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

Authors
  • Whelan, Michael1
  • Renda, Christina2
  • Hohenadel, Karin2
  • Buchan, Sarah2, 3
  • Murti, Michelle2, 3
  • 1 Communicable Diseases, Emergency Preparedness and Response, Public Health Ontario, 661 University Avenue, 17th Floor, Toronto, ON, M5G 1M1, Canada. [email protected] , (Canada)
  • 2 Communicable Diseases, Emergency Preparedness and Response, Public Health Ontario, 661 University Avenue, 17th Floor, Toronto, ON, M5G 1M1, Canada. , (Canada)
  • 3 Dalla Lana School of Public Health, University of Toronto, Health Sciences Building 155 College Street, 6th Floor, Toronto, ON, M5T 3M7, Canada. , (Canada)
Type
Published Article
Journal
Canadian journal of public health = Revue canadienne de sante publique
Publication Date
Oct 01, 2020
Volume
111
Issue
5
Pages
752–760
Identifiers
DOI: 10.17269/s41997-020-00295-5
PMID: 32096013
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

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. 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. 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. 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.

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