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Amenu K, McIntyre KM, Moje N, Knight-Jones T, Rushton J, Grace D. Approaches for disease prioritization and decision-making in animal health, 2000-2021: a structured scoping review. Front Vet Sci 2023; 10:1231711. [PMID: 37876628 PMCID: PMC10593474 DOI: 10.3389/fvets.2023.1231711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 09/06/2023] [Indexed: 10/26/2023] Open
Abstract
This scoping review identifies and describes the methods used to prioritize diseases for resource allocation across disease control, surveillance, and research and the methods used generally in decision-making on animal health policy. Three electronic databases (Medline/PubMed, Embase, and CAB Abstracts) were searched for articles from 2000 to 2021. Searches identified 6, 395 articles after de-duplication, with an additional 64 articles added manually. A total of 6, 460 articles were imported to online document review management software (sysrev.com) for screening. Based on inclusion and exclusion criteria, 532 articles passed the first screening, and after a second round of screening, 336 articles were recommended for full review. A total of 40 articles were removed after data extraction. Another 11 articles were added, having been obtained from cross-citations of already identified articles, providing a total of 307 articles to be considered in the scoping review. The results show that the main methods used for disease prioritization were based on economic analysis, multi-criteria evaluation, risk assessment, simple ranking, spatial risk mapping, and simulation modeling. Disease prioritization was performed to aid in decision-making related to various categories: (1) disease control, prevention, or eradication strategies, (2) general organizational strategy, (3) identification of high-risk areas or populations, (4) assessment of risk of disease introduction or occurrence, (5) disease surveillance, and (6) research priority setting. Of the articles included in data extraction, 50.5% had a national focus, 12.3% were local, 11.9% were regional, 6.5% were sub-national, and 3.9% were global. In 15.2% of the articles, the geographic focus was not specified. The scoping review revealed the lack of comprehensive, integrated, and mutually compatible approaches to disease prioritization and decision support tools for animal health. We recommend that future studies should focus on creating comprehensive and harmonized frameworks describing methods for disease prioritization and decision-making tools in animal health.
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Affiliation(s)
- Kebede Amenu
- Global Burden of Animal Diseases (GBADs) Programme, University of Liverpool, Liverpool, United Kingdom
- Department of Microbiology, Immunology and Veterinary, Public Health, College of Veterinary Medicine and Agriculture, Addis Ababa University, Bishoftu, Ethiopia
- Animal and Human Health Program, International Livestock Research Institute (ILRI), Addis Ababa, Ethiopia
| | - K. Marie McIntyre
- Global Burden of Animal Diseases (GBADs) Programme, University of Liverpool, Liverpool, United Kingdom
- Department of Livestock and One Health, Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, United Kingdom
- Modelling, Evidence and Policy Group, School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Nebyou Moje
- Department of Biomedical Sciences, College of Veterinary Medicine and Agriculture, Addis Ababa University, Bishoftu, Ethiopia
| | - Theodore Knight-Jones
- Global Burden of Animal Diseases (GBADs) Programme, University of Liverpool, Liverpool, United Kingdom
- Animal and Human Health Program, International Livestock Research Institute (ILRI), Addis Ababa, Ethiopia
| | - Jonathan Rushton
- Global Burden of Animal Diseases (GBADs) Programme, University of Liverpool, Liverpool, United Kingdom
- Department of Livestock and One Health, Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, United Kingdom
| | - Delia Grace
- Global Burden of Animal Diseases (GBADs) Programme, University of Liverpool, Liverpool, United Kingdom
- Food and Markets Department, Natural Resources Institute, University of Greenwich, London, United Kingdom
- Animal and Human Health Program, International Livestock Research Institute (ILRI), Nairobi, Kenya
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Rossi G, Grohn YT, Schukken YH, Smith RL. The effect of Mycobacterium avium ssp. paratuberculosis infection on clinical mastitis occurrence in dairy cows. J Dairy Sci 2017; 100:7446-7454. [PMID: 28711261 DOI: 10.3168/jds.2017-12721] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2017] [Accepted: 05/23/2017] [Indexed: 12/21/2022]
Abstract
Endemic diseases can be counted among the most serious sources of losses for livestock production. In dairy farms in particular, one of the most common diseases is Johne's disease, caused by Mycobacterium avium ssp. paratuberculosis (MAP). Infection with MAP causes direct costs because it affects milk production, but it has also been suspected to increase the risk of clinical mastitis (CM) among infected animals. This might contribute to further costs for farmers. We asked whether MAP infection represents a risk factor for CM and, in particular, whether CM occurrences were more common in MAP-infected animals. Our results, obtained by survival analysis, suggest that MAP-infected cows had an increased probability of experiencing CM during lactation. These results highlight the need to account for the interplay of infectious diseases and other health conditions in economic and epidemiological modeling. In this case, accounting for MAP-infected cows having an increased CM occurrence might have nonnegligible effects on the estimated benefit of MAP control.
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Affiliation(s)
- G Rossi
- Department of Pathobiology, University of Illinois College of Veterinary Medicine, Urbana 61802.
| | - Y T Grohn
- Department of Population Medicine and Diagnostic Sciences, Cornell University College of Veterinary Medicine, Ithaca, NY 14850
| | - Y H Schukken
- Department of Population Medicine and Diagnostic Sciences, Cornell University College of Veterinary Medicine, Ithaca, NY 14850; GD Animal Health, 7400 AA, Deventer, the Netherlands; Department of Animal Sciences, Wageningen University, Wageningen, the Netherlands
| | - R L Smith
- Department of Pathobiology, University of Illinois College of Veterinary Medicine, Urbana 61802
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Down PM, Bradley AJ, Breen JE, Browne WJ, Kypraios T, Green MJ. A Bayesian micro-simulation to evaluate the cost-effectiveness of interventions for mastitis control during the dry period in UK dairy herds. Prev Vet Med 2016; 133:64-72. [PMID: 27720028 PMCID: PMC5073076 DOI: 10.1016/j.prevetmed.2016.09.012] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2016] [Revised: 09/10/2016] [Accepted: 09/13/2016] [Indexed: 11/16/2022]
Abstract
Importance of the dry period with respect to mastitis control is now well established although the precise interventions that reduce the risk of acquiring intramammary infections during this time are not clearly understood. There are very few intervention studies that have measured the clinical efficacy of specific mastitis interventions within a cost-effectiveness framework so there remains a large degree of uncertainty about the impact of a specific intervention and its costeffectiveness. The aim of this study was to use a Bayesian framework to investigate the cost-effectiveness of mastitis controls during the dry period. Data were assimilated from 77 UK dairy farms that participated in a British national mastitis control programme during 2009-2012 in which the majority of intramammary infections were acquired during the dry period. The data consisted of clinical mastitis (CM) and somatic cell count (SCC) records, herd management practices and details of interventions that were implemented by the farmer as part of the control plan. The outcomes used to measure the effectiveness of the interventions were i) changes in the incidence rate of clinical mastitis during the first 30days after calving and ii) the rate at which cows gained new infections during the dry period (measured by SCC changes across the dry period from <200,000cells/ml to >200,000cells/ml). A Bayesian one-step microsimulation model was constructed such that posterior predictions from the model incorporated uncertainty in all parameters. The incremental net benefit was calculated across 10,000 Markov chain Monte Carlo iterations, to estimate the cost-benefit (and associated uncertainty) of each mastitis intervention. Interventions identified as being cost-effective in most circumstances included selecting dry-cow therapy at the cow level, dry-cow rations formulated by a qualified nutritionist, use of individual calving pens, first milking cows within 24h of calving and spreading bedding evenly in dry-cow yards. The results of this study highlighted the efficacy of specific mastitis interventions in UK conditions which, when incorporated into a costeffectiveness framework, can be used to optimize decision making in mastitis control. This intervention study provides an example of how an intuitive and clinically useful Bayesian approach can be used to form the basis of an on-farm decision support tool.
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Affiliation(s)
- P M Down
- University of Nottingham, School of Veterinary Medicine and Science, Sutton Bonington Campus, Loughborough LE12 5RD, United Kingdom.
| | - A J Bradley
- University of Nottingham, School of Veterinary Medicine and Science, Sutton Bonington Campus, Loughborough LE12 5RD, United Kingdom; Quality Milk Management Services Ltd, Cedar Barn, Easton Hill, Easton, Wells BA5 1DU, United Kingdom
| | - J E Breen
- University of Nottingham, School of Veterinary Medicine and Science, Sutton Bonington Campus, Loughborough LE12 5RD, United Kingdom; Quality Milk Management Services Ltd, Cedar Barn, Easton Hill, Easton, Wells BA5 1DU, United Kingdom
| | - W J Browne
- Graduate School of Education and Centre for Multilevel modelling, University of Bristol, 35 Berkeley Square, Bristol BS8 1JA, United Kingdom
| | - T Kypraios
- University of Nottingham, School of Mathematical Sciences, University Park, Nottingham NG7 2RD, United Kingdom
| | - M J Green
- University of Nottingham, School of Veterinary Medicine and Science, Sutton Bonington Campus, Loughborough LE12 5RD, United Kingdom
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Archer SC, Hudson CD, Green MJ. Use of Stochastic Simulation to Evaluate the Reduction in Methane Emissions and Improvement in Reproductive Efficiency from Routine Hormonal Interventions in Dairy Herds. PLoS One 2015; 10:e0127846. [PMID: 26061424 PMCID: PMC4465033 DOI: 10.1371/journal.pone.0127846] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2015] [Accepted: 04/20/2015] [Indexed: 11/29/2022] Open
Abstract
This study predicts the magnitude and between herd variation in changes of methane emissions and production efficiency associated with interventions to improve reproductive efficiency in dairy cows. Data for 10,000 herds of 200 cows were simulated. Probability of conception was predicted daily from the start of the study (parturition) for each cow up to day 300 of lactation. Four scenarios of differing first insemination management were simulated for each herd using the same theoretical cows: A baseline scenario based on breeding from observed oestrus only, synchronisation of oestrus for pre-set first insemination using 2 methods, and a regime using prostaglandin treatments followed by first insemination to observed oestrus. Cows that did not conceive to first insemination were re-inseminated following detection of oestrus. For cows that conceived, gestation length was 280 days with cessation of milking 60 days before calving. Those cows not pregnant after 300 days of lactation were culled and replaced by a heifer. Daily milk yield was calculated for 730 days from the start of the study for each cow. Change in mean reproductive and economic outputs were summarised for each herd following the 3 interventions. For each scenario, methane emissions were determined by daily forage dry matter intake, forage quality, and cow replacement risk. Linear regression was used to summarise relationships. In some circumstances improvement in reproductive efficiency using the programmes investigated was associated with reduced cost and methane emissions compared to reliance on detection of oestrus. Efficiency of oestrus detection and the time to commencement of breeding after calving influenced variability in changes in cost and methane emissions. For an average UK herd this was a saving of at least £50 per cow and a 3.6% reduction in methane emissions per L of milk when timing of first insemination was pre-set.
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Affiliation(s)
- Simon C. Archer
- School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington, Leicestershire, United Kingdom
- * E-mail:
| | - Christopher D. Hudson
- School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington, Leicestershire, United Kingdom
| | - Martin J. Green
- School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington, Leicestershire, United Kingdom
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