Critical Findings on COVID-19 Select Publications from the National Academies of Sciences, Engineering, and Medicine (2021) / Chapter Skim
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EVALUATING DATA TYPES: A Guide for Decision Makers using Data to Understand the Extent and Spread of COVID-19
EVALUATING DATA TYPES: A Guide for Decision Makers using Data to Understand the Extent and Spread of COVID-19
Pages 60-74
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From page 60... ...
* This rapid expert consultation was produced through the Societal Experts Action Network (SEAN)
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From page 61... ...
The seven data types are: the number of confirmed cases, hospitalizations, emergency department visits, reported confirmed COVID-19 deaths, excess deaths, fraction of viral tests that are positive, and representative prevalence surveys (including both viral and antibody tests)
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From page 62... ...
Specific limitations and cautions that apply to data on COVID-19 This rapid expert consultation addresses the assessment of the seven data types and the implications of those assessments for decision making; it does not recommend specific policy actions. Specific features of the disease and response to the pandemic have implications for understanding this assessment of data types.
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From page 63... ...
over prior comparable time periods as a measure of the total number of deaths that may be directly or indirectly attributable to COVID-19 • Fraction of viral tests that are positive as a measure of the total number of currently infected persons • Representative prevalence surveys (including both viral and antibody tests) administered to a representative sample of a defined population to estimate the percentage of persons in that population either currently or formerly positive for COVID-19 Given the rapid evolution of understanding of the virus that causes COVID-19, additional data types are emerging.
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Table 1 shows the seven data types listed above against the five criteria for assessing their reliability and validity. Check marks indicate that a data type generally meets a criterion, while the triangles denote the need for caution, meaning that the questions listed above under a criterion should be asked to better understand the quality of the data.
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From page 65... ...
⚠ ⚠ ⚠ ⚠ ⚠ Fraction of viral tests that are positive Key Implication for Decision Making: These data may not be an adequate measure of prevalence, depending on testing criteria. If mainly symptomatic people are tested, this figure is expected to overestimate the true community prevalence.
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From page 66... ...
As the volume of testing expands to include populations with less severe symptoms and asymptomatic individuals, this measure will be increasingly useful for determining the prevalence of COVID-19. • Representativeness: The number of confirmed cases per 1,000 people per week, month, or year (i.e., the rate of confirmed cases)
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From page 67... ...
However, those with the most severe disease may seek care in the ED regardless of these considerations. • Bias: Diagnoses made in the ED may be modified subsequently and may underestimate or overestimate actual COVID-19 cases, especially given time lags in processing of tests.
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From page 68... ...
, leading to errors in calculated death rates by race and ethnicity. • Time: Local health authorities initially report deaths quickly, but the final, complete, cleaned data may take time to produce.
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From page 69... ...
While the total number of deaths is reasonably accurate, it is difficult to calculate "excess deaths" because deaths in each year reflect unique public health phenomena. As a result, computing excess deaths is a statistical procedure that entails comparing current deaths with expected deaths based on historical averages, and the magnitude of the excess will depend on the time period chosen for comparison.
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From page 70... ...
There will be some time lag involved, however, in mounting and interpreting such a survey. While prevalence surveys in general, such as surveys of health care workers or convenience samples (defined in footnote 7 below)
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• Bias: If prevalence surveys are based on representative samples and if the sensitivity and specificity of the viral tests are known, bias due to errors in the tests can be corrected using well-known statistical formulas. It is important to make these corrections so that unbiased estimates can be obtained; see footnote 1 (Biemer and Lyberg, 2008)
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. Washington State's actual coronavirus death toll may be higher than current tallies, health officials say.
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We thank Oxiris Barbot, New York City Department Health and Mental Hygiene; Paul Biemer, RTI International and University of North Carolina, Chapel Hill; Ron Carlee, Old Dominion University; Jeffrey Eaton, Imperial College London; Thomas Farley, Philadelphia Department of Public Health; William Hanage, Harvard T.H. Chan School of Public Health; Stéphane Helleringer, The Johns Hopkins University; Claude-Alix Jacob, Cambridge Public Health Department; Nancy Krieger, Harvard T.H.
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FEIT, Deputy Executive Director DBASSE ADRIENNE STITH BUTLER, Associate Board Director EMILY P BACKES, Senior Program Officer DARA SHEFSKA, Associate Program Officer PAMELLA ATAYI, Program Coordinator Copyright National Academy of Sciences.
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