Introduction This report presents projections of population from 2015 to 2025 by age and sex for Illinois, Chicago and Illinois counties produced for the Certificate of Need (CON) Program. As actual future population trends are unknown, the projected numbers should not be considered a precise prediction of the future population; rather, these projections, calculated under a specific set of assumptions, indicate the levels of population that would result if our assumptions about each population component (births, deaths and net migration) hold true. The assumptions used in this report, and the details presented below, generally assume a continuation of current trends. Methodology These projections were produced using a demographic cohort-component projection model. In this model, each component of population change – birth, death and net migration – is projected separately for each five-year birth cohort and sex. The cohort – component method employs the following basic demographic balancing equation: P1 = P0 + B – D + NM Where: P1 = Population at the end of the period; P0 = Population at the beginning of the period; B = Resident births during the period; D = Resident deaths during the period; and NM = Net migration (Inmigration – Outmigration) during the period. The model roughly works as follows: for every five-year projection period, the base population, disaggregated by five-year age groups and sex, is “survived” to the next five-year period by applying the appropriate survival rates for each age and sex group; next, net migrants by age and sex are added to the survived population. The population under 5 years of age is generated by applying age specific birth rates to the survived females in childbearing age (15 to 49 years). Base Population These projections began with the July 1, 2010 population estimates by age and sex produced by the U.S. Census Bureau. The most recent census population of April 1, 2010 was the base for July 1, 2010 population estimates. Special Populations In 19 counties, the college dormitory population or adult inmates in correctional facilities accounted for 5 percent or more of the total population of the county; these counties were considered as special counties. There were six college dorm counties (Champaign, Coles, DeKalb, Jackson, McDonough and McLean) and 13 correctional facilities counties (Bond, Brown, Crawford, Fayette, Fulton, Jefferson, Johnson, Lawrence, Lee, Logan, Montgomery, Perry and Randolph) that qualified as special counties. When projecting the population, these special populations were first subtracted from the base populations for each special county; then they were added back to the projected population to produce the total population projections by age and sex. The base special population by age and sex from the 2010 population census was used for this purpose with the assumption that this population will remain the same throughout each projection period. Mortality Future deaths were projected by applying age and sex specific survival rates to each age and sex specific base population. The assumptions on survival rates were developed on the basis of trends of mortality rates in the individual life tables constructed for each level of geography for 1989-1991, 1999-2001 and 2009-2011. The application of five-year survival rates provides a projection of the number of persons from the initial population expected to be alive in five years. Resident deaths data by age and sex from 1989 to 2011 were provided by the Illinois Center for Health Statistics (ICHS), Illinois Department of Public Health. Fertility Total fertility rates (TFRs) were first computed for each county. For most counties, the projected 2015 TFRs were computed as the average of the 2000 and 2010 TFRs. 2010 or 2015 rates were retained for 2020 projections, depending on the birth trend of each county. The age-specific birth rates (ASBR) were next computed for each county by multiplying the 2010 ASBR by each projected TFR. Total births were then projected for each county by applying age-specific birth rates to the projected female population of reproductive ages (15 to 49 years). The total births were broken down by sex, using an assumed sex-ratio at birth. These births were survived five years applying assumed survival ratios to get the projected population for the age group 0-4. For the special counties, special populations by age and sex were taken out before computing age-specific birth rates. The resident birth data used to compute age-specific birth rates for 1989-1991, 1999-2001 and 2009-2011 came from ICHS. Births to females younger than 15 years of age were added to those of the 15-19 age group and births to women older than 49 years of age were added to the 45-49 age group. Net Migration Migration is the major component of population change in Illinois, Chicago and Illinois counties. The state is experiencing a significant loss of population through internal (domestic migration within the U.S.) net migration. Unlike data on births and deaths, migration data based on administrative records are not available on a regular basis. Most data on migration are collected through surveys or indirectly from administrative records (IRS individual tax returns). For this report, net migration trends have been reviewed using data from different sources and methods (such as residual method) from the University of Wisconsin, Madison, Illinois Department of Public Health, individual exemptions data from the Internal Revenue Service, and survey data from the U.S. Census Bureau. On the basis of knowledge gained through this review and of levels of net migration from different sources, assumptions have been made that Illinois will have annual net migrants of -40, 000, -35,000 and -30,000 during 2010-2015, 2015-2020 and 2020-2025, respectively. These figures have been distributed among the counties, using age and sex distribution of net migrants during 1995-2000. The 2000 population census was the last decennial census, which included the question “Where did you live five years ago?” The age and sex distribution of the net migrants was derived, using answers to this question. The net migration for Chicago has been derived independently, using census survival method for 1990-2000 and 2000-2010 under the assumption that the annual net migration for Chicago will be -40,000, -30,000 and -25,000 for 2010-2015, 2015-2020 and 2020-2025, respectively. The age and sex distribution from the 2000-2010 net migration was used to distribute the net migrants for the projection periods. Conclusion These projections were prepared for use by the Certificate of Need (CON) Program; they are produced using evidence-based techniques, reasonable assumptions and the best available input data. However, as assumptions of future demographic trends may contain errors, the resulting projections are unlikely to be free of errors. In general, projections of small areas are less reliable than those for larger areas, and the farther in the future projections are made, the less reliable they may become. When possible, these projections should be regularly reviewed and updated, using more recent birth, death and migration data.

This data is based on population projections (6L) provided by the National Center for Veterans Statistics and Analysis, published in 2018.

This is the state summary of veterans' statistics for Oregon for fiscal year 2021. There are tables for facilities, expenditures, and healthcare and benefits provided by the VA. The demographics data include age, period of service, gender, race, educational attainment, and personal income, as well as projected changes in veteran demographics.National Center for Veterans Analysis and Statistics, Contact: www.va.gov/vetdataSources: VA Veteran Population Projection Model, VA Geographic Distribution of Expenditures, VA Annual Benefits Report, U.S. Census Bureau, American Community Survey

Veterans as a Percent of County Population (FY2019)VA Facilities (as of 3/30/2021)VA Expenditures FY 2019 (in thousands)Veteran Population (as of 9/30/2019) Age Distribution Period of Service Household Income Educational AttainmentVA Healthcare and Benefits (as of 9/30/2020)Population Projections Population Changes Veteran Population Projection By Age and Year Veteran Population Projection By Gender and Year Veteran Population Projection By Period of Service and Year Veteran Population Projection By Race/Ethnicity and Year Age Distribution over Time -- WashingtonNational Center for Veterans Analysis and Statistics, Contact: www.va.gov/vetdataSources: VA Veteran Population Projection Model, VA Geographic Distribution of Expenditures, VA Annual Benefits Report, U.S. Census Bureau, American Community Survey

Veterans as a Percent of County Population (FY2019)VA Facilities (as of 3/30/2021)VA Expenditures FY 2019 (in thousands)Veteran Population (as of 9/30/2019) Age Distribution Period of Service Household Income Educational AttainmentVA Healthcare and Benefits (as of 9/30/2020)Population Projections Population Changes Veteran Population Projection By Age and Year Veteran Population Projection By Gender and Year Veteran Population Projection By Period of Service and Year Veteran Population Projection By Race/Ethnicity and Year Age Distribution over Time -- OhioNational Center for Veterans Analysis and Statistics, Contact: www.va.gov/vetdataSources: VA Veteran Population Projection Model, VA Geographic Distribution of Expenditures, VA Annual Benefits Report, U.S. Census Bureau, American Community Survey

Population mortality forecasts are widely used for allocating public health expenditures, setting research priorities, and evaluating the viability of public pensions, private pensions, and health care financing systems. In part because existing methods seem to forecast worse when based on more information, most forecasts are still based on simple linear extrapolations that ignore known biological risk factors and other prior information. We adapt a Bayesian hierarchical forecasting model capable of including more known health and demographic information than has previously been possible. This leads to the first age- and sex-specific forecasts of American mortality that simultaneously incorporate, in a formal statistical model, the effects of the recent rapid increase in obesity, the steady decline in tobacco consumption, and the well known patterns of smooth mortality age profiles and time trends. Formally including new information in forecasts can matter a great deal. For example, we estimate an increase in male life expectancy at birth from 76.2 years in 2010 to 79.9 years in 2030, which is 1.8 years greater than the U.S. Social Security Administration projection and 1.5 years more than U.S. Census projection. For females, we estimate more modest gains in life expectancy at birth over the next twenty years from 80.5 years to 81.9 years, which is virtually identical to the Social Security Administration projection and 2.0 years less than U.S. Census projections. We show that these patterns are also likely to greatly affect the aging American population structure. We offer an easy-to-use approach so that researchers can include other sources of information and potentially improve on our forecasts too. Website See also: Mortality Studies

These data are to supplement the following in-press publication:

Zoraghein, H., and O'Neill B. (2020). U.S. state-level projections of the spatial distribution of population consistent with Shared Socioeconomic Pathways. Sustainability.

The data herein were generated using the population_gravity model which can be found here: https://github.com/IMMM-SFA/population_gravity

CONTENTS:

zoraghein-oneill_population_gravity_inputs_outputs.zip

contains a directory for each U.S. state for inputs and outputs

inputs contain the following:

_1km.tif: Urban and Rural population GeoTIF rasters at a 1km resolution

value per grid cell: number of humans (float)

crs: EPSG:102003 - USA_Contiguous_Albers_Equal_Area_Conic - Projected

nodata value: -3.40282e+38

_mask_short_term.tif: Mask GeoTIF rasters at a 1km resolution that contain values from 0.0 to 1.0 for each 1 km grid cell to help calculate suitability depending on topographic and land use and land cover characteristics

value per grid cell: values from 0.0 to 1.0 (float) that are generated from topographic and land use and land cover characteristics to inform suitability as outlined in the companion publication

crs: EPSG:102003 - USA_Contiguous_Albers_Equal_Area_Conic - Projected

nodata value: -3.40282e+38

_popproj.csv: Population projection CSV files for urban, rural, and total population (number of humans; float) for SSPs 2, 3, and 5 for years 2010-2100

_coordinates.csv: CSV file containing the coordinates for each 1 km grid cell within the target state. File includes a header with the fields XCoord, YCoord, FID.,Where data types and field descriptions are as follows: (XCoord, float, X coordinate in meters),(YCoord, float, Y coordinate in meters),(FID, int, Unique feature id)

_within_indices.txt: text file containing a file structured as a Python list (e.g. [0, 1]) that contains the index of each grid cell when flattened from a 2D array to a 1D array for the target state.

_params.csv: CSV file containing the calibration parameters (alpha_rural, beta_rural, alpha_urban, beta_urban; float) for the population_gravity model for each year from 2010-2100 in 10-year time-steps as described in the companion publication

outputs contain the following:

jones_oneill directory; these are the comparison datasets used to build Figures 7 and 8 in the companion publication

contains three directories: SSP2, SSP3, and SSP5 that each contain a GeoTIF representing total population (number of humans; float) at 1km resolution for years 2050 and 2100.

1kmtotal_jones_oneill.tif:

value per grid cell: number of humans (float)

crs: EPSG:102003 - USA_Contiguous_Albers_Equal_Area_Conic - Projected

nodata value: -3.40282e+38

model directory; these are the model outputs from population_gravity for SSP2, SSP3, and SSP5 that each contain a GeoTIF representing urban, rural, and total population (number of humans; float) at 1km resolution for years 2020-2100 in 10-year time-steps.

1km_jones_oneill.tif:

value per grid cell: number of humans (float)

crs: EPSG:102003 - USA_Contiguous_Albers_Equal_Area_Conic - Projected

nodata value: -3.40282e+38

zoraghein-oneill_population_gravity_national-ssp-maps.zip

Results of the population_gravity model mosaicked to the National scale at a 1km resolution and the comparison Jones and O'Neill research. These are used to generate Figure 6 of the companion paper

National_1km_jones_oneill.tif:

value per grid cell: number of humans (float)

crs: EPSG:102003 - USA_Contiguous_Albers_Equal_Area_Conic - Projected

nodata value: -3.40282e+38

National_1km_.tif:

value per grid cell: number of humans (float)

crs: EPSG:102003 - USA_Contiguous_Albers_Equal_Area_Conic - Projected

nodata value: -3.40282e+38

In 2023, the median age of the population of the United States was 39.2 years. While this may seem quite young, the median age in 1960 was even younger, at 29.5 years. The aging population in the United States means that society is going to have to find a way to adapt to the larger numbers of older people. Everything from Social Security to employment to the age of retirement will have to change if the population is expected to age more while having fewer children. The world is getting older It’s not only the United States that is facing this particular demographic dilemma. In 1950, the global median age was 23.6 years. This number is projected to increase to 41.9 years by the year 2100. This means that not only the U.S., but the rest of the world will also have to find ways to adapt to the aging population.

Belgian National Burden of Disease Study

Projections

To produce these projections, we used a Bayesian modeling approach via Integrated Nested Laplace Approximation (INLA) to analyze past trends and extend them into future estimates. An automated model selection process identified the best-fitting model for each disease, ensuring robust and accurate projections. Additionally, population projections from the Federal Planning Bureau were incorporated to account for expected changes in population structure, allowing us to anticipate shifts in disease prevalence and Years Lived with Disability (YLD) across age, sex, and regional categories. More details on the applied methodology can be found in the BeBOD projection protocol.

Estimates of the burden of disease

Prevalence

Our estimates are based on the GBD cause list for morbidity by IHME. We first select for each of the 33 causes, the most suitable local data source as described in the general protocol. Next, we calculate the prevalence by year, region, age, and sex, to obtain a prevalence for each of the included diseases.

Years Lived with Disability

In addition to calculating the number of prevalent cases, we also calculate Years Lived with Disability (YLDs) as a measure of morbidity. YLDs are calculated as the product of the number of prevalent cases with the disability weight (DW), averaged over the different health states of the disease. The DWs reflect the relative reduction in quality of life, on a scale from 0 (perfect health) to 1 (death). We calculate YLDs using the Global Burden of Disease DWs.

In terms of population size, the sex ratio in the United States favors females, although the gender gap is remaining stable. In 2010, there were around 5.17 million more women, with the difference projected to decrease to around 3 million by 2027.

Gender ratios by U.S. state In the United States, the resident population was estimated to be around 331.89 million in 2021. The gender distribution of the nation has remained steady for several years, with women accounting for approximately 51.1 percent of the population since 2013. Females outnumbered males in the majority of states across the country in 2020, and there were eleven states where the gender ratio favored men.

Metro areas by population National differences between male and female populations can also be analyzed by metropolitan areas. In general, a metropolitan area is a region with a main city at its center and adjacent communities that are all connected by social and economic factors. The largest metro areas in the U.S. are New York, Los Angeles, and Chicago. In 2019, there were more women than men in all three of those areas, but Jackson, Missouri was the metro area with the highest share of female population.

These data were compiled to help understand how climate change may impact dryland pinyon-juniper ecosystems in coming decades, and how resource management might be able to minimize those impacts. Objective(s) of our study were to model the demographic rates of PJ woodlands to estimate the areas that may decline in the future vs. those that will be stable. We quantified populations growth rates across broad geographic areas, and identified the relative roles of recruitment and mortality in driving potential future changes in population viability in 5 tree species that are major components of these dry forests. We used this demographic model to project pinyon-juniper population stability under future climate conditions, assess how robust these projected changes are, and to identify where on the landscape management strategies that decrease tree competition would effectively resist population decline. These data represent estimated recruitment, mortality and population growth across the distribution of five common pinyon-juniper species across the US Southwest. These data were collected by the US Forest service in their monitoring program, which is a systematic survey of forested regions across the entire US. Our data is from western US states, including AZ, CA, CO, ID, MT, NM, ND, NV, OR, SD, TX, UT, and was collected between 2000-2007, depending on state census collection times. These data were collected by the Forest Inventory and Analysis program of the USDA US Forest Service. Within each established plot, all adult trees greater than 12.7 cm (5 in.) diameter at breast height (DBH) are assigned unique tags and tracked within four, 7.32 m (24 ft.) radius subplots. All saplings <12.7 cm & > 2.54 cm (1 in.) DBH are assigned unique tags and tracked within four, 2.07 m (6.8 ft.) radius microplots within the larger adult plots. Finally, seedlings <2.54 cm DBH are counted within the same microplots as the saplings. Two censuses were conducted 10 years apart in each plot. These data can be used to inform how tree species have unique responses to changing climate conditions and how management actions, like tree density reduction, may effectively resist transformation away from pinyon-juniper woodland to other ecosystem types.

A figure depicting the projected population structure under the conditions of A(03-06); and projections of the site summary matrices A(CR15), A(CR20), A(CR30), and A(PR15).

The statistic shows the total population in the United States from 2015 to 2021, with projections up until 2027. In 2021, the total population of the U.S. amounted to approximately 332.18 million inhabitants.

The United States' economy over the last decade

The United States of America is the world’s largest national economy and the second most prominent trader globally, trailing just behind China. The country is also one of the most populated countries in the world, trailing only China and India. The United States' economy prospers primarily due to having a plentiful amount of natural resources and advanced infrastructure to cope with the production of goods and services, as well as the population and workforce to enable high productivity. Efficient productivity led to a slight growth in GDP almost every year over the past decade, despite undergoing several economic hardships towards the late 2000's.

In addition, the United States holds arguably one of the most important financial markets, with the majority of countries around the world having commercial connections with American companies. Dependency on a single market like the United States has however caused several global dilemmas, most evidently seen during the 2008 financial crisis. What initially started off as a bursting of the U.S. housing bubble lead to a worldwide recession and the necessity to reform national economics. The global financial crisis affected the United States most drastically, especially within the unemployment market as well as national debt, which continued to rise due to the United States having to borrow money in order to stimulate its economy.

IM3 SELECT Urbanization Data Urban fraction is provided in TIF files projected on the WGS84 datum at coarse (1/8 degree) and downscaled (1km) resolutions across the globe for each of three Shared Socioeconomic Pathway (SSP) scenarios corresponding to SSP2, SSP3, and SSP5; and each of two population scenarios corresponding to the default population scenario and an updated population scenario with more detailed projections for the United States. The population projections are provided as CSV files. Folder structure: default_population population urban_fraction coarse SSP2 SSP3 SSP5 downscaled SSP2 SSP3 SSP5united_states_updated_population population urban_fraction coarse SSP2 SSP3 SSP5 downscaled SSP2 SSP3 SSP5 Urban fraction data was produced using the SELECT model v1.0.0 (Gao, J. & O'Neill, B.C. 2019). Default population data derived from Gao, J. & O'Neill, B.C. 2020. Original model outputs produced using the default population data are available from Gao, J. 2020. Updated United States population data derived from Zoraghein, H. & O'Neill, B.C. 2020. Other SELECT input files available at Gao, J. & O'Neill, B.C. 2022.

The statistic shows the share of U.S. population, by race and Hispanic origin, in 2016 and a projection for 2060. As of 2016, about 17.79 percent of the U.S. population was of Hispanic origin. Race and ethnicity in the U.S. For decades, America was a melting pot of the racial and ethnical diversity of its population. The number of people of different ethnic groups in the United States has been growing steadily over the last decade, as has the population in total. For example, 35.81 million Black or African Americans were counted in the U.S. in 2000, while 43.5 million Black or African Americans were counted in 2017.

The median annual family income in the United States in 2017 earned by Black families was about 50,870 U.S. dollars, while the average family income earned by the Asian population was about 92,784 U.S. dollars. This is more than 15,000 U.S. dollars higher than the U.S. average family income, which was 75,938 U.S. dollars.

The unemployment rate varies by ethnicity as well. In 2018, about 6.5 percent of the Black or African American population in the United States were unemployed. In contrast to that, only three percent of the population with Asian origin was unemployed.

As of June 2025, 24.2 percent of Facebook users in the United States were aged between 25 and 34 years, making up Facebook’s largest audience in the country. Overall, almost 19 percent of users belonged to the 18 to 24-year age group. Does everyone in the U.S. use Facebook? In 2024, there were approximately 250 million Facebook users in the U.S., a figure which is projected to steadily increase, and reach 262.8 million by 2028. Social media users in the United States have a very high awareness of the social media giant. Expectedly, 94 percent of users had heard of the brand in 2025. Although the vast majority of U.S. social networkers knew of Facebook, the likeability of the platform was not so impressive at 68 percent. Nonetheless, usage, loyalty, and buzz around the brand remained relatively high. Facebook, Meta, and the metaverse A strategic rebranding from Facebook to Meta Platforms in late 2021 boded well for the company in Mark Zuckerberg’s attempt to be strongly linked to the metaverse, and to be considered more than just a social media company. According to a survey conducted in the U.S. in early 2022, Meta Platforms is the brand that Americans most associated with the metaverse.  

In 2024, white Americans remained the largest racial group in the United States, numbering just over 254 million. Black Americans followed at nearly 47 million, with Asians totaling around 23 million. Hispanic residents, of any race, constituted the nation’s largest ethnic minority. Despite falling fertility, the U.S. population continues to edge upward and is expected to reach 342 million in 2025. International migrations driving population growth The United States’s population growth now hinges on immigration. Fertility rates have long been in decline, falling well below the replacement rate of 2.1. On the other hand, international migration stepped in to add some 2.8 million new arrivals to the national total that year. Changing demographics and migration patterns Looking ahead, the U.S. population is projected to grow increasingly diverse. By 2060, the Hispanic population is expected to grow to 27 percent of the total population. Likewise, African Americans will remain the largest racial minority at just under 15 percent.