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Systematic Review

Seroprevalence of Coxiella burnetii in Occupational Settings: A Meta-Analysis of Italian Studies

1
AUSL–IRCCS di Reggio Emilia, Servizio di Prevenzione e Sicurezza Negli Ambienti di Lavoro (SPSAL), Local Health Unit of Reggio Emilia, 42122 Reggio Emilia, Italy
2
Department of Experimental and Clinical Medicine, University of Florence, 50134 Florence, Italy
3
Department of Medicine DAME—Division of Pediatrics, University of Udine, 33100 Udine, Italy
4
Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy
*
Author to whom correspondence should be addressed.
Zoonotic Dis. 2023, 3(1), 38-51; https://doi.org/10.3390/zoonoticdis3010005
Submission received: 6 January 2023 / Revised: 11 February 2023 / Accepted: 14 February 2023 / Published: 16 February 2023
(This article belongs to the Special Issue Feature Papers of Zoonotic Diseases 2021–2022)

Abstract

:

Simple Summary

Q fever is a disease caused by the bacteria Coxiella burnetii. This pathogen usually infects some animals (i.e., goats, sheep, and cattle), their birth products (i.e., placenta, amniotic fluid), as well as urine, feces, and milk of infected animals. Albeit quite rarely, humans can get infected by breathing in dust that has been contaminated by feces of infected animals, handling products contaminated by urine and birth products, as well as by drinking the milk from infected animals. In other words, people working in farm settings are at high risk of developing this disease. Interestingly, the actual occurrence of this disease is unclear, as some people never get sick, and the large majority of the infected only develop flu-like symptoms including fever, chills, fatigue, and muscle pain.

Abstract

Coxiella burnetii (C. burnetii) can cause a serious human disease known as Q Fever (QF). Our study summarized seroprevalence data from occupational settings in Italy, a country characterized by low notification rates of QF (17 cases between 2015 and 2021). Through systematic research on 3 databases (PubMed, EMBASE, MedRxiv), all studies including seroprevalence rates of C. burnetii in Italy were retrieved, and their results summarized and compared. We identified a total of 7 articles for a total of 1178 workers, mostly from agricultural settings. A pooled seroprevalence of 44.0% (95% Confidence Interval [95%CI] 27.6 to 61.8) was calculated. Subgroup estimates ranged from 2.8% (95%CI 0.9–6.3) in forestry rangers to 49.2% (95%CI 26.8–72.0) in livestock farmers, and peaked at 73.7% (95%CI 56.9–86.6) and 75.9% (95%CI 13.4–98.5) in abattoir workers and veterinary professionals, respectively. Seroprevalence rates for C. burnetii largely exceeded the official notification rates, suggesting its substantial underreporting in Italy.

1. Introduction

Q Fever (QF) is a zoonotic infectious disease with global distribution caused by the obligate intracellular bacterium Coxiella burnetii (C. burnetii) [1,2], a small (0.2 to 0.4 µm × 0.4 to 1.0 µm) gram-negative pathogen belonging to the Coxiellaceae family [3]. C. burnetii infects a wide range of domesticated and wild animals, and human cases result from contact via airborne routes, after the organism settles in dust and becomes aerosolized [3,4,5].
With a case fatality ratio ranging between 0.9% to 2.4%, QF in humans rarely represents a deadly disease, but it is usually acknowledged as a debilitating one [3,6,7,8]. Even though up to 60% of incident cases may go unnoticed, the large majority of remaining cases evolves into mild syndromes characterized by common and unspecific symptoms such as fever, asthenia, chills, headache, myalgia, skin rashes, sweating, nausea, vomiting, and diarrhea [2,3], and in some cases may evolve into pneumonia, hepatitis, myocarditis, and even meningoencephalitis (Acute QF); moreover, QF may also develop into a chronic disease (Chronic QF) characterized by endocarditis, vascular alterations, hepatitis, and chronic pulmonary lesions [2,3,9,10,11].
Since its discovery in 1935 among Australian abattoir workers [3,11,12], QF has been characterized as an occupational infectious diseases associated with the livestock industry, laboratory personnel [5,11,12], and, in general, with occupational tasks leading to the close contact with animals [13], particularly sheep and goats [13,14], as the pathogen largely remains both uncontrolled and highly prevalent in livestock [3]. In these settings, seroprevalence can range from 30% to 70% [1]. Still, a considerable share of cases occurs in individuals not occupationally exposed to livestock but reporting any contact with wildlife, where several species represent an effective reservoir for C. burnetii [3,11,12,15,16,17,18,19,20,21,22]. As a consequence, three types of reservoirs can be distinguished, including: (a) domestic animals (e.g., ruminants such as goats, sheep and cattle); (b) wild animals (mostly rodents, then small mammals, birds, and reptiles); and (c) ticks, that maintain the viability of micro-organisms in nature [13,14,18,23].
Since the large majority of acute cases can be exclusively detectable by seroconversion [1,23], the true incidence of QF in the general population as well as in high-risk occupational groups remains disputed, even in the European Union (EU), for several reasons. First of all, reporting is not universally compulsory: as a consequence, a considerable share of incident cases may go unreported to the competent Health authorities [24], leading to the general underestimation of the true burden of disease [1,2,13,14,23,25]. Moreover, the clinical case definition is not consistently shared by all EU/EAA Countries. For instance, between 2017 and 2021, a total of 3559 cases have occurred in the 27 EU countries: of them, 710 have been reported from France alone (19.9% of all incident cases), and 500 from Germany (14.0%) [24]. Interestingly, not only is the notification of QF mandatory in Germany and voluntary in France, but the very same case definition is quite heterogenous [23,24]. In other words, the occasional absence of symptoms, the low clinical suspicion, and the need for serological diagnostic tests, all of them characterized by different techniques and cut-off points, may lead to general underdiagnosis of this disease [5,23,26]. In turn, the underestimation of the actual QF burden may lead to the inappropriate definition of preventive measures, even in high-risk groups, ultimately leading to the persistence of this disorders as an occupational health threat [2,4,13,15,16].
With a total of 17 cases of QF officially reported between 2015 and 2021 (58.8% occurring in age group 25 to 65 years), Italy is usually acknowledged as being at very low risk of infections from C. burnetii [9,17,20,23,27,28], but their actual occurrence largely remains undefined [17,23,28]. As around 3.9% of the Italian workforce is to date involved in the agricultural sector (compared to the 2.4% of France and 1.4% of Germany) [29,30], the crude number of workers potentially exposed to C. burnetii is consistent, suggesting the substantial underreporting of incident cases. Through a systematic review, our study aimed to ascertain the published measurement of seroprevalence for C. burnetii in Italy, and to reconcile possible variations in seroprevalence rates with occupational exposures.

2. Materials and Methods

This systematic review has been conducted following the PRISMA (Prepared Items for Systematic Reviews and Meta-Analysis) guidelines [31,32], and was registered in PROSPERO with the progressive number CRD42023367891.
The research concepts were preliminarily defined according to the “PICO” (Patient/Population/Problem; Intervention; Control/Comparator; Outcome) strategy, as reported in Table 1. More precisely, the population of interest (P) was identified in workers potentially exposed to C. burnetii; the investigated result (I) was identified in the seroprevalence of biomarkers for any previous exposure to C. burnetii; the control (C) was defined as the healthy individuals not occupationally exposed (where available); and the outcome (O) was identified in the seroprevalence of previous C. burnetii infection among occupational exposed individuals, as a proxy for the risk of QF in occupational settings.
Two conventional scientific databases (i.e., PubMed and EMBASE) and the preprint repository MedRxiv were searched through a combination of the following keywords: (“Q Fever” OR “Coxiella burnetii” OR “Coxiella”) AND (“Italy” OR “Italian”) AND (“epidemiology” OR “seroprevalence” OR “prevalence” OR “frequency” OR “occurrence”). No chronological restrictions were applied.
Documents eligible for review were original research publications available online or through inter-library loan, including case studies, cohort studies, case-control studies, and cross-sectional studies. Retrieved documents were excluded if: (1) full text was not available to the reviewers; (2) articles were written in a language not understood by reviewers (i.e., Italian, English, German, French, or Spanish); (3) reports lacked timeframe (i.e., the prevalence year) and geographical settings, or it was only vaguely defined; and (4) a proper definition of the occupational settings was lacking.
Retrieved entries were initially screened for their titles in terms of relevance to the subject. Titles that were considered consistent with the research outline were subsequently analyzed by their abstracts. If the content was in turn consistent with the design of the present review, full-text versions of eligible articles were independently read by two investigators (AB and FM). Disagreements were resolved by consensus between the two reviewers; where they did not reach consensus, input from a third investigator (MR) was obtained.
Data abstracted included: (a) Settings of the study: prevalence year, Region; (b) Occupational settings of the sampled cases; (c) Total number of prevalent cases; (d) Number of reference population (if available); and (e) Characteristics of the serologic assay.
Retrieved studies were then rated about their potential risk of bias by means of the National Toxicology Program (NTP)’s Office of Health Assessment and Translation (OHAT) handbook and respective risk of bias (ROB) tool [33,34]. Briefly, the ROB tool evaluates the internal validity of a given study in order to assess whether the study’s design and conduct have compromised the credibility of the link between exposure and outcome. In its current version, OHAT ROB tool covers six possible sources of bias (i.e., participant selection, confounding, attrition/exclusion, detection, selective reporting, and other sources) with potential answers ranging from “definitely low”, “probably low”, and “probably high” to “definitely high”. Interestingly, OHAT ROB tool does not apply an overall rating for each study, and OHAT handbook also recommends that even studies with “probably high” or “definitely high” ratings should not be removed from consideration of the overall body of evidence.
Characteristics of the included studies were initially summarized through descriptive analysis, with subsequent calculation of crude prevalence figures. If a study did not include raw data, either as number of prevalent cases or referent population, such figures were reverse-calculated from available data. Pooled prevalence estimates were then calculated by means of prevalent cases per 100 population.
Meta-analysis of retrieved studies was performed through a random effect model in order to cope with the presumptive heterogeneity in design of the included reports. The amount of inconsistency between studies was estimated by means of I2 statistic (i.e., the percentage of total variation across studies that is due to heterogeneity rather than chance). For the aims of this study, I2 values were categorized as follows: 0 to 25% low heterogeneity; 26% to 50% moderate heterogeneity; and ≥ 50% substantial heterogeneity. Contour-enhanced funnel plots and radial plots were generated in order to visually assess potential publication bias and small study bias, respectively. Funnel plot asymmetry was eventually assessed by means of the Egger test statistic. All calculations were performed in R (version 4.0.3) [35], and RStudio (version 1.4.1717; Rstudio, PBC; Boston, MA, USA) software by means of the meta package (version 4.9-9).

3. Results

Results of the inquiry are summarized in Figure 1. Briefly, a total pool of 313 entries (i.e., 142 from PubMed; 6 from MedRxiv; and 165 from EMBASE) were initially retrieved; of them, 107 were duplicated entries, therefore being removed. The remaining 206 records were then screened by title and abstract (65.8% of the original pool), and a total of 184 entries (58.8% of the original pool) were eventually removed from the analyses. The remaining 22 entries were assessed and reviewed by full-text.
Eventually, 15 of them were excluded due to not fitting the inclusion criteria. The remaining 7 papers were eventually included in qualitative and quantitative analysis (2.2% of the initial sample).
The retrieved studies (range: 2006–2022) [13,14,18,25,26,36,37] included a total of 16 estimates from occupational settings and 3 reference groups. Of the aforementioned studies, 3 were based on immunofluorescence assay (IFA), 3 on enzyme-linked immunoassay (ELISA), and one complement fixation test (CFT).
A total of 1670 specimens from individuals living and working in 6 Italian regions (i.e., Lombardy, Friuli Venezia Giulia, Tuscany, Apulia, Lombardy, and Sicily; Appendix A Figure A1) and belonging to 9 distinctive occupational groups were retrieved. Of them, 1314 were potentially exposed to C. burnetii because of their professional tasks (78.7% of the sample), and more precisely, agricultural workers operating as livestock farmers (No. 158, 9.5% of the total sample); agricultural workers having mixed tasks, including caring for animals (No. 223, 13.4%); agricultural workers not caring for animals (No. 32, 1.9%); forestry rangers (No. 181, 10.8%); forestry workers (No. 538, 32.2%); veterinarians (No. 94, 5.6%); abattoir workers (No. 38, 2.3%); laboratory workers (No. 20, 1.2%); and geologists and agronomists (No. 30, 1.8%) (Table 2).
Interestingly, while two studies [13,25] also estimated seroprevalence for C. burnetii in healthy blood donors from the very same geographic areas, with a total of 42 subjects over 322 samples (13.0%; actual range from 13.6% [13] to 14.3% [25]), the study from Stufano et al. included administrative workers (No. 34) not exposed to C. burnetii in occupational settings [26]. Overall, a total of 356 not-occupationally exposed individuals were reported and represented the reference group (21.3% of the total sample).
A detailed description of the risk of bias (ROB) assessment on retrieved studies is summarized in Table 3 and Figure 2.
Overall, the overall quality of retrieved studies was largely unsatisfying. First of all, in nearly all studies, how the sample was ultimately recruited remains largely unclear. Moreover, in the large majority of retrieved reports, the very same exposure is not clearly stated. For example, in the study by Aquilini et al. [18] we are only briefed that the sampled individuals (i.e., 507) were “forestry workers”, and even though Authors did recall an estimate for the exposure assessment, it was not reported through the actual tasks performed by study participants, and the risk were summarized in terms of exposure to milk, ticks, and animals. Similarly, the study of Fenga et al. reports on the seroprevalence for C. burnetii among “laboratory workers” [25]. It is quite unclear whether the participants had any occupational interaction with specimens possibly contaminated by the pathogen. Moreover, more than half of the included studies were based on laboratory techniques such as IFA [13,18,37] and even CFT [14], that are far less sensitive and specific than ELISA [25,26,36]. Eventually, the analysis of the possible role of non-occupational exposures was not clearly addressed in the majority of the assessed studies, while no clear reporting bias could be identified among the pooled reports.
In the meta-analysis, the pooled seroprevalence for C. burnetii was estimated through a random-effect model at 44.0% (95% Confidence Interval [95%CI] 27.6–61.8) (Figure 3), with substantial heterogeneity across the assessed subgroups. For instance, individual estimates ranged from 2.8% (95%CI 0.9–6.3) in forestry rangers to 20.0% (95%CI 5.7–43.7) in laboratory personnel, and 25.1% (95%CI 21.6–28.9) among forestry workers; 26.7% (95%CI 12.3–45.9) in agronomists and geologists, 31.2% (95%CI 16.1–49.0) in agricultural workers not caring for animals, 49.2% (95%CI 26.8–72.0) in farmers having mixed tasks that included breeding animals, 64.2% (95%CI 44.0–80.4) in livestock farmers, and peaked at 73.7% (95%CI 56.9–86.6) and 75.9% (95%CI 13.4–98.5) in abattoir workers and veterinary professionals, respectively. The estimates were affected by substantial heterogeneity (tau2 = 2.004; tau = 1.4157; I2 = 92.4% [95%CI, 89.3%; 94.6%], Q = 197.88, p < 0.001).
Interestingly, by assuming non-occupationally exposed individuals as the reference group (Figure 4), a substantially higher occurrence of seropositivity was reported among agricultural workers in all of the three subgroups: livestock farmers (Risk Ratio [RR] 4.506, 95%CI 3.381 to 6.006), individuals performing mixed tasks (RR 2.965, 95%CI 2.187 to 4.019), and non-breeders (RR 2.270, 95%CI 1.276 to 4.039). An increased risk was also associated with forestry workers (RR 1.823, 95%CI 1.353 to 2.457), abattoir workers (RR 5.343, 95%CI 3.897 to 7.387), veterinary professionals (RR 3.710 95%CI 2.676 to 5.144), as well as for geologists and agronomists (RR 1.937, 95%CI 1.014 to 3.703). On the contrary, a reduced risk was associated with individuals working as forestry rangers (RR 0.201, 95%CI 0.081 to 0.495), and no substantial differences with non-exposed individuals were identified among laboratory and technical professionals (RR 1.453, 95%CI 0.582 to 3.625).
When seroprevalence was compared by the parent Italian region, after the exclusion of non-exposed individuals, it ranged between 2.8% from Friuli Venezia Giulia to 49.0% in Apulia (Appendix A Figure A1). Assuming workers from Lombardy (the most populated Italian region, with around 10,000,000 inhabitants, that is 1/6 of the total Italian population) as the reference group, the risk ratio for reporting seroprevalence on C. burnetii ranged between RR 0.063 (95%CI 0.026 to 0.154) in the North-Eastern Region of Friuli Venezia Giulia to RR 0.568 (95%CI 0.432 to 0.746) in Tuscany and RR 0.980 (95%CI 0.751 to 1.278) in Sicily, with the greatest estimates for Basilicata (RR 1.087, 95%CI 0.767 to 1.593) and Apulia (RR 1.119, 95%CI 0.861 to 1.453) (Figure 5).
When dealing with the assessment of the potential publication and small study bias, mixed results were retrieved. On the one hand, the radial plot calculated by single prevalence estimates (Figure 6), hinted towards no effects for the small size of samples included in the estimates. On the other hand, the substantial asymmetry of the corresponding funnel plot (Figure 7) suggested a residual publication bias, that was substantially rejected by the Eggers’s test (t = −1.50, df = 14, intercept = 2.5879, SE = 1.7307, p-value = 0.157).

4. Discussion

In our systematic review and meta-analysis, a pooled prevalence of seropositivity for C. burnetii on occupationally exposed individuals was estimated at 44.0% (95%CI 27.6 to 61.8). The risk for seropositivity was greatest among veterinary professionals, followed by abattoir workers, livestock farmers, agricultural workers, geologists/agronomists, and forestry workers, while laboratory professionals had no substantially increased risk, and forestry rangers exhibited a conversely reduced seropositivity risk.
In other words, the present study is consistent with some previous reports, suggesting that seroprevalence for C. burnetii among professionals working with animals or involved in meat processing may be up to 50% [1,6,7,10,16,19,38,39,40], largely exceeding estimates in non-occupationally exposed individuals from the index areas (where prevalence estimates ranging between 13.6% and 14.3% were retrieved) [13,25], and national figures for non-occupational exposed individuals [6,19,37,41,42]. In this regard, it should be stressed that according to official reports, between 2015 and 2021, a total of 17 cases of QF have been reported to Italian health authorities, for an annual reference rate well below 0.1 cases per 100,000 persons [23,24]. These figures are clearly inconsistent with animal records, as provided by the joint European Centre for Disease prevention and Control (ECDC) and European Food Safety Authority (EFSA) report on Zoonoses in the European Union for 2021 [24], where seroprevalence rates of 20.9% in sheep, 17.9% in goats, and 15.1% in cattle were provided. Therefore, when pooled estimates are compared to official figures, QF in Italy appears as substantially underestimated, with notification rates reasonably representing the tip of a very larger burden of disease, particularly in occupational settings. Occupational physicians, i.e., the medical professionals responsible for health promotion in the workplaces [43], should therefore be made aware of the actual transmission of C. burnetii in occupational settings, promoting appropriate surveillance programs and improving the adherence to proper preventive measures among at-risk workers.
In fact, not only do pooled estimates substantially mirror several international reports on this pathogen [1,6,7,10,16,19,38,39,41,44], but they are also quite consistent with some previous Italian reports, where the relatively high occurrence of this pathogen in various agricultural settings was highlighted [20,45]. For example, by 1992, 13.1% of 99 sampled farms, 4.4% of sampled animals, and 6.5% of raw milk samples from the Apennine region of Emilia Romagna were contaminated by C. burnetii [20]. Similarly, a more recent study from the Autonomous Province of Bolzano reported an overall seroprevalence of 13.6% for cattle, 11.7% for sheep, and 7.9% for goats [45]. Moreover, in 2003 a large outbreak of QF among prison inmates did occur in the Como area following the exposure to dust contaminated by a passing flock of sheep [27,28].
As a consequence, the status of QF in Italian agricultural and forestry workers would be quite similar to other zoonotic pathogens of occupational interest, e.g., Hantaviruses, Tick-borne Encephalitis virus, and Borrelia burgdoferi (the causal agent of Lyme disease) [46,47,48,49,50]. More precisely, our analyses stress the strong association of seroprevalence for C. burnetii with the meat industry (more precisely, for abattoir personnel) and farm tasks that cause the interaction between men and animals, i.e., animal breeders, but also with veterinarians [1,14,23]. On the contrary, even though C. burnetii may be acquired as a tick-borne disease [13,18], the relatively low seroprevalence in forestry rangers was not unexpected, for several reasons. For one, despite their role in maintaining the wildlife reservoir, ticks are only marginal agents of Q fever in humans. As a consequence, a marginal transmission in these specific settings was largely foreseen. Moreover, the estimates on forestry rangers were mostly obtained through the study by Cinco et al. [36], whose results should be carefully assessed as obtained through the complement fixation test, whose sensitivity and specificity are quite lower than those associated with IFA and ELISA.
Limitations. Despite the potential interest, our study is affected by several limitations. First of all, the studies that were retrieved and included in the analyses were mostly of limited quality, as summarized by the ROB tools. In this regard, two main shortcomings should be stressed. On the one hand, nearly all studies did not report how the sample was eventually recruited. In other words, the actual representativity of the assessed occupational groups, even in the targeted areas, remains unclear. On the other hand, as previously stressed for other infectious diseases affecting agricultural workers [46,47,51], the job description could fail to appreciate other exposures, possibly associated with residential and environmental factors, as agricultural settings hardly dichotomize occupational and residential environments [46,47,52,53]. Moreover, the studies were also quite heterogenous in geographical terms, sample size, and sampling strategy [13,18,25,37]. As a consequence, retrieved estimates could only be limitedly comparable.
Third, as the collected studies were performed across a very broad timespan (2000 to 2022) and laboratory techniques were quite heterogeneous (i.e., IFA, CFT, and ELISA), we cannot rule out whether differences in estimates are the result of the specificities of the diagnostic procedures or the actual seroprevalence [2]. Fourth, the pooled population was quite small, both in general and when dealing with individual occupational groups, which is not representative of Italian workforce [14].

5. Conclusions

In conclusion, further studies are required in order to better understand the actual occurrence of C. burnetii infections in Italian, not only in occupational settings, but also in the general population. Even though the large majority of cases clearly occur either asymptomatically or pauci-symptomatically [2,16,43,45], more accurately tailored interventions for at-risk workers should be implemented in terms of surveillance programs and informative campaigns on the appropriate preventive measures.

Author Contributions

Conceptualization, M.R. and A.B.; methodology, M.R. and F.M.; software, M.R.; validation, S.C., M.R. and F.M.; formal analysis, S.C.; investigation, M.R.; resources, M.R.; data curation, S.C.; writing—original draft preparation, S.C. and A.B.; writing—review and editing, S.C. and A.B.; visualization, M.R.; supervision, F.M.; project administration, F.M.; funding acquisition, F.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Raw data are available from the Study Authors.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Figure A1. Geographical areas involved in the studies included in the analyses.
Figure A1. Geographical areas involved in the studies included in the analyses.
Zoonoticdis 03 00005 g0a1

References

  1. Woldeyohannes, S.M.; Gilks, C.F.; Baker, P.; Perkins, N.R.; Reid, S.A. Seroprevlance of Coxiella Burnetii among Abattoir and Slaughterhouse Workers: A Meta-Analysis. One Health 2018, 6, 23–28. [Google Scholar] [CrossRef] [PubMed]
  2. España, P.P.; Uranga, A.; Cillóniz, C.; Torres, A. Q Fever (Coxiella Burnetii). Semin. Respir. Crit. Care Med. 2020, 41, 509–521. [Google Scholar] [CrossRef] [PubMed]
  3. González-Barrio, D.; Ruiz-Fons, F. Coxiella Burnetii in Wild Mammals: A Systematic Review. Transbound. Emerg. Dis. 2019, 66, 662–671. [Google Scholar] [CrossRef]
  4. Bailly, S.; Hozé, N.; Bisser, S.; Zhu-Soubise, A.; Fritzell, C.; Fernandes-Pellerin, S.; Mbouangoro, A.; Rousset, D.; Djossou, F.; Cauchemez, S.; et al. Transmission Dynamics of Q Fever in French Guiana: A Population-Based Cross-Sectional Study. Lancet Reg. Health-Am. 2022, 16, 100385. [Google Scholar] [CrossRef] [PubMed]
  5. Zakeri, A.; Montaseri, M.; Shekarforoush, S.S. Prevalence and Risk Factors Associated with Q Fever Infection in Slaughterhouse Workers in Fars Province, Iran. Int. Arch. Occup. Environ. Health, 2023; epub ahead of print. [Google Scholar] [CrossRef]
  6. Dragan, A.L.; Voth, D.E. Coxiella Burnetii: International Pathogen of Mystery. Microbes Infect. 2020, 22, 100–110. [Google Scholar] [CrossRef] [PubMed]
  7. De Souza Ribeiro Mioni, M.; Costa, F.B.; Ribeiro, B.L.D.; Teixeira, W.S.R.; Pelicia, V.C.; Labruna, M.B.; Rousset, É.; Sidi-Boumedine, K.; Thiéry, R.; Megid, J. Coxiella Burnetii in Slaughterhouses in Brazil: A Public Health Concern. PLoS ONE 2020, 15, e0241246. [Google Scholar] [CrossRef]
  8. Woldeyohannes, S.M.; Gilks, C.F.; Baker, P.; Perkins, N.R.; Reid, S.A. Q Fever awareness and risk profiles among agricultural show attendees. Aust. J. Rural Health 2022, 30, 601–607. [Google Scholar] [CrossRef]
  9. Tringali, G.; Mansueto, S. Epidemiology of Q Fever in Italy and in Other Mediterranean Countries. Ser. A Med. Microbiol. Infect. Dis. Virol. Parasitol. 1987, 267, 20–25. [Google Scholar] [CrossRef] [PubMed]
  10. Georgiev, M.; Afonso, A.; Neubauer, H.; Needham, H.; Thiéry, R.; Rodolakis, A.; Roest, H.J.; Stärk, K.D.; Stegeman, J.A.; Vellema, P.; et al. Review Articles Q Fever in Humans and Farm Animals in Four European Countries, 1982 to 2010. Eurosurveillance 2013, 18, 20407. [Google Scholar] [CrossRef]
  11. Ruppanner, R.; Brooks, D.; Morrish, D.; Spinelli, J.; Franti, C.E.; Behymer, D.E. Q Fever Hazards from Sheep and Goats Used in Research. Arch. Environ. Health 1982, 37, 103–110. [Google Scholar] [CrossRef]
  12. Monno, R.; Fumarola, L.; Trerotoli, P.; Cavone, D.; Massaro, T.; Spinelli, L.; Rizzo, C.; Musti, M. Seroprevalence of Q-Fever, Brucellosis and Leptospirosis in Farmers and Agricultural Workers in Bari, Southern Italy. Clin. Microbiol. Infect. 2009, 15, 142–143. [Google Scholar] [CrossRef] [Green Version]
  13. Monno, R.; Fumarola, L.; Trerotoli, P.; Cavone, D.; Giannelli, G.; Rizzo, C.; Ciceroni, L.; Musti, M. Seroprevalence of Q Fever, Brucellosis and Leptospirosis in Farmers and Agricultural Workers in Bari, Southern Italy. Ann. Agric. Environ. Med. 2009, 16, 205–209. Available online: https://www.aaem.pl/Seroprevalence-of-Q-fever-brucellosis-and-leptospirosis-in-farmers-and-agricultural-workers-in-Bari-Southern-Italy-,71564,0,2.html (accessed on 4 January 2023). [CrossRef] [Green Version]
  14. Tabibi, R.; Baccalini, R.; Barassi, A.; Bonizzi, L.; Brambilla, G.; Consonni, D.; Melzi D’eril, G.; Romanò, L.; Sokooti, M.; Somaruga, C.; et al. Occupational Exposure to Zoonotic Agents among Agricultural Workers in Lombardy Region, Northern Italy. Ann. Agric. Environ. Med. 2013, 20, 676–681. [Google Scholar]
  15. van der Giessen, J.; Vlaanderen, F.; Kortbeek, T.; Swaan, C.; van den Kerkhof, H.; Broens, E.; Rijks, J.; Koene, M.; De Rosa, M.; Uiterwijk, M.; et al. Signalling and responding to zoonotic threats using a One Health approach: A decade of the Zoonoses Structure in the Netherlands, 2011 to 2021. Eurosurveillance 2022, 27, 2200039. [Google Scholar] [CrossRef]
  16. Groten, T.; Kuenzer, K.; Moog, U.; Hermann, B.; Maier, K.; Boden, K. Who Is at Risk of Occupational Q Fever: New Insights from a Multi-Profession Cross-Sectional Study. BMJ Open 2020, 10, e030088. [Google Scholar] [CrossRef] [Green Version]
  17. Selvaggi, T.M.; Rezza, G.; Scagnelli, M.; Rigoli, R.; Rassu, M.; de Lalla, F.; Pellizzer, G.P.; Tramarin, A.; Bettini, C.; Zampieri, L.; et al. Investigation of a Q-Fever Outbreak in Northern Italy. Eur. J. Epidemiol. 1996, 12, 403–408. [Google Scholar] [CrossRef]
  18. Aquilini, D.; Parola, P.; Salvo, E.; Paladini, A. Seroepidemiology of the Rickettioses, Human Granulocytic Ehrlichiosis, Lyme Disease, Q Fever, and Tularemia in Forestry Workers in Tuscany, Italy. J. Spirochetal Tick-Borne Dis. 2000, 7, 35–41. [Google Scholar]
  19. Dal Pozzo, F.; Martinelle, L.; Léonard, P.; Renaville, B.; Renaville, R.; Thys, C.; Smeets, F.; Czaplicki, G.; van Esbroeck, M.; Saegerman, C. Q Fever Serological Survey and Associated Risk Factors in Veterinarians, Southern Belgium, 2013. Transbound. Emerg. Dis. 2017, 64, 959–966. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  20. Martini, M.; Baldelli, R.; de Calboli, L.P. An Epidemiological Study on Q Fever in the Emilia-Romagna Region, Italy. Zent. Bakteriol. 1994, 280, 416–422. [Google Scholar] [CrossRef] [PubMed]
  21. Sellens, E.; Norris, J.M.; Dhand, N.K.; Heller, J.; Hayes, L.; Gidding, H.F.; Willaby, H.; Wood, N.; Graves, S.; Bosward, K.L. Willingness of veterinarians in Australia to recommend Q fever vaccination in veterinary personnel: Implications for workplace health and safety compliance. PLoS ONE 2018, 13, e0198421. [Google Scholar] [CrossRef] [Green Version]
  22. Acharya, D.; Park, J.H.; Chun, J.H.; Kim, M.Y.; Yoo, S.J.; Lewin, A.; Lee, K. Seroepidemiologic Evidence of Q Fever and Associated Factors among Workers in Veterinary Service Laboratory in South Korea. PLoS Negl. Trop. Dis. 2022, 16, e0010054. [Google Scholar] [CrossRef] [PubMed]
  23. European Centre for Disease Prevention and Control (ECDC). Annual Epidemiological Report 2019. Q Fever; European Centre for Disease Prevention and Control (ECDC): Stockholm, Sweden, 2021.
  24. European Food Safety Authority (EFSA); European Centre for Disease Prevention and Control (ECDC). The European Union One Health 2021 Zoonoses Report. EFSA J. 2022, 20, 7666. [Google Scholar] [CrossRef]
  25. Fenga, C.; Gangemi, S.; de Luca, A.; Calimeri, S.; lo Giudice, D.; Pugliese, M.; Licitra, F.; Alibrandi, A.; Costa, C. Seroprevalence and Occupational Risk Survey for Coxiella Burnetii among Exposed Workers in Sicily, Southern Italy. Int. J. Occup. Med. Health 2015, 28, 901–907. [Google Scholar] [CrossRef]
  26. Stufano, A.; Iatta, R.; Sgroi, G.; Jahantigh, H.R.; Cagnazzo, F.; Flöel, A.; Lucchese, G.; Loconsole, D.; Centrone, F.; Mendoza-Roldan, J.A.; et al. Seroprevalence of Vector-Borne Pathogens in Outdoor Workers from Southern Italy and Associated Occupational Risk Factors. Parasit Vectors 2022, 15, 264. [Google Scholar] [CrossRef] [PubMed]
  27. Santoro, D.; Giura, R.; Colombo, M.C.; Antonelli, P.; Gramegna, M.; Gandola, O.; Gridabilla, G. Q Fever in Como, Northern Italy. Emerg. Infect. Dis. 2004, 10, 159–160. [Google Scholar] [CrossRef] [Green Version]
  28. Starnini, G.; Caccamo, F.; Farchi, F.; Babudieri, S.; Brunetti, B.; Rezza, G. An Outbreak of Q Fever in a Prison in Italy. Epidemiol. Infect. 2005, 133, 377–380. [Google Scholar] [CrossRef] [PubMed]
  29. Eurostat. European Economic Statistics—2010 Edition, 1st ed.; Gerstberger, C., Ed.; European Union: Luxembourg, 2011; ISBN 9789279161902. [Google Scholar]
  30. Maucorps, A.; Münch, A.; Brkanovic, S.; Schuh, B.; Dwyer, J.; Vigani, M.; Khafagy, A.; Coto Sauras, M.; Deschellette, P.; Lopez, A.; et al. The EU Farming Employment: Current Challenges and Future Prospects, Policy Department for Structural and Cohesion Policies Directorate-General for Internal Policies PE; Agriculture and Rural Development: Brussels, Belgium, 2019. [Google Scholar]
  31. Moher, D.; Liberati, A.; Tetzlaff, J.; Altman, D.G.; Altman, D.; Antes, G.; Atkins, D.; Barbour, V.; Barrowman, N.; Berlin, J.A.; et al. Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. PLoS Med. 2009, 6, e1000097. [Google Scholar] [CrossRef] [Green Version]
  32. Rethlefsen, M.L.; Kirtley, S.; Waffenschmidt, S.; Ayala, A.P.; Moher, D.; Page, M.J.; Koffel, J.B. PRISMA-S: An Extension to the PRISMA Statement for Reporting Literature Searches in Systematic Reviews. Syst. Rev. 2021, 10, 39. [Google Scholar] [CrossRef]
  33. Office of Health Assessment and Translation (OHAT); Division of the National Toxicology Program; National Institute of Environmental Health Sciences. Handbook for Conducting a Literature-Based Health Assessment Using OHAT Approach for Systematic Review and Evidence Integration; National Toxicology Program: Research Triangle Park, NC, USA, 2019. [Google Scholar]
  34. Eick, S.M.; Goin, D.E.; Chartres, N.; Lam, J.; Woodruff, T.J. Assessing Risk of Bias in Human Environmental Epidemiology Studies Using Three Tools: Different Conclusions from Different Tools. Syst. Rev. 2020, 9, 249. [Google Scholar] [CrossRef] [PubMed]
  35. R Development Core Team. R a Language and Environment for Statistical Computing: Reference Index; R Foundation for Statistical Computing: Vienna, Austria, 2010; ISBN 3900051070. [Google Scholar]
  36. Cinco, M.; Luzzati, R.; Mascioli, M.; Floris, R.; Brouqui, P. Serological Evidence of Rickettsia Infections in Forestry Rangers in North-Eastern Italy. Clin. Microbiol. Infect. 2006, 12, 493–495. [Google Scholar] [CrossRef] [Green Version]
  37. Verso, M.G.; Vesco, G.; Villari, S.; Galluzzo, P.; Gargano, V.; Matranga, D.; de Marchis, P.; Picciotto, D. Analysis of Seroprevalence against Coxiella Burnetii in a Sample of Farm Workers in Western Sicily. Ann. Agric. Environ. Med. 2016, 23, 71–74. [Google Scholar] [CrossRef] [PubMed]
  38. Cruz, R.; Santos, C.; Esteves, F.; Ferreira, A.S.; Vasconcelos-Nóbrega, C.; Mega, C.; Albuquerque, C.; Teixeira-de-Lemos, E.; Coelho, A.C.; Vala, H.; et al. Occupationally Acquired Q Fever in Shepherds and Sheep Milk Cheesemakers. Ecohealth 2020, 17, 255–257. [Google Scholar] [CrossRef] [PubMed]
  39. Sellens, E.; Bosward, K.L.; Norris, J.M.; Wood, N.; Heller, J.; Graves, S.; Gidding, H.F. Coxiella Burnetii Seroprevalence in Unvaccinated Veterinary Workers in Australia: Evidence to Support Q Fever Vaccination. Zoonoses Public Health 2020, 67, 79–88. [Google Scholar] [CrossRef] [PubMed]
  40. Park, J.H.; do Hwang, S.; Acharya, D.; Lee, S.H.; Hwang, K.J.; Yoo, S.J.; Lee, K. Seroreactivity and Risk Factors Associated with Coxiella Burnetii Infection among Cattle Slaughterhouse Workers in South Korea. Int. J. Environ. Res. Public Health 2018, 15, 2264. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  41. Szymańska-Czerwińska, M.; Galińska, E.M.; Niemczuk, K.; Knap, J.P. Prevalence of Coxiella Burnetii Infection in Humans Occupationally Exposed to Animals in Poland. Vector-Borne Zoonotic Dis. 2015, 15, 261–267. [Google Scholar] [CrossRef] [Green Version]
  42. O’Neill, T.J.; Sargeant, J.M.; Poljak, Z. The Effectiveness of Coxiella Burnetii Vaccines in Occupationally Exposed Populations: A Systematic Review and Meta-Analysis. Zoonoses Public Health 2014, 61, 81–96. [Google Scholar] [CrossRef]
  43. Manzoli, L.; Sotgiu, G.; Magnavita, N.; Durando, P.; Barchitta, M.; Carducci, A.; Conversano, M.; de Pasquale, G.; Dini, G.; Firenze, A.; et al. Evidence-Based Approach for Continuous Improvement of Occupational Health. Epidemiol. Prev. 2015, 39, 81–85. [Google Scholar]
  44. Meadows, S.; Jones-Bitton, A.; McEwen, S.A.; Jansen, J.; Patel, S.N.; Filejski, C.; Menzies, P. Coxiella Burnetii (Q Fever) Seropositivity and Associated Risk Factors in Sheep and Goat Farm Workers in Ontario, Canada. Vector-Borne Zoonotic Dis. 2016, 16, 643–649. [Google Scholar] [CrossRef]
  45. Tavella, A.; Trevisiol, K.; Morosetti, G.; Fobatscher, C.; Piffer, C.; Fugatti, A. Q Fever: The Importance of Surveillance in the Autonomous Province of Bolzano (Italy). Int. J. Infect. Dis. 2014, 21, 188–189. [Google Scholar] [CrossRef] [Green Version]
  46. Riccò, M.; Ferraro, P.; Peruzzi, S.; Balzarini, F.; Ranzieri, S. Hantaviruses in Agricultural and Forestry Workers: Knowledge, Attitudes and Practices in Italian Physicians. Trop. Med. Infect. Dis. 2021, 6, 169. [Google Scholar] [CrossRef]
  47. Riccò, M.; Peruzzi, S.; Ranzieri, S.; Magnavita, N. Occupational Hantavirus Infections in Agricultural and Forestry Workers: A Systematic Review and Metanalysis. Viruses 2021, 13, 2150. [Google Scholar] [CrossRef] [PubMed]
  48. Riccò, M. Epidemiology of Tick-Borne Encephalitis in North-Eastern Italy (2017–2020): International Insights from National Notification Reports. Acta Biomed. 2021, 92, e2021229. [Google Scholar] [PubMed]
  49. Riccò, M.; Bragazzi, N.L.; Vezzosi, L.; Balzarini, F.; Colucci, E.; Veronesi, L.; Riccò, M.; Bragazzi, N.L.; Vezzosi, L.; Balzarini, F.; et al. Knowledge, Attitudes, and Practices on Tick-Borne Human Diseases and Tick-Borne Encephalitis Vaccine among Farmers from North-Eastern Italy (2017). J. Agromed. 2020, 25, 73–85. [Google Scholar] [CrossRef] [PubMed]
  50. Riccò, M.; Gualerzi, G.; Ranzieri, S.; Ferraro, P.; Bragazzi, N.L. Knowledge, Attitudes, Practices (KAP) of Italian Occupational Physicians towards Tick Borne Encephalitis. Trop. Med. Infect. Dis. 2020, 5, 117. [Google Scholar] [CrossRef] [PubMed]
  51. Riccò, M.; Peruzzi, S.; Ranzieri, S.; Balzarini, F.; Valente, M.; Marchesi, F.; Bragazzi, N.L. Hantavirus Infections in Italy: Not Reported Doesn’t Mean Inexistent. Authors and Affiliations. Acta Biomed. 2020, 92, e2021324. [Google Scholar]
  52. Riccò, M.; Vezzosi, L.; Mezzoiuso, A.G. Occupational Eye Injury in the Agricultural Settings: A Retrospective Study from North-Eastern Italy. Acta Biomed. 2020, 90, 457–467. [Google Scholar]
  53. Riccò, M.; Vezzosi, L.; Balzarini, F.; Gualerzi, G.; Valente, M.; Bragazzi, N.L. Air Temperatures and Occupational Injuries in the Agricultural Settings: A Report from Northern Italy (Po River Valley, 2013–2017). Acta Biomed. 2020, 91, e2020094. [Google Scholar] [CrossRef]
Figure 1. Flow chart of included studies.
Figure 1. Flow chart of included studies.
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Figure 2. Summary of risk of bias assessment according to the National Toxicology Program (NTP)’s Office of Health Assessment and Translation (OHAT) handbook and respective risk of bias (ROB) tool [33,34].
Figure 2. Summary of risk of bias assessment according to the National Toxicology Program (NTP)’s Office of Health Assessment and Translation (OHAT) handbook and respective risk of bias (ROB) tool [33,34].
Zoonoticdis 03 00005 g002
Figure 3. Forrest plot for retrieved seroprevalence studies about C. burnetii infection in Italy, from occupational settings. A pooled seroprevalence of 44.0% (95% Confidence Interval [95%CI] 27.6 to 61.8) was eventually calculated, with substantial heterogeneity (tau2 = 2.004; tau = 1.4157; I2 = 92.4% [95%CI, 89.3%; 94.6%], Q = 197.88, p < 0.001) [13,14,18,25,26,36,37].
Figure 3. Forrest plot for retrieved seroprevalence studies about C. burnetii infection in Italy, from occupational settings. A pooled seroprevalence of 44.0% (95% Confidence Interval [95%CI] 27.6 to 61.8) was eventually calculated, with substantial heterogeneity (tau2 = 2.004; tau = 1.4157; I2 = 92.4% [95%CI, 89.3%; 94.6%], Q = 197.88, p < 0.001) [13,14,18,25,26,36,37].
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Figure 4. Forest plot representing risk ratio on main occupational groups compared to non-occupationally exposed workers (49 cases out of a total of 356 donors, 13.8%). Note: RR = Risk Ratio; 95%CI = 95% Confidence Interval.
Figure 4. Forest plot representing risk ratio on main occupational groups compared to non-occupationally exposed workers (49 cases out of a total of 356 donors, 13.8%). Note: RR = Risk Ratio; 95%CI = 95% Confidence Interval.
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Figure 5. Forest plot representing risk ratio on main occupational groups by parent Italian region, and assuming Lombardy region (42 positive cases over a total of 96 samples; 43.8%) as the reference one. Note: RR = Risk Ratio; 95%CI = 95% Confidence Interval.
Figure 5. Forest plot representing risk ratio on main occupational groups by parent Italian region, and assuming Lombardy region (42 positive cases over a total of 96 samples; 43.8%) as the reference one. Note: RR = Risk Ratio; 95%CI = 95% Confidence Interval.
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Figure 6. Radial plots for estimates included in the meta-analysis. Visual inspection reported all the estimates as substantially scattered across the regression line, suggesting no significant small study effect.
Figure 6. Radial plots for estimates included in the meta-analysis. Visual inspection reported all the estimates as substantially scattered across the regression line, suggesting no significant small study effect.
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Figure 7. Border-enhanced funnel plots for estimates included in the meta-analysis. Visual inspection of contour-enhanced funnel plots suggested the substantial evidence of publication bias, but this was substantially rejected by Egger test (i.e., t = −1.50, df = 14, intercept = 2.5879, SE = 1.7307, p-value = 0.157).
Figure 7. Border-enhanced funnel plots for estimates included in the meta-analysis. Visual inspection of contour-enhanced funnel plots suggested the substantial evidence of publication bias, but this was substantially rejected by Egger test (i.e., t = −1.50, df = 14, intercept = 2.5879, SE = 1.7307, p-value = 0.157).
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Table 1. PICO worksheet.
Table 1. PICO worksheet.
ItemDefinition
Population of interestWorkers potentially exposed to C. burnetii
Investigated resultSeroprevalence of biomarkers for previous exposure to C. burnetii
ControlHealthy individuals not occupationally exposed
OutcomeSeroprevalence of previous infection of C. burnetii among occupationally exposed individuals; risk of Q Fever in occupational settings.
Table 2. Summary of retrieved seroprevalence studies for Q Fever in Italy, occupational settings.
Table 2. Summary of retrieved seroprevalence studies for Q Fever in Italy, occupational settings.
Study (Year)RegionOccupational SettingsProcedureSubjects
(No.)
Positive
(No., %)
Reference
Aquilini et al. (2000)TuscanyForestry WorkersIFA507126, 24.9%[18]
Cinco et al. (2006)Friuli Venezia GiuliaForestry RangersELISA1815, 2.8%[36]
Monno et al. (2009)ApuliaAnimal Farmers
(breeders)
IFA5042, 84.0%[13]
Agricultural Workers (mixed tasks)6640, 60.6%
Veterinarians1212, 100%
Healthy donors
(Controls)
28038, 13.6%
Tabibi et al. (2013)LombardyAnimal Farmers (breeders)CFT6432, 50.0%[14]
Agricultural Workers
(non breeders)
3210, 31.2%
Fenga et al. (2015)SicilyVeterinariansELISA3828, 73.7%[25]
Abattoir workers3828, 73.7%
Animal Farmers (breeders)4424, 54.5%
Laboratory workers204, 20.0%
Healthy donors
(Controls)
426, 14.3%
Verso et al. (2016)SicilyAgricultural Workers
(mixed tasks)
IFA12630, 23.8%[37]
Stufano et al. (2022)Apulia and BasilicataForestry workersELISA319, 29.0%[26]
Farmers3121, 67.7%
Veterinarians448, 18.2%
Geologists/Agronomists308, 26.7%
Administrative employees
(Controls)
345, 14.7%
Note: IFA = Immunofluorescence Assay; CFT = complement fixation test; ELISA = enzyme-linked immunoassay.
Table 3. Tabular representation for the Risk of Bias (ROB) assessment according to the National Toxicology Program (NTP)’s Office of Health Assessment and Translation (OHAT) handbook and respective risk of bias (ROB) tool [33,34]. Note: D1: possibility of selection bias; D2: exposure assessment; D3: outcome assessment; D4: confounding factors; D5: reporting bias; D6: other bias. Zoonoticdis 03 00005 i002Zoonoticdis 03 00005 i002 = definitively high; Zoonoticdis 03 00005 i002 = probably high; Zoonoticdis 03 00005 i001 = probably low; Zoonoticdis 03 00005 i001Zoonoticdis 03 00005 i001 = definitively low.
Table 3. Tabular representation for the Risk of Bias (ROB) assessment according to the National Toxicology Program (NTP)’s Office of Health Assessment and Translation (OHAT) handbook and respective risk of bias (ROB) tool [33,34]. Note: D1: possibility of selection bias; D2: exposure assessment; D3: outcome assessment; D4: confounding factors; D5: reporting bias; D6: other bias. Zoonoticdis 03 00005 i002Zoonoticdis 03 00005 i002 = definitively high; Zoonoticdis 03 00005 i002 = probably high; Zoonoticdis 03 00005 i001 = probably low; Zoonoticdis 03 00005 i001Zoonoticdis 03 00005 i001 = definitively low.
StudyRISK OF BIAS
D1D2D3D4D5D6
Aquilini et al. (2000) [18]Zoonoticdis 03 00005 i002Zoonoticdis 03 00005 i002Zoonoticdis 03 00005 i002Zoonoticdis 03 00005 i002Zoonoticdis 03 00005 i002Zoonoticdis 03 00005 i002Zoonoticdis 03 00005 i001Zoonoticdis 03 00005 i001
Cinco et al. (2006) [36]Zoonoticdis 03 00005 i002Zoonoticdis 03 00005 i002Zoonoticdis 03 00005 i001Zoonoticdis 03 00005 i001Zoonoticdis 03 00005 i002Zoonoticdis 03 00005 i001Zoonoticdis 03 00005 i001
Monno et al. (2009) [13]Zoonoticdis 03 00005 i002Zoonoticdis 03 00005 i002Zoonoticdis 03 00005 i002Zoonoticdis 03 00005 i002Zoonoticdis 03 00005 i001Zoonoticdis 03 00005 i001
Tabibi et al. (2013) [14]Zoonoticdis 03 00005 i002Zoonoticdis 03 00005 i002Zoonoticdis 03 00005 i002Zoonoticdis 03 00005 i002Zoonoticdis 03 00005 i002Zoonoticdis 03 00005 i002Zoonoticdis 03 00005 i001Zoonoticdis 03 00005 i001
Fenga et al. (2015) [25]Zoonoticdis 03 00005 i002Zoonoticdis 03 00005 i002Zoonoticdis 03 00005 i002Zoonoticdis 03 00005 i001Zoonoticdis 03 00005 i001Zoonoticdis 03 00005 i002Zoonoticdis 03 00005 i001Zoonoticdis 03 00005 i001
Verso et al. (2016) [37]Zoonoticdis 03 00005 i002Zoonoticdis 03 00005 i002Zoonoticdis 03 00005 i002Zoonoticdis 03 00005 i002Zoonoticdis 03 00005 i001Zoonoticdis 03 00005 i001
Stufano et al. (2022) [26]Zoonoticdis 03 00005 i001Zoonoticdis 03 00005 i001Zoonoticdis 03 00005 i001Zoonoticdis 03 00005 i001Zoonoticdis 03 00005 i001Zoonoticdis 03 00005 i001Zoonoticdis 03 00005 i001Zoonoticdis 03 00005 i001Zoonoticdis 03 00005 i001
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Riccò, M.; Baldassarre, A.; Corrado, S.; Marchesi, F. Seroprevalence of Coxiella burnetii in Occupational Settings: A Meta-Analysis of Italian Studies. Zoonotic Dis. 2023, 3, 38-51. https://doi.org/10.3390/zoonoticdis3010005

AMA Style

Riccò M, Baldassarre A, Corrado S, Marchesi F. Seroprevalence of Coxiella burnetii in Occupational Settings: A Meta-Analysis of Italian Studies. Zoonotic Diseases. 2023; 3(1):38-51. https://doi.org/10.3390/zoonoticdis3010005

Chicago/Turabian Style

Riccò, Matteo, Antonio Baldassarre, Silvia Corrado, and Federico Marchesi. 2023. "Seroprevalence of Coxiella burnetii in Occupational Settings: A Meta-Analysis of Italian Studies" Zoonotic Diseases 3, no. 1: 38-51. https://doi.org/10.3390/zoonoticdis3010005

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