open access publication

Article, 2024

Rationalising development of classification systems describing livestock production systems for disease burden analysis within the Global Burden of Animal Diseases programme

Research in Veterinary Science, ISSN 1532-2661, 0034-5288, Volume 168, Page 105102, 10.1016/j.rvsc.2023.105102

Contributors

Li, Yin 0000-0001-5720-5267 (Corresponding author) [1] [2] Mcintyre, Kirsty Marie 0000-0003-1360-122X [3] [4] Rasmussen, Philip 0000-0002-1936-7254 [5] [6] Gilbert, William 0000-0002-7450-7643 [4] Chaters, Gemma L 0000-0001-9652-6194 [4] Raymond, Kassy [7] Jemberu, Wudu Temesgen 0000-0002-3769-307X [8] [9] Larkins, Andrew 0000-0002-2741-1059 [2] Patterson, Grace T 0000-0001-6672-3920 [7] Kwok, Stephen [2] Kappes, Alexander James [10] Mayberry, Dianne Elizabeth 0000-0003-1584-8066 [1] Schrobback, Peggy 0000-0003-0526-1659 [1] Acosta, Mario Herrero [11] Stacey, Deborah Ann 0000-0002-2019-9905 [7] Huntington, Benjamin [4] Bruce, Mieghan M 0000-0003-3176-2094 [2] Knight-Jones, Theodore J D [12] Rushton, Jonathan R 0000-0001-5450-4202 [4]

Affiliations

  1. [1] Commonwealth Scientific and Industrial Research Organisation
  2. [NORA names: Australia; Oceania; OECD];
  3. [2] Murdoch University
  4. [NORA names: Australia; Oceania; OECD];
  5. [3] Newcastle University
  6. [NORA names: United Kingdom; Europe, Non-EU; OECD];
  7. [4] University of Liverpool
  8. [NORA names: United Kingdom; Europe, Non-EU; OECD];
  9. [5] University of Copenhagen
  10. [NORA names: KU University of Copenhagen; University; Denmark; Europe, EU; Nordic; OECD];

Abstract

The heterogeneity that exists across the global spectrum of livestock production means that livestock productivity, efficiency, health expenditure and health outcomes vary across production systems. To ensure that burden of disease estimates are specific to the represented livestock population and people reliant upon them, livestock populations need to be systematically classified into different types of production system, reflective of the heterogeneity across production systems. This paper explores the data currently available of livestock production system classifications and animal health through a scoping review as a foundation for the development of a framework that facilitates more specific estimates of livestock disease burdens. A top-down framework to classification is outlined based on a systematic review of existing classification methods and provides a basis for simple grouping of livestock at global scale. The proposed top-down classification framework, which is dominated by commodity focus of production along with intensity of resource use, may have less relevance at the sub-national level in some jurisdictions and will need to be informed and adapted with information on how countries themselves categorize livestock and their production systems. The findings in this study provide a foundation for analysing animal health burdens across a broad level of production systems. The developed framework will fill a major gap in how livestock production and health are currently approached and analysed.

Keywords

Global, analysis, animal health, animal health burden, animals, burden, burden analysis, burden of disease estimates, classification, classification framework, classification method, classification system, commodity, commodity focus, countries, data, development, development of classification systems, disease, disease burden, disease burden analysis, disease estimates, disease programmes, efficiency, estimation, expenditure, findings, focus, framework, gap, global burden, global scale, global spectrum, group, groups of livestock, health, health burden, health expenditure, health outcomes, heterogeneity, information, intensity, intensity of resource use, jurisdictions, levels, levels of production systems, livestock, livestock population, livestock production, livestock production systems, method, outcomes, people, population, production, production systems, programme, relevance, resource use, review, scale, scope, scoping review, study, sub-national level, system, system classification, systematic review, top-down framework, use

Funders

  • Bill & Melinda Gates Foundation

Data Provider: Digital Science