Crude birth rates, age-specific fertility rates and total fertility rates (live births), 2000 to most recent year.

The dataset contains the birth rates calculated with municipal detail for 1,000 residents in the period January - October of the year 2022. The source of the data is Istat - Demographic Report

This dataset contains California’s adolescent birth rate (ABR) by county, age group and race/ethnicity using aggregated years 2014-2016. The ABR is calculated as the number of live births to females aged 15-19 divided by the female population aged 15-19, multiplied by 1,000. Births to females under age 15 are uncommon and thus added to the numerator (total number of births aged 15-19) in calculating the ABR for aged 15-19. The categories by age group are aged 18-19 and aged 15-17; births occurring to females under aged 15 are added to the numerator for aged 15-17 in calculating the ABR for this age group. The race and ethnic groups in this table utilized five mutually exclusive race and ethnicity categories. These categories are Hispanic and the following Non-Hispanic categories of Multi-Race, Black, American Indian (includes Eskimo and Aleut), Asian and Pacific Islander (includes Hawaiian) combined, and White. Note that there are birth records with missing race/ethnicity or categorized as “Other” and not shown in the dataset but included in the ABR calculation overall.

Number of births per 1,000 women. Note:Teenage birth rates are calculated using the Australian female Estimated Resident Population (ERP) aged 15–19 as the denominator.

Check out the PhilaStats Vital Statistics Dashboard for the City of Philadelphia, for interactive maps and charts of vital statistics and trends in natality (births), mortality (deaths), and population for Philadelphia residents. See also the technical notes for the creation and visualization of Philadelphia's Vital Statistics. View metadata for key information about this dataset.Vital statistics are annually published calculations on birth and death records that facilitate the tracking of important health and population trends in Philadelphia over time. Public officials, researchers, and citizens alike may use vital statistics to plan for population shifts and healthcare needs, to perform research, and to stay informed and up-to-date on the natality and mortality trends in our City. The vital statistics dataset consists of natality and mortality data on Philadelphia City residents for each year of finalized data available, back to 2011 for births and 2012 for deaths. Citywide metrics and metrics by Philadelphia Planning District are provided for both natality and mortality metrics. A population estimates table is also provided, which includes the population counts used to calculate some metrics.The Vital Statistics - Natality dataset is also available in this citywide table.For questions about this dataset, contact epi@phila.gov. For technical assistance, email maps@phila.gov.

The Russian Fertility Database of the International Laboratory for Population and Health of HSE University contains fertility rates in Russia for the period from 1946 to 2022 and for women born in 1932-1988. The Russian Fertility Database is primarily oriented to the experts involved in demographic analysis. The data are presented in *.xlsx format.

All indicators presented in the database are calculated on the basis of population statistics data from the Federal State Statistics Service. Birth rates for 1946-1958 are calculated on the basis of the numbers of births by birth order and mother's age for 1946-1958 and population data for 1946-1958 presented in the book Andreev E.M., Darsky L.E., Kharkova T.L. (1998) Demographic History of Russia: 1927-1959. M.: Informatika. 187 p. Birth rates for 1959-2022 are calculated on the basis of the numbers of births by birth order and mother's age for 1959-2022 and data on the age distribution of the population for 1959-2023.

Annual average birth data by zip code from the Michigan Department of Community Health, Vital Statistics. Covers years 2009-11, 2010-12, and 2011-13. Birth rates were calculated by Kurt Metzger. Birth Rate = Number of births per 1,000 Women 15-44 years of age (2010 Census).

Analysis of ‘BirthData 2010 13 WayneZips’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/f71b8987-9136-4cc5-92a4-590f882f0df5 on 26 January 2022.

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

Annual average birth data by zip code from the Michigan Department of Community Health, Vital Statistics. Covers years 2009-11, 2010-12, and 2011-13. Birth rates were calculated by Kurt Metzger. Birth Rate = Number of births per 1,000 Women 15-44 years of age (2010 Census).

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

Live births by local authority of usual residence of mother, General Fertility Rates and Total Fertility Rates. The general fertility rate (GFR) is the number of live births per 1,000 women aged 15-44. Rates are based on the most up-to-date population estimates. The General Fertility Rate (GFR) is the number of live births per 1,000 women aged 15-44. The Total Fertility Rate (TFR) is the average number of live children that a group of women would bear if they experienced the age-specific fertility rates of the calendar year in question throughout their childbearing lifespan. The TFR has been calculated using the number of live births and the mid-year population estimates (sub-nationally) and the projections (nationally) for women by single year of age. This generally produces a better match of births to those at risk of having births. However, local authority level population estimates are only considered reliable in five-year age bands. Thus, especially in small local authorities, it should be noted that rates computed using single year of age data may produce spurious results. City of London has been grouped with Hackney after 2004. Read more on the ONS website ONS have also produces birth statistics for mid-year for small areas.

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.

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.

This dataset contains statistics about births and fertility rates for Australia, states and territories, and sub-state regions. It includes all births that occurred and were registered in Australia, including births to mothers whose place of usual residence was overseas. Estimated resident populations (ERPs) are used as denominators to calculate fertility rates and are based on the results of the 2016 Census. This dataset uses the ABS Statistical Area Level 2 (SA2) boundaries of the Australian Statistical Geography Standard (ASGS) 2016. For more information such as the scope, coverage and exclusions used in this dataset please visit the Australian Bureau of Statistics (ABS) methodology documentation. AURIN has spatially enabled the original data from the ABS with the 2016 SA2 boundaries.

This dataset presents the estimated number of live births in each country for the most recent reference year, based on the 2024 revision of the UN Population Division’s World Population Prospects. Live birth estimates are a key demographic indicator, used for planning health services, calculating health coverage indicators, and understanding population growth trends. These figures support maternal and newborn health monitoring and workforce planning at national and global levels.Data Source:UN Population Division World Population Prospects: https://population.un.org/wpp/Download/StandardData Dictionary: The data is collated with the following columns:Column headingContent of this columnPossible valuesRefNumerical counter for each row of data, for ease of identification1+CountryShort name for the country195 countries in total – all 194 WHO member states plus PalestineISO3Three-digit alphabetical codes International Standard ISO 3166-1 assigned by the International Organization for Standardization (ISO). e.g. AFG (Afghanistan)ISO22 letter identifier code for the countrye.g. AF (Afghanistan)ICM_regionICM Region for countryAFR (Africa), AMR (Americas), EMR (Eastern Mediterranean), EUR (Europe), SEAR (South east Asia) or WPR (Western Pacific)CodeUnique project code for each indicator:GGTXXnnnGG=data group e.g. OU for outcomeT = N for novice or E for ExpertXX = identifier number 00 to 30nnn = identifier name eg mmre.g. OUN01sbafor Outcome Novice Indicator 01 skilled birth attendance Short_nameIndicator namee.g. maternal mortality ratioDescriptionText description of the indicator to be used on websitee.g. Maternal mortality ratio (maternal deaths per 100,000 live births)Value_typeDescribes the indicator typeNumeric: decimal numberPercentage: value between 0 & 100Text: value from list of text optionsY/N: yes or noValue_categoryExpect this to be ‘total’ for all indicators for Phase 1, but this could allow future disaggregation, e.g. male/female; urban/ruraltotalYearThe year that the indicator value was reported. For most indicators, we will only report if 2014 or more recente.g. 2020Latest_Value‘LATEST’ if this is the most recent reported value for the indicator since 2014, otherwise ‘No’. Useful for indicators with time trend data.LATEST or NOValueIndicator valuee.g. 99.8. NB Some indicators are calculated to several decimal places. We present the value to the number of decimal places that should be displayed on the Hub.SourceFor Caesarean birth rate [OUN13cbr] ONLY, this column indicates the source of the data, either OECD when reported, or UNICEF otherwise.OECD or UNICEFTargetHow does the latest value compare with Global guidelines / targets?meets targetdoes not meet targetmeets global standarddoes not meet global standardRankGlobal rank for indicator, i.e. the country with the best global score for this indicator will have rank = 1, next = 2, etc. This ranking is only appropriate for a few indicators, others will show ‘na’1-195Rank out ofThe total number of countries who have reported a value for this indicator. Ranking scores will only go as high as this number.Up to 195TrendIf historic data is available, an indication of the change over time. If there is a global target, then the trend is either getting better, static or getting worse. For mmr [OUN04mmr] and nmr [OUN05nmr] the average annual rate of reduction (arr) between 2016 and latest value is used to determine the trend:arr <-1.0 = getting worsearr >=-1.0 AND <=1.0 = staticarr >1.0 = getting betterFor other indicators, the trend is estimated by comparing the average of the last three years with the average ten years ago:decreasing if now < 95% 10 yrs agoincreasing if now > 105% 10 yrs agostatic otherwiseincreasingdecreasing Or, if there is a global target: getting better,static,getting worseNotesClarification comments, when necessary LongitudeFor use with mapping LatitudeFor use with mapping DateDate data uploaded to the Hubthe following codes are also possible values:not reported does not apply don’t knowThis is one of many datasets featured on the Midwives’ Data Hub, a digital platform designed to strengthen midwifery and advocate for better maternal and newborn health services.

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/

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.

This dataset provides a global gridded (5 arc-min resolution) detailed annual net-migration dataset for 2000-2019. We also provide global annual birth and death rate datasets – that were used to estimate the net-migration – for same years. The dataset is presented in details, with some further analyses, in the following publication. Please cite this paper when using data.

Niva et al. 2023. World's human migration patterns in 2000-2019 unveiled by high-resolution data. Nature Human Behaviour 7: 2023–2037. Doi: https://doi.org/10.1038/s41562-023-01689-4

You can explore the data in our online net-migration explorer: https://wdrg.aalto.fi/global-net-migration-explorer/

Short introduction to the data

For the dataset, we collected, gap-filled, and harmonised:

a comprehensive national level birth and death rate datasets for altogether 216 countries or sovereign states; and

sub-national data for births (data covering 163 countries, divided altogether into 2555 admin units) and deaths (123 countries, 2067 admin units).

These birth and death rates were downscaled with selected socio-economic indicators to 5 arc-min grid for each year 2000-2019. These allowed us to calculate the 'natural' population change and when this was compared with the reported changes in population, we were able to estimate the annual net-migration. See more about the methods and calculations at Niva et al (2023).

We recommend using the data either over multiple years (we provide 3, 5 and 20 year net-migration sums at gridded level) or then aggregated over larger area (we provide adm0, adm1 and adm2 level geospatial polygon files). This is due to some noise in the gridded annual data.

Due to copy-right issues we are not able to release all the original data collected, but those can be requested from the authors.

List of datasets

Birth and death rates:

raster_birth_rate_2000_2019.tif: Gridded birth rate for 2000-2019 (5 arc-min; multiband tif)

raster_death_rate_2000_2019.tif: Gridded death rate for 2000-2019 (5 arc-min; multiband tif)

tabulated_adm1adm0_birth_rate.csv: Tabulated sub-national birth rate for 2000-2019 at the division to which data was collected (subnational data when available, otherwise national)

tabulated_ adm1adm0_death_rate.csv: Tabulated sub-national death rate for 2000-2019 at the division to which data was collected (subnational data when available, otherwise national)

Net-migration:

raster_netMgr_2000_2019_annual.tif: Gridded annual net-migration 2000-2019 (5 arc-min; multiband tif)

raster_netMgr_2000_2019_3yrSum.tif: Gridded 3-yr sum net-migration 2000-2019 (5 arc-min; multiband tif)

raster_netMgr_2000_2019_5yrSum.tif: Gridded 5-yr sum net-migration 2000-2019 (5 arc-min; multiband tif)

raster_netMgr_2000_2019_20yrSum.tif: Gridded 20-yr sum net-migration 2000-2019 (5 arc-min)

polyg_adm0_dataNetMgr.gpkg: National (adm 0 level) net-migration geospatial file (gpkg)

polyg_adm1_dataNetMgr.gpkg: Provincial (adm 1 level) net-migration geospatial file (gpkg) (if not adm 1 level division, adm 0 used)

polyg_adm2_dataNetMgr.gpkg: Communal (adm 2 level) net-migration geospatial file (gpkg) (if not adm 2 level division, adm 1 used; and if not adm 1 level division either, adm 0 used)

Files to run online net migration explorer

masterData.rds and admGeoms.rds are related to our online ‘Net-migration explorer’ tool (https://wdrg.aalto.fi/global-net-migration-explorer/). The source code of this application is available in https://github.com/vvirkki/net-migration-explorer. Running the application locally requires these two .rds files from this repository.

Metadata

Grids:

Resolution: 5 arc-min (0.083333333 degrees)

Spatial extent: Lon: -180, 180; -90, 90 (xmin, xmax, ymin, ymax)

Coordinate ref system: EPSG:4326 - WGS 84

Format: Multiband geotiff; each band for each year over 2000-2019

Units:

Birth and death rates: births/deaths per 1000 people per year

Net-migration: persons per 1000 people per time period (year, 3yr, 5yr, 20yr, depending on the dataset)

Geospatial polygon (gpkg) files:

Spatial extent: -180, 180; -90, 83.67 (xmin, xmax, ymin, ymax)

Temporal extent: annual over 2000-2019

Coordinate ref system: EPSG:4326 - WGS 84

Format: gkpk

Units:

Net-migration: persons per 1000 people per year

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”.

For current version see: https://www.sandiegocounty.gov/content/sdc/hhsa/programs/phs/maternal_child_family_health_services/MCFHSstatistics.html

Infant Mortality - Cohort Dataset Note: The Infant Mortality Rate is infant deaths (under one year of age) per 1,000 live births, by geography. Numerator represents infant's race/ethnicity. Denominator represents mother's race/ethnicity.

***API: Asian/Pacific Islander. ***AIAN: American Indian/Alaska Native. Blank Cells: Rates not calculated for fewer than 5 events. Rates not calculated in cases where infant's zip code of residence is unknown.

Sources: State of California, Department of Public Health, Death Statistical Master Files (before 2014), California Comprehensive Death Files (2014 and later), and Birth Statistical Master Files. Prepared by: County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics Unit, 2019.

Interpretation: "There were 5 infant deaths per 1,000 live births in Geography X".

Data Guide, Dictionary, and Codebook: https://www.sandiegocounty.gov/content/dam/sdc/hhsa/programs/phs/CHS/Community%20Profiles/Public%20Health%20Services%20Codebook_Data%20Guide_Metadata_10.2.19.xlsx

Birth and mortality rates of the population of the City of Turin calculated for the year 2015.

Abstract

Objective: Preterm birth is the primary driver of neonatal mortality worldwide, but it is defined by gestational age (GA) which is challenging to accurately assess in low-resource settings. In a commitment to reducing preterm birth while reinforcing and strengthening facility, routine data sources, the East Africa Preterm Birth Initiative (PTBi-EA) chose eligibility criteria that combined GA and birth weight. This analysis evaluated the quality of the GA data as recorded in maternity registers in PTBi-EA study facilities and the validity of the PTBi-EA eligibility criteria.

Methods: We conducted a retrospective analysis of maternity register data from March – September 2016. GA data from 23 study facilities in Migori, Kenya and the Busoga Region of Uganda were evaluated for completeness (variable present), consistency (recorded versus calculated GA), and plausibility (falling within the 3^rd and 97^th birth weight percentiles for GA of the INTERGROWTH-21^st Newborn Birth Weight Standards). Preterm birth rates were calculated using: 1) recorded GA <37 weeks, 2) recorded GA <37 weeks, excluding implausible GAs, 3) birth weight <2500g, and 4) PTBi-EA eligibility criteria of <2500g and between 2500g and 3000g if the recorded GA is <37 weeks.

Results: In both countries, GA was the least recorded variable in the maternity register (77.6%). Recorded and calculated GA (Kenya only) were consistent in 29.5% of births. Implausible GAs accounted for 11.7% of births. The four preterm birth rates were 1) 14.5%, 2) 10.6%, 3) 9.6%, 4) 13.4%.

Conclusions: Maternity register GA data presented quality concerns in PTBi-EA study sites. The PTBi-EA eligibility criteria of <2500g and between 2500g and 3000g if the recorded GA is <37 weeks adjusted for these concerns by using both birth weight and GA, balancing issues of accuracy and completeness with practical applicability.