The following datasets are based on the children and youth (under age 21) beneficiary population and consist of aggregate Mental Health Service data derived from Medi-Cal claims, encounter, and eligibility systems. These datasets were developed in accordance with California Welfare and Institutions Code (WIC) § 14707.5 (added as part of Assembly Bill 470 on 10/7/17). Please contact BHData@dhcs.ca.gov for any questions or to request previous years’ versions of these datasets. Note: The Performance Dashboard AB 470 Report Application Excel tool development has been discontinued. Please see the Behavioral Health reporting data hub at https://behavioralhealth-data.dhcs.ca.gov/ for access to dashboards utilizing these datasets and other behavioral health data.

States report information from two reporting populations: (1) The Served Population which is information on all youth receiving at least one independent living services paid or provided by the Chafee Program agency, and (2) Youth completing the NYTD Survey. States survey youth regarding six outcomes: financial self-sufficiency, experience with homelessness, educational attainment, positive connections with adults, high-risk behaviors, and access to health insurance. States collect outcomes information by conducting a survey of youth in foster care on or around their 17th birthday, also referred to as the baseline population. States will track these youth as they age and conduct a new outcome survey on or around the youth's 19th birthday; and again on or around the youth's 21st birthday, also referred to as the follow-up population. States will collect outcomes information on these older youth at ages 19 or 21 regardless of their foster care status or whether they are still receiving independent living services from the State. Depending on the size of the State's foster care youth population, some States may conduct a random sample of the baseline population of the 17-year-olds that participate in the outcomes survey so that they can follow a smaller group of youth as they age. All States will collect and report outcome information on a new baseline population cohort every three years. Units of Response: Current and former youth in foster care Type of Data: Administrative Tribal Data: No Periodicity: Annual Demographic Indicators: Ethnicity;Race;Sex SORN: Not Applicable Data Use Agreement: https://www.ndacan.acf.hhs.gov/datasets/request-dataset.cfm Data Use Agreement Location: https://www.ndacan.acf.hhs.gov/datasets/order_forms/termsofuseagreement.pdf Granularity: Individual Spatial: United States Geocoding: FIPS Code

The provided News dataset consists of 12,800 news titles that have been categorized into two groups: safe and unsafe, specifically with regard to their appropriateness for teenagers and kids. This dataset likely aims to facilitate the development of models or algorithms that can classify news titles based on their suitability for young audiences.

Categorizing news titles as safe or unsafe for teenagers and kids suggests a concern for the content's age-appropriateness and potential impact on young readers. The term "safe" in this context implies that the news title contains content that is considered suitable, non-offensive, and aligned with ethical guidelines for teenagers and kids. Conversely, the term "unsafe" suggests that the news title may include content that could be harmful, inappropriate, or unsuitable for young audiences.

The National Longitudinal Study of Adolescent Health (Add Health) is a longitudinal study of a nationally representative sample of adolescents in grades 7-12 in the United States during the 1994-1995 school year. Add Health combines longitudinal survey data on respondents' social, economic, psychological and physical well-being with contextual data on the family, neighborhood, community, school, friendships, peer groups, and romantic relationships, providing unique opportunities to study how social environments and behaviors in adolescence are linked to health and achievement outcomes in young adulthood. Public-use biomarker data has been added.

Data is available from four instruments in Wave I (conducted from September 1994 through December 1995), two surveys in Wave II (conducted from April 1996 through August 1996), several sources in Wave III (collected from August 2001 through April 2002), and one in-home interview in Wave IV (conducted from January 2008 through February 2009). Data from Wave V, conducted during 2016-2018 as a mixed-mode survey to collect information on health status and indicators of chronic disease, is available upon application approval only.

This data set belongs to:Beyens, I., Pouwels, J. L., van Driel, I. I., Keijsers, L., & Valkenburg, P. M. (2020). The effect of social media on well-being differs from adolescent to adolescent. Scientific Reports. doi:10.1038/s41598-020-67727-7The design, sampling and analysis plan of the study are available on the Open Science Framework (OSF) at https://osf.io/nhks2.For more information, please contact the authors at i.beyens@uva.nl or info@project-awesome.nl.

About this dataset

Context

The dataset tabulates the data for the Valencia, PA population pyramid, which represents the Valencia population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Valencia, PA, is 33.9.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Valencia, PA, is 149.7.
• Total dependency ratio for Valencia, PA is 183.6.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Valencia, PA is 0.7.

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 for the Valencia population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Valencia for the selected age group is shown in the following column.
• Population (Female): The female population in the Valencia for the selected age group is shown in the following column.
• Total Population: The total population of the Valencia for the selected age group is shown in the following column.

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 Valencia Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the data for the Maryland population pyramid, which represents the Maryland population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Maryland, is 28.2.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Maryland, is 24.9.
• Total dependency ratio for Maryland is 53.1.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Maryland is 4.0.

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 for the Maryland population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Maryland for the selected age group is shown in the following column.
• Population (Female): The female population in the Maryland for the selected age group is shown in the following column.
• Total Population: The total population of the Maryland for the selected age group is shown in the following column.

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 Maryland Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the data for the Bay County, FL population pyramid, which represents the Bay County population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Bay County, FL, is 27.1.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Bay County, FL, is 28.3.
• Total dependency ratio for Bay County, FL is 55.5.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Bay County, FL is 3.5.

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 for the Bay County population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Bay County for the selected age group is shown in the following column.
• Population (Female): The female population in the Bay County for the selected age group is shown in the following column.
• Total Population: The total population of the Bay County for the selected age group is shown in the following column.

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 Bay County Population by Age. You can refer the same here

README Structural and functional MRI dataset from the Adolescent Health and Development in Context (AHDC) study in Columbus, Ohio with 2 waves of data

Authors: Baldwin M. Way (Principal Investigator), Christopher R. Browning, Dylan D. Wagner, Jodi L. Ford, Bethany Boettner, Ping Bai.

Study Overview These data were collected as part of a longitudinal study of adolescent health and well-being collected in Columbus, Ohio (Adolescent Health and Development in Context Study). The larger goals of the project (R01DA042080) were to understand how geospatial exposures predicted substance use. More specifically, Specific Aims 1a & 1b were to longitudinally and cross-sectionally determine how neural function and structure is reshaped by EtV in the community (1a) and substance use (1b). Specific Aim 2 was to use baseline as well as longitudinal neural changes to predict subsequent substance use and identify neural mediators. Specific Aim 3 was to identify risk and resilience factors that alter the effects of community EtV on the neural embedding of EtV as well as the neural prediction of substance use outcomes. The participants in this longitudinal neuroimaging study were recruited from the Adolescent Health and Development in Context study (Boettner, B., Browning, C. R., & Calder, C. A. (2019). Feasibility and validity of geographically explicit ecological momentary assessment with recall‐aided space‐time budgets. Journal of Research on Adolescence, 29(3), 627-645.).

Inquiries about this dataset should be directed to: way.37@osu.edu This dataset is licensed under the Creative Commons Zero (CC0) v1.0 License.

Study Area The study area is a contiguous space within the Interstate 270 loop outerbelt freeway, encompassing a majority of the city of Columbus as well as several suburban municipalities.

Sampling The sampling frame was based on a combination of a vendor-provided list of households in the study area with high probability of meeting eligibility criteria and directory data from public school districts represented in the study area. Households were mailed a letter or postcard describing the study, followed by interviewer calls to the household to solicit participation in the study. Once eligibility was confirmed with the household, one randomly selected youth aged 11-17 and one primary caregiver (English speaking) were recruited to participate in the study.

The racial/ethnic identity of the first wave of the AHDC study was 1,405 youth with 47% white, 38% Black, 5% Hispanic/Latino, 8% multiracial, and 2% Asian. The sample closely approximates the population in the study area with respect to household income of families with children and youth racial/ethnic composition, with the exception that the AHDC sample has a somewhat higher percent of youth who identified as Black compared with the 2009-2013 American Community Survey (ACS) estimates of the area.

For Wave 3 (the first neuroimaging wave), in addition to the participants recruited from the original sample, a refresher sample was recruited. These participants were recruited from within the families of the original sample (i.e. siblings) as well as using the same methods of recruiting the initial wave from low-income census tracts as well as tabling at schools in these tracts.

Study Design The study employs a prospective cohort design in which the data on youth and caregivers were collected at multiple time points. The Wave 1 field period began in spring 2014 and was completed in summer 2016. Wave 2 was conducted between January and December 2016. Wave 3 (the first imaging wave) was conducted between July of 2018 and March of 2020, concluding with the cessation of in person activities due to COVID-19 related restrictions on in-person activities. Wave 4 was run between July and October of 2020. Wave 5 was run between March and July of 2021. Wave 6 (the 2nd imaging wave) was run between May of 2022 and January of 2024.

Study Procedures Within each wave, participant data were collected over a weeklong period. An Entrance Survey with both a focal youth and his or her caregiver was followed by a seven-day smartphone-based Global Positioning System (GPS) tracking and Ecological Momentary Assessment (EMA) data collection period (EMA Week), and either a final Exit Survey at the end of the week (Waves 1 and 2) or a session at the Center for Cognitive and Behavioral Brain Imaging at the Ohio State University (Waves 3 and 6). Waves 4 and 5 were slightly different due to restrictions on in-person activity. Wave 4 consisted of a phone interview and online survey that was completed remotely with participants downloading an app on their phone for responding to EMAs and GPS tracking. Wave 5 only consisted of an online survey and the responding to EMAs with GPS tracking.

The Entrance Survey was collected at the initial in-home visit with adolescent participants and their caregivers. It included a wide range of measures across social, economic, psychological, health, and behavioral domains. Both adolescent and caregiver participants reported on geographic location of and experiences at routine activities (e.g. school, work, church, stores, relative’s house).

The real-time Ecological Momentary Assessment (EMA) surveys were collected via self-administered survey on project-provided smartphones. The study phones also passively collected GPS spatial coordinates during the seven-day EMA collection period. Youth respondents were prompted up to five times a day, and asked to report on their location, network partner presence, risk behaviors such as substance use, mood, surrounding social climate, and sleep patterns.

Waves 1 and 2: A second visit, the Exit Survey, gathered follow-up information about the EMA week. The youth completed an interactive Space-Time Budget with the interviewer to collect detailed activity data on five days – the three most recent weekdays and two weekend days. The processed GPS data results in summarized stationary and travel periods during those five days, along with activity types and network partner presence. Concurrently, caregivers completed a self-administered survey about perceptions of social climate and safety in their neighborhood and at other routine locations.

Waves 3 and 6: The second visit at the conclusion of the week of GPS tracking and EMA sampling was conducted at the Ohio State Center for Cognitive and Behavioral Brain Imaging. Participants completed an initial battery of questionnaires before scanning as well as had the option of providing a hair sample for cortisol or substance use measurement and blood sample for measurement of immune related markers. Participants also completed questionnaires after the scan.

Participants 309 youths participated in the initial home interviews in Wave 3. 290 of these youths came to the imaging center and 271 adolescents were successfully scanned. Of these 271, 158 were in Wave 1 of the AHDC study, while 113 were part of the refresher sample and were thus new to Wave 3.

For Wave 6, there were 144 individuals who came to the imaging center and 120 were successfully scanned. Of these, 110 were also scanned at wave 3, while 10 of these were individuals who were scanned for the first time.

MRI Tasks In the first wave of imaging data (2018 to 2020; Wave 3 of the AHDC parent study), the task sequence was the same for all youths. The time of each run is listed after each and then in parentheses is the number of subjects after quality control checks (e.g. motion). 1. MPRAGE: 6:58 min (n = 249) 2. T2: 3:36 min 3. Resting State Scan (eyes open, rest): 5 min 4. Emotional Faces Task (Surprise, Angry, Fear, Neutral): 4:30 min x 2 runs (n = 214) 5. Cue Reactivity Task (Food, Marijuana, Flavored E-Cigs, Alcohol, and Outdoor images): 5:40 min x 2 runs (n = 215) 6. DTI: 6:55 min 7. Resting State Scan (eyes open, rest): 5 min 8. Field Map: 1:33 min 9. Monetary Incentive Delay Task: 5:23 min x 2 runs (n = 207) 10. Working Memory Task: 4:51 min x 2 runs (n = 183) These latter two tasks used the same Eprime script as used in the ABCD study.

In the second imaging wave run between 2022 and 2024 (Wave 6 overall), there was a slight change to the task order for all participants in order to reduce the probability of youths falling asleep during the first resting state scan. The scan order for the second wave of imaging data was T1, T2, Emotional Faces Task, Cue Reactivity, Resting State 1, MID, Resting State 2, Field Map, Nback task.

Caution This dataset is for research purposes only. The data have been anonymized, and users must not perform analyses aimed at re-identifying individual subjects.

Acknowledgements. We are grateful to all of the youth and their caregivers who participated in the study. The Adolescent Health and Development in Context study (Waves 1 and 2) was funded by the National Institutes for Drug Abuse (R01DA032371; Browning, PI) as well as the Eunice Kennedy Shriver National Institute on Child Health and Human Development (Boettner, R03HD096182; Calder, R01HD088545; Hayford, the Ohio State University Institute for Population Research, 2P2CHD058484), and the William T. Grant Foundation). Participants for the imaging data (Waves 3 and 6) were recruited from this sample, which was generously supported by a grant from the National Institutes of Drug Abuse (R01DA042080; Way, PI). There were two waves of data collected during COVID (Waves 4 and 5) that were funded by a supplemental grant from the National Institutes of Drug Abuse (DA042080-03S1; Way, PI). Assay of head hair samples for cortisol during the imaging waves (Waves 3 and 6) was funded by a grant from the John Templeton Foundation (ID: 61803; Way, PI). Head hair cortisol and salivary cortisol collection and assays for Waves 1 and 2 were funded by R21DA034960 (Ford, PI).

This data set contains estimated teen birth rates for age group 15–19 (expressed per 1,000 females aged 15–19) by county and year. DEFINITIONS Estimated teen birth rate: Model-based estimates of teen birth rates for age group 15–19 (expressed per 1,000 females aged 15–19) for a specific county and year. Estimated county teen birth rates were obtained using the methods described elsewhere (1,2,3,4). These annual county-level teen birth estimates “borrow strength” across counties and years to generate accurate estimates where data are sparse due to small population size (1,2,3,4). The inferential method uses information—including the estimated teen birth rates from neighboring counties across years and the associated explanatory variables—to provide a stable estimate of the county teen birth rate. Median teen birth rate: The middle value of the estimated teen birth rates for the age group 15–19 for counties in a state. Bayesian credible intervals: A range of values within which there is a 95% probability that the actual teen birth rate will fall, based on the observed teen births data and the model. NOTES Data on the number of live births for women aged 15–19 years were extracted from the National Center for Health Statistics’ (NCHS) National Vital Statistics System birth data files for 2003–2015 (5). Population estimates were extracted from the files containing intercensal and postcensal bridged-race population estimates provided by NCHS. For each year, the July population estimates were used, with the exception of the year of the decennial census, 2010, for which the April estimates were used. Hierarchical Bayesian space–time models were used to generate hierarchical Bayesian estimates of county teen birth rates for each year during 2003–2015 (1,2,3,4). The Bayesian analogue of the frequentist confidence interval is defined as the Bayesian credible interval. A 100*(1-α)% Bayesian credible interval for an unknown parameter vector θ and observed data vector y is a subset C of parameter space Ф such that 1-α≤P({C│y})=∫p{θ │y}dθ, where integration is performed over the set and is replaced by summation for discrete components of θ. The probability that θ lies in C given the observed data y is at least (1- α) (6). County borders in Alaska changed, and new counties were formed and others were merged, during 2003–2015. These changes were reflected in the population files but not in the natality files. For this reason, two counties in Alaska were collapsed so that the birth and population counts were comparable. Additionally, Kalawao County, a remote island county in Hawaii, recorded no births, and census estimates indicated a denominator of 0 (i.e., no females between the ages of 15 and 19 years residing in the county from 2003 through 2015). For this reason, Kalawao County was removed from the analysis. Also , Bedford City, Virginia, was added to Bedford County in 2015 and no longer appears in the mortality file in 2015. For consistency, Bedford City was merged with Bedford County, Virginia, for the entire 2003–2015 period. Final analysis was conducted on 3,137 counties for each year from 2003 through 2015. County boundaries are consistent with the vintage 2005–2007 bridged-race population file geographies (7). SOURCES National Center for Health Statistics. Vital statistics data available online, Natality all-county files. Hyattsville, MD. Published annually. For details about file release and access policy, see NCHS data release and access policy for micro-data and compressed vital statistics files, available from: http://www.cdc.gov/nchs/nvss/dvs_data_release.htm. For natality public-use files, see vital statistics data available online, available from: https://www.cdc.gov/nchs/data_access/vitalstatsonline.htm. National Center for Health Statistics. U.S. Census populations with bridged race categories. Estimated population data available. Postcensal and intercensal files. Hyattsville, MD

The PATH Study was launched in 2011 to inform the Food and Drug Administration's regulatory activities under the Family Smoking Prevention and Tobacco Control Act (TCA). The PATH Study is a collaboration between the National Institute on Drug Abuse (NIDA), National Institutes of Health (NIH), and the Center for Tobacco Products (CTP), Food and Drug Administration (FDA). The study sampled over 150,000 mailing addresses across the United States to create a national sample of people who use or do not use tobacco. 45,971 adults and youth constitute the first (baseline) wave, Wave 1, of data collected by this longitudinal cohort study. These 45,971 adults and youth along with 7,207 "shadow youth" (youth ages 9 to 11 sampled at Wave 1) make up the 53,178 participants that constitute the Wave 1 Cohort. Respondents are asked to complete an interview at each follow-up wave. Youth who turn 18 by the current wave of data collection are considered "aged-up adults" and are invited to complete the Adult Interview. Additionally, "shadow youth" are considered "aged-up youth" upon turning 12 years old, when they are asked to complete an interview after parental consent. At Wave 4, a probability sample of 14,098 adults, youth, and shadow youth ages 10 to 11 was selected from the civilian, noninstitutionalized population (CNP) at the time of Wave 4. This sample was recruited from residential addresses not selected for Wave 1 in the same sampled Primary Sampling Unit (PSU)s and segments using similar within-household sampling procedures. This "replenishment sample" was combined for estimation and analysis purposes with Wave 4 adult and youth respondents from the Wave 1 Cohort who were in the CNP at the time of Wave 4. This combined set of Wave 4 participants, 52,731 participants in total, forms the Wave 4 Cohort. At Wave 7, a probability sample of 14,863 adults, youth, and shadow youth ages 9 to 11 was selected from the CNP at the time of Wave 7. This sample was recruited from residential addresses not selected for Wave 1 or Wave 4 in the same sampled PSUs and segments using similar within-household sampling procedures. This "second replenishment sample" was combined for estimation and analysis purposes with the Wave 7 adult and youth respondents from the Wave 4 Cohorts who were at least age 15 and in the CNP at the time of Wave 7. This combined set of Wave 7 participants, 46,169 participants in total, forms the Wave 7 Cohort. Please refer to the Restricted-Use Files User Guide that provides further details about children designated as "shadow youth" and the formation of the Wave 1, Wave 4, and Wave 7 Cohorts. Dataset 0002 (DS0002) contains the data from the State Design Data. This file contains 7 variables and 82,139 cases. The state identifier in the State Design file reflects the participant's state of residence at the time of selection and recruitment for the PATH Study. Dataset 1011 (DS1011) contains the data from the Wave 1 Adult Questionnaire. This data file contains 2,021 variables and 32,320 cases. Each of the cases represents a single, completed interview. Dataset 1012 (DS1012) contains the data from the Wave 1 Youth and Parent Questionnaire. This file contains 1,431 variables and 13,651 cases. Dataset 1411 (DS1411) contains the Wave 1 State Identifier data for Adults and has 5 variables and 32,320 cases. Dataset 1412 (DS1412) contains the Wave 1 State Identifier data for Youth (and Parents) and has 5 variables and 13,651 cases. The same 5 variables are in each State Identifier dataset, including PERSONID for linking the State Identifier to the questionnaire and biomarker data and 3 variables designating the state (state Federal Information Processing System (FIPS), state abbreviation, and full name of the state). The State Identifier values in these datasets represent participants' state of residence at the time of Wave 1, which is also their state of residence at the time of recruitment. Dataset 1611 (DS1611) contains the Tobacco Universal Product Code (UPC) data from Wave 1. This data file contains 32 variables and 8,601 cases. This file contains UPC values on the packages of tobacco products used or in the possession of adult respondents at the time of Wave 1. The UPC values can be used to identify and validate the specific products used by respondents and augment the analyses of the characteristics of tobacco products used

About this dataset

Context

The dataset tabulates the data for the Vancouver, WA population pyramid, which represents the Vancouver population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Vancouver, WA, is 27.4.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Vancouver, WA, is 24.5.
• Total dependency ratio for Vancouver, WA is 51.9.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Vancouver, WA is 4.1.

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 for the Vancouver population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Vancouver for the selected age group is shown in the following column.
• Population (Female): The female population in the Vancouver for the selected age group is shown in the following column.
• Total Population: The total population of the Vancouver for the selected age group is shown in the following column.

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 Vancouver Population by Age. You can refer the same here

Analysis of ‘Percentage of Births in High Poverty for Adolescents’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/d664cebf-c0bd-4d3e-8923-14451d09c8a2 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

This dataset contains an indicator concerning births among adolescents aged 15-19: Percentage of Births in High Poverty Area (PBHP). Early childbearing is an important public health issue that can be addressed by monitoring surveillance data such as percentage of births in high poverty area (PBHP). This data, particularly across small areas such as Medical Service Areas, are a valuable part of surveillance that informs program planning efforts targeting localized needs. The indicator (PBHP) is stratified by adolescent mothers' race and Hispanic ethnicity. The race and Hispanic ethnic groups in this table utilize four mutually exclusive race and ethnicity categories. These categories are Hispanic (HISP) and the following Non-Hispanic categories of Black, Asian, and White. Data should not be compared to previous data where birth rates were presented by Medical Service Study Area due to differences in methodology and population data sources. A link to the full report about these current data can be found here http://www.cdph.ca.gov/data/statistics/Documents/150603CAABRPRBPOVbymssaapprovedCM.pdf

--- Original source retains full ownership of the source dataset ---

The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Chad: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49).

There is also a tiled version of this dataset that may be easier to use if you are interested in many countries.

As of April 2024, around 16.5 percent of global active Instagram users were men between the ages of 18 and 24 years. More than half of the global Instagram population worldwide was aged 34 years or younger.

              Teens and social media

              As one of the biggest social networks worldwide, Instagram is especially popular with teenagers. As of fall 2020, the photo-sharing app ranked third in terms of preferred social network among teenagers in the United States, second to Snapchat and TikTok. Instagram was one of the most influential advertising channels among female Gen Z users when making purchasing decisions. Teens report feeling more confident, popular, and better about themselves when using social media, and less lonely, depressed and anxious.
              Social media can have negative effects on teens, which is also much more pronounced on those with low emotional well-being. It was found that 35 percent of teenagers with low social-emotional well-being reported to have experienced cyber bullying when using social media, while in comparison only five percent of teenagers with high social-emotional well-being stated the same. As such, social media can have a big impact on already fragile states of mind.

This dataset contains all former foster youth enrolled in Medi-Cal. The Medi-Cal Former Foster Youth (FFY) program provides full scope Medi-Cal to individuals up to the age of 26 who were in foster care at age 18 or older.

The Survey of Youth in Residential Placement (SYRP) is the only national survey that gathers data directly from youth in the juvenile justice system. The Office of Juvenile Justice and Delinquency Prevention (OJJDP) designed the survey in 2000 and 2001 to survey offender youth between the ages of 10 and 20. SYRP asks the youth about their backgrounds, offense histories and problems; the facility environment; experiences in the facility; experiences with alcohol and drugs; experiences of victimization in placement; medical needs and services received; and their expectations for the future. SYRP research provides answers to a number of questions about the characteristics and experiences of youth in custody including: Who are the youth in placement? What are their offenses? What are their family backgrounds? What are their expectations for the future? How are youth grouped in living units and programs? What activities are available in each facility? How accessible are social, emotional, and legal supports? What is the quality of the youth-staff relationships? How clear are the facility's rules? How clear is the facility's commitment to justice and due process? What methods of control and discipline do staff use? SYRP's findings are based on anonymous interviews with a nationally representative sample of youth in custody during the spring of 2003 using audio computer-assisted self-interview (ACASI) technology. SYRP is the latest addition to two ongoing data collections that OJJDP designed and implemented in the 1990s. It joins the Census of Juveniles in Residential Placement and the Juvenile Residential Facility Census to provide updated statistics on youth in custody in the juvenile justice system. SYRP bulletins, reports, and a simplified online analysis tool are available from the SYRP Project Web site.

About this dataset

Context

The dataset tabulates the data for the Petersburg, AK population pyramid, which represents the Petersburg population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Petersburg, AK, is 26.9.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Petersburg, AK, is 36.0.
• Total dependency ratio for Petersburg, AK is 62.9.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Petersburg, AK is 2.8.

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 for the Petersburg population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Petersburg for the selected age group is shown in the following column.
• Population (Female): The female population in the Petersburg for the selected age group is shown in the following column.
• Total Population: The total population of the Petersburg for the selected age group is shown in the following column.

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 Petersburg Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the data for the Many, LA population pyramid, which represents the Many population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Many, LA, is 29.1.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Many, LA, is 39.4.
• Total dependency ratio for Many, LA is 68.5.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Many, LA is 2.5.

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 for the Many population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Many for the selected age group is shown in the following column.
• Population (Female): The female population in the Many for the selected age group is shown in the following column.
• Total Population: The total population of the Many for the selected age group is shown in the following column.

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 Many Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the data for the Delhi, LA population pyramid, which represents the Delhi population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey 5-Year estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Delhi, LA, is 28.3.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Delhi, LA, is 24.5.
• Total dependency ratio for Delhi, LA is 52.9.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Delhi, LA is 4.1.

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

Variables / Data Columns

• Age Group: This column displays the age group for the Delhi population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Delhi for the selected age group is shown in the following column.
• Population (Female): The female population in the Delhi for the selected age group is shown in the following column.
• Total Population: The total population of the Delhi for the selected age group is shown in the following column.

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 Delhi Population by Age. You can refer the same here