Spatial and temporal epidemiological analysis in the Big Data era

Pfeiffer, Dirk U.; Stevens, Kim

doi:10.1016/j.prevetmed.2015.05.012

Cited by 61 publications

(53 citation statements)

References 114 publications

(107 reference statements)

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“…More research is needed to develop and adapt “big data” analytic methods. 43 The ideal scenario for the study of associations between dietary factors and numerous health and disease outcomes would be a life course study from fetal development to later life, and collection of multiple exposures. It is now time to consider the data capture capabilities, data composition infrastructure, and analytic techniques that would be required for such studies.…”

Section: Future Research Needsmentioning

confidence: 99%

The National Cancer Institute’s Dietary Assessment Primer: A Resource for Diet Research

Thompson¹,

Kirkpatrick²,

Subar³

et al. 2015

Journal of the Academy of Nutrition and Dietetics

238

201

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This monograph describes the National Cancer Institute's (NCI) Dietary Assessment Primer, a web resource developed to help researchers choose the best available dietary assessment approach to achieve their research objective. All self-report instruments have error, but understanding the nature of that error can lead to better assessment, analysis, and interpretation of results. The Primer includes profiles of the major self-report dietary assessment instruments including guidance on the best uses of each instrument; discussion of validation and measurement error generally and with respect to each instrument; guidance for choosing a dietary assessment approach for different research questions; and additional resources such as a glossary, references, and overviews of specific/important issues in the field. This monograph also describes some future research needs in the field of dietary assessment.

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Section: Future Research Needsmentioning

confidence: 99%

The National Cancer Institute’s Dietary Assessment Primer: A Resource for Diet Research

Thompson¹,

Kirkpatrick²,

Subar³

et al. 2015

Journal of the Academy of Nutrition and Dietetics

238

201

View full text Add to dashboard Cite

show abstract

“…Furthermore, such disparities in coding rates between clinics may encourage improved coding if these data are communicated back to the clinics. As with all analyses based on big data, inherent biases stemming from multi-source data collection must be considered (7). …”

Section: Discussionmentioning

confidence: 99%

“…The advantage of this approach is that it can be fully integrated in the veterinary workflow and captures information directly from the practice management software, thus minimizing time-consuming additional data entry and the use of linked software or websites. However, only recently resources have become available to process such large volumes of data in real-time (6, 7). …”

Section: Introductionmentioning

confidence: 99%

Evidence in Practice – A Pilot Study Leveraging Companion Animal and Equine Health Data from Primary Care Veterinary Clinics in New Zealand

Muellner¹,

Muellner²,

Gates

et al. 2016

Front. Vet. Sci.

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Veterinary practitioners have extensive knowledge of animal health from their day-to-day observations of clinical patients. There have been several recent initiatives to capture these data from electronic medical records for use in national surveillance systems and clinical research. In response, an approach to surveillance has been evolving that leverages existing computerized veterinary practice management systems to capture animal health data recorded by veterinarians. Work in the United Kingdom within the VetCompass program utilizes routinely recorded clinical data with the addition of further standardized fields. The current study describes a prototype system that was developed based on this approach. In a 4-week pilot study in New Zealand, clinical data on presentation reasons and diagnoses from a total of 344 patient consults were extracted from two veterinary clinics into a dedicated database and analyzed at the population level. New Zealand companion animal and equine veterinary practitioners were engaged to test the feasibility of this national practice-based health information and data system. Strategies to ensure continued engagement and submission of quality data by participating veterinarians were identified, as were important considerations for transitioning the pilot program to a sustainable large-scale and multi-species surveillance system that has the capacity to securely manage big data. The results further emphasized the need for a high degree of usability and smart interface design to make such a system work effectively in practice. The geospatial integration of data from multiple clinical practices into a common operating picture can be used to establish the baseline incidence of disease in New Zealand companion animal and equine populations, detect unusual trends that may indicate an emerging disease threat or welfare issue, improve the management of endemic and exotic infectious diseases, and support research activities. This pilot project is an important step toward developing a national surveillance system for companion animals and equines that moves beyond emerging infectious disease detection to provide important animal health information that can be used by a wide range of stakeholder groups, including participating veterinary practices.

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“…When combined with spatial and temporal epidemiological analyses, such methods generate new knowledge on disease events and risks. Big data is at the crucial point of being increasingly utilised and applied in animal health, has the capability to fill knowledge gaps, and the ability to allow for the effective management of animal-and human-health risks (58).…”

Section: Crowdsourced and Internet-based Data Collectionmentioning

confidence: 99%

Technologies for capturing and analysing animal health data in near real time

Holmstrom¹,

Beckham²

2017

Rev. Sci. Tech. OIE

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Information technologies are rapidly advancing the way in which animal health data and information are collected, analysed and shared in order to support animal health management, disease surveillance and response, and decision-making. However, the full potential of these technologies for early detection and response to natural or intentional disease events has not been fully realised in animal health at the global level. This paper discusses advances made in these technologies and examples of how they have been applied in animal health for near real-time data collection and analysis. The technologies reviewed include: i) mobile health (mHealth) technologies, wireless sensors and biosensors for remote data collection; ii) crowdsourced and Internet-based data collection; and iii) electronic health (eHealth) technologies for data integration and analysis. Experiences of implementing these technologies, and challenges with their use, are also discussed so as to provide recommendations on their future application in animal health. The world is ripe with opportunities to develop and enhance mHealth and eHealth technologies that are cost effective and capable of near real-time data collection and analysis. Such technologies have been shown to be valuable and capable of being implemented in both developing and developed countries, and ultimately will strengthen disease surveillance and reporting across the globe. International mechanisms and data standards are needed to facilitate the sharing and analysis of animal and human health data between countries. Identifying ways in which animal and human health data collection and analysis can be better integrated within a 'One Health' approach will enhance the coordination and capability of disease detection and response at the human-animal interface.

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Spatial and temporal epidemiological analysis in the Big Data era

Cited by 61 publications

References 114 publications

The National Cancer Institute’s Dietary Assessment Primer: A Resource for Diet Research

The National Cancer Institute’s Dietary Assessment Primer: A Resource for Diet Research

Evidence in Practice – A Pilot Study Leveraging Companion Animal and Equine Health Data from Primary Care Veterinary Clinics in New Zealand

Technologies for capturing and analysing animal health data in near real time

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