Wednesday, January 11, 2012

Are Spatiotemporal Methods Useful for Early Detection?

To answer the question in the title, let us start with the quotation from Fricker [1]: “Returning to the original question of whether statistical methods are useful for early event detection, I suggest that we really don’t know yet. That is, whether the systems and their associated detection algorithms can be modified so that they appropriately minimize false positive signals while maintaining sufficient sensitivity to actual outbreaks is still an open question”. It was also cited in our post “Biosurveillance in Crisis” of December 2011. This quotation is related to the purely temporal approach that is implemented in most current syndromic surveillance systems, by using statistical process control (SPC) methods. The temporal, univariate methodology is well-developed, widely used and technically much simpler than all existing spatiotemporal approaches. Actually, spatiotemporal  surveillance is a generalization of purely temporal one and as such it inherits all the challenges of the latter. In addition, spatiotemporal methods have both theoretical and practical challenges of their own. Therefore, whether statistical methods are useful for early event detection within spatiotemporal biosurveillance still is an open question even to the greater extent, than for temporal surveillance. Thus, as to early detection, spatiotemporal methods are unlikely to provide any advantages over temporal ones.

We have come to the above conclusion merely by comparison. The more important argumentation is as follows. In November 2011, the CDC overhauled its nationwide biosurveillance program, Biosense (see [2]). One of the most important components of this overhaul is giving more power and initiative to local jurisdictions - now  they have ownership of the data and the earliest checking of it.  “Local and state health departments have the best relationship with providers. They understand the context in which an event has happened, and they understand their population more than anybody else. So if we can make sure they have ownership of that data and the initial vetting of it is there, that would be the basis to truly start stitching a regional and national picture,” said Taha Kass-Hout, the CDC’s deputy director for information science and program manager for BioSense. Also Kass-Hout said: “BioSense will help the community ‘open for business’. That is, any health department in the country could ask their providers to share healthcare information with them in a meaningful ready to use environment. That will remove a lot of the barriers from the providers as well as the health departments.”

With this new data-sharing approach, in which local health departments – not the CDC – maintain ownership of their data in the form of daily counts of visits to local providers: emergency departments of city hospitals, affiliated clinics and doctor offices; and a new understanding of who is responsible for early analysis of this data and eventually for early decision making and response; it becomes clear that this new Biosense environment is ideal for purely temporal approaches applied at a very local, city level.

Now, let us briefly describe how a typical and one of the most commonly used spatiotemporal method, SaTScan works:

1.      A global region designated for surveillance (it could be the whole country or just a large geographical region, etc.) is subdivided into sub-regions.

2.      For each sub-region and a specified syndrome, the data are collected, typically in the form of visit counts during some baseline period comprising some most recent days.

3.      Then SaTScan searches for statistically significant clusters by comparing these counts in a certain geographical area with its neighboring areas. The algorithm is based on computing a likelihood ratio-based scan statistic and is using randomization to obtain p values (see, for example [3])

4.      See more details about SaTScan, its practical problems such as performance evaluation, computational time, etc, in [3] - [5].

Without considering any technicality, it is easy to see that SaTScan is a global method by design and by implementation, and as such it is left beyond the redesigned Biosense program, with its emphasis on local data ownership, local early analysis and local decision making and response. As says Burkom in [6]:   “I entered a recent project anticipating an application of scan statistics, but in the course of requirements and data analysis and give-and-take among the lead epidemiologist, implementers, and developers, we adopted a solution based on Bonferroni-limited multiple adaptive control charts”.

Thus, it has to be acknowledged that SaTScan as a typical spatiotemporal biosurveillance method can hardly be useful for early outbreak detection. As to situational awareness, it has to be based on some ability to predict the future development of the outbreak, which in turn should be based on some theoretical, epidemiological model. Since there is no any epidemiological component in the SaTScan methodology, it is unlikely that this approach could be helpful for situational awareness either. Most probably, SaTScan can be more successful in the static situations or slowly developing processes such as geographical distribution of cancer, diabetes, liver diseases etc., and also in non-health related applications: history, astronomy, demography among others (for more details, see [7]).


[1] Fricker, R. D. (2011a). Some methodological issues in biosurveillance. Statistics in Medicine, [full text]
[2] Goth, G. (2011). A new age of biosurveillance is upon us.,1
[3] Shmueli, G. and Burkom, H. S. (2010). Statistical challenges facing early outbreak detection in biosurveillance. Technometrics, 52(1), pp. 39-51.                                                                                                            
[4] Fricker, R. D. (2010). Biosurveillance: detecting, tracking, and mitigating the
effects of natural disease and bioterrorism
[5] Fraker, S. E. (2007). Evaluation of Scan Methods Used in the Monitoring of Public Health Surveillance Data (Dissertation)  
[6] Burkom, H. S. (2011). Comments on ‘Some methodological issues in biosurveillance’ Medicine. 2011,30 pp. 426—429                                    
 [7] SaTScan Bibliography (2011).

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