Student and School Characteristics Associated With COVID-19-Related Learning Decline Among Middle and High School Students in K-12 Schools.

BACKGROUND: COVID-19-disrupted schools, including shifts to virtual learning which may have impacted academic progress. This study assessed characteristics associated with changes in academic grades (before and during the pandemic) for different learning modalities for US students ages 13-19. METHODS: Students (N = 2152) completed a web survey on school-related experiences during the 2020-2021 school year. County social vulnerability and SARS-CoV-2 transmission data were merged with survey data. Multivariable logistic regression analysis for grade change was conducted with student and school characteristics for each learning modality, controlling for community characteristics. RESULTS: Greater proportions of remote/virtual (34.4%) and hybrid (30.1%) learning students reported grade decline compared to in-person students (19.9%). Among in-person students, odds of reporting same/improved grades were 65% lower among non-Hispanic black students and 66% lower among non-Hispanic students from other races, compared to non-Hispanic white students. Among hybrid students, odds of reporting same/improved grades for students reporting anxiety were 47% lower than students without anxiety, and odds of reporting same/improved grades among students reporting substance use were 40% lower than students not reporting substance use. Among remote/virtual students, odds of reporting same/improved grades among students with depression were 62% lower than odds of students not reporting depression symptoms. Remote/virtual students who received school-provided educational services also had 1.55 times the odds of reporting same/improved grades, compared to remote/virtual students not receiving these services. CONCLUSIONS: Academic grades were negatively impacted during COVID-19 and learning mode may have contributed. Understanding these impacts is critical to student health and academic achievement.

The National Center for Health Statistics' 2015 and 2016 Research and Development Surveys.

Objective: This report provides a general description of the background and operation of the first two rounds of the Research and Development Survey (RANDS), a series of cross-sectional surveys from probability-sampled commercial survey panels. The Division of Research and Methodology of the National Center for Health Statistics (NCHS) conducted the first two rounds of RANDS in 2015 and 2016. RANDS 1 and 2 are being used primarily for question design evaluation and for investigating statistical methodologies for estimation. Methods: NCHS contracted with Gallup, Inc. to conduct RANDS 1 in Fall 2015 and RANDS 2 in Spring 2016. RANDS 1 and 2 were conducted using a web survey mode and included survey questions from the National Health Interview Survey (NHIS) that were specifically chosen to provide comparison and evaluation of the survey methodology properties of web surveys and traditional household surveys. In this report, some demographic and health estimates are provided from both sources to describe the RANDS data. Results: In RANDS 1, 2,304 out of the original 9,809 invited panel members completed the survey, for a completion rate of 23.5%. In RANDS 2, 2,480 of the initial 8,231 invited respondents completed the survey, for a completion rate of 30.1%. RANDS 1 and 2 participants were similar to the quarterly NHIS participants with respect to sex, census region, and whether they had worked for pay in the previous week. Other characteristics varied, including age, race and ethnicity, and income. Most health estimates differed between RANDS and NHIS. Public-use versions of the RANDS data can be found at: https://www.cdc.gov/nchs/rands. Conclusion: RANDS is an ongoing platform for research to understand the properties of probability-sampled recruited panels of primarily web users, investigating and developing statistical methods for using such data in conjunction with large nationally representative health surveys, and for extending question-design evaluations.

Disparities in Incidence of COVID-19 Among Underrepresented Racial/Ethnic Groups in Counties Identified as Hotspots During June 5-18, 2020 - 22 States, February-June 2020.

During January 1, 2020-August 10, 2020, an estimated 5 million cases of coronavirus disease 2019 (COVID-19) were reported in the United States.* Published state and national data indicate that persons of color might be more likely to become infected with SARS-CoV-2, the virus that causes COVID-19, experience more severe COVID-19-associated illness, including that requiring hospitalization, and have higher risk for death from COVID-19 (1-5). CDC examined county-level disparities in COVID-19 cases among underrepresented racial/ethnic groups in counties identified as hotspots, which are defined using algorithmic thresholds related to the number of new cases and the changes in incidence.† Disparities were defined as difference of ≥5% between the proportion of cases and the proportion of the population or a ratio ≥1.5 for the proportion of cases to the proportion of the population for underrepresented racial/ethnic groups in each county. During June 5-18, 205 counties in 33 states were identified as hotspots; among these counties, race was reported for ≥50% of cumulative cases in 79 (38.5%) counties in 22 states; 96.2% of these counties had disparities in COVID-19 cases in one or more underrepresented racial/ethnic groups. Hispanic/Latino (Hispanic) persons were the largest group by population size (3.5 million persons) living in hotspot counties where a disproportionate number of cases among that group was identified, followed by black/African American (black) persons (2 million), American Indian/Alaska Native (AI/AN) persons (61,000), Asian persons (36,000), and Native Hawaiian/other Pacific Islander (NHPI) persons (31,000). Examining county-level data disaggregated by race/ethnicity can help identify health disparities in COVID-19 cases and inform strategies for preventing and slowing SARS-CoV-2 transmission. More complete race/ethnicity data are needed to fully inform public health decision-making. Addressing the pandemic's disproportionate incidence of COVID-19 in communities of color can reduce the community-wide impact of COVID-19 and improve health outcomes.

Trends in Number and Distribution of COVID-19 Hotspot Counties - United States, March 8-July 15, 2020.

The geographic areas in the United States most affected by the coronavirus disease 2019 (COVID-19) pandemic have changed over time. On May 7, 2020, CDC, with other federal agencies, began identifying counties with increasing COVID-19 incidence (hotspots) to better understand transmission dynamics and offer targeted support to health departments in affected communities. Data for January 22-July 15, 2020, were analyzed retrospectively (January 22-May 6) and prospectively (May 7-July 15) to detect hotspot counties. No counties met hotspot criteria during January 22-March 7, 2020. During March 8-July 15, 2020, 818 counties met hotspot criteria for ≥1 day; these counties included 80% of the U.S. population. The daily number of counties meeting hotspot criteria peaked in early April, decreased and stabilized during mid-April-early June, then increased again during late June-early July. The percentage of counties in the South and West Census regions* meeting hotspot criteria increased from 10% and 13%, respectively, during March-April to 28% and 22%, respectively, during June-July. Identification of community transmission as a contributing factor increased over time, whereas identification of outbreaks in long-term care facilities, food processing facilities, correctional facilities, or other workplaces as contributing factors decreased. Identification of hotspot counties and understanding how they change over time can help prioritize and target implementation of U.S. public health response activities.

National Center for Health Statistics Guidelines for Analysis of Trends.

Many reports present analyses of trends over time based on multiple years of data from National Center for Health Statistics (NCHS) surveys and the National Vital Statistics System (NVSS). Trend analyses of NCHS data involve analytic choices that can lead to different conclusions about the trends. This report discusses issues that should be considered when conducting a time trend analysis using NCHS data and presents guidelines for making trend analysis choices. Trend analysis issues discussed include: choosing the observed time points to include in the analysis, considerations for survey data and vital records data (record level and aggregated), a general approach for conducting trend analyses, assorted other analytic issues, and joinpoint regression. This report provides 12 guidelines for trend analyses, examples of analyses using NCHS survey and vital records data, statistical details for some analysis issues, and SAS and SUDAAN code for specification of joinpoint regression models. Several an lytic choices must be made during the course of a trend analysis, and the choices made can affect the results. This report highlights the strengths and limitations of different choices and presents guidelines for making some of these choices. While this report focuses on time trend analyses, the issues discussed and guidelines presented are applicable to trend analyses involving other ordinal and interval variables.

National Center for Health Statistics Data Presentation Standards for Proportions.

The National Center for Health Statistics (NCHS) disseminates information on a broad range of health topics through diverse publications. These publications must rely on clear and transparent presentation standards that can be broadly and efficiently applied. Standards are particularly important for large, cross-cutting reports where estimates cannot be individually evaluated and indicators of precision cannot be included alongside the estimates. This report describes the NCHS Data Presentation Standards for Proportions. The multistep NCHS Data Presentation Standards for Proportions are based on a minimum denominator sample size and on the absolute and relative widths of a confidence interval calculated using the Clopper-Pearson method. Proportions (usually multiplied by 100 and expressed as percentages) are the most commonly reported estimates in NCHS reports.

A Note on the Effect of Data Clustering on the Multiple-Imputation Variance Estimator: A Theoretical Addendum to , JOS.

Multiple imputation is a popular approach to handling missing data. Although it was originally motivated by survey nonresponse problems, it has been readily applied to other data settings. However, its general behavior still remains unclear when applied to survey data with complex sample designs, including clustering. Recently, Lewis et al. (2014) compared single- and multiple-imputation analyses for certain incomplete variables in the 2008 National Ambulatory Medicare Care Survey, which has a nationally representative, multistage, and clustered sampling design. Their study results suggested that the increase of the variance estimate due to multiple imputation compared with single imputation largely disappears for estimates with large design effects. We complement their empirical research by providing some theoretical reasoning. We consider data sampled from an equally weighted, single-stage cluster design and characterize the process using a balanced, one-way normal random-effects model. Assuming that the missingness is completely at random, we derive analytic expressions for the within- and between-multiple-imputation variance estimators for the mean estimator, and thus conveniently reveal the impact of design effects on these variance estimators. We propose approximations for the fraction of missing information in clustered samples, extending previous results for simple random samples. We discuss some generalizations of this research and its practical implications for data release by statistical agencies.

Multiple chronic conditions among US adults who visited physician offices: data from the National Ambulatory Medical Care Survey, 2009.

Most research on adults with chronic conditions focuses on a single disease or condition, such as hypertension or diabetes, rather than on multiple chronic conditions (MCC). Our study's objective was to compare physician office visits by adults with MCC with visits by adults without MCC, by selected patient demographic characteristics. We also identified the most prevalent dyads and triads of chronic conditions among these patients. We used the National Ambulatory Medical Care Survey, a nationally representative survey of office visits to nonfederal physicians and used 13 of the 20 conditions defined by the National Strategic Framework on Multiple Chronic Conditions. Descriptive estimates were generated and significant differences were tested. In 2009, an estimated 326 million physician office visits, were made by adults aged 18 years or older with MCC representing 37.6% of all medical office visits by adults. Hypertension was the most prevalent chronic condition that appeared in the top 5 MCC dyads and triads, by sex and age groups. The number of visits by patients with MCC increased with age and was greater for men than for women and for adults with public rather than private insurance. Physicians were more likely to prescribe medications at office visits made by patients with MCC. Physician office visits by adults with MCC were not evenly distributed by demographic characteristics.