Teenagers are the 2nd largest group of people affected by social media addiction. Teens ages 13 to 18 years old spend a significant amount of their free time on social media with an average of 3 hours a day.

According to a survey conducted in the United States in 2023, ** percent of social media users aged between 13 and 17 years used YouTube, down from ** percent in 2022. As for TikTok, ** percent of U.S. teens used the app, down from ** percent in 2022. Additionally, Snapchat, Instagram, X (formerly Twitter), and Twitch all saw a slight ******* in usage amongst teens in the United States. Facebook and WhatsApp saw increases in usage among this demographic.

A 2023 survey found that half of the teenagers in the United States between 15 and 17 years used the internet almost constantly. The share of teens between 13 and 14 years going online frequently was lower, 40 percent. Overall, 46 percent of U.S. teens surveyed said they used the internet almost continuously.

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).

The goal of this study was to test specific hypotheses illustrating the relationships among serious victimization experiences, the mental health effects of victimization, substance abuse/use, and delinquent behavior in adolescents. The study assessed familial and nonfamilial types of violence. It was designed as a telephone survey of American youth aged 12-17 living in United States households and residing with a parent or guardian. One parent or guardian in each household was interviewed briefly to establish rapport, secure permission to interview the targeted adolescent, and to ensure the collection of comparative data to examine potential nonresponse bias from households without adolescent participation. All interviews with both parents and adolescents were conducted using Computer-Assisted Telephone Interviewing (CATI) technology. From the surveys of parents and adolescents, the principal investigators created one data file by attaching the data from the parents to the records of their respective adolescents. Adolescents were asked whether violence and drug abuse were problems in their schools and communities and what types of violence they had personally witnessed. They were also asked about other stressful events in their lives, such as the loss of a family member, divorce, unemployment, moving to a new home or school, serious illness or injury, and natural disaster. Questions regarding history of sexual assault, physical assault, and harsh physical discipline elicited a description of the event and perpetrator, extent of injuries, age at abuse, whether alcohol or drugs were involved, and who was informed of the incident. Information was also gathered on the delinquent behavior of respondents and their friends, including destruction of property, assault, theft, sexual assault, and gang activity. Other questions covered history of personal and family substance use and mental health indicators, such as major depression, post-traumatic stress disorders, weight changes, sleeping disorders, and problems concentrating. Demographic information was gathered from the adolescents on age, race, gender, number of people living in household, and grade in school. Parents were asked whether they were concerned about violent crime, affordable child care, drug abuse, educational quality, gangs, and the safety of their children at school. In addition, they were questioned about their own victimization experiences and whether they discussed personal safety issues with their children. Parents also supplied demographic information on gender, marital status, number of children, employment status, education, race, and income.

In 2022, around 20.3 percent of teenagers between ages 16 and 19 were employees while enrolled at school in the United States. This is an increase from the previous year, when 19.4 percent of teenagers were working while in school.

This dataset contains California’s adolescent birth rate (ABR) by county, age group and race/ethnicity using aggregated years 2014-2016. The ABR is calculated as the number of live births to females aged 15-19 divided by the female population aged 15-19, multiplied by 1,000. Births to females under age 15 are uncommon and thus added to the numerator (total number of births aged 15-19) in calculating the ABR for aged 15-19. The categories by age group are aged 18-19 and aged 15-17; births occurring to females under aged 15 are added to the numerator for aged 15-17 in calculating the ABR for this age group. The race and ethnic groups in this table utilized five mutually exclusive race and ethnicity categories. These categories are Hispanic and the following Non-Hispanic categories of Multi-Race, Black, American Indian (includes Eskimo and Aleut), Asian and Pacific Islander (includes Hawaiian) combined, and White. Note that there are birth records with missing race/ethnicity or categorized as “Other” and not shown in the dataset but included in the ABR calculation overall.

Interactive Summary Health Statistics for Teens provide estimates of selected health topics for youth aged 12-17 years based on final data from the National Health Interview Survey— Teen.

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.

90% of people aged 18-29 use social media in some form. 15% of people aged 23-38 admit that they are addicted to social media.

In spring 2022, it was found that teenagers from the U.S. spent just *** percent of their video content time watching cable TV, down from ** percent in fall 2018. Netflix and YouTube were the most popular video platforms among teenagers in the United States.

Statistical data on teen vaping prevalence and addiction risks based on CDC surveys and health research

Vaccination Coverage among Adolescents (13-17 Years) • Data on adolescent vaccination coverage and selected sociodemographic characteristics by State, HHS Region, and the United States from the National Immunization Survey-Teen (NIS-Teen). • Additional information available at https://www.cdc.gov/vaccines/imz-managers/coverage/teenvaxview/index.html

In this post, I'll give you all the social media addiction statistics you need to be aware of to moderate your social media use.

Between 2015 and 2023, internet usage by teenagers in the United States declined, whereas the share of those who went online constantly almost doubled, from 24 to 46 percent. As of October 2023, the percentage of those using the internet only several times a week or less often was three percent.

This is historical data. The update frequency has been set to "Static Data" and is here for historic value. Updated on 8/14/2024 Teen Birth Rate - This indicator shows the rate of births to teens ages 15-19 years (per 1,000 population). Teen pregnancy is linked to a host of social problems such as poverty, lack of overall child well-being, out-of-wedlock births, lack of responsible fatherhood, health issues, school failure, child abuse and neglect and at-risk behaviors. Link to Data Details

Statistics on youth in foster care reported in compliance with Local Law 145 amended by City Council. Cells with one to five youth are not shown to protect anonymity.

56.8% of the world’s total population is active on social media.

Identifies Tempe youth regarding substance misuse and other problematic youth behaviors, utilizing the Arizona Youth Survey that is administered by the Arizona Criminal Justice Commission, on behalf of the State of Arizona, biennially to Arizona 8th, 10th and 12th grade students. This alllows for data driven decision making to provide comprehensive youth substance use prevention educations to youth, parents, educators, and community stakeholders. This data also assist in creating goals and objectives to support Tempe youth along with securing grant funding from federal and state agencies. This page provides data for the Youth Drug Use and Misuse performance measure. The performance measure dashboard is available at 1.21 Youth Drug Use and MisuseAdditional InformationSource: Arizona Criminal Justice Commission Statistical Analysis CenterContact: Bernadette CogginsContact E-Mail: Bernadette_Coggins@tempe.govData Source Type: Excel; csvPreparation Method: Data extracted from Arizona Youth Survey, then manually compiled by outcomePublish Frequency: Every 2 years Publish Method: ManualData Dictionary (update pending)

Key statistics about mental health challenges facing military teens, as illustrated in the infographic on this page.