Advances in acquiring and analyzing the spatial attributes of data have greatly enhanced the potential utility of wildlife disease surveillance data for addressing problems of ecological or economic importance. We present an approach for using wildlife disease surveillance data to identify areas for (or of) intervention, to spatially delineate paired treatment and control areas, and then to analyze these nonrandomly selected sites in a meta-analysis framework via before-after-control impact (BACI) estimates of effect size. We apply these methods to evaluate the effectiveness of attempts to reduce chronic wasting disease (CWD) prevalence through intensive localized culling of mule deer (Odocoileus hemionus) in north-central Colorado, USA. Areas where surveillance data revealed high prevalence or case clusters were targeted by state wildlife management agency personnel for focal scale (on average <17 km2) culling, primarily via agency sharpshooters. Each area of sustained culling that we could also identify as unique by cluster analysis was considered a potential treatment area. Treatment areas, along with spatially paired control areas that we constructed post hoc in a case-control design (collectively called "management evaluation sites"), were then delineated using home range estimators. Using meta-BACI analysis of CWD prevalence data for all management evaluation sites, the mean effect size (change of prevalence on treatment areas minus change in prevalence on their paired control areas) was 0.03 (SE = 0.03); mean effect size on treatment areas was not greater than on paired control areas. Excluding cull samples from prevalence estimates or allowing for an equal or greater two-year lag in system responses to management did not change this outcome. We concluded that management benefits were not evident, although whether this represented true ineffectiveness or was a result of lack of data or insufficient duration of treatment could not be discerned. Based on our observations, we offer recommendations for designing a management experiment with 80% power to detect a 0.10 drop in prevalence over a 6-12-year period.