This dataset contains death counts by sex, age group, race/ethnicity, and selected cause of death. For more information, check out: http://www.health.ny.gov/statistics/vital_statistics/.

This data collection includes information about the cause of all recorded deaths occurring in the United States, Puerto Rico, the Virgin Islands, Guam, American Samoa, and the Commonwealth of the Northern Marianas during 2003. Data are provided concerning underlying causes of death, multiple conditions that caused the death, place of death, residence of the deceased (e.g., region, division, state, county), whether an autopsy was performed, and the month and day of the week of the death. In addition, data are supplied on the sex, race, age, marital status, education, usual occupation, and origin or descent of the deceased. Along with the combined Territory Public-Use file for each year, a subset based on state of occurrence has been created for Puerto Rico, Virgin Islands, American Samoa, Guam, and Northern Marianas. The multiple cause of death fields were coded from the MANUAL OF THE INTERNATIONAL STATISTICAL CLASSIFICATION OF DISEASES AND RELATED HEALTH PROBLEMS, TENTH REVISION (ICD-10).

This dataset contains death counts for selected causes of death by county and region. For more information, check out: http://www.health.ny.gov/statistics/vital_statistics/.

This dataset tracks the updates made on the dataset "Vital Statistics Deaths by Resident County, Region, and Selected Cause of Death: Beginning 2003" as a repository for previous versions of the data and metadata.

This dataset describes drug poisoning deaths at the county level by selected demographic characteristics and includes age-adjusted death rates for drug poisoning from 1999 to 2015. Deaths are classified using the International Classification of Diseases, Tenth Revision (ICD–10). Drug-poisoning deaths are defined as having ICD–10 underlying cause-of-death codes X40–X44 (unintentional), X60–X64 (suicide), X85 (homicide), or Y10–Y14 (undetermined intent). Estimates are based on the National Vital Statistics System multiple cause-of-death mortality files (1). Age-adjusted death rates (deaths per 100,000 U.S. standard population for 2000) are calculated using the direct method. Populations used for computing death rates for 2011–2015 are postcensal estimates based on the 2010 U.S. census. Rates for census years are based on populations enumerated in the corresponding censuses. Rates for noncensus years before 2010 are revised using updated intercensal population estimates and may differ from rates previously published. Estimate does not meet standards of reliability or precision. Death rates are flagged as “Unreliable” in the chart when the rate is calculated with a numerator of 20 or less. Death rates for some states and years may be low due to a high number of unresolved pending cases or misclassification of ICD–10 codes for unintentional poisoning as R99, “Other ill-defined and unspecified causes of mortality” (2). For example, this issue is known to affect New Jersey in 2009 and West Virginia in 2005 and 2009 but also may affect other years and other states. Estimates should be interpreted with caution. Smoothed county age-adjusted death rates (deaths per 100,000 population) were obtained according to methods described elsewhere (3–5). Briefly, two-stage hierarchical models were used to generate empirical Bayes estimates of county age-adjusted death rates due to drug poisoning for each year during 1999–2015. These annual county-level estimates “borrow strength” across counties to generate stable estimates of death rates where data are sparse due to small population size (3,5). Estimates are unavailable for Broomfield County, Colo., and Denali County, Alaska, before 2003 (6,7). Additionally, Bedford City, Virginia was added to Bedford County in 2015 and no longer appears in the mortality file in 2015. County boundaries are consistent with the vintage 2005-2007 bridged-race population file geographies (6).

Abstract Background Mortality due to external causes is among of the leading causes of death worldwide, with great expression in young age groups. Objective: To describe the profile of deaths from external causes in a southern municipality of Brazil and verify its trend in the last years. Method It was carried out a retrospective study of external causes deaths using mortality rates by gender, age group and cause of with quasi Poisson regression analysis. Results Mortality coefficient was 46 per 100,000 inhabitants prevailing transport accidents (40%) and aggressions (30.5%). Increase of 53% in mortality rate was detected with variations by gender, age group and type. Conclusion Social policies are necessary encompassing intersectoral actions and taking into account the specificities in determination of each age group’s types of death.

This dataset tracks the updates made on the dataset "Vital Statistics Deaths by Resident County, Region, and Selected Cause of Death: Beginning 2003" as a repository for previous versions of the data and metadata.

In 2023, there were around 97,231 deaths in the United States due to unintentional drug overdose. Drug overdose, motor vehicle traffic accidents, and falls are the leading causes of unintentional injury death in the United States.

This table provides the age-standardized mortality rates per 100,000 population, for the three selected causes of death and all causes combined. The three selected causes of death are Circulatory System, Neoplasms and External Causes (Injury). Age standardization is a technique applied to make rates comparable across groups with different age distributions. A simple rate is defined as the number of people with a particular condition divided by the whole population. An age-standardized rate is defined as the number of people with a condition divided by the population within each age group. Standardizing (adjusting) the rate across age groups allows a more accurate comparison between populations that have different age structures. Age standardization is typically done when comparing rates across time periods, different geographic areas, and or population sub-groups (e.g. ethnic group). This indicator dataset contains information at both Local Geographic Area (for example, Lacombe, Red Deer - North, Calgary - West Bow, etc.) and Alberta levels. Local geographic area refers to 132 geographic areas created by Alberta Health (AH) and Alberta Health Services (AHS) based on census boundaries. This table is the part of "Alberta Health Primary Health Care - Community Profiles" report published February 2013

The North Carolina State Center for Health Services (SCHS) collects yearly vital statistics. The Odum Institute holds vital statistics beginning in 1968 for births, fetal deaths, deaths, birth/infant deaths, marriages and divorce. Public marriage and divorce data are available through 1999 only.This study focuses on deaths in North Carolina in 2003. Death is defined as the permanent disappearance of any evidence of life at any time after live birth. This definition excludes fetal death s. The data kept for deaths includes the age, race, marital status, and sex of the individual; date, time, cause and location of death; and mode of burial. The data is strictly numerical, there is no identifying information given about the individuals.

In 2023, the death rate from drug overdose in the United States stood at around 29 per 100,000 population, while the death rate from motor vehicle traffic accidents was 12.5 per 100,000 population. Drug overdose, motor vehicle traffic accidents, and falls are the leading causes of unintentional injury death in the United States.

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.

This table identifies all state-level causes of death that were at least twice the national rate in each of the periods 1999-2003, 2004-2008, and 2009-2013. Data are based on the 113 Cause of Death list and are based on the CDC's Underlying Cause of Death file accessible at: http://wonder.cdc.gov/ucd-icd10.html.

Standardized number of deaths from colon cancer in women per 100,000 inhabitants, Small City healthcare region, Flanders, period 2003-2012. For this group of causes of death from colon cancer with ICD-10 codes C18-C21 (death due to cancer of the colon, rectum or anus), an analysis was made for the period 2003-2012. This concerns the average number of inhabitants in the region during the period 2003-2012. All figures refer exclusively to residents of the Flemish Region. Foreigners and residents of the Walloon Region or the Brussels-Capital Region who died in the Flemish Region are therefore not included. They also do not appear in the population denominators. Average annual number of deaths in the region for the selected cause of death for the period 2003-2012. This concerns direct standardization and is expressed as "number of deaths per 100,000 persons of a standard population". This method is used in these maps (entire mortality atlas) and in comparisons between Flanders and Europe. The legend is constructed as follows: the midpoint of each interval (group boundaries) is 10% lower than the next group, and 10% higher than the previous group. https://www.zorg-en-gezondheid.be/number-overledens -per-region-2014Definition of small town care region: A care region is a geographically defined area. With a view to stimulating and organizing cooperation between health facilities and welfare facilities and determining the programming, the Flemish Government divides the Flemish Region into care regions. It takes into account existing partnerships and their specific characteristics and respects provincial boundaries. The Flemish government also pays attention to the availability and accessibility of health facilities or welfare facilities for the user. Zorgregiolayer Kleine Stad divides the area of ​​the Flemish Region into 59 areas. The Brussels Capital Region can be included as an additional area in certain cases, when the Flemish government has jurisdiction in the Brussels Capital Region.

This table identifies all state-level causes of death that were at least five times the national rate in at least one of the periods 1999-2003, 2004-2008, and 2009-2013. Data are based on the 113 Cause of Death list and are based on the CDC's Underlying Cause of Death file accessible at: http://wonder.cdc.gov/ucd-icd10.html.

Standardized number of deaths from breast cancer per 100,000 inhabitants (women), care region sublevel 2, Flanders, period 2003-2012. For this cause of death group breast cancer ICD-10 code C50, an analysis was made for the period 2003-2012. This concerns the average number inhabitants in the region during the period 2003-2012. All figures refer exclusively to residents of the Flemish Region. Foreigners and residents of the Walloon Region or the Brussels-Capital Region who died in the Flemish Region are therefore not included. They also do not appear in the population denominators. Average annual number of deaths in the region for the selected cause of death for the period 2003-2012. This concerns direct standardization and is expressed as "number of deaths per 100,000 persons of a standard population". This method is used in these maps (entire mortality atlas) and in comparisons between Flanders and Europe. The legend is constructed as follows: the midpoint of each interval (group boundaries) is 10% lower than the next group, and 10% higher than the previous group. https://www.zorg-en-gezondheid.be/number-overledens -per-region-2014Definition of healthcare region sublevel 2: A healthcare region is a geographically defined area. With a view to stimulating and organizing cooperation between health facilities and welfare facilities and determining the programming, the Flemish Government divides the Flemish Region into care regions. It takes into account existing partnerships and their specific characteristics and respects provincial boundaries. The Flemish government also pays attention to the availability and accessibility of health facilities or welfare facilities for the user. There are different hierarchical levels of demarcation. Care region layer Sublevel 2 divides the Flemish Region into 111 areas. The Brussels Capital Region can be included as an additional area in certain cases, when the Flemish government has jurisdiction in the Brussels Capital Region.

Objective: To delineate the mortality trends of malignant tumors, heart disease and cerebrovascular disease in China.Methods: The crude mortality from 2003 to 2019 was derived from the China Health Statistical Yearbook, and the mortality rates were analyzed through joinpoint regression supplemented by descriptive statistics and χ2 tests.Results: The fitting model of age-standardized mortality due to malignant tumors showed three joinpoints. The APCs from 2003 to 2005, 2005–2008, 2008–2012 and 2012–2019 were −11.00%, 9.63%, −4.67% and −1.40%, respectively, and the AAPC was −1.54%. The mortality rate of cerebrovascular disease consistently decreased (APC = AAPC = −0.98%). In the subgroup analyses, significant differences were observed between sexes and regions. The mortality rate of heart disease among rural females exhibited an upward trend (APC = AAPC = 2.33%). Older adults aged over 75 years had the highest mortality rates and the most drastic change.Conclusion: The three diseases had variable change trends. The government should focus more on policies that promote the equalization of basic public health services. Continuous education on heart disease, which includes not only beneficial behaviors but also knowledge of first aid, should be strengthened for rural females.

Analysis of ‘NCHS - Drug Poisoning Mortality by County: United States’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/3452f1d5-5a52-4f78-8ff8-02a7f7bff7fc on 12 February 2022.

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

This dataset contains model-based county estimates for drug-poisoning mortality.

Deaths are classified using the International Classification of Diseases, Tenth Revision (ICD–10). Drug-poisoning deaths are defined as having ICD–10 underlying cause-of-death codes X40–X44 (unintentional), X60–X64 (suicide), X85 (homicide), or Y10–Y14 (undetermined intent).

Estimates are based on the National Vital Statistics System multiple cause-of-death mortality files (1). Age-adjusted death rates (deaths per 100,000 U.S. standard population for 2000) are calculated using the direct method. Populations used for computing death rates for 2011–2016 are postcensal estimates based on the 2010 U.S. census. Rates for census years are based on populations enumerated in the corresponding censuses. Rates for noncensus years before 2010 are revised using updated intercensal population estimates and may differ from rates previously published.

Death rates for some states and years may be low due to a high number of unresolved pending cases or misclassification of ICD–10 codes for unintentional poisoning as R99, “Other ill-defined and unspecified causes of mortality” (2). For example, this issue is known to affect New Jersey in 2009 and West Virginia in 2005 and 2009 but also may affect other years and other states. Drug poisoning death rates may be underestimated in those instances.

Smoothed county age-adjusted death rates (deaths per 100,000 population) were obtained according to methods described elsewhere (3–5). Briefly, two-stage hierarchical models were used to generate empirical Bayes estimates of county age-adjusted death rates due to drug poisoning for each year. These annual county-level estimates “borrow strength” across counties to generate stable estimates of death rates where data are sparse due to small population size (3,5). Estimates for 1999-2015 have been updated, and may differ slightly from previously published estimates. Differences are expected to be minimal, and may result from different county boundaries used in this release (see below) and from the inclusion of an additional year of data. Previously published estimates can be found here for comparison.(6) Estimates are unavailable for Broomfield County, Colorado, and Denali County, Alaska, before 2003 (7,8). Additionally, Clifton Forge County, Virginia only appears on the mortality files prior to 2003, while Bedford City, Virginia was added to Bedford County in 2015 and no longer appears in the mortality file in 2015. These counties were therefore merged with adjacent counties where necessary to create a consistent set of geographic units across the time period. County boundaries are largely consistent with the vintage 2005-2007 bridged-race population file geographies, with the modifications noted previously (7,8).

REFERENCES 1. National Center for Health Statistics. National Vital Statistics System: Mortality data. Available from: http://www.cdc.gov/nchs/deaths.htm.

1. CDC. CDC Wonder: Underlying cause of death 1999–2016. Available from: http://wonder.cdc.gov/wonder/help/ucd.html.

2. Rossen LM, Khan D, Warner M. Trends and geographic patterns in drug-poisoning death rates in the U.S., 1999–2009. Am J Prev Med 45(6):e19–25. 2013.

3. Rossen LM, Khan D, Warner M. Hot spots in mortality from drug poisoning in the United States, 2007–2009. Health Place 26:14–20. 2014.

4. Rossen LM, Khan D, Hamilton B, Warner M. Spatiotemporal variation in selected health outcomes from the National Vital Statistics System. Presented at: 2015 National Conference on Health Statistics, August 25, 2015, Bethesda, MD. Available from: http://www.cdc.gov/nchs/ppt/nchs2015/Rossen_Tuesday_WhiteOak_BB3.pdf.

5. Rossen LM, Bastian B, Warner M, and Khan D. NCHS – Drug Poisoning Mortality by County: United States, 1999-2015. Available from: https://data.cdc.gov/NCHS/NCHS-Drug-Poisoning-Mortality-by-County-United-Sta/pbkm-d27e.

6. National Center for Health Statistics. County geog

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

Data set of annual questionnaires of a long-term prospective study of 1,337 former Johns Hopkins University medical students to identify precursors of premature cardiovascular disease and hypertension. The purpose of the study has broadened, however, as the cohort has aged. The study has been funded for 15 years. Participants were an average of 22 years of age at entry and have been followed to an average age of 69 years. Data are collected through annual questionnaires, supplemented with phone calls and substudies. Self-reports of diseases and risk factors have been validated. Every year from 1988 to 2003, anywhere from 2 to 6 questionnaires have been administered, in categories such as the following, which repeat periodically: Morbidity, Supplemental Illness, Health Behavior, Family and Career, Retirement, Job Satisfaction, Blood Pressure and Weight, Medications, Work Environment, Social Network, Diabetes, Osteoarthritis, Health Locus of Control, Preventive Health Services, General Health, Functional Limitations, Memory Functioning, Smoking, Religious Beliefs and Practices, Links with Administrative Data, National Death Index searches for all nonrespondents * Dates of Study: 1946-2003 * Study Features: Longitudinal * Sample Size: 1,337 (1946)

Intentional low turns were the most common cause of fatal skydiving accidents in the United States between 2003 and 2022, accounting for nearly 15 percent of the total number of fatalities. Equipment problems were the second most common causes of fatalities for this extreme sport in the U.S..