This statistic depicts the age distribution in the United States from 2014 to 2024. In 2024, about 17.32 percent of the U.S. population fell into the 0-14 year category, 64.75 percent into the 15-64 age group and 17.93 percent of the population were over 65 years of age. The increasing population of the United States The United States of America is one of the most populated countries in the world, trailing just behind China and India. A total population count of around 320 million inhabitants and a more-or-less steady population growth over the past decade indicate that the country has steadily improved its living conditions and standards for the population. Leading healthier lifestyles and improved living conditions have resulted in a steady increase of the life expectancy at birth in the United States. Life expectancies of men and women at birth in the United States were at a record high in 2012. Furthermore, a constant fertility rate in recent years and a decrease in the death rate and infant mortality, all due to the improved standard of living and health care conditions, have helped not only the American population to increase but as a result, the share of the population younger than 15 and older than 65 years has also increased in recent years, as can be seen above.

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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this page each of these datasets since 2009 can be accessed, though please remember that the older versions have been superceded.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)

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.

Abstract

The primary objective of the 2012 Indonesia Demographic and Health Survey (IDHS) is to provide policymakers and program managers with national- and provincial-level data on representative samples of all women age 15-49 and currently-married men age 15-54.

The 2012 IDHS was specifically designed to meet the following objectives: • Provide data on fertility, family planning, maternal and child health, adult mortality (including maternal mortality), and awareness of AIDS/STIs to program managers, policymakers, and researchers to help them evaluate and improve existing programs; • Measure trends in fertility and contraceptive prevalence rates, and analyze factors that affect such changes, such as marital status and patterns, residence, education, breastfeeding habits, and knowledge, use, and availability of contraception; • Evaluate the achievement of goals previously set by national health programs, with special focus on maternal and child health; • Assess married men’s knowledge of utilization of health services for their family’s health, as well as participation in the health care of their families; • Participate in creating an international database that allows cross-country comparisons that can be used by the program managers, policymakers, and researchers in the areas of family planning, fertility, and health in general

Geographic coverage

National coverage

Analysis unit

• Household
• Women age 15-49
• Ever married men age 15-54
• Never married men age 15-24

Kind of data

Sample survey data [ssd]

Sampling procedure

Indonesia is divided into 33 provinces. Each province is subdivided into districts (regency in areas mostly rural and municipality in urban areas). Districts are subdivided into subdistricts, and each subdistrict is divided into villages. The entire village is classified as urban or rural.

The 2012 IDHS sample is aimed at providing reliable estimates of key characteristics for women age 15-49 and currently-married men age 15-54 in Indonesia as a whole, in urban and rural areas, and in each of the 33 provinces included in the survey. To achieve this objective, a total of 1,840 census blocks (CBs)-874 in urban areas and 966 in rural areas-were selected from the list of CBs in the selected primary sampling units formed during the 2010 population census.

Because the sample was designed to provide reliable indicators for each province, the number of CBs in each province was not allocated in proportion to the population of the province or its urban-rural classification. Therefore, a final weighing adjustment procedure was done to obtain estimates for all domains. A minimum of 43 CBs per province was imposed in the 2012 IDHS design.

Refer to Appendix B in the final report for details of sample design and implementation.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2012 IDHS used four questionnaires: the Household Questionnaire, the Woman’s Questionnaire, the Currently Married Man’s Questionnaire, and the Never-Married Man’s Questionnaire. Because of the change in survey coverage from ever-married women age 15-49 in the 2007 IDHS to all women age 15-49 in the 2012 IDHS, the Woman’s Questionnaire now has questions for never-married women age 15-24. These questions were part of the 2007 Indonesia Young Adult Reproductive Survey questionnaire.

The Household and Woman’s Questionnaires are largely based on standard DHS phase VI questionnaires (March 2011 version). The model questionnaires were adapted for use in Indonesia. Not all questions in the DHS model were adopted in the IDHS. In addition, the response categories were modified to reflect the local situation.

The Household Questionnaire was used to list all the usual members and visitors who spent the previous night in the selected households. Basic information collected on each person listed includes age, sex, education, marital status, education, and relationship to the head of the household. Information on characteristics of the housing unit, such as the source of drinking water, type of toilet facilities, construction materials used for the floor, roof, and outer walls of the house, and ownership of various durable goods were also recorded in the Household Questionnaire. These items reflect the household’s socioeconomic status and are used to calculate the household wealth index. The main purpose of the Household Questionnaire was to identify women and men who were eligible for an individual interview.

The Woman’s Questionnaire was used to collect information from all women age 15-49. These women were asked questions on the following topics: • Background characteristics (marital status, education, media exposure, etc.) • Reproductive history and fertility preferences • Knowledge and use of family planning methods • Antenatal, delivery, and postnatal care • Breastfeeding and infant and young children feeding practices • Childhood mortality • Vaccinations and childhood illnesses • Marriage and sexual activity • Fertility preferences • Woman’s work and husband’s background characteristics • Awareness and behavior regarding HIV-AIDS and other sexually transmitted infections (STIs) • Sibling mortality, including maternal mortality • Other health issues

Questions asked to never-married women age 15-24 addressed the following: • Additional background characteristics • Knowledge of the human reproduction system • Attitudes toward marriage and children • Role of family, school, the community, and exposure to mass media • Use of tobacco, alcohol, and drugs • Dating and sexual activity

The Man’s Questionnaire was administered to all currently married men age 15-54 living in every third household in the 2012 IDHS sample. This questionnaire includes much of the same information included in the Woman’s Questionnaire, but is shorter because it did not contain questions on reproductive history or maternal and child health. Instead, men were asked about their knowledge of and participation in health-careseeking practices for their children.

The questionnaire for never-married men age 15-24 includes the same questions asked to nevermarried women age 15-24.

Cleaning operations

All completed questionnaires, along with the control forms, were returned to the BPS central office in Jakarta for data processing. The questionnaires were logged and edited, and all open-ended questions were coded. Responses were entered in the computer twice for verification, and they were corrected for computeridentified errors. Data processing activities were carried out by a team of 58 data entry operators, 42 data editors, 14 secondary data editors, and 14 data entry supervisors. A computer package program called Census and Survey Processing System (CSPro), which was specifically designed to process DHS-type survey data, was used in the processing of the 2012 IDHS.

Response rate

The response rates for both the household and individual interviews in the 2012 IDHS are high. A total of 46,024 households were selected in the sample, of which 44,302 were occupied. Of these households, 43,852 were successfully interviewed, yielding a household response rate of 99 percent.

Refer to Table 1.2 in the final report for more detailed summarized results of the of the 2012 IDHS fieldwork for both the household and individual interviews, by urban-rural residence.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors, and (2) sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2012 Indonesia Demographic and Health Survey (2012 IDHS) to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2012 IDHS is only one of many samples that could have been selected from the same population, using the same design and identical size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling error is a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

A sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95 percent of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2012 IDHS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulae. The computer software used to calculate sampling errors for the 2012 IDHS is a SAS program. This program used the Taylor linearization method

Israel Employment Rate: Age 15 & Over: Trend: Female data was reported at 57.248 % in Oct 2018. This records a decrease from the previous number of 57.374 % for Sep 2018. Israel Employment Rate: Age 15 & Over: Trend: Female data is updated monthly, averaging 55.911 % from Jan 2012 (Median) to Oct 2018, with 82 observations. The data reached an all-time high of 57.560 % in Jun 2018 and a record low of 53.186 % in Jan 2012. Israel Employment Rate: Age 15 & Over: Trend: Female data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Global Database’s Israel – Table IL.G023: Employment Rate.

Israel Employment Rate: Age 15 & Over: Trend: Male data was reported at 65.192 % in Oct 2018. This records a decrease from the previous number of 65.247 % for Sep 2018. Israel Employment Rate: Age 15 & Over: Trend: Male data is updated monthly, averaging 65.552 % from Jan 2012 (Median) to Oct 2018, with 82 observations. The data reached an all-time high of 66.282 % in May 2017 and a record low of 64.230 % in Jan 2012. Israel Employment Rate: Age 15 & Over: Trend: Male data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Global Database’s Israel – Table IL.G023: Employment Rate.

Israel Employment Rate: Age 25-64: Trend data was reported at 77.349 % in Oct 2018. This records an increase from the previous number of 77.344 % for Sep 2018. Israel Employment Rate: Age 25-64: Trend data is updated monthly, averaging 76.119 % from Jan 2012 (Median) to Oct 2018, with 82 observations. The data reached an all-time high of 77.915 % in May 2018 and a record low of 73.675 % in Jan 2012. Israel Employment Rate: Age 25-64: Trend data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Global Database’s Israel – Table IL.G023: Employment Rate.

BackgroundFood insecurity, the uncertain ability to access adequate food, can limit adherence to dietary measures needed to prevent and manage cardiometabolic conditions. However, little is known about temporal trends in food insecurity among those with diet-sensitive cardiometabolic conditions.MethodsWe used data from the Continuous National Health and Nutrition Examination Survey (NHANES) 2005–2012, analyzed in 2015–2016, to calculate trends in age-standardized rates of food insecurity for those with and without the following diet-sensitive cardiometabolic conditions: diabetes mellitus, hypertension, coronary heart disease, congestive heart failure, and obesity.Results21,196 NHANES participants were included from 4 waves (4,408 in 2005–2006, 5,607 in 2007–2008, 5,934 in 2009–2010, and 5,247 in 2011–2012). 56.2% had at least one cardiometabolic condition, 24.4% had 2 or more, and 8.5% had 3 or more. The overall age-standardized rate of food insecurity doubled during the study period, from 9.06% in 2005–2006 to 10.82% in 2007–2008 to 15.22% in 2009–2010 to 18.33% in 2011–2012 (p for trend < .001). The average annual percentage change in food insecurity for those with a cardiometabolic condition during the study period was 13.0% (95% CI 7.5% to 18.6%), compared with 5.8% (95% CI 1.8% to 10.0%) for those without a cardiometabolic condition, (parallelism test p = .13). Comparing those with and without the condition, age-standardized rates of food insecurity were greater in participants with diabetes (19.5% vs. 11.5%, p < .0001), hypertension (14.1% vs. 11.1%, p = .0003), coronary heart disease (20.5% vs. 11.9%, p < .001), congestive heart failure (18.4% vs. 12.1%, p = .004), and obesity (14.3% vs. 11.1%, p < .001).ConclusionsFood insecurity doubled to historic highs from 2005–2012, particularly affecting those with diet-sensitive cardiometabolic conditions. Since adherence to specific dietary recommendations is a foundation of the prevention and treatment of cardiometabolic disease, these results have important implications for clinical management and public health.

Graph and download economic data for Infra-Annual Labor Statistics: Working-Age Population Total: From 25 to 54 Years for Israel (LFWA25TTILM647N) from Jan 2012 to Jun 2025 about Israel, working-age, 25 to 54 years, and population.

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.

The average age of the Swedish population grew slightly over the past decade. Women's average age was always higher than men's in Sweden within the considered time period. In 2022, the average age of women reached 42.6 years, and the average age of men was 40.9 years.

Israel Employment Rate: Age 25-64: Trend: Female data was reported at 73.830 % in Sep 2018. This records a decrease from the previous number of 73.974 % for Aug 2018. Israel Employment Rate: Age 25-64: Trend: Female data is updated monthly, averaging 71.286 % from Jan 2012 (Median) to Sep 2018, with 81 observations. The data reached an all-time high of 73.974 % in Aug 2018 and a record low of 68.072 % in Jan 2012. Israel Employment Rate: Age 25-64: Trend: Female data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Global Database’s Israel – Table IL.G023: Employment Rate.

Israel Employment Rate: Age 25-64: Trend: Male data was reported at 81.117 % in Sep 2018. This records an increase from the previous number of 81.072 % for Aug 2018. Israel Employment Rate: Age 25-64: Trend: Male data is updated monthly, averaging 81.015 % from Jan 2012 (Median) to Sep 2018, with 81 observations. The data reached an all-time high of 82.455 % in Mar 2018 and a record low of 79.386 % in Jan 2012. Israel Employment Rate: Age 25-64: Trend: Male data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Global Database’s Israel – Table IL.G023: Employment Rate.

TITLE: Hospitalization for Falls, Trends, Age 65 or Older, SA, 2012-15 - FALLHSP65OVSA1215

SUMMARY: Counts and Crude Rates of Hospitalization for Falls, for Ages 65 and Older in New Mexico Small Areas, for individual years 2012 to 2015 and trends. UPDATED 5/28/19

SOURCE: New Mexico Hospital Inpatient Discharge Data, NM Department of Health (Preliminary Query System)

Population Estimates: University of New Mexico, Geospatial and Population Studies (GPS) Program, http://gps.unm.edu/.

via New Mexico Department of Health's NM-IBIS web site (http://ibis.health.state.nm.us)

NOTES: Trends are classified as No Change if the percent of the rate change is within plus or minus one standard error mean (5.9).

PREPARED BY: EMcRae_NMCDC; T Scharmen, NM Department of Health, thomas.scharmen@state.nm.us

FEATURE SERVICE: https://nmcdc.maps.arcgis.com/home/item.html?id=b1504a826cca4223a908b986c01f8c9b

NEW MEXICO VARIABLE DEFINITION

9999 SANO Small Area Number

NEW MEXICO SANAME Small Area Name

3603 H12 Number of Hospitalizations of Persons Age 65 or Older, 2012

3881 H13 Number of Hospitalizations of Persons Age 65 or Older, 2013

3400 H14 Number of Hospitalizations of Persons Age 65 or Older, 2014

3349 H15 Number of Hospitalizations of Persons Age 65 or Older, 2015

14233 H1215 Number of Hospitalizations of Persons Age 65 or Older, 2012 thru 2015

295719 P12 Population of Persons Age 65 or Older, 2012

307390 P13 Population of Persons Age 65 or Older, 2013

319657 P14 Population of Persons Age 65 or Older, 2014

332077 P15 Population of Persons Age 65 or Older, 2015

1254844 P1215 Population of Persons Age 65 or Older, 2012 thru 2015

121.8 R12 Rate of Hospitalizations per 10,000 Persons Age 65 or Older, 2012

126.3 R13 Rate of Hospitalizations per 10,000 Persons Age 65 or Older, 2013

106.4 R14 Rate of Hospitalizations per 10,000 Persons Age 65 or Older, 2014

100.8 R15 Rate of Hospitalizations per 10,000 Persons Age 65 or Older, 2015

113.4 R1215 Rate of Hospitalizations per 10,000 Persons Age 65 or Older, 2012 thru 2015

117.9 CILL12 Lower Confidence Interval for rate, 2012

122.3 CILL13 Lower Confidence Interval for rate, 2013

102.8 CILL14 Lower Confidence Interval for rate, 2014

97.4 CILL15 Lower Confidence Interval for rate, 2015

111.6 CILL1215 Lower Confidence Interval for rate, 2012 thru 2015

125.8 CIUL12 Upper Confidence Interval for rate, 2012

130.2 CIUL13 Upper Confidence Interval for rate, 2013

109.9 CIUL14 Upper Confidence Interval for rate, 2014

104.2 CIUL15 Upper Confidence Interval for rate, 2015

115.3 CIUL1215 Upper Confidence Interval for rate, 2012 thru 2015

-21 DR15_12 Numeric Change in Hospitalization Rate, 2015 minus 2012

-17.2 PDR15_12 Percent Change in Hospitalization Rate, 2015 minus 2012 / 2012 x 100

DECREASED PDRTREND Trend in Percent Change in Hospitalization Rate (see Notes)

About this dataset

Context

The dataset tabulates the population of Shelton by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Shelton. The dataset can be utilized to understand the population distribution of Shelton by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Shelton. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Shelton.

Key observations

Largest age group (population): Male # 55-59 years (2,012) | Female # 55-59 years (1,862). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

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

Age groups:

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

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Shelton population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Shelton is shown in the following column.
• Population (Female): The female population in the Shelton is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Shelton for each age group.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

This dataset is a part of the main dataset for Shelton Population by Gender. You can refer the same here

GLA 2012 round ward-level population projections by 5yr age groups using 2009 SHLAA-based housing trajectories. These differ from the standard ward projections in that development data is used to distribute population at ward level, but the overall borough-level projection is constrained to the 2012 round Trend-based projection found here. Ward projections consistent with the 2012 round SHLAA-based borough projections can be found here.

There is a custom age range tool available for this data.

For links to the GLA's full range of demographic projections click here.

Year, month, region, gender, age: 0-4 years old, age: 5-9 years old, age: 10-14 years old, age: 15-19 years old, age: 20-24 years old, age: 25-29 years old , Age: 30-34 years old, Age: 35-39 years old, Age: 40-44 years old, Age: 45-49 years old, Age: 50-54 years old, Age: 55-59 years old, Age: 60-64 years old, Age: 65-69 years old, Age: 70-74 years old, Age: 75-79 years old, Age: 80-84 years old, Age: 85-89 years old, Age: 90-94 years old, Age: 95-99 years old, Age: 95-99 years old : Over 100 years old

About this dataset

Context

The dataset tabulates the population of Laurens County by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Laurens County. The dataset can be utilized to understand the population distribution of Laurens County by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Laurens County. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Laurens County.

Key observations

Largest age group (population): Male # 10-14 years (2,012) | Female # 10-14 years (2,117). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

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

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Laurens County population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Laurens County is shown in the following column.
• Population (Female): The female population in the Laurens County is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Laurens County for each age group.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

This dataset is a part of the main dataset for Laurens County Population by Gender. You can refer the same here

This short report uses 2002 to 2012 National Survey on Drug Use and Health (NSDUH) to assess trends in past month smokeless tobacco initiation and use by gender and age group among those aged 12 or older.