Capture-recapture study to estimate the true incidence of tuberculosis in Portugal, 2018

Abstract

Abstract Accurate estimation of the true number of cases of an infectious disease is essential to plan and efficiently allocate available resources. This study aims to improve the Portuguese surveillance system for tuberculosis (TB) by identifying gaps in TB epidemiological surveillance at the national level. We estimated annual TB incidence using a capture-recapture method to assess the sensitivity of national TB surveillance. Using probabilistic record linkage between two data sources, the National Epidemiological Surveillance System (SINAVE) and National Tuberculosis Program Surveillance System (SVIG-TB), we extracted TB diagnosed cases data for calendar year 2018. All reported TB cases were included, classified as confirmed, probable or possible. A two-source capture-recapture analysis using a log-linear model was performed to estimate the number of unobserved TB cases in Portugal and of the proportion identified by the current TB surveillance system. Between the two datasets, we found 896 TB cases (of a total of 2170 cases) that could not be matched (37.5% SINAVE only, 62.5% SVIG only). Based on the log-linear model, it was estimated that there were 148 unobserved TB cases (95% confidence interval 127.96 - 171.31). Therefore, the estimated true number of TB cases in 2018 is 2318, so current surveillance has a sensitivity of 93.6%. Based on these findings, the TB incidence in Portugal is estimated to be 22.55 cases per 100 000 inhabitants. Capture-recapture methods are useful in estimating annual TB incidence in high-resource settings. Although the two TB surveillance systems capture the majority of TB cases in Portugal, we might still be underestimating the true number of TB cases. Because TB is a high impact infectious disease, precise incidence estimates are crucial to allocate treatment and prevention resources and guide health policies. Key messages CRC method showed that Portugal is a TB low incidence country. Epidemiological surveillance systems should have a high sensitivity in order to allocate efficiently resources available.