INTRODUCTION
Noroviruses (NoV) – non-enveloped, single-stranded RNA viruses – are a leading cause of non-bacterial gastroenteritis and are responsible for an estimated 218000 deaths each year in children aged <5 years and 1·1 million hospitalizations worldwide [Reference Patel1]. NoV infection has an average incubation period between 24 and 48 h and average illness duration between 12 and 60 h [Reference Kaplan2]. NoV infection may induce vomiting, diarrhoea, mild fever, abdominal cramping, and nausea in infected individuals. The primary mode of transmission is faecal–oral and occurs through ingestion of contaminated water, consumption of contaminated food, or direct contact with environmental surfaces or infected persons. As few as ten viral particles can cause infection (ID50=18 viruses) [Reference Teunis3], and NoV can persist in the environment even after disinfection (reviewed in [Reference Leon, Moe and Potter4] and [Reference Leon5]). NoV are genetically classified into five genogroups, of which GII, GI, and GIV (rarely), in order of greatest to lowest numbers, are responsible for human outbreaks [Reference Zheng6]. Within the GII genogroup, GII.4 strains are responsible for most human NoV outbreaks, including pandemics [Reference Leon, Moe and Potter4].
Despite the ability to detect and classify NoV through antibody-based assays (since 1985) and genomic amplification assays (since 1992), many fundamental questions regarding NoV outbreak characteristics have either been incompletely addressed or remain unanswered from analysis of surveillance data. To be reliable sources of data for epidemiological research, surveillance systems rely on management teams (e.g. restaurants, hotels) and healthcare administrators (e.g. hospitals, long-term care facilities) to consistently notify public health authorities when they suspect an infectious disease outbreak has occurred, so that appropriate follow-up activities (e.g. laboratory confirmation, epidemiological study, etc.) can be conducted. Primary cases can then be defined as those individuals suspected to be infected by a common vehicle and confirmed via a laboratory or clinical diagnosis. Primary attack rates can then be defined as the proportion of individuals who become ill following direct contact with a person or object infected with NoV divided by the number of individuals at risk. By virtue of their large sample size (more outbreaks), large studies of norovirus outbreak epidemiology derived from such surveillance data [Reference Lopman7, Reference Verhoef8] are able to provide highly precise measures of effect (characterized by narrower error intervals).
In addition to these large norovirus epidemiological studies, other published studies describe data from: (1) a subset of outbreaks that were reported to or by public health authorities for additional genotyping [Reference Fankhauser9–Reference Tseng12], (2) outbreaks that have been aggregated as part of smaller, more focused NoV surveillance networks (e.g. vessel sanitation programme for norovirus outbreaks on cruise ships) [Reference Widdowson13], (3) active surveillance [Reference Kaplan14], or (4) outbreaks that form the foundations of pilot NoV surveillance networks in some countries, such as France [Reference Bon15] or Switzerland [Reference Fretz16]. In general, these studies suggest that a majority of reported NoV outbreaks occur in healthcare facilities and foodservice settings [Reference Fankhauser9–Reference Tseng12, Reference Bon15, Reference Fretz16]. In addition, foodborne and waterborne outbreaks have often been associated with high attack rates [Reference Blanton11, Reference Fretz16]; however, while a number of studies characterize food-associated outbreaks, compared to outbreaks in other settings, as having smaller numbers of primary cases and persons at risk [Reference Fankhauser9–Reference Blanton11], some studies suggest contradictory conclusions [Reference Kaplan14]. Furthermore, a higher proportion of GII strains, compared to GI strains, was reported in a majority of outbreaks across several studies [Reference Fankhauser9, Reference Blanton11, Reference Widdowson13, Reference Bon15, Reference Noda, Fukuda and Nishio17] and was commonly associated with healthcare settings [Reference Fankhauser9, Reference Blanton11, Reference Bon15]. In general, GII.4 outbreaks were associated with lower attack rates [Reference Noda, Fukuda and Nishio17]. Knowledge gaps from previous studies include: (1) lack of statistical comparison of the number of cases, persons at risk, or attack rate across outbreak settings and modes of transmission, (2) limited analyses of attack rate differences between and among the NoV genogroups that most commonly infect humans, i.e. GI and GII, and (3) infrequent use of statistical adjustment or multivariate modelling to study the influence of multiple variables simultaneously on an outcome.
Based on data abstracted from a systematic review of published NoV outbreak reports, this investigation seeks to address these knowledge gaps in NoV outbreak epidemiology and expand on previous research by exploring the relationship between potential risk factors for NoV outbreaks worldwide using multivariate models.
METHODS
Article identification
Literature searches for the terms ‘norovirus’ and ‘outbreak’ were performed in PubMed, EMBASE, and Web of Knowledge. MeSH terms were expanded in PubMed when available and Boolean operators were used to include all possible term forms. The complete search strategy is included as online Supplementary material (see Appendix S1).
Article screening
Our literature search identified 2435 non-duplicate articles (Fig. 1). Two reviewers independently assessed each article for inclusion. The inclusion criteria required that articles (1) be published in an article format (e.g. citations of conference abstracts were excluded), (2) be published entirely in English, (3) describe human NoV outbreaks, and (4) have at least one case from each NoV outbreak confirmed by reverse transcriptase–polymerase chain reaction (RT–PCR). Furthermore, the articles were required to have explicitly stated the number of primary cases (the article was excluded if the number of NoV-positive stools was reported instead of the number of primary cases). The exclusion criteria eliminated articles that documented sporadic cases of NoV or that were published prior to the development of RT–PCR methodology for NoV detection in 1992. Discrepancies between each pair of reviews were resolved by consensus or a third investigator. Rather than impose a uniform case definition on the articles, we adopted each author's case definition. Relevant articles were allowed to contain information on more than one outbreak and data were abstracted separately for each outbreak. However, outbreaks reported in multiple publications were only recorded once.
Fig. 1. Flow diagram of searches, screening, and selection of eligible articles for creation of Norovirus outbreak database. The 250 articles meeting inclusion criteria contained 902 outbreaks published between December 1993 and May 2011. Outbreaks spanned the period from December 1983 to March 2010. Arrows indicate the temporal order of the steps represented in the grey rectangles. NoV, Norovirus; RT–PCR, reverse transcriptase–polymerase chain reaction.
NoV outbreak database
A standardized Epi Info™ survey version 3.5.1 (CDC, USA) was used to abstract data on as many as 74 variables (when available) into a Microsoft Access database. These variables included, but were not limited to: mode of transmission (described below), vehicle (person or object infected with or contaminated by NoV that promotes transmission of virus to healthy individuals or contamination of additional objects or surfaces with virus), genetic classification (genogroup and genotype/cluster), outbreak beginning and end dates, location (described below), outbreak setting (described below), incubation period (length of time between viral infection and symptom presentation), illness duration, case ages, number of primary cases and persons at risk (described below), primary attack rates, and description of any action taken to stop the outbreak.
During data abstraction, calculations were limited to addition or subtraction (e.g. adding staff and guest cases to generate a total primary case number for a hotel outbreak). Reported numbers of laboratory-confirmed and clinically identified primary cases were also combined. Data were cleaned using SAS™ version 9.2 (SAS Institute, USA). Outbreak seasonality was derived from the month of occurrence and the hemisphere of the affected country. Modes of transmission were categorized as follows: foodborne (e.g. consumption of contaminated shellfish or foods contaminated during production or preparation by an infected food handler), waterborne (e.g. drinking or recreational water), person-to-person (direct contact with infected vomitus or other aerosolized virus particles), and environmental (direct contact with contaminated surfaces). Outbreak settings were categorized as follows: foodservice (e.g. restaurants), healthcare (e.g. hospitals, long-term care facilities, psychiatric institutions), leisure (e.g. vacations, cruises, hotels, recreational activities), school/daycare (e.g. schools, daycare facilities, universities), and other (e.g. residential, community-wide outbreaks, military, and prison settings). Outbreaks were classified as GI or GII if the associated strains were identified exclusively as belonging to one genogroup or the other; outbreaks associated with GI and GII strains combined were classified accordingly, as long as one strain from the outbreak was identified as belonging to GI and another was identified as belonging to GII. To avoid counting an outbreak more than once, an outbreak associated with strains from multiple genogroups was not classified as belonging to GI individually or GII individually. To include the maximum number of outbreaks in the analysis, an algorithm was constructed in SAS to calculate number of persons at risk for direct infection with NoV (primary cases÷primary attack rate) and primary attack rate (primary cases÷persons at risk) for outbreaks from which those variables were not abstracted.
Statistical analysis
All analyses were conducted with SAS™ version 9.2. Analyses of primary attack rate were restricted to outbreaks that simultaneously contained data for all three of the following variables: primary cases, persons at risk, and primary attack rate. Analyses of genogroup were restricted to outbreaks that contained data on whether the outbreak was a GI individually, GII individually, or GI+GII combined outbreak. Continuous variables were non-normal even after transformations and therefore were expressed as the median and interquartile range (IQR). Multiple groups of continuous data were compared with a Kruskal–Wallis test and post-hoc adjustments for multiple comparisons using Dunn's test [Reference Elliott and Hynan18]. Multiple groups of categorical data were compared by first analysing the groups with an overall χ2 test. Post-hoc 2×2 χ2 analyses with Tukey-style adjustment for multiple comparisons were subsequently performed using the compprop macro for SAS™ [Reference Elliott and Reisch19].
Independent variables included in the multivariate regression models [primary attack rate (linear), occurrence of GI or GII strains in outbreaks (logistic)] were determined a priori: mode of transmission, outbreak setting, season, outbreak year, action taken in response (as described in article), genogroup (linear), and primary attack rate (logistic). Multivariate regression models only included outbreaks that had values for all variables in the models. One model excluded outbreaks with more than one transmission route, while another considered these multiple transmission outbreaks as separate, independent outbreaks – wherein each outbreak was associated with a single transmission route. An interaction assessment was not performed because of the large number of pairwise interaction terms, low statistical power to evaluate all such interactions, and the potential for instability in model estimates.
For linear models, collinearity was considered to be present if the variance inflation factor associated with any covariate exceeded 10. For the logistic models, multicollinearity was considered to be present if any condition index obtained using the collin macro for SAS™ [Reference Davis, Moolgavkar and Prentice20] exceeded 15. Collinear variables were removed from all adjusted models. Penalized maximum-likelihood estimation techniques [Reference Heinze and Schemper21] were employed after examination of the full logistic model revealed a quasi-complete separation of data-points (due to few observations in some categories of one or more stratified covariates).
RESULTS
We analysed 902 eligible outbreaks. These outbreaks occurred between December 1983 and March 2010 and were abstracted from 250 articles published between December 1993 and May 2011 (Fig. 1, Supplementary Appendix S2). Of the 896 outbreaks documenting year of occurrence, 71% occurred between 2000 and 2010. More than 90% of outbreaks occurred in the northern hemisphere and 45% took place during winter.
Transmission route and setting
Transmission route data was available for 565 outbreaks; 89 (16%) involved two or more modes of transmission. Multiple transmission outbreaks were replicated and segregated into their individual component modes of transmission, resulting in 666 observations for the transmission route sub-analysis (Table 1). Setting data were abstracted from 830 outbreaks (Table 1). Reported outbreaks were most commonly attributed to foodborne transmission (362/666, 54%) and foodservice settings (294/830, 35%), whereas the fewest number of outbreaks was associated with environmental transmission (60/666, 9%) or reported in school or daycare settings (80/830, 10%). As described in the Methods section, settings were consolidated to enhance statistical power, but within each setting, there were subcategories that comprised more than 15% of the reported setting. ‘School/Daycare’ was comprised of school (64/80, 80%) and daycare (16/80, 20%) outbreaks. Similarly, ‘Healthcare’ was comprised of hospitals (107/225, 48%) and long-term care facilities (112/225, 50%). ‘Other’ included private homes (30/91, 33%), community-wide outbreaks (20/91, 22%) and military settings (15/91, 16%). In general, outbreaks due to foodborne transmission or foodservice setting were significantly associated with the lowest number of primary cases and persons at risk, but were significantly associated with the highest attack rate compared to other modes of transmission or outbreak settings. In addition, waterborne transmission attack rates were significantly higher than those associated with person-to-person or environmental transmission. Healthcare-related outbreaks were significantly associated with fewer primary cases and had the lowest attack rates.
Table 1. Comparative analysis of the number of primary cases, persons at risk and primary attack rate by mode of transmission and outbreak setting
IQR, Interquartile range.
* Complete information was not available for all outbreaks. Summary statistics were calculated for a subset of the outbreaks, described in the ‘Outbreaks’ column, with information on all three of the following variables: the number of primary cases, persons at risk, and primary attack rate.
† Foodborne outbreaks included those associated with Norovirus infection following consumption of shellfish or other foods contaminated during production or preparation by an infected food handler. Person-to-person outbreaks included those associated with Norovirus infection following direct contact with vomitus or other aerosolized viral particles. Waterborne outbreaks included those associated with ingestion of infected drinking or recreational water. Environmental outbreaks included those associated with Norovirus infection following direct physical contact with contaminated surfaces.
‡ Up to three modes of transmission may have been listed per outbreak. Outbreaks may therefore be listed under more than one mode of transmission.
§ Significantly different from foodborne transmission (P<0·05).
∥ Significantly different from waterborne transmission (P<0·05).
¶ Foodservice settings included restaurants, wedding receptions and other catered functions. Healthcare settings included hospitals, psychiatric institutions, long-term care facilities. Leisure settings included hotels, cruise ships, tour buses, camps, and ski lodges. School/daycare settings included primary, secondary, elementary, middle and high schools, as well as daycare facilities, colleges, and universities. Other outbreak settings included residential, military and prison settings.
# Significantly different from foodservice settings (P<0·05).
$ Significantly different from healthcare settings (P<0·05).
We assessed 580 outbreaks that recorded information on primary attack rate, ranging from <1% to 100%, for significant bivariate relationships. Foodborne and waterborne outbreaks, as well as outbreaks in foodservice settings and outbreaks associated with both GI and GII strains, had significantly higher attack rates compared to their referent categories (Table 2). By contrast, healthcare-related and GII cluster 4 (GII.4) NoV outbreaks, as well as outbreaks in which authors described a corrective action, had significantly lower attack rates.
Table 2. Unadjusted and adjusted multiple linear regression coefficients modelling primary attack rate as a function of potential norovirus outbreak risk factors
s.e., Standard error; GI, genogroup I; GII, genogroup II.
* Unadjusted analyses excluded outbreaks with multiple modes of transmission.
† P<0·05.
‡ GII outbreaks with no cluster data were excluded from unadjusted model.
In the multivariate linear regression model where multiple transmission outbreaks were excluded (Table 2), or where they were included in a model as an extra dummy variable (data not shown), no covariates were significantly associated with a higher or lower attack rate. However, winter season was borderline significant in the model where multiple transmission routes were classified as an extra dummy variable (P=0·056, data not shown). In the model that included multiple transmission routes, where we classified outbreaks of more than one mode of transmission as multiple, independent outbreaks of a single transmission type (see Methods section), outbreaks in foodservice settings and in winter were associated with higher attack rates, compared to their referent categories.
To assess whether additional covariates were significant if the power (sample size) of the models was increased, we included outbreaks that lacked data for the ‘setting’ variable and found that, in either adjusted model, foodborne outbreaks were associated with higher attack rates compared to their referent categories (data not shown). In the model where outbreaks with multiple modes of transmission were included, winter outbreaks were also associated with higher attack rates (data not shown). When we included outbreaks that lacked data for the ‘mode of transmission’ variable in either adjusted model, outbreaks in foodservice settings had significantly higher attack rates (data not shown). In addition, the ‘outbreak year’ variable had a significant negative relationship with attack rate – outbreaks that occurred more recently were associated with lower attack rates (data not shown). Furthermore, in the model where outbreaks with multiple modes of transmission were included, spring outbreaks were associated with higher attack rates (data not shown).
Genogroup, transmission, and setting
We analysed 754 outbreaks that had genogroup data and 581 that had cluster-level data. Compared to outbreaks associated with GI strains (99/754, 13%) and multiple strains (GI+GII, 92/754, 12%), outbreaks associated with GII strains had the highest proportion (563/754, 75%). GII.4 was reported in one third of outbreaks associated with a single strain (genogroup and cluster).
In general, compared to GII, GI outbreaks were significantly more likely to be associated with waterborne transmission [Tables 3 and 4; adjusted OR (aOR) 0·19, 95% confidence interval (CI) 0·05–0·67]. A significantly smaller proportion of waterborne and foodborne outbreaks was associated with GII (including GII.4) strains compared to other modes of transmission (Table 3). However, a significantly higher proportion of waterborne and foodborne outbreaks was associated with multiple strains (GI+GII) appearing simultaneously. GII outbreaks, compared to GI, were significantly associated with healthcare setting (Tables 3 and 4; aOR 33·67, 95% CI 1·76–644·18). The 95% CI around the healthcare setting aOR was large because all 47 healthcare-related outbreaks in the model were associated with GII strains. Similarly, a higher proportion of NoV outbreaks in healthcare-related and leisure settings was associated with GII.4 strains compared to other settings (Table 3). GII outbreaks, compared to GI, were also significantly associated with winter compared to autumn (aOR 5·34, 95% CI 1·94–14·73; Table 4). To assess whether additional covariates were significant if the power (sample size) of the models was increased, we included outbreaks lacking either setting or transmission data in the fully adjusted models but found no additional significant associations beyond those described (data not shown).
Table 3. Comparative analysis of norovirus outbreaks associated with genogroup by mode of transmission and outbreak setting
GI, Genogroup I; GII, genogroup II.
* Up to three modes of transmission may have been listed per outbreak. Outbreaks may therefore be listed under more than one mode of transmission. Percentages may sum to more than 100 due to rounding.
† Includes all GII strains including those lacking a specific GII cluster designation.
‡ Percentages and total exclude segment of GII strains lacking a specific GII cluster designation.
§ Significantly different from waterborne transmission types (P<0·05).
∥ Significantly different from foodborne transmission types (P<0·05).
¶ Significantly different from healthcare settings (P<0·05).
# Significantly different from school/daycare settings (P<0·05).
$ Significantly different from foodservice settings (P<0·05).
Table 4. Unadjusted and adjusted odds ratios modelling the likelihood of whether a genogroup I or genogroup II norovirus strain is associated with an outbreak that is characterized by the presence of potential norovirus risk factors
OR, Odds ratio; CI, confidence interval.
* Unadjusted analyses excluded outbreaks with multiple modes of transmission.
† Significant odds ratios >1 represent that the odds an outbreak was caused by a GII strain were greater than the odds an outbreak was caused by a GI strain.
‡ P<0·05.
DISCUSSION
The goal of this analysis was to examine relationships between primary attack rate, genogroup, mode of transmission, outbreak setting, and other NoV outbreak characteristics. In general, we found the number of primary cases and persons at risk to be significantly lower in outbreaks related to food and healthcare settings. Attack rates were significantly higher in outbreaks related to food, water, and in winter. By contrast, the attack rate was lower in healthcare-related outbreaks. Regarding genogroup, outbreaks in healthcare settings and in winter were more likely to be associated with GII strains, while waterborne outbreaks were more likely to be associated with GI strains. These results were largely consistent with published literature [Reference Fankhauser9, Reference Blanton11, Reference Kaplan14, Reference Bon15].
Our finding that food-related outbreaks were associated with the fewest primary cases and persons at risk (Table 1) suggests that viral transmission within a confined space, such as a restaurant, may limit the primary cases and persons at risk to only those persons sharing that space. By contrast, the number of primary cases or persons at risk may be larger, compared to food-related outbreaks, in waterborne outbreaks in which contaminated drinking water is distributed throughout a community or person-to-person outbreaks where an infected person becomes a mobile source of exposure. Improved global surveillance networks for foodborne outbreaks, such as FoodNet in the USA [Reference Scallan22], the NoroNet network in Europe [Reference Verhoef8], and OzFoodNet in Australia [Reference Kirk23], may allow for the detection of smaller outbreaks.
Previous research by Kaplan et al. [Reference Kaplan14] supports our observation that foodborne and waterborne outbreaks have significantly greater attack rates compared to person-to-person and environmental outbreaks (Tables 1, 2). Foodborne and waterborne outbreaks may have greater attack rates due to: (1) consumption of higher infectious doses of NoV, and (2) more accurate identification of persons at risk (i.e. all persons eating a food item vs. all persons who may have had contact with an infected person). As mentioned previously, active monitoring by targeted surveillance systems may lead to more accurate detection and classification of NoV food- and water-related outbreaks [Reference Scallan22, Reference Yoder24]. In a manner similar to other published reports [Reference Johnston25, Reference Zingg26], our analysis also indicated that outbreaks in healthcare settings were associated with low attack rates (Table 1). Several factors may account for this finding, including the limited mobility of infected persons in hospitals and long-term care facilities, requirements in the USA for active hospital infection surveillance, the likelihood of more interventions associated with person-to-person spread within the institutional environment, and the implicit financial and quality improvement incentives to encourage healthcare facilities to reduce the incidence of nosocomial NoV outbreaks [Reference Zingg26].
In contrast to Kroneman et al. [Reference Kroneman27] – who did not observe a significant multivariate association between season and genogroup – we observed that outbreaks in winter were more likely to be caused by GII strains than outbreaks in autumn (Table 4). As mentioned previously, the clustering of people indoors during seasonal cold weather, combined with an absence of herd immunity to circulating NoV strains [Reference Gallimore28, Reference Lindesmith, Donaldson and Baric29], may facilitate person-to-person NoV transmission. We also found that GII strains, particularly GII.4, were more likely to be present in outbreaks that occurred in healthcare settings. A higher proportion of hospitalized patients may be infected with NoV in winter because, in winter, hospitals may receive a larger intake of patients (e.g. respiratory diseases), which may facilitate the spread of NoV within an enclosed environment [Reference Lopman7]. Furthermore, elderly populations and patients with pre-existing conditions may be more susceptible to GII.4 strains, which have been shown to mutate quickly [Reference Fankhauser9, Reference Kroneman27]. A 2006 study by Chan et al. suggests that individuals with NoV GII infection may shed higher concentrations of virus than individuals with NoV GI infection [Reference Chan30]. Recent findings published by Lee et al. suggest that the elderly and persons with pre-existing conditions may shed NoV for longer periods of time than healthy young people [Reference Lee31]. Therefore, shedding higher concentrations of virus for longer periods of time may greatly favour transmission of GII strains over GI strains.
Finally, we observed that GI strains were significantly more likely to have been transmitted via water than by other routes of transmission. While the majority of strains implicated in foodborne, person-to-person, and environmental outbreaks belonged to GII (Table 3), waterborne strains were more likely to belong to GI (Tables 3, 4). This finding is consistent with previous research [Reference Lysen32]. While previous work suggests that a representative GI strain is less stable on surfaces than a representative GII strain [Reference Liu33], it is possible that GI strains are more stable in water than GII strains. GI NoV stored in groundwater was still infectious in human volunteers after 2 months [Reference Seitz34] and GI NoV RNA stored in groundwater was still detectable by RT–PCR after 588 days [Reference Charles35].
Limitations and strengths
There were three main limitations to this study. First, as is the case with many systematic reviews, our study was influenced by publication bias. We abstracted data of several reports from active surveillance that were published. Publications were more likely from outbreaks with novel findings than small, relatively common outbreaks (e.g. healthcare-related settings). However, most infectious disease outbreaks are reported by passive surveillance. Passive surveillance has its own biases. Outbreak data collected may not be as comprehensive in passive vs. active surveillance. Some outbreaks may not be recognized because illness is mild or because cases disperse (e.g. restaurants). In contrast, outbreaks may be recognized when cases are in communication with each other (e.g. weddings). Publication bias also resulted from the overrepresentation of outbreak data from five countries (Japan, USA, Sweden, UK, The Netherlands) that accounted for approximately two-thirds of the reported outbreaks. Care should be taken when generalizing these results to countries with limited or extensive surveillance. For example, some countries may only be able to investigate outbreaks in one setting (e.g. foodservice or healthcare-related) or may have better surveillance of foodborne and waterborne outbreaks because it is easier to implicate a contaminated food item or water source than an environmental or person-to-person source.
Second, because we treated outbreaks with more than one mode of transmission as multiple, independent outbreaks of a single transmission type, we increased the effective sample size in these models and therefore some non-significant variables may have become significant. These findings may still be biologically valid but additional analyses of data from outbreaks with multiple transmission routes are needed to validate these findings. Third, as our data came from different authors, all outbreaks did not have the same variables. This limited the number of analysed outbreaks and may have increased the likelihood of misclassification bias (e.g. an outbreak that reported only genogroup-level information would not have been analysed as a GII.4 outbreak).
There were three key strengths to this analysis. First, we employed a rigorous study protocol that required validation by at least two individuals following article selection and data abstraction. Second, this analysis incorporates a greater number of countries than any other review of published NoV outbreak studies [Reference Lopman7, Reference Fankhauser9, Reference Kroneman27]. Third, the size of our database and multivariate modelling allowed us to control for a variety of variables. Furthermore, publication bias probably minimally influenced the multivariate analysis identifying factors associated with either GI or GII outbreaks (Table 4) because an outbreak was neither any more likely nor any less likely to be published simply because it was associated with a GI or a GII strain.
Implications
There are several important implications from this study. First, point-source outbreaks (e.g. food) were associated with the highest illness burden, primary attack rates, and proportion of outbreaks, and were more likely to be caused by strains from multiple genogroups. These results suggest that several primer sets should be used in the detection of NoV from point-source outbreak specimens. Second, healthcare settings were associated with low primary attack rates, which is probably due to effective infection control practices in healthcare facilities. Third, NoV outbreak attack rates may fluctuate by seasons and one NoV genogroup may predominate over another in outbreaks, particularly in winter.
NOTE
Supplementary material accompanies this paper on the Journal's website (http://journals.cambridge.org/hyg).
ACKNOWLEDGEMENTS
The authors acknowledge assistance with our systematic review from Emory University librarians Barbara Abu-Zeid and Mia White (to J.E.M., J.J.R., J.R.F.) and statistical assistance from Emory University Professor Dr David Kleinbaum (to J.E.M, B.W.D., J.S.L.). We also thank Elisabeth Honorat, MPH, whose master's thesis, ‘Epidemiology of norovirus outbreaks’ (Emory University, 2007) inspired the current work.
This work was partially supported by STAR grant RD83172701 from the U.S. Environmental Protection Agency (to C.L.M., J.N.S.E.), grant 1K01AI087724-01 from the NIAID at the NIH, grant 2010-85212-20608 from the National Institute of Food and Agriculture at the U.S. Department of Agriculture, and the Emory University Global Health Institute (to J.S.L.). We also acknowledge financial support from the Practical Experience Program at the Emory University Rollins School of Public Health (to J.E.M.), and the Federal Work Study program at the U.S. Department of Education (to B.W.D.).
DECLARATION OF INTEREST
None.
