| Literature DB >> 24589227 |
Dennis Muhanguzi1, Kim Picozzi, Jan Hatendorf, Michael Thrusfield, Susan Christina Welburn, John David Kabasa, Charles Waiswa.
Abstract
BACKGROUND: Tick-borne diseases (TBDs) present a major economic burden to communities across East Africa. Farmers in East Africa must use acaracides to target ticks and prevent transmission of tick-borne diseases such as anaplasmosis, babesiosis, cowdriosis and theileriosis; the major causes of cattle mortality and morbidity. The costs of controlling East Coast Fever (ECF), caused by Theileria parva, in Uganda are significant and measures taken to control ticks, to be cost-effective, should take into account the burden of disease. The aim of the present work was to estimate the burden presented by T. parva and its spatial distribution in a crop-livestock production system in Eastern Uganda.Entities:
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Year: 2014 PMID: 24589227 PMCID: PMC3973879 DOI: 10.1186/1756-3305-7-91
Source DB: PubMed Journal: Parasit Vectors ISSN: 1756-3305 Impact factor: 3.876
Figure 1Geographical location of Tororo District (+ sample sites).
Percentage distribution by age, sex and breed (N = 2,658)
| | | | | | |
| 0-12 months | 397 | 17 | 4.3 | Ref | |
| 13-24 months | 579 | 30 | 5.2 | 1.2 | 0.6-2.6 |
| 25-36 months | 452 | 22 | 4.9 | 1.1 | 0.7-2.0 |
| >36 months | 1230 | 64 | 5.2 | 1.2 | 0.7-2.1 |
| | | | | | |
| Female | 1393 | 67 | 4.8 | Ref | |
| Male | 1069 | 58 | 5.4 | 1.1 | 0.8-1.5 |
| Neutered | 196 | 8 | 4.1 | 0.8 | 0.4-1.8 |
| | | | | | |
| Boran × African short horn Zebu (Nkedi) | 2570 | 129 | 5.0 | Ref | |
| Boran × Holstein Friesian | 44 | 2 | 4.5 | 0.9 | 0.3-2.8 |
| African short horn Zebu (Nkedi) | 44 | 2 | 4.5 | 0.9 | 0.2-3.8 |
Village (Herd) level prevalence in Tororo District
| Alupe B | 60 | 5 | 8.3 | 2.8-18.4 |
| Atapara-Kaleu | 155 | 14 | 9.0 | 5.0-14.7 |
| Chawolo-Sirongo B | 188 | 8 | 4.3 | 1.9-8.2 |
| Dida | 100 | 1 | 1.0 | 0.0-5.4 |
| Kadanya | 132 | 6 | 4.5 | 1.7-9.6 |
| Kajalau Central & South | 64 | 3 | 4.7 | 1.0-13.1 |
| Kasoli A | 200 | 23 | 11.5 | 7.4-16.8 |
| Kirewa Zone | 132 | 2 | 1.5 | 0.2-5.4 |
| Macharimeri | 180 | 6 | 3.3 | 1.2-7.1 |
| Mailombiri/Molo-Akisim | 103 | 4 | 3.9 | 1.1-9.7 |
| Mikwana/Kijwala | 169 | 0 | 0.0 | 0.0-2.2 |
| Munyinyi-Magelo | 164 | 19 | 11.6 | 7.1-17.5 |
| Ngeta A | 127 | 4 | 3.1 | 0.9-7.9 |
| Nyabanja zone | 139 | 1 | 0.7 | 0.0-4.0 |
| Nyafumba A&B | 94 | 3 | 3.2 | 0.7-9.0 |
| Oriyoyi A | 124 | 11 | 8.9 | 4.5-15.3 |
| Pabendo (Sere A) | 76 | 0 | 0.0 | 0.0-4.7 |
| Pamaraka | 80 | 0 | 0.0 | 0.0-4.5 |
| Pasaya | 104 | 2 | 1.9 | 0.2-6.8 |
| Rubuleri | 91 | 0 | 0.0 | 0.0-4.0 |
| Segero-Ojulai | 100 | 5 | 5.0 | 1.6-11.3 |
| Singisi | 76 | 16 | 21.1 | 12.5-31.9 |
aBinomial exact confidence intervals for individual villages adjusted for within village correlation using Generalized estimating equation (GEE) models for the overall estimate.
Figure 2Prevalence of in Tororo District; September to December 2011. Shows the spatial distribution of T. parva in Tororo district between September and December 2011 interpolated using 22 point prevalences in the 22 study villages to create a district wide spatial effect. An inverse distance weighted interpolation on the analyst extension of ArcMap 10.1 was used to generate a continuous T. parva prevalence map on a red colour ramp. Parameters were set so that for each pixel in the continuous raster an average prevalence was calculated based on all prevalence values at village level. Being a weighted average, the weight is higher for villages near the pixel and lower for more distant villages. A default exponent value of 2 was chosen such that each pixel value was taken to be the sum of observed prevalence values, which were first divided by the squared distance between villages. The result was then divided by the number of observations and multiplied by the sum of distances to generate a continuous T. parva prevalence map over the 22 individual point prevalences.