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Principles and practice of Clinical parasitology - Gillespie S.

Gillespie S. Principles and practice of Clinical parasitology - Wiley publishing , 2001. - 675 p.
ISBN 0-471-97729-2
Download (direct link): principlesandpracticeofclin2001.pdf
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Fig 216 Global Poisson probability map for bancroftian filariasis case prevalences. The map shows Pj>mean values, and may be interpreted by considering that there is a ‘high probability’ (p >0.90) that the prevalence estimated in each black area is higher than the mean global value (MGV); there is ‘equivocal evidence’ that the risk of each dark-shaded area is higher than the MGV (p=0.50-0.90) and that of each light shaded area is lower than the MGV (p = 0.10-0.50); and, finally, there is a ‘high probability’ that the risk of each medium shaded area is lower than the MGV (p<0.10). (Note: caste probabilities for all the other endemic Pacific Island countries lay between 0.50 and 0.90)
probability scale replaces the high spatial variation of the original map with a more homogeneous pattern in the ‘between-country’ distribution of cases, the results also confirm the impression from Figure 2.15A that the underlying case rate for the disease is not constant across the world. Instead, the case rates exhibit strong regional variations, with more countries in Africa and the Pacific Island region (not shown) with probabilities of infection and disease higher than the global mean rate, compared to countries in Asia or South America (Figure 2.16). This finding of a significant regional influence on spatial variation suggests that separate analytical maps based on regional mean rates will be required to identify anomalous or priority counties within each endemic region. They also argue for a geographically targeted strategy for filariasis control.
The Effect of Diurnal Temperature Differences on Bancroftian Filariasis Distribution
Thomson et al. (1996) used remotely sensed data on diurnal temperature differences (dT) in conjunction with spatial data on case prevalences from 297 villages within the Southern Nile Delta, and showed that this environmental variable may underlie the observed spatial distribution of lymphatic filariasis, at least within their study region. dTs indicate surface and subsurface moisture contained in the soil and plant canopy, and hence may act as a surrogate for the abundance of the mosquito vector, Culex quinquefasciatus. Satellite image data from NOAA-AVHRR were analysed to determine dTs for the southern Nile Delta, while the case prevalence and locational data for each of the
F'g 217 Mean diurnal temperature differences of southern Nile delta, 16 August 1990, with study village sites superimposed according to bancroftian filariasis prevalence category
297 villages were inputted into a GIS. Point dT values for each village were obtained by averaging the values for 3x3 pixel areas (10km2) centred on the corresponding longitude and latitude of each village. The digitized filariasis prevalence data were superimposed on the dT map and assigned to each of four prevalence categories, 0.5%, 5%, 15% and 25%, respectively (Figure 2.17). The association between village dT value and prevalence category was investigated using stepwise polychotomous logistic regression, which indicated a significant relationship between the two variables. Similar
applications of dT maps or other remotely sensed data to delineate areas of risk with Bancroftian filariasis in other disease-endemic regions await study.
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