Cervical Cancer Risk Factors for Biopsy: This Dataset is Obtained from UCI Repository and kindly acknowledged! This file contains a List of Risk Factors for Cervical Cancer leading to a Biopsy Examination! About 11,000 new cases of invasive cervical cancer are diagnosed each year in the U.S. However, the number of new cervical cancer cases has been declining steadily over the past decades. Although it is the most preventable type of cancer, each year cervical cancer kills about 4,000 women in the U.S. and about 300,000 women worldwide. In the United States, cervical cancer mortality rates plunged by 74% from 1955 - 1992 thanks to increased screening and early detection with the Pap test. AGE Fifty percent of cervical cancer diagnoses occur in women ages 35 - 54, and about 20% occur in women over 65 years of age. The median age of diagnosis is 48 years. About 15% of women develop cervical cancer between the ages of 20 - 30. Cervical cancer is extremely rare in women younger than age 20. However, many young women become infected with multiple types of human papilloma virus, which then can increase their risk of getting cervical cancer in the future. Young women with early abnormal changes who do not have regular examinations are at high risk for localized cancer by the time they are age 40, and for invasive cancer by age 50. SOCIOECONOMIC AND ETHNIC FACTORS Although the rate of cervical cancer has declined among both Caucasian and African-American women over the past decades, it remains much more prevalent in African-Americans -- whose death rates are twice as high as Caucasian women. Hispanic American women have more than twice the risk of invasive cervical cancer as Caucasian women, also due to a lower rate of screening. These differences, however, are almost certainly due to social and economic differences. Numerous studies report that high poverty levels are linked with low screening rates. In addition, lack of health insurance, limited transportation, and language difficulties hinder a poor woman’s access to screening services. HIGH SEXUAL ACTIVITY Human papilloma virus (HPV) is the main risk factor for cervical cancer. In adults, the most important risk factor for HPV is sexual activity with an infected person. Women most at risk for cervical cancer are those with a history of multiple sexual partners, sexual intercourse at age 17 years or younger, or both. A woman who has never been sexually active has a very low risk for developing cervical cancer. Sexual activity with multiple partners increases the likelihood of many other sexually transmitted infections (chlamydia, gonorrhea, syphilis).Studies have found an association between chlamydia and cervical cancer risk, including the possibility that chlamydia may prolong HPV infection. FAMILY HISTORY Women have a higher risk of cervical cancer if they have a first-degree relative (mother, sister) who has had cervical cancer. USE OF ORAL CONTRACEPTIVES Studies have reported a strong association between cervical cancer and long-term use of oral contraception (OC). Women who take birth control pills for more than 5 - 10 years appear to have a much higher risk HPV infection (up to four times higher) than those who do not use OCs. (Women taking OCs for fewer than 5 years do not have a significantly higher risk.) The reasons for this risk from OC use are not entirely clear. Women who use OCs may be less likely to use a diaphragm, condoms, or other methods that offer some protection against sexual transmitted diseases, including HPV. Some research also suggests that the hormones in OCs might help the virus enter the genetic material of cervical cells. HAVING MANY CHILDREN Studies indicate that having many children increases the risk for developing cervical cancer, particularly in women infected with HPV. SMOKING Smoking is associated with a higher risk for precancerous changes (dysplasia) in the cervix and for progression to invasive cervical cancer, especially for women infected with HPV. IMMUNOSUPPRESSION Women with weak immune systems, (such as those with HIV / AIDS), are more susceptible to acquiring HPV. Immunocompromised patients are also at higher risk for having cervical precancer develop rapidly into invasive cancer. DIETHYLSTILBESTROL (DES) From 1938 - 1971, diethylstilbestrol (DES), an estrogen-related drug, was widely prescribed to pregnant women to help prevent miscarriages. The daughters of these women face a higher risk for cervical cancer. DES is no longer prsecribed.

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.

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.

Annual percent change and average annual percent change in age-standardized cancer incidence rates since 1984 to the most recent diagnosis year. The table includes a selection of commonly diagnosed invasive cancers, as well as in situ bladder cancer. Cases are 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) from 1992 to the most recent data year and on the International Classification of Diseases, ninth revision (ICD-9) from 1984 to 1991.

Some racial and ethnic categories are suppressed for privacy and to avoid misleading estimates when the relative standard error exceeds 30% or the unweighted sample size is less than 50 respondents. Margins of error are estimated at the 90% confidence level.

Data Source: Centers for Disease Control and Prevention (CDC). Behavioral Risk Factor Surveillance System Survey (BRFSS) Data

Why This Matters

Colorectal cancer is the third leading cause of cancer death in the U.S. for men and women. Although colorectal cancer is most common among people aged 65 to 74, there has been an increase in incidences among people aged 40 to 49.

Nationally, Black people are disproportionately likely to both have colorectal cancer and die from it. Hispanic residents, and especially those with limited English proficiency, report having the lowest rate of colorectal cancer screenings.

Racial disparities in education, poverty, health insurance coverage, and English language proficiency are all factors that contribute to racial gaps in receiving colorectal cancer screenings. Increased colorectal cancer screening utilization has been shown to nearly erase the racial disparities in the death rate of colorectal cancer.

The District Response

The Colorectal Cancer Control Program (DC3C) aims to reduce colon cancer incidence and mortality by increasing colorectal cancer screening rates among District residents.

DC Health’s Cancer and Chronic Disease Prevention Bureau works with healthcare providers to improve the use of preventative health services and provide colorectal cancer screening services.

DC Health maintains the District of Columbia Cancer Registry (DCCR) to track cancer incidences, examine environmental substances that cause cancer, and identify differences in cancer incidences by age, gender, race, and geographical location.

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.

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 )

Some racial and ethnic categories are suppressed for privacy and to avoid misleading estimates when the relative standard error exceeds 30% or the unweighted sample size is less than 50 respondents.

Data Source: Centers for Disease Control and Prevention (CDC). Behavioral Risk Factor Surveillance System Survey (BRFSS) Data

Why This Matters

Breast cancer is the most commonly diagnosed cancer in women and people assigned female at birth (AFAB) and the second leading cause of cancer death in the U.S. Breast cancer screenings can save lives by helping to detect breast cancer in its early stages when treatment is more effective.

While non-Hispanic white women and AFAB individuals are more likely to be diagnosed with breast cancer than their counterparts of other races and ethnicities, non-Hispanic Black women and AFAB individuals die from breast cancer at a significantly higher rate than their counterparts races and ethnicities.

Later-stage diagnoses and prolonged treatment duration partly explain these disparities in mortality rate. Structural barriers to quality health care, insurance, education, affordable housing, and sustainable income that disproportionately affect communities of color also drive racial inequities in breast cancer screenings and mortality.

The District Response

Project Women Into Staying Healthy (WISH) provides free breast and cervical cancer screenings to uninsured or underinsured women and AFAB adults aged 21 to 64. Patient navigation, transportation assistance, and cancer education are also provided.

DC Health’s Cancer and Chronic Disease Prevention Bureau works with healthcare providers to improve the use of preventative health services and provide breast cancer screening services.

DC Health maintains the District of Columbia Cancer Registry (DCCR) to track cancer incidences, examine environmental substances that cause cancer, and identify differences in cancer incidences by age, gender, race, and geographical location.

SUMMARYThis analysis, designed and executed by Ribble Rivers Trust, identifies areas across England with the greatest levels of obesity, inactivity and inactivity/obesity-related illnesses. Please read the below information to gain a full understanding of what the data shows and how it should be interpreted.The analysis incorporates data relating to the following:Obesity/inactivity-related illnesses (asthma, cancer, chronic kidney disease, coronary heart disease, depression, diabetes mellitus, hypertension, stroke and transient ischaemic attack)Excess weight in children and obesity in adults (combined)Inactivity in children and adults (combined)The analysis was designed with the intention that this dataset could be used to identify locations where investment could encourage greater levels of activity. In particular, it is hoped the dataset will be used to identify locations where the creation or improvement of accessible green/blue spaces and public engagement programmes could encourage greater levels of outdoor activity within the target population, and reduce the health issues associated with obesity and inactivity.ANALYSIS METHODOLOGY1. Obesity/inactivity-related illnessesThe 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)- Depression (in adults aged 18+)- 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 areaOf the GPs that covered part of that MSOA: the percentage of patients registered with each GP that have that illness The 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 8 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.2. Excess weight in children and obesity in adults (combined)For each MSOA, the number and percentage of children in Reception and Year 6 with excess weight was combined with population data (up to age 17) to estimate the total number of children with excess weight.The first part of the analysis detailed in section 1 was used to estimate the number of adults with obesity in each MSOA, based on GP-level statistics.The percentage of each MSOA’s adult population (aged 18+) with obesity was estimated, using GP-level data (see section 1 above). 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 adult patients registered with each GP that are obeseThe estimated percentage of each MSOA’s adult population with obesity was then combined with Office for National Statistics Mid-Year Population Estimates (2019) data for MSOAs, to estimate the number of adults in each MSOA with obesity.The estimated number of children with excess weight and adults with obesity were combined with population data, to give the total number and percentage of the population with excess weight.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 excess weight/obesityB) the NUMBER of people within that MSOA who are estimated to have excess weight/obesityAn 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 excess weight/obesity, compared to other MSOAs. In other words, those are areas where a large number of people are predicted to suffer from excess weight/obesity, and where those people make up a large percentage of the population, indicating there is a real issue with that excess weight/obesity within the population and the investment of resources to address that issue could have the greatest benefits.3. Inactivity in children and adultsFor each administrative district, the number of children and adults who are inactive was combined with population data to estimate the total number and percentage of the population that are inactive.Each district was assigned a relative score between 1 and 0 (1 = worst, 0 = best) based on:A) the PERCENTAGE of the population within that district who are estimated to be inactiveB) the NUMBER of people within that district who are estimated to be inactiveAn 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 district predicted to be inactive, compared to other districts. In other words, those are areas where a large number of people are predicted to be inactive, and where those people make up a large percentage of the population, indicating there is a real issue with that inactivity within the population and the investment of resources to address that issue could have the greatest benefits.Summary datasetAn average of the scores calculated in sections 1-3 was taken, and converted to a relative score between 1 and 0 (1= worst, 0 = best). The closer the score to 1, the greater the number and percentage of people suffering from obesity, inactivity and associated illnesses. I.e. these are areas where there are a large number of people (both children and adults) who are obese, inactive and suffer from obesity/inactivity-related illnesses, and where those people make up a large percentage of the local population. These are the locations where interventions could have the greatest health and wellbeing benefits for the local population.LIMITATIONS1. For data recorded at the GP practice level, 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 ‘Levels of obesity, inactivity and associated illnesses: Summary (England). Areas with data missing’ 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, 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

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;

The aim of the National Prostate Cancer Audit (NPCA) is to evaluate the patterns of care and outcomes for patients with prostate cancer in England and Wales, and to support services to improve the quality of care. National guidelines underpin the management of patients with prostate cancer and the NPCA evaluates current patterns of care against these standards including guidance and quality standards from the National Institute for Health and Care Excellence (NICE).

The information presented here reports on prostate cancer services in England and Wales, showing variation across providers.

We report results from all six of our performance indicators for both England and Wales, using the most recently available data to the audit. Four performance indicators:

• proportion of men with low-risk localised cancer undergoing radical prostate cancer treatment

• proportion of men with high-risk/locally advanced disease undergoing radical prostate cancer treatment

• proportion of patients experiencing at least one genitourinary (GU) complication requiring a procedural/surgical intervention within 2 years of radical prostatectomy

• proportion of patients receiving a procedure of the large bowel and a diagnosis indicating radiation toxicity up to 2 years following radical prostate radiotherapy (RT)

require risk stratification using the Gleason score, which is not currently available in the Rapid Cancer Registration Dataset (RCRD) therefore, to include these, we have used the National Cancer Registration Dataset (NCRD) in England. The most recently available data to the audit from the NCRD in England is between 1st January 2021 and 31st December 2021.

In Wales, the data we receive includes the Gleason score, and the most recently available data to the audit covers patients newly diagnosed with prostate cancer between 1st April 2022 and 31st March 2023.

Previous analysis has shown that RCRD underestimated the proportion of men diagnosed with metastatic disease when compared to the NCRD, therefore we have used the NCRD in England to report this indicator. This means we report on different time frames for England and Wales. The proportion of patients who had an emergency readmission within 90 days of radical prostate cancer surgery however can be accurately calculated using the RCRD. Therefore, to compare rates between England and Wales, we selected the same timeframe for this indicator.

To report on the national picture of prostate cancer services, we use the most recently available data in England from the RCRD between 1st January 2023 and 31st December 2023, and in Wales between 1st April 2022 and 31st March 2023. To report on inequalities in England (age, ethnicity and deprivation), we use the most recently available data from the RCRD between 1st January 2021 and 31st December 2023.

Individual provider results and reports are available enabling regional and national comparisons to support local QI.

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.

SUMMARYThis analysis, designed and executed by Ribble Rivers Trust, identifies areas across England with the greatest levels of physical and mental 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)- Depression (in adults aged 18+)- 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 illness The 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 8 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.

IntroductionChronic infection with hepatitis C virus (HCV) is an established risk factor for liver cancer. Although several epidemiologic studies have evaluated the risk of extrahepatic malignancies among people living with HCV, due to various study limitations, results have been heterogeneous.MethodsWe used data from the British Columbia Hepatitis Testers Cohort (BC-HTC), which includes all individuals tested for HCV in the Province since 1990. We assessed hepatic and extrahepatic cancer incidence using data from BC Cancer Registry. Standardized incidence ratios (SIR) comparing to the general population of BC were calculated for each cancer site from 1990 to 2016.ResultsIn total, 56,823 and 1,207,357 individuals tested positive and negative for HCV, respectively. Median age at cancer diagnosis among people with and without HCV infection was 59 (interquartile range (IQR): 53-65) and 63 years (IQR: 54-74), respectively. As compared to people living without HCV, a greater proportion of people living with HCV-infection were men (66.7% vs. 44.7%, P-value

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;

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.

The proportion of women eligible for screening who have had a test with a recorded result at least once in the previous 36 months.RationaleBreast screening supports early detection of cancer and is estimated to save 1,400 lives in England each year. This indicator provides an opportunity to incentivise screening promotion and other local initiatives to increase coverage of breast screening.Improvements in coverage would mean more breast cancers are detected at earlier, more treatable stages.Breast screening supports early detection of cancer and is estimated to save 1,400 lives in England each year. This indicator provides an opportunity to incentivise screening promotion and other local initiatives to increase coverage of breast screening.Improvements in coverage would mean more breast cancers are detected at earlier, more treatable stages.Definition of numeratorTested women (numerator) is the number of eligible women aged 53 to 70 registered with a GP with a screening test result recorded in the past 36 months.Definition of denominatorEligible women (denominator) is the number of women aged 53 to 70 years resident in the area (determined by postcode of residence) who are eligible for breast screening at a given point in time, excluding those whose recall has been ceased for clinical reasons (for example, due to previous bilateral mastectomy).CaveatsData for ICBs are estimated from local authority data. In most cases ICBs are coterminous with local authorities, so the ICB figures are precise. In cases where local authorities cross ICB boundaries, the local authority data are proportionally split between ICBs, based on population located in each ICB.The affected ICBs are:Bath and North East Somerset, Swindon and Wiltshire;Bedfordshire, Luton and Milton Keynes;Buckinghamshire, Oxfordshire and Berkshire West;Cambridgeshire and Peterborough;Frimley;Hampshire and Isle of Wight;Hertfordshire and West Essex;Humber and North Yorkshire;Lancashire and South Cumbria;Norfolk and Waveney;North East and North Cumbria;Suffolk and North East Essex;Surrey Heartlands;Sussex;West Yorkshire.Please be aware that the April 2019 to March 2020, April 2020 to March 2021 and April 2021 to March 2022 data covers the time period affected by the COVID19 pandemic and therefore data for this period should be interpreted with caution.This indicator gives screening coverage by local authority . This is not the same as the indicator based on population registered with primary care organisations which include patients wherever they live. This is likely to result in different England totals depending on selected (registered or resident) population footprint.The indicator excludes women outside the target age range for the screening programme who may self refer for screening.Standards say "Women who are ineligible for screening due to having had a bilateral mastectomy, women who are ceased from the programme based on a ‘best interests’ decision under the Mental Capacity Act 2005 or women who make an informed choice to remove themselves from the screening programme will be removed from the numerator and denominator.There are a number of categories of women in the eligible age range who are not registered with a GP and subsequently not called for screening as they are not on the Breast Screening Select (BS Select) database. Screening units have a responsibility to maximise coverage of eligible women in their target population and should therefore be accessible to women in this category through self referral and GP referral ."This indicator gives screening coverage by local authority . This is not the same as the indicator based on population registered with primary care organisations which include patients wherever they live. This is likely to result in different England totals depending on selected (registered or resident) population footprint.

computer-assisted telephone interview (CATI); mail questionnaireThe data available for download are not weighted and users will need to weight the data prior to analysis. Users who plan to do inferential statistical testing using the data should utilize a statistical program that can incorporate the replicate weights included in the dataset. Additional information about sampling, interviewing, sampling error, weighting, and the universe of each question may be found in the codebook.This data collection utilized a split frame where approximately half of the sample completed the survey by telephone through random digit dial (RDD) and half completed it through the mail as a paper and pencil questionnaire. Users can analyse the data with only the RDD respondents, only the mail respondents, or both, as indicated by the variable SAMPFLAG. For each type of analysis, users will need to supply the proper final weight to get population estimates and replicate weights to calculate the correct variance.Variable names containing more than 16 characters were truncated in order to be compatible with current statistical programs. Therefore, variable names may differ slightly from those in the original documentation.The formats of the weight and replicate weight variables were adjusted to fit the width of the values present in these variables, and the variables REGION and DIVISION were converted from character to numeric.To protect respondent confidentiality, open-ended responses containing information on respondent's occupation in variables HC03WHERESEE2_OS and HD05OCCUPATIO_OS were blanked.ICPSR created a unique sequential record identifier variable named CASEID. The Health Information National Trends Survey (HINTS) collects nationally representative data about the American public's access to and use of cancer-related information. The 2007 HINTS survey is the third in an ongoing biannual series and provides information on the changing patterns, needs, and behavior in seeking and supplying cancer information and explores how cancer risks are perceived. Respondents were asked about the ways in which they obtained health information, their use of health care services, their views about medical information and research, and their beliefs about cancer. A series of questions specifically addressed cervical cancer, colon cancer, and the Human Papillomavirus (HPV). Information was also collected on physical and mental health status, diet, physical activity, sun exposure, history of cancer, tobacco use, and whether respondents had health insurance. Demographic variables include sex, age, race, education level, employment status, marital status, household income, number of people living in the household, ownership of residence, and whether respondents were born in the United States. For the CATI data collection, the sample design was a list-assisted RDD sample and one adult in the household was sampled for the extended interview using an algorithm designed to minimize intrusiveness. The mail survey included a stratified sample selected from a list of addresses that oversampled for minorities. Sampled addresses were matched to a database of listed telephone numbers, with 50 percent of the cases successfully matched to a telephone number. Matches in which a telephone number was both appended to an address-sample address and included in the RDD sample were deleted from the address sample. Please refer to the codebook documentation for more information on sample design. Every sampled adult who completed a questionnaire in HINTS 2007 received three full-sample weights and three sets of replicate-sample weights. Two of the three types of weights correspond to the type of samples - the address-sample weight (MWGT0) and the RDD sample weight (RWGT0). The address-sample weight is missing for a case in the RDD sample and vice versa. The sample-specific weights are used to calculate estimates based on data from one of the two samples. The third type of weight is a composite weight (CWGT0) which is used to calculate estimates based on the data from both samples. Please refer to the codebook documentation for more information on weighting. Response Rates: The overall response rate for the RDD sample was 24.23 percent, while the overall response rate for the address-sample was 30.99 percent. Please refer to the codebook documentation for more information on response rates. The civilian, noninstitutionalized population of the United States aged 18 years and older. Datasets: DS1: Health Information National Trends Survey (HINTS), 2007

Cancer, the second-leading cause of mortality, kills 16% of people worldwide. Unhealthy lifestyles, smoking, alcohol abuse, obesity, and a lack of exercise have been linked to cancer incidence and mortality. However, it is hard. Cancer and lifestyle correlation analysis and cancer incidence and mortality prediction in the next several years are used to guide people’s healthy lives and target medical financial resources. Two key research areas of this paper are Data preprocessing and sample expansion design Using experimental analysis and comparison, this study chooses the best cubic spline interpolation technology on the original data from 32 entry points to 420 entry points and converts annual data into monthly data to solve the problem of insufficient correlation analysis and prediction. Factor analysis is possible because data sources indicate changing factors. TSA-LSTM Two-stage attention design a popular tool with advanced visualization functions, Tableau, simplifies this paper’s study. Tableau’s testing findings indicate it cannot analyze and predict this paper’s time series data. LSTM is utilized by the TSA-LSTM optimization model. By commencing with input feature attention, this model attention technique guarantees that the model encoder converges to a subset of input sequence features during the prediction of output sequence features. As a result, the model’s natural learning trend and prediction quality are enhanced. The second step, time performance attention, maintains We can choose network features and improve forecasts based on real-time performance. Validating the data source with factor correlation analysis and trend prediction using the TSA-LSTM model Most cancers have overlapping risk factors, and excessive drinking, lack of exercise, and obesity can cause breast, colorectal, and colon cancer. A poor lifestyle directly promotes lung, laryngeal, and oral cancers, according to visual tests. Cancer incidence is expected to climb 18–21% between 2020 and 2025, according to 2021. Long-term projection accuracy is 98.96 percent, and smoking and obesity may be the main cancer causes.