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.

Among many types of cancers, to date, lung cancer remains one of the deadliest cancers around the world. Many researchers, scientists, doctors, and people from other fields continuously contribute to this subject regarding early prediction and diagnosis. One of the significant problems in prediction is the black-box nature of machine learning models. Though the detection rate is comparatively satisfactory, people have yet to learn how a model came to that decision, causing trust issues among patients and healthcare workers. This work uses multiple machine learning models on a numerical dataset of lung cancer-relevant parameters and compares performance and accuracy. After comparison, each model has been explained using different methods. The main contribution of this research is to give logical explanations of why the model reached a particular decision to achieve trust. This research has also been compared with a previous study that worked with a similar dataset and took expert opinions regarding their proposed model. We also showed that our research achieved better results than their proposed model and specialist opinion using hyperparameter tuning, having an improved accuracy of almost 100% in all four models.

Data set from- What Defines Quality of Life for Older Patients Diagnosed with Cancer? A Qualitative Study

Abstract of the study: The treatment of cancer can have a significant impact on quality of life in older patients and this needs to be taken into account in decision making. However, quality of life can consist of many different components with varying importance between individuals. We set out to assess how older patients with cancer define quality of life and the components that are most significant to them. This was a single-centre, qualitative interview study. Patients aged 70 years or older with cancer were asked to answer open-ended questions: What makes life worthwhile? What does quality of life mean to you? What could affect your quality of life? Subsequently, they were asked to choose the five most important determinants of quality of life from a predefined list: cognition, contact with family or with community, independence, staying in your own home, helping others, having enough energy, emotional well-being, life satisfaction, religion and leisure activities. Afterwards, answers to the open-ended questions were independently categorized by two authors. The proportion of patients mentioning each category in the open-ended questions were compared to the predefined questions. Overall, 63 patients (median age 76 years) were included. When asked, “What makes life worthwhile?”, patients identified social functioning (86%) most frequently. Moreover, to define quality of life, patients most frequently mentioned categories in the domains of physical functioning (70%) and physical health (48%). Maintaining cognition was mentioned in 17% of the open-ended questions and it was the most commonly chosen option from the list of determinants (72% of respondents). In conclusion, physical functioning, social functioning, physical health and cognition are important components in quality of life. When discussing treatment options, the impact of treatment on these aspects should be taken into consideration.

Reference of research paper: Seghers PAL, Kregting JA, van Huis-Tanja LH, Soubeyran P, O'Hanlon S, Rostoft S, Hamaker ME, Portielje JEA. What Defines Quality of Life for Older Patients Diagnosed with Cancer? A Qualitative Study. Cancers. 2022; 14(5):1123. https://doi.org/10.3390/cancers14051123

Content of the data set: The first Tab describes what questions were asked, the second tab shows all individual anonymised answers to the open questions, the fourth shows the definitions that were used to classify all answers. Q1-Q4 show how the answers were categorised.

This publication reports on newly diagnosed cancers registered in England in addition to cancer deaths registered in England during 2020. It includes this summary report showing key findings, spreadsheet tables with more detailed estimates, and a methodology document.

India IN: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Female data was reported at 19.800 NA in 2016. This records a decrease from the previous number of 20.000 NA for 2015. India IN: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Female data is updated yearly, averaging 21.200 NA from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 23.400 NA in 2000 and a record low of 19.800 NA in 2016. India IN: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Female data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s India – Table IN.World Bank.WDI: Health Statistics. Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS).; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

SUMMARYThis analysis, designed and executed by Ribble Rivers Trust, identifies areas across England with the greatest levels of physical illnesses that are linked with obesity and inactivity. 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:- Asthma (in persons of all ages)- Cancer (in persons of all ages)- Chronic kidney disease (in adults aged 18+)- Coronary heart disease (in persons of all ages)- Diabetes mellitus (in persons aged 17+)- Hypertension (in persons of all ages)- Stroke and transient ischaemic attack (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.For each of the above illnesses, the percentage of each MSOA’s population with that illness 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 area- Of the GPs that covered part of that MSOA: the percentage of patients registered with each GP that have that illnessThe estimated percentage of each MSOA’s population with each illness 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 each illness, within the relevant age range.For each illness, 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 that illnessB) the NUMBER of people within that MSOA who are estimated to have that illnessAn 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 predicted to have that illness, compared to other MSOAs. In other words, those are areas where a large number of people are predicted to suffer from an illness, and where those people make up a large percentage of the population, indicating there is a real issue with that illness within the population and the investment of resources to address that issue could have the greatest benefits.The scores for each of the 7 illnesses were added together then converted to a relative score between 1 – 0 (1 = worst, 0 = best), to give an overall score for each MSOA: a score close to 1 would indicate that an area has high predicted levels of all obesity/inactivity-related illnesses, and these are areas where the local population could benefit the most from interventions to address those illnesses. A score close to 0 would indicate very low predicted levels of obesity/inactivity-related illnesses and therefore interventions might not be required.LIMITATIONS1. GPs do not have catchments that are mutually exclusive from each other: they overlap, with some geographic areas being covered by 30+ practices. This dataset should be viewed in combination with the ‘Health and wellbeing statistics (GP-level, England): Missing data and potential outliers’ dataset to identify where there are areas that are covered by multiple GP practices but at least one of those GP practices did not provide data. Results of the analysis in these areas should be interpreted with caution, particularly if the levels of obesity/inactivity-related illnesses appear to be significantly lower than the immediate surrounding areas.2. 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).3. 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.4. 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 obesity/inactivity-related illnesses, rather than interpreting the boundaries between areas as ‘hard’ boundaries that mark definite divisions between areas with differing levels of these illnesses. 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 outliersDOWNLOADING 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.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.CaBA HEALTH & WELLBEING EVIDENCE BASEThis dataset forms part of the wider CaBA Health and Wellbeing Evidence Base.

Note: DPH is updating and streamlining the COVID-19 cases, deaths, and testing data. As of 6/27/2022, the data will be published in four tables instead of twelve. The COVID-19 Cases, Deaths, and Tests by Day dataset contains cases and test data by date of sample submission. The death data are by date of death. This dataset is updated daily and contains information back to the beginning of the pandemic. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Cases-Deaths-and-Tests-by-Day/g9vi-2ahj. The COVID-19 State Metrics dataset contains over 93 columns of data. This dataset is updated daily and currently contains information starting June 21, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-State-Level-Data/qmgw-5kp6 . The COVID-19 County Metrics dataset contains 25 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-County-Level-Data/ujiq-dy22 . The COVID-19 Town Metrics dataset contains 16 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Town-Level-Data/icxw-cada . To protect confidentiality, if a town has fewer than 5 cases or positive NAAT tests over the past 7 days, those data will be suppressed. COVID-19 cases and associated deaths that have been reported among Connecticut residents, broken down by race and ethnicity. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. Deaths reported to the either the Office of the Chief Medical Examiner (OCME) or Department of Public Health (DPH) are included in the COVID-19 update. The following data show the number of COVID-19 cases and associated deaths per 100,000 population by race and ethnicity. Crude rates represent the total cases or deaths per 100,000 people. Age-adjusted rates consider the age of the person at diagnosis or death when estimating the rate and use a standardized population to provide a fair comparison between population groups with different age distributions. Age-adjustment is important in Connecticut as the median age of among the non-Hispanic white population is 47 years, whereas it is 34 years among non-Hispanic blacks, and 29 years among Hispanics. Because most non-Hispanic white residents who died were over 75 years of age, the age-adjusted rates are lower than the unadjusted rates. In contrast, Hispanic residents who died tend to be younger than 75 years of age which results in higher age-adjusted rates. The population data used to calculate rates is based on the CT DPH population statistics for 2019, which is available online here: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Population/Population-Statistics. Prior to 5/10/2021, the population estimates from 2018 were used. Rates are standardized to the 2000 US Millions Standard population (data available here: https://seer.cancer.gov/stdpopulations/). Standardization was done using 19 age groups (0, 1-4, 5-9, 10-14, ..., 80-84, 85 years and older). More information about direct standardization for age adjustment is available here: https://www.cdc.gov/nchs/data/statnt/statnt06rv.pdf Categories are mutually exclusive. The category “multiracial” includes people who answered ‘yes’ to more than one race category. Counts may not add up to total case counts as data on race and ethnicity may be missing. Age adjusted rates calculated only for groups with more than 20 deaths. Abbreviation: NH=Non-Hispanic. Data on Connecticut deaths were obtained from the Connecticut Deaths Registry maintained by the DPH Office of Vital Records. Cause of death was determined by a death certifier (e.g., physician, APRN, medical

Nigeria NG: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Male data was reported at 20.900 NA in 2016. This records an increase from the previous number of 20.800 NA for 2015. Nigeria NG: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Male data is updated yearly, averaging 21.000 NA from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 22.600 NA in 2000 and a record low of 20.800 NA in 2015. Nigeria NG: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Male data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Nigeria – Table NG.World Bank.WDI: Health Statistics. Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS).; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

This dataset presents the footprint of participation statistics in the National Bowel Cancer Screening Program (NBCSP) for people aged 50 to 74. The NBCSP began in 2006. It aims to reduce morbidity and mortality from bowel cancer by actively recruiting and screening the eligible target population for early detection or prevention of the disease. The data spans the years of 2014-2017 and is aggregated to 2015 Department of Health Primary Health Network (PHN) areas, based on the 2011 Australian Statistical Geography Standard (ASGS). Cancer is one of the leading causes of illness and death in Australia. Cancer screening programs aim to reduce the impact of selected cancers by facilitating early detection, intervention and treatment. Australia has three cancer screening programs: BreastScreen Australia National Cervical Screening Program (NCSP) National Bowel Cancer Screening Program (NBCSP) The National cancer screening programs participation data presents the latest cancer screening participation rates and trends for Australia's 3 national cancer screening programs. The data has been sourced from the Australian Institute of Health and Welfare (AIHW) analysis of National Bowel Cancer Screening Program register data, state and territory BreastScreen Australia register data and state and territory cervical screening register data. For further information about this dataset, visit the data source:Australian Institute of Health and Welfare - National Cancer Screening Programs Participation Data Tables. Please note: AURIN has spatially enabled the original data using the Department of Health - PHN Areas. Participation rates represent the percentage of people invited to screen through the NBCSP during the relevant 2-year period, who returned a completed screening test within that period or by 30 June of the following year. The number of individuals invited to screen excludes those who deferred or opted out without completing their screening test. PHN areas were assigned to NBCSP invitees using an SA1 to PHN correspondence. Those invitees without reliable SA1 details were mapped with a postcode to PHN correspondence instead, which may lead to some minor inaccuracies in results. Some invitee SA1 codes and postcodes cannot be attributed to a PHN. These invitees were included in an 'Unknown' group where applicable. Some postcodes cross PHN boundaries, leading to slight inaccuracies. The time period of some PHN data presented is prior to the initiation of PHNs, which were in established in June 2015. Biennial screening for those aged 50-74 is not fully rolled out. During the time period reported, the specific ages invited within the 50-74 age range included 50, 54, 55, 58, 60, 64, 65, 68, 70, 72 and 74. These results calculate participation rates using the new NBCSP performance indicator specifications. This indicator now measures a 2-year invitation period and also excludes those who opted off or suspended participation. Therefore, these results cannot be compared to rates reported prior to 2014. NBCSP participation rates per area are not related to bowel cancer incidence rates.

Saudi Arabia SA: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70 data was reported at 16.400 % in 2016. This records a decrease from the previous number of 16.500 % for 2015. Saudi Arabia SA: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70 data is updated yearly, averaging 17.900 % from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 18.900 % in 2000 and a record low of 16.400 % in 2016. Saudi Arabia SA: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Saudi Arabia – Table SA.World Bank: Health Statistics. Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS).; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted Average;

The number of females who participated in a breast cancer screening program and there proportion of the relevant population, as well as the number of people diagnosed with breast cancer as a rate of those who participated, 2010-2011 (NSW, Vic, Qld, SA & WA). Source: Compiled by PHIDU based on data from BreastScreen NSW, BreastScreen Vic, BreastScreen Qld, BreastScreen WA - 2010 and 2011.The Dataset also contains the number of females who participated in a cervical cancer screening program and there proportion of the relevant population, as well as the number of the people diagnosed with low/high cervical cancer as a rate of those who participated, 2010-2011 (NSW, Vic, Qld, SA, WA & ACT). Source: Compiled by PHIDU based on data from the NSW Department of Health and NSW Central Cancer Registry, 2011 and 2012; Victorian Cervical Cytology Registry, 2011 and 2012; Queensland Health Cancer Services Screening Branch, 2011 and 2012; SA Cervix Screening Program, 2011 and 2012; Western Australia Cervical Cytology Register, 2011 and 2012; and ACT Cytology Register, 2011 and 2012.For both sets of screening if a women was screened more than twice in the two year period she is counted once only (all entries that were classified as not shown, not published or not applicable were assigned a null value; no data was provided for Maralinga Tjarutja LGA, in South Australia). The data is by LGA 2015 profile (based on the LGA 2011 geographic boundaries). For more information on statistics used please refer to the PHIDU website, available from: http://phidu.torrens.edu.au/

PurposeAnaplastic thyroid carcinoma (ATC) and primary squamous cell carcinoma of the thyroid (PSCCTh) have similar histological findings and are currently treated using the same approaches; however, the characteristics and prognosis of these cancers are poorly researched. The objective of this study was to determine the differences in characteristics between ATC and PSCCTh and establish prognostic models.Patients and MethodsAll variables of patients with ATC and PSCCTh, diagnosed from 2004–2015, were retrieved from the Surveillance, Epidemiology, and End Results Program (SEER) database. Percentage differences for categorical data were compared using the Chi-square test. Kaplan-Meier curves, log-rank test, and Cox-regression for survival analysis, and C-index value was used to evaluate the performance of the prognostic models.ResultsAfter application of the inclusion and exclusion criteria, a total of 1164 ATC and 124 PSCCTh patients, diagnosed from 2004 to 2015, were included in the study. There were no differences in sex, ethnicity, age, marital status, or percentage of proximal metastases between the two cancers; however, radiotherapy, chemotherapy, incidence of surgical treatment, and presence of multiple primary tumors were higher in patients with ATC than those with PSCCTh. Further cancer-specific survival (CSS) of patients with PSCCTh was better than that of patients with ATC. Prognostic factors were not identical for the two cancers. Multivariate Cox model analysis indicated that age, sex, radiotherapy, chemotherapy, surgery, multiple primary tumors, marital status, and distant metastasis status are independent prognostic factors for CSS in patients with ATC, while for patients with PSCCTh, the corresponding factors are age, radiotherapy, multiple primary tumors, and surgery. The C-index values of the two models were both > 0.8, indicating that the models exhibited good discriminative ability.ConclusionPrognostic factors influencing CSS were not identical in patients with ATC and PSCCTh. These findings indicate that different clinical treatment and management plans are required for patients with these two types of thyroid cancer.

Ivory Coast CI: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Male data was reported at 28.200 NA in 2016. This records a decrease from the previous number of 28.500 NA for 2015. Ivory Coast CI: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Male data is updated yearly, averaging 27.700 NA from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 28.500 NA in 2015 and a record low of 25.200 NA in 2000. Ivory Coast CI: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Male data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Ivory Coast – Table CI.World Bank.WDI: Health Statistics. Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS).; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

Rank, number of deaths, percentage of deaths, and age-specific mortality rates for the leading causes of death, by age group and sex, 2000 to most recent year.

This dataset presents the footprint of participation statistics in the National Cervical Screening Program (NCSP) for women aged 20 to 69, by age group. The NCSP began in 1991. It aims to reduce cervical cancer cases, illness and deaths in Australia. The data spans the years of 2014-2016 and is aggregated to Statistical Area Level 3 (SA3) from the 2011 Australian Statistical Geography Standard (ASGS).

Cancer is one of the leading causes of illness and death in Australia. Cancer screening programs aim to reduce the impact of selected cancers by facilitating early detection, intervention and treatment. Australia has three cancer screening programs:

• BreastScreen Australia

• National Cervical Screening Program (NCSP)

• National Bowel Cancer Screening Program (NBCSP)

The National cancer screening programs participation data presents the latest cancer screening participation rates and trends for Australia's 3 national cancer screening programs. The data has been sourced from the Australian Institute of Health and Welfare (AIHW) analysis of National Bowel Cancer Screening Program register data, state and territory BreastScreen Australia register data and state and territory cervical screening register data.

For further information about this dataset, visit the data source:Australian Institute of Health and Welfare - National Cancer Screening Programs Participation Data Tables.

Please note:

• AURIN has spatially enabled the original data.

• Participation in the NCSP for this report was defined as the percentage of women in the population aged 20-69 who had at least one Pap test in a 2-year period. Participation rates were calculated using the average of the Australian Bureau of Statistics (ABS) Estimated Resident Population (ERP) for females aged 20-69 for the relevant 2-year reporting period adjusted for the estimated proportion of women who have had a hysterectomy.

• An SA3 was assigned to women using a postcode to SA3 correspondence. Because these are based only on postcode, these data will be less accurate than those published by individual states and territories.

• Postcode is used for mailing purposes and may not reflect where a woman resides.

• Some postcodes (and hence women) cannot be attributed to an SA3 and therefore these women were excluded from the analysis. This is most noticeable in the Northern Territory but affects all states and territories to some degree.

• SA3s with a numerator less than 20 or a denominator less than 100 have been suppressed.

• SA3 data for Blue Mountains - South, Christmas Island, Cocos (Keeling) Islands, Cotter - Namadgi, Fyshwick - Piallago - Hume, Illawarra Catchment Reserve, Jervis Bay, and Lord Howe Island were excluded due to reliability concerns from low numbers in these regions.

• Some duplication may occur where the same test is reported to the cervical screening register in two or more jurisdictions. This may lead to erroneous results when focusing on smaller geographical areas. This may affect border areas more than others.

• Totals may not sum due to rounding.

• Data are preliminary and subject to change.

• The 2014-2015 period covers 1 January 2014 to 31 December 2015, and the 2015-2016 period covers 1 January 2015 to 31 December 2016.

Kenya KE: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70 data was reported at 13.400 % in 2016. This records an increase from the previous number of 13.300 % for 2015. Kenya KE: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70 data is updated yearly, averaging 13.400 % from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 17.300 % in 2000 and a record low of 13.300 % in 2015. Kenya KE: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Kenya – Table KE.World Bank: Health Statistics. Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS).; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted Average;

資料來源：本署癌症篩檢資料庫，篩檢統計至2014.02.08。 備註：1.篩檢率計算方式：近2年曾接受過大腸癌篩檢人數 / 當年12月50-69歲人口數。 2.2010年起，全面提供50-69歲民眾2年1次糞便潛血檢查。 Source：Cancer Screening Database, HPA. Data by February, 8, 2014. Note：1.Colorectal cancer screening rate: Percentage of people aged 50-69 reporting at Least 1 iFOBT in the past 2 years. Note：2.Screening policy: The government has incorporated colorectal cancer screening into preventive health care services for people aged 50-69 with at least 1 iFOBT within the past 2 years.

Abstract copyright UK Data Service and data collection copyright owner.The Opinions and Lifestyle Survey (formerly known as the ONS Opinions Survey or Omnibus) is an omnibus survey that began in 1990, collecting data on a range of subjects commissioned by both the ONS internally and external clients (limited to other government departments, charities, non-profit organisations and academia).Data are collected from one individual aged 16 or over, selected from each sampled private household. Personal data include data on the individual, their family, address, household, income and education, plus responses and opinions on a variety of subjects within commissioned modules. The questionnaire collects timely data for research and policy analysis evaluation on the social impacts of recent topics of national importance, such as the coronavirus (COVID-19) pandemic and the cost of living, on individuals and households in Great Britain. From April 2018 to November 2019, the design of the OPN changed from face-to-face to a mixed-mode design (online first with telephone interviewing where necessary). Mixed-mode collection allows respondents to complete the survey more flexibly and provides a more cost-effective service for customers. In March 2020, the OPN was adapted to become a weekly survey used to collect data on the social impacts of the coronavirus (COVID-19) pandemic on the lives of people of Great Britain. These data are held in the Secure Access study, SN 8635, ONS Opinions and Lifestyle Survey, Covid-19 Module, 2020-2022: Secure Access. From August 2021, as coronavirus (COVID-19) restrictions were lifting across Great Britain, the OPN moved to fortnightly data collection, sampling around 5,000 households in each survey wave to ensure the survey remains sustainable. The OPN has since expanded to include questions on other topics of national importance, such as health and the cost of living. For more information about the survey and its methodology, see the ONS OPN Quality and Methodology Information webpage.Secure Access Opinions and Lifestyle Survey dataOther Secure Access OPN data cover modules run at various points from 1997-2019, on Census religion (SN 8078), cervical cancer screening (SN 8080), contact after separation (SN 8089), contraception (SN 8095), disability (SNs 8680 and 8096), general lifestyle (SN 8092), illness and activity (SN 8094), and non-resident parental contact (SN 8093). See Opinions and Lifestyle Survey: Secure Access for details. Main Topics:Each month's questionnaire consists of two elements: core questions, covering demographic information, are asked each month together with non-core questions that vary from month to month. The non-core questions for this month were: Televisions (Module 177): this module was asked on behalf of the Department of National Heritage, to ascertain how many households have a television that did not work at the time and did not have another TV set that did work, and whether they intended to get the broken television set repaired in the next seven days after the interview took place. ACAS awareness (Module 187): this module was asked on behalf of ACAS, the Advisory, Conciliation and Arbitration Service, who wished to know how many people had heard of them and how many had a realistic idea of what sort of organisation they are and what they do. The module was asked of all respondents in paid employment. Second homes (Module 4): this module was asked on behalf of the Department of Environment, Transport and the Regions (DETR). It has appeared in previous Omnibus surveys in a slightly different form. The module queried respondents on ownership of a second home by any member of the household and reasons for having the second home. Expectation of house price changes (Module 137): this module asks respondents' views on changes to house prices in the next year and next five years. Fire safety (Module 33): this module covers fire safety and was asked in connection with Fire Safety Week. Questions assess awareness of fire risks and fire safety measures the respondent has taken. Lone mothers (Module 184): this module was asked on behalf of the Department of Social Security. The questions were taken from a British attitudes survey and compare attitudes towards mothers living in couples with children of varying ages with attitudes towards lone mothers. Smoking (Module 130): this module assesses people's smoking habits, past and present, attitudes to smoking in different scenarios, and awareness of cigarette advertising. Unemployment risk (Module 183): this module was asked on behalf of the Centre for Research in Social Policy at Loughborough University. The questions were designed to investigate respondents' assessment of the risks of being unemployed, their attitude towards unemployment insurance and their recent experience of unemployment. Contraception (Module 170): the Special Licence version of this module is held under SN 6475. PEPs and TESSAs (Module 185): this module was asked on behalf of the Inland Revenue, to gain more information about the distribution of PEPs and TESSAs and in particular the extent to which the two groups overlap. Multi-stage stratified random sample Face-to-face interview 1997 ACCIDENTS ADULTS ADVERTISING ADVICE AGE ARBITRATION ASTHMA ATTITUDES BANK ACCOUNTS CANCER CARDIOVASCULAR DISE... CAUSES OF DEATH CHILD BENEFITS CHILD CARE CHILD DAY CARE CHILDREN CINEMA COHABITATION COLOUR TELEVISION R... COMPANIES CONFLICT RESOLUTION COOKING EQUIPMENT COSTS COT DEATHS COURTS CREDIT CARD USE CULTURAL EVENTS Consumption and con... DIABETES DISEASES ECONOMIC ACTIVITY ECONOMIC VALUE EDUCATIONAL BACKGROUND ELECTRICAL EQUIPMENT EMPLOYEES EMPLOYMENT EMPLOYMENT CONTRACTS EMPLOYMENT HISTORY EMPLOYMENT PROGRAMMES ETHNIC GROUPS EXPENDITURE Economic conditions... FAMILY MEMBERS FINANCIAL SERVICES FIRE PROTECTION EQU... FULL TIME EMPLOYMENT FURNISHED ACCOMMODA... Family life and mar... GENDER GENERAL PRACTITIONERS GRANTS HEADS OF HOUSEHOLD HEALTH HEALTH CONSULTATIONS HEALTH PROFESSIONALS HEARING HEATING SYSTEMS HOLIDAYS HOME CONTENTS INSUR... HOME OWNERSHIP HOME SELLING HOSPITAL SERVICES HOURS OF WORK HOUSEHOLDS HOUSES HOUSING TENURE HUMAN SETTLEMENT Health behaviour Housing ILL HEALTH INCOME INCOME TAX INDUSTRIES INFLATION INFORMATION MATERIALS INFORMATION SOURCES INHERITANCE INSURANCE INTEREST FINANCE INVESTMENT Income JOB HUNTING JUDGMENTS LAW LABOUR RELATIONS LANDLORDS Labour relations co... MANAGERS MARITAL STATUS MARRIAGE DISSOLUTION MASS MEDIA MEDICAL CENTRES MEDICAL INSURANCE MEDICAL PRESCRIPTIONS MORTGAGES MOTHERS MOTOR VEHICLES ONE PARENT FAMILIES ORGANIZATIONS PARENTS PART TIME EMPLOYMENT PASSIVE SMOKING PENSIONS PERSONNEL PLACE OF RESIDENCE PRESCHOOL CHILDREN PRICES PRIVATE SECTOR PUBLIC HOUSES PUBLIC INFORMATION PUBLIC SERVICE BUIL... RADIO RECRUITMENT RENTED ACCOMMODATION RESPIRATORY TRACT D... RESTAURANTS RETIREMENT SAVINGS SCHOOLCHILDREN SCHOOLS SECOND HOMES SELF EMPLOYED SHOPS SICK LEAVE SMOKING SMOKING CESSATION SMOKING RESTRICTIONS SOCIAL HOUSING SOCIAL SECURITY BEN... SPORTING EVENTS SPOUSE S ECONOMIC A... SPOUSE S EMPLOYMENT SPOUSES STATE AID SUPERVISORS Social behaviour an... TELEPHONE HELP LINES TELEVISION ADVERTISING TELEVISION RECEIVERS TERMINATION OF SERVICE TIED HOUSING TOBACCO TRAINING TRAVEL UNEMPLOYMENT UNFURNISHED ACCOMMO... UNMARRIED MOTHERS UNWAGED WORKERS Unemployment VOCATIONAL EDUCATIO... WAGES WORKERS RIGHTS WORKING MOTHERS WORKPLACE property and invest...

資料來源：本署癌症篩檢資料庫，篩檢統計至2014.02.08。 備註：1.篩檢率計算方式：近2年曾接受過口腔癌篩檢人數 / (當年12月人口數*抽菸或嚼檳榔比例)。 2.2010年起，全面提供30歲以上嚼檳榔或吸菸民眾2年1次口腔黏膜檢查。 Source：Cancer Screening Database, HPA. Data by February, 8, 2014. Note：1.Oral cancer screening rate: Percentage of people aged 30 or over who chew betel quid or smoke reporting a oral mucosa test in the past 2 years. 2.Screening policy: The government has incorporated oral cancer screening into preventive health care services for people aged 30 or over who chew betel quid or smoke with once every 2 years since 2010.

About 20–30% of early-stage breast cancer patients suffer relapses after surgery. To identify such high-risk patients, many signatures have been reported, but they lack robustness in data measured on different platforms. Here, we developed a signature which is robust across multiple profiling platforms, and identified reproducible omics features characterizing metastasis of estrogen receptor (ER)-positive breast cancer from the Gene Expression Omnibus database with the aid of the signature. Based on the stable within-sample relative expression orderings (REOs), we constructed a signature consisting of five gene pairs, named 5-GPS, whose REOs were significantly correlated with relapse-free survival using the univariate Cox regression model. Using 5-GPS, patients were classified into the low-risk and high-risk groups. Patients in the high-risk group have worse survival compared to those in the low-risk group using Kaplan-Meier curve analysis with the log-rank test. Applying 5-GPS to the RNA-sequencing data of stage I-IV breast cancer samples archived in The Cancer Genome Atlas (TCGA), we found that the proportion of the high-risk patients increases with the stage. The proposed REO-based signature shows potential in identifying early-stage ER+ breast cancer patients with high risk of relapse after surgery.