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Aim This study was conducted to determine the knowledge and practice of pregnant women attending specialist antenatal clinics (ANCs) concerning malaria, ITN (insecticide-treated net) utilization and antimalarial treatment. Subjects and methods The study recruited 225 women who voluntarily presented at ANCs in Jimma town, Ethiopia. A pre-tested questionnaire was administered. Results A great majority (98.2%; 221/225) of the respondents were aware of malaria. Among them, 77.4% (174/225) indicated mosquito bites as a mode of malaria transmission. Overall, 94.3% (212/225) and 98.3% (221/225) of pregnant women had knowledge about ITNs and antimalarials, respectively. Overall, 76.8% (173/225) and 57.4% (129/ 225) of the study participants erroneously indicated mosquito bites and stagnant water as causes of malaria, respectively. In general, the majority of pregnant women were unable to distinguish between malaria transmission and cause. Chi-square analysis revealed a strong association between the educational status of the pregnant women and their daily usage of ITNs (P=0.001; χ 2 =22.9; df=8). Conclusion The results clearly suggest that although the majority of the pregnant women had ample awareness of malaria, ITN usage and antimalarial treatment, a sizable faction still had misconceptions and misunderstandings. Therefore, appropriate communication strategies should be designed and implemented among the marginalized and most vulnerable section of society, especially through health education campaigns, in order to have a constructive outcome in the near future.

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Microbial source tracking (MST) methods need to be rapid, inexpensive and accurate. Unfortunately, many MST methods provide a wealth of information that is difficult to interpret by the regulators who use this information to make decisions. This paper describes the use of classification tree analysis to interpret the results of a MST method based on fatty acid methyl ester (FAME) profiles of Escherichia coli isolates, and to present results in a format readily interpretable by water quality managers. Raw sewage E. coli isolates and animal E. coli isolates from cow, dog, gull, and horse were isolated and their FAME profiles collected. Correct classification rates determined with leaveone-out cross-validation resulted in an overall low correct classification rate of 61%. A higher overall correct classification rate of 85% was obtained when the animal isolates were pooled together and compared to the raw sewage isolates. Bootstrap aggregation or adaptive resampling and combining of the FAME profile data increased correct classification rates substantially. Other MST methods may be better suited to differentiate between different fecal sources but classification tree analysis has enabled us to distinguish raw sewage from animal E. coli isolates, which previously had not been possible with other multivariate methods such as principal component analysis and cluster analysis.

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