The Centers for Disease Control and Prevention (CDC) define surveillance to be the ongoing, systematic collection, analysis, interpretation, and dissemination of data about a health-related event for use in public health action to reduce morbidity and mortality and to improve health 1. To control and prevent disease, it is surely important to be vigilant for infectious disease outbreaks or geographic areas of notably high chronic disease incidence. Indeed this is a primary aim of public health surveillance, and explains in part why surveillance plays an integral role in public health practice 2.

When caring for a single patient, the clinician understandably desires as much diagnostic information as possible, and at the highest possible level of precision. Analogously, a public health professional is concerned with diagnosing a public ailment, and should similarly desire all available information with the greatest possible level of precision. Thus it is noteworthy, in the context of public health surveillance, that for reasons of privacy, information is sometimes destroyed or intentionally degraded before being proffered to the analyst.

The argument to protect patient data for reasons of privacy could also be used to shield these data from clinicians. In a clinical setting, we choose not to protect the privacy of the patient by hiding relevant information from the clinician, because it is patently silly to do so. However, we often suffer from a similarly framed argument to obscure population level data, even when addressing matters of concern to the public health.

We argue that one important reason to retain important, specific information such as precise location is that the "requisite" aggregation for privacy necessarily reduces the power available for outbreak detection. To balance the cost of this and other troubles for spatial analysis 3, aggregation does indeed make it more difficult to identify individual patients. This is crucial if the data are made publicly available or if there are other reasons to safeguard privacy, but it also makes an already challenging surveillance task even more difficult.

A growing body of literature addresses statistical protection of privacy and its effects on analysis of surveillance data. Cox has written a useful survey of the general problem of confidentiality within small geographic areas, and the impacts of privacy concerns on public health policy and practice 4.

Armstrong et al. thoroughly discuss the design and implementation of several different approaches to protect privacy in the context of spatial analyses 5. Importantly, methods were evaluated both on the impact on analysis as well as the effectiveness of preserving confidentiality. Yet the restriction of the quantitative assessment to the Cuzick-Edwards test statistic 6, which is no longer commonly used for spatial surveillance 78, limits the application of this knowledge to a surveillance setting. Further, data with exact locations were not considered for this evaluation.

Waller and colleagues have written extensively on factors that may influence power of cluster detection methods. For example, they have studied the effects of geographic scale on focused tests of clustering 910, and the importance of cluster location amidst a heterogeneous underlying population 11. Notably, this group has investigated more than one statistical method, using several different measures for evaluation. However these studies generally use focused tests of clustering, where a putative exposure source has been identified a priori, whereas surveillance purposes typically require a general test of clustering 12.

Just as we trust clinicians and hospital personnel with sensitive and confidential information, so too, one can argue, we should find trustworthy individuals to handle surveillance data responsibly.

Informatics-based approaches offer a potential compromise to the trade-off between privacy and surveillance utility. For example, development of automated surveillance algorithms might allow sensitive data to be analyzed without human intervention 13. But in order to evaluate the benefit that such an approach might provide, we must first better understand the costs in performance that the obfuscation or destruction of information may cause.

We reported briefly 14 that there is an undesirable loss of power to detect disease outbreaks when the spatial information provided is degraded from a continuous scale of measurement to a coarser, aggregate level. For example, often only a patient's ZIP code is available to a surveillance system, instead of the patient's listed residential address. Similar results have appeared in contemporaneous work 15, and a recent paper by the same group further confirms this basic premise 16. However, those studies focused solely on exact locations compared to a single level of aggregation.

In our present work, we add to these previous results by considering multiple levels of aggregation. Using synthetic data, we systematically quantify the loss of cluster detection performance as a function of spatial resolution, while limiting confounding influences from a variety of complex factors that affect spatial analyses. We may interpret these results relative to geographic scales we might encounter while surveilling a large metropolitan city. In this way, we attempt to clarify the price one pays for aggregation, and in turn to better inform future policy decision-makers.

Figures 3 through 6 illustrates our results. For all three sets of simulations, power decreases as the size of aggregation regions increases. These simulated clusters are sufficiently large so that the power to detect for all three cluster radii is nearly 100% when exact locations are used; this decreases to roughly 40% at the coarsest level of aggregation, which corresponds to a more than halving of the probability of successful detection (Figure 3).

Using exact locations, the false detection rate is approximately 2%. In the presence of any level of aggregation, the false detection rate increases to nearly 20% or higher in all of our simulations (Figure 4). This rate appears to increase slowly for greater levels of aggregation.

We further evaluate the effect of aggregation on the sensitivity and specificity of SaTScan (Figures 5 and 6). While performance is nearly ideal when using exact locations, the proportion of false negatives rises to almost 50% at the coarsest level of aggregation. In concordance with our earlier results, sensitivity tends to decrease as spatial aggregation increases, while the false positive fraction (1 minus specificity) follows an inverse and nearly monotonic association.

Our results are noteworthy for a number of reasons. First, we have used more than two levels of aggregation in an effort to estimate the incremental effect of this aggregation on the power of cluster detection. Second, we have further investigated the effect of aggregation on the rate of false detection. Finally, when viewed in the context of similar studies, our results add to a body of evidence that the underlying relationships reported appear robust to differing geographies and population distributions.

Our calculation of power and false detection differs from the same measures as otherwise used in an important way. We expect a certain proportion of spurious "clusters" to arise by chance alone. Thus we have placed an additional requirement on what we denote a successful identification of a cluster, namely that the identified cluster be proximal to the true cluster as determined by the simulation design. Because our simulations involve only one cluster per data set, an identification far from the true cluster is genuinely spurious and must be considered a false detection in this context. Indeed, for practical purposes such an identification might divert resources for investigation to a geographic area not related to the true outbreak or cluster present in the data.

To place our results in context, consider the metropolitan Boston area. The city and adjacent suburbs can be enclosed in a circle of radius roughly 7, 500 meters. Although the size of city ZIP codes and census tracts varies, an approximate median radius for Boston ZIP codes is roughly 1, 500 meters, or 20% of the region radius. Boston census tracts have an approximate median radius of 500 meters, or 6.7% of the region radius. Thus census tract and ZIP code aggregation of Boston data corresponds roughly to our first and penultimate levels of aggregation respectively. Likewise, the simulated clusters of radii 0.025, 0.05, and 0.10 correspond to disease outbreaks smaller than one census tract, about one census tract, or several census tracts (perhaps a small ZIP code) respectively.

The number of false detections rose well above the nominal alpha level when spatial data were aggregated. Interestingly, the level of aggregation does not appear to be a major contributor to false alarms; rather, there is an immediate increase upon aggregation above the nominal false alarm rate, with little additional increase for further aggregation. To our knowledge, this has not been reported previously. Since false alarms form a major limitation to the actionable consequences of cluster detection, this issue should be considered carefully. Even in situations where loss of power is not severe, the increase in false detection rates may impose further limits of the utility of spatial methods when using aggregated data.

Our study is limited in several ways. We have only included an evaluation of SaTScan as a test of clustering, although we have seen similar results using other methods 14. The use of synthetic data is both helpful and harmful to generalizability of results. There are few populations that even approximate a homogeneous and uniform distribution, and thus the simulated data sets do not reflect a realistic surveillance scenario. However, using a homogeneous distribution removes some of the potentially confounding interactions between cluster location, geography, population distribution, and spatial methods. Thus despite its limitations, our study contributes to an understanding of the complex association between spatial resolution and power of detection.

We chose not to investigate spatio-temporal methods (implemented for example with a space-time scan, also available using SaTScan). Space-time interactions imply greater complexity when considering effects of spatial aggregation (or indeed, temporal aggregation), and the potential parameter space of simulation studies increases greatly as well. For this and other reasons, the effect of spatial aggregation (or indeed, temporal aggregation) in a cluster detection context remains an area for further investigation.

The author(s) declare that they have no competing interests.

AO and MP conceived of the study, participated in the design, and drafted the manuscript. AO, CJ, and JM were responsible for statistical programming and data analysis. All authors read and approved the final manuscripts.