Volume 69, Issue 5 pp. e1213-e1230

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A systematic review, meta-analysis and meta-regression of the global prevalence of Toxoplasma gondii infection in wild marine mammals and associations with epidemiological variables

Man-Yao Li,

Man-Yao Li

Marine College, Shandong University, Weihai, Shandong, PR China

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Xiao-Nan Gao,

Xiao-Nan Gao

Marine College, Shandong University, Weihai, Shandong, PR China

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Jun-Yang Ma,

Jun-Yang Ma

Marine College, Shandong University, Weihai, Shandong, PR China

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Hany M. Elsheikha,

Corresponding Author

Hany M. Elsheikha

[email protected]

orcid.org/0000-0003-3303-930X

Faculty of Medicine and Health Sciences, School of Veterinary Medicine and Science, University of Nottingham, Loughborough, UK

Faculty of Veterinary Medicine, University of Liège, Sart Tilman B43, Liège, Belgium

Correspondence

Wei Cong, Marine College, Shandong University, Weihai, Shandong 264209, PR China.

Email: [email protected]

Hany M. Elsheikha, Faculty of Medicine and Health Sciences, School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington Campus, Loughborough, UK.

Email: [email protected]

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Wei Cong,

Corresponding Author

Wei Cong

[email protected]

orcid.org/0000-0003-0471-1689

Marine College, Shandong University, Weihai, Shandong, PR China

Correspondence

Wei Cong, Marine College, Shandong University, Weihai, Shandong 264209, PR China.

Email: [email protected]

Hany M. Elsheikha, Faculty of Medicine and Health Sciences, School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington Campus, Loughborough, UK.

Email: [email protected]

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Man-Yao Li,

Man-Yao Li

Marine College, Shandong University, Weihai, Shandong, PR China

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Xiao-Nan Gao,

Xiao-Nan Gao

Marine College, Shandong University, Weihai, Shandong, PR China

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Jun-Yang Ma,

Jun-Yang Ma

Marine College, Shandong University, Weihai, Shandong, PR China

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Hany M. Elsheikha,

Corresponding Author

Hany M. Elsheikha

[email protected]

orcid.org/0000-0003-3303-930X

Faculty of Medicine and Health Sciences, School of Veterinary Medicine and Science, University of Nottingham, Loughborough, UK

Faculty of Veterinary Medicine, University of Liège, Sart Tilman B43, Liège, Belgium

Correspondence

Wei Cong, Marine College, Shandong University, Weihai, Shandong 264209, PR China.

Email: [email protected]

Hany M. Elsheikha, Faculty of Medicine and Health Sciences, School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington Campus, Loughborough, UK.

Email: [email protected]

Search for more papers by this author

Wei Cong,

Corresponding Author

Wei Cong

[email protected]

orcid.org/0000-0003-0471-1689

Marine College, Shandong University, Weihai, Shandong, PR China

Correspondence

Wei Cong, Marine College, Shandong University, Weihai, Shandong 264209, PR China.

Email: [email protected]

Hany M. Elsheikha, Faculty of Medicine and Health Sciences, School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington Campus, Loughborough, UK.

Email: [email protected]

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First published: 23 February 2022

https://doi.org/10.1111/tbed.14493

Citations: 7

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Abstract

Toxoplasma gondii infection in wild marine mammals is a growing problem and is associated with adverse impacts on marine animal and public health. This systematic review, meta-analysis and meta-regression estimates the global prevalence of T. gondii infection in wild marine mammals and analyses the association between T. gondii infection and epidemiological variables. PubMed, Web of Science, Science Direct, China National Knowledge Infrastructure, and Wanfang Data databases were searched until 30 May 2021. Eighty-four studies (n = 14,931 wild marine mammals from 15 families) were identified from literature. The overall pooled prevalence of T. gondii infection was 22.44% [3848/14,931; 95% confidence interval (CI): 17.29–28.04]. The prevalence in adult animals 21.88% (798/3119; 95% CI: 13.40–31.59) was higher than in the younger age groups. North America had a higher prevalence 29.92% (2756/9243; 95% CI: 21.77–38.77) compared with other continents. At the country level, the highest prevalence was found in Spain 44.26% (19/88; 95%CI: 5.21–88.54). Regarding climatic variables, the highest prevalence was found in areas with a mean annual temperature >20°C 36.28% (171/562; 95% CI: 6.36–73.61) and areas with an annual precipitation > 800 mm 26.92% (1341/5042; 95% CI: 18.20–36.59). The subgroup and meta-regression analyses showed that study-level covariates, including age, country, continent, and mean temperature, partly explained the between-study heterogeneity. Further studies are needed to investigate the source of terrestrial to aquatic dissemination of T. gondii oocysts, the fate of this parasite in marine habitat and its effects on wild marine mammals.

1 INTRODUCTION

Toxoplasma gondii is a worldwide protozoan parasite, which is highly prevalent in human population. This water-borne and food-borne zoonotic parasite has a broad intermediate host range and can infect many terrestrial and marine mammals (Dubey, 2010). Despite its complex life cycle, felines are the only definitive hosts of T. gondii. Infected cats excrete thousands of environmentally resistant oocysts with their faeces, which contaminate the environment (Elsheikha et al., 2020; Frenkel et al., 1975; Yilmaz & Hopkins, 1972). Surface runoff can carry T. gondii oocysts from land to ocean (Conrad et al., 2005; Poulle et al., 2021; Shapiro et al., 2015; VanWormer et al., 2014). T. gondii oocysts is widely distributed in the marine environment due to its strong adaptability. These oocysts can survive for up to 24 months in seawater under laboratory conditions and can sporulate in seawater and remain infectious to mice for months (Lindsay et al. 2003; Lindsay & Dubey, 2009).

The influx of T. gondii oocysts from land to sea can have a negative impact on marine mammals (Fayer et al., 2004; Poulle et al., 2021). Marine invertebrates can trap and retain T. gondii oocysts via filtering seawater or ingesting seaweed (Mazzillo et al., 2013) and migratory filter-feeding fish can retain infective oocysts in their digestive tract (Massie et al., 2010). These filter feeders are common food sources of marine mammals (Cabezón et al., 2011; Obusan et al., 2015) and thus represent an important source of infection by T. gondii oocysts (Mazzillo et al., 2013; Poulle et al., 2021). T. gondii-infected marine mammals manifest as meningoencephalitis, multiple organ necrosis and haemorrhage (Lapointe et al., 1998; Roe et al., 2013). Abortion, attributed to toxoplasmosis, has been reported in a sea otter from California (Shapiro et al., 2016). Toxoplasmosis is also a common cause of mortality of sea otters and Hawaiian monk seals (Dubey, 2010; Dubey et al., 2020; Miller et al., 2020).

T. gondii is one of the most common food-borne pathogens (Wei et al., 2021). In countries such as Japan and Faroe Islands, wild marine mammals, such as small cetaceans (e.g. whales and dolphins) are used as food sources (Vail et al., 2020) and local communities are at risk of acquiring infection via ingestion of uncooked meat of marine mammals harboring T. gondii. Serological evidence of T. gondii infection in wild marine mammals has been reported in the Pacific Rim (Burgess et al., 2018), Southern Ocean (Núñez-Egido et al., 2020), Philippine Islands (Obusan et al., 2019) and Sub-Antarctic area (Michael et al., 2016), which raises concerns of the adverse impact of T. gondii infection on the health of marine mammals and humans. Previous studies have brought new insights to T. gondii epidemiology and its negative impact on wild marine mammals and public health, highlighting the need to generate more data and tools to understand the extent of and variables associated with T. gondii infection in wild marine animals. This knowledge is pivotal for understanding of the transmission dynamics and monitoring of T. gondii infection within and among wild marine mammals.

In the present systematic review and meta-analysis, we identified the global pooled prevalence of T. gondii infection among wild marine mammals. In addition, we investigated the correlation between T. gondii infection and epidemiological variables, including year of publication, detection method, age, gender, habitat, level of economic status, geographical information (continent and country) and climate factors (temperature and precipitation).

2 MATERIALS AND METHODS

This study was conducted in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) reporting guideline (Moher et al., 2009; Table S1).

2.1 Literature search strategy

We searched five electronic databases, including the Web of Science, Medline, Science Direct, China National Knowledge Infrastructure (CNKI), and Wanfang Data for articles published until 30 May 2021. The search was performed using a combination of Medical Subject Heading (MeSH) terms (e.g. ‘Dolphin’, ‘Trichechus’, ‘Otters’, ‘Porpoises’, ‘Sea Lions’, ‘Seals, Earless’, ‘Whales’, ‘Dugong’, ‘Toxoplasma’) and text (e.g. ‘Marine mammal’ and ‘Polar bear’). The retrieval formulas are shown in Table S2. Additionally, we searched reference lists in the identified studies for relevant studies. We did not apply any restrictions regarding the date of publication or geographical location. However, search was focused only on English language articles.

2.2 Article selection criteria

Studies were eligible if they are related to T. gondii infection in wild marine mammals, reported the prevalence of T. gondii infection, results are based on individual rather than pooled samples, and cross-sectional studies published up to 30 May 2021. Studies were excluded if they did not provide enough or original information, case reports, review papers, conference abstracts, other non–full-length articles, conducted on captive animals and non–English language publications. The identified articles were managed using EndNote (Version: X9 Clarivate Analytics, USA).

2.3 Study selection and data extraction

After two authors (M-Y. L., X-N. G.) screened titles and abstracts for initial eligibility, J-Y. M. and W.C. independently reviewed the full texts of potentially eligible articles for inclusion and exclusion criteria. Any disagreements were resolved by consulting H.E. The selected studies were reviewed for type of study, publication year, detection method, marine mammal species, habitat, gender, age, economic development level and geographic information (continent, and country). In addition, information on mean annual temperature and annual precipitation was obtained from the National Centers for Environmental Information (https://gis.ncdc.noaa.gov/maps/ncei/cdo/monthly/). All extracted information was recorded in Microsoft Excel 2019 (Microsoft, Redmond, WA, USA).

2.4 Study quality assessment

The quality of the included studies was assessed independently by two authors (M-Y L, X-N G) using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) method (Guyatt et al., 2008; Wang et al., 2021). The quality assessment included five criteria: clarity of the study aim, study contains information on the gender or age, detection method is clearly described, a sample size > 100 and known sampling time. Each criterion was represented by 1 point. The study that met the five criteria were given a score of 5 points. Based on this analysis, studies were rated as having a low quality (0−1 point), moderate quality (2−3 points) and those with 4−5 points were considered high quality (Gong et al., 2020a).

2.5 Statistical analysis

All data analyses were performed using R-based software v3.6.3 (‘R core team, R: A language and environment for statistical computing’, R core team 2018). The R codes used for data analysis are shown in Table S3. To fit the data to Gaussian distribution before meta-analysis, four conversion methods were used, including logarithmic conversion (PLN), logit transformation (PLOGIT), arcsine transformation (PAS) and double-arcsine transformation (PFT). Data normality was checked by Shapiro-Wilk test. The closer the ‘W’ is to the value 1 (maximum value of this statistic) and p values ≥ .05, the closer the fit to Gaussian normal distribution.

Due to the anticipated heterogeneity between and within studies, we used a random effects model which assumes that the true effect is not the same across studies. We calculated the Cochran Q value and the inconsistency I² statistic. The between-study heterogeneity was deemed significant if the p value of the Q test was < 0.10, or if the I² statistic was >50%. The result of meta-analysis was visualized using a forest plot. The Egger's bias test at p < 0 .05 and the funnel plot asymmetry were used to detect any publication bias. The influence of each included study on meta-analysis results was examined using the leave-out-one approach, where one study was deleted at a time from the meta-analysis and other studies were analysed (Gong et al., 2020b; Wang et al., 2021; Wei et al., 2021).

To investigate the source of heterogeneity, we performed meta-regression and subgroup meta-analyses, based on study-level characteristics as potential explanatory sources, including year of publication (1997–2010 vs. after 2010), detection method (immunohistochemical vs. serological and nucleic-acid based), age (pups vs. juveniles and adults), gender (female vs. male), habitat (aquatic vs. subaquatic and terrestrial habitats) and level of economic development (developed vs. developing countries). In addition, we used the publication year as a covariate and related variables for joint analysis to explain some of the heterogeneity caused by publication year, and R² represented heterogeneity explained by the covariate (Wang et al., 2021; Wei et al., 2021).

Given the global scale of the meta-analysis and the anticipated variations between geographical regions and climatic conditions, we extended the subgroup and meta-regression analyses to geographical region and climatic factors. This included country (Canada vs. 7 other countries), continent (Antarctica vs. other continents), mean annual temperature (<0°C vs. higher temperatures) and annual precipitation (<200 mm vs. higher precipitation levels). Reliable estimation of the between-study heterogeneity using the random-effects model requires many studies (Guolo, 2012). Therefore, meta-regression analysis of the epidemiological variables was only conducted when three or more studies were available.

3 RESULTS

3.1 Characteristics of the included studies

Searching five electronic databases and identification of the eligible studies were performed using the selection strategy outlined in Figure 1. We identified 2680 reports, of which 84 studies met eligibility criteria and were included in the analyses (Table S4). Summary characteristics of the 84 included studies are shown in Table 1. One of the eligible studies was obtained from the reference list of White et al. (2013). According to the quality criteria, 59 studies were rated as having high quality and 25 were of moderate quality.

Details are in the caption following the image — **FIGURE 1**
Open in figure viewer PowerPoint

PRISMA flow chart shows the steps used for article screening and selection

TABLE 1. Summary of the main characteristics of the eligible studies on Toxoplasma gondii infection in wild marine mammals

No.	Reference	No. positive/total examined	Marine mammals	Detection method(s)	Study design	Quality score	Study quality^a
	Africa
1	Lane et al. (2014)	0/40	Delphinidae,	Immunohistochemical	Cross-sectional	4	High
2	Namroodi et al. (2018)	30/36	Phocidae	Serological	Cross-sectional	4	High
	Antarctica
3	Jensen et al. (2012)	40/102	Otariidae	Serological	Cross-sectional	4	High
			Phocidae
4	Rengifo-Herrera et al. (2012)	28/211	Otariidae	Serological	Cross-sectional	4	High
			Phocidae
5	Tryland et al. (2012)	0/116	Otariidae	Serological	Cross-sectional	5	High
			Phocidae
	North America
6	Tocidlowski et al. (1997)	46/103	Mustelidae	Serological	Cross-sectional	5	High
7	Cole et al. (2000)	15/67	Mustelidae	Bioassay	Cross-sectional	3	Middle
8	Mikaelian et al. (2000)	6/22	Monodontidae	Serological	Cross-sectional	3	Middle
9	Lambourn et al. (2001)	29/380	Phocidae	Serological	Cross-sectional	4	High
10	Miller et al. (2002a)	115/223	Mustelidae	Serological	Cross-sectional	4	High
11	Miller et al. (2002b)	65/243	Mustelidae	Serological	Cross-sectional	4	High
12	Dubey et al. (2003)	328/761	Mustelidae,	Serological	Cross-sectional	4	High
			Phocidae,
			Otariidae,
			Delphinidae,
			Odobenidae,
			Monodontidae
13	Hanni et al. (2003)	36/175	Mustelidae	Serological	Cross-sectional	4	High
14	Measures et al. (2004)	127/328	Phocidae	Serological	Cross-sectional	5	High
15	Miller et al. (2004)	35/219	Mustelidae	Serological	Cross-sectional	3	Middle
16	Dubey et al. (2005)	146/146	Delphinidae	Serological	Cross-sectional	5	High
17	Rah et al. (2005)	30/500	Ursidae	Serological	Cross-sectional	5	High
18	Littnan et al. (2006)	2/18	Phocidae	Serological	Cross-sectional	2	Middle
19	Aguirre et al. (2007)	2/67	Phocidae	Serological	Cross-sectional	3	Middle
20	Gaydos et al. (2007)	7/40	Mustelidae	Serological	Cross-sectional	3	Middle
21	Thomas et al. (2007)	17/344	Mustelidae	Serological	Cross-sectional	4	High
22	Dubey et al. (2008)	27/52	Delphinidae	Serological	Cross-sectional	3	Middle
23	Brancato et al. (2009)	18/30	Mustelidae	Serological	Cross-sectional	3	Middle
24	Johnson et al. (2009)	56/118	Mustelidae	Serological	Cross-sectional	4	High
25	Kirk et al. (2010)	18/136	Ursidae	Serological	Cross-sectional	5	High
26	Gibson et al. (2011)	85/161	Otariidae	Nucleic acid based	Cross-sectional	4	High
			Phocidae
			Phocoenidae
			Delphinidae
			Physeteridae
			Mustelidae
27	Goldstein et al. (2011)	6/163	Mustelidae	Serological	Cross-sectional	4	High
28	Hueffer et al. (2011)	0/116	Phocidae	Serological	Cross-sectional	4	High
29	Simon et al. (2011)	86/828	Phocidae	Nucleic acid based,	Cross-sectional	5	High
				serological
30	Bossart et al. (2012)	1/28	Trichechidae	Serological	Cross-sectional	4	High
31	White et al. (2013)	29/30	Mustelidae	Serological	Cross-sectional	3	Middle
32	Greig et al. (2014)	14/283	Phocidae	Serological	Cross-sectional	4	High
33	Carlson-Bremer et al. (2015)	536/1630	Otariidae	Serological	Cross-sectional	4	High
34	Al-Adhami et al. (2016)	34/194	Phocidae,	Serological	Cross-sectional	4	High
			Odobenidae,
			Balaenidae,
			Monodontidae
35	Bauer et al. (2016)	0/34	Phocidae	Serological	Cross-sectional	4	High
36	Smith et al. (2016)	3/44	Trichechidae	Serological	Cross-sectional	3	Middle
37	Atwood et al. (2017)	33/138	Ursidae	Serological	Cross-sectional	4	High
38	Burgess et al. (2018)	184/710	Mustelidae	Serological	Cross-sectional	4	High
39	Verma et al. (2018)	65/70	Mustelidae	Serological	Cross-sectional	3	Middle
40	Bachand et al. (2019)	12/61	Phocidae	Serological	Cross-sectional	3	Middle
41	Reiling et al. (2019)	32/81	Phocidae	Nucleic acid based	Cross-sectional	3	Middle
42	Shapiro et al. (2019)	135/135	Mustelidae	Bioassay	Cross-sectional	4	High
43	Miller et al. (2020)	334/542	Mustelidae	Serological	Cross-sectional	4	High
44	Sanders II et al. (2020)	53/220	Mustelidae	Nucleic acid based	Cross-sectional	4	High
	South America
45	Santos et al. (2011)	82/95	Iniidae	Serological	Cross-sectional	4	High
46	Sulzner et al. (2012)	8/112	Trichechidae	Serological	Cross-sectional	4	High
47	McAloose et al. (2016)	0/21	Balaenidae	Immunohistochemical	Cross-sectional	2	Middle
48	Costa-Silva et al. (2019)	3/185	Delphinidae	Immunohistochemical	Cross-sectional	5	High
49	Calvo-Mac et al. (2020)	1/19	Mustelidae	Serological	Cross-sectional	3	Middle
	Europe
50	Oksanen et al. (1998)	0/645	Phocidae	Serological	Cross-sectional	4	High
51	Cabezón et al. (2004)	11/58	Phocoenidae	Serological	Cross-sectional	3	Middle
			Delphinidae
52	Sobrino et al. (2007)	6/6	Mustelidae	Serological	Cross-sectional	3	Middle
53	Alekseev et al. (2009)	46/221	Delphinidae	Serological	Cross-sectional	4	High
			Monodontidae
54	Forman et al. (2009)	8/101	Ziphiidae,	Serological	Cross-sectional	4	High
			Delphinidae,
			Phocoenidae,
			Balaenopteridae
55	Oksanen et al. (2009)	98/527	Ursidae	Serological	Cross-sectional	5	High
56	Di Guardo et al. (2010)	4/8	Delphinidae	Serological	Cross-sectional	4	High
57	Jensen et al. (2010)	163/704	Ursidae	Serological	Cross-sectional	5	High
58	Pretti et al. (2010)	3/4	Delphinidae	Nucleic acid based,	Cross-sectional	4	High
				serological
59	Cabezón et al. (2011)	14/103	Phocidae	Serological	Cross-sectional	5	High
60	Chadwick et al. (2013)	108/271	Mustelidae	Serological	Cross-sectional	5	High
61	Naidenko et al. (2013)	2/26	Ursidae	Serological	Cross-sectional	4	High
62	Bernal-Guadarrama et al. (2014)	2/24	Delphinidae	Serological	Cross-sectional	3	Middle
63	Blanchet et al. (2014)	0/20	Phocoenidae	Nucleic acid based	Cross-sectional	4	High
64	Profeta et al. (2015)	10/70	Delphinidae,	Serological	Cross-sectional	3	Middle
			Balaenopteridae,
			Physeteridae,
			Ziphiidae
65	Hermosilla et al. (2016)	0/5	Physeteridae	Nucleic acid based	Cross-sectional	4	High
66	van de Velde et al. (2016)	121/292	Mustelidae,	Serological	Cross-sectional	4	High
			Phocoenidae,
			Delphinidae,
			Balaenopteridae,
			Phocidae,
			Ziphiidae,
			Physeteridae,
67	Waap et al. (2016)	0/2	Mustelidae	Serological	Cross-sectional	3	Middle
68	Alekseev et al. (2017)	11/78	Monodontidae	Serological	Cross-sectional	3	Middle
69	Santoro et al. (2017)	0/6	Mustelidae	Nucleic acid based	Cross-sectional	4	High
70	Smallbone et al. (2017)	168/654	Mustelidae	Serological	Cross-sectional	5	High
71	Pintore et al. (2018)	7/45	Delphinidae,	Nucleic acid based	Cross-sectional	3	Middle
			Balaenopteridae
72	Scotter et al. (2019)	6/39	Odobenidae	Serological	Cross-sectional	4	High
73	Núñez-Egido et al. (2020)	0/70	Otariidae	Serological	Cross-sectional	4	High
74	Terracciano et al. (2020)	6/26	Delphinidae	Serological	Cross-sectional	3	Middle
	Asia
75	Murata et al. (2004)	7/59	Delphinidae	Serological	Cross-sectional	3	Middle
76	Omata et al. (2006)	0/8	Delphinidae	Nucleic acid based	Cross-sectional	4	High
77	Fujii et al. (2007)	3/77	Phocidae	Serological	Cross-sectional	4	High
78	Obusan et al. (2015)	7/23	Delphinidae	Nucleic acid based	Cross-sectional	4	High
79	Obusan et al. (2019)	20/28	Delphinidae	Nucleic acid based	Cross-sectional	4	High
			Physeteridae,	Serological
			Balaenopteridae
	Oceania
80	Omata et al. (2005)	33/58	Delphinidae	Serological	Cross-sectional	3	Middle
81	Lynch et al. (2011)	0/104	Otariidae	Serological	Cross-sectional	5	High
82	Roe et al. (2013)	17/28	Delphinidae	Immunohistochemical	Cross-sectional	4	High
83	Michael et al. (2016)	3/50	Otariidae	Serological	Cross-sectional	4	High
84	Wong et al. (2020)	5/114	Dugongidae	Serological	Cross-sectional	5	High

^a The quality scores identified studies as high-quality (4–5 points), moderate-quality (2–3 points) and low-quality (0–1 point).

3.2 Risk of publication bias and sensitivity analysis

The conversion result of PAS was closer to normal distribution (Table S5), and therefore we chose the combination result of PAS conversion for meta-analysis. The use of the random-effects model was appropriate for the analysis because between-study heterogeneity was significant (I² = 98%) (Figure 2). The funnel plot showed that all studies are distributed symmetrically, indicating no evidence of publication bias (Figure 3). The results of Egger's test further confirmed the lack of evidence of publication bias among the included studies (p = 0.9134, Figure 4, Table 2). The sensitivity analysis indicated that there was no significant effect on the meta-analysis findings after omitting one study at a time, indicating that the results are reliable (Figure 5).

TABLE 2. Egger’ s test result for publication bias

Slope	Bias	SE bias	t	df	p Value
0.5032	−0.1685	1.5444	−0.11	82	.9134

3.3 Primary outcome

The primary outcome of this systematic review was to assess the prevalence of T. gondii infection in wild marine mammals from a global perspective. The study included 14,931 wild animals from 15 families. The overall pooled prevalence of T. gondii infection in wild marine mammals was 22.44% [3848/14,931, 95% confidence interval (CI): 17.29%−28.04%; Figure 2]. We compared the prevalence of T. gondii infection among the 15 families of marine animals studied. Roughly similar pooled prevalences were detected in Balaenopteridae 13.9% (4/215; 95% CI: 0.00–67.83), Odobenidae 14.37% (18/126; 95% CI: 4.30–28.46), Otariidae 13.99% (591/2185; 95% CI: 2.46–31.90), Phocidae 15.72% (595/4097; 95% CI: 9.31–23.34) and Phocoenidae 15.30% (65/262; 95% CI: 0.00–47.99). The prevalence of T. gondii infection in other animal families is shown in Table 3.

TABLE 3. Pooled prevalence and heterogeneity statistics of T. gondii infection in various families of wild marine mammals

Family	No. of studies	No. examined	No. positive	% (95% CI)	χ²	p Value	I² (%)
					Heterogeneity statistics
Balaenidae	2	23	1	6.23 (0.00–79.38)	4.12	.04	75.7
Balaenopteridae	6	215	4	13.90 (0.00–67.83)	24.97	<.01	80.0
Delphinidae	21	1066	505	36.99 (15.63–61.06)	1090.72	<.01	98.2
Dugongidae	1	114	5	4.39 (1.25–9.06)	–	–	–
Iniidae	2	96	82	65.08 (0.00–100.00)	3.72	.05	73.1
Monodontidae	6	310	34	9.49 (2.15–19.91)	19.48	<.01	74.3
Mustelidae	26	4574	1624	40.59 (28.90–52.80)	1443.56	<.01	98.3
Odobenidae	3	126	18	14.37 (4.30–28.46)	7.32	.03	72.7
Otariidae	9	2185	591	13.99 (2.46–31.90)	388.04	<.01	97.9
Phocidae	22	4097	595	15.72 (9.31–23.34)	730.84	<.01	97.1
Phocoenidae	5	262	65	15.30 (0.00–47.99)	67.54	<.01	94.1
Physeteridae	7	32	3	3.24 (0.00–18.64)	6.86	.33	12.5
Trichechidae	3	184	12	6.23 (2.94–10.45)	0.27	.87	0.00
Ursidae	6	1555	285	18.05 (7.82–31.20)	161.50	<.01	96.9
Ziphiidae	5	49	20	27.80 (2.43–61.73)	7.81	.1	48.8

Abbreviations: CI = confidence interval; I² = heterogeneity index; χ² = heterogeneity statistic.
Note: p Value < 0.05 is statistically significant.

3.4 Secondary outcome

The subgroup and meta-regression analyses revealed that four covariates, including age (Table 4), country, continent and mean temperature (Table 5), can partly explain between-study heterogeneity (p < 0.05). In the age subgroup, the prevalence of T. gondii infection in adult animals 21.88% (798/3119; 95% CI: 13.40–31.59) was higher compared with the prevalence of the two younger age groups (Table 4). Of all continents, North America had the highest prevalence with 29.92% (2756/9243; 95% CI: 21.77–38.77). Of all countries, the highest prevalence was found in Spain 44.26% (19/88; 95% CI: 5.21–88.54), followed by the United States 30.93% (2459/7720; 95% CI: 21.54–41.19) and United Kingdom 25.28% (415/1390; 95% CI: 14.41–38.02). Regarding the climatic factors, the prevalence in areas with an annual mean temperature >20°C, 36.28% (171/562; 95% CI: 6.36–73.61) was significantly higher compared with other areas with lower temperatures (Table 5).

TABLE 4. Pooled prevalence, heterogeneity statistics, and meta-regression of sociodemographic variables associated with T. gondii infection in wild marine mammals

						Heterogeneity statistics			Univariate meta-regression
		No. of studies	No. examined	No. positive	% (95% CI)	χ²	p Value	I² (%)	p Value	Coefficient (95% CI)	R² (%)
Publication year									.211	0.085 (−0.048 to 0.219)	0.00
	1997–2010	33	6448	1507	26.90 (18.23–36.56)	2123.97	0	98.5
	After 2010	51	8483	2341	19.70 (13.47–26.78)	2798.30	0	98.2
Detection method									.111	−0.235 (−0.524 to 0.054)	1.34
	Immunohistochemical	4	274	20	6.28 (0.00–31.49)	66.92	<.01	95.5
	Serological	72	13901	3485	22.75 (17.49–28.48)	4065.41	0	98.3
	Nucleic acid-based	12	748	204	16.23 (4.31–33.78)	295.73	<.01	96.3
Age									.043	−0.142 (−0.279 to −0.005)	1.70
	Pup	23	764	96	2.94 (0.00–10.13)	144.59	<.01	84.8
	Juvenile	25	1235	222	12.39 (5.37–21.05)	209.66	<.01	88.6
	Adult	31	3119	798	21.88 (13.40–31.59)	919.22	<.01	96.7
Gender									.935	0.007 (−0.154 to 0.167)	0.00
	Female	28	2429	583	20.53 (11.56–31.02)	755.09	<.01	96.4
	Male	28	2506	620	19.10 (9.40–30.58)	741.02	<.01	96.4
Habitat									.771	0.020 (−0.112 to 0.1516)	0.00
	Aquatic	34	2351	731	22.32 (10.66–36.78)	1959.44	0	98.3
	Subaquatic	50	10982	2828	23.51 (17.17–30.51)	3216.31	0	98.5
	Terrestrial	6	1555	285	17.97 (7.91–31.00)	162.99	<.01	96.9
Economic status									.848	0.016 (−0.150 to 0.183)	0.00
	Developed country	69	12793	3392	21.98 (16.53–27.97)	3934.03	0	98.3
	Developing country	15	1140	256	20.63 (8.00–37.23)	533.20	<.01	97.4

Abbreviations: CI = Confidence interval; I² = heterogeneity index; R² = proportion of between-study variance explained; χ² = heterogeneity statistic.
Note: p Value < .05 is statistically significant.

TABLE 5. Pooled prevalence, heterogeneity statistics, and meta-regression of geographical and climatic factors associated with T. gondii infection in wild marine mammals

						Heterogeneity			Univariate meta-regression
		No. studies	No. examined	No. positive	% (95% CI)	χ²	p value	I² (%)	p–value	Coefficient (95% CI)	R² (%)
Country									.0142	0.167 (0.033 to 0.300)	3.22
	Canada	7	1523	297	20.55 (10.09–33.56)	138.60	<.01	95.7
	Italy	6	159	30	21.07 (9.51–35.70)	15.71	<.01	68.2
	Japan	3	144	10	5.33 (0.65–14.12)	5.25	.07	61.9
	Norway	5	1596	255	7.40 (0.18–23.72)	302.22	<.01	98.7
	Russia	5	523	72	10.84 (5.10–18.39)	21.80	<.01	81.7
	Spain	3	88	19	44.26 (5.21–88.54)	32.11	<.01	93.8
	United Kingdom	5	1390	415	25.28 (14.41–38.02)	93.71	<.01	95.7
	USA	34	7720	2459	30.93 (21.54–41.19)	2786.59	0	98.8
Continent									.005	0.176 (0.053 to 0.300)	3.13
	Antarctica	3	429	68	11.73 (0.00–41.07)	100.77	<.01	98.0
	Asia	5	195	37	18.06 (1.97–45.17)	62.53	<.01	93.6
	Europe	27	3922	807	15.02 (9.41–21.67)	624.79	<.01	95.8
	North America	40	9243	2756	29.92 (21.77–38.77)	3023.94	0	98.7
	Oceania	5	351	56	17.73 (1.13–47.72)	147.22	<.01	97.3
	South America	5	432	94	12.92 (0.00–53.38)	316.25	<.01	98.7
Mean temperature (°C)									<.001	−0.251 (−0.3810 to −0.1211)	12.12
	<0	25	4098	536	9.68 (5.38–14.97)	561.18	<.01	95.7
	0–10	25	2843	677	22.16 (13.07–32.70)	829.54	<.01	97.1
	10–15	21	1930	654	33.48 (19.49–48.96)	750.38	<.01	97.3
	15–20	18	5103	1766	31.76 (19.41–45.49)	1461.90	<.01	98.8
	>20	8	562	171	36.28 (6.36–73.61)	532.74	<.01	98.7
Precipitation (mm)									.085	−0.120(−0.2570 to 0.0164)	4.17
	<200	7	1507	408	20.06 (7.67–36.28)	252.08	<.01	97.6
	200–400	24	4571	1476	24.87 (14.02–37.44)	1558.14	<.01	98.5
	400–800	25	3692	617	15.14 (8.22–23.48)	816.31	<.01	97.1
	>800	44	5042	1341	26.92 (18.20–36.59)	2149.76	0	98.0

Abbreviations: CI = confidence interval; I² = heterogeneity index; R² = proportion of between-study variance explained; χ² = heterogeneity statistic.
Note: p Value < .05 is statistically significant.

The prevalence in areas with an annual precipitation >800 mm 26.92% (1341/5042; 95% CI: 18.20–36.59) was higher than other areas with a lower precipitation (Table 5). However, the differences were not statistically significant (p = 0.085). No significant difference (p = 0.848; Table 4) was detected in the prevalence between developed countries 21.98% (3392/12,793; 95% CI: 16.53–27.97) and developing countries 20.63% (256/1140; 95% CI: 8.00–37.23). Likewise, publication year, detection method, gender and habitat did not have any significant influence on the prevalence of T. gondii infection in wild marine mammals.

Four types of methods used for detecting T. gondii infection in wild marine mammals were reported in the 84 analysed studies, including bioassay, immunohistochemical, serological and nucleic acid-based. As shown in Table 1, the primary testing method was serological, followed by nucleic acid-based amplification, immunohistochemical method and bioassay. Serological methods, included latex agglutination test (LAT), modified agglutination test (MAT), direct agglutination test (DAT), indirect fluorescence antibody test (IFAT), dye test (DT), enzyme-linked immunosorbent assay (ELISA), indirect haemagglutination assay (IHA) and immunoblotting assay. Nucleic acid-based amplification methods included nested polymerase chain reaction (nested PCR), reverse transcription-polymerase chain reaction (RT-PCR) and quantitative real-time polymerase chain reaction (qPCR).

4 DISCUSSION

The global pooled prevalence of T. gondii infection in wild marine mammals was 22.44%, supporting growing evidence that T. gondii infection is widespread in wild marine mammals. T. gondii prevalence varied considerably, from 3.24% to 65.08%, between wild marine mammal families. The factors responsible for this large variation in the reported T. gondii prevalence is not known but is likely to be related to different detection methods and different feeding habits of marine mammals. Our analysis included 15 families of marine mammals with different prey choices, which can influence the risk of exposure to T. gondii infection (Johnson et al., 2009).

Marine mammals with high T. gondii prevalence rates, such as Odobenidae and Ursidae (polar bears) prey on seals and whales (Gjertz & Wiig, 1992; Mckinney et al., 2017; Thiemann et al., 2008), and are more likely to be infected via consumption of prey tissue containing T. gondii. The low prevalence rates 4.39% and 6.23% detected in herbivore marine mammals Dugongidae and Trichechidae, respectively, may be attributed to limited exposure to infection via ingestion of oocysts from contaminated seawater or seagrass (Wong et al., 2020; Wyrosdick et al., 2017). The combined prevalence in Mustelidae (mainly river otters and sea otters) was 40.59%. Marine invertebrates particularly mollusks are a common food source for these animals (Miller et al., 2002a, 2020). Since marine bivalve shellfish can filter and retain T. gondii oocysts from seawater (Cong et al., 2019, 2021; Coupe et al., 2018), and they provide more opportunities for exposure of Mustelidae to T. gondii (Krusor et al., 2015).

The particularly high prevalence detected in Iniidae (65.08%) may be related to the type of aquatic environment. The Iniidae (Amazon River dolphin) is the only freshwater mammalian group identified in our analysis. Surface runoff contaminated with cat faeces facilitates the spread of T. gondii oocysts from land to the aquatic habitat of the Amazon River dolphin, increasing the risk of infection. The lowest infection rate (3.24%) detected in Physeteridae (sperm whales) is likely because whales inhabit the open ocean (Rendell & Frantzis, 2016), which is less affected by surface runoff pollution. This observation is consistent with a previous study (Hermosilla et al., 2016).

One of the limitations of meta-analysis of epidemiological studies is data heterogeneity, which can be related to sampling frame (Ni et al., 2020; Wang et al., 2021) or different detection methods (Gong et al., 2021). Subgroup analysis revealed no significant differences between publication years or detection methods (p > 0.05, Table 4). Detection of T. gondii infection has historically been achieved using a number of methods. Serological and immunohistochemical methods are useful; however, cross-reactivity may lead to unreliable results (Gong et al., 2021; Miller et al., 2002b). Nucleic acid-based amplification methods can provide high detection rates and specific identification of T. gondii compared with other methods (Su et al., 2010). However, they are expensive, require specialized instruments and do not distinguish between viable and non-viable oocysts (Dubey et al., 2020).

Although gender has been considered a potential risk factor in previous meta-analysis of T. gondii infection in cattle (Gong et al., 2020a) and goats (Wei et al., 2021), there was no significant difference in the prevalence between male and female wild marine mammals in our study. Regarding the habitat subgroup, the prevalence in subaquatic animals (23.51%) was nearly similar to that in aquatic animals (22.32%), whereas terrestrial animals had a lower prevalence (17.97%). In our study, the subgroup of terrestrial animals included polar bear, which is the largest terrestrial predator in the circumpolar region (Naidenko et al., 2013). It is unlikely for bears to become infected via surface runoff because of the limited presence of cats in the arctic. Also, the arctic regions have water masses that are relatively isolated from global ocean circulation, which further reduces the risk of water-borne infection by T. gondii oocysts. However, accumulation of T. gondii oocysts in estuarine environment during Spring may lead to pollution of the arctic coast, and thus increasing exposure to infection (Simon et al., 2013). Additionally, transmission of T. gondii between polar bears and other animal species can occur even in the absence of cats (Prestrud et al., 2010).

The economic development level had no significant effect on T. gondii prevalence. While this may initially suggest a lack of influence of the economic disadvantage in developing countries on the prevalence of T. gondii infection in wild marine mammals, this trend was not consistent. For example, Iran had a very high prevalence (83.33%) of T. gondii in wild marine mammals compared with that reported in developed countries (Table 5). That said, results based on one study in Iran cannot represent the situation in developing countries.

Interestingly, meta-regression analysis identified age, country, continent and annual mean temperature as variables significantly associated with T. gondii infection in wild marine mammals. The prevalence was significantly higher in adult marine mammals compared with that of the juvenile and pup groups, suggesting age-dependent increase of T. gondii infection. It is reasonable to assume that the longer an animal remains alive, the greater the risk of being exposed to infection. A similar finding was reported in terrestrial mammals (horses: Li et al., 2020; cattle: Gong et al., 2020a; goats: Wei et al., 2021; pigs: Foroutan et al., 2019; wild boars: Rostami et al., 2017).

We noted differences in the biogeographic patterns of T. gondii prevalence. The highest infection rate in wild marine mammals was detected in Spain (44.26%), followed by the United States (30.93%) and United Kingdom (25.28%). At the continent level, the highest prevalence was found in North America. Because felines are the only definitive host of T. gondii (Elmore et al., 2010), differences in the prevalence between geographical regions may be related to differences in the density of cats between various regions. A previous meta-analysis of T. gondii infection in felids (Montazeri et al., 2020) showed that the highest infection rate of domestic cats was found in Australia (52%), while the highest infection rate of wild felids was detected in Africa (74%). Regional differences in prevalence may also be related to variations in aquatic dispersion of T. gondii oocysts driven by ocean currents (Figure 6), which can influence the distribution of T. gondii oocysts in seawater, resulting in different levels of T. gondii contamination in marine environment (Poulle et al., 2021). However, the effect of dispersion by ocean currents on the biogeography of T. gondii infection in marine environment remains to be elucidated.

We considered whether there is a relationship between T. gondii prevalence and climatic factors in each geographical region. The analysis revealed statistical differences between mean temperatures, with the highest (36.28%) and lowest (9.68%) prevalence associated with > 20°C and <0°C, respectively. This result was consistent with a previous meta-analysis of chronic toxoplasmosis in pregnant women (Rostami et al., 2021). Temperature affects sporulation and infectivity of T. gondii oocysts (Shapiro et al., 2019).

The study used a robust methodology in accordance with the PRISMA statement to extensively search five electronic databases. However, the study has potential limitations. First, we did not search other bibliographic databases (e.g. Cochrane Library, Embase, Scopus) and, thus, our systematic review may not be representative of all relevant research in the field. Second, the relatively small number of the included studies and the high level of heterogeneity identified among studies should be considered when interpreting the results. Third, it remains to be confirmed whether the association between the identified variables and T. gondii infection in wild marine mammals is valid in populations not represented in our meta-analysis.

5 CONCLUSION

In this systematic review and meta-analysis, the worldwide pooled prevalence of T. gondii infection in wild marine mammals was 22.44%. This result adds to the growing evidence base suggesting that T. gondii infection in wild marine mammals is not restricted to certain geographical regions, highlighting the broad impact of T. gondii on marine ecosystem, marine animal health and public health. Further research is required to determine the prevalence of T. gondii infection in wild marine mammals in under-studied areas, particularly in Africa, Asia, Oceania and South America. Future research is also needed to investigate the dissemination routes of T. gondii oocysts from land to sea and the effect of ocean currents on the fate of the parasite in marine environment. Our results consolidate current knowledge of T. gondii infection and associated epidemiologic characteristics in wild marine mammals. This knowledge is useful for predicting disease risk and for establishing measures to limit the impact of toxoplasmosis on wild marine mammals.

ACKNOWLEDGEMENTS

This research was supported by the Young Scholars Program of Shandong University, Weihai (Grant no. 20820211009) and the Postdoctoral Innovation Project of Shandong Province (Grant no. 202001005).

CONFLICT OF INTEREST

The authors have no conflicts of interest to declare.

ETHICAL APPROVAL

No ethical approval was required.

Open Research

DATA AVAILABILITY STATEMENT

All data, models, and code generated or used during the study appear in the submitted article.

Supporting Information

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Filename	Description
tbed14493-sup-0001-TableS1.docx18.8 KB	TABLE S1. PRISMA Checklist for meta-analysis
tbed14493-sup-0002-TableS2.docx18.2 KB	TABLE S2. Retrieval formulas in database
tbed14493-sup-0003-TableS3.docx12.5 KB	TABLE S3. R code for meta-analysis
tbed14493-sup-0004-TableS4.docx12.1 KB	TABLE S4. Normal distribution test for the normal rate and different transformations of the normal rate

A systematic review, meta-analysis and meta-regression of the global prevalence of Toxoplasma gondii infection in wild marine mammals and associations with epidemiological variables

Abstract

1 INTRODUCTION