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

• Published in: Cadernos de saúde pública 2021-01, Vol.37 (7), p.e00292320-e00292320
• Main Authors: Carvalho, Carolina Abreu de, Carvalho, Vitória Abreu de, Campos, Marcos Adriano Garcia, Oliveira, Bruno Luciano Carneiro Alves de, Diniz, Eduardo Moraes, Santos, Alcione Miranda dos, Souza, Bruno Feres de, Silva, Antônio Augusto Moura da
• Format: Article
• Language: English
• Subjects: COVID-19; Data Accuracy; Health Policy & Services; Mortality Registries; SARS-CoV-2
• ISSN: 0102-311X; 1678-4464; 1678-4464
• DOI: 10.1590/0102-311x00292320

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. La propuesta de este estudio es describir la demora en la notificación de muertes por COVID-19, en la ciudad São Luís, Maranhão, Brasil, y demostrar su impacto en el seguimiento puntual, así como en el modelaje de la pandemia de COVID-19. Un objetivo secundario fue confirmar el alcance, donde la previsión inmediata es capaz de mejorar el retraso en la notificación de las muertes. Analizamos los datos de muertes por COVID-19 diariamente en el Boletín Epidemiológico de la Secretaría de Estado de la Salud de Maranhão y calculamos los atrasos notificados desde el 23 de marzo al 29 de agosto, 2020. Con el fin de ilustrar el impacto del retraso en la notificación de muertes, y para probar la efectividad de la predicción inmediata bayesiana en la mejora de los datos de calidad, ajustamos un modelo jerárquico bayesiano semi-mecanicista. Solo un 17.8% de las muertes se notificaron sin atrasos o el día después, mientras que un 40.5% se vieron retr