National Health Interview Survey, COVID-19, and Online Data Collection Platforms: Adaptations, Tradeoffs, and New Directions.

High-quality data are accurate, relevant, and timely. Large national health surveys have always balanced the implementation of these quality dimensions to meet the needs of diverse users. The COVID-19 pandemic shifted these balances, with both disrupted survey operations and a critical need for relevant and timely health data for decision-making. The National Health Interview Survey (NHIS) responded to these challenges with several operational changes to continue production in 2020. However, data files from the 2020 NHIS were not expected to be publicly available until fall 2021. To fill the gap, the National Center for Health Statistics (NCHS) turned to 2 online data collection platforms-the Census Bureau's Household Pulse Survey (HPS) and the NCHS Research and Development Survey (RANDS)-to collect COVID-19‒related data more quickly. This article describes the adaptations of NHIS and the use of HPS and RANDS during the pandemic in the context of the recently released Framework for Data Quality from the Federal Committee on Statistical Methodology. (Am J Public Health. 2021;111(12):2167-2175. https://doi.org/10.2105/AJPH.2021.306516).

Efficiency and Quality of Data Collection Among Public Mental Health Surveys Conducted During the COVID-19 Pandemic: Systematic Review.

BACKGROUND: The World Health Organization has recognized the importance of assessing population-level mental health during the COVID-19 pandemic. During a global crisis such as the COVID-19 pandemic, a timely surveillance method is urgently needed to track the impact on public mental health. OBJECTIVE: This brief systematic review focused on the efficiency and quality of data collection of studies conducted during the COVID-19 pandemic. METHODS: We searched the PubMed database using the following search strings: ((COVID-19) OR (SARS-CoV-2)) AND ((Mental health) OR (psychological) OR (psychiatry)). We screened the titles, abstracts, and texts of the published papers to exclude irrelevant studies. We used the Newcastle-Ottawa Scale to evaluate the quality of each research paper. RESULTS: Our search yielded 37 relevant mental health surveys of the general public that were conducted during the COVID-19 pandemic, as of July 10, 2020. All these public mental health surveys were cross-sectional in design, and the journals efficiently made these articles available online in an average of 18.7 (range 1-64) days from the date they were received. The average duration of recruitment periods was 9.2 (range 2-35) days, and the average sample size was 5137 (range 100-56,679). However, 73% (27/37) of the selected studies had Newcastle-Ottawa Scale scores of <3 points, which suggests that these studies are of very low quality for inclusion in a meta-analysis. CONCLUSIONS: The studies examined in this systematic review used an efficient data collection method, but there was a high risk of bias, in general, among the existing public mental health surveys. Therefore, following recommendations to avoid selection bias, or employing novel methodologies considering both a longitudinal design and high temporal resolution, would help provide a strong basis for the formation of national mental health policies.

Advancements in the National Vital Statistics System to Meet the Real-Time Data Needs of a Pandemic.

The National Center for Health Statistics' (NCHS's) National Vital Statistics System (NVSS) collects, processes, codes, and reviews death certificate data and disseminates the data in annual data files and reports. With the global rise of COVID-19 in early 2020, the NCHS mobilized to rapidly respond to the growing need for reliable, accurate, and complete real-time data on COVID-19 deaths. Within weeks of the first reported US cases, NCHS developed certification guidance, adjusted internal data processing systems, and stood up a surveillance system to release daily updates of COVID-19 deaths to track the impact of the COVID-19 pandemic on US mortality. This report describes the processes that NCHS took to produce timely mortality data in response to the COVID-19 pandemic. (Am J Public Health. 2021;111(12):2133-2140. https://doi.org/10.2105/AJPH.2021.306519).

Considerations for Improving Reporting and Analysis of Date-Based COVID-19 Surveillance Data by Public Health Agencies.

More than a year after the first domestic COVID-19 cases, the United States does not have national standards for COVID-19 surveillance data analysis and public reporting. This has led to dramatic variations in surveillance practices among public health agencies, which analyze and present newly confirmed cases by a wide variety of dates. The choice of which date to use should be guided by a balance between interpretability and epidemiological relevance. Report date is easily interpretable, generally representative of outbreak trends, and available in surveillance data sets. These features make it a preferred date for public reporting and visualization of surveillance data, although it is not appropriate for epidemiological analyses of outbreak dynamics. Symptom onset date is better suited for such analyses because of its clinical and epidemiological relevance. However, using symptom onset for public reporting of new confirmed cases can cause confusion because reporting lags result in an artificial decline in recent cases. We hope this discussion is a starting point toward a more standardized approach to date-based surveillance. Such standardization could improve public comprehension, policymaking, and outbreak response. (Am J Public Health. 2021;111(12):2127-2132. https://doi.org/10.2105/AJPH.2021.306520).

COVID-19 Pandemic Impact on the National Health Care Surveys.

While underscoring the need for timely, nationally representative data in ambulatory, hospital, and long-term-care settings, the COVID-19 pandemic posed many challenges to traditional methods and mechanisms of data collection. To continue generating data from health care and long-term-care providers and establishments in the midst of the COVID-19 pandemic, the National Center for Health Statistics had to modify survey operations for several of its provider-based National Health Care Surveys, including quickly adding survey questions that captured the experiences of providing care during the pandemic. With the aim of providing information that may be useful to other health care data collection systems, this article presents some key challenges that affected data collection activities for these national provider surveys, as well as the measures taken to minimize the disruption in data collection and to optimize the likelihood of disseminating quality data in a timely manner. (Am J Public Health. 2021;111(12):2141-2148. https://doi.org/10.2105/AJPH.2021.306514).

COVID-19 Pandemic and Indigenous Representation in Public Health Data.

Public Health 3.0 calls for the inclusion of new partners and novel data to bring systemic change to the US public health landscape. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has illuminated significant data gaps influenced by ongoing colonial legacies of racism and erasure. American Indian and Alaska Native (AI/AN) populations and communities have been disproportionately affected by incomplete public health data and by the COVID-19 pandemic itself. Our findings indicate that only 26 US states were able to calculate COVID-19‒related death rates for AI/AN populations. Given that 37 states have Indian Health Service locations, we argue that public health researchers and practitioners should have a far larger data set of aggregated public health information on AI/AN populations. Despite enormous obstacles, local Tribal facilities have created effective community responses to COVID-19 testing, tracking, and vaccine administration. Their knowledge can lead the way to a healthier nation. Federal and state governments and health agencies must learn to responsibly support Tribal efforts, collect data from AI/AN persons in partnership with Indian Health Service and Tribal governments, and communicate effectively with Tribal authorities to ensure Indigenous data sovereignty. (Am J Public Health. 2021;111(S3): S208-S214. https://doi.org/10.2105/AJPH.2021.306415).

Keep Moving Forward: Health Informatics and Information Management beyond the COVID-19 Pandemic.

OBJECTIVES: To identify gaps and challenges in health informatics and health information management during the COVID-19 pandemic. To describe solutions and offer recommendations that can address the identified gaps and challenges. METHODS: A literature review of relevant peer-reviewed and grey literature published from January 2020 to December 2020 was conducted to inform the paper. RESULTS: The literature revealed several themes regarding health information management and health informatics challenges and gaps: information systems and information technology infrastructure; data collection, quality, and standardization; and information governance and use. These challenges and gaps were often driven by public policy and funding constraints. CONCLUSIONS: COVID-19 exposed complexities related to responding to a world-wide, fast moving, quickly spreading novel virus. Longstanding gaps and ongoing challenges in the local, national, and global health and public health information systems and data infrastructure must be addressed before we are faced with another global pandemic.

Alternative Methods for Grouping Race and Ethnicity to Monitor COVID-19 Outcomes and Vaccination Coverage.

Population-based analyses of COVID-19 data, by race and ethnicity can identify and monitor disparities in COVID-19 outcomes and vaccination coverage. CDC recommends that information about race and ethnicity be collected to identify disparities and ensure equitable access to protective measures such as vaccines; however, this information is often missing in COVID-19 data reported to CDC. Baseline data collection requirements of the Office of Management and Budget's Standards for the Classification of Federal Data on Race and Ethnicity (Statistical Policy Directive No. 15) include two ethnicity categories and a minimum of five race categories (1). Using available COVID-19 case and vaccination data, CDC compared the current method for grouping persons by race and ethnicity, which prioritizes ethnicity (in alignment with the policy directive), with two alternative methods (methods A and B) that used race information when ethnicity information was missing. Method A assumed non-Hispanic ethnicity when ethnicity data were unknown or missing and used the same population groupings (denominators) for rate calculations as the current method (Hispanic persons for the Hispanic group and race category and non-Hispanic persons for the different racial groups). Method B grouped persons into ethnicity and race categories that are not mutually exclusive, unlike the current method and method A. Denominators for rate calculations using method B were Hispanic persons for the Hispanic group and persons of Hispanic or non-Hispanic ethnicity for the different racial groups. Compared with the current method, the alternative methods resulted in higher counts of COVID-19 cases and fully vaccinated persons across race categories (American Indian or Alaska Native [AI/AN], Asian, Black or African American [Black], Native Hawaiian or Other Pacific Islander [NH/PI], and White persons). When method B was used, the largest relative increase in cases (58.5%) was among AI/AN persons and the largest relative increase in the number of those fully vaccinated persons was among NH/PI persons (51.6%). Compared with the current method, method A resulted in higher cumulative incidence and vaccination coverage rates for the five racial groups. Method B resulted in decreasing cumulative incidence rates for two groups (AI/AN and NH/PI persons) and decreasing cumulative vaccination coverage rates for AI/AN persons. The rate ratio for having a case of COVID-19 by racial and ethnic group compared with that for White persons varied by method but was <1 for Asian persons and >1 for other groups across all three methods. The likelihood of being fully vaccinated was highest among NH/PI persons across all three methods. This analysis demonstrates that alternative methods for analyzing race and ethnicity data when data are incomplete can lead to different conclusions about disparities. These methods have limitations, however, and warrant further examination of potential bias and consultation with experts to identify additional methods for analyzing and tracking disparities when race and ethnicity data are incomplete.

Managing Pandemics with Health Informatics: Successes and Challenges.

INTRODUCTION: The novel COVID-19 pandemic struck the world unprepared. This keynote outlines challenges and successes using data to inform providers, government officials, hospitals, and patients in a pandemic. METHODS: The authors outline the data required to manage a novel pandemic including their potential uses by governments, public health organizations, and individuals. RESULTS: An extensive discussion on data quality and on obstacles to collecting data is followed by examples of successes in clinical care, contact tracing, and forecasting. Generic local forecast model development is reviewed followed by ethical consideration around pandemic data. We leave the reader with thoughts on the next inevitable outbreak and lessons learned from the COVID-19 pandemic. CONCLUSION: COVID-19 must be a lesson for the future to direct us to better planning and preparing to manage the next pandemic with health informatics.

Variation in Reporting of the Race and Ethnicity of COVID-19 Cases and Deaths Across US States: April 12, 2020, and November 9, 2020.

Despite growing evidence that COVID-19 is disproportionately affecting communities of color, state-reported racial/ethnic data are insufficient to measure the true impact.We found that between April 12, 2020, and November 9, 2020, the number of US states reporting COVID-19 confirmed cases by race and ethnicity increased from 25 to 50 and 15 to 46, respectively. However, the percentage of confirmed cases reported with missing race remained high at both time points (29% on April 12; 23% on November 9). Our analysis demonstrates improvements in reporting race/ethnicity related to COVID-19 cases and deaths and highlights significant problems with the quality and contextualization of the data being reported.We discuss challenges for improving race/ethnicity data collection and reporting, along with opportunities to advance health equity through more robust data collection and contextualization. To mitigate the impact of COVID-19 on racial/ethnic minorities, accurate and high-quality demographic data are needed and should be analyzed in the context of the social and political determinants of health.