Parasite Epidemiology and Control最新文献

Background: In low-and-middle income countries, national representative household surveys such as the Demographic and Health Surveys (DHS) and the Malaria Indicator Surveys (MIS) are routinely carried out to assess the malaria risk and the coverage of related interventions. A two-stage sampling design was used to identify clusters and households within each cluster. To ensure confidentiality, DHS made the data available after jittering (displacement) of the geographical coordinates of the clusters, shifting their original locations within a radius of 10 km. Our study assessed the influence of jittering on the estimates of the geographical distribution of malaria risk and on the effects of malaria control interventions using data from the latest MIS in Cameroon.

Methods: We generated one hundred datasets by jittering the original MIS data. For each dataset, climatic factors were extracted at the jittered locations and Bayesian geostatistical variable selection was applied to identify the most important climatic predictors and malaria intervention coverage indicators. The models were adjusted for potential confounding effects of socio-economic factors. Bayesian kriging based on the selected models was used to estimate the geographical distribution of malaria risk. The influence of jittering was analysed using results of the variable selection and the Bayesian credible intervals of the regression coefficients.

Results: Geostatistical variable selection was sensitive to jittering. Among the important predictors identified in the true data, distance to water bodies and presence of forest were mostly influenced by the jittering. Altitude and vegetation index were the least affected predictors. The various sets of selected environmental factors were able to capture the main spatial patterns of the disease risk, but the jittering increased the prediction error. The parameter estimates of the effects of socio-economic factors and intervention indicators were relatively stable in the simulated data.

Conclusion: In Cameroon, the malaria risk estimates obtained from the jittered data were comparable to the ones generated using the true locations; however, jittering modified our interpretation of the relationship between environmental predictors and malaria transmission.

The trypanosomosis remains unresolved due to its impact on various hosts, leading to production losses in Ethiopia. In the Southwest of Oromia, multiple livestock species share grazing land in tsetse-infested areas. Thus, a cross-sectional study was conducted from December 2020 to December 2021 to determine the prevalence and associated risk factors of trypanosomosis in bovines, small ruminants, and equines, as well as the distribution of the vector in the Dabo Hana district of Southwest Oromia, Ethiopia. A vector survey was carried out using 60 monoconical traps placed at intervals ranging from about 100 to 200 m. Out of the 1441 flies captured, 86.2 % were Glossina, 7.84 % were Stomoxys, and 5.96 % were Tabanus. The overall apparent density of flies was 12 flies per trap per day. Among the 1242 caught Glossina species, 85 % were identified as G. tachinoides and 15 % as G. m. submorsitans. The average age of male tsetse flies was 28 days, and the overall infection rate of trypanosomes in tsetse flies was 4.8 %. A total of 701 blood samples (190 from bovines, 384 from small ruminants, and 127 from equines) were analyzed using buffy coat and Giemsa techniques. The prevalence of trypanosomosis was found to be 10 % in bovines, 4.2 % in small ruminants, and 3.1 % in equines. A significant difference (P < 0.05) in trypanosome infection was observed among the three host species, as well as with respect to the age and body condition of the animals. The predominant cause of infection was T. congolense, accounting for 74.4 % of cases. The mean packed cell volume (PCV) values of infected bovines, small ruminants, and equines were significantly lower (P < 0.05) compared to those of non-infected animals. Trypanosomosis is a major livestock disease in the study area. The findings provide valuable insights into the prevalence and infection rates of trypanosomosis, identify the affected species, and highlight significant risk factors, such as age, body condition, and vector distribution. Implementing sustainable and integrated practices for trypanosomosis control is crucial, and conducting molecular techniques in different seasons is also recommended.

Uganda started implementing mass drug administration against schistosomiasis in 2003, with district used as an implementation unit. This resulted into misclassification of communities into wrong risk levels, under-or-over treatment and over request of praziquantel (PZQ) drugs. The objective of the current study was to reviewing the community data available at World Health Organization/ESPEN database to understand the status of schistosomiasis and identify pockets with infection. The decision tree assessment tool was used to analyzed schistosomiasis epidemiological data of 7501 communities. Before validation, the schistosomiasis endemicity status of 79 % of communities was not known. After validation, 58.6 %, 22.6 % and 16.3 % of communities were not endemic, had low and moderate endemicity status. Of 2362 communities classified having high endemicity using a district as implementation unit, 41.6 %, 12.7 % and 17.3 % of them were not endemic, had low and moderate endemicity, while only 22.7 % had high endemicity. Using the new treatment guidelines, 2,875,006 school aged children were adequately treated, 18,235 were under-treated and 2,250,013 were over treated. The results show a considerable change in endemicity status when communities were used as an implementation unit compared to district. Thus, the country control programme is recommended to use communities as implementation unit.