Modeling causes of death: an integrated approach using CODEm

Background: Data on causes of death by age and sex are a critical input into health decision-making. Priority setting in public health should be informed not only by the current magnitude of health problems but by trends in them. However, cause of death data are often not available or are subject to substantial problems of comparability. We propose five general principles for cause of death model development, validation, and reporting.Methods: We detail a specific implementation of these principles that is embodied in an analytical tool - the Cause of Death Ensemble model (CODEm) - which explores a large variety of possible models to estimate trends in causes of death. Possible models are identified using a covariate selection algorithm that yields many plausible combinations of covariates, which are then run through four model classes. The model classes include mixed effects linear models and spatial-temporal Gaussian Process Regression models for cause fractions and death rates. All models for each cause of death are then assessed using out-of-sample predictive validity and combined into an ensemble with optimal out-of-sample predictive performance.Results: Ensemble models for cause of death estimation outperform any single component model in tests of root mean square error, frequency of predicting correct temporal trends, and achieving 95% coverage of the prediction interval. We present detailed results for CODEm applied to maternal mortality and summary results for several other causes of death, including cardiovascular disease and several cancers.Conclusions: CODEm produces better estimates of cause of death trends than previous methods and is less susceptible to bias in model specification. We demonstrate the utility of CODEm for the estimation of several major causes of death

Topographic and stochastic influences on pāhoehoe lava lobe emplacement

A detailed understanding of pāhoehoe emplacement is necessary for developing accurate models of flow field development, assessing hazards, and interpreting the significance of lava morphology on Earth and other planetary surfaces. Active pāhoehoe lobes on Kīlauea Volcano, Hawai'i, were examined on 21–26 February 2006 using oblique time series stereo-photogrammetry and differential global positioning system measurements. During this time, the local discharge rate for peripheral lava lobes was generally constant at 0.0061±0.0019 m3/s, but the areal coverage rate of the lobes exhibited a periodic increase every 4.13±0.64 min. This periodicity is attributed to the time required for the pressure within the liquid lava core to exceed the cooling-induced strength of its margins. The pāhoehoe flow advanced through a series of down-slope and cross-slope breakouts, which began as ∼0.2-m-thick units (i.e., toes) that coalesced and inflated to become approximately meter-thick lobes. The lobes were thickest above the lowest points of the initial topography and above shallow to reverse-facing slopes, defined relative to the local flow direction. The flow path was typically controlled by high-standing topography, with the zone directly adjacent to the final lobe margin having an average relief that was a few centimeters higher than the lava-inundated region. This suggests that toe-scale topography can, at least temporarily, exert strong controls on pāhoehoe flow paths by impeding the lateral spreading of the lobe. Observed cycles of enhanced areal spreading and inflated lobe morphology are also explored using a model that considers the statistical likelihood of sequential breakouts from active flow margins and the effects of topographic barriers