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Background: Spatial and temporal analysis can be used to identify disease outbreaks within small areas of a community, but have been largely absent from past gonorrhea epidemiology studies. From 2010-2012, methods were employed to exhibit the importance of both space and time in the understanding of gonorrhea incidence patterns.
Methods: Three methods were implemented to account for the spatial and temporal interactions of gonorrhea within the population. Both an ordinary least squares regression and spatial lag regression models were used to determine the socio-demographic factors most closely associated with high gonorrhea rates, the latter indicating the influence of space. Secondly, the Getis-Ord Gi* statistic was implemented to identify census blocks where gonorrhea rates were considered to be clustered. Finally a multi-region cumulative sum (CUSUM) method was utilized to determine when the number of cases exceeded a threshold, indicating a spatiotemporal pattern of gonorrhea.
Results: While preserving the spatial context, the percentage of African-Americans within a neighborhood was still found to be the most influential demographic characteristic in determining the spatial distribution of gonorrhea. The Getis-Ord Gi*statistic was able to identify regional hotspots of gonorrhea on an annual basis while simultaneously displaying a trend of convergence on the East Side of Buffalo, NY over time. The temporal analysis yielded an understanding of when and where the number of gonorrhea cases exceeded the expectation, results displayed similar geographic trends to the cluster analysis.
Conclusions: The Getis-Ord statistic can identify small scale areas that require immediate implementation of disease control strategies. Spatial regression presented a more geographically relevant understanding of the socio-demographic factors driving gonorrhea which can aid in targeted outreach to specific groups of people. The CUSUM method can provide public health officials with insight into the spatiotemporal nature of gonorrhea, ultimately aiding in disease surveillance and mitigation policies.