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Systematic Review

Detection of Bovine Respiratory Syncytial Virus in Cattle: A Systematic Review and Meta-Analysis

by
Gebremeskel Mamu Werid
1,
Ashenafi Kiros Wubshet
2,
Teshale Teklue Araya
3,
Darren Miller
1,
Farhid Hemmatzadeh
4,
Michael P. Reichel
5 and
Kiro Petrovski
1,4,*
1
Davies Livestock Research Centre, School of Animal & Veterinary Sciences, University of Adelaide, Roseworthy Campus, Roseworthy, SA 5371, Australia
2
Shandong Province Binzhou Animal Husbandry and Veterinary Institute, A3 Floor, No. 777 Changjiang Wu Road, Bincheng District, Binzhou 256606, China
3
Mekelle Agricultural Research Center, Mekelle P.O. Box 492, Ethiopia
4
Australian Centre for Antimicrobial Resistance Ecology, School of Animal & Veterinary Sciences, University of Adelaide, Roseworthy Campus, Roseworthy, SA 5371, Australia
5
Department of Population Medicine and Diagnostic Sciences, Cornell University College of Veterinary Medicine, Ithaca, NY 14853, USA
*
Author to whom correspondence should be addressed.
Ruminants 2024, 4(4), 491-514; https://doi.org/10.3390/ruminants4040035
Submission received: 10 September 2024 / Revised: 22 October 2024 / Accepted: 23 October 2024 / Published: 29 October 2024
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Simple Summary

Bovine respiratory syncytial virus (BRSV) is a major contributor to cattle respiratory diseases. The study systematically reviewed 110 studies to assess BRSV prevalence and detection rates. Results showed that BRSV prevalence varies with the detection method: 0.62 for antibody-based, 0.05 for antigen-based, and 0.03 for nucleic acid-based methods. Key factors influencing prevalence included location, herd size, and co-infections. These findings indicate the complexity of BRSV detection.

Abstract

Bovine respiratory syncytial virus (BRSV) is an economically important pathogen of cattle and contributes to the bovine respiratory disease complex (BRDC). Despite individual studies investigating BRSV prevalence, risk factors, and detection methodologies, a systematic review and meta-analysis have been lacking. The aim of the current study was to conduct a systematic review and meta-analysis to determine the prevalence and detection rate of BRSV and identify associated risk factors. Additionally, the study aimed to explore the variability in BRSV prevalence based on different detection methods and associated risk factors. Adhering to PRISMA guidelines, data from three databases—Web of Science, PubMed, and Scopus—were systematically retrieved, screened and extracted. Out of 2790 initial studies, 110 met the inclusion criteria. The study found that prevalence and detection rates varied based on the detection methods used (antibody, antigen, and nucleic acid), study populations, production systems, and geographic locations. Findings were reported as a pooled proportion. The pooled proportion, hereafter referred to as prevalence or detection rate, was determined by calculating the ratio of cattle that tested positive for BRSV to the total number of cattle tested. Key findings include a pooled prevalence of 0.62 for antibody-based methods, 0.05 for antigen-based methods, and 0.09 (adjusted to 0.03) for nucleic acid-based methods. Detection rates in BRDC cases also varied, with antibody methods showing a rate of 0.34, antigen methods 0.16, and nucleic acid methods 0.13. The certainty of evidence of the meta-analysis results, assessed using GRADE, was moderate for antibody detection methods and low for antigen and nucleic acid methods. The study identified significant risk factors and trends affecting BRSV prevalence, such as geographical location, herd size, age, and co-infections. The results of the current study showed the complexity of understanding BRSV prevalence in different settings. The variability in BRSV prevalence based on detection methods and associated risk factors, such as geographic location and herd size, highlights the need for tailored approaches to detect and manage BRSV accurately.

1. Introduction

Bovine respiratory syncytial virus (BRSV) is one of the primary pathogens of the bovine respiratory disease complex (BRDC) [1]. BRSV is linked to economic and animal welfare concerns in the cattle industry. BRSV is a negative-sense, single-stranded, enveloped RNA virus that belongs to the Orthopneumovirus genus within the Pneumoviridae family [2]. BRSV has been classified into four groups, A, B, AB, and an intermediate group, based on antigenic differences, and into six distinct genetic clusters [3].
BRSV, transmitted through aerosols and contact, primarily affects the respiratory tract and reduces cattle productivity [4,5,6]. While the virus targets the epithelial cells of the respiratory tract, the extensive damage predominantly comes from the host’s immune response. By compromising its protective barrier, this destruction of epithelial cells renders the respiratory system more susceptible to secondary bacterial infections [7]. The clinical spectrum of BRSV varies from subtle, subclinical infections to severe respiratory signs, leading to outbreaks with morbidity and, in some cases, mortality [8,9]. Without co-infections, even in severe cases, affected calves usually recover within 10 days [10].
Reinfection with BRSV is common, and infection can occur despite the presence of maternal antibodies [11,12]. Subclinical reinfection is widely regarded as the primary contributor to BRSV transmissions [9,13]. Multiple factors, including environmental conditions, herd management, and cattle physiological status, can modulate BRSV prevalence [14,15,16,17,18,19], affecting disease transmission and subsequent management. Moreover, the role of co-infections complicates the clinical and epidemiological picture of BRSV, as they can significantly modify the disease outcome. The complex interaction between BRSV and other respiratory pathogens such as bovine alphaherpesvirus 1(BHV1), Bovine parainfluenza-3 virus (BPI3V), bovine viral diarrhea virus (BVDV), and Histophillus somni indicates the importance of understanding these intricacies for holistic disease management [17,20,21].
The relationship between antibody levels and BRSV infection is more complex than merely indicating past infections. In calves, the presence of neutralising antibodies did not prevent infection but did lessen the severity of the disease [22]. An inverse correlation was observed between antibody levels targeting BRSV and disease intensity [22]. Furthermore, a higher level of mucosal antibodies offered defence against the disease, while serum antibodies reduced disease severity [13]. On the other hand, the severity of clinical signs correlated with the presence of BRSV-specific IgE in both serum and lymph [23], which indicated the role of BRSV-specific IgE in exacerbating disease severity, thereby complicating the role of immunoglobulins in BRSV infections.
Significant advancements have been achieved in the detection of BRDC pathogens. However, there are significant gaps in our understanding of BRSV detection and prevalence. For instance, in clinical cases, BRSV is most consistently detected in the cranioventral parts of the lung [24]. Moreover, bronchoalveolar lavage (BAL) samples have been observed to have slightly higher BRSV levels than nasal swabs [25,26]. The virus induces weak B-cell and T-cell responses [27,28], making cattle vulnerable to repeated reinfections. Another challenge is the short duration of virus shedding, which typically does not extend beyond six days [29,30]. This transient nature of infection, combined with the vulnerability of the virus to degradation, emphasises the crucial relationship between sample type, transportation, storage, and overall detection accuracy [31].
Effective management and prevention of BRSV depends on understanding its prevalence. However, prevalence estimates are influenced by the variety of detection methods, each with its own sensitivity and specificity. Fluorescent Antibody Testing (FAT or IFAT) detects the virus using fluorescently labelled antibodies, Enzyme-Linked Immunosorbent Assay (ELISA) operates on antigen-antibody interactions, and Polymerase Chain Reaction (PCR) detects nucleic acids. In addition, isolation of BRSV has been challenging, even in PCR-confirmed samples [25,31,32,33,34]. Such variability in detection presents challenges to accurately assessing the BRSV prevalence. The interaction between these detection methods and multiple risk factors influencing BRSV prevalence is also poorly documented. Hence, the objectives of this systematic review and meta-analysis were to determine the variations in BRSV prevalence or detection rates when assessed through different detection methods and identify key risk factors influencing prevalence.

2. Materials and Methods

2.1. Literature Search Strategy

The research questions, search methodologies, screening procedures, and presentation of findings were conducted in line with the guidelines set by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) (S2) [35]. For the databases Web of Science, PubMed, and Scopus, advanced search queries targeting studies on “bovine respiratory syncytial virus” in cattle or bovine were applied to titles, abstracts, and keywords, and all articles published until 19 May 2023 at 9:00 am were retrieved. In PubMed, the query was “bovine respiratory syncytial virus” [All Fields]) AND (“cattl” [MeSH Terms] OR “cattl” [All Fields]) OR “bovin” [All Fields]). In Web of Science, the query string was (TS = (“bovine respiratory syncytial virus”)) AND (TS = (“cattle”) OR TS = (“bovine”)). For Scopus, the search query used was TITLE-ABS-KEY “bovine respiratory syncytial virus” AND “cattle” OR “bovine”)).
The study followed a review protocol registered at https://osf.io/zqxba (DOI: https://doi.org/10.17605/OSF.IO/ZQXBA, accessed on 3 June 2024). In brief, the first two authors systematically retrieved, reviewed, and extracted all articles, ensuring their quality using the Covidence platform (https://app.covidence.org, accessed on 26 September 2023). Any discrepancies were resolved through consultation with a third senior author. The consensus data were then included in the analysis. The collected data were screened based on specified inclusion and exclusion criteria (Table 1). The study population included cattle from any production system, breed, region, or age group, and studies reporting the prevalence or detection rate of BRSV were considered for inclusion. Although articles published primarily in English were considered, studies in other languages were also included if translations were available.
Only peer-reviewed articles with sample sizes greater than 30 and involving at least two farms were included.
On the other hand, the exclusion criteria excluded reviews, commentaries, opinion pieces, and editorials. Articles that focused on species other than cattle or lacked laboratory-confirmed prevalence or detection rates of BRSV were also excluded. Furthermore, articles lacking sufficient information regarding the type and origin of data used for analysis and those with duplicates or overlapping data were excluded.

2.2. Data Extraction and Quality Assessment

Key variables such as publication year, author list, country of origin, study design, production system type (beef, dairy, mixed, or others), total number of cattle or herds tested, number of BRSV-positive cattle or herds, age group, BRSV detection method, and sample type were systematically extracted from all included articles using the Covidence data extraction tool (https://app.covidence.org, accessed on 26 September 2023). For cohort or observational studies, data from day 0 were included in the analysis. When a study has epidemiological data from multiple years, only data from the most recent year were included in the analysis. Articles that used different BRSV detection methods were treated as separate entries in the meta-analysis, provided all samples were derived from the same initial study population. Entries without clear descriptions of population characteristics and sampling strategies were excluded from the final analysis.
The quality of the selected articles was assessed using the Joanna Briggs Institute Qualitative Assessment and Review Instrument (JBI-QARI; https://jbi.global/critical-appraisal-tools, accessed on 6 September 2023).

2.3. Qualitative Data Selection and Analysis

The Nvivo 20.0 software [36] was used to systematically identify, code, and categorise thematically the risk factors and associated variables. Data organised by thematic area was exported to MS Word for further analysis.

2.4. Statistical and Meta-Analysis

Detection methods were divided into three groups based on the analyte identified. Competitive and indirect ELISA, IFAT, and neutralisation tests were classified under the antibody detection method. The antigen detection group included antigen capture, direct and Sandwich ELISA, immunohistochemistry (IHC), and direct fluorescence antibody test (DFAT). On the other hand, reverse transcriptase polymerase chain reaction (RT-PCR), real-time RT-PCR and nested RT-PCR were classified under the nucleic acid detection method. However, based on the type of analyte identified in the article, competitive ELISA was classified either as an antigen or an antibody detection method.
The study analysed various sample types, including bronchoalveolar lavage, nasal swabs, blood, and lung tissue. Samples referred to as nasopharyngeal, nasal, or deep nasopharyngeal swabs were grouped and classified as ‘nasal swab samples’, while those described as bronchoalveolar, lung, or tracheobronchial lavage were classified as ‘bronchoalveolar lavage samples.
For the meta-analysis, the study population was classified into three categories: (1) “Randomly Selected Samples,” consisting of animals without respiratory symptoms; (2) “Longitudinal (Observational and Experimental),” including animals from observational or case-control studies; and (3) “BRDC Cases,” which included animals showing respiratory symptoms, cattle identified as BRDC cases in the primary study, or those involved in BRDC outbreaks. Health status was included as a categorical variable representing these different groups. Each study was categorised based on the health status information provided in the original studies. Subgroup analyses were used to explore the impact of health status on BRSV prevalence. Specifically, separate meta-analyses were conducted for each health status group to account for differences between BRD and non-BRD cases.
The meta-analysis was conducted using R packages ‘meta’ and ‘metafor’ [37] by applying the inverse variance method with the DerSimonian-Laird estimator for tau2. Prediction intervals were determined using the t-distribution, and the Freeman-Tukey double arcsine transformation was used to stabilise the variance [38]. In the current study, prevalence was defined as the proportion of cattle found to have BRSV at the time of testing or at the beginning of the study, and it is used for cross-sectional and longitudinal studies with a general population. However, detection rate refers to the proportion of BRSV-positive cases, specifically in cattle populations with BRDC or suspected of BRDC. The primary effect measure was identified using the DerSimonian-Laird random-effects model and expressed as a pooled proportion. The pooled proportion, referred to as prevalence or detection rate, was calculated by dividing the number of cattle testing positive for BRSV by the total number of cattle tested and was presented with a 95% confidence interval. Given the expected heterogeneity across articles, a random-effects model was employed in the aggregated analysis [37,38]. The degree of heterogeneity was assessed using the I2 statistic [39], complemented by the Q test [40].
Potential sources of heterogeneity were investigated through subgroup analysis, focusing on detection methods (antibody, antigen, nucleic acid, and virus isolation) and cattle production systems (beef cattle, dairy cattle, and local breeds), with each subgroup comprised of at least five studies.
Effect size distribution was visually assessed using Graphic Display of Heterogeneity (GOSH) plots [41]. Given the non-normal distribution of epidemiologic data, the DBSCAN algorithm was employed to identify outliers [42]. Outliers identified by both influence analysis and DBSCAN were excluded from the dataset. After the outliers were removed, a sensitivity analysis was carried out to assess the robustness of the findings, and the meta-analysis was repeated to verify the consistency of the results.
The potential existence of publication bias was evaluated with Peters’ [43] and Egger’s [44] tests and a p < 0.05 was considered statistically significant.

2.5. Assessment of Certainty of Evidence

The overall certainty of evidence for the meta-analysis results was assessed using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) framework [45].

3. Results

3.1. Study Selection and Characteristics

The included articles were from 34 countries, with 32 reporting at the animal-level (Supplementary) and 2 focusing on the herd-level. Out of 4466 retrieved articles from three databases, 1676 duplicates were removed, leaving 2790 articles to be screened. Of the 2790 articles primarily screened by title and abstract, 2545 were excluded as they did not meet the inclusion criteria, leaving 245 articles. Of the 245 articles flagged for full-text retrieval, 182 were successfully retrieved and evaluated; 110 were selected for data extraction, though 11 were subsequently excluded upon quality assessment. Ultimately, a total of 99 articles were included in the meta-analysis (Figure 1). The articles included comprised longitudinal (n = 12), cross-sectional (n = 49), case series (n = 29), and diagnostic test accuracy articles (n = 9). Based on the detection method, 52, 12, and 34 articles used antibody, antigen, and nucleic acid detection methods, respectively. The articles were categorised based on age, cattle production systems, and study populations. Only 21 out of 99 articles have the term “bovine respiratory syncytial virus” in the title, possibly indicating that BRSV was not the primary focus of many articles.

3.2. Prevalence of BRSV

The rate at which BRSV was detected varied based on age group, detection method, production system type, geographical location, and population type. However, no publication bias was detected using Peters’ and Egger’s tests. The global distribution of BRSV in randomly selected cattle populations and cattle with signs of BRDC varied based on the detection method used (Figure 2).

3.2.1. Prevalence of Bovine Respiratory Syncytial Virus Detected by Antibody-Based Detection Methods

The meta-analysis, aimed at evaluating the prevalence of BRSV using antibody-based detection methods, initially included 36 articles with 18,594 observations. The pre-outlier removal analysis revealed a pooled prevalence of 0.63. Subsequently, after excluding outliers, the analysis was refined to include 33 articles with 15,071 observations, which slightly adjusted the pooled prevalence to 0.64 (Figure 3A and Table 2). Both analyses involved subgroup analysis based on age group, production system, and study population type. Subgroup analysis across age groups (calves, mixed/unknown, adults), production systems (beef cattle, dairy cattle, mixed/unknown), and study population types (randomly selected samples, longitudinal (observational and experimental studies)) revealed consistent BRSV prevalence (Supplementary Figure S2). No significant differences were observed within these subgroups in pre- and post-outlier removal analyses.

3.2.2. Prevalence of Bovine Respiratory Syncytial Virus Detected by Antigen Detection Methods

Only two articles were found that used antigen detection methods to evaluate the prevalence of BRSV in randomly selected samples. The analysis was based on a relatively small dataset comprising 494 observations and 24 BRSV-positive cases. A pooled prevalence of BRSV 0.05 (95% CI: 0.00 to 0.30) with a moderate level of heterogeneity (I2 = 34.9%), a tau value of 0.02, and an H statistic of 1.24 was found.

3.2.3. Prevalence of Bovine Respiratory Syncytial Virus Detected by Nucleic Acid-Based Detection Methods

The analysis used nucleic acid detection methods to assess the prevalence of BRSV in randomly selected samples. Initially, the analysis included 10 articles with 3626 observations, revealing a pooled prevalence of 0.09.
Following the detection and removal of outliers, the analysis included eight articles with 3159 observations, which led to a slight decrease in the pooled prevalence to 0.03, a narrower prediction interval of 0.00 to 0.15, and a reduced heterogeneity of 90% (Figure 4A, Table 3 and Supplementary Figure S3A).
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Figure 4. Comparison of bovine respiratory syncytial virus detection rates using nucleic acid detection method: demonstrating a low proportion of positive cattle in the randomly sampled cattle populations compared to those with bovine respiratory disease complex [1,26,32,33,34,47,50,93,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120]. (A) prevalence in the randomly selected cattle populations, and (B) detection rate in cattle with bovine respiratory disease complex.
Figure 4. Comparison of bovine respiratory syncytial virus detection rates using nucleic acid detection method: demonstrating a low proportion of positive cattle in the randomly sampled cattle populations compared to those with bovine respiratory disease complex [1,26,32,33,34,47,50,93,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120]. (A) prevalence in the randomly selected cattle populations, and (B) detection rate in cattle with bovine respiratory disease complex.
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Table 3. Sensitivity of the pooled prevalence of BRSV using nucleic acid detection method.
Table 3. Sensitivity of the pooled prevalence of BRSV using nucleic acid detection method.
Variable NameBefore Outlier RemovalAfter Outlier Removal
Number of studies108 *
Number of observations36263159
Number of events32979
Pooled prevalence0.09 (95% CI: 0.01 to 0.23)0.03 (95% CI: 0.01 to 0.06)
Prediction interval0.00 to 0.670.00 to 0.15
Heterogeneity (I2)98.9% (tau2 = 0.07, H = 9.49)89.9% (tau2 = 0.01, H = 3.14)
* Outliers removed: [34,110].

3.3. BRSV Detection Rate Among Bovine Respiratory Disease Complex (BRDC) Cases

3.3.1. Detection Rate of BRSV Using Antibody Detection Methods in BRD Cases

The meta-analysis, utilising antibody detection methods to assess the BRSV detection rate, initially involved 15 articles comprising 3926 observations. The pooled detection rate was found to be 0.38, with a substantial heterogeneity (I2 = 98.5%). The prediction interval ranged from 0.00 to 0.92, reflecting significant diversity in detection rate rates across different settings.
After outlier removal, the refined analysis included 14 articles with 3798 observations. This adjustment led to a marginal decrease in the pooled detection rate to 0.34 and a reduction in heterogeneity (I2 = 98.0%). The prediction interval narrowed slightly to 0.01–0.83 (Table 4 and Figure 3B).
The dataset included no subgroup for production systems (only beef cattle) and no population type (only BRDC cases). Hence, a comparison of age groups was conducted, which revealed no significant difference in the prevalence of BRSV between adults and calves. Meta-analysis investigating BRSV detection rate using antibody detection methods included 14 articles, encompassing 3798 observations and identified 1104 BRSV-positive cases. The overall pooled detection rate from the random effects model was estimated at 0.34, with a wide prediction interval ranging from 0.01 to 0.83.
The test for subgroup differences did not detect significant differences across the diverse study populations (Q = 2.75, p > 0.05), with both the “calves Subgroup” (n = 7) and the “adult Subgroup” (n = 3) showing a detection rate of 0.25 (Supplementary Figure S4).

3.3.2. Detection Rate of the Bovine Respiratory Syncytial Virus Using Antigen Detection Methods in Cattle with Bovine Respiratory Disease Complex Cases

The study utilised the antigen detection method to evaluate the BRSV detection rate in samples from BRDC tests. We analysed the data before and after removing outliers to understand their impact on the study outcomes. Initially, the analysis included 12 articles with 1050 observations and 225 BRSV-positive cases. The pooled detection rate was 0.23, with a prediction interval ranging from 0.00 to 0.88, indicating substantial variability among the articles. The dataset showed high heterogeneity (I2 = 96.9%, tau = 0.30), as reflected in the significant Q test value (p < 0.05). After removing outliers, the analysis comprised 11 articles with 978 observations and 157 BRSV cases. This led to a decrease in the pooled detection rate to 0.16 and a narrower prediction interval of 0.00 to 0.56. The heterogeneity was reduced but remained high (I2 = 91.5%, tau = 0.18; Table 5 and Figure 5).
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Figure 5. Prevalence of bovine respiratory syncytial virus using antigen detection methods in cattle with bovine respiratory disease complex [121,122,123,124,125,126,127,128,129,130].
Figure 5. Prevalence of bovine respiratory syncytial virus using antigen detection methods in cattle with bovine respiratory disease complex [121,122,123,124,125,126,127,128,129,130].
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Table 5. Sensitivity of the pooled detection rate of BRSV using antigen detection method.
Table 5. Sensitivity of the pooled detection rate of BRSV using antigen detection method.
Variable NameBefore Outlier RemovalAfter Outlier Removal
Number of studies1211 *
Number of observations1050978
Pooled detection rate0.23 (95% CI: 0.08 to 0.42)0.16 (95% CI: 0.08 to 0.28)
Prediction interval0.00 to 0.880.00 to 0.56
Heterogeneity (I2)96.9% (tau = 0.30)91.5% (tau = 0.18)
* Outlier removed: [131].
In the subgroup analysis of the BRSV detection rate using antigen detection, involving 11 articles with 978 observations and 157 events, we explored variations across age groups, detection methods, and production systems. The overall pooled detection rate was 0.16, with a prediction interval from 0.00 to 0.56, reflecting high heterogeneity (I2 = 91.5%).
The age group analysis indicated that calves had a detection rate of 0.23 with 82.2% heterogeneity, while mixed or unknown groups showed a detection rate of 0.18 with 94.0% heterogeneity. Adults had a notably lower detection rate of 0.08, with heterogeneity at 92.5%. However, these differences across age groups were not statistically significant (Q = 1.40, p > 0.05).
In the detection method subgroup, antigen ELISA showed a detection rate of 0.08 (I2 = 93.4%), FAT indicated a detection rate of 0.17 (I2 = 92.1%), and IHC reported 0.21 (I2 = 89.3%). The differences between these methods were not significant (Q = 1.00, p > 0.05).
The analysis by production system revealed significant differences: beef cattle had a detection rate of 0.07 with no heterogeneity, dairy cattle showed 0.19 with heterogeneity at 92.9%, and mixed or unknown systems indicated a detection rate of 0.30 with 84.1% heterogeneity (Q = 19.16, p < 0.05; Supplementary Figure S5).

3.3.3. Detection Rate of Bovine Respiratory Syncytial Virus Using Nucleic Acid Detection Methods in Cattle with Bovine Respiratory Disease Complex Cases

The meta-analysis assessing BRSV detection rate using nucleic acid detection, the initial analysis of 24 articles (6618 observations, 908 BRSV cases) revealed a pooled detection rate of 0.18, with considerable variability indicated by a wide prediction interval of 0.00 to 0.55 and high heterogeneity (I2 = 97.4%, tau2 = 0.04).
After removing outliers, the analysis, which included 22 articles with 6495 observations and 813 cases, showed a reduced detection rate of 0.13 and a narrower prediction interval of 0.00 to 0.41 (Table 6 and Figure 4B). The heterogeneity remained high (I2 = 96.3%), albeit slightly reduced, highlighting the influence of outliers on detection rate estimates and study variability.
Meta-analysis on BRSV detection rate, employing nucleic acid detection, incorporated 22 articles involving 6495 observations and 813 positive cases. The overall pooled detection rate was 0.13, with substantial variability indicated by a prediction interval of 0.00 to 0.41 and high heterogeneity (I2 = 96.3%) (Table 4, Figure 4B).
Subgroup analysis by production system revealed beef cattle (5 articles) with a detection rate of 0.13 and heterogeneity at 89.4%, dairy cattle (7 articles) with a detection rate of 0.14 and heterogeneity at 96.8%, and mixed or unknown (10 articles) with a detection rate of 0.12 and heterogeneity at 97.1%. No significant differences were found between these production systems (Q = 0.08, p > 0.05).
Age group analysis showed calves (10 articles) with a detection rate of 0.15 and heterogeneity at 97.4% and mixed or unknown (12 articles) with a detection rate of 0.11 and heterogeneity at 94.2%. There were no significant differences between these age groups (Q = 0.37, p > 0.05; Supplementary Figure S6).

3.4. Herd-Level Prevalence of Bovine Respiratory Syncytial Virus

For articles involving herds without BRDC cases, the meta-analysis included 18 articles with 3706 observations and 2598 events. Of these, 16 articles were carried out using the antibody detection method, while only 2 used the nucleic acid detection method. The pooled prevalence of BRSV was 0.84 (95% CI: 0.69 to 0.95). A subgroup analysis by detection method did not show a significant difference between these groups (Q = 2.23, p = 0.14; Figure 6A).
Seven articles involving herds with BRDC cases, with 189 observations and 70 events, were analysed. Of these, one article used antigen-based detection, four used antibody-based detection, and two articles used nucleic acid-based detection. The prevalence in this subgroup was 0.44 (95% CI: 0.20 to 0.70; Figure 6B).
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Figure 6. The proportion of herds tested positive for bovine respiratory syncytial virus: (A) in the randomly selected cattle populations, and (B) in herds with bovine respiratory disease complex [9,15,16,17,18,46,50,51,53,54,55,61,62,63,66,68,71,73,110,119,129,132].
Figure 6. The proportion of herds tested positive for bovine respiratory syncytial virus: (A) in the randomly selected cattle populations, and (B) in herds with bovine respiratory disease complex [9,15,16,17,18,46,50,51,53,54,55,61,62,63,66,68,71,73,110,119,129,132].
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3.5. Factors Influencing BRSV Prevalence

Several risk factors that can influence the prevalence of BRSV were detected in the present study. The identified risk factors were age, certain demographic characteristics, co-infection, and farm management (Table 7). While cohort studies that specifically focused on the risk factors of BRSV are limited, existing evidence highlights varying magnitudes of influence from different risk factors on BRSV prevalence.
An increased risk of BRSV infection (RR = 1.8) was reported in regions with a beef herd density exceeding ten per 100 km2 compared to those with densities of less than ten herds per 100 km2 (RR = 0.51) [19]. Herd size further influenced BRSV seropositivity. Farms housing more than 20 cattle showed a higher risk (RR = 1.48) than smaller herds [76]. Similarly, herds with 100–300 cattle had a higher odds ratio (OR = 1.42) compared to those with 50–100 cattle. The risk level was most elevated for herds with 300–500 cattle, with an OR of 4.5 [134]. In contrast, a reduced risk for herds sized between 63 and 115, with an OR of 0.16, while herds marginally over 62 showed an OR of 0.27 [46].
Co-infections were another risk factor that significantly affected BRSV prevalence. Cattle seropositive to BHV1 or BVDV faced an elevated risk (RR = 1.2) relative to their uninfected peers [17]. Furthermore, BRSV risk was associated with BPI3V co-infection (OR = 13.4) [21].
Age group was found to be another determinant of BRSV seropositivity. Cattle older than one year faced a higher risk (RR = 1.44) than calves younger than 12 months [17]. Similarly, cattle under 12 months old had a significantly reduced risk, with an OR of 0.072, compared to cattle over 48 months. Those between 13 and 48 months also showed a reduced risk at an OR of 0.501 [18].
Clinical signs were also correlated with BRSV risk. Cattle exhibiting respiratory signs had an OR of 1, while asymptomatic cattle showed a decreased risk with an OR of 0.41 [18].
Season and geographic location were also found to affect the prevalence of BRSV. Moreover, when used as a reference with an OR of 1, cattle from southern regions of Sweden showed marked differences against those from central and northern regions, which showed ORs of 0.45 and 0.13, respectively [133]. Season was associated with BRSV prevalence, with cattle sampled in winter at a higher risk, with an OR of 10.3, especially when compared to their autumn-sampled counterparts; in contrast, the spring-summer period carried a reduced risk, with an OR of 0.3 [21].

3.6. Assessment of Certainty of Evidence

The certainty of evidence for the meta-analysis of BRSV detection in cattle, assessed using the GRADE approach, varied by detection method. Antibody-based methods provided moderate certainty due to consistent findings but high heterogeneity. Antigen and nucleic acid-based methods had low certainty, attributed to limited studies, wide prediction intervals, and substantial variability. Overall, antibody detection methods showed higher reliability, while antigen and nucleic acid methods require further research to improve evidence quality.

4. Discussion

The current systematic review and meta-analysis provide an overview of the global prevalence of BRSV. The analysis showed significant variability in BRSV prevalence at both the animal and herd levels, influenced by age groups, cattle production systems, detection methods, and geographic regions. The prevalence of BRSV at the animal level varied significantly depending on the method of detection, with antibody detection at the highest prevalence at 0.64, followed by antigen detection at 0.048 and nucleic acid at 0.03. In BRDC cases, the detection rate of BRDC was 0.34, 0.16, and 0.13 when detected using antibody, antigen, and nucleic acid methods, respectively. At the herd level, BRSV prevalence was 0.84, and no significant difference was observed among the detection methods. This study also revealed considerable heterogeneity in results across different variables, including age groups, cattle production systems, and geographic regions. Moreover, certain study characteristics, such as geographic location, population type, and study year, were significantly associated with the reported BRSV prevalence. Although the study population consisted of unvaccinated cattle, it is worth noting that BRSV vaccines are available and widely used in practice. However, their effectiveness can vary, particularly in young calves with maternal antibodies, which may contribute to challenges in BRSV control [137].
The analysis indicated that animal-level BRSV prevalence varied significantly depending on the detection method, with antibody detection yielding the highest prevalence, followed by antigen and nucleic acid detection. The GRADE assessment indicated moderate certainty for antibody-based detection methods due to consistent findings across studies, though high heterogeneity persisted, suggesting that antibody detection is relatively reliable for assessing BRSV prevalence, particularly in studies targeting past infections or broader epidemiological surveys. The observed variations in the prevalence estimates highlight the significance of carefully choosing the detection method, as each strategy focuses on a different aspect of the virus to estimate the prevalence of infection in a population and may display varying degrees of reliability [138].
When addressing BRSV prevalence in the context of BRDC, the choice of detection method must align with the specific goal of the test due to the intrinsic sensitivities and specificities of each method [33,139,140]. Antigen detection methods, such as antigen capture ELISA and IHC, could effectively identify active infections by detecting viral proteins. These methods are more specific to current infections but may be less sensitive if the antigen levels are low or the sample handling is suboptimal [141,142]. For broader epidemiological studies or determining the exposure history of a herd, the antibody-based detection method might be preferred over the other methods as it captures both current and past infections [13,139,143,144]. In comparison to other acute respiratory viruses, such as BHV-1 and BVDV, the systemic antibody responses induced by BRSV are weaker, potentially linked to the characteristics of BRSV infection [30,145]. However, the role of these weaker antibody responses in disease severity is unclear, as they do not appear to correlate with the severity of clinical signs [146]. Thus, understanding and aligning each method to the detection objective is critical for accurate BRSV detection within BRDC scenarios.
Compared to the randomly selected cattle populations (0.64 post-outlier removal, the reduced antibody detection rate in BRDC cases (0.34 post-outlier removal) can be attributed primarily to immune response variation. BRSV infection predisposes animals to secondary bacterial infections, enhancing bacterial adherence to respiratory epithelial cells [147]. The virus primarily replicates in ciliated epithelium and type II pneumocytes, modulating the immune response towards a Th2 bias and suppressing interferon production [145]. BRSV modulates the immune response to evade a robust CD8+ cytotoxic T-cell response [145], potentially impacting its detectability, and this immunomodulation can lead to IgE responses [30] and increased susceptibility to secondary infections. BRSV infection also impairs pulmonary clearance of inhaled antigens, potentially contributing to allergic sensitisation and chronic inflammatory lung disease [148]. Moreover, in the acute phase of BRDC, animals may not have developed a sufficiently robust antibody response [29], contributing to lower detection rates. The co-infections typical in BRDC cases could also uniquely influence the immune response to BRSV [145,149]. However, the observed lower detection rate of nucleic acid methods for BRSV in both randomly selected cattle populations and BRDC cases, compared to antibody and antigen detection methods, is multifactorial. Nucleic acid methods such as RT-PCR are highly sensitive to the active phase of viral replication, making them less effective in detecting past or latent infections than antibody methods, which can identify long-lasting immune responses. In BRDC, where multiple pathogens coexist, the fluctuating viral loads can impede the effectiveness of nucleic acid detection for BRSV [145,150]. The inherent instability or lability of the virus, especially in samples collected from the field or those subjected to transport to diagnostic labs [151,152], further challenges the reliability of nucleic acid methods. Moreover, the presence of neutralising antibodies, which can mask viral presence, contributes to the comparatively lower detection rates of nucleic acid methods [142], indicating the complexities in nucleic acid-based detection of BRSV and the need for caution when interpreting these results [153,154].
Using the antibody detection method, the observed relatively lower detection rate of BRSV in BRDC cases, compared to the randomly selected cattle populations, could be attributed to the complex and multifaceted nature of BRDC. In BRDC, multiple pathogens may interact synergistically or antagonistically, potentially complicating the detection of individual pathogens such as BRSV [155]. This complexity can mask or alter the typical presentation and detectability of BRSV, leading to reduced detection rates in BRDC cases. No significant differences were observed across different study populations in the subgroup analysis. Both calves and adults showed a similar detection rate of 0.25 when assessed using the antibody detection method. This outcome challenges the assumption that age significantly affects BRSV prevalence within BRDC cases [17], indicating that other factors, including regional and local elements such as cattle management practices, climate and local virus strains, might influence BRSV prevalence [15,21,76,132].
Multiple risk factors significantly influencing the prevalence of BRSV in cattle were identified in the current study. Age emerged as a significant risk factor, with older cattle having a higher prevalence, possibly due to extended exposure over time [14,16,17,18,21,46,76]. However, as calves infected while possessing passively derived antibodies may not undergo seroconversion [22], care should be taken when interpreting age-based seroprevalence. Similarly, the size of the herd played a role, with larger herds showing a higher likelihood of BRSV positivity [14,16,46,76,88,89,132,133,134], possibly because larger herd sizes can lead to overcrowding, which not only induces stress, potentially compromising or suppressing immunity but also results in higher contact rates, thereby accelerating viral transmission. A consistent association was observed between BRSV prevalence and observable respiratory signs, as well as historical exposure or recent occurrences of respiratory diseases [14,16,21,134,135,136], indicating the role of BRSV in BRDC.
Moreover, the density and proximity of neighbouring farms, possibly due to higher risks of cross-contamination, were identified as significant determinants of BRSV prevalence [15,19,65,132]. BRSV was frequently found in co-infections with other bovine pathogens such as BHV1, BPI3V, BVDV, and bovine adenovirus 3 (BAV3) [17,20,21], further complicating BRDC. Geographical factors such as location and altitude also indicated prevalence variations [15,76,132]. The introduction or purchase of cattle from other herds increased the odds of seropositivity [14,76]. The BRSV prevalence was also found to depend on the season, with higher BRSV prevalence during colder seasons [21,76], suggesting cooler environmental conditions may enhance the stability and survival of the virus [61,62]. Cold weather can lead to closer proximity of animals for warmth and shelter, increasing the likelihood of transmission [62]. Moreover, the lower temperatures may facilitate the persistence of the virus in the environment, thereby increasing exposure risk and contributing to the observed seasonal prevalence patterns. Moreover, cattle with higher milk production levels were associated with higher odds of BRSV seropositivity [134], which could be linked to the physiological stresses of high-yielding cattle. Distinct BRSV subgroups, particularly A and AB, were associated with severe BRDC cases [128], indicating the presence of potential viral strain variations in virulence. These multifaceted findings highlight the complex nature of BRSV risk factors and their implications for disease management and prevention. However, while these factors were drawn from both direct evidence and suggestions in the literature, it is essential to note that some of the evidence, such as the association of milk production levels with BRSV prevalence, may require further research before being considered as established risk factors for BRSV.
The current study has several potential limitations, such as observed heterogeneity among the included articles, which may still have unaccounted variables. The analysis included articles of varied quality, potentially impacting the results. The reliance of the current study on secondary data made it susceptible to inaccuracies present in the original articles, and its findings, influenced by regional factors such as country of origin, may not be universally generalisable.

5. Conclusions

This systematic review and meta-analysis documented BRSV prevalence in cattle populations, highlighting the significant role of multiple factors such as age groups, detection methods, geographic location, and study design. The analysis showed substantial variability in BRSV prevalence, underlining the influence of both biological and operational factors across different settings. These findings indicate the importance of continuous surveillance and adaptive management strategies. As BRSV remains a major challenge in the global cattle industry, this analysis offers essential insights for guiding future research and developing effective prevention and control measures.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/ruminants4040035/s1, Supplementary Figures S1–S6.

Author Contributions

Conceptualisation, K.P. and G.M.W.; methodology, T.T.A., G.M.W., A.K.W. and K.P.; formal analysis, G.M.W.; resources, K.P.; data curation, G.M.W.; writing—original draft preparation, G.M.W.; writing—review and editing, K.P., F.H., D.M., M.P.R., T.T.A. and A.K.W.; supervision, K.P., F.H. and M.P.R. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All datasets used in this study are readily accessible upon request to the authors.

Acknowledgments

The first author would like to extend gratitude to the University of Adelaide Research Scholarship.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Steps followed for article screening.
Figure 1. Steps followed for article screening.
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Figure 2. Global distribution of bovine respiratory syncytial virus based on antibody, antigen, nucleic acid and virus isolation (VI) detection methods in randomly selected cattle populations (A) and cattle showing signs of bovine respiratory disease complex (B).
Figure 2. Global distribution of bovine respiratory syncytial virus based on antibody, antigen, nucleic acid and virus isolation (VI) detection methods in randomly selected cattle populations (A) and cattle showing signs of bovine respiratory disease complex (B).
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Figure 3. Comparison of bovine respiratory syncytial virus seropositivity using antibody detection method: demonstrating a higher proportion of seropositive cattle in the randomly sampled cattle populations compared to those cattle with bovine respiratory disease complex [9,15,16,17,18,20,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94]. (A) prevalence in the randomly selected cattle populations, and (B) detection rate in cattle with bovine respiratory disease complex.
Figure 3. Comparison of bovine respiratory syncytial virus seropositivity using antibody detection method: demonstrating a higher proportion of seropositive cattle in the randomly sampled cattle populations compared to those cattle with bovine respiratory disease complex [9,15,16,17,18,20,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94]. (A) prevalence in the randomly selected cattle populations, and (B) detection rate in cattle with bovine respiratory disease complex.
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Table 1. Study inclusion and exclusion criteria.
Table 1. Study inclusion and exclusion criteria.
CategoryCriteria
Type of study (Inclusion)Both cross-sectional and longitudinal epidemiological studies.
Target population (Inclusion)Studies focused on non-vaccinated cattle, including all production systems (e.g., beef, dairy).
Outcome measures (Inclusion)Studies that report the prevalence, incidence, or distribution of bovine respiratory syncytial virus.
Diagnostic method (Inclusion)Studies that used laboratory-confirmed diagnostic methods for bovine respiratory syncytial virus detection.
Sample size (Inclusion)Studies with a sample size of at least 30 cattle and 2 farms or herds.
Publication language (Inclusion)Studies published in English or those available with English translations.
Publication type (Inclusion)Studies published in peer-reviewed journals.
Data integrity (Inclusion)Studies with original or reanalysing previously published data that have data collection date, sample size, or denominator for each reported prevalence or rate.
Exclusion criteriaNon-cattle study populations. Vaccinated cattle. Sample sizes smaller than 30. Non-peer-reviewed publications, including conference proceedings and book chapters. Non-epidemiological studies. Studies lacking key details.
Table 2. Sensitivity of the pooled prevalence of BRSV using antibody detection method.
Table 2. Sensitivity of the pooled prevalence of BRSV using antibody detection method.
Variable NamePre-Outlier RemovalPost-Outlier Removal
Number of studies36 *33
Number of observations18,59415,071
Pooled prevalence0.63 (95% CI: 0.53 to 0.72)0.64 (95% CI: 0.54 to 0.73)
Prediction interval0.13 to 0.990.23 to 0.95
Heterogeneity (I2)99.3% (tau2 = 0.07, H = 11.81)98.6% (tau2 = 0.04, H = 8.59)
* Outliers removed: [61,70,80].
Table 4. Sensitivity of the pooled detection rate of BRSV using antibody detection method.
Table 4. Sensitivity of the pooled detection rate of BRSV using antibody detection method.
Variable NameBefore Outlier RemovalAfter Outlier Removal
Number of studies1514 *
Number of observations39263798
Pooled detection rate0.38 (95% CI: 0.19 to 0.60)0.34 (95% CI: 0.16 to 0.54)
Prediction interval0.00 to 0.920.01 to 0.83
Heterogeneity (I2)98.5% (tau2 = 0.07, H = 8.23)98.0% (tau2 = 0.05, H = 7.14)
* Outlier removed: [81].
Table 6. Sensitivity of the pooled detection rate of BRSV using nucleic acid detection method.
Table 6. Sensitivity of the pooled detection rate of BRSV using nucleic acid detection method.
Variable NameBefore Outlier RemovalAfter Outlier Removal
Number of studies2422 *
Number of observations66186495
Pooled detection rate0.18 (95% CI: 0.09 to 0.29)0.13 (95% CI: 0.07 to 0.20)
Prediction interval0.00 to 0.550.00 to 0.41
Heterogeneity (I2)97.4% (tau2 = 0.04, H = 6.20)96.3% (tau2 = 0.02, H = 5.18)
* Outliers removed: [1,116].
Table 7. Summary of risk factors of bovine respiratory syncytial virus (BRSV) infection in cattle.
Table 7. Summary of risk factors of bovine respiratory syncytial virus (BRSV) infection in cattle.
Thematic Area Description Reference
Age Older cattle had higher odds of being seropositive[14,16,17,18,21,46,76]
Herd sizeLarge herds had higher odds of being BRSV positive.[14,16,46,76,88,89,132,133,134]
Respiratory signsThe presence of respiratory signs was associated with a higher prevalence of BRSV.[14,21,135,136]
History of respiratory diseaseCattle previously exposed to BRSV or recent disease occurrence had higher odds of being seropositive.[16,21,134]
Farm characteristics Distance between farms or farm density of the area affected the prevalence of BRSV.[15,19,65,132]
Co-infection BRSV was associated with coinfection to BHV1 *, BPI3V ¥, BVDV π, and BAV3 £[17,20,21]
Geographic location Geographic location, including farm altitude, affected BRSV prevalence [15,76,132]
Source of an animalThe purchase of animals or introduction of cattle to a herd from another herd increased the odds of being seropositive.[14,76]
Season of the yearIncreased prevalence in winter (colder season), compared to summer season.[21,76]
Milk production Cattle with higher milk production was associated with higher seroprevalence. [134]
Serotype/genotypeBRSV subgroups A and AB were associated with severe respiratory disease[128]
* Bovine alphaherpesvirus 1; ¥ Bovine parainfluenza-3 virus; π Bovine viral diarrhea virus; £ Bovine adenovirus 3.
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Werid, G.M.; Wubshet, A.K.; Araya, T.T.; Miller, D.; Hemmatzadeh, F.; Reichel, M.P.; Petrovski, K. Detection of Bovine Respiratory Syncytial Virus in Cattle: A Systematic Review and Meta-Analysis. Ruminants 2024, 4, 491-514. https://doi.org/10.3390/ruminants4040035

AMA Style

Werid GM, Wubshet AK, Araya TT, Miller D, Hemmatzadeh F, Reichel MP, Petrovski K. Detection of Bovine Respiratory Syncytial Virus in Cattle: A Systematic Review and Meta-Analysis. Ruminants. 2024; 4(4):491-514. https://doi.org/10.3390/ruminants4040035

Chicago/Turabian Style

Werid, Gebremeskel Mamu, Ashenafi Kiros Wubshet, Teshale Teklue Araya, Darren Miller, Farhid Hemmatzadeh, Michael P. Reichel, and Kiro Petrovski. 2024. "Detection of Bovine Respiratory Syncytial Virus in Cattle: A Systematic Review and Meta-Analysis" Ruminants 4, no. 4: 491-514. https://doi.org/10.3390/ruminants4040035

APA Style

Werid, G. M., Wubshet, A. K., Araya, T. T., Miller, D., Hemmatzadeh, F., Reichel, M. P., & Petrovski, K. (2024). Detection of Bovine Respiratory Syncytial Virus in Cattle: A Systematic Review and Meta-Analysis. Ruminants, 4(4), 491-514. https://doi.org/10.3390/ruminants4040035

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