Estimating Mode of Transport in Daily Mobility during the COVID-19 Pandemic Using a Multinomial Logistic Regression Model.

At the beginning of 2020 there was a spinning point in the travel behavior of people around the world because of the pandemic and its consequences. This paper analyzes the specific behavior of travelers commuting to work or school during the COVID-19 pandemic based on a sample of 2000 respondents from two countries. We obtained data from an online survey, applying multinomial regression analysis. The results demonstrate the multinomial model with an accuracy of almost 70% that estimates the most used modes of transport (walking, public transport, car) based on independent variables. The respondents preferred the car as the most frequently used means of transport. However, commuters without car prefer public transport to walking. This prediction model could be a tool for planning and creating transport policy, especially in exceptional cases such as the limitation of public transport activities. Therefore, predicting travel behavior is essential for policymaking based on people's travel needs.

The development of a tool to predict temperature-exposure of incinerated teeth using colourimetric and hydroxyapatite crystal size data.

This study presents a novel tool to predict temperature-exposure of incinerated pig teeth as a proxy for understanding impacts of fire on human teeth. Previous studies on the estimation of temperature-exposure of skeletal elements have been limited to that of heat-exposed bone. This predictive tool was developed using a multinomial regression model of colourimetric and hydroxyapatite crystal size variables using data obtained from unheated pig teeth and teeth incinerated at 300 °C, 600 °C, 800 °C and 1000 °C. An additional variable based on the observed appearance of the tooth was included in the tool. This enables the tooth to be classified as definitely burnt (600 °C-1000 °C) or uncertain (27 °C/300 °C). As a result, the model predicting the temperature-exposure of the incinerated teeth had an accuracy of 95%. This tool is a holistic, robust and reliable approach to estimate temperature of heat-exposed pig teeth, with high accuracy, and may act as a valuable proxy to estimate heat exposure for human teeth in forensic casework.

Haplotype heterogeneity and low linkage disequilibrium reduce reliable prediction of genotypes for the ­α 3.7I form of α-thalassaemia using genome-wide microarray data.

Background: The -α 3.7I-thalassaemia deletion is very common throughout Africa because it protects against malaria. When undertaking studies to investigate human genetic adaptations to malaria or other diseases, it is important to account for any confounding effects of α-thalassaemia to rule out spurious associations. Methods: In this study we have used direct α-thalassaemia genotyping to understand why GWAS data from a large malaria association study in Kilifi Kenya did not identify the α-thalassaemia signal. We then explored the potential use of a number of new approaches to using GWAS data for imputing α-thalassaemia as an alternative to direct genotyping by PCR. Results: We found very low linkage-disequilibrium of the directly typed data with the GWAS SNP markers around α-thalassaemia and across the haemoglobin-alpha ( HBA) gene region, which along with a complex haplotype structure, could explain the lack of an association signal from the GWAS SNP data. Some indirect typing methods gave results that were in broad agreement with those derived from direct genotyping and could identify an association signal, but none were sufficiently accurate to allow correct interpretation compared with direct typing, leading to confusing or erroneous results. Conclusions: We conclude that going forwards, direct typing methods such as PCR will still be required to account for α-thalassaemia in GWAS studies.

Market-based tools for a plastic waste reduction policy in agriculture: A case study in the south of Italy.

The 2018 European Strategy for Plastics in a Circular Economy identifies a set of actions to reduce plastic waste in the EU in the near future. To implement this strategic view, appropriate policy tools need to be identified within Member Countries. The present work aims at investigating farmers' attitudes towards the use of traditional market-based tools (i.e. subsidies and tax-credits) as well as other initiatives such as a pay-back mechanism under an Extended Producer Responsibility (EPR) to reduce plastic waste in agriculture. We analyse the case of the province of Foggia, an area which is recognised as being the largest plain in southern Italy. We consider a survey of 1,783 farmers and a multinomial regression model to infer on the probability of adopting the above policy tools. Key results suggest that the choice of each policy tool would be affected by the type of plastic waste generated. In particular, plastic packaging and plastic films would be likely to affect the probability to opt for a subsidy. In contrast, other types of plastic waste mainly generated by cereal crops activities (e.g. plastic bags and bottles for fertilisers and chemicals) would favour the adoption of a tax-credit mechanism. As for other aspects, horticulture production and the proximity to a collection site for waste disposal would increase the probability of adopting an EPR policy to contribute to plastic waste reduction.

Philippine Costs in Oncology (PESO): Describing the economic impact of cancer on Filipino cancer patients using the ASEAN costs in oncology study dataset

@#<p style="text-align: justify;"><strong>BACKGROUND: </strong>Cancers are among the top causes of mortality in the Philippines. The treatment regimens are also costly and put Filipinos at risk of financial catastrophe. The economic impact, however, has not been documented.</p><p style="text-align: justify;"><strong>OBJECTIVE:</strong> This analysis aimed to describe the economic impact of cancer in the Philippines and analyze predictors of financial catastrophe among Filipino cancer patients.</p><p style="text-align: justify;"><strong>METHOD:</strong> The analysis used the dataset from the ASEAN costs in oncology study, a prospective study of adult cancer patients in Southeast Asia. Cancer patients were recruited at time of diagnosis and were monitored in terms of health outcomes, costs, and quality of life. Multinomial regression models were generated to assess predictors of death and financial catastrophe.</p><p style="text-align: justify;"><strong>RESULTS:</strong> Information from 909 respondents in the Philippines was included in the analysis. Overall, 240 (26.4%) of the cohort were dead at the end of the study while 40.6% were still alive at Month 12 but had experienced financial catastrophe. Mean combined Month 3 and Month 12 out-of-pocket expenditure was PhP181,789.00 (n = 458, sd = 348,717.47). Belonging to higher income groups (vs. belonging to the lowest two) was significantly associated with lower risk of financial catastrophe. Insurance did not confer significant change in risk of death or financial catastrophe.</p><p style="text-align: justify;"><strong>CONCLUSION:</strong> Cancer can be a significant economic burden for Filipinos leading to financial catastrophe. Insurance mechanisms at the time of study failed to protect against catastrophe.</p>