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. 2026 Apr 24;12(3):e70759. doi: 10.1002/vms3.70759

Continental Prevalence of Tuberculosis Infection and Associated Risk Factors From African Cattle: Systematic Review and Meta‐Analysis

Khasapane NG 1,✉, Nkhebenyane SJ 1, Ramatla T 1,2
PMCID: PMC13108713  PMID: 42030422

ABSTRACT

Introduction

Bovine tuberculosis (bTB) is a chronic zoonotic disease affecting cattle of all age groups, including wild animals. The objective of this systematic review and meta‐analysis was to provide a comprehensive and unbiased summary on the distribution of bTB and its associated risk factors in Africa.

Methods

We gathered and systematically reviewed literature concerning the prevalence of bTB in Africa, published in English from January 2000 to October 2024 in three databases, and a meta‐analysis was conducted using the Comprehensive Meta‐Analysis software. A total of 513 studies were retrieved, and at the end of the screening, 30 full texts were eligible for inclusion in the meta‐analysis.

Results

The results of this study revealed an overall pooled prevalence estimate (PPE) of bTB in Africa was 6.9%, with a higher burden being in Nigeria (9.2%) and Ethiopia (6.7%), respectively. Furthermore, immunoassays had a PPE of 31.9%. The results further showed that milk samples had the highest PPE with 17.9%. Our multivariable regression showed that overall bTB may be associated with the breed and body condition score (BCS). However, exploratory univariable regression showed that bTB may be associated with the Fulani breed, semi‐intensive production level and medium herd size.

Conclusion

The current research findings enable African nations to adopt targeted, cost‐effective and sustainable bTB control strategies that are more likely to succeed than generic, one‐size‐fits‐all programs by mapping prevalence, identifying risk factors and understanding socio‐economic contexts.

Keywords: Africa, bovine tuberculosis, cattle, Mycobacterium bovis, prevalence

Wildlife and cattle is affected by bovine tuberculosis (bTB), which presents a zoonotic risk. The current systematic review and meta‐analysis uses global data, uncovering a high prevalence in specific regions and pinpointing major risk factors for transmission. Results stress the necessity of targeted monitoring and control tactics to limit the spread of bTB and safeguard animal and public health.

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1. Introduction

Members of the Mycobacterium tuberculosis complex, primarily Mycobacterium bovis, are responsible for the chronic bacterial disease known as bovine tuberculosis (bTB) in cattle. Cattle are the primary source of human infection for bTB, a serious zoonotic disease. In addition, it impacts wild boar, deer and antelope, as well as various domesticated animals like sheep, goats, horses, pigs, dogs and cats. Direct contact with infected animals, as well as the handling and consumption of animal products and by‐products, are the main ways that humans become infected (WHO 2023).

bTB is found with variable frequency throughout the world. BTB has considerable economic and public health importance (Azami and Zinsstag 2018). Worldwide, it results in economic losses of up to $3 billion annually (Schiller et al. 2010). The cost of bTB is mostly associated with animal production losses in affluent nations, such as higher mortality and decreased production of milk and meat (Azami and Zinsstag 2018). For nations with a large cattle population, like Ethiopia, estimates of these losses have been made (Schiller et al. 2010).

Furthermore, bTB contributes significantly to human TB, with zoonotic transmission likely accounting for up to 10% of human TB infections worldwide (Carruth et al. 2016). According to a WHO report from 2018, M. bovis was responsible for 143,000 incident TB cases in people globally (WOAH 2019). Humans typically contract the disease by eating contaminated raw meat and unpasteurized milk or by coming into close contact with infected animals (WOAH 2019; Cressey et al. 2006; Radostits et al. 2000). There have been numerous reports of M. bovis infections in people in Africa, including Kenya (Kerubo et al. 2022).

Crucially, most developing nations consider bTB to be a neglected tropical illness, mostly because of inadequate hygiene standards and a lack of control strategies (Tuberculosis 2009). However, the successful implementation of the test and slaughter policies of afflicted animals has reduced the issues caused by bTB in the majority of affluent countries (Humblet et al. 2009). On the other hand, test and slaughter policies have failed in Africa, which is home to over half of the world's cattle population, mostly because the government has not provided enough money to compensate impacted farmers (Biet et al. 2005). As a result, the disease persists unchecked, which significantly hinders both the production of livestock and the global traffic in cattle and their products (Azami et al. 2018; Belete et al. 2021). Risk variables included introducing new animals into the herd, having a history of bTB infection, and having poor herd management (Kouengoua et al. 2024). According to De Garine‐Wichatitsky et al. (2013), it is challenging to eradicate zoonotic diseases because their epidemiology is poorly understood and they spread at the human–wild animal–animal interface (González‐Barrio 2022). According to the law, bTB is a communicable illness that must be reported (Mpouam et al. 2021). Developing successful national TB surveillance and control plans, particularly in developing nations, requires accurate data on the burden of bTB.

Few systematic reviews and meta‐analyses have been conducted on tuberculosis in animals in Nigeria (Ahmad et al. 2023), in Bangladesh (1970–2023) (Islam et al. 2024), in Tanzania (Alonso et al. 2016) and in India (Srinivasan et al. 2019). However, there is no recent information on comprehensive data to estimate the prevalence of bTB on the African continent. Thus, this current systematic review and meta‐analysis sought to provide a recent comprehensive prevalence of bTB in Africa.

2. Materials and Methods

2.1. Study Design and Systematic Review Protocol

This systematic review and meta‐analysis were conducted following the Preferred Reporting Items for Systematic Reviews and Meta‐analyses (PRISMA) guidelines (Page et al. 2021). Data extraction, screening and analysis involved searching database systems for potentially pertinent articles, assessing their relevance and evaluating whether they were acceptable for review. This review was conducted using published studies conducted in Africa.

2.2. Search Strategy

The systematic literature search was performed using the following keyword and Boolean operator (AND and OR) combination: ‘Prevalence’ AND ‘Mycobacterium bovis’ AND ‘Cow OR Cattle’ AND ‘Africa’ OR ‘Cote d'Ivoire’ OR ‘Ivory coast’ AND ‘Congo OR democratic Republic of the congo’ OR ‘Zaire’ AND ‘Djibouti’ AND ‘Egypt’ AND ‘Malawi’ AND ‘Mali’ AND ‘Mauritania’ AND ‘Mauritius’ AND ‘Mayotte’ AND ‘Morocco’ AND ‘Mozambique’ AND ‘Algeria’ AND ‘Angola’ AND ‘Benin’ AND ‘Botswana’ AND ‘Burkina Faso’ AND ‘Burundi’ AND ‘Cameroon’ AND ‘Cape Verde’ AND ‘Central African republic’ AND ‘Chad’ AND ‘Comoros’ AND ‘Equatorial guinea’ AND ‘Eritrea’ AND ‘Ethiopia’ AND ‘Gabon’ AND ‘Gambia’ AND ‘Ghana’ AND ‘guinea OR Guinea‐bissau’ AND ‘Kenya’ AND ‘Lesotho’ AND ‘Liberia’ AND ‘Libya’ AND ‘Madagascar’ AND ‘Namibia’ AND ‘Niger’ AND ‘Nigeria’ AND ‘Reunion’ AND ‘Rwanda’ AND ‘Saint Helena’ AND ‘São Tomé and Príncipe’ AND ‘Senegal’ AND ‘Seychelles’ AND ‘Sierra Leone’ AND ‘Somalia’ AND ‘South Africa’ AND ‘South Sudan’ AND ‘Sudan’ AND ‘Swaziland’ AND ‘Tanzania’ AND ‘Togo’ AND ‘Tunisia’ AND ‘Uganda’ AND ‘Zambia’ AND ‘Zimbabwe’. The bibliographic databases PubMed (https://pubmed.ncbi.nlm.nih.gov/), African Journals Online (https://www.ajol.info/index.php/ajol/Gsearch) and ScienceDirect (https://www.sciencedirect.com/) were searched, using the above‐indicated combination of terms.

2.3. Inclusion and Exclusion Criteria

The studies that were selected underwent additional screening after a thorough examination of their titles and abstracts. The following requirements had to be met by all the articles that were included in the study: (1) original research articles conducted in Africa; (2) the full texts were available; (3) the publication was in English; (4) studies must report the sample size; (5) studies conducted only in cattle; (6) the prevalence of M. bovis is clearly stated; (7) studies conducted from the years 2000 to 2024; and (8) reviews, meta‐analyses and case reports were excluded. Studies that did not meet the specified requirements were excluded. No attempt was made to obtain any further information or to retrieve unpublished studies from the authors of the original manuscripts.

2.4. Data Extraction

Published articles identified in this study were retrieved, screened and reviewed by two authors (N.G.K. and T.R.). Disagreements between the researchers were resolved through discussions to reach a consensus. The retrieved studies were managed in Microsoft Excel (Microsoft Corporation, Redmond, WA, USA) and EndNote version 20 (1500 Spring Garden Street, Fourth Floor, Philadelphia, PA 19130, USA). Upon the compilation of the search titles and abstracts from the three databases, duplicate records were removed. This was followed by screening of the titles and abstracts for eligibility; then the full texts of eligible studies were evaluated using the same criteria listed above. Finally, data were extracted from text, tables and graphs.

2.5. Quality Assessment of the Studies

Two authors (N.G.K. and T.R.) independently evaluated the quality of the studies using the Joanna Briggs Institute's (JBI) Critical Appraisal Tool for prevalence studies (Ramatla et al. 2023). The JBI tool has ‘Yes’, ‘No’, ‘Unclear’ or ‘Not applicable’ question types, and scores were assigned as 1 for ‘Yes’ and 0 for ‘No’. Questions that were deemed irrelevant to this study were excluded. The final checklist contained the following questions concerning: (1) Was the sample frame appropriate to address the target population? (2) Were study participants recruited in an appropriate way? (3) Was the sample size adequate? (4) Were the study subjects and setting described in detail? (5) Was the data analysis conducted with sufficient coverage of the identified sample? (6) Were valid methods used for the identification of the condition? (7) Was the condition measured in a standard, reliable way for all participants? (8) Was there appropriate statistical analysis? (9) Was the response rate adequate, and, if not, was the low response rate managed appropriately? The number of ‘Yes’ scores for each study was added, and the percentage score was computed by dividing by the total number of questions. The studies were classified as low quality (less than 50% score), moderate quality (50%–74%) and high quality (≥75%) (Tawana et al. 2023). Inconsistencies in the scoring between the two reviewers were discussed and resolved.

2.6. Data Synthesis and Data Analysis

Comprehensive Meta‐Analysis Software ver.4.0 program (https://www.meta‐analysis.com/) was used to conduct the meta‐analysis on the prevalence of bTB and risk factors associated with it. The pooled prevalence estimate (PPE) with 95% confidence interval (CI) was created using the random‐effects model. Heterogeneity between studies was measured using the I 2 statistic. No heterogeneity is indicated by a number around 0%, whereas low, moderate and high heterogeneities are indicated by values near 25%, 50% and 75%, respectively. The heterogeneities between studies from a chi‐squared test of the null hypothesis that there is no heterogeneity are shown by the p values. The meta‐analysis did not include subgroup analyses with less than three trials. Subgroup analysis was conducted on the basis of the country, year of publication and diagnostic and confirmatory testing methods. For meta‐regression, each covariate underwent univariable analysis. Because the test (meta‐regression) having a low power, 0.25, was deemed significant. The multivariable meta‐regression analysis included all components with significant p values. To check for symmetry, publication bias was assessed using funnel plots, which were then supplemented by Egger's regression test and the Begg and Mazumdar rank correlation test (Ramatla et al. 2023; Begg and Mazumdar 1994; Egger et al. 1997). A p value of less than 0.05 was deemed statistically significant for every test.

3. Results

3.1. Search Results

A total of 513 articles were retrieved from the database search. After removing the duplicates (n = 125), studies not in English (n = 49) and studies not in Africa (n = 41). A total of 298 studies were screened on the basis of title and abstract. Subsequently, from the full‐text evaluation of the 92 studies, 62 were excluded for various reasons, including study design (only cross‐sectional was selected), unclear information on the outcome of interest and a minimal sample size (Figure 1). Finally, only 30 studies were included for data extraction and analysis (Table S1). The quality assessment score from the JBI critical appraisal ranges from 1 to 9. The lowest score was found only in five studies (Table S2).

FIGURE 1.

FIGURE 1

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PRISMA flow diagram demonstrating the search and selection process of included studies in the review.

3.2. Characteristics of Eligible Studies

For the purpose of this study, the sample size per study ranged from 99 to 3236, and all studies were published between 01 January 2000 and October 2024. Our subgroup analysis further revealed that more studies were conducted extensively between the years 2016 and 2020, with 13 studies, whereas between 2021 and 2024, only 8 studies were conducted. Fewer studies were conducted between 2011 and 2015, with five studies, and between 2006 and 2010, with four studies. The majority of the studies (n = 18) employed the CITT test for identification of M. bovis, followed by Ziehl–Neelsen acid‐fast staining (n = 8), the bTB antibody test and genotyping (4 each), and immunoassays (n = 3). Moreover, it is worth noting that a majority of the studies used multiple methods for diagnosing M. bovis.

Of the 30 peer‐reviewed articles included in this study, 13 were from Ethiopia (Almaw et al. 2021; Ameni et al. 2008; Demelash et al. 2009; Demissie et al. 2021; Gumi et al. 2012; Hussein et al. 2024; Kemal et al. 2019; Mekonnen et al. 2019; Nuru et al. 2015; Robi et al. 2024; Woldemariam et al. 2016; Woldemariam et al. 2020; Zeweld 2014), eight articles from Nigeria (Atuman et al. 2018; Bikom et al. 2021; Damina et al. 2011; Lawan et al. 2020; Makeri et al. 2018; Ofukwu et al. 2010; Usman et al. 2017; Yohanna et al. 2008), two articles from each of Mozambique (Machado et al. 2018; Moiane et al. 2014) and one study from each of the following countries: Tanzania and Kenya (Shirima et al. 2003; Gathogo et al. 2012), Burkina Faso (Dahourou et al. 2023), South Africa (Mareledwane et al. 2022), Uganda (Nalapa et al. 2017), Ghana (Amemor et al. 2017) and Morocco (Yahyaoui Azami et al. 2018) (Figure 2).

FIGURE 2.

FIGURE 2

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A map showing the number of studies included in the study per country that investigated the prevalence of bovine tuberculosis in Africa (https://www.mapchart.net/world.html [accessed on 30 September 2025]). A summary of studies was conducted between 2000 and 2024.

3.3. Overall PPEs

An overall PPE of bTB in Africa was observed to be 8.1% (95% CI: 5.1–12.8); a significant difference between study heterogeneities was observed from the 30 eligible studies with 4964 samples, as shown in Table 1 (Figure 3).

TABLE 1.

Overall meta‐analysis statistics for overall prevalence and subgroup analysis.

Factor Number of studies Number positives % Prevalence I 2 (95%CI) τ 2 Publication bias
Overall prevalence 30 4964 8.1 98.828 0.999 0.000
Confirmatory methods
Immunoassays 3 1203 31.9 99.668 2.015 0.000
Test kits 4 208 15.7 90.840 0.246 0.000
Genotyping 4 320 5.7 99.816 14.042 0.000
Neelsen acid‐fast staining 6 235 2.9 99.205 1.448 0.000
Type of samples 0.000
Blood/Serum 6 1286 18.3 7.1–39.7 1.339 0.000
Lesions 7 1978 5.9 3–11.3 0.873 0.000
Milk 3 200 17.9 1.4–77.6 5.401 0.000
Skin 16 1407 5.5 2.5–11.9 2.888 0.000
Mixed (nasal secretions, faeces and urine) 1 20 — — — —
Countries 0.000
Ethiopia 13 1377 6.7 97.157 0.487 0.000
Nigeria 8 328 9.2 91.037 0.295 0.000
Mozambique 2 328 — — — —
Tanzania 1 9 — — — —
Burkina Faso 1 80 — — — —
South Africa 1 318 — — — —
Ghana 1 38 — — — —
Morocco 1 282 — — — —
Uganda 1 153 — — — —
Kenya 1 23 — — — —
Years 0.000
Before/On 2015 8 1335 5.7 99.72 1.502 0.000
After 2015 22 2008 9.5 99.329 2.506 0.000
2006–2010 4 851 9.2 99.781 1.75 0.000
2011–2015 5 484 3.5 99.435 4.060 0.000
2016–2020 14 1135 10.2 99.611 1.253 0.000
2021–2024 8 821 8.6 99.640 4.622 0.000
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FIGURE 3.

FIGURE 3

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Forest plot depicting the overall included studies on the meta‐analysis.

3.4. Meta‐Analysis of Subgroups

The subgroup results, as shown in Table 1, reveal that immunoassays had the highest PPE of 31.9%, followed by test kits (15.7%), genotyping (5.7%) and Ziehl–Neelsen acid‐fast staining (2.9%), even though some had fewer studies. On the basis of the type of sample collected for screening and diagnosing bTB, the meta‐analysis results showed that the highest PPE was from blood or serum samples with 18.3%, followed by milk samples (17.9%), whereas skin samples had the lowest PPE of 5.5%.

The point prevalence estimates of bTB according to countries were able to be generated from two countries only, where Nigeria was found to have the highest PPE of 9.2%. However, Ethiopia had the lowest PPE of 6.7%. Countries like Mozambique, Tanzania, Burkina Faso, South Africa, Ghana, Morocco, Uganda and Kenya were not included in the meta‐analysis due to the small number of studies (fewer than three studies) (Figure 4).

FIGURE 4.

FIGURE 4

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The prevalence of bTB by country in Africa.

A total of 11 studies with a PPE of 5.7% were conducted on or before the year 2015, whereas after the year 2015, 23 studies were conducted with a PPE of 9.5%. However, further analysis on years was done, looking at heterogeneity per 5 years. Our data revealed that between 2006 and 2010, five studies were conducted with PPE of 9.2%, whereas between 2011 and 2015, six studies were conducted with PPE of 3.5%. Furthermore, between 2016 and 2020, 14 studies were conducted with a PPE of 10.2%, and lastly, between 2021 and 2024, nine studies were conducted with a PPE of 9.6% (Table 1).

3.5. Meta‐Regression Analysis

A univariable meta‐regression was conducted to determine the effect of the covariates as moderators of the cumulative prevalence. Factors, such as age, body score condition, herd size, sex, farm environment/production system and breed, were used as the moderators in the meta‐regression. The univariable analysis showed that the Fulani breed, semi‐intensive production level and medium herd size had the highest value of R 2 of 40%–99% and p < 0.005 being observed. The multivariable meta‐regression analysis revealed that bTB may be associated with the breed (p < 0.030) and body condition score (BCS) (p < 0.000). The proportion (R 2) of each of the moderators’ effects on heterogeneity with their corresponding p values is shown in Table 2.

TABLE 2.

Univariate and multivariate meta‐regression analysis of bovine tuberculosis (bTB) risk factors in the African continent.

Univariate analysis Multivariate analysis
Covariates R2 p value R 2 (%) p value
Breed 17 0.030 **
Local breed 20 0.217
Cross breed 6 0.512
Exotic breed 0.9 0.850
Fulani breed 99 0.050 **
Gudali breed 93 0.160
Body condition score (BCS) 44.2 0.000 **
Good BCS 15.6 0.258
Medium BCS 87.7 0.000 **
Poor BCS 37.4 0.079 *
Age 4.0 0.182
Young 1.9 0.493
Adults 8.6 0.288
Old 24.2 0.398
Production systems 20.9 0.116
Intensive 36.7 0.393
Semi‐intensive 90.2 0.013 **
Herd size 11.8 0.588
Small 24.6 0.144
Medium 45.7 0.045 **
Large 15.2 0.338
Sex 7.1 0.114
Male 17.4 0.084 *
Female 5.8 0.333
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Note: R 2—determines the proportion of variance in the dependent variable that can be explained by the independent variable.

*

Significant at 25% significance level.

**

Significant at 5% significance level.

3.6. Publication Bias

The Begg and Mazumdar rank correlation test demonstrated no significant publishing bias for practically all parameters, except for risk factors such as adults (5–9 years) at p < 0.04 and male cattle at p < 0.01 (Figure 5A,B).

FIGURE 5.

FIGURE 5

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(A and B) Funnel plots indicating risk of publication bias from adult (5–9 years) at p < 0.04 (A) and male cattle at p < 0.01 (B).

4. Discussion

Animal tuberculosis is a paradigmatic shared disease at the wildlife–livestock interface, and the condition has complex global epidemiological scenarios impacting the livestock industry and human health. In the present study, we aimed to assess the prevalence of M. bovis and its associated factors in African cattle through systematically reviewing the articles from 2000 to 2024 and further using a meta‐analysis approach to determine the PPEs and other statistical solutions. The PPE of an overall result in this study was higher than that which was reported by Carneiro et al. (2021) in Brazil (3.0%); however, it was lower than that of Abdel‐Moein et al. (2016) in Egypt (14.7%) in cattle, whereas it was almost similar to the prevalence in buffaloes (8.8%) in the similar study.

The current study further revealed that the comparative intradermal tuberculin test was the most used technique for identification and diagnosis of bTB hence, the overall PPE of the current study was based on it. All studies that used primarily conventional‐based identification methods reported significantly higher prevalence of zoonotic TB than studies that used primarily genotyping‐based methods. We also observed a high variation in reported prevalence among studies conducted in the same country where the methodology differed. For instance, in Mexico, Lopez‐Rocha et al. (2013) found a low prevalence of M. bovis (2.1%) using genotyping‐based methods, whereas the three conventional‐based studies from Mexico showed prevalence that reached up to 30% M. bovis among the total number of TB cases (Torres‐Gonzalez et al. 2016). Similarly, the highest (76.7%) and lowest (0.42%) prevalence were reported from two African countries, Tunisia by Siala et al. (2017) and Ethiopia by Firdessa et al. (2012), respectively.

However, again, the study from Tunisia was performed using conventional‐based methods, whereas the one from Ethiopia utilized genotyping for strain identification. Likewise, the influence of identification techniques was prominent in previous studies done in Ethiopia, Nigeria and Tanzania, where the median proportion of M. bovis cases was significantly higher than for the findings of other recent molecular‐based studies (Rodrigues et al. 2022; Cadmus et al. 2006; Kidane et al. 2015). For example, the specificity of a PCR‐based method using primers JB21/22 for identification of M. bovis was shown to be low by Sales et al. (2014). A retrospective analysis of TB cases in the Netherlands during 1993–2007 (Majoor et al. 2011) and the United States, from 1995 to 2005, showed an average prevalence of 1.4% (Hlavsa et al. 2008). Müller et al. (2013) projected that the pooled contribution of zoonotic disease in the African region was estimated to be 2.8% of clinically confirmed cases.

In addition, overcrowding itself was claimed to stress cattle and compromise their ability to thrive with the disease. Lesions in intensively managed cattle were predominantly found in the lung and associated lymph nodes, whereas mesenteric lymph nodes are the primary foci in grazing cattle, suggesting the mode of transmission of the disease in the respective production systems (Kaneene et al. 2006). Our results were similar to those of Hussein et al. (2024), who noted that animals showed significantly higher odds of testing positive for tuberculosis based on BCS, with the odds being 11.4 times greater. Consistent with the findings of a prior study by Admasu et al. (2014), however, this finding contradicts the results obtained by Mamo et al. (2013), who reported no significant association between the BCS and bTB positivity. Furthermore, in terms of age, Hamed et al. (2021) contradicted our results, as they have noted as well that there was a significant increase in risk with increasing age up to 6 years, then a decrease. The decrease in infection at a later stage may be due to the development of an anergic state or due to excess mortality of infected animals.

Moreover, cow breeds play a significant role in determining the level of risk of contracting M. bovis; however, our results differed from Hamed et al. (2021), where the authors claim that the Holstein–Friesian breed showed higher prevalence than Simmental and Brown. The prevalence at the animal level was significantly associated with different locations of farms. It might be due to climatic condition differences in the farms and stress on cattle when kept in poor management and overcrowded environments (Kemal et al. 2019). In addition, the current study identified herd size as a potential risk factor for likelihood of bTB test‐positivity at the farm level. The size of herds has been reported as a risk factor for bTB (Wright et al. 2015; Picasso et al. 2017); however, in our study another explanation might be the study design in which more cattle were tested in larger herds, which increases the herd‐level sensitivity of the SICTT in larger herds. More than two‐thirds of the tested farms practised intensive farming, of which most were larger herds (>10 cattle). Therefore, the chances of within‐herd transmission of bTB in these herds are very high and can result in a very high within‐herd prevalence (Islam et al. 2024). This is demonstrated in two older (>10 year) cattle farms in the Dhaka City Corporation area, in which approximately 45% or 46% of the cattle were found to be positive for bTB.

In addition, cattle from semi‐intensive production systems in Africa are more infected with M. bovis as compared to other production systems. Perhaps, this reflects the effect of the production systems on the prevalence of bTB in the country and warrants the need for prioritization of control towards intensive or semi‐intensive dairy production systems. Similar observations were made in Tanzania and Uganda (Shirima et al. 2003; Inangolet et al. 2008). Previously, it was reported that intensive farming facilitates transmission of bTB among cattle as individual animals are kept in close proximity to one another (Constable et al. 2016).

5. Limitations

The current study has the following limitation: The study could not directly associate the risk of M. bovis to human beings; hence, zoonotic studies are required. Furthermore, to understand the prevalence of this infection, animals such as buffaloes are also required.

6. Conclusions and Recommendations

The study found that estimates of TB prevalence varied significantly across Africa, suggesting that TB burdens varied by region. Therefore, to successfully reduce the TB burden and prioritize resource allocation for control efforts, targeted treatments are advised in districts with greater prevalence. Improved diagnostic instruments and additional testing techniques may be required to guarantee precise diagnosis and surveillance. Furthermore, thorough and precise data collection, including targeted sampling and testing, is needed, as the actual prevalence of bTB may be underestimated. The sensitivity and specificity of the faulty diagnostic procedure for bTB must be adjusted for within the framework of a possible control or surveillance program in order to produce accurate estimates of bTB prevalence, which will then shape the development of TB control strategies.

It would be advantageous to give cattle vaccination enough thought as a strategic TB preventive and control program, tailored to the African context (Zumla et al. 2020; Buddle et al. 2018). For instance, the United Kingdom and Spain are now considering vaccination against M. tuberculosis infection in animals and wildlife, even though the use of BCG vaccination against TB in cattle is generally prohibited by European Union legislation due to the possibility of inducing a cellular immune response, which could interfere with diagnosis and complicate eradication efforts (Balseiro et al. 2020; APH 2023). Standardized record‐keeping is also advised for reporting animal TB cases in order to produce enough information to support a thorough inquiry.

Author Contributions

Khasapane N.G.: writing – original draft, conceptualization, methodology, project administration, software, investigation, data curation. Nkhebenyane S.J.: writing – review and editing, supervision, project administration. Ramatla T.: writing – review and editing, conceptualization, methodology, formal analysis, supervision, validation.

Funding

This research was funded by the Central University of Technology under the University Capacity Development Grant (UCDG).

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Supporting Table 1: Studies included for data extraction and analysis.

Supporting Table 2: The quality assessment score from the JBI critical appraisal.

Supporting Table 3: PRISMA 2020 Checklist.

Supporting Table 4: PRISMA 2020 for Abstract Checklist.

VMS3-12-e70759-s001.DOCX (63.4KB, DOCX)

Acknowledgements

This is a short text to acknowledge the contributions of specific colleagues, institutions or agencies that aided the efforts of the authors.

Data Availability Statement

The original contributions presented in the study are included in the article/Supporting Information section; further enquiries can be directed to the corresponding author.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supporting Table 1: Studies included for data extraction and analysis.

Supporting Table 2: The quality assessment score from the JBI critical appraisal.

Supporting Table 3: PRISMA 2020 Checklist.

Supporting Table 4: PRISMA 2020 for Abstract Checklist.

VMS3-12-e70759-s001.DOCX (63.4KB, DOCX)

Data Availability Statement

The original contributions presented in the study are included in the article/Supporting Information section; further enquiries can be directed to the corresponding author.

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