Prevalence and risk factors of neglected zoonoses in Ethiopian small ruminants: a focus on Q fever, brucellosis, chlamydiosis, and toxoplasmosis

Abstract

Neglected zoonoses, including Q fever, brucellosis, chlamydiosis, and toxoplasmosis, pose significant health risks to both animals and humans, particularly in low-resource settings. This study assessed their seroprevalence and risk factors in small ruminants across five Ethiopian districts. Among the 1,402 animals tested, 16.5% were seropositive for Coxiella burnetii (Q fever), 6.8% were seropositive for Brucella spp., 8.8% were seropositive for Chlamydia abortus, and 11.4% were seropositive for Toxoplasma gondii, with 5.3% showing mixed infections. At the flock level, 76.8% harbored at least one pathogen, and 45.2% tested positive for multiple infections. Mixed-effects logistic regression identified key risk factors. Animals in lowland pastoral systems had a significantly lower risk of C. burnetii exposure (OR: 0.2; 95% CI: 0.08–0.6, p = 0.01). Similarly, households that culled abortive animals presented a reduced infection risk (OR: 0.6; 95% CI: 0.4–0.9, p = 0.05). In contrast, the agropastoral system was linked to a lower likelihood of Brucella exposure (OR: 5.8; 95% CI: 2.1–16.5; p = 0.001). The risk of toxoplasmosis was greater in mixed crop–livestock systems (OR: 10.4; 95% CI: 1.0–109; p = 0.05) and sheep and goat mixed flocks (OR: 7.6; 95% CI: 1.0–61.2; P = 0.05). The high seroprevalence of these zoonoses in Ethiopian small ruminants underscores their significant public health and economic impact. The widespread flock-level burden and frequent coinfections highlight ongoing transmission risks, reproductive losses, and challenges in disease control. Variations in exposure across production systems emphasize the role of management practices in disease dynamics. Given the multipathogen burden, targeted interventions should move beyond single-disease approaches and adopt integrated control strategies within a One Health framework to mitigate risks for both livestock and human populations.

Introduction

In Ethiopia, smallholder sheep and goats play a vital role in household livelihoods, food security, and the national economy [1]. These animals are important for ensuring that households and communities have a reliable supply of meat, milk, wool, and skin. They are valuable sources of income for smallholder farmers and can be cashed in to provide for household needs, education, and healthcare with minimal investment and lower maintenance costs [2]. Furthermore, they hold significant social and cultural importance and are crucial in empowering women, as women have better control over sheep and goats than other larger ruminants do [3].

However, livestock diseases that impact productivity undermine sheep and goat production, and some infectious diseases also present a zoonotic risk to human health. Studies have identified zoonoses such as Q fever, brucellosis, toxoplasmosis, and chlamydiosis as among the most prevalent diseases affecting small ruminants in Ethiopia [4–7]. These diseases not only impact animal health but also contribute to human cases in the country [8].

These zoonotic diseases are transmitted primarily through direct contact with infected animals, consumption of contaminated animal products, and inhalation of contaminated aerosols or dust [9]. Individuals at greater risk include those who work closely with animals, such as farmers and veterinarians, as well as consumers who handle or consume animal products [10]. These factors lead to serious health consequences, particularly for vulnerable populations such as children, elderly individuals, and those with compromised immune systems [11]. In countries with low to middle incomes, such as Ethiopia, the scarcity of public and veterinary services, coupled with low awareness and inadequate practices for preventing zoonotic diseases [12], can lead to prolonged and pronounced impacts from these diseases.

Livestock diseases have a significant impact on smallholder farmers, leading to livestock losses from morbidity, mortality, and reduced reproductive performance [13]. Morbidity leads to decreased production of meat, milk, and other animal products, whereas mortality results in the loss of valuable breeding stock and reduces the overall herd size [1]. Reproductive issues, such as abortion and infertility, further exacerbate these losses, limiting the ability of farmers to generate income and sustain their livelihoods [14, 15]. The reduced availability of animal-sourced foods also contributes to malnutrition, particularly in vulnerable populations [16]. Additionally, the costs associated with treating sick animals and implementing disease prevention measures, such as vaccination and biosecurity, add to the financial strain on smallholder producers, making it challenging for them to invest in other aspects of their farming operations.

Despite the high prevalence and impact of zoonotic diseases in Ethiopia, little is known about these issues in Ethiopia, with limited ongoing research on these issues. A better understanding of the epidemiology of these zoonotic diseases will provide valuable insights into their burden and the need for action. In this study, we assessed the seroprevalence of key zoonoses in small ruminants in Ethiopia, identifying risk factors that can inform control policies.

Materials and methods

Description of the study area and population

A study was conducted in the Abergele and Zequalla districts of the Amhara regional state, Yabello, and Elwaya districts of the Oromia regional state, and Doyogana District of the central Ethiopian region (former SNNP region) (Fig. 1). The Abergele and Zequala districts represent an agro-pastoral production system where livestock are predominantly reared, with low crop farming productivity due to rainfall scarcity. Yabello and Elwaya districts represented the lowland pastoral production system. These districts are characterized by sparsely populated pastoral rangelands where the subsistence of pastoralists is based mainly on livestock and livestock products. Doyogana District represents a highland mixed crop‒livestock production system. In this area, livestock husbandry and rain-fed cropping are closely interlinked, with production synergies and the spread of risks and income across crop and livestock outputs.

Fig. 1.

Map of the study districts and sampling sites in Ethiopia

Profile of the study unit

All the examined animals were raised in extensive traditional management systems. The average flock size was 35 for goats (with a median of 26) and 12 for sheep (with a median of 7). Figures 2a and b show violin plots of small ruminant flock sizes, categorized by flock type and production system. None of the studied animals were vaccinated against any of the diseases tested, and there is no vaccination programme against these diseases in these populations in Ethiopia.

Fig. 2.

Violin plots illustrate the distribution of small ruminant flock sizes by flock type (Fig. 2a) and production system (Fig. 2b). The violin plots display the density distribution of flock sizes, with wider sections representing higher data concentrations. The box shows the interquartile range (IQR) with the median line, while the whiskers indicate the range of data outside the IQR. The blue dot indicates the mean value for each category. Blue error bars indicate the bootstrapped 95% confidence intervals around the mean

Sampling methods and sample size determination

The study districts were selected based on specific criteria, such as their agroecology and production systems, their potential for sheep and/or goat production, and the significance of these animals to the livelihoods of the households in the areas [17]. Two peasant associations (PAs) were chosen from each district, except for Doyogana, where three PAs were selected because of the inability to obtain a sufficient number of animals for sampling per household.

The animals in this study were selected from among 154 smallholder farmers who were randomly chosen from a group of 318 household surveys conducted between July 2018 and February 2019 via the random function in Microsoft Excel. Within each household, 5 goats and 3 sheep aged six months or older were randomly chosen for serum sample collection. As a result, 1226 animals were sampled from 133 sheep flocks and 90 goat flocks. In addition, blood samples were collected from 176 randomly selected animals across three breeding farms. The initial sample size was determined based on an expected seroprevalence of 50% to maximize the sample size with a 95% confidence level and a 5% margin of error. After accounting for a clustered sampling of animals within households, the calculated sample size was inflated by 45% to increase the precision. The sample size calculations were described in detail in a previous publication [18].

Sample collection and transportation

Blood was collected from the jugular vein in 10 ml sterile plain vacutainer tubes. During blood sample collection, trained veterinarians conducted structured interviews with owners and attendants to gather information about individual animals and flocks. The interviews were conducted in the local language. The collected information included biological variables such as age, sex, species, parity, and body condition. Additionally, management-related aspects, such as culling practices, breeding methods, flock characteristics, flock size, farming practices, and pasture management, were recorded. The local administration details, including the Peasant Association, Kebele, and district, were also documented. Animal age was determined via dentition, and body condition was evaluated via examination of various body parts following the methodology established by Abebe and Yami [19].

The serum was then separated from the clotted blood by centrifugation at 1500 × g for 10 min. The serum samples were kept in cryovial tubes, transported to the laboratory in an ice box with cold packs, and then stored at -20 °C until analysis. The Animal Health Institute (formerly known as the National Animal Health Diagnostic and Investigation Center) in Sebeta, Ethiopia, conducted the laboratory analyses.

Serological analysis

Commercial enzyme-linked immunosorbent assays (ELISAs) were used to detect antibodies against targeted pathogens. The following antibody test kits were used: the C. burnetti Antibody Test Kit, the C. abortus Antibody Test Kit, the T. gondii Antibody Test Kit, all from IDEXX^® (Switzerland AG, 3097 Liebefeld-Bern Switzerland), and for Brucella spp., the Svanovir TM Brucella-Ab c-ELISA test kits (Svanova Biotech, Uppsala, Sweden) were used. The detailed test procedures were described elsewhere by Alemayehu et al. [18].

Data management and analyses

Epidemiological information collected from the flock and animal was cross-matched and combined with the laboratory results in Microsoft Excel version 15. The outcomes of interest were the seroprevalence of Q fever, brucellosis, chlamydiosis, and toxoplasmosis in the examined animals and flocks. A flock was considered positive if at least one animal was seropositive for the pathogens under investigation. The frequency distribution was used to organize and summarize the distribution of seroprevalences according to predictor variables. Data cleaning and statistical analyses were conducted in Stata 17 (Stata SE/17, Stata Corp., College Station, TX, USA). The violin plots were created via R Studio with the “ggplot2” package.

Risk factor analysis

Risk factor analyses were performed on data concerning animals from smallholder farmers owing to the nonrandom selection of breeding farms. Variables related to individual animal-level factors (age, sex, species, parity, and BCS) and flock-level factors (breeding male ownership, flock type, aborting dam culling practice, flock size, grazing pasture ownership, and production system) were identified as potential predictors. The regression analyses included districts and households as random variables to account for the nesting of households within districts and the clustering of animals in the households. The mixed-effects logistic regression (MELR) model was fitted to account for this clustering. The intraclass correlation coefficient (ICC) was used to analyze the data structure and determine the proportion of total variance in the seroprevalence of selected zoonoses attributed to district- and household-level factors. The Akaike information criterion (AIC) was employed to evaluate the goodness of fit of the model. The stepwise backward elimination method was applied until all variables in the model were statistically significant at a p value ≤ 0.05. The likelihood ratio test (LRT) was used to determine if adding or removing predictors significantly improved the model fit. The final models controlled the effects of animal age.

Results

Seroprevalence of Q fever, Brucella, Chlamydia, and Toxoplasma

Figure 3 illustrates the prevalence of four pathogens at the animal and flock level, categorized by three production systems in Ethiopia. Among the 1,402 animals examined, 16.5% (95% CI: 14.6–18.5) tested positive for Coxiella burnetii (Q fever), 6.8% (95% CI: 5.6–8.2) for Brucella spp., 8.8% (95% CI: 7.5–10.4) for Chlamydia abortus, and 11.4% (95% CI: 9.7–13.3) for Toxoplasma gondii antibodies. In addition, 5.3% (95% CI: 4.2–6.6) of the examined animals presented evidence of mixed infection.

Fig. 3.

Seroprevalence of neglected zoonoses in small ruminants across five districts in Ethiopia. The figure illustrates the prevalence of C. burnetii, Brucella spp., C. abortus, and T. gondii at both individual and flock levels aggregated by production system. The red bars indicate animal-level prevalence, while the blue bars represent flock-level prevalence. MCL = Mixed Crop-Livestock

The overall flock-level seroprevalence for the four pathogens was 44.3% (95% CI: 36.7–49.7) for C. burnetii, 26.8% (95% CI: 21.4–32.9) for Brucellas spp., 33.8% (95% CI: 27.9–40.2) for C. abortus, and 48.0% (95% CI: 48.0–41.0) for T. gondii. A total of 76.8% (95% CI: 70.8–81.8) of the flocks were positive for at least one of the tested zoonotic pathogens, and 45.2% (95% CI: 38.8–51.7) of the flocks were positive for multiple pathogens.

Table 1 presents the seroprevalence at both the flock and individual levels, categorized by flock type. In the smallholder farms, C. burnetii was detected in 16%, Brucella spp. in 6.9%, C. abortus in 7.8%, and T. gondii in 12.1% of the animals. In government breeding farms, C. burnetii had a prevalence of 19.7%, Brucella spp. had a prevalence of 6.2%, C. abortus had a prevalence of 15.7%, and T. gondii had a prevalence of 5.8%.

Table 1.

Seroprevalence of zoonotic pathogens in small ruminants at individual and flock levels, categorized by flock type

Pathogens	Flock type	Flock level (n = 228)					Individual animal level
		Number tested	Number positive	Prevalence (%)	95% CI		Number of animals tested	Number positive	Prevalence (%)	95% CI
C. burnetii	Smallholder	223	96	43.0	36.7	49.7	1,224	196	16.0	14.1	18.2
	Breeding	5	5	100	.	.	178	35	19.7	14.5	26.2
	Overall	228	101	44.3	37.9	50.8	1,402	231	16.5	14.6	18.5
Brucella spp.	Smallholder	223	57	25.6	20.2	31.7	1,224	84	6.9	5.6	8.4
	Breeding	5	4	80.0	30.6	97.3	178	11	6.2	3.5	10.8
	Overall	228	61	26.8	21.4	32.9	1,402	95	6.8	5.6	8.2
C. abortus	Smallholder	223	73	32.7	26.9	39.2	1,224	96	7.8	6.5	9.5
	Breeding	5	4	80.0	30.6	97.3	178	28	15.7	11.1	21.8
	Overall	228	77	33.8	27.9	40.2	1,402	124	8.8	7.5	10.4
T. gondii	Smallholder	192	91	47.4	40.4	54.5	1,062	129	12.1	10.3	14.3
	Breeding	4	3	75.0	23.5	96.7	138	8	5.8	2.9	11.2
	Overall	196	94	48.0	41.0	55.0	1,200	137	11.4	9.7	13.3
Mixed exposure	Smallholder	223	99	44.4	38.0	51.0	1,224	59	4.8	3.8	6.2
	Breeding	5	4	80.0	30.6	97.3	178	15	8.4	5.1	13.5
	Overall	228	103	45.2	38.8	51.7	1,402	74	5.3	4.2	6.6
At least one pathogen exposure	Smallholder	223	170	76.2	70.2	81.4
	Breeding	5	5	100.0	.	.
	Overall	228	175	76.8	70.8	81.8

Legend: Seroprevalence of Coxiella burnetii (Q fever), Brucella spp., Chlamydia abortus, and Toxoplasma gondii in small ruminants from smallholder farms and breeding farms in Ethiopia, presented as percentages with 95% CI. The table presents prevalence estimates at both individual and flock levels, stratified by flock type. CI = Confidence Interval

Risk factors associated with Coxiella burnetii seroprevalence

Table 2 presents the results of the univariate MELR analyses. Among the potential predictor variables examined, livestock age, sex, parity, and production system were found to be associated with C. burnetii exposure. However, the final MELR model, adjusting for the potential confounding effects of animal age, showed a significant association with the culling practices of aborted animals and production systems only (Table 3). Animals raised in lowland pastoral production systems (OR: 0.2; 95% CI: 0.2–1; P: 0.007) and those from households that frequently culled aborted animals (OR: 0.6; 95% CI: 0.4–1.0; P: 0.048) presented a reduced likelihood of exposure to C. burnetii.

Table 2.

Univariable analysis of factors associated with Coxiella burnetii seroprevalence in small ruminants

Predictors	Categories	Number tested	Number positive	Prevalence (%)	Unadjusted OR (95% CI)			P Value
Animal species	Caprine	1,031	181	17.6	1
	Ovine	195	14	7.2	1.1	0.7	1.7	0.7
Age categories	Young	129	3	2.3	1
	Adult	1,097	192	17.5	5.7	1.7	19.3	0.0
Sex	Female	1,031	181	17.6	1
	Male	195	14	7.2	0.5	0.3	1.0	0.0
Parity	1–3	275	40	14.6	1
	4–6	213	46	21.6	1.7	1.0	2.8	0.0
	> 6	538	95	17.7	1.3	0.8	2.0	0.2
Body condition	Poor	603	89	14.8	1
	Medium	548	96	17.5	1.2	0.8	1.7	0.3
	Good	75	10	13.3	0.8	0.3	1.8	0.5
Breeding male ownership	No	169	19	11.2	1
	Yes	1,057	176	16.7	1.6	0.8	3.2	0.2
Flock type	Goat	87	13	14.9	1
	Sheep	189	0	0.0	1
	Mixed	950	182	19.2	1.3	0.8	2.0	0.3
Aborted dam culling practice	Keep	333	72	21.6	1
	Cull	893	123	13.8	0.6	0.4	1.0	0.1
Flock size	< 15	592	77	13.0	1
	15–30	307	66	21.5	1.0	0.6	1.7	0.9
	> 30	327	52	15.9	0.8	0.4	1.3	0.3
Grazing land pasture	Private	143	7	4.9
	Communal	771	125	16.2	0.6	0.1	2.4	0.5
	Both	312	63	20.2	0.8	0.2	3.4	0.8
Production system	Agropastoral	488	106	21.7	1
	Pastoral	544	89	16.4	0.2	0.0	0.6	0.0
	MCL	194	0	0.0	-	-	-	-

Legend: Results of the univariable mixed-effects logistic regression analysis assessing potential risk factors for C.burnetii seroprevalence in small ruminants. The table presents predictors, categorized variables, number of animals tested, number of seropositive cases, prevalence (%), unadjusted OR with 95% CI, and corresponding p-values. MCL = Mixed Crop-Livestock, OR = Odds Ratio, CI = Confidence Interval

Table 3.

Multivariable mixed-effects logistic regression analysis of risk factors for Coxiella burnetii seroprevalence in small ruminants

Predictors	Categories	Number tested	number positive	Prevalence (%)	Adjusted OR (95% CI)			P value
Age categories	Young	129	3	2.3	1
	Adult	1,097	192	17.5	6.1	1.8	20.6	0.00
Aborted dam culling practice	Keep	333	72	21.6	1
	Cull	893	123	13.8	0.6	0.4	0.9	0.05
Production system	Agropastoral	488	106	21.7	1
	Pastoral	544	89	16.4	0.2	0.08	0.6	0.01
	MCL	194	0	0.0	-
ICC-district					0.06
ICC-household					0.13

Legend: Results of the multivariable mixed-effects logistic regression model assessing risk factors for Coxiella burnetii seroprevalence in small ruminants controlled for the effect of animal age. The table includes predictors, categorized variables, number of animals tested, adjusted OR with 95% CI, and p-values. The model accounts for clustering at the flock and district levels. MCL = Mixed Crop-Livestock, OR = Odds Ratio, CI = Confidence Interval, ICC = Intra Clustering Coefficient

From the analysis at the district and household levels, the ICC values were 0.06 and 0.13, indicating that 6% and 13% of the variance in the seroprevalence of Q fever was attributable to district-level variation and between-household variation within the district, respectively.

Risk factors associated with Brucella spp. seroprevalence

Table 4 presents the results of univariate and multivariable MELR analyses that illustrate the relationships between the prevalence of Brucella spp. and its potential predictors. The analyses revealed that body condition, male ownership, and production system were associated with Brucella spp. exposure. The multivariable MELR analysis, which accounted for animal age, confirmed these factors as the primary risk factors for brucella exposure (Table 5). Animals with medium body weight conditions were less likely to test positive for brucella than were those with poor body weight conditions (OR: 0.5; 95% CI: 0.3–0.9; P: 0.03). Moreover, the presence of a breeding male in the flock reduced the risk of brucella exposure by twofold compared with flocks without one (OR: 0.5; 95% CI: 0.2-1; P: 0.07). Additionally, animals in the agropastoral production system presented a lower risk of brucella exposure than did those in the pastoral production system (OR: 5.8; 95% CI: 2.1–16.5; P: 0.00) and MCL (OR: 32.5; 95% CI: 11-92.5; P: 0.00).

Table 4.

Univariate mixed-effect logistic regression analyses of factors associated with Brucella spp. seroprevalence in small ruminants

Predictors	Categories	Number tested	Number positive	Prevalence (%)	Unadjusted OR (95% CI)			P Value
Animal species	Caprine	1031	29	3.4
	Ovine	195	55	14.5	1.1	0.4	2.5	0.91
Age categories	Young	129	20	15.5
	Adult	1097	64	5.8	0.9	0.4	1.8	0.77
Sex	Female	1031	65	6.3
	Male	195	19	9.7	0.7	0.3	1.3	0.24
Parity	1–3	275	20	7.3
	4–6	213	9	4.2	0.5	0.2	1.3	0.17
	> 6	538	36	6.7	0.9	0.5	1.7	0.81
Body condition	Poor	603	53	8.8
	Medium	548	26	4.7	0.6	0.3	1.0	0.05
	Good	75	5	6.7	0.8	0.3	2.7	0.76
Breeding male ownership	No	169	17	10.1
	Yes	1057	67	6.3	0.5	0.2	1.0	0.05
Flock type	Goat	87	1	1.2
	Sheep	189	46	24.3	10.3	0.5	210.9	0.13
	Mixed	950	37	3.9	2.0	0.2	18.4	0.56
Aborted dam culling practice	Keep	333	18	5.4
	Cull	893	66	7.4	0.8	0.4	1.7	0.57
Flock size	< 15	592	64	10.8
	15–30	307	9	2.9	0.8	0.3	2.2	0.72
	> 30	327	11	3.4	0.7	0.3	1.8	0.46
Grazing pasture	Private	143	27	18.9
	Communal	771	43	5.6	1.7	0.7	4.2	0.24
	Both	312	14	4.5	1.0	0.4	2.7	0.97
Production system	Agropastoral	488	6	1.2
	Pastoral	544	30	5.5	5.3	1.9	14.4	0.00
	MCL	194	48	24.7	32.6	11.8	89.9	0.00

Legend: Results of the univariable mixed-effects logistic regression analysis assessing potential risk factors for Brucella seroprevalence in small ruminants. The table presents predictors, categorized variables, number of animals tested, number of seropositive cases, prevalence (%), unadjusted OR with 95% CI, and corresponding p-values. MCL = Mixed Crop-Livestock, OR = Odds Ratio, CI = Confidence Interval

Table 5.

Multivariable mixed-effects logistic regression analysis of risk factors for Brucella spp. seroprevalence in small ruminants

Predictors	Categories	Number tested	Number positive	Prevalence (%)	Adjusted OR (95% CI)			P value
Age categories	Young	129	20	15.5
	Adult	1097	64	5.8	1.0	0.5	2.0	0.94
Body condition	Poor	603	53	8.8
	Medium	548	26	4.7	0.5	0.3	0.9	0.03
	Good	75	5	6.7	0.7	0.2	2.0	0.46
Breeding male ownership	No	169	17	10.1
	Yes	1057	67	6.3	0.5	0.2	1.1	0.07
Production system	Agropastoral	488	6	1.2
	Pastoral	544	30	5.5	5.8	2.1	16.5	0.00
	MCL	194	48	24.7	32.5	11.4	92.5	0.00
ICC-district					0.0
ICC-household					0.18

Legend: Results of the multivariable mixed-effects logistic regression model assessing risk factors for Brucella spp. seroprevalence in small ruminants controlled for the effect of animal age. The table includes predictors, categorized variables, number of animals tested, adjusted OR with 95% CI, and p-values. The model accounts for clustering at the flock and district levels. MCL = Mixed Crop-Livestock, OR = Odds Ratio, CI = Confidence Interval, ICC = Intra Clustering Coefficient

The analysis revealed that differences between households within a district were the main contributors to the variance in the seroprevalence of brucellosis in the study districts (18%).

Risk factors associated with Chlamydia abortus seroprevalence

Table 6 presents the results of simple MELR analyses illustrating the relationship between the prevalence of and its potential predictors. The analyses revealed that none of the variables were significantly associated with chlamydia exposure.

Table 6.

Univariate mixed-effect logistic regression analyses of factors associated with Chlamydia abortus seroprevalence in small ruminants

Predictors	Categories	Number tested	Number positive	Prevalence (%)	Unadjusted OR (95% CI)			P Value
Animal species	Caprine	1031	58	6.9
	Ovine	195	38	10.0	1.4	0.8	2.5	0.29
Age categories	Young	129	15	11.6
	Adult	1097	81	7.4	0.9	0.5	1.7	0.76
Sex	Female	1031	78	7.6
	Male	195	18	9.2	0.9	0.5	1.6	0.71
Parity	1–3	275	22	8.0
	4–6	213	14	6.6	0.8	0.4	1.5	0.44
	> 6	538	42	7.8	0.9	0.5	1.6	0.79
Body condition	Poor	603	45	7.5
	Medium	548	44	8.0	1.3	0.8	2.0	0.33
	Good	75	7	9.3	1.5	0.6	3.5	0.40
Breeding male ownership	No	169	13	7.7
	Yes	1057	83	7.9	0.8	0.4	1.5	0.49
Flock type	Goat	87	2	2.3
	Sheep	189	25	13.2	4.1	0.7	24.1	0.12
	Mixed	950	69	7.3	2.4	0.5	10.6	0.25
Aborted dam culling practice	Keep	333	26	7.8
	Cull	893	70	7.8	0.9	0.6	1.5	0.73
Flock size	< 15	592	50	8.5
	15–30	307	19	6.2	0.8	0.4	1.4	0.41
	> 30	327	27	8.3	1.1	0.6	1.9	0.75
Grazing pasture	Private	143	20	14.0
	Communal	771	52	6.7	0.7	0.3	1.4	0.27
	Both	312	24	7.7	0.8	0.4	1.7	0.55
Production system	Agropastoral	488	40	8.2
	Pastoral	544	30	5.5	0.6	0.2	1.6	0.34
	MCL	194	26	13.4	1.7	0.5	5.2	0.39

Legend: Results of the univariable mixed-effects logistic regression analysis assessing potential risk factors for Chlamydia abortus seroprevalence in small ruminants. The table presents predictors, categorized variables, number of animals tested, number of seropositive cases, prevalence (%), unadjusted OR with 95% CI, and corresponding p-values. MCL = Mixed Crop-Livestock, OR = Odds Ratio, CI = Confidence Interval

Risk factors associated with Toxoplasma gondii seroprevalence

Table 7 displays the findings from both univariate and multivariable MELR analyses, highlighting the associations between T. gondii prevalence and its potential predictors. The analyses indicated that factors such as flock type, grazing land, and production system significantly (P ≤ 0.05) affected Toxoplasma exposure. The multivariable model adjusted for animal age revealed that flock type and production system had a significant association (p ≤ 0.05) with Toxoplasma exposure (Table 8). Compared with the agro-pastoral production system, the MCL production system increased the odds of Toxoplasma exposure by 10.4 times (OR: 10.4; 95% CI: 1.0–109; P: 0.05). Compared with a goat-only flock, being in a mixed flock increased the odds of Toxoplasma exposure by 7.6 times (OR: 7.6; 95% CI: 1.0–61.2; P: 0.05).

Table 7.

Univariate mixed-effect logistic regression analyses of factors associated with Toxoplasma gondii seroprevalence in small ruminants

Predictors	Categories	Number tested	number positive	%	Unadjusted OR (95% CI)			P Value
Animal species	Caprine	775	80	10.3
	Ovine	289	49	17.0	1.2	0.7	1.9	0.58
Age categories	Young	82	6	7.3
	Adult	982	123	12.5	3.1	1.2	8.3	0.02
Sex	Female	926	111	12.0
	Male	138	18	13.0	0.8	0.5	1.5	0.49
Parity	1–3	248	31	12.5
	4–6	185	21	11.4	0.9	0.5	1.7	0.80
	> 6	488	58	11.9	1.0	0.6	1.6	0.91
Body condition	Poor	511	44	8.6
	Medium	489	70	14.3	1.3	0.8	2.1	0.25
	Good	64	15	23.4	2.0	0.9	4.4	0.08
Breeding male ownership	No	155	27	17.4
	Yes	909	102	11.2	0.8	0.4	1.4	0.37
Flock type	Goat	87	1	1.2
	Sheep	102	22	21.6	10.3	0.8	126.3	0.07
	Mixed	875	106	12.1	7.9	1.0	63.4	0.05
Aborted dam culling practice	Keep	318	42	13.2
	Cull	746	87	11.7	0.8	0.5	1.3	0.36
Flock size	< 15	509	73	14.3
	15–30	281	24	8.5	1.0	0.6	1.9	0.92
	> 30	274	32	11.7	1.4	0.8	2.6	0.25
Grazing pasture	Private	71	18	25.4
	Communal	702	93	13.3	0.6	0.2	1.5	0.24
	Both	291	18	6.2	0.3	0.1	0.9	0.03
Production system	Agropastoral	416	26	6.3
	Pastoral	544	79	14.5	2.2	0.7	7.3	0.19
	MCL	104	24	23.1	4.4	1.0	18.8	0.05

Legend: Results of the univariable mixed-effects logistic regression analysis assessing potential risk factors for T. gondii seroprevalence in small ruminants. The table presents predictors, categorized variables, number of animals tested, number of seropositive cases, prevalence (%), unadjusted OR with 95% CI, and corresponding p-values. MCL = Mixed Crop-Livestock, OR = Odds Ratio, CI = Confidence Interval

Table 8.

Multivariable mixed-effects logistic regression analysis of risk factors for T. gondi seroprevalence in small ruminants

Predictors	Categories	Number tested	number positive	Prevalence (%)	Adjusted OR 95% CI			P value
Age categories	Young	82	6	7.3
	Adult	982	123	12.5	3.3	1.3	8.9	0.02
Flock type	Goat	87	1	1.2
	Sheep	102	22	21.6	3.4	0.2	61.1	0.39
	Mixed	875	106	12.1	7.6	1	61.2	0.05
Production system	Agropastoral	416	26	6.3
	Pastoral	544	79	14.5	1.8	0.6	6.9	0.34
	MCL	104	24	23.1	10.4	1	26.1	0.05
ICC-district					0.07
ICC-household					0.09

Legend: Results of the multivariable mixed-effects logistic regression model assessing risk factors for T. gondii seroprevalence in small ruminants controlled for the effect of animal age. The table includes predictors, categorized variables, number of animals tested, adjusted OR with 95% CI, and p-values. The model accounts for clustering at the flock and district levels. MCL = Mixed Crop-Livestock, OR = Odds Ratio, CI = Confidence Interval, ICC= Intra cluster coefficient

Discussions

This study provides important insights into the prevalence of C. burnetii, Brucella spp., C. abortus, and T. gondii in sheep and goats in Ethiopia, highlighting both single and multiple exposures to these zoonotic pathogens. Despite the known importance of these diseases in zoonotic disease surveillance and resource allocation, they remain largely unmonitored in the country [6].

The seroprevalence of C. burnetii in small ruminants reported in this study is lower than that reported in previous findings in the country [4, 7, 20–22]. This discrepancy in seroprevalence rates may be influenced by factors such as geographical variations, animal management practices, and the specific populations examined. Nonetheless, the observed seroprevalence indicates a notable presence of C. burnetii infection among sheep and goat populations in the study areas, highlighting the need for surveillance and control measures. Moreover, the study revealed that a significant percentage of small ruminant flocks in agro-pastoral and pastoral systems in Ethiopia had at least one animal that tested positive for C. burnetii antibodies. This suggests that infection is widespread in these production systems, posing significant risks to the health and productivity of small ruminants as well as to the public, particularly for those who work closely with livestock, such as farmers, veterinarians, and slaughterhouse workers [23, 24].

The overall Brucella spp. seroprevalence reported in this study aligns with previous reports in various agroecological and small ruminant production systems in Ethiopia [25–28]. However, this prevalence is higher than findings from other studies in Ethiopia, which reported lower seroprevalence rates [29–34]. The seroprevalence at the flock level found in this study is consistent with the results of Teklue et al. [29] but higher than those reported in previous studies [35–37]. The observed variations in Brucella seroprevalence across Ethiopian studies can be attributed to multiple factors tied to regional epidemiology, livestock management practices, and methodological differences.

The seroprevalence of C. abortus reported in this study aligns with findings from the pastoral regions of Borana [4] but is lower than the prevalence observed in highland sheep populations in Ethiopia [5]. The flock-level seroprevalence of chlamydial infections in small ruminants across Ethiopia indicates that chlamydiosis is prevalent in various production systems. This situation underscores the necessity for targeted surveillance and preventive strategies to mitigate the disease’s impact. Chlamydial infections can result in significant economic losses due to reproductive failure and increased veterinary expenses [38, 39]. Additionally, the zoonotic potential of these infections [40] highlights the critical need for monitoring and controlling them in small ruminants, as well as raising awareness regarding the risks of human transmission and associated preventive measures.

The overall toxoplasmosis seroprevalence reported in this study is lower than the prevalence rate reported in different parts of Ethiopia [5, 41–43]. The seroprevalence variation in our study could be attributed to geographical differences, management practices, sampling techniques, and diagnostic methods [41, 44, 45].

The study revealed that most of the flocks were exposed to at least one zoonotic pathogen, with nearly half testing positive for multiple pathogens. These findings are consistent with those of previous studies showing that Ethiopia’s livestock sector is burdened by zoonotic diseases, resulting in economic losses and public health concerns [4, 5, 46–49]. Farming communities, especially those with close contact, are particularly vulnerable. Limited veterinary services and poor farm biosecurity further exacerbate coinfection, increasing morbidity and mortality and complicating disease control efforts [50]. The coexistence of multiple zoonotic diseases, combined with restricted access to health care, presents critical public health and economic challenges for poor and marginalized rural communities [49, 51, 52].

Serological studies provide important insights into the disease burden within livestock populations of LMICs with limited diagnostic capacities [53, 54]. Seropositivity indicates prior pathogen exposure but does not confirm current infection, necessitating careful interpretation. In the absence of vaccination among the studied groups, antibody detection suggests past infections, highlighting potential sources of transmission within livestock systems and farming communities. Implementing integrated surveillance and intervention strategies can enhance disease control, protect public health, and improve livestock productivity [23, 55, 56].

This study revealed that breeding farms have higher rates of exposure to pathogens such as C. burnetii, Brucella spp., and C. abortus than smallholder flocks do. This is alarming because breeding farms, which distribute breeding stock, can inadvertently transmit infectious diseases to smallholder flocks [57]. While breeding farms play a crucial role in the spread of improved breeds and technologies [17], they need to enforce strict biosecurity measures and vaccination programs for breeding animals against major pathogens to safeguard the health of flocks and the well-being of farmers. Additionally, breeding farms should regularly test for key diseases to effectively identify and manage infected animals [58]. This proactive approach can help minimize the risk of spreading infections to smallholder flocks, which often lack resources for comprehensive disease management.

Our study revealed a significant difference in C. burnetii seroprevalence across production systems, which is consistent with studies of management practices, herd density, and environmental factors related to pathogen transmission [59, 60]. The higher seroprevalence in lowland pastoral and agropastoral production systems may result from increased flock contact at communal grazing and watering points, facilitating transmission through direct contact and environmental contamination [61, 62]. Additionally, Ethiopia’s lowlands experience dry conditions that can increase airborne spread, contributing to increased seroprevalence [63, 64].

This study revealed that households that frequently culled aborted animals had a lower likelihood of C. burnetii exposure. Since C. burnetii is a major cause of abortion in small ruminants, with infected animals shedding bacteria in feces, urine, and milk for extended periods, culling reduces infection sources and transmission cycles [62, 65, 66]. This finding aligns with previous studies demonstrating that removing shedders helps control transmission within flocks [67–70].

Animals in medium body condition were less likely to test positive for Brucella spp. than those in poor condition were, which aligns with previous studies [71, 72]. While poor body condition may impair immune defenses, this relationship is not universal. The high seroprevalence observed may be linked to brucellosis-induced reproductive issues, such as abortions, stillbirths, and infertility, which contribute to overall health decline [73]. Chronic infections can also cause prolonged stress, reduce nutrient intake, impair immune function, and lead to weight loss [74].

Flocks with a breeding male presented a twofold lower risk of Brucella exposure than those without. The use of shared sires, which are common in livestock breeding programs, including community-based breeding programs (CBBPs) [17, 75], increases the risk of disease transmission [57]. A pilot sire certification scheme, which is based on the estimated breeding value and technical requirements, is underway [76]. This certification should include screening for key sexually transmitted diseases and vaccination against known reproductive diseases prevalent in the country.

The study revealed that animals in the agropastoral production system have a lower risk of Brucella exposure than do those in the pastoral and mixed crop-livestock (MCL) systems. This finding aligns with other findings indicating that the seroprevalence of Brucella can be affected by the production system [31, 47, 77]. In contrast, some studies have reported that animals in MCL systems have a lower risk of Brucella exposure than do those in pastoral and agropastoral systems. This might be related to factors such as inadequate biosecurity practices, pasture management, mixed species interactions, environmental contamination, and the cultural practices of the farmers in MCL systems. The contradictory findings regarding the risk of Brucella exposure in MCL systems highlight the need for further investigation.

The MELR analysis revealed no significant predictors of C. abortus. However, previous studies have linked exposure to factors such as species, age, reproductive disorders, and management practices [78–82]. This discrepancy may be due to unmeasured factors such as environmental conditions, seasonality, biosecurity, and socioeconomic variables. These findings highlight the need for longitudinal studies to monitor flock dynamics and identify emerging risk factors over time.

Compared with single-species flocks, mixed rearing of sheep and goats increased the risk of T. gondii exposure, likely due to increased animal density and increased contact, facilitating cross-species transmission [83]. The greater risk of animal exposure to Toxoplasma in MCL production systems than in pastoral production systems may be associated with flock size and management practices. MCL farmers typically keep smaller flocks in the same area for an extended period, increasing exposure to environmental contamination from infected dams. Additionally, grazing areas in MCL systems are often located near farmer residential areas with relatively high cat activity, increasing the risk of oocyst contamination. In contrast, pastoral flocks often graze in remote areas, reducing cat interactions and exposure risk. This study supports research findings in Ethiopia and elsewhere [41, 84, 85].

Conclusions

This study provides essential epidemiological data on neglected zoonoses in small ruminants, identifies key risk factors that influence their spread, and stresses the need for integrated control measures targeting multiple pathogens. These insights are vital for improving livestock health, protecting public health, and minimizing economic losses in affected communities. The findings of this study highlight the high prevalence of zoonotic pathogens among small ruminant populations in Ethiopia, which poses significant public health risks due to their zoonotic potential. The elevated evidence of infection rates at the flock level demonstrated a high risk of pathogen transmission within flocks, emphasizing the necessity for flock-level interventions to control disease spread. Additionally, the study identified specific management practices and production systems as critical factors contributing to zoonotic exposure risks, providing actionable insights for targeted interventions. Research has also revealed evidence of mixed infections, with nearly half of the flocks testing positive for multiple pathogens. This complicates disease management and increases the likelihood of severe health impacts on both animals and humans, highlighting the need for integrated control strategies. Future interventions should target multiple pathogens simultaneously rather than adopting a single-pathogen approach. This suggests the complexity of zoonotic disease dynamics in small ruminants and advocates for comprehensive strategies to mitigate public health and socioeconomic impacts.

Author contributions

G.A. and B.W. conceptualized the study. G.A., B.A., H.D., D.T., and A.K. participated in data curation. BT coordinated the laboratory analysis. GA analyzed the data and wrote the manuscript together with B.W. G.M. and T.K.J. supervised the work, and all the authors contributed to the conception, design, and revision of the manuscript. All the authors read and approved the final manuscript.

Funding

This work was conducted as part of the CGIAR Initiative on Sustainable Animal Productivity. CGIAR research is supported by contributions to the CGIAR Trust Fund. CGIAR is a global research partnership for a food-secure future dedicated to transforming food, land, and water systems in a climate crisis. G.A. was supported through a DAAD PhD scholarship.

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

This study was approved by the College of Veterinary Medicine, Addis Ababa University, and the Animal Agriculture Research Ethics Review Committee (Certificate Ref. No: VM/ERC/05/08/11/2018). Before conducting the research, the farmers were informed of the purpose of the study, and consent was sought from the household representative. Written informed consent for participation was not required for this study per the national legislation and institutional requirements.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.