Seasonal influenza is one of the most common infectious diseases with great disease burden. Public health professionals have given many afford to estimate the accurate the disease burden and monitor temporal trends of seasonal influenza. However current influenza surveillance system in South Korea could not provide full functions on disease burden estimation and temporal trends monitoring. There is a need for supporting structure to make robust surveillance.
In aim 1, we aimed to estimate the burden of influenza and its related disease based on billing information from the national health insurance service – national sample cohort. We found that rural area of South Korea has more disease burden compared to the urban area and age under 5 had the highest burden of influenza infection.
In aim 2, we assess the timeliness of influenza epidemiological information from billing system compared to current sentinel surveillance as temporal trends monitoring. We did not observe any delays of influenza out-patients activity compared to current sentinel surveillance in peak time and cross-correlation value comparison. We could not fully apply aberration time comparison since aberration signals highly depended on model sensitivity and specificity and model selection process itself.
In aim 3, we were able to perform influenza temporal trends association analysis by subpopulations. The Seoul Capital Area showed the early signs of influenza activity in peak and cross-correlation time comparison. Age 6 to 15 showed the earlier sign of influenza activity while age over 65 showed the later sing of influenza activity.
We were able to estimate the burden of influenza with different case definitions and provided stratified disease burden as WHO guided. We did not see any delays of influenza out-patient activity from billing system compared to the current sentinel surveillance. Moreover, we observed potential temporal associations of influenza activity by different subpopulations.
A surveillance system based on solely billing information cannot be perfect by itself. Instead, combinations of surveillance structures with a different source for disease information can be called a robust surveillance system. The surveillance system should have different arrangements with the various data source to make concordance of observation
