Le Infezioni in Medicina, n. 3, 292-311, 2024

doi: 10.53854/liim-3203-4

REVIEWS

Prevalence of latent tuberculosis infection (LTBI) in healthcare workers in Latin America and the Caribbean: systematic review and meta-analysis

Edinson Dante Meregildo-Rodriguez1,2,3, Mariano Ortiz-Pizarro4, Martha Genara Asmat-Rubio3,5, Mayra Janett Rojas-Benites6,7, Gustavo Adolfo Vásquez-Tirado7,8

1Escuela de Medicina, Universidad César Vallejo, Trujillo, Perú;

2Infectious and Communicable Diseases Research Group (ICDRG), Universidad César Vallejo, Trujillo, Perú;

3Hospital Regional Lambayeque, Chiclayo, Perú;

4Escuela de Odontología, Universidad Católica Santo Toribio de Mogrovejo, Chiclayo, Perú;

5Escuela de Farmacia y Bioquímica, Universidad Nacional de Trujillo, Trujillo, Perú;

6Escuela de Posgrado, Universidad Privada Antenor Orrego, Trujillo, Perú;

7Hospital Regional Docente de Trujillo, Trujillo, Perú;

8Escuela de Medicina, Universidad Privada Antenor Orrego, Trujillo, Perú

Article received 3 June 2024, accepted 14 August 2024

Corresponding author

Edinson Dante Meregildo-Rodriguez

E-mail: edmeregildo@ucvvirtual.edu.pe

SummaRY

Background: Tuberculosis remains a significant global health concern, and healthcare workers (HCWs) face a high risk of acquiring latent tuberculosis infection (LTBI) through occupational exposure. In the Latin American and Caribbean (LAC) region, where the burden of tuberculosis is substantial, understanding the prevalence of LTBI among HCWs is crucial for effective infection control measures. Therefore, we conducted a systematic review and meta-analysis to estimate the prevalence of LTBI among HCWs in LAC countries.

Methods: Our search included MEDLINE, Scopus, EMBASE, Web of Science, and Google Scholar databases, focusing on relevant English-language records. We looked for observational studies from inception until December 2023.

Results: Our analysis included 38 studies representing 15,236 HCWs and 6,728 LTBI cases. These studies spanned the period from 1994 to 2023 and were conducted in Brazil, Peru, Cuba, Colombia, Trinidad and Tobago, Mexico, and Chile. The mean prevalence of LTBI among HCWs was 35.32% (range 17.86-56.00%) for interferon-gamma release assay (IGRA) and 43.67% (range 6.68-70.29%) for tuberculin skin test (TST). The pooled prevalence of LTBI among HCWs was 34.5% (95% CI 25.4-44.1%) for IGRA and 43.0% (95% CI 35.5-50.7%) for TST. When considering both IGRA and TST tests, the overall prevalence of LTBI among HCWs was 40.98% (95% CI 34.77-47.33%). LTBI was associated with longer lengths of employment and exposure to patients, family members, or any person with TB. Additionally, older HCWs faced a higher risk of LTBI. Specific professional roles (such as nurses, nurse technicians, or physicians), smoking, and deficient TB infection control measures increased the likelihood of LTBI. However, information regarding gender and BCG vaccination status showed discordance among studies.

Conclusion: Our findings underscore a substantial burden of LTBI among HCWs in LAC countries. Implementing adequate infection control measures is essential to prevent and control transmission within healthcare settings.

Keywords: Latent tuberculosis, health personnel, systematic review, meta-analysis, Latin America and Caribbean countries.

INTRODUCTION

Tuberculosis (TB) represents a global health emergency and stands as the world’s second leading cause of death from a single infectious agent after coronavirus disease (COVID-19) [1, 2]. Tuberculosis ranks among the top ten causes of mortality globally, causes almost twice as many deaths as HIV/AIDS, and is the most widespread infectious agent disease [1, 3].

In 2022, the global estimate for new tuberculosis cases reached 10.6 million, with 3.1% reported in the Americas WHO region [4]. The estimated number of TB-related deaths was 1.6 million, with the Americas witnessing the highest increase in TB-related deaths between 2015 and 2021 at 31% [4]. The countries with the highest incidence rates were Haiti, Peru, and Bolivia, while Saint Lucia, Antigua and Barbuda, Jamaica, Barbados, and Grenada reported the lowest incidence rates. Regarding mortality rates due to TB, Guyana, Haiti, and Bolivia recorded the highest rates, whereas Jamaica, Cuba, Bahamas, Costa Rica, Barbados, El Salvador, and Saint Vincent and the Grenadines reported the lowest mortality rates [4].

Latent tuberculosis infection (LTBI) is a persistent immune response in the absence of clinical disease manifestations following stimulation by Mycobacterium tuberculosis (MTB) antigens [5]. In 1999, the World Health Organization (WHO) estimated that one-third of the world’s population had LTBI. However, this percentage has recently been updated to one-fourth [6]. About 5-10% of LTBI cases, especially within the first two years, have the potential to progress to active TB [7, 8]. Certain variables may heighten the likelihood of LTBI reactivation. For example, HIV infection, hemodialysis, immunosuppression, cancer, diabetes mellitus, and other conditions can increase the risk [5, 7, 9]. Treatment can effectively reduce the risk of progression from LTBI to active TB by 60-90% [10].

Health care workers (HCWs), and even health care students, are at an increased risk of developing LTBI and active tuberculosis due to their continuous occupational exposure to MTB [11-16]. Tuberculosis represents a significant occupational risk for HCWs, especially in regions with a high prevalence of the disease, such as the LAC region. Health care facilities lacking adequate infection prevention and control procedures should be particularly concerned about this situation [12, 17, 18]. The mean prevalence of LTBI in HCWs in underdeveloped nations is 54% (range 18-80%) [13, 19-21]. The risk of occupational-related LTBI among HCWs depends upon various factors, including the local tuberculosis incidence, the healthcare facility’s characteristics, the HCWs’ activities, and the effectiveness of preventive and control measures [17, 18, 20, 22, 23].

The most crucial tuberculosis prevention and control pillars are early case detection, diagnosis, and effective treatment [2, 24]. The TB epidemic must be contained by 2035, and addressing latent tuberculosis infection (LTBI) is crucial through screening and TB preventative therapy (TPT) [25]. TPT is recommended in HCWs and heath care students because recent converters have a significantly increased risk of developing LTBI into active tuberculosis [7, 26-28]. However, for many national TB programs in resource-constrained nations already struggling with active TB screening and treatment, the costs of scaling up LTBI screening and TPT programs are prohibitive [25]. Therefore, we aimed to conduct this systematic review and meta-analysis to estimate the prevalence of LTBI among HCWs in the LAC region. This study’s results will help clarify the epidemiology of TB in HCWs, one of the populations with the highest risk of developing the disease [27, 28]. Planning public strategies to safeguard HCWs will be made more accessible using this information. Furthermore, this may serve as a starting point for further research.

MATERIALS AND METHODS

This research aligns with the PRISMA, AMSTAR 2, and MOSE guidelines for systematic reviews. We registered the protocol in PROSPERO (CRD42023470345) [29-31].

Sources of Information and Search strategy. First, we used three broad concepts to identify the medical subject headings and keywords in the databases: “latent tuberculosis,” “healthcare workers,” and “occupational exposure.” Then, we formulated our research question using a PECO strategy: Population: “Healthcare workers” OR “Healthcare workers” OR “Health Personnel” OR “medical staff” OR “hospital staff” OR “Physician” OR “nurse” OR “community health worker” from “Latin America and the Caribbean countries”; Exposure: “occupational exposure” OR “infectious disease transmission” OR “occupational disease” OR “nosocomial exposure”; Comparator: nil; Outcome: “latent tuberculosis infection” OR “LTBI” OR “latent tuberculosis,” OR “tuberculin test” OR “interferon-gamma release test” OR “interferon-gamma release assay” OR “IGRA.” All keywords were searched for in the title, abstract, and keywords.

We conducted a systematic electronic search using MEDLINE (PubMed), Scopus, EMBASE, Web of Science, and Google Scholar. We screened each database using thesaurus, controlled language terms (MeSH, Emtree, etc.), free vocabulary terms, and synonyms. We combined these terms using Boolean operators following our PECO question. In addition, we conducted manual secondary screening of the references s in primary and secondary studies. There were no restrictions on language or publication year. The complete search strategy details are provided in Table S1, Supplementary Materials.

Inclusion and exclusion criteria. We looked for observational (cohort, case-control, and cross-sectional) studies from inception until December 31, 2023. We defined the target population as HCWs whose occupations involved having either direct contact with patients (physicians, nursing staff and assistants, therapists, community health agents, medical interns, etc.) or indirect or no contact with patients who have been exposed to infected material or environment (e.g., laboratory workers, cleaning staff, administrative employees). Following our protocol and PECO question, we only included studies conducted in the LAC region. Besides, the LTBI status should have been investigated using IGRA or TST tests as part of routine examinations or screenings. Studies were only included whether IGRA and TST were recorded separately. We excluded studies in which more than 50% of the participants included were health sciences students, except medical interns and medical residents. Students have different exposure risks than hired HCWs from health facilities. Therefore, the LTBI risk and the prevalence of LTBI in students differs from that of formal HCWs, as was shown in three meta-analyses of studies conducted in high TB burden countries [18, 21, 32]. We excluded case reports, case series, narrative reviews, editorials, comments, conference reports, and duplicated publications. Abstracts were only considered if they included all the relevant information and the full text was unavailable. We imported the primary and secondary search articles into Mendeley Reference Manager®. Then, retrieved papers were exported into a spreadsheet for a second full-text screening. Our selection process is detailed in Figure 1.

Figure 1 - PRISMA 2020 flow diagram.

Study selection. After duplicate removal, retrieved records were imported into the Rayyan® tool. Then, these publications were screened and individually examined by four blinded and independent researchers (MOP, MGAR, MJRB, and GAVT). The studies were selected by consensus, and the leading researcher acted as the arbitrator (EDMR) in case of discordance. All the articles collected were examined using the terms of the PECO strategy and the inclusion and exclusion criteria.

Data extraction. The same researchers who performed the selection process examined and extracted relevant details of each study using a prescribed form: authors, publication date, country, study design, study period, research setting, occupational group, age, diagnostic method, diagnostic test (TST or IGRA), diagnostic test cut-off values, risk factors for LTBI, the proportion of positive TST or IGRA results, the total number of participants and cases, measures of association (prevalence or incidence), and, the confounding factors. Extracted data from each paper were recorded in a spreadsheet. Finally, the reviewers compared the extracted data with each other, and if there was a discrepancy, the lead researcher solved it if necessary. If essential data were missing, the lead researcher sent at least two emails to the corresponding author.

Data synthesis, meta-analysis, and meta-regression. We pooled absolute frequencies (the number of total participants and the number of cases or events) and relative frequencies (proportions or prevalences), along with their respective 95 percent confidence intervals (95% CI). We performed the meta-analysis using R-4.3.2 and RStudio® 2023.09.1 software and the libraries meta, dmetar, and metafor; functions metagen and metaprop; and inverse variance method with Restricted Maximum-Likelihood (REML) for tau2. For meta-analysis, we considered each subgroup analysis a separate study for studies reporting effect measures stratified into different subgroups.

Our protocol stated that we would examine heterogeneity among studies with Cochran’s Q test and Higgins I2 statistic, using a fixed effects model if heterogeneity were not statistically significant (p>0.10, I2 statistics < 40%). Otherwise, we would use a random effects model [33]. The potential subgroups analyzed were the study design (case-control, cohort, or cross-sectional study), the Continent of origin where the study was conducted (South America, the Caribbeans, or North America), the year of publication expressed as Decades (1990-1999, 2000-2009, 2010-2019, or 2020-2024), the diagnostic test cut-off points of TST, and the occupational group of the HCWs.

We generated forest plots for each group comparison. We conducted sensitivity and influence analysis and meta-regression to assess heterogeneity. For this purpose, we use the functions of meta package: “find outliers”, “influence analysis” with Baujat plots, and “leave-one-out” according to I2 and size effect. These analyses, helps to identify studies that have a significant impact on heterogeneity and the overall effect in a meta-analysis [34]. Furthermore, in addition to a funnel plot, we performed ranktest (rank correlation test for funnel plot asymmetry), Egger’s test, trim-and-fill funnel plot, and a classic fail-safe N analysis to assess the risk of publication bias [34].

Risk of bias assessment. We assessed individual papers’ bias risk following the Newcastle-Ottawa scale (NOS) [35].

GRADE assessment. The same researchers who performed the selection and extraction process independently evaluated the certainty of the evidence (CoE) of each study based on the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) criteria [36, 37]. The lead researcher resolved any reviewer discrepancies between the reviewers.

RESULTS

Figure 1 shows the study section process. We identified 146 records, all of which were retrieved from databases. All were observational (23 cross-sectional, 11 prospective, and 2 retrospective cohort) studies. After record removal and exclusion of 7 studies, we found 55 studies for retrieval and 48 for assessing eligibility. Of these studies, 13 were excluded for different reasons (Supplementary Material, Table S2). Then, 36 different studies were included in this systematic review. We later excluded a study from the meta-analysis because it did not detail the study population or prevalence [38]. As was stated in our protocol, for the purpose of the meta-analysis, we added to these remaining 35 primary studies 3 articles [39-41] that reported different subgroups (TST and IGRA) with their respective effect measure for each subgroup (Table 1). Of these 38 studies, 29 used TST, and 9 used IGRA.

This systematic review included studies conducted between 1994 to 2023. Of the 36 primary studies, 16 were conducted in Brazil, 7 in Peru, 5 in Cuba, 3 in Colombia, 2 in Trinidad and Tobago, 2 in Mexico, and 1 in Chile. For the analysis, the country of origin of these studies was grouped into three continents (north America, south America, and the Caribbeans). Considering the 36 studies, 7 used the IGRA test, 27 used TST, and 2 used both. Of the 36 primary studies included, 23 were cross-sectional (CSS), 11 were prospective cohort (PCS), and 2 were retrospective cohort studies (RCS). These studies included a total of 15,236 HCWs and 6,728 cases of LTBI. The effect measure reported in the cross-sectional studies was prevalence, which was reported in all studies. In the cohort studies (either prospective or retrospective), the authors reported the results of QFT-G or TST testing for tuberculosis infection at baseline (prevalence), and new cases were reported after a particular follow-up time (incidence). Therefore, we collected information reported from the studies.

The type of participant also varied widely among studies. Some studies recruited only clinical staff (direct patient caregivers), such as doctors, nurses, or technical personnel; other researchers included administrative and paramedical staff-other recruited students in health sciences who were rotating or practicing and community health workers.

Table 1 - General characteristics of included studies.

Prevalence of LTBI among HCWs. Considering the results of the 38 studies, the mean prevalence of LTBI among HCWs was 35.32% (range from 17.86% to 56.00%) for IGRA and 43.67% (range from 6.68% to 70.29%) for TST. This difference was statistically significant (Xi2 = 76.18, p = 0.0001). The pooled prevalence of LTBI among HCWs was 34.5% (95% CI 25.4-44.1%) for IGRA (Figure 2a) and 43.0% (95% CI 35.5-50.7%) for TST (Figure 2b). This difference was not statistically significant (Xi2 = 0.467, p = 0.4943). The pooled prevalence of LTBI among HCWs, including IGRA and TST tests, was 40.98% (95% CI 34.77-47.33%) (Figure 2c).

Figure 2a - Forest plot of the pooled prevalence of LTBI in HCWs based on IGRA results.

Figure 2b - Forest plot of the pooled prevalence of LTBI in HCWs on TST results.

Figure 2c - Forest plot of the pooled prevalence of LTBI in HCWs, based on the combined prevalence from IGRA and TST results.

Heterogeneity. Statistical heterogeneity was significant (I2 = 98.7%, p = 0) for pooled prevalence. The influence analysis, sensitivity analysis, and leave-one-out tests did not significantly impact the overall estimate. Furthermore, we performed meta-regression analyses to explore potential sources of between-study heterogeneity. The moderators’ test was not statistically significant according to the type of test (IGRA or TST) (p = 0.9849); however, it was statistically significant for the kind of study design (CCS, PCS, or RCS) (p = 0.0094), the subcontinent (South America, North America, or the Caribbeans) (p = 0.0094) and the year of publication of the study (p = 0.0087). That is, these last three variables accounted for variability between studies. Similarly, the test for residual heterogeneity was also statistically significant for all of these four variables (p = 0.0001), suggesting that our model was not well specified and other moderating variables not taken into account should be considered (Supplementary materials, Table S3).

Using a random-effects model and the function “find outliers,” we identified 21 studies as outliers (Soto 2017, Alonso 2001, Sedamano 2020, Meregildo 2023, Baboolal 2010, Borroto 2011, Borroto 2015, Franco 2006, Prado 2017, de Souza 2014, Roth 2005, Molina-Gamboa 1994, Bonifacio 2002, Garcia-Garcia 2001, da Costa 2010, Ruiz Morales 2015, Lopes 2008, Lacerda 2017, Lima 2020, Martínez-Hall 2015, and Ochoa 2017). The exclusion of these studies left 17 papers, but heterogeneity remained significant (I2 = 73.8%, p = 0.0001).

Subgroup analysis with a random effects model showed that the prevalence of LTBI among HCWS did not differ according to the test (IGRA or TST) (test for subgroup differences, p = 0.1716) or design type of study (test for subgroup differences, p = 0.9855). However, the prevalence of LTBI among HCWS did differ according to the Continent where the study was conducted and the Decade of publication (test for subgroup differences, p <0.0001 for all these variables). Due to the lack of information, we could not conduct a subgroup analysis according to the occupational group of HCWs or the diagnostic test cut-off values for the TST.

Publication bias. Our funnel plot shows a slight concentration of points at the top and right of the graph (Figure 3a). This could suggest some degree of publication bias. Consequently, we conducted an Egger’s test, trim-and-fill, and a classic fail-safe N analysis. The Egger test (regression test for funnel plot asymmetry) did not suggest a publication bias risk (p = 0.2017). Likewise, considering a reference or threat criterion (5 * k + 10 = 200), the Rosenthal approach (observed significance level p < 0.001, target significance level p = 0.05, Fail-safe N = 136,169) and the Rosenberg approach (observed significance level p < 0.001, target significance level p = 0.1, Fail-safe N = 132,281) suggested that publication bias was not a threat to the existence of a significant effect size in this meta-analysis. Likewise, the trim-and-fill method added 13 hypothetical missing studies on the right side of the funnel plot (Figure 3b). However, the effect size remained similar (prevalence 0.5405; 95% CI 0.4608-0.6192), and the heterogeneity remained significant (I2 = 99.3%, p = 0.001).

Figure 3a - The funnel plot shows the studies included in the meta-analysis, focusing on LTBI prevalence in HCWs. The analysis is based on both IGRA and TST results.

Figure 3b - The trim-and-fill analysis shows the studies included in the meta-analysis (gray dots) and hypothetically added studies (white dots), focusing on LTBI prevalence in HCWs. The analysis is based on IGRA and TST results.

Factors associated with LTBI in HCWs. Risk factors for LTBI in HCWs varied widely among studies. In most studies, LTBI was associated with longer employment length, as reported in 14 studies [17, 19, 38, 40, 42-51]. However, the years of service as HCWs related to greater risk of LBTI were as short as 30 months to more than 30 years [52, 53]. Having had exposure with a patient, a family member [40] or any person with TB increased the risk of LTBI. Similarly, according to 7 studies, the older the HCWs, the higher the risk of LTBI [19, 23, 38, 40-42, 46, 49, 54-56, 53, 55, 57-59]. Regarding the occupational group, 7 papers reported that being nurses or nurse technicians increased the likelihood of LTBI and only one article reported that physicians and nurses had a higher risk of LTBI [41, 48, 52, 55, 57, 60, 61]. Smoking associated with the risk of LTBI was reported in 3 papers [40, 41, 57, 62]. Concerning gender, results were discordant. Some studies reported a higher risk of LTBI in males, but others in females [40, 46, 47, 49, 58]. Likewise, information about BCG status was inconsistent. Some studies described that a BCG scar or the antecedent of BCG vaccination was associated with higher rates of LTBI however, other studies reported that the absence of a BCG scar was associated with a higher prevalence of LTBI [40, 41, 47, 53-55, 57]. Other factors associated with increased risk of LTBI were deficient TB infection control measures [41, 52, 55, 57, 63], irregular use of N95 masks, and work in the emergency or radiology departments, among others.

Although all studies included were observational, for most studies, the risk of bias was low (Table 2).

Table 2 - Factors associated to LTBI in HCWs in Latin America and the Caribbean.

GRADE assessment. We upgraded the level of CoE because the number of studies (38), participants (15,236 HCWs), and events (6,728 cases of LTBI) included was important, and the risk of bias in most studies was low. Similarly, indirectness (the included studies compared similar outcomes), imprecision, and publication bias did not significantly impact the CoE. Conversely, inconsistency (high heterogeneity, different definitions of HCWs and reported prevalences, etc.) downgraded the CoE. Consequently, the CoE, according to the GRADE criteria was very low.

DISCUSSION

HCWs with LTBI not only jeopardize their own health but also can compromise patient safety and community well-being. To the best of our knowledge, this is the first systematic review and meta-analysis assessing the prevalence and risk factors of LTBI in HCWs in the LAC region. Our findings reveal that more than one-third of HCWs in the region are affected by LTBI. Specifically, the mean prevalence of LTBI among HCWs was 6.68-70.29% (mean 43.67%) by TST and 17.86-56.00% (mean 35.32%) by IGRA. This difference was statistically significant (p = 0.0001). According to our metanalysis, the pooled prevalence of LTBI in HCWs was 43.0% (95% CI 35.5-50.7%) by TST and 34.5% (95% CI 25.4-44.1) by IGRA. This difference was not statistically significant (p = 0.4943). Furthermore, the pooled prevalence of LTBI among HCWs, including both IGRA and TST tests, was 40.98% (95% CI 34.77-47.33%).

The difference in the prevalence of LTBI as determined by the two methods (TST and IGRA) can be explained by the fact that many LAC countries routinely administer BCG vaccination, which can cause cross-reactions with TST [69, 70, 74]. As a result, studies using TST typically report a higher prevalence of LTBI in HCWs than those using IGRA. The evidence suggests that including IGRAs in testing strategies seems cost-effective in terms of the cost-effectiveness or cost-utility relationship. Still, this result is dependent upon the underlying LTBI prevalence rates. IGRAs appear to be cost-effective for high-risk populations in high-income countries. However, its cost-effectiveness in low- to middle-income countries and countries with a high burden of tuberculosis still needs to be investigated [75-77]. Neither TST nor IGRA predicted the subsequent development of active TB among household contacts of pulmonary TB patients during follow-up. However, considering the cost and other logistics, TST remains the most preferred method for LTBI diagnosis in resource-limited, high TB-burden settings [78].

The IGRA test was designed to complement the LTBI infection diagnosis and increase sensitivity and specificity by quantifying the interferon-gamma produced by sensitized T-cells in response to contact with specific M. tuberculosis antigens [79]. The U.S. Food and Drug Administration (FDA) has approved two TB blood tests: QuantiFERON®-TB Gold Plus (QFT-Plus) and T-SPOT® TB test (T-Spot) [79, 80]. Nevertheless, IGRA results are not univocal. First, the IGRA cannot distinguish between past and current M. tuberculosis infection and LTBI and active tuberculosis [79, 81]. Second, IGRA tests perform well in immunocompetent individuals over five years of age; however, their clinical efficacy is affected in immunocompromised individuals due to reduced and fluctuating interferon-gamma synthesis, which could cause indeterminate results in this population [82-86]. Third, the assessment of LTBI by IGRAs is based on response to specific antigens: early secreted antigenic target 6 kDa (ESAT-6) and culture filtrate protein 10 kDa (CFP-10), localized in one particular genomic area of M. tuberculosis, called the region of difference (RD1) [87, 88]. Although other atypical mycobacteria such as Mycobacterium kansasii, Mycobacterium marinum, and Mycobacterium szulgai may induce positive IGRAs due to RD1 homology with Mycobacterium tuberculosis [89], IGRA results are not influenced by previous BCG vaccination [90]. Therefore, IGRA has higher specificity than TST, particularly in recent contacts with smear-positive patients [91, 94].

The most critical cornerstones for the prevention and control of tuberculosis are early diagnosis and treatment of cases [4]. Addressing LTBI through screening and TB preventive treatment (TPT) is crucial to stop this epidemic [25]. Since the risk of progression of LTBI into active tuberculosis is notably higher in recent converters, this is why TPT is indicated in this group [26]. However, the costs to scale up LTBI screening and TPT programs are prohibitive for many national TB programs in resource-limited countries of the LAC region, which are already struggling to provide active TB screening and treatment 7, [25]. Indeed, in Peru, the Tuberculosis Control Program does not contemplate an active search for LTBI cases, not even in the highest-risk populations such as HCWs [19, 67].

Risk factors for LTBI in HCWs varied widely among studies. In most studies, LTBI was associated with more extended employment length, as reported in 14 studies [17, 19, 38, 40, 42-51]. Surprisingly, the risk of LTBI was observed even in HCWs with relatively short service periods, ranging from 30 months to more than 30 years. Exposure to patients [family members, or any person with TB increased the likelihood of LTBI. Additionally, findings from 7 studies indicated that older HCWs faced a higher risk of LTBI [19, 23, 38, 40, 41, 46, 49, 53-59]. Regarding occupational groups, seven papers highlighted that being nurses or nurse technicians was associated with an elevated likelihood of LTBI [41, 48, 52, 55, 57, 60, 61] only one article reported that physicians and nurses had a higher risk of LTBI [55]. Furthermore, smoking was identified as a risk factor for LTBI in 3 papers [40, 41, 57, 62]. These findings illuminate the severity of the problem and emphasize the urgent requirement to implement effective strategies for preventing and controlling tuberculosis within healthcare settings.

At least seven systematic reviews have explored the prevalence or incidence of LTBI in HCWs [18, 21, 32, 95-98]. Among these, three assessed LTBI in HCWs without distinguishing between countries based on risk or disease burden [95-97]. One study focused solely on HCWs in low-incidence countries [98], while three papers specifically addressed LTBI in HCWs in low- and middle-income countries (LMICs) [18, 21, 32]. As a result, our findings align better with the latter studies.

Joshi et al. conducted a systematic review to summarize evidence regarding the occurrence and prevalence of LTBI and active disease among HCWs in LMICs. Their study also evaluated the impact of various preventive strategies implemented. The researchers identified 42 articles, which included 51 studies. From this data, they extracted information on LTBI and disease incidence, prevalence, and risk factors among HCWs. On average, the prevalence of LTBI among HCWs was 54%, with a range of 33% to 79%. Estimates of the annual risk of LTBI varied from 0.5% to 14.3%.

Additionally, the annual incidence of tuberculosis disease in HCWs ranged from 69 to 5,780 cases per 100,000 individuals. Comparing HCWs to the general population, the attributable risk for TB disease ranged from 25 to 5,361 cases per 100,000 per year. Certain work locations, such as inpatient TB facilities, laboratories, internal medicine departments, and emergency facilities, were associated with a higher risk of acquiring TB disease. Occupational categories, including radiology technicians, patient attendants, nurses, ward attendants, paramedics, and clinical officers, also faced an elevated risk. In conclusion, Joshi and colleagues’ review highlighted that TB is a significant occupational concern for HCWs in LMICs, underscoring the necessity of designing and implementing straightforward, effective, and affordable TB infection-control programs with these countries [18].

In 2019, Apriani et al. published a systematic review and meta-analysis, searching through three databases. They included 85 studies involving 32,630 participants from 26 LMICs. The study focused on the prevalence of LTBI among HCWs, using both TST and the IGRA. The TST-based prevalence of LTBI ranged from 14% to 98% (with a mean of 49%), while the IGRA-based prevalence ranged from 9% to 86% (with a mean of 39%). As expected, countries with a tuberculosis incidence of ≥300 per 100,000 population had the highest LTBI prevalence. The pooled prevalence of LTBI based on TST was 55% (95% CI 41-69%), and for IGRA it was 56% (95% CI 39-73%). Additionally, the annual incidence estimated from TST results varied from 1% to 38% (with a mean of 17%), while the yearly incidence estimated from IGRA ranged from 10% to 30% (with a mean of 18%). Factors such as years of work, work location, TB contact, and job category were associated with the prevalence and incidence of LTBI. Notably, only 15 studies reported on infection control measures in healthcare facilities, and their implementation was limited. In conclusion, the authors emphasized that HCWs in LMICs remain at an increased risk of acquiring LTBI, necessitating urgent and robust implementation of infection control measures [21].

In 2019, Nasreen S and their colleagues conducted a systematic review and meta-analysis to estimate the prevalence of LTBI among HCWs in countries with a high burden of tuberculosis. They searched four databases and grey literature, examining 990 records. From these, they selected 18 studies conducted in seven high-burden countries involving 10,078 participants. Among these participants, TST results were available for 9,545 individuals. The researchers reported that the pooled prevalence of LTBI among HCWs was 47% (95% CI 34-60%, I2 99.6%). Additionally, subgroup analyses based on the country of study revealed that the prevalence of LTBI was lowest in Brazil (37%) and highest in South Africa (64%). Furthermore, when examining specific groups within HCWs, they found that the prevalence of LTBI among medical and nursing students was 26% (95% CI 6-46%, I2 99.3%). In contrast, the prevalence among all HCWs was 57% (95% CI 44-70%, I2 99.1%). Data on LTBI incidence was available for HCWs in four countries. The cumulative incidence ranged from 2.8% among Brazilian medical students to 38% among all HCWs in South Africa. In conclusion, the authors emphasized that there is a substantial burden of LTBI among healthcare workers in countries with a high prevalence of tuberculosis. They recommend implementing adequate infection control measures to prevent and manage transmission within healthcare settings [32].

Compared to the studies by Joshi et al., Apriani et al., and Nasreen et al., in general, our research on the prevalence of LTBI among healthcare workers in Latin America and the Caribbean (LAC) shows both similarities and differences [18, 21, 32]. While these three studies report higher average and pooled prevalence rates of LTBI in HCWS, our overall estimate (40.98%) is slightly lower. Still, it aligns with the specific estimate for Brazil reported by Nasreen et al. (37%). Like the three studies, we identified risk factors such as longer employment duration and exposure to TB patients, underscoring the need for enhanced infection control measures. However, our study adds a unique focus on LAC, revealing variability in LTBI prevalence across different countries and highlighting the urgency of implementing regionally tailored strategies for infection control in these settings.

While this meta-analysis provides valuable insights into the epidemiology of LTBI among HCWs in Latin America and the Caribbean, it does have several limitations that warrant consideration. The high heterogeneity observed across studies underscores the need for caution when interpreting pooled prevalence estimates. Moreover, all the included studies were observational, which limits causal inference and highlights the need for further prospective research to elucidate underlying risk factors and transmission dynamics. We did not estimate the cumulative incidences of LTBI in HCWs because most of the studies (25 out of 36) were either cross-sectional or retrospective cohort studies, making it methodologically incorrect to do so. Another potential limitation of our study is the inclusion of overlapping samples tested with both the TST and the IGRA within the same studies. Although this overlap might introduce a minor bias by contributing to a spuriously small variance estimate, the effect is expected to be minimal given the number of studies analyzed [33]. Specifically, the overlap in samples tested with both TST and IGRA is not complete across all studies. T benefits of including the results from various tests outweigh this limitation, providing a more comprehensive analysis of the data [99, 100]. Nonetheless, our findings underscore the urgent need for comprehensive TB control measures in healthcare settings. These measures should include enhanced infection control protocols, targeted screening strategies, and vaccination programs tailored to the unique needs of HCWs in the region.

CONCLUSIONS

Our systematic review and meta-analysis revealed a high prevalence of LTBI among HCWs in Latin America and the Caribbean, necessitating urgent action. The substantial burden of LTBI among HCWs, the backbone of our healthcare systems, underscores the need for targeted interventions and robust infection control measures. Our findings highlight the importance of comprehensive TB control programs that address identified risk factors and promote best practices to protect HCWs and prevent TB transmission. Further research is required to understand the geographical and temporal variations in LTBI prevalence among HCWs in the region and to evaluate the cost-effectiveness of different TB control interventions in these healthcare settings.

Conflict of interest

None to declare.

External grants and funding

None to declare.

SUPPLEMENTARY MATERIALS

Table S1 - Search strategy

Table S2 - Excluded studies and the reason for their exclusion.

Table S3 - Meta-regression analysis of the studies included in the metanalysis assessing the pooled prevalence of LTBI in HCWs in Latin America and the Caribbean.

REFERENCES

  1. Kanchar A, Swaminathan S. Tuberculosis Control: WHO Perspective and Guidelines. Indian J Pediatr. 2019; 86(8): 703-706. doi: 10.1007/s12098-019-02989-2.
  2. Reid MJA, Arinaminpathy N, Bloom A, et al. Building a tuberculosis-free world: The Lancet Commission on tuberculosis. Lancet. 2019; 393(10178): 1331-1384. doi: 10.1016/S0140-6736(19)30024-8.
  3. Gopalaswamy R, Shanmugam S, Mondal R, et al. Of tuberculosis and non-tuberculous mycobacterial infections - a comparative analysis of epidemiology, diagnosis and treatment. J Biomed Sci. 2020; 27(1): 74. doi: 10.1186/s12929-020-00667-6.
  4. World Health Organization (WHO). Global tuberculosis report 2023. [cited 23 Jan 2024]. Available: https://www.who.int/publications/i/item/9789240083851.
  5. Kiazyk S, Ball TB. Latent tuberculosis infection: An overview. Can Commun Dis Rep. 2017; 43(3-4): 62-66. doi: 10.14745/ccdr.v43i34a01.
  6. Cohen A, Mathiasen VD, Schön T, et al. The global prevalence of latent tuberculosis: a systematic review and meta-analysis. Eur Respir J. 2019; 54(3): 1900655. doi:10.1183/13993003.00655-2019.
  7. Christopoulos AI, Diamantopoulos AA, Dimopoulos PA, et al. Risk factors for tuberculosis in dialysis patients: A prospective multi-center clinical trial. BMC Nephrol. 2009; 10: 36. doi:10.1186/1471-2369-10-36.
  8. Cardona PJ, Ruiz-Manzano J. On the nature of Mycobacterium tuberculosis-latent bacilli. Eur Respir J. 2004; 24(6): 1044-1051. doi: 10.1183/09031936.04.00072604.
  9. Sidhu A, Verma G, Humar A, et al. Outcome of latent tuberculosis infection in solid organ transplant recipients over a 10-year period. Transplantation. 2014; 98: 671-675. doi:10.1097/TP.0000000000000133.
  10. World Health Organization (WHO). Global Tuberculosis Programme (GTB) GRC. Latent tuberculosis infection: updated and consolidated guidelines for programmatic management. Geneva: World Health Organization; 2018. Licence: CC BY-NC-SA 3.0 IGO. 15 Feb 2018 [cited 25 Jan 2024]. Available: https://www.who.int/publications/i/item/9789241550239.
  11. Verso MG, Serra N, Ciccarello A, et al. Latent tuberculosis infection among healthcare students and postgraduates in a Mediterranean Italian area: What correlation with work exposure? Int J Environ Res Public Health. 2020; 17(1): 137. doi:10.3390/ijerph17010137.
  12. Almufty HB, Abdulrahman IS, Merza MA. Latent tuberculosis infection among healthcare workers in Duhok province: From screening to prophylactic treatment. Trop Med Infect Dis. 2019; 4(2): 85. doi:10.3390/tropicalmed4020085.
  13. Belo C, Naidoo S. Prevalence and risk factors for latent tuberculosis infection among healthcare workers in Nampula Central Hospital, Mozambique. BMC Infect Dis. 2017; 17(1): 408. doi:10.1186/s12879-017-2516-4.
  14. Guo H-Y, Zhong Q-H, Zhou J, et al. Risk of prevalence of latent tuberculosis infection in health care workers-an idiographic meta-analysis from a Chinese perspective. J Thorac Dis. 2021; 13(4): 2378-2392. doi:10.21037/jtd-20-1612.
  15. H SNF, Manoharan A, Koh WM, et al. Facilitators and barriers to latent tuberculosis infection treatment among primary healthcare workers in Malaysia: a qualitative study. BMC Health Serv Res. 2023; 23(1): 914. doi: 10.1186/s12913-023-09937-z.
  16. Corbett EL, Muzangwa J, Chaka K, et al. Nursing and Community Rates of Mycobacterium tuberculosis Infection among Students in Harare, Zimbabwe. Clin Infect Dis. 2007; 44(3): 317-323. doi:10.1086/509926.
  17. Baussano I, Nunn P, Williams B, et al. Tuberculosis among health care workers. Emerg Infect Dis. 2011; 17(3): 488-494. doi:10.3201/eid1703.100947.
  18. Joshi R, Reingold AL, Menzies D, et al. Tuberculosis among healthcare workers in low- and middle-income countries: a systematic review. PLoS Med. 2006; 3(12): e494. doi: 10.1371/journal.pmed.0030494.
  19. Soto Cabezas MG, Munayco Escate CV, Chávez Herrera J, et al. Prevalence of latent tuberculosis infection in health workers from primary health care centers in Lima, Peru. Rev Peru Med Exp Salud Publica. 2017; 34(4): 649-654. doi:10.17843/rpmesp.2017.344.3035
  20. Meregildo-Rodriguez ED, Yuptón-Chávez V, Asmat-Rubio MG, et al. Latent tuberculosis infection (LTBI) in healthcare workers: a cross-sectional study at a northern Peruvian hospital. Front Med (Lausanne). 2023; 10. doi:10.3389/fmed.2023.1295299.
  21. Apriani L, McAllister S, Sharples K, et al. Latent tuberculosis infection in healthcare workers in low- and middle-income countries: an updated systematic review. Eur Respir J. 2019; 53(4): 1801789. doi: 10.1183/­13993003.01789-2018.
  22. Menzies D, Joshi R, Pai M. Risk of tuberculosis infection and disease associated with work in health care settings. Int J Tuberc Lung Dis. 2007; 11(6): 593-605.
  23. Hernández M, Casar C, García P, et al. Latent tuberculosis infection screening in healthcare workers in four large hospitals in Santiago, Chile. Rev Chilena Infectol. 2014; 31(3): 254-260. doi:10.4067/S0716-10182014000300002.
  24. Narasimhan P, Wood J, Macintyre CR, et al. Risk factors for tuberculosis. Pulm Med. 2013; 2013: 828939. doi: 10.1155/2013/828939.
  25. Kota NT, Shrestha S, Kashkary A, et al. The Global Expansion of LTBI Screening and Treatment Programs: Exploring Gaps in the Supporting Economic Evidence. Pathogens. 2023; 12(3): 500. https://doi.org/10.3390/pathogens12030500.
  26. Calixto-Aguilar LS, Manrique-Zegarra M, Gotuzzo-Herencia E, et al. Behaviors in response to the tuberculin skin test conversion in medical students from a university in Lima, Peru. Rev Peru Med Exp Salud Publica. 2016; 33(2): 283-287. doi:10.17843/rpmesp.2016.332.2216.
  27. Centers for Disease Control, National Center for HIV P, Hepatitis V, Prevention T. Latent tuberculosis infection: a guide for primary health care providers. Available from: https://www.cdc.gov/tb/media/pdfs/Latent-TB-Infection-A-Guide-for-Primary-Health-Care-Providers.pdf
  28. Sterling TR, Njie G, Zenner D, et al. Guidelines for the Treatment of Latent Tuberculosis Infection: Recommendations from the National Tuberculosis Controllers Association and CDC, 2020. MMWR Recomm Rep. 2020; 69(1): 1-11. doi: 10.15585/mmwr.rr6901a1.
  29. Moher D, Liberati A, Tetzlaff J, et al; PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 2009; 6(7): e1000097. doi: 10.1371/journal.pmed.1000097.
  30. Shea BJ, Reeves BC, Wells G, et al. AMSTAR 2: A critical appraisal tool for systematic reviews that include randomised or non-randomised studies of healthcare interventions, or both. BMJ (Online). 2017; 358: j4008. doi:10.1136/bmj.j4008.
  31. Brooke BS, Schwartz TA, Pawlik TM. MOOSE Reporting guidelines for meta-analyses of observational studies. JAMA Surg. 2021; 156(8): 787-788. doi:10.1001/jamasurg.2021.0522.
  32. Nasreen S, Shokoohi M, Malvankar-Mehta MS. Prevalence of Latent Tuberculosis among Health Care Workers in High Burden Countries: A Systematic Review and Meta-Analysis. PLoS One. 2016; 11(10): e0164034. doi:10.1371/journal.pone.0164034.
  33. Higgins JPT, Thomas J, Chandler J, et al. Cochrane Handbook for Systematic Reviews of Interventions version 6.4 (updated August 2023). Cochrane, 2023. Available: www.training.cochrane.org/handbook.
  34. Harrer M, Cuijpers P, Furukawa TA, et al. Doing Meta-Analysis With R: A Hands-On Guide. 1st ed. Boca Raton, FL and London: Chapman & Hall/CRC Press; 2021. Available: https://www.routledge.com/Doing-Meta-Analysis-with-R-A-Hands-On-Guide/Harrer-Cuijpers-Furukawa-Ebert/p/book/9780367610074.
  35. Wells G, Shea B, O’Connell D, et al. Ottawa Hospital Research Institute. [cited 23 Jan 2024]. Available: https://www.ohri.ca/programs/clinical_epidemiology/oxford.asp.
  36. Granholm A, Alhazzani W, Møller MH. Use of the GRADE approach in systematic reviews and guidelines. BJA. 2019; 123(5): 554-559. https://doi.org/10.1016/j.bja.2019.08.015.
  37. Meader N, King K, Llewellyn A, et al. A checklist designed to aid consistency and reproducibility of GRADE assessments: Development and pilot validation. Syst Rev. 2014; 3: 82. doi:10.1186/2046-4053-3-82.
  38. Borroto S, González E, Doval A, et al. Latent tuberculosis infection in health care workers of Cuban health facilities: Risk assessing and results of an intervention. Trans R Soc Trop Med Hyg. 2019; 113: S248-S249. doi:10.1093/trstmh/trz097.
  39. Baboolal S, Ramoutar D, Akpaka PE. Comparison of the QuantiFERON®-TB Gold assay and tuberculin skin test to detect latent tuberculosis infection among target groups in Trinidad & Tobago. Rev Panam Salud Publica. 2010; 28(1): 36-42.
  40. de Souza FM, do Prado TN, Pinheiro J dos S, et al. Comparison of Interferon-γ Release Assay to Two Cut-Off Points of Tuberculin Skin Test to Detect Latent Mycobacterium tuberculosis Infection in Primary Health Care Workers. PLoS One. 2014; 9(8): e102773. doi: 10.1371/journal.pone.0102773.
  41. Ochoa J, León AL, Ramírez IC, et al. Prevalence of tuberculosis infection in healthcare workers of the public hospital network in Medellín, Colombia: a Bayesian approach. Epidemiol Infect. 2017; 145(6): 1095-1106. doi:10.1017/S0950268816003150.
  42. Alonso-Echanove J, Granich RM, Laszlo A, et al. Occupational Transmission of Mycobacterium tuberculosis to Health Care Workers in a University Hospital in Lima, Peru. Clin Infect Dis. 2001; 33(5): 589-596. doi: 10.1086/321892.
  43. Sedamano J, Schwalb A, Cachay R, et al. Prevalence of positive TST among healthcare workers in high-burden TB setting in Peru. BMC Public Health. 2020; 20(1): 612. doi:10.1186/s12889-020-08756-9.
  44. Ju Wang J-D, Zhang Xu CM, Lopez LM, et al. Factors Related with Latent TB Among Health Care Workers of a Peruvian University Hospital. D21 Latent Tuberculosis Infection. Am Thorac Soc. 2015; 191: A5421-A5421 doi:10.1164/ajrccm-conference.2015.191.1_MeetingAbstracts.A5421.
  45. Borroto Gutiérrez S, Sevy Court JI, Fumero Leru M, et al. Tuberculosis risk assessment in the staff of the National University Pneumologic Hospital of Havana. Rev Cubana Med Trop. 2012; 64(1): 55-60.
  46. Borroto Gutiérrez S, Martínez Alvarez AM, Guanche Garcell H, et al. Tuberculosis risk in the staff of three clinical surgical hospitals at Havana city. Rev Cubana Med Trop. 2015. 2015; 67(1): 59-74.
  47. Franco C, Zanetta DM. Assessing occupational exposure as risk for tuberculous infection at a teaching hospital in São Paulo, Brazil. Int J Tuberc Lung Dis. 2006; 10(4): 384-389.
  48. Costa H, Malaspina AC, Mello F, et al. Tuberculosis in a Psychiatric Hospital in the state of Goiás, Brazil. J Bras Pneumol. 2006; 32(6): 566-572.
  49. Meregildo-Rodriguez ED, Yuptón-Chávez V, Asmat-Rubio MG, et al. Latent tuberculosis infection (LTBI) in healthcare workers: a cross-sectional study at a northern Peruvian hospital. Front Med (Lausanne). 2023;10: 1295299. doi:10.3389/fmed.2023.1295299.
  50. Lopes LKO, Teles SA, Souza ACS, et al. Tuberculosis risk among nursing professionals from Central Brazil. Am J Infect Control. 2008; 36(2): 148-151. doi:10.1016/j.ajic.2007.01.013.
  51. Lacerda TC, Souza FM, Prado TND, et al. Tuberculosis infection among primary health care workers. J Bras Pneumol. 2017; 43(6): 416-423. doi: 10.1590/S1806-37562016000000211.
  52. da Costa PA, Trajman A, Mello FC, et al. Administrative measures for preventing Mycobacterium tuberculosis infection among healthcare workers in a teaching hospital in Rio de Janeiro, Brazil. J Hosp Infect. 2009; 72(1): 57-64. doi: 10.1016/j.jhin.2009.01.016.
  53. Molina-Gamboa J, Fivera-Morales I, Ponce-de-León-Rosales S. Prevalence of Tuberculin Reactivity among Healthcare Workers from a Mexican Hospital. Infect Control Hosp Epidemiol. 1994; 15(5): 319-320. doi:10.1086/646920.
  54. Roth VR, Garrett DO, Laserson KF, et al. A multicenter evaluation of tuberculin skin test positivity and conversion among health care workers in Brazilian hospitals. Int J Tuberc Lung Dis. 2005; 9(12): 1335-1342. doi: https://doi.org/10.15446/rsap.v17n3.38210.
  55. García-García M de L, Jiménez-Corona A, Jiménez-Corona ME, et al. Factors Associated With Tuberculin Reactivity in Two General Hospitals in Mexico. Infect Control Hosp Epidemiol. 2001; 22(2): 88-93. doi: 10.1086/­501869.
  56. Moreira TR, Zandonade E, Maciel ELN. Risco de infecção tuberculosa em agentes comunitários de saúde. Rev Saude Publica. 2010; 44(2): 332-338. doi:10.1590/S0034-89102010000200014.
  57. Prado TN Do, Riley LW, Sanchez M, et al. Prevalencia de infección latente de la tuberculosis y factores de riesgo entre profesionales de salud en la atención primaria en Brasil. Cad Saude Publica. 2017; 33(12): e00154916. doi:10.1590/0102-311X00154916.
  58. Orrett FA. Prevalence of tuberculin skin test reactivity among health care workers at a teaching hospital in Trinidad. Clin Microbiol Infect. 2000; 6(1): 45-48. doi:10.1046/j.1469-0691.2000.00015-2.x.
  59. Ruiz-Morales Á, Hidalgo-Martínez P, Pulido-Arenas J, et al. Tuberculin skin test conversion and association with occupation and demographic characteristics in workers at San Ignacio University Hospital. Rev. Salud Pública. 2015; 17(3): 443-449. doi:10.15446/rsap.v17n3.38210.
  60. Roth VR, Garrett DO, Laserson KF, et al. A multicenter evaluation of tuberculin skin test positivity and conversion among health care workers in Brazilian hospitals. Int J Tuberc Lung Dis. 2005; 9(12): 1335-1342. doi: https://doi.org/10.15446/rsap.v17n3.38210.
  61. Severo KGP, Oliveira JDS, Carneiro M, et al. Latent tuberculosis in nursing professionals of a Brazilian hospital. J Occup Med Toxicol. 2011; 6(1): 15. doi:10.1186/1745-6673-6-15.
  62. Oliveira SMVL, Honner MR, Paniago AMM, et al. Prevalence of mycobacterium tuberculosis among professionals in a university hospital, Mato Grosso do Sul, 2004. Rev. Latino-Am. Enfermagem. 2007; 15(6): 1120-1124. doi: https://doi.org/10.1590/S0104-11692007000600010
  63. Escombe AR, Huaroto L, Ticona E, et al. Tuberculosis transmission risk and infection control in a hospital emergency department in Lima, Peru. Int J Tuberc Lung Dis. 2010; 14(9): 1120-1126.
  64. Alonso-Echanove J, Granich RM, Laszlo A, et al. Occupational Transmission of Mycobacterium tuberculosis to Health Care Workers in a University Hospital in Lima, Peru. Clin. Infect. Dis. 2001; 33(5): 589-596 doi: https://doi.org/10.1086/321892.
  65. Borroto S, Gámez D, Díaz D, et al. Latent tuberculosis infection among health care workers at a general hospital in Santiago de Cuba. IJTLD. 2011; 15(11): 1510-1514. doi:10.5588/ijtld.10.0333.
  66. Busatto C, Nunes L de S, Valim AR de M, et al. Tuberculosis among prison staff in Rio Grande do Sul. Rev Bras Enferm. 2017; 70(2): 370-375. doi:10.1590/0034-7167-2016-0012.
  67. Bonifacio N, Saito M, Gilman RH, et al. High Risk for Tuberculosis in Hospital Physicians, Peru. Emerg Infect Dis. 2002; 8(7): 747-748. doi:10.3201/eid0807.010506.
  68. Spíndola De Miranda S, Campos De Oliveira A, Santos AX, et al. Positive tuberculin test and risk of infection by Mycobacterium tuberculosis in a tuberculosis clinic settled in an upright building, in Minas Gerais, Brazil. Rev Med Chile. 2012; 140(8): 1022-1027. doi: http://dx.doi.org/10.4067/S0034-98872012000800008.
  69. Encinales L, Zuñiga J, Granados-Montiel J, et al. Humoral immunity in tuberculin skin test anergy and its role in high-risk persons exposed to active tuberculosis. Mol Immunol. 2010; 47(5): 1066-1073. doi:10.1016/j.molimm.2009.11.005.
  70. Rodrigues PM, Moreira R, Moraes AKL, et al. Mycobacterium tuberculosis infection among community health workers involved in TB control. J Bras Pneumol. 2009; 35(4): 351-358 doi: https://doi.org/10.1590/S1806-37132009000400009.
  71. Lima OC, Souza FM, Prado TND, et al. Analysis of the incidence of latent Mycobacterium tuberculosis infection among primary health care professionals in two Brazilian capitals. J Bras Pneumol. 2020; 46(2): e20190201. doi: 10.36416/1806-3756/e20190201.
  72. Martínez Hall D, Borroto Gutiérrez S, Arroyo Rojas L, González Ochoa E. Evaluation of latent tuberculosis infection risk in primary health care workers. Rev Cubana Med Trop. 2015; 67(1): 11-19.
  73. Rabahi MF, Junqueira-Kipnis AP, dos Reis MCG, et al. Humoral response to HspX and GlcB to previous and recent infection by Mycobacterium tuberculosis. BMC Infect Dis. 2007; 7: 148. doi: 10.1186/1471-2334-7-148.
  74. Zwerling A, Behr MA, Verma A, et al. The BCG World Atlas: A Database of Global BCG Vaccination Policies and Practices. PLoS Med. 2011; 8(3): e1001012. doi:10.1371/journal.pmed.1001012.
  75. Nienhaus A, Schablon A, Costa JT, et al. Systematic review of cost and cost-effectiveness of different TB-screening strategies. BMC Health Serv Res. 2011; 11: 247. doi:10.1186/1472-6963-11-247.
  76. Sousa S, Rocha D, Silva JC, et al. Comparing the cost-effectiveness of two screening strategies for latent tuberculosis infection in Portugal. Pulmonology. 2021; 27(6): 493-499. doi:10.1016/j.pulmoe.2021.04.002.
  77. Mahon J, Beale S, Holmes H, et al. A systematic review of cost-utility analyses of screening methods in latent tuberculosis infection in high-risk populations. BMC Pulm Med. 2022; 22(1): 375. doi:10.1186/s12890-022-02149-x.
  78. Sharma SK, Vashishtha R, Chauhan LS, et al. Comparison of TST and IGRA in Diagnosis of Latent Tuberculosis Infection in a High TB-Burden Setting. PLoS One. 2017; 12(1): e0169539. doi:10.1371/journal.pone.0169539.
  79. Lalvani A, Pareek M. Interferon gamma release assays: principles and practice. Enferm Infecc Microbiol Clin. 2010; 28(4): 245-252. doi:10.1016/j.eimc.2009.05.012.
  80. Centers for Disease Control and Prevention (CDC). Clinical Testing Guidance for Tuberculosis: Interferon Gamma Release Assay. 2024 [cited 6 Aug 2024]. Available: https://www.cdc.gov/tb/hcp/testing-diagnosis/interferon-gamma-release-assay.html?CDC_AAref_Val=https://www.cdc.gov/tb/publications/factsheets/testing/IGRA.htm.
  81. Chen H, Nakagawa A, Takamori M, et al. Diagnostic accuracy of the interferon-gamma release assay in acquired immunodeficiency syndrome patients with suspected tuberculosis infection: a meta-analysis. Infection. 2022; 50: 597-606. doi:10.1007/s15010-022-01789-9.
  82. Lahey T, Czechura T, Crabtree S, et al. Greater preexisting interferon responses to mycobacterial antigens and lower bacillary load during HIV-associated tuberculosis. J Infect Dis. 2013;208(10):1629-33. doi: 10.1093/infdis/jit396.
  83. Redelman-Sidi G, Sepkowitz KA. IFN-γ Release Assays in the Diagnosis of Latent Tuberculosis Infection among Immunocompromised Adults. Am J Respir Crit Care Med. 2013; 188(4): 422-431. doi:10.1164/rccm.201209-1621CI.
  84. Carvalho ACC, Schumacher RF, Bigoni S, et al. Contact investigation based on serial interferon-gamma release assays (IGRA) in children from the hematology-oncology ward after exposure to a patient with pulmonary tuberculosis. Infection. 2013; 41(1): 827-831. doi:10.1007/s15010-013-0450-y.
  85. Kamiya H, Ikushima S, Kondo K, et al. Diagnostic performance of interferon-gamma release assays in elderly populations in comparison with younger populations. J Infect Chemother. 2013; 19(2): 217-222. doi: 10.1007/s10156-012-0480-x.
  86. Sollai S, Galli L, de Martino M, et al. Systematic review and meta-analysis on the utility of Interferon-gamma release assays for the diagnosis of Mycobacterium tuberculosis infection in children: a 2013 update. BMC Infect Dis. 2014; 14 Suppl 1(Suppl 1): S6. doi:10.1186/1471-2334-14-S1-S6.
  87. Demkow U. Interferon gamma based tests as a new tool in diagnosis of latent tuberculosis. Pneumonol Alergol Pol. 2011; 79(4): 261-263. doi: https://doi.org/10.5603/ARM.27643.
  88. Borkowska DI, Napiórkowska AM, Brzezińska SA, et al. From Latent Tuberculosis Infection to Tuberculosis. News in Diagnostics (QuantiFERON-Plus). Pol J Microbiol. 2017; 66(1): 5-8. doi:10.5604/17331331.1234987.
  89. Augustynowicz-Kopeć E, Siemion-Szcześniak I, Zabost A, et al. Interferon Gamma Release Assays in Patients with Respiratory Isolates of Non-Tuberculous Mycobacteria - a Preliminary Study. Pol J Microbiol. 2019; 68(1): 15-19. doi:10.21307/pjm-2019-002.
  90. Gudjónsdóttir MJ, Kötz K, Nielsen RS, et al. Relation between BCG vaccine scar and an interferon-gamma release assay in immigrant children with “positive” tuberculin skin test (≥10 mm). BMC Infect Dis. 2016; 16(1): 540. doi:10.1186/s12879-016-1872-9.
  91. Horvat RT. Gamma Interferon Assays Used in the Diagnosis of Tuberculosis. Clin Vaccine Immunol. 2015; 22(8): 845-849. doi: 10.1128/CVI.00199-15.
  92. Gonzślez-Moreno J, García-Gasalla M, Gállego-Lezaun C, et al. Role of QuantiFERON® -TB Gold In-Tube in tuberculosis contact investigation: experience in a tuberculosis unit. Infect Dis. 2015; 47(4): 244-251. doi:10.3109/00365548.2014.987813.
  93. Zhang Y, Zhou G, Shi W, et al. Comparing the diagnostic performance of QuantiFERON-TB Gold Plus with QFT-GIT, T-SPOT.TB and TST: a systematic review and meta-analysis. BMC Infect Dis. 2023; 23(1): 40. doi:10.1186/s12879-023-08008-2.
  94. Arias Guillén M. Avances en el diagnóstico de la infección tuberculosa. Arch Bronconeumol. 2011; 47(10): 521-530. doi:10.1016/j.arbres.2011.06.018.
  95. da Silva EH, Lima E, Dos Santos TR, et al. Prevalence and incidence of tuberculosis in health workers: A systematic review of the literature. Am J Infect Control. 2022; 50(7): 820-827. doi: 10.1016/j.ajic.2022.01.021.
  96. Uden L, Barber E, Ford N, et al. Risk of Tuberculosis Infection and Disease for Health Care Workers: An Updated Meta-Analysis. Open Forum Infect Dis. 2017; 4(3): ofx137. doi:10.1093/OFID/OFX137.
  97. Lee S, Lee W, Kang SK. Tuberculosis infection status and risk factors among health workers: an updated systematic review. Ann Occup Environ Med. 2021; 33: e17. doi: 10.35371/aoem.2021.33.e17.
  98. Peters C, Kozak A, Nienhaus A, et al. Risk of Occupational Latent Tuberculosis Infection among Health Personnel Measured by Interferon-Gamma Release Assays in Low Incidence Countries-A Systematic Review and Meta-Analysis. Int J Environ Res Public Health. 2020; 17(2): 581. doi: 10.3390/ijerph17020581.
  99. Gutierrez-Arias R, Pieper D, Lunny C, et al. Strategies used to manage overlap of primary study data by exercise-related overviews: protocol for a systematic methodological review. BMJ Open. 2023; 13(4): e069906. doi: 10.1136/bmjopen-2022-069906.
  100. Lunny C, Pieper D, Thabet P, et al. Managing overlap of primary study results across systematic reviews: practical considerations for authors of overviews of reviews. BMC Med Res Methodol. 2021; 21:140(1): 140. doi: 10.1186/s12874-021-01269-y.