1 Réseau National de Santé Publique, 14 rue du Val d'Osne, 94410 Saint Maurice, France.
2 Centre National de Référence des Salmonella et des Shigella, Institut Pasteur, 28 Rue du Docteur Roux, 75724 Paris, Cedex 15, France.
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ABSTRACT |
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capture-recapture; disease outbreaks; food poisoning; Salmonella infections
Abbreviations: AIC, Akaike Information Criterion; BIC, Bayesian Information Criterion; CI, confidence interval; DDASS, Direction Départementale des Affaires Sanitaires et Sociales; DSV, Direction des Services Vétérinaires; MA, Ministry of Agriculture; NPHN, National Public Health Network; NRC, National Salmonella and Shigella Reference Center
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INTRODUCTION |
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Studies performed in Great Britain have shown that the economic impact of foodborne Salmonella outbreaks is far from negligible (12, 13
). Therefore, assessment of the true human and economic burden of Salmonella infection, particularly outbreaks, requires a precise knowledge of its incidence. This is particularly true in France, where data on Salmonella outbreaks are derived from passive mandatory notification of foodborne outbreaks, which is known from anecdotal data to have a very low sensitivity (11
). In this study, we quantified the level of underascertainment of foodborne Salmonella outbreaks by estimating the true number of outbreaks that occurred in France in 1995, using the capture-recapture method applied to three different sources of information.
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MATERIALS AND METHODS |
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Any foodborne outbreak must be reported by the physician, the head of the family, or the chief of the affected community to the district public health department (Direction Départementale des Affaires Sanitaires et Sociales (DDASS)) or the district veterinary service (Direction des Services Vétérinaires (DSV)). After an investigation, the DDASS or the DSV sends a report to the NPHN or the MA, respectively, where it is validated and used for a national annual surveillance summary. Although it is recommended, collaboration between the DDASS and the DSV is not systematic. For both mandatory notification systems, a foodborne outbreak is defined as illness in at least two persons with digestive symptoms that can be attributed to the same food source. For each foodborne outbreak, information is collected on the postal code of occurrence, the date of onset, the date and place of meal consumption, the suspect food items, the organism implicated (and, if it is Salmonella, the serotype), the number of patients affected, and the type of outbreak (family or community).
The NRC receives Salmonella isolates from public and private microbiology laboratories for serotyping (14). The sending of isolates to the NRC is voluntary. For each isolate, information is collected on the laboratory postal code, the dates of isolation and of receipt at the NRC, and the serotype. If the isolate is associated with an outbreak, information on the number of patients affected and the type of outbreak (family or community) is also collected. The NRC defines a foodborne Salmonella outbreak as the isolation of Salmonella associated with the occurrence of other cases of gastroenteritis in a defined population or community. A common food origin is not always reported in the NRC data.
Identification of matches
Because foodborne outbreaks are not recorded in each system with a common identifier, we developed matching criteria between the three systems (NPHN, MA, and NRC) according to data common to all systems. A match between the NPHN and the MA had to have the same postal code of occurrence and the same date of onset (1 day). Potential matches were confirmed if the investigation reports sent to the NPHN and the MA had similar characteristics (date, place of occurrence, number of patients, serotype, and implicated food). Date of onset is not recorded at the NRC (dates of isolation and of receipt of the isolate are available); thus, we had to use other criteria to identify matches between the NRC and the other two sources. They were based on the following: the postal code of occurrence; the delay between the date of onset of the outbreak (data from the NPHN and MA) and either the date of isolation of Salmonella recorded by the NRC (delay 1) or the date of receipt of Salmonella at the NRC (delay 2); and, finally, the distribution by quartile of these delays, determined in three previous studies of Salmonella isolates for which the three dates had been collected (1517
) (table 1). Matches between the NPHN and the NRC and between the MA and the NRC had to have 1) the same postal code, 2) a date of onset prior to the date of Salmonella isolation and/or the date of receipt of the isolate at the NRC, 3) delay 1 and/or delay 2 included in one of the quartiles of their estimated distribution, and 4) the same serotype. Although data on the date of isolation or the date of receipt for Salmonella were not all complete, the matches could be identified with either a single delay or both. Since a single notification could match with several others, we considered as a true match the one with the shortest delay 1 and/or delay 2. Matches between the three sources were identified by comparing matches between the NPHN and the MA, the NPHN and the NRC, and the MA and the NRC.
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To take into account the dependencies between the sources and potential variable catchability, we estimated the number of outbreaks by log-linear modeling (22, 23
). We used a stepwise procedure with the BMDP 4F program (BMDP Statistical Software, Inc., Los Angeles, California). The choice of the final model was based on the likelihood ratio statistic (G2), the Akaike Information Criterion (AIC), and the Bayesian Information Criterion (BIC), which are functions of the likelihood ratio statistic, with AIC = G2 - 2 df and BIC = G2 - (log N/2
) df, where df is degrees of freedom and N is the number of foodborne outbreaks observed (24
). The optimal model was the model with the lowest AIC and BIC values. The weighted average of the BIC ("weighted BIC") of all estimates provided by each model was calculated as suggested by Drapper (25
).
We also stratified the analysis by serotype (S. enteritidis, S. typhimurium, and other serotypes) and type of outbreak (family or community). The total variance was calculated by adding the variance of each stratum. The 95 percent confidence interval was calculated using the method suggested by Hook and Regal (24). The sensitivity of each source is the number of foodborne outbreaks reported to the source divided by the total number estimated by the final log-linear model. Sensitivity was also calculated for each stratum of serotype and type of outbreak. The representativeness was assessed by comparing, by stratification variables, the distribution of each source to the distribution estimated by log-linear modeling using the goodness-of-fit
2 test (26
).
Qualitative assessment of the dependency between the NPHN and the MA
In addition to the log-linear method, we evaluated the dependency between the two mandatory notification systems through a survey of a random sample of 22 districts, for both public health (DDASS) and veterinary (DSV) departments. In each district selected, officers of the DDASS and DSV were interviewed independently by telephone, using a standardized questionnaire, on their handling of foodborne outbreak notifications and their collaboration.
Positive predictive value of the definition used by the NRC
Because notifications to the NPHN and the MA are validated by a systematic procedure, we assumed that their positive predictive value was 100 percent. For the NRC, information about the index meal is not always complete and validated. Therefore, we estimated the positive predictive value of a prospective sample of foodborne Salmonella outbreaks reported to the NRC between February 1 and May 31, 1997. We contacted laboratories of isolation and case physicians by telephone to collect further information on the reported outbreaks. An outbreak was considered truly foodborne if it fulfilled the case definition used for mandatory notification. The positive predictive value obtained was used to correct the estimate of the total number of foodborne Salmonella outbreaks estimated by the capture-recapture analysis, as suggested by LaPorte et al. (27).
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RESULTS |
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The median delay 1 for matched outbreaks was 3.5 days (range, 019 days) between the NPHN and the NRC and 3 days (range, 118 days) between the MA and the NRC. Delay 2 was similar (10 days) for matches between the NPHN and the NRC (range, 237 days) and matches between the MA and the NRC (range, 233 days).
In the two-source capture-recapture analysis, the estimate of the total number of foodborne Salmonella outbreaks for the NPHN-MA pair was 3.5 times smaller than the estimates for NPHN-NRC and MA-NRC (table 2). This indicates a strong dependence between the NPHN and the MA, with, among the outbreaks notified to the NRC, an odds ratio between NPHN and MA data of 12.2 (95 percent confidence interval (CI): 5.7, 26.2). The survey of the DDASS and DSV confirmed the strong positive dependency between the two mandatory notification systems: Of the 22 districts surveyed, 10 DDASS and DSV districts indicated that very good collaboration was in place for all types of outbreaks (family or community), 10 had established good collaboration for community outbreaks only, and in two districts the DDASS and the DSV did not collaborate.
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After correction with the positive predictive value of the NRC, we estimated that 757 (95 percent CI: 651, 863) foodborne Salmonella outbreaks had occurred in France in 1995 (table 4). The sensitivity of the three surveillance systems was estimated at 15 percent for the NPHN, 10 percent for the MA, and 50 percent for the NRC. S. enteritidis outbreaks were reported more often to the NPHN (19 percent) than outbreaks of S. typhimurium (8 percent) and other serotypes (11 percent) (table 4). There were almost three times fewer reports of family outbreaks (5 percent) reported to the MA than community outbreaks (13 percent). Compared with the estimates obtained by the capture-recapture analysis (table 5), there was a statistically significant difference in the serotype distribution for the NPHN (p = 0.001), with an overrepresentation of S. enteritidis (71 percent vs. 54 percent), and in the types of outbreaks for the MA (p = 2 x 10-5), with many fewer family outbreaks reported (52 percent vs. 74 percent).
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DISCUSSION |
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To use the capture-recapture method accurately, several conditions must be met (24). The definition of the event studied should be the same for each source (29
, 30
), a condition that was not met for the NRC. The lower specificity of the definition of the NRC would have induced false positive notifications and therefore an overestimate of outbreaks. This was corrected with the positive predictive value of the case definition of the NRC, as suggested by LaPorte et al. (27
). Since the notifications to the two mandatory notification sources were routinely validated, we assumed that their positive predictive values were 100 percent.
Since none of the three notification systems shared a unique identifier, we identified matches according to the postal code and date of onset for one pair of sources (NPHN-MA) and according to the postal code and delays between date of onset, date of Salmonella isolation, or receipt by the NRC for the other two pairs of sources (NPHN-NRC and MA-NRC). This matching algorithm, which we did not validate, might have induced either false negative or false positive matches and therefore an under- or overestimated number of outbreaks, particularly for those between the sources NPHN-NRC and MA-NRC. However, it is possible that the opposite effects of false positive matches and false negative matches cancelled each other out (31). In addition, the distribution of delay 1 and delay 2 for matched outbreaks between these two pairs of sources was homogeneous (data not shown). Because the NPHN and the MA sources have more common criteria, the specificity and sensitivity of the matching procedure between these two sources was probably less problematic. Furthermore, as Egeland et al. did in another study (32
), we were able to validate all identified matches by comparing the reports of the two sources. This method allowed us to minimize the number of false positives, but it did not rule the false negatives out.
The qualitative investigation confirmed the positive dependency found statistically between the NPHN and the MA in the capture-recapture analysis. Such studies are recommended for assessment of the dependency between two sources when this dependency cannot be assessed using statistical analysis (33). Furthermore, it was useful to understand the mechanisms of the dependency. Since the respective and complementary roles of the NPHN and the MA are both defined by the same regulatory act, we expected to find a strong positive dependency between both sources.
To take into account the dependencies between the three sources and the variable catchability in the different sources, we performed a log-linear analysis. To select the final model, we did not use the "principle of parsimony" (24), which would have ignored the interactions between the NPHN and the serotype and between the MA and the type of foodborne outbreak. The model that included these two interaction terms had the best BIC and AIC criteria and gave the same estimate as the one without these two interaction terms. In addition, the "weighted BIC" proposed by Hook and Regal (24
, 34
) gave the same estimate. A two-source analysis between the NRC and the NPHN-MA merged together gave an estimate similar to that of the final log-linear model. This simple approach developed by Wittes and colleagues (20
, 21
) could be used in future analyses. However, the log-linear model remains relevant for analysis of the dependencies between sources and the interactions with covariables, when at least three sources are considered (24
).
Serotype and type of outbreak (community and family) introduced variable catchability within the NPHN and MA data, respectively. S. enteritidis was better reported to the NPHN than S. typhimurium or other serotypes, and community outbreaks were more often reported to the MA than family outbreaks. This observation may be explained by more systematic requesting of stool cultures by district health officers when the assumed index meal contains eggs or egg products. In the same way, the DSV probably pays less attention to family outbreaks than to community outbreaks. Analysis of the variable catchability by region would be of interest; however, this was not possible because of the low number of outbreaks in some regions. Moreover, it was not possible to stratify on the size of the outbreaks. Although the data were available in each source, information on this variable was not considered valid, because there were important variations in numbers of patients for the same foodborne outbreak reported to at least two sources (laboratory-confirmed cases versus clinical cases, for example).
Our study estimated the number of outbreaks in which at least one patient had a positive Salmonella stool culture. Therefore, it still underestimated the true number of foodborne Salmonella outbreaks, because it did not take into account outbreaks in which patients did not visit a clinical practitioner or did not have a stool culture, or those for which a common food exposure was not identified (35). This estimation does not include the number of sporadic cases. In France, it is estimated that 46 percent of patients who consult a general practitioner for acute gastroenteritis have a stool culture, and Salmonella is the most frequently isolated agent (36
, 37
). Using the algorithm proposed by Chalker and Blaser (38
), which adjusts for the problems mentioned above, the true estimate is probably in excess of 2,000 foodborne Salmonella outbreaks yearly.
Assessment of the actual number of foodborne Salmonella outbreaks is a necessary preliminary step for assessing its public health burden. Whatever the design used, studies of the burden of foodborne Salmonella infection, including estimation of the number of sporadic cases and the economic impact, are needed to foster prevention and control efforts in food production, distribution, and consumption (39). Analyses conducted according to characteristics such as the type of outbreak are very helpful for adjusting the targeting of prevention and control. Because routine surveillance underestimates the number of family outbreaks more than community outbreaks, less priority is given by public health and food safety authorities for the control of the former. Our quantitative approach, based on routine surveillance data, is a first step in the assessment of the true burden of this unresolved public health problem in France.
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ACKNOWLEDGMENTS |
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The authors acknowledge Dr. E. Delarocque-Astagneau (National Public Health Network), S. Haeghebaert (National Public Health Network), and F. Lequerrec (Ministry of Agriculture) for validating foodborne outbreaks. The authors also thank the district health and veterinary officers for investigating foodborne outbreaks and forwarding data to the National Public Health Network and the Ministry of Agriculture, respectively.
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NOTES |
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REFERENCES |
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