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Gonorrhea cluster detection in Manitoba, Canada: Spatial, temporal, and spatio-temporal analysis
Amin Abed1, Mahmoud Torabi1, Zeinab Mashreghi2
1Department of Community Health Sciences, University of Manitoba, Winnipeg, Manitoba, R3E 0W3, Canada.
Gonorrhea infections in Manitoba show distinct spatial and temporal patterns, with clusters in northern regions and central Winnipeg. Public health efforts must target these high-risk areas for effective control.
Area of Science:
- Public Health
- Epidemiology
- Spatial Analysis
Background:
- Gonorrhea is the second most common sexually transmitted infection in Canada.
- Manitoba's 2018 incidence rate was triple the national average.
- Understanding infection patterns is crucial for targeted interventions.
Purpose of the Study:
- To investigate spatial, temporal, and spatio-temporal patterns of Gonorrhea infection in Manitoba.
- To identify high-risk geographic areas and time periods for Gonorrhea transmission.
- To inform public health strategies for prevention and control.
Main Methods:
- Utilized individual-level, laboratory-confirmed administrative data from Manitoba Health (2000-2016).
- Employed spatial autocorrelation analysis across 96 regional health authority districts.
- Conducted temporal and spatio-temporal cluster analysis.
Main Results:
- Identified significant clustering of Gonorrhea infections, particularly in northern Manitoba and central Winnipeg.
- Observed seasonal trends with increased infections in late summer and fall.
- Detected spatio-temporal clusters, including a major one in northern Manitoba (2006-2014) and a secondary one in central Winnipeg (2004-2012).
- Noted higher infection rates in females at younger ages and a rise in repeat infections (16% in 2016).
Conclusions:
- Gonorrhea transmission in Manitoba exhibits significant spatial, temporal, and spatio-temporal variations.
- Findings highlight the need for localized prevention and control measures.
- Data supports targeted public health interventions and resource allocation in identified high-risk clusters.
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