Matching HIV, TB, viral hepatitis and STD surveillance data: Identification of infectious disease syndemics in New York city

Objectives: A syndemic is infection with two or more diseases which interact to worsen the health impact of either disease alone. We sought to identify syndemics in NYC by matching the HIV, TB, viral hepatitis, and STD surveillance databases.

Methods: Deterministic matching was used to link cases of disease reported to the NYC Department of Health and Mental Hygiene (DOHMH). We included incident cases of TB, chlamydia, gonorrhea, and syphilis diagnosed between 1/1/00 and 12/31/10; and prevalent and incident cases of HIV, hepatitis B, and hepatitis C, excluding those known to have died before 2000. A line-listed, de-identified dataset was created for analysis, including non-matching and matching records.

Results: 955,944 non-unique individuals were in the analytic dataset; 12% matched to at least one other disease. Persons with syphilis were most likely to match to at least one other disease (64%), and those with hepatitis B were the least likely to match (11%). Four diseases had the highest percentage of matches with HIV: syphilis (50% matching to HIV), hepatitis C (15%), TB (14%), hepatitis B (5%). People with a reported case of gonorrhea had the highest percentage of matches with chlamydia (46%); and chlamydia had the highest percentages of matches with gonorrhea (14%). People with HIV had the greatest overlap with hepatitis C (16%).

Conclusions: In the absence of integrated surveillance systems, matching surveillance data can help us understand the prevalence of infectious disease syndemics, and to appropriately target services to address them. Matching can also create opportunities for collaboration between disease-specific programs within health departments.