In 2023, the birth rate for women aged 15 to 19 years in the Central African Republic was *** per 1,000 women of that age, the highest adolescent birth rate of any country worldwide. This statistic shows the leading 20 countries based on adolescent birth rate in 2023, per 1,000 women aged 15 to 19 years.

This map shows the teen pregnancy rate per 1,000 females age 15 to 17 by county. Counties are shaded based on quartile distribution. The lighter shaded counties have a lower percentage of teen pregnancy. The darker shaded counties have a higher percentage of teen pregnancy. New York State Community Health Indicator Reports (CHIRS) were developed in 2012, and are updated annually to consolidate and improve data linkages for the health indicators included in the County Health Assessment Indicators (CHAI) for all communities in New York. The CHIRS present data for more than 300 health indicators that are organized by 15 different health topics. Data if provided for all 62 New York State counties, 11 regions (including New York City), the State excluding New York City, and New York State. For more information, check out: http://www.health.ny.gov/statistics/chac/indicators/. The "About" tab contains additional details concerning this dataset.

In 2022, Bulgaria had the highest share of births to teenage mothers in Europe, at almost 10.2 percent of all births in the country. Furthermore, in Slovakia and Moldova, over five percent of births in both countries were to mothers aged below 20 years. The share of teenage births was particularly low in Switzerland, Andorra, and Norway. Falling teenage births In Europe, the share of births to teenage mothers has been trending downwards. Across the whole European region, the share of adolescent births fell from almost *** percent in 1980 to ***** percent in 2021. More specifically, in the European Union, teenagers accounted for fewer than *** percent of all births in 2021. Access to contraception In developed countries, the average age for women giving birth has increased over time, and in general, women are choosing to have fewer children. One of the main reasons is improved access to contraception, which allows women greater autonomy over their bodies. Luxembourg, which was rated as having the best access to modern contraception, also has the highest average childbearing age in Europe. Next on the contraception ranking; Belgium, France, and the UK also had a mean age of around ** for mothers.

This is one of three datasets related to the Prevention Agenda Tracking Indicators county level data posted on this site. Each dataset consists of county level data for 68 health tracking indicators and sub-indicators for the Prevention Agenda 2013-2017: New York State’s Health Improvement Plan. A health tracking indicator is a metric through which progress on a certain area of health improvement can be assessed. The indicators are organized by the Priority Area of the Prevention Agenda as well as the Focus Area under each Priority Area. Each dataset includes tracking indicators for the five Priority Areas of the Prevention Agenda 2013-2017. The most recent year dataset includes the most recent county level data for all indicators. The trend dataset includes the most recent county level data and historical data, where available. Each dataset also includes the Prevention Agenda 2017 state targets for the indicators. Sub-indicators are included in these datasets to measure health disparities among socioeconomic groups. For more information, check out: http://www.health.ny.gov/prevention/prevention_agenda/2013-2017/ and https://www.health.ny.gov/PreventionAgendaDashboard, or go to the “About” tab.

Teenage pregnancy remains a critical issue in Kenya, with 15% of girls aged 15–19 having been pregnant. Counties in western Kenya experience high teenage pregnancy rates (22–30%) along with high HIV prevalence and widespread poverty. Long-term consequences of teenage pregnancy have been documented in high-income countries, but evidence from the Global South is lacking. Here, we examined the association between teenage pregnancy and adult socio-economic functioning in western Kenya using cross-sectional survey data from Migori County, Kenya. We categorized women into three groups: adult mothers (first child ≥20 years), teenage mothers to 1 child (had 1 child before age 20), and teenage mothers to 2 + children (had 2 or more children before age 20). We then compared adult socioeconomic and health outcomes of these groups. We found that among 6,089 mothers, 45.2% had their first child during adolescence. Compared to adult mothers, teenage mothers were significantly less likely to complete primary education: a 12.2 percentage point (pp) reduction (95% CI: -14.9, -9.4) among teenage mothers to 1 child and 27.6 pp reduction (95% CI: -31.4, -23.8) among teenage mothers to 2 + children. Teenage mothers were also more likely to have loans and experience food insecurity. The risk of experiencing the death of a child increased from 3.4% among adult mothers to 15.3% among teenage mothers to 2 + children, a 4.5-fold increase (p 

This table provides data on the number of conceptions which resulted in either a live or still birth or termination by legal abortion. Conception figures are estimates ,derived by combining information from birth registrations and notifications of legal abortions. They do not include miscarriages or illegal abortions.The date of conception is estimated using recorded gestation for abortions and stillbirths, and by assuming 38 weeks for live births. The conceptions figures relate to residents in an area.

Colombia CO: Teenage Mothers data was reported at 17.400 % in 2015. This records a decrease from the previous number of 19.500 % for 2010. Colombia CO: Teenage Mothers data is updated yearly, averaging 17.400 % from Dec 1986 (Median) to 2015, with 7 observations. The data reached an all-time high of 20.500 % in 2005 and a record low of 12.800 % in 1990. Colombia CO: Teenage Mothers data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Colombia – Table CO.World Bank.WDI: Social: Health Statistics. Teenage mothers are the percentage of women ages 15-19 who already have children or are currently pregnant.;Demographic and Health Surveys.;Weighted average;

Purpose:This dataset contains the Crude Rate of live births to teens age 15-17 / 1,000 female population age 15-17 per Colorado Census Tract (2019-2023). Data taken directly from the 2019-2023 Colorado Birth Dataset. Numerator and denominator data are calculated from the 2019-2023 Colorado Department of Public Health and Environment Colorado Live Birth Statistics.Update Schedule and URL:This dataset is updated annually (September) and is provided using death data directly assembled from the Colorado Department of Public Health and Environment Colorado Live Birth Statistics. For inquiries about vital statistics or for data requests contact cdphe.healthstatistics@state.co.us, or use the data request system.Fields Description:GEOID: 11-digit Census Tract FIPS Identifier COUNTY: County NameNAME: Census Tract NameTF_ADJRATE: Crude Rate of Live Births to Females Age 15-17 / 1,000 Female Population Age 15-17 (2019-2023, Colorado Live Birth Statistics)TF_L95CI: Teen Fertility Rate Lower 95% Confidence IntervalTF_U95CI: Teen Fertility Rate Upper 95% Confidence IntervalTF_STATEADJRATE: Statewide Crude Rate of Live Births to Females Age 15-17 / 1,000 Female Population Age 15-17 (2019-2023, Colorado Live Birth Statistics)TF_SL95CI: Statewide Teen Fertility Rate Lower 95% Confidence IntervalTF_SU95CI: Statewide Teen Fertility Rate Upper 95% Confidence IntervalTF_DISPLAY: Teen Fertility Rate Census Tract Map Display Designation (Estimate is Higher Than State Average Confidence Interval, Lower Than State Average Confidence Interval, Not Different Than State Average Confidence Interval, No Events or Data Suppressed)

SUMMARY This table contains data about women, ages 15 to 50, pregnant people, infants, children, and youths, up to age 24. It contains information about a wide range of health topics, including medical conditions, nutrition, dehydration, oral health, mental health, safety, access to health care, and basic needs, like housing. Local, county-level prevalence rates, time trends, and health disparities about national public health priorities, including preterm birth, infant death, childhood obesity, adolescent depression and substance use, and high blood pressure, diabetes, and kidney disease in young adults. The population data is from the 2023-2024 San Francisco Maternal Child and Adolescent Health needs assessment and is published on the Open Data Portal to share with community partners, plan services, and promote health. For more information see: Maternal, Child, and Adolescent Health Homepage Maternal, Child, and Adolescent Health Reports HOW THE DATASET IS CREATED The Maternal, Child, and Adolescent Health (MCAH) Needs Assessment for San Francisco included review of a wide range of citywide population data covering a ten-year span, from 2014 to 2023. Data from over 83,000 birth records, 59,000 death records, 261,000 emergency room visits, 66,000 hospital admissions, and 90,000 newborn screening discharges were gathered, along with citywide data from child welfare records, health screenings in childcare and schools, DMV records of first-time drivers, school surveys, and a state-run mailed survey of recent births (California Department of Public Health MIHA survey). The datasets provided information about approximately 700 health conditions. Each health condition was described in terms of the number of people affected or cases, and the rate affected, stratified by age, sex, race-ethnicity, insurance status, zip code, and time period. Rates were calculated by dividing the number of people or events by the population group estimate (e.g., total births or census estimates), then multiplying by 100 or 1,000 depending on the measure. Each rate was presented with its 95% confidence interval to support users to compare any two rates, either between groups or over time. Two rates differ “significantly” if their 95% confidence intervals do not overlap. The present dataset summarizes the group-level results for any age-, sex-, race-, insurance-, zip code-, and/or period-specific group that included at least 20 people or cases. Causes of death, health conditions that affected over 1000 people in the time frame, problems that got worse over time, and health disparities by insurance, race-ethnicity and/or zip code were flagged for the MCAH Needs Assessment. UPDATE PROCESS The dataset will be updated manually, bi-annually, each December and June. HOW TO USE THIS DATASET Population data from the MCAH needs assessment are shared in several formats, including aggregated datasets on DataSF.gov, downloadable PDF summary reports by age group, interactive online visualizations, data tables, trend graphs, and maps. Information about each variable is available in a linked data dictionary. The definition of each numerator and denominator depends on data source, life stage, and time. Health conditions may not be directly comparable across life stage, if the numerator definition includes age- or pregnancy-specific diagnosis codes (e.g. diabetes hospitalization). For small groups or rare conditions, consider combining time periods and/or groups. Data are suppressed if fewer than 20 cases happened in the group and period. Group-specific rates are available if the matched group-specific census estimates (denominator) were available. Census estim

Abstract

Unsafe abortion was one of the major causes of complications, leading to Liberia's high maternal mortality ratio (1,072 deaths per 100,000 live births). The Ministry of Health highly prioritized reducing the high rates of maternal and neonatal deaths in the country. Among national efforts to improve access to and quality of reproductive, maternal, neonatal, child, and adolescent health (RMNCAH) services was the prevention of unsafe abortion and morbidity and mortality from unsafe abortions because nearly 6 out of 10 girls were mothers before age 19. In addition, adolescent pregnancy contributed to high maternal mortality and high neonatal mortality. Nevertheless, there was little information available on abortion incidence, burden and costs of managing complications from unsafe abortions, and the quality of post-abortion care. Data were critical for government and civil society stakeholders to design effective policies and guidance to reduce maternal morbidity and mortality from unsafe abortions and to advocate for increased access to comprehensive abortion care (inclusive of safe abortion for legal indications and post-abortion care) in Liberia.

Objectives: The overall aim of the study was to determine the incidence of induced abortions, severity and magnitude of abortion-related complications, quality of PAC, and cost burden of unsafe abortion on the health systems in Liberia.

Methodology: A mixed-method cross-sectional design was applied to determine the incidence of abortion in Liberia. This research design was employed using the Abortion Incidence Complication Method (AICM). This widely applied indirect method had produced robust estimates of abortion incidence in a range of contexts. The study comprised five (5) different surveys, namely: 1) Health Facility Survey (HFS), 2) Prospective Morbidity Survey, 3) Knowledgeable Informant Survey, 4) Quality of PAC survey, and 5) Post Abortion Care (PAC) Costing Survey. The Health Facility Survey was implemented at sampled public facilities using a nationally representative, stratified, random sampling approach to determine the incidence of induced abortion and abortion complications in Liberia. The Health Facility Survey also included a quality survey to assess the quality of post-abortion care. The Prospective Morbidity Survey was about women seeking PAC in health facilities and providers, including a patient chart review to assess the severity of complications. It also collected data on decision-making, care-seeking pathways, and awareness of the country's abortion law. For the Knowledgeable Informant Survey, a sample of health sector stakeholders who were knowledgeable about abortion/PAC in Liberia were interviewed. Data from this component generated the multiplier to inform the incidence of abortion. The PAC costing study targeted health facility administrators to estimate the costs of PAC at facilities and the national level.

Geographic coverage

National coverage

Analysis unit

knowledgeable healthcare providers

Universe

senior health providers, who are knowledgeable about the provision of PAC

Sampling procedure

The sample frame for the health facility survey included all public clinics, health centers, and hospitals based on the assumption that all public facilities can provide some level of PAC as per the current Liberia National Guidelines for Comprehensive Abortion Care (2019). The sample size for public facilities was drawn from the MOH Master Facility Listing (updated October 8, 2020). According to the most updated Master Facility Listing, there are 467 functional public facilities. After eliminating specialized facilities (e.g., specific infectious disease facilities, rehabilitation centers, etc.) and military/paramilitary facilities (barrack, prisons, etc.), a sample frame of 436 functional public facilities across the 15 counties in Liberia was generated. These 436 facilities include 21 hospitals (5%), 37 health centers (8%), and 378 clinics (87%). All of the public hospitals (i.e., 100%) were included in the survey because of the high PAC patient load and service volume, 70% of health centers, and 10% of clinics were also randomly sampled for the HFS (considerations being PAC service volume and staffing of clinicians at those levels).

The study sample was distributed amongst the 15 counties by determining what proportion of the total eligible facilities in the country was situated in a county disaggregated by levels. We multiplied the percentage of facilities at each level in the county by the sample size to determine county allocation of the sample size. To select the actual facility for inclusion in the sample and study, we isolated the facilities by region and level. For each level and region, we ordered the list of facilities in alphabetical order and following that order, attach serial numbers to the facilities. Using the Stata program, we generated random numbers of the ordered facilities. Any facility so randomly selected was included in the sample and study.

As for private facilities, there were no standard formula for estimating the sample to include in the HFS. Facilities were purposely selected based on consideration of the level of care, the number of facilities available in each level of care, facility catchment population, number of PAC cases (reported in DHIS-2 for 2020), and logistical considerations. Twenty private facilities (11 hospitals, 3 health centers, 6 clinics) were purposively selected because of the volume of PAC consultations at those facilities.

Sampling deviation

Health facility survey (HFS): One hundred and thirty-two (132) health facilities were sampled, but only 128 health facilities responded.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was written in english with a primary purpose of the HFS is to estimate the number of women who receive treatment in facilities for abortion-related complications. The HFS will be a statistically representative survey of all health facilities in Kenya classified as having the capacity to provide PAC services. In each selected health facility, a senior health provider, who is knowledgeable about the provision of PAC, is interviewed.

Cleaning operations

the software used was survey CTO for data colllection, the data was later downloaded in STATA format.

Response rate

97.0%

Sampling error estimates

N/A

Relationship between individual risk factors and adolescent pregnancy among girls attending secondary school, Siaya County, western Kenya, 2017–2018.