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

Note: DPH is updating and streamlining the COVID-19 cases, deaths, and testing data. As of 6/27/2022, the data will be published in four tables instead of twelve.

The COVID-19 Cases, Deaths, and Tests by Day dataset contains cases and test data by date of sample submission. The death data are by date of death. This dataset is updated daily and contains information back to the beginning of the pandemic. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Cases-Deaths-and-Tests-by-Day/g9vi-2ahj.

The COVID-19 State Metrics dataset contains over 93 columns of data. This dataset is updated daily and currently contains information starting June 21, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-State-Level-Data/qmgw-5kp6 .

The COVID-19 County Metrics dataset contains 25 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-County-Level-Data/ujiq-dy22 .

The COVID-19 Town Metrics dataset contains 16 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Town-Level-Data/icxw-cada . To protect confidentiality, if a town has fewer than 5 cases or positive NAAT tests over the past 7 days, those data will be suppressed.

This dataset includes a count and rate per 100,000 population for COVID-19 cases, a count of COVID-19 molecular diagnostic tests, and a percent positivity rate for tests among people living in community settings for the previous two-week period. Dates are based on date of specimen collection (cases and positivity).

A person is considered a new case only upon their first COVID-19 testing result because a case is defined as an instance or bout of illness. If they are tested again subsequently and are still positive, it still counts toward the test positivity metric but they are not considered another case.

Percent positivity is calculated as the number of positive tests among community residents conducted during the 14 days divided by the total number of positive and negative tests among community residents during the same period. If someone was tested more than once during that 14 day period, then those multiple test results (regardless of whether they were positive or negative) are included in the calculation.

These case and test counts do not include cases or tests among people residing in congregate settings, such as nursing homes, assisted living facilities, or correctional facilities.

These data are updated weekly and reflect the previous two full Sunday-Saturday (MMWR) weeks (https://wwwn.cdc.gov/nndss/document/MMWR_week_overview.pdf).

DPH note about change from 7-day to 14-day metrics: Prior to 10/15/2020, these metrics were calculated using a 7-day average rather than a 14-day average. The 7-day metrics are no longer being updated as of 10/15/2020 but the archived dataset can be accessed here: https://data.ct.gov/Health-and-Human-Services/COVID-19-case-rate-per-100-000-population-and-perc/s22x-83rd

As you know, we are learning more about COVID-19 all the time, including the best ways to measure COVID-19 activity in our communities. CT DPH has decided to shift to 14-day rates because these are more stable, particularly at the town level, as compared to 7-day rates. In addition, since the school indicators were initially published by DPH last summer, CDC has recommended 14-day rates and other states (e.g., Massachusetts) have started to implement 14-day metrics for monitoring COVID transmission as well.

With respect to geography, we also have learned that many people are looking at the town-level data to inform decision making, despite emphasis on the county-level metrics in the published addenda. This is understandable as there has been variation within counties in COVID-19 activity (for example, rates that are higher in one town than in most other towns in the county).

Additional notes: As of 11/5/2020, CT DPH has added antigen testing for SARS-CoV-2 to reported test counts in this dataset. The tests included in this dataset include both molecular and antigen datasets. Molecular tests reported include polymerase chain reaction (PCR) and nucleic acid amplicfication (NAAT) tests.

The population data used to calculate rates is based on the CT DPH population statistics for 2019, which is available online here: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Population/Population-Statistics. Prior to 5/10/2021, the population estimates from 2018 were used.

Data suppression is applied when the rate is <5 cases per 100,000 or if there are <5 cases within the town. Information on why data suppression rules are applied can be found online here: https://www.cdc.gov/cancer/uscs/technical_notes/stat_methods/suppression.htm

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

BackgroundSedentary behavior is thought to pose different risks to those attributable to physical inactivity. However, few studies have examined the association between physical activity and sitting time with cancer incidence within the same population.MethodsWe followed 38,154 healthy Norwegian adults in the Nord-Trøndelag Health Study (HUNT) for cancer incidence from 1995–97 to 2014. Cox proportional hazards regression was used to estimate risk of site-specific and total cancer incidence by baseline sitting time and physical activity.ResultsDuring the 16-years follow-up, 4,196 (11%) persons were diagnosed with cancer. We found no evidence that people who had prolonged sitting per day or had low levels of physical activity had an increased risk of total cancer incidence, compared to those who had low sitting time and were physically active. In the multivariate model, sitting ≥8 h/day was associated with 22% (95% CI, 1.05–1.42) higher risk of prostate cancer compared to sitting 16.6 MET-h/week). The joint effects of physical activity and sitting time the indicated that prolonged sitting time increased the risk of CRC independent of physical activity in men.ConclusionsOur findings suggest that prolonged sitting and low physical activity are positively associated with colorectal-, prostate- and lung cancer among men. Sitting time and physical activity were not associated with cancer incidence among women. The findings emphasizing the importance of reducing sitting time and increasing physical activity.

Context

According to the American Thoracic Society and the American Lung Association:

Pneumonia is the world’s leading cause of death among children under 5 years of age.

Pneumonia killed approximately 2,400 children a day in 2015.

Pneumonia killed an estimated 880,000 children under the age of five in 2016.

More than 150,000 people are estimated to die from lung cancer each year.

Infections, including pneumonia, are the second most common cause of death in people with lung cancer.

From a recent study by the Association of American Medical Colleges (AAMC):

“The physician workforce shortages that our nation is facing are being felt even more acutely as we mobilize on the front lines to combat the COVID-19 national emergency.” --David J. Skorton, MD, AAMC president and CEO

The demographic that is going to suffer most from this shortage is patients over age 65: "While the national population is projected to grow by 10.4% during the 15 years covered by the study, the over-65 population is expected to grow by 45.1%."

Content

For the original dataset, click here.

For the sorted dataset needed to run this notebook, click here.

• CONTENT: 5856 Posterior to Anterior (PA) Chest X-ray images from pediatric patients of one to five years old from Guangzhou Women and Children’s Medical Center, Guangzhou. All chest X-ray imaging was performed as part of patients’ routine clinical care.

• PROCESS: “For the analysis of chest X-ray images, all chest radiographs were initially screened for quality control by removing all low quality or unreadable scans. The diagnoses for the images were then graded by two expert physicians before being cleared for training the AI system. In order to account for any grading errors, the evaluation set was also checked by a third expert.” (page 12)

Acknowledgements

Here's a link to an example project using this dataset: https://github.com/Luv2bnanook44/flatiron_phase4_project

This dataset was preprocessed from this Kaggle dataset from Paul Mooney: https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia

The National Cancer Registration and Analysis Service (NCRAS) at Public Health England supplies cancer registration data to NHS Digital. This data is available to be linked to other data held by NHS Digital in order to provide notifications on an individual's cancer status, be available to support research studies and to identify potential research participants for clinical trials.

NCRAS is the population-based cancer registry for England. It collects, quality assures and analyses data on all people living in England who are diagnosed with malignant and pre-malignant neoplasms, with national coverage since 1971.

The Cancer Registration dataset comprises England data to the present day, and Welsh data up to April 2017.

Timescales for dissemination of agreed data can be found under 'Our Service Levels' at the following link: https://digital.nhs.uk/services/data-access-request-service-dars/data-access-request-service-dars-process Standard response

BackgroundStrategies to increase physical activity (PA) and improve nutrition would contribute to substantial health benefits in the population, including reducing the risk of several types of cancers. The increasing accessibility of digital technologies mean that these tools could potentially facilitate the improvement of health behaviours among young people.ObjectiveWe conducted a review of systematic reviews to assess the available evidence on digital interventions aimed at increasing physical activity and good nutrition in sub-populations of young people (school-aged children, college/university students, young adults only (over 18 years) and both adolescent and young adults (<25 years)).MethodsSearches for systematic reviews were conducted across relevant databases including KSR Evidence (www.ksrevidence.com), Cochrane Database of Systematic Reviews (CDSR) and Database of Abstracts of Reviews of Effects (DARE; CRD). Records were independently screened by title and abstract by two reviewers and those deemed eligible were obtained for full text screening. Risk of bias (RoB) was assessed with the Risk of Bias Assessment Tool for Systematic Reviews (ROBIS) tool. We employed a narrative analysis and developed evidence gap maps.ResultsTwenty-four reviews were included with at least one for each sub-population and employing a range of digital interventions. The quality of evidence was limited with only one of the 24 of reviews overall judged as low RoB. Definitions of “digital intervention” greatly varied across systematic reviews with some reported interventions fitting into more than one category (i.e., an internet intervention could also be a mobile phone or computer intervention), however definitions as reported in the relevant reviews were used. No reviews reported cancer incidence or related outcomes. Available evidence was limited both by sub-population and type of intervention, but evidence was most pronounced in school-aged children. In school-aged children eHealth interventions, defined as school-based programmes delivered by the internet, computers, tablets, mobile technology, or tele-health methods, improved outcomes. Accelerometer-measured (Standardised Mean Difference [SMD] 0.33, 95% Confidence Interval [CI]: 0.05 to 0.61) and self-reported (SMD: 0.14, 95% CI: 0.05 to 0.23) PA increased, as did fruit and vegetable intake (SMD: 0.11, 95% CI: 0.03 to 0.19) (review rated as low RoB, minimal to considerable heterogeneity across results). No difference was reported for consumption of fat post-intervention (SMD: −0.06, 95% CI: −0.15 to 0.03) or sugar sweetened beverages(SSB) and snack consumption combined post-intervention (SMD: −0.02, 95% CI:–0.10 to 0.06),or at the follow up (studies reported 2 weeks to 36 months follow-up) after the intervention (SMD:–0.06, 95% CI: −0.15 to 0.03) (review rated low ROB, minimal to substantial heterogeneity across results). Smartphone based interventions utilising Short Messaging Service (SMS), app or combined approaches also improved PA measured using objective and subjective methods (SMD: 0.44, 95% CI: 0.11 to 0.77) when compared to controls, with increases in total PA [weighted mean difference (WMD) 32.35 min per day, 95% CI: 10.36 to 54.33] and in daily steps (WMD: 1,185, 95% CI: 303 to 2,068) (review rated as high RoB, moderate to substantial heterogeneity across results). For all results, interpretation has limitations in terms of RoB and presence of unexplained heterogeneity.ConclusionsThis review of reviews has identified limited evidence that suggests some potential for digital interventions to increase PA and, to lesser extent, improve nutrition in school-aged children. However, effects can be small and based on less robust evidence. The body of evidence is characterised by a considerable level of heterogeneity, unclear/overlapping populations and intervention definitions, and a low methodological quality of systematic reviews. The heterogeneity across studies is further complicated when the age (older vs. more recent), interactivity (feedback/survey vs. no/less feedback/surveys), and accessibility (type of device) of the digital intervention is considered. This underscores the difficulty in synthesising evidence in a field with rapidly evolving technology and the resulting challenges in recommending the use of digital technology in public health. There is an urgent need for further research using contemporary technology and appropriate methods.

Age-standardised proportion of adults (16+) who met the recommended guidelines of consuming five or more portions of fruit and vegetables a day by gender. To help reduce the risk of deaths from chronic diseases such as heart disease, stroke, and cancer. The Five-a-day programme was introduced to increase fruit and vegetable consumption within the general population. Its central message is that people should eat at least five portions of fruit and vegetables a day; that a variety of fruit and vegetables should be consumed and that fresh, frozen, canned and dried fruit, vegetables and pulses all count in making up these portions. The programme includes educational initiatives to increase awareness of the Five-a-day message and the benefits of fruit and vegetable consumption, along with more direct schemes to increase access to fruit and vegetables, such as the school fruit scheme and community initiatives. Monitoring of fruit and vegetable consumption is key to evaluating the success of the policy, both at the level of individual schemes and at a more general level. The England average, at the 95% confidence level (LCL = lower confidence interval; UCL = upper confidence interval). Related to: National Indicator Library - NHS England Digital (editor note: was https://indicators.ic.nhs.uk/webview/)

This table shows working age population that has a disability and Employment, unemployment, economic activity and inactivity rates by disability (includes Equalities Act Core disabled, DDA & work-limiting disabled) The definition of ‘disability’ under the Equality Act 2010 shows a person has a disability if: they have a physical or mental impairment the impairment has a substantial and long-term adverse effect on their ability to perform normal day-to-day activities For the purposes of the Act, these words have the following meanings: 'substantial' means more than minor or trivial 'long-term' means that the effect of the impairment has lasted or is likely to last for at least twelve months (there are special rules covering recurring or fluctuating conditions) 'normal day-to-day activities' include everyday things like eating, washing, walking and going shopping There are additional provisions relating to people with progressive conditions. People with HIV, cancer or multiple sclerosis are protected by the Act from the point of diagnosis. People with some visual impairments are automatically deemed to be disabled. 18/03/2015 Data has been reweighted in line with the latest ONS estimates. 2013 data is not available for disability measures from this survey. Due to changes in the health questions on the Annual Population Survey there is quite a large discontinuity in the estimates from the Apr 2012 to Mar 2013 period onwards. These became available again from the Apr 2013 to March 2014 period as new variables. 95% confidence interval of percent figure (+/-).

This study is open to adults with different types of advanced cancer (solid tumours). The study is also open to patients with diffuse large B-cell lymphoma in whom previous treatment was not successful. In some countries, adolescents who are at least 15 years old and who are diagnosed with NUT carcinoma can also participate. No standard treatment exists for this rare and aggressive form of cancer.The purpose of this study is to find out the highest dose of BI 894999 that people can tolerate.BI 894999 is tested for the first time in humans. Participants take tablets once daily. The study also tests whether participants can tolerate BI 894999 better when taken continuously or with breaks in between.Participants can stay in the study as long as they benefit from the treatment and can tolerate it.The doctors also regularly check the general health of the participants.

These data are being released in BETA version to facilitate early access to the study for research purposes. This collection has not been fully processed by NACDA or ICPSR at this time; the original materials provided by the principal investigator were minimally processed and converted to other file types for ease of use. As the study is further processed and given enhanced features by ICPSR, users will be able to access the updated versions of the study. Please report any data errors or problems to user support and we will work with you to resolve any data related issues. The National Health Interview Survey (NHIS) is conducted annually and sponsored by the National Center for Health Statistics (NCHS), which is part of the U.S. Public Health Service. The purpose of the NHIS is to obtain information about the amount and distribution of illness, its effects in terms of disability and chronic impairments, and the kinds of health services people receive across the United States population through the collection and analysis of data on a broad range of health topics. The redesigned NHIS questionnaire introduced in 1997 (see National Health Interview Survey, 1997 [ICPSR 2954]) consists of a core that remains largely unchanged from year to year, plus an assortment of supplements varying from year to year. The 2010 NHIS Core consists of three modules: Family, Sample Adult, and Sample Child. The datasets derived from these modules include Household Level, Family Level, Person Level, Injury/Poison Episode Level, Injury/Poison Verbatim Level, Sample Adult Level, and Sample Child level. The 2010 NHIS supplements consist of stand alone datasets for Cancer Level and Quality of Life data derived from the Sample Adult core and Disability Questions Tests 2010 Level derived from the Family core questionnaire. Additional supplementary questions can be found in the Sample Child dataset on the topics of cancer, immunization, mental health, and mental health services and in the Sample Adult dataset on the topics of epilepsy, immunization, and occupational health. Part 1, Household Level, contains data on type of living quarters, number of families in the household responding and not responding, and the month and year of the interview for each sampling unit. Parts 2-5 are based on the Family Core questionnaire. Part 2, Family Level, provides information on all family members with respect to family size, family structure, health status, limitation of daily activities, cognitive impairment, health conditions, doctor visits, hospital stays, health care access and utilization, employment, income, participation in government assistance programs, and basic demographic information. Part 3, Person Level, includes information on sex, age, race, marital status, education, family income, major activities, health status, health care costs, activity limits, and employment status. Parts 4 and 5, Injury/Poisoning Episode Level and Injury/Poisoning Verbatim Level, consist of questions about injuries and poisonings that resulted in medical consultations for any family members and contains information about the external cause and nature of the injury or poisoning episode and what the person was doing at the time of the injury or poisoning episode, in addition to the date and place of occurrence. A randomly-selected adult in each family was interviewed for Part 6, Sample Adult Level, regarding specific health issues, the relation between employment and health, health status, health care and doctor visits, limitation of daily activities, immunizations, and behaviors such as smoking, alcohol consumption, and physical activity. Demographic information, including occupation and industry, also was collected. The respondents to Part 6 also completed Part 7, Cancer Level, which consists of a set of supplemental questions about diet and nutrition, physical activity, tobacco, cancer screening, genetic testing, family history, and survivorship. Part 8, Sample Child Level, provides information from an adult in the household on medical conditions of one child in the household, such as developmental or intellectual disabilities, respiratory problems, seizures, allergies, and use of special equipment like hearing aids, braces, or wheelchairs. Parts 9 through 13 comprise the additional Supplements and Paradata for the 2010 NHIS. Part 9, Disability Questions Tests 2010 Level

Disability and mobility data for London and Rest of the UK, for working age (16-64) and all adults (16+). Data includes population with mobility difficulties, people who use special equipment to help be mobile, people with a mobility impairment, and people who currently have 'DDA' Disability. The definition of ‘DDA disability’ under the Equality Act 2010 shows a person has a disability if: they have a physical or mental impairment the impairment has a substantial and long-term adverse effect on their ability to perform normal day-to-day activities For the purposes of the Act, these words have the following meanings: 'substantial' means more than minor or trivial 'long-term' means that the effect of the impairment has lasted or is likely to last for at least twelve months (there are special rules covering recurring or fluctuating conditions) 'normal day-to-day activities' include everyday things like eating, washing, walking and going shopping There are additional provisions relating to people with progressive conditions. People with HIV, cancer or multiple sclerosis are protected by the Act from the point of diagnosis. People with some visual impairments are automatically deemed to be disabled. Find out more about the Life Opportunities Survey (LOS).

This dataset provides statistics on the number of discharged cases, deaths, and the average daily stay at Hamad Medical Corporation hospitals in Qatar. The information is categorized by disease type using ICD (International Classification of Diseases) codes, and spans multiple years. It offers insights into hospital performance, disease burden, and patient outcomes across a wide spectrum of medical conditions including infectious diseases, cancer, cardiovascular issues, and maternal health.

This data originates from the Public Health Outcomes tool currently presents data for available indicators for upper tier local authority levels, collated by Public Health England (PHE). The data currently published here are the baselines for the Public Health Outcomes Framework, together with more recent data where these are available. The baseline period is 2010 or equivalent, unless these data are unavailable or not deemed to be of sufficient quality. The first data were published in this tool as an official statistics release in November 2012. Future official statistics updates will be published as part of a quarterly update cycle in August, November, February and May. The definition, rationale, source information, and methodology for each indicator can be found within the spreadsheet. Data included in the spreadsheet: 0.1i - Healthy life expectancy at birth0.1ii - Life Expectancy at 650.1ii - Life Expectancy at birth0.2i - Slope index of inequality in life expectancy at birth based on national deprivation deciles within England0.2ii - Number of upper tier local authorities for which the local slope index of inequality in life expectancy (as defined in 0.2iii) has decreased0.2iii - Slope index of inequality in life expectancy at birth within English local authorities, based on local deprivation deciles within each area0.2iv - Gap in life expectancy at birth between each local authority and England as a whole0.2v - Slope index of inequality in healthy life expectancy at birth based on national deprivation deciles within England0.2vii - Slope index of inequality in life expectancy at birth within English regions, based on regional deprivation deciles within each area1.01i - Children in poverty (all dependent children under 20)1.01ii - Children in poverty (under 16s)1.02i - School Readiness: The percentage of children achieving a good level of development at the end of reception1.02i - School Readiness: The percentage of children with free school meal status achieving a good level of development at the end of reception1.02ii - School Readiness: The percentage of Year 1 pupils achieving the expected level in the phonics screening check1.02ii - School Readiness: The percentage of Year 1 pupils with free school meal status achieving the expected level in the phonics screening check1.03 - Pupil absence1.04 - First time entrants to the youth justice system1.05 - 16-18 year olds not in education employment or training1.06i - Adults with a learning disability who live in stable and appropriate accommodation1.06ii - % of adults in contact with secondary mental health services who live in stable and appropriate accommodation1.07 - People in prison who have a mental illness or a significant mental illness1.08i - Gap in the employment rate between those with a long-term health condition and the overall employment rate1.08ii - Gap in the employment rate between those with a learning disability and the overall employment rate1.08iii - Gap in the employment rate for those in contact with secondary mental health services and the overall employment rate1.09i - Sickness absence - The percentage of employees who had at least one day off in the previous week1.09ii - Sickness absence - The percent of working days lost due to sickness absence1.10 - Killed and seriously injured (KSI) casualties on England's roads1.11 - Domestic Abuse1.12i - Violent crime (including sexual violence) - hospital admissions for violence1.12ii - Violent crime (including sexual violence) - violence offences per 1,000 population1.12iii- Violent crime (including sexual violence) - Rate of sexual offences per 1,000 population1.13i - Re-offending levels - percentage of offenders who re-offend1.13ii - Re-offending levels - average number of re-offences per offender1.14i - The rate of complaints about noise1.14ii - The percentage of the population exposed to road, rail and air transport noise of 65dB(A) or more, during the daytime1.14iii - The percentage of the population exposed to road, rail and air transport noise of 55 dB(A) or more during the night-time1.15i - Statutory homelessness - homelessness acceptances1.15ii - Statutory homelessness - households in temporary accommodation1.16 - Utilisation of outdoor space for exercise/health reasons1.17 - Fuel Poverty1.18i - Social Isolation: % of adult social care users who have as much social contact as they would like1.18ii - Social Isolation: % of adult carers who have as much social contact as they would like1.19i - Older people's perception of community safety - safe in local area during the day1.19ii - Older people's perception of community safety - safe in local area after dark1.19iii - Older people's perception of community safety - safe in own home at night2.01 - Low birth weight of term babies2.02i - Breastfeeding - Breastfeeding initiation2.02ii - Breastfeeding - Breastfeeding prevalence at 6-8 weeks after birth2.03 - Smoking status at time of delivery2.04 - Under 18 conceptions2.04 - Under 18 conceptions: conceptions in those aged under 162.06i - Excess weight in 4-5 and 10-11 year olds - 4-5 year olds2.06ii - Excess weight in 4-5 and 10-11 year olds - 10-11 year olds2.07i - Hospital admissions caused by unintentional and deliberate injuries in children (aged 0-14 years)2.07i - Hospital admissions caused by unintentional and deliberate injuries in children (aged 0-4 years)2.07ii - Hospital admissions caused by unintentional and deliberate injuries in young people (aged 15-24)2.08 - Emotional well-being of looked after children2.09i - Smoking prevalence at age 15 - current smokers (WAY survey)2.09ii - Smoking prevalence at age 15 - regular smokers (WAY survey)2.09iii - Smoking prevalence at age 15 - occasional smokers (WAY survey)2.09iv - Smoking prevalence at age 15 years - regular smokers (SDD survey)2.09v - Smoking prevalence at age 15 years - occasional smokers (SDD survey)2.12 - Excess Weight in Adults2.13i - Percentage of physically active and inactive adults - active adults2.13ii - Percentage of physically active and inactive adults - inactive adults2.14 - Smoking Prevalence2.14 - Smoking prevalence - routine & manual2.15i - Successful completion of drug treatment - opiate users2.15ii - Successful completion of drug treatment - non-opiate users2.16 - People entering prison with substance dependence issues who are previously not known to community treatment2.17 - Recorded diabetes2.18 - Admission episodes for alcohol-related conditions - narrow definition2.19 - Cancer diagnosed at early stage (Experimental Statistics)2.20i - Cancer screening coverage - breast cancer2.20ii - Cancer screening coverage - cervical cancer2.21i - Antenatal infectious disease screening – HIV coverage2.21iii - Antenatal Sickle Cell and Thalassaemia Screening - coverage2.21iv - Newborn bloodspot screening - coverage2.21v - Newborn Hearing screening - Coverage2.21vii - Access to non-cancer screening programmes - diabetic retinopathy2.21viii - Abdominal Aortic Aneurysm Screening2.22iii - Cumulative % of the eligible population aged 40-74 offered an NHS Health Check2.22iv - Cumulative % of the eligible population aged 40-74 offered an NHS Health Check who received an NHS Health Check2.22v - Cumulative % of the eligible population aged 40-74 who received an NHS Health check2.23i - Self-reported well-being - people with a low satisfaction score2.23ii - Self-reported well-being - people with a low worthwhile score2.23iii - Self-reported well-being - people with a low happiness score2.23iv - Self-reported well-being - people with a high anxiety score2.23v - Average Warwick-Edinburgh Mental Well-Being Scale (WEMWBS) score2.24i - Injuries due to falls in people aged 65 and over2.24ii - Injuries due to falls in people aged 65 and over - aged 65-792.24iii - Injuries due to falls in people aged 65 and over - aged 80+3.01 - Fraction of mortality attributable to particulate air pollution3.02 - Chlamydia detection rate (15-24 year olds)3.02 - Chlamydia detection rate (15-24 year olds)3.03i - Population vaccination coverage - Hepatitis B (1 year old)3.03i - Population vaccination coverage - Hepatitis B (2 years old)3.03iii - Population vaccination coverage - Dtap / IPV / Hib (1 year old)3.03iii - Population vaccination coverage - Dtap / IPV / Hib (2 years old)3.03iv - Population vaccination coverage - MenC3.03ix - Population vaccination coverage - MMR for one dose (5 years old)3.03v - Population vaccination coverage - PCV3.03vi - Population vaccination coverage - Hib / Men C booster (5 years)3.03vi - Population vaccination coverage - Hib / MenC booster (2 years old)3.03vii - Population vaccination coverage - PCV booster3.03viii - Population vaccination coverage - MMR for one dose (2 years old)3.03x - Population vaccination coverage - MMR for two doses (5 years old)3.03xii - Population vaccination coverage - HPV3.03xiii - Population vaccination coverage - PPV3.03xiv - Population vaccination coverage - Flu (aged 65+)3.03xv - Population vaccination coverage - Flu (at risk individuals)3.04 - People presenting with HIV at a late stage of infection3.05i - Treatment completion for TB3.05ii - Incidence of TB3.06 - NHS organisations with a board approved sustainable development management plan3.07 - Comprehensive, agreed inter-agency plans for responding to health protection incidents and emergencies4.01 - Infant mortality4.02 - Tooth decay in children aged 54.03 - Mortality rate from causes considered preventable4.04i - Under 75 mortality rate from all cardiovascular diseases4.04ii - Under 75 mortality rate from cardiovascular diseases considered preventable4.05i - Under 75 mortality rate from cancer4.05ii - Under 75 mortality rate from cancer considered preventable4.06i - Under 75 mortality rate from liver disease4.06ii - Under 75 mortality rate from liver disease considered preventable4.07i - Under 75 mortality rate from respiratory disease4.07ii - Under 75 mortality rate from respiratory disease considered preventable4.08 - Mortality

Content

The hospital readmission rate PUF presents nation-wide information about inpatient hospital stays that occurred within 30 days of a previous inpatient hospital stay (readmissions) for Medicare fee-for-service beneficiaries. The readmission rate equals the number of inpatient hospital stays classified as readmissions divided by the number of index stays for a given month. Index stays include all inpatient hospital stays except those where the primary diagnosis was cancer treatment or rehabilitation. Readmissions include stays where a beneficiary was admitted as an inpatient within 30 days of the discharge date following a previous index stay, except cases where a stay is considered always planned or potentially planned. Planned readmissions include admissions for organ transplant surgery, maintenance chemotherapy/immunotherapy, and rehabilitation.

This dataset has several limitations. Readmissions rates are unadjusted for age, health status or other factors. In addition, this dataset reports data for some months where claims are not yet final. Data published for the most recent six months is preliminary and subject to change. Final data will be published as they become available, although the difference between preliminary and final readmission rates for a given month is likely to be less than 0.1 percentage point.

Data Source: The primary data source for these data is the CMS Chronic Condition Data Warehouse (CCW), a database with 100% of Medicare enrollment and fee-for-service claims data. For complete information regarding data in the CCW, visit http://ccwdata.org/index.php. Study Population: Medicare fee-for-service beneficiaries with inpatient hospital stays.

Context

This is a dataset hosted by the Centers for Medicare & Medicaid Services (CMS). The organization has an open data platform found here and they update their information according the amount of data that is brought in. Explore CMS's Data using Kaggle and all of the data sources available through the CMS organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

Cover photo by Justyn Warner on Unsplash
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This dataset is distributed under NA

Each biopharmaceutical research and new drug development investigation is targeted at discovering novel and potent medications for managing specific ailments. Thus, to discover and develop new potent medications, it should be performed sequentially or step by step. This is because drug development is a lengthy and risky work that requires significant money, resources, and labor. Breast and lung cancer contributes to the death of millions of people throughout the world each year, according to the report of the World Health Organization, and has been a public threat worldwide, although the global medical sector is developed and updated day by day. However, no proper treatment has been found until now. Therefore, this research has been conducted to find a new bioactive molecule to treat breast and lung cancer—such as natural myricetin and its derivatives—by using the latest and most authentic computer-aided drug-design approaches. At the beginning of this study, the biological pass prediction spectrum was calculated to select the target protein. It is noted that the probability of active (Pa) score is better in the antineoplastic (Pa: 0.788–0.938) in comparison with antiviral (Pa: 0.236–0.343), antibacterial (Pa: 0.274–0.421), and antifungal (Pa: 0.226–0.508). Thus, cancerous proteins, such as in breast and lung cancer, were picked up, and the computational investigation was continued. Furthermore, the docking score was found to be -7.3 to -10.4 kcal/mol for breast cancer (standard epirubicin hydrochloride, -8.3 kcal/mol), whereas for lung cancer, the score was -8.2 to -9.6 kcal/mol (standard carboplatin, -5.5 kcal/mol). The docking score is the primary concern, revealing that myricetin derivatives have better docking scores than standard chemotherapeutic agents epirubicin hydrochloride and carboplatin. Finally, drug-likeness, ADME, and toxicity prediction were fulfilled in this investigation, and it is noted that all the derivatives were highly soluble in a water medium, whereas they were totally free from AMES toxicity, hepatotoxicity, and skin sensitization, excluding only ligands 1 and 7. Thus, we proposed that the natural myricetin derivatives could be a better inhibitor for treating breast and lung cancer.

The VITamin D and OmegA-3 TriaL (VITAL) is a randomized clinical trial in 25,871 U.S. men and women investigating whether taking daily dietary supplements of vitamin D3 (2000 IU) or omega-3 fatty acids (Omacor® fish oil, 1 gram) reduces the risk of developing cancer, heart disease, and stroke in people who do not have a prior history of these illnesses. The 5-year intervention phase (study pill-taking, median 5.3 years) has ended; post-intervention observational follow-up of study participants is ongoing.

Abstract (en): The purpose of the National Health Interview Survey (NHIS) is to obtain information about the amount and distribution of illness, its effects in terms of disability and chronic impairments, and the kinds of health services people receive. Implementation of a redesigned NHIS, consisting of a basic module, a periodic module, and a topical module, began in 1997 (See NATIONAL HEALTH INTERVIEW SURVEY, 1997 [ICPSR 2954]). This final release of the 2000 NHIS contains the Household, Family, Person, Sample Adult, Sample Child, and Immunization, and Injury and Poison data files from the basic module. The 2000 NHIS also contains the Cancer Control Module (included in the Sample Adult File, Part 4), which corresponds to the Cancer Supplements of 1987 and 1992 and examines such items as diet and nutrition, use of herbal supplements, Hispanic acculturation, genetic testing, and family history. Each record in the Household-Level File (Part 1) of the basic module contains data on the type of living quarters, number of families in the household responding and not responding, and the month and year of the interview for each eligible sampling unit. The Family-Level File (Part 2) is made up of reconstructed variables from the person-level data of the basic module and includes information on sex, age, race, marital status, Hispanic origin, education, veteran status, family income, family size, major activities, health status, activity limits, and employment status, along with industry and occupation. As part of the basic module, the Person-Level File (Part 3) provides information on all family members with respect to health status, limitation of daily activities, cognitive impairment, and health conditions. Also included are data on years at current residence, region variables, height, weight, bed days, doctor visits, hospital stays, and health care access and utilization. A randomly-selected adult in each family was interviewed for the Sample Adult File (Part 4) regarding respiratory conditions, renal conditions, AIDS, joint symptoms, health status, limitation of daily activities, and behaviors such as smoking, alcohol consumption, and physical activity. The Sample Child File (Part 5) provides information from a knowledgeable adult in the household on medical conditions of one child in the household, such as respiratory problems, seizures, allergies, and use of special equipment such as hearing aids, braces, or wheelchairs. Also included are questions regarding child behavior, the use of mental health services, and Attention Deficit Hyperactivity Disorder (ADHD). The Child Immunization File (Part 6) presents information from shot records and supplies vaccination status, along with the number and dates of shots, and information about the chicken pox vaccine. The Injury and Poison Data File (Part 7) contains episode-level data for injuries and poisonings and the Injury and Poison Verbatim File (Part 8) contains verbatim comments for both injuries and poisonings. Civilian, noninstitutionalized population of the 50 United States and the District of Columbia. The NHIS uses a stratified multistage probability design. The sample for the NHIS is redesigned every decade using population data from the most recent decennial census. A redesigned sample was implemented in 1995. This new design includes a greater number of primary sampling units (PSUs) (from 198 in 1994 to 358), and a more complicated nonresponse adjustment based on household screening and oversampling of Black and Hispanic persons, for more reliable estimates of these groups. 2006-03-30 File cb03381-all_volume_2 was removed from dataset 10 and flagged as a study-level file, so that it will accompany all downloads. Dataset 10 was then empty, and was deleted.2006-03-30 File cb03381-all_volume_1 was removed from dataset 9 and flagged as a study-level file, so that it will accompany all downloads. Dataset 9 was then empty, and was deleted.2006-03-30 File MAN3381.ALL.PDF was removed from any previous datasets and flagged as a study-level file, so that it will accompany all downloads.2006-03-30 File QU3381.ALL.PDF was removed from any previous datasets and flagged as a study-level file, so that it will accompany all downloads.2005-11-04 On 2005-03-14 new files were added to one or more datasets. These files included additional setup files as well as one or more of the following: SAS program, SAS transport, SPSS portable, and Stata system files. The metadata record was revis...

This round of Euro-Barometer surveys queried respondents on standard Euro-Barometer measures, such as how satisfied they were with their present life, whether they attempted to persuade others close to them to share their views on subjects they held strong opinions about, whether they discussed political matters, what their country's goals should be for the next ten or fifteen years, and how they viewed the need for societal change. The surveys also focused on health problems. Questions about smoking examined whether the respondent had heard of the European Code Against Cancer and whether the respondent smoked. Smokers were asked what tobacco products they used, how many cigarettes they smoked in a day, and whether they planned to cut down on their tobacco consumption. Queries focusing on other health issues included respondents' subjective ratings of their health and diet, the basis for their foodstuff selections, the extent and impact of alcohol consumption on their driving, the extent of the problem of drinking and driving, how the problem of drinking and driving would be best addressed, and respondents' own use of alcohol. Opinions on alcohol and drug abuse were elicited through questions such as what type of problem the respondent considered alcohol and drug use to be, whether current measures were enough to solve abuse, what measures should be taken to solve the problems, the respondent's knowledge of drugs and the use of drugs, drug use among acquaintances, and how drug testing should be implemented. AIDS-related items focused on how the respondent thought AIDS could be contracted and which manner of transmission the respondent most feared, which interventions should be used to eliminate or to slow the spread of AIDS, which interventions should be undertaken by the European Community, how best to handle those who had AIDS or were HIV-positive, whether the respondent personally knew anyone with AIDS/HIV+, how the emergence and spread of AIDS had changed the respondent's personal habits, and what precautions were effective against contracting AIDS. Questions concerning the respondent's work history asked whether there had been periods without work lasting more than a year. A series of items focused on the longest period without pay: how long the period was, the age of the respondent during this period, the main reason for leaving the previous job, what the previous occupation was and whether it was part-time, what the new occupation was and whether it was part-time, and how the level of the new occupation compared to the previous occupation. The interaction of raising children and pursuing a career was investigated through questions including how many children the respondent had, what effect changes in family life had on working life, whether the respondent worked full- or part-time while raising children, and whether the respondent would prefer to care for children full-time, care for children part-time and work part-time, or work full-time. A series of questions pertained to the period prior to the respondent's first three children attending school: whether the respondent worked during this period, what the respondent's occupation was, the attributes of the occupation that concerned the family, the attributes of the partner's occupation that concerned the family, who the primary caregivers were, whether the partner was the primary caregiver, and whether there were difficulties making last-minute arrangements for child care. Additional information was gathered on family income, number of people residing in the home, size of locality, home ownership, region of residence, occupation of the head of household, and the respondent's age, sex, occupation, education, religion, religiosity, subjective social class standing, political party and union membership, and left-right political self-placement. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR09577.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

Content

This data collection consists of images acquired during chemoradiotherapy of 20 locally-advanced, non-small cell lung cancer patients. The images include four-dimensional (4D) fan beam (4D-FBCT) and 4D cone beam CT (4D-CBCT). All patients underwent concurrent radiochemotherapy to a total dose of 64.8-70 Gy using daily 1.8 or 2 Gy fractions.

Acknowledgements

Thanks to The Cancer Imaging Archive for publicly providing an open data source. Special recognition to Hugo, Geoffrey D., Weiss, Elisabeth, Sleeman, William C., Balik, Salim, Keall, Paul J., Lu, Jun, & Williamson, Jeffrey F. (2016). Data from 4D Lung Imaging of NSCLC Patients. The Cancer Imaging Archive. http://doi.org/10.7937/K9/TCIA.2016.ELN8YGLE.

Inspiration

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