Spatial analysis and mapping of sexually transmitted diseases to optimise intervention and prevention strategies

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Abstract
Objective: We analysed and mapped the distribution of four reportable sexually transmitted diseases, chlamydial infection/non-gonococcal urethritis (chlamydial infection), gonorrhoea, primary and secondary syphilis (syphilis), and HIV infection, for Wake County, North Carolina, to optimise an intervention. Methods: We used STD surveillance data reported to Wake County, for the year 2000 to analyse and map STD rates. STD rates were mathematically represented as a spatial random field. We analysed spatial variability by calculating and modelling covariance functions of random field theory. Covariances are useful in assessing spatial patterns of disease locally and at a distance. We combined observed STD rates and appropriate covariance models using a geostatistical method called kriging, to predict STD rates and associated prediction errors for a grid covering Wake County. Final disease estimates were interpolated using a spline with tension and mapped to generate a continuous surface of infection. Results: Lower incidence STDs exhibited larger spatial variability and smaller neighbourhoods of influence than higher incidence STDs. Each reported STD had a clustered spatial distribution with one primary core area of infection. Core areas overlapped for all four STDs. Conclusions: Spatial heterogeneity within STD suggests that STD specific prevention strategies should not be targeted uniformly across Wake County, but rather to core areas. Overlap of core areas among STDs suggests that intervention and prevention strategies can be combined to target multiple STDs effectively. Geostatistical techniques are objective, population level approaches to spatial analysis and mapping that can be used to visualise disease patterns and identify emerging outbreaks.