Population based cancer incidence rates were abstracted from National Cancer Institute, State Cancer Profiles for all available counties in the United States for which data were available. This is a national county-level database of cancer data that are collected by state public health surveillance systems. All-site cancer is defined as any type of cancer that is captured in the state registry data, though non-melanoma skin cancer is not included. All-site age-adjusted cancer incidence rates were abstracted separately for males and females. County-level annual age-adjusted all-site cancer incidence rates for years 2006–2010 were available for 2687 of 3142 (85.5%) counties in the U.S. Counties for which there are fewer than 16 reported cases in a specific area-sex-race category are suppressed to ensure confidentiality and stability of rate estimates; this accounted for 14 counties in our study. Two states, Kansas and Virginia, do not provide data because of state legislation and regulations which prohibit the release of county level data to outside entities. Data from Michigan does not include cases diagnosed in other states because data exchange agreements prohibit the release of data to third parties. Finally, state data is not available for three states, Minnesota, Ohio, and Washington. The age-adjusted average annual incidence rate for all counties was 453.7 per 100,000 persons. We selected 2006–2010 as it is subsequent in time to the EQI exposure data which was constructed to represent the years 2000–2005. We also gathered data for the three leading causes of cancer for males (lung, prostate, and colorectal) and females (lung, breast, and colorectal). The EQI was used as an exposure metric as an indicator of cumulative environmental exposures at the county-level representing the period 2000 to 2005. A complete description of the datasets used in the EQI are provided in Lobdell et al. and methods used for index construction are described by Messer et al. The EQI was developed for the period 2000– 2005 because it was the time period for which the most recent data were available when index construction was initiated. The EQI includes variables representing each of the environmental domains. The air domain includes 87 variables representing criteria and hazardous air pollutants. The water domain includes 80 variables representing overall water quality, general water contamination, recreational water quality, drinking water quality, atmospheric deposition, drought, and chemical contamination. The land domain includes 26 variables representing agriculture, pesticides, contaminants, facilities, and radon. The built domain includes 14 variables representing roads, highway/road safety, public transit behavior, business environment, and subsidized housing environment. The sociodemographic environment includes 12 variables representing socioeconomics and crime. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Human health data are not available publicly. EQI data are available at: https://edg.epa.gov/data/Public/ORD/NHEERL/EQI. Format: Data are stored as csv files. This dataset is associated with the following publication: Jagai, J., L. Messer, K. Rappazzo , C. Gray, S. Grabich , and D. Lobdell. County-level environmental quality and associations with cancer incidence#. Cancer. John Wiley & Sons Incorporated, New York, NY, USA, 123(15): 2901-2908, (2017).

SUMMARYThis analysis, designed and executed by Ribble Rivers Trust, identifies areas across England with the greatest levels of cancer (in persons of all ages). Please read the below information to gain a full understanding of what the data shows and how it should be interpreted.ANALYSIS METHODOLOGYThe analysis was carried out using Quality and Outcomes Framework (QOF) data, derived from NHS Digital, relating to cancer (in persons of all ages).This information was recorded at the GP practice level. However, GP catchment areas are not mutually exclusive: they overlap, with some areas covered by 30+ GP practices. Therefore, to increase the clarity and usability of the data, the GP-level statistics were converted into statistics based on Middle Layer Super Output Area (MSOA) census boundaries.The percentage of each MSOA’s population (all ages) with cancer was estimated. This was achieved by calculating a weighted average based on:The percentage of the MSOA area that was covered by each GP practice’s catchment areaOf the GPs that covered part of that MSOA: the percentage of registered patients that have that illness The estimated percentage of each MSOA’s population with cancer was then combined with Office for National Statistics Mid-Year Population Estimates (2019) data for MSOAs, to estimate the number of people in each MSOA with cancer, within the relevant age range.Each MSOA was assigned a relative score between 1 and 0 (1 = worst, 0 = best) based on:A) the PERCENTAGE of the population within that MSOA who are estimated to have cancerB) the NUMBER of people within that MSOA who are estimated to have cancerAn average of scores A & B was taken, and converted to a relative score between 1 and 0 (1= worst, 0 = best). The closer to 1 the score, the greater both the number and percentage of the population in the MSOA that are estimated to have cancer, compared to other MSOAs. In other words, those are areas where it’s estimated a large number of people suffer from cancer, and where those people make up a large percentage of the population, indicating there is a real issue with cancer within the population and the investment of resources to address that issue could have the greatest benefits.LIMITATIONS1. GP data for the financial year 1st April 2018 – 31st March 2019 was used in preference to data for the financial year 1st April 2019 – 31st March 2020, as the onset of the COVID19 pandemic during the latter year could have affected the reporting of medical statistics by GPs. However, for 53 GPs (out of 7670) that did not submit data in 2018/19, data from 2019/20 was used instead. Note also that some GPs (997 out of 7670) did not submit data in either year. This dataset should be viewed in conjunction with the ‘Health and wellbeing statistics (GP-level, England): Missing data and potential outliers’ dataset, to determine areas where data from 2019/20 was used, where one or more GPs did not submit data in either year, or where there were large discrepancies between the 2018/19 and 2019/20 data (differences in statistics that were > mean +/- 1 St.Dev.), which suggests erroneous data in one of those years (it was not feasible for this study to investigate this further), and thus where data should be interpreted with caution. Note also that there are some rural areas (with little or no population) that do not officially fall into any GP catchment area (although this will not affect the results of this analysis if there are no people living in those areas).2. Although all of the obesity/inactivity-related illnesses listed can be caused or exacerbated by inactivity and obesity, it was not possible to distinguish from the data the cause of the illnesses in patients: obesity and inactivity are highly unlikely to be the cause of all cases of each illness. By combining the data with data relating to levels of obesity and inactivity in adults and children (see the ‘Levels of obesity, inactivity and associated illnesses: Summary (England)’ dataset), we can identify where obesity/inactivity could be a contributing factor, and where interventions to reduce obesity and increase activity could be most beneficial for the health of the local population.3. It was not feasible to incorporate ultra-fine-scale geographic distribution of populations that are registered with each GP practice or who live within each MSOA. Populations might be concentrated in certain areas of a GP practice’s catchment area or MSOA and relatively sparse in other areas. Therefore, the dataset should be used to identify general areas where there are high levels of cancer, rather than interpreting the boundaries between areas as ‘hard’ boundaries that mark definite divisions between areas with differing levels of cancer.TO BE VIEWED IN COMBINATION WITH:This dataset should be viewed alongside the following datasets, which highlight areas of missing data and potential outliers in the data:Health and wellbeing statistics (GP-level, England): Missing data and potential outliersLevels of obesity, inactivity and associated illnesses (England): Missing dataDOWNLOADING THIS DATATo access this data on your desktop GIS, download the ‘Levels of obesity, inactivity and associated illnesses: Summary (England)’ dataset.DATA SOURCESThis dataset was produced using:Quality and Outcomes Framework data: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital.GP Catchment Outlines. Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital. Data was cleaned by Ribble Rivers Trust before use.MSOA boundaries: © Office for National Statistics licensed under the Open Government Licence v3.0. Contains OS data © Crown copyright and database right 2021.Population data: Mid-2019 (June 30) Population Estimates for Middle Layer Super Output Areas in England and Wales. © Office for National Statistics licensed under the Open Government Licence v3.0. © Crown Copyright 2020.COPYRIGHT NOTICEThe reproduction of this data must be accompanied by the following statement:© Ribble Rivers Trust 2021. Analysis carried out using data that is: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital; © Office for National Statistics licensed under the Open Government Licence v3.0. Contains OS data © Crown copyright and database right 2021. © Crown Copyright 2020.CaBA HEALTH & WELLBEING EVIDENCE BASEThis dataset forms part of the wider CaBA Health and Wellbeing Evidence Base.

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

The number of new cases, age-standardized rates and average age at diagnosis of cancers 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). Cancer incidence rates are age-standardized using the direct method and the final 2011 Canadian postcensal population structure. Random rounding of case counts to the nearest multiple of 5 is used to prevent inappropriate disclosure of health-related information.

Age-standardised rate of mortality from oral cancer (ICD-10 codes C00-C14) in persons of all ages and sexes per 100,000 population.RationaleOver the last decade in the UK (between 2003-2005 and 2012-2014), oral cancer mortality rates have increased by 20% for males and 19% for females1Five year survival rates are 56%. Most oral cancers are triggered by tobacco and alcohol, which together account for 75% of cases2. Cigarette smoking is associated with an increased risk of the more common forms of oral cancer. The risk among cigarette smokers is estimated to be 10 times that for non-smokers. More intense use of tobacco increases the risk, while ceasing to smoke for 10 years or more reduces it to almost the same as that of non-smokers3. Oral cancer mortality rates can be used in conjunction with registration data to inform service planning as well as comparing survival rates across areas of England to assess the impact of public health prevention policies such as smoking cessation.References:(1) Cancer Research Campaign. Cancer Statistics: Oral – UK. London: CRC, 2000.(2) Blot WJ, McLaughlin JK, Winn DM et al. Smoking and drinking in relation to oral and pharyngeal cancer. Cancer Res 1988; 48: 3282-7. (3) La Vecchia C, Tavani A, Franceschi S et al. Epidemiology and prevention of oral cancer. Oral Oncology 1997; 33: 302-12.Definition of numeratorAll cancer mortality for lip, oral cavity and pharynx (ICD-10 C00-C14) in the respective calendar years aggregated into quinary age bands (0-4, 5-9,…, 85-89, 90+). This does not include secondary cancers or recurrences. Data are reported according to the calendar year in which the cancer was diagnosed.Counts of deaths for years up to and including 2019 have been adjusted where needed to take account of the MUSE ICD-10 coding change introduced in 2020. Detailed guidance on the MUSE implementation is available at: https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/articles/causeofdeathcodinginmortalitystatisticssoftwarechanges/january2020Counts of deaths for years up to and including 2013 have been double adjusted by applying comparability ratios from both the IRIS coding change and the MUSE coding change where needed to take account of both the MUSE ICD-10 coding change and the IRIS ICD-10 coding change introduced in 2014. The detailed guidance on the IRIS implementation is available at: https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/bulletins/impactoftheimplementationofirissoftwareforicd10causeofdeathcodingonmortalitystatisticsenglandandwales/2014-08-08Counts of deaths for years up to and including 2010 have been triple adjusted by applying comparability ratios from the 2011 coding change, the IRIS coding change and the MUSE coding change where needed to take account of the MUSE ICD-10 coding change, the IRIS ICD-10 coding change and the ICD-10 coding change introduced in 2011. The detailed guidance on the 2011 implementation is available at https://webarchive.nationalarchives.gov.uk/ukgwa/20160108084125/http://www.ons.gov.uk/ons/guide-method/classifications/international-standard-classifications/icd-10-for-mortality/comparability-ratios/index.htmlDefinition of denominatorPopulation-years (aggregated populations for the three years) for people of all ages, aggregated into quinary age bands (0-4, 5-9, …, 85-89, 90+)

Mortality Rates for Lake County, Illinois. Explanation of field attributes: Average Age of Death – The average age at which a people in the given zip code die. Cancer Deaths – Cancer deaths refers to individuals who have died of cancer as the underlying cause. This is a rate per 100,000. Heart Disease Related Deaths – Heart Disease Related Deaths refers to individuals who have died of heart disease as the underlying cause. This is a rate per 100,000. COPD Related Deaths – COPD Related Deaths refers to individuals who have died of chronic obstructive pulmonary disease (COPD) as the underlying cause. This is a rate per 100,000.

AbstractIn Italy, approximately 400.000 new cases of malignant tumors are recorded every year. The average of annual deaths caused by tumors, according to the Italian Cancer Registers, is about 3.5 deaths and about 2.5 per 1,000 men and women respectively, for a total of about 3 deaths every 1,000 people. Long-term (at least a decade) and spatially detailed data (up to the municipality scale) are neither easily accessible nor fully available for public consultation by the citizens, scientists, research groups, and associations. Therefore, here we present a ten-year (2009–2018) database on cancer mortality rates (in the form of Standardized Mortality Ratios, SMR) for 23 cancer macro-types in Italy on municipal, provincial, and regional scales. We aim to make easily accessible a comprehensive, ready-to-use, and openly accessible source of data on the most updated status of cancer mortality in Italy for local and national stakeholders, researchers, and policymakers and to provide researchers with ready-to-use data to perform specific studies. Methods For a given locality, year, and cause of death, the SMR is the ratio between the observed number of deaths (Om) and the number of expected deaths (Em): SMR = Om/Em (1) where Om should be an available observational data and Em is estimated as the weighted sum of age-specific population size for the given locality (ni) per age-specific death rates of the reference population (MRi): Em = sum(MRi x ni) (2) MRi could be provided by a public health organization or be estimated as the ratio between the age-specific number of deaths of reference population (Mi) to the age-specific reference population size (Ni): MRi = Mi/Ni (3) Thus, the value of Em is weighted by the age distribution of deaths and population size. SMR assumes value 1 when the number of observed and expected deaths are equal. Following eqns. (1-3), the SMR was computed for single years of the period 2009-2018 and for single cause of death as defined by the International ICD-10 classification system by using the following data: age-specific number of deaths by cause of reference population (i.e., Mi) from the Italian National Institute of Statistics (ISTAT, (http://www.istat.it/en/, last access: 26/01/2022)); age-specific census data on reference population (i.e., Ni) from ISTAT; the observed number of deaths by cause (i.e., Om) from ISTAT; the age-specific census data on population (ni); the SMR was estimated at three different level of aggregation: municipal, provincial (equivalent to the European classification NUTS 3) and regional (i.e., NUTS2). The SMR was also computed for the broad category of malignant tumors (i.e. C00-C979, hereinafter cancer macro-type C), and for the broad category of malignant tumor plus non-malignant tumors (i.e. C00-C979 plus D0-D489, hereinafter cancer macro-type CD). Lower 90% and 95% confidence intervals of 10-year average values were computed according to the Byar method.

This dataset contains Age-Adjusted Rate, Confidence Interval, Average Annual Count, and Trend field information for US States for the average 5 year span from 2012 to 2016.Data is segmented by sex and age, with fields describing the sex and age group tabulated.For more information, visit statecancerprofiles.cancer.gov Data NotationsState Cancer Registries may provide more current or more local data.† Incidence rates (cases per 100,000 population per year) are age-adjusted to the 2000 US standard population seer.cancer.gov/stdpopulations/stdpop.19ages.html. Rates are for invasive cancer only (except for bladder cancer which is invasive and in situ) or unless otherwise specified. Rates calculated using SEER*Stat. [seer.cancer.gov/seerstat]Population counts for denominators are based on Census populations as modified [seer.cancer.gov/popdata] by NCI. The 1969-2016 US Population Data File [seer.cancer.gov/popdata] is used for SEER and NPCR incidence rates.‡ Incidence data come from different sources. Due to different years of data availability, most of the trends are AAPCs based on APCs but some are APCs calculated in SEER*Stat. Please refer to the source for each area for additional information. Rates and trends are computed using different standards for malignancy. For more information see malignant.html.^ All Stages refers to any stage in the Surveillance, Epidemiology, and End Results (SEER) summary stage [seer.cancer.gov/tools/ssm].Healthy People 2020 Objectives [www.healthypeople.gov]provided by the Centers for Disease Control and Prevention [www.cdc.gov]. Michigan Data do not include cases diagnosed in other states for those states in which the data exchange agreement specifically prohibits the release of data to third parties.Trend Data not available for Nevada.Data Source Field Key:(1) Source: CDC's National Program of Cancer Registries Cancer Surveillance System (NPCR-CSS) November 2018 data submission and SEER November 2018 submission as published in United States Cancer Statistics nccd.cdc.gov/uscs Source: State Cancer Registry and the CDC's National Program of Cancer Registries Cancer Surveillance System (NPCR-CSS) November 2018 data submission. State rates include rates from metropolitan areas funded by SEER [seer.cancer.gov/registries].(6) Source: State Cancer Registry and the CDC's National Program of Cancer Registries Cancer Surveillance System (NPCR-CSS) November 2018 data submission.(7) Source: SEER November 2018 submission.8 Source: Incidence data provided by the SEER Program. [seer.cancer.gov] AAPCs are calculated by the Joinpoint Regression Program [surveillance.cancer.gov/joinpoint] and are based on APCs. Data are age-adjusted to the 2000 US standard population www.seer.cancer.gov/stdpopulations/single_age.html. Rates are for invasive cancer only (except for bladder cancer which is invasive and in situ) or unless otherwise specified. Population counts for denominators are based on Census populations as modified by NCI. The 1969-2017 US Population Data [seer.cancer.gov/popdata] File is used with SEER November 2018 data. Please note that the data comes from different sources. Due to different years [statecancerprofiles.cancer.gov/historicaltrend/differences.html] of data availability, most of the trends are AAPCs based on APCs but some are APCs calculated in SEER*Stat. [seer.cancer.gov/seerstat] Please refer to the source for each graph for additional information. Some data are not available [http://statecancerprofiles.cancer.gov/datanotavailable.html] for combinations of geography, cancer site, age, and race/ethnicity.

Age standardized rate of cancer incidence, by selected sites of cancer and sex, three-year average, census metropolitan areas.

Two datasets that explore causes of death due to cancer in South Africa, drawing on data from the Revised Burden of Disease estimates for the Comparative Risk Factor Assessment for South Africa, 2000.

The number and percentage of deaths due to cancer by cause are ranked for persons, males and females in the tables below.

Lung cancer is the leading cause of cancer in SA accounting for 17% of all cancer deaths. This is followed by oesophagus Ca which accounts for 13%, cervix cancer accounting for 8%, breast cancer accounting for 8% and liver cancer which accounts for 6% of all cancers. Many more males suffer from lung and oesophagus cancer than females.

This dataset is sourced from Public Health England and consists of the percentage of people in the resident population eligible for cervical screening who were screened adequately within the previous years (2010 to 2016) for bowel, cervical and breast cancer.

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

Proportion of patients examined within a standardized course of care out of the total number of people who are expected to receive a cancer diagnosis during the year. The calculation is based on data from the National Board of Health and Welfare. Standardized course of care (SVF) is a national approach that aims to reduce unnecessary waiting and uncertainty for the patient. All SVFs start with a well-founded suspicion of cancer. What is a well-founded suspicion, how it should be investigated and how long this may take, is stated in the national care program for each cancer diagnosis. The time from well-founded suspicion to the start of treatment is measured in the same way throughout the country. An SVF describes the investigations to be carried out in the event of suspicion of a particular cancer disease, as well as the maximum time limits within which the investigations must be completed and the first treatment must be started. The denominator consists of the total number of people who are expected to receive a cancer diagnosis during the year. The calculation is based on the number of cancer cases in the last three years with data from the Cancer Registry at the National Board of Health and Welfare.

The population of Middle Tennessee was assessed using publically available data collected in 2009 describing demographic and breast cancer-related characteristics of the population.*The value for each breast cancer risk factor was determined for each Middle Tennessee County, and counties were then ranked in numerical order from lowest to highest. The numerically ranked counties were then subdivided into quartiles, such that the three counties with the lowest risk factor values were placed in Quartile 1, and those with the highest were placed in Quartile 4. The range of risk factor values encompassed by each quartile are shown.1The percentage of the total female population in the county that is over the age of 50 years (a surrogate for menopause).2The breast cancer incidence per 100,000 women. 3Breast cancer mortality per 100,000 women.4The percentage of all breast cancers that were diagnosed at Stage IV.5The percentage of all breast cancers that were diagnosed without a prior mammographic screening.6The percentage of the female population lacking any form of health insurance.7The median household income.8The percentage of the population possessing higher than a high school level education.9The percentage of the population that is not Caucasian.

To investigate the global incidence of prostate cancer with special attention to the changing age structures. Data regarding the cancer incidence and population statistics were retrieved from the International Agency for Research on Cancer in World Health Organization. Eight developing and developed jurisdictions in Asia and the Western countries were selected for global comparison. Time series were constructed based on the cancer incidence rates from 1988 to 2007. The incidence rate of the population aged ≥ 65 was adjusted by the increasing proportion of elderly population, and was defined as the “aging-adjusted incidence rate”. Cancer incidence and population were then projected to 2030. The aging-adjusted incidence rates of prostate cancer in Asia (Hong Kong, Japan and China) and the developing Western countries (Costa Rica and Croatia) had increased progressively with time. In the developed Western countries (the United States, the United Kingdom and Sweden), we observed initial increases in the aging-adjusted incidence rates of prostate cancer, which then gradually plateaued and even decreased with time. Projections showed that the aging-adjusted incidence rates of prostate cancer in Asia and the developing Western countries were expected to increase in much larger extents than the developed Western countries.

Abstract Objective: To identify the socioepidemiologic and histopathologic patterns of lung cancer patients in the Middle Euphrates region. Patients and Methods: This study analyzed medical information from lung cancer patients at the Middle Euphrates Cancer Center in Iraq from January 2018 to December 2023. Demographic information (age, gender, residency, and education level) as well as clinical details (histopathological categorization) were obtained. The inclusion criteria included all confirmed lung cancer cases, while cases with inadequate data or non-lung cancer diagnosis were omitted. The data were analyzed using IBM SPSS Statistics (version 26). The data summarized using descriptive statistics, and chi-square tests used to identify correlations between categorical variables at a significance level of p < 0.05. Ethical approval was obtained from the relevant institutional review board. Results: A total of 1162 patients were included with mean age at diagnosis(64.47±11.45) years. Majority of patients are over 60 years (64.4%), followed by (40–60 years), 34%, and the least affected group is under 40 years (1.6%). Males account for the majority of cases (68%), while females about 32%, with male:female ratio that fluctuate around 2:1. Illiterate patients and those with low education levels represent the largest proportion accounting for about 87.9% of the study population. Squamous Cell Carcinoma (SCC) is the most frequent subtype (41.7%), followed closely by Adenocarcinoma (AC) at 37%, and Small Cell Lung Cancer (SCLC), 10.5%. Although SCC is the predominant subtype overall, AC incidence is increasing overtime (from 31.7% in 2018 to 41.4% in 2023) with predominance in females, younger and higher educated groups. While the percentage of SCLC and other less common subgroups remained relatively stable over time, there is a significant reduction in NSCLC-NOS diagnoses (from 11.1% in 2018 to 3.2% in 2023). Conclusions: In Iraq, specifically in the Middle Euphrates region, lung cancer is a major public health issue in the elder age groups. The two main subtypes, SCC and AC, are the main contributors, with obvious increment in AC cases in the recent years. The shifting trends indicate the urgent need for improved screening strategies, focused preventative initiatives, and customized treatment plans in view of changing risk profiles.

One woman in nine can expect to develop breast cancer during her lifetime and one in 25 will die from the disease. Statistically low incidences of breast cancer are found in Newfoundland and Labrador, the territories, and northern areas of most provinces. Otherwise, each province has one or more pockets of significantly high breast cancer incidence. These are often located in more southerly areas, but they do not seem to be restricted to either urban or rural areas alone. Breast cancer rates are a health status indicator. They can be used to help assess health conditions. Health status refers to the state of health of a person or group, and measures causes of sickness and death. It can also include people’s assessment of their own health.

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 )

Objective: While Hungary is often reported to have the highest incidence and mortality rates of lung cancer, until 2018 no nationwide epidemiology study was conducted to confirm these trends. The objective of this study was to estimate the occurrence of lung cancer in Hungary based on a retrospective review of the National Health Insurance Fund (NHIF) database.Methods: Our retrospective, longitudinal study included patients aged ≥20 years who were diagnosed with lung cancer (ICD-10 C34) between 1 Jan 2011 and 31 Dec 2016. Age-standardized incidence and mortality rates were calculated using both the 1976 and 2013 European Standard Populations (ESP).Results: Between 2011 and 2016, 6,996 – 7,158 new lung cancer cases were recorded in the NHIF database annually, and 6,045 – 6,465 all-cause deaths occurred per year. Age-adjusted incidence rates were 115.7–101.6/100,000 person-years among men (ESP 1976: 84.7–72.6), showing a mean annual change of − 2.26% (p = 0.008). Incidence rates among women increased from 48.3 to 50.3/100,000 person-years (ESP 1976: 36.9–38.0), corresponding to a mean annual change of 1.23% (p = 0.028). Age-standardized mortality rates varied between 103.8 and 97.2/100,000 person-years (ESP 1976: 72.8–69.7) in men and between 38.3 and 42.7/100,000 person-years (ESP 1976: 27.8–29.3) in women.Conclusion: Age-standardized incidence and mortality rates of lung cancer in Hungary were found to be high compared to Western-European countries, but lower than those reported by previous publications. The incidence of lung cancer decreased in men, while there was an increase in incidence and mortality among female lung cancer patients.