Age-adjustment mortality rates are rates of deaths that are computed using a statistical method to create a metric based on the true death rate so that it can be compared over time for a single population (i.e. comparing 2006-2008 to 2010-2012), as well as enable comparisons across different populations with possibly different age distributions in their populations (i.e. comparing Hispanic residents to Asian residents). Age adjustment methods applied to Montgomery County rates are consistent with US Centers for Disease Control and Prevention (CDC), National Center for Health Statistics (NCHS) as well as Maryland Department of Health and Mental Hygiene’s Vital Statistics Administration (DHMH VSA). PHS Planning and Epidemiology receives an annual data file of Montgomery County resident deaths registered with Maryland Department of Health and Mental Hygiene’s Vital Statistics Administration (DHMH VSA). Using SAS analytic software, MCDHHS standardizes, aggregates, and calculates age-adjusted rates for each of the leading causes of death category consistent with state and national methods and by subgroups based on age, gender, race, and ethnicity combinations. Data are released in compliance with Data Use Agreements between DHMH VSA and MCDHHS. This dataset will be updated Annually.

This dataset of U.S. mortality trends since 1900 highlights childhood mortality rates by age group for age at death. Age-adjusted death rates (deaths per 100,000) after 1998 are calculated based on the 2000 U.S. standard population. Populations used for computing death rates for 2011–2017 are postcensal estimates based on the 2010 census, estimated as of July 1, 2010. Rates for census years are based on populations enumerated in the corresponding censuses. Rates for noncensus years between 2000 and 2010 are revised using updated intercensal population estimates and may differ from rates previously published. Data on age-adjusted death rates prior to 1999 are taken from historical data (see References below). Age groups for childhood death rates are based on age at death. SOURCES CDC/NCHS, National Vital Statistics System, historical data, 1900-1998 (see https://www.cdc.gov/nchs/nvss/mortality_historical_data.htm); CDC/NCHS, National Vital Statistics System, mortality data (see http://www.cdc.gov/nchs/deaths.htm); and CDC WONDER (see http://wonder.cdc.gov). REFERENCES National Center for Health Statistics, Data Warehouse. Comparability of cause-of-death between ICD revisions. 2008. Available from: http://www.cdc.gov/nchs/nvss/mortality/comparability_icd.htm. National Center for Health Statistics. Vital statistics data available. Mortality multiple cause files. Hyattsville, MD: National Center for Health Statistics. Available from: https://www.cdc.gov/nchs/data_access/vitalstatsonline.htm. Kochanek KD, Murphy SL, Xu JQ, Arias E. Deaths: Final data for 2017. National Vital Statistics Reports; vol 68 no 9. Hyattsville, MD: National Center for Health Statistics. 2019. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr68/nvsr68_09-508.pdf. Arias E, Xu JQ. United States life tables, 2017. National Vital Statistics Reports; vol 68 no 7. Hyattsville, MD: National Center for Health Statistics. 2019. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr68/nvsr68_07-508.pdf. National Center for Health Statistics. Historical Data, 1900-1998. 2009. Available from: https://www.cdc.gov/nchs/nvss/mortality_historical_data.htm.

Context

IHME United States Mortality Rates by County 1980-2014: National - All. (Deaths per 100,000 population)

To quickly get started creating maps, like the one below, see the Quick Start R kernel.

https://storage.googleapis.com/montco-stats/kaggleNeoplasms.png" alt="NeoplasmsMap">

How the Dataset was Created

This Dataset was created from the Excel Spreadsheet, which can be found in the download. Or, you can view the source here. If you take a look at the row for United States, for the column Mortality Rate, 1980*, you'll see the set of numbers 1.52 (1.44, 1.61). Numbers in parentheses are 95% uncertainty. The 1.52 is an age-standardized mortality rate for both sexes combined (deaths per 100,000 population).

In this Dataset 1.44 will be placed in the named column Mortality Rage, 1989 (Min)* and 1.61 is in column named Mortality Rate, 1980 (Max)* . For information on how these Age-standardized mortality rates were calculated, see the December JAMA 2016 article, which you can download for free.

https://storage.googleapis.com/montco-stats/kaggleUSMort.png" alt="Spreadsheet">

Reference

JAMA Full Article

Video Describing this Study (Short and this is worth viewing)

Data Resources

How Americans Die May Depend On Where They Live, by Anna Maria Barry-Jester (FiveThirtyEight)

Interactive Map from healthdata.org

IHME Data

Acknowledgements

This Dataset was provided by IHME

Institute for Health Metrics and Evaluation 2301 Fifth Ave., Suite 600, Seattle, WA 98121, USA Tel: +1.206.897.2800 Fax: +1.206.897.2899 © 2016 University of Washington

This is a source dataset for a Let's Get Healthy California indicator at https://letsgethealthy.ca.gov/. Infant Mortality is defined as the number of deaths in infants under one year of age per 1,000 live births. Infant mortality is often used as an indicator to measure the health and well-being of a community, because factors affecting the health of entire populations can also impact the mortality rate of infants. Although California’s infant mortality rate is better than the national average, there are significant disparities, with African American babies dying at more than twice the rate of other groups. Data are from the Birth Cohort Files. The infant mortality indicator computed from the birth cohort file comprises birth certificate information on all births that occur in a calendar year (denominator) plus death certificate information linked to the birth certificate for those infants who were born in that year but subsequently died within 12 months of birth (numerator). Studies of infant mortality that are based on information from death certificates alone have been found to underestimate infant death rates for infants of all race/ethnic groups and especially for certain race/ethnic groups, due to problems such as confusion about event registration requirements, incomplete data, and transfers of newborns from one facility to another for medical care. Note there is a separate data table "Infant Mortality by Race/Ethnicity" which is based on death records only, which is more timely but less accurate than the Birth Cohort File. Single year shown to provide state-level data and county totals for the most recent year. Numerator: Infants deaths (under age 1 year). Denominator: Live births occurring to California state residents. Multiple years aggregated to allow for stratification at the county level. For this indicator, race/ethnicity is based on the birth certificate information, which records the race/ethnicity of the mother. The mother can “decline to state”; this is considered to be a valid response. These responses are not displayed on the indicator visualization.

The Global Subnational Infant Mortality Rates, Version 2.01 consist of Infant Mortality Rate (IMR) estimates for 234 countries and territories, 143 of which include subnational Units. The data are benchmarked to the year 2015 (Version 1 was benchmarked to the year 2000), and are drawn from national offices, Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), and other sources from 2006 to 2014. In addition to Infant Mortality Rates, Version 2.01 includes crude estimates of births and infant deaths, which could be aggregated or disaggregated to different geographies to calculate infant mortality rates at different scales or resolutions, where births are the rate denominator and infant deaths are the rate numerator. Boundary inputs are derived primarily from the Gridded Population of the World, Version 4 (GPWv4) data collection. National and subnational data are mapped to grid cells at a spatial resolution of 30 arc-seconds (~1 km) (Version 1 has a spatial resolution of 1/4 degree, ~28 km at the equator), allowing for easy integration with demographic, environmental, and other spatial data.

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)

Analysis of ‘Strategic Measure_Infant mortality rate (number of deaths of infants younger than 1-year-old per 1,000 live births)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/2a716fbb-f883-4a74-ad0b-9d984a06b758 on 26 January 2022.

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

This dataset includes counts of infant births and deaths within Austin city limits by year. The counts are calculated into an infant mortality rate for each year. Both infant deaths and infant births are reported through the Office of Vital Records.

View more details and insights related to this data set on the story page: https://data.austintexas.gov/stories/s/HE-B-3-Infant-mortality-rate-number-of-deaths-of-i/jwg4-2djc/

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

Calculation Of Mortality Rates From Respiratory And Circulatory Diseases
This dataset falls under the category Environmental Data Other.
It contains the following data: Mortality rates according to respiratory and circulatory diseases calculated in the project "Air quality and its effects on the health of the population of the ten municipalities of the Aburra Valley, 2008 - 2015".
This dataset was scouted on 2022/01/18 as part of a data sourcing project conducted by TUMI. License information might be outdated: Check original source for current licensing. The data can be accessed using the following URL / API Endpoint: https://datosabiertos.metropol.gov.co/dataset/c%C3%A1lculo-tasas-de-mortalidad-de-enfermedades-respiratorias-y-circulatoriasSee URL for data access and license information.

VITAL SIGNS INDICATOR Life Expectancy (EQ6)

FULL MEASURE NAME Life Expectancy

LAST UPDATED April 2017

DESCRIPTION Life expectancy refers to the average number of years a newborn is expected to live if mortality patterns remain the same. The measure reflects the mortality rate across a population for a point in time.

DATA SOURCE State of California, Department of Health: Death Records (1990-2013) No link

California Department of Finance: Population Estimates Annual Intercensal Population Estimates (1990-2010) Table P-2: County Population by Age (2010-2013) http://www.dof.ca.gov/Forecasting/Demographics/Estimates/

CONTACT INFORMATION vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator) Life expectancy is commonly used as a measure of the health of a population. Life expectancy does not reflect how long any given individual is expected to live; rather, it is an artificial measure that captures an aspect of the mortality rates across a population. Vital Signs measures life expectancy at birth (as opposed to cohort life expectancy). A statistical model was used to estimate life expectancy for Bay Area counties and Zip codes based on current life tables which require both age and mortality data. A life table is a table which shows, for each age, the survivorship of a people from a certain population.

Current life tables were created using death records and population estimates by age. The California Department of Public Health provided death records based on the California death certificate information. Records include age at death and residential Zip code. Single-year age population estimates at the regional- and county-level comes from the California Department of Finance population estimates and projections for ages 0-100+. Population estimates for ages 100 and over are aggregated to a single age interval. Using this data, death rates in a population within age groups for a given year are computed to form unabridged life tables (as opposed to abridged life tables). To calculate life expectancy, the probability of dying between the jth and (j+1)st birthday is assumed uniform after age 1. Special consideration is taken to account for infant mortality. For the Zip code-level life expectancy calculation, it is assumed that postal Zip codes share the same boundaries as Zip Code Census Tabulation Areas (ZCTAs). More information on the relationship between Zip codes and ZCTAs can be found at https://www.census.gov/geo/reference/zctas.html. Zip code-level data uses three years of mortality data to make robust estimates due to small sample size. Year 2013 Zip code life expectancy estimates reflects death records from 2011 through 2013. 2013 is the last year with available mortality data. Death records for Zip codes with zero population (like those associated with P.O. Boxes) were assigned to the nearest Zip code with population. Zip code population for 2000 estimates comes from the Decennial Census. Zip code population for 2013 estimates are from the American Community Survey (5-Year Average). The ACS provides Zip code population by age in five-year age intervals. Single-year age population estimates were calculated by distributing population within an age interval to single-year ages using the county distribution. Counties were assigned to Zip codes based on majority land-area.

Zip codes in the Bay Area vary in population from over 10,000 residents to less than 20 residents. Traditional life expectancy estimation (like the one used for the regional- and county-level Vital Signs estimates) cannot be used because they are highly inaccurate for small populations and may result in over/underestimation of life expectancy. To avoid inaccurate estimates, Zip codes with populations of less than 5,000 were aggregated with neighboring Zip codes until the merged areas had a population of more than 5,000. In this way, the original 305 Bay Area Zip codes were reduced to 218 Zip code areas for 2013 estimates. Next, a form of Bayesian random-effects analysis was used which established a prior distribution of the probability of death at each age using the regional distribution. This prior is used to shore up the life expectancy calculations where data were sparse.

Contains equation used to calculate death rates for farms. Data held within the Department of Agriculture

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

US: Maternal Mortality Ratio: National Estimate: per 100,000 Live Births data was reported at 28.000 Ratio in 2013. This records an increase from the previous number of 13.000 Ratio for 2007. US: Maternal Mortality Ratio: National Estimate: per 100,000 Live Births data is updated yearly, averaging 13.000 Ratio from Dec 1996 (Median) to 2013, with 3 observations. The data reached an all-time high of 28.000 Ratio in 2013 and a record low of 7.600 Ratio in 1996. US: Maternal Mortality Ratio: National Estimate: per 100,000 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. Maternal mortality ratio is the number of women who die from pregnancy-related causes while pregnant or within 42 days of pregnancy termination per 100,000 live births.; ; UNICEF, State of the World's Children, Childinfo, and Demographic and Health Surveys.; ;

This dataset includes counts of infant births and deaths within Austin city limits by year. The counts are calculated into an infant mortality rate for each year. Both infant deaths and infant births are reported through the Office of Vital Records. View more details and insights related to this data set on the story page: https://data.austintexas.gov/stories/s/HE-B-3-Infant-mortality-rate-number-of-deaths-of-i/jwg4-2djc/

Fetal mortality occurs after 20 weeks of gestation and before labor. Infant mortality occurs before the first year of age and is a sum of Neonatal (the first 28 days after birth) and Postneonatal (from 28 days up to 1 year) mortality. Rates are calculated per every 1000 births; rates are not available for disaggregated race/ethnicities. Fetal and infant mortality values are available for given race/ethnicities. Connecticut Department of Public Health collects and reports data annually. CTData.org carries 1-, 3- and 5-Year aggregations.

The dataset contains information on various demographic and health indicators for different countries. It is organized into several columns, each providing essential information about these countries. Here's a description of each column:

1. Country: This column represents the names of different countries or regions included in the dataset. Each row corresponds to a specific country or region, and this column serves as the identifier for each entry.

2. Life Expectancy Males: This column contains data on the average life expectancy of males in each of the listed countries. Life expectancy is a crucial health indicator and provides an estimate of the average number of years a male can expect to live, given current mortality rates and health conditions.

3. Life Expectancy Females: Similar to the "Life Expectancy Males" column, this column provides data on the average life expectancy of females in the same countries. It reflects the average number of years a female can expect to live, considering the prevailing health and mortality conditions.

4. Birth Rate: The "Birth Rate" column contains information about the birth rate in each country. Birth rate is a demographic indicator that represents the number of live births per 1,000 people in a given population over a specific period, usually a year. It can provide insights into a country's population growth or decline.

5. Death Rate: This column presents data on the death rate in each of the listed countries. The death rate is another crucial demographic indicator and represents the number of deaths per 1,000 people in a population over a specific period, often a year. It helps gauge the overall health and mortality conditions within a country.

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.

This data presents national-level provisional maternal mortality rates based on a current flow of mortality and natality data in the National Vital Statistics System. Provisional rates which are an early estimate of the number of maternal deaths per 100,000 live births, are shown as of the date specified and may not include all deaths and births that occurred during a given time period (see Technical Notes). A maternal death is the death of a woman while pregnant or within 42 days of termination of pregnancy irrespective of the duration and the site of the pregnancy, from any cause related to or aggravated by the pregnancy or its management, but not from accidental or incidental causes. In this data visualization, maternal deaths are those deaths with an underlying cause of death assigned to International Statistical Classification of Diseases, 10th Revision (ICD-10) code numbers A34, O00–O95, and O98–O99. The provisional data include reported 12 month-ending provisional maternal mortality rates overall, by age, and by race and Hispanic origin. Provisional maternal mortality rates presented in this data visualization are for “12-month ending periods,” defined as the number of maternal deaths per 100,000 live births occurring in the 12-month period ending in the month indicated. For example, the 12-month ending period in June 2020 would include deaths and births occurring from July 1, 2019, through June 30, 2020. Evaluation of trends over time should compare estimates from year to year (June 2020 and June 2021), rather than month to month, to avoid overlapping time periods. In the visualization and in the accompanying data file, rates based on death counts less than 20 are suppressed in accordance with current NCHS standards of reliability for rates. Death counts between 1-9 in the data file are suppressed in accordance with National Center for Health Statistics (NCHS) confidentiality standards. Provisional data presented on this page will be updated on a quarterly basis as additional records are received. Previously released estimates are revised to include data and record updates received since the previous release. As a result, the reliability of estimates for a 12-month period ending with a specific month will improve with each quarterly release and estimates for previous time periods may change as new data and updates are received.

Stroke Mortality Rates in the US (Age-Standardized) 2012-2014

State/Territory and County Data

By US Open Data Portal, data.gov [source]

About this dataset

This dataset contains primary stroke mortality data from 2012 to 2014 among US adults aged 35+ across all states/territories and counties. Data is age-standardized and county rates are spatially smoothed to provide a better and more accurate view of the prevalence of mortality due to stroke. The data evaluation can be further divided by gender, race/ethnicity, stratification category 1, stratification 1, stratification category 2, or stratification 2. All data is sourced from the National Vital Statistics System (NVSS) ensuring it's accuracy and reliability. For even more information regarding heart disease related deaths as well as methodology employed in mapping such occurrences visit the Interactive Atlas of Heart Disease and Stroke. Looking deeper into these numbers may reveal hidden trends that could lead us closer towards reducing stroke related mortality in adults across our nation!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

The U.S. Stroke Mortality Rates (Age-Standardized) 2012-2014 dataset provides stroke mortality rates for adults aged 35 and over living in the United States from 2012 to 2014. This dataset is an ideal resource for examining the impact of stroke at a local or national level.

This guide will provide an introduction to understanding and using this data correctly, as well as highlighting some potential areas of investigation it may be used for:

• Understanding the Context: The first step towards understanding this data is to take a close look at its features and categories. These include year, location, geography level, data source, class, topic, value type/unit/ footnote symbol and stratification category/stratification which allow you to view data through multiple ways (e.g., by age group or by race).

  You can also filter your results with these attributes including specific years or different locations in order explore particular conditions within a certain area or year range (e.g., how many stroke related deaths occurred among blacks in California between 2012 – 2014?). It’s important to note that all county age-standardized rates are spatially smoothed — meaning each county rate is adjusted taking into account nearby counties — so the results you get might reflect wider regional trends more than actual localized patterns associated with individual counties.)

• Accessing & Previewing Data: Once you have familiarised yourself with the library concept behind this dataset it’s time access it's contents directly! To download your desired subset inside Kaggle platform just open up csv file titled 'csv- 1'. Alternatively ,you can use other open source tools such as Exasol Analytic Database technology (available on built-in 'notebook' feature) if you want work on even larger datasets with more processing power come into play ! Inside visualization tab users will be able view chart graphs( pie charts histograms etc ) from their query results .And once completed feel free export their respective visuals SVG PNG PDF formats too .

• Finding Answers: With all these processes complete ,you now should have plenty of datasets ready go in advance - great start but what does story tell us ? Well break things down compare different groups slices look at correlations trends deviations across various demographic filters questions about causal effects become much easier answer ! Leave creative freedom your side let those numbers feel ! So try pose some interesting interesting hypothesis determine how above factors could change across different states spend hours going through wealth

Research Ideas

• Utilizing location-specific stroke mortality data to pinpoint areas that need targeted public health interventions and outreach.
• Analyzing the correlation between age-standardized stroke mortality rates and demographic data, such as gender, race/ethnicity or socioeconomic status.
• Creating strategies focused on reducing stroke mortality in high risk demographic groups based on findings from the datasets geographical and sociological analysis tools

Acknowledgements

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

License

Unknown License - Please check the dataset description for more information.

Columns

File: csv-1.csv | Column name | Description ...

Fetal mortality occurs after 20 weeks of gestation and before labor. Infant mortality occurs before the first year of age and is a sum of Neonatal (the first 28 days after birth) and Postneonatal (from 28 days up to 1 year) mortality. Rates are calculated per every 1000 births; rates are not available for disaggregated race/ethnicities. Fetal and infant mortality values are available for given race/ethnicities. Connecticut Department of Public Health collects and reports data annually. CTData.org carries 1-, 3- and 5-Year aggregations.

description:

All birth data by race before 1980 are based on race of the child; starting in 1980, birth data by race are based on race of the mother. Birth data are used to calculate infant mortality rate.

https://www.cdc.gov/nchs/data-visualization/mortality-trends/

All birth data by race before 1980 are based on race of the child; starting in 1980, birth data by race are based on race of the mother. Birth data are used to calculate infant mortality rate.

https://www.cdc.gov/nchs/data-visualization/mortality-trends/