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?:abstract
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Testing representative populations to determine the prevalence or percent of the population with active SARS-Cov-2 infection and/or antibodies to infection is being recommended as essential for making public policy decisions to open-up or to continue enforcing national, state and local government rules to \'shelter-in-place\'. However, all laboratory tests are imperfect and have estimates of sensitivity and specificity less than 100% - in some cases considerably less than 100%. That error will lead to biased prevalence estimates. If the true prevalence is low, possibly in the range of 1-5%, then testing error will lead to a constant background of bias that will most likely be larger and possibly much larger than the true prevalence itself. As a result, what is needed is a method for adjusting prevalence estimates for testing error. In this paper we outline methods for adjusting prevalence estimates for testing error both prospectively in studies being planned and retrospectively in studies that have been conducted. The methods if employed would also help to harmonize study results within countries and world-wide. Adjustment can lead to more accurate prevalence estimates and to better policy decisions. However, adjustment will not improve the accuracy of an individual test.
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Testing representative populations to determine the prevalence or the percentage of the population with active severe acute respiratory syndrome coronavirus 2 infection and/or antibodies to infection is being recommended as essential for making public policy decisions to ease restrictions or to continue enforcing national, state, and local government rules to shelter in place However, all laboratory tests are imperfect and have estimates of sensitivity and specificity less than 100%-in some cases, considerably less than 100% That error will lead to biased prevalence estimates If the true prevalence is low, possibly in the range of 1%-5%, then testing error will lead to a constant background of bias that most likely will be larger, and possibly much larger, than the true prevalence itself As a result, what is needed is a method for adjusting prevalence estimates for testing error Methods are outlined in this article for adjusting prevalence estimates for testing error both prospectively in studies being planned and retrospectively in studies that have been conducted If used, these methods also would help harmonize study results within countries and worldwide Adjustment can lead to more accurate prevalence estimates and to better policy decisions However, adjustment will not improve the accuracy of an individual test
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