Low fat-free mass as a marker of mortality in community-dwelling healthy elderly subjects†

Background: low fat-free mass has been related to high mortality in patients. This study evaluated the relationship between body composition of healthy elderly subjects and mortality. Methods: in 1999, 203 older subjects underwent measurements of body composition by bioelectrical impedance analysis, Charlson co-morbidity index and estimation of energy expenditure through physical activity by a validated questionnaire. These measurements were repeated in 2002, 2005 and 2008 in all consenting subjects. Mortality data between 1999 and 2010 were retrieved from the local death registers. The relationship between mortality and the last indexes of fat and fat-free masses was analysed by multiple Cox regression models. Results: women's and men's data at last follow-up were: age 81.1±5.9 and 80.9±5.8 years, body mass index 25.3±4.6 and 26.1±3.4kg/m2, fat-free mass index 16.4±1.8 and 19.3±1.9kg/m2 and fat mass index 9.0±3.2 and 6.8±2.0kg/m2. Fifty-eight subjects died between 1999 and 2010. The fat-free mass index (hazard ratio 0.77; 95% confidence interval 0.63-0.95) but not the fat mass index, predicted mortality in addition to sex and Charlson index. The multiple Cox regression model explained 31% of the variance of mortality. Conclusion: a low fat-free mass index is an independent risk factor of mortality in elderly subjects, healthy at the time of body composition measuremen

Predictors of residual antimalarial drugs in the blood in community surveys in Tanzania.

Understanding pattern of antimalarials use at large scale helps ensuring appropriate use of treatments and preventing the spread of resistant parasites. We estimated the proportion of individuals in community surveys with residual antimalarials in their blood and identified the factors associated with the presence of the most commonly detected drugs, lumefantrine and/or desbutyl-lumefantrine (LF/DLF) or sulfadoxine-pyrimethamine (SP). A cross-sectional survey was conducted in 2015 in three regions of Tanzania with different levels of malaria endemicity. Interviews were conducted and blood samples collected through household surveys for further antimalarial measurements using liquid chromatography coupled to tandem mass spectrometry. In addition, diagnosis and treatment availability was investigated through outlet surveys. Multilevel mixed effects logistic regression models were used to estimate odds ratios for having LF/DLF or SP in the blood. Amongst 6391 participants, 12.4% (792/6391) had LF/DLF and 8.0% (510/6391) SP in the blood. Factors associated with higher odds of detecting LF/DLF in the blood included fever in the previous two weeks (OR = 2.6, p<0.001), living in districts of higher malaria prevalence (OR = 1.5, p<0.001) and living in a ward in which all visited drug stores had artemisinin-based combination therapies in stocks (OR = 2.7, p = 0.020). Participants in older age groups were less likely to have LF/DLF in the blood (OR = 0.9, p<0.001). Factors associated with higher odds of having SP in the blood included being pregnant (OR = 4.6, p<0.001), living in Mwanza (OR = 3.9, p<0.001 compared to Mbeya), fever in the previous two weeks (OR = 1.7, p<0.001) and belonging to older age groups (OR = 1.2, p<0.001). The most significant predictors identified were expected. History of fever in the past two weeks and young age were significant predictors of LF/DLF in the blood, which is encouraging. Antimalarial drug pressure was high and hence the use of recommended first-line drugs in combination with malaria Rapid Diagnostics Tests should be promoted to ensure appropriate treatment

The Second Competition on Spatial Statistics for Large Datasets

In the last few decades, the size of spatial and spatio-temporal datasets in many research areas has rapidly increased with the development of data collection technologies. As a result, classical statistical methods in spatial statistics are facing computational challenges. For example, the kriging predictor in geostatistics becomes prohibitive on traditional hardware architectures for large datasets as it requires high computing power and memory footprint when dealing with large dense matrix operations. Over the years, various approximation methods have been proposed to address such computational issues, however, the community lacks a holistic process to assess their approximation efficiency. To provide a fair assessment, in 2021, we organized the first competition on spatial statistics for large datasets, generated by our {\em ExaGeoStat} software, and asked participants to report the results of estimation and prediction. Thanks to its widely acknowledged success and at the request of many participants, we organized the second competition in 2022 focusing on predictions for more complex spatial and spatio-temporal processes, including univariate nonstationary spatial processes, univariate stationary space-time processes, and bivariate stationary spatial processes. In this paper, we describe in detail the data generation procedure and make the valuable datasets publicly available for a wider adoption. Then, we review the submitted methods from fourteen teams worldwide, analyze the competition outcomes, and assess the performance of each team