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.

Analysis of ‘Smoking-Attributable Mortality, Morbidity, and Economic Costs (SAMMEC) - Smoking-Attributable Mortality (SAM)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/8d02cc25-7e9d-4739-8e14-1dae7dd12c28 on 27 January 2022.

--- Dataset description provided by original source is as follows ---

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.

--- Original source retains full ownership of the source dataset ---

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

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.

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

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.

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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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.

Age-standardised rate of mortality from oral cancer (ICD-10 codes C00-C14) in persons of all ages and sexes per 100,000 population.RationaleOver the last decade in the UK (between 2003-2005 and 2012-2014), oral cancer mortality rates have increased by 20% for males and 19% for females1Five year survival rates are 56%. Most oral cancers are triggered by tobacco and alcohol, which together account for 75% of cases2. Cigarette smoking is associated with an increased risk of the more common forms of oral cancer. The risk among cigarette smokers is estimated to be 10 times that for non-smokers. More intense use of tobacco increases the risk, while ceasing to smoke for 10 years or more reduces it to almost the same as that of non-smokers3. Oral cancer mortality rates can be used in conjunction with registration data to inform service planning as well as comparing survival rates across areas of England to assess the impact of public health prevention policies such as smoking cessation.References:(1) Cancer Research Campaign. Cancer Statistics: Oral – UK. London: CRC, 2000.(2) Blot WJ, McLaughlin JK, Winn DM et al. Smoking and drinking in relation to oral and pharyngeal cancer. Cancer Res 1988; 48: 3282-7. (3) La Vecchia C, Tavani A, Franceschi S et al. Epidemiology and prevention of oral cancer. Oral Oncology 1997; 33: 302-12.Definition of numeratorAll cancer mortality for lip, oral cavity and pharynx (ICD-10 C00-C14) in the respective calendar years aggregated into quinary age bands (0-4, 5-9,…, 85-89, 90+). This does not include secondary cancers or recurrences. Data are reported according to the calendar year in which the cancer was diagnosed.Counts of deaths for years up to and including 2019 have been adjusted where needed to take account of the MUSE ICD-10 coding change introduced in 2020. Detailed guidance on the MUSE implementation is available at: https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/articles/causeofdeathcodinginmortalitystatisticssoftwarechanges/january2020Counts of deaths for years up to and including 2013 have been double adjusted by applying comparability ratios from both the IRIS coding change and the MUSE coding change where needed to take account of both the MUSE ICD-10 coding change and the IRIS ICD-10 coding change introduced in 2014. The detailed guidance on the IRIS implementation is available at: https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/bulletins/impactoftheimplementationofirissoftwareforicd10causeofdeathcodingonmortalitystatisticsenglandandwales/2014-08-08Counts of deaths for years up to and including 2010 have been triple adjusted by applying comparability ratios from the 2011 coding change, the IRIS coding change and the MUSE coding change where needed to take account of the MUSE ICD-10 coding change, the IRIS ICD-10 coding change and the ICD-10 coding change introduced in 2011. The detailed guidance on the 2011 implementation is available at https://webarchive.nationalarchives.gov.uk/ukgwa/20160108084125/http://www.ons.gov.uk/ons/guide-method/classifications/international-standard-classifications/icd-10-for-mortality/comparability-ratios/index.htmlDefinition of denominatorPopulation-years (aggregated populations for the three years) for people of all ages, aggregated into quinary age bands (0-4, 5-9, …, 85-89, 90+)

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

This data shows premature deaths (Age under 75) from Circulatory Disease, numbers and rates by gender, as 3-year moving-averages.

Circulatory diseases include heart diseases and stroke, and others. Socio-economic and lifestyle factors are associated with circulatory disease deaths and inequalities in circulatory disease rates. Modifiable risk factors include smoking, excess weight, diet, and physical inactivity.

Directly Age-Standardised Rates (DASR) are shown in the data, where numbers are sufficient, so that death rates can be directly compared between areas. The DASR calculation applies Age-specific rates to a Standard (European) population to cancel out possible effects on crude rates due to different age structures among populations, thus enabling direct comparisons of rates.

A limitation on using mortalities as a proxy for prevalence of health conditions is that mortalities may give an incomplete view of health conditions in an area, as ill-health might not lead to premature death.

Data source: NHS Digital Compendium hub, dataset unique identifier P00395. This data is updated annually.

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.Chronic obstructive pulmonary disease (COPD) refers to a group of diseases, including emphysema and chronic bronchitis, that create airflow blockages in the lungs. Exposure to tobacco smoke is an important risk factor for COPD. Cities and communities can take an active role in curbing tobacco use and reducing COPD 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.

Smoking-attributable deaths, events, and directs costs.

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.

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.

ObjectivesIn addition to harms caused to individuals who smoke, second-hand smoke (SHS or passive smoke) is an important public health issue. We aim to estimate the extent of preventable deaths due to tobacco and SHS exposure in Southeast Asia.MethodsData were from the Global Burden of Disease Study 2019. We analysed data from Southeast Asia, including Cambodia, Indonesia, Laos, Malaysia, Maldives, Mauritius, Myanmar, Philippines, Seychelles, Sri Lanka, Thailand, Timor-Leste, and Vietnam.ResultsIn 2019, there were 728,500 deaths attributable to tobacco in Southeast Asia, with 128,200 deaths attributed to SHS exposure. The leading causes of preventable deaths were ischemic heart disease, stroke, diabetes mellitus, lower respiratory infections, chronic obstructive pulmonary disease, tracheal, bronchus, and lung cancer. Among deaths attributable to tobacco, females had higher proportions of deaths attributable to SHS exposure than males in Southeast Asia.ConclusionThe burden of preventable deaths in a year due to SHS exposure in Southeast Asia is substantial. The implementation and enforcement of smoke-free policies should be prioritized to reduce the disease burden attributed to passive smoking in Southeast Asia.

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.

a. PYs: person-yearsb. incidence: number per 100,000 person-years.The difference of incidence in specific causes of deaths between smoker and non-smoker group were calculated using Wilcox (Gehan) Statistic by survival life table analysis.Cause-specific mortality by smoking status (N = 162,098).

Years of life lost (YLLs) due to premature mortality, disability, and total DALYs.