摘要：Objectives. Patient data that includes precise locations can reveal patients’ identities, whereas data aggregated into administrative regions may preserve privacy and confidentiality. We investigated the effect of varying degrees of address precision (exact latitude and longitude vs the center points of zip code or census tracts) on detection of spatial clusters of cases. Methods. We simulated disease outbreaks by adding supplementary spatially clustered emergency department visits to authentic hospital emergency department syndromic surveillance data. We identified clusters with a spatial scan statistic and evaluated detection rate and accuracy. Results. More clusters were identified, and clusters were more accurately detected, when exact locations were used. That is, these clusters contained at least half of the simulated points and involved few additional emergency department visits. These results were especially apparent when the synthetic clustered points crossed administrative boundaries and fell into multiple zip code or census tracts. Conclusions. The spatial cluster detection algorithm performed better when addresses were analyzed as exact locations than when they were analyzed as center points of zip code or census tracts, particularly when the clustered points crossed administrative boundaries. Use of precise addresses offers improved performance, but this practice must be weighed against privacy concerns in the establishment of public health data exchange policies. With the widespread deployment of virtually real-time population health monitoring systems, including syndromic surveillance systems, 1 , 2 there has been an increasing focus on spatial cluster detection as a means of identifying disease outbreaks. These spatial epidemiological methods rely on knowledge of patient locations to detect unusual disease clusters. Patients’ home addresses are recorded in hospital administrative data, but use of this precise information raises privacy concerns. 3 – 5 Consequently, many surveillance systems have begun to use regional locations, such as zip code centroids (center points). 6 – 11 However, this practice can distort the spatial distribution of the original, nonaggregated data, which may adversely affect subsequent spatial analyses. 3 Therefore, it is important to study the potential effect that aggregating data to centroids may have on the statistical analyses underlying these systems. 12 , 13 Although there is compelling justification to accurately monitor clinical data for public health purposes, it is important to protect identifiable patient information. The Privacy Rule of the Health Insurance Portability and Accountability Act 14 requires that disclosed health information be restricted to the minimum necessary to satisfy its intended purpose. The minimum amount of information necessary for effective syndromic surveillance has not been well investigated. However, the issue has been explored in the context of cancer surveillance. A recent study revealed few differences when late-stage breast and prostate cancer results were compared for different area-specific units (town, census tract, block group) and exact coordinates (the study’s objective was not to search for small area clusters). 15 An earlier study showed that small clusters did not characterize breast cancer incidence rates in the region assessed. 16 The current practice in syndromic surveillance, in which there is great interest in detecting small, localized clusters, is to store patient locations as either latitude and longitude coordinates of home addresses or, more commonly, as points within administrative regions such as zip code areas or census tracts. The latter practice presumably results in patients being less identifiable as individuals, although extent of anonymity is certain to vary. 17 , 18 A recent study using simulated risk data showed that, even when anonymity is ensured, assigning individuals to census tracts results in maps that do not accurately portray disease risk. 19 The goal of this study was to investigate the effects of blurring identifiable patient data by converting a patient’s home address from an exact location to a regional centroid. We assessed outbreak detection by adding synthetic, spatially clustered emergency department visits to authentic background hospital emergency department surveillance data, creating semisynthetic data. 20 The clusters were placed in a region densely populated by patients. In previous work, we found that small clusters near hospitals were difficult to detect. 21 Yet, one goal of a real-time surveillance system is to detect unusual events early, possibly when only a few individuals have been affected. Depending on the nature of the outbreak, early detection may be critical in minimizing morbidity and mortality. We used a spatial scan statistic 22 , 23 to determine whether the simulated clusters could be detected. Pilot work indicated that this metric would detect relatively small, compact clusters in the present data. We examined 2 dimensions of cluster detection. One was detection rate, defined simply as the percentage of the semisynthetic data sets containing clusters detected by the spatial scan statistic. The other was accuracy, which we assessed by comparing characteristics of detected clusters with characteristics of simulated clusters. Transferring addresses to the centroids of administrative regions might increase detection rates by essentially amplifying clusters when many cases are concentrated at a single point. By contrast, detection might be more difficult in this case because not only would the simulated cluster points be concentrated, so would points from the background emergency department data.
