Co-infections of respiratory pathogens and gastro-intestinal parasites in smallholder pig production systems in Uganda

doi:10.21203/rs.3.rs-2234357/v1

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Research Article

Co-infections of respiratory pathogens and gastro-intestinal parasites in smallholder pig production systems in Uganda

https://doi.org/10.21203/rs.3.rs-2234357/v1

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published 22 Feb, 2023

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A cross-sectional study was conducted to identify factors for infections of pigs with key respiratory pathogens: porcine circovirus type 2 (PCV2), porcine reproductive and respiratory syndrome virus (PPRSv), Mycoplasma hyopneumoniae (M. hyo), Actinobacillus pleuropneumoniae (App) and gastro-intestinal (GIT) parasites in Uganda. A structured questionnaire was used to collect data on management practices associated with infections. Ninety (90) farms and 259 pigs were sampled. Sera were screened against 4 pathogens using commercial ELISA tests. The Baerman’s method was used to identify parasite species in faecal samples. Logistic regression was done to identify risk factors for infections. Cluster analysis was done using a hierarchical K-means partitioning algorithm. Results showed individual animal seroprevalence of PCV2 was 6.9% (95% CI 3.7–11.1), PRRSv 13.8% (95% CI 8.8–19.6), M. hyo 6.4% (95% CI 3.5–10.5) and App 30.4% (95% CI 24.8–36.5). The prevalences of Ascaris spp was 12.7% (95% CI 8.6–16.8), Strongyles spp was 16.2% (95% CI 11.7–20.7) and Eimeria spp was 56.4% (95% CI 50.3–62.4). Pigs infested with Ascaris spp were more likely to test positive to PCV2, odds ratio (OR) 1.86 (CI 1.31–2.60; p = 0.0002). For M. hyo, infection with Strongyles spp was a risk factor (OR 12.9, p < 0.001). Pigs that had parasite infestations were more likely (Strongyles spp. and Ascaris spp. ORs 3.5 and 3.4, p < 0.001, respectively) to have respiratory co-infections. Overall, 3 farm clusters were characterized by housing, biosecurity practices and pathogens. This study provides evidence that improving hygiene and biosecurity is critical in reducing pathogen incidence in herds.

Co-infections

helminths

pigs

pathogens

respiratory

Uganda

In Uganda, pig production has grown rapidly in recent years from approx. 0.7 million pigs in 1990 to 4.2 million in 2017 (UBOS 2018). This reflects a rise in the demand for pork (Ouma et al. 2014), which offers significant opportunities to pig producers for livelihoods improvement. In Uganda’s current production systems, the lack of implementation of biosecurity measures constitute key factors for the spread of swine diseases such as African swine fever (Muhanguzi et al. 2012; Muhangi et al. 2014; Dione et al. 2016). Recent studies reveal that among diseases, respiratory and gastro-intestinal (GIT) helminth infections are common in Ugandan pigs contributing to the disease burden and thus affecting productivity in the sector (Ikwap et al. 2014; Roesel et al. 2017). In Lira district, Uganda, a recent multi-pathogen study revealed occurrence of Mycoplasma hyopneumoniae (M. hyo), Actinobacillus pleuropneumoniae (App), Leptospira spp., porcine reproductive and respiratory syndrome virus (PPRSv) and porcine circovirus (PCV2) type 2 (Dione et al. 2018). Other studies confirmed presence of PCV2 in Ugandan pigs (Jonsson 2013; Ojok et al. 2013; Eneku et al. 2018). Three main production systems are identified: farrow to finish, farrow to wean and wean to finish. In some farms, pigs are often not segregated by age groups and are fed together. Coupled with low biosecurity, this exposes younger pigs to infectious diseases. In Uganda, no pig vaccines were available and no vaccination was done against any pig disease during this study.

To-date, no information exists on husbandry and management factors associated with important repiratory pathogen infections in Uganda’s smallholder pig systems. This hampers the design of effective interventions at farm level. Evidence from previous studies shows Metastrongylus spp. and Ascaris spp. compromise lung function due to the damage induced by their migratory larvae, thereby excerbating the effect of other viral and bacterial agents (Adedeji et al. 1989; Brewer and Greve 2011). This interaction increases disease duration and/or severity, with associated negative effects on productivity (Thacker et al. 1999). This study was designed to (i) identify risk factors for co-infections with respiratory pathogens (ii) examine associations between gastro-intestinal parasite infestations and key respiratory pathogens (PCV2, PRRSv, M. hyo and App) and (iii) investigate associations between pathogens occurrence, farm management and biosecurity practices, with a view to inform control and preventive measures at herd level.

Study area

This study was done in Lira district, mid-northern Uganda, where the International Livestock Research Institute (ILRI) previously implemented a smallholder pig value chain development project (SPVCD) since 2011. In this project, a value chains assessment was conducted to select study sites using pig density, poverty levels and market access (Ouma 2017). Our study used market access to select subcounties based on value chain domains into rural production for urban consumption (R-U) and urban production for urban (U-U) consumption (Ouma 2017). The total pig population in Lira district was estimated to be 30,000 in 2020 (DVO pers. com). Pigs are produced under housed, tethered and free-range systems (Kungu et al. 2019). Under these systems, pigs are housed in permanent or temporal structures made of cement, wood or papyrus. Tethering is when pigs are tied on a rope (on a pole) to graze around the homestead, while free-range is when pigs are allowed to freely roam in the neighborhood in search of own feeds and water. Routine preventive measures such as anthelmintics are generally not practiced, until pigs show visible signs of illness. In these smallholder pig production systems, biosecurity and hygiene practices are generally poor, which partly explains a high disease incidence in herds.

Study Design, Sampling Of Subcounties, Parishes And Villages

A cross-sectional serologic study was conducted from October to December 2018. We used multistage sampling to select subcounties and villages. In the first stage, four subcounties were selected (from a total of 9): two (Central division and Railways) representing U-U consumption and two (Adekokwok and Ngetta) representing R-U consumption. In stage two, two (2) villages with the highest pig density were selected for the study.

Sample Size Determination

To determine the sample size, a formula for simple random sampling was used (Dohoo et al. 2003). A previous study in Lira district found a seroprevalence of M. hyo in pigs of 20.9% (Dione et al. 2018). Adjusting for test sensitivity and specificity, true prevalence was computed to be 24%. The required sample size of pigs was obtained from Eq. (1):

n = \({Z}_{\alpha /2}^{2}pq/{d}^{2}\)----------------------------------------------- Eq. (1)

where n = is the required sample size; Z_α is the standard z-score from a normal distribution (1.96), p = estimated prevalence of disease (24%) and q = 1-p (76%); d = allowable error (6%). Using this formula, an unadjusted sample size of 195 pigs was computed. To adjust for within-farm clustering, we sampled 3 pigs per herd, thus the design effect (Deff) was obtained from Eq. 2 below:

\(\text{D}\text{e}\text{f}\text{f}=1+icc ({n}_{1}-1)\) --------------------------------------- Eq. (2)

where icc is the intra-cluster corrrelation (0.2) for respiratory disease (Dohoo et al. 2003), n₁ is the number of pigs sampled per herd (3), thus the Deff calculated is 1.4. The adjusted sample size was calculated from the equation: N = n₁ x number of pigs sampled per cluster/herd (3). From this, the adjusted sample size of 273 pigs was derived.

Sampling Of Farms And Pigs

In each selected village, a list of pig keeping households/farms was obtained from the district veterinary office and the area local councils. Random sampling of farms was done, until the required sample size was obtained. We sampled farms regardless of health status or anthelmintic treatments to examine differences in farm husbandry practices and how these influence occurrences of specific pathogens in farms. Using a sampling frame of all pig farmers generated with field research assistants, three (3) pigs per herd were sampled until the required sample size was reached. Only pigs ≥ 2.5 months old were selected for sampling, since pigs below that age are reported to retain maternal antibodies to PCV2 and PRRSv post weaning, which could interfere with serologic tests (Opriessnig et al. 2004; Gillespie et al. 2009). App-acquired colostral antibodies were reported to decay within 2 months postpartum (Vigre et al. 2003). We sampled pigs from 2.5 months and above regardless of health status, clinical signs or feed types given to pigs.

Data Collection Methods

A structured questionnaire with closed questions was designed, pre-tested by the first author in Mukono district and revised before use. Research assistants were trained in its use before it was administered to each household head or farm manager. To ensure consistency, all questions were translated to a local language spoken in the area (Langi). The questionnaire captured data on potential risk factors for infection with respiratory pathogens.

Blood Sample Collection And Storage

Each pig was properly restrained as described in the ILRI Standard Operating Procedures (SOPs) manual, section 2, part (c) & (d) (ILRI 2004). Smaller pigs were restrained by hand, while larger ones were restrained with a metallic pig catcher (Model BZ002, MG. Livestock, Shandong, China) placed behind the upper incisor teeth and the snout raised upwards. Blood was then collected from the cranial vena cava or jugular vein, using a 21G, 1.5" needle into plain 5 mL BD® vacutainer tubes. The tubes were labeled with animal identification details and then placed in an ice box at 4–6°C. After collection, samples were delivered (within 3 hours) to the district veterinary laboratory for temporary storage. Blood samples were left to stand at room temperature (20°C) overnight and serum harvested the following day into 2 mL cryotubes (Sarstedt®, Germany), labelled and stored in a fridge at -20°C until testing.

Serological Analysis Of Sera

In the lab, sera were screened using ELISA assays according to manufacturers’ instructions for each pathogen: M. hyo and App-ApxIV (IDDEXX, Westbrook, Maine, USA); for PRRSv and PCV2 assays (Krishgen Biosystems, India). Cut-off sample to positive ratios (S/P%) for M. hyo were > 0.40 (positive) and < 0.30 (negative), App were ≥ 50% (positive) and < 40% (negative). PCV2 and PRRSv S/P cut-off ratios for positive and negative samples were ≥ 0.2 and < 0.2 respectively. Suspect samples were re-tested. Test sensitivity (Se) and specificity (Sp) for M. hyo ELISA were 85.6% and 99.6% respectively; Se and Sp for App-ApxIV Ab ELISA test were 97.8% and 100%, respectively. Se and Sp for PRRSv were 94.0% and 94.0%, while for PCV2 Se and Sp were 92.0% and 94.0% respectively. Test Se and Sp were used to calculate true prevalence of respiratory infections at α = 0.05 significance level.

Faecal Sample Collection And Analysis

Faecal samples (~ 3gr) were collected from the rectum of each pig using gloved hands into 10 mL plastic containers, labelled and placed in ice box at 4 ºC. Samples were taken to the district veterinary lab for temporary storage at 4 ºC. Samples were transferred to the central diagnostic laboratory, College of Veterinary Medicine, Animal Resources and Biosecurity (CoVAB), Makerere University for analysis, one week after collection. Helminth species were identified using the Baermann method (MAFF 1986). Faecal egg counts in eggs per gram of faeces (EPGs) were quantified using McMaster technique (MAFF 1986).

Data Analysis And Presentation

Data was coded and entered into Excel 16.0 and any errors in entry corrected by cross-checking with questionnaires. RStudio was used for data analysis and presentation (R Core Team 2019). True prevalence was computed by adjusting apparent prevalence using prevalence 0.2.0 package in R, taking into account test sensitivities and specificities (R Core Team 2019). Multivariable logistic regression analysis of risk factors for each pathogen was performed. The response variable was the ELISA test result, with predictors: pig age and husbandry practices (house type, parasites, drug use, pig mixing, hygiene score and drainage). The model below was fitted to predict respiratory infections, as a function of pig characteristics and husbandry practices (house type, parasites, drug use, pig age, pig mixing, hygiene and drainage):

\(\text{ln}\frac{\widehat{p}}{(1-\widehat{p)}}={\beta }_{0}+ {\beta }_{1}{x}_{1i}+{\beta }_{2}{x}_{2i}\dots .....{\beta }_{p}{x}_{pi }\) --------------------------Eq. (3)

where \(\text{ln}\frac{\widehat{p}}{(1-\widehat{p)}}\) is the expected log of odds of infection, β₀ is the model intercept; β_1, β_2, are regression coefficients, while x_1i, x_2i, are the respective explanatory variables. Interaction terms were tested for each model and confounding was checked by inclusion and exclusion of variables to observe a change in model coefficients. A cumulative link mixed effects (CLMM) model was fitted to estimate the odds of co-infection (2 or more pathogens) with farm as a random effect. The CLMM model from R package ‘ordinal’ was used to select 9 variables for cluster analysis. Only significant variables (at p < 0.05) were retained in the model. However, pig age and sex were dropped because they both run across all farms, which maximizes between-cluster homogeneity. A hybrid hierarchical K-means (hkmeans) partitioning algorithm was used to identify and characterize farm clusters. Residual plots and R-square statistics were used to assess the fitted models.

In all four subcounties, a total of 259 pigs were sampled from 90 farms. Data of 12 pigs was incomplete (missing values) as such was excluded from the analysis. The median age category of the respondents was 36–50 years, with an age range of 24–70 years. Of the 90 respondents, 41 (45.5%) were males while 49 (54.5%) were females. The median herd size per housed herds was 11 pigs, and for tethered herds was 4 pigs. Male pigs constituted 53.7% (n = 139) of pigs in the sample, while females were 46.3% (n = 120). The median age of sampled pigs was 5 months, and the age range was from 2.5 to 15 months. Table 1 below shows a summary of demographic characteristics.

Table 1

Demographic characteristics of respondents
Characteristic	Category	Males n (%)	Females n (%)	Total n (%)
Age (yrs)	18–35	17 (54.1)	20 (45.9)	37 (100.0)
	36–50	14 (46.7)	16 (53.3)	30 (100.0)
	51–75	10 (43.5)	13 (56.5)	23 (100.0)
Location / Subcounty	Adekokwok (peri-urban)	18 (38.3)	29 (61.7)	47 (100.0)
	Central division (urban)	5 (50.0)	5 (50.0)	10 (100.0)
	Ngetta (rural)	9 (50.0)	9 (50.0)	18 (100.0)
	Railways div. (peri-urban)	9 (60.0)	6 (40.0)	15 (100.0)
Education level	None	1 (33.3)	2 (66.7)	3 (100.0)
	Primary	10 (24.4)	31 (75.6)	41(100.0)
	Secondary	15 (68.2)	7 (31.8)	22 (100.0)
	Tertiary	15 (62.5)	9 (37.5)	24 (100.0)
Occupation	Farmer	29 (44.6)	36 (55.4)	65 (100.0)
	Business	7 (46.7)	8 (53.3)	15 (100.0)
	Employee	3 (42.85)	4 (57.15)	7 (100.0)
	Others	2 (66.7)	1 (33.3)	3 (100.0)
Management system	Housed	26 (48.15)	28 (51.85)	54 (100.0)
Management system	Tethered	15 (41.66 )	21 (58.34)	36 (100.0)
Breeds kept	Local	4 (50.0)	4 (50.0)	8 (100.0)
	Exotic	7 (58.34)	5 (41.66)	12 (100.0)
	Cross-bred	28 (43.75)	36 (56.25)	64 (100.0)
	Mixed	2 (33.3)	4 (66.7)	6 (100.0)

Prevalence Of Respiratory Pathogens And Git Parasites

The Table 2 below shows true herd and individual level prevalence of selected respiratory pathogens at respective 95% confidence intervals. Results showed that App was the most prevalent pathogen, while PCV2 was the least prevalent at both pig and herd level. Of the GIT parasites, Eimeria spp was the most prevalent while Trichuris spp was the least prevalent parasite found.

Table 2

True prevalence of tested respiratory pathogens and helminths in pigs
Pathogen	Individual pig level (n = 259)		Herd level (n = 90)
Pathogen	% prevalence	95% conf. int.	% prevalence	95% conf. int.
PCV2	3.50	0.4–7.7	23.9	15.6–33.1
PRRSv	14.0	9.1–19.5	32.6	23.4–42.6
M. hyo	6.8	3.8–10.6	16.2	9.5–24.6
App	30.5	25.0–36.2	45.7	35.6–55.9
Ascaris spp	12.7	9.2–17.3	28.9	20.5–38.9
Strongyles spp	16.2	12.2–21.2	34.4	25.4–44.7
Trichuris spp	1.5	0.6–3.9	4.4	1.7–10.7
Eimeria spp	56.4	50.3–62.3	81.1	71.8–87.8

Conf. int = confidence interval

Prevalence Of Co-infections

Co-infections with two or more pathogens were observed in this study. Among respiratory pathogens, the highest prevalence of co-infections were between PRRSv and App, followed by between PCV2 and App. For co-infections between respiratory pathogens and parasites, highest co-infections occured between Eimeria spp, followed by Strongyles spp and respiratory pathogens. Only 5 pigs were co-infected with 3 pathogens and that 42.5% (n = 110) of pigs sampled had at least 2 co-infections. Table 3 below shows a summary of co-infections.

Table 3

Prevalence of co-infections between respiratory pathogens and GIT helminths
Pathogen	PCV2% (n)	PRRSv % (n)	M. hyo % (n)	App % (n)	Total co-infections, %
PCV2	-	1.93 (n = 5)	1.15 (n = 3)	3.47 (n = 9)	6.56 (n = 17)
PRRSv	1.93 (n = 5)	-	1.54 n = 4)	5.80 (n = 15)	9.26 (n = 24)
M. hyo	1.15 (n = 3)	1.54 (n = 4)	-	1.54 (n = 4)	4.24 (n = 11)
App	3.47 (n = 9)	5.80 (n = 15)	1.54 (n = 4)	-	10.81 (n = 28)
Ascaris spp	3.08 (n = 8)	3.47 (n = 9)	2.31 (n = 6)	5.40 (n = 14)	14.28 (n = 37)
Strongyles spp	2.31 (n = 6)	3.08 (n = 8)	3.86 (n = 10)	6.17 (n = 16)	15.44 (n = 40)
Eimeria spp	5.40 (n = 14)	11.96 (n = 31)	3.86 (n = 10)	21.23 (n = 55)	42.47 (n = 110)

Note: only five pigs (1.93%) were co-infected with 3 resp. pathogens (PCV2, PRRSv & M. hyo; PCV2, PRRSv & App; and PRRSv, M. hyo & App).

Multivariable Logistic Regression Model Of Risk Factors For Individual Respiratory Infections

Results showed that parasite infections, pig confinement (only for App), drug use and pig age were significant predictors of respiratory infections (Table 4). Drugs used by farmers were antibiotics, anthelmintics and multivitamins.

Table 4

Multivariable logistic regression model of risk factors for individual respiratory infections
Pathogen	Variable	Coeff	Std error	OR	95% CI	z-value	p-value
PCV2	(Intercept)	-2.4358	0.3214	0.087	0.044 - 0.157	-7.578	3.52e-14***
	Ascaris spp. infection	1.5590	0.4949	4.753	1.738–2.382	3.150	0.00163**
	No pig mixing	-0.690	0.485	0.501	0.182–1.260	-1.421	0.155
PRRSv	(Intercept)	-2.530	0.726	0.079	0.017–0.307	-3.485	0.000***
	Pig age	0.181	0.069	1.199	1.046–1.378	2.614	0.008**
	Herd size (> 20 pigs)	0.016	0.005	1.016	1.005–1.028	2.935	0.003**
	Hygiene score_1	1.003	0.584	2.728	0.953–9.949	1.716	0.086.
	Hygiene score_2	0.030	0.775	1.031	0.216–4.938	0.039	0.968
	Drug use	-2.125	0.669	0.119	0.028–0.406	-3.176	0.001**
	Farmer sex (fem.)	-1.207	0.430	0.299	0.126–0.692	-2.803	0.005**
	Drug use*farmer sex (females)	2.021	0.937	7.550	1.116–47.929	2.157	0.031*
M. hyo	(Intercept)	-3.5154	0.4771	0.0297	0.010–0.068	-7.369	1.72e-13***
	Strongyles spp	2.5628	0.5824	12.971	4.300-44.171	4.401	1.08e-05***
	Drainage	-0.7957	0.6885	0.4512	0.096–1.570	-1.156	0.248
App	(Intercept)	-2.881	0.50961	0.056	0.019–0.145	-5.654	1.56e-08 ***
	Housed	1.138	0.358	3.122	1.591–6.541	3.178	0.00148 **
	Pig age > 6 mnths	0.12313	0.055	1.131	1.014–1.263	2.205	0.02745 *
	Eimeria spp	0.793	0.303	2.212	1.232–4.061	2.620	0.00880 **

Drugs used by farmers: antibiotics, anthelmintics and multivitamins. Hygiene score_0 = poor, hygiene score_1 = moderate; Hygiene score_2 = best. Hygiene score was made by physical observation of the level of hygiene in pens during sampling. *=p < 0.05, **=p < 0.01, ***=p < 0.001.

A Cumulative Link Mixed Model For Mixed Respiratory Infections With Farm As Random Effect

Table 5 below shows a cumulative link mixed model (from R packages ‘ordinal, factoextra’) of factors for respiratory co-infections with farm as a random effect. This model was fitted to predict co-infections (regardless of pathogens involved), as this has synergistic effects in the induction of respiratory disease. The model shows that farmer occupation, wall, floor type and no contacts with outside pigs were protective against co-infection, while pig age, management method (confined vs tethered pigs) and helminth infestations increased the risks of respiratory co-infections.

Table 5

A cumulative link mixed model for respiratory co-infections in pigs
Independent variable	Estimate	Std. error	OR	95% CI	z-value	p-value
Thresholds 0\|1	3.02	0.69	20.62	5.26–8.08e + 01	4.34	-
1\|2	5.55	0.77	257.54	56.80–1.16e + 03	7.19	-
2\|3	7.68	0.91	2185.51	366.93–1.30e + 04	8.44	-
Occupation_2	-1.14	0.40	0.32	0.14–7.08e-01	-2.81	0.004**
Occupation_3	-1.82	0.69	0.16	0.04–6.27e-01	-2.63	0.008**
Occupation_4	-0.38	0.79	0.68	0.14–3.20e + 00	-0.48	0.626
Pig age	0.20	0.05	1.23	1.10–1.38e + 00	3.57	0.0003***
Herd size	0.01	0.00	1.01	1.00–1.02e + 00	2.95	0.003**
Floor type cement	-1.14	0.45	0.31	0.13–7.79e-01	-2.50	0.012*
Floor type raised	-1.51	0.65	0.22	0.06–7.95e-01	-2.31	0.020*
Wall type 1 (mud)	2.88	0.81	17.91	3.66–8.76e + 01	3.56	0.0003***
Wall type 2 (timber)	1.10	0.56	3.03	1.00–9.13e + 00	1.97	0.048*
Wall type 3 (bricks)	0.35	0.48	1.42	0.55–3.66e + 00	0.73	0.468
Hygiene score 1	0.83	0.46	2.31	0.93–5.73e + 00	1.80	0.070.
Hygiene score 2	0.96	0.59	2.62	0.81–8.48e + 00	1.61	0.106
Cleaning frequency 1	0.83	0.50	2.30	0.85–6.20e + 00	1.65	0.099.
Cleaning frequency 2	1.88	0.48	6.58	2.54–1.70e + 01	3.89	0.000***
Cleaning frequency 3	1.46	0.44	4.32	1.80–1.04e + 01	3.27	0.001**
No contacts	-0.78	0.45	0.45	0.18–1.10e + 00	-1.73	0.082.
Strongyles spp	1.25	0.37	3.50	1.68–7.29e + 00	3.35	0.0008***
Ascaris spp	1.23	0.42	3.45	1.49–7.95e + 00	2.91	0.003**

For categorical independent variables, higher or better quality were coded with higher values; contacts with outside pigs was coded as (1 = no, 0 = yes); Occupation_2 = business, Occupation_3 = employee, Occupation_4 = other; continuous variables (pig age, herd size) were entered as counts. Differences in practices between farms account for the random effect. OR = Odds ratio, *= p < 0.05, **= p < 0.01, ***= p < 0.001.

Cluster Analysis

In this method, the hierarchical K-means initially uses a K-means algorithm to determine the number of clusters, and then employs it to perform hierarchical clustering (Tung-Shou et al. 2005). The method generates a tree-like dendrogram and a cluster plot used to visualize the clusters. Results of the cluster analysis revealed three distinct clusters of farms (Fig. 1).

Table 6 below shows a description of the 3 clusters revealed by the algorithm. Farms were clustered by husbandry practices, biosecurity level and respiratory pathogens.

Table 6

Summary description of clusters of farms identified
Cluster #	Cluster category	No. of farms, n (%)	Farm cluster characteristics
One (1)	Housed, cemented floors or raised wooden platforms; good housing, biosecurity level group	27 (30.0)	These had low herd sizes (mean ± SD = 7.5 ± 4.0), had cemented floors or raised wooden platforms, good biosecurity, best floors, good drainage systems and hygiene scores. Compared to cluster 2, these were sparsely clustered together. Farms in cluster 1 were least affected by mixed parasites and respiratory pathogens.
Two (2)	Tethered, poor “housing” and biosecurity level group	56 (62.2)	These farms tether pigs, characterized by low herd sizes (mean ± SD, 5.0 ± 3.3), poor biosecurity (poor hygiene, drainage). Farms in this cluster were more affected by M. hyo, had high mixed parasites (Ascaris spp, Strongyles spp) infestations. Farms in this cluster were most predominant and closely packed together
Three (3)	Housed, cemented floors, poor to moderate housing, biosecurity level group	7 (7.7)	They had highest herd sizes (mean ± SD, 78 ± 54), floor types were mostly made of concrete, had poor hygiene scores and drainage systems, and lower frequency of cleaning. These farms were fewer, kept more younger pigs (compared to cluster 1 & 2 farms) and affected most by mixed respiratory infections. However, cluster 3 farms were least affected by parasite infections, but more affected by App. Compared to those in cluster 1 and 2, cluster 3 farm characteristics were unique and were sparsely clustered.

The following variables were used to define biosecurity level: fencing (1 = present or 0 = absent); use of disinfectant(s) on the farm (1 = yes, 0 = no), rearing of different age groups in the same pen (0 = yes or 1 = no), cleaning frequency (0 = Never, 1 = 1-2times/week, 2 = 3–4 times/week, 3 = daily), isolate sick pigs (1 = yes or 0 = no). The total biosecurity level/score was computed as the sum of individual variable scores with higher codes given to “best” or recommended practices.

These results highlight widespread occurrence of selected respiratory pathogens in pigs in the study area. At both individual and herd level, App was found to be of highest prevalence, followed by PRRSv, PCV2 and lastly, M. hyo (Table 2). These findings are comparable with those from a recent study (Dione et al. 2018). However, compared to the findings of other studies (Jonsson 2013; Ojok et al. 2013; Eneku et al. 2018), our study found a lower PCV2 seroprevalence. This may be due to differences in the sampling procedures, diagnostic methods used and the type of production system from which pigs were sampled. In our study, sampling was done in households that confined their pigs in pig sheds or tethered around homesteads, while (Jonsson 2013) sampled pigs from a wildlife-livestock interface (near Murchison Falls national park), which probably exposed them to a higher risk of infection from other roaming or wild pigs. Eneku et al., (2018) sampled pigs that presented with clinical signs of PCV2 and therefore had a higher probability of PCV2 detection while another research team (Ojok et al. 2013) sampled pig tissues from a local abattoir.

The PCV2 seroprevalence at individual pig and herd level found in this study was lower than (54% and 78%, respectively) reported in Mozambique (Laisse et al. 2018). Differences in the sampled population could account for variations in PCV2 prevalence, as the Mozambiqan study was done in slaughter places, which were likely to be older animals compared to the pigs sampled in this study. The results from this study also show that PCV2 seroprevalence was higher (6.9% vs 1.4%) than that found in Nigeria (Aiki-Raji et al. 2018), lower than (15.9%) that was found in South Africa (Afolabi et al. 2017).

This study revealed a higher PRRSv seroprevalence compared to the previous findings (Dione et al. 2018). This could suggest either increased herd to herd transmission over the past few years, since PRRSv transmission can occur via several routes (Otake et al. 2010), and/or because the virus can remain in affected herds as a persistent infection (Murtaugh and Genzow 2011). The finding that the odds of testing seropositive to PPRSv rises with increase in pig age is in consonance with findings from previous studies which showed that neutralising and anti-PRRSv IgG antibodies can remain persistent for several months (Nelson et al. 1994; Murtaugh et al. 2002). The increased odds of seropositivity to PRRSv due to lack of regular preventive treatments against bacterial and helminth infections was demonstrated in this study. The role of PRRSv in inducing severe disease during co-infections with other pathogens has been previously reported (Halbur et al. 1996; Thacker et al. 1999). This suggests regular prophylactic treatments are important in reducing the risk of opportunistic co-infections. A similar observation was made for App, in which infection was dose-dependent, accounting for increased incidence in older pigs (Marsteller and Fenwick 1999).

The effect of herd size was highlighted in this study. The observation that larger herds (> 20 pigs) increased the odds of PRRSv infection may be related with increased stocking density, as PRRSv is known to be highly infectious. The increase in the odds of PRRSv detection may also be due to its tendency to remain as a persistent infection after entry into a herd (Pileri and Mateu 2016).

Co-infections in this study were lower than in other studies (Gillespie et al. 2009). Co-infections between PRRSv and App were the most prevalent, followed by PCV2 and App. The effect of PCV2 co-infection with other pathogens in increasing the severity and incidence of PCV2-associated disease has been reported in previous studies (Opriessnig et al. 2004; Fablet et al. 2012; Segalés et al. 2013). Other studies reveal a diversity of pathogens involved in respiratory disease (Qin et al. 2018). The cumulative link model showed that better floor types (cement or raised platform) had protective effects against co-infections (Table 5). Similarly, changing from the use of mud to timber as material for the wall significantly reduced the odds of co-infections. Hygiene score and the frequency of cleaning of pens (while protective on single infections) did not appear to have any effect on the odds of co-infections. Probably some farmers did not provide honest answers to a question on how often they cleaned pig pens weekly.

This study revealed associations of particular respiratory pathogens and GIT parasite infections in pigs (Table 4). These results are comparable with findings from a study in southwest Uganda which reported a high prevalence of GIT helminth infections (Roesel et al. 2017). The increased odds of Ascaris spp infection in tethered pigs illustrates the importance of biosecurity (e.g. confining pigs) in reducing the risk of infection. Ascaris spp. has been shown to compromise lung function through immunomodulatory mechanisms, thereby excerbating the effect of other viral and bacterial agents, as well as increase disease severity (Adedeji et al. 1989; Brewer and Greve 2011). While pigs infected with Eimeria spp may show no observable clinical signs, Eimeria spp have been reported to cause diarrhea in piglets as they damage intestinal mucosa increasing susceptibility to other pathogens.

In general, results showed that biosecurity variables had a significant influence on pathogen occurrence in farms. Farms in cluster 3 with poor hygiene and lower frequency of cleaning were more likely to have pathogen co-infections (Tables 5 & 6). These findings agree with previous studies which revealed that good housing, hygiene and reduced stress play a significant role in minimising the effects of diseases such as PCVAD (Gillespie et al. 2009; Cargill 2019). In contrast, farms in clusters 1 (Table 6, Fig. 1), which had the best hygiene and biosecurity scores, were less affected by mixed pathogens. Farms in cluster 2 (free range) which had poor hygiene and biosecurity scores were mostly affected by GIT parasite infestations. Poor drainage and hygiene in cluster 1 and 2 farms may have raised the risk of re-infections with parasite eggs from contaminated feeds and water. In addition, the higher parasite infestations observed in cluster 1 and 2 farms may also have been due to lack of implementation of routine preventive measures such as deworming (compared to cluster 3 farms). These environmental stressors (poor hygiene, rearing of different age groups in the same pens, overcrowding and poor nutrition, etc.) are known to suppress immunological responses and therefore impede a pig’s ability to fight off infection (Cargill 2019). The role of good ventilation and proper cleaning practices in improving indoor air quality by reducing microbial contamination with respiratory pathogens has been documented (Banhazi et al. 2008; Cargill 2019). Overall, the results of the cluster analysis are in agreement with the logistic regression models which demonstrated the importance of improved hygiene and biosecurity in reducing mixed respiratory infections.

Strengths And Limitations Of The Study

The principal investigator oversaw all aspects of data collection, laboratory analysis of samples, data entry and analysis, ensuring quality assurance. However, potential bias may have been introduced by innacurate responses provided by some respondents to some questions in the questionnaire and misclassification errors of pigs due to imperfect assay sensitivities and specificities. Also, the time lapse between faecal sample collection and lab analysis may have affected the sensitivity of the Baerman’s test, as ability of larvae to hatch was reduced. The detection of antibodies may not reflect actual infection because antibody responses occur earlier than GIT helminth infections at the time of sampling. This may have led to overestimation of the observed relationships.

This study highlights widespread occurrence of economically important respiratory pathogens in pigs in the study area. This may likely reflect the situation in swine herds in eastern and northern Uganda, where production systems are largely similar. The negative correlations between biosecurity variables and mixed pathogens signifies the role of improved biosecurity in reducing the risks of co-infections. In addition, the clustering of farms of poor biosecurity level with pathogens provides further support to the above evidence. Further studies to identify PCV2 and PRRSv genotypes that circulate in pigs in this region, as well as quantify their economic impacts on swine productivity are warranted to guide the design of effective interventions.

Acknowledgements

We thank Jane Poole and Nicholas Ndiwa (Research Methods Group, ILRI Nairobi) for their guidance in statistical analyses. The assistance received from the district veterinary officer of Lira district, Dr Ogwal Anthony and extension officers, Cecil Podpodo and Benard Okello during data collection is highly appreciated. We thank staff of Central Diagnostic Laboratory of CoVAB, Makerere University (Dickson, Edrine, Jacky and Kanyike) for their support during laboratory analyses of samples. We thank and appreciate the cooperation of pig farmers in Lira district for their voluntary acceptance in this study.

Funding

This work was supported by the German Academic Exchange Service (DAAD) as Peter Oba’s PhD Research fellowship through International Livestock Research Institute (ILRI). This research was conducted as part of the CGIAR Research Program on Livestock, we thank donors who support its work through their contributions to the CGIAR Trust Fund. The funding agency had no role in the preparation of this manuscript.

Conflict of interest/Competing Interests

The authors have no relevant financial or non-financial interests to disclose.

Data Availability Statement

The original contributions presented in the study are publicly available. The dataset analyzed for this study can be found here: https://data.ilri.org/portal/dataset/multipathogen-survey-and-risk-factors.

Author contributions

Michel M. Dione, Barbara Wieland and Peter Oba conceived the study; Michel M. Dione, Frank N. Mwiine, Joseph Erume, Barbara Wieland and Peter Oba designed the study; Peter Oba collected the data; Peter Oba and Michel M. Dione analyzed and interpreted the data; Peter Oba wrote the first draft of the manuscript; All authors reviewed subsequent drafts and approved the final manuscript for submission.

Ethics approvals

This study involved a questionnaire-based survey of farmers as well as blood and fecal sampling of pigs. The study protocol was assessed and approved by the Institutional Review Board (IRB no. SBLS/REC/18/008) of Makerere University’s College of Veterinary Medicine, Animal Resources and Biosecurity (CoVAB), Uganda National Council of Science and Technology (UNCST reg no. A590), ILRI’s Institutional Research Ethics Committee (IREC no. IREC2018-23) and from ILRI’s Institutional Animal Care and Use Committee (IACUC2018-22). Pig sampling was done by trained veterinarians who adhered to ethical standards of animal welfare.

Consent to participate

Informed (written) consent was obtained from all research participants included in the study by signing consent forms (translated into a local language) given to them.

Consent for publication

Not applicable for this study.

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Co-infections of respiratory pathogens and gastro-intestinal parasites in smallholder pig production systems in Uganda

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Abstract

Figures

Introduction

Materials And Methods

Study area

Study Design, Sampling Of Subcounties, Parishes And Villages

Sample Size Determination

Sampling Of Farms And Pigs

Data Collection Methods

Blood Sample Collection And Storage

Serological Analysis Of Sera

Faecal Sample Collection And Analysis

Data Analysis And Presentation

Results

Prevalence Of Respiratory Pathogens And Git Parasites

Prevalence Of Co-infections

Multivariable Logistic Regression Model Of Risk Factors For Individual Respiratory Infections

A Cumulative Link Mixed Model For Mixed Respiratory Infections With Farm As Random Effect

Cluster Analysis

Discussion

Strengths And Limitations Of The Study

Conclusions

Declarations

References

Additional Declarations

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