The National Survey of Children’s Health (NSCH) is sponsored by the Maternal and Child Health Bureau of the Health Resources and Services Administration, an Agency in the U.S. Department of Health and Human Services.The NSCH examines the physical and emotional health of children ages 0-17 years of age. Special emphasis is placed on factors related to the well-being of children. These factors include access to - and quality of - health care, family interactions, parental health, neighborhood characteristics, as well as school and after-school experiences.The NSCH is also designed to assess the prevalence and impact of special health care needs among children in the US and explores the extent to which children with special health care needs (CSHCN) have medical homes, adequate health insurance, access to needed services, and adequate care coordination. Other topics may include functional difficulties, transition services, shared decision-making, and satisfaction with care. Information is collected from parents or caregivers who know about the child's health.

The table NSCH 2020 Screener is part of the dataset National Survey of Children's Health (NSCH), available at https://redivis.com/datasets/c4gx-9ytmbqmdz. It contains 95035 rows across 40 variables.

This dataset tracks the updates made on the dataset "National Survey of Children’s Health (NSCH) – Vision and Eye Health Surveillance" as a repository for previous versions of the data and metadata.

The table NSCH 2020 Topical is part of the dataset National Survey of Children's Health (NSCH), available at https://redivis.com/datasets/c4gx-9ytmbqmdz. It contains 42777 rows across 443 variables.

2016-17 merged. This dataset is a de-identified summary table of vision and eye health data indicators from the National Survye of Chilrens Health (NSCH), stratified by all available combinations of age group, race/ethnicity, gender, risk factor and state. NSCH is a telephone survey conducted by the National Center for Health Statistics at CDC (currently conducted by the U.S. Census Bureau) that examines the physical and emotional health of children 0-17 years of age. Approximate sample size is 95,000 over two rounds of data collection. Data were suppressed for cell sizes less than 30 persons, or where the relative standard error more than 30% of the mean. Detailed information on VEHSS NSCH analyses can be found on the VEHSS NSCH webpage (cdc.gov/visionhealth/vehss/data/national-surveys/national-survey-of-childrens-health.html). Additional information about NSCH can be found on the NSCH website (http://childhealthdata.org/learn/NSCH). The VEHSS NSCH dataset was last updated in November 2019.

The National Survey of Children's Health, funded by the Maternal and Child Health Bureau (MCHB), is a module of the State and Local Area Integrated Telephone Survey (SLAITS) that is conducted by the National Center for Health Statistics (NCHS) at the Centers for Disease Control and Prevention (CDC). The survey was conducted to assess how well each state, and the nation as a whole, met MCHB's strategic plan goals and national performance measures. These goals include providing national leadership for maternal and child health, promoting an environment that supports maternal and child health, eliminating health barriers and disparities, improving the health infrastructure and systems of care, assuring quality care, working with states and communities to plan and implement policies and programs to improve the social, emotional, and physical environment, and acquiring the best available evidence to develop and promote guidelines and practices to assure a social, emotional, and physical environment that supports the health and well-being of women and children. The National Survey of Children's Health (NSCH) was designed to produce national- and state-specific prevalence estimates for a variety of physical, emotional, and behavioral health indicators and measures of children's experiences with the health care system. Respondents were asked an extensive battery of questions about the family, including parental health, stress and coping behaviors, family activities, and parental concerns about their children, as well as their perceptions of the child's neighborhood. Demographic information includes race, gender, family income, and education level.

Analysis of ‘National Survey of Children’s Health (NSCH) – Vision and Eye Health Surveillance’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/d1b71df1-9af9-45ab-848f-abb1f821bd62 on 12 February 2022.

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

2016-17 merged. This dataset is a de-identified summary table of vision and eye health data indicators from the National Survye of Chilrens Health (NSCH), stratified by all available combinations of age group, race/ethnicity, gender, risk factor and state. NSCH is a telephone survey conducted by the National Center for Health Statistics at CDC (currently conducted by the U.S. Census Bureau) that examines the physical and emotional health of children 0-17 years of age. Approximate sample size is 95,000 over two rounds of data collection. Data were suppressed for cell sizes less than 30 persons, or where the relative standard error more than 30% of the mean. Detailed information on VEHSS NSCH analyses can be found on the VEHSS NSCH webpage (cdc.gov/visionhealth/vehss/data/national-surveys/national-survey-of-childrens-health.html). Additional information about NSCH can be found on the NSCH website (http://childhealthdata.org/learn/NSCH). The VEHSS NSCH dataset was last updated in November 2019.

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

The table NSCH 2019 Screener is part of the dataset National Survey of Children's Health (NSCH), available at https://redivis.com/datasets/c4gx-9ytmbqmdz. It contains 67625 rows across 40 variables.

The table NSCH 2016 Topical is part of the dataset National Survey of Children's Health (NSCH), available at https://redivis.com/datasets/c4gx-9ytmbqmdz. It contains 50212 rows across 422 variables.

Data Downloads: Area Health Resource FilesData Downloads: BHW Clincian DashboardsData Downloads: BHW Program Applicant and Award DataData Downloads: GrantsData Downloads: Health Center Service Delivery and Look Alike SitesData Downloads: Health Professions Training ProgramsData Downloads: Maternal and Child Health BureauData Downloads: National Health Service Corps (NHSC), Nurse Corps, and Substance Use Disorder Treatment and Recovery (STAR) and other ProgramsData Downloads: Nursing Workforce Survey DataData Downloads: Organ Donation and TransplantationData Downloads: Ryan White HIV/AIDS ProgramData Downloads: Shortage Areas Data Downloads: Uniform Data SystemData Downloads: Workforce ProjectionsData by GeographyHRSA Fact SheetsNational Survey of Organ Donation Attitudes and PracticesNational Survey of Children’s Health (NSCH) and National Survey of Children with Special Health Care Needs ChartbooksNational Survey of Children's Health (NSCH)Donor Registry DataTransplant Activity Report

Introduction: Screentime is ubiquitous with children and parents concerned and anxious about its effect on the well-being of their children. This project uses the 2020 data from the National Survey of Children’s Health (NSCH) to determine if there is a correlation between the amount of weekday screentime in children ages 17 and younger and reported instances of mental health treatment and mental health treatment needed. Objectives: The primary objective of this project is to determine if there is a correlation between screentime and the mental health of children, ages 17 and younger. Methods: This project utilizes 2020 data from the NSCH, specifically the survey information collected about children ages 17 and younger on screentime, mental health professional treatment, and age of the child. Screentime refers to weekday time spent in front of a TV, computer, cellphone, or other electronic device watching programs, playing games, accessing the internet or using social media. After analyzing the three aforementioned variables, the percentage of mental health treatment occurrences by age group per screen time category indicates whether there is a correlation between children’s screentime and their mental health. Results: Preschool-aged (0-5 years old) children who spent 2 hours per weekday in front of a screen had the highest occurrence of mental health treatment, doubling the other categories of screentime. In school-aged (6-13 years old) children, there is a rise in mental health treatment needed as screentime increases. In adolescent (14-17 years old) children, there is a significant increase in the occurrence of mental health treatment as screentime increases, where 60% of adolescents who require mental health treatment spent four or more hours in front of a screen. Conclusions: There is a correlation between increased screentime and the occurrence of mental health treatment in children, particularly with the Adolescent (14-17 years old) age group.

Adverse childhood experiences (ACEs) are linked to adverse health outcomes for adults and children in the United States. The prevalence of critically ill children who are exposed to ACEs is not known. Our objective was to compare the frequency of ACEs of critically ill children with that of the general pediatric population of Georgia and the United States using publicly available National Survey of Children’s Health (NSCH) data. The impact of ACEs on patient-reported outcome measures of emotional, social, and physical health in critically ill children is not known. We sought to determine whether a higher total number of ACEs was associated with poorer patient-reported measures of emotional, social, and physical health. We conducted a prospective cross-sectional study of children < 18 years of age who were admitted to a 36-bed free-standing, quaternary academic pediatric intensive care unit in Atlanta, Georgia from June 2020—December 2021. Parents of patients who were admitted to the pediatric intensive care unit completed a survey regarding their child’s ACEs, health care use patterns, and patient-reported outcome measures (PROMIS) of emotional, social, and physical health. Prevalence estimates of ACEs were compared with national and state data from the NSCH using Rao-Scott Chi-square tests. PROMIS measures reported within the PICU cohort were compared with population normed T-scores. The association of cumulative ACEs within the PICU cohort with patient-reported outcomes of emotional, social, and physical health were evaluated with a t-test. Among the 84 participants, 54% had ≥ 1 ACE, 29% had ≥ 2 ACEs, and 10% had ≥ 3 ACEs. Children with ≥ 2 ACEs had poorer anxiety and family relationship T-scores compared to those with ≤ 1 ACE. Given the high burden of ACEs in critically ill children, screening for ACEs may identify vulnerable children that would benefit from interventions and support to mitigate the negative effects of ACEs and toxic stress on emotional, social, and physical health.

The table NSCH 2016 Implicate is part of the dataset National Survey of Children's Health (NSCH), available at https://redivis.com/datasets/c4gx-9ytmbqmdz. It contains 50212 rows across 8 variables.

Grounded in a strength-based (asset) model, this study explores the racial disparities in students’ learning and well-being during the pandemic. Linking the U.S. national/state databases of education and health, it examines whole-child outcomes and related factors—remote learning and protective community. It reveals race/ethnicity-stratified, state-level variations of learning and well-being losses in the midst of school accountability turnover. This data file includes aggregate state-level data derived from the NAEP and NSCH datasets, including all 50 U.S. states' pre-pandemic and post-pandemic measures of whole-child development outcomes (academic proficiency, socioemotional wellness, and physical health) as well as environmental conditions (remote learning and protective community) among school-age children. Methods To address the research questions, this study examines repeated cross-sectional datasets with nation/state-representative samples of school-age children. For academic achievement measures, the National Assessment of Educational Progress (NAEP) 2019 and 2022 datasets are used to assess nationally representative samples of 4th-grade and 8th-grade students’ achievement in reading and math (http://www.nces.ed.gov/nationsreportcard). In 2019, the NAEP samples included: 150,600 fourth graders from 8,300 schools and 143,100 eighth graders from 6,950 schools. In 2022, the NAEP samples included: (1) for reading, 108,200 fourth graders from 5,780 schools and 111,300 eighth graders from 5,190 schools; (2) for math, 116,200 fourth graders from 5,780 schools and 111,000 eighth graders from 5,190 schools. Data are weighted to be representative of the US population of students in grades 4 and 8, each for the entire nation and every state. Results are reported as average scores on a 0 to 500 scale and as percentages of students performing at or above the NAEP achievement levels: NAEP Basic, NAEP Proficient, and NAEP Advanced. In this study, we focus on changes in the percentages of students at or above the NAEP Basic level, which is the minimum competency level expected for all students across the nation. As a supplement to the NAEP assessment data, this study uses the NAEP School Dashboard (see https://ies.ed.gov/schoolsurvey/mss-dashboard/), which surveyed approximately 3,500 schools each month at grades 4 and 8 each during the pandemic period of January through May 2021: 46 states/jurisdictions participated, and 4,100 of 6,100 sampled schools responded. This study uses state-level information on the percentages of students who received in-person vs. remote/hybrid instructional modes. The school-reported remote learning enrollment rate is highly correlated with the NAEP survey student-reported remote learning experience (during 2021) across grades and subjects (r = .82 for grade 4 reading, r = .81 for grade 4 math, r = .79 for grade 8 reading, r = .83 for grade 8 math). These strong positive correlations provide supporting evidence for the cross-validation of remote learning measures at the state level. For socioemotional wellness and physical health measures, the National Survey of Children’s Health (NSCH) data are used. The 2018/19 surveys involved about 356,052 households screened for age-eligible children, and 59,963 child-level questionnaires were completed. The 2020/21 surveys involved about 199,840 households screened for age-eligible children, and 93,669 child-level questionnaires were completed. Our analysis focuses on school-age children (ages 6-17) in the data. In addition, the NSCH data are also used to assess the quality of protective and nurturing environment for child development across family, school, and neighborhood settings (see Appendix).

For more recent aggregated data reports on childhood obesity in NM, visit NM Healthy Kids Healthy Communities Program, NMDOH: https://www.nmhealth.org/about/phd/pchb/hknm/TitleChildhood Obese and Overweight Estimates, NM Counties 2016 - NMCHILDOBESITY2017SummaryCounty level childhood overweight and obese estimates for 2016 in New Mexico. *Most recent data known to be available on childhood obesity*NotesThis map shows NM County estimated rates of childhood overweight and obesity. US data is available upon request. Published in May, 2022. Data is most recent known sub-national obesity data set. If you know of another resource or more recent, please reach out. emcrae@chi-phi.orgSourceData set produced from the American Journal of Epidemiology and with authors and contributors out of the University of South Carolina, using data from the National Survey of Children's Health. Journal SourceZgodic, A., Eberth, J. M., Breneman, C. B., Wende, M. E., Kaczynski, A. T., Liese, A. D., & McLain, A. C. (2021). Estimates of childhood overweight and obesity at the region, state, and county levels: A multilevel small-area estimation approach. American Journal of Epidemiology, 190(12), 2618–2629. https://doi.org/10.1093/aje/kwab176 Journal article uses data fromThe United States Census Bureau, Associate Director of Demographic Programs, National Survey of Children’s Health 2020 National Survey of Children's Health Frequently Asked Questions. October 2021. Available from:https://www.census.gov/programs-surveys/nsch/data/datasets.htmlGIS Data Layer prepared byEMcRae_NMCDCFeature Servicehttps://nmcdc.maps.arcgis.com/home/item.html?id=80da398a71c14539bfb7810b5d9d5a99AliasDefinitionregionRegion NationallystateState (data set is NM only but national data is available upon request)fips_numCounty FIPScountyCounty NamerateRate of Obesitylower_ciLower Confidence Intervalupper_ciUpper Confidence IntervalfipstxtCounty FIPS text

For more recent aggregated data reports on childhood obesity in NM, visit NM Healthy Kids Healthy Communities Program, NMDOH: https://www.nmhealth.org/about/phd/pchb/hknm/TitleChildhood Obese and Overweight Estimates, NM Counties 2016 - NMCHILDOBESITY2017SummaryCounty level childhood overweight and obese estimates for 2016 in New Mexico. *Most recent data known to be available on childhood obesity*NotesThis map shows NM County estimated rates of childhood overweight and obesity. US data is available upon request. Published in May, 2022. Data is most recent known sub-national obesity data set. If you know of another resource or more recent, please reach out. emcrae@chi-phi.orgSourceData set produced from the American Journal of Epidemiology and with authors and contributors out of the University of South Carolina, using data from the National Survey of Children's Health. Journal SourceZgodic, A., Eberth, J. M., Breneman, C. B., Wende, M. E., Kaczynski, A. T., Liese, A. D., & McLain, A. C. (2021). Estimates of childhood overweight and obesity at the region, state, and county levels: A multilevel small-area estimation approach. American Journal of Epidemiology, 190(12), 2618–2629. https://doi.org/10.1093/aje/kwab176 Journal article uses data fromThe United States Census Bureau, Associate Director of Demographic Programs, National Survey of Children’s Health 2020 National Survey of Children's Health Frequently Asked Questions. October 2021. Available from:https://www.census.gov/programs-surveys/nsch/data/datasets.htmlGIS Data Layer prepared byEMcRae_NMCDCFeature Servicehttps://nmcdc.maps.arcgis.com/home/item.html?id=80da398a71c14539bfb7810b5d9d5a99AliasDefinitionregionRegion NationallystateState (data set is NM only but national data is available upon request)fips_numCounty FIPScountyCounty NamerateRate of Obesitylower_ciLower Confidence Intervalupper_ciUpper Confidence IntervalfipstxtCounty FIPS text

From April 30 to May 27, 2024, some 17.6 percent of U.S. adults reported symptoms of anxiety disorder in the past two weeks. Between May 2020 and March 2021, the share of U.S. adults who reported symptoms of anxiety disorder in the past two weeks consistently stayed above 30 percent, peaking at 37.2 percent in November 2020. The COVID-19 pandemic-related hardships and stress can be associated with the increase in the prevalence of anxiety disorder between 2020 and 2021. U.S. population with anxiety Anxiety disorders are the most common mental illnesses in the United States. Generalized anxiety disorder, panic disorder, specific phobias, and separation anxiety disorder are among the several forms of anxiety disorders. Women are more likely than men to suffer from anxiety disorder. Throughout one's lifetime, 26 percent of men and 40 percent of women in the U.S. suffer from anxiety problems. In 2021, roughly seven percent of the U.S. population suffered from anxiety disorder. U.S. children with anxiety According to the National Survey of Children’s Health (NSCH) report, between 2016 and 2019, approximately 10 percent of children and adolescents aged three to 17 had received a diagnosis of anxiety issues. The likelihood of ever being diagnosed with anxiety disorder increased with age, prevalence of anxiety was 13.7 percent among those aged between 12 and 17 years. Anxiety prevalence estimates in children were higher among White children. However, no significant differences were found in anxiety prevalence by gender and poverty level.

VEHSS Composite Prevalence Estimates 2017, 2019, 2021, 2022. This dataset contains estimates of the prevalence of visual acuity loss and major eye diseases generated using a Bayesian meta-analytic modeling approach that combines information from multiple data sources to produce comprehensive estimates of prevalence by age, race, and gender at the national, state and county levels. These composite prevalence estimates are the primary surveillance measures developed by the Centers for Disease Control and Prevention’s Vision & Eye Health Surveillance System (VEHSS).

For more information about these estimates including summary tables and maps, methods, and links to related publications visit https://www.cdc.gov/visionhealth/vehss/estimates/index.html To view this data in the VEHSS interactive data visualization application, visit https://ddt-vehss.cdc.gov/ and search for “VEHSS Composite Prevalence Estimate”.

Visual Acuity Loss: Visual acuity loss prevalence estimates represent best-corrected visual acuity in the better-seeing eye and are included in rows where Category=’Measured Visual Acuity’. Rows with Subgroup = ‘Any vision loss' represents any impairment or blindness of 20/40 or worse; rows with Subgroup = 'US-defined blindness' refers to the subset of vision loss that is 20/200 or worse.

Age Related Macular Degeneration: The age-related macular degeneration (AMD) estimates represent AMD as measured with retinal imaging examination, and are included in rows where Category = ‘Age Related Macular Degeneration’. The Subgroup ‘Vision threatening AMD’ includes patients with geographic atrophy, wet-form AMD, or choroidal neovascularization in either eye. The Subgroup ‘Non-vision threatening AMD’ includes patients with early or intermediate dry-form AMD defined as retinal pigment epithelium abnormalities or drusen ≥125 µm in the worse-affected eye, and do not have vision threatening AMD.

Diabetic Retinopathy: The diabetic retinopathy (DR) estimates represent DR as measured with retinal imaging examination, and are included in rows where Category=’Diabetic Eye Diseases’. The Subgroup ‘Vision threatening DR’ includes patients with severe non-proliferative DR, proliferative DR, and diabetic macular edema. The Subgroup ‘Non-vision threatening DR’ is defined as patients with mild-moderate non-proliferative DR or unspecified DR, and do not have vision threatening DR.

Glaucoma: The glaucoma estimates represent glaucoma as measured with retinal imaging examination and are included in rows where Category=’Glaucoma’. The Subgroup ‘Vision affecting glaucoma’ includes people with glaucoma and abnormal visual field. The Subgroup ‘Non-vision affecting glaucoma’ is defined as people with glaucoma without an abnormal visual field.

Age Groups: The VEHSS Composite Prevalence Estimates are available by major age groups (All ages, ages 0-17, 18-39, 40-64, 65-84, 85+) and detailed (5-year) age groups, which are indicated by the text “by detailed age groups” in the ‘Indicator’ field.

Prevalence Data Type: These estimates are also available as crude (Data_Value_Type = ‘Crude Prevalence’) or adjusted data (Data_Value_Type=’Adjusted Prevalence). Crude Prevalence is the estimate of the actual number and percentage of people living with each condition. Adjusted Prevalence estimates are adjusted to match the national population by age, race/ethnicity, and gender. Adjusted prevalence estimates can be used to help identify disparities in prevalence between geographic areas that are not explained by differences in demographic characteristics.

Data Sources: Data sources for VEHSS Composite Prevalence Estimates include the National Health and Nutrition Examination Survey (NHANES), the American Community Survey (ACS), the National Survey of Children’s Health (NSCH), the Behavioral Risk Factor Surveillance System (BRFSS), Medicare Fee-For-Service claims, the Transformed Medicaid Statistical Information System, MarketScan commercial insurance

BackgroundJuvenile idiopathic arthritis (JIA) is a prevalent chronic rheumatological condition in children, with reported prevalence ranging from 12. 8 to 45 per 100,000 and incidence rates from 7.8 to 8.3 per 100,000 person-years. The diagnosis of JIA can be challenging due to its symptoms, such as joint pain and swelling, which can be similar to other conditions (e.g., joint pain can be associated with growth in children and adolescents).MethodsThe National Survey of Children's Health (NSCH) database (2016–2021) of the United States was used in the current study. The NSCH database is funded by the Health Resources and Services Administration and Child Health Bureau and surveyed in all 50 states plus the District of Columbia. A total of 223,195 children aged 0 to 17 were analyzed in this study. A least absolute shrinkage and selection operator (LASSO) logistic regression and stepwise logistic regression were used to select the predictors, which were used to create the nomograms to predict JIA.ResultsA total of 555 (248.7 per 100,000) JIA cases were reported in the NSCH. In the LASSO model, the receiver operating characteristic curve demonstrated excellent discrimination, with an area under the curve (AUC) of 0.9002 in the training set and 0.8639 in the validation set. Of the 16 variables selected by LASSO, 13 overlapped with those from the stepwise model. The regression achieved an AUC of 0.9130 in the training set and 0.8798 in the validation set. Sensitivity, specificity, and accuracy were 79.1%, 90.2%, and 90.2% in the training set, and 69.0%, 90.9%, and 90.8% in the validation set.DiscussionUsing two well-validated predictor models, we developed nomograms for the early prediction of JIA in children based on the NSCH database. The tools are also available for parents and health professionals to utilize these nomograms. Our easy-to-use nomograms are not intended to replace the standard diagnostic methods. Still, they are designed to assist parents, clinicians, and researchers in better-estimating children's potential risk of JIA. We advise individuals utilizing our nomogram model to be mindful of potential pre-existing selection biases that may affect referrals and diagnoses.

Abbreviations: AGRE, the Autism Genetic Resource Exchange; SSC, the Simons Simplex Collection; AC, the Autism Consortium; NSCH, the 2007 National Survey of Children’s Health; Wt. %, weighted percentage; CI, confidence interval.aGenetic Collaborative Samples (AGRE, SSC, and AC) combined.bUnweighted number of children.