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).

In 2022, Poland reported 330.5 deaths from cancer per 100,000 population, the highest cancer mortality rate in Europe. Hungary followed with 327.8 cancer deaths per 100,000, and then Croatia with 319.6 cancer deaths per 100,000 population. This statistic displays the cancer mortality rate in Europe in 2022, by country (per 100,000 population).

In 2022, the mortality rate of breast cancer in women in Europe was **** per 100,000 women. Cyprus had the highest mortality rate at **** per 100,000, followed by Slovakia with **** per 100,000 women. Conversely, Spain had the lowest mortality rate at **** per 100,000. This statistic depicts the mortality rate of breast cancer in Europe in 2022 in women population, by country.

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.

In 2022, the mortality rate of prostate cancer in Europe was **** per 100,000. Estonia had the highest mortality rate at **** per 100,000, followed by Latvia with **** per 100,000 men. Conversely, Italy had the lowest mortality rate at **** per 100,000. This statistic depicts the mortality rate of prostate cancer Europe in 2022, by country.

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

Users can access data about cancer statistics, specifically incidence and mortality worldwide for the 27 major types of cancer. Background Cancer Mondial is maintained by the Section of Cancer Information (CIN) of International Agency for Research on Cancer by the World Health Organization. Users can access CIN databases including GLOBOCAN, CI5(Cancer Incidence in Five Continents), WHO, ACCIS(Automated Childhood Cancer Information System), ECO (European Cancer Observatory), NORDCAN and Survcan. User functionality Users can access a variety of databases. CIN Databases: GLOBOCAN provides acces s to the most recent estimates (for 2008) of the incidence of 27 major cancers and mortality from 27 major cancers worldwide. CI5 (Cancer Incidence in Five Continents) provides access to detailed information on the incidence of cancer recorded by cancer registries (regional or national) worldwide. WHO presents long time series of selected cancer mortality recorded in selected countries of the world. Collaborative projects: ACCIS (Automated Childhood Cancer Information System) provides access to data on cancer incidence and survival of children collected by European cancer registries. ECO (European Cancer Observatory) provides access to the estimates (for 2008) of the incidence of, and mortality f rom 25 major cancers in the countries of the European Union (EU-27). NORDCAN presents up-to-date long time series of cancer incidence, mortality, prevalence and survival from 40 cancers recorded by the Nordic countries. SurvCan presents cancer survival data from cancer registries in low and middle income regions of the world. Data Notes Data is available in different formats depending on which type of data is accessed. Some data is available in table, PDF, and html formats. Detailed information about the data is available.

In 2022, the mortality rate of colorectal cancer in Europe was, among men, **** per 100,000, while among women it stood at **** per 100,000. For men, Croatia had the highest mortality rate at **** per 100,000, while Luxembourg had the lowest at **** per 100,000. For women, Croatia also had the highest mortality rate at **** per 100,000, while Austria had the lowest at **** per 100,000. This statistic depicts the mortality rate of colorectal cancer in Europe in 2022, by country and gender.

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

Figure 2.8 5-year relative survival rates from selected cancers 1998-2003 to 2008-2013, Ireland and selected OECD countries

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.

Blood cancer survival rates vary widely across the globe — and the contrast between India and developed nations is both eye-opening and urgent. While countries like the U.S., UK, and Germany report survival rates of 60–75% for common blood cancers such as leukaemia, lymphoma, and myeloma, India’s figures remain significantly lower, often ranging between 30 and 40%.This gap is driven by several critical factors: late diagnosis, limited access to advanced treatments, lack of awareness, and uneven healthcare infrastructure. Yet, with timely detection and modern therapies like CAR T-cell therapy and bone marrow transplant, these numbers can improve — and lives can be saved.At bmtnext.com, BMT NEXT is working to close this survival gap by offering world-class care, cutting-edge treatment options, and personalised support for every patient. Our mission is to ensure that patients in India receive the same level of care and hope as those in the most advanced healthcare systems.It’s time to bridge the divide — and BMT NEXT is leading the way.

Despite substantial improvements in survival from childhood cancer during the last decades, there are indications that survival rates for several cancer types are no longer improving. Moreover, evidence accumulates suggesting that socioeconomic and sociodemographic factors may have an impact on survival also in high-income countries. The aim of this review is to summarize the findings from studies on social factors and survival in childhood cancer. Several types of cancer and social factors are included in order to shed light on potential mechanisms and identify particularly affected groups. A literature search conducted in PubMed identified 333 articles published from December 2012 until June 2018, of which 24 fulfilled the inclusion criteria. The findings are diverse; some studies found no associations but several indicated a social gradient with higher mortality among children from families of lower socioeconomic status (SES). There were no clear suggestions of particularly vulnerable subgroups, but hematological malignancies were most commonly investigated. A wide range of social factors have been examined and seem to be of different importance and varying between studies. However, potential underlying mechanisms linking a specific social factor to childhood cancer survival was seldom described. This review provides some support for a relationship between lower parental SES and worse survival after childhood cancer, which is a finding that needs further attention. Studies investigating predefined hypotheses involving specific social factors within homogenous cancer types are lacking and would increase the understanding of mechanisms involved, and allow targeted interventions to reduce health inequalities.

In 2010, cancer deaths accounted for more than 15% of all deaths worldwide, and this fraction is estimated to rise in the coming years. Increased cancer mortality has been observed in immigrant populations, but a comprehensive analysis by country of birth has not been conducted. We followed all individuals living in Sweden between 1961 and 2009 (7,109,327 men and 6,958,714 women), and calculated crude cancer mortality rates and age-standardized rates (ASRs) using the world population for standardization. We observed a downward trend in all-site ASRs over the past two decades in men regardless of country of birth but no such trend was found in women. All-site cancer mortality increased with decreasing levels of education regardless of sex and country of birth (p for trend

Published as part of Health in Ireland: Key Trends 2016 (Department of Health) Published as part of Health in Ireland: Key Trends 2016 (Department of Health)

In 2022, the mortality rate of pancreatic cancer in Europe was, among men, **** per 100,000, while among women it stood at ** per 100,000. For men, Hungary had the highest mortality rate at **** per 100,000, while Ireland had the lowest at **** per 100,000. For women, Hungary also had the highest mortality rate at ** per 100,000, while Luxembourg had the lowest at ** per 100,000. This statistic depicts the mortality rate of pancreatic cancer in Europe in 2022, by country and gender.

BackgroundIn most developed countries, the number of cancer survivors is expected to increase in the coming decades because of rising incidence and survival rates and an aging population. These patients are heterogeneous in terms of health service demands: from recently diagnosed patients requiring first-course therapy to patients with extensive care needs and severe disabilities to long-term survivors who only need minimal care. Therefore, in terms of providing healthcare planners and policymakers with useful indicators for addressing policies according to health service demands, it is worth supplying updated measures of prevalence for groups of patients based on the level of care they require. The aim of this paper is to illustrate a new method for estimating short-term projections of cancer prevalence by phase of care that applies to areas covered by cancer registration.MethodsThe proposed method combines linear regression models to project limited duration prevalence derived from cancer registry data and a session of the freely available software COMPREV to estimate the projected complete prevalence into three distinct clinically relevant phases of care: initial, continuing, and final. The method is illustrated and validated using data from the Veneto region in Italy for breast, colorectal, and lung cancers.ResultsPrevalence is expected to increase in 2015-2026 for all considered cancer sites and sexes, with average annual variations spanning from 2.6% for women with lung cancer to 0.5% for men with colorectal cancer. The only exception is lung cancer prevalence in men, which shows an average annual decrease of 1.9%. The majority of patients are in the continuing phase of care, followed by the initial and final phases, except for lung cancer, where the final phase of care prevails over the initial one.DiscussionThe paper proposes a method for estimating (short-term) future cancer healthcare needs that is based on user-friendly and freely available software and linear regression models. Validation results confirm the applicability of our method to the most frequent cancer types, provided that cancer registry data with at least 15 years of registration are available. Evidence from this method is addressed to policymakers for planning future cancer care, thus improving the cancer survivorship experience for patients and caregivers.

BackgroundOvarian cancer is a challenging disease to diagnose and treat effectively with five-year survival rates below 50%. Previous patient experience research in high-income countries highlighted common challenges and opportunities to improve survival and quality of life for women affected by ovarian cancer. However, no comparable data exist for low-and middle-income countries, where 70% of women with the disease live. This study aims to address this evidence gap.MethodsThis is an observational multi-country study set in low- and middle-income countries. We aim to recruit over 2000 women diagnosed with ovarian cancer across multiple hospitals in 24 countries in Asia, Africa and South America. Country sample sizes have been calculated (n = 70–96 participants /country), taking account of varying national five-year disease prevalence rates. Women within five years of their diagnosis, who are in contact with participating hospitals, are invited to take part in the study. A questionnaire has been adapted from a tool previously used in high-income countries. It comprises 57 multiple choice and two open-ended questions designed to collect information on demographics, women’s knowledge of ovarian cancer, route to diagnosis, access to treatments, surgery and genetic testing, support needs, the impact of the disease on women and their families, and their priorities for action. The questionnaire has been designed in English, translated into local languages and tested according to local ethics requirements. Questionnaires will be administered by a trained member of the clinical team.ConclusionThis study will inform further research, advocacy, and action in low- and middle-income countries based on tailored approaches to the national, regional and global challenges and opportunities. In addition, participating countries can choose to repeat the study to track progress and the protocol can be adapted for other countries and other diseases.

Differences in cancer survival between the Nordic countries have previously been reported. The aim of this study was to examine whether these differences in outcome remain, based on updated information from five national cancer registers. The data used for the analysis was from the NORDCAN database focusing on nine common cancers diagnosed 1990–2016 in Denmark, Finland, Iceland, Norway and Sweden with maximum follow-up through 2017. Relative survival (RS) was estimated at 1 and 5 years using flexible parametric RS models, and percentage point differences between the earliest and latest years available were calculated. A consistent improvement in both 1- and 5-year RS was found for most studied sites across all countries. Previously observed differences between the countries have been attenuated. The improvements were particularly pronounced in Denmark that now has cancer survival similar to the other Nordic countries. The reasons for the observed improvements in cancer survival are likely multifactorial, including earlier diagnosis, improved treatment options, implementation of national cancer plans, uniform national cancer care guidelines and standardized patient pathways. The previous survival disadvantage in Denmark is no longer present for most sites. Continuous monitoring of cancer survival is of importance to assess the impact of changes in policies and the effectiveness of health care systems.

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)