However, it served as an additional confirmatory indicator for identifying outliers. Empirical Bayesian smoothing forced the rates towards the center (average) and increased the likelihood of clustering. However, for any particular region within the state, one may use a regional global mean for smoothing and then use the local Moran's I to identify local outliers. Thus, the frame of reference was the average rate across all the counties in the state of Texas. Thirdly, we used the state global mean for smoothing because this was conducted from the state's perspective. It also reduced the likelihood of concluding that there was clustering. We used the global smoothing method which computes the rates using the global mean (as against the local mean) of the rates because it was a better smoother. The formular for the empirical Bayesian smoothing is Ŕ = μ + ś(r - μ), where Ŕ is the new smoothed rate estimate, μ is the global population-weighted mean, ś is the shrinkage factor, and r is the level incidence rate (see Waller and Gotway for more details). To reduce this, empirical Bayesian smoothing, which was proposed by Clayton and Kaldor was applied to the computed raw rates. The corollary to this is that the rates may not fully represent the relative magnitude of the underlying risks if compared with other counties with high population base.

Raw rates derived from different counties across a region may result in unstable rates because of the small number of cases from small population base counties. Thus, indirectly, ESDA may help to identify disparities in chlamydia surveillance systems. In view of this, differences in the incidence rates may be the result of differences in existing surveillance systems. Majority of chlamydia cases are asymptomatic prompting recommendations for routine screening for young women by individuals and organizations. Finer spatial units (cities or census tracts) may provide more location-specific information that can inform the design and implementation stages of existing or future interventions. Our choice of counties as the unit of analysis was based on availability of data. The primary objective of this study was to use ESDA methods to identify and monitor Bayesian-smoothed chlamydia incidence rates using county-level data from the state of Texas. The ideas and motivation for the application of these methods to STD were drawn from pioneering works in the area of ESDA by Luc Anselin and others on juvenile crime and cancer rates, among others. It is also valuable to be able to assess and develop surveillance systems that can immediately and effectively pick up warning signs of increases in any particular STD. These methods can aid health officials to design more location-specific prevention programs that take into account global and local spatial influences. ESDA can also assist in identifying and monitoring hot spots ('problem counties') that may not be obvious otherwise. At the state-level, ESDA methods can be used by state health officials to monitor spatial and temporal variations in rates using counties as spatial units. An emerging approach to achieving this end is the application of Exploratory Spatial Data Analysis (ESDA) methods which draws from the field of spatial statistics.

Several previous studies have recommended that the design and implementation of effective interventions to control or prevent sexually transmitted diseases (STDs) should be grounded on a good understanding of the existing and emerging spatiotemporal patterns because STDs are characterized by geographic patterns. Thus, it is a public health problem that has attracted public attention, albeit not as much as would be desired. In men, chlamydia has been associated with urethritis and other complications such as epididymitis and acute proctitis. Untreated chlamydial infections in women have been associated with more serious reproductive complications such as pelvic inflammatory disease (PID), ectopic pregnancy, tubal infertility, and chronic pelvic pain. Chlamydia is the most prevalent reportable disease in the United States with an estimated 2.8 million cases each year.

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