About this dataset

Context

The dataset tabulates the United States population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for United States. The dataset can be utilized to understand the population distribution of United States by age. For example, using this dataset, we can identify the largest age group in United States.

Key observations

The largest age group in United States was for the group of age 30 to 34 years years with a population of 22.71 million (6.86%), according to the ACS 2018-2022 5-Year Estimates. At the same time, the smallest age group in United States was the 80 to 84 years years with a population of 6.25 million (1.89%). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 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

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the United States is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of United States total population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 United States Population by Age. You can refer the same here

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

About this dataset

Context

The dataset tabulates the Live Oak population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Live Oak. The dataset can be utilized to understand the population distribution of Live Oak by age. For example, using this dataset, we can identify the largest age group in Live Oak.

Key observations

The largest age group in Live Oak, CA was for the group of age 15 to 19 years years with a population of 893 (9.64%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Live Oak, CA was the 80 to 84 years years with a population of 103 (1.11%). 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

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Live Oak is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Live Oak total population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Live Oak Population by Age. You can refer the same here

Number of people 80+ living in special forms of accommodation divided by number of inhabitants 80+ 31/12. The year 2013-2020 refers to an average of 12 months of the year, from 2021 a municipality’s individual median. As of 2007, the number is derived from the National Board of Health and Welfare’s individual statistics. Until 2006 from the National Board of Health and Welfare’s quantitative statistics. It refers to all directing.

This Dataset provides comprehensive demographic information on global populations from 1950 to the present. It offers insights into various aspects of population dynamics, including population counts, gender ratios, birth and death rates, life expectancy, and migration patterns.

Column Descriptions:

SortOrder: Numeric identifier for sorting.

LocID: Location identifier.

Notes: Additional notes or comments (blank in this dataset).

ISO3_code: ISO 3-character country code.

ISO2_code: ISO 2-character country code.

SDMX_code: Statistical Data and Metadata Exchange code.

LocTypeID: Location type identifier.

LocTypeName: Location type name.

ParentID: Identifier for the parent location.

Location: Name of the location.

VarID: Identifier for the variant.

Variant: Type of population variant.

Time: Year or time period.

TPopulation1Jan: Total population on January 1st.

TPopulation1July: Total population on July 1st.

TPopulationMale1July: Total male population on July 1st.

TPopulationFemale1July: Total female population on July 1st.

PopDensity: Population density (people per square kilometer).

PopSexRatio: Population sex ratio (male/female).

MedianAgePop: Median age of the population.

NatChange: Natural change in population.

NatChangeRT: Natural change rate (per 1,000 people).

PopChange: Population change.

PopGrowthRate: Population growth rate (percentage).

DoublingTime: Time for population to double (in years).

Births: Total number of births.

Births1519: Births to mothers aged 15-19.

CBR: Crude birth rate (per 1,000 people).

TFR: Total fertility rate (average number of children per woman).

NRR: Net reproduction rate.

MAC: Mean age at childbearing.

SRB: Sex ratio at birth (male/female).

Deaths: Total number of deaths.

DeathsMale: Total male deaths.

DeathsFemale: Total female deaths.

CDR: Crude death rate (per 1,000 people).

LEx: Life expectancy at birth.

LExMale: Life expectancy for males at birth.

LExFemale: Life expectancy for females at birth.

LE15: Life expectancy at age 15.

LE15Male: Life expectancy for males at age 15.

LE15Female: Life expectancy for females at age 15.

LE65: Life expectancy at age 65.

LE65Male: Life expectancy for males at age 65.

LE65Female: Life expectancy for females at age 65.

LE80: Life expectancy at age 80.

LE80Male: Life expectancy for males at age 80.

LE80Female: Life expectancy for females at age 80.

InfantDeaths: Number of infant deaths.

IMR: Infant mortality rate (per 1,000 live births).

LBsurvivingAge1: Children surviving to age 1.

Under5Deaths: Number of deaths under age 5.

NetMigrations: Net migration rate (per 1,000 people).

CNMR: Crude net migration rate.

How to Use the Dataset:

1. Researchers can analyze demographic trends, birth and death rates, and population growth over time.
2. Policymakers can use population data to inform decisions on healthcare, education, and social services.
3. Data scientists can visualize and model population dynamics for various regions.
4. Journalists can use the dataset to report on global population trends and disparities.
5. Educators can incorporate real-world population data into lessons and research.

Please upvote and show your support if you find this dataset valuable for your research or analysis. Your feedback and contributions help make this dataset more accessible to the Kaggle community. Thank you!

Context

Data set taken from WHO: See Life Tables by Country (CUBA) & Life Expectancy at Birth (CUBA) Detailed information on year-wise deaths by age group, and population left alive by age group - this data permits calculations of Life Expectancies for Cuba. This is data for a lecture on computation of life-expectancies, which is part of a course on Real Statistics: An Islamic Approach. Lecture linked below provides further details on how to compute life expectancies from this data: Computing Life Expectancies from Mortality Tables.

Content

Rows 3 to 21 provide Age-Specific death rates for 5 year groups 0-5. 5-10, and so on up to 80-85, and 85+ Rows 22 to 40 provide probability of dying in each of these same age-categories. Rows 41 to 59 provide Number of people left alive in each of these 5- year age groups Rows 60 to 78 provide number of people who die in each of these age categories Rows 79 to 97 provide number of person-years lived by each of these 5-year age cohorts Rows 98 to 116 provide number of person-years lived ABOVE given age group Rows 117 to 135 provide life expectancy within each age category

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

Life expectancy at birth and at age 65, by sex, on a three-year average basis.

Number of deaths and mortality rates, by age group, sex, and place of residence, 1991 to most recent year.

Estimated number of persons on July 1, by 5-year age groups and gender, and median age, for Canada, provinces and territories.

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

About

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

Column Description

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

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

Foto von kazi arifuzzaman auf Unsplash

Abstract

The fourth edition of the Global Findex offers a lens into how people accessed and used financial services during the COVID-19 pandemic, when mobility restrictions and health policies drove increased demand for digital services of all kinds.

The Global Findex is the world's most comprehensive database on financial inclusion. It is also the only global demand-side data source allowing for global and regional cross-country analysis to provide a rigorous and multidimensional picture of how adults save, borrow, make payments, and manage financial risks. Global Findex 2021 data were collected from national representative surveys of about 128,000 adults in more than 120 economies. The latest edition follows the 2011, 2014, and 2017 editions, and it includes a number of new series measuring financial health and resilience and contains more granular data on digital payment adoption, including merchant and government payments.

The Global Findex is an indispensable resource for financial service practitioners, policy makers, researchers, and development professionals.

Geographic coverage

People living in remote or difficult-to-access rural areas were excluded. The excluded area represents approximately 2 percent of the total population.

Analysis unit

Individual

Kind of data

Observation data/ratings [obs]

Sampling procedure

In most developing economies, Global Findex data have traditionally been collected through face-to-face interviews. Surveys are conducted face-to-face in economies where telephone coverage represents less than 80 percent of the population or where in-person surveying is the customary methodology. However, because of ongoing COVID-19 related mobility restrictions, face-to-face interviewing was not possible in some of these economies in 2021. Phone-based surveys were therefore conducted in 67 economies that had been surveyed face-to-face in 2017. These 67 economies were selected for inclusion based on population size, phone penetration rate, COVID-19 infection rates, and the feasibility of executing phone-based methods where Gallup would otherwise conduct face-to-face data collection, while complying with all government-issued guidance throughout the interviewing process. Gallup takes both mobile phone and landline ownership into consideration. According to Gallup World Poll 2019 data, when face-to-face surveys were last carried out in these economies, at least 80 percent of adults in almost all of them reported mobile phone ownership. All samples are probability-based and nationally representative of the resident adult population. Phone surveys were not a viable option in 17 economies that had been part of previous Global Findex surveys, however, because of low mobile phone ownership and surveying restrictions. Data for these economies will be collected in 2022 and released in 2023.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households. Each eligible household member is listed, and the hand-held survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender.

In traditionally phone-based economies, respondent selection follows the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies where mobile phone and landline penetration is high, a dual sampling frame is used.

The same respondent selection procedure is applied to the new phone-based economies. Dual frame (landline and mobile phone) random digital dialing is used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digital dialing is used in economies with limited to no landline presence (less than 20 percent).

For landline respondents in economies where mobile phone or landline penetration is 80 percent or higher, random selection of respondents is achieved by using either the latest birthday or household enumeration method. For mobile phone respondents in these economies or in economies where mobile phone or landline penetration is less than 80 percent, no further selection is performed. At least three attempts are made to reach a person in each household, spread over different days and times of day.

Sample size for Albania is 1000.

Mode of data collection

Face-to-face [f2f]

Research instrument

Questionnaires are available on the website.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar. 2022. The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. Washington, DC: World Bank.

This dataset and map service provides information on the U.S. Housing and Urban Development's (HUD) low to moderate income areas. The term Low to Moderate Income, often referred to as low-mod, has a specific programmatic context within the Community Development Block Grant (CDBG) program. Over a 1, 2, or 3-year period, as selected by the grantee, not less than 70 percent of CDBG funds must be used for activities that benefit low- and moderate-income persons. HUD uses special tabulations of Census data to determine areas where at least 51% of households have incomes at or below 80% of the area median income (AMI). This dataset and map service contains the following layer.

The collection of patient sexual orientation and gender identity information is crucial in identifying and addressing disparities in healthcare access, quality, and outcomes for sexual and gender minority individuals. While some studies have explored patients’ willingness to disclose this information in specific settings, little is known about response rates in digital health applications. In light of the growing use of digital health, including virtual care, we sought to determine whether adults would respond to optional sexual orientation and gender identity fields during registration for a digital health application offered through their employer-provided benefits. We analyzed response rates for sexual orientation and gender identity by age, race and ethnicity, and region among individuals over age 17 between September 9th and December 31, 2022. Our study, which included over 41,000 commercially-insured adults from all 50 states, found that nearly 80% were willing to report their sexual orientation and gender identity. However, we observed higher nonresponse rates among older adults and individuals living in central and southern regions, with no consistent pattern by race and ethnicity. Our findings indicate that digital health applications could be a valuable resource for collecting this data from a diverse group of adults. Nevertheless, digital health companies must ensure that they use the data responsibly, identifying quality improvement initiatives and contributing to research that can inform health policies for sexual and gender minority individuals.

This data originates from the Public Health Outcomes tool currently presents data for available indicators for upper tier local authority levels, collated by Public Health England (PHE). The data currently published here are the baselines for the Public Health Outcomes Framework, together with more recent data where these are available. The baseline period is 2010 or equivalent, unless these data are unavailable or not deemed to be of sufficient quality. The first data were published in this tool as an official statistics release in November 2012. Future official statistics updates will be published as part of a quarterly update cycle in August, November, February and May. The definition, rationale, source information, and methodology for each indicator can be found within the spreadsheet. Data included in the spreadsheet: 0.1i - Healthy life expectancy at birth0.1ii - Life Expectancy at 650.1ii - Life Expectancy at birth0.2i - Slope index of inequality in life expectancy at birth based on national deprivation deciles within England0.2ii - Number of upper tier local authorities for which the local slope index of inequality in life expectancy (as defined in 0.2iii) has decreased0.2iii - Slope index of inequality in life expectancy at birth within English local authorities, based on local deprivation deciles within each area0.2iv - Gap in life expectancy at birth between each local authority and England as a whole0.2v - Slope index of inequality in healthy life expectancy at birth based on national deprivation deciles within England0.2vii - Slope index of inequality in life expectancy at birth within English regions, based on regional deprivation deciles within each area1.01i - Children in poverty (all dependent children under 20)1.01ii - Children in poverty (under 16s)1.02i - School Readiness: The percentage of children achieving a good level of development at the end of reception1.02i - School Readiness: The percentage of children with free school meal status achieving a good level of development at the end of reception1.02ii - School Readiness: The percentage of Year 1 pupils achieving the expected level in the phonics screening check1.02ii - School Readiness: The percentage of Year 1 pupils with free school meal status achieving the expected level in the phonics screening check1.03 - Pupil absence1.04 - First time entrants to the youth justice system1.05 - 16-18 year olds not in education employment or training1.06i - Adults with a learning disability who live in stable and appropriate accommodation1.06ii - % of adults in contact with secondary mental health services who live in stable and appropriate accommodation1.07 - People in prison who have a mental illness or a significant mental illness1.08i - Gap in the employment rate between those with a long-term health condition and the overall employment rate1.08ii - Gap in the employment rate between those with a learning disability and the overall employment rate1.08iii - Gap in the employment rate for those in contact with secondary mental health services and the overall employment rate1.09i - Sickness absence - The percentage of employees who had at least one day off in the previous week1.09ii - Sickness absence - The percent of working days lost due to sickness absence1.10 - Killed and seriously injured (KSI) casualties on England's roads1.11 - Domestic Abuse1.12i - Violent crime (including sexual violence) - hospital admissions for violence1.12ii - Violent crime (including sexual violence) - violence offences per 1,000 population1.12iii- Violent crime (including sexual violence) - Rate of sexual offences per 1,000 population1.13i - Re-offending levels - percentage of offenders who re-offend1.13ii - Re-offending levels - average number of re-offences per offender1.14i - The rate of complaints about noise1.14ii - The percentage of the population exposed to road, rail and air transport noise of 65dB(A) or more, during the daytime1.14iii - The percentage of the population exposed to road, rail and air transport noise of 55 dB(A) or more during the night-time1.15i - Statutory homelessness - homelessness acceptances1.15ii - Statutory homelessness - households in temporary accommodation1.16 - Utilisation of outdoor space for exercise/health reasons1.17 - Fuel Poverty1.18i - Social Isolation: % of adult social care users who have as much social contact as they would like1.18ii - Social Isolation: % of adult carers who have as much social contact as they would like1.19i - Older people's perception of community safety - safe in local area during the day1.19ii - Older people's perception of community safety - safe in local area after dark1.19iii - Older people's perception of community safety - safe in own home at night2.01 - Low birth weight of term babies2.02i - Breastfeeding - Breastfeeding initiation2.02ii - Breastfeeding - Breastfeeding prevalence at 6-8 weeks after birth2.03 - Smoking status at time of delivery2.04 - Under 18 conceptions2.04 - Under 18 conceptions: conceptions in those aged under 162.06i - Excess weight in 4-5 and 10-11 year olds - 4-5 year olds2.06ii - Excess weight in 4-5 and 10-11 year olds - 10-11 year olds2.07i - Hospital admissions caused by unintentional and deliberate injuries in children (aged 0-14 years)2.07i - Hospital admissions caused by unintentional and deliberate injuries in children (aged 0-4 years)2.07ii - Hospital admissions caused by unintentional and deliberate injuries in young people (aged 15-24)2.08 - Emotional well-being of looked after children2.09i - Smoking prevalence at age 15 - current smokers (WAY survey)2.09ii - Smoking prevalence at age 15 - regular smokers (WAY survey)2.09iii - Smoking prevalence at age 15 - occasional smokers (WAY survey)2.09iv - Smoking prevalence at age 15 years - regular smokers (SDD survey)2.09v - Smoking prevalence at age 15 years - occasional smokers (SDD survey)2.12 - Excess Weight in Adults2.13i - Percentage of physically active and inactive adults - active adults2.13ii - Percentage of physically active and inactive adults - inactive adults2.14 - Smoking Prevalence2.14 - Smoking prevalence - routine & manual2.15i - Successful completion of drug treatment - opiate users2.15ii - Successful completion of drug treatment - non-opiate users2.16 - People entering prison with substance dependence issues who are previously not known to community treatment2.17 - Recorded diabetes2.18 - Admission episodes for alcohol-related conditions - narrow definition2.19 - Cancer diagnosed at early stage (Experimental Statistics)2.20i - Cancer screening coverage - breast cancer2.20ii - Cancer screening coverage - cervical cancer2.21i - Antenatal infectious disease screening – HIV coverage2.21iii - Antenatal Sickle Cell and Thalassaemia Screening - coverage2.21iv - Newborn bloodspot screening - coverage2.21v - Newborn Hearing screening - Coverage2.21vii - Access to non-cancer screening programmes - diabetic retinopathy2.21viii - Abdominal Aortic Aneurysm Screening2.22iii - Cumulative % of the eligible population aged 40-74 offered an NHS Health Check2.22iv - Cumulative % of the eligible population aged 40-74 offered an NHS Health Check who received an NHS Health Check2.22v - Cumulative % of the eligible population aged 40-74 who received an NHS Health check2.23i - Self-reported well-being - people with a low satisfaction score2.23ii - Self-reported well-being - people with a low worthwhile score2.23iii - Self-reported well-being - people with a low happiness score2.23iv - Self-reported well-being - people with a high anxiety score2.23v - Average Warwick-Edinburgh Mental Well-Being Scale (WEMWBS) score2.24i - Injuries due to falls in people aged 65 and over2.24ii - Injuries due to falls in people aged 65 and over - aged 65-792.24iii - Injuries due to falls in people aged 65 and over - aged 80+3.01 - Fraction of mortality attributable to particulate air pollution3.02 - Chlamydia detection rate (15-24 year olds)3.02 - Chlamydia detection rate (15-24 year olds)3.03i - Population vaccination coverage - Hepatitis B (1 year old)3.03i - Population vaccination coverage - Hepatitis B (2 years old)3.03iii - Population vaccination coverage - Dtap / IPV / Hib (1 year old)3.03iii - Population vaccination coverage - Dtap / IPV / Hib (2 years old)3.03iv - Population vaccination coverage - MenC3.03ix - Population vaccination coverage - MMR for one dose (5 years old)3.03v - Population vaccination coverage - PCV3.03vi - Population vaccination coverage - Hib / Men C booster (5 years)3.03vi - Population vaccination coverage - Hib / MenC booster (2 years old)3.03vii - Population vaccination coverage - PCV booster3.03viii - Population vaccination coverage - MMR for one dose (2 years old)3.03x - Population vaccination coverage - MMR for two doses (5 years old)3.03xii - Population vaccination coverage - HPV3.03xiii - Population vaccination coverage - PPV3.03xiv - Population vaccination coverage - Flu (aged 65+)3.03xv - Population vaccination coverage - Flu (at risk individuals)3.04 - People presenting with HIV at a late stage of infection3.05i - Treatment completion for TB3.05ii - Incidence of TB3.06 - NHS organisations with a board approved sustainable development management plan3.07 - Comprehensive, agreed inter-agency plans for responding to health protection incidents and emergencies4.01 - Infant mortality4.02 - Tooth decay in children aged 54.03 - Mortality rate from causes considered preventable4.04i - Under 75 mortality rate from all cardiovascular diseases4.04ii - Under 75 mortality rate from cardiovascular diseases considered preventable4.05i - Under 75 mortality rate from cancer4.05ii - Under 75 mortality rate from cancer considered preventable4.06i - Under 75 mortality rate from liver disease4.06ii - Under 75 mortality rate from liver disease considered preventable4.07i - Under 75 mortality rate from respiratory disease4.07ii - Under 75 mortality rate from respiratory disease considered preventable4.08 - Mortality

BackgroundQuitline counseling is an effective method for supporting smoking cessation, offering personalized and accessible assistance. Tobacco use is a significant public health issue among people living with HIV. In Vietnam, over 50% of men living with HIV use tobacco. However, there is limited research on Quitline use and retention rates in this population and a lack of research on factors associated with retention in Quitline counseling. The study aims to evaluate the factors associated with retention in Quitline counseling for smoking cessation among HIV-positive smokers receiving care at HIV outpatient clinics in Vietnam.MethodThe study analyzed data from a randomized controlled trial (RCT) that compared the effectiveness of three smoking cessation interventions for smokers living with HIV at 13 Outpatient Clinics in Ha Noi. A total of 221 smokers aged 18 and above living with HIV participated in Arm 1 of the RCT, which included screening for tobacco use (Ask), health worker-delivered brief counseling (Assist), and proactive referral to Vietnam’s national Quitline (AAR), in which the Quitline reached out to the patient to engage them in up to 10 sessions of smoking cessation counseling. Retention in Quitline counseling was defined as participating in more than five counseling calls. The study used bivariate and logistic regression analyses to explore the associations between retention and other factors.ResultsFifty-one percent of HIV-positive smokers completed more than five counseling sessions. Smokers living with HIV aged 35 or older (OR = 5.53, 95% CI 1.42–21.52), who had a very low/low tobacco dependence level (OR = 2.26, 95% CI 1.14–4.51), had a lower score of perceived importance of quitting cigarettes (OR = 0.87, 95% CI 0.76–0.99), had a household ban or partial ban on cigarettes smoking (OR = 2.58, 95% CI 1.39–4.80), and had chosen a quit date during the Quitline counseling (OR = 3.0, 95% CI 1.63–5.53) were more likely to retain in the Quitline counseling than those smokers living with HIV whose ages were less than 35, who had a high/very high tobacco dependence level, had a higher score of perception of the importance of quitting cigarettes, did not have a household ban on cigarettes smoking, and did not choose a quit date during counseling.ConclusionThere is a high retention rate in Quitline counseling services among PLWHs receiving care at HIV outpatient clinics. Tailoring interventions to the associated factors such as age, tobacco dependence, perceived importance of quitting, household smoking bans, and setting a quit date during counseling may improve engagement and outcomes, aiding in the reduction of smoking prevalence among HIV-positive individuals.

This web map displays data from the voter registration database as the percent of registered voters by census tract in King County, Washington. The data for this web map is compiled from King County Elections voter registration data for the years 2013-2019. The total number of registered voters is based on the geo-location of the voter's registered address at the time of the general election for each year. The eligible voting population, age 18 and over, is based on the estimated population increase from the US Census Bureau and the Washington Office of Financial Management and was calculated as a projected 6 percent population increase for the years 2010-2013, 7 percent population increase for the years 2010-2014, 9 percent population increase for the years 2010-2015, 11 percent population increase for the years 2010-2016 & 2017, 14 percent population increase for the years 2010-2018 and 17 percent population increase for the years 2010-2019. The total population 18 and over in 2010 was 1,517,747 in King County, Washington. The percentage of registered voters represents the number of people who are registered to vote as compared to the eligible voting population, age 18 and over. The voter registration data by census tract was grouped into six percentage range estimates: 50% or below, 51-60%, 61-70%, 71-80%, 81-90% and 91% or above with an overall 84 percent registration rate. In the map the lighter colors represent a relatively low percentage range of voter registration and the darker colors represent a relatively high percentage range of voter registration. PDF maps of these data can be viewed at King County Elections downloadable voter registration maps. The 2019 General Election Voter Turnout layer is voter turnout data by historical precinct boundaries for the corresponding year. The data is grouped into six percentage ranges: 0-30%, 31-40%, 41-50% 51-60%, 61-70%, and 71-100%. The lighter colors represent lower turnout and the darker colors represent higher turnout. The King County Demographics Layer is census data for language, income, poverty, race and ethnicity at the census tract level and is based on the 2010-2014 American Community Survey 5 year Average provided by the United States Census Bureau. Since the data is based on a survey, they are considered to be estimates and should be used with that understanding. The demographic data sets were developed and are maintained by King County Staff to support the King County Equity and Social Justice program. Other data for this map is located in the King County GIS Spatial Data Catalog, where data is managed by the King County GIS Center, a multi-department enterprise GIS in King County, Washington. King County has nearly 1.3 million registered voters and is the largest jurisdiction in the United States to conduct all elections by mail. In the map you can view the percent of registered voters by census tract, compare registration within political districts, compare registration and demographic data, verify your voter registration or register to vote through a link to the VoteWA, Washington State Online Voter Registration web page.

Moldova's first Demographic and Health Survey (2005 MDHS) is a nationally representative sample survey of 7,440 women age 15-49 and 2,508 men age 15-59 selected from 400 sample points (clusters) throughout Moldova (excluding the Transnistria region). It is designed to provide data to monitor the population and health situation in Moldova; it includes several indicators which follow up on those from the 1997 Moldova Reproductive Health Survey (1997 MRHS) and the 2000 Multiple Indicator Cluster Survey (2000 MICS). The 2005 MDHS used a two-stage sample based on the 2004 Population and Housing Census and was designed to produce separate estimates for key indicators for each of the major regions in Moldova, including the North, Center, and South regions and Chisinau Municipality. Unlike the 1997 MRHS and the 2000 MICS surveys, the 2005 MDHS did not cover the region of Transnistria. Data collection took place over a two-month period, from June 13 to August 18, 2005. The survey obtained detailed information on fertility levels, abortion levels, marriage, sexual activity, fertility preferences, awareness and use of family planning methods, breastfeeding practices, nutritional status of women and young children, childhood mortality, maternal and child health, adult health, and awareness and behavior regarding HIV infection and other sexually transmitted diseases. Hemoglobin testing was conducted on women and children to detect the presence of anemia. Additional features of the 2005 MDHS include the collection of information on international emigration, language preference for reading printed media, and domestic violence. The 2005 MDHS was carried out by the National Scientific and Applied Center for Preventive Medicine, hereafter called the National Center for Preventive Medicine (NCPM), of the Ministry of Health and Social Protection. ORC Macro provided technical assistance for the MDHS through the USAID-funded MEASURE DHS project. Local costs of the survey were also supported by USAID, with additional funds from the United Nations Children's Fund (UNICEF), the United Nations Population Fund (UNFPA), and in-kind contributions from the NCPM. MAIN RESULTS CHARACTERISTICS OF RESPONDENTS Ethnicity and Religion. Most women and men in Moldova are of Moldovan ethnicity (77 percent and 76 percent, respectively), followed by Ukrainian (8-9 percent of women and men), Russian (6 percent of women and men), and Gagauzan (4-5 percent of women and men). Romanian and Bulgarian ethnicities account for 2 to 3 percent of women and men. The overwhelming majority of Moldovans, about 95 percent, report Orthodox Christianity as their religion. Residence and Age. The majority of respondents, about 58 percent, live in rural areas. For both sexes, there are proportionally more respondents in age groups 15-19 and 45-49 (and also 45-54 for men), whereas the proportion of respondents in age groups 25-44 is relatively lower. This U-shaped age distribution reflects the aging baby boom cohort following World War II (the youngest of the baby boomers are now in their mid-40s), and their children who are now mostly in their teens and 20s. The smaller proportion of men and women in the middle age groups reflects the smaller cohorts following the baby boom generation and those preceding the generation of baby boomers' children. To some degree, it also reflects the disproportionately higher emigration of the working-age population. Education. Women and men in Moldova are universally well educated, with virtually 100 percent having at least some secondary or higher education; 79 percent of women and 83 percent of men have only a secondary or secondary special education, and the remainder pursues a higher education. More women (21 percent) than men (16 percent) pursue higher education. Language Preference. Among women, preferences for language of reading material are about equal for Moldovan (37 percent) and Russian (35 percent) languages. Among men, preference for Russian (39 percent) is higher than for Moldovan (25 percent). A substantial percentage of women and men prefer Moldovan and Russian equally (27 percent of women and 32 percent of men). Living Conditions. Access to electricity is almost universal for households in Moldova. Ninety percent of the population has access to safe drinking water, with 86 percent in rural areas and 96 percent in urban areas. Seventy-seven percent of households in Moldova have adequate means of sanitary disposal, with 91 percent of households in urban areas and only 67 percent in rural areas. Children's Living Arrangements. Compared with other countries in the region, Moldova has the highest proportion of children who do not live with their mother and/or father. Only about two-thirds (69 percent) of children under age 15 live with both parents. Fifteen percent live with just their mother although their father is alive, 5 percent live with just their father although their mother is alive, and 7 percent live with neither parent although they are both alive. Compared with living arrangements of children in 2000, the situation appears to have worsened. FERTILITY Fertility Levels and Trends. The total fertility rate (TFR) in Moldova is 1.7 births. This means that, on average, a woman in Moldova will give birth to 1.7 children by the end of her reproductive period. Overall, fertility rates have declined since independence in 1991. However, data indicate that fertility rates may have increased in recent years. For example, women of childbearing age have given birth to, on average, 1.4 children at the end of their childbearing years. This is slightly less than the total fertility rate (1.7), with the difference indicating that fertility in the past three years is slightly higher than the accumulation of births over the past 30 years. Fertility Differentials. The TFR for rural areas (1.8 births) is higher than that for urban areas (1.5 births). Results show that this urban-rural difference in childbearing rates can be attributed almost exclusively to younger age groups. CONTRACEPTION Knowledge of Contraception. Knowledge of family planning is nearly universal, with 99 percent of all women age 15-49 knowing at least one modern method of family planning. Among all women, the male condom, IUD, pills, and withdrawal are the most widely known methods of family planning, with over 80 percent of all women saying they have heard of these methods. Female sterilization is known by two-thirds of women, while periodic abstinence (rhythm method) is recognized by almost six in ten women. Just over half of women have heard of the lactational amenorrhea method (LAM), while 40-50 percent of all women have heard of injectables, male sterilization, and foam/jelly. The least widely known methods are emergency contraception, diaphragm, and implants. Use of Contraception. Sixty-eight percent of currently married women are using a family planning method to delay or stop childbearing. Most are using a modern method (44 percent of married women), while 24 percent use a traditional method of contraception. The IUD is the most widely used of the modern methods, being used by 25 percent of married women. The next most widely used method is withdrawal, used by 20 percent of married women. Male condoms are used by about 7 percent of women, especially younger women. Five percent of married women have been sterilized and 4 percent each are using the pill and periodic abstinence (rhythm method). The results show that Moldovan women are adopting family planning at lower parities (i.e., when they have fewer children) than in the past. Among younger women (age 20-24), almost half (49 percent) used contraception before having any children, compared with only 12 percent of women age 45-49. MATERNAL HEALTH Antenatal Care and Delivery Care. Among women with a birth in the five years preceding the survey, almost all reported seeing a health professional at least once for antenatal care during their last pregnancy; nine in ten reported 4 or more antenatal care visits. Seven in ten women had their first antenatal care visit in the first trimester. In addition, virtually all births were delivered by a health professional, in a health facility. Results also show that the vast majority of women have timely checkups after delivering; 89 percent of all women received a medical checkup within two days of the birth, and another 6 percent within six weeks. CHILD HEALTH Childhood Mortality. The infant mortality rate for the 5-year period preceding the survey is 13 deaths per 1,000 live births, meaning that about 1 in 76 infants dies before the first birthday. The under-five mortality rate is almost the same with 14 deaths per 1,000 births. The near parity of these rates indicates that most all early childhood deaths take place during the first year of life. Comparison with official estimates of IMRs suggests that this rate has been improving over the past decade. NUTRITION Breastfeeding Practices. Breastfeeding is nearly universal in Moldova: 97 percent of children are breastfed. However the duration of breast-feeding is not long, exclusive breastfeeding is not widely practiced, and bottle-feeding is not uncommon. In terms of the duration of breastfeeding, data show that by age 12-15 months, well over half of children (59 percent) are no longer being breastfed. By age 20-23 months, almost all children have been weaned. Exclusive breastfeeding is not widely practiced and supplementary feeding begins early: 57 percent of breastfed children less than 4 months are exclusively breastfed, and 46 percent under six months are exclusively breastfeed. The remaining breastfed children also consume plain water, water-based liquids or juice, other milk in addition to breast milk, and complimentary foods. Bottle-feeding is fairly widespread in Moldova; almost one-third (29 percent) of infants under 4 months old are fed with a bottle with

Description

This dataset contains the county-wise vote share of the United States presidential election of 2020, and in the future 2024, the main advantage of the dataset is that it contains various important county statistics such as the counties racial composition, median and mean income, income inequality, population density, education level, population and the counties occupational distribution.

_Imp: this dataset will be updated as the 2024 results come in, I will also be adding more county demographic data, if you have any queries or suggestions please feel free to comment _

Motivation

The reasons for constructing this dataset are many, however the prime reason was to aggregate all the data on counties along with the election result data for easy analysis in one place. I noticed that Kaggle contains no datasets with detailed county information, and that using the US census bureau site is pretty difficult and time consuming to extract data so it would be better to have a pre-prepared table of data

Columns

• The first columns contain information on the county and state
• The next columns contain the 2020 vote both raw and %
• The next columns contain the education level of the county population
• Following that we have information about the income and income inequality in the county
• Then we have the county racial composition
• The counties population and population density
• The final columns contain information about the distribution of occupations in the county

National and subnational mid-year population estimates for the UK and its constituent countries by administrative area, age and sex (including components of population change, median age and population density).