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Roblin DW, Jones JW, Fuller CH. Pollen Exposure and Associated Healthcare Utilization: A Population-based Study Using Health Maintenance Organization Data in the Washington, DC, Area. Ann Am Thorac Soc 2021; 18:1642-1649. [PMID: 33794139 PMCID: PMC8522299 DOI: 10.1513/annalsats.202008-976oc] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2020] [Accepted: 02/24/2021] [Indexed: 11/20/2022] Open
Abstract
Rationale: Most studies of the healthcare utilization impact of pollen exposure have focused on emergency department visits or hospital admissions. However, other frequent but lower cost services-phone calls and e-mails to providers and office visits-may also be affected. Objectives: The objective of our study was to estimate the impact of tree and grass pollen exposures on respiratory-related healthcare utilization across a range of medical services, including calls and e-mails to providers, nonurgent face-to-face visits, urgent and emergent care visits, and hospitalizations. Methods: We conducted a retrospective observational study of daily tree and grass pollen counts linked to electronic health records of Kaiser Permanente beneficiaries in the metropolitan Washington, DC, area for 2013-2014. Results: The proportion of Kaiser Permanente beneficiaries with respiratory-related healthcare utilization was significantly greater (for P ⩽ 0.05) given a 1 standard deviation increase in same-day pollen exposure. For tree pollen, a 1 standard deviation increase in same-day pollen exposure was associated with relative increases in utilization ranging from 1.77% (95% confidence interval [CI], 0.07-4.17%) for urgent and emergent care visits to 12.84% (95% CI, 11.02-14.65%) for provider calls/e-mails. For grass pollen exposure, a 1 standard deviation increase in same-day pollen exposure was associated with relative increases in utilization ranging from 1.42% (95% CI, 0.39-2.46) for provider face-to-face visits to 11.09% (95% CI, 9.26-12.92) for provider calls/e-mails. Conclusions: Increased pollen exposure was associated with increases in healthcare utilization across a range of services, with relatively higher increases in provider calls/e-mails and lower increases in emergent or acute care. If climate change increases intensity and geographic scope of pollen exposure as predicted and if this study's estimates of association of peak pollen exposure on healthcare utilization are generalizable, then the impact of climate change on healthcare utilization may be significant.
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Affiliation(s)
- Douglas W. Roblin
- Mid-Atlantic Permanente Research Institute, Kaiser Permanente, Rockville, Maryland
| | - Jordan W. Jones
- Economic Research Service, U.S. Department of Agriculture, Kansas City, Missouri; and
| | - Christina H. Fuller
- Department of Population Health Sciences, Georgia State University School of Public Health, Atlanta, Georgia
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2
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Hyllestad S, Amato E, Nygård K, Vold L, Aavitsland P. The effectiveness of syndromic surveillance for the early detection of waterborne outbreaks: a systematic review. BMC Infect Dis 2021; 21:696. [PMID: 34284731 PMCID: PMC8290622 DOI: 10.1186/s12879-021-06387-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Accepted: 07/06/2021] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Waterborne outbreaks are still a risk in high-income countries, and their early detection is crucial to limit their societal consequences. Although syndromic surveillance is widely used for the purpose of detecting outbreaks days earlier than traditional surveillance systems, evidence of the effectiveness of such systems is lacking. Thus, our objective was to conduct a systematic review of the effectiveness of syndromic surveillance to detect waterborne outbreaks. METHOD We searched the Cochrane Library, Medline/PubMed, EMBASE, Scopus, and Web of Science for relevant published articles using a combination of the keywords 'drinking water', 'surveillance', and 'waterborne disease' for the period of 1990 to 2018. The references lists of the identified articles for full-text record assessment were screened, and searches in Google Scholar using the same key words were conducted. We assessed the risk of bias in the included articles using the ROBINS-I tool and PRECEPT for the cumulative body of evidence. RESULTS From the 1959 articles identified, we reviewed 52 articles, of which 18 met the eligibility criteria. Twelve were descriptive/analytical studies, whereas six were simulation studies. There is no clear evidence for syndromic surveillance in terms of the ability to detect waterborne outbreaks (low sensitivity and high specificity). However, one simulation study implied that multiple sources of signals combined with spatial information may increase the timeliness in detecting a waterborne outbreak and reduce false alarms. CONCLUSION This review demonstrates that there is no conclusive evidence on the effectiveness of syndromic surveillance for the detection of waterborne outbreaks, thus suggesting the need to focus on primary prevention measures to reduce the risk of waterborne outbreaks. Future studies should investigate methods for combining health and environmental data with an assessment of needed financial and human resources for implementing such surveillance systems. In addition, a more critical thematic narrative synthesis on the most promising sources of data, and an assessment of the basis for arguments that joint analysis of different data or dimensions of data (e.g. spatial and temporal) might perform better, should be carried out. TRIAL REGISTRATION PROSPERO: International prospective register of systematic reviews. 2019. CRD42019122332 .
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Affiliation(s)
- Susanne Hyllestad
- Department of Infection Control and Preparedness, Norwegian Institute of Public Health, Oslo, Norway.
- Faculty of Medicine, University of Oslo, Institute of Health and Society, Oslo, Norway.
| | - Ettore Amato
- Department of Infection Control and Preparedness, Norwegian Institute of Public Health, Oslo, Norway
| | - Karin Nygård
- Department of Infection Control and Preparedness, Norwegian Institute of Public Health, Oslo, Norway
| | - Line Vold
- Department of Infection Control and Preparedness, Norwegian Institute of Public Health, Oslo, Norway
| | - Preben Aavitsland
- Department of Infection Control and Preparedness, Norwegian Institute of Public Health, Oslo, Norway
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Faverjon C, Carmo LP, Berezowski J. Multivariate syndromic surveillance for cattle diseases: Epidemic simulation and algorithm performance evaluation. Prev Vet Med 2019; 172:104778. [PMID: 31586719 DOI: 10.1016/j.prevetmed.2019.104778] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2019] [Revised: 09/18/2019] [Accepted: 09/18/2019] [Indexed: 10/25/2022]
Abstract
Multivariate Syndromic Surveillance (SyS) systems that simultaneously assess and combine information from different data sources are especially useful for strengthening surveillance systems for early detection of infectious disease epidemics. Despite the strong motivation for implementing multivariate SyS and there being numerous methods reported, the number of operational multivariate SyS systems in veterinary medicine is still very small. One possible reason is that assessing the performance of such surveillance systems remains challenging because field epidemic data are often unavailable. The objective of this study is to demonstrate a practical multivariate event detection method (directionally sensitive multivariate control charts) that can be easily applied in livestock disease SyS, using syndrome time series data from the Swiss cattle population as an example. We present a standardized method for simulating multivariate epidemics of different diseases using four diseases as examples: Bovine Virus Diarrhea (BVD), Infectious Bovine Rhinotracheitis (IBR), Bluetongue virus (BTV) and Schmallenberg virus (SV). Two directional multivariate control chart algorithms, Multivariate Exponentially Weighted Moving Average (MEWMA) and Multivariate Cumulative Sum (MCUSUM) were compared. The two algorithms were evaluated using 12 syndrome time series extracted from two Swiss national databases. The two algorithms were able to detect all simulated epidemics around 4.5 months after the start of the epidemic, with a specificity of 95%. However, the results varied depending on the algorithm and the disease. The MEWMA algorithm always detected epidemics earlier than the MCUSUM, and epidemics of IBR and SV were detected earlier than epidemics of BVD and BTV. Our results show that the two directional multivariate control charts are promising methods for combining information from multiple time series for early detection of subtle changes in time series from a population without producing an unreasonable amount of false alarms. The approach that we used for simulating multivariate epidemics is relatively easy to implement and could be used in other situations where real epidemic data are unavailable. We believe that our study results can support the implementation and assessment of multivariate SyS systems in animal health.
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Affiliation(s)
- Céline Faverjon
- Veterinary Public Health Institute, Vetsuisse Faculty, University of Bern, Liebefeld, Switzerland.
| | - Luís Pedro Carmo
- Veterinary Public Health Institute, Vetsuisse Faculty, University of Bern, Liebefeld, Switzerland
| | - John Berezowski
- Veterinary Public Health Institute, Vetsuisse Faculty, University of Bern, Liebefeld, Switzerland
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4
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Yuan M, Boston-Fisher N, Luo Y, Verma A, Buckeridge DL. A systematic review of aberration detection algorithms used in public health surveillance. J Biomed Inform 2019; 94:103181. [PMID: 31014979 DOI: 10.1016/j.jbi.2019.103181] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Revised: 04/16/2019] [Accepted: 04/17/2019] [Indexed: 12/21/2022]
Abstract
The algorithms used for detecting anomalies have evolved substantially over the last decade to take advantage of advances in informatics and to accommodate changes in surveillance data. We identified 145 studies since 2007 that evaluated statistical methods used to detect aberrations in public health surveillance data. For each study, we classified the analytic methods and reviewed the evaluation metrics. We also summarized the practical usage of the detection algorithms in public health surveillance systems worldwide. Traditional methods (e.g., control charts, linear regressions) were the focus of most evaluation studies and continue to be used commonly in practice. There was, however, an increase in the number of studies using forecasting methods and studies applying machine learning methods, hidden Markov models, and Bayesian framework to multivariate datasets. Evaluation studies demonstrated improved accuracy with more sophisticated methods, but these methods do not appear to be used widely in public health practice.
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Affiliation(s)
- Mengru Yuan
- Clinical and Health Informatics Research Group, McGill University, 1140 Pine Avenue West, Montreal, QC H3A 1A3, Canada
| | - Nikita Boston-Fisher
- Clinical and Health Informatics Research Group, McGill University, 1140 Pine Avenue West, Montreal, QC H3A 1A3, Canada
| | - Yu Luo
- Clinical and Health Informatics Research Group, McGill University, 1140 Pine Avenue West, Montreal, QC H3A 1A3, Canada
| | - Aman Verma
- Clinical and Health Informatics Research Group, McGill University, 1140 Pine Avenue West, Montreal, QC H3A 1A3, Canada
| | - David L Buckeridge
- Clinical and Health Informatics Research Group, McGill University, 1140 Pine Avenue West, Montreal, QC H3A 1A3, Canada.
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Texier G, Allodji RS, Diop L, Meynard JB, Pellegrin L, Chaudet H. Using decision fusion methods to improve outbreak detection in disease surveillance. BMC Med Inform Decis Mak 2019; 19:38. [PMID: 30837003 PMCID: PMC6402142 DOI: 10.1186/s12911-019-0774-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2018] [Accepted: 02/18/2019] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND When outbreak detection algorithms (ODAs) are considered individually, the task of outbreak detection can be seen as a classification problem and the ODA as a sensor providing a binary decision (outbreak yes or no) for each day of surveillance. When they are considered jointly (in cases where several ODAs analyze the same surveillance signal), the outbreak detection problem should be treated as a decision fusion (DF) problem of multiple sensors. METHODS This study evaluated the benefit for a decisions support system of using DF methods (fusing multiple ODA decisions) compared to using a single method of outbreak detection. For each day, we merged the decisions of six ODAs using 5 DF methods (two voting methods, logistic regression, CART and Bayesian network - BN). Classical metrics of accuracy, prediction and timelines were used during the evaluation steps. RESULTS In our results, we observed the greatest gain (77%) in positive predictive value compared to the best ODA if we used DF methods with a learning step (BN, logistic regression, and CART). CONCLUSIONS To identify disease outbreaks in systems using several ODAs to analyze surveillance data, we recommend using a DF method based on a Bayesian network. This method is at least equivalent to the best of the algorithms considered, regardless of the situation faced by the system. For those less familiar with this kind of technique, we propose that logistic regression be used when a training dataset is available.
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Affiliation(s)
- Gaëtan Texier
- French Armed Forces Center for Epidemiology and Public Health (CESPA), SSA, Camp de Sainte Marthe, 13568, Marseille, France. .,UMR VITROME, IRD, AP-HM, SSA, IHU-Méditerranée Infection, Aix Marseille Univ, 13005, Marseille, France.
| | - Rodrigue S Allodji
- French Armed Forces Center for Epidemiology and Public Health (CESPA), SSA, Camp de Sainte Marthe, 13568, Marseille, France.,CESP, Univ. Paris-Sud, UVSQ, INSERM, Université Paris-Saclay, Villejuif, France.,Cancer and Radiation Team, Gustave Roussy Cancer Center, F-94805, Villejuif, France
| | - Loty Diop
- International Food Policy Research Institute (IFPRI), Regional Office for West and Central Africa Regional Office, 24063, Dakar, Sénégal
| | - Jean-Baptiste Meynard
- French Armed Forces Center for Epidemiology and Public Health (CESPA), SSA, Camp de Sainte Marthe, 13568, Marseille, France.,UMR 912 - SESSTIM - INSERM/IRD/Aix-Marseille Université, 13385, Marseille, France
| | - Liliane Pellegrin
- French Armed Forces Center for Epidemiology and Public Health (CESPA), SSA, Camp de Sainte Marthe, 13568, Marseille, France.,UMR VITROME, IRD, AP-HM, SSA, IHU-Méditerranée Infection, Aix Marseille Univ, 13005, Marseille, France
| | - Hervé Chaudet
- French Armed Forces Center for Epidemiology and Public Health (CESPA), SSA, Camp de Sainte Marthe, 13568, Marseille, France.,UMR VITROME, IRD, AP-HM, SSA, IHU-Méditerranée Infection, Aix Marseille Univ, 13005, Marseille, France
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Beard R, Wentz E, Scotch M. A systematic review of spatial decision support systems in public health informatics supporting the identification of high risk areas for zoonotic disease outbreaks. Int J Health Geogr 2018; 17:38. [PMID: 30376842 PMCID: PMC6208014 DOI: 10.1186/s12942-018-0157-5] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2018] [Accepted: 10/19/2018] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Zoonotic diseases account for a substantial portion of infectious disease outbreaks and burden on public health programs to maintain surveillance and preventative measures. Taking advantage of new modeling approaches and data sources have become necessary in an interconnected global community. To facilitate data collection, analysis, and decision-making, the number of spatial decision support systems reported in the last 10 years has increased. This systematic review aims to describe characteristics of spatial decision support systems developed to assist public health officials in the management of zoonotic disease outbreaks. METHODS A systematic search of the Google Scholar database was undertaken for published articles written between 2008 and 2018, with no language restriction. A manual search of titles and abstracts using Boolean logic and keyword search terms was undertaken using predefined inclusion and exclusion criteria. Data extraction included items such as spatial database management, visualizations, and report generation. RESULTS For this review we screened 34 full text articles. Design and reporting quality were assessed, resulting in a final set of 12 articles which were evaluated on proposed interventions and identifying characteristics were described. Multisource data integration, and user centered design were inconsistently applied, though indicated diverse utilization of modeling techniques. CONCLUSIONS The characteristics, data sources, development and modeling techniques implemented in the design of recent SDSS that target zoonotic disease outbreak were described. There are still many challenges to address during the design process to effectively utilize the value of emerging data sources and modeling methods. In the future, development should adhere to comparable standards for functionality and system development such as user input for system requirements, and flexible interfaces to visualize data that exist on different scales. PROSPERO registration number: CRD42018110466.
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Affiliation(s)
- Rachel Beard
- College of Health Solutions, Arizona State University, Phoenix, AZ USA
- Center for Environmental Health Engineering, Biodesign Institute, Arizona State University, Tempe, AZ USA
| | - Elizabeth Wentz
- School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ USA
| | - Matthew Scotch
- College of Health Solutions, Arizona State University, Phoenix, AZ USA
- Center for Environmental Health Engineering, Biodesign Institute, Arizona State University, Tempe, AZ USA
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7
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Hopkins RS, Tong CC, Burkom HS, Akkina JE, Berezowski J, Shigematsu M, Finley PD, Painter I, Gamache R, Vilas VJDR, Streichert LC. A Practitioner-Driven Research Agenda for Syndromic Surveillance. Public Health Rep 2017; 132:116S-126S. [PMID: 28692395 DOI: 10.1177/0033354917709784] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Syndromic surveillance has expanded since 2001 in both scope and geographic reach and has benefited from research studies adapted from numerous disciplines. The practice of syndromic surveillance continues to evolve rapidly. The International Society for Disease Surveillance solicited input from its global surveillance network on key research questions, with the goal of improving syndromic surveillance practice. A workgroup of syndromic surveillance subject matter experts was convened from February to June 2016 to review and categorize the proposed topics. The workgroup identified 12 topic areas in 4 syndromic surveillance categories: informatics, analytics, systems research, and communications. This article details the context of each topic and its implications for public health. This research agenda can help catalyze the research that public health practitioners identified as most important.
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Affiliation(s)
- Richard S Hopkins
- 1 Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, FL, USA
| | - Catherine C Tong
- 2 International Society for Disease Surveillance, Braintree, MA, USA
| | - Howard S Burkom
- 3 Applied Physics Laboratory, Johns Hopkins University, Laurel, MD, USA
| | - Judy E Akkina
- 4 Center for Epidemiology and Animal Health, Veterinary Services, Animal and Plant Health Inspection Service, US Department of Agriculture, Fort Collins, CO, USA
| | - John Berezowski
- 5 Veterinary Public Health Institute, University of Bern, Bern, Switzerland
| | - Mika Shigematsu
- 6 International Biological and Chemical Threat Reduction Program, Sandia National Laboratories, Albuquerque, NM, USA.,7 National Institute of Infectious Diseases, Tokyo, Japan
| | - Patrick D Finley
- 8 Department of Operations Research and Computational Analysis, Sandia National Laboratories, Albuquerque, NM, USA
| | - Ian Painter
- 9 Department of Health Services, School of Public Health, University of Washington, Seattle, WA, USA.,10 Gamache Consulting, Rockville, MD, USA
| | - Roland Gamache
- 11 School of Veterinary Medicine, University of Surrey, Kent, UK.,12 Center for Population Health Information Technology, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
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Cross-Disciplinary Consultancy to Enhance Predictions of Asthma Exacerbation Risk in Boston. Online J Public Health Inform 2016; 8:e199. [PMID: 28210420 PMCID: PMC5302473 DOI: 10.5210/ojphi.v8i3.6902] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
This paper continues an initiative conducted by the International Society for Disease Surveillance with funding from the Defense Threat Reduction Agency to connect near-term analytical needs of public health practice with technical expertise from the global research community. The goal is to enhance investigation capabilities of day-to-day population health monitors. A prior paper described the formation of consultancies for requirements analysis and dialogue regarding costs and benefits of sustainable analytic tools. Each funded consultancy targets a use case of near-term concern to practitioners. The consultancy featured here focused on improving predictions of asthma exacerbation risk in demographic and geographic subdivisions of the city of Boston, Massachusetts, USA based on the combination of known risk factors for which evidence is routinely available. A cross-disciplinary group of 28 stakeholders attended the consultancy on March 30-31, 2016 at the Boston Public Health Commission. Known asthma exacerbation risk factors are upper respiratory virus transmission, particularly in school-age children, harsh or extreme weather conditions, and poor air quality. Meteorological subject matter experts described availability and usage of data sources representing these risk factors. Modelers presented multiple analytic approaches including mechanistic models, machine learning approaches, simulation techniques, and hybrids. Health department staff and local partners discussed surveillance operations, constraints, and operational system requirements. Attendees valued the direct exchange of information among public health practitioners, system designers, and modelers. Discussion finalized design of an 8-year de-identified dataset of Boston ED patient records for modeling partners who sign a standard data use agreement.
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9
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Vial F, Wei W, Held L. Methodological challenges to multivariate syndromic surveillance: a case study using Swiss animal health data. BMC Vet Res 2016; 12:288. [PMID: 27998276 PMCID: PMC5168866 DOI: 10.1186/s12917-016-0914-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2015] [Accepted: 12/06/2016] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND In an era of ubiquitous electronic collection of animal health data, multivariate surveillance systems (which concurrently monitor several data streams) should have a greater probability of detecting disease events than univariate systems. However, despite their limitations, univariate aberration detection algorithms are used in most active syndromic surveillance (SyS) systems because of their ease of application and interpretation. On the other hand, a stochastic modelling-based approach to multivariate surveillance offers more flexibility, allowing for the retention of historical outbreaks, for overdispersion and for non-stationarity. While such methods are not new, they are yet to be applied to animal health surveillance data. We applied an example of such stochastic model, Held and colleagues' two-component model, to two multivariate animal health datasets from Switzerland. RESULTS In our first application, multivariate time series of the number of laboratories test requests were derived from Swiss animal diagnostic laboratories. We compare the performance of the two-component model to parallel monitoring using an improved Farrington algorithm and found both methods yield a satisfactorily low false alarm rate. However, the calibration test of the two-component model on the one-step ahead predictions proved satisfactory, making such an approach suitable for outbreak prediction. In our second application, the two-component model was applied to the multivariate time series of the number of cattle abortions and the number of test requests for bovine viral diarrhea (a disease that often results in abortions). We found that there is a two days lagged effect from the number of abortions to the number of test requests. We further compared the joint modelling and univariate modelling of the number of laboratory test requests time series. The joint modelling approach showed evidence of superiority in terms of forecasting abilities. CONCLUSIONS Stochastic modelling approaches offer the potential to address more realistic surveillance scenarios through, for example, the inclusion of times series specific parameters, or of covariates known to have an impact on syndrome counts. Nevertheless, many methodological challenges to multivariate surveillance of animal SyS data still remain. Deciding on the amount of corroboration among data streams that is required to escalate into an alert is not a trivial task given the sparse data on the events under consideration (e.g. disease outbreaks).
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Affiliation(s)
- Flavie Vial
- Veterinary Public Health Institute, Vetsuisse Faculty, University of Bern, Bern, Switzerland
- Epi-connect, Skogås, Sweden
| | - Wei Wei
- Department Biostatistics, Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Leonhard Held
- Department Biostatistics, Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
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10
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Cooper GF, Villamarin R, Rich Tsui FC, Millett N, Espino JU, Wagner MM. A method for detecting and characterizing outbreaks of infectious disease from clinical reports. J Biomed Inform 2014; 53:15-26. [PMID: 25181466 DOI: 10.1016/j.jbi.2014.08.011] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2014] [Revised: 08/04/2014] [Accepted: 08/22/2014] [Indexed: 11/30/2022]
Abstract
Outbreaks of infectious disease can pose a significant threat to human health. Thus, detecting and characterizing outbreaks quickly and accurately remains an important problem. This paper describes a Bayesian framework that links clinical diagnosis of individuals in a population to epidemiological modeling of disease outbreaks in the population. Computer-based diagnosis of individuals who seek healthcare is used to guide the search for epidemiological models of population disease that explain the pattern of diagnoses well. We applied this framework to develop a system that detects influenza outbreaks from emergency department (ED) reports. The system diagnoses influenza in individuals probabilistically from evidence in ED reports that are extracted using natural language processing. These diagnoses guide the search for epidemiological models of influenza that explain the pattern of diagnoses well. Those epidemiological models with a high posterior probability determine the most likely outbreaks of specific diseases; the models are also used to characterize properties of an outbreak, such as its expected peak day and estimated size. We evaluated the method using both simulated data and data from a real influenza outbreak. The results provide support that the approach can detect and characterize outbreaks early and well enough to be valuable. We describe several extensions to the approach that appear promising.
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Affiliation(s)
- Gregory F Cooper
- Real-time Outbreak and Disease Surveillance (RODS) Laboratory, Department of Biomedical Informatics, University of Pittsburgh, 5607 Baum Boulevard, Pittsburgh, PA 15206-3701, USA.
| | - Ricardo Villamarin
- Real-time Outbreak and Disease Surveillance (RODS) Laboratory, Department of Biomedical Informatics, University of Pittsburgh, 5607 Baum Boulevard, Pittsburgh, PA 15206-3701, USA
| | - Fu-Chiang Rich Tsui
- Real-time Outbreak and Disease Surveillance (RODS) Laboratory, Department of Biomedical Informatics, University of Pittsburgh, 5607 Baum Boulevard, Pittsburgh, PA 15206-3701, USA
| | - Nicholas Millett
- Real-time Outbreak and Disease Surveillance (RODS) Laboratory, Department of Biomedical Informatics, University of Pittsburgh, 5607 Baum Boulevard, Pittsburgh, PA 15206-3701, USA
| | - Jeremy U Espino
- Real-time Outbreak and Disease Surveillance (RODS) Laboratory, Department of Biomedical Informatics, University of Pittsburgh, 5607 Baum Boulevard, Pittsburgh, PA 15206-3701, USA
| | - Michael M Wagner
- Real-time Outbreak and Disease Surveillance (RODS) Laboratory, Department of Biomedical Informatics, University of Pittsburgh, 5607 Baum Boulevard, Pittsburgh, PA 15206-3701, USA
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Dyck R, Islam MS, Zargar A, Mohapatra A, Sadiq R. Application of data fusion in human health risk assessment for hydrocarbon mixtures on contaminated sites. Toxicology 2012; 313:160-73. [PMID: 23219588 DOI: 10.1016/j.tox.2012.11.010] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2012] [Revised: 11/09/2012] [Accepted: 11/24/2012] [Indexed: 11/27/2022]
Abstract
The exposure and toxicological data used in human health risk assessment are obtained from diverse and heterogeneous sources. Complex mixtures found on contaminated sites can pose a significant challenge to effectively assess the toxicity potential of the combined chemical exposure and to manage the associated risks. A data fusion framework has been proposed to integrate data from disparate sources to estimate potential risk for various public health issues. To demonstrate the effectiveness of the proposed data fusion framework, an illustrative example for a hydrocarbon mixture is presented. The Joint Directors of Laboratories Data Fusion architecture was selected as the data fusion architecture and Dempster-Shafer Theory (DST) was chosen as the technique for data fusion. For neurotoxicity response analysis, neurotoxic metabolites toxicological data were fused with predictive toxicological data and then probability-boxes (p-boxes) were developed to represent the toxicity of each compound. The neurotoxic response was given a rating of "low", "medium" or "high". These responses were then weighted by the percent composition in the illustrative F1 hydrocarbon mixture. The resulting p-boxes were fused according to DST's mixture rule of combination. The fused p-boxes were fused again with toxicity data for n-hexane. The case study for F1 hydrocarbons illustrates how data fusion can help in the assessment of the health effects for complex mixtures with limited available data.
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Affiliation(s)
- Roberta Dyck
- School of Engineering, Okanagan Campus, The University of British Columbia, 3333 University Way, Kelowna, BC V1V 1V7, Canada.
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