Health, United States is an annual report on trends in health statistics, find more information at http://www.cdc.gov/nchs/hus.htm.

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.

This table provides statistical information about people in Canada by their demographic, social and economic characteristics as well as provide information about the housing units in which they live.

This dataset documents cardiovascular disease (CVD) death rates, relative and absolute excess death rates, and trends. Specifically, this report presents county (or county equivalent) estimates of CVD death rates in 2000-2020, trends during 2010-2019, and relative and absolute excess death rates in 2020 by age group (ages 35–64 years, ages 65 years and older). All estimates were generated using a Bayesian spatiotemporal model and a smoothed over space, time, and 10-year age groups. Rates are age-standardized in 10-year age groups using the 2010 US population. Data source: National Vital Statistics System.

This dataset provides the distribution of the total population in Qatar by age groups (0-14, 15-64, and 65+) for the census years 2010 and 2020, showing population numbers and their corresponding percentages.

The information above provides insights on the average age of the French population between the years 2010 and 2023. We can thus observe that the average age of the French has continued to increase over the ten years presented: from **** years on average, the French are passed to **** years of average age.

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 chart shows the trend in health care coverage status among NY residents age 18 and over by gender from 2007 to 2010. Behavioral Risk Factor Surveillance System (BRFSS) sample data were used to generate annual percentages of non-institutionalized adult (18+) NYS residents with/without health insurance coverage. Health care coverage percentages are provided for 2007 forward, and are available for a range of demographic groups (New York City/Rest of State; Sex; Race/Ethnicity; Age; Education; Income; Disability Status; Employment Status; Mental Health Status). BRFSS is a random digit dialing (RDD) phone survey of the health status and health behaviors of adult NYS residents. The sample covers between 6,000 and 9,000 completed interviews annually. For more information, check out: http://www.health.ny.gov/statistics/brfss/. The "About" tab contains additional details concerning this dataset.

This layer shows the age statistics in Tucson by neighborhood, aggregated from block level data, between 2010-2019. For questions, contact GIS_IT@tucsonaz.gov. The data shown is from Esri's 2019 Updated Demographic estimates.Esri's U.S. Updated Demographic (2019/2024) Data - Population, age, income, sex, race, home value, and marital status are among the variables included in the database. Each year, Esri's Data Development team employs its proven methodologies to update more than 2,000 demographic variables for a variety of U.S. geographies.Additional Esri Resources:Esri DemographicsU.S. 2019/2024 Esri Updated DemographicsEssential demographic vocabularyPermitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

This dataset documents rates and trends in local hypertension-related cardiovascular disease (CVD) death rates. Specifically, this report presents county (or county equivalent) estimates of hypertension-related CVD death rates in 2000-2019 and trends during two intervals (2000-2010, 2010-2019) by age group (ages 35–64 years, ages 65 years and older), race/ethnicity (non-Hispanic American Indian/Alaska Native, non-Hispanic Asian/Pacific Islander, non-Hispanic Black, Hispanic, non-Hispanic White), and sex (female, male). The rates and trends were estimated using a Bayesian spatiotemporal model and a smoothed over space, time, and demographic group. Rates are age-standardized in 10-year age groups using the 2010 US population. Data source: National Vital Statistics System.

BackgroundLife expectancy at birth in the United States will likely surpass 80 years in the coming decade. Yet recent studies suggest that longevity gains are unevenly shared across age and socioeconomic groups. First, mortality in midlife has risen among non-Hispanic whites. Second, low-educated whites have suffered stalls (men) or declines (women) in adult life expectancy, which is significantly lower than among their college-educated counterparts. Estimating the number of life years lost or gained by age and cause of death, broken down by educational attainment, is crucial in identifying vulnerable populations.Methods and FindingsUsing U.S. vital statistics data from 1990 to 2010, this study decomposes the change in life expectancy at age 25 by age and cause of death across educational attainment groups, broken down by race and gender. The findings reveal that mortality in midlife increased for white women (and to a lesser extent men) with 12 or fewer years of schooling, accounting for most of the stalls or declines in adult life expectancy observed in those groups. Among blacks, mortality declined in nearly all age and educational attainment groups. Although an educational gradient was found across multiple causes of death, between 60 and 80 percent of the gap in adult life expectancy was explained by cardiovascular diseases, smoking-related diseases, and external causes of death. Furthermore, the number of life years lost to smoking-related, external, and other causes of death increased among low- and high school-educated whites, explaining recent stalls or declines in longevity.ConclusionsLarge segments of the American population—particularly low- and high school-educated whites under age 55—are diverging from their college-educated counterparts and losing additional years of life to smoking-related diseases and external causes of death. If this trend continues, old-age mortality may also increase for these birth cohorts in the coming decades.

This dataset tracks the updates made on the dataset "Selected Trend Table from Health, United States, 2011. Health conditions among children under 18 years of age, by selected characteristics: United States, average annual, selected years 1997 - 1999 through 2008 - 2010" as a repository for previous versions of the data and metadata.

This dataset documents rates and trends in heart disease and stroke mortality. Specifically, this report presents county (or county equivalent) estimates of heart disease and stroke death rates in 2000-2019 and trends during two intervals (2000-2010, 2010-2019) by age group (ages 35–64 years, ages 65 years and older), race/ethnicity (non-Hispanic American Indian/Alaska Native, non-Hispanic Asian/Pacific Islander, non-Hispanic Black, Hispanic, non-Hispanic White), and sex (women, men). The rates and trends were estimated using a Bayesian spatiotemporal model and a smoothed over space, time, and demographic group. Rates are age-standardized in 10-year age groups using the 2010 US population. Data source: National Vital Statistics System.

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Excel age range creator for GLA Projections data

This Excel based tool enables users to query the raw single year of age data so that any age range can easily be calculated without having to carry out often complex, and time consuming formulas that could also be open to human error. Each year the GLA demography team produce sets of population projections. On this page each of these datasets since 2009 can be accessed, though please remember that the older versions have been superceded. From 2012, data includes population estimates and projections between 2001 and 2041 for each borough plus Central London (Camden, City of London, Kensington & Chelsea, and Westminster), Rest of Inner Boroughs, Inner London, Outer London and Greater London.

The full raw data by single year of age (SYA) and gender are available as Datastore packages at the links below.

How to use the tool: Simply select the lower and upper age range for both males and females (starting in cell C3) and the spreadsheet will return the total population for the range.

Tip: You can copy and paste the boroughs you are interested in to another worksheet by clicking: Edit then Go To (or Control + G), then Special, and Visible cells only. Then simply copy and 'paste values' of the cells to a new location.

Warning: The ethnic group and ward files are large (around 35MB), and may take some time to download depending on your bandwidth.

Find out more about GLA population projections on the GLA Demographic Projections page

BOROUGH PROJECTIONS

GLA 2009 Round London Plan Population Projections (January 2010) (SUPERSEDED)

GLA 2009 Round (revised) London Plan Population Projections (August 2010) (SUPERCEDED)

GLA 2009 Round (revised) SHLAA Population Projections (August 2010) (SUPERCEDED)

GLA 2010 Round SHLAA Population Projections (February 2011) (SUPERCEDED)

GLA 2011 Round SHLAA Population Projections, High Fertility (December 2011) (SUPERCEDED)

GLA 2011 Round SHLAA Population Projections, Standard Fertility (January 2012) (SUPERCEDED)

GLA 2012 Round SHLAA Population Projections, (December 2012)(SUPERCEDED)

GLA 2012 Round Trend Based Population Projections, (December 2012) (SUPERCEDED)

GLA 2012 Round SHLAA Borough Projections incorporating DCLG 2011 household formation rates, (June 2013) (SUPERCEDED)

GLA 2013 Round Trend Based Population Projections - High (December 2013) (SUPERCEDED)

GLA 2013 Round Trend Based Population Projections - Central (December 2013) (SUPERCEDED)

GLA 2013 Round Trend Based Population Projections - Low (December 2013) (SUPERCEDED)

GLA 2013 Round SHLAA Based Population Projections (February 2014) (SUPERCEDED) Spreadsheet now includes national comparator data from ONS.

GLA 2013 Round SHLAA Based Capped Population Projections (March 2014) (SUPERCEDED) Spreadsheet includes national comparator data from ONS.

GLA 2014 Round Trend-based, Short-Term Migration Scenario Population Projections (April 2015) Spreadsheet includes national comparator data from ONS.

GLA 2014 Round Trend-based, Long-Term Migration Scenario Population Projections (April 2015) Spreadsheet includes national comparator data from ONS.

GLA 2014 Round SHLAA DCLG Based Long Term Migration Scenario Population Projections (April 2015) Spreadsheet includes national comparator data from ONS.

GLA 2014 Round SHLAA Capped Household Size Model Short Term Migration Scenario Population Projections (April 2015) Spreadsheet includes national comparator data from ONS. This is the recommended file to use.

WARD PROJECTIONS

GLA 2008 round (High) Ward Projections (March 2009) (SUPERSEDED)

GLA 2009 round (revised) London Plan Ward Projections (August 2010) (SUPERCEDED)

GLA 2010 round SHLAA Ward Projections (February 2011) (SUPERCEDED)

GLA 2011 round SHLAA Standard Ward Projections (January 2012) (SUPERCEDED)

GLA 2011 round SHLAA High Ward Projections (January 2012) (SUPERCEDED)

GLA 2012 round SHLAA based Ward Projections (March 2013) (XLS) (SUPERCEDED)

GLA 2012 round SHLAA Ward Projections (March 2013) (XLS) (SUPERCEDED)

GLA 2013 round SHLAA Ward Projections (March 2014) (SUPERCEDED)

GLA 2013 round SHLAA Capped Ward Projections (March 2014) (SUPERCEDED)

GLA 2014 round SHLAA Capped Household Size Model Short Term Migration Scenario Ward Projections (April 2015) This is the recommended file to use.

ETHNIC GROUP PROJECTIONS FOR LOCAL AUTHORITIES

GLA 2012 Round SHLAA Ethnic Group Borough Projections - Interim (May 2013) (SUPERCEDED)

GLA 2012 Round Trend Based Ethnic Group Borough Projections - Interim (May 2013) (SUPERCEDED)

GLA 2012 Round SHLAA Based Ethnic Group Borough Projections - Final (Nov 2013) (SUPERCEDED)

GLA 2012 Round Trend Based Ethnic Group Borough Projections - Final (Nov 2013) (SUPERCEDED)

GLA 2013 Round SHLAA Capped Ethnic Group Borough Projections (August 2014)

This dataset of U.S. mortality trends since 1900 highlights trends in age-adjusted death rates for five selected major causes of death. Age-adjusted death rates (deaths per 100,000) after 1998 are calculated based on the 2000 U.S. standard population. Populations used for computing death rates for 2011–2017 are postcensal estimates based on the 2010 census, estimated as of July 1, 2010. Rates for census years are based on populations enumerated in the corresponding censuses. Rates for noncensus years between 2000 and 2010 are revised using updated intercensal population estimates and may differ from rates previously published. Data on age-adjusted death rates prior to 1999 are taken from historical data (see References below). Revisions to the International Classification of Diseases (ICD) over time may result in discontinuities in cause-of-death trends. SOURCES CDC/NCHS, National Vital Statistics System, historical data, 1900-1998 (see https://res1wwwd-o-tcdcd-o-tgov.vcapture.xyz/nchs/nvss/mortality_historical_data.htm); CDC/NCHS, National Vital Statistics System, mortality data (see http://www.cdc.gov/nchs/deaths.htm); and CDC WONDER (see http://wonder.cdc.gov). REFERENCES National Center for Health Statistics, Data Warehouse. Comparability of cause-of-death between ICD revisions. 2008. Available from: http://www.cdc.gov/nchs/nvss/mortality/comparability_icd.htm. National Center for Health Statistics. Vital statistics data available. Mortality multiple cause files. Hyattsville, MD: National Center for Health Statistics. Available from: https://res1wwwd-o-tcdcd-o-tgov.vcapture.xyz/nchs/data_access/vitalstatsonline.htm. Kochanek KD, Murphy SL, Xu JQ, Arias E. Deaths: Final data for 2017. National Vital Statistics Reports; vol 68 no 9. Hyattsville, MD: National Center for Health Statistics. 2019. Available from: https://res1wwwd-o-tcdcd-o-tgov.vcapture.xyz/nchs/data/nvsr/nvsr68/nvsr68_09-508.pdf. Arias E, Xu JQ. United States life tables, 2017. National Vital Statistics Reports; vol 68 no 7. Hyattsville, MD: National Center for Health Statistics. 2019. Available from: https://res1wwwd-o-tcdcd-o-tgov.vcapture.xyz/nchs/data/nvsr/nvsr68/nvsr68_07-508.pdf. National Center for Health Statistics. Historical Data, 1900-1998. 2009. Available from: https://res1wwwd-o-tcdcd-o-tgov.vcapture.xyz/nchs/nvss/mortality_historical_data.htm.

Thailand TH: Age Dependency Ratio: % of Working-Age Population data was reported at 40.237 % in 2017. This records an increase from the previous number of 40.067 % for 2016. Thailand TH: Age Dependency Ratio: % of Working-Age Population data is updated yearly, averaging 55.820 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 90.722 % in 1969 and a record low of 39.085 % in 2010. Thailand TH: Age Dependency Ratio: % of Working-Age Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Thailand – Table TH.World Bank.WDI: Population and Urbanization Statistics. Age dependency ratio is the ratio of dependents--people younger than 15 or older than 64--to the working-age population--those ages 15-64. Data are shown as the proportion of dependents per 100 working-age population.; ; World Bank staff estimates based on age distributions of United Nations Population Division's World Population Prospects: 2017 Revision.; Weighted average; Relevance to gender indicator: this indicator implies the dependency burden that the working-age population bears in relation to children and the elderly. Many times single or widowed women who are the sole caregiver of a household have a high dependency ratio.

Abstract

The 2010 Armenia Demographic and Health Survey (2010 ADHS) is the third in a series of nationally representative sample surveys designed to provide information on population and health issues. It is conducted in Armenia under the worldwide Demographic and Health Surveys program. Specifically, the 2010 ADHS has a primary objective of providing current and reliable information on fertility levels, marriage, sexual activity, fertility preferences, awareness and use of family planning methods, breastfeeding practices, nutritional status of young children, childhood mortality, maternal and child health, and awareness and behavior regarding AIDS and other sexually transmitted infections (STIs). The survey obtained detailed information on these issues from women of reproductive age and, for certain topics, from men as well.

The 2010 ADHS results are intended to provide information needed to evaluate existing social programs and to design new strategies to improve health of and health services for the people of Armenia. Data are presented by region (marz) wherever sample size permits. The information collected in the 2010 ADHS will provide updated estimates of basic demographic and health indicators covered in the 2000 and 2005 surveys.

The long-term objective of the survey includes strengthening the technical capacity of major government institutions, including the NSS. The 2010 ADHS also provides comparable data for longterm trend analysis in Armenia because the 2000, 2005, and 2010 surveys were implemented by the same organisation and used similar data collection procedures. It also adds to the international database of demographic and health–related information for research purposes.

The 2010 ADHS was conducted by the National Statistical Service (NSS) and the MOH of Armenia from October 5 through December 25, 2010.

Kind of data

Sample survey data

Sampling procedure

The sample was designed to permit detailed analysis-including the estimation of rates of fertility, infant/child mortality, and abortion-at the national level, for Yerevan, and for total urban and total rural areas separately. Many indicators can also be estimated at the regional (marz) level.

A representative probability sample of 7,580 households was selected for the 2010 ADHS sample. The sample was selected in two stages. In the first stage, 308 clusters were selected from a list of enumeration areas in a subsample of a master sample derived from the 2001 Population Census frame. In the second stage, a complete listing of households was carried out in each selected cluster. Households were then systematically selected for participation in the survey.

All women age 15-49 who were either permanent residents of the households in the 2010 ADHS sample or visitors present in the household on the night before the survey were eligible to be interviewed. Interviews were completed with 5,922 women. In addition, in a subsample of one-third of all of the households selected for the survey, all men age 15-49 were eligible to be interviewed if they were either permanent residents or visitors present in the household on the night before the survey. Interviews were completed with 1,584 men.

Appendix A of the Final Report provides additional information on the sample design of the 2010 Armenia DHS.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were used in the ADHS: a Household Questionnaire, a Woman’s Questionnaire, and a Man’s Questionnaire. The Household Questionnaire and the individual questionnaires were based on model survey instruments developed in the MEASURE DHS program and questionnaires used in the previous 2005 ADHS. The model questionnaires were adapted for use by NSS and MOH. Suggestions were also sought from a number of nongovernmental organizations (NGOs). The questionnaires were developed in English and translated into Armenian. They were pretested in July 2010.

The Household Questionnaire was used to list all usual members of and visitors to the selected households and to collect information on the socioeconomic status of the household. The first part of the Household Questionnaire collected for each household member or visitor information on their age, sex, educational attainment, and relationship to the head of household. This information provided basic demographic data for Armenian households. It also was used to identify the women and men who were eligible for an individual interview (i.e., women and men age 15-49). In the second part of the Household Questionnaire, there were questions on housing characteristics (e.g., the flooring material, the source of water, and the type of toilet facilities), on ownership of a variety of consumer goods, and on other aspects of the socioeconomic status of the household. In addition, the Household Questionnaire was used to obtain information on each child’s birth registration, ask questions about child discipline and child labor, and record height and weight measurements of children under age 5.

The Woman’s Questionnaire obtained information from women age 15-49 on the following topics: - Background characteristics - Pregnancy history - Antenatal, delivery, and postnatal care - Knowledge, attitudes, and use of contraception - Reproductive and adult health - Childhood mortality - Health and health care utilization - Vaccinations of children under age 5 - Episodes of diarrhea and respiratory illness of children under age 5 - Breastfeeding and weaning practices - Marriage and recent sexual activity - Fertility preferences - Knowledge of and attitudes toward AIDS and other sexually transmitted diseases - Woman’s work and husband’s background characteristics

The Man’s Questionnaire, administered to men age 15-49, focused on the following topics: - Background characteristics - Health and health care utilization - Marriage and recent sexual activity - Attitudes toward and use of condoms - Knowledge of and attitudes toward AIDS and other sexually transmitted diseases - Attitudes toward women’s status

Cleaning operations

Data Processing

The processing of the ADHS results began shortly after fieldwork commenced. Completed questionnaires were returned regularly from the field to NSS headquarters in Yerevan, where they were entered and edited by data processing personnel who were specially trained for this task. The data processing personnel included a supervisor, a questionnaire administrator (who ensured that the expected number of questionnaires from all clusters was received), several office editors, 12 data entry operators, and a secondary editor. The concurrent processing of the data was an advantage because the senior DHS technical staff were able to advise field teams of problems detected during the data entry. In particular, tables were generated to check various data quality parameters. As a result, specific feedback was given to the teams to improve performance. The data entry and editing phase of the survey was completed in March 2011.

Response rate

A total of 7,580 households were selected in the sample, of which 7,043 were occupied at the time of the fieldwork. The main reason for the difference is that some of the dwelling units that were occupied during the household listing operation were either vacant or the household was away for an extended period at the time of interviewing. The number of occupied households successfully interviewed was 6,700, yielding a household response rate of 95 percent. The household response rate in urban areas (94 percent) was slightly lower than in rural areas (97 percent).

In these households, a total of 6,059 eligible women were identified; interviews were completed with 5,922 of these women, yielding a response rate of 98 percent. In one-third of the households, a total of 1,641 eligible men were identified, and interviews were completed with 1,584 of these men, yielding a response rate of 97 percent. Response rates are slightly lower in urban areas (97 percent for women and 96 percent for men) than in rural areas where rates were 99 and 97 percent, respectively.

Sampling error estimates

Detailed information on sampling errors is provided in Appendix B of the Final Report.

Excel Age-Range creator for Office for National Statistics (ONS) Mid year population estimates (MYE) covering each year between 1999 and 2016

These files take into account the revised estimates for 2002-2010 released in April 2013 down to Local Authority level and the post 2011 estimates based on the Census results. Scotland and Northern Ireland data has not been revised, so Great Britain and United Kingdom totals comprise the original data for these plus revised England and Wales figures.

This Excel based tool enables users to query the single year of age raw data so that any age range can easily be calculated without having to carry out often complex, and time consuming formulas that could also be open to human error. Simply select the lower and upper age range for both males and females and the spreadsheet will return the total population for the range. Please adhere to the terms and conditions of supply contained within the file.

Tip: You can copy and paste the rows you are interested in to another worksheet by using the filters at the top of the columns and then select all by pressing Ctrl+A. Then simply copy and paste the cells to a new location.

ONS Mid year population estimates

Open Excel tool (London Boroughs, Regions and National, 1999-2016)

Also available is a custom-age tool for all geographies in the UK. Open the tool for all UK geographies (local authority and above) for: 2010, 2011, 2012, 2013, 2014 and 2015.

This full MYE dataset by single year of age (SYA) age and gender is available as a Datastore package here.

Ward Level Population estimates

Single year of age population tool for 2002 to 2015 for all wards in London.

New 2014 Ward boundary estimates

Ward boundary changes in May 2014 only affected three London boroughs - Hackney, Kensington and Chelsea, and Tower Hamlets. The estimates between 2001-2013 have been calculated by the GLA by taking the proportion of a the old ward that falls within the new ward based on the proportion of population living in each area at the 2011 Census. Therefore, these estimates are purely indicative and are not official statistics and not endorsed by ONS. From 2014 onwards, ONS began publishing official estimates for the new ward boundaries. Download here.

This table provides statistical information about people in Canada by their demographic, social and economic characteristics as well as provide information about the housing units in which they live.

This dataset documents rates and trends in heart disease and stroke mortality. Specifically, this report presents county (or county equivalent) estimates of heart disease and stroke death rates in 2000-2019 and trends during two intervals (2000-2010, 2010-2019) by age group (ages 35–64 years, ages 65 years and older), race/ethnicity (non-Hispanic American Indian/Alaska Native, non-Hispanic Asian/Pacific Islander, non-Hispanic Black, Hispanic, non-Hispanic White), and sex (women, men). The rates and trends were estimated using a Bayesian spatiotemporal model and a smoothed over space, time, and demographic group. Rates are age-standardized in 10-year age groups using the 2010 US population. Data source: National Vital Statistics System.