Leveraging Ethnic Backgrounds to Improve Collection of Race, Ethnicity, and Language Data.

INTRODUCTION: Healthcare disparities may be exacerbated by upstream incapacity to collect high-quality and accurate race, ethnicity, and language (REaL) data. There are opportunities to remedy these data barriers. We present the Denver Health (DH) REaL initiative, which was implemented in 2021. METHODS: Denver Health is a large safety net health system. After assessing the state of REaL data at DH, we developed a standard script, implemented training, and adapted our electronic health record to collect this information starting with an individual's ethnic background followed by questions on race, ethnicity, and preferred language. We analyzed the data for completeness after REaL implementation. RESULTS: A total of 207,490 patients who had at least one in-person registration encounter before and after the DH REaL implementation were included in our analysis. There was a significant decline in missing values for race (7.9%-0.5%, p < .001) and for ethnicity (7.6%-0.3%, p < .001) after implementation. Completely of language data also improved (3%-1.6%, p < .001). A year after our implementation, we knew over 99% of our cohort's self-identified race and ethnicity. CONCLUSIONS: Our initiative significantly reduced missing data by successfully leveraging ethnic background as the starting point of our REaL data collection.

Consistency of Physician Data Across Health Insurer Directories.

This study assesses the consistency of information across publicly available physician directories from 5 large national health insurers.

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).

Estimation of the Width of Uncertainty in Care Consumption and Costs When Using Common Data Collection Tools in Economic Evaluations: A Benchmark for Sensitivity Analyses.

OBJECTIVES: This study aimed to evaluate the uncertainty related to the use of common collection tools to assess costs in economic evaluations compared with an exhaustive administrative database. METHODS: A pragmatic study was performed using preexisting cost-effectiveness studies. Patients were probabilistically matched with themselves in the French National Health Data System (Système National des Données de Santé [SNDS]), and all their reimbursed hospital and ambulatory care data during the study were extracted. Outcomes included the ratio of the number of each type of resources consumed using trial data (case report forms for ambulatory care and local hospital data for hospital care) versus the SNDS and the ratio of corresponding costs. Mean ratios and 95% confidence intervals (CIs) were calculated using bootstrapping. The impact of the collection tool on the result of the economic evaluation was calculated with the difference in costs between the 2 treatment arms with both collection methods. RESULTS: Five cost-effectiveness studies were included in the analysis. A total of 397 patients had the SNDS hospital data, and 321 had ambulatory care data. Common collection tools underestimated hospital admissions by 13% (95% CI 8-20), corresponding costs by 5% (95% CI 2-14), and ambulatory acts by 41% (95% CI 33-51), with large variations in costs depending on the study. There was no change in the economic conclusion in any study. CONCLUSIONS: The use of common collection tools underestimates healthcare resource consumption and its associated costs, particularly for ambulatory care. Our results could provide useful evidence-based estimates to inform sensitivity analyses' parameters in future cost-effectiveness analyses.

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.

Environmental Distractions during Unsupervised Remote Digital Cognitive Assessment.

The current demand for cognitive assessment cannot be met with traditional in-person methods, warranting the need for remote unsupervised options. However, lack of visibility into testing conditions and effort levels limit the utility of existing remote options. This retrospective study analyzed the frequency of and factors associated with environmental distractions during a brief digital assessment taken at home by 1,442 adults aged 23-84. Automated scoring algorithms flagged low data capture. Frequency of environmental distractions were manually counted on a per-frame and per-trial basis. A total of 7.4% of test administrations included distractions. Distractions were more frequent in men (41:350) than women (65:1,092) and the average age of distracted participants (51.7) was lower than undistracted participants (57.8). These results underscore the challenges associated with unsupervised cognitive assessment. Data collection methods that enable review of testing conditions are needed to confirm quality, usability, and actionability.

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.

Explaining the concept of maternal health information verification and assessment during pregnancy: a qualitative study.

BACKGROUND: Pregnant women use information sources for their own health and health of their children. However, despite the importance of trusting the information sources, pregnant women may not have the ability to verify the maternal health information, which could have negative consequences for their health. The purpose of this study was to explain the concept of maternal health information verification and assessment in pregnant women according to their experiences and perception. METHODS: This is a qualitative study that was conducted in 2017 in Tehran, Iran. The participants in this study consisted of 19 pregnant women who were selected by purposeful sampling. To collect data, semi-structured, in-depth and face to face interviews were conducted with participants and continued until saturation of data. Conventional content analysis method was used to analyze the data and to identify concepts and synthesize them into general classes. MAXQDA software version 10 was used to manage the data. RESULTS: In the process of data analysis, the concept of verification and assessment of maternal health information in pregnancy was explained in two main categories, including "Validity of information resources" and "Reliance on information resources." The category of Validity of information resources had two subcategories of valid and invalid sources, and the main category of Reliance on information resources had two subcategories of indicators of assurance, and confusion and trying to obtain assurance. CONCLUSION: The results indicated that pregnant women used various sources and indicators, as well as different evaluation methods to obtain information and verify it, especially when they are confused. Thus, health authorities and healthcare professionals should provide appropriate programs to familiarize mothers with credible sources, train pregnant women on standards and practices for judging the accuracy of information, and create a safe margin of information.

Health Equity Beyond Data: Health Care Worker Perceptions of Race, Ethnicity, and Language Data Collection in Electronic Health Records.

BACKGROUND: Recent research and policy initiatives propose addressing the social determinants of health within clinical settings. One such strategy is the expansion of routine data collection on patient Race, Ethnicity, and Language (REAL) within electronic health records (EHRs). Although previous research has examined the general views of providers and patients on REAL data, few studies consider health care workers' perceptions of this data collection directly at the point of care, including how workers understand REAL data in relation to health equity. OBJECTIVE: This qualitative study examines a large integrated delivery system's implementation of REAL data collection, focusing on health care workers' understanding of REAL and its impact on data's integration within EHRs. RESULTS: Providers, staff, and administrators expressed apprehension over REAL data collection due to the following: (1) disagreement over data's significance, including the expected purpose of collecting REAL items; (2) perceived barriers to data retrieval, such as the lack of standardization across providers and national tensions over race and immigration; and (3) uncertainty regarding data's use (clinical decision making vs. system research) and dissemination (with whom the data may be shared; eg, public agencies, other providers, and insurers). CONCLUSION: Emerging racial disparities associated with COVID-19 highlight the high stakes of REAL data collection. However, numerous barriers to health equity remain. Health care workers need greater institutional support for REAL data and related EHR initiatives. Despite data collection's central importance to policy objectives of disparity reduction, data mandates alone may be insufficient for achieving health equity.

The importance of data source in prescription drug monitoring program research.

OBJECTIVE: To develop a legal research protocol for identifying various measures of prescription drug monitoring program (PDMP) start dates, apply the protocol to create a useable PDMP database, and test whether the different legal databases that are meant to contain the same information produce divergent results when used in an illustrative empirical exercise. DATA SOURCES: Original research from state statutes, regulations, policy statements, and interviews; alternative PDMP data from the National Alliance for Model State Drug Laws and Prescription Drug Abuse Policy System; claims from a 40 percent random sample of Medicare beneficiaries, 2006-2014. STUDY DESIGN: Collaborative research effort among a group of lawyers to develop protocol. Legal research to produce an original database of dates state PDMP laws: (a) were enacted, (b) became operational, and (c) required query before prescribing controlled substances. Descriptive analyses characterize differences in dates of enactment, operation, and must query requirements. Regression analyses estimating, for each beneficiary annually any opioid prescription received in a calendar year, among other measures. Estimates conducted on under age 65 and full Medicare population. DATA COLLECTION/EXTRACTION METHODS: PDMP legal databases were linked to annual Medicare claims. PRINCIPAL FINDINGS: An original database differs from commonly used, publicly available data. Outcomes tested depend on the measure of PDMP date used and differ by data source. Must-query laws show the largest effects among all the laws tested. CONCLUSIONS: Data choices likely have had large consequences for study results and may explain contradictory outcomes in prior research. Researchers must understand and report protocol for dates used in PDMP research to ensure that results are internally consistent and verifiable.

Reacting to crises: The COVID-19 impact on biostatistics/epidemiology.

Most crises, though difficult and challenging to address, offer opportunities for change and for development of new perspectives or approaches to deal with traditional strategies. The reaction to and the managing of the COVID-19 pandemic has provided a platform for evaluating how we quantify disease prevalence, incidence, time courses and sequellae as well as how well we plan, design, analyze and interpret health care associated data, including clinical trials and electronic medical records and health claims data. Whether the Covid-19 crisis provides opportunities to advance the fields of biostatistics and epidemiology in select ways remains to be seen. This article describes three areas of crises experienced by the author during a career in the regulation of pharmaceutical products and how they were responded to. Some suggestions for potential future opportunities in reaction to the Covid-19 crises are provided.