Assessing the reporting of Dengue, Chikungunya and Zika to the National Surveillance System in Colombia from 2014-2017: A Capture-recapture analysis accounting for misclassification of arboviral diagnostics

Author summary Colombia is one of the countries most affected by dengue, chikungunya, and Zika in the Americas and are diseases of mandatory reporting to the national surveillance system. Surveillance systems are vital components of disease control programs to understand disease burden, trends, and to detect outbreaks. However, underreporting can bias estimates and greatly reduce surveillance data utility. We used a capture-recapture analysis to identify cases that were diagnosed at the healthcare facilities (capture) and those that were reported to SIVIGILA (recapture) in order to assess the degree of overlapping between diagnosis and reporting. Our study was conducted in three different endemicity settings and showed that the overall reporting of arboviruses was variable in magnitude and dependent on several factors including age of the case, year of diagnosis, and type of health care provider. We also identify the need to correct for misclassification when estimating arboviral burden based on clinical diagnosis in endemic settings using data from passive surveillance systems. Background Chikungunya, dengue, and Zika are three different arboviruses which have similar symptoms and are a major public health issue in Colombia. Despite the mandatory reporting of these arboviruses to the National Surveillance System in Colombia (SIVIGILA), it has been reported that the system captures less than 10% of diagnosed cases in some cities. Methodology/Principal findings To assess the scope and degree of arboviruses reporting in Colombia between 2014-2017, we conducted an observational study of surveillance data using the capture-recapture approach in three Colombian cities. Using healthcare facility registries (capture data) and surveillance-notified cases (recapture data), we estimated the degree of reporting by clinical diagnosis. We fit robust Poisson regressions to identify predictors of reporting and estimated the predicted probability of reporting by disease and year. To account for the potential misclassification of the clinical diagnosis, we used the simulation extrapolation for misclassification (MC-SIMEX) method. A total of 266,549 registries were examined. Overall arboviruses' reporting ranged from 5.3% to 14.7% and varied in magnitude according to age and year of diagnosis. Dengue was the most notified disease (21-70%) followed by Zika (6-45%). The highest reporting rate was seen in 2016, an epidemic year. The MC-SIMEX corrected rates indicated underestimation of the reporting due to the potential misclassification bias. Conclusions These findings reflect challenges on arboviruses' reporting, and therefore, potential challenges on the estimation of arboviral burden in Colombia and other endemic settings with similar surveillance systems.

Automated summary

Colombia is heavily affected by dengue, chikungunya, and Zika, but surveillance data reveals biases in reporting, highlighting the need for corrections in order to accurately estimate the disease burden.