Redefining syndromic surveillance

Development and Evaluation of Syndromic Surveillance Definitions for Fall- and Hip Fracture-Related Emergency Department Visits Among Adults Aged 65 Years and Older, United States 2017-2018

OBJECTIVE

To develop syndromic surveillance definitions for unintentional fall- and hip fracture-related emergency department (ED) visits among older adults (aged ≥65 years) for use in the Centers for Disease Control and Prevention's National Syndromic Surveillance Program (NSSP) data and compare the percentage of ED visits captured using these new syndromes with ED visits from the Healthcare Cost and Utilization Project Nationwide Emergency Department Sample (HCUP-NEDS), a nationally representative administrative data set.

DESIGN/SETTING

Syndromic definitions were developed using chief complaint terms and discharge diagnosis codes in NSSP data. The percentages of ED visits among older adults related to falls and hip fractures in NSSP were compared with the percentages in HCUP-NEDS in 2017 and 2018.

MEASURES

Prevalence ratios were calculated as the relative difference in the percentage of ED visits related to falls or hip fractures in NSSP compared with HCUP-NEDS. Counts and percentages calculated using HCUP-NEDS were weighted to produce nationally representative estimates. Data were analyzed overall and by sex and age group.

RESULTS

The percentage of ED visits among older adults related to falls in NSSP was 12% less in 2017 (10.81%) and 7% less in 2018 (11.42%) compared with HCUP-NEDS (2017: 12.30%; 2018: 12.26%). The percentage of ED visits among older adults related to hip fractures in NSSP was 41% less in 2017 (0.65%) and 30% less in 2018 (0.76%) compared with HCUP-NEDS (2017: 1.10%; 2018: 1.09%). In both 2017 and 2018, a higher percentage of ED visits among older women and adults aged 85 years or older were related to falls or hip fractures compared with older men and younger age groups across both data sets.

CONCLUSION

A smaller percentage of older adults' ED visits met the falls and hip fracture definitions in NSSP compared with HCUP-NEDS in 2017 and 2018. However, demographic trends remained similar across both data sets.

Suspicious symptom monitoring for leprosy: an optimal practice for early detection under a low endemic situation in Zhejiang Province, China

BACKGROUND

Leprosy is a chronic infectious disease that causes disabilities and deformities. Early detection is a major strategy for leprosy control. This study reported a new practice of suspicious symptom monitoring for early detection of leprosy.

METHODS

A descriptive and comparative analysis between a non-strategy group of pre-implementation of suspicious symptom monitoring in 2005-2011 and a strategy group of strategy implementation in 2012-2018 was conducted through indicators of the number of times of misdiagnoses, delayed period, proportion of early detected cases, and proportion of disabilities.

RESULT

Compared with the non-strategy group in 2005-2011, the median number of times of misdiagnoses was decreased from two times to zero times (z = 4.387, P < 0.001), and the median delayed period of newly detected cases were shortened from 24 months to 13 months (z = 2.381, P < 0.001), the proportion of early detected cases was increased from 43.7% to 75.2% (χ²  = 29.464, P < 0.001), the proportion of grade 2 disabilities was decreased from 28.6% in the highest year of 2005 to 4.0% in the lowest year of 2014, and the average proportion of disabilities was decreased from 33.5% to 17.6% (χ²  = 9.421, P = 0.002) in the strategy group in 2012-2018, respectively.

CONCLUSION

Suspicious symptom monitoring promoted early detection of cases by reducing the number of times misdiagnosis of leprosy patients, shortening the delayed period, increasing the proportion of early detection, and decreasing the proportion of disabilities. It is an important and recommendable public health strategy for leprosy prevention and control in a low epidemic condition.

Strategies to Address the Lack of Labeled Data for Supervised Machine Learning Training With Electronic Health Records: Case Study for the Extraction of Symptoms From Clinical Notes

BACKGROUND

Automated extraction of symptoms from clinical notes is a challenging task owing to the multidimensional nature of symptom description. The availability of labeled training data is extremely limited owing to the nature of the data containing protected health information. Natural language processing and machine learning to process clinical text for such a task have great potential. However, supervised machine learning requires a great amount of labeled data to train a model, which is at the origin of the main bottleneck in model development.

OBJECTIVE

The aim of this study is to address the lack of labeled data by proposing 2 alternatives to manual labeling for the generation of training labels for supervised machine learning with English clinical text. We aim to demonstrate that using lower-quality labels for training leads to good classification results.

METHODS

We addressed the lack of labels with 2 strategies. The first approach took advantage of the structured part of electronic health records and used diagnosis codes (International Classification of Disease-10th revision) to derive training labels. The second approach used weak supervision and data programming principles to derive training labels. We propose to apply the developed framework to the extraction of symptom information from outpatient visit progress notes of patients with cardiovascular diseases.

RESULTS

We used >500,000 notes for training our classification model with International Classification of Disease-10th revision codes as labels and >800,000 notes for training using labels derived from weak supervision. We show that the dependence between prevalence and recall becomes flat provided a sufficiently large training set is used (>500,000 documents). We further demonstrate that using weak labels for training rather than the electronic health record codes derived from the patient encounter leads to an overall improved recall score (10% improvement, on average). Finally, the external validation of our models shows excellent predictive performance and transferability, with an overall increase of 20% in the recall score.

CONCLUSIONS

This work demonstrates the power of using a weak labeling pipeline to annotate and extract symptom mentions in clinical text, with the prospects to facilitate symptom information integration for a downstream clinical task such as clinical decision support.

Enhancing the value of meat inspection records for broiler health and welfare surveillance: longitudinal detection of relational patterns

Background

Abattoir data are under-used for surveillance. Nationwide surveillance could benefit from using data on meat inspection findings, but several limitations need to be overcome. At the producer level, interpretation of meat inspection findings is a notable opportunity for surveillance with relevance to animal health and welfare. In this study, we propose that discovery and monitoring of relational patterns between condemnation conditions co-present in broiler batches at meat inspection can provide valuable information for surveillance of farmed animal health and welfare.

Results

Great Britain (GB)-based integrator meat inspection records for 14,045 broiler batches slaughtered in nine, four monthly intervals were assessed for the presence of surveillance indicators relevant to broiler health and welfare. K-means and correlation-based hierarchical clustering, and association rules analyses were performed to identify relational patterns in the data. Incidence of condemnation showed seasonal and temporal variation, which was detected by association rules analysis. Syndrome-related and non-specific relational patterns were detected in some months of meat inspection records. A potentially syndromic cluster was identified in May 2016 consisting of infection-related conditions: pericarditis, perihepatitis, peritonitis, and abnormal colour. Non-specific trends were identified in some months as an unusual combination of condemnation reasons in broiler batches.

Conclusions

We conclude that the detection of relational patterns in meat inspection records could provide producer-level surveillance indicators with relevance to broiler chicken health and welfare.

School-based surveillance of acute infectious disease in children: a systematic review

BACKGROUND

Syndromic surveillance systems are an essential component of public health surveillance and can provide timely detection of infectious disease cases and outbreaks. Whilst surveillance systems are generally embedded within healthcare, there is increasing interest in novel data sources for monitoring trends in illness, such as over-the-counter purchases, internet-based health searches and worker absenteeism. This systematic review considers the utility of school attendance registers in the surveillance of infectious disease outbreaks and occurrences amongst children.

METHODS

We searched eight databases using key words related to school absence, infectious disease and syndromic surveillance. Studies were limited to those published after 1st January 1995. Studies based in nursery schools or higher education settings were excluded. Article screening was undertaken by two independent reviewers using agreed eligibility criteria. Data extraction was performed using a standardised data extraction form. Outcomes included estimates of absenteeism, correlation with existing surveillance systems and associated lead or lag times.

RESULTS

Fifteen studies met the inclusion criteria, all of which were concerned with the surveillance of influenza. The specificity of absence data varied between all-cause absence, illness absence and syndrome-specific absence. Systems differed in terms of the frequency of data submissions from schools and the level of aggregation of the data. Baseline rates of illness absence varied between 2.3-3.7%, with peak absences ranging between 4.1-9.8%. Syndrome-specific absenteeism had the strongest correlation with other surveillance systems (r = 0.92), with illness absenteeism generating mixed results and all-cause absenteeism performing the least well. A similar pattern of results emerged in terms of lead and lag times, with influenza-like illness (ILI)-specific absence providing a 1-2 week lead time, compared to lag times reported for all-cause absence data and inconsistent results for illness absence data.

CONCLUSION

Syndrome-specific school absences have potential utility in the syndromic surveillance of influenza, demonstrating good correlation with healthcare surveillance data and a lead time of 1-2 weeks ahead of existing surveillance measures. Further research should consider the utility of school attendance registers for conditions other than influenza, to broaden our understanding of the potential application of this data for infectious disease surveillance in children.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO 2019 CRD42019119737.

An equine veterinary surveillance network for the UK horse population

April Lawson and Gina Pinchbeck of the University of Liverpool introduce a new initiative that will use electronic health records to create an evidence base for equine research and surveillance.

Estimating COVID-19 cases and outbreaks on-stream through phone calls

One of the main problems in controlling COVID-19 epidemic spread is the delay in confirming cases. Having information on changes in the epidemic evolution or outbreaks rise before laboratory-confirmation is crucial in decision making for Public Health policies. We present an algorithm to estimate on-stream the number of COVID-19 cases using the data from telephone calls to a COVID-line. By modelling the calls as background (proportional to population) plus signal (proportional to infected), we fit the calls in Province of Buenos Aires (Argentina) with coefficient of determination R ² > 0.85. This result allows us to estimate the number of cases given the number of calls from a specific district, days before the laboratory results are available. We validate the algorithm with real data. We show how to use the algorithm to track on-stream the epidemic, and present the Early Outbreak Alarm to detect outbreaks in advance of laboratory results. One key point in the developed algorithm is a detailed track of the uncertainties in the estimations, since the alarm uses the significance of the observables as a main indicator to detect an anomaly. We present the details of the explicit example in Villa Azul (Quilmes) where this tool resulted crucial to control an outbreak on time. The presented tools have been designed in urgency with the available data at the time of the development, and therefore have their limitations which we describe and discuss. We consider possible improvements on the tools, many of which are currently under development.

Improving the Utility of Voluntary Ovine Fallen Stock Collection and Laboratory Diagnostic Submission Data for Animal Health Surveillance Purposes: A Development Cycle

There are calls from policy-makers and industry to use existing data sources to contribute to livestock surveillance systems, especially for syndromic surveillance. However, the practical implications of attempting to use such data sources are challenging; development often requires incremental steps in an iterative cycle. In this study the utility of business operational data from a voluntary fallen stock collection service was investigated, to determine if they could be used as a proxy for the mortality experienced by the British sheep population. Retrospectively, Scottish ovine fallen stock collection data (2011-2014) were transformed into meaningful units for analysis, temporal and spatial patterns were described, time-series methods and a temporal aberration detection algorithm applied. Distinct annual and spatial trends plus seasonal patterns were observed in the three age groups investigated. The algorithm produced an alarm at the point of an historic known departure from normal (April 2013) for two age groups, across Scotland as a whole and in specific postcode areas. The analysis was then extended. Initially, to determine if similar methods could be applied to ovine fallen stock collections from England and Wales for the same time period. Additionally, Scottish contemporaneous laboratory diagnostic submission data were analyzed to see if they could provide further insight for interpretation of statistical alarms. Collaboration was required between the primary data holders, those with industry sector knowledge, plus veterinary, epidemiological and statistical expertise, in order to turn data and analytical outcomes into potentially useful information. A number of limitations were identified and recommendations were made as to how some could be addressed in order to facilitate use of these data as surveillance "intelligence." e.g., improvements to data collection and provision. A recent update of the fallen stock collections data has enabled a longer temporal period to be analyzed, with evidence of changes made in line with the recommendations. Further development will be required before a functional system can be implemented. However, there is potential for use of these data as: a proxy measure for mortality in the sheep population; complementary components in a future surveillance system, and to inform the design of additional surveillance system components.

Evaluation of Emergency Department-Based Surveillance Systems at 2 Healthcare Facilities After Hurricane Maria: Puerto Rico, 2017-2018

OBJECTIVES

Hurricane Maria caused catastrophic damage in Puerto Rico, increasing the risk for morbidity and mortality in the post-impact period. We aimed to establish a syndromic surveillance system to describe the number and type of visits at 2 emergency health-care settings in the same hospital system in Ponce, Puerto Rico.

METHODS

We implemented a hurricane surveillance system by interviewing patients with a short questionnaire about the reason for visit at a hospital emergency department and associated urgent care clinic in the 6 mo after Hurricane Maria. We then evaluated the system by comparing findings with data from the electronic medical record (EMR) system for the same time period.

RESULTS

The hurricane surveillance system captured information from 5116 participants across the 2 sites, representing 17% of all visits captured in the EMR for the same period. Most visits were associated with acute illness/symptoms (79%), followed by injury (11%). The hurricane surveillance and EMR data were similar, proportionally, by sex, age, and visit category.

CONCLUSIONS

The hurricane surveillance system provided timely and representative data about the number and type of visits at 2 sites. This system, or an adapted version using available electronic data, should be considered in future disaster settings.

Evaluation of the trends in the incidence of infectious diseases using the syndromic surveillance system, early warning and response unit, Mongolia, from 2009 to 2017: a retrospective descriptive multi-year analytical study

Background

In recent times, emerging and re-emerging infectious diseases are posing a public health threat in developing countries, and vigilant surveillance is necessary to prepare against these threats. Analyses of multi-year comprehensive infectious disease syndrome data are required in Mongolia, but have not been conducted till date. This study aimed to describe the trends in the incidence of infectious disease syndromes in Mongolia during 2009–2017 using a nationwide syndrome surveillance system for infectious diseases established in 2009.

Methods

We analyzed time trends using monthly data on the incidence of infectious disease syndromes such as acute fever with rash (AFR), acute fever with vesicular rash (AFVR), acute jaundice (AJ), acute watery diarrhea (AWD), acute bloody diarrhea (ABD), foodborne disease (FD) and nosocomial infection (NI) reported from January 1, 2009 to December 31, 2017. Time series forecasting models based on the data up to 2017 estimated the future trends in the incidence of syndromes up to December 2020.

Results

During the study, the overall prevalence of infectious disease syndromes was 71.8/10,000 population nationwide. The average number of reported infectious disease syndromes was 14,519 (5229-55,132) per year. The major types were AFR (38.7%), AFVR (31.7%), AJ (13.9%), ABD (10.2%), and AWD (1.8%), accounting for 96.4% of all reported syndromes. The most prevalent syndromes were AJ between 2009 and 2012 (59.5–48.7%), AFVR between 2013 and 2014 (54.5–59%), AFR between 2015 and 2016 (67.6–65.9%), and AFVR in 2017 (62.2%). There were increases in the prevalence of AFR, with the monthly number of cases being 37.7 ± 6.1 during 2015–2016; this could be related to the measles outbreak in Mongolia during that period. The AFVR incidence rate showed winter’s multiplicative seasonal fluctuations with a peak of 10.6 ± 2 cases per 10,000 population in 2017. AJ outbreaks were identified in 2010, 2011, and 2012, and these could be associated with hepatitis A outbreaks. Prospective time series forecasting showed increasing trends in the rates of AFVR and ABD.

Conclusions

The evidence-based method for infectious disease syndromes was useful in gaining an understanding of the current situation, and predicting the future trends of various infectious diseases in Mongolia.

Issues in Building a Nursing Home Syndromic Surveillance System with Textmining: Longitudinal Observational Study

Background

New nursing homes (NH) data warehouses fed from residents’ medical records allow monitoring the health of elderly population on a daily basis. Elsewhere, syndromic surveillance has already shown that professional data can be used for public health (PH) surveillance but not during a long-term follow-up of the same cohort.

Objective

This study aimed to build and assess a national ecological NH PH surveillance system (SS).

Methods

Using a national network of 126 NH, we built a residents’ cohort, extracted medical and personal data from their electronic health records, and transmitted them through the internet to a national server almost in real time. After recording sociodemographic, autonomic and syndromic information, a set of 26 syndromes was defined using pattern matching with the standard query language-LIKE operator and a Delphi-like technique, between November 2010 and June 2016. We used early aberration reporting system (EARS) and Bayes surveillance algorithms of the R surveillance package (Höhle) to assess our influenza and acute gastroenteritis (AGE) syndromic data against the Sentinelles network data, French epidemics gold standard, following Centers for Disease Control and Prevention surveillance system assessment guidelines.

Results

By extracting all sociodemographic residents’ data, a cohort of 41,061 senior citizens was built. EARS_C3 algorithm on NH influenza and AGE syndromic data gave sensitivities of 0.482 and 0.539 and specificities of 0.844 and 0.952, respectively, over a 6-year period, forecasting the last influenza outbreak by catching early flu signals. In addition, assessment of influenza and AGE syndromic data quality showed precisions of 0.98 and 0.96 during last season epidemic weeks’ peaks (weeks 03-2017 and 01-2017) and precisions of 0.95 and 0.92 during last summer epidemic weeks’ low (week 33-2016).

Conclusions

This study confirmed that using syndromic information gives a good opportunity to develop a genuine French national PH SS dedicated to senior citizens. Access to senior citizens’ free-text validated health data on influenza and AGE responds to a PH issue for the surveillance of this fragile population. This database will also make possible new ecological research on other subjects that will improve prevention, care, and rapid response when facing health threats.

The Impact of Law on Syndromic Disease Surveillance Implementation

CONTEXT

Legal environments influence how health information technologies are implemented in public health practice settings. Syndromic disease surveillance (SyS) is a relatively new approach to surveillance that depends heavily on health information technologies to achieve rapid awareness of disease trends. Evidence suggests that legal concerns have impeded the optimization of SyS.

OBJECTIVES

To (1) understand the legal environments in which SyS is implemented, (2) determine the perceived legal basis for SyS, and (3) identify perceived legal barriers and facilitators to SyS implementation.

DESIGN

Multisite case study in which 35 key informant interviews and 5 focus groups were conducted with 75 SyS stakeholders. Interviews and focus groups were audio recorded, transcribed, and analyzed by 3 coders using thematic content analysis. Legal documents were reviewed.

SETTING

Seven jurisdictions (5 states, 1 county, and 1 city) that were purposively selected on the basis of SyS capacity and legal environment.

PARTICIPANTS

Health department directors, SyS system administrators, legal counsel, and hospital personnel.

RESULTS

Federal (eg, HIPAA) and state (eg, notifiable disease reporting) laws that authorize traditional public health surveillance were perceived as providing a legal basis for SyS. Financial incentives for hospitals to satisfy Meaningful Use regulations have eased concerns about the legality of SyS and increased the number of hospitals reporting SyS data. Legal issues were perceived as barriers to BioSense 2.0 (the federal SyS program) participation but were surmountable.

CONCLUSION

Major legal reforms are not needed to promote more widespread use of SyS. The current legal environment is perceived by health department and hospital officials as providing a firm basis for SyS practice. This is a shift from how law was perceived when SyS adoption began and has policy implications because it indicates that major legal reforms are not needed to promote more widespread use of the technology. Beyond SyS, our study suggests that federal monetary incentives can ameliorate legal concerns regarding novel health information technologies.

Syndromic surveillance for West Nile virus using raptors in rehabilitation

Background

Wildlife rehabilitation centers routinely gather health-related data from diverse species. Their capability to signal the occurrence of emerging pathogens and improve traditional surveillance remains largely unexplored. This paper assessed the utility for syndromic surveillance of raptors admitted to The Raptor Center (TRC) to signal circulation of West Nile Virus (WNV) in Minnesota between 1990 and 2014. An exhaustive descriptive analysis using grouping time series structures and models of interrupted times series was conducted for indicator subsets.

Results

A total of 13,080 raptors were monitored. The most representative species were red-tailed hawks, great horned owls, Cooper’s hawks, American kestrels and bald eagles. Results indicated that temporal patterns of accessions at the TRC changed distinctively after the incursion of WNV in 2002. The frequency of hawks showing WNV-like signs increased almost 3 times during July and August, suggesting that monitoring of hawks admitted to TRC with WNV-like signs could serve as an indicator of WNV circulation. These findings were also supported by the results of laboratory diagnosis.

Conclusions

This study demonstrates that monitoring of data routinely collected by wildlife rehabilitation centers has the potential to signal the spread of pathogens that may affect wild, domestic animals and humans, thus supporting the early detection of disease incursions in a region and monitoring of disease trends. Ultimately, data collected in rehabilitation centers may provide insights to efficiently allocate financial and human resources on disease prevention and surveillance.

Evaluation of the ability of standardized supports to improve public health response to syndromic surveillance for respiratory diseases in Canada

Background

Despite widespread implementation of syndromic surveillance systems within public health agencies, previous studies of the implementation and use of these systems have indicated that the functions and responses taken in response to syndromic surveillance data vary widely according to local context and preferences. The objective of the Syndromic Surveillance Evaluation Study was to develop and implement standardized supports in local public health agencies in Ontario, Canada, and evaluate the ability of these supports to affect actions taken as part of public health communicable disease control programs.

Methods

Local public health agencies (LPHA) in Ontario, which used syndromic surveillance based on emergency department visits for respiratory disease, were recruited and randomly allocated to the study intervention or control group. The intervention group health agencies received standardized supports in terms of a standardized aberrant event detection algorithm and a response protocol dictating steps to investigate and assess the public health significance of syndromic surveillance alerts. The control group continued with their pre-existing syndromic surveillance infrastructure and processes. Outcomes were assessed using logbooks, which collected quantitative and qualitative information about alerts received, investigation steps taken, and public health responses. The study was conducted prospectively for 15 months (October 2013 to February 2015).

Results

Fifteen LPHAs participated in the study (n = 9 intervention group, n = 6 control group). A total of 1,969 syndromic surveillance alerts were received by all LPHAs. Variations in the types and amount of responses varied by LPHA, in particularly differences were noted by the size of the health unit. Smaller health units had more challenges to both detect and mount a response to any alerts. LPHAs in the control group were more likely to declare alerts to have public health significance and to initiate any action. Regression models using repeated measures showed an interaction between the year (Year 1 versus Year 2) and the intervention as well as an interaction between year and sustained nature of the alert. Both of these were linked to the control health units reporting more “watchful waiting”.

Conclusions

This study raises questions about the effectiveness of using standardized protocols to improve the performance of syndromic surveillance in a decentralized public health system. Despite efforts to create standardized protocols and engage public health agencies in the process, no significant differences in the effective use of syndromic alerts were observed beyond year 1. It also raises questions about the minimum capacity of the agency and minimum population size that are required for an effective response.

Use of syndromic surveillance data to monitor poisonings and drug overdoses in state and local public health agencies

BACKGROUND

The incidence of poisoning and drug overdose has risen rapidly in the USA over the last 16 years. To inform local intervention approaches, local health departments (LHDs) in North Carolina (NC) are using a statewide syndromic surveillance system that provides timely, local emergency department (ED) and Emergency Medical Services (EMS) data on medication and drug overdoses.

OBJECTIVE

The purpose of this article is to describe the development and use of a variety of case definitions for poisoning and overdose implemented in NC's syndromic surveillance system and the impact of the system on local surveillance initiatives.

DESIGN, SETTING, PARTICIPANTS

Thirteen new poisoning and overdose-related case definitions were added to NC's syndromic surveillance system and LHDs were trained on their use for surveillance purposes. Twenty-one LHDs were surveyed on the utility and impact of these new case definitions.

RESULTS/CONCLUSIONS

Ninety-one per cent of survey respondents (n = 29) agreed or strongly agreed that their ability to access timely ED data was vital to inform community-level overdose prevention work. Providing LHDs with access to local, timely data to identify pockets of need and engage stakeholders facilitates the practice of informed injury prevention and contributes to the reduction of injury incidence in their communities.

Post-disaster health impact of natural hazards in the Philippines in 2013

BACKGROUND

In 2011, the Health Emergency Management Bureau (HEMB) created the Surveillance for Post Extreme Emergencies and Disasters (SPEED), a real-time syndromic surveillance system that allows the early detection and monitoring of post-disaster disease trends. SPEED can assist health leaders in making informed decisions on health systems affected by disasters. There is a need for further validation of current concepts in post-disaster disease patterns in respect to actual field data. This study aims to evaluate the temporal post-disaster patterns of selected diseases after a flood, an earthquake, and a typhoon in the Philippines in 2013.

METHODOLOGY

We analyzed the 21 syndromes provided by SPEED both separately and grouped into injuries, communicable diseases, and non-communicable diseases (NCDs) by calculating daily post-disaster consultation rates for up to 150 days post-disaster. These were compared over time and juxtaposed according to the type of disaster.

RESULTS

Communicable diseases were found to be the predominant syndrome group in all three disaster types. The top six syndromes found were: acute respiratory infections, open wounds, bruises and burns, high blood pressure, skin disease, fever, and acute watery diarrhea.

DISCUSSION

Overall, the results aligned with the country's morbidity profile. Within 2 months, the clear gradation of increasing syndrome rates reflected the severity (flood

CONCLUSIONS

Most post-disaster syndromes may be addressed by prevention, early diagnosis, and early treatment. Health needs differ in response and recovery phases.

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Key Words

• disasters
• infectious disease
• injuries
• non-communicable diseases
• syndromic surveillance

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MESH Headings Collapse

Grants

• 001 World Health Organization

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Affiliation(s)

Miguel Antonio Salazar Institute of Public Health, Heidelberg University, Heidelberg, Germany
Arturo Pesigan World Health Organization Office in Sri Lanka, Colombo, Sri Lanka
Ronald Law Health Emergency Management Bureau, Department of Health, Republic of the Philippines, Manila, Philippines
Volker Winkler Institute of Public Health, Heidelberg University, Heidelberg, Germany;

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A practical approach to designing syndromic surveillance systems for livestock and poultry

The field of animal syndromic surveillance (SyS) is growing, with many systems being developed worldwide. Now is an appropriate time to share ideas and lessons learned from early SyS design and implementation. Based on our practical experience in animal health SyS, with additions from the public health and animal health SyS literature, we put forward for discussion a 6-step approach to designing SyS systems for livestock and poultry. The first step is to formalise policy and surveillance goals which are considerate of stakeholder expectations and reflect priority issues (1). Next, it is important to find consensus on national priority diseases and identify current surveillance gaps. The geographic, demographic, and temporal coverage of the system must be carefully assessed (2). A minimum dataset for SyS that includes the essential data to achieve all surveillance objectives while minimizing the amount of data collected should be defined. One can then compile an inventory of the data sources available and evaluate each using the criteria developed (3). A list of syndromes should then be produced for all data sources. Cases can be classified into syndrome classes and the data can be converted into time series (4). Based on the characteristics of the syndrome-time series, the length of historic data available and the type of outbreaks the system must detect, different aberration detection algorithms can be tested (5). Finally, it is essential to develop a minimally acceptable response protocol for each statistical signal produced (6). Important outcomes of this pre-operational phase should be building of a national network of experts and collective action and evaluation plans. While some of the more applied steps (4 and 5) are currently receiving consideration, more emphasis should be put on earlier conceptual steps by decision makers and surveillance developers (1-3).

Using Bayes' rule to define the value of evidence from syndromic surveillance

In this work we propose the adoption of a statistical framework used in the evaluation of forensic evidence as a tool for evaluating and presenting circumstantial “evidence” of a disease outbreak from syndromic surveillance. The basic idea is to exploit the predicted distributions of reported cases to calculate the ratio of the likelihood of observing n cases given an ongoing outbreak over the likelihood of observing n cases given no outbreak. The likelihood ratio defines the Value of Evidence (V). Using Bayes' rule, the prior odds for an ongoing outbreak are multiplied by V to obtain the posterior odds. This approach was applied to time series on the number of horses showing clinical respiratory symptoms or neurological symptoms. The separation between prior beliefs about the probability of an outbreak and the strength of evidence from syndromic surveillance offers a transparent reasoning process suitable for supporting decision makers. The value of evidence can be translated into a verbal statement, as often done in forensics or used for the production of risk maps. Furthermore, a Bayesian approach offers seamless integration of data from syndromic surveillance with results from predictive modeling and with information from other sources such as disease introduction risk assessments.

Defining syndromes using cattle meat inspection data for syndromic surveillance purposes: a statistical approach with the 2005-2010 data from ten French slaughterhouses

Background

The slaughterhouse is a central processing point for food animals and thus a source of both demographic data (age, breed, sex) and health-related data (reason for condemnation and condemned portions) that are not available through other sources. Using these data for syndromic surveillance is therefore tempting. However many possible reasons for condemnation and condemned portions exist, making the definition of relevant syndromes challenging.

The objective of this study was to determine a typology of cattle with at least one portion of the carcass condemned in order to define syndromes. Multiple factor analysis (MFA) in combination with clustering methods was performed using both health-related data and demographic data.

Results

Analyses were performed on 381,186 cattle with at least one portion of the carcass condemned among the 1,937,917 cattle slaughtered in ten French abattoirs. Results of the MFA and clustering methods led to 12 clusters considered as stable according to year of slaughter and slaughterhouse. One cluster was specific to a disease of public health importance (cysticercosis). Two clusters were linked to the slaughtering process (fecal contamination of heart or lungs and deterioration lesions). Two clusters respectively characterized by chronic liver lesions and chronic peritonitis could be linked to diseases of economic importance to farmers. Three clusters could be linked respectively to reticulo-pericarditis, fatty liver syndrome and farmer’s lung syndrome, which are related to both diseases of economic importance to farmers and herd management issues. Three clusters respectively characterized by arthritis, myopathy and Dark Firm Dry (DFD) meat could notably be linked to animal welfare issues. Finally, one cluster, characterized by bronchopneumonia, could be linked to both animal health and herd management issues.

Conclusion

The statistical approach of combining multiple factor analysis with cluster analysis showed its relevance for the detection of syndromes using available large and complex slaughterhouse data. The advantages of this statistical approach are to i) define groups of reasons for condemnation based on meat inspection data, ii) help grouping reasons for condemnation among a list of various possible reasons for condemnation for which a consensus among experts could be difficult to reach, iii) assign each animal to a single syndrome which allows the detection of changes in trends of syndromes to detect unusual patterns in known diseases and emergence of new diseases.