The differences between males and females in life expectancy at birth are decomposed by selected causes of death. Changes in mortality rates for a given cause of death change over time and contribute to the overall change in life expectancy.

Age-Adjusted Death Rates

Death Rates and Life Expectancy in the United States, 2011-2013

By Health [source]

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How to use the dataset

In order to use this dataset, start by selecting a particular set of variables to investigate. You can choose from Measure Names (e.g., Death Rates or Life Expectancy), Race (e.g., All Races), Sex (Male/Female) and Year (2011-2013). Once you have selected your desired variables, you can begin analyzing the data by looking at mortality rates and life expectancy averages amongst different populations in the United States over time.

You may also wish to perform more detailed analyses such as identifying trends or examining correlations between features, regional disparities in mortality rates or changes in average life expectancies over time. If so, you can do so by creating line graphs plotted against one or more independent variables such as Race and Sex to see how demographics impact these statistics overall and on a yearly basis using the Year variable computed from July 1st 2010 estimates

Research Ideas

• Analyzing mortality and life expectancy trends among certain races and sexes over time.
• Examining the effects of different socioeconomic factors on death rates and life expectancies.
• Making predictions about future mortality rates and average life expectancies with machine learning algorithms

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Open Database License (ODbL) v1.0 - You are free to: - Share - copy and redistribute the material in any medium or format. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices. - No Derivatives - If you remix, transform, or build upon the material, you may not distribute the modified material. - No additional restrictions - You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

Columns

File: rows.csv | Column name | Description | |:----------------------------|:----------------------------------------------------------------------| | Measure Names | The type of measure being reported. (String) | | Race | The race of the population being reported. (String) | | Sex | The gender of the population being reported. (String) | | Year | The year the data was collected. (Integer) | | Average Life Expectancy | The average life expectancy of the population being reported. (Float) | | Mortality | The mortality rate of the population being reported. (Float) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Health.

The data used in this paper are the number of deaths and exposure to risk, which can be obtained directly from the Human Mortality Database. It is provided for both genders, male and female as well as the total population. The data is presented by single age ranging from 0 to 109, and age 110+ denotes those at higher ages for a particular year.

This dataset documents rates and trends in local hypertension-related cardiovascular disease (CVD) death rates. Specifically, this report presents county (or county equivalent) estimates of hypertension-related CVD death rates in 2000-2019 and trends during two intervals (2000-2010, 2010-2019) by age group (ages 35–64 years, ages 65 years and older), race/ethnicity (non-Hispanic American Indian/Alaska Native, non-Hispanic Asian/Pacific Islander, non-Hispanic Black, Hispanic, non-Hispanic White), and sex (female, male). The rates and trends were estimated using a Bayesian spatiotemporal model and a smoothed over space, time, and demographic group. Rates are age-standardized in 10-year age groups using the 2010 US population. Data source: National Vital Statistics System.

Abstract

The Health Inequality Project uses big data to measure differences in life expectancy by income across areas and identify strategies to improve health outcomes for low-income Americans.

Section 7

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each percentile of the national income distribution. Both race-adjusted and unadjusted estimates are reported.

Source

Section 13

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each percentile of the national income distribution separately by year. Both race-adjusted and unadjusted estimates are reported.

Source

Section 6

This dataset was created on 2020-01-10 18:53:00.508 by merging multiple datasets together. The source datasets for this version were:

Commuting Zone Life Expectancy Estimates by year: CZ-level by-year life expectancy estimates for men and women, by income quartile

Commuting Zone Life Expectancy: Commuting zone (CZ)-level life expectancy estimates for men and women, by income quartile

Commuting Zone Life Expectancy Trends: CZ-level estimates of trends in life expectancy for men and women, by income quartile

Commuting Zone Characteristics: CZ-level characteristics

Commuting Zone Life Expectancy for larger populations: CZ-level life expectancy estimates for men and women, by income ventile

Section 15

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each quartile of the national income distribution by state of residence and year. Both race-adjusted and unadjusted estimates are reported.

Source

Section 11

This table reports US mortality rates by gender, age, year and household income percentile. Household incomes are measured two years prior to the mortality rate for mortality rates at ages 40-63, and at age 61 for mortality rates at ages 64-76. The “lag” variable indicates the number of years between measurement of income and mortality.

Observations with 1 or 2 deaths have been masked: all mortality rates that reflect only 1 or 2 deaths have been recoded to reflect 3 deaths

Source

Section 3

This table reports coefficients and standard errors from regressions of life expectancy estimates for men and women at age 40 for each quartile of the national income distribution on calendar year by commuting zone of residence. Only the slope coefficient, representing the average increase or decrease in life expectancy per year, is reported. Trend estimates for both race-adjusted and unadjusted life expectancies are reported. Estimates are reported for the 100 largest CZs (populations greater than 590,000) only.

Source

Section 9

This table reports life expectancy estimates at age 40 for Males and Females for all countries. Source: World Health Organization, accessed at: http://apps.who.int/gho/athena/

Source

Section 10

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each quartile of the national income distribution by county of residence. Both race-adjusted and unadjusted estimates are reported. Estimates are reported for counties with populations larger than 25,000 only

Source

Section 2

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each quartile of the national income distribution by commuting zone of residence and year. Both race-adjusted and unadjusted estimates are reported. Estimates are reported for the 100 largest CZs (populations greater than 590,000) only.

Source

Section 8

This table reports US population and death counts by age, year, and sex from various sources. Counts labelled “dm1” are derived from the Social Security Administration Data Master 1 file. Counts labelled “irs” are derived from tax data. Counts labelled “cdc” are derived from NCHS life tables.

Source

Section 12

This table reports numerous county characteristics, compiled from various sources. These characteristics are described in the county life expectancy table.

Two variables constructed by the Cen

Note: Starting April 27, 2023 updates change from daily to weekly. Summary The cumulative number of probable COVID-19 deaths among Maryland residents by gender: Female; Male; Unknown. Description The MD COVID-19 - Probable Deaths by Gender Distribution data layer is a collection of the statewide confirmed and probable COVID-19 related deaths that have been reported each day by the Vital Statistics Administration by gender. A death is classified as probable if the person's death certificate notes COVID-19 to be a probable, suspect or presumed cause or condition. Probable deaths are not yet been confirmed by a laboratory test. Some data on deaths may be unavailable due to the time lag between the death, typically reported by a hospital or other facility, and the submission of the complete death certificate. Confirmed deaths are available from the MD COVID-19 - Confirmed Deaths by Gender Distribution data layer. Terms of Use The Spatial Data, and the information therein, (collectively the "Data") is provided "as is" without warranty of any kind, either expressed, implied, or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted, nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct, indirect, incidental, consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data, nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

This dataset contains the Infant Mortality Rates (IMR) across various years, states, genders such as male and female, and regions such as urban and rural. Data for some smaller states prior to 2004 is not available due to inadequacy of samples. For some states like Kerala and Delhi, there are instances when no deaths were reported. This has been highlighted in the notes column.

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 ".".

Canada CA: Mortality Rate: Under-5: Female: per 1000 Live Births data was reported at 4.700 Ratio in 2023. This stayed constant from the previous number of 4.700 Ratio for 2022. Canada CA: Mortality Rate: Under-5: Female: per 1000 Live Births data is updated yearly, averaging 7.000 Ratio from Dec 1960 (Median) to 2023, with 64 observations. The data reached an all-time high of 28.600 Ratio in 1960 and a record low of 4.700 Ratio in 2023. Canada CA: Mortality Rate: Under-5: Female: per 1000 Live Births data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Canada – Table CA.World Bank.WDI: Social: Health Statistics. Under-five mortality rate, female is the probability per 1,000 that a newborn female baby will die before reaching age five, if subject to female age-specific mortality rates of the specified year.;Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.;Weighted average;Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys. Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation. This is a sex-disaggregated indicator for Sustainable Development Goal 3.2.1 [https://unstats.un.org/sdgs/metadata/].

Estimated annual number of deaths by 5-year age groups and gender for Canada, provinces and territories.

This dataset provides comprehensive information on under-five deaths in India over the period from 1970 to 2021. It presents data on the number of deaths among children under the age of five, categorized by gender (female and male), and also includes the total count of under-five deaths for each year.

Columns:

Time period: This column represents the time span for which the data is recorded. It includes years ranging from 1970 to 2021, providing a yearly breakdown of under-five mortality data.

Female: The "Female" column presents the number of under-five deaths among females in India for each year from 1970 to 2021. It offers insights into the mortality trends for female children under the age of five during the specified time period.

Male: The "Male" column displays the number of under-five deaths among males in India for each year from 1970 to 2021. It offers insights into the mortality trends for male children under the age of five during the specified time period.

Total: The "Total" column shows the total number of under-five deaths (combined females and males) in India for each year from 1970 to 2021. This column provides an overall picture of under-five mortality rates in the country over the specified time period.

Mortality rates for men and women stratified by baseline CD4+ cell count and age.

Please see FAQ for latest information on COVID-19 Data Hub data flows: https://covid-19.geohive.ie/pages/helpfaqs.Notice:See the Technical Data Issues section in the FAQ for information about issues in data: https://covid-19.geohive.ie/pages/helpfaqs.Deaths: From 16th May 2022 onwards, reporting of Notified Deaths will be weekly (each Wednesday) with deaths notified since the previous Wednesday reported. This is based on the date on which a death was notified on CIDR, not the date on which the death occurred. Data on deaths by date of death is available on the new HPSC Epidemiology of COVID-19 Data Hub https://epi-covid-19-hpscireland.hub.arcgis.com/.Notice:

Please be advised that on 29th April 2021, the 'Aged65up' and 'HospitalisedAged65up' fields were removed from this table. The three fields 'Aged65to74', 'Aged75to84', and 'Aged85up' replace the 'Aged65up' field.The three fields 'HospitalisedAged65to74', 'HospitalisedAged75to84' and 'HospitalisedAged85up' replace the 'HospitalisedAged65up' field.Please be advised that on the week beginning 1st March 2021, the values in the following fields in this table were set to zero: 'CommunityTransmission' , 'CloseContact', 'TravelAbroad' and ‘ClustersNotified’. ----------------------------------------------------------------------This feature service contains the up to date Covid-19 Daily Statistics as well as the Profile of Covid-19 Daily Statistics for Ireland, as reported by the Health Protection Surveillance Centre.The Covid-19 Daily Statistics are updated once a week, each Wednesday, which includes data for the full time series. Data on deaths is updated once a week, each Wednesday, which includes data for the full time series.The further breakdown of these counts (age, gender, transmission, etc.) is part of a Daily Statistics Profile of Covid-19, to help identify patterns and trends.The primary Date applies to the following fields:ConfirmedCovidCases, TotalConfirmedCovidCases, ConfirmedCovidDeaths, TotalCovidDeaths, ConfirmedCovidRecovered,SevenDayAverageCases.The StatisticProfileDate applies to the following fields:CovidCasesConfirmed, HospitalisedCovidCases, RequiringICUCovidCases, HealthcareWorkersCovidCases,Clusters Notified,HospitalisedAged5,HospitalisedAged5to14,HospitalisedAged15to24,HospitalisedAged25to34,HospitalisedAged35to44,HospitalisedAged45to54,HospitalisedAged55to64,HospitalisedAged65to74,HospitalisedAged75to84,HospitalisedAged85up,Male, Female, Unknown,Aged1to4, Aged5to14, Aged15to24, Aged25to34, Aged35to44, Aged45to54, Aged55to64, Aged65to74,Aged75to84,Aged85up,MedianAgeCommunityTransmission, CloseContact, TravelAbroad, Total Deaths by Date of Death,Deaths by Date of Death.

This table contains 52020 series, with data for years 1996 - 1996 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (170 items: Canada; Newfoundland and Labrador; Health and Community Services St. John's Region; Newfoundland and Labrador; Health and Community Services Eastern Region; Newfoundland and Labrador ...), Sex (3 items: Both sexes; Males; Females ...), Selected causes of death (ICD-9) (17 items: Total; all causes of death; All malignant neoplasms (cancers);Lung cancer; Colorectal cancer ...), Characteristics (6 items: Number of deaths; Low 95% confidence interval; number of deaths; Mortality; High 95% confidence interval; number of deaths ...).

Mortality rates (qx) values from the national life tables release, presented in time series format. These statistics are for males and females for England, Wales, Scotland, Northern Ireland and the UK.

United States US: Mortality Rate: Under-5: Male: per 1000 Live Births data was reported at 7.200 Ratio in 2017. This records a decrease from the previous number of 7.400 Ratio for 2015. United States US: Mortality Rate: Under-5: Male: per 1000 Live Births data is updated yearly, averaging 8.000 Ratio from Dec 1990 (Median) to 2017, with 5 observations. The data reached an all-time high of 12.500 Ratio in 1990 and a record low of 7.200 Ratio in 2017. United States US: Mortality Rate: Under-5: Male: per 1000 Live Births data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Health Statistics. Under-five mortality rate, male is the probability per 1,000 that a newborn male baby will die before reaching age five, if subject to male age-specific mortality rates of the specified year.; ; Estimates Developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.; Weighted average; Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.

This table contains 70641 series, with data for years 1997 - 1997 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (167 items: Canada; Health and Community Services Eastern Region; Newfoundland and Labrador; Newfoundland and Labrador; Health and Community Services St. John's Region; Newfoundland and Labrador ...), Sex (3 items: Both sexes; Males; Females ...), Selected causes of death (ICD-9) (17 items: Total; all causes of death; All malignant neoplasms (cancers);Lung cancer; Colorectal cancer ...), Characteristics (9 items: Number of deaths; Low 95% confidence interval; number of deaths; Mortality; High 95% confidence interval; number of deaths ...).

This dataset compromises all country data included in the UN Inter-agency Group for Child Mortality Estimation (IGME) database (https://childmortality.org/data, downloaded June 2019).

It includes:

Reference area: name of the country

Indicator: child mortality indicator (neonatal mortality, infant mortality, under-5 mortality and mortality rate age 5 to 14)

Sex: sex of the child (male, female and total)

Series name: name of survey/census/VR [note: UN IGME estimates, i.e. not source data, are identified as "UN IGME estimate" in this field]

Series year: year of survey/census/VR series

Observation value: value of indicator from survey/census/VR

Observation status: indicates whether the data point is included or excluded for estimation [status of "normal" indicates UN IGME estimate, i.e. not source data]

Series Category: category of survey/census/VR, and can be:

DHS [Demographic and Health Survey]

MIS [Malaria Indicator Survey]

AIS [AIDS Indicator Survey]

Interim DHS

Special DHS

NDHS [National DHS]

WFS [World Fertility Survey]

MICS [Multiple Indicator Cluster Survey]

NMICS [National MICS]

RHS [Reproductive Health Survey]

PAP [Pan Arab Project for Child or Pan Arab Project for Family Health or Gulf Famly Health Survey]

LSMS [Living Standard Measurement Survey]

Panel [Dual record, multiround/follow-up survey and longitudinal/panel survey]

Census

VR [Vital Registration]

SVR [Sample Vital Registration]

Others [e.g. Life Tables]

Series type: the type of calculation method used to derive the indicator value (direct, indirect, household deaths, life table and vital records)

Standard error: sampling standard error of the observation value

Series method: data collection method, and can be:

Survey/census with Full Birth Histories

Survey/census with Summary Birth Histories

Survey/census with Household death

Vital Registration

Other

Lower and upper bound: the lower and upper bounds of 90% uncertainty interval of UN IGME estimates (for estimates only, i.e., not source data).

The dataset is used in the following paper:

Ezbakhe, F. and Pérez-Foguet, A. (2019) Levels and trends in child mortality: a compositional approach. Demographic Research (Under Review)

Abstract (en): These data are a collection of demographic statistics for the populations of 125 countries or areas throughout the world, prepared by the Statistical Office of the United Nations. The units of analysis are both country and data year. The primary source of data is a set of questionnaires sent monthly and annually to national statistical services and other appropriate government offices. Data include statistics on approximately 50 types of causes of death for the years 1966 through 1974 for males, females, and total populations. Causes of death in 125 countries or areas throughout the world between the years 1966 and 1974. 2005-11-04 On 2005-03-14 new files were added to one or more datasets. These files included additional setup files as well as one or more of the following: SAS program, SAS transport, SPSS portable, and Stata system files. The metadata record was revised 2005-11-04 to reflect these additions. The causes of death are classified according to the 6th, 7th, and 8th versions of an abbreviated list of the World Health Organization's INTERNATIONAL STATISTICAL CLASSIFICATION OF DISEASES, INJURIES, AND CAUSES OF DEATH. Therefore, data for causes of death are not necessarily comparable across countries or data years. Users should refer to Variable 5 in the Variable List for full discussion of this problem. Users interested in comparing deaths for countries or years that use different versions of the Abbreviated list should consult two publications: A. Joan Klebba, and Alice B. Dolman. COMPARABILITY OF MORTALITY STATISTICS FOR THE SEVENTH AND EIGHTH REVISIONS OF THE INTERNATIONAL CLASSIFICATION OF DISEASES, UNITED STATES. Rockville, MD: United States Department of Health, Education, and Welfare. Public Health Service. Health Services and Mental Health Administration. National Center for Health Statistics, 1975, and World Health Organization. MANUAL OF THE INTERNATIONAL STATISTICAL CLASSIFICATION OF DISEASES, INJURIES, AND CAUSES OF DEATH. Geneva, Switzerland: World Health Organization, 1967.The user should note that countries have data covering a variety of time spans (the maximum span being 1965-1973), and the data have not been padded to supply missing data codes for those years for which a country does not have data. Thus, Egypt has data for years 1965 through 1972, while Kenya has data for only 1970. (See Appendix D in the codebook to determine the years for which a country has data.)It is important that any user of these data consult the United Nations' DEMOGRAPHIC YEARBOOK, 1976, for further explanation of the data's limitations. Certain countries have modified reporting procedures which are presented in both the footnotes and the technical notes accompanying the tables in the Yearbook. There is no way to identify these problems using only the machine-readable data.In order to eliminate unnecessary repetition of identifying information, data were merged so that each record now contains all the data for a country for one particular year. In this process, breakdowns of deaths by ethnic group and/or urban/rural classification were omitted since only a few countries provided such information. Each record now contains the data for the number of deaths from each cause of death for male, female, and total.While the data appear to be in a rectangular matrix, such is not the case. This occurs because different versions of the abbreviated list are referenced in different data years. The lack of a rectangular data matrix does little to restrict the manageability of the dataset. See codebook for examples.While the data have been reformatted and documented by ICPSR staff, there has been no attempt to verify the accuracy and consistency of the data received from the U.N. Statistical Office.

Rank, number of deaths, percentage of deaths, and age-specific mortality rates for the leading causes of death, by age group and sex, 2000 to most recent year.