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.

This dataset includes teen birth rates for females by age group, race, and Hispanic origin in the United States since 1960.

Data availability varies by race and ethnicity groups. All birth data by race before 1980 are based on race of the child. Since 1980, birth data by race are based on race of the mother. For race, data are available for Black and White births since 1960, and for American Indians/Alaska Native and Asian/Pacific Islander births since 1980. Data on Hispanic origin are available since 1989. Teen birth rates for specific racial and ethnic categories are also available since 1989. From 2003 through 2015, the birth data by race were based on the “bridged” race categories (5). Starting in 2016, the race categories for reporting birth data changed; the new race and Hispanic origin categories are: Non-Hispanic, Single Race White; Non-Hispanic, Single Race Black; Non-Hispanic, Single Race American Indian/Alaska Native; Non-Hispanic, Single Race Asian; and, Non-Hispanic, Single Race Native Hawaiian/Pacific Islander (5,6). Birth data by the prior, “bridged” race (and Hispanic origin) categories are included through 2018 for comparison.

National data on births by Hispanic origin exclude data for Louisiana, New Hampshire, and Oklahoma in 1989; New Hampshire and Oklahoma in 1990; and New Hampshire in 1991 and 1992. Birth and fertility rates for the Central and South American population includes other and unknown Hispanic. Information on reporting Hispanic origin is detailed in the Technical Appendix for the 1999 public-use natality data file (see ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Dataset_Documentation/DVS/natality/Nat1999doc.pdf).

In 2023, the birth rate among Hispanic teenagers aged 15 to 19 years was **** per 1,000 women. In comparison, the birth rate among non-Hispanic Asian teens was just *** per 1,000. This statistic shows birth rates among teenagers and young adult women in the U.S. aged 15 to 19 in 1991 to 2023, by race/ethnicity.

Title VI of the Civil Rights Act and the Executive Order on Environmental Justice (#12898) do not provide specific guidance to evaluate EJ issues within a region's transportation planning process. Therefore, MPOs must devise their own methods for ensuring that EJ issues are investigated and evaluated in transportation decision-making. In 2001, DVRPC developed an EJ technical assessment to identify direct and disparate impacts of its plans, programs, and planning process on defined population groups in the Delaware Valley region. This assessment, called the Indicators of Potential Disadvantage Methodology, is utilized in a variety of DVRPC plans and programs. DVRPC currently assesses the following population groups, defined by the U.S. Census Bureau:

Youth

Older Adults

Female

Racial Minority

Ethnic Minority

Foreign-Born

Disabled

Limited English Proficiency

Low-Income Census tables used to gather data from the 2018-2022 American Community Survey 5-Year Estimates Using U.S. Census American Community Survey data, the population groups listed above are identified and located at the census tract level. Data is gathered at the regional level, combining populations from each of the nine counties, for either individuals or households, depending on the indicator. From there, the total number of persons in each demographic group is divided by the appropriate universe (either population or households) for the nine-county region, providing a regional average for that population group. Any census tract that meets or exceeds the regional average level, or threshold, is considered an EJ-sensitive tract for that group. Census tables used to gather data from the 2018-2022 American Community Survey 5-Year Estimates. For more information and for methodology, visit DVRPC's website:http://www.dvrpc.org/GetInvolved/TitleVI/ For technical documentation visit DVRPC's GitHub IPD repo: https://github.com/dvrpc/ipd Source of tract boundaries: 2020 US Census Bureau, TIGER/Line Shapefiles Note: Tracts with null values should be symbolized as "Insufficient or No Data". Data Dictionary for Attributes: (Source = DVRPC indicates a calculated field) Field Alias Description Source year IPD analysis year DVRPC geoid20 11-digit tract GEOID Census tract identifier ACS 5-year statefp 2-digit state GEOID FIPS Code for State ACS 5-year countyfp 3-digit county GEOID FIPS Code for County ACS 5-year tractce Tract number Tract Number ACS 5-year name Tract number Census tract identifier with decimal places ACS 5-year namelsad Tract name Census tract name with decimal places ACS 5-year d_class Disabled percentile class Classification of tract's disabled percentage as: well below average, below average, average, above average, or well above average calculated d_est Disabled count estimate Estimated count of disabled population ACS 5-year d_est_moe Disabled count margin of error Margin of error for estimated count of disabled population ACS 5-year d_pct Disabled percent estimate Estimated percentage of disabled population ACS 5-year d_pct_moe Disabled percent margin of error Margin of error for percentage of disabled population ACS 5-year d_pctile Disabled percentile Tract's regional percentile for percentage disabled calculated d_score Disabled percentile score Corresponding numeric score for tract's disabled classification: 0, 1, 2, 3, 4 calculated em_class Ethnic minority percentile class Classification of tract's Hispanic/Latino percentage as: well below average, below average, average, above average, or well above average calculated em_est Ethnic minority count estimate Estimated count of Hispanic/Latino population ACS 5-year em_est_moe Ethnic minority count margin of error Margin of error for estimated count of Hispanic/Latino population ACS 5-year em_pct Ethnic minority percent estimate Estimated percentage of Hispanic/Latino population calculated em_pct_moe Ethnic minority percent margin of error Margin of error for percentage of Hispanic/Latino population calculated em_pctile Ethnic minority percentile Tract's regional percentile for percentage Hispanic/Latino calculated em_score Ethnic minority percentile score Corresponding numeric score for tract's Hispanic/Latino classification: 0, 1, 2, 3, 4 calculated f_class Female percentile class Classification of tract's female percentage as: well below average, below average, average, above average, or well above average calculated f_est Female count estimate Estimated count of female population ACS 5-year f_est_moe Female count margin of error Margin of error for estimated count of female population ACS 5-year f_pct Female percent estimate Estimated percentage of female population ACS 5-year f_pct_moe Female percent margin of error Margin of error for percentage of female population ACS 5-year f_pctile Female percentile Tract's regional percentile for percentage female calculated f_score Female percentile score Corresponding numeric score for tract's female classification: 0, 1, 2, 3, 4 calculated fb_class Foreign-born percentile class Classification of tract's foreign born percentage as: well below average, below average, average, above average, or well above average calculated fb_est Foreign-born count estimate Estimated count of foreign born population ACS 5-year fb_est_moe Foreign-born count margin of error Margin of error for estimated count of foreign born population ACS 5-year fb_pct Foreign-born percent estimate Estimated percentage of foreign born population calculated fb_pct_moe Foreign-born percent margin of error Margin of error for percentage of foreign born population calculated fb_pctile Foreign-born percentile Tract's regional percentile for percentage foreign born calculated fb_score Foreign-born percentile score Corresponding numeric score for tract's foreign born classification: 0, 1, 2, 3, 4 calculated le_class Limited English proficiency percentile class Classification of tract's limited english proficiency percentage as: well below average, below average, average, above average, or well above average calculated le_est Limited English proficiency count estimate Estimated count of limited english proficiency population ACS 5-year le_est_moe Limited English proficiency count margin of error Margin of error for estimated count of limited english proficiency population ACS 5-year le_pct Limited English proficiency percent estimate Estimated percentage of limited english proficiency population ACS 5-year le_pct_moe Limited English proficiency percent margin of error Margin of error for percentage of limited english proficiency population ACS 5-year le_pctile Limited English proficiency percentile Tract's regional percentile for percentage limited english proficiency calculated le_score Limited English proficiency percentile score Corresponding numeric score for tract's limited english proficiency classification: 0, 1, 2, 3, 4 calculated li_class Low-income percentile class Classification of tract's low income percentage as: well below average, below average, average, above average, or well above average calculated li_est Low-income count estimate Estimated count of low income (below 200% of poverty level) population ACS 5-year li_est_moe Low-income count margin of error Margin of error for estimated count of low income population ACS 5-year li_pct Low-income percent estimate Estimated percentage of low income (below 200% of poverty level) population calculated li_pct_moe Low-income percent margin of error Margin of error for percentage of low income population calculated li_pctile Low-income percentile Tract's regional percentile for percentage low income calculated li_score Low-income percentile score Corresponding numeric score for tract's low income classification: 0, 1, 2, 3, 4 calculated oa_class Older adult percentile class Classification of tract's older adult percentage as: well below average, below average, average, above average, or well above average calculated oa_est Older adult count estimate Estimated count of older adult population (65 years or older) ACS 5-year oa_est_moe Older adult count margin of error Margin of error for estimated count of older adult population ACS 5-year oa_pct Older adult percent estimate Estimated percentage of older adult population (65 years or older) ACS 5-year oa_pct_moe Older adult percent margin of error Margin of error for percentage of older adult population ACS 5-year oa_pctile Older adult percentile Tract's regional percentile for percentage older adult calculated oa_score Older adult percentile score Corresponding numeric score for tract's older adult classification: 0, 1, 2, 3, 4 calculated rm_class Racial minority percentile class Classification of tract's non-white percentage as: well below average, below average, average, above average, or well above average calculated rm_est Racial minority count estimate Estimated count of non-white population ACS 5-year rm_est_moe Racial minority count margin of error Margin of error for estimated count of non-white population ACS 5-year rm_pct Racial minority percent estimate Estimated percentage of non-white population calculated rm_pct_moe Racial minority percent margin of error Margin of error for percentage of non-white population calculated rm_pctile Racial minority percentile Tract's regional percentile for percentage non-white calculated rm_score Racial minority percentile score Corresponding numeric score for tract's non-white classification: 0, 1, 2, 3, 4 calculated tot_pp Total population estimate Estimated total population of tract (universe [or denominator] for youth, older adult, female, racial minoriry, ethnic minority, & foreign born) ACS 5-year tot_pp_moe Total population margin of error Margin of error for estimated total population of tract ACS 5-year y_class Youth percentile class Classification of tract's youth percentage as: well below average, below average, average, above average, or well above average calculated y_est Youth count

Teenage birth rate is number of live births among females ages 15 to 19 years per 1,000 females in that age group in a year. Data are for Santa Clara County residents. The measure is summarized for total county population by race/ethnicity. Teenage birth rates are presented for females ages 15 to 17, 18 to 19 and 15 to 19 years. Data trends are from year 2000 to 2015. Source: Santa Clara County Public Health Department, 2000-2015 Birth Statistical Master File; U.S. Census Bureau, 2010 Census.METADATA:Notes (String): Lists table title, notes, sourcesYear (Numeric): Year of birthAge group (String): Lists the age of mother at the time of birth: 15 to 17, 18 to 19 and 15 to 19 years.Category (String): Lists the category representing the data: Santa Clara County is for total population, race/ethnicity: African American, Asian/Pacific Islander, Latino and White (non-Hispanic White only).Rate per 1,000 females in the age group (Numeric): Teen birth rate is number of live births to mothers ages 15 to 19 years at the time of birth per 1,000 females in that age group in a year. Rate based on birth count less than 6 in a year in the area are not presented.

Cumulative Risks of Foster Care Placement from Birth to Age 18 for All U.S. Children and White, Black, Hispanic, Asian, and Native American Children, 2005.

Abstract (en): This special topic poll, conducted March 31-April 9, 2005, is part of a continuing series of monthly surveys that solicit public opinion on the presidency and on a range of other political and social issues. A national sample of 1,586 respondents aged 14 to 24 years was surveyed, including oversamples of African American youth, Hispanic youth, and 14- to 20-year olds. Despite being termed a monthly poll, the foci of this poll were the opinions and judgments of teenagers and young adults about various aspects of the education system and process in the United States. Views were sought on the most important problem facing young people, the highest level of education respondents hoped to achieve, the highest level they expected to actually achieve, and whether a college degree was necessary to "get ahead". Respondents were asked about their plans after high school, the quality of their high school and its teachers and staff, whether their high school education was adequately preparing them for college and/or the job market, what measures respondents took or would like take to improve their chances of getting into the college of their choice, the importance of grade point averages and performance on standardized tests in getting into college, and their ability to get information about educational opportunities. Similar questions were asked of those respondents who were college students, regarding assistance received from college professors, the importance of internships, and whether college was adequately preparing them to get a well-paying job after graduation. Additional questions addressed MTV's involvement in issues concerning young people and how much impact MTV could have in raising awareness among young people about the importance of education. Demographic information includes age, race, sex, education, employment status, ethnicity, parents' education, perceived social class, level of religious participation, religious preference, whether respondents considered themselves to be an evangelical or born-again Christian, and the presence of other household members between the ages of 14 and 24. The data contain weights that should be used for analysis. All oversampled groups were weighted to their proper proportion in the total sample. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Created online analysis version with question text.; Checked for undocumented or out-of-range codes.. Teen and young adult population of the United States aged 14 through 24 who have a telephone at home. A national sample of 1,586 respondents aged 14 to 24 years was surveyed. These respondents were part of nationwide representative samples identified in households previously interviewed by CBS News and from RDD samples drawn from targeted areas. The 262 African American respondents, 200 Hispanic respondents, and 1,200 respondents aged 14 to 20 years in this poll included an oversample to provide larger bases for analysis. 2010-04-27 Corrected the dates of collection, PI, and oversample information and releasing the full product suite including question text. telephone interviewThe data contain oversamples of African Americans, Hispanics, and 14- to 20-years olds, as identified in the OSMP variable.Interviews were collected in both English and Spanish, as indicated in the variable HISP.The CASEID variable was reformatted in order to make it a unique identifier.Truncated value label in variables Q2, Q4, and Q5 were corrected.This data collection was produced by CBS News, New York, NY.

Baseline comparisons of demographic characteristics by intervention condition.

Gene variants in the UGT1A1 gene are strongly associated with circulating bilirubin levels in several populations, as well as other variants of modest effect across the genome. However, the effects of such variants are unknown regarding the Native American ancestry of the admixed Latino population. Our objective was to assess the Native American genetic determinants of serum bilirubin in Chilean admixed adolescents using the local ancestry deconvolution approach. We measured total serum bilirubin levels in 707 adolescents of the Chilean Growth and Obesity Cohort Study (GOCS) and performed high-density genotyping using the Illumina-MEGA array (>1.7 million genotypes). We constructed a local ancestry reference panel with participants from the 1000 Genomes Project, the Human Genome Diversity Project, and our GOCS cohort. Then, we inferred and isolated haplotype tracts of Native American, European, or African origin to perform genome-wide association studies. In the whole cohort, the rs887829 variant and others near UGT1A1 were the unique signals achieving genome-wide statistical significance (b = 0.30; p = 3.34 × 10−57). After applying deconvolution methods, we found that significance is also maintained in Native American (b = 0.35; p = 3.29 × 10−17) and European (b = 0.28; p = 1.14 × 10−23) ancestry components. The rs887829 variant explained a higher percentage of the variance of bilirubin in the Native American (37.6%) compared to European ancestry (28.4%). In Native American ancestry, carriers of the TT genotype of this variant averaged 4-fold higher bilirubinemia compared to the CC genotype (p = 2.82 × 10−12). We showed for the first time that UGT1A1 variants are the primary determinant of bilirubin levels in Native American ancestry, confirming its pan-ethnic relevance. Our study illustrates the general value of the local ancestry deconvolution approach to assessing isolated ancestry effects in admixed populations.

About a fifth of Hispanic high school students in the United States were overweight between 2016 and 2017, making it the ethnic group with the highest percentage of overweight high school students. Female obesity rates were considerably higher than those of male students for the black and Hispanic groups during the measured period.

Overweight and obese U.S. adults

U.S. overweight rates in adults differed slightly from those of U.S. high school students in 2017. That year, the African American population had the highest overweight and obesity rates of any race or ethnicity, closely followed by American Indians/Alaska Natives and Hispanics. Over 73 percent of all African American adults in the country were either overweight or obese. In 2018, the highest rates of obesity among African Americans could be found in states, such as Mississippi, Arkansas, and Tennessee.

Overweight youth worldwide

Many children and adolescents in other countries, such as New Zealand, Greece, and Italy, also struggle with overweight and obesity. In New Zealand, for example, over forty percent of boys and girls, up to age 19, were overweight or obese in 2016. In the same year, less than ten percent of Indian children and teenagers were overweight.

In 2023, it was estimated that nearly 61 percent of those aged 26 to 29 years in the United States were current alcohol drinkers, the highest rate of all age groups. Those aged 26 to 29 also had the highest rates of binge alcohol use. Although the legal age to consume alcohol in the United States is 21, around 31.1 percent of those aged 18 to 20 years were already current alcohol users. Binge drinking in the United States Binge drinking is commonly defined as consuming five or more alcoholic drinks on one occasion for men and four or more drinks for women. Binge drinking is most common among adults in their 20s, and more common among Hispanics and Whites than other races or ethnicities. The states with the highest prevalence of binge drinking are North Dakota, Montana, and Iowa, while Alabama has the lowest prevalence of binge drinking of all U.S. states. In 2022, almost 22 percent of the population of North Dakota binge drank in the past 30 days, with the overall prevalence rate in the United States around 17 percent at that time. High school alcohol use Although alcohol use among teens remains a problem, the annual prevalence of alcohol use among those in grades 8, 10, and 12 has decreased dramatically over the past two decades. In 2023, it was estimated that a combined total of 30 percent of those in grades 8, 10, and 12 had used alcohol in the past year. In comparison, the annual prevalence rate of alcohol use among these grades was just over 67 percent in the year 1991. The 30-day prevalence of alcohol use for these grades has also decreased significantly. In 1991, around 40 percent of those in grades 8, 10, and 12 used alcohol in the past month, while in 2023 this rate dropped to just 14.3 percent.