Notification of malaria cases in the Brazilian Amazon Basin from 2010 to 2020: an analysis of the reporting times

• Published in: Malaria journal 2023-02, Vol.22 (1), p.49-49, Article 49
• Main Authors: Ayala, Mario J C, Valiati, Naiara C M, Bastos, Leonardo S, Villela, Daniel A M
• Format: Article
• Language: English
• Subjects: Analysis; Brazil - epidemiology; Decision making; Disease transmission; Health surveillance; Human diseases; Humans; Incidence; Malaria; Malaria - epidemiology; Malaria - prevention & control; Mathematical models; Population Surveillance - methods; Public health; Random variables; Reporting times; Retrospective Studies; Skewness; Standard deviation; Statistical models; Vector-borne diseases
• ISSN: 1475-2875; 1475-2875
• DOI: 10.1186/s12936-023-04464-y

As controlling malaria transmission remains a public-health challenge in the Brazilian Amazon basin, the National Surveillance System for Malaria (SIVEP-MALARIA) has registered malaria notifications for over fifteen years helping in the decision-making on control and elimination. As a surveillance database, the system is prone to reporting delays, and knowledge about reporting patterns is essential in decisions. This study contains an analysis of temporal and state trends of reporting times in a total of 1,580,617 individual malaria reports from January 2010 to December 2020, applying procedures for statistical distribution fitting. A nowcasting technique was applied to show an estimation of number of cases using a statistical model of reporting delays. Reporting delays increased over time for the states of Amazonas, Rondônia, Roraima, and Pará. Amapá has maintained a similar reporting delay pattern, while Acre decreased reporting delay between 2010 and 2020. Predictions were more accurate in states with lower reporting delays. The temporal evolution of reporting delays only showed a decrease in malaria reports in Acre from 2010 to 2020. Malaria notifications may take days or weeks to enter the national surveillance database. The reporting times are likely to impact incidence estimation over periods when data is incomplete, whilst the impact of delays becomes smaller for retrospective analysis. Short-term assessments for the estimation of malaria incidence from the malaria control programme must deal with reporting delays.