Aligning valid research outcomes with stakeholder values-what do they need for decision-making?

This paper is derived from a presentation given by the first author at the 2024 Symposium for the Calvin Schwabe Award, presented to Dr. Jan Sargeant for Lifetime Achievement in Veterinary Epidemiology and Preventive Medicine. Researchers must work toward ensuring validity throughout the research process, but we also should ensure that our resulting outcomes are specified to appropriately inform and enable decision-making by the end-users. Given the scope and diversity of topics addressed by veterinary researchers, the potential beneficiaries or stakeholders of our research also varies. Stakeholders or end-users may include veterinary practitioners, other researchers, livestock owners, "pet parents," government officials, corporate entities, or the general public in the case of public health or food security and safety issues. Current research in animal agriculture provides an opportunity to consider research outcomes in a sustainability framework which concurrently values social, economic, and environment impacts of animal health and management decisions. In companion animals, contemporary issues of affordability and access to care, quality of life, or compliance effects on efficacy, also extend the spectrum of relevant research outcomes. In these cases, traditional measures of animal health, such as morbidity, mortality, or weight gain, may not be the most relevant for the end-users. Furthermore, if studies are not designed and analyzed with well-defined primary outcomes that are informed by stakeholders' values, but rather post-hoc considerations of these values are made based on indirect or surrogate measures, there is the potential to incorporate error and bias into our conclusions and the end-users' decision-making processes.

Exposure variables in veterinary epidemiology: are they telling us what we think they are?

This manuscript summarizes a presentation delivered by the first author at the 2024 symposium for the Calvin Schwabe Award for Lifetime Achievement in Veterinary Epidemiology and Preventive Medicine, which was awarded to Dr. Jan Sargeant. Epidemiologic research plays a crucial role in understanding the complex relationships between exposures and health outcomes. However, the accuracy of the conclusions drawn from these investigations relies upon the meticulous selection and measurement of exposure variables. Appropriate exposure variable selection is crucial for understanding disease etiologies, but it is often the case that we are not able to directly measure the exposure variable of interest and use proxy measures to assess exposures instead. Inappropriate use of proxy measures can lead to erroneous conclusions being made about the true exposure of interest. These errors may lead to biased estimates of associations between exposures and outcomes. The consequences of such biases extend beyond research concerns as health decisions can be made based on flawed evidence. Recognizing and mitigating these biases are essential for producing reliable evidence that informs health policies and interventions, ultimately contributing to improved population health outcomes. To address these challenges, researchers must adopt rigorous methodologies for exposure variable selection and validation studies to minimize measurement errors.

What question are we trying to answer? Embracing causal inference.

This study summarizes a presentation at the symposium for the Calvin Schwabe Award for Lifetime Achievement in Veterinary Epidemiology and Preventive Medicine, which was awarded to the first author. As epidemiologists, we are taught that "correlation does not imply causation." While true, identifying causes is a key objective for much of the research that we conduct. There is empirical evidence that veterinary epidemiologists are conducting observational research with the intent to identify causes; many studies include control for confounding variables, and causal language is often used when interpreting study results. Frameworks for studying causes include the articulation of specific hypotheses to be tested, approaches for the selection of variables, methods for statistical estimation of the relationship between the exposure and the outcome, and interpretation of that relationship as causal. When comparing observational studies in veterinary populations to those conducted in human populations, the application of each of these steps differs substantially. The a priori identification of exposure-outcome pairs of interest are less common in observational studies in the veterinary literature compared to the human literature, and prior knowledge is used to select confounding variables in most observational studies in human populations, whereas data-driven approaches are the norm in veterinary populations. The consequences of not having a defined exposure-outcome hypotheses of interest and using data-driven analytical approaches include an increased probability of biased results and poor replicability of results. A discussion by the community of researchers on current approaches to studying causes in observational studies in veterinary populations is warranted.

OssaNMA: An R package for using information from network meta-analyses to optimize the power and sample allocation of a new two-arm trial.

Randomized clinical trials (RCTs) are designed for measuring the effectiveness of the treatments and testing a hypothesis regarding the relative effect between two or more treatments. Trial designers are often interested in maximizing power when the total sample size is fixed or minimizing the required total sample size to reach a pre-specified power. One approach to maximizing power proposed by previous researchers is to leverage prior evidence using meta-analysis (NMA) to inform the sample size determination of a new trial. For example, researchers may be interested in designing a two-arm trial comparing treatments A and B which are already in the existing trial network but do not have any direct comparison. The researchers' intention is to incorporate the result into an existing network for meta-analysis. Here we develop formulas to address these options and use simulations to validate our formula and evaluate the performance of different analysis methods in terms of power. We also implement our proposed method into the R package OssaNMA and publish an R Shiny app for the convenience of the application. The goal of the package is to enable researchers to readily adopt the proposed approach which can improve the power of an RCT and is therefore resource-saving. In the R Shiny app, We also provide the option to include the cost of each treatment which would enable researchers to compare the total treatment cost associated with each design and analysis approach. Further, we explore the effect of allocation to treatment group on study power when the a priori plan is to incorporate the new trial result into an existing network for meta-analysis.

Optimal trial design selection: a comparative analysis between two-arm and three-arm trials incorporating network meta-analysis for evaluating a new treatment.

BACKGROUND: Planning the design of a new trial comparing two treatments already in a network of trials with an a-priori plan to estimate the effect size using a network meta-analysis increases power or reduces the sample size requirements. However, when the comparison of interest is between a treatment already in the existing network (old treatment) and a treatment that hasn't been studied previously (new treatment), the impact of leveraging information from the existing network to inform trial design has not been extensively investigated. We aim to identify the most powerful trial design for a comparison of interest between an old treatment A and a new treatment Z, given a fixed total sample size. We consider three possible designs: a two-arm trial between A and Z ('direct two-arm'), a two-arm trial between another old treatment B and Z ('indirect two-arm'), and a three-arm trial among A, B, and Z. METHODS: We compare the standard error of the estimated effect size between treatments A and Z for each of the three trial designs using formulas. For continuous outcomes, the direct two-arm trial always has the largest power, while for a binary outcome, the minimum variances among the three trial designs are conclusive only when [Formula: see text]. Simulation studies are conducted to demonstrate the potential for the indirect two-arm and three-arm trials to outperform the direct two-arm trial in terms of power under the condition of [Formula: see text]. RESULTS: Based on the simulation results, we observe that the indirect two-arm and three-arm trials have the potential to be more powerful than a direct two-arm trial only when [Formula: see text]. This power advantage is influenced by various factors, including the risk of the three treatments, the total sample size, and the standard error of the estimated effect size from the existing network meta-analysis. CONCLUSIONS: The standard two-arm trial design between two treatments in the comparison of interest may not always be the most powerful design. Utilizing information from the existing network meta-analysis, incorporating an additional old treatment into the trial design through an indirect two-arm trial or a three-arm trial can increase power.

When we shouldn't borrow information from an existing network of trials for planning a new trial.

Introduction: To achieve higher power or increased precision for a new trial, methods based on updating network meta-analysis (NMA) have been proposed by researchers. However, this approach could potentially lead to misinterpreted results and misstated conclusions. This work aims to investigate the potential inflation of type I error risk when a new trial is conducted only when, based on a p-value of the comparison in the existing network, a "promising" difference between two treatments is noticed. Methods: We use simulations to evaluate the scenarios of interest. In particular, a new trial is to be conducted independently or depending on the results from previous NMA in various scenarios. Three analysis methods are applied to each simulation scenario: with the existing network, sequential analysis and without the existing network. Results: For the scenario that the new trial will be conducted only when a promising finding (p-value <5%) is indicated by the existing network, the type I error risk increased dramatically (38.5% in our example data) when analyzed with the existing network and sequential analysis. The type I error is controlled at 5% when analyzing the new trial without the existing network. Conclusion: If the intention is to combine a trial result with an existing network of evidence, or if it is expected that the trial will eventually be included in a network meta-analysis, then the decision that a new trial is performed should not depend on a statistically "promising" finding indicated by the existing network.

Connecting a disconnected trial network with a new trial: optimizing the estimation of a comparative effect in a network meta-analysis.

BACKGROUND: In network meta-analysis, estimation of a comparative effect can be performed for treatments that are connected either directly or indirectly. However, disconnected trial networks may arise, which poses a challenge to comparing all available treatments of interest. Several modeling approaches attempt to compare treatments from disconnected networks but not without strong assumptions and limitations. Conducting a new trial to connect a disconnected network can enable calculation of all treatment comparisons and help researchers maximize the value of the existing networks. Here, we develop an approach to finding the best connecting trial given a specific comparison of interest. METHODS: We present formulas to quantify the variation in the estimation of a particular comparative effect of interest for any possible connecting two-arm trial. We propose a procedure to identify the optimal connecting trial that minimizes this variation in effect estimation. RESULTS: We show that connecting two treatments indirectly might be preferred to direct connection through a new trial, by leveraging information from the existing disconnected networks. Using a real network of studies on the use of vaccines in the treatment of bovine respiratory disease (BRD), we illustrate a procedure to identify the best connecting trial and confirm our findings via simulation. CONCLUSION: Researchers wishing to conduct a connecting two-arm study can use the procedure provided here to identify the best connecting trial. The choice of trial that minimizes the variance of a comparison of interest is network dependent and it is possible that connecting treatments indirectly may be preferred to direct connection.

The standards of reporting randomized trials in pets (PetSORT): Methods and development processes.

Background: Reporting of clinical trials conducted in client- and shelter-owned dog and cat populations is not optimal, which inhibits the ability to assess the reliability and validity of trial findings and precludes the ability to include some trials in evidence synthesis. Objective: To develop a reporting guideline for parallel group and crossover trials that addresses the unique features and reporting requirements for trials conducted in client- and shelter-owned dog and cat populations. Design: Consensus statement. Setting: Virtual. Participants: Fifty-six experts from North America, the United Kingdom, Europe, and Australia working in academia, government (research and regulatory agencies), industry, and clinical veterinary practice. Methods: A steering committee created a draft checklist for reporting criteria based upon the CONSORT statement and the CONSORT extensions for reporting of abstracts and crossover trials. Each item was presented to the expert participants and was modified and presented again until >85% of participants were in agreement about the inclusion and wording of each item in the checklist. Results: The final PetSORT checklist consists of 25 main items with several sub-items. Most items were modifications of items contained in the CONSORT 2010 checklist or the CONSORT extension for crossover trials, but 1 sub-item pertaining to euthanasia was created de novo. Conclusion: The methods and processes used to develop this guideline represent a novel departure from those used to create other reporting guidelines, by using a virtual format. The use of the PetSORT statement should improve reporting of trials conducted in client- and shelter-owned dogs and cats and published in the veterinary research literature.

The standards of reporting trials in pets (PetSORT): Explanation and elaboration.

Well-designed randomized controlled trials (RCTs) provide the best evidence of the primary research designs for evaluating the effectiveness of interventions. However, if RCTs are incompletely reported, the methodological rigor with which they were conducted cannot be reliably evaluated and it may not be possible to replicate the intervention. Missing information also may limit the reader's ability to evaluate the external validity of a trial. Reporting guidelines are available for clinical trials in human healthcare (CONSORT), livestock populations (REFLECT), and preclinical experimental research involving animals (ARRIVE 2.0). The PetSORT guidelines complement these existing guidelines, providing recommendations for reporting controlled trials in pet dogs and cats. The rationale and scientific background are explained for each of the 25 items in the PetSORT reporting recommendations checklist, with examples from well-reported trials.

Using information from network meta-analyses to optimize the power and sample allocation of a subsequent trial with a new treatment.

BACKGROUND: A critical step in trial design is determining the sample size and sample allocation to ensure the proposed study has sufficient power to test the hypothesis of interest: superiority, equivalence, or non-inferiority. When data are available from prior trials and leveraged with the new trial to answer the scientific questions, the value of society's investment in prior research is increased. When prior information is available, the trial design including the sample size and allocation should be adapted accordingly, yet the current approach to trial design does not utilize such information. Ensuring we maximize the value of prior research is essential as there are always constraints on resources, either physical or financial, and designing a trial with adequate power can be a challenge. METHODS: We propose an approach to increasing the power of a new trial by incorporating evidence from a network meta-analysis into the new trial design and analysis. We illustrate the methodology through an example network meta-analysis, where the goal is to identify the optimal allocation ratio for the new three-arm trial, which involves the reference treatment, the new treatment, and the negative control. The primary goal of the new trial is to show that the new treatment is non-inferior to the reference treatment. It may also be of interest to know if the new treatment is superior to the negative control. We propose an optimal treatment allocation strategy which is derived from minimizing the standard error of the log odds ratio estimate of the comparison of interest. We conducted a simulation study to assess the proposed methods to design a new trial while borrowing information from the existing network meta-analysis and compare it to even allocation methods. RESULTS: Using mathematical derivation and simulations, we document that our proposed approach can borrow information from a network meta-analysis to modify the treatment allocation ratio and increase the power of the new trial given a fixed total sample size or to reduce the total sample size needed to reach a desired power. CONCLUSIONS: When prior evidence about the hypotheses of interest is available, the traditional equal allocation strategy is not the most powerful approach anymore. Our proposed methodology can improve the power of trial design, reduce the cost of trials, and maximize the utility of prior investments in research.

Watch your language: An exploration of the use of causal wording in veterinary observational research.

Observational research may be conducted to predict an outcome or to identify associations between an intervention or risk factor (an "exposure") and an outcome. However, the end goal of observational research often is to identify exposures that can be manipulated to improve an outcome, meaning that the aim is identify causal relationships. Causal inference from observational studies may be appropriate when an exposure-outcome of interest is identified, causal reasoning is used to identify confounders, confounders are adequately controlled, and theoretical issues, such as temporality, are considered. If these conditions are not met, causal inference cannot be made in an observational study. The objective of our study was to explore the use of causal language in veterinary observational studies, and to compare the use of causal language between studies that appear to be predictive or associational in purpose vs. those that appear to be exploring causal relationships. The dataset comprised 200 observational studies in veterinary species published between 2020 and 2022. The majority (117 out of 200) were cross-sectional studies. There were 48 studies that described an exposure-outcome of interest, and we considered these studies to be exploring potential causal relationships; of note, this liberal categorization would be anticipated to overestimate the proportion of studies suitably designed for causal inference. Overall, 172 studies (86%) used causal wording in at least one section of the article. Causal language was used in 128/152 (84%) of studies exploring predictions or associations; this language implies causation when it is not appropriate to do so. In studies designed such that causal inference might be possible, 44/48 (92%) used causal language in one or more sections. There were no substantive differences in the use of causal wording between observational study designs, exposure types, or whether the first author's affiliation was a country in which English is an official language. There is a need for authors of veterinary observational studies to explicitly state the purpose of the study (associational, predictive, or causal), and to use causal wording appropriately based on the aim of the study.

Invited review: Maximizing value and minimizing waste in clinical trial research in dairy cattle: Selecting interventions and outcomes to build an evidence base.

Clinical trials are a valuable study design for evaluating interventions when it is ethical and feasible for investigators to randomly allocate study animals to intervention groups. Researchers may choose to evaluate the comparative efficacy of intervention groups for their effect on outcomes that are relevant to the specific objectives of their trial. However, the results across multiple trials on the same intervention and with the same outcome should be considered when making decisions on whether to use an intervention, because the results of a single trial are subject to sampling error and do not reflect all biological variability. The objective of this review was to provide an overview of important concepts when selecting intervention groups and outcomes within a randomized controlled trial, and when building a body of evidence for intervention efficacy across multiple trials. Empirical evidence is presented to highlight that integrating and interpreting the efficacy of an intervention across trials is hindered by a lack of replication of interventions across trials. Inconsistency in the outcomes and their measurement among trials also limits the ability to build a body of evidence for the efficacy of interventions. The development of core outcome sets for specific topic areas in dairy science, updated as necessary, may improve consistency across trials and aid in the development of a body of evidence for evidence-based decision-making.

Levels of Evidence, Quality Assessment, and Risk of Bias: Evaluating the Internal Validity of Primary Research.

Clinical decisions in human and veterinary medicine should be based on the best available evidence. The results of primary research are an important component of that evidence base. Regardless of whether assessing studies for clinical case management, developing clinical practice guidelines, or performing systematic reviews, evidence from primary research should be evaluated for internal validity i.e., whether the results are free from bias (reflect the truth). Three broad approaches to evaluating internal validity are available: evaluating the potential for bias in a body of literature based on the study designs employed (levels of evidence), evaluating whether key study design features associated with the potential for bias were employed (quality assessment), and applying a judgement as to whether design elements of a study were likely to result in biased results given the specific context of the study (risk of bias assessment). The level of evidence framework for assessing internal validity assumes that internal validity can be determined based on the study design alone, and thus makes the strongest assumptions. Risk of bias assessments involve an evaluation of the potential for bias in the context of a specific study, and thus involve the least assumptions about internal validity. Quality assessment sits somewhere between the assumptions of these two. Because risk of bias assessment involves the least assumptions, this approach should be used to assess internal validity where possible. However, risk of bias instruments are not available for all study designs, some clinical questions may be addressed using multiple study designs, and some instruments that include an evaluation of internal validity also include additional components (e.g., evaluation of comprehensiveness of reporting, assessments of feasibility or an evaluation of external validity). Therefore, it may be necessary to embed questions related to risk of bias within existing quality assessment instruments. In this article, we overview the approaches to evaluating internal validity, highlight the current complexities, and propose ideas for approaching assessments of internal validity.

Publication and accessibility of results of controlled trials in dairy science.

Research allows for the discovery of new knowledge and is integral to evidence-based decision-making. However, research is only useful if it is available. The aim of this study was to explore publication and accessibility of full-text reports for controlled trials (experimental studies) conducted in dairy cattle. We determined the proportion of controlled trials presented as abstracts at the 2015 Joint Annual Meeting of the American Dairy Science Association and the American Society of Animal Science or the 2015 American Association of Bovine Practitioners Annual Conference that were subsequently published. Factors associated with publication or non-publication in a peer-reviewed journal were evaluated using risk ratios. For trials that were subsequently published, we compared the sample size, numerical results, and inference between the conference abstract and the subsequent publication. Approximately half of the trials (177 out of 380) reported at conferences were subsequently published. Source conference, whether the conference abstract results were described as preliminary, whether there was at least one positive outcome, author affiliation, whether the trial involved deliberate disease induction, and total sample size were not strongly associated with subsequent publication. For trials that were published, the sample size differed between the conference proceedings and full publications for 22%, the numerical results differed in 29%, and the inference differed for 11%. We also evaluated whether trials included in 9 recent systematic reviews were in English and were available without subscription or cost. Of the 390 trials included in recent systematic reviews, approximately 40% were available only through subscription or access fee. These results suggest that publication and accessibility of research results is suboptimal, representing an area of wastage in dairy cattle research. Researchers should ensure that they publish the results of trials comprehensively in searchable publications, even if the results are not novel or do not detect expected differences, and, when possible, make the results available freely.

Current use of biologic therapies for musculoskeletal disease: A survey of board-certified equine specialists.

OBJECTIVE: To evaluate the use of mesenchymal stem cells (MSCs), autologous conditioned serum (ACS), platelet-rich plasma (PRP), and autologous protein solution (APS) for the treatment of equine musculoskeletal disease by diplomates of the American College of Veterinary Surgery (ACVS), and American College of Veterinary Sports Medicine and Rehabilitation (ACVSMR). STUDY DESIGN: Cross-sectional study. SAMPLE POPULATION: Diplomates (n = 423). METHODS: An email link was sent to ACVS and ACVMR diplomates. A survey contained 59 questions regarding demographics, as well as indications, frequency, adverse effects, and limitations of use. Responses were analyzed using Fisher's exact test. RESULTS: One hundred and fifty four surveys were analyzed. Years in practice and type of practice were not associated with biologic therapy use. PRP was the most used therapy (120/137; 87.5%). PRP and MSCs were most often administered intralesionally while ACS and APS were most often administered intra-articularly. ACS (50/104; 48.1%) treatment was repeated commonly within 2 weeks of initial injection. MSCs (39/90; 43.3%) and PRP (38/100; 38%) were commonly repeated 1-2 months after initial injection and APS was typically repeated >4 months after initial injection (21/53; 39.6%). Local inflammation and expense were the most common adverse effect and limitation of use. CONCLUSION: Diplomates most commonly utilized PRP and MSC intralesionally for soft-tissue injuries, and ACS and ACP intra-articularly for joint injury. Protocols for repeated administration varied widely. Local inflammation was a clinical concern with the use of biologics. CLINICAL SIGNIFICANCE: Biologic therapies are used commonly by ACVS and ACVSMR diplomates for soft tissue and joint disease.

Salmonella detection in commercially prepared livestock feed and the raw ingredients and equipment used to manufacture the feed: A systematic review and meta-analysis.

Salmonella contamination of livestock feed is a serious veterinary and public health issue. In this study we used a systematic review to assess the prevalence and characterization of Salmonella isolates detected in raw feed components, feed milling equipment and finished feed from 97 studies published from 1955 to 2020 across seven global regions. Eighty-five studies were included in a meta-analyses to estimate the combined prevalence of Salmonella detection and to compare the risk of contamination associated with different sample types. We found the overall combined prevalence estimate of Salmonella detection was 0.14 with a prevalence of 0.18 in raw feed components, 0.09 in finished feed and 0.08 in feed milling equipment. Animal based raw feed components were 3.9 times more likely to be contaminated with Salmonella than plant based raw feed components. Differences between studies accounted for 99 % of the variance in the prevalence estimate for all sample types and there was no effect of region on the prevalence estimates. The combined prevalence of Salmonella detection in raw feed components decreased from 0.25 in 1955 to 0.11 in 2019. The proportion of Salmonella isolates that were resistant to antimicrobials was largest for amikacin (0.20), tetracycline (0.18) streptomycin (0.17), cefotaxime (0.14) and sulfisoxazole (0.11). The prevalence of Salmonella contamination of animal feed varies widely between individual studies and is an ongoing challenge for the commercial feed industry. Control relies on the vigilant monitoring and control of Salmonella in each individual mill.

A likelihood ratio test for the homogeneity of between-study variance in network meta-analysis.

BACKGROUND: Network meta-analysis (NMA) is a statistical method used to combine results from several clinical trials and simultaneously compare multiple treatments using direct and indirect evidence. Statistical heterogeneity is a characteristic describing the variability in the intervention effects being evaluated in the different studies in network meta-analysis. One approach to dealing with statistical heterogeneity is to perform a random effects network meta-analysis that incorporates a between-study variance into the statistical model. A common assumption in the random effects model for network meta-analysis is the homogeneity of between-study variance across all interventions. However, there are applications of NMA where the single between-study assumption is potentially incorrect and instead the model should incorporate more than one between-study variances. METHODS: In this paper, we develop an approach to testing the homogeneity of between-study variance assumption based on a likelihood ratio test. A simulation study was conducted to assess the type I error and power of the proposed test. This method is then applied to a network meta-analysis of antibiotic treatments for Bovine respiratory disease (BRD). RESULTS: The type I error rate was well controlled in the Monte Carlo simulation. We found statistical evidence (p value = 0.052) against the homogeneous between-study variance assumption in the network meta-analysis BRD. The point estimate and confidence interval of relative effect sizes are strongly influenced by this assumption. CONCLUSIONS: Since homogeneous between-study variance assumption is a strong assumption, it is crucial to test the validity of this assumption before conducting a network meta-analysis. Here we propose and validate a method for testing this single between-study variance assumption which is widely used for many NMA.

A Bayesian latent class mixture model with censoring for correlation analysis in antimicrobial resistance across populations.

BACKGROUND: The emergence of antimicrobial resistance across populations is a global threat to public health. Surveillance programs often monitor human and animal populations to evaluate trends of emergence in these populations. Many national level antibiotic resistance surveillance programs quantify the proportion of resistant bacteria as a means of monitoring emergence and control measures. The reason for monitoring these different populations are many, including interest in similar changes in resistance which might provide insight into emergence and control options. METHODS: In this research, we developed a method to quantify the correlation in antimicrobial resistance across populations, for the conventionally unnoticed mean shift of the susceptible bacteria. With the proposed Bayesian latent class mixture model with censoring and multivariate normal hierarchy, we address several challenges associated with analyzing the minimum inhibitory concentration data. RESULTS: Application of this approach to the surveillance data from National Antimicrobial Resistance Monitoring System led to a detection of positive correlation in the central tendency of azithromycin resistance of the susceptible populations from Salmonella serotype Typhimurium across food animal and human populations. CONCLUSIONS: Our proposed approach has been shown to be accurate and superior to the commonly used naïve estimation by simulation studies. Further implementation of this Bayesian model could serve as a useful tool to indicate the co-existence of antimicrobial resistance, and potentially a need of clinical intervention.

Completeness of reporting of systematic reviews in the animal health literature: A meta-research study.

Systematic reviews are a valuable tool for evaluating the efficacy of interventions and for quantifying associations. To be properly assessed, reviews must be comprehensively reported. The primary objective of this study was to evaluate the completeness of reporting of systematic reviews and meta-analyses in animal health. The secondary objective was to further characterize methods for literature searches and risk of bias assessments and to document whether the risk of bias component represented an assessment of risk of bias, study quality, or levels of evidence based on the primary studies included. The dataset comprised 91 systematic reviews or meta-analyses of interventions or exposures with at least one health outcome measured at the animal or animal byproduct level, in any companion or food animal species and published between 2014 and 2018. Two reviewers independently collected information on whether each item in the PRISMA reporting guidelines was reported, with disagreements resolved by consensus. There was considerable variability in the completeness of reporting among reviews; some items, such as eligibility criteria for inclusion, were reported in most reviews (>65 %). Other items were not consistently reported; for instance, in 60 % (54) of the reviews there was no information provided on the sample size of individual studies, populations, interventions and comparators, outcomes, or follow up period. Although 89 % (81) of systematic reviews with meta-analysis included the effect size estimate and confidence intervals, it was not possible to determine which study designs were included for 30 % (14) of reviews. Results from individual PRISMA item questions were combined to determine whether all aspects of each recommended item were reported; 71 % of items were adequately reported in less than half the systematic reviews without a meta-analysis, 35 % of the items were adequately reported in less than half the systematic reviews with a meta-analysis, and 71 % of items were adequately reported in less than half of the meta-analyses without a systematic review component. An assessment of individual study level bias was included in 64 % of the reviews, although this component included an evaluation of risk of bias (35 reviews), study quality (25 reviews), or levels of evidence based on study design (12 reviews). Reporting guidelines or clinical guidelines were inappropriately used to assess risk of bias in 9 reviews. Overall, the results of this study reveal that reporting of systematic reviews in the animal health literature is suboptimal and improvements are needed to enhance utility of these reviews.

Non-antibiotic Approaches for Disease Prevention and Control in Nursery Pigs: A Scoping Review.

Swine producers are encouraged to practice antibiotic stewardship by reducing their use of antibiotics belonging to classes of medical importance to humans. We conducted a scoping review of non-antibiotic approaches in the form of products or management practices that might prevent or control disease and thus reduce the need for antibiotics in nursery pigs. Our objectives were to systematically describe the research on this broad topic for the North American context, identify specific topics that could feasibly support systematic reviews, and identify knowledge gaps. A search of multiple databases identified 11,316 articles and proceedings for relevance screening. From these, 441 eligible clinical trials and observational studies were charted. The majority were clinical trials (94%). Study results from EU countries were mostly communicated through journal articles, whereas study results from the USA were mostly communicated through conference proceedings. Interventions and health outcomes were diverse. The two most frequent intervention categories were feed additives and piglet vaccines. The three most frequent outcomes reported were diarrhea, mortality, and indices of vaccine immunity. There were 13 specific topics comprising various feed additives and vaccines that might feasibly support systematic reviews. There were relatively few studies in which interventions were compared with antibiotic comparison groups and relatively few studies evaluating management practices.