Person:

Kulldorff, Martin

Background: 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). 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. Methods: To 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. Results: The 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). Conclusions: Spatial 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.

Background: Clinical microbiology laboratories worldwide constitute an invaluable resource for monitoring emerging threats and the spread of antimicrobial resistance. We studied the growing number of biochemical tests routinely performed on clinical isolates to explore their value as epidemiological markers. Methodology/Principal Findings Microbiology laboratory results from January 2009 through December 2011 from a 793-bed hospital stored in WHONET were examined. Variables included patient location, collection date, organism, and 47 biochemical and 17 antimicrobial susceptibility test results reported by Vitek 2. To identify biochemical tests that were particularly valuable (stable with repeat testing, but good variability across the species) or problematic (inconsistent results with repeat testing), three types of variance analyses were performed on isolates of K. pneumonia: descriptive analysis of discordant biochemical results in same-day isolates, an average within-patient variance index, and generalized linear mixed model variance component analysis. Results: 4,200 isolates of K. pneumoniae were identified from 2,485 patients, 32% of whom had multiple isolates. The first two variance analyses highlighted SUCT, TyrA, GlyA, and GGT as “nuisance” biochemicals for which discordant within-patient test results impacted a high proportion of patient results, while dTAG had relatively good within-patient stability with good heterogeneity across the species. Variance component analyses confirmed the relative stability of dTAG, and identified additional biochemicals such as PHOS with a large between patient to within patient variance ratio. A reduced subset of biochemicals improved the robustness of strain definition for carbapenem-resistant K. pneumoniae. Surveillance analyses suggest that the reduced biochemical profile could improve the timeliness and specificity of outbreak detection algorithms. Conclusions: The statistical approaches explored can improve the robust recognition of microbial subpopulations with routinely available biochemical test results, of value in the timely detection of outbreak clones and evolutionarily important genetic events.

Background: Guillain-Barré Syndrome (GBS) can be triggered by gastrointestinal or respiratory infections, including influenza. During the 2009 influenza A (H1N1) pandemic in the United States, monovalent inactivated influenza vaccine (MIV) availability coincided with high rates of wildtype influenza infections. Several prior studies suggested an elevated GBS risk following MIV, but adjustment for antecedent infection was limited. Methods: We identified patients enrolled in health plans participating in the Vaccine Safety Datalink and diagnosed with GBS from July 2009 through June 2011. Medical records of GBS cases with 2009–10 MIV, 2010–11 trivalent inactivated influenza vaccine (TIV), and/or a medically-attended respiratory or gastrointestinal infection in the 1 through 141 days prior to GBS diagnosis were reviewed and classified according to Brighton Collaboration criteria for diagnostic certainty. Using a case-centered design, logistic regression models adjusted for patient-level time-varying sources of confounding, including seasonal vaccinations and infections in GBS cases and population-level controls. Results: Eighteen confirmed GBS cases received vaccination in the 6 weeks preceding onset, among 1.27 million 2009–10 MIV recipients and 2.80 million 2010–11 TIV recipients. Forty-four confirmed GBS cases had infection in the 6 weeks preceding onset, among 3.77 million patients diagnosed with medically-attended infection. The observed-versus-expected odds that 2009–10 MIV/2010–11 TIV was received in the 6 weeks preceding GBS onset was odds ratio = 1.54, 95% confidence interval (CI), 0.59–3.99; risk difference = 0.93 per million doses, 95% CI, −0.71–5.16. The association between GBS and medically-attended infection was: odds ratio = 7.73, 95% CI, 3.60–16.61; risk difference = 11.62 per million infected patients, 95% CI, 4.49–26.94. These findings were consistent in sensitivity analyses using alternative infection definitions and risk intervals for prior vaccination shorter than 6 weeks. Conclusions: After adjusting for antecedent infections, we found no evidence for an elevated GBS risk following 2009–10 MIV/2010–11 TIV influenza vaccines. However, the association between GBS and antecedent infection was strongly elevated.