Spatial Clustering of Porcine Cysticercosis in Mbulu District, Northern Tanzania

Abstract

BackgroundPorcine cysticercosis is caused by a zoonotic tapeworm, Taenia solium, which causes serious disease syndromes in human. Effective control of the parasite requires knowledge on the burden and pattern of the infections in order to properly direct limited resources. The objective of this study was to establish the spatial distribution of porcine cysticercosis in Mbulu district, northern Tanzania, to guide control strategies.Methodology/Principal FindingsThis study is a secondary analysis of data collected during the baseline and follow-up periods of a randomized community trial aiming at reducing the incidence rate of porcine cysticercosis through an educational program. At baseline, 784 randomly selected pig-keeping households located in 42 villages in 14 wards were included. Lingual examination of indigenous pigs aged 2–12 (median 8) months, one randomly selected from each household, were conducted. Data from the control group of the randomized trial that included 21 of the 42 villages were used for the incidence study. A total of 295 pig-keeping households were provided with sentinel pigs (one each) and reassessed for cysticercosis incidence once or twice for 2–9 (median 4) months using lingual examination and antigen ELISA. Prevalence of porcine cysticercosis was computed in Epi Info 3.5. The prevalence and incidence of porcine cysticercosis were mapped at household level using ArcView 3.2. K functions were computed in R software to assess general clustering of porcine cysticercosis. Spatial scan statistics were computed in SatScan to identify local clusters of the infection. The overall prevalence of porcine cysticercosis was 7.3% (95% CI: 5.6, 9.4; n = 784). The K functions revealed a significant overall clustering of porcine cysticercosis incidence for all distances between 600 m and 5 km from a randomly chosen case household based on Ag-ELISA. Lingual examination revealed clustering from 650 m to 6 km and between 7.5 and 10 km. The prevalence study did not reveal any significant clustering by this method. Spatial scan statistics found one significant cluster of porcine cysticercosis prevalence (P = 0.0036; n = 370). In addition, the analysis found one large cluster of porcine cysticercosis incidence based on Ag-ELISA (P = 0.0010; n = 236) and two relatively small clusters of incidence based on lingual examination (P = 0.0012 and P = 0.0026; n = 241). These clusters had similar spatial location and included six wards, four of which were identified as high risk areas of porcine cysticercosis.Conclusion/SignificanceThis study has identified local clusters of porcine cysticercosis in Mbulu district, northern Tanzania, where limited resources for control of T. solium could be directed. Further studies are needed to establish causes of clustering to institute appropriate interventions.

Evidence of Yersinia pestis DNA in rodents in plague outbreak foci in Mbulu and Karatu Districts, northern Tanzania

“…The 2012 National Census showed a population of 320,279 people comprised of 161,548 men and 158,731 women with an average of 6 people per household. Crop and livestock production are the most important economic activities, employing more than 90% of the total labour force (Ngowi et al, 2010).…”

A scoping review of spatial cluster analysis techniques for point-event data

“…It has been suggested that a study area's boundary extent can affect the likelihood for detecting a true cluster (Jacquez and Greiling, 2003;Wheeler, 2007). Papers in our review illustrate this effect, which detected clusters that span several km in diameter throughout a rural area (Ngowi et al, 2010), or multiple contiguous neighbourhoods throughout a metropolitan area (Chaix et al, 2006). Our method test with pedestrian injury illustrated this particular effect when applying the spatial scan statistic to the same dataset only changed by an increased sample size and study area size (Fig.…”

“…The primary use of K-function analysis was exploring the presence and scale of spatial clustering of the selected exposure variables (Austin et al, 2005;Hillier et al, 2009;Day and Pearce, 2011). The K-function was also used to assess the spatial structure of a distribution before conducting local analyses of spatial clustering (Han et al, 2004;Broman et al, 2006;Wheeler, 2007;Epp et al, 2010;Ngowi et al, 2010;Poljak et al, 2010). Knowing the scale and structure of the spatial dependency among data helps the user confirm whether local analyses are required as well as provide an approximation of spatial weight specifications.…”

“…Over half of papers in this review applied the spatial scan statistic to examine the spatial patterns of address location data (Andrade et al, 2004;Brooker et al, 2004;Han et al, 2004;Polack et al, 2005;Bautista et al, 2006;Chaix et al, 2006;Ernst et al, 2006;Pollack et al, 2006;Sarkar et al, 2007;Wheeler, 2007;Pasma, 2008;Warden, 2008;Huang et al, 2009;Meliker et al, 2009;Tanser et al, 2009;Epp et al, 2010;Ngowi et al, 2010;Poljak et al, 2010;Westercamp et al, 2010;Winskill et al, 2011). Of the reviewed articles, 83% applied a Bernoulli model spatial scan statistic to case-control data; two of those articles also used other models in SatScan that can be applied to address location data, the discrete normal continuous model ) and the discrete poisson continuous model (Ngowi et al, 2010), ordinal model (Westercamp et al, 2010) and the multinomial model (Westercamp et al, 2010).…”

“…Of the reviewed articles, 83% applied a Bernoulli model spatial scan statistic to case-control data; two of those articles also used other models in SatScan that can be applied to address location data, the discrete normal continuous model ) and the discrete poisson continuous model (Ngowi et al, 2010), ordinal model (Westercamp et al, 2010) and the multinomial model (Westercamp et al, 2010). A clear categorical distinction between applications was the scale of the study area in which the spatial scan statistic was used; large-scale (state, regional and metropolitan areas) versus small-scale (small areas, neighbourhoods and villages).…”