All together now: aggregating multiple records to develop a person-based dataset to integrate and enhance infectious disease surveillance in Ontario, Canada

Abstract

Abstract Setting Syndemics occur when two or more health conditions interact to increase morbidity and mortality and are exacerbated by social, economic, environmental, and political factors. Routine provincial surveillance in Ontario assesses and reports on the epidemiology of single infectious diseases separately. Therefore, we aimed to develop a method that allows disease overlaps to be examined routinely as a path to better understanding and addressing syndemics in Ontario. Intervention We extracted data for individuals with a record of chlamydia, gonorrhea, infectious syphilis, hepatitis B and C, HIV/AIDS, invasive group A streptococcal disease (iGAS), or tuberculosis in Ontario’s reportable disease database from 1990 to 2018. We transformed the data into a person-based integrated surveillance dataset retaining individuals (clients) with at least one record between 2006 and 2018. Outcomes The resulting dataset had 659,136 unique disease records among 470,673 unique clients. Of those clients, 23.1% had multiple disease records with 50 being the most for one client. We described the frequency of disease overlaps; for example, 34.7% of clients with a syphilis record had a gonorrhea record. We quantified known overlaps, finding 1274 clients had gonorrhea, infectious syphilis, and HIV/AIDS records, and potentially emerging overlaps, finding 59 clients had HIV/AIDS, hepatitis C, and iGAS records. Implications Our novel person-based integrated surveillance dataset represents a platform for ongoing in-depth assessment of disease overlaps such as the relative timing of disease records. It enables a more client-focused approach, is a step towards improved characterization of syndemics in Ontario, and could inform other jurisdictions interested in adopting similar approaches.