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

This dataset comprises 9105 individual critically ill patients across 5 United States medical centers, accessioned throughout 1989-1991 and 1992-1994. Each row concerns hospitalized patient records who met the inclusion and exclusion criteria for nine disease categories: acute respiratory failure, chronic obstructive pulmonary disease, congestive heart failure, liver disease, coma, colon cancer, lung cancer, multiple organ system failure with malignancy, and multiple organ system failure with sepsis. The goal is to determine these patients' 2- and 6-month survival rates based on several physiologic, demographics, and disease severity information. It is an important problem because it addresses the growing national concern over patients' loss of control near the end of life. It enables earlier decisions and planning to reduce the frequency of a mechanical, painful, and prolonged dying process.

For what purpose was the dataset created?

To develop and validate a prognostic model that estimates survival over a 180-day period for seriously ill hospitalized adults (phase I of SUPPORT) and to compare this model's predictions with those of an existing prognostic system and with physicians' independent estimates (SUPPORT phase II).

Who funded the creation of the dataset?

Funded by the Robert Wood Johnson Foundation

What do the instances in this dataset represent?

The instances represent records of critically ill patients admitted to United States hospitals with advanced stages of serious illness.

Are there recommended data splits?

No recommendation, standard train-test split could be used. Can use three-way holdout split (i.e., train-validation-test) when doing model selection.

Does the dataset contain data that might be considered sensitive in any way?

Yes. There is information about race, gender, income, and education level.

Was there any data preprocessing performed?

No. Due to the high percentage of missing values, there are a couple of recommended imputation values: According to the HBiostat Repository (https://hbiostat.org/data/repo/supportdesc, Professor Frank Harrell) the following default values have been found to be useful in imputing missing baseline physiologic data: Baseline Variable Normal Fill-in Value - Serum albumin (alb) 3.5 - PaO2/FiO2 ratio (pafi) 333.3 - Bilirubin (bili) 1.01 - Creatinine (crea) 1.01 - bun 6.51 - White blood count (wblc) 9 (thousands) - Urine output (urine) 2502 There are 159 patients surviving 2 months for whom there were no patient or surrogate interviews. These patients have missing sfdm2.

Additional Information

Data sources are medical records, personal interviews, and the National Death Index (NDI). For each patient administrative records data, clinical data and survey data were collected. The objective of the SUPPORT project was to improve decision-making in order to address the growing national concern over the loss of control that patients have near the end of life and to reduce the frequency of a mechanical, painful, and prolonged process of dying. SUPPORT comprised a two-year prospective observational study (Phase I) followed by a two-year controlled clinical trial (Phase II). Phase I of SUPPORT collected data from patients accessioned during 1989-1991 to characterize the care, treatment preferences, and patterns of decision-making among critically ill patients. It also served as a preliminary step for devising an intervention strategy for improving critically-ill patients' care and for the construction of statistical models for predicting patient prognosis and functional status. An intervention was implemented in Phase II of SUPPORT, which accessioned patients during 1992-1994. The Phase II intervention provided physicians with accurate predictive information on future functional ability, survival probability to six months, and patients' preferences for end-of-life care. Additionally, a skilled nurse was provided as part of the intervention to elicit patient preferences, provide prognoses, enhance understanding, enable palliative care, and facilitate advance planning. The intervention was expected to increase communication, resulting in earlier decisions to have orders against resuscitation, decrease time that patients spent in undesirable states (e.g., in the Intensive Care Unit, on a ventilator, and in a coma), increase physician understanding of patients' preferences for care, decrease patient pain, and decrease hospital resource use. Data collection in both phases of SUPPORT consisted of questionnaires administered to patients, their surrogates, and physicians, plus chart reviews for abstracting clinical, treatment, and decision information. Phase II also collected information regarding the implementation of the intervention, such as patient-specific logs maintained by nurses assigned to patients as part of the intervention. SUPPORT patients were fol...

Smoking is so common, and feels so familiar, that it can be hard to grasp just how large the impact is. Every year, around 8 million people die prematurely as a result of smoking.1 This means that about one in seven deaths worldwide are due to smoking.2 Millions more live in poor health because of it.

Smoking primarily contributes to early deaths through heart diseases and cancers. Globally, more than one in five cancer deaths are attributed to smoking.

This means tobacco kills more people every day than terrorism kills in a year.

Smoking is a particularly large problem in high-income countries. There, cigarette smoking is the most important cause of preventable disease and death. This is especially true for men: they account for almost three-quarters of deaths from smoking.

The impact of smoking is devastating on the individual level. In case you need some motivation to stop smoking: The life expectancy of those who smoke regularly is about 10 years lower than that of non-smokers.

It’s also devastating on the aggregate level. In the past 30 years more than 200 million have died from smoking. Looking into the future, epidemiologists Prabhat Jha and Richard Peto estimate that “If current smoking patterns persist, tobacco will kill about 1 billion people this century.”

It is on us to prevent this.

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 <8 h/day. Further, men with low physical activity (≤8.3 MET-h/week) had 31% (95% CI, 1.00–1.70) increased risk of colorectal cancer (CRC) and 45% (95% CI, 1.01–2.09) increased risk of lung cancer compared to participants with a high physical activity (>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.

Although the link between sugar-sweetened beverages (SSB) and pancreatic cancer has been suggested for its insulin-stimulating connection, most epidemiological studies showed inconclusive relationship. Whether the result was limited by sample size is explored. This prospective study followed 491,929 adults, consisting of 235,427 men and 256,502 women (mean age: 39.9, standard deviation: 13.2), from a health surveillance program and there were 523 pancreatic cancer deaths between 1994 and 2017. The individual identification numbers of the cohort were matched with the National Death file for mortality, and Cox models were used to assess the risk. The amount of SSB intake was recorded based on the average consumption in the month before interview by a structured questionnaire. We classified the amount of SSB intake into 4 categories: 0–<0.5 serving/day, ≥0.5–<1 serving per day, ≥1–<2 servings per day, and ≥2 servings per day. One serving was defined as equivalent to 12 oz and contained 35 g added sugar. We used the age and the variables at cohort enrolment as the reported risks of pancreatic cancers. The cohort was divided into 3 age groups, 20–39, 40–59, and ≥60. We found young people (age <40) had higher prevalence and frequency of sugar-sweetened beverages than the elderly. Those consuming 2 servings/day had a 50% increase in pancreatic cancer mortality (HR = 1.55, 95% CI: 1.08–2.24) for the total cohort, but a 3-fold increase (HR: 3.09, 95% CI: 1.44–6.62) for the young. The risk started at 1 serving every other day, with a dose–response relationship. The association of SSB intake of ≥2 servings/day with pancreatic cancer mortality among the total cohort remained significant after excluding those who smoke or have diabetes (HR: 2.12, 97% CI: 1.26–3.57), are obese (HR: 1.57, 95% CI: 1.08–2.30), have hypertension (HR: 1.90, 95% CI: 1.20–3.00), or excluding who died within 3 years after enrollment (HR: 1.67, 95% CI: 1.15–2.45). Risks remained in the sensitivity analyses, implying its independent nature. We concluded that frequent drinking of SSB increased pancreatic cancer in adults, with highest risk among young people.

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

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 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.

Overview This dataset contains historical daily prices for Pfizer which is largest pharmaceutical company, headquartered at United States. Pfizer primarily develops medication to treat various diseases including cancer.

It contains stock prices from 15th May 2019 till present date (5 years data).

Data Structure:

1) Date - specifies the trading date 2) Open - opening price for that date 3) High - maximum price during the day 4) Low - minimum price during the day 5) Close - close price during the day 6) Adj Close - close price adjusted for both dividends and splits 6) Volume - the number of shares transaction during a given day

Kindly use the dataset for exploring the data and analyzing.

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 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

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.

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).

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.

Objectives: Early in the COVID-19 pandemic, people with underlying comorbidities were overrepresented in hospitalised cases of COVID-19, but the relationship between comorbidity and COVID-19 outcomes was complicated by potential confounding by age. This review therefore sought to characterise the international evidence base available in the early stages of the pandemic on the association between comorbidities and progression to severe disease, critical care, or death, after accounting for age, among hospitalised patients with COVID-19.Methods: We conducted a rapid, comprehensive review of the literature (to 14 May 2020), to assess the international evidence on the age-adjusted association between comorbidities and severe COVID-19 progression or death, among hospitalised COVID-19 patients – the only population for whom studies were available at that time.Results: After screening 1,100 studies, we identified 14 eligible for inclusion. Overall, evidence for obesity and cancer increasing risk of severe disease or death was most consistent. Most studies found that having at least one of obesity, diabetes mellitus, hypertension, heart disease, cancer, or chronic lung disease was significantly associated with worse outcomes following hospitalisation. Associations were more consistent for mortality than other outcomes. Increasing numbers of comorbidities and obesity both showed a dose-response relationship. Quality and reporting were suboptimal in these rapidly conducted studies, and there was a clear need for additional studies using population-based samples.Conclusions: This review summarises the most robust evidence on this topic that was available in the first few months of the pandemic. It was clear at this early stage that COVID-19 would go on to exacerbate existing health inequalities unless actions were taken to reduce pre-existing vulnerabilities and target control measures to protect groups with chronic health conditions.

About BRFSS dataset

The Behavioral Risk Factor Surveillance System (BRFSS) is a collaborative project between all of the states in the United States (US) and participating US territories and the Centers for Disease Control and Prevention (CDC). The BRFSS is administered and supported by CDC’s Population Health Surveillance Branch, under the Division of Population Health at the National Center for Chronic Disease Prevention and Health Promotion. BRFSS is an ongoing surveillance system designed to measure behavioral risk factors for the non-institutionalized adult population (18 years of age and older) residing in the US. The BRFSS was initiated in 1984, with 15 states collecting surveillance data on risk behaviors through monthly telephone interviews. Over time, the number of states participating in the survey increased; by 2001, 50 states, the District of Columbia, Puerto Rico, Guam, and the US Virgin Islands were participating in the BRFSS. Today, all 50 states, the District of Columbia, Puerto Rico, and Guam collect data annually and American Samoa, Federated States of Micronesia, and Palau collect survey data over a limited point- in-time (usually one to three months). In this document, the term “state” is used to refer to all areas participating in BRFSS, including the District of Columbia, Guam, and the Commonwealth of Puerto Rico.

Table of Contents

Main Survey - Section 0 - Record Identification

_state: State Fips Code fmonth: File Month idate: Interview Date imonth: Interview Month iday: Interview Day iyear: Interview Year dispcode: Final Disposition seqno: Annual Sequence Number _psu: Primary Sampling Unit ctelenum: Correct Telephone Number? pvtresd1: Private Residence? colghous: Do You Live In College Housing? stateres: Resident Of State cellfon3: Cellular Telephone ladult: Are You 18 Years Of Age Or Older? numadult: Number Of Adults In Household nummen: Number Of Adult Men In Household numwomen: Number Of Adult Women In Household

Main Survey - Section 1 - Health Status

genhlth: General Health

Main Survey - Section 2 - Healthy Days - Health-Related Quality of Life

physhlth: Number Of Days Physical Health Not Good menthlth: Number Of Days Mental Health Not Good poorhlth: Poor Physical Or Mental Health

Main Survey - Section 3 - Health Care Access

hlthpln1: Have Any Health Care Coverage persdoc2: Multiple Health Care Professionals medcost: Could Not See Dr. Because Of Cost checkup1: Length Of Time Since Last Routine Checkup

Main Survey - Section 4 - Inadequate Sleep

sleptim1: How Much Time Do You Sleep

Main Survey - Section 5 - Hypertension Awareness

bphigh4: Ever Told Blood Pressure High bpmeds: Currently Taking Blood Pressure Medication

Main Survey - Section 6 - Cholesterol Awareness

bloodcho: Ever Had Blood Cholesterol Checked cholchk: How Long Since Cholesterol Checked toldhi2: Ever Told Blood Cholesterol High

Main Survey - Section 7 - Chronic Health Conditions

cvdinfr4: Ever Diagnosed With Heart Attack cvdcrhd4: Ever Diagnosed With Angina Or Coronary Heart Disease cvdstrk3: Ever Diagnosed With A Stroke asthma3: Ever Told Had Asthma asthnow: Still Have Asthma chcscncr: (Ever Told) You Had Skin Cancer? chcocncr: (Ever Told) You Had Any Other Types Of Cancer? chccopd1: (Ever Told) You Have (Copd) Chronic Obstructive Pulmonary Disease, Emphysema Or havarth3: Told Have Arthritis addepev2: Ever Told You Had A Depressive Disorder chckidny: (Ever Told) You Have Kidney Disease? diabete3: (Ever Told) You Have Diabetes

Main Survey - Section 8 - Demographics

veteran3: Are You A Veteran marital: Marital Status children: Number Of Children In Household educa: Education Level employ1: Employment Status income2: Income Level weight2: Reported Weight In Pounds height3: Reported Height In Feet And Inches numhhol2: Household Telephones numphon2: Residential Phones cpdemo1: Do You Have A Cell Phone For Personal Use? cpdemo4: What Percent Of All Calls Are Received On Your Cell Phone? internet: Internet Use In The Past 30 Days? renthom1: Own Or Rent Home sex: Respondents Sex pregnant: Pregnancy Status qlactlm2: Activity Limitation Due To Health Problems useequip: Health Problems Requiring Special Equipment blind: Blind Or Difficulty Seeing decide: Difficulty Concentrating Or Remembering diffwalk: Difficulty Walking Or Climbing Stairs diffdres: Difficulty Dressing Or Bathing diffalon: Difficulty Doing Errands Alone

Main Survey - Section 9 - Tobacco Use

smoke100: Smoked At Least 100 Cigarettes smokday2: Frequency Of Days Now Smoking stopsmk2: Stopped Smoking In Past 12 Months lastsmk2: Interval Since Last Smoked usenow3: Use Of Smokeless Tobacco Products

Main Survey - Section 10 - Alcohol Consumption

alcday5: Days In Past 30 Had Alcoholic Beverage avedrnk2: Avg Alcoholic Drinks Per Day In Past 30 drnk3ge5: Binge Drinking maxdrnks: Most Drinks On ...

Dietary exposure of Hong Kong adults to mycotoxins and their metabolites including aflatoxins (AFs), ochratoxin A (OTA), fumonisins (FNs), deoxynivalenol (DON), acetyldeoxynivalenols (AcDONs) and zearalenone (ZEA) was estimated using the Total Diet Study (TDS) approach to assess the associated health risk to the local people. Sixty commonly consumed food items, collected in four seasons, were sampled and prepared as consumed. These mycotoxins were primarily found at low levels. The highest mean levels (upper bound) were: AFs, 1.50 µg kg–1 in legumes, nuts and seed; OTA, 0.22 µg kg–1 in sugars and confectionery; FNs, 9.76 µg kg–1 in cereals and their products; DON and AcDONs, 33.1 µg kg–1 in cereals and their products; and ZEA, 53.8 µg kg–1 in fats and oils. The estimated dietary exposures of Hong Kong adults to the mycotoxins analysed were well below the respective health-based guidance values, where available. For AFs, the upper-bound exposure for high consumers is 0.0049 µg kg bw–1 day–1, which was estimated to contribute to about 7.7 (< 1%) of liver cancer cases when compared with 1222 liver cancer cases per year in Hong Kong. The percentage contributions of the estimated 95th percentile dietary exposures (lower and upper bound) to the health-based guidance values of individual mycotoxins were: ochratoxin A, 3.6–9.2%; fumonisins, 0.04–8.5%; deoxynivalenol and acetyldeoxynivalenols, 21.7–28.2%; and zearalenone 3.3–34.5%. The findings indicate that dietary exposures to all the mycotoxins analysed in this study were unlikely to pose an unacceptable health risk to the Hong Kong population.