnormal growths or tumors. The non-invasive nature of these tests and their ability to provide quick results make them a preferred choice among healthcare

Counties were ranked for each risk factor associated with breast cancer in numerical order according to data presented in Table 2 and Table 3. Based on their numerical ranking in each dataset category, each county was assigned a risk factor quartile score, with 1 indicating the lowest quartile, and 4 indicating the highest quartile. The quartile score for breast cancer mortality rate and breast cancer incidence rate was weighted double. The sum of the quartile scores of each category was caluclated for each county to generate the integrated quartile score. A high integrated quartile score is intended to reflect the county with the greatest need of breast cancer-related resources aimed at reducing breast cancer mortality.

BackgroundThe nationwide HUN-CANCER EPI study examined cancer incidence and mortality rates in Hungary from 2011 to 2019.MethodsUsing data from the National Health Insurance Fund (NHIF) and Hungarian Central Statistical Office (HCSO), our retrospective study analyzed newly diagnosed malignancies between Jan 1, 2011, and Dec 31, 2019. Age-standardized incidence and mortality rates were calculated for all and for different tumor types using both the 1976 and 2013 European Standard Populations (ESP).FindingsThe number of newly diagnosed cancer cases decreased from 60,554 to 56,675 between 2011–2019. Age-standardized incidence rates were much lower in 2018, than previously estimated (475.5 vs. 580.5/100,000 person-years [PYs] in males and 383.6 vs. 438.5/100,000 PYs in females; ESP 1976). All-site cancer incidence showed a mean annual decrease of 1.9% (95% CI: 2.4%-1.4%) in men and 1.0% (95% CI:1.42%-0.66%) in women, parallel to mortality trends (-1.6% in males and -0.6% in females; ESP 2013). In 2018, the highest age-standardized incidence rates were found for lung (88.3), colorectal (82.2), and prostate cancer (62.3) in men, and breast (104.6), lung (47.7), and colorectal cancer (45.8) in women. The most significant decreases in incidence rates were observed for stomach (4.7%), laryngeal (4.4%), and gallbladder cancers (3.5%), with parallel decreases in mortality rates (3.9%, 2.7% and 3.2%, respectively).InterpretationWe found a lower incidence of newly diagnosed cancer cases for Hungary compared to previous estimates, and decreasing trends in cancer incidence and mortality, in line with global findings and the declining prevalence of smoking.

BACKGROUND Comprehensive analyses of statistical data on breast cancer incidence, mortality, and associated risk factors are of great value for decision-making related to reducing the disease burden of breast cancer. METHODS: Based on data from the Annual Report of China Tumour Registry and the Global Burden of Disease (GBD), we conducted summary and trend analyses of incidence and mortality rates of breast cancer in Chinese women from 2014 to 2018 for urban and rural areas in the whole, eastern, central, and western parts of the country, and projected the incidence and mortality rates of breast cancer for 2019 in comparison with the GBD 2019 estimates. And the comparative risk assessment framework estimated risk factors contributing to breast cancer deaths and disability-adjusted life years (DALYs) from GBD. RESULTS: The Annual Report of the Chinese Tumour Registry showed that showed that the mortality rate of breast cancer declined and the incidence rate remained largely unchanged from 2014 to 2018. There was a significant increasing trend in incidence rates among urban and rural women in eastern China and rural women in central China, whereas there was a significant decreasing trend in mortality rates among rural women in China. The two data sources have some differences in their predictions of breast cancer in China in 2019. The GBD data estimated the age-standard DALYs rates of high body-mass index, high fasting plasma glucose and diet high in red meat, which are the top three risk factors attributable to breast cancer in Chinese women, to be 29.99/100,000, 13.66/100,000 and 13.44/100,000, respectively. Conclusion: The trend of breast cancer incidence and mortality rates shown in the Annual Report of China Tumour Registry indicates that China has achieved remarkable results in reducing the burden of breast cancer, but there is still a need to further improve breast cancer screening and early diagnosis and treatment, and to improve the system of primary prevention. The GBD database provides risk factors for breast cancer in the world, Asia, and China, and lays the foundation for research on effective measures to reduce the burden of breast cancer.

Background: Rising expenditure for new cancer medicines is accelerating concerns that their costs will become unsustainable for universal healthcare access. Moreover, early market access of new oncology medicines lacking appropriate clinical evaluation generates uncertainty over their cost-effectiveness and increases expenditure for unknown health gain. Patient-level data can complement clinical trials and generate better evidence on the effectiveness, safety and outcomes of these new medicines in routine care. This can support policy decisions including funding. Consequently, there is a need for improving datasets for establishing real-world outcomes of newly launched oncology medicines.Aim: To outline the types of available datasets for collecting patient-level data for oncology among different European countries. Additionally, to highlight concerns regarding the use and availability of such data from a health authority perspective as well as possibilities for cross-national collaboration to improve data collection and inform decision-making.Methods: A mixed methods approach was undertaken through a cross-sectional questionnaire followed-up by a focus group discussion. Participants were selected by purposive sampling to represent stakeholders across different European countries and healthcare settings. Descriptive statistics were used to analyze quantifiable questions, whilst content analysis was employed for open-ended questions.Results: 25 respondents across 18 European countries provided their insights on the types of datasets collecting oncology data, including hospital records, cancer, prescription and medicine registers. The most available is expenditure data whilst data concerning effectiveness, safety and outcomes is less available, and there are concerns with data validity. A major constraint to data collection is the lack of comprehensive registries and limited data on effectiveness, safety and outcomes of new medicines. Data ownership limits data accessibility as well as possibilities for linkage, and data collection is time-consuming, necessitating dedicated staff and better systems to facilitate the process. Cross-national collaboration is challenging but the engagement of multiple stakeholders is a key step to reach common goals through research.Conclusion: This study acts as a starting point for future research on patient-level databases for oncology across Europe. Future recommendations will require continued engagement in research, building on current initiatives and involving multiple stakeholders to establish guidelines and commitments for transparency and data sharing.

Background : Substantial differences between countries were observed in terms of Covid-19 death tolls during the past two years. It was of interest to find out how the epidemiologic and/or demographic history of the population may have had a role in the high prevalence of the Covid-19 in some countries. Objective : This observational study aimed to investigate possible relations between Covid-19 death numbers in 39 countries and the prepandemic history of epidemiologic and demographic conditions. Methods : We sought the Covid-19 death toll in 39 countries in Europe, America, Africa, and Asia. Records (2019) of epidemiologic (Cancer, Alzheimer's disease) and demographic (natality, mortality, and fetility rates, percentage of people aged 65 and over) parameters as well as data on alcohol intake per capita were retrieved from official web pages. Data was analysed by simple linear or polynomial regression by the mean of Microsoft Excell software (2016). Results : When Covid-19 death numbers were plotted against the geographic latitude of each country, a bell-shaped curve was obtained for both the first and second years (coefficient of determination R2=0.38) of the pandemic. In a similar manner, bell-shaped curves were obtained when latitudes were plotted against the scores of (cancer plus Alzheimer's disease, R² = 0,65,), the percentage of advanced age (R² = 0,52,) and the alcohol intake level (R² = 0,64,). Covid-19 death numbers were positively correlated to the scores of (cancer plus Alzheimer's disease) (R2= 0.41, P= 1.61x10-5), advanced age (R2= 0.38, P= 4.09x10-5) and alcohol intake (R2= 0.48, P= 1.55x10-6). Instead, inverted bell-shaped curves were obtained when latitudes were plotted against the birth rate/mortality rate ratio (R² = 0,51) and the fetility rate (R² = 0,33). In addition, Covid-19 deaths were negatively correlated with the birth rate/mortality rate ratio (R2= 0.67) and fertility rate (R2= 0.50). Conclusion : The results show that the 39 countries in both hemisphers in this study have different patterns of epidemiologic and demographic factors, and that the negative history of epidemiologic and demographic factors of the northern hemisphere countries, as well as their high alcohol intake, were very correlated with their Covid-19 death tolls. Hence, also nutritional habits may have had a role in the general health status of people in regard to their immunity against the coronavirus.

Users can access data about cancer incidence and mortality for all the countries of the world as of 2008. Background GloboCan is a project of the International Agency for Research on Cancer and the World Health Organization (WHO). GloboCan presents estimates of the burden of cancer in 184 countries or territories around the world. User functionality GloboCan provides access to the most recent estimates (from 2008) of the incidence and mortality of 27 major cancers. Users can create fact sheets or do online analysis to create tables, graphs, maps, and predictions. Users c an choose to create tables by population or by cancer type. Covariates for analysis include age group, sex, and continent. Users are able to choose between mortality and incidence statistics. Users can choose to create age specific cancer curves, bar charts, maps, and pie charts. The prediction option allows the user to estimate the future burden of a selected cancer in selected population for a selected year. Data Notes Data sources and methods are clearly outlined on the “Data Sources and Methods” section of the website. Users are able to download their online analysis in PDF or html format. GloboCan uses the definitions outlined in the United Nations, World Population Prospects, 2008 revision (except Cyprus located in Southern Europe and Taiwan is located in Eastern Asia).

Death rate of a population adjusted to a standard age distribution. As most causes of death vary significantly with people's age and sex, the use of standardised death rates improves comparability over time and between countries, as they aim at measuring death rates independently of different age structures of populations. The standardised death rates used here are calculated on the basis of a standard European population (defined by the World Health Organization). Detailed data for 65 causes of death are available in the database (under the heading 'Data').

The relatively high incidence of cervical cancer in women at older ages is an issue in countries performing cervical screening for decades. Controversy remains on when and how to cease screening. Existing population-based studies on effectiveness of cervical screening at older ages have not considered women’s screening history. We performed a nationwide cohort study to investigate the incidence of cervical cancer after age 60 and its association with cervical screening at ages 61-65, stratified by screening history at ages 51-60. Using the Total Population Register, we identified women born between January 1919 and December 1945, resident in Sweden since age 51. According to the year that each county started the electronic record of cervical screening and women’s resident county, we further identified 569,132 women that have cervical screening record available since age 51. Women’s screening records, cervical cancer occurrence, and level of education were retrieved from the Swedish National Cervical Screening Registry, the National Cancer Register, and LISA (Longitudinal integration database for health insurance and labour market studies) respectively. We presented the cumulative incidence of cervical cancer from age 61-80 by using competing risk regression models, and compared the hazard ratio of cervical cancer by screening status at ages 61-65 from Cox models, adjusted for birth cohort and level of education, conditioning on screening history in their 50s. We find that Cervical screening at ages 61-65 is associated with a statistically significant reduction of subsequent cervical cancer risk for women unscreened, or screened with abnormalities, in their 50s. In women screened negative in their 50s, the risk for future cancer is not sizeable, and the risk reduction associated with continued screening appears limited. These findings should inform the current debate regarding age and criteria to discontinue cervical screening.

Purpose:

In order to provide evidence for age and criteria to discontinue cervical screening, we use this data to investigate the impact of cervical screening at ages 61-65 on cervical cancer incidence and stage at ages 61-80, stratifying by screening history at ages 51-60.

This data comprises women born between January 1919 and December 1945, resident in Sweden since age 51, and having cervical screening record available since age 51. It contains the following variables: - Seq_nr: sequence number indicating each individual woman, from 1 to 569,132. - Edu_cat: level of education in three categories: 1=low (less than high school); 2=high school; 3=university exam and above; .=missing. Data are retrieved from LISA (Longitudinal integration database for health insurance and labour market studies). - Birth_cat: five categories of birth-year: 1=1919-1925; 2=1926-1930; 3=1931-1935; 4=1936-1940; 5=1941-1945. - Scr_51_60: Screening history at ages 51-60, in five categories: 1=adequately screened, negative; 2=inadequately screened, negative; 3=unscreened; 4=having low-grade abnormality; 5=having high-grade abnormality. Data are retrieved from the Swedish National Cervical Screening Registry. - Age_first_scr_6165: age at having the first screening test at ages 61-65. (Missing value indicates there is no screening test at ages 61-65). Data are retrieved from the Swedish National Cervical Screening Registry. - Orgscr_county: If in the county that had more than 40% women being screened at ages 61-65: 0=no; 1=yes. - Age_entry: age when entering the cohort, which is 61 for all women. - Age_exit: age when the follow-up is finished. - Cx_fail: the event of finishing follow-up: 1=having cervical cancer; 2=competing events (death or having total hysterectomy); 3=censoring (emigration, turning age 81, or 2011-12-31). The information is retrieved from the Swedish National Cancer Registry (cervical cancer), Cause of Death Register (death), Patient Register (hysterectomy), and Migration Register (emigration). The dataset also includes three variables created by Swedish National Dataservice (SND-study, SND-dataset, SND-version).

Directly age-standardised registration rate for oral cancer (ICD-10 C00-C14), in persons of all ages, per 100,000 2013 European Standard PopulationRationaleTobacco is a known risk factor for oral cancers (1). In England, 65% of hospital admissions (2014–15) for oral cancer and 64 % of deaths (2014) due to oral cancer were attributed to smoking (2). Oral cancer registration is therefore a direct measure of smoking-related harm. Given the high proportion of these registrations that are due to smoking, a reduction in the prevalence of smoking would reduce the incidence of oral cancer.Towards a Smokefree Generation: A Tobacco Control Plan for England states that tobacco use remains one of our most significant public health challenges and that smoking is the single biggest cause of inequalities in death rates between the richest and poorest in our communities (3).In January 2012 the Public Health Outcomes Framework was published, then updated in 2016. Smoking and smoking related death plays a key role in two of the four domains: Health Improvement and Preventing premature mortality (4).References:(1) GBD 2013 Risk Factors Collaborators. Global, regional and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risk factors in 188 countries, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. The Lancet 2015; 386:10010 2287–2323. (2) Statistics on smoking, England 2016, May 2016; http://content.digital.nhs.uk/catalogue/PUB20781 (3) Towards a Smokefree Generation: A Tobacco Control Plan for England, July 2017 https://www.gov.uk/government/publications/towards-a-smoke-free-generation-tobacco-control-plan-for-england (4) Public Health Outcomes Framework 2016 to 2019, August 2016; https://www.gov.uk/government/publications/public-health-outcomes-framework-2016-to-2019 Definition of numeratorCancer registrations for oral cancer (ICD-10, C00-C14) in the calendar years 2007-09 to 2017-2019. The National Cancer Registration and Analysis Service collects data relating to each new diagnosis of cancer that occurs in England. This does not include secondary cancers. Data are reported according to the calendar year in which the cancer was diagnosed.Definition of denominatorPopulation-years (ONS mid-year population estimates aggregated for the respective years) for people of all ages, aggregated into quinary age bands (0-4, 5-9,…, 85-89, 90+).CaveatsReviews of the quality of UK cancer registry data 1, 2 have concluded that registrations are largely complete, accurate and reliable. The data on cancer registration ‘quality indicators’ (mortality to incidence ratios, zero survival cases and unspecified site) demonstrate that although there is some variability, overall ascertainment and reliability is good. However cancer registrations are continuously being updated, so the number of registrations for each year may not be complete, as there is a small but steady stream of late registrations, some of which only come to light through death certification.1. Huggett C (1995). Review of the Quality and Comparability of Data held by Regional Cancer Registries. Bristol: Bristol Cancer Epidemiology Unit incorporating the South West Cancer Registry. 2. Seddon DJ, Williams EMI (1997). Data quality in population based cancer registration. British Journal of Cancer 76: 667-674.The data presented here replace versions previously published. Population data and the European Standard Population have been revised. ONS have provided an explanation of the change in standard population (available at http://www.ons.gov.uk/ons/guide-method/user-guidance/health-and-life-events/revised-european-standard-population-2013--2013-esp-/index.html )

According to the World Cancer Report 2020 published by the World Health Organization's Institute for Research on Cancer (IARC), there will be 19.29 million new cancer cases and 9.96 million deaths globally in 2020, of which 4.569 million new cases and 3.003 million deaths will occur in China, accounting for 23.7% and 30.2% of the global new cases and deaths, respectively. Among them, China had 4.569 million new cancer cases and 3.003 million deaths, accounting for 23.7% and 30.2% of the global new cases and deaths respectively. China has become the largest country in the world in terms of new cancer cases and deaths.Nasopharyngeal cancer is a kind of malignant tumor with a very high clinical incidence rate, and it is at the top of the list of malignant tumors in otorhinolaryngology. Due to the deep and hidden nasopharyngeal part, the complex relationship with the surrounding area, and the differences in clinical manifestations, early diagnosis is very difficult, and it is very easy to miss the optimal time of treatment due to missed or misdiagnosis. Due to the unique anatomical location and tumor biological behavior of nasopharyngeal cancer, simultaneous radiotherapy has been the main treatment for nasopharyngeal cancer, followed by radiotherapy, chemotherapy, targeted therapy, surgery, and traditional Chinese medicine.Early tumor diagnosis refers to the use of rapid and easy methods to screen out a very small number of tumor high-risk groups from a large number of target populations that appear healthy and have not yet developed symptoms, which can detect tumors early and reduce the risk of morbidity, especially for cancer types with high morbidity and mortality rates and a long developmental cycle, such as lung, gastric, and colorectal cancers. From a global perspective, China's cancer incidence and mortality rates are at a high level, and there are multiple reasons for this phenomenon - medical technology needs to be improved, the quality of the living environment is poor, the routine of life is irregular, and living habits are poor. Compared with chronic diseases such as cardiovascular disease and diabetes, tumor is a "fatal disease" that requires early diagnosis and treatment, and the earlier the diagnosis, the greater the hope of cure. To integrate the data resources and results of early diagnosis of nasopharyngeal cancer and to promote related research, a literature review and information extraction analysis were carried out, and a biomarker-based early diagnosis database of nasopharyngeal cancer was constructed to assist the early diagnosis of nasopharyngeal cancer. The database covers the types of biomarkers, name, specificity, sensitivity, AUC, cell lines used, sample type, sample size, references, and their links. The database contains many types of biomarkers and is a powerful tool for early screening and diagnosis of nasopharyngeal cancer.

Background: A cancer patient's quality of life (QoL) is the perception of their physical, functional, psychological, and social well-being as well as their mental and emotional state. QoL is one of the most important factors to consider when a person is being treated for cancer and during follow-up. The present study aimed to understand the status of QoL of cancer patients and determine the factors affecting it. Methods: This cross-sectional study was conducted among 210 cancer patients attending the oncology unit of a medical college, within a 4-month consecutive time period in 2022. Data were collected by using the Bengali version of the European Organization for Research and Treatment of Cancer questionnaire. Results: The present study reported a high number of female cancer patients (67.6%). Breast cancer was more common among females (31.43%) while lung and upper respiratory tract cancer was among males (19.05). Most of the patients in the present study were diagnosed with cancer in the past year (86.19%). The functional scales' overall mean scores varied from 54.92 for physical functioning to 38.89 for social functioning. The highest symptom scale score was for financial issues (63.02), while the lowest was for diarrhea (33.01). The overall QoL of cancer patients in the present study was 47.98 which was 45.71 for males and 49.10 for females respectively.
Conclusion: The overall QoL was poor in cancer patients in the present study compared to the developed countries. There was a low score for QoL for social and emotional function. Financial difficulty was the primary reason behind low QoL in the symptom scale. If the government supports cancer patients by providing subsidies for treatment and health insurance policies, cancer patients will benefit and QoL will improve. Methods The study proposal and consent form were approved by the Ethics Committee. The present study was conducted in the Oncology Unit of a medical college within a 4-month consecutive time period in 2022. The expected number of new cancer patients visiting the department was 400 during the study period. We chose p = 0.50, q = 0.50, Z = 1.96, and E = 0.04 for N = 400, and the minimum sample size was calculated to be n = 196. For the purpose of this study, permission was sought from European Organization for Research and Treatment of Cancer (EORTC) to use the Bengali version of their EORTC QLQ C30 questionnaire. EORTC provided the research tool and scoring manuals for the study. The 30-item questionnaire covers 15 domains which consist of five functioning scales (physical functioning, social functioning, role functioning, emotional functioning, and cognitive functioning) and nine symptom scales (fatigue, pain, nausea/vomiting, dyspnea, sleep disturbances, appetite loss, diarrhea, constipation, and financial difficulties) and one global health status/ quality of life scale.(Aaronson et al., 1993) Strong scores on the functioning and global health status/QoL scales on the 100-point meter suggest high QoL, whereas high scores on the symptom scales indicate a high symptom burden.(Fayers PM et al., 2001) Data were collected 2 days each week. All adult patients who came to the outpatient clinic and all patients newly admitted to the inpatient clinic on those days were administered the questionnaire in person by the first author. The study objective was explained to the patients and verbal consent was obtained. Patients who were interviewed for this study previously, those who could not provide consent (unconscious), patients with suspected cancer but without a confirmed report, and patients less than 18 years of age were excluded. Socio-demographic characteristics such as age at treatment, gender, marital status, religion, economic status, and education were obtained from the patients. The information on clinical status such as the site of the primary tumor, stage of the tumor, and type of treatment was recorded from the clinical documentation.

Intro

This open dataset aggregates all known cause of deaths (diseases and accidents). It contains sex and age group-specific deaths. The novelty lies in the entity relations using weighted biological causations and not only correlations. Data for France are curated and ready. Data for the USA in work (see Tasks). To help review, curate or correct any information from the dataset, please see Tasks and/or use the Kaggle discussion section of this dataset. Thank you.

Novelties/Differentiations

1)causality relations between all diseases/causes and detailed data (m/f, age group) in a single dataset 2)dataset focuses on causality/biology mechanisms and excludes correlations without causation (correlation does not mean causation) 3)add dimensions and causations between concepts: root -> indirect(s) -> direct e.g. "infection by VIH" -> "AIDS" -> "infection" -> "pneumonia" -> "low oxygen in blood"

How to cite/reuse this work?

Mentioned this source as ""Multi-Level causality relations of underlying of deaths with age, sex and country stratifications, Schicklin, C., Version [version number]. https://doi.org/10.34740/kaggle/dsv/2161283 Retrieved [month] [day], [year]"". Before publishing any rework, reuse, commercially or non-commercially, please send the info to the contact contributor https://www.kaggle.com/cedricschicklin "Contact User". This is a non-blocking step for information and data tracking only. For reuse in entity relation analysis, it is recommended to use the JSON parsable "predecessor_array".

Quality Charter

Data sources for each total deaths country-specific are available in sources_total_deaths_[COUNTRY] or computed via causality. Data sources for each causality mechanisms are available in sources_total_deaths_[COUNTRY]. Weights in the predecessor arrays are computed using total deaths for a country and are not recomputed per sub-group. "Unknown"-like concepts in the dataset means that the concept has not discovered yet as per the best knowledge of contributors from available scientific information. quality columns indicate if the source is extensive, and among others, level on knowledge. Please see the column header description for more information. @contributors: only mention allowed data sources (check if copyrighted, and at minimum reusable for non-commercial use), if any, add copyright specific information in the source column after the source data.

Format

CSV with JSON arrays. The decimal sign is the point. The comma is only allowed in source CSV quoted text e.g. [{"predecessor_id":"ischemia","impact":"0.601"},{"predecessor_id":"kidney cancer","impact":"0.142"},{"predecessor_id":"diabetes","impact":"0.256"}]

Disclaimer

This compilation of data is intended for data scientists only and not for patients. Please use this project for health statistical reporting and analysis only. For health issues, please consult a medical doctor. This compilation is a collaborative work distributed without any warranty. Clause de non-responsabilité: les autheurs et les évaluateurs ne sont pas responsable de l'usage qui pourrait être fait des informations données ci-après.

Abstract copyright UK Data Service and data collection copyright owner. The Organisation for Economic Co-operation and Development (OECD) Health Statistics offers the most comprehensive source of comparable statistics on health and health systems across OECD countries. It is an essential tool for health researchers and policy advisors in governments, the private sector and the academic community, to carry out comparative analyses and draw lessons from international comparisons of diverse health care systems. Within UKDS.Stat the data are presented in the following databases: Health status This datasets presents internationally comparable statistics on morbidity and mortality with variables such as life expectancy, causes of mortality, maternal and infant mortality, potential years of life lost, perceived health status, infant health, dental health, communicable diseases, cancer, injuries, absence from work due to illness. The annual data begins in 2000. Non-medical determinants of health This dataset examines the non-medical determinants of health by comparing food, alcohol, tobacco consumption and body weight amongst countries. The data are expressed in different measures such as calories, grammes, kilo, gender, population. The data begins in 1960. Healthcare resources This dataset includes comparative tables analyzing various health care resources such as total health and social employment, physicians by age, gender, categories, midwives, nurses, caring personnel, personal care workers, dentists, pharmacists, physiotherapists, hospital employment, graduates, remuneration of health professionals, hospitals, hospital beds, medical technology with their respective subsets. The statistics are expressed in different units of measure such as number of persons, salaried, self-employed, per population. The annual data begins in 1960. Healthcare utilisation This dataset includes statistics comparing different countries’ level of health care utilisation in terms of prevention, immunisation, screening, diagnostics exams, consultations, in-patient utilisation, average length of stay, diagnostic categories, acute care, in-patient care, discharge rates, transplants, dialyses, ICD-9-CM. The data is comparable with respect to units of measures such as days, percentages, population, number per capita, procedures, and available beds. Health Care Quality Indicators This dataset includes comparative tables analyzing various health care quality indicators such as cancer care, care for acute exacerbation of chronic conditions, care for chronic conditions and care for mental disorders. The annual data begins in 1995. Pharmaceutical market This dataset focuses on the pharmaceutical market comparing countries in terms of pharmaceutical consumption, drugs, pharmaceutical sales, pharmaceutical market, revenues, statistics. The annual data begins in 1960. Long-term care resources and utilisation This dataset provides statistics comparing long-term care resources and utilisation by country in terms of workers, beds in nursing and residential care facilities and care recipients. In this table data is expressed in different measures such as gender, age and population. The annual data begins in 1960. Health expenditure and financing This dataset compares countries in terms of their current and total expenditures on health by comparing how they allocate their budget with respect to different health care functions while looking at different financing agents and providers. The data covers the years starting from 1960 extending until 2010. The countries covered are Australia, Austria, Belgium, Canada, Chile, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Israel, Italy, Japan, Korea, Luxembourg, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, Turkey, United Kingdom, and United States. Social protection This dataset introduces the different health care coverage systems such as the government/social health insurance and private health insurance. The statistics are expressed in percentage of the population covered or number of persons. The annual data begins in 1960. Demographic references This dataset provides statistics regarding general demographic references in terms of population, age structure, gender, but also in term of labour force. The annual data begins in 1960. Economic references This dataset presents main economic indicators such as GDP and Purchasing power parities (PPP) and compares countries in terms of those macroeconomic references as well as currency rates, average annual wages. The annual data begins in 1960. These data were first provided by the UK Data Service in November 2014.

DOI The Prevention and Screening Innovation Project towards Elimination of Cervical Cancer (PRESCRIP-TEC) research project contributes to the evidence-base for the WHO strategy to eliminate cervical cancer as a public health problem. The project implements an innovative approach in cervical cancer screening, including direct treatment and follow-up, for women in resource-poor or hard-to-reach settings, by improving availability, accessibility, acceptability and quality of services. PRESCRIP-TEC focuses on implementation research into secondary prevention of cervical cancer in different settings in four countries over three continents: Bangladesh and India in Asia, Uganda in Africa, and Slovakia in Eastern Europe. The project builds on interventions with promising or proven effectiveness including cost-effectiveness: - hrHPV based screening is cost-effective when adequate coverage is reached. - Self-swab for hrHPV leads to higher uptake of screening compared to sampling by clinicians. - Visual inspection with acetic acid (VIA) is an approved screening method by the WHO and is part of the national cervical cancer prevention programme in Uganda, Bangladesh, India; in India AI to support VIA screening was shown to be effective in detecting VIA positive lesions. The dataset concerns the following research question per target country: Which are the client-related factors for accessibility and acceptability and adherence to the enhanced screening protocol? Eligible women for the study were women who were 1) living in the study areas and based on their age eligible for cervical cancer screening according to national guidelines, 2) not pregnant, 3) not screened in the previous period whether it was defined based on the national screening policy, and 4) having the cognitive abilities to understand and answer to the questions and give informed consent. Convenience and random sampling were used for selection of respondents and different steps for recruitment were undertaken in each country. In Uganda convenience sampling was applied and eligible women were invited for participation. In Bangladesh sampling at household level was done. Households in the study areas were included for participation if females, husbands and mothers in law from wanted to participate. First convenience sampling was applied and thereafter random sampling was followed. In India, consecutive sampling was applied to approach eligible women and decision makers across various centerscentres. Decision makers were interviewed from the same household when they were considered by the women as being a decision maker. Male decision makers were either husband, son, father or father-in-law and female decision makers were mother or mother-in-law. In Slovak Republic the snowball sampling method was applied to approach eligible women from marginalised Roma communities. Partners or relatives of participating women were approached for participation as well. The African Women Awareness of Cancer (AWACAN) questionnaire for measuring women’s awareness of breast and cervical cancer (Moodley, 2020) was adapted for use within this study. The PRESCRIP-TEC research team selected the questions solely addressing cervical cancer awareness from the AWACAN survey and developed a AWACAN survey suitable for decision-makers by modifying the selected AWACAN questions. For female respondents, the AWACAN instrument included five socio-demographic questions and five questions on the history concerning cervical cancer screening and treatment and one question on household decision-making. For both females and household-decision makers the adapted survey questionnaire included a total of 53 questions. The tool measures cervical cancer awareness in the following domains: risk factors, symptoms, lay beliefs, confidence in appraisal, help-seeking behaviours, and barriers to health care. The questionnaire is a mix of open, closed and multiple-choice questions. Questions about risk factors are only asked to respondents if they indicate to have heard about cervical cancer. Knowledge scores of risk factors and symptoms were are calculated by assigning 1 point to each "Yes" response and a 0 to "No" response. The cumulative score for knowledge of correct risk factors ranges from 0 to 11 and the cumulative score for knowledge of all symptoms ranges from 0 to 12. The AWACAN tool has shown to be reliable and valid for use in Sub-Saharan Africa (Moodley, 2019) and to our knowledge the instrument has not been used previously in Bangladesh, India and Slovak Republic. The AWACAN survey was conducted in four countries, in Bangladesh, India, Uganda and Slovak Republic in the context of the PRESCRIP-TEC project.

Total-Other-Finance-Cost Time Series for RaySearch Laboratories AB (publ). RaySearch Laboratories AB (publ), a medical technology company, provides software solutions for cancer treatment worldwide. The company offers RayStation, a treatment planning system for cancer centers; RayCare, an oncology information system for clinics; RayIntelligence, cloud-based oncology analytics system that cancer centers can use to gather, structure and analyze data; and RayCommand, a treatment control system designed to link the treatment machine and the treatment planning and oncology information systems. It also provides µ-RayStation* (Micro-RayStation), a software platform for planning and evaluation in small animal irradiation research; RayPlan, a treatment planning system; DrugLog, which verifies the identity and concentration of compounded injectables before administered to a patient; RayMigrate, allows users to convert Pinnacle patient data to RayStation format and/or to DICOM format; and machine learning and liver ablation solutions. It has collaboration with Vision RT for integration of MapRT with RayStation treatment planning system. The company was founded in 2000 and is headquartered in Stockholm, Sweden.

Background: Rising expenditure for new cancer medicines is accelerating concerns that their costs will become unsustainable for universal healthcare access. Moreover, early market access of new oncology medicines lacking appropriate clinical evaluation generates uncertainty over their cost-effectiveness and increases expenditure for unknown health gain. Patient-level data can complement clinical trials and generate better evidence on the effectiveness, safety and outcomes of these new medicines in routine care. This can support policy decisions including funding. Consequently, there is a need for improving datasets for establishing real-world outcomes of newly launched oncology medicines.Aim: To outline the types of available datasets for collecting patient-level data for oncology among different European countries. Additionally, to highlight concerns regarding the use and availability of such data from a health authority perspective as well as possibilities for cross-national collaboration to improve data collection and inform decision-making.Methods: A mixed methods approach was undertaken through a cross-sectional questionnaire followed-up by a focus group discussion. Participants were selected by purposive sampling to represent stakeholders across different European countries and healthcare settings. Descriptive statistics were used to analyze quantifiable questions, whilst content analysis was employed for open-ended questions.Results: 25 respondents across 18 European countries provided their insights on the types of datasets collecting oncology data, including hospital records, cancer, prescription and medicine registers. The most available is expenditure data whilst data concerning effectiveness, safety and outcomes is less available, and there are concerns with data validity. A major constraint to data collection is the lack of comprehensive registries and limited data on effectiveness, safety and outcomes of new medicines. Data ownership limits data accessibility as well as possibilities for linkage, and data collection is time-consuming, necessitating dedicated staff and better systems to facilitate the process. Cross-national collaboration is challenging but the engagement of multiple stakeholders is a key step to reach common goals through research.Conclusion: This study acts as a starting point for future research on patient-level databases for oncology across Europe. Future recommendations will require continued engagement in research, building on current initiatives and involving multiple stakeholders to establish guidelines and commitments for transparency and data sharing.