This dataset contains counts of live births for California counties based on information entered on birth certificates. Final counts are derived from static data and include out of state births 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 births that occurred during the time period.

The final data tables include both births that occurred in California regardless of the place of residence (by occurrence) and births to California residents (by residence), whereas the provisional data table only includes births that occurred in California regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by parent giving birth's age, parent giving birth's race-ethnicity, and birth place type. See temporal coverage for more information on which strata are available for which years.

Abstract copyright UK Data Service and data collection copyright owner.

The National Child Development Study (NCDS) is a continuing longitudinal study that seeks to follow the lives of all those living in Great Britain who were born in one particular week in 1958. The aim of the study is to improve understanding of the factors affecting human development over the whole lifespan.

The NCDS has its origins in the Perinatal Mortality Survey (PMS) (the original PMS study is held at the UK Data Archive under SN 2137). This study was sponsored by the National Birthday Trust Fund and designed to examine the social and obstetric factors associated with stillbirth and death in early infancy among the 17,000 children born in England, Scotland and Wales in that one week. Selected data from the PMS form NCDS sweep 0, held alongside NCDS sweeps 1-3, under SN 5565.

Survey and Biomeasures Data (GN 33004):
To date there have been nine attempts to trace all members of the birth cohort in order to monitor their physical, educational and social development. The first three sweeps were carried out by the National Children's Bureau, in 1965, when respondents were aged 7, in 1969, aged 11, and in 1974, aged 16 (these sweeps form NCDS1-3, held together with NCDS0 under SN 5565). The fourth sweep, also carried out by the National Children's Bureau, was conducted in 1981, when respondents were aged 23 (held under SN 5566). In 1985 the NCDS moved to the Social Statistics Research Unit (SSRU) - now known as the Centre for Longitudinal Studies (CLS). The fifth sweep was carried out in 1991, when respondents were aged 33 (held under SN 5567). For the sixth sweep, conducted in 1999-2000, when respondents were aged 42 (NCDS6, held under SN 5578), fieldwork was combined with the 1999-2000 wave of the 1970 Birth Cohort Study (BCS70), which was also conducted by CLS (and held under GN 33229). The seventh sweep was conducted in 2004-2005 when the respondents were aged 46 (held under SN 5579), the eighth sweep was conducted in 2008-2009 when respondents were aged 50 (held under SN 6137) and the ninth sweep was conducted in 2013 when respondents were aged 55 (held under SN 7669).

Four separate datasets covering responses to NCDS over all sweeps are available. National Child Development Deaths Dataset: Special Licence Access (SN 7717) covers deaths; National Child Development Study Response and Outcomes Dataset (SN 5560) covers all other responses and outcomes; National Child Development Study: Partnership Histories (SN 6940) includes data on live-in relationships; and National Child Development Study: Activity Histories (SN 6942) covers work and non-work activities. Users are advised to order these studies alongside the other waves of NCDS.

From 2002-2004, a Biomedical Survey was completed and is available under End User Licence (EUL) (SN 8731) and Special Licence (SL) (SN 5594). Proteomics analyses of blood samples are available under SL SN 9254.

Linked Geographical Data (GN 33497):
A number of geographical variables are available, under more restrictive access conditions, which can be linked to the NCDS EUL and SL access studies.

Linked Administrative Data (GN 33396):
A number of linked administrative datasets are available, under more restrictive access conditions, which can be linked to the NCDS EUL and SL access studies. These include a Deaths dataset (SN 7717) available under SL and the Linked Health Administrative Datasets (SN 8697) available under Secure Access.

Additional Sub-Studies (GN 33562):
In addition to the main NCDS sweeps, further studies have also been conducted on a range of subjects such as parent migration, unemployment, behavioural studies and respondent essays. The full list of NCDS studies available from the UK Data Service can be found on the NCDS series access data webpage.

How to access genetic and/or bio-medical sample data from a range of longitudinal surveys:
For information on how to access biomedical data from NCDS that are not held at the UKDS, see the CLS Genetic data and biological samples webpage.

Further information about the full NCDS series can be found on the Centre for Longitudinal Studies website.

The National Child Development Deaths Dataset, 1958-2014: Special Licence Access contains data on known deaths among members of...

Number and percentage of live births, by month of birth, 1991 to most recent year.

HELP International have been able to raise around $ 10 million. Now the NGO needs to decide how to allocate this money strategically and effectively. Hence, your Job as a Data scientist is to categorize the countries using some factors to suggest the countries that NGOs need to focus on the most.

Id | Features | Description --|:---------|:----------- 1|**Country:** | Name of the country
2|**Child_Mort:** | Death of children under 5 years of age per 1000 live births 3|**Exports:** | Exports of goods and services per capita. Given as %age of the GDP per capita 4|**Health:** | Total health spending per capita. Given as %age of GDP per capita 5|**Imports:** |Imports of goods and services per capita. Given as %age of the GDP per capita 6|**Income:** | Net annual income per person 7|**Inflation:** | The measurement of the annual growth rate of the Total GDP 8|**Life_Expec:** | The average number of years a new born child would live if the current mortality patterns are to remain the same 9|**Total_Fer:** | The number of children that would be born to each woman if the current age-fertility rates remain the same 10|**GDPP:** | The GDP per capita. Calculated as the Total GDP divided by the total population.

Estimated number of persons on July 1, by 5-year age groups and gender, and median age, for Canada, provinces and territories.

This statistical release makes available the most recent monthly data on NHS-funded maternity services in England, using data submitted to the Maternity Services Data Set (MSDS). This is the latest report from the newest version of the data set, MSDS.v.2, which has been in place since April 2019. The new data set was a significant change which added support for key policy initiatives such as continuity of carer, as well as increased flexibility through the introduction of new clinical coding. This was a major change, so data quality and coverage initially reduced from the levels seen in earlier publications. MSDS.v.2 data completeness improved over time, and we are looking at ways of supporting further improvements. This publication also includes the National Maternity Dashboard, which can be accessed via the link below. Data derived from SNOMED codes is used in some measures such as those for birthweight, and others will follow in later publications. SNOMED data is also included in some of the published Clinical Quality Improvement Metrics (CQIMs), where rules have been applied to ensure measure rates are calculated only where data quality is high enough. System suppliers are at different stages of development and delivery to trusts. In some cases, this has limited the aspects of data that can be submitted in the MSDS. To help Trusts understand to what extent they met the Clinical Negligence Scheme for Trusts (CNST) Maternity Incentive Scheme (MIS) Data Quality Criteria for Safety Action 2 Year 6, we have been producing a CNST Scorecard Dashboard showing trust performance against this criteria. The final results for the CNST MIS Y6 SA2 assessment, using July 2024 data, are now available in this dashboard, and can be accessed via the link below. This dashboard also includes data for a few non-CNST MSDS data quality priorities and last month we introduced into the dashboard a new data quality measure on birth site code recording, in accordance with Maternity and Neonatal Programme priorities. This new measure will not be assessed as part of the Maternity Incentive Scheme. This month, a small improvement was made to how the CQIMReadmissions metric uses discharge date information and this has resulted in a small change in the data output. As a result, the published CQIMReadmissions figures from this month's publication onwards are not fully comparable to the figures from earlier months. Last month, MSDS metrics published to support Saving Babies Lives Care Bundle (SBLCB) monitoring were updated to align with the contents of SBLCB version 3. As a result some SBLCB version 2 metrics have been removed from the Measures file and others have been renamed to align with SBLCB version 3 naming conventions. More information about the CQIMReadmissions change and the MSDS metrics published to support SBLCB are available in the accompanying Metadata file. The percentages presented in this report are based on rounded figures and therefore may not total to 100%.

About this dataset

Context

The dataset tabulates the United States population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for United States. The dataset can be utilized to understand the population distribution of United States by age. For example, using this dataset, we can identify the largest age group in United States.

Key observations

The largest age group in United States was for the group of age 25-29 years with a population of 22,854,328 (6.93%), according to the 2021 American Community Survey. At the same time, the smallest age group in United States was the 80-84 years with a population of 5,932,196 (1.80%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the United States is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of United States total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for United States Population by Age. You can refer the same here

This is a publication on maternity activity in English NHS hospitals. This report examines data relating to delivery and birth episodes in 2023-24, and the booking appointments for these deliveries. This annual publication covers the financial year ending March 2024. Data is included from both the Hospital Episodes Statistics (HES) data warehouse and the Maternity Services Data Set (MSDS). HES contains records of all admissions, appointments and attendances for patients admitted to NHS hospitals in England. The HES data used in this publication are called 'delivery episodes'. The MSDS collects records of each stage of the maternity service care pathway in NHS-funded maternity services, and includes information not recorded in HES. The MSDS is a maturing, national-level dataset. In April 2019, the MSDS transitioned to a new version of the dataset. This version, MSDS v2.0, is an update that introduced a new structure and content - including clinical terminology, in order to meet current clinical practice and incorporate new requirements. It is designed to meet requirements that resulted from the National Maternity Review, which led to the publication of the Better Births report in February 2016. This is the fifth publication of data from MSDS v2.0 and data from 2019-20 onwards is not directly comparable to data from previous years. This publication shows the number of HES delivery episodes during the period, with a number of breakdowns including by method of onset of labour, delivery method and place of delivery. It also shows the number of MSDS deliveries recorded during the period, with a breakdown for the mother's smoking status at the booking appointment by age group. It also provides counts of live born term babies with breakdowns for the general condition of newborns (via Apgar scores), skin-to-skin contact and baby's first feed type - all immediately after birth. There is also data available in a separate file on breastfeeding at 6 to 8 weeks. For the first time information on 'Smoking at Time of Delivery' has been presented using annual data from the MSDS. This includes national data broken down by maternal age, ethnicity and deprivation. From 2025/2026, MSDS will become the official source of 'Smoking at Time of Delivery' information and will replace the historic 'Smoking at Time of Delivery' data which is to become retired. We are currently undergoing dual collection and reporting on a quarterly basis for 2024/25 to help users compare information from the two sources. We are working with data submitters to help reconcile any discrepancies at a local level before any close down activities begin. A link to the dual reporting in the SATOD publication series can be found in the links below. Information on how all measures are constructed can be found in the HES Metadata and MSDS Metadata files provided below. In this publication we have also included an interactive Power BI dashboard to enable users to explore key NHS Maternity Statistics measures. The purpose of this publication is to inform and support strategic and policy-led processes for the benefit of patient care. This report will also be of interest to researchers, journalists and members of the public interested in NHS hospital activity in England. Any feedback on this publication or dashboard can be provided to enquiries@nhsdigital.nhs.uk, under the subject “NHS Maternity Statistics”.

Birthday Paradox Visitor Data

Exploring Probability and Patterns of Day of the Week Birthdays

By data.world's Admin [source]

About this dataset

This dataset contains daily visitor-submitted birthdays and associated data from an ongoing experimentation known as the Birthday Paradox. Be enlightened as you learn how many people have chosen the same day of their birthday as yours. Get a better perspective on how this phenomenon varies day-to-day, including recent submissions within the last 24 hours. This experiment is published under the MIT License, giving you access to detailed information behind this perplexing cognitive illusion. Find out now why the probability of two people in the same room having birthday matches is much higher than one might expect!

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How to use the dataset

This dataset provides data on the Birthday Paradox Visitor Experiments. It contains information such as daily visitor-submitted birthdays, the total number of visitors who have submitted birthdays, the total number of visitors who guessed the same day as their birthday, and more. This dataset can be used to analyze patterns in visitor behavior related to the Birthday Paradox Experiment.

In order to use this dataset effectively and efficiently, it is important to understand its fields and variables:
- Updated: The date when this data was last updated
- Count: The total number of visitors who have submitted birthdays
- Recent: The number of visitors who have submitted birthdays in the last 24 hours
- binnedDay: The day of the week for a given visitor's birthday submission
- binnedGuess: The day of week that a given visitor guessed their birthday would fall on 6) Tally: Total number of visitors who guessed same day as their birthday 7) binnedTally: Total number of visitors grouped by guess day

To begin using this dataset you should first filter your data based on desired criteria such as date range or binnedDay. For instance, if you are interested in analyzing Birthady Paradox Experiment results for Monday submissions only then you can filter your data by binnedDay = 'Monday'. Then further analyze your filtered query by examining other fields such as binnedGuess and comparing it with tally or binnedTally results accordingly. For example if we look at Monday entries above we should compare 'Monday' tallies with 'Tuesday' guesses (or any other weekday). ` Furthermore understanding updates from recent field can also provide interesting insights into user behavior related to Birthady Paradox Experiment -- trackingt recent entries may yield valuable trends over time.

By exploring various combinations offields available in this dataset users will be ableto gain a better understandingof how user behaviordiffers across different daysofweek both within a singledayandover periodsoftimeaccordingtodifferent criteria providedbythisdataset

Research Ideas

• Analyzing the likelihood of whether a person will guess their own birthday correctly.
• Estimating which day of the week is seeing the most number of visitors submitting their birthdays each day and analyzing how this varies over time.
• Investigating how likely it is for two people from different regions to have the same birthday by comparing their respective submission rates on each day of the week

Acknowledgements

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

License

See the dataset description for more information.

Columns

File: data.csv | Column name | Description | |:----------------|:-----------------------------------------------------------------------------------| | updated | The date and time the data was last updated. (DateTime) | | count | The total number of visitor submissions. (Integer) | | recent | The number of visitor submissions in the last 24 hours. (Integer) | | binnedDay | The day of the week the visitor submitted their birthday. (String) | | binnedGuess | The day of the week the visitor guessed their birthday. (String) | | tally | The total number of visitor guesses that matched their actual birthdays. (Integer) | | binnedTally | The day of the week the visitor guessed their birthday correctly. (String) |

Acknowledgement...

The National Child Development Study (NCDS) is a continuing longitudinal study that seeks to follow the lives of all those living in Great Britain who were born in one particular week in 1958. The aim of the study is to improve understanding of the factors affecting human development over the whole lifespan.

The NCDS has its origins in the Perinatal Mortality Survey (PMS) (the original PMS study is held at the UK Data Archive under SN 2137). This study was sponsored by the National Birthday Trust Fund and designed to examine the social and obstetric factors associated with stillbirth and death in early infancy among the 17,000 children born in England, Scotland and Wales in that one week. Selected data from the PMS form NCDS sweep 0, held alongside NCDS sweeps 1-3, under SN 5565.

Survey and Biomeasures Data (GN 33004):
To date there have been ten attempts to trace all members of the birth cohort in order to monitor their physical, educational and social development. The first three sweeps were carried out by the National Children's Bureau, in 1965, when respondents were aged 7, in 1969, aged 11, and in 1974, aged 16 (these sweeps form NCDS1-3, held together with NCDS0 under SN 5565). The fourth sweep, also carried out by the National Children's Bureau, was conducted in 1981, when respondents were aged 23 (held under SN 5566). In 1985 the NCDS moved to the Social Statistics Research Unit (SSRU) - now known as the Centre for Longitudinal Studies (CLS). The fifth sweep was carried out in 1991, when respondents were aged 33 (held under SN 5567). For the sixth sweep, conducted in 1999-2000, when respondents were aged 42 (NCDS6, held under SN 5578), fieldwork was combined with the 1999-2000 wave of the 1970 Birth Cohort Study (BCS70), which was also conducted by CLS (and held under GN 33229). The seventh sweep was conducted in 2004-2005 when the respondents were aged 46 (held under SN 5579), the eighth sweep was conducted in 2008-2009 when respondents were aged 50 (held under SN 6137), the ninth sweep was conducted in 2013 when respondents were aged 55 (held under SN 7669), and the tenth sweep was conducted in 2020-24 when the respondents were aged 60-64 (held under SN 9412).

A Secure Access version of the NCDS is available under SN 9413, containing detailed sensitive variables not available under Safeguarded access (currently only sweep 10 data). Variables include uncommon health conditions (including age at diagnosis), full employment codes and income/finance details, and specific life circumstances (e.g. pregnancy details, year/age of emigration from GB).

Four separate datasets covering responses to NCDS over all sweeps are available. National Child Development Deaths Dataset: Special Licence Access (SN 7717) covers deaths; National Child Development Study Response and Outcomes Dataset (SN 5560) covers all other responses and outcomes; National Child Development Study: Partnership Histories (SN 6940) includes data on live-in relationships; and National Child Development Study: Activity Histories (SN 6942) covers work and non-work activities. Users are advised to order these studies alongside the other waves of NCDS.

From 2002-2004, a Biomedical Survey was completed and is available under Safeguarded Licence (SN 8731) and Special Licence (SL) (SN 5594). Proteomics analyses of blood samples are available under SL SN 9254.

Linked Geographical Data (GN 33497):
A number of geographical variables are available, under more restrictive access conditions, which can be linked to the NCDS EUL and SL access studies.

Linked Administrative Data (GN 33396):
A number of linked administrative datasets are available, under more restrictive access conditions, which can be linked to the NCDS EUL and SL access studies. These include a Deaths dataset (SN 7717) available under SL and the Linked Health Administrative Datasets (SN 8697) available under Secure Access.

Multi-omics Data and Risk Scores Data (GN 33592)
Proteomics analyses were run on the blood samples collected from NCDS participants in 2002-2004 and are available under SL SN 9254. Metabolomics analyses were conducted on respondents of sweep 10 and are available under SL SN 9411. Polygenic indices are available under SL SN 9439. Derived summary scores have been created that combine the estimated effects of many different genes on a specific trait or characteristic, such as a person's risk of Alzheimer's disease, asthma, substance abuse, or mental health disorders, for example. These scores can be combined with existing survey data to offer a more nuanced understanding of how cohort members' outcomes may be shaped.

Additional Sub-Studies (GN 33562):
In addition to the main NCDS sweeps, further studies have also been conducted on a range of subjects such as parent migration, unemployment, behavioural studies and respondent essays. The full list of NCDS studies available from the UK Data Service can be found on the NCDS series access data webpage.

How to access genetic and/or bio-medical sample data from a range of longitudinal surveys:
For information on how to access biomedical data from NCDS that are not held at the UKDS, see the CLS Genetic data and biological samples webpage.

Further information about the full NCDS series can be found on the Centre for Longitudinal Studies website.

SN 9412 - National Child Development Study: Age 62, Sweep 10, 2019-2024
The NCDS Age 62 Survey, (or 'Life in Your Early 60s' Survey as known to study members) was conducted between 2019 and 2024 when participants were aged 61-65 years. This sweep was designed and managed by the Centre for Longitudinal Studies (CLS) at the UCL Social Research Institute. Interviewer fieldwork was conducted by NatCen and Verian (formerly Kantar). Health visits were conducted by NatCen and INUVI. The Age 62 Survey involved an interview, a health visit, two paper self-completion questionnaires and an online dietary questionnaire.

The broad aim of the Age 62 Survey was to collect information which would aid the understanding of the lifelong factors affecting retirement and ageing. This survey also had a biomedical focus with physical measurements and assessments being conducted for the first time since the Age 44 biomedical sweep. The data collection built on the extensive data collected previously from birth and across the lifetime of study members and will facilitate comparisons with other generations as they reach the same life stage, allowing for study of social change.

The study was initially planned and designed to be conducted in-person. Fieldwork commenced in January 2020 but was subsequently paused in March 2020 due to the COVID-19 pandemic. As in-person interviewing was not feasible until early 2022, the protocol was adapted so that interviews could be conducted by video-call. Interviewer fieldwork restarted by video call in spring 2021 until April 2022 when it was feasible to return to in-person interviewing. The video mode option continued to be available if requested by a cohort member or was required due to interviewer capacity issues in a particular area.

Once mainstage fieldwork was complete, those who had not participated were invited to complete a short version of the questionnaire via web (known as the ‘mop-up’ survey). Cohort members who completed the survey between January-March 2020, were also invited to take part in the mop-up survey in order establish how their circumstances might have changed since the pandemic. Emigrants were not invited to take part in the main survey but were invited to take part in this short web-survey.

A full account of the survey development and fieldwork procedures can be found in the National Child Development Study technical report and appendices produced by NatCen Social Research, which accompanies this data.

Latest edition information
For the second edition (October 2025), the Biomeasures dataset has been updated. Variables related to weight, waist and hip measurements have been added and some of the grip strength variables have been updated.

The National Child Development Study (NCDS) is a continuing longitudinal study that seeks to follow the lives of all those living in Great Britain who were born in one particular week in 1958. The aim of the study is to improve understanding of the factors affecting human development over the whole lifespan.

The NCDS has its origins in the Perinatal Mortality Survey (PMS) (the original PMS study is held at the UK Data Archive under SN 2137). This study was sponsored by the National Birthday Trust Fund and designed to examine the social and obstetric factors associated with stillbirth and death in early infancy among the 17,000 children born in England, Scotland and Wales in that one week. Selected data from the PMS form NCDS sweep 0, held alongside NCDS sweeps 1-3, under SN 5565.

Survey and Biomeasures Data (GN 33004):
To date there have been ten attempts to trace all members of the birth cohort in order to monitor their physical, educational and social development. The first three sweeps were carried out by the National Children's Bureau, in 1965, when respondents were aged 7, in 1969, aged 11, and in 1974, aged 16 (these sweeps form NCDS1-3, held together with NCDS0 under SN 5565). The fourth sweep, also carried out by the National Children's Bureau, was conducted in 1981, when respondents were aged 23 (held under SN 5566). In 1985 the NCDS moved to the Social Statistics Research Unit (SSRU) - now known as the Centre for Longitudinal Studies (CLS). The fifth sweep was carried out in 1991, when respondents were aged 33 (held under SN 5567). For the sixth sweep, conducted in 1999-2000, when respondents were aged 42 (NCDS6, held under SN 5578), fieldwork was combined with the 1999-2000 wave of the 1970 Birth Cohort Study (BCS70), which was also conducted by CLS (and held under GN 33229). The seventh sweep was conducted in 2004-2005 when the respondents were aged 46 (held under SN 5579), the eighth sweep was conducted in 2008-2009 when respondents were aged 50 (held under SN 6137), the ninth sweep was conducted in 2013 when respondents were aged 55 (held under SN 7669), and the tenth sweep was conducted in 2020-24 when the respondents were aged 60-64 (held under SN 9412).

A Secure Access version of the NCDS is available under SN 9413, containing detailed sensitive variables not available under Safeguarded access (currently only sweep 10 data). Variables include uncommon health conditions (including age at diagnosis), full employment codes and income/finance details, and specific life circumstances (e.g. pregnancy details, year/age of emigration from GB).

Four separate datasets covering responses to NCDS over all sweeps are available. National Child Development Deaths Dataset: Special Licence Access (SN 7717) covers deaths; National Child Development Study Response and Outcomes Dataset (SN 5560) covers all other responses and outcomes; National Child Development Study: Partnership Histories (SN 6940) includes data on live-in relationships; and National Child Development Study: Activity Histories (SN 6942) covers work and non-work activities. Users are advised to order these studies alongside the other waves of NCDS.

From 2002-2004, a Biomedical Survey was completed and is available under Safeguarded Licence (SN 8731) and Special Licence (SL) (SN 5594). Proteomics analyses of blood samples are available under SL SN 9254.

Linked Geographical Data (GN 33497):
A number of geographical variables are available, under more restrictive access conditions, which can be linked to the NCDS EUL and SL access studies.

Linked Administrative Data (GN 33396):
A number of linked administrative datasets are available, under more restrictive access conditions, which can be linked to the NCDS EUL and SL access studies. These include a Deaths dataset (SN 7717) available under SL and the Linked Health Administrative Datasets (SN 8697) available under Secure Access.

Multi-omics Data and Risk Scores Data (GN 33592)
Proteomics analyses were run on the blood samples collected from NCDS participants in 2002-2004 and are available under SL SN 9254. Metabolomics analyses were conducted on respondents of sweep 10 and are available under SL SN 9411. Polygenic indices are available under SL SN 9439. Derived summary scores have been created that combine the estimated effects of many different genes on a specific trait or characteristic, such as a person's risk of Alzheimer's disease, asthma, substance abuse, or mental health disorders, for example. These scores can be combined with existing survey data to offer a more nuanced understanding of how cohort members' outcomes may be shaped.

Additional Sub-Studies (GN 33562):
In addition to the main NCDS sweeps, further studies have also been conducted on a range of subjects such as parent migration, unemployment, behavioural studies and respondent essays. The full list of NCDS studies available from the UK Data Service can be found on the NCDS series access data webpage.

How to access genetic and/or bio-medical sample data from a range of longitudinal surveys:
For information on how to access biomedical data from NCDS that are not held at the UKDS, see the CLS Genetic data and biological samples webpage.

Further information about the full NCDS series can be found on the Centre for Longitudinal Studies website.

The National Child Development Study: Linked Health Administrative Datasets (Hospital Episode Statistics), England, 1997-2023: Secure Access includes data files from the NHS Digital HES database for those cohort members who provided consent to health data linkage in the Age 50 sweep. The HES database contains information about all hospital admissions in England. The following linked HES data are available:

1) Accident and Emergency (A&E)
The A&E dataset details each attendance to an Accident and Emergency care facility in England, between 01-04-2007 and 31-03-2020 (inclusive). It includes major A&E departments, single speciality A&E departments, minor injury units and walk-in centres in England.

2) Admitted Patient Care (APC)
The APC data summarises episodes of care for admitted patients, where the episode occurred between 01-04-1997 and 31-03-2023 (inclusive).

3) Critical Care (CC)
The CC dataset covers records of critical care activity between 01-04-2009 and 31-03-2023 (inclusive).

4) Out Patient (OP)
The OP dataset lists the outpatient appointments between 01-04-2003 and 31-03-2023 (inclusive).

5) Emergency Care Dataset (ECDS)
The ECDS lists the emergency care appointments between 01-04-2020 and 31-03-2023 (inclusive).

6) Consent data
The consents dataset describes consent to linkage, and is current at the time of deposit.

CLS/ NHS Digital Sub-licence agreement
NHS Digital has given CLS permission for onward sharing of the NCDS/HES dataset via the UKDS Secure Lab. In order to ensure data minimisation, NHS Digital requires that researchers only access the HES variables needed for their approved research project. Therefore, the HES linked data provided by the UKDS to approved researchers will be subject to sub-setting of variables. The researcher will need to request a specific sub-set of variables from the NCDS/HES data dictionary, which will subsequently be made available within their UKDS Secure Account. Once the researcher has finished their research, the UKDS will delete the tailored dataset for that specific project. Any party wishing to access the data deposited at the UK Data Service will be required to enter into a Licence agreement with CLS (UCL), in addition to the agreements signed with the UKDS, provided in the application pack.

CLS Hospital Episode Statistics data access update July 2025

From March 2027, HES data linked to all four CLS studies will no longer be available via the UK Data Service. For projects ending before March 2027, uses should continue to apply via UKDS. However, if access to a wider range of linked Longitudinal Population Studies data is needed, UKLLC might be more suitable. For projects ending after March 2027, users must apply via UKLLC.

Latest edition information
For the third

This dataset of U.S. mortality trends since 1900 highlights the differences in age-adjusted death rates and life expectancy at birth by race and sex. 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). Life expectancy data are available up to 2017. Due to changes in categories of race used in publications, data are not available for the black population consistently before 1968, and not at all before 1960. More information on historical data on age-adjusted death rates is available at https://www.cdc.gov/nchs/nvss/mortality/hist293.htm. 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.

UK residents by broad country of birth and citizenship groups, broken down by UK country, local authority, unitary authority, metropolitan and London boroughs, and counties. Estimates from the Annual Population Survey.

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.

Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_6150f21b0892b3fdde546d2a1af2af82/view

This is a publication on maternity activity in English NHS hospitals. This report examines data relating to delivery and birth episodes in 2022-23, and the booking appointments for these deliveries. This annual publication covers the financial year ending March 2023. Data is included from both the Hospital Episodes Statistics (HES) data warehouse and the Maternity Services Data Set (MSDS). HES contains records of all admissions, appointments and attendances for patients admitted to NHS hospitals in England. The HES data used in this publication are called 'delivery episodes'. The MSDS collects records of each stage of the maternity service care pathway in NHS-funded maternity services, and includes information not recorded in HES. The MSDS is a maturing, national-level dataset. In April 2019 the MSDS transitioned to a new version of the dataset. This version, MSDS v2.0, is an update that introduced a new structure and content - including clinical terminology, in order to meet current clinical practice and incorporate new requirements. It is designed to meet requirements that resulted from the National Maternity Review, which led to the publication of the Better Births report in February 2016. This is the fourth publication of data from MSDS v2.0 and data from 2019-20 onwards is not directly comparable to data from previous years. This publication shows the number of HES delivery episodes during the period, with a number of breakdowns including by method of onset of labour, delivery method and place of delivery. It also shows the number of MSDS deliveries recorded during the period, with breakdowns including the baby's first feed type, birthweight, place of birth, and breastfeeding activity; and the mothers' ethnicity and age at booking. There is also data available in a separate file on breastfeeding at 6 to 8 weeks. The count of Total Babies includes both live and still births, and previous changes to how Total Babies and Total Deliveries were calculated means that comparisons between 2019-20 MSDS data and later years should be made with care. Information on how all measures are constructed can be found in the HES Metadata and MSDS Metadata files provided below. In this publication we have also included an interactive Power BI dashboard to enable users to explore key NHS Maternity Statistics measures. The purpose of this publication is to inform and support strategic and policy-led processes for the benefit of patient care. This report will also be of interest to researchers, journalists and members of the public interested in NHS hospital activity in England. Any feedback on this publication or dashboard can be provided to enquiries@nhsdigital.nhs.uk, under the subject “NHS Maternity Statistics”.

Abstract

During October 1996 Statistics South Africa recorded the details of people living in more than nine million households in South Africa, as well as those in hostels, hotels and prisons. Census 1996 was the first nation wide census since the splitting up of the country under apartheid after 1970 and sought to apply the same methodology to everyone: visiting the household, and obtaining details about all its members from a representative who was either interviewed, or else filled in the questionnaire in their language of choice.

Geographic coverage

The survey had national coverage

Analysis unit

Households and individuals

Universe

The survey covered households and household members in households in the nine provinces of South Africa.

Kind of data

Sample survey data

Sampling procedure

A sample of 1600 Enumerator Areas (EA's) was produced in conjunction with the sample for the 1996 Population Census post-enumeration survey. A two stage sampling procedure was applied in the following manner.

The first stratification was done by province, as well as by type of EA (formal or informal urban areas, commercial farms, traditional authority areas or other non-urban areas). Originally eight hundred EA's were allocated to each strata by province proportionately. Later some adjustments were made to ensure adequate representation of smaller provinces such as the Northern Cape. Independent systematic samples of EA's were drawn for each stratum within each province. The sampling frame that was used was constructed from the preliminary database of EA's which was established during the demarcation and listing phase of the 1996 population census. In the second phase 10 households were drawn from each EA on the western and eastern side of the EA drawn for the post enumeration survey. This meant 10 households per EA in 1600 different EA's, that is 16 000 households in total.

Mode of data collection

Face-to-face [f2f]

Research instrument

The data files in the October Household Survey 1996 (OHS 1996) correspond to the following sections in the questionnaire:

House: Data from FLAP, Section 1 and Section 7 Person: Data from Section 2 Worker: Data from Section 3 Migrant: Data from Section 4 Death: Data from Section 5 Births: Data from Section 6 - This data had a considerable number of problems and will not be published. Income: Data from Section 7 (included in House) Domestic: Data from Section 8

Data appraisal

Questionnaire: The October Household Survey 1996 questionnaire had incorrect FLAP data. No Population Group question was indicated on the FLAP. DataFirst notified Statistics SA who supplied a corrected questionnaire which is the one now available with the dataset.

Household IDs: In the previous version of the 1996 October Household Survey dataset archived by DataFirst the HHID were not unique. This was corrected in the first version disseminated by DataFirst, version 1. Version 1.1 keeps this correction, but data users should check versions not obtained from DataFirst and replace these with the latest version available from DataFirst.

Linking Files: The Metadata for the OHS 1996 provides an explanation for merging the files in the files in the OHS 1996 dataset: "The data from different files can be linked on the basis of the record identifiers. The record identifiers are composed of the first few fields in each file. Each record contains the three fields Magisterial District, Enumeration area, and Visiting point number. These eleven digits together constitute a unique household identifier. All records with a given household identifier, no matter which file they are in, belong to the same household. For individuals, a further two digits constituting the Person number, when added to the household identifier, creates a unique individual identifier. Again, these can be used to link records from the PERSON and WORK files. The syntax needed to merge information from different files will differ according to the statistical package used (October Household Survey 1996: Metadata: General Notes: 2).” According to the above, to generate household IDs it is necessary to use a combination of magisterial district number (mdnumber), enumeration area number (eanumber) and visiting point number (vpnumber). To generate person IDs it is necessary to use the above with the person number (personnu).

These variables are named as such in the OHS 1996 House, OHS 1996 Births, OHS 1996 Migrant, OHS 1996 Deaths, OHS 1996 Household Income Other, OHS 1996 Other, OHS 1996 Domestic and OHS 1996 Flap data files. However, in the OHS 1996 Worker and OHS 1996 Person data files the variable for magisterial district number is “distr”, the variable for Enumeration Area is “ea” and the variable for visiting point number is called "visp”. The variable for person number in these files is called “respno”.

The metadata provided to DataFirst with this dataset does not discuss these changes.

October Household Survey 1996 Births file: Births data was collected by Section 6 of the OHS 1996 questionnaire, completed for all women younger than 55 years who had ever given birth. The metadata for this survey from Statistics SA states that “This data had a considerable number of problems and will not be published” The dataset provided by DataFirst therefore does not include the original “births” file. Those in possession of this file from unofficial versions of the dataset should note the following problems with the data in the OHS 1996 births file:

Variable name: eegender Question 6.2: Is/was (the child) a boy or a girl? Valid range: 1 (boy) - 2 (girl) Data quality issue: There is a third response value of 0 with no description

Variable name: livinghh Question 6.4: If alive: Is (the child) currently living with this household? Valid range: 1 (yes) - 2 (no) Data quality issue: This variable has an additional response value (0), which has no description

Variable name: agealive Question 6.5: If alive: How old is he/she? This question was asked of all women younger than 55 years who have ever given birth to provide the age of their living children. Data quality issue: responses range from 0-77 for age of child (assuming age 99 is for missing responses) which is outside the plausible range.

Variable name: agenaliv Question 6.6: If dead: How old was (the child) when he/she died? Data quality issue: The format of the age at death variable is not clear

Variable name: datebirt Question 6.7: [All children]: In what year and month was (the child) born? Data quality issue: There are problems with the format of the date of birth variable

Variable name: wherebor Question 6.8: [All children]: Where was (the child) born? Data quality issue: There are only three options for the place of birth in the questionnaire (in a hospital, in a clinic and elsewhere), but the data has 10 response values (0-9) with no explanation for this in the metadata.

Variable name: regstere Question 6.9 [All children] Was the birth registered? Valid range: 1(yes) - 2 (no) Data quality issue: There are 4 response values (0-3) for this variable

US Presidents Dataset

US Presidents: Birth, Death, Terms, and Parties

By Throwback Thursday [source]

About this dataset

The dataset also contains essential personal information, including each president's date of birth and date of death. Additionally, it includes specific details about when each president took office and when they left office.

Furthermore, the dataset provides insight into where each president was born and where they ultimately passed away. This includes information on both the cities and states associated with their births and deaths.

With this extensive collection of data on US presidents throughout history, researchers can analyze trends related to education backgrounds, regional representation among presidents' origins and final resting places, as well as political party distributions throughout different eras in American history

How to use the dataset

• Number: The numerical order of the US Presidents

  • This column provides the sequential number assigned to each President. You can use this information to quickly identify specific presidents within the dataset.

• Colleges: The colleges or universities attended by the US Presidents

  • In this column, you can find details about which colleges or universities each President attended during their academic years.

• Birth City: The city where the US Presidents were born

  • This column lists the birth city of each President. It can be interesting to explore patterns or similarities between their places of birth.

• Birth State: The state where the US Presidents were born

  • Similar to Birth City, this column contains information about which state each President was born in.

• Birth Date: The date of birth for each President

  • Discovering famous birthdays has always been intriguing! Explore this column for insights into when these influential figures were born.

• Death City: The city where the US Presidents died

  • Uncover notable locations by exploring where each President passed away using this data column.

• Death State: The state where the US Presidents died

  • Just like Death City, you can gain insights into important locations associated with Presidential deaths through this data field.

• Death Date: The date of death for each President

  • Although it is a solemn topic, knowing when these historical figures passed away offers context within their lifetime.

• Left Office :The date when people left office

• Took Office:The date when US Presidents took office.

• Party: The political party affiliation of the US Presidents

  • Understanding the political party affiliations of each President can reveal interesting trends, patterns, and shifts in party dominance over time.

By utilizing this dataset and interpreting these columns, you can gain valuable insights into the lives and backgrounds of the US Presidents. Additionally, this information also allows for comparisons between presidents based on various factors such as birthplace or educational background.

Feel free to leverage visualizations, statistical analyses or create your research questions to dive deeper into this data!

Remember that using dates from different columns together will help you organize and analyze the

Research Ideas

• analyzing the relationship between the colleges attended by US Presidents and their political affiliations
• studying the impact of geographical factors, such as birth cities and states, on presidential careers or political ideologies
• examining trends in terms served and the length of time between taking office and leaving office for different political parties

Acknowledgements

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

License

See the dataset description for more information.

Columns

File: ThrowbackDataThursday Week 8 - US Presidents.csv | Column name | Description | |:----------------|:--------------------------------------------------------------------------------------| | Number | The numerical order of each US President. (Numeric) | | Colleges | Information about the colleges or universities attended by each President. (Text) | | Birth City | The city where each President was born. (Text) | | Birth State | The state where each President was born. (Text) | | ...

Abstract

Changes in socio-economic status and lifestyle behaviors among the adult population in South Africa not only in urban areas but also in rural South Africa, have led to increased prevalence of chronic diseases and associated risk factors, together with an epidemic of some infectious diseases. Researchers at the University of the North (now University of Limpopo Turfloop campus) established The Dikgale centre for Health and Demographic surveillance system in 1996 funded by a core grant from NUFU, Norway.

The broad aim of the Dikgale HDSS is to provide information to improve health of the people in Limpopo province and to assist the local government in making effective health care policy. As few data are available on the prevalence of diseases in rural and peri-urban areas of Limpopo province, the initial objective of the HDSS was to establish a field site where the incidence and prevalence of diseases could be assessed. It was therefore necessary to collect longitudinal demographic data (e.g. mortality, fertility, migration) on the population. To this end, three primary subjects are observed longitudinally in Dikgale HDSS: physical structures (e.g. homesteads, clinics and schools), households and individuals. The information about these subjects, and all related information, was at first stored in Access and later in a single MSSQL Server database, in a truly longitudinal way-i.e. not as a series of cross-sections.

The surveillance area is located in the Capricorn district, Limpopo province approximately 40 km from Polokwane, the capital city of Limpopo province and 15-50 km from the University of Limpopo (Turfloop campus). The site covers an area of approximately 200 square kilometers. The initially the total population was 8000 but the field site was expanded in 2010 and now includes approximately 36,000 people who are members of approximately 7000 households. The households are present in 15 villages of varying sizes. The population is predominantly Northern Sotho speaking. All households have electricity. Some households have piped water either inside the house or in their yards, but most fetch water from taps situated at strategic points in the villages. Most households have a pit latrine in their yards. A large proportion of adults are migrant workers, while others work as farm laborers on neighboring farms, or as domestic workers in nearby towns. Many are pensioners. The unemployment rate in the area is high.

To fulfil the eligibility criteria for the Dikgale HDSS cohort, individuals must be a member of a household within the surveillance area but not necessarily resident within it. Crucially, this means that Dikgale HDSS collects information on resident and non-resident members of households and makes a distinction between membership (self-defined on the basis of links to other household members) and residency (residing at a physical structure within the surveillance area at a particular point in time). Individuals can be members of more than one household at any point in time (e.g. polygamous married men whose wives maintain separate households). To be consistent with similar datasets from other INDEPTH Member centres, this data set contains data from resident members only.

During data collection, households are visited by fieldworkers and information supplied by a single key informant. All births, deaths and migrations of household members are recorded. If household members have moved internally within the surveillance area, such moves are reconciled and the internal migrant retains the original identifier associated with him/her.

Geographic coverage

Demographic surveillance area situated in Capricorn District 40 km north- east of Polokwane the capital city of Limpopo province, and 20-30 km from the University of Limpopo. The area is approximately 310 square kilometers.

Analysis unit

Individual

Universe

• Resident household members of households within the demographic surveillance area.

Immigrants are defined by intention to become resident, but actual residence episodes of less than 180 days are censored. Outmigrants are defined by intention to become resident elsewhere, but actual periods of non-residence less than 180 days are censored. Children born to resident women are considered resident by default, irrespective of actual place of birth. The dataset contains the events of all individuals ever resident during the study period (only covering the original field site of 8000 people (1 January,1996 to 31 December 2013).

Kind of data

Event history data

Frequency of data collection

Once a year

Sampling procedure

This dataset is not based on a sample, it contains information from the complete demographic surveillance area.

Sampling deviation

Not applicable.

Mode of data collection

Proxy Respondent [proxy]

Research instrument

1) Bounded structure registration (BSR) or update (BSU) form - Used to register characteristics of the BS - Updates characteristics of the BS - Information as at previous round is preprinted

2) Household registration (HHR) or update (HHU) form - Used to register characteristics of the HH - Used to update information about the composition of the household - Information pre-printed of composition and all registered households as at previous

3) Household Membership Registration (HMR) or update (HMU) - Used to link individuals to households - Information preprinted of member status observations as at previous

4) Individual registration form (IDR) - Used to uniquely identify each individual - Mainly to ensure members with multiple household memberships are appropriately captured

5) Migration notification form (MGN) - Used to record change in the BS of residency of individuals or households - Migrants are tracked and updated in the database

6) Pregnancy history form (PGH) & pregnancy outcome notification form (PON) - Records details of pregnancies and their outcomes - Only if woman is a new member - Only if woman has never completed WHL or WGH

7) Death notification form (DTN) - Records all deaths that have recently occurred - Includes information about time, place, circumstances and possible cause of death

Cleaning operations

On data entry data consistency and plausibility were checked by 455 data validation rules at database level. If data validation failure was due to a data collection error, the questionnaire was referred back to the field for revisit and correction.

No imputations were done on the resulting micro data set, except for:

a. If an out-migration (OMG) event is followed by a homestead entry event (ENT) and the gap between OMG event and ENT event is greater than 180 days, the ENT event was changed to an in-migration event (IMG).

b. If an out-migration (OMG) event is followed by a homestead entry event (ENT) and the gap between OMG event and ENT event is less than 180 days, the OMG event was changed to an homestead exit event (EXT) and the ENT event date changed to the day following the original OMG event.

c. If a homestead exit event (EXT) is followed by an in-migration event (IMG) and the gap between the EXT event and the IMG event is greater than 180 days, the EXT event was changed to an out-migration event (OMG).

d. If a homestead exit event (EXT) is followed by an in-migration event (IMG) and the gap between the EXT event and the IMG event is less than 180 days, the IMG event was changed to an homestead entry event (ENT) with a date equal to the day following the EXT event.

e. If the last recorded event for an individual is homestead exit (EXT) and this event is more than 180 days prior to the end of the surveillance period, then the EXT event is changed to an out-migration event (OMG).

Response rate

On an average, it is 99% over the years in all rounds

Sampling error estimates

Not Applicable.

Data appraisal

CentreId MetricTable QMetric Illegal Legal Total Metric RunDate ZA021 MicroDataCleaned Starts 52379 2017-05-20 14:35
ZA021 MicroDataCleaned Transitions 0 110666 110666 0 2017-05-20 14:35
ZA021 MicroDataCleaned Ends 52379 2017-05-20 14:35
ZA021 MicroDataCleaned SexValues 6 110660 110666 0 2017-05-20 14:35
ZA021 MicroDataCleaned DoBValues 12 110654 110666 0 2017-05-20 14:35

This map shows the access to mental health providers in every county and state in the United States according to the 2024 County Health Rankings & Roadmaps data for counties, states, and the nation. It translates the numbers to explain how many additional mental health providers are needed in each county and state. According to the data, in the United States overall there are 319 people per mental health provider in the U.S. The maps clearly illustrate that access to mental health providers varies widely across the country.The data comes from this County Health Rankings 2024 layer. An updated layer is usually published each year, which allows comparisons from year to year. This map contains layers for 2024 and also for 2022 as a comparison. County Health Rankings & Roadmaps (CHR&R), a program of the University of Wisconsin Population Health Institute with support provided by the Robert Wood Johnson Foundation, draws attention to why there are differences in health within and across communities by measuring the health of nearly all counties in the nation. This map's layers contain 2024 CHR&R data for nation, state, and county levels. The CHR&R Annual Data Release is compiled using county-level measures from a variety of national and state data sources. CHR&R provides a snapshot of the health of nearly every county in the nation. A wide range of factors influence how long and how well we live, including: opportunities for education, income, safe housing and the right to shape policies and practices that impact our lives and futures. Health Outcomes tell us how long people live on average within a community, and how people experience physical and mental health in a community. Health Factors represent the things we can improve to support longer and healthier lives. They are indicators of the future health of our communities. Some example measures are:Life ExpectancyAccess to Exercise OpportunitiesUninsuredFlu VaccinationsChildren in PovertySchool Funding AdequacySevere Housing Cost BurdenBroadband AccessTo see a full list of variables, definitions and descriptions, explore the Fields information by clicking the Data tab here in the Item Details of this layer. For full documentation, visit the Measures page on the CHR&R website. Notable changes in the 2024 CHR&R Annual Data Release:Measures of birth and death now provide more detailed race categories including a separate category for ‘Native Hawaiian or Other Pacific Islander’ and a ‘Two or more races’ category where possible. Find more information on the CHR&R website.Ranks are no longer calculated nor included in the dataset. CHR&R introduced a new graphic to the County Health Snapshots on their website that shows how a county fares relative to other counties in a state and nation. Data Processing:County Health Rankings data and metadata were prepared and formatted for Living Atlas use by the CHR&R team. 2021 U.S. boundaries are used in this dataset for a total of 3,143 counties. Analytic data files can be downloaded from the CHR&R website.