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Article Abstract

Health information is particularly essential in times of pandemics in which rapid response is crucial for political and stakeholder decision-making processes, and therefore the availability of data as well as its quality analysis are necessary. This study aimed to describe the completeness and quality of the e-Sistema Único de Saúde (SUS) Health Surveillance database (SUS Vigilância em Saúde) of the state of Espírito Santo, Brazil, from the notification of deaths from corana virus disease 2019 (COVID-19) from January 2020 to June 2021. A descriptive population-based register study was conducted from the analysis of the completeness of secondary data from the record of deaths from COVID-19, retrieved from the e-SUS Vigilância em Saúde (Health Surveillance) (VS) database of the state of Espírito Santo, Brazil, from January 2020 to June 2021. A total of 11,359 death records from COVID-19 via e-SUS VS in the state of Espírito Santo, Brazil, were evaluated. The score used to assess incompleteness was the 1 proposed by Romero and Cunha which classifies as excellent (when < 5%), good (between 5% and 10%), regular (between 10% and 20%), poor (between 20% and 50%), and very poor (when > 50%), according to the percentage of the absence of information. Descriptive statistical analyses were conducted in the Stata program, version 15.1. "Case identification" variables, and "condition" variables were classified as excellent completeness. Among the evolution variables, only "hospitalization" was classified as regular. Among the laboratory variables, only the polymerase chain reaction presented excellent completeness, while the "rapid test" and "serologies for immunoglobulin G, and immunoglobulin M" variables were classified as good completeness. It is concluded that most of the variables available in e-SUS VS of the state of Espírito Santo, Brazil, of notification of deaths from COVID-19 in 2020 presented excellent completeness, confirming the excellent quality of the state database.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10035550PMC
http://dx.doi.org/10.1097/MD.0000000000033343DOI Listing

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