Estimating the Under-Five Mortality Rate Using a Bayesian Hierarchical Time Series Model

Background: Millennium Development Goal 4 calls for a reduction in the under-five mortality rate by two-thirds between 1990 and 2015, which corresponds to an annual rate of decline of 4.4%. The United Nations Inter-Agency Group for Child Mortality Estimation estimates under-five mortality in every country to measure progress. For the majority of countries, the estimates within a country are based on the assumption of a piece-wise constant rate of decline. Methods and Findings: This paper proposes an alternative method to estimate under-five mortality, such that the underlying rate of change is allowed to vary smoothly over time using a time series model. Information about the average rate of decline and changes therein is exchanged between countries using a Bayesian hierarchical model. Cross-validation exercises suggest that the proposed model provides credible bounds for the under-five mortality rate that are reasonably well calibrated during the observation period. The alternative estimates suggest smoother trends in under-five mortality and give new insights into changes in the rate of decline within countries. Conclusions: The proposed model offers an alternative modeling approach for obtaining estimates of under-five mortality which removes the restriction of a piece-wise linear rate of decline and introduces hierarchy to exchange information between countries. The newly proposed estimates of the rate of decline in under-5 mortality and the uncertaint

Simulation study for analysis of binary responses in the presence of extreme case problems

<p>Abstract</p> <p>Background</p> <p>Estimates of variance components for binary responses in presence of extreme case problems tend to be biased due to an under-identified likelihood. The bias persists even when a normal prior is used for the fixed effects.</p> <p>Methods</p> <p>A simulation study was carried out to investigate methods for the analysis of binary responses with extreme case problems. A linear mixed model that included a fixed effect and random effects of sire and residual on the liability scale was used to generate binary data. Five simulation scenarios were conducted based on varying percentages of extreme case problems, with true values of heritability equal to 0.07 and 0.17. Five replicates of each dataset were generated and analyzed with a generalized prior (<b>g-prior</b>) of varying weight.</p> <p>Results</p> <p>Point estimates of sire variance using a normal prior were severely biased when the percentage of extreme case problems was greater than 30%. Depending on the percentage of extreme case problems, the sire variance was overestimated when a normal prior was used by 36 to 102% and 25 to 105% for a heritability of 0.17 and 0.07, respectively. When a g-prior was used, the bias was reduced and even eliminated, depending on the percentage of extreme case problems and the weight assigned to the g-prior. The lowest Pearson correlations between true and estimated fixed effects were obtained when a normal prior was used. When a 15% g-prior was used instead of a normal prior with a heritability equal to 0.17, Pearson correlations between true and fixed effects increased by 11, 20, 23, 27, and 60% for 5, 10, 20, 30 and 75% of extreme case problems, respectively. Conversely, Pearson correlations between true and estimated fixed effects were similar, within datasets of varying percentages of extreme case problems, when a 5, 10, or 15% g-prior was included. Therefore this indicates that a model with a g-prior provides a more adequate estimation of fixed effects.</p> <p>Conclusions</p> <p>The results suggest that when analyzing binary data with extreme case problems, bias in the estimation of variance components could be eliminated, or at least significantly reduced by using a g-prior.</p

The Ground-Dwelling Arthropod Community of Península Valdés in Patagonia, Argentina

This is the first study based on a planned and intensive sampling effort that describes the community composition and structure of the ground-dwelling arthropod assemblage of Península Valdés (Patagonia). It was carried out using pitfall traps, opened for two weeks during the summers of 2005, 2006 and 2007. A total of 28, 111 individuals were caught. Ants (Hymenoptera: Formicidae) dominated this community, followed by beetles (Coleoptera) and spiders (Araneae). The most abundant species were Pheidole bergi Mayr (Hymenoptera: Formicidae) and Blapstinus punctulatus Solier (Coleoptera: Tenebrionidae). Two new species were very recently described as new based on specimens collected during this study: Valdesiana curiosa Carpintero, Dellapé & Cheli (Hemiptera, Miridae) and Anomaloptera patagonica Dellapé & Cheli (Hemiptera, Oxycarenidae). The order Coleoptera was the most diverse taxa. The distribution of abundance data was best described by the logarithmic series model both at the family and species levels, suggesting that ecological relationships in this community could be controlled by a few factors. The community was dominated by predators from a trophic perspective. This suggests that predation acts as an important factor driving the distribution and abundances of surface-dwelling arthropods in this habitat and as such serves as a key element in understanding desert, above-ground community structure. These findings may also be useful for management and conservation purposes in arid Patagonia

A Bayesian method for calculating real-time quantitative PCR calibration curves using absolute plasmid DNA standards

<p>Abstract</p> <p>Background</p> <p>In real-time quantitative PCR studies using absolute plasmid DNA standards, a calibration curve is developed to estimate an unknown DNA concentration. However, potential differences in the amplification performance of plasmid DNA compared to genomic DNA standards are often ignored in calibration calculations and in some cases impossible to characterize. A flexible statistical method that can account for uncertainty between plasmid and genomic DNA targets, replicate testing, and experiment-to-experiment variability is needed to estimate calibration curve parameters such as intercept and slope. Here we report the use of a Bayesian approach to generate calibration curves for the enumeration of target DNA from genomic DNA samples using absolute plasmid DNA standards.</p> <p>Results</p> <p>Instead of the two traditional methods (classical and inverse), a Monte Carlo Markov Chain (MCMC) estimation was used to generate single, master, and modified calibration curves. The mean and the percentiles of the posterior distribution were used as point and interval estimates of unknown parameters such as intercepts, slopes and DNA concentrations. The software WinBUGS was used to perform all simulations and to generate the posterior distributions of all the unknown parameters of interest.</p> <p>Conclusion</p> <p>The Bayesian approach defined in this study allowed for the estimation of DNA concentrations from environmental samples using absolute standard curves generated by real-time qPCR. The approach accounted for uncertainty from multiple sources such as experiment-to-experiment variation, variability between replicate measurements, as well as uncertainty introduced when employing calibration curves generated from absolute plasmid DNA standards.</p

Thermodynamical Control by Frequent Quantum Measurements

Heat flow between a large ``bath'' and a smaller system brings them progressively closer to thermal equilibrium while increasing their entropy. Deviations from this trend are fluctuations involving a small fraction of a statistical ensemble of systems interacting with the bath: in this respect, quantum and classical thermodynamics are in agreement. Can there be drastic differences between them? Here we address a distinctly quantum mechanical setting that displays such differences: disturbances of thermal equilibrium between two-level systems (TLS) and a bath by frequent and brief quantum (non-demolishing) measurements of the TLS energy-states. If the measurements are frequent enough to induce either the Zeno or the anti-Zeno regime, namely, the slowdown or speedup of the TLS relaxation, then the resulting entropy and temperature of both the system and the bath are found to be completely unrelated to what is expected by standard thermodynamical rules that hold for memoryless baths. The practical advantage of these anomalies is the possibility of very fast control of heat and entropy, allowing cooling and state-purification of quantum systems much sooner than their thermal equilibration time.Comment: 10 Pages. Pre-submission version of Nature {\bf 452}, 724 (2008). Includes Supplementary Informatio

Bottom mixed layer oxygen dynamics in the Celtic Sea

The seasonally stratified continental shelf seas are highly productive, economically important environments which are under considerable pressure from human activity. Global dissolved oxygen concentrations have shown rapid reductions in response to anthropogenic forcing since at least the middle of the twentieth century. Oxygen consumption is at the same time linked to the cycling of atmospheric carbon, with oxygen being a proxy for carbon remineralisation and the release of CO2. In the seasonally stratified seas the bottom mixed layer (BML) is partially isolated from the atmosphere and is thus controlled by interplay between oxygen consumption processes, vertical and horizontal advection. Oxygen consumption rates can be both spatially and temporally dynamic, but these dynamics are often missed with incubation based techniques. Here we adopt a Bayesian approach to determining total BML oxygen consumption rates from a high resolution oxygen time-series. This incorporates both our knowledge and our uncertainty of the various processes which control the oxygen inventory. Total BML rates integrate both processes in the water column and at the sediment interface. These observations span the stratified period of the Celtic Sea and across both sandy and muddy sediment types. We show how horizontal advection, tidal forcing and vertical mixing together control the bottom mixed layer oxygen concentrations at various times over the stratified period. Our muddy-sand site shows cyclic spring-neap mediated changes in oxygen consumption driven by the frequent resuspension or ventilation of the seabed. We see evidence for prolonged periods of increased vertical mixing which provide the ventilation necessary to support the high rates of consumption observed

Towards causal benchmarking of bias in face analysis algorithms

Measuring algorithmic bias is crucial both to assess algorithmic fairness, and to guide the improvement of algorithms. Current methods to measure algorithmic bias in computer vision, which are based on observational datasets, are inadequate for this task because they conflate algorithmic bias with dataset bias. To address this problem we develop an experimental method for measuring algorithmic bias of face analysis algorithms, which manipulates directly the attributes of interest, e.g., gender and skin tone, in order to reveal causal links between attribute variation and performance change. Our proposed method is based on generating synthetic ``transects'' of matched sample images that are designed to differ along specific attributes while leaving other attributes constant. A crucial aspect of our approach is relying on the perception of human observers, both to guide manipulations, and to measure algorithmic bias. Besides allowing the measurement of algorithmic bias, synthetic transects have other advantages with respect to observational datasets: they sample attributes more evenly allowing for more straightforward bias analysis on minority and intersectional groups, they enable prediction of bias in new scenarios, they greatly reduce ethical and legal challenges, and they are economical and fast to obtain, helping make bias testing affordable and widely available. We validate our method by comparing it to a study that employs the traditional observational method for analyzing bias in gender classification algorithms. The two methods reach different conclusions. While the observational method reports gender and skin color biases, the experimental method reveals biases due to gender, hair length, age, and facial hair

The Ground-Dwelling Arthropod Community of Península Valdés in Patagonia, Argentina

This is the first study based on a planned and intensive sampling effort that describes the community composition and structure of the ground-dwelling arthropod assemblage of Península Valdés (Patagonia). It was carried out using pitfall traps, opened for two weeks during the summers of 2005, 2006 and 2007. A total of 28, 111 individuals were caught. Ants (Hymenoptera: Formicidae) dominated this community, followed by beetles (Coleoptera) and spiders (Araneae). The most abundant species were Pheidole bergi Mayr (Hymenoptera: Formicidae) and Blapstinus punctulatus Solier (Coleoptera: Tenebrionidae). Two new species were very recently described as new based on specimens collected during this study: Valdesiana curiosa Carpintero, Dellapé & Cheli (Hemiptera, Miridae) and Anomaloptera patagonica Dellapé & Cheli (Hemiptera, Oxycarenidae). The order Coleoptera was the most diverse taxa. The distribution of abundance data was best described by the logarithmic series model both at the family and species levels, suggesting that ecological relationships in this community could be controlled by a few factors. The community was dominated by predators from a trophic perspective. This suggests that predation acts as an important factor driving the distribution and abundances of surface-dwelling arthropods in this habitat and as such serves as a key element in understanding desert, above-ground community structure. These findings may also be useful for management and conservation purposes in arid Patagonia

Ecological Modeling of Aedes aegypti (L.) Pupal Production in Rural Kamphaeng Phet, Thailand

Background - Aedes aegypti (L.) is the primary vector of dengue, the most important arboviral infection globally. Until an effective vaccine is licensed and rigorously administered, Ae. aegypti control remains the principal tool in preventing and curtailing dengue transmission. Accurate predictions of vector populations are required to assess control methods and develop effective population reduction strategies. Ae. aegypti develops primarily in artificial water holding containers. Release recapture studies indicate that most adult Ae. aegypti do not disperse over long distances. We expect, therefore, that containers in an area of high development site density are more likely to be oviposition sites and to be more frequently used as oviposition sites than containers that are relatively isolated from other development sites. After accounting for individual container characteristics, containers more frequently used as oviposition sites are likely to produce adult mosquitoes consistently and at a higher rate. To this point, most studies of Ae. aegypti populations ignore the spatial density of larval development sites. Methodology - Pupal surveys were carried out from 2004 to 2007 in rural Kamphaeng Phet, Thailand. In total, 84,840 samples of water holding containers were used to estimate model parameters. Regression modeling was used to assess the effect of larval development site density, access to piped water, and seasonal variation on container productivity. A varying-coefficients model was employed to account for the large differences in productivity between container types. A two-part modeling structure, called a hurdle model, accounts for the large number of zeroes and overdispersion present in pupal population counts. Findings - The number of suitable larval development sites and their density in the environment were the primary determinants of the distribution and abundance of Ae. aegypti pupae. The productivity of most container types increased significantly as habitat density increased. An ecological approach, accounting for development site density, is appropriate for predicting Ae. aegypti population levels and developing efficient vector control program

New approaches to measuring anthelminthic drug efficacy: parasitological responses of childhood schistosome infections to treatment with praziquantel

By 2020, the global health community aims to control and eliminate human helminthiases, including schistosomiasis in selected African countries, principally by preventive chemotherapy (PCT) through mass drug administration (MDA) of anthelminthics. Quantitative monitoring of anthelminthic responses is crucial for promptly detecting changes in efficacy, potentially indicative of emerging drug resistance. Statistical models offer a powerful means to delineate and compare efficacy among individuals, among groups of individuals and among populations.; We illustrate a variety of statistical frameworks that offer different levels of inference by analysing data from nine previous studies on egg counts collected from African children before and after administration of praziquantel.; We quantify responses to praziquantel as egg reduction rates (ERRs), using different frameworks to estimate ERRs among population strata, as average responses, and within strata, as individual responses. We compare our model-based average ERRs to corresponding model-free estimates, using as reference the World Health Organization (WHO) 90 % threshold of optimal efficacy. We estimate distributions of individual responses and summarize the variation among these responses as the fraction of ERRs falling below the WHO threshold.; Generic models for evaluating responses to anthelminthics deepen our understanding of variation among populations, sub-populations and individuals. We discuss the future application of statistical modelling approaches for monitoring and evaluation of PCT programmes targeting human helminthiases in the context of the WHO 2020 control and elimination goals