Environmental management for malaria control: knowledge and practices in Mvomero, Tanzania.

Environmental conditions play an important role in the transmission of malaria; therefore, regulating these conditions can help to reduce disease burden. Environmental management practices for disease control can be implemented at the community level to complement other malaria control methods. This study assesses current knowledge and practices related to mosquito ecology and environmental management for malaria control in a rural, agricultural region of Tanzania. Household surveys were conducted with 408 randomly selected respondents from 10 villages and qualitative data were collected through focus group discussions and in-depth interviews. Results show that respondents are well aware of the links between mosquitoes, the environment, and malaria. Most respondents stated that cleaning the environment around the home, clearing vegetation around the home, or draining stagnant water can reduce mosquito populations, and 63% of respondents reported performing at least one of these techniques to protect themselves from malaria. It is clear that many respondents believe that these environmental management practices are effective malaria control methods, but the actual efficacy of these techniques for controlling populations of vectors or reducing malaria prevalence in the varying ecological habitats in Mvomero is unknown. Further research should be conducted to determine the effects of different environmental management practices on both mosquito populations and malaria transmission in this region, and increased participation in effective techniques should be promoted.

Knowledge Modeling for The Outcome of Brain Stereotactic Radiosurgery

Purpose: To build a model that will predict the survival time for patients that were treated with stereotactic radiosurgery for brain metastases using support vector machine (SVM) regression.

Methods and Materials: This study utilized data from 481 patients, which were equally divided into training and validation datasets randomly. The SVM model used a Gaussian RBF function, along with various parameters, such as the size of the epsilon insensitive region and the cost parameter (C) that are used to control the amount of error tolerated by the model. The predictor variables for the SVM model consisted of the actual survival time of the patient, the number of brain metastases, the graded prognostic assessment (GPA) and Karnofsky Performance Scale (KPS) scores, prescription dose, and the largest planning target volume (PTV). The response of the model is the survival time of the patient. The resulting survival time predictions were analyzed against the actual survival times by single parameter classification and two-parameter classification. The predicted mean survival times within each classification were compared with the actual values to obtain the confidence interval associated with the model’s predictions. In addition to visualizing the data on plots using the means and error bars, the correlation coefficients between the actual and predicted means of the survival times were calculated during each step of the classification.

Results: The number of metastases and KPS scores, were consistently shown to be the strongest predictors in the single parameter classification, and were subsequently used as first classifiers in the two-parameter classification. When the survival times were analyzed with the number of metastases as the first classifier, the best correlation was obtained for patients with 3 metastases, while patients with 4 or 5 metastases had significantly worse results. When the KPS score was used as the first classifier, patients with a KPS score of 60 and 90/100 had similar strong correlation results. These mixed results are likely due to the limited data available for patients with more than 3 metastases or KPS scores of 60 or less.

Conclusions: The number of metastases and the KPS score both showed to be strong predictors of patient survival time. The model was less accurate for patients with more metastases and certain KPS scores due to the lack of training data.