Modelling malaria treatment practices in Bangladesh using spatial statistics

<p>Abstract</p> <p>Background</p> <p>Malaria treatment-seeking practices vary worldwide and Bangladesh is no exception. Individuals from 88 villages in Rajasthali were asked about their treatment-seeking practices. A portion of these households preferred malaria treatment from the National Control Programme, but still a large number of households continued to use drug vendors and approximately one fourth of the individuals surveyed relied exclusively on non-control programme treatments. The risks of low-control programme usage include incomplete malaria treatment, possible misuse of anti-malarial drugs, and an increased potential for drug resistance.</p> <p>Methods</p> <p>The spatial patterns of treatment-seeking practices were first examined using hot-spot analysis (Local Getis-Ord Gi statistic) and then modelled using regression. Ordinary least squares (OLS) regression identified key factors explaining more than 80% of the variation in control programme and vendor treatment preferences. Geographically weighted regression (GWR) was then used to assess where each factor was a strong predictor of treatment-seeking preferences.</p> <p>Results</p> <p>Several factors including tribal affiliation, housing materials, household densities, education levels, and proximity to the regional urban centre, were found to be effective predictors of malaria treatment-seeking preferences. The predictive strength of each of these factors, however, varied across the study area. While education, for example, was a strong predictor in some villages, it was less important for predicting treatment-seeking outcomes in other villages.</p> <p>Conclusion</p> <p>Understanding where each factor is a strong predictor of treatment-seeking outcomes may help in planning targeted interventions aimed at increasing control programme usage. Suggested strategies include providing additional training for the Building Resources across Communities (BRAC) health workers, implementing educational programmes, and addressing economic factors.</p

B-4 Greenhouse Emissions from Tofu Production

Background: Tofu is perceived as healthy and ecofriendly protein-rich food, but little is known about the carbon footprint generated by this soy product. Objectives: The purpose of this study was to evaluate the emissions of greenhouse gases (GHG) generated by the production of tofu. Methods: We performed a life cycle assessment (LCA) to calculate the greenhouse gases emissions (GHG) generated by tofu using SimaPro 7. Our LCA calculations include materials and energy inputs required to produce tofu: whole soybeans, water, electricity, natural gas, transportation and packaging materials. The functional unit: 1 kg of tofu. The boundary is from cradle to factory gate. Results: The total GHG emissions per one kilogram of tofu produced are 893 g of CO2eq. Monte Carlo simulations shows that the CO2eq estimation is robust. The GHG emissions are mainly generated by whole soybeans (50%), natural gas (27%), packaging (13%), transportation (6%) and electricity (4%). Conclusion: Tofu is a protein rich food that generates relatively low GHG emissions when compared to protein-rich animal foods. Tofu generates 22 to 34 times less greenhouse gas emissions than beef products. Thus, tofu is a suitable food to consume by people who intend to reduce their carbon footprint by dietary choices