Health risk behaviors practiced during adolescence often persist into adulthood and contribute to the leading causes of morbidity and mortality in the United States. Youth health behavior data at the national, state, territorial, tribal, and local levels help monitor the effectiveness of public health interventions designed to promote adolescent health. The Youth Risk Behavior Surveillance System (YRBSS) is the largest public health surveillance system in the United States, monitoring a broad range of health-related behaviors among high school students. YRBSS includes a nationally representative Youth Risk Behavior Survey (YRBS) and separate state, local school district, territorial, and tribal school-based YRBSs. This overview report describes the surveillance system and the 2019 survey methodology, including sampling, data collection procedures, response rates, data processing, weighting, and analyses presented in this MMWR Supplement. A 2019 YRBS participation map, survey response rates, and student demographic characteristics are included. In 2019, a total of 78 YRBSs were administered to high school student populations across the United States (national and 44 states, 28 local school districts, three territories, and two tribal governments), the greatest number of participating sites with representative data since the surveillance system was established in 1991. The nine reports in this MMWR Supplement are based on national YRBS data collected during August 2018-June 2019. A full description of 2019 YRBS results and downloadable data are available (https://www.cdc.gov/healthyyouth/data/yrbs/index.htm). Efforts to improve YRBSS and related data are ongoing and include updating reliability testing for the national questionnaire, transitioning to electronic survey administration (e.g., pilot testing for a tablet platform), and exploring innovative analytic methods to stratify data by school-level socioeconomic status and geographic location. Stakeholders and public health practitioners can use YRBS data (comparable across national, state, tribal, territorial, and local jurisdictions) to estimate the prevalence of healthrelated behaviors among different student groups, identify student risk behaviors, monitor health behavior trends, guide public health interventions, and track progress toward national health objectives.

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this report was posted as an MMWR Early Release on the MMWR website (https://www.cdc.gov/mmwr).Transmission of SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19), is ongoing in many communities throughout the United States. Although case-based and syndromic surveillance are critical for monitoring the pandemic, these systems rely on persons obtaining testing or reporting a COVID-19-like illness. Using serologic tests to detect the presence of SARS-CoV-2 antibodies is an adjunctive strategy that estimates the prevalence of past infection in a population. During April 28-May 3, 2020, coinciding with the end of a statewide shelter-in-place order, CDC and the Georgia Department of Public Health conducted a serologic survey in DeKalb and Fulton counties in metropolitan Atlanta to estimate SARS-CoV-2 seroprevalence in the population. A two-stage cluster sampling design was used to randomly select 30 census blocks in each county, with a target of seven participating households per census block. Weighted estimates were calculated to account for the probability of selection and adjusted for age group, sex, and race/ethnicity. A total of 394 households and 696 persons participated and had a serology result; 19 (2.7%) of 696 persons had SARS-CoV-2 antibodies detected. The estimated weighted seroprevalence across these two metropolitan Atlanta counties was 2.5% (95% confidence interval [CI] = 1.4-4.5). Non-Hispanic black participants more commonly had SARS-CoV-2 antibodies than did participants of other racial/ethnic groups (p<0.01). Among persons with SARS-CoV-2 antibodies, 13 (weighted % = 49.9; 95% CI = 24.4-75.5) reported a COVID-19-compatible illness,* six (weighted % = 28.2; 95% CI = 11.9-53.3) sought medical care for a COVID-19-compatible illness, and five (weighted % = 15.7; 95% CI = 5.1-39.4) had been tested for SARS-CoV-2 infection, demonstrating that many of these infections would not have been identified through case-based

Adolescence is an important period of risk for substance use initiation and substance use-related adverse outcomes. To examine youth substance use trends and patterns, CDC analyzed data from the 2009-2019 Youth Risk Behavior Survey. This report presents estimated prevalence of current (i.e., previous 30-days) marijuana use, prescription opioid misuse, alcohol use, and binge drinking and lifetime prevalence of marijuana, synthetic marijuana, cocaine, methamphetamine, heroin, injection drug use, and prescription opioid misuse among U.S. high school students. Logistic regression and Joinpoint analyses were used to assess 2009-2019 trends. Prevalence of current and lifetime substance use by demographics, frequency of use, and prevalence of co-occurrence of selected substances among students reporting current prescription opioid misuse are estimated using 2019 data. Multivariable logistic regression analysis was used to determine demographic and substance use correlates of current prescription opioid misuse. Current alcohol, lifetime cocaine, methamphetamine, heroin, and injection drug use decreased during 2009-2019. Lifetime use of synthetic marijuana (also called synthetic cannabinoids) decreased during 2015-2019. Lifetime marijuana use increased during 2009-2013 and then decreased during 2013-2019. In 2019, 29.2% reported current alcohol use, 21.7% current marijuana use, 13.7% current binge drinking, and 7.2% current prescription opioid misuse. Substance use varied by sex, race/ethnicity, grade, and sexual minority status (lesbian, gay, or bisexual). Use of other substances, particularly current use of alcohol (59.4%) and marijuana (43.5%), was common among students currently misusing prescription opioids. Findings highlight opportunities for expanding evidence-based prevention policies, programs, and practices that aim to reduce risk factors and strengthen protective factors related to youth substance use, in conjunction with ongoing initiatives for combating the opioid crisis.

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BackgroundAccurate estimation of gestational age is important for both clinical and public health purposes. Estimates of gestational age using fetal ultrasound measurements are considered most accurate but are frequently unavailable in low- and middle-income countries. The objective of this study was to assess the validity of last menstrual period and Farr neonatal examination estimates of gestational age, compared to ultrasound estimates, in a large cohort of women in Vietnam.MethodsData for this analysis come from a randomized, placebo-controlled micronutrient supplementation trial in Vietnam. We analyzed 912 women with ultrasound and prospectively-collected last menstrual period estimates of gestational age and 685 women with ultrasound and Farr estimates of gestational age. We used the Wilcoxon signed rank sum test to assess differences in gestational age estimated by last menstrual period or Farr examination compared to ultrasound and computed the intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC) to quantify agreement between methods. We computed the Kappa coefficient (κ) to quantify agreement in preterm, term and post-term classification.ResultsThe median gestational age estimated by ultrasound was 273.9 days. Gestational age was slightly overestimated by last menstrual period (median 276.0 days, P < 0.001) and more greatly overestimated by Farr examination (median 286.7 days, P < 0.001). Gestational age estimates by last menstrual period and ultrasound were moderately correlated (ICC = 0.78) and concordant (CCC = 0.63), whereas gestational age estimates by Farr examination and ultrasound were weakly correlated (ICC = 0.26) and concordant (CCC = 0.05). Last menstrual period and ultrasound estimates of gestational age were within ± 14 days for 88.4% of women; Farr and ultrasound estimates were within ± 14 days for 55.8% of women. Last menstrual period and ultrasound estimates of gestational age had higher agreement in term classification (κ = 0.41) than Farr and ultrasound (κ = 0.05).ConclusionIn this study of women in Vietnam, we found last menstrual period provided a more accurate estimate of gestational age than the Farr examination when compared to ultrasound. These findings provide useful information about the utility and accuracy of different methods to estimate gestational age and suggest last menstrual period may be preferred over Farr examination in settings where ultrasound is unavailable.Trial registrationThe trial was registered at ClinicalTrials.Gov as NCT01665378 on August 13, 2012.Electronic supplementary materialThe online version of this article (doi:10.1186/s12884-016-1192-5) contains supplementary material, which is available to authorized users.

Objectives Federal open-data initiatives that promote increased sharing of federally collected data are important for transparency, data quality, trust, and relationships with the public and state, tribal, local, and territorial partners. These initiatives advance understanding of health conditions and diseases by providing data to researchers, scientists, and policymakers for analysis, collaboration, and use outside the Centers for Disease Control and Prevention (CDC), particularly for emerging conditions such as COVID-19, for which data needs are constantly evolving. Since the beginning of the pandemic, CDC has collected person-level, de-identified data from jurisdictions and currently has more than 8 million records. We describe how CDC designed and produces 2 de-identified public datasets from these collected data. Methods We included data elements based on usefulness, public request, and privacy implications; we suppressed some field values to reduce the risk of re-identification and exposure of confidential information. We created datasets and verified them for privacy and confidentiality by using data management platform analytic tools and R scripts. Results Unrestricted data are available to the public through Data.CDC.gov, and restricted data, with additional fields, are available with a data-use agreement through a private repository on GitHub.com. Practice Implications Enriched understanding of the available public data, the methods used to create these data, and the algorithms used to protect the privacy of de-identified people allow for improved data use. Automating data-generation procedures improves the volume and timeliness of sharing data.