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 dataset contains the number of cases, number of in hospital/30 day deaths, observed, expected and risk- adjusted mortality rates for cardiac surgery and percutaneous coronary interventions (PCI) by hospital. Regions represent where the hospitals are located. The initial Health Data NY dataset includes patients discharged between January 1, 2008, and December 31, 2010. Analyses of risk-adjusted mortality rates and associated risk factors are provided for 2010 and for the three-year period from 2008 through 2010. For PCI, analyses of all cases, non-emergency cases (which represent the majority of procedures) and emergency cases are included. Subsequent year reports data will be appended to this dataset. For more information check out: http://www.health.ny.gov/health_care/consumer_information/cardiac_surgery/ or go to the “About” tab.

Morbidity and mortality of children

This dataset contains counts of deaths for California counties based on information entered on death certificates. Final counts are derived from static data and include out-of-state deaths to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all deaths that occurred during the time period. Deaths involving injuries from external or environmental forces, such as accidents, homicide and suicide, often require additional investigation that tends to delay certification of the cause and manner of death. This can result in significant under-reporting of these deaths in provisional data.

The final data tables include both deaths that occurred in each California county regardless of the place of residence (by occurrence) and deaths to residents of each California county (by residence), whereas the provisional data table only includes deaths that occurred in each county regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by age, gender, race-ethnicity, and death place type. Deaths due to all causes (ALL) and selected underlying cause of death categories are provided. See temporal coverage for more information on which combinations are available for which years.

The cause of death categories are based solely on the underlying cause of death as coded by the International Classification of Diseases. The underlying cause of death is defined by the World Health Organization (WHO) as "the disease or injury which initiated the train of events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury." It is a single value assigned to each death based on the details as entered on the death certificate. When more than one cause is listed, the order in which they are listed can affect which cause is coded as the underlying cause. This means that similar events could be coded with different underlying causes of death depending on variations in how they were entered. Consequently, while underlying cause of death provides a convenient comparison between cause of death categories, it may not capture the full impact of each cause of death as it does not always take into account all conditions contributing to the death.

Download the latest version of the Glossary and Methodology File

Number of deaths and age-specific mortality rates for selected grouped causes, by age group and sex, 2000 to most recent year.

Death rate has been age-adjusted by the 2000 U.S. standard populaton. All-cause mortality is an important measure of community health. All-cause mortality is heavily driven by the social determinants of health, with significant inequities observed by race and ethnicity and socioeconomic status. Black residents have consistently experienced the highest all-cause mortality rate compared to other racial and ethnic groups. During the COVID-19 pandemic, Latino residents also experienced a sharp increase in their all-cause mortality rate compared to White residents, demonstrating a reversal in the previously observed mortality advantage, in which Latino individuals historically had higher life expectancy and lower mortality than White individuals despite having lower socioeconomic status on average. The disproportionately high all-cause mortality rates observed among Black and Latino residents, especially since the onset of the COVID-19 pandemic, are due to differences in social and economic conditions and opportunities that unfairly place these groups at higher risk of developing and dying from a wide range of health conditions, including COVID-19.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

Mortality and morbidity rate and its distribution, as reflected by age and gender structures and geographical distribution, are essential data for the setting up of economic and social development plans.This statistic represents child mortality rate by nationality and gender

Annual data on death registrations by area of usual residence in the UK. Summary tables including age-standardised mortality rates.

2005-2009. SAMMEC - Smoking-Attributable Mortality, Morbidity, and Economic Costs. Smoking-attributable expenditures (SAEs) are excess health care expenditures attributable to cigarette smoking by type of service among adults ages 19 years of age and older.

Number of deaths caused by external causes of morbidity and mortality, by age group and sex, 2000 to most recent year.

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 ---

The number of maternal deaths and maternal mortality rates for selected causes, 2000 to most recent year.

Maternal mortality is widely considered an indicator of overall population health and the status of women in the population. DOHMH uses multiple methods including death certificates, vital records linkage, medical examiner records, and hospital discharge data to identify all pregnancy-associated deaths (deaths that occur during pregnancy or within a year of the end of pregnancy) of New York state residents in NYC each year. DOHMH convenes the Maternal Mortality and Morbidity Review Committee (M3RC), a multidisciplinary and diverse group of 40 members that conducts an in-depth, expert review of each pregnancy-associated death of New York state residents occurring in NYC from both clinical and social determinants of health perspectives. The data in this table come from vital records and the M3RC review process. Data are not cross-classified on all variables: cause of death data are available by the relation to pregnancy (pregnancy-related, pregnancy-associated but not related, unable to determine), race/ethnicity and borough of residence data are each separately available for the total number of pregnancy-associated deaths and pregnancy-related deaths only.

Women are, on average, more often absent from work for health reasons than men, but live longer. This conflicting pattern suggests that the gender absenteeism gap arises partly from factors unrelated to objective health. An overlooked explanation is that men and women might have different preferences for absenteeism due to different attitudes to, for example, risk. Using detailed administrative data on absenteeism, hospitalizations, and mortality, we evaluate the existence of gender-specific preferences for absenteeism and analyze whether these differences are socially determined. We find robust evidence of gender differences in absenteeism that cannot be explained by poorer objective health among women.

UNICEF's country profile for India, including under-five mortality rates, child health, education and sanitation data.

This dataset comprises new and accumulated cases and death episodes for each Brazilian municipality, by epidemiological week.

Criteria for confirmed cases: * Final classification (variable CLASSI_FIN) = 5 * Antigenic test result (variable AN_SARS2) = 1 * RT-PCR test result (variable AN_SARS2) = 1 For death episodes: * confirmed cases that progressed to death (variable EVOLUCAO = 2) * death from other causes (variable EVOLUCAO = 3) Reference date for cases: * symptom onset date (variable DT_SIN_PRI) Reference date for death episodes: * case evolution date (variable DT_EVOLUCA) * for missing dates, the closest date was used: case closing date, ICU discharge date, ICU entry date, testing date, notification date Age groups follow a five-years interval Phase and peak variables were created based on epidemiological weeks.

This dataset was used as part project - Evaluating Effects of Social Inequalities on the COVID-19 Pandemic in Brazil. Maria Yury Ichihara and colleagues at the Centre for Data and Knowledge Integration for Health (Cidacs) at Fiocruz in Brazil created a social disparities index to measure inequalities relevant to the COVID-19 pandemic, such as unequal access to healthcare, to identify regions that are more vulnerable to infection and to better focus prevention efforts.

In Brazil, markers of inequality are associated with COVID-19 morbidity and mortality. They developed the index with available COVID-19 surveillance data, hosted on the Cidacs platform, and built a public data visualisation dashboard to share the index and patterns of COVID-19 incidence and mortality with the broader community. This enabled health managers and policymakers to monitor the pandemic situation in the most vulnerable populations and target social and health interventions.

Permissions to use this dataset must be obtained from the Ministry of Health Brazil.

The USGS National Wildlife Health Center's (NWHC) EPIZOO database is a long term data set that documents over40 years of information on epizootics (epidemics) in wildlife. EPIZOO tracks die-offs throughout the United States and territories, primarily in migratory birds and endangered species. Data include locations, dates, species involved, history, population numbers, total numbers of sick and dead animals, and diagnostic information. Regular data are available from 1975 to the present; some data are available from earlier years. These data represent the most comprehensive documentation of the geographic occurrence of diseases in free-ranging wildlife in existence today. The data are collected from a reporting network developed at NWHC as well as from collaborators across the North American continent.

This dataset tracks the updates made on the dataset "Smoking-Attributable Mortality, Morbidity, and Economic Costs (SAMMEC) - Smoking-Attributable Mortality (SAM) Glossary and Methodology" as a repository for previous versions of the data and metadata.

To create the dataset, the top 10 countries leading in the incidence of COVID-19 in the world were selected as of October 22, 2020 (on the eve of the second full of pandemics), which are presented in the Global 500 ranking for 2020: USA, India, Brazil, Russia, Spain, France and Mexico. For each of these countries, no more than 10 of the largest transnational corporations included in the Global 500 rating for 2020 and 2019 were selected separately. The arithmetic averages were calculated and the change (increase) in indicators such as profitability and profitability of enterprises, their ranking position (competitiveness), asset value and number of employees. The arithmetic mean values of these indicators for all countries of the sample were found, characterizing the situation in international entrepreneurship as a whole in the context of the COVID-19 crisis in 2020 on the eve of the second wave of the pandemic. The data is collected in a general Microsoft Excel table. Dataset is a unique database that combines COVID-19 statistics and entrepreneurship statistics. The dataset is flexible data that can be supplemented with data from other countries and newer statistics on the COVID-19 pandemic. Due to the fact that the data in the dataset are not ready-made numbers, but formulas, when adding and / or changing the values in the original table at the beginning of the dataset, most of the subsequent tables will be automatically recalculated and the graphs will be updated. This allows the dataset to be used not just as an array of data, but as an analytical tool for automating scientific research on the impact of the COVID-19 pandemic and crisis on international entrepreneurship. The dataset includes not only tabular data, but also charts that provide data visualization. The dataset contains not only actual, but also forecast data on morbidity and mortality from COVID-19 for the period of the second wave of the pandemic in 2020. The forecasts are presented in the form of a normal distribution of predicted values and the probability of their occurrence in practice. This allows for a broad scenario analysis of the impact of the COVID-19 pandemic and crisis on international entrepreneurship, substituting various predicted morbidity and mortality rates in risk assessment tables and obtaining automatically calculated consequences (changes) on the characteristics of international entrepreneurship. It is also possible to substitute the actual values identified in the process and following the results of the second wave of the pandemic to check the reliability of pre-made forecasts and conduct a plan-fact analysis. The dataset contains not only the numerical values of the initial and predicted values of the set of studied indicators, but also their qualitative interpretation, reflecting the presence and level of risks of a pandemic and COVID-19 crisis for international entrepreneurship.