Abstract
BackgroundUtilizing highly precise spatial resolutions within disease outbreak detection, such as the patients’ address, is most desirable as this provides the actual residential location of the infected individual(s). However, this level of precision is not always readily available or only available for purchase, and when utilized, increases the risk of exposing protected health information. Aggregating data to less precise scales (e.g., ZIP code or county centroids) may mitigate this risk but at the expense of potentially masking smaller isolated high risk areas.MethodsTo experimentally examine the effect of spatial data resolution on space-time cluster detection, we extracted administrative medical claims data for 122500 viral lung episodes occurring during 2007–2010 in Tennessee. We generated 10000 spatial datasets with varying cluster location, size and intensity at the address-level. To represent spatial data aggregation (i.e., reduced resolution), we then created 10000 corresponding datasets both at the ZIP code and county level for a total of 30000 datasets. Using the space-time permutation scan statistic and the SaTScan™ cluster software, we evaluated statistical power, sensitivity and positive predictive values of outbreak detection when using exact address locations compared to ZIP code and county level aggregations.ResultsThe power to detect disease outbreaks did not largely diminish when using spatially aggregated data compared to more precise address information. However, aggregations negatively impacted the ability to more accurately determine the exact spatial location of the outbreak, particularly in smaller clusters (<800 km2).ConclusionsSpatial aggregations do not necessitate a loss of power or sensitivity; rather, the relationship is more complex and involves simultaneously considering relative risk within the cluster and cluster size. The likelihood of spatially over-estimating outbreaks by including geographical areas outside the actual disease cluster increases with aggregated data.
Highlights
Complete and timely reporting of infectious diseases is important for effective outbreak detection, which is in part based on statistically analyzing a study area for unexpected spatial and/ or temporal clusters of cases
Studying disease clusters at the most precise level of spatial resolution, such as the patients’ address, is most desirable as this provides the actual residential location of the infected individual. This level of data precision is not always readily available or only available for purchase, and when utilized, it increases the risk of exposing protected health information (PHI) perhaps without the patient’s knowledge or consent
Using the episode treatment groups (ETGs) methodology, baseline data consisted of viral lung infection episodes with and without comorbidities occurring from January 1, 2007–December 31, 2010 within the proposed study area collected from electronic administrative claims data
Summary
Complete and timely reporting of infectious diseases is important for effective outbreak detection, which is in part based on statistically analyzing a study area for unexpected spatial and/ or temporal clusters of cases. Studying disease clusters at the most precise level of spatial resolution, such as the patients’ address, is most desirable as this provides the actual residential location of the infected individual This level of data precision is not always readily available or only available for purchase, and when utilized, it increases the risk of exposing protected health information (PHI) perhaps without the patient’s knowledge or consent. Utilizing highly precise spatial resolutions within disease outbreak detection, such as the patients’ address, is most desirable as this provides the actual residential location of the infected individual(s) This level of precision is not always readily available or only available for purchase, and when utilized, increases the risk of exposing protected health information. Aggregating data to less precise scales (e.g., ZIP code or county centroids) may mitigate this risk but at the expense of potentially masking smaller isolated high risk areas
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