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.

https://news.yale.edu/sites/default/files/styles/featured_media/public/ynews-cancer-healthy_137381816.jpg?itok=HN73dW20&c=a75e254fe1da31f2732f6b0d7bce1413" alt="Cancer">

The dataset appears to contain information on the risk of developing or dying from various types of cancer for both males and females.

The columns include:

Gender: The type of cancer or category (e.g., "Any cancer", "Bladder", etc.). Risk of developing (Male): The percentage risk and the equivalent "one in _ person" statistic. Risk of dying (Male): The percentage risk and the equivalent "one in _ person" statistic. Risk of developing (Woman): The percentage risk and the equivalent "one in _ person" statistic. Risk of dying (Woman): The percentage risk and the equivalent "one in _ person" statistic.

Columns in the Dataset Gender Risk of developing (Male): Percentage Risk of developing (Male): One in _ Person Risk of dying (Male): Percentage Risk of dying (Male): One in _ Person Risk of developing (Woman): Percentage Risk of developing (Woman): One in _ Person Risk of dying (Woman): Percentage Risk of dying (Woman): One in _ Person

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

Background This study sought to describe the pattern of complementary/alternative medicine (CAM) use among a group of patients with advanced breast cancer, to examine the main reasons for their CAM use, to identify patient's information sources and their communication pattern with their physicians. Methods Face-to-face structured interviews of patients with advanced-stage breast cancer at a comprehensive oncology center. Results Seventy three percent of patients used CAM; relaxation/meditative techniques and herbal medicine were the most common. The most commonly cited primary reason for CAM use was to boost the immune system, the second, to treat cancer; however these reasons varied depending on specific CAM therapy. Friends or family members and mass media were common primary information source's about CAM. Conclusions A high proportion of advanced-stage breast cancer patients used CAM. Discussion with doctors was high for ingested products. Mass media was a prominent source of patient information. Credible sources of CAM information for patients and physicians are needed.

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.

IntroductionThe objective of this study is to compare different methods for measuring breast density, both visual assessments and automated volumetric density, in a breast cancer screening setting. These measures could potentially be implemented in future screening programmes, in the context of personalised screening or screening evaluation.Materials and MethodsDigital mammographic exams (N = 992) of women participating in the Dutch breast cancer screening programme (age 50–75y) in 2013 were included. Breast density was measured in three different ways: BI-RADS density (5th edition) and with two commercially available automated software programs (Quantra and Volpara volumetric density). BI-RADS density (ordinal scale) was assessed by three radiologists. Quantra (v1.3) and Volpara (v1.5.0) provide continuous estimates. Different comparison methods were used, including Bland-Altman plots and correlation coefficients (e.g., intraclass correlation coefficient [ICC]).ResultsBased on the BI-RADS classification, 40.8% of the women had ‘heterogeneously or extremely dense’ breasts. The median volumetric percent density was 12.1% (IQR: 9.6–16.5) for Quantra, which was higher than the Volpara estimate (median 6.6%, IQR: 4.4–10.9). The mean difference between Quantra and Volpara was 5.19% (95% CI: 5.04–5.34) (ICC: 0.64). There was a clear increase in volumetric percent dense volume as BI-RADS density increased. The highest accuracy for predicting the presence of BI-RADS c+d (heterogeneously or extremely dense) was observed with a cut-off value of 8.0% for Volpara and 13.8% for Quantra.ConclusionAlthough there was no perfect agreement, there appeared to be a strong association between all three measures. Both volumetric density measures seem to be usable in breast cancer screening programmes, provided that the required data flow can be realized.

Source: Cancer Screening Database, HPA. Note: Cervical cancer screening rate: Percentage of women aged 30 or over reporting a pap smear in the past 3 years.

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 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 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.

• A PHN was assigned to women using a postcode to PHN 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 a PHN 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.

• Totals may not sum due to rounding.

• The time period of some PHN data presented is prior to the initiation of PHNs, which were in established in June 2015.

• 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.

• 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.

• PHN205 Murray includes Albury, NSW.

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.

This dataset presents the footprint of female cancer mortality statistics in Australia for all cancers combined and the 11 top cancer groupings (breast, cervical, colorectal, leukaemia, lung, lymphoma, melanoma of the skin, ovary, pancreas, thyroid and uterus) and their respective ICD-10 codes. The data spans the years 2009-2013 and is aggregated to Greater Capital City Statistical Areas (GCCSA) from the 2011 Australian Statistical Geography Standard (ASGS). Mortality data refer to the number of deaths due to cancer in a given time period. Cancer deaths data are sourced from the Australian Institute of Health and Welfare (AIHW) 2013 National Mortality Database (NMD).

Breast density is a radiologic feature that reflects fibroglandular tissue content relative to breast area or volume, and it is a breast cancer risk factor. This study employed deep learning approaches to identify histologic correlates in radiologically-guided biopsies that may underlie breast density and distinguish cancer among women with elevated and low density. Data access: Datasets supporting figure 2, tables 2 and 3 and supplementary table 2 of the published article are publicly available in the figshare repository, as part of this data record (https://doi.org/10.6084/m9.figshare.9786152). These datasets are contained in the zip file NPJ FigShare.zip. Datasets supporting figure 3, table 1 and supplementary table 1 of the published article are not publicly available to protect patient privacy, but can be made available on request from Dr. Gretchen L. Gierach, Senior Investigator, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA, email address: gierachg@mail.nih.gov. Study description and aims: The study aimed to identify tissue correlates of breast density that may be important for distinguishing malignant from benign biopsy diagnoses separately among women with high and low breast density, to help inform cancer risk stratification among women undergoing a biopsy following an abnormal mammogram. Haematoxylin and eosin (H&E)-stained digitized images from image-guided breast biopsies (n=852 patients) were evaluated. Breast density was assessed as global and localized fibroglandular volume (%). A convolutional neural network characterized H&E composition. 37 features were extracted from the network output, describing tissue quantities and morphological structure. A random forest regression model was trained to identify correlates most predictive of fibroglandular volume (n=588). Correlations between predicted and radiologically quantified fibroglandular volume were assessed in 264 independent patients. A second random forest classifier was trained to predict diagnosis (invasive vs. benign); performance was assessed using area under receiver-operating characteristics curves (AUC). For more details on the methodology please see the published article. Study approval: The Institutional Review Boards at the NCI and the University of Vermont approved the protocol for this project for either active consenting or a waiver of consent to enrol participants, link data and perform analytical studies. Dataset descriptions: Data supporting figure 2: Datasets Figure 2A H&E.jpg, Figure 2A Mammogram.jpg, Figure 2B H&E.jpg and Figure 2B Mammogram.jpg are in .jpg file format and consist of histological whole slide H&E images and corresponding full-field digital mammograms from patients whose biopsies yielded diagnoses of atypical ductal hyperplasia and invasive carcinoma. Data supporting figure 3: Dataset Figure 3.xls is in .xls file format and contains raw data used to generate the Receiver Operating Characteristic (ROC) curves for the prediction of invasive cancer among women with high percent global fibroglandular volume, low percent global fibroglandular volume, high percent localized fibroglandular volume and low percent localized fibroglandular volume. Data supporting table 1: Dataset Table1_analysis.sas7bdat is in SAS file format and contains the characteristics of study participants in the BREAST Stamp Project, who were referred for an image-guided breast biopsy, stratified by the training and testing sets (n = 852). Data supporting table 2: Datasets Global FGV.xls (accompanying Global FGV.png file) and Localized FGV.xls (accompanying Localized FGV.png file) are in .xls file format and the accompanying files are in .png file format. The data contain histologic features identified in the random forest model for the prediction of global and localized % fibroglandular volume. Data supporting table 3: Datasets HighGlobal_feature_importance.xls, HighGlobal_feature_importance.pdf, HighLocal_feature_importance.xls, HighLocal_feature_importance.pdf, LowGlobal_feature_importance.xls, LowGlobal_feature_importance.pdf, LowLocal_feature_importance.xls, LowLocal_feature_importance.pdf are in .xls file format. The accompanying figures generated from the data in the .xls files are in .pdf file format. These files contain histologic features identified in the random forest model for the prediction of invasive cancer status among women with high vs. low % fibroglandular volume. Data supporting supplementary table 1: Datasets testfeatures.xls and trainfeatures.xls are in .xls file format and include the distribution and description of the 37 histologic features extracted from the convolutional neural network deep learning output in the H&E stained whole slide images from the training and testing sets. Data supporting supplementary table 2: Datasets All_samples_global.xls, All_samples_global.png, All_samples_local.xls, All_samples_local.png, PostMeno_global.xls, PostMeno_global.png, PostMeno_local.xls, PostMeno_local.png, PreMeno_global.xls, PreMeno_global.png, PreMeno_local.xls, PreMeno_local.png are in .xls file format. The accompanying figures generated from the data in the .xls files are in .png file format. These data include the histologic features identified in the random forest model that included BMI for the prediction of global and localized % fibroglandular volume.Software needed to access the data: Data files in SAS file format require the SAS software to be accessed.

Prevalences in percent of the 2168 men and women who had the cancer diagnoses breast cancer and prostate cancer and the periodontal inflammation gingivitis and periodontitis.

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/

Background: Breast cancer continues to be a public health issue in Botswana. However, there is limited evidence on the association of risk factors with the stages at which diagnosis is done. This study provides evidence on association between risk factors and the stages at which breast cancer is diagnosed among adult females in Botswana.Methods: A cross‐sectional study of 211 Botswana adult women with confirmed breast cancer at public oncology centers was conducted over 10 months in 2022. Data on known risk factors was collected, and statistical tests performed using STATA.Results: The median age of participants at the time of first diagnosis was 50 years. Forty-six percent (46%) of the diagnosed women had advanced stages of breast cancer. Univariate analysis showed significant association of the following four factors with late breast cancer diagnosis; single and never married (OR 0.18, 95% CI: 0.036-0.932), history of iregular menses (OR 1.63, 95% CI: 1.013-2.627), breast cup size (OR 0.57, 0.336-0.968), and age at first full-time pregnancy (OR 0.86, 0.606-1.209). In a bivariate analysis, occupation (p = 0.029), age at first full-term pregnancy (p = 0.042) and type of current breast cancer (p = 0.002) were observed to be associated with late breast cancer diagnosis among women in Botswana. In a multivariate analysis, known second degree family history (OR 0.34, 95% CI: 0.129-0.893) and ductal carcinoma (OR 2.56, 95% CI: 1.083-6.071) were significant predictors of late breast cancer diagnosis among women in four cancer designated catchment centres in Botswana.Conclusion: Women in Botswana present with advanced stages of breast cancer at time of first diagnosis. The risk factors associated with this delayed diagnosis have been identified. We recommend upscaled campaigns targeting women to raise awareness of risk factors and the importance of early detection and access to care.

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.

Davidson county ZIP codes were ranked for each risk factor in numerical order according to: breast cancer incidence per 100,000 women, percentage of the female population over the age of 50 years, breast cancer mortality rate per 100,000 women, rate of Stage IV diagnosis, annual median income per household, the percentage of the female population lacking health insurance, and the percentage of the non-white population. Based on their numerical ranking in each dataset category, each ZIP code was assigned a risk factor quartile score, with 1 indicating the lowest quartile, and 4 indicating the highest quartile for each risk factor. The quartile score for breast cancer mortality rate was weighted double. The sum of the quartile scores of each category was calculated for each ZIP code to generate the integrated quartile score. A high integrated quartile score is intended to identify ZIP codes with the greatest need of breast cancer-related resources aimed at reducing breast cancer mortality.

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

Dataset

This dataset was created by Zahra Alavi

Contents

This dataset presents the footprint of participation statistics in BreastScreen Australia for women ages 50 to 74, by age group. The national breast cancer screening program, BreastScreen Australia began in 1991. It aims to reduce illness and death from breast cancer using screening mammography for early detection of unsuspected breast cancer in women. The data spans the years of 2014-2016 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 women in the population aged 50-74 screened by BreastScreen Australia over 2 calendar years. The population denominator was the average of the Australian Bureau of Statistics (ABS) Estimated Resident Population (ERP) for females aged 50-74 within the relevant geographical area for the relevant 2-year reporting period.

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

• Some postcodes (and hence women) cannot be attributed to a PHN 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.

• Totals may not sum due to rounding.

• The time period of some PHN data presented is prior to the initiation of PHNs, which were in established in June 2015.

• BreastScreen Australia changed its target age group from 50-69 years to 50-74 years from July 2013; participation is reported for both the previous and current target age groups to allow comparison of trends with previously reported data.

• Data are preliminary and subject to change.

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

• PHN205 Murray includes Albury, NSW

Each variable was assessed for each ZIP code within each county. Based on The Pearson's Correlation between the indicated variables was calculated using datapoints obtained for each ZIP code within each county.