Delay in death reporting affects timely monitoring and modeling of the Covid-19 pandemic

Bibliographic Data

ID5167474
AuthorsCarolina Abreu De Carvalho (0000-0001-7900-4642, Universidade Federal do Maranhão), Vitória Abreu De Carvalho (0000-0002-7831-5761, Universidade Federal do Maranhão), Marcos Adriano Garcia Campos (0000-0001-8924-1203, Universidade Federal do Maranhão), Bruno Luciano Carneiro Alves De Oliveira (0000-0001-8053-7972, Universidade Federal do Maranhão), Eliezer M Diniz (0000-0002-6201-1771, Universidade Federal do Maranhão), Alcione Miranda Dos Santos (0000-0001-9711-0182, Universidade Federal do Maranhão), Bruno Feres De Souza (0000-0003-1997-4983, Universidade Federal do Maranhão), Antonio Augusto Moura Da Silva (0000-0003-4968-5138, Universidade Federal do Maranhão)
Year2021
Volume37
Issue7
Pagese00292320-e00292320
Publication date2021-01-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueCadernos de Saude Publica (JOURNAL)
Journal identifiersISSN: 0102-311X • E-ISSN: 1678-4464
PublisherFapUNIFESP (SciELO) (PUBLISHER)
DOI10.1590/0102-311x00292320
PMID34406216
OpenAlexW3195023886
SCIELO_PIDS0102-311X2021000705018
LanguageEN
Citations received8
References cited13

This study describes the COVID-19 death reporting delay in the city of São Luís, Maranhão State, Brazil, and shows its impact on timely monitoring and modeling of the COVID-19 pandemic, while seeking to ascertain how nowcasting can improve death reporting delay. We analyzed COVID-19 death data reported daily in the Epidemiological Bulletin of the State Health Secretariat of Maranhão and calculated the reporting delay from March 23 to August 29, 2020. A semi-mechanistic Bayesian hierarchical model was fitted to illustrate the impact of death reporting delay and test the effectiveness of a Bayesian Nowcasting in improving data quality. Only 17.8% of deaths were reported without delay or the day after, while 40.5% were reported more than 30 days late. Following an initial underestimation due to reporting delay, 644 deaths were reported from June 7 to August 29, although only 116 deaths occurred during this period. Using the Bayesian nowcasting technique partially improved the quality of mortality data during the peak of the pandemic, providing estimates that better matched the observed scenario in the city, becoming unusable nearly two months after the peak. As delay in death reporting can directly interfere with assertive and timely decision-making regarding the COVID-19 pandemic, the Brazilian epidemiological surveillance system must be urgently revised and notifying the date of death must be mandatory. Nowcasting has proven somewhat effective in improving the quality of mortality data, but only at the peak of the pandemic

Business · Coronavirus disease 2019 (COVID-19) · Data quality · Disease · Geography · Medical emergency · Nowcasting · Pandemic · COVID-19 and healthcare impacts · COVID-19 epidemiological studies · Data-Driven Disease Surveillance · Epidemiology · Medicine

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