From the mid-19th century until today, life expectancy at birth in the United States has roughly doubled, from 39.4 years in 1850 to 79.6 years in 2025. It is estimated that life expectancy in the U.S. began its upward trajectory in the 1880s, largely driven by the decline in infant and child mortality through factors such as vaccination programs, antibiotics, and other healthcare advancements. Improved food security and access to clean water, as well as general increases in living standards (such as better housing, education, and increased safety) also contributed to a rise in life expectancy across all age brackets. There were notable dips in life expectancy; with an eight year drop during the American Civil War in the 1860s, a seven year drop during the Spanish Flu empidemic in 1918, and a 2.5 year drop during the Covid-19 pandemic. There were also notable plateaus (and minor decreases) not due to major historical events, such as that of the 2010s, which has been attributed to a combination of factors such as unhealthy lifestyles, poor access to healthcare, poverty, and increased suicide rates, among others. However, despite the rate of progress slowing since the 1950s, most decades do see a general increase in the long term, and current UN projections predict that life expectancy at birth in the U.S. will increase by another nine years before the end of the century.

A global phenomenon, known as the demographic transition, has seen life expectancy from birth increase rapidly over the past two centuries. In pre-industrial societies, the average life expectancy was around 24 years, and it is believed that this was the case throughout most of history, and in all regions. The demographic transition then began in the industrial societies of Europe, North America, and the West Pacific around the turn of the 19th century, and life expectancy rose accordingly. Latin America was the next region to follow, before Africa and most Asian populations saw their life expectancy rise throughout the 20th century.

Life expectancy in the United Kingdom was below 39 years in the year 1765, and over the course of the next two and a half centuries, it is expected to have increased by more than double, to 81.1 by the year 2020. Although life expectancy has generally increased throughout the UK's history, there were several times where the rate deviated from its previous trajectory. These changes were the result of smallpox epidemics in the late eighteenth and early nineteenth centuries, new sanitary and medical advancements throughout time (such as compulsory vaccination), and the First world War and Spanish Flu epidemic in the 1910s.

This table contains 2394 series, with data for years 1991 - 1991 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...), Population group (19 items: Entire cohort; Income adequacy quintile 1 (lowest);Income adequacy quintile 2;Income adequacy quintile 3 ...), Age (14 items: At 25 years; At 30 years; At 40 years; At 35 years ...), Sex (3 items: Both sexes; Females; Males ...), Characteristics (3 items: Life expectancy; High 95% confidence interval; life expectancy; Low 95% confidence interval; life expectancy ...).

Life expectancy in India was 25.4 in the year 1800, and over the course of the next 220 years, it has increased to almost 70. Between 1800 and 1920, life expectancy in India remained in the mid to low twenties, with the largest declines coming in the 1870s and 1910s; this was because of the Great Famine of 1876-1878, and the Spanish Flu Pandemic of 1918-1919, both of which were responsible for the deaths of up to six and seventeen million Indians respectively; as well as the presence of other endemic diseases in the region, such as smallpox. From 1920 onwards, India's life expectancy has consistently increased, but it is still below the global average.

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/

U.S. Census Bureau: Decennial Census ZCTA Population (2000-2010) http://factfinder.census.gov

U.S. Census Bureau: American Community Survey 5-Year Population Estimates (2013) http://factfinder.census.gov

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 that can be compared across time and populations. More information about the determinants of life expectancy that may lead to differences in life expectancy between neighborhoods can be found in the Bay Area Regional Health Inequities Initiative (BARHII) Health Inequities in the Bay Area report at http://www.barhii.org/wp-content/uploads/2015/09/barhii_hiba.pdf. 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 http://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). ACS estimates are adjusted using Decennial Census data for more accurate population estimates. An adjustment factor was calculated using the ratio between the 2010 Decennial Census population estimates and the 2012 ACS 5-Year (with middle year 2010) population estimates. This adjustment factor is particularly important for ZCTAs with high homeless population (not living in group quarters) where the ACS may underestimate the ZCTA population and therefore underestimate the life expectancy. 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. ZIP Code 94103, representing Treasure Island, was dropped from the dataset due to its small population and having no bordering ZIP Codes. In this way, the original 305 Bay Area ZIP Codes were reduced to 217 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.

The life expectancy for men aged 65 years in the U.S. has gradually increased since the 1960s. Now men in the United States aged 65 can expect to live 18.2 more years on average. Women aged 65 years can expect to live around 20.7 more years on average. Life expectancy in the U.S. As of 2023, the average life expectancy at birth in the United States was 78.39 years. Life expectancy in the U.S. had steadily increased for many years but has recently dropped slightly. Women consistently have a higher life expectancy than men but have also seen a slight decrease. As of 2023, a woman in the U.S. could be expected to live up to 81.1 years. Leading causes of death The leading causes of death in the United States include heart disease, cancer, unintentional injuries, and cerebrovascular diseases. However, heart disease and cancer account for around 42 percent of all deaths. Although heart disease and cancer are the leading causes of death for both men and women, there are slight variations in the leading causes of death. For example, unintentional injury and suicide account for a larger portion of deaths among men than they do among women.

Life table data for "Bounce backs amid continued losses: Life expectancy changes since COVID-19"

cc-by Jonas Schöley, José Manuel Aburto, Ilya Kashnitsky, Maxi S. Kniffka, Luyin Zhang, Hannaliis Jaadla, Jennifer B. Dowd, and Ridhi Kashyap. "Bounce backs amid continued losses: Life expectancy changes since COVID-19".

These are CSV files of life tables over the years 2015 through 2021 across 29 countries analyzed in the paper "Bounce backs amid continued losses: Life expectancy changes since COVID-19".

40-lifetables.csv

Life table statistics 2015 through 2021 by sex, region and quarter with uncertainty quantiles based on Poisson replication of death counts. Actual life tables and expected life tables (under the assumption of pre-COVID mortality trend continuation) are provided.

30-lt_input.csv

Life table input data.

id: unique row identifier

region_iso: iso3166-2 region codes

sex: Male, Female, Total

year: iso year

age_start: start of age group

age_width: width of age group, Inf for age_start 100, otherwise 1

nweeks_year: number of weeks in that year, 52 or 53

death_total: number of deaths by any cause

population_py: person-years of exposure (adjusted for leap-weeks and missing weeks in input data on all cause deaths)

death_total_nweeksmiss: number of weeks in the raw input data with at least one missing death count for this region-sex-year stratum. missings are counted when the week is implicitly missing from the input data or if any NAs are encounted in this week or if age groups are implicitly missing for this week in the input data (e.g. 40-45, 50-55)

death_total_minnageraw: the minimum number of age-groups in the raw input data within this region-sex-year stratum

death_total_maxnageraw: the maximum number of age-groups in the raw input data within this region-sex-year stratum

death_total_minopenageraw: the minimum age at the start of the open age group in the raw input data within this region-sex-year stratum

death_total_maxopenageraw: the maximum age at the start of the open age group in the raw input data within this region-sex-year stratum

death_total_source: source of the all-cause death data

death_total_prop_q1: observed proportion of deaths in first quarter of year

death_total_prop_q2: observed proportion of deaths in second quarter of year

death_total_prop_q3: observed proportion of deaths in third quarter of year

death_total_prop_q4: observed proportion of deaths in fourth quarter of year

death_expected_prop_q1: expected proportion of deaths in first quarter of year

death_expected_prop_q2: expected proportion of deaths in second quarter of year

death_expected_prop_q3: expected proportion of deaths in third quarter of year

death_expected_prop_q4: expected proportion of deaths in fourth quarter of year

population_midyear: midyear population (July 1st)

population_source: source of the population count/exposure data

death_covid: number of deaths due to covid

death_covid_date: number of deaths due to covid as of

death_covid_nageraw: the number of age groups in the covid input data

ex_wpp_estimate: life expectancy estimates from the World Population prospects for a five year period, merged at the midpoint year

ex_hmd_estimate: life expectancy estimates from the Human Mortality Database

nmx_hmd_estimate: death rate estimates from the Human Mortality Database

nmx_cntfc: Lee-Carter death rate projections based on trend in the years 2015 through 2019

Deaths

source:

STMF input data series (https://www.mortality.org/Public/STMF/Outputs/stmf.csv)

ONS for GB-EAW pre 2020

CDC for US pre 2020

STMF:

harmonized to single ages via pclm

pclm iterates over country, sex, year, and within-year age grouping pattern and converts irregular age groupings, which may vary by country, year and week into a regular age grouping of 0:110

smoothing parameters estimated via BIC grid search seperately for every pclm iteration

last age group set to [110,111)

ages 100:110+ are then summed into 100+ to be consistent with mid-year population information

deaths in unknown weeks are considered; deaths in unknown ages are not considered

ONS:

data already in single ages

ages 100:105+ are summed into 100+ to be consistent with mid-year population information

PCLM smoothing applied to for consistency reasons

CDC:

The CDC data comes in single ages 0:100 for the US. For 2020 we only have the STMF data in a much coarser age grouping, i.e. (0, 1, 5, 15, 25, 35, 45, 55, 65, 75, 85+). In order to calculate life-tables in a manner consistent with 2020, we summarise the pre 2020 US death counts into the 2020 age grouping and then apply the pclm ungrouping into single year ages, mirroring the approach to the 2020 data

Population

source:

for years 2000 to 2019: World Population Prospects 2019 single year-age population estimates 1950-2019

for year 2020: World Population Prospects 2019 single year-age population projections 2020-2100

mid-year population

mid-year population translated into exposures:

if a region reports annual deaths using the Gregorian calendar definition of a year (365 or 366 days long) set exposures equal to mid year population estimates

if a region reports annual deaths using the iso-week-year definition of a year (364 or 371 days long), and if there is a leap-week in that year, set exposures equal to 371/364*mid_year_population to account for the longer reporting period. in years without leap-weeks set exposures equal to mid year population estimates. further multiply by fraction of observed weeks on all weeks in a year.

COVID deaths

source: COVerAGE-DB (https://osf.io/mpwjq/)

the data base reports cumulative numbers of COVID deaths over days of a year, we extract the most up to date yearly total

External life expectancy estimates

source:

World Population Prospects (https://population.un.org/wpp/Download/Files/1_Indicators%20(Standard)/CSV_FILES/WPP2019_Life_Table_Medium.csv), estimates for the five year period 2015-2019

Human Mortality Database (https://mortality.org/), single year and age tables

This table contains mortality indicators by sex for Canada and all provinces except Prince Edward Island. These indicators are derived from three-year complete life tables. Mortality indicators derived from single-year life tables are also available (table 13-10-0837). For Prince Edward Island, Yukon, the Northwest Territories and Nunavut, mortality indicators derived from three-year abridged life tables are available (table 13-10-0140).

For most of the world, throughout most of human history, the average life expectancy from birth was around 24. This figure fluctuated greatly depending on the time or region, and was higher than 24 in most individual years, but factors such as pandemics, famines, and conflicts caused regular spikes in mortality and reduced life expectancy. Child mortality The most significant difference between historical mortality rates and modern figures is that child and infant mortality was so high in pre-industrial times; before the introduction of vaccination, water treatment, and other medical knowledge or technologies, women would have around seven children throughout their lifetime, but around half of these would not make it to adulthood. Accurate, historical figures for infant mortality are difficult to ascertain, as it was so prevalent, it took place in the home, and was rarely recorded in censuses; however, figures from this source suggest that the rate was around 300 deaths per 1,000 live births in some years, meaning that almost one in three infants did not make it to their first birthday in certain periods. For those who survived to adolescence, they could expect to live into their forties or fifties on average. Modern figures It was not until the eradication of plague and improvements in housing and infrastructure in recent centuries where life expectancy began to rise in some parts of Europe, before industrialization and medical advances led to the onset of the demographic transition across the world. Today, global life expectancy from birth is roughly three times higher than in pre-industrial times, at almost 73 years. It is higher still in more demographically and economically developed countries; life expectancy is over 82 years in the three European countries shown, and over 84 in Japan. For the least developed countries, mostly found in Sub-Saharan Africa, life expectancy from birth can be as low as 53 years.

This submission includes publicly available data extracted in its original form. Please reference the Related Publication listed here for source and citation information. If you have questions about the underlying data stored here, please contact CDC-INFO at https://www.cdc.gov/cdc-info/forms/contact-us.html. If you have questions or recommendations related to this metadata entry and extracted data, please contact the CAFE Data Management team at: climatecafe@bu.edu. "Life expectancy estimates from the National Center for Health Statistics provide a reliable snapshot of population health and mortality in the United States. National life expectancy estimates are calculated using period (current) life tables. Life tables are used to measure mortality, survivorship, and the life expectancy of a population at varying ages. Period life tables estimate how many more years a group of people who are currently at a particular age – any age from birth to 100 or more – can expect to live if the mortality patterns in a given year remain the same over the rest of their lives. Life tables can also be used to compare how life expectancy has improved or declined over time. National-level life tables are released annually, as well as every 10 years (decennially) around the U.S. population census." [Quote from: https://www.cdc.gov/nchs/nvss/life-expectancy.htm]

National Records of Scotland Guidance;What is ‘period’ life expectancyAll of the estimates presented in this report are ‘period’ life expectancy. They are calculated assuming that mortality rates for each age group in the time period (here 2021-2023) are constant throughout a person’s life. Period life expectancy is often described as how long a baby born now could expect to live if they experienced today’s mortality rates throughout their lifetime. It is very unlikely that this would be the case as it means that future changes in things such as medicine and legislation are not taken into consideration.Period life expectancy is not an accurate prediction of how long a person born today will actually live, but it is a useful measure of population health at a point in time and is most useful for comparing trends over time, between areas of a country and with other countries.How national life expectancy is calculatedThe latest life expectancy figures are calculated from the mid-year population estimates for Scotland and the number of deaths registered in Scotland during 2021, 2022, and 2023. Life expectancy for Scotland is calculated for each year of age and represents the average number of years that someone of that age could expect to live if death rates for each age group remained constant over their lifetime. Life expectancy in Scotland is calculated as a three-year average, produced by combining deaths and population data for the three-year period. Three years of data are needed to provide large enough numbers to make these figures accurate and lessen the effect of very ‘good’ or ‘bad’ years. Throughout this publication, the latest life expectancy figures refer to 2021-2023 period. How sub-national life expectancy is calculatedWe calculate life expectancy for areas within Scotland using a very similar method to the national figures but with a few key differences. Firstly, we use age groups rather than single year of age. This is to increase the population size of each age group to reduce fluctuations and ensure accurate calculation of mortality rates. Secondly, we use a maximum age group of 90+ whereas the national figures are calculated up to age 100. These are known as ‘abridged life tables.’ Because these methods produce slightly different figures, we also calculate a Scotland figure using the abridged method to allow for accurate comparisons between local areas for example. This Scotland figure is only for comparison and does not replace the headline national figure. You can read more information about the methods in this publication in our methodology guide on the NRS website. Uses of life expectancyLife expectancy at birth is a very useful indicator of mortality conditions across a population at a particular point in time. It also provides an objective means of comparing trends in mortality over time, between areas of a country and with other countries. This is used to monitor and investigate health inequalities and to set public health targets. Life expectancy is also used to inform pensions policy, research and teaching.

Abstract

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

Section 7

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

Source

Section 13

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

Source

Section 6

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

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

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

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

Commuting Zone Characteristics: CZ-level characteristics

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

Section 15

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

Source

Section 11

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

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

Source

Section 3

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

Source

Section 9

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

Source

Section 10

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

Source

Section 2

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

Source

Section 8

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

Source

Section 12

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

Two variables constructed by the Cen

The infant mortality rate is defined as the number of deaths of children under one year of age, expressed per 1 000 live births. Some of the international variation in infant mortality rates is due to variations among countries in registering practices for premature infants. The United States and Canada are two countries which register a much higher proportion of babies weighing less than 500g, with low odds of survival, resulting in higher reported infant mortality. In Europe, several countries apply a minimum gestational age of 22 weeks (or a birth weight threshold of 500g) for babies to be registered as live births. This indicator is measured in terms of deaths per 1 000 live births.

This indicator is a summary measure of premature mortality, providing an explicit way of weighting deaths occurring at younger ages, which may be preventable. The calculation of Potential Years of Life Lost (PYLL) involves summing up deaths occurring at each age and multiplying this with the number of remaining years to live up to a selected age limit (age 75 is used in OECD Health Statistics). In order to assure cross-country and trend comparison, the PYLL are standardised, for each country and each year. The total OECD population in 2010 is taken as the reference population for age standardisation. This indicator is presented as a total and per gender. It is measured in years lost per 100 000 inhabitants (total), per 100 000 men and per 100 000 women, aged 0-69.

Life expectancy at birth is defined as how long, on average, a newborn can expect to live, if current death rates do not change. However, the actual age-specific death rate of any particular birth cohort cannot be known in advance. If rates are falling, actual life spans will be higher than life expectancy calculated using current death rates. Life expectancy at birth is one of the most frequently used health status indicators. Gains in life expectancy at birth can be attributed to a number of factors, including rising living standards, improved lifestyle and better education, as well as greater access to quality health services. This indicator is presented as a total and per gender and is measured in years.

Period survival tables (per period of 5 years) by gender and age for the population of the Netherlands.

The table shows how many boys or girls from a group of 100 thousand newborns will reach the age of ½, 1½, 2½ etc. years. It can also be seen how old these children will be on average.

The following breakdowns are possible: — Mortality rate by sex and age; — Living (table population) by gender and age; — Deceased (table population) by gender and age; — Life expectancy by gender and age.

Data available from period 1861 to 1866 to period 2006 to 2011.

Status of the figures: All figures in the table are final.

Changes as at 31 March 2016: None, this table has been discontinued.

When will there be new figures? No longer applicable. This table is followed by the Life Expectancy Table; gender, age (per year and per period of 5 years). See paragraph 3.

Life expectancy in Sweden was 36 in the year 1765, and over the course of the next 255 years, it is expected to have increased to 82.6 by 2020. Although life expectancy has generally increased throughout Sweden's history, there was a lot of fluctuation around the turn of the nineteenth century due to The Napoleonic Wars and First Cholera Epidemic, and again in the 1910s due to the Spanish Flu Epidemic.

Period and Cohort Mortality rates (qx) for England using the high life expectancy variant by single year of age 0 to 100.

About

The 2024 Revision of World Population Prospects is the twenty-eighth edition of official United Nations population estimates and projections that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat. It presents population estimates from 1950 to the present for 237 countries or areas, underpinned by analyses of historical demographic trends. This latest assessment considers the results of 1,910 national population censuses conducted between 1950 and 2023, as well as information from vital registration systems and from 3,189 nationally representative sample surveys. The 2024 revision also presents population projections to the year 2100 that reflect a range of plausible outcomes at the global, regional and national levels.

Column Description

• Total Population, as of 1 January (thousands)
• Total Population, as of 1 July (thousands)
• Male Population, as of 1 July (thousands)
• Female Population, as of 1 July (thousands)
• Population Density, as of 1 July (persons per square km) (UPDATED on 14 July 2022)
• Population Sex Ratio, as of 1 July (males per 100 females)
• Median Age, as of 1 July (years)
• Natural Change, Births minus Deaths (thousands)
• Rate of Natural Change (per 1,000 population)
• Population Change (thousands)
• Population Growth Rate (percentage)
• Population Annual Doubling Time (years)
• Births (thousands)
• Births by women aged 15 to 19 (thousands)
• Crude Birth Rate (births per 1,000 population)
• Total Fertility Rate (live births per woman)
• Net Reproduction Rate (surviving daughters per woman)
• Mean Age Childbearing (years)
• Sex Ratio at Birth (males per 100 female births)
• Total Deaths (thousands)
• Male Deaths (thousands)
• Female Deaths (thousands)
• Crude Death Rate (deaths per 1,000 population)
• Life Expectancy at Birth, both sexes (years)
• Male Life Expectancy at Birth (years)
• Female Life Expectancy at Birth (years)
• Life Expectancy at Age 15, both sexes (years)
• Male Life Expectancy at Age 15 (years)
• Female Life Expectancy at Age 15 (years)
• Life Expectancy at Age 65, both sexes (years)
• Male Life Expectancy at Age 65 (years)
• Female Life Expectancy at Age 65 (years)
• Life Expectancy at Age 80, both sexes (years)
• Male Life Expectancy at Age 80 (years)
• Female Life Expectancy at Age 80 (years)
• Infant Deaths, under age 1 (thousands)
• Infant Mortality Rate (infant deaths per 1,000 live births)
• Live Births Surviving to Age 1 (thousands)
• Under-Five Deaths, under age 5 (thousands)
• Under-Five Mortality (deaths under age 5 per 1,000 live births)
• Mortality before Age 40, both sexes (deaths under age 40 per 1,000 live births)
• Male Mortality before Age 40 (deaths under age 40 per 1,000 male live births)
• Female Mortality before Age 40 (deaths under age 40 per 1,000 female live births)
• Mortality before Age 60, both sexes (deaths under age 60 per 1,000 live births)
• Male Mortality before Age 60 (deaths under age 60 per 1,000 male live births)
• Female Mortality before Age 60 (deaths under age 60 per 1,000 female live births)
• Mortality between Age 15 and 50, both sexes (deaths under age 50 per 1,000 alive at age 15)
• Male Mortality between Age 15 and 50 (deaths under age 50 per 1,000 males alive at age 15)
• Female Mortality between Age 15 and 50 (deaths under age 50 per 1,000 females alive at age 15)
• Mortality between Age 15 and 60, both sexes (deaths under age 60 per 1,000 alive at age 15)
• Male Mortality between Age 15 and 60 (deaths under age 60 per 1,000 males alive at age 15)
• Female Mortality between Age 15 and 60 (deaths under age 60 per 1,000 females alive at age 15)
• Net Number of Migrants (thousands)
• Net Migration Rate (per 1,000 population)

Copyright © 2024 by United Nations, made available under a Creative Commons license CC BY 3.0 IGO: http://creativecommons.org/licenses/by/3.0/igo/ Suggested citation: United Nations, Department of Economic and Social Affairs, Population Division (2024). World Population Prospects 2024, Online Edition.

Foto von kazi arifuzzaman auf Unsplash

Created for the 2023-2025 State of Black Los Angeles County (SBLA) interactive report. Countywide Statistical Areas (CSA) are current as of October 2023.

Fields ending in _yr1 were calculated for the original 2021-2022 SBLA report, while fields ending in _yr2 or without a year suffix were calculated for the 2023-2025 version. Eviction Filings per 100 (eviction_filings_per100) and Life Expectancy (life_expectancy) did not have updated data and are the same data shown in the Year 1 report.

Population and demographic data are from US Census American Community Survey (ACS) 5-year estimates, aggregated up from census tract or block group to CSA. Year 1 data are from 2020, year 2 data are from 2022.

Poverty Data (200% FPL) are from LA County ISD-eGIS Demographics. Year 1 data are from 2021, Year 2 are from 2022.

The 2023-2025 report includes several new indicators that are calculated as the percent of countywide population by race that resides in a geographic area of interest. Population for these indicators is estimated based on intersection with census block group centroids. These indicators are:

Indicator

Fields

Source

Health Professional Shortage Areas (HPSA) for Primary Care

hpsa_primary_pct hpsa_primary_black_pct

LA County DPH https://data.lacounty.gov/datasets/lacounty::health-professional-shortage-area-primary-care/about

Health Professional Shortage Areas (HPSA) for Mental Health

hpsa_mental_pct hpsa_mental_black_pct

LA County DPH https://data.lacounty.gov/datasets/lacounty::health-professional-shortage-area-mental-health/about

Concentrated Disadvantage

cd_pct cd_black_pct

LA County ISD-Enterprise GIS https://egis-lacounty.hub.arcgis.com/datasets/lacounty::concentrated-disadvantage-index-2022/explore

Firearm Dealers

firearm_dl_count (count of dealers in CSA) firearm_dl_per10000 (rate of dealers per 10,000)

LA County DPH Office of Violence Prevention (OVP)

High and Very High Park Need Areas

parks_need_pct parks_need_black_pct

LA County Parks Needs Assessment Plus (PNA+) https://lacounty.maps.arcgis.com/apps/instant/media/index.html?appid=3d0ef36720b447dcade1ab87a2cc80b9

High Quality Transit Areas

hqta_pct hqta_black_pct

SCAG https://lacounty.maps.arcgis.com/home/item.html?id=43e6fef395d041c09deaeb369a513ca1

High Walkability Areas

walk_total_pct walk_black_pct

EPA Walkability Index https://www.epa.gov/smartgrowth/smart-location-mapping#walkability

High Poverty and High Segregation Areas

highpovseg_total_pct highpovseg_black_pct

CTCAC/HCD Opportunity Area Maps https://www.treasurer.ca.gov/ctcac/opportunity.asp

LA County Arts Investments

arts_dollars (total $$ for CSA) arts_dollars_percap (investment dollars per capita)

LA County Department of Arts and Culture https://lacountyartsdata.org/#maps

Strong Start (areas with at least 9 Strong Start indicators)

strongstart_total_pct strongstart_black_pct

CA Strong Start Index https://strongstartindex.org/map

For more information about the purpose of this data, please contact CEO-ARDI.

For more information about the configuration of this data, please contact ISD-Enterprise GIS.

VITAL SIGNS INDICATOR Street Pavement Condition (T16)

FULL MEASURE NAME Pavement condition index (PCI)

LAST UPDATED May 2017

DESCRIPTION Street pavement condition, more commonly referred to as the pavement condition index (PCI), reflects the quality of pavement on local streets and roads in the region. Calculated using a three-year moving average, PCI ranges from zero (failed) to 100 (brand-new) and has been used as a regional indicator of pavement preservation for over a decade.

DATA SOURCE Metropolitan Transportation Commission: StreetSaver

CONTACT INFORMATION vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator) Pavement condition index (PCI) relies upon a three-year moving average for regional, county, and city PCI to improve the reliability of the PCI data on an annual basis. The index ranges from 0 to 100, with 0 representing a failed road and 100 representing a brand-new facility. Segment PCI data is collected on a rolling basis but is imputed for interim years based on facility age and treatments using the MTC StreetSaver system. Due to the lack of reported PCI data in 2006, the city of Palo Alto is not included in the Regional Distribution chart.