BackgroundMore than 75% of the total area of Ethiopia is malarious, making malaria the leading public health problem in Ethiopia. The aim of this study was to investigate the prevalence rate and the associated socio-economic, geographic and demographic factors of malaria based on the rapid diagnosis test (RDT) survey results.MethodsFrom December 2006 to January 2007, a baseline malaria indicator survey in Amhara, Oromiya and Southern Nation Nationalities and People (SNNP) regions of Ethiopia was conducted by The Carter Center. This study uses this data. The method of generalized linear model was used to analyse the data and the response variable was the presence or absence of malaria using the rapid diagnosis test (RDT).ResultsThe analyses show that the RDT result was significantly associated with age and gender. Other significant covariates confounding variables are source of water, trip to obtain water, toilet facility, total number of rooms, material used for walls, and material used for roofing. The prevalence of malaria for households with clean water found to be less. Malaria rapid diagnosis found to be higher for thatch and stick/mud roof and earth/local dung plaster floor. Moreover, spraying anti-malaria to the house was found to be one means of reducing the risk of malaria. Furthermore, the housing condition, source of water and its distance, gender, and ages in the households were identified in order to have two-way interaction effects.ConclusionIndividuals with poor socio-economic conditions are positively associated with malaria infection. Improving the housing condition of the household is one of the means of reducing the risk of malaria. Children and female household members are the most vulnerable to the risk of malaria. Such information is essential to design improved strategic intervention for the reduction of malaria epidemic in Ethiopia.
Background: Malaria is one of the oldest and deadliest infectious diseases in humans. Many mathematical models of malaria have been developed during the past century, and applied to potential interventions. However, malaria remains uncontrolled and is increasing in many areas, as are vector and parasite resistance to insecticides and drugs.
BackgroundAnaemia is one of the significant public health problems among children in the world. Understanding risk factors of anaemia provides more insight to the nature and types of policies that can be put up to fight anaemia. We estimated the prevalence and risk factors of anaemia in a population-based, cross-sectional survey.MethodologyBlood samples from 11,711 children aged between 6 months and 14 years were collected using a single-use, spring-loaded, sterile lancet to make a finger prick. Anaemia was measured based on haemoglobin concentration level. The generalized linear model framework was used to analyse the data, in which the response variable was either a child was anemic or not anemic.ResultsThe overall prevalence of anaemia among the children in Kenya was estimated to be 28.8%. The risk of anaemia was found to decrease with age progressively with increase in each year of age; children below 1 year were at highest risk of anaemia. The risk of anaemia was significantly higher in male than female children. Mothers with secondary and above education had a protective effect on the risk of anaemia on their children. Malaria diagnosis status of a child was positively associated with risk anaemia.ConclusionControlling co-morbidity of malaria and improving maternal knowledge are potential options for reducing the burden of anaemia.
BackgroundRandom survival forest (RSF) models have been identified as alternative methods to the Cox proportional hazards model in analysing time-to-event data. These methods, however, have been criticised for the bias that results from favouring covariates with many split-points and hence conditional inference forests for time-to-event data have been suggested. Conditional inference forests (CIF) are known to correct the bias in RSF models by separating the procedure for the best covariate to split on from that of the best split point search for the selected covariate.MethodsIn this study, we compare the random survival forest model to the conditional inference model (CIF) using twenty-two simulated time-to-event datasets. We also analysed two real time-to-event datasets. The first dataset is based on the survival of children under-five years of age in Uganda and it consists of categorical covariates with most of them having more than two levels (many split-points). The second dataset is based on the survival of patients with extremely drug resistant tuberculosis (XDR TB) which consists of mainly categorical covariates with two levels (few split-points).ResultsThe study findings indicate that the conditional inference forest model is superior to random survival forest models in analysing time-to-event data that consists of covariates with many split-points based on the values of the bootstrap cross-validated estimates for integrated Brier scores. However, conditional inference forests perform comparably similar to random survival forests models in analysing time-to-event data consisting of covariates with fewer split-points.ConclusionAlthough survival forests are promising methods in analysing time-to-event data, it is important to identify the best forest model for analysis based on the nature of covariates of the dataset in question.
Mark-release-recapture (MRR) experiments were conducted with emerging Anopheles gambiae s.l. and Anophelesfunestus Giles at Jaribuni and Mtepeni in Kilifi, along the Kenyan Coast. Of 739 and 1246 Anopheles released at Jaribuni and Mtepeni, 24.6 and 4.33% were recaptured, respectively. The daily survival probability was 0.96 for An. funestus and 0.95 for An. gambiae in Jaribuni and 0.83 and 0.95, respectively, in Mtepeni. The maximum flight distance recorded was 661 m. The high survival probability of An. gambiae and An. funestus estimated accounts for the continuous transmission of malaria along the Kenyan coast. This study also shows that the release of young, emergent female Anopheles improves the recapture rates and may be a better approach to MRR studies.
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