Infant Mortality Rate by Maternal Race/Ethnicity for New York City, 2007-2016 Counts of infant deaths (age <1 year) are based on NYC death certificates. The rate is calculated using the counts of infant deaths as the numerator and the count of live births from NYC birth certificates as the denominator.

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/

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

Historical dataset showing U.S. infant mortality rate by year from 1950 to 2025.

The Mortality - Infant Deaths (from Linked Birth / Infant Death Records) online databases on CDC WONDER provide counts and rates for deaths of children under 1 year of age, occuring within the United States to U.S. residents. Information from death certificates has been linked to corresponding birth certificates. Data are available by county of mother's residence, child's age, underlying cause of death, sex, birth weight, birth plurality, birth order, gestational age at birth, period of prenatal care, maternal race and ethnicity, maternal age, maternal education and marital status. Data are available since 1995. The data are produced by the National Center for Health Statistics.

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.

Infant and Neonatal Mortality Rates

United States, 1915-2013

By Health [source]

About this dataset

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How to use the dataset

This dataset contains important insight on the infant and neonatal mortality rate in the United States between 1915 to 2013. As such, it can be used to analyze trends in infant and neonatal mortality over time as well as draw comparisons between different states or regions.

Before you start diving into this data set, here are a few useful tips for exploring its contents: - Explore the columns: First, get familiar with the columns of this data set by reviewing their descriptions listed above; these will help you understand what each column offers so that you can make informed decisions when analyzing the data. - Determine which level of geography makes sense: Will your analysis focus on state-level data or looking at national trends? Make sure to select only those points relevant to your project to avoid getting overwhelmed with unnecessary information. - Choose a metric for measuring progress: Decide on your metrics for success and use those values as benchmark points when exploring information from this dataset . For example, if tracking changes over time is important in your analysis then choose “Year” from this dataset’s available fields and start sorting by that value within each geographical area of interest (for example states). Further utilize statistical measures such as averages or medians from “Mortality Rate” field to determine progress/regress in each segment over time periods of interests (again choosing year values).
- Prioritize visualization techniques appropriately: Upon gathering all needed information then focus on how best present it – using tables or graphs? Tables offer a great way to track details while visuals like charts & maps provide insights into larger trajectories that might not be clear through tables alone; ensuring both types are part of your analysis will ensure maximum clarity & accuracywith presentation yielding maximum impact & understanding among target audiences!

Research Ideas

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: NCHS_-_Infant_and_neonatal_mortality_rates_United_States_1915-2013.csv | Column name | Description | |:-------------------|:---------------------------------------------------| | Type | The type of mortality rate being measured (String) | | Year | The year the mortality rate was measured (Integer) | | Mortality Rate | The mortality rate per 1,000 live births (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.

Rates are infants (under 1 year) and neonatal (under 28 days) deaths per 1,000 live births.

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

Historical dataset showing Virgin Islands (U.S.) infant mortality rate by year from N/A to N/A.

United States US: Mortality Rate: Infant: per 1000 Live Births data was reported at 5.600 Ratio in 2016. This records a decrease from the previous number of 5.700 Ratio for 2015. United States US: Mortality Rate: Infant: per 1000 Live Births data is updated yearly, averaging 10.000 Ratio from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 25.900 Ratio in 1960 and a record low of 5.600 Ratio in 2016. United States US: Mortality Rate: Infant: 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. Infant mortality rate is the number of infants dying before reaching one year of age, per 1,000 live births in a given 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.

Historical dataset showing North America infant mortality rate by year from 1950 to 2025.

This topic is no longer available in the NCHS Data Query System (DQS). Search, visualize, and download other estimates from over 120 health topics with DQS, available from: https://www.cdc.gov/nchs/dataquery/index.htm. Data on on average annual infant mortality rates in the United States and U.S. dependent areas, by race and Hispanic origin of mother, state, and territory. Data are from Health, United States. SOURCE: National Center for Health Statistics, National Vital Statistics System, Linked Birth/Infant Death Data Set.

NCHS - Infant and neonatal mortality rates: United States, 1915-2013

  Description

Rates are infants (under 1 year) and neonatal (under 28 days) deaths per 1,000 live births. https://www.cdc.gov/nchs/data-visualization/mortality-trends/

  Dataset Details

Publisher: Centers for Disease Control and Prevention Temporal Coverage: 1915/2013 Geographic Coverage: United States Last Modified: 2025-04-21 Contact: National Center for Health Statistics (cdcinfo@cdc.gov)… See the full description on the dataset page: https://huggingface.co/datasets/HHS-Official/nchs-infant-and-neonatal-mortality-rates-united-st.

Data on infant, neonatal, postneonatal, fetal, and perinatal mortality rates by selected characteristics of the mother. Please refer to the PDF or Excel version of this table in the HUS 2019 Data Finder (https://www.cdc.gov/nchs/hus/contents2019.htm) for critical information about measures, definitions, and changes over time. SOURCE: NCHS, National Vital Statistics System, public-use Linked Birth/Infant Death Data Set, public-use Fetal Death File, and public-use Birth File. For more information on the National Vital Statistics System, see the corresponding Appendix entry at https://www.cdc.gov/nchs/data/hus/hus19-appendix-508.pdf.

United States US: Mortality Rate: Infant: Male: per 1000 Live Births data was reported at 6.000 Ratio in 2017. This records a decrease from the previous number of 6.200 Ratio for 2015. United States US: Mortality Rate: Infant: Male: per 1000 Live Births data is updated yearly, averaging 6.800 Ratio from Dec 1990 (Median) to 2017, with 5 observations. The data reached an all-time high of 10.400 Ratio in 1990 and a record low of 6.000 Ratio in 2017. United States US: Mortality Rate: Infant: 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 United States – Table US.World Bank.WDI: Health Statistics. Infant mortality rate, male is the number of male infants dying before reaching one year of age, per 1,000 male live births in a given 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.

Global Health Outcomes Data

Impact on Mortality Rates and Malnutrition in Countries Around the World

By Humanitarian Data Exchange [source]

About this dataset

This dataset provides comprehensive insights into critical health conditions around the world, such as mortality rate, malnutrition levels, and frequency of preventable diseases. It documents the prevalence of life-threatening diseases like malaria and tuberculosis, and are tracked alongside key health indicators like adult mortality rates, HIV prevalence, physicians per 10,000 people ratio and public health expenditures. Such metrics provide us with an accurate picture of how developed healthcare systems are in certain countries which ultimately leads to improvements in public policy formation and awareness amongst decision-makers. With this data it is possible to observe disparities between different regions of the world which can help inform global strategies for providing equitable care globally. This dataset is a valuable source for researchers interested in understanding global health trends over time or seeking to evaluate regional differences within countries

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How to use the dataset

This dataset provides comprehensive global health outcome data for countries around the world. It includes vital information such as infant mortality rates, child malnutrition rates, adult mortality rates, deaths due to malaria and tuberculosis, HIV prevalence rates, life expectancy at age 60 and public health expenditure. This dataset can be used to gain valuable insight into the challenges faced by different countries in providing a good quality of life for their citizens.

To use this dataset, first identify what questions you need answered and what outcomes you are looking to measure. You may want to look at specific disease-based indicators (e.g. malaria or tuberculosis), health-related indicators (e.g., nutrition), or overall population markers (e.g., life expectancy).

Then decide which data points from the provided fields will help answer your questions and provide the results needed - e.g,. infant mortality rate or HIV prevalence rate - extracting these values from relevant columns like “Infants lacking immunization (% of one-year-olds) Measles 2013” or “HIV prevalence, adult (% ages 15Ð49) 2013” respectively

Next extract other columnwise relevant information - e.g., country name — that could also aid your analysis using tools like Excel or Python's Pandas library; sorting through them based on any metric desired — e..g,, physicians per 10k people — while being mindful that some data points are missing in some cases (denoted by NA).

Finally perform basic analyses with either your own scripting language, like R/Python libraries' numerical functions with accompanying visuals/graphs etc if elucidating trends is desired; drawing meaningful conclusions about overall state of global health outcomes accordingly before making informed decisions thereafter if needed too!

Research Ideas

• Create a world health map to visualize the differences in health outcomes across different countries and regions.
• Develop an AI-based decision support tool that identifies optimal public health policies or interventions based on these metrics for different countries.
• Design a dashboard or web app that displays and updates this data in real-time, to allow users to compare the current state of global health indicators and benchmark them against historical figures

Acknowledgements

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

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: health-outcomes-csv-1.csv | Column name | Description | |:-------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------| | Country | The name of the country. (String) ...

Historical dataset showing Central America infant mortality rate by year from 1950 to 2025.

This dataset tracks the updates made on the dataset "NCHS - Infant Mortality Rates, by Race: United States, 1915-2013" as a repository for previous versions of the data and metadata.

Forecast: Infant Mortality Rate in the US 2022 - 2026 Discover more data with ReportLinker!

This collection contains five modified data sets with mortality, population, and other demographic information for five American cities (Baltimore, Maryland; Boston, Massachusetts; New Orleans, Louisiana; New York City (Manhattan only), New York; and Philadelphia, Pennsylvania) from the early 19th century to the early 20th century. Mortality was represented by an annual crude death rate (deaths per 1000 population per year). The population was linearly interpolated from U.S. Census data and state census data (for Boston and New York City). All data sets include variables for year, total deaths, census populations, estimated annual linearly interpolated populations, and crude death rate. The Baltimore data set (DS0001) also provides birth and death rate variables based on race and slave status demographics, as well as a variable for stillbirths. The Philadelphia data set (DS0005) also includes variables for total births, total infant deaths, crude birth rate, and infant deaths per 1,000 live births.