This dataset displays tobacco-related deaths in the City of Austin between 2006 and 2018 and includes year of death, gender, age, race/ethnicity and whether tobacco contributed to the death (yes or probably contributed). Data are sourced from the City of Austin's Office of Vital Records. The contribution of tobacco to a death is indicated using a checkbox on the death certificate (marked by the individual filling out the death certificate). [NOTE: Race/ethnicity data are missing for December 2018 due to electronic death records system errors]

2005-2009. SAMMEC - Smoking-Attributable Mortality, Morbidity, and Economic Costs. Smoking-attributable mortality (SAM) is the number of deaths caused by cigarette smoking based on diseases for which the U.S. Surgeon General has determined that cigarette smoking is a causal factor.

This is a source dataset for a Let's Get Healthy California indicator at https://letsgethealthy.ca.gov/. Adult smoking prevalence in California, males and females aged 18+, starting in 2012. Caution must be used when comparing the percentages of smokers over time as the definition of ‘current smoker’ was broadened in 1996, and the survey methods were changed in 2012. Current cigarette smoking is defined as having smoked at least 100 cigarettes in lifetime and now smoking every day or some days. Due to the methodology change in 2012, the Centers for Disease Control and Prevention (CDC) recommend not conducting analyses where estimates from 1984 – 2011 are compared with analyses using the new methodology, beginning in 2012. This includes analyses examining trends and changes over time. (For more information, please see the narrative description.) The California Behavioral Risk Factor Surveillance System (BRFSS) is an on-going telephone survey of randomly selected adults, which collects information on a wide variety of health-related behaviors and preventive health practices related to the leading causes of death and disability such as cardiovascular disease, cancer, diabetes and injuries. Data are collected monthly from a random sample of the California population aged 18 years and older. The BRFSS is conducted by Public Health Survey Research Program of California State University, Sacramento under contract from CDPH. The survey has been conducted since 1984 by the California Department of Public Health in collaboration with the Centers for Disease Control and Prevention (CDC). In 2012, the survey methodology of the California BRFSS changed significantly so that the survey would be more representative of the general population. Several changes were implemented: 1) the survey became dual-frame, with both cell and landline random-digit dial components, 2) residents of college housing were eligible to complete the BRFSS, and 3) raking or iterative proportional fitting was used to calculate the survey weights. Due to these changes, estimates from 1984 – 2011 are not comparable to estimates from 2012 and beyond. Center for Disease Control and Policy (CDC) and recommend not conducting analyses where estimates from 1984 – 2011 are compared with analyses using the new methodology, beginning in 2012. This includes analyses examining trends and changes over time.Current cigarette smoking was defined as having smoked at least 100 cigarettes in lifetime and now smoking every day or some days. Prior to 1996, the definition of current cigarettes smoking was having smoked at least 100 cigarettes in lifetime and smoking now.

This dataset contains three smoking related indicators.

Rates of self reported four-week smoking quitters

Smoking quit rates per 100,000 available from the HNA.

- These quarterly reports present provisional results from the monitoring of the NHS Stop Smoking Services (NHS SSS) in England. This report includes information on the number of people setting a quit date and the number who successfully quit at the 4 week follow-up. Data for London presented with England comparator. PCT level data available from NHS.

Number of Deaths Attributable to Smoking per 100,000 population by borough

Deaths attributable to smoking, directly age-sex standardised rate for persons aged 35 years +. Causes of death considered to be related to smoking are: various cancers, cardiovascular and respiratory diseases, and diseases of the digestive system.

Numbers of adults smoking by borough

Prevalence of smoking among persons aged 18 years and over.
- Population who currently smoke, are ex-smokers, or never smoked by borough. This includes cigarette, cigar or pipe smokers. Data by age is also provided for London with a UK comparator.

Relevant links: http://www.hscic.gov.uk/Article/1685

http://www.apho.org.uk/default.aspx?QN=HP_DATATABLES

BackgroundTobacco smoking is a major risk factor for many diseases. We sought to quantify the burden of tobacco-smoking-related deaths in Asia, in parts of which men's smoking prevalence is among the world's highest.Methods and FindingsWe performed pooled analyses of data from 1,049,929 participants in 21 cohorts in Asia to quantify the risks of total and cause-specific mortality associated with tobacco smoking using adjusted hazard ratios and their 95% confidence intervals. We then estimated smoking-related deaths among adults aged ≥45 y in 2004 in Bangladesh, India, mainland China, Japan, Republic of Korea, Singapore, and Taiwan—accounting for ∼71% of Asia's total population. An approximately 1.44-fold (95% CI = 1.37–1.51) and 1.48-fold (1.38–1.58) elevated risk of death from any cause was found in male and female ever-smokers, respectively. In 2004, active tobacco smoking accounted for approximately 15.8% (95% CI = 14.3%–17.2%) and 3.3% (2.6%–4.0%) of deaths, respectively, in men and women aged ≥45 y in the seven countries/regions combined, with a total number of estimated deaths of ∼1,575,500 (95% CI = 1,398,000–1,744,700). Among men, approximately 11.4%, 30.5%, and 19.8% of deaths due to cardiovascular diseases, cancer, and respiratory diseases, respectively, were attributable to tobacco smoking. Corresponding proportions for East Asian women were 3.7%, 4.6%, and 1.7%, respectively. The strongest association with tobacco smoking was found for lung cancer: a 3- to 4-fold elevated risk, accounting for 60.5% and 16.7% of lung cancer deaths, respectively, in Asian men and East Asian women aged ≥45 y.ConclusionsTobacco smoking is associated with a substantially elevated risk of mortality, accounting for approximately 2 million deaths in adults aged ≥45 y throughout Asia in 2004. It is likely that smoking-related deaths in Asia will continue to rise over the next few decades if no effective smoking control programs are implemented.Please see later in the article for the Editors' Summary

Tobacco smoking causes cardiovascular diseases, lung disease, and various cancers. Understanding the population-based characteristics associated with smoking and the cause of death is important to improve survival. This study sought to evaluate the differential impact of smoking on cardiac or non-cardiac death according to age. Data from 514,866 healthy adults who underwent national health screening in South Korea were analyzed. The participants were divided into three groups: never-smoker, ex-smoker or current smoker according to the smoking status. The incidence rates and hazard ratios (HRs) of cardiac or non-cardiac deaths according to smoking status and age groups during the 10-year follow-up were calculated to evaluate the differential risk of smoking. Over the follow-up period, 6,192 and 24,443 cardiac and non-cardiac deaths had occurred, respectively. The estimated incidence rate of cardiac and non-cardiac death gradually increased in older age groups and was higher in current smokers and ex-smokers than that in never-smokers among all age groups. After adjustment of covariates, the HRs for cardiac death of current smokers compared to never-smokers were the highest in individuals in their 40’s (1.82; 95% CI, 1.45–2.28); this gradually decreased to 0.96 (95% CI, 0.67–1.38) in individuals >80 years. In contrast, the HRs for non-cardiac death peaked in individuals in their 50’s, (HR 1.69, 95% CI 1.57–1.82) and was sustained in those >80 years (HR 1.40, 95% CI 1.17–1.69). Ex-smokers did not show elevated risk of cardiac death compared to never-smokers in any age group, whereas they showed significantly higher risk of non-cardiac death in their 60’s and 70’s (HR, 1.29; 95% CI, 1.19–1.39; HR 1.22, 95% CI, 1.12–1.32, respectively). Acute myocardial infarction and lung cancer showed patterns similar to those of cardiac and non-cardiac death, respectively. Smoking was associated with higher relative risk of cardiac death in the middle-aged group and non-cardiac death in the older age group. Ex-smokers in the older age group had elevated risk of non-cardiac death. To prevent early cardiac death and late non-cardiac death, smoking cessation should be emphasized as early as possible.

Deaths related to smoking for Greater London. Deaths are expressed as the rate per 100,000 for the period 2005 to 2007. data sourced from the Guardian (http://www.guardian.co.uk/world-government-data/search?q=uk+smoking+in+2007&facet_year=2010) and data.gov.uk (http://data.london.gov.uk/datastore/package/deaths-smoking#). Boundary data is from OS Open Data which has been tweaked and augmented to have the ONS codes to join the two datasets (done in ArcGIS). GIS vector data. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2012-06-27 and migrated to Edinburgh DataShare on 2017-02-21.

This data shows the percentage of adults (age 18 and over) who are current smokers.

Smoking is the single biggest cause of preventable death and illnesses, and big inequalities exist between and within communities. Smoking is a major risk factor for many diseases, such as lung cancer, chronic obstructive pulmonary disease (COPD, bronchitis and emphysema) and heart disease. It is also associated with cancers in other organs.

Smoking is a modifiable lifestyle risk factor. Preventing people from starting smoking is important in reducing the health harms and inequalities.

This data is based on the Office for National Statistics (ONS) Annual Population Survey (APS). In this dataset particularly at district level there may be inherent statistical uncertainty in some data values. Thus as with many other datasets, this data should be used together with other data and resources to obtain a fuller picture.

Data source: Public Health England, Public Health Outcomes Framework (PHOF) indicator 2.14. This data is updated annually.

This data shows the percentage of adults (age 18 and over) who are current smokers. Smoking is the single biggest cause of preventable death and illnesses, and big inequalities exist between and within communities. Smoking is a major risk factor for many diseases, such as lung cancer, chronic obstructive pulmonary disease (COPD, bronchitis and emphysema) and heart disease. It is also associated with cancers in other organs. Smoking is a modifiable lifestyle risk factor. Preventing people from starting smoking is important in reducing the health harms and inequalities. This data is based on the Office for National Statistics (ONS) Annual Population Survey (APS). The percentage of adults is not age-standardised. In this dataset particularly at district level there may be inherent statistical uncertainty in some data values. Thus as with many other datasets, this data should be used together with other data and resources to obtain a fuller picture. Data source: Office for Health Improvement and Disparities (OHID) Public Health Outcomes Framework (PHOF) indicator 92443 (Number 15). This data is updated annually.

For current version see: https://www.sandiegocounty.gov/content/sdc/hhsa/programs/phs/community_health_statistics/CHSU_Mortality.html#smoking

This dataset presents smoking attributable deaths for San Diego County by condition and overall categories for those 35 years of age and older.

2014-2016. For data by HHSA Region or archived years, please visit www.sdhealthstatistics.com

Methods: Fractions by the Centers for Disease Control, Smoking‐Attributable Mortality, Morbidity, and Economic Costs (SAMMEC) System. http://www.ncbi.nlm.nih.gov/books/NBK294316/table/ch12.t4/?report=objectonly
Note: Deaths with unknown age or sex were not included in the analysis. Deaths were pulled using 2016 ICD 10 codes. Source: California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Vital Records Business Intelligence System (2016). Prepared by County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics Unit, 2019.

Note: COPD = chronic obstructive pulmonary disease. a - Other cancers consist of cancers of the lip, pharynx and oral cavity, esophagus, stomach, pancreas, larynx, cervix uteri (women), kidney and renal pelvis, bladder, liver, colon and rectum, and acute myeloid leukemia.
b - Other heart disease comprised of rheumatic heart disease, pulmonary heart disease, and other forms of heart disease.
c - Cerebrovascular diseases ICD-10 Codes: I60-I69 d - Other vascular diseases are comprised of atherosclerosis, aortic aneurysm, and other arterial diseases. e - Pulmonary diseases consists of pneumonia, influenza, emphysema, bronchitis, and chronic airways obstruction.
f - Prenatal conditions (All Ages) comprised of ICD-10 codes: K55.0, P00.0, P01.0, P01.1, P01.5, P02.0, P02.1, P02.7, P07.0–P07.3, P10.2, P22.0–P22.9, P25.0–P27.9, P28.0, P28.1, P36.0–P36.9, P52.0–P52.3, and P77 (Dietz et al. 2010).
g - Sudden Infant Death Syndrome ((All Ages) ICD-10 code R95

Tobacco smoking is one of the largest preventable causes of death and disease in Australia. In 2017-18, 13.8% of adults aged 18 years and over were daily smokers (2.6 million people), down from 14.5% in 2014-15. The decrease is a continuation of the trend over the past two decades, in 1995, 23.8% of adults were daily smokers.

Additionally the proportion of adults who have never smoked is increasing over time, from 49.4% in 2007-08 to 52.6% in 2014-15 and 55.7% in 2017-18.

Smoking has been proven to negatively affect health in a multitude of ways.Smoking has been found to harm nearly every organ of the body, cause many diseases, as well as reducing the life expectancy of smokers in general. As of 2018, smoking has been considered the leading cause of preventable morbidity and mortality in the world, continuing to plague the world’s overall health.

According to a World Health Organization report, the number of deaths caused by smoking will reach 10 million by 2030.

Evidence-based treatment for assistance in smoking cessation had been proposed and promoted. however, only less than one third of the participants could achieve the goal of abstinence. Many physicians found counseling for smoking cessation ineffective and time-consuming, and did not routinely do so in daily practice. To overcome this problem, several factors had been proposed to identify smokers who had a better chance of quitting, including the level of nicotine dependence, exhaled carbon monoxide (CO) concentration, cigarette amount per day, the age at smoking initiation, previous quit attempts, marital status, emotional distress, temperament and impulsivity scores, and the motivation to stop smoking. However, individual use of these factors for prediction could lead to conflicting results that were not straightforward enough for the physicians and patients to interpret and apply. Providing a prediction model might be a favorable way to understand the chance of quitting smoking for each individual smoker. Health outcome prediction models had been developed using methods of machine learning over recent years.

A group of scientists are working on predictive models with smoking status as the prediction target.Your task is to help them create a machine learning model to identify the smoking status of an individual using bio-signals

Dataset Description - age : 5-years gap height(cm) weight(kg) waist(cm) : Waist circumference length eyesight(left) eyesight(right) hearing(left) hearing(right) systolic : Blood pressure relaxation : Blood pressure fasting blood sugar Cholesterol : total triglyceride HDL : cholesterol type LDL : cholesterol type hemoglobin Urine protein serum creatinine AST : glutamic oxaloacetic transaminase type ALT : glutamic oxaloacetic transaminase type Gtp : γ-GTP dental caries smoking

Adolescents Who Use Tobacco Products - This indicator shows the percentage of adolescents (public high school students) who used any tobacco product in the last 30 days. Preventing youth from using tobacco products is critical to improving the health of Marylanders. This highly addictive behavior can lead to costly illnesses and death to users and those exposed to secondhand smoke. Link to Data Details

Smoking is a leading preventable cause of chronic diseases like circulatory disease, cancer, and chronic lung conditions, worsening outcomes in acute illnesses. Despite reductions from public health campaigns, 13-16% of the UK population still smoke, with higher rates in hospital admissions. Smoking-related admissions cost over £870,000 annually, prompting a focus on smoking cessation, particularly in secondary care, where targeted interventions are effective. Influenza often leads to severe complications in hospitals, such as ICU admission and death, especially in older adults and those with chronic respiratory conditions. Smoking increases risks of mortality and ICU admission in influenza cases, but UK-specific data, especially on seasonal influenza, is limited. Updated data on high-risk groups, including smokers, is crucial to guide interventions. This dataset of 26,047 admissions between Jan 2018 and Jul 2024 with influenza, includes demography, serial physiology, assessments, diagnostic codes (ICD-10 & SNOMED-CT), initial presentation, ventilation, ICU transfers, prescriptions and outcomes. Geography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. Data set availability: Data access is available via the PIONEER Hub for projects which will benefit the public or patients. This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes. Data access can be provided to NHS, academic, commercial, policy and third sector organisations. Applications from SMEs are welcome. There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee. Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details. Available supplementary data: Matched controls; ambulance and community data. Unstructured data (images). We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements. Available supplementary support: Analytics, model build, validation & refinement; A.I. support. Data partner support for ETL (extract, transform & load) processes. Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run. Consultancy with clinical, patient & end-user and purchaser access/ support. Support for regulatory requirements. Cohort discovery. Data-driven trials and “fast screen” services to assess population size.

Background: Maternal pregnancy smoking have adverse perinatal outcomes and the relationship between maternal smoking and neonatal death have not been fully elucidated. We aimed to examine the risk of neonatal death in relation to maternal smoking and to quantify potential mediators of these associations. Methods: We did a population-based cohort study using Period Linked Birth-Infant Death data from 2016 to 2019 in the US National Vital Statistics System. The exposure was maternal smoking status. The main outcome was neonatal death. Association between maternal smoking and neonatal death was estimated through logistic regression. Mediation analysis was performed to assess the extent to which the association between maternal smoking and neonatal death was mediated by neonatal complications. Results: The final sample consisted of 14717020 mothers with live singleton births. The overall neonatal mortality rate was 2.2 per 1000 live births. Maternal pregnancy smoking was associated with an increased risk of neonatal death (aOR, 1.33 [95%CI, 1.28-1.38]; P

Cardiovascular diseases (CVDs) are the number 1 cause of death globally, taking an estimated 17.9 million lives each year, which accounts for 31% of all deaths worlwide. Heart failure is a common event caused by CVDs and this dataset contains 12 features that can be used to predict mortality by heart failure.

Most cardiovascular diseases can be prevented by addressing behavioural risk factors such as tobacco use, unhealthy diet and obesity, physical inactivity and harmful use of alcohol using population-wide strategies.

People with cardiovascular disease or who are at high cardiovascular risk (due to the presence of one or more risk factors such as hypertension, diabetes, hyperlipidaemia or already established disease) need early detection and management wherein a machine learning model can be of great help.

Attribute Information:

Thirteen (13) clinical features: - age: age of the patient (years) - anaemia: decrease of red blood cells or hemoglobin (boolean) - high blood pressure: if the patient has hypertension (boolean) - creatinine phosphokinase (CPK): level of the CPK enzyme in the blood (mcg/L) - diabetes: if the patient has diabetes (boolean) - ejection fraction: percentage of blood leaving the heart at each contraction (percentage) - platelets: platelets in the blood (kiloplatelets/mL) - sex: woman or man (binary) - serum creatinine: level of serum creatinine in the blood (mg/dL) - serum sodium: level of serum sodium in the blood (mEq/L) - smoking: if the patient smokes or not (boolean) - time: follow-up period (days) - [target] death event: if the patient deceased during the follow-up period (boolean)

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1996-2010. Centers for Disease Control and Prevention (CDC). State Tobacco Activities Tracking and Evaluation (STATE) System. BRFSS Survey Data. The BRFSS is a continuous, state-based surveillance system that collects information about modifiable risk factors for chronic diseases and other leading causes of death. The data for the STATE System were extracted from the annual BRFSS surveys from participating states. Tobacco topics included are cigarette smoking status, cigarette smoking prevalence by demographics, cigarette smoking frequency, and quit attempts. NOTE: these data are not to be compared with BRFSS data collected 2011 and forward, as the methodologies were changed. Please refer to the FAQs / Methodology sections for more details.

Death rate has been age-adjusted by the 2000 U.S. standard population. Single-year data are only available for Los Angeles County overall, Service Planning Areas, Supervisorial Districts, City of Los Angeles overall, and City of Los Angeles Council Districts.Lung cancer is a leading cause of cancer-related death in the US. People who smoke have the greatest risk of lung cancer, though lung cancer can also occur in people who have never smoked. Most cases are due to long-term tobacco smoking or exposure to secondhand tobacco smoke. Cities and communities can take an active role in curbing tobacco use and reducing lung cancer by adopting policies to regulate tobacco retail; reducing exposure to secondhand smoke in outdoor public spaces, such as parks, restaurants, or in multi-unit housing; and improving access to tobacco cessation programs and other preventive services.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

2011-2019. Centers for Disease Control and Prevention (CDC). State Tobacco Activities Tracking and Evaluation (STATE) System. BRFSS Survey Data. The BRFSS is a continuous, state-based surveillance system that collects information about modifiable risk factors for chronic diseases and other leading causes of death. The data for the STATE System were extracted from the annual BRFSS surveys from participating states. Tobacco topics included are cigarette and e-cigarette use prevalence by demographics, cigarette and e-cigarette use frequency, and quit attempts. NOTE: these data are not to be compared with BRFSS data collected 2010 and prior, as the methodologies were changed. Please refer to the FAQs / Methodology sections for more details.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

Data for cities, communities, and City of Los Angeles Council Districts were generated using a small area estimation method which combined the survey data with population benchmark data (2022 population estimates for Los Angeles County) and neighborhood characteristics data (e.g., U.S. Census Bureau, 2017-2021 American Community Survey 5-Year Estimates). Adults included in this indicator are current cigarette smokers. Current smokers are defined as adults who smoked at least 100 cigarettes in their lifetime and currently smoke.Tobacco use is a leading preventable cause of premature death and disability. Cities and communities can curb tobacco use by adopting policies to regulate tobacco retail and reduce exposure to secondhand smoke in outdoor public spaces, such as parks, restaurants, or in multi-unit housing.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.