1. Introduction
Group B Streptococcus (GBS) is a highly contagious intramammary pathogen in dairy cattle responsible for clinical and subclinical mastitis and major economic losses in the dairy industry (1, 2). Beyond bovine infection, GBS can also cause serious human diseases, such as urinary tract infections, pneumonia, bacteremia, and meningitis (3). Its prevalence on dairy farms varies widely across regions, from < 10% in Northern Europe to approximately 50% in South America and > 90% in China (4, 5).
The mammary gland is the main site of infection, but extramammary reservoirs, such as the gastrointestinal and genital tracts, contribute to transmission through contaminated milking processes. Mastitis remains one of the most costly dairy diseases, and a large proportion of antimicrobials (60% - 70%) are used for its control (6). However, high levels of antimicrobial resistance have been reported among mastitis pathogens, including GBS, along with concerns about antibiotic residues in milk that may affect human health (7).
Given its veterinary and public health importance, this study systematically reviews the diagnosis, prevention, and treatment of GBS to reduce its economic and health impacts in dairy cattle.
2. Methods
This study was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (8).
2.1. Search Strategy
A comprehensive search was conducted in PubMed, Scopus, Embase, Web of Science, ScienceDirect, Google Scholar, and internal sources for studies published between January 2000 and December 2022. Both MeSH and free-text terms were used without language or study-design restrictions. Search terms were adapted for each database. English- and Persian-language studies were eligible. Persian-language studies were screened and translated when needed for extraction. Three reviewers independently screened the studies, with disagreements resolved by consensus.
2.2. Inclusion Criteria
Studies were eligible if they reported the prevalence of GBS in dairy cattle or milk samples using culture or PCR methods. The unit of analysis (cow level, quarter level, herd level, or milk-sample level) was extracted as reported in each study and considered during synthesis. Clinical mastitis was defined as the presence of visible clinical signs, whereas subclinical mastitis was defined according to diagnostic indicators such as somatic cell count (SCC), California Mastitis Test (CMT), or bacteriological findings, based on the definitions used in the original studies. When studies included mixed animal types or diagnostic approaches, only clearly reported dairy cattle data with complete denominators were included in the analysis.
2.3. Exclusion Criteria
Publications were excluded if 1) they failed to use the standard method to isolate Group B Streptococcus; 2) they had an irrelevant sampling source (eg, women, neonates, or men); or 3) they were reviews, meta-analyses, systematic reviews, or repeated publications of the same study.
2.4. Data Collection
After the literature search and screening process, eligible original studies conducted in Iran and other countries were included in the analysis, whereas nonoriginal papers were excluded. Relevant data, such as authorship, year, country, sample size, study type, prevalence, antibiotics studied, and animal status, were extracted. Discrepancies encountered during data collection were discussed until consensus was achieved (Figure 1).
Figure 1.
Flow diagram of the research process
2.5. Quality Assessment and Statistical Analyses
Study quality was assessed using the 9-item checklist proposed by Hoy et al. (9), and each study was categorized according to its potential risk of bias. To estimate pooled prevalence values, a random-effects meta-analysis based on the DerSimonian-Laird approach was applied. The Freeman-Tukey double-arcsine transformation was used to stabilize variance across studies, including studies reporting zero-event data. Statistical heterogeneity among studies was evaluated using Cochran Q test and the I2 statistic. Subgroup analyses were conducted according to variables such as diagnostic technique, mastitis status, sample type, and sampling level. Additional exploratory analyses were performed whenever substantial heterogeneity was identified. All statistical procedures were completed using STATA version 11.2.
3. Results
According to Figure 1, 1,691 records were identified through database searches. After removing 33 duplicate records, 1,658 records remained for title and abstract screening. Following screening, 1,323 records were excluded. The full texts of 335 articles were assessed for eligibility, of which 101 were excluded because of unclear methods, sample size, or results, and 35 were excluded because of an inappropriate population or source. Finally, 115 studies were included in the review.
3.1. Prevalence of Group B Streptococcus in Dairy Cows and Milk Samples
Prevalence estimates were synthesized according to the reported unit of analysis, and differences in sampling level and diagnostic methods were considered during interpretation. The prevalence of GBS among dairy cows was 5.9% (95% CI, 0.044 - 0.080) based on 89 studies. Based on culture tests, the prevalence was 5.9% (95% CI, 0.046 - 0.074) from 81 studies; using PCR, it was 16.2% (95% CI, 0.043 - 0.452) from 11 studies (Table 1). For each pooled estimate, the corresponding I2, Q statistic, and prediction interval (when heterogeneity was substantial) are reported to reflect between-study variability. A study-level summary of the risk-of-bias assessment is provided in Table 1.
Table 1.
Prevalence Status of Group B Streptococcus in Dairy Cows and Milk Samples
| Author/Year (Ref.) | Country | Milk Sample/ %GBS | Cow Sample/ %GBS | Test Method | Quality Assessment |
|---|---|---|---|---|---|
| Abboud/ 2021 (10) | Lebanon | 90/ 12.2 | 77/ 14.3 | Culture | Low risk |
| Abd El-Tawab/ 2017 (11) | Egypt | 124/ 29.8 | 124/ 29.8 | Culture | Low risk |
| ABD EL-RADY/ 2009 (12) | Egypt | 1400/ 1.8 | 350/ 7.1 | Culture | Low risk |
| Abed/2021 (13) | Egypt | 1060/ 10.3 | 270/ 40.4 | Culture | Low risk |
| Abera/ 2013 (14) | Ethiopia | 264/ 3.0 | 66/ 12.1 | Culture | Low risk |
| Abera/ 2012 (15) | Ethiopia | 766/ 1.0 | 201/ 4.0 | Culture | Low risk |
| Abrahmsén/ 2014 (16) | Uganda | 760/ 2.0 | 195/ 7.7 | Culture | Low risk |
| Ahmed/ 2020 (17) | Egypt | 50/ 2.0 | 50/ 2.0 | Culture | Low risk |
| Ait-Kaki/ 2019 (18) | Algeria | 100/ 3.0 | 100/ 3.0 | Culture | Low risk |
| Alekish/ 2013 (19) | Jordan | - | 205/ 5.9 | Culture | Low risk |
| Alemu/ 2013 (20) | Ethiopia | 165/ 9.7 | 69/ 23.2 | Culture | Low risk |
| Ali/ 2011 (21) | Pakistan | 2400/ 1.1 | 600/ 4.3 | Culture | Low risk |
| Almaw/ 2008 (22) | Ethiopia | 1347/ 0.9 | 351/ 3.4 | Culture | Low risk |
| Amin/ 2011 (23) | Egypt | 240/ 6.3 | 240/ 6.3 | Culture | Low risk |
| Amin/ 2017 (24) | Ethiopia | 127/ 16.5 | 384/ 5.5 | Culture | Low risk |
| Ayano/ 2013 (25) | Ethiopia | - | 546/ 7.0 | Culture | Low risk |
| Azevedo/ 2016 (26) | Portugal | 92/ 18.5 | 92/ 18.5 | PCR | Low risk |
| Barrett/ 2005 (27) | Ireland | 285/ 2.5 | 285/ 2.5 | Culture | Low risk |
| Bedane/ 2012 (28) | Ethiopia | 1840/ 8.7 | 460/ 34.8 | Culture | Low risk |
| Belayneh/ 2013 (29) | Ethiopia | 1172/ 1.6 | 303/ 6.3 | Culture | Low risk |
| Bengtsson/ 2009 (30) | Sweden | 987/ 0.6 | 669/ 0.9 | Culture | Low risk |
| Bi/ 2016 (5) | China | 894/ 92.2 | - | PCR | |
| Bortolami/ 2015 (31) | Italy | 380/ 6.6 | 98/ 25.5 | Culture | Low risk |
| Bosward/ 2016 (32) | Australia | 120/ 90.8 | 120/ 90.8 | Culture | Low risk |
| Botrel/ 2010 (33) | France | 1770/ 0.9 | - | Culture | Low risk |
| Carvalho-Castro/ 2017 (34) | Brazil | - | 65/ 90.8 | PCR | Low risk |
| Cervinkova/ 2013 (35) | Czech Republic | 669/ 0.7 | - | Culture | Low risk |
| Cheng/ 2010 (36) | China | 100/ 27.0 | 1740/ 1.6 | Culture | Low risk |
| Cicconi-Hogan/ 2013 (37) | United States | 290/ 2.1 | - | PCR | Low risk |
| Cobo-Angel/ 2018 (1) | Colombia | 1809/ 18.1 | 1712/ 19.1 | Culture | Low risk |
| Cobo-Angel/ 2019 (4) | Colombia | 2092/ 12.5 | - | Culture | Low risk |
| Cortinhas/ 2016 (38) | Brazil | - | 358/ 6.7 | Culture | Low risk |
| Cvetnić/ 2016 (39) | Croatia | 1540/ 0.2 | 385/ 0.8 | Culture | Low risk |
| Deng/ 2021 (40) | Netherlands | 317/ 18.0 | 81/ 70.4 | Culture | Low risk |
| Dereje/ 2018 (41) | Ethiopia | 484/ 1.0 | 186/ 2.7 | Culture | Low risk |
| Deressa/ 2013 (42) | Ethiopia | - | 384/ 1.0 | Culture | Low risk |
| Dieser/ 2014 (43) | Argentina | 2296/ 2.1 | 2228/ 2.2 | Culture | Low risk |
| Ding/ 2016 (44) | China | 360/ 15.8 | 360/ 15.8 | PCR | Low risk |
| Duguma/ 2014 (45) | Ethiopia | 340/ 6.5 | 90/ 24.4 | Culture | Low risk |
| Đuričić/ 2020 (46) | Croatia | 1216/ 0.1 | 78/ 1.3 | Culture | Low risk |
| Dyson/ 2022 (47) | Australia | 2572/ 0.2 | 2651/ 0.2 | Culture | Low risk |
| Effendi/ 2018 (48) | Indonesia | 173/ 20.8 | 173/ 20.8 | Culture | Low risk |
| Elhaig/ 2015 (49) | Egypt | 1240/ 4.8 | 310/ 19.4 | Culture | Low risk |
| Elias/ 2012 (50) | Brazil | 247/ 39.7 | - | Culture | Low risk |
| El-Jakee/ 2013 (51) | Egypt | - | 300/ 19.3 | PCR | Low risk |
| Favero/ 2015 (52) | Brazil | - | 231/ 24.2 | Culture | Low risk |
| Ferguson/ 2007 (53) | Italy | 18711/ 1.2 | 9721/ 2.3 | Culture | Low risk |
| Fosgate/ 2013 (54) | South Africa | 1848/ 2.9 | 173/ 31.2 | Culture | Low risk |
| Oliveira/ 2015 (55) | Brazil | 12464/ 7.0 | 9789/ 8.9 | Culture | Low risk |
| Ostensson/ 2013 (56) | Vietnam | 458/ 21.0 | - | Culture | Low risk |
| Palii/ 2020 (57) | Ukraine | - | 75/ 5.3 | Culture | Low risk |
| Pankaj/ 2013 (58) | India | 326/ 1.2 | 82/ 4.9 | Culture | Low risk |
| Persson/ 2011 (59) | Sweden | 583/ 0.2 | 583/ 0.2 | Culture | Low risk |
| Pol/ 2007 (60) | United States | 5672/ 1.6 | 1453/ 6.1 | Culture | Low risk |
| Ramirez/ 2014 (61) | Colombia | 71915/ 4.1 | - | Culture | Low risk |
| Rato/ 2013 (62) | Portugal | 444/ 13.5 | 365/ 16.4 | Culture | Low risk |
| Reinoso/ 2010 (63) | Argentina | 1223/ 0.9 | - | PCR | Low risk |
| Reyes/ 2015 (64) | Colombia | 1104/ 27.9 | Culture | Low risk | |
| Riekerink/ 2010 (65) | Canada | 904/ 0.7 | - | Culture | Low risk |
| Rossi/ 2018 (66) | Brazil | 1164/ 15.8 | - | Culture | Low risk |
| Saed/ 2020 (67) | Egypt | 191/ 17.8 | 128/ 26.6 | Culture | Low risk |
| Schwarz/ 2010 (68) | Germany | 145065/ 0.3 | Culture | Low risk | |
| Seid/ 2015 (69) | Ethiopia | 1422/ 1.1 | 358/ 4.2 | Culture | Low risk |
| Sharma/ 2006 (70) | India | 423/ 1.4 | 109/ 5.5 | Culture | Low risk |
| Shome/ 2012 (71) | India | 209/ 8.1 | - | PCR | Low risk |
| Singh/ 2018 (72) | India | 69/ 62.3 | - | PCR | Low risk |
| Soltau/ 2017 (73) | Germany | 6335/ 0.6 | - | Culture | Low risk |
| Song/ 2020 (74) | China | 1153/ 4.3 | - | Culture | Low risk |
| Suriyasathaporn/ 2011 (75) | Thailand | 133/ 2.3 | 68/ 4.4 | Culture | Low risk |
| Teklesilasie/ 2014 (76) | Ethiopia | 1412/ 1.3 | 365/ 5.2 | Culture | Low risk |
| Tenhagen/ 2006 (77) | Germany | - | 751/ 0.4 | Culture | Low risk |
| Tenhagen/ 2009 (78) | Germany | 6915/ 0.1 | - | Culture | Low risk |
| Tian/ 2019 (79) | China | 735/ 3.0 | - | Culture | Low risk |
| Timonen/ 2017 (80) | Estonia | 522/ 14.4 | 522/ 14.4 | PCR | Low risk |
| Tomazi/ 2018 (81) | Brazil | 4212/ 5.9 | 2637/ 9.4 | Culture | Low risk |
| Vakkamaki/ 2017 (82) | Finland | 117511/ 0.4 | 331764/ 0.2 | PCR | Low risk |
| Verbeke/ 2014 (83) | Finland | 845/ 0.4 | 677/ 0.4 | Culture | Low risk |
| Workineh/ 2002 (84) | Ethiopia | 396/ 2.3 | 186/ 4.8 | Culture | Low risk |
| Wraight/ 2003 (85) | Australia | 440/ 1.6 | - | Culture | Low risk |
| Gao Jian/ 2012 (86) | China | 3288/ 2.8 | 3190/ 2.9 | Culture | Low risk |
| Gharari / 2014 (87) | Iran | 700/ 39.7 | - | Culture | Low risk |
| Gillespie/ 2005 (88) | America | 192/ 0.3 | - | PCR | Low risk |
| Gitau/ 2014 (89) | Kenya | 241/ 5.8 | 234/ 6.0 | Culture | Low risk |
| Gonçalves/ 2018 (90) | Brazil | 584/ 3.9 | 584/ 3.9 | Culture | Low risk |
| Green/ 2005 (91) | England | 960/ 0.1 | 480/ 0.1 | Culture | Low risk |
| Guélat-Brechbuehl/ 2010 (92) | Switzerland | 2538/ 0.2 | - | Culture | Low risk |
| Hameed/ 2007 (93) | Poland | 704/ 0.7 | - | Culture | Low risk |
| Hernandez/ 2021 (2) | Argentina | 1500/ 3.7 | 1500/ 3.7 | PCR | Low risk |
| Holko/ 2019 (7) | Slovakia | 633/ 4.7 | 512/ 5.9 | Culture | Low risk |
| Holmoy/ 2018 (94) | Norway | 595/ 6.2 | 578/ 6.4 | Culture | Low risk |
| Holmøy/ 2019 (95) | Norway | 25136/ 2.3 | 11485/ 5.1 | Culture | Low risk |
| Idriss/ 2014 (96) | Slovakia | 390/ 1.5 | - | Culture | Low risk |
| Ikiz/ 2013 (97) | Turkey | 270/ 9.3 | 132/ 18.9 | Culture | Low risk |
| Karimuribo/ 2008 (98) | Tanzania | 1500/ 0.8 | 434/ 2.8 | Culture | Low risk |
| Keneth I. K./ 2014 (99) | Uganda | - | 124/ 4.8 | Culture | Low risk |
| Kilyenyi/ 2021 (100) | Tanzania | 745/ 2.4 | 195/ 9.2 | Culture | Low risk |
| Kivaria/ 2007 (101) | Tanzania | 1964/ 13.6 | 1365/ 19.6 | Culture | Low risk |
| Krukowski/ 2020 (102) | Poland | 38852/ 2.6 | 22587/ 4.5 | Culture | Low risk |
| Lago/ 2011 (103) | Canada | 449/ 0.1 | 442/ 0.1 | Culture | Low risk |
| Lakew/ 2019 (104) | Ethiopia | 1536/ 0.3 | 384/ 12.0 | Culture | Low risk |
| Lakew/ 2009 (105) | Ethiopia | 892/ 1.9 | 223/ 7.6 | Culture | Low risk |
| Leelahapongsathon/ 2014 (106) | Thailand | 1827/ 2.0 | 467/ 7.9 | Culture | Low risk |
| Levison/ 2016 (107) | Canada | 936/ 0.1 | 743/ 0.1 | Culture | Low risk |
| Lin/ 2021 (108) | China | 379/ 11.1 | - | PCR | Low risk |
| Mahmmod/ 2013 (109) | Denmark | 2456/ 2.2 | 614/ 8.6 | Culture | Low risk |
| Makovec/ 2003 (110) | America | 77172/ 4.5 | - | Culture | Low risk |
| Mbindyo/ 2020 (111) | Kenya | 1580/ 0.3 | 395/ 1.0 | Culture | Low risk |
| Memon/ 2012 (112) | China | 1660/ 1.6 | 415/ 6.3 | Culture | Low risk |
| Moatamedi/ 2007 (113) | Iran | 120/ 15.8 | - | Culture | Low risk |
| Moges/ 2011 (114) | Ethiopia | - | 322/ 8.1 | Culture | Low risk |
| Mpatswenumugabo/ 2017 (115) | Rwanda | - | 123/ 3.3 | Culture | Low risk |
| Mureithi/ 2016 (116) | Kenya | 688/ 1.9 | 172/ 7.6 | Culture | Low risk |
| Nam/ 2009 (117) | Korea | 12508/ 0.0 | 12508/ 0.0 | Culture | Low risk |
| Manning/ 2010 (118) | United States | - | 115/ 1.7 | Culture | Low risk |
Among dairy cows, the prevalence of GBS was 6.0% (95% CI, 4.4 - 8.2) in cattle across 86 studies, whereas buffalo showed a prevalence of 4.2% (95% CI, 3.2 - 5.7) in 3 studies. Based on breed, prevalence was 5.3% (95% CI, 4.8 - 5.9) in local breeds, 5.8% (95% CI, 3.1 - 10.7) in crossbreeds, and 7.7% (95% CI, 4.0 - 14.4) in Holstein cattle (Figure 2).
Figure 2.
Prevalence of GBS in dairy cows according to animal species and breed
Among 74 studies reporting mastitis cases, 35% (95% CI, 0.285 - 0.422) of dairy cows had mastitis, including 16.6% (95% CI, 0.075 - 0.329) with clinical mastitis and 48.5% (95% CI, 0.434 - 0.537) with subclinical mastitis.
The prevalence of GBS in milk samples across 102 studies was 3.1% (95% CI, 0.025 - 0.040). Based on culture tests, the prevalence across 89 studies was 2.8% (95% CI, 0.022 - 0.035), and using PCR across 16 studies, it was 12.6% (95% CI, 0.034 - 0.368). Furthermore, the prevalence of GBS in cattle milk was 3% (95% CI, 0.024 - 0.039), whereas in buffalo milk it was 1.1% (95% CI, 0.008 - 0.016).
3.2. Geographic Distribution of Group B Streptococcus in Dairy Cows and Milk Samples
Across the reviewed studies, the highest frequency of GBS in dairy cows was 31.2% (95% CI, 0.248 - 0.385) in South Africa, based on one study, followed by 23.2% (95% CI, 0.157 - 0.329) in Colombia, based on two studies. The highest prevalence of GBS in milk samples was 26.5% (95% CI, 0.095 - 0.551) in Iran, based on two studies, followed by 20.8% (95% CI, 0.154 - 0.275) in Indonesia, based on one study (Table 2).
Table 2.
Geographic Distribution of GBS Prevalence in Dairy Cows and Milk Samples a
| Sample Type | Continent | Country | No. of Studies | Pooled Prevalence (%) | 95% CI |
|---|---|---|---|---|---|
| Dairy cow | Africa | South Africa | 1 | 31.2 | 24.8 - 38.5 |
| Dairy cow | Americas | Colombia | 2 | 23.2 | 15.7 - 32.9 |
| Milk sample | Asia | Iran | 2 | 26.5 | 9.5 - 55.1 |
| Milk sample | Asia | Indonesia | 1 | 20.8 | 15.4 - 27.5 |
a Abbreviation: CI, confidence interval.
3.3. Temporal Trends in Group B Streptococcus Prevalence
The prevalence of GBS in dairy cows varied between 2001 and 2022, with the highest prevalence observed in 2021 (20.2%; 95% CI, 5.9 - 14.2). Peaks were also observed in 2002, 2007, 2013, 2015, and 2021 (Figure 3). Similarly, temporal analysis of GBS in milk samples revealed a peak prevalence of 10.8% (95% CI, 6.2 - 18.2) in 2018 during the period from 2001 to 2022 (Figure 4).
Figure 3.
Temporal trends in the prevalence of GBS in dairy cows, 89 studies
3.4. Antimicrobial Susceptibility
Among studies reporting antibiotic resistance of GBS isolated from dairy cows and milk samples, resistance prevalence varied across agents. Resistance to penicillin was 20% (95% CI, 0.063 - 0.482) based on 10 studies. Resistance to tetracycline was 39.4% (95% CI, 0.177 - 0.664) based on 7 studies, erythromycin resistance was 36.7% (95% CI, 0.160 - 0.638) based on 7 studies, clindamycin resistance was 42.9% (95% CI, 0.192 - 0.704) based on 7 studies, and levofloxacin resistance was 23.6% (95% CI, 0.07 - 0.562) based on 3 studies. Because some pooled estimates were derived from only a small number of studies, particularly for levofloxacin, these results should be interpreted with caution.
4. Discussion
This systematic review and meta-analysis identified a global prevalence of GBS in dairy cows of 5.9% by culture and 16.2% by PCR, with mastitis affecting 35% of cows and having a significant impact on animal health and production. The prevalence of GBS in milk samples was estimated at 3.1%. Although some external literature suggests possible transmission to humans through farm exposure (118) or consumption of contaminated products (119), this review primarily synthesized prevalence data in cattle and milk. Direct evidence of transmission pathways and the extent of human health risk from bovine GBS were not the primary focus of the included studies and require further investigation. Comparison of these findings with the higher global prevalence observed in pregnant women, 10% - 40%, (120, 121, 122) highlights differences in GBS epidemiology between host species. Livestock generally show lower rates, although findings vary widely across studies (13, 32, 39, 40, 47, 107), likely reflecting differences in control programs and management practices. Additionally, GBS prevalence is lower in buffaloes than in dairy cows (4.2% vs 6%), possibly because of greater resistance to mastitis (49).
Regarding antimicrobial susceptibility, notable resistance was observed, particularly to penicillin (20%) and tetracycline (39.4%), among other agents. However, the considerable uncertainty in pooled resistance estimates for some antibiotics, because of the limited number of contributing studies (eg, only 3 studies for levofloxacin), warrants cautious interpretation. These findings underscore the need for ongoing surveillance and improved antimicrobial stewardship in dairy farming to mitigate the burden of GBS and the emergence of resistance.
