Suitability of bovine portion condemnations at provincially-inspected abattoirs in Ontario Canada for food animal syndromic surveillance

ABSTRACT: BACKGROUND: Abattoir condemnations may play an important role in a food animal syndromic surveillance system. Portion condemnation data may be particularly useful, as these data can provide more specific information on health outcomes than whole carcass condemnation data. Various seasonal, secular, disease, and non-disease factors have been previously identified to be associated with whole carcass condemnation rates in Ontario provincial abattoirs; and if ignored, may bias the results of quantitative disease surveillance methods. The objective of this study was to identify various seasonal, secular, and abattoir characteristic factors that may be associated with bovine portion condemnation rates and compare how these variables may differ from previously identified factors associated with bovine whole carcass condemnation rates. RESULTS: Data were collected from the Ontario Ministry of Agriculture, Food and Rural Affairs (OMAFRA) and the Ontario Cattlemen's Association regarding "parasitic liver" and pneumonic lung condemnation rates for different cattle classes, abattoir compliance ratings, and the monthly sales-yard price for commodity classes from 2001-2007. To control for clustering by abattoirs, multi-level Poisson modeling was used to investigate the association between the following variables and "parasitic liver" as well as pneumonic lung condemnation rates: year, season, annual abattoir audit rating, geographic region, annual abattoir operating time, annual total number of animals processed, animal class, and commodity sales price. CONCLUSIONS: In this study, "parasitic liver" condemnation rates were associated with year, season, animal class, audit rating, and region. Pneumonic lung condemnation rates were associated with year, season, animal class, region, audit rating, number of cattle processed per year, and number of weeks abattoirs processed cattle. Unlike previous models based on whole carcass condemnations, commodity price was not associated with partial condemnations in this study. The results identified material-specific predictor variables for condemnation rates. This is important for syndromic surveillance based on abattoir data and should be modeled and controlled for during quantitative surveillance analysis on a portion specific basis.     

Linking Human Health and Livestock Health: A “One-Health” Platform for Integrated Analysis of Human Health,Livestock Health,and Economic Welfare in Livestock Dependent Communities

Background

For most rural households in sub-Saharan Africa, healthy livestock play a key role in averting the burden associated with zoonotic diseases, and in meeting household nutritional and socio-economic needs. However, there is limited understanding of the complex nutritional, socio-economic, and zoonotic pathways that link livestock health to human health and welfare. Here we describe a platform for integrated human health, animal health and economic welfare analysis designed to address this challenge. We provide baseline epidemiological data on disease syndromes in humans and the animals they keep, and provide examples of relationships between human health, animal health and household socio-economic status.

Method

We designed a study to obtain syndromic disease data in animals along with economic and behavioral information for 1500 rural households in Western Kenya already participating in a human syndromic disease surveillance study. Data collection started in February 2013, and each household is visited bi-weekly and data on four human syndromes (fever, jaundice, diarrhea and respiratory illness) and nine animal syndromes (death, respiratory, reproductive, musculoskeletal, nervous, urogenital, digestive, udder disorders, and skin disorders in cattle, sheep, goats and chickens) are collected. Additionally, data from a comprehensive socio-economic survey is collected every 3 months in each of the study households.

Findings

Data from the first year of study showed 93% of the households owned at least one form of livestock (55%, 19%, 41% and 88% own cattle, sheep, goats and chickens respectively). Digestive disorders, mainly diarrhea episodes, were the most common syndromes observed in cattle, goats and sheep, accounting for 56% of all livestock syndromes, followed by respiratory illnesses (18%). In humans, respiratory illnesses accounted for 54% of all illnesses reported, followed by acute febrile illnesses (40%) and diarrhea illnesses (5%). While controlling for household size, the incidence of human illness increased 1.31-fold for every 10 cases of animal illness or death observed (95% CI 1.16–1.49). Access and utilization of animal source foods such as milk and eggs were positively associated with the number of cattle and chickens owned by the household. Additionally, health care seeking was correlated with household incomes and wealth, which were in turn correlated with livestock herd size.

Conclusion

This study platform provides a unique longitudinal dataset that allows for the determination and quantification of linkages between human and animal health, including the impact of healthy animals on human disease averted, malnutrition, household educational attainment, and income levels.     

Suitability and limitations of portion-specific abattoir data as part of an early warning system for emerging diseases of swine in Ontario

Background  

Abattoir data have the potential to provide information for geospatial disease surveillance applications, but the quality of the data and utility for detecting disease outbreaks is not well understood. The objectives of this study were to 1) identify non-disease factors that may bias these data for disease surveillance and 2) determine if major disease events that took place during the study period would be captured using multi-level modelling and scan statistics. We analyzed data collected at all provincially-inspected abattoirs in Ontario, Canada during 2001-2007. During these years there were outbreaks of porcine circovirus-associated disease (PCVAD), porcine reproductive and respiratory syndrome (PRRS) and swine influenza that produced widespread disease within the province. Negative binomial models with random intercepts for abattoir, to account for repeated measurements within abattoirs, were created. The relationships between partial carcass condemnation rates for pneumonia and nephritis with year, season, agricultural region, stock price, and abattoir processing capacity were explored. The utility of the spatial scan statistic for detecting clusters of high partial carcass condemnation rates in space, time, and space-time was investigated.     

Disparities in Beef Tapeworm Identification Rates in the Abattoirs of Gauteng Province,South Africa: A Descriptive Epidemiologic Study

Background

Bovine Taenia saginata cysticercus infections (also called bovine cysticercosis or beef measles) is usually diagnosed in cattle only during post-mortem meat inspection. The aim of this study was to investigate the identification rates of these infections in and to identify predictors/determinants of variations in the identification rates in abattoirs in Gauteng province, South Africa.

Methods

Retrospective data for over 1.4 million cattle carcasses inspected in 26 abattoirs between January 2010 and December 2013 were used for the study. The identification rates (proportion of bovine Taenia saginata cysticercus positive carcasses) were computed and generalized estimating equations used to identify predictors/determinants of identification rates.

Results

The overall identification rate was 0.70% (95% CI: 0.45, 0.95). Significantly (p< 0.05) lower rates were reported during summer (0.55%) than other seasons. Some geographic areas reported significantly (p<0.05) higher rates than others. The identification rates in high throughput abattoirs was significantly (p<0.05) higher (RR: 9.4; 95% CI: 4.7–19.1) than in low throughput abattoirs. Similarly, the identification rates among animals from feedlots were significantly (p<0.05) higher (RR: 1.6; 95% CI: 1.7–3.5) than those from non-feedlot sources. No significant (p>0.05) association was identified between identification rates and either the number of meat inspectors per abattoir or the provider of inspection services.

Conclusion

Although no significant association was found between identification rates and provider of inspection services, follow-up studies will need to be done to specifically investigate the potential conflict of interest arising from the fact that abattoir owners hire meat inspection services directly. Capture of abattoir surveillance data needs to include farm address and for each case to be reported separately. Finally, information on the type of identified cysts (alive or calcified) need to be collected to help better estimate risk to consumers. This study provides useful baseline data to guide future studies, surveillance and control efforts.     

ISS-An Electronic Syndromic Surveillance System for Infectious Disease in Rural China

Background

syndromic surveillance system has great advantages in promoting the early detection of epidemics and reducing the necessities of disease confirmation, and it is especially effective for surveillance in resource poor settings. However, most current syndromic surveillance systems are established in developed countries, and there are very few reports on the development of an electronic syndromic surveillance system in resource-constrained settings.

Objective

this study describes the design and pilot implementation of an electronic surveillance system (ISS) for the early detection of infectious disease epidemics in rural China, complementing the conventional case report surveillance system.

Methods

ISS was developed based on an existing platform ‘Crisis Information Sharing Platform’ (CRISP), combining with modern communication and GIS technology. ISS has four interconnected functions: 1) work group and communication group; 2) data source and collection; 3) data visualization; and 4) outbreak detection and alerting.

Results

As of Jan. 31^st 2012, ISS has been installed and pilot tested for six months in four counties in rural China. 95 health facilities, 14 pharmacies and 24 primary schools participated in the pilot study, entering respectively 74256, 79701, and 2330 daily records into the central database. More than 90% of surveillance units at the study sites are able to send daily information into the system. In the paper, we also presented the pilot data from health facilities in the two counties, which showed the ISS system had the potential to identify the change of disease patterns at the community level.

Conclusions

The ISS platform may facilitate the early detection of infectious disease epidemic as it provides near real-time syndromic data collection, interactive visualization, and automated aberration detection. However, several constraints and challenges were encountered during the pilot implementation of ISS in rural China.     

Use of spatiotemporal analysis of laboratory submission data to identify potential outbreaks of new or emerging diseases in cattle in Great Britain

Background  

New and emerging diseases of livestock may impact animal welfare, trade and public health. Early detection of outbreaks can reduce the impact of these diseases by triggering control measures that limit the number of cases that occur. The aim of this study was to investigate whether prospective spatiotemporal methods could be used to identify outbreaks of new and emerging diseases in scanning surveillance data. SaTScan was used to identify clusters of unusually high levels of submissions where a diagnosis could not be reached (DNR) using different probability models and baselines. The clusters detected were subjected to a further selection process to reduce the number of false positives and a more detailed epidemiological analysis to ascertain whether they were likely to represent real outbreaks.     

Evaluation of Outbreak Detection Performance Using Multi-Stream Syndromic Surveillance for Influenza-Like Illness in Rural Hubei Province,China: A Temporal Simulation Model Based on Healthcare-Seeking Behaviors

Background

Syndromic surveillance promotes the early detection of diseases outbreaks. Although syndromic surveillance has increased in developing countries, performance on outbreak detection, particularly in cases of multi-stream surveillance, has scarcely been evaluated in rural areas.

Objective

This study introduces a temporal simulation model based on healthcare-seeking behaviors to evaluate the performance of multi-stream syndromic surveillance for influenza-like illness.

Methods

Data were obtained in six towns of rural Hubei Province, China, from April 2012 to June 2013. A Susceptible-Exposed-Infectious-Recovered model generated 27 scenarios of simulated influenza A (H1N1) outbreaks, which were converted into corresponding simulated syndromic datasets through the healthcare-behaviors model. We then superimposed converted syndromic datasets onto the baselines obtained to create the testing datasets. Outbreak performance of single-stream surveillance of clinic visit, frequency of over the counter drug purchases, school absenteeism, and multi-stream surveillance of their combinations were evaluated using receiver operating characteristic curves and activity monitoring operation curves.

Results

In the six towns examined, clinic visit surveillance and school absenteeism surveillance exhibited superior performances of outbreak detection than over the counter drug purchase frequency surveillance; the performance of multi-stream surveillance was preferable to signal-stream surveillance, particularly at low specificity (Sp <90%).

Conclusions

The temporal simulation model based on healthcare-seeking behaviors offers an accessible method for evaluating the performance of multi-stream surveillance.     

Biosecurity aspects of cattle production in Western Uganda,and associations with seroprevalence of brucellosis,salmonellosis and bovine viral diarrhoea

Background

Many low-income countries have a human population with a high number of cattle owners depending on their livestock for food and income. Infectious diseases threaten the health and production of cattle, affecting both the farmers and their families as well as other actors in often informal value chains. Many infectious diseases can be prevented by good biosecurity. The objectives of this study were to describe herd management and biosecurity routines with potential impact on the prevalence of infectious diseases, and to estimate the burden of infectious diseases in Ugandan cattle herds, using the seroprevalence of three model infections.

Results

Farmer interviews (n?=?144) showed that biosecurity measures are rarely practised. Visitors’ hand-wash was used by 14%, cleaning of boots or feet by 4 and 79% put new cattle directly into the herd. During the 12 months preceding the interviews, 51% of farmers had cattle that died and 31% had noticed abortions among their cows. Interestingly, 72% were satisfied with the health status of their cattle during the same time period. The prevalence (95% CI) of farms with at least one seropositive animal was 16.7% (11.0;23.8), 23.6% (16.9;31.4), and 53.4% (45.0;61.8) for brucella, salmonella and BVD, respectively.A poisson regression model suggested that having employees looking after the cattle, sharing pasture with other herds, and a higher number of dead cattle were associated with a herd being positive to an increasing number of the diseases. An additive bayesian network model with biosecurity variables and a variable for the number of diseases the herd was positive to resulted in three separate directed acyclic graphs which illustrate how herd characteristics can be grouped together. This model associated the smallest herd size with herds positive to a decreasing number of diseases and having fewer employees.

Conclusion

There is potential for improvement of biosecurity practices in Ugandan cattle production. Salmonella, brucella and BVD were prevalent in cattle herds in the study area and these infections are, to some extent, associated with farm management practices.

     

Efficacy of European starling control to reduce <Emphasis Type="Italic">Salmonella enterica</Emphasis> contamination in a concentrated animal feeding operation in the Texas panhandle

Background  

European starlings (Sturnus vulgaris) are an invasive bird species known to cause damage to plant and animal agriculture. New evidence suggests starlings may also contribute to the maintenance and spread of diseases within livestock facilities. Identifying and mitigating the risk pathways that contribute to disease in livestock is necessary to reduce production losses and contamination of human food products. To better understand the impact starlings have on disease transmission to cattle we assessed the efficacy of starling control as a tool to reduce Salmonella enterica within a concentrated animal feeding operation. We matched a large facility, slated for operational control using DRC-1339 (3-chloro-4-methylaniline hydrochloride, also 3-chloro p-toluidine hydrochloride, 3-chloro-4-methylaniline), with a comparable reference facility that was not controlling birds. In both facilities, we sampled cattle feed, cattle water and cattle feces for S. enterica before and after starling control operations.     

Devising an Indicator to Detect Mid-Term Abortions in Dairy Cattle: A First Step Towards Syndromic Surveillance of Abortive Diseases

Bovine abortion surveillance is essential for human and animal health because it plays an important role in the early warning of several diseases. Due to the limited sensitivity of traditional surveillance systems, there is a growing interest for the development of syndromic surveillance. Our objective was to assess whether, routinely collected, artificial insemination (AI) data could be used, as part of a syndromic surveillance system, to devise an indicator of mid-term abortions in dairy cattle herds in France. A mid-term abortion incidence rate (MAIR) was computed as the ratio of the number of mid-term abortions to the number of female-weeks at risk. A mid-term abortion was defined as a return-to-service (i.e. a new AI) taking place 90 to 180 days after the previous AI. Weekly variations in the MAIR in heifers and parous cows were modeled with a time-dependent Poisson model at the département level (French administrative division) during the period of 2004 to 2010. The usefulness of monitoring this indicator to detect a disease-related increase in mid-term abortions was evaluated using data from the 2007–2008 episode of bluetongue serotype 8 (BT8) in France. An increase in the MAIR was identified in heifers and parous cows in 47% (n = 24) and 71% (n = 39) of the départements. On average, the weekly MAIR among heifers increased by 3.8% (min-max: 0.02–57.9%) when the mean number of BT8 cases that occurred in the previous 8 to 13 weeks increased by one. The weekly MAIR among parous cows increased by 1.4% (0.01–8.5%) when the mean number of BT8 cases occurring in the previous 6 to 12 weeks increased by one. These results underline the potential of the MAIR to identify an increase in mid-term abortions and suggest that it is a good candidate for the implementation of a syndromic surveillance system for bovine abortions.     

Herd and cow characteristics affecting the odds of veterinary treatment for disease – a multilevel analysis

Background  

Research has indicated that a number of different factors affect whether an animal receives treatment or not when diseased. The aim of this paper was to evaluate if herd or individual animal characteristics influence whether cattle receives veterinary treatment for disease, and thereby also introduce misclassification in the disease recording system.     

Potential Use of School Absenteeism Record for Disease Surveillance in Developing Countries,Case Study in Rural Cambodia

Background

Disease surveillance allows prospective monitoring of patterns in disease incidence in the general community, specific institutions (e.g. hospitals, elderly care homes), and other important population subgroups. Surveillance activities are now routinely conducted in many developed countries and in certain easy-to-reach areas of the developing ones. However due to limited health resources, population in rural area that consisted of the most the vulnerable groups are not under surveillance. Cheaper alternative ways for disease surveillance were needed in resource-limited settings.

Methods and Findings

In this study, a syndromic surveillance system using disease specific absenteeism rates was established in 47 pre-schools with 1,417 students 3–6 y of age in a rural area of Kampot province, Cambodia. School absenteeism data were collected via short message service. Data collected between 1st January and 31st December 2012 was used for system evaluation for future potential use in larger scale. The system appeared to be feasible and acceptable in the rural study setting. Moderate correlation was found between rates of school absenteeism due to illness and the reference data on rates of attendance at health centers in persons <16 y (maximum cross-correlation coefficient = 0.231 at lag = −1 week).

Conclusions

School absenteeism data is pre-existing, easily accessible and requires minimum time and resources after initial development, and our results suggest that this system may be able to provide complementary data for disease surveillance, especially in resource limited settings where there is very little information on illnesses in the community and traditional surveillance systems are difficult to implement. An important next step is to validate the syndromic data with other forms of surveillance including laboratory data.     

Assessing syndromic surveillance of cardiovascular outcomes from emergency department chief complaint data in New York City

Background

Prospective syndromic surveillance of emergency department visits has been used for near-real time tracking of communicable diseases to detect outbreaks or other unexpected disease clusters. The utility of syndromic surveillance for tracking cardiovascular events, which may be influenced by environmental factors and influenza, has not been evaluated. We developed and evaluated a method for tracking cardiovascular events using emergency department free-text chief complaints.

Methodology/Principal Findings

There were three phases to our analysis. First we applied text processing algorithms based on sensitivity, specificity, and positive predictive value to chief complaint data reported by 11 New York City emergency departments for which ICD-9 discharge diagnosis codes were available. Second, the same algorithms were applied to data reported by a larger sample of 50 New York City emergency departments for which discharge diagnosis was unavailable. From this more complete data, we evaluated the consistency of temporal variation of cardiovascular syndromic events and hospitalizations from 76 New York City hospitals. Finally, we examined associations between particulate matter ≤2.5 µm (PM_2.5), syndromic events, and hospitalizations. Sensitivity and positive predictive value were low for syndromic events, while specificity was high. Utilizing the larger sample of emergency departments, a strong day of week pattern and weak seasonal trend were observed for syndromic events and hospitalizations. These time-series were highly correlated after removing the day-of-week, holiday, and seasonal trends. The estimated percent excess risks in the cold season (October to March) were 1.9% (95% confidence interval (CI): 0.6, 3.2), 2.1% (95% CI: 0.9, 3.3), and 1.8% (95%CI: 0.5, 3.0) per same-day 10 µg/m³ increase in PM_2.5 for cardiac-only syndromic data, cardiovascular syndromic data, and hospitalizations, respectively.

Conclusions/Significance

Near real-time emergency department chief complaint data may be useful for timely surveillance of cardiovascular morbidity related to ambient air pollution and other environmental events.     

Exploratory Analysis of Methods for Automated Classification of Laboratory Test Orders into Syndromic Groups in Veterinary Medicine

Background

Recent focus on earlier detection of pathogen introduction in human and animal populations has led to the development of surveillance systems based on automated monitoring of health data. Real- or near real-time monitoring of pre-diagnostic data requires automated classification of records into syndromes–syndromic surveillance–using algorithms that incorporate medical knowledge in a reliable and efficient way, while remaining comprehensible to end users.

Methods

This paper describes the application of two of machine learning (Naïve Bayes and Decision Trees) and rule-based methods to extract syndromic information from laboratory test requests submitted to a veterinary diagnostic laboratory.

Results

High performance (F₁-macro = 0.9995) was achieved through the use of a rule-based syndrome classifier, based on rule induction followed by manual modification during the construction phase, which also resulted in clear interpretability of the resulting classification process. An unmodified rule induction algorithm achieved an F_1-micro score of 0.979 though this fell to 0.677 when performance for individual classes was averaged in an unweighted manner (F_1-macro), due to the fact that the algorithm failed to learn 3 of the 16 classes from the training set. Decision Trees showed equal interpretability to the rule-based approaches, but achieved an F_1-micro score of 0.923 (falling to 0.311 when classes are given equal weight). A Naïve Bayes classifier learned all classes and achieved high performance (F_1-micro = 0.994 and F_1-macro = .955), however the classification process is not transparent to the domain experts.

Conclusion

The use of a manually customised rule set allowed for the development of a system for classification of laboratory tests into syndromic groups with very high performance, and high interpretability by the domain experts. Further research is required to develop internal validation rules in order to establish automated methods to update model rules without user input.     

Potential Risk of Regional Disease Spread in West Africa through Cross-Border Cattle Trade

Background

Transboundary animal movements facilitate the spread of pathogens across large distances. Cross-border cattle trade is of economic and cultural importance in West Africa. This study explores the potential disease risk resulting from large-scale, cross-border cattle trade between Togo, Burkina Faso, Ghana, Benin, and Nigeria for the first time.

Methods and Principal Findings

A questionnaire-based survey of livestock movements of 226 cattle traders was conducted in the 9 biggest cattle markets of northern Togo in February-March 2012. More than half of the traders (53.5%) operated in at least one other country. Animal flows were stochastically simulated based on reported movements and the risk of regional disease spread assessed. More than three quarters (79.2%, range: 78.1–80.0%) of cattle flowing into the market system originated from other countries. Through the cattle market system of northern Togo, non-neighbouring countries were connected via potential routes for disease spread. Even for diseases with low transmissibility and low prevalence in a given country, there was a high risk of disease introduction into other countries.

Conclusions

By stochastically simulating data collected by interviewing cattle traders in northern Togo, this study identifies potential risks for regional disease spread in West Africa through cross-border cattle trade. The findings highlight that surveillance for emerging infectious diseases as well as control activities targeting endemic diseases in West Africa are likely to be ineffective if only conducted at a national level. A regional approach to disease surveillance, prevention and control is essential.     

First-Time Detection of Mycobacterium bovis in Livestock Tissues and Milk in the West Bank,Palestinian Territories

Background

Bovine tuberculosis, bTB, is classified by the WHO as one of the seven neglected zoonontic diseases that cause animal health problems and has high potential to infect humans. In the West Bank, bTB was not studied among animals and the prevalence of human tuberculosis caused by M. bovis is unknown. Therefore, the aim of this study was to estimate the prevalence of bTB among cattle and goats and identify the molecular characteristics of bTB in our area.

Methodology/principal findings

A total of 208 tissue samples, representing 104 animals, and 150 raw milk samples, obtained from cows and goats were examined for the presence of mycobacteria. The tissue samples were collected during routine meat inspection from the Jericho abattoir. DNA was extracted from all samples, milk and tissue biopsies (n = 358), and screened for presence of TB DNA by amplifying a 123-bp segment of the insertion sequence IS6110. Eight out of 254 animals (3.1%) were found to be TB positive based on the IS6110-PCR. Identification of M. bovis among the positive TB samples was carried out via real time PCR followed by high resolution melt curve analysis, targeting the A/G transition along the oxyR gene. Spoligotyping analysis revealed a new genotype of M. bovis that was revealed from one tissue sample.

Significance

Detection of M. bovis in tissue and milk of livestock suggests that apparently healthy cattle and goats are a potential source of infection of bTB and may pose a risk to public health. Hence, appropriate measures including meat inspection at abattoirs in the region are required together with promotion of a health campaign emphasizing the importance of drinking pasteurized milk. In addition, further studies are essential at the farm level to determine the exact prevalence of bTB in goats and cattle herds in the West Bank and Israel.     

Enhanced understanding of infectious diseases by fusing multiple datasets: a case study on malaria in the Western Brazilian Amazon region

Background

A common challenge to the study of several infectious diseases consists in combining limited cross-sectional survey data, collected with a more sensitive detection method, with a more extensive (but biased) syndromic sentinel surveillance data, collected with a less sensitive method. Our article describes a novel modeling framework that overcomes this challenge, resulting in enhanced understanding of malaria in the Western Brazilian Amazon.

Methodology/Principal Findings

A cohort of 486 individuals was monitored using four cross-sectional surveys, where all participants were sampled regardless of symptoms (aggressive-active case detection), resulting in 1,383 microscopy and 1,400 polymerase chain reaction tests. Data on the same individuals were also obtained from the local surveillance facility (i.e., passive and active case detection), totaling 1,694 microscopy tests. Our model accommodates these multiple pathogen and case detection methods. This model is shown to outperform logistic regression in terms of interpretability of its parameters, ability to recover the true parameter values, and predictive performance. We reveal that the main infection determinant was the extent of forest, particularly during the rainy season and in close proximity to water bodies, and participation on forest activities. We find that time residing in Acrelandia (as a proxy for past malaria exposure) decreases infection risk but surprisingly increases the likelihood of reporting symptoms once infected, possibly because non-naïve settlers are only susceptible to more virulent Plasmodium strains. We suggest that the search for asymptomatic carriers should focus on those at greater risk of being infected but lower risk of reporting symptoms once infected.

Conclusions/Significance

The modeling framework presented here combines cross-sectional survey data and syndromic sentinel surveillance data to shed light on several aspects of malaria that are critical for public health policy. This framework can be adapted to enhance inference on infectious diseases whenever asymptomatic carriers are important and multiple datasets are available.     

Syndromic Surveillance for Local Outbreaks of Lower-Respiratory Infections: Would It Work?

Background

Although syndromic surveillance is increasingly used to detect unusual illness, there is a debate whether it is useful for detecting local outbreaks. We evaluated whether syndromic surveillance detects local outbreaks of lower-respiratory infections (LRIs) without swamping true signals by false alarms.

Methods and Findings

Using retrospective hospitalization data, we simulated prospective surveillance for LRI-elevations. Between 1999–2006, a total of 290762 LRIs were included by date of hospitalization and patients place of residence (>80% coverage, 16 million population). Two large outbreaks of Legionnaires disease in the Netherlands were used as positive controls to test whether these outbreaks could have been detected as local LRI elevations. We used a space-time permutation scan statistic to detect LRI clusters. We evaluated how many LRI-clusters were detected in 1999–2006 and assessed likely causes for the cluster-signals by looking for significantly higher proportions of specific hospital discharge diagnoses (e.g. Legionnaires disease) and overlap with regional influenza elevations. We also evaluated whether the number of space-time signals can be reduced by restricting the scan statistic in space or time. In 1999–2006 the scan-statistic detected 35 local LRI clusters, representing on average 5 clusters per year. The known Legionnaires'' disease outbreaks in 1999 and 2006 were detected as LRI-clusters, since cluster-signals were generated with an increased proportion of Legionnaires disease patients (p:<0.0001). 21 other clusters coincided with local influenza and/or respiratory syncytial virus activity, and 1 cluster appeared to be a data artifact. For 11 clusters no likely cause was defined, some possibly representing as yet undetected LRI-outbreaks. With restrictions on time and spatial windows the scan statistic still detected the Legionnaires'' disease outbreaks, without loss of timeliness and with less signals generated in time (up to 42% decline).

Conclusions

To our knowledge this is the first study that systematically evaluates the performance of space-time syndromic surveillance with nationwide high coverage data over a longer period. The results show that syndromic surveillance can detect local LRI-outbreaks in a timely manner, independent of laboratory-based outbreak detection. Furthermore, since comparatively few new clusters per year were observed that would prompt investigation, syndromic hospital-surveillance could be a valuable tool for detection of local LRI-outbreaks.     

Assessment of a Syndromic Surveillance System Based on Morbidity Data: Results from the Oscour? Network during a Heat Wave

Background

Syndromic surveillance systems have been developed in recent years and are now increasingly used by stakeholders to quickly answer questions and make important decisions. It is therefore essential to evaluate the quality and utility of such systems. This study was designed to assess a syndromic surveillance system based on emergency departments'' (ED) morbidity rates related to the health effects of heat waves. This study uses data collected during the 2006 heat wave in France.

Methods

Data recorded from 15 EDs in the Ile-de-France (Paris and surrounding area) from June to August, 2006, were transmitted daily via the Internet to the French Institute for Public Health Surveillance. Items collected included diagnosis (ICD10), outcome, and age. Several aspects of the system have been evaluated (data quality, cost, flexibility, stability, and performance). Periods of heat wave are considered the most suitable time to evaluate the system.

Results

Data quality did not vary significantly during the period. Age, gender and outcome were completed in a comprehensive manner. Diagnoses were missing or uninformative for 37.5% of patients. Stability was recorded as being 99.49% for the period overall. The average cost per day over the study period was estimated to be €287. Diagnoses of hyperthermia, malaise, dehydration, hyponatremia were correlated with increased temperatures. Malaise was most sensitive in younger and elderly adults but also the less specific. However, overall syndrome groups were more sensitive with comparable specificity than individual diagnoses.

Conclusion

This system satisfactorily detected the health impact of hot days (observed values were higher than expected on more than 90% of days on which a heat alert was issued). Our findings should reassure stakeholders about the reliability of health impact assessments during or following such an event. These evaluations are essential to establish the validity of the results of syndromic surveillance systems.