Invasive pneumococcal disease in tuscany region, Italy, 2016–2017: Integrating multiple data sources to investigate underreporting

Abstract

Invasive pneumococcal disease (IPD) is a vaccine-preventable disease characterized by the presence of Streptococcus pneumoniae in normally sterile sites. Since 2007, Italy has implemented an IPD national surveillance system (IPD-NSS). This system suffers from high rates of underreporting. To estimate the level of underreporting of IPD in 2016–2017 in Tuscany (Italy), we integrated data from IPD-NSS and two other regional data sources, i.e., Tuscany regional microbiological surveillance (Microbiological Surveillance and Antibiotic Resistance in Tuscany, SMART) and hospitalization discharge records (HDRs). We collected (1) notifications to IPD-NSS, (2) SMART records positive for S. pneumoniae from normally sterile sites, and (3) hospitalization records with IPD-related International Classification of Diseases, Ninth Revision, Clinical Modification (ICD9) codes in discharge diagnoses. We performed data linkage of the three sources to obtain a combined surveillance system (CSS). Using the CSS, we calculated the completeness of the three sources and performed a three-source log-linear capture–recapture analysis to estimate total IPD underreporting. In total, 127 IPD cases were identified from IPD-NSS, 320 were identified from SMART, and 658 were identified from HDRs. After data linkage, a total of 904 unique cases were detected. The average yearly CSS notification rate was 12.1/100,000 inhabitants. Completeness was 14.0% for IPD-NSS, 35.4% for SMART, and 72.8% for HDRs. The capture–recapture analysis suggested a total estimate of 3419 cases of IPD (95% confidence interval (CI): 1364–5474), corresponding to an underreporting rate of 73.7% (95% CI: 34.0–83.6) for CSS. This study shows substantial underreporting in the Tuscany IPD surveillance system. Integration of available data sources may be a useful approach to complement notification-based surveillance and provide decision-makers with better information to plan effective control strategies against IPD.