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2.
J Med Internet Res ; 23(2): e25118, 2021 02 10.
Article in English | MEDLINE | ID: covidwho-1575984

ABSTRACT

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.


Subject(s)
COVID-19 , Data Collection/standards , Health Surveys/standards , Mental Health , Cross-Sectional Studies , Data Collection/methods , Humans , Pandemics , SARS-CoV-2
3.
Am J Public Health ; 111(S3): S208-S214, 2021 10.
Article in English | MEDLINE | ID: covidwho-1496723

ABSTRACT

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).


Subject(s)
Alaskan Natives/statistics & numerical data , COVID-19/epidemiology , Public Health , United States Indian Health Service/statistics & numerical data , COVID-19 Testing , COVID-19 Vaccines/therapeutic use , Data Collection/standards , Humans , SARS-CoV-2 , United States/epidemiology
5.
Yearb Med Inform ; 30(1): 75-83, 2021 Aug.
Article in English | MEDLINE | ID: covidwho-1392941

ABSTRACT

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.


Subject(s)
COVID-19 , Information Management , Medical Informatics , Data Accuracy , Data Collection/standards , Humans , Public Health Administration , Public Health Practice/legislation & jurisprudence , United States
7.
MMWR Morb Mortal Wkly Rep ; 70(32): 1075-1080, 2021 Aug 13.
Article in English | MEDLINE | ID: covidwho-1355296

ABSTRACT

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.


Subject(s)
COVID-19/ethnology , Data Analysis , /statistics & numerical data , Bias , COVID-19/prevention & control , COVID-19/therapy , COVID-19 Vaccines/administration & dosage , Data Collection/standards , Health Status Disparities , Healthcare Disparities/ethnology , Humans , Treatment Outcome , United States/epidemiology , Vaccination Coverage/statistics & numerical data
8.
Yearb Med Inform ; 30(1): 17-25, 2021 Aug.
Article in English | MEDLINE | ID: covidwho-1196868

ABSTRACT

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.


Subject(s)
COVID-19/prevention & control , Data Collection , Medical Informatics , Artificial Intelligence , COVID-19/diagnosis , Contact Tracing , Data Collection/standards , Forecasting , Health Care Rationing , Health Workforce , Humans , Pandemics/prevention & control , Telemedicine
9.
Am J Public Health ; 111(6): 1141-1148, 2021 06.
Article in English | MEDLINE | ID: covidwho-1186632

ABSTRACT

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.


Subject(s)
COVID-19 , Mandatory Reporting , Mortality/trends , /statistics & numerical data , COVID-19/epidemiology , COVID-19/mortality , Data Collection/standards , Health Status Disparities , Humans , Minority Groups/statistics & numerical data , United States
10.
13.
Am J Nurs ; 121(3): 14-15, 2021 Mar 01.
Article in English | MEDLINE | ID: covidwho-1101874

ABSTRACT

Slipshod data collection and poor follow-up defeat efforts to track infections and mitigate risk.


Subject(s)
COVID-19/mortality , Health Personnel/statistics & numerical data , Data Collection/standards , Global Health , Humans , Pandemics , SARS-CoV-2
14.
J Med Internet Res ; 23(3): e22219, 2021 03 02.
Article in English | MEDLINE | ID: covidwho-1088863

ABSTRACT

Coincident with the tsunami of COVID-19-related publications, there has been a surge of studies using real-world data, including those obtained from the electronic health record (EHR). Unfortunately, several of these high-profile publications were retracted because of concerns regarding the soundness and quality of the studies and the EHR data they purported to analyze. These retractions highlight that although a small community of EHR informatics experts can readily identify strengths and flaws in EHR-derived studies, many medical editorial teams and otherwise sophisticated medical readers lack the framework to fully critically appraise these studies. In addition, conventional statistical analyses cannot overcome the need for an understanding of the opportunities and limitations of EHR-derived studies. We distill here from the broader informatics literature six key considerations that are crucial for appraising studies utilizing EHR data: data completeness, data collection and handling (eg, transformation), data type (ie, codified, textual), robustness of methods against EHR variability (within and across institutions, countries, and time), transparency of data and analytic code, and the multidisciplinary approach. These considerations will inform researchers, clinicians, and other stakeholders as to the recommended best practices in reviewing manuscripts, grants, and other outputs from EHR-data derived studies, and thereby promote and foster rigor, quality, and reliability of this rapidly growing field.


Subject(s)
COVID-19/epidemiology , Data Collection/methods , Electronic Health Records , Data Collection/standards , Humans , Peer Review, Research/standards , Publishing/standards , Reproducibility of Results , SARS-CoV-2/isolation & purification
15.
Psychiatr Serv ; 72(1): 86-88, 2021 01 01.
Article in English | MEDLINE | ID: covidwho-1060599

ABSTRACT

To address the global mental health crisis exacerbated by the COVID-19 pandemic, an urgent need has emerged to transform the accessibility, efficiency, and quality of mental health care. The next suite of efforts to transform mental health care must foster the implementation of "learning organizations," that is, organizations that continuously improve patient-centered care through ongoing data collection. The concept of learning organizations is highly regarded, but the key features of such organizations, particularly those providing mental health care, are less well defined. Using telepsychiatry care as an example, the authors of this Open Forum concretely describe the key building blocks for operationalizing a learning organization in mental health care to set a research agenda for services transformation.


Subject(s)
Data Collection , Health Services Research/organization & administration , Mental Health Services/organization & administration , Patient-Centered Care/organization & administration , Psychiatry/organization & administration , Quality Improvement/organization & administration , Telemedicine/organization & administration , COVID-19 , Data Collection/standards , Health Services Research/standards , Humans , Implementation Science , Mental Health Services/standards , Organizations , Patient-Centered Care/standards , Psychiatry/standards , Quality Improvement/standards , Stakeholder Participation , Telemedicine/standards
16.
Med Care ; 59(5): 379-385, 2021 05 01.
Article in English | MEDLINE | ID: covidwho-1059643

ABSTRACT

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.


Subject(s)
Data Collection/standards , Electronic Health Records/standards , Health Equity , Health Personnel/psychology , Language , Perception , Confidentiality , Humans , Interviews as Topic , Qualitative Research , Social Determinants of Health
17.
BMJ Glob Health ; 6(1)2021 01.
Article in English | MEDLINE | ID: covidwho-1015667

ABSTRACT

In-person interactions have traditionally been the gold standard for qualitative data collection. The COVID-19 pandemic required researchers to consider if remote data collection can meet research objectives, while retaining the same level of data quality and participant protections. We use four case studies from the Philippines, Zambia, India and Uganda to assess the challenges and opportunities of remote data collection during COVID-19. We present lessons learned that may inform practice in similar settings, as well as reflections for the field of qualitative inquiry in the post-COVID-19 era. Key challenges and strategies to overcome them included the need for adapted researcher training in the use of technologies and consent procedures, preparation for abbreviated interviews due to connectivity concerns, and the adoption of regular researcher debriefings. Participant outreach to allay suspicions ranged from communicating study information through multiple channels to highlighting associations with local institutions to boost credibility. Interviews were largely successful, and contained a meaningful level of depth, nuance and conviction that allowed teams to meet study objectives. Rapport still benefitted from conventional interviewer skills, including attentiveness and fluency with interview guides. While differently abled populations may encounter different barriers, the included case studies, which varied in geography and aims, all experienced more rapid recruitment and robust enrollment. Reduced in-person travel lowered interview costs and increased participation among groups who may not have otherwise attended. In our view, remote data collection is not a replacement for in-person endeavours, but a highly beneficial complement. It may increase accessibility and equity in participant contributions and lower costs, while maintaining rich data collection in multiple study target populations and settings.


Subject(s)
COVID-19 , Data Collection , Interpersonal Relations , Africa South of the Sahara , Data Accuracy , Data Collection/methods , Data Collection/standards , Humans , India , Internet , Pandemics , Philippines , Physical Distancing , Qualitative Research , SARS-CoV-2
18.
Soc Sci Med ; 265: 113549, 2020 11.
Article in English | MEDLINE | ID: covidwho-970135

ABSTRACT

Governments around the world have made data on COVID-19 testing, case numbers, hospitalizations and deaths openly available, and a breadth of researchers, media sources and data scientists have curated and used these data to inform the public about the state of the coronavirus pandemic. However, it is unclear if all data being released convey anything useful beyond the reputational benefits of governments wishing to appear open and transparent. In this analysis we use Ontario, Canada as a case study to assess the value of publicly available SARS-CoV-2 positive case numbers. Using a combination of real data and simulations, we find that daily publicly available test results probably contain considerable error about individual risk (measured as proportion of tests that are positive, population based incidence and prevalence of active cases) and that short term variations are very unlikely to provide useful information for any plausible decision making on the part of individual citizens. Open government data can increase the transparency and accountability of government, however it is essential that all publication, use and re-use of these data highlight their weaknesses to ensure that the public is properly informed about the uncertainty associated with SARS-CoV-2 information.


Subject(s)
COVID-19/epidemiology , Government , Health Communication/standards , Uncertainty , Data Collection/standards , Humans , Models, Theoretical , Ontario/epidemiology , Pandemics , Risk Assessment , SARS-CoV-2
20.
Contemp Clin Trials ; 102: 106214, 2021 03.
Article in English | MEDLINE | ID: covidwho-917234

ABSTRACT

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.


Subject(s)
Biostatistics , COVID-19/epidemiology , Data Collection/methods , Epidemiology/organization & administration , Acquired Immunodeficiency Syndrome/drug therapy , Acquired Immunodeficiency Syndrome/epidemiology , Anti-HIV Agents/therapeutic use , Clinical Trials as Topic/organization & administration , Cooperative Behavior , Data Collection/standards , Drug Development/organization & administration , Drug Industry/organization & administration , Efficiency, Organizational , Epidemiology/standards , Humans , Incidence , Pandemics , Prevalence , SARS-CoV-2 , Time Factors , United States , United States Food and Drug Administration/organization & administration
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