This dataset contains estimates of mortality rates due to the major causes of death among the population of New York City, starting 2007. The estimated data for crude and age-adjusted mortality rates due the major causes of death are described by gender and race/ethnicity of the population groups.

Number of deaths, crude mortality rates and age standardized mortality rates (based on 2021 estimated population) for selected grouped causes, by sex, 2000 to most recent year.

The Mortality - Multiple Cause of Death data on CDC WONDER are county-level national mortality and population data spanning the yehttps://healthdata.gov/d/2sz9-6c59ars 1999-2006. These data are available in two separate data sets: one data set for years 1999-2004 with 3 race groups, and another data set for years 2005-2006 with 4 race groups and 3 Hispanic origin categories. Data are based on death certificates for U.S. residents. Each death certificate contains a single underlying cause of death, up to twenty additional multiple causes, and demographic data. The number of deaths, crude death rates, age-adjusted death rates, standard errors and 95% confidence intervals for death rates can be obtained by place of residence (total U.S., state, and county), age group (including infants), race, Hispanic ethnicity (years 2005-2006 only), sex, year of death, and cause-of-death (4-digit ICD-10 code or group of codes). The data are produced by the National Center for Health Statistics.

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

Lung Cancer Deaths reports the number, crude rate, and age-adjusted mortality rate (AAMR) of deaths due to lung cancer.

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

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

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

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

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

COVID-19 cases and associated deaths that have been reported among Connecticut residents, broken down by race and ethnicity. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. Deaths reported to the either the Office of the Chief Medical Examiner (OCME) or Department of Public Health (DPH) are included in the COVID-19 update.

The following data show the number of COVID-19 cases and associated deaths per 100,000 population by race and ethnicity. Crude rates represent the total cases or deaths per 100,000 people. Age-adjusted rates consider the age of the person at diagnosis or death when estimating the rate and use a standardized population to provide a fair comparison between population groups with different age distributions. Age-adjustment is important in Connecticut as the median age of among the non-Hispanic white population is 47 years, whereas it is 34 years among non-Hispanic blacks, and 29 years among Hispanics. Because most non-Hispanic white residents who died were over 75 years of age, the age-adjusted rates are lower than the unadjusted rates. In contrast, Hispanic residents who died tend to be younger than 75 years of age which results in higher age-adjusted rates.

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

Rates are standardized to the 2000 US Millions Standard population (data available here: https://seer.cancer.gov/stdpopulations/). Standardization was done using 19 age groups (0, 1-4, 5-9, 10-14, ..., 80-84, 85 years and older). More information about direct standardization for age adjustment is available here: https://www.cdc.gov/nchs/data/statnt/statnt06rv.pdf

Categories are mutually exclusive. The category “multiracial” includes people who answered ‘yes’ to more than one race category. Counts may not add up to total case counts as data on race and ethnicity may be missing. Age adjusted rates calculated only for groups with more than 20 deaths. Abbreviation: NH=Non-Hispanic.

Data on Connecticut deaths were obtained from the Connecticut Deaths Registry maintained by the DPH Office of Vital Records. Cause of death was determined by a death certifier (e.g., physician, APRN, medical examiner) using their best clinical judgment. Additionally, all COVID-19 deaths, including suspected or related, are required to be reported to OCME. On April 4, 2020, CT DPH and OCME released a joint memo to providers and facilities within Connecticut providing guidelines for certifying deaths due to COVID-19 that were consistent with the CDC’s guidelines and a reminder of the required reporting to OCME.25,26 As of July 1, 2021, OCME had reviewed every case reported and performed additional investigation on about one-third of reported deaths to better ascertain if COVID-19 did or did not cause or contribute to the death. Some of these investigations resulted in the OCME performing postmortem swabs for PCR testing on individuals whose deaths were suspected to be due to COVID-19, but antemortem diagnosis was unable to be made.31 The OCME issued or re-issued about 10% of COVID-19 death certificates and, when appropriate, removed COVID-19 from the death certificate. For standardization and tabulation of mortality statistics, written cause of death statements made by the certifiers on death certificates are sent to the National Center for Health Statistics (NCHS) at the CDC which assigns cause of death codes according to the International Causes of Disease 10th Revision (ICD-10) classification system.25,26 COVID-19 deaths in this report are defined as those for which the death certificate has an ICD-10 code of U07.1 as either a primary (underlying) or a contributing cause of death. More information on COVID-19 mortality can be found at the following link: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Mortality/Mortality-Statistics

Data are subject to future revision as reporting changes.

Starting in July 2020, this dataset will be updated every weekday.

Additional notes: A delay in the data pull schedule occurred on 06/23/2020. Data from 06/22/2020 was processed on 06/23/2020 at 3:30 PM. The normal data cycle resumed with the data for 06/23/2020.

A network outage on 05/19/2020 resulted in a change in the data pull schedule. Data from 5/19/2020 was processed on 05/20/2020 at 12:00 PM. Data from 5/20/2020 was processed on 5/20/2020 8:30 PM. The normal data cycle resumed on 05/20/2020 with the 8:30 PM data pull. As a result of the network outage, the timestamp on the datasets on the Open Data Portal differ from the timestamp in DPH's daily PDF reports.

Starting 5/10/2021, the date field will represent the date this data was updated on data.ct.gov. Previously the date the data was pulled by DPH was listed, which typically coincided with the date before the data was published on data.ct.gov. This change was made to standardize the COVID-19 data sets on data.ct.gov.

The Mortality - Multiple Cause of Death data on CDC WONDER are county-level national mortality and population data spanning the years 1999-2009. Data are based on death certificates for U.S. residents. Each death certificate contains a single underlying cause of death, up to twenty additional multiple causes (Boolean set analysis), and demographic data. The number of deaths, crude death rates, age-adjusted death rates, standard errors and 95% confidence intervals for death rates can be obtained by place of residence (total U.S., region, state, and county), age group (including infants and single-year-of-age cohorts), race (4 groups), Hispanic ethnicity, sex, year of death, and cause-of-death (4-digit ICD-10 code or group of codes, injury intent and mechanism categories, or drug and alcohol related causes), year, month and week day of death, place of death and whether an autopsy was performed. The data are produced by the National Center for Health Statistics.

Context

COVID-19 Cases and Deaths by Race/Ethnicity

Content

COVID-19 cases and associated deaths that have been reported among Connecticut residents, broken down by race and ethnicity. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. Deaths reported to either the Office of the Chief Medical Examiner (OCME) or Department of Public Health (DPH) are included in the COVID-19 update.

The following data show the number of COVID-19 cases and associated deaths per 100,000 population by race and ethnicity. Crude rates represent the total cases or deaths per 100,000 people. Age-adjusted rates consider the age of the person at diagnosis or death when estimating the rate and use a standardized population to provide a fair comparison between population groups with different age distributions. Age-adjustment is important in Connecticut as the median age of among the non-Hispanic white population is 47 years, whereas it is 34 years among non-Hispanic blacks, and 29 years among Hispanics. Because most non-Hispanic white residents who died were over 75 years of age, the age-adjusted rates are lower than the unadjusted rates. In contrast, Hispanic residents who died tend to be younger than 75 years of age which results in higher age-adjusted rates.

The age-adjusted rates are directly standardized using the 2018 ASRH Connecticut population estimate denominators (available here: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Population/Annual-State--County-Population-with-Demographics).

Rates are standardized to the 2000 US Millions Standard population (data available here: https://seer.cancer.gov/stdpopulations/). Standardization was done using 19 age groups (0, 1-4, 5-9, 10-14, ..., 80-84, 85 years and older). More information about direct standardization for age adjustment is available here: https://www.cdc.gov/nchs/data/statnt/statnt06rv.pdf Categories are mutually exclusive. The category “multiracial” includes people who answered ‘yes’ to more than one race category. Counts may not add up to total case counts as data on race and ethnicity may be missing. Age-adjusted rates calculated only for groups with more than 20 deaths. Abbreviation: NH=Non-Hispanic.

This dataset will be updated on a daily basis. Data are subject to future revision as reporting changes.

Starting in July 2020, this dataset will be updated every weekday.

Additional notes: A delay in the data pull schedule occurred on 06/23/2020. Data from 06/22/2020 was processed on 06/23/2020 at 3:30 PM. The normal data cycle resumed with the data for 06/23/2020.

A network outage on 05/19/2020 resulted in a change in the data pull schedule. Data from 5/19/2020 was processed on 05/20/2020 at 12:00 PM. Data from 5/20/2020 was processed on 5/20/2020 8:30 PM. The normal data cycle resumed on 05/20/2020 with the 8:30 PM data pull. As a result of the network outage, the timestamp on the datasets on the Open Data Portal differs from the timestamp in DPH's daily PDF reports.

Acknowledgements

Thanks to catalog.data.gov.

The CDC WONDER Mortality - Underlying Cause of Death online database is a county-level national mortality and population database spanning the years since 1979 -2008. The number of deaths, crude death rates, age-adjusted death rates, standard errors and 95% confidence intervals for death rates can be obtained by place of residence (total U.S., Census region, Census division, state, and county), age group (including infant age groups), race (years 1979-1998: White, Black, and Other; years 1999-2008: American Indian or Alaska Native, Asian or Pacific Islander, Black or African American, and White), Hispanic origin (years 1979-1998: not available; years 1999-present: Hispanic or Latino, not Hispanic or Latino, Not Stated), gender, year of death, and underlying cause of death (years 1979-1998: 4-digit ICD-9 code and 72 cause-of-death recode; years 1999-present: 4-digit ICD-10 codes and 113 cause-of-death recode, as well as the Injury Mortality matrix classification for Intent and Mechanism), and urbanization level of residence (2006 NCHS urban-rural classification scheme for counties). The Compressed Mortality data are produced by the National Center for Health Statistics.

The United States Cancer Statistics (USCS) online databases in WONDER provide cancer incidence and mortality data for the United States for the years since 1999, by year, state and metropolitan areas (MSA), age group, race, ethnicity, sex, childhood cancer classifications and cancer site. Report case counts, deaths, crude and age-adjusted incidence and death rates, and 95% confidence intervals for rates. The USCS data are the official federal statistics on cancer incidence from registries having high-quality data and cancer mortality statistics for 50 states and the District of Columbia. USCS are produced by the Centers for Disease Control and Prevention (CDC) and the National Cancer Institute (NCI), in collaboration with the North American Association of Central Cancer Registries (NAACCR). Mortality data are provided by the Centers for Disease Control and Prevention (CDC), National Center for Health Statistics (NCHS), National Vital Statistics System (NVSS).

1. Database contents

The Russian Short-Term Mortality Fluctuations database (RusSTMF) contains a series of standardized and crude death rates for men, women and both sexes for Russia as a whole and its regions for the period from 2000 to 2021.

All the output indicators presented in the database are calculated based on data of deaths registered by the Vital Registry Office. The weekly death counts are calculated based on depersonalized individual data provided by the Russian Federal State Statistics Service (Rosstat) at the request of the HSE. Time coverage: 03.01.2000 (Week 1) – 31.12.2021 (Week 1148)

2. A brief description of the input data on deaths

Date of death: date of occurrence

Unit of time: week

First and last days of the week: Monday – Sunday

First and last week of the year: The weeks are organized according to ISO 8601:2004 guidelines. Each week of the year, including the first and last, contains 7 days. In order to get 7-day weeks, the days of previous years are included in this first week (if January 1 fell on Tuesday, Wednesday or Thursday) or in the last calendar week (if December 31 fell on Thursday, Friday or Saturday).

Age groups: the entire population

Sex: men, women, both sexes (men and women combined)

Restrictions and data changes: data on deaths in the Pskov region were excluded for weeks 9-13 of 2012

Note: Deaths with an unknown date of occurrence (unknown year, month, or day) account for about 0.3% of all deaths and are excluded from the calculation of week-age-specific and standardized death rates.

3. Description of the week-specific mortality rates data file

Week-specific standardized death rates for Russia as a whole and its regions are contained in a single data file presented in .csv format. The format of data allows its uploading into any system for statistical analysis. Each record (row) in the data file contains data for one calendar year, one week, one territory, one sex.

The decimal point is dot (.)

The first element of the row is the territory code ("PopCode" column), the second element is the year ("Year" column), the third element ("Week" column) is the week of the year, the fourth element ("Sex" column) is sex (F – female, M – male, B – both sexes combined). This is followed by a column "CDR" with the value of the crude death rate and "SDR" with the value of the standardized death rate. If the indicator cannot be calculated for some combination of year, sex, and territory, then the corresponding meaningful data elements in the data file are replaced with ".".

This data is compiled by the Cook County Department of Public Health using data from the Illinois Department of Public Health Vital Statistics. It includes the annual number of deaths, crude and age-adjusted death rates by selected causes of death. Further analysis is available by age group, race/ethnicity, gender and decedent's place of residence in suburban Cook County at the time of their death. Table of Contents and other information can be found at http://opendocs.cookcountyil.gov/docs/Death_Table_of_Contents2_jh9b-icit.pdf. Note: * Counts suppressed for events between 1 and 4, - Rates not calculated for events less than 20

Around 7.7% of Americans have asthma, including 20.2 million adults and 4.6 million children. This study examines asthma mortality trends and disparities across U.S. demographic and geographic groups from 1999 to 2020. A retrospective analysis was conducted using the CDC WONDER database to examine asthma-related deaths in the U.S. from 1999 to 2020. Age-adjusted mortality rates (AAMRs) and crude mortality rates (CMRs) per 100,000 were calculated. Trends and annual percent changes (APCs) were assessed overall and stratified by sex, race, region, and age. From 1999 to 2020, the U.S. recorded 221 161 asthma-related deaths (AAMR: 3.07), mostly in medical facilities. Mortality declined from 1999 to 2018 (APC: −1.53%) but surged from 2018 to 2020 (APC: 28.63%). Females, NH Blacks, and NH American Indians had the highest mortality rates. Older adults (≥65) had the greatest burden, with younger groups showing notable increases post-2018. Rural areas and the West reported slightly higher rates than urban and other regions. Hawaii and the District of Columbia had the highest AAMRs, while Florida and Nevada had the lowest. Asthma-related mortality in the U.S. declined until 2018 but sharply increased from 2018 to 2020, with rises across all demographic groups, regions, and settings. Females, NH Blacks, and older adults consistently had higher mortality rates, while younger age groups showed recent alarming increases. Targeted interventions are urgently needed to address inequities and recent mortality surges.

Users can search this database pertaining to respiratory conditions such as asthma, pneumonia, bronchitis, and tuberculosis. BackgroundThe National Occupational Respiratory Mortality System (NORMS) is developed and maintained by National Institute of Occupational Safety and Health (NIOSH) of the Centers for Disease Control and Prevention (CDC). This surveillance system includes respiratory conditions such as: asthma, pneumonia, bronchitis, tuberculosis, lung cancer, and silicosis, among others. User FunctionalityUsers can generate national- or occupation-specific queries. Users can gener ate tables, charts and maps containing the summary statistics such as number of deaths, crude death rates, age-adjusted death rates, and years of potential life lost (YPLL ). Users can also download the dataset and/or data queries into Microsoft Excel. Data NotesThis website provides data history regarding revisions to the dataset. Data from additional sources (i.e., population estimates, comparative standard population, and life-table values) are also available. National mortality data is derived from the National Center for Health Statistics (NCHS) multiple cause of death records. These data are updated annually since 1968, unless otherwise indicated. Data are available on national, state, and county levels. The most recent d ata available is from 2007.

BackgroundEven in low and middle income countries most deaths occur in older adults. In Europe, the effects of better education and home ownership upon mortality seem to persist into old age, but these effects may not generalise to LMICs. Reliable data on causes and determinants of mortality are lacking. Methods and FindingsThe vital status of 12,373 people aged 65 y and over was determined 3–5 y after baseline survey in sites in Latin America, India, and China. We report crude and standardised mortality rates, standardized mortality ratios comparing mortality experience with that in the United States, and estimated associations with socioeconomic factors using Cox's proportional hazards regression. Cause-specific mortality fractions were estimated using the InterVA algorithm. Crude mortality rates varied from 27.3 to 70.0 per 1,000 person-years, a 3-fold variation persisting after standardisation for demographic and economic factors. Compared with the US, mortality was much higher in urban India and rural China, much lower in Peru, Venezuela, and urban Mexico, and similar in other sites. Mortality rates were higher among men, and increased with age. Adjusting for these effects, it was found that education, occupational attainment, assets, and pension receipt were all inversely associated with mortality, and food insecurity positively associated. Mutually adjusted, only education remained protective (pooled hazard ratio 0.93, 95% CI 0.89–0.98). Most deaths occurred at home, but, except in India, most individuals received medical attention during their final illness. Chronic diseases were the main causes of death, together with tuberculosis and liver disease, with stroke the leading cause in nearly all sites. ConclusionsEducation seems to have an important latent effect on mortality into late life. However, compositional differences in socioeconomic position do not explain differences in mortality between sites. Social protection for older people, and the effectiveness of health systems in preventing and treating chronic disease, may be as important as economic and human development. Please see later in the article for the Editors' Summary

Death statistics (i) Number of Deaths for Different Sexes and Crude Death Rate for the Period from 1981 to 2023 (ii) Age-standardised Death Rate (Overall and by Sex) for the Period from 1981 to 2023 (iii) Age-specific Death Rate for Year 2013 and 2023 (iv) Death Rates by Leading Causes of Death for the Period from 2001 to 2023 (v) Number of Deaths by Leading Causes of Death for the Period from 2001 to 2023 (vi) Age-standardised Death Rates by Leading Causes of Death for the Period from 2001 to 2023 (vii) Late Foetal Mortality Rate for the Period from 1981 to 2023 (viii) Perinatal Mortality Rate for the Period from 1981 to 2023 (ix) Neonatal Mortality Rate for the Period from 1981 to 2023 (x) Infant Mortality Rate for the Period from 1981 to 2023 (xi) Number of Maternal Deaths for the Period from 1981 to 2023 (xii) Maternal Mortality Ratio for the Period from 1981 to 2023

Number of deaths, crude mortality rates and age standardized mortality rates (based on 1991 population) for selected grouped causes, by sex, 2000 to 2013.

Crude, age-specific, and age-standardized COVID-19 mortality rates per 100,000 person-years for non-Hispanic White, non-Hispanic Black, Hispanic, non-Hispanic American Indian or Alaska Native, and non-Hispanic Asian or Pacific Islander populations, and age-specific mortality rate ratios and rate differences per 100,000 person-years.

BackgroundThe global COVID-19 pandemic is still ongoing, and cross-country and cross-period variation in COVID-19 age-adjusted case fatality rates (CFRs) has not been clarified. Here, we aimed to identify the country-specific effects of booster vaccination and other features that may affect heterogeneity in age-adjusted CFRs with a worldwide scope, and to predict the benefit of increasing booster vaccination rate on future CFR.MethodCross-temporal and cross-country variations in CFR were identified in 32 countries using the latest available database, with multi-feature (vaccination coverage, demographic characteristics, disease burden, behavioral risks, environmental risks, health services and trust) using Extreme Gradient Boosting (XGBoost) algorithm and SHapley Additive exPlanations (SHAP). After that, country-specific risk features that affect age-adjusted CFRs were identified. The benefit of booster on age-adjusted CFR was simulated by increasing booster vaccination by 1–30% in each country.ResultsOverall COVID-19 age-adjusted CFRs across 32 countries ranged from 110 deaths per 100,000 cases to 5,112 deaths per 100,000 cases from February 4, 2020 to Jan 31, 2022, which were divided into countries with age-adjusted CFRs higher than the crude CFRs and countries with age-adjusted CFRs lower than the crude CFRs (n = 9 and n = 23) when compared with the crude CFR. The effect of booster vaccination on age-adjusted CFRs becomes more important from Alpha to Omicron period (importance scores: 0.03–0.23). The Omicron period model showed that the key risk factors for countries with higher age-adjusted CFR than crude CFR are low GDP per capita and low booster vaccination rates, while the key risk factors for countries with higher age-adjusted CFR than crude CFR were high dietary risks and low physical activity. Increasing booster vaccination rates by 7% would reduce CFRs in all countries with age-adjusted CFRs higher than the crude CFRs.ConclusionBooster vaccination still plays an important role in reducing age-adjusted CFRs, while there are multidimensional concurrent risk factors and precise joint intervention strategies and preparations based on country-specific risks are also essential.

Number of deaths, crude mortality rates and age standardized mortality rates (based on 2011 population) for selected grouped causes, by sex, 2000 to most recent year.