The Centers for Disease Control and Prevention (CDC) operates 2 surveillance systems for NoV outbreaks in the United States—the National Outbreak Reporting System (NORS) and CaliciNet. NORS is a comprehensive reporting system for all enteric disease outbreaks in the United States, regardless of cause or transmission mode (9). NORS data include general outbreak characteristics, patient demographics, symptoms, and clinical outcomes. CaliciNet is an electronic laboratory surveillance network that collects information on genetic sequences of NoVs implicated in outbreaks (11). CaliciNet laboratories perform molecular typing of NoV strains by using standardized laboratory protocols for reverse transcription PCR followed by sequence analysis. Sequence data are then uploaded into a central database to monitor national trends in circulating NoV strains. Information about outbreaks is often reported by state health departments to CDC several months after outbreaks occur; therefore, both NORS and CaliciNet data may be subject to substantial reporting delays.

Beginning in August 2012, a network of 5 sentinel states was established to improve the timeliness of NoV outbreak reporting through NORS and CaliciNet and thereby allow near real-time assessment of NoV activity. These 5 states (Minnesota, Ohio, Oregon, Tennessee, and Wisconsin) include ≈33 million residents or 11% of the total US population spread across several regions of the country (20). In addition, these 5 states had historically the highest per capita reporting rates for NoV outbreaks and therefore were least likely to be affected by underreporting biases. State health departments that participate in this network–the Norovirus Sentinel Testing and Tracking (NoroSTAT) network–report suspected NoV outbreaks through NORS and CaliciNet within 7 business days of notification of the outbreak to the state health department. NoroSTAT reporting allows NoV strain data uploaded through CaliciNet to be rapidly linked with epidemiologic characteristics of outbreaks reported through NORS by using consistent outbreak identifiers in each system. Aside from more timely and complete data reporting, outbreak reporting practices and case definitions among NoroSTAT participants otherwise remained unchanged.

The present study was restricted to data reported to NORS and CaliciNet by NoroSTAT states on all confirmed and suspected NoV outbreaks with first illness onset dates during August 1, 2012–April 16, 2013. To compare the level of outbreak activity in the 2012–13 season with that in previous years, we extracted data for the same time during the 2 previous seasons (August 1, 2011–April 16, 2012 and August 1, 2010–April 16, 2011) from NORS for the NoroSTAT states. A confirmed NoV outbreak is defined as >2 cases of similar enteric illness associated with a common exposure that are laboratory confirmed for NoV by reverse transcription PCR, enzyme immunoassay, or electron microscopy. A suspected NoV outbreak is defined as >2 cases of similar enteric illness associated with a common exposure in which NoV was the suspected causative agent, which is determined by each reporting site (e.g., supportive clinical or epidemiologic information or outbreaks with only 1 positive NoV specimen).

NoV genotype and sequence data were extracted from CaliciNet, and epidemiologic data, including demographics, symptoms, and outcome data, were extracted from NORS. Season onset was defined as the week by which at least 10% of the total number of NoV outbreaks during August 1–April 16 of each year had occurred. Seasonal peak in NoV outbreak activity was defined as the month with the highest number of NoV outbreaks. Season duration was defined as the number of weeks between which 10% and 90% of the total number of NoV outbreaks had occurred.

For comparison of GII.4 Sydney outbreak characteristics with those attributed to other NoV strains, outbreaks for which the NoV strain was reported through CaliciNet were grouped into 2 categories: GII.4 Sydney and non–GII.4 Sydney. A third category consisted of outbreaks reported by NoroSTAT states only through NORS (i.e., for which strain data were not available). Data on demographic characteristics, symptoms, and clinical outcomes were not available from all outbreaks. To minimize potential biases from underreporting, we included demographic characteristics, symptoms, and outcomes and analyzed them only when available for >10% of reported illnesses across all reported outbreaks.

To test the increase in proportion of NoV outbreaks attributed to the GII.4 Sydney strain by month during the 2012–13 season, we used the χ² test for trend. Categorical variables were compared by calculating rate ratios with 95% CIs. Statistical significance was set at p<0.05 by using the mid-p exact test. Analyses were performed by using SAS v9.3 (SAS Institute Inc., Cary, NC, USA).

Ohio collects statewide syndromic surveillance data from 178 emergency department and urgent care facilities (referred to collectively herein as emergency departments) into a system called EpiCenter. This system is used by state and local public health agencies to detect, track, and characterize health events, such as pandemic influenza, outbreaks, environmental exposures, and potential bioterrorism, in real-time. The system gathers information on patient symptoms with identification removed and automatically alerts public health officials when an unusual pattern or trend is occurring. We used EpiCenter data for August 1, 2010–April 16, 2013, to measure the weekly proportion of patients with gastrointestinal syndrome, defined as the chief complaint that included the following symptom keywords: abdominal, diarrhea not watery/bloody, diarrhea watery/bloody, nausea, and vomiting. To assess the correlation between the proportion of emergency department visits attributed to gastrointestinal illness and the number of NoV outbreaks, we used the Pearson correlation coefficient.

During August 1, 2012–April 16, 2013, a total of 637 NoV outbreaks were reported by Minnesota, Ohio, Oregon, Tennessee, and Wisconsin. Season onset was similar in 2012–13 compared with 2011–12 and 2010–11 (October 24 vs. November 7 and October 3, respectively, Figure 1). By April 16, the cumulative number of outbreaks was slightly higher in the 2012–13 season than in the 2011–12 and 2010–11 seasons (637 vs. 536 and 432, respectively). However, the data varied considerably between the states. The cumulative number of outbreaks increased in 3 states (Oregon, Tennessee, and Ohio), Wisconsin data were similar for each season, and Minnesota reported a decreased cumulative number of outbreaks (Technical Appendix Figure). Peak outbreak activity occurred in January 2013 (152 outbreaks) and was 16% higher than the average of peak month outbreak activity in the 2 preceding seasons (148 outbreak in January 2012 and 113 outbreaks in January 2011). Season duration was 21 weeks in 2012–13, compared with 18 weeks in 2011–12 and 22 weeks in 2010–11.

Sequence data were available for 358 (56%) of the 637 outbreaks. Outbreaks with sequence data were more likely to have reported the mode of transmission as foodborne, and transmission was more likely to have occurred at restaurants and less likely to have occurred at schools (data not shown). Other characteristics were similar between the outbreaks with and without sequence data. Of these, 226 (63%) were attributed to the GII.4 Sydney strain. The proportion of outbreaks attributed to GII.4 Sydney increased from 8% in September 2012 to 82% in March 2013 (Figure 2, χ² test for trend; p<0.001). In December 2012, GII.4 Sydney became the predominant strain, accounting for 44 (66%) of the 67 outbreaks reported that month from which sequence data were available. GII.4 Sydney accounted for most sequenced outbreaks in all 5 states: 16 (57%) of 28 in Minnesota, 50 (56%) of 89 in Ohio, 57 (61%) of 94 in Oregon, 27 (73%) of 37 in Tennessee, and 76 (69%) of 110 in Wisconsin. The proportion of GII.4 Sydney outbreaks in the states with an increased cumulative number of outbreaks in 2012–2013 was not significantly different from the proportion in those states without an increased number of outbreaks (61% vs. 67%, respectively; p = 0.27).

Overall, the most commonly identified mode of transmission was person-to-person, which occurred in 481 (75.5%) of 637 outbreaks (Table 1). The proportions of different modes of transmission were similar among GII.4 Sydney and non–GII.4 Sydney outbreaks. Healthcare settings were reported most frequently across all outbreaks. However, GII.4 Sydney outbreaks occurred more frequently in healthcare settings (long-term care facilities and hospitals) than did non–GII.4 Sydney outbreaks (75.2% vs. 47.7%, respectively; rate ratio [RR] 1.58; 95% CI 1.30–1.91). GII.4 Sydney outbreaks occurred less frequently in schools and childcare centers than did non–GII.4 Sydney outbreaks (1.8% vs. 21.2%, respectively; RR 0.08; 95% CI 0.03–0.23). The proportion of outbreaks that occurred at restaurants or banquet facilities was similar for GII.4 Sydney and non–GII.4 Sydney outbreaks. Outbreaks for which sequence data were available were similar with respect to setting and transmission mode to those without sequence data.

Aggregate patient data were available for 310 (49%) of the 637 NoV outbreaks (9,018 patients); 104 of the outbreaks were GII.4 Sydney outbreaks, 74 were non–GII.4 Sydney outbreaks, and 132 were reported only through NORS without genotype data. The proportion of patients >50 years of age was higher in GII.4 Sydney outbreaks than in non–GII.4 Sydney outbreaks (65.9% vs. 43.9%, respectively; RR 1.50; 95% CI 1.39–1.62; Table 2).

Patients from GII.4 Sydney outbreaks reported diarrhea slightly more frequently than patients from outbreaks associated with non–GII.4 Sydney viruses (84.8% vs 75.7%, respectively; RR 1.12; 95% CI 1.09–1.15; Table 2). In contrast, vomiting, fever, and abdominal cramps were reported by a lower proportion of patients from GII.4 Sydney outbreaks than those from non–GII.4 Sydney outbreaks (Table 2).

A higher proportion of patients required outpatient visits among those affected by GII.4 Sydney outbreaks than among those in non–GII.4 Sydney outbreaks (7.9% vs. 4.3%, respectively; RR 1.81; 95% CI 1.33–2.47). We observed 9 (0.4%) deaths among 2,324 patients associated with GII.4 Sydney outbreaks for whom we had mortality data, compared with 3 (0.2%) deaths reported among 1,706 patients associated with non–Sydney outbreaks (RR 2.20, 95% CI 0.60–8.12; Table 2). Hospitalization and emergency department visits occurred at similar proportions in GII.4 Sydney and non–GII.4 Sydney outbreaks.

On average, the proportion of total weekly emergency department visits reported through Ohio EpiCenter that were classified into the gastrointestinal syndrome category was 13.3% (median 12.9%, range 11.1%–15.9%; Figure 3). The proportion of weekly gastrointestinal emergency department visits was strongly correlated with NoV outbreaks reported from Ohio (Pearson correlation coefficient; r = 0.68). The magnitude and timing of seasonal increase in the proportion of emergency department visits for gastrointestinal syndrome during the 2012–13 season were similar to those of previous seasons.