No lab is an island: universal coding of laboratory test names.

The local laboratory with a local client-base, that never needs to exchange information with any outside entity, is a dying breed. As marketing channels, animal movement, and reporting requirements become increasingly national and international, the need to communicate about laboratory tests and results grows. Local and proprietary names of laboratory tests often fail to communicate enough detail to distinguish between similar tests. To avoid a lengthy description of each test, laboratories need the ability to assign codes that, although not sufficiently user-friendly for day-to-day use, contain enough information to translate between laboratories and even languages. The Logical Observation Identifiers Names and Codes (LOINC) standard provides such a universal coding system. Each test-each atomic observation-is evaluated on 6 attributes that establish its uniqueness at the level of clinical-or epidemiologic-significance. The analyte detected, analyte property, specimen, and result scale combine with the method of analysis and timing (for challenge and metabolic type tests) to define a unique LOINC code. Equipping laboratory results with such universal identifiers creates a world of opportunity for cross-institutional data exchange, aggregation, and analysis, and presents possibilities for data mining and artificial intelligence on a national and international scale. A few challenges, relatively unique to regulatory veterinary test protocols, require special handling.

Challenges and Opportunities Developing Mathematical Models of Shared Pathogens of Domestic and Wild Animals.

Diseases that affect both wild and domestic animals can be particularly difficult to prevent, predict, mitigate, and control. Such multi-host diseases can have devastating economic impacts on domestic animal producers and can present significant challenges to wildlife populations, particularly for populations of conservation concern. Few mathematical models exist that capture the complexities of these multi-host pathogens, yet the development of such models would allow us to estimate and compare the potential effectiveness of management actions for mitigating or suppressing disease in wildlife and/or livestock host populations. We conducted a workshop in March 2014 to identify the challenges associated with developing models of pathogen transmission across the wildlife-livestock interface. The development of mathematical models of pathogen transmission at this interface is hampered by the difficulties associated with describing the host-pathogen systems, including: (1) the identity of wildlife hosts, their distributions, and movement patterns; (2) the pathogen transmission pathways between wildlife and domestic animals; (3) the effects of the disease and concomitant mitigation efforts on wild and domestic animal populations; and (4) barriers to communication between sectors. To promote the development of mathematical models of transmission at this interface, we recommend further integration of modern quantitative techniques and improvement of communication among wildlife biologists, mathematical modelers, veterinary medicine professionals, producers, and other stakeholders concerned with the consequences of pathogen transmission at this important, yet poorly understood, interface.

An approach for de-identification of point locations of livestock premises for further use in disease spread modeling.

We describe a method for de-identifying point location data used for disease spread modeling to allow data custodians to share data with modeling experts without disclosing individual farm identities. The approach is implemented in an open-source software program that is described and evaluated here. The program allows a data custodian to select a level of de-identification based on the K-anonymity statistic. The program converts a file of true farm locations and attributes into a file appropriate for use in disease spread modeling with the locations randomly modified to prevent re-identification based on location. Important epidemiological relationships such as clustering are preserved to as much as possible to allow modeling similar to those using true identifiable data. The software implementation was verified by visual inspection and basic descriptive spatial analysis of the output. Performance is sufficient to allow de-identification of even large data sets on desktop computers available to any data custodian.

An epidemiologic simulation model of the spread and control of highly pathogenic avian influenza (H5N1) among commercial and backyard poultry flocks in South Carolina, United States.

Epidemiologic simulation modeling of highly pathogenic avian influenza (HPAI) outbreaks provides a useful conceptual framework with which to estimate the consequences of HPAI outbreaks and to evaluate disease control strategies. The purposes of this study were to establish detailed and informed input parameters for an epidemiologic simulation model of the H5N1 strain of HPAI among commercial and backyard poultry in the state of South Carolina in the United States using a highly realistic representation of this poultry population; to estimate the consequences of an outbreak of HPAI in this population with a model constructed from these parameters; and to briefly evaluate the sensitivity of model outcomes to several parameters. Parameters describing disease state durations; disease transmission via direct contact, indirect contact, and local-area spread; and disease detection, surveillance, and control were established through consultation with subject matter experts, a review of the current literature, and the use of several computational tools. The stochastic model constructed from these parameters produced simulated outbreaks ranging from 2 to 111 days in duration (median 25 days), during which 1 to 514 flocks were infected (median 28 flocks). Model results were particularly sensitive to the rate of indirect contact that occurs among flocks. The baseline model established in this study can be used in the future to evaluate various control strategies, as a tool for emergency preparedness and response planning, and to assess the costs associated with disease control and the economic consequences of a disease outbreak.

The etiology of social change.

A fundamental aspect of human beings is that they learn. The process of learning and what is learned are impacted by a number of factors, both cognitive and social; that is, humans are boundedly rational. Cognitive and social limitations interact, making it difficult to reason about how to provide information to impact what humans know, believe, and do. Herein, we use a multi-agent dynamic-network simulation system, Construct, to conduct such reasoning. In particular, we ask, What media should be used to provide information to most impact what people know, believe, and do, given diverse social structures? All simulated agents are boundedly rational both at the cognitive and social level, and so are subject to factors such as literacy, education, and the breadth of their social network. We find that there is no one most effective intervention; rather, to be effective, messages and the media used to spread the message need to be selected for the population being addressed. Typically, a multimedia campaign is critical.

Sending data to a central repository.

The Veterinary Medical DataBases (VMDB) has been collecting data from participating institutions for 40 years. We have recently started a program to collect clinical case data from not only veterinary schools but from veterinary practitioners as well. In so doing, we are using commonly accepted means of data representation and transmission. Thus, we use the Standard Nomenclature of Medicine (SNOMED) as a standard vocabulary language and Health Level 7 (HL 7) as a standard transmission protocol. We report on the planning and implementation of these standards and the problems encountered in their implementation.

Connecticut RxData project.

The Connecticut Hospital Association has developed a data resource for inpatient pharmacy information. The RxData project collects drug dispensing information from member hospitals and joins it to administrative discharge data in the Chime database. The resulting dataset is useful for descriptive epidemiology of drug use patterns as well as surveillance, quality improvement, and some hypothesis testing. The drug identity is derived from the National Drug Code submitted by hospital pharmacies. A drug reference file is used together with these codes for hierarchical analysis. Data are accepted from participating facilities in a variety of formats and mapped to a common schema. The program uses locally developed roll-up logic to overcome the lack of consistent standards for recording inpatient drug order and dispense information. Dispensing records at different levels of aggregation are collected from source pharmacy information systems and converted to a standard "regimen" based on continuous dispensing of the same drug. The resulting record structure allows direct comparison of data from dissimilar systems. Data are currently available for eleven acute care hospitals and most of their associated emergency and outpatient surgery facilities. The program is expected to expand to cover most if not all Connecticut hospitals over the next two to three years.