| Literature DB >> 36174979 |
Hyeshin Hwang1, Younghye Ro1,2, Hyunkyoung Lee3, Jihyeon Kim3, Kyunghyun Lee3, Eun-Jin Choi3, You-Chan Bae3, ByungJae So3, Dohoon Kwon4, Ho Kim4, Inhyung Lee1,5.
Abstract
BACKGROUND: Since 2013, the number of requests for diagnosis for horses based on neurological symptoms has increased rapidly in South Korea. The affected horses have commonly exhibited symptoms of acute seasonal hindlimb ataxia. A previous study from 2015-2016 identified Setaria digitata as the causative agent.Entities:
Keywords: Epidemiology; ataxia; horses; mosquito vectors; setaria nematode
Mesh:
Year: 2022 PMID: 36174979 PMCID: PMC9523341 DOI: 10.4142/jvs.22045
Source DB: PubMed Journal: J Vet Sci ISSN: 1229-845X Impact factor: 1.603
The test results of the neurotropic viruses and parasites on 50 diseased and 155 cohabiting horses
| Tests | Viruses and parasites | Diseased horses | Cohabitating horses |
|---|---|---|---|
| Blood PCR | EHV-1 | - | - |
| EHV-4 | - | - | |
| EIV | - | - | |
| JEV | - | - | |
| WNV | - | - | |
| Nasal swab PCR | EHV-1 | - | - |
| EHV-4 | - | - | |
| EIV | - | - | |
| SN titer or ELISA | EHV-1 | Variablea | Variablea |
| EHV-4 | Variablea | Variablea | |
| Parasite eggs | Strongyles | 8 | 26 |
| - | - | ||
| - | - | ||
| - | - | ||
| - | 5 | ||
| - | 1 | ||
| - | 6 | ||
| - | 1 | ||
| Larvae | - | 3 |
PCR, polymerase chain reaction; EHV-1, equine herpesvirus-1; EHV-4, equine herpesvirus-4; EIV, equine influenza virus; JEV, Japanese encephalitis virus; WNV, West Nile virus; SN, serum neutralization; ELISA, enzyme-linked immunosorbent assay.
aNegative to > 256.
Univariable analysis of environmental risk factors for hindlimb ataxia of horses from South Korea
| Variables | Univariable analysis | |||||
|---|---|---|---|---|---|---|
| Coefficient | Standard error | Odds ratio | 95% confidence interval | |||
| Existence of cattle ranches in a 2 km vicinity | 0.005a | |||||
| No | Ref. | |||||
| Yes | 1.764 | 0.629 | 5.83 | 1.7–20 | ||
| # of paddy fields in a 2 km vicinity | 0.003a | |||||
| < 2 | Ref. | |||||
| ≥ 2 | 1.75 | 0.593 | 5.76 | 1.8–18.42 | ||
| < 0.5 km from a river | 0.011a | |||||
| No | Ref. | |||||
| Yes | 1.545 | 0.608 | 4.69 | 1.42–15.42 | ||
| Mountainous terrain | 0.412 | |||||
| No | Ref. | |||||
| Yes | −0.454 | 0.553 | 0.63 | 0.21–1.88 | ||
| City | 0.057a | |||||
| No | Ref. | |||||
| Yes | −1.175 | 0.617 | 0.31 | 0.09–1.03 | ||
| # of reservoirs in a 2 km vicinity | 0.300 | |||||
| < 2 | Ref. | |||||
| ≥ 2 | −0.598 | 0.577 | 0.55 | 0.18–1.7 | ||
| # of ponds in a 2 km vicinity | 0.529 | |||||
| < 2 | Ref. | |||||
| ≥ 2 | −0.345 | 0.548 | 0.71 | 0.24–2.07 | ||
| Existence of fishing ponds in a 2 km vicinity | 0.596 | |||||
| No | Ref. | |||||
| Yes | −0.377 | 0.713 | 0.69 | 0.17–2.77 | ||
a p < 0.2 in the univariable analysis were then considered for inclusion in a multivariable regression model.
Multivariable logistic regression model of environmental risk factors for hindlimb ataxia of horses from South Korea
| Variables | Multivariable analysis | |||||
|---|---|---|---|---|---|---|
| Coefficient | Standard error | Odds ratio | 95% confidence interval | |||
| Existence of cattle ranches in a 2 km vicinity | 0.549 | |||||
| No | Ref. | |||||
| Yes | 0.513 | 0.845 | 1.67 | 0.32–8.76 | ||
| # of paddy fields in a 2 km vicinity | 0.041a | |||||
| < 2 | ||||||
| ≥ 2 | 1.587 | 0.799 | 4.89 | 1.03–23.43 | ||
| < 0.5 km from a river | 0.01a | |||||
| No | ||||||
| Yes | 1.733 | 0.722 | 5.66 | 1.38–23.29 | ||
| City | 0.408 | |||||
| No | ||||||
| Yes | −0.644 | 0.772 | 0.53 | 0.12–2.39 | ||
a p < 0.05.
Baseline characteristics of horse ranch environment
| Variables | Case ranches (n = 41) | Control ranches (n = 20) | ||
|---|---|---|---|---|
| Existence of cattle ranches in a 2 km vicinity | 0.008 | |||
| No | 6 (14.6) | 10 (50.0) | ||
| Yes | 35 (85.4) | 10 (50.0) | ||
| # of paddy fields in a 2 km vicinity | 0.005 | |||
| < 2 | 10 (24.4) | 13 (65.0) | ||
| ≥ 2 | 31 (75.6) | 7 (35.0) | ||
| < 0.5 km from a river | 0.018 | |||
| No | 16 (39.0) | 15 (75.0) | ||
| Yes | 25 (61.0) | 5 (25.0) | ||
| Mountainous terrain | 0.582 | |||
| No | 21 (51.2) | 8 (40.0) | ||
| Yes | 20 (48.8) | 12 (60.0) | ||
| City | 0.102 | |||
| No | 34 (82.9) | 12 (60.0) | ||
| Yes | 7 (17.1) | 8 (40.0) | ||
| # of reservoirs in a 2 km vicinity | 0.454 | |||
| < 2 | 30 (73.2) | 12 (60.0) | ||
| ≥ 2 | 11 (26.8) | 8 (40.0) | ||
| # of ponds in a 2 km vicinity | 0.722 | |||
| < 2 | 24 (58.5) | 10 (50.0) | ||
| ≥ 2 | 17 (41.5) | 10 (50.0) | ||
| Existence of fishing ponds in a 2 km vicinity | 0.870 | |||
| No | 35 (85.4) | 16 (80.0) | ||
| Yes | 6 (14.6) | 4 (20.0) | ||
Values are presented as number (%).