Veronica J. Berrocal

Space-Time Data fusion Under Error in Computer Model Output: An Application to Modeling Air Quality

We provide methods that can be used to obtain more accurate environmental exposure assessment. In particular, we propose two modeling approaches to combine monitoring data at point level with numerical model output at grid cell level, yielding improved prediction of ambient exposure at point level. Extending our earlier downscaler model (Berrocal, V. J., Gelfand, A. E., and Holland, D. M. (2010b). A spatio-temporal downscaler for outputs from numerical models. Journal of Agricultural, Biological and Environmental Statistics 15, 176-197), these new models are intended to address two potential concerns with the model output. One recognizes that there may be useful information in the outputs for grid cells that are neighbors of the one in which the location lies. The second acknowledges potential spatial misalignment between a station and its putatively associated grid cell. The first model is a Gaussian Markov random field smoothed downscaler that relates monitoring station data and computer model output via the introduction of a latent Gaussian Markov random field linked to both sources of data. The second model is a smoothed downscaler with spatially varying random weights defined through a latent Gaussian process and an exponential kernel function, that yields, at each site, a new variable on which the monitoring station data is regressed with a spatial linear model. We applied both methods to daily ozone concentration data for the Eastern US during the summer months of June, July and August 2001, obtaining, respectively, a 5% and a 15% predictive gain in overall predictive mean square error over our earlier downscaler model (Berrocal et al., 2010b). Perhaps more importantly, the predictive gain is greater at hold-out sites that are far from monitoring sites.

Combining spatial statistical and ensemble information in probabilistic weather forecasts

Abstract Forecast ensembles typically show a spread–skill relationship, but they are also often underdispersive, and therefore uncalibrated. Bayesian model averaging (BMA) is a statistical postprocessing method for forecast ensembles that generates calibrated probabilistic forecast products for weather quantities at individual sites. This paper introduces the spatial BMA technique, which combines BMA and the geostatistical output perturbation (GOP) method, and extends BMA to generate calibrated probabilistic forecasts of whole weather fields simultaneously, rather than just weather events at individual locations. At any site individually, spatial BMA reduces to the original BMA technique. The spatial BMA method provides statistical ensembles of weather field forecasts that take the spatial structure of observed fields into account and honor the flow-dependent information contained in the dynamical ensemble. The members of the spatial BMA ensemble are obtained by dressing the weather field forecasts from t...

Probabilistic quantitative precipitation field forecasting using a two-stage spatial model

Abstract : Short-range forecasts of precipitation fields are required in a wealth of agricultural, hydrological, ecological and other applications. Forecasts from numerical weather prediction models are often biased and do not provide uncertainty information. Here we present a postprocessing technique for such numerical forecasts that produces correlated probabilistic forecasts of precipitation accumulation at multiple sites simultaneously. The statistical model is a spatial version of a two-stage model that describes the distribution of precipitation with a mixture of a point mass at zero and a Gamma density for the continuous distribution of precipitation accumulation. Spatial correlation is captured by assuming that two Gaussian processes drive precipitation occurrence and precipitation amount, respectively. The first process is latent and governs precipitation occurrence via a truncation. The second process explains the spatial correlation in precipitation accumulation. It is related to precipitation via a site-specific transformation function, so to retain the marginal right-skewed distribution of precipitation while modeling spatial dependence. Both processes take into account the information contained in the numerical weather forecast and are modeled as stationary, isotropic spatial processes with an exponential correlation function. The two-stage spatial model was applied to forecasts of daily precipitation accumulation over the Pacific Northwest in 2004, at a prediction horizon of 48 hours. The predictive distributions from the two-stage spatial model were calibrated and sharp, and out-performed reference forecasts for spatially composite and areally averaged quantities.

A bivariate space-time downscaler under space and time misalignment.

Ozone and particulate matter PM(2.5) are co-pollutants that have long been associated with increased public health risks. Information on concentration levels for both pollutants come from two sources: monitoring sites and output from complex numerical models that produce concentration surfaces over large spatial regions. In this paper, we offer a fully-model based approach for fusing these two sources of information for the pair of co-pollutants which is computationally feasible over large spatial regions and long periods of time. Due to the association between concentration levels of the two environmental contaminants, it is expected that information regarding one will help to improve prediction of the other. Misalignment is an obvious issue since the monitoring networks for the two contaminants only partly intersect and because the collection rate for PM(2.5) is typically less frequent than that for ozone.Extending previous work in Berrocal et al. (2009), we introduce a bivariate downscaler that provides a flexible class of bivariate space-time assimilation models. We discuss computational issues for model fitting and analyze a dataset for ozone and PM(2.5) for the ozone season during year 2002. We show a modest improvement in predictive performance, not surprising in a setting where we can anticipate only a small gain.

Probabilistic Weather Forecasting for Winter Road Maintenance

Winter road maintenance is one of the main tasks for the Washington State Department of Transportation. Anti-icing, that is, the preemptive application of chemicals, is often used to keep the roadways free of ice. Given the preventive nature of anti-icing, accurate predictions of road ice are needed. Currently, anti-icing decisions are usually based on deterministic weather forecasts. However, the costs of the two kinds of errors are highly asymmetric because the cost of a road closure due to ice is much greater than that of taking anti-icing measures. As a result, probabilistic forecasts are needed to optimize decision making. We propose two methods for forecasting the probability of ice formation. Starting with deterministic numerical weather predictions, we model temperature and precipitation using distributions centered around the bias-corrected forecasts. This produces a joint predictive probability distribution of temperature and precipitation, which then yields the probability of ice formation, defined here as the occurrence of precipitation when the temperature is below freezing. The first method assumes that temperatures, as well as precipitation, at different spatial locations are conditionally independent given the numerical weather predictions. The second method models the spatial dependence between forecast errors at different locations. The model parameters are estimated using a Bayesian approach via Markov chain Monte Carlo. We evaluate both methods by comparing their probabilistic forecasts with observations of ice formation for Interstate Highway 90 in Washington State for the 2003–2004 and 2004–2005 winter seasons. The use of the probabilistic forecasts reduces costs by about 50% when compared to deterministic forecasts. The spatial method improves the reliability of the forecasts, but does not result in further cost reduction when compared to the first method.

Treatment of acute gout: A systematic review

OBJECTIVE Acute gout is traditionally treated with NSAIDs, corticosteroids, and colchicine; however, subjects have multiple comorbidities that limit the use of some conventional therapies. We systematically reviewed the published data on the pharmacologic and non-pharmacologic agents used for the treatment of acute gouty arthritis. METHODS A systematic search was performed using PubMed and Cochrane database through May 2013. We included only randomized controlled trials (RCTs) that included NSAIDs, corticosteroids, colchicine, adrenocorticotropic hormone (ACTH), interleukin-1 (IL-1) inhibitors, topical ice, or herbal supplements. RESULTS Thirty articles were selected for systematic review. The results show that NSAIDs and COX-2 inhibitors are effective agents for the treatment of acute gout attacks. Systemic corticosteroids have similar efficacy to therapeutic doses of NSAIDs, with studies supporting oral and intramuscular use. ACTH is suggested to be efficacious in acute gout. Oral colchicine demonstrated to be effective, with low-dose colchicine demonstrating a comparable tolerability profile as placebo and a significantly lower side effect profile to high-dose colchicine. The IL-1β inhibitory antibody, canakinumab, was effective for the treatment of acute attacks in subjects refractory to and in those with contraindications to NSAIDs and/or colchicine. However, rilonacept was demonstrated to be not as effective, and there are no RCTs for the use of anakinra. CONCLUSION NSAIDs, COX-2 selective inhibitors, corticosteroids, colchicine, ACTH, and canakinumab have evidence to suggest efficacy in treatment of acute gout.

The American College of Rheumatology Provisional Composite Response Index for Clinical Trials in Early Diffuse Cutaneous Systemic Sclerosis.

Early diffuse cutaneous systemic sclerosis (dcSSc) is characterized by rapid changes in the skin and internal organs. The objective of this study was to develop a composite response index in dcSSc (CRISS) for use in randomized controlled trials (RCTs).

Combination of echocardiographic and pulmonary function test measures improves sensitivity for diagnosis of systemic sclerosis-associated pulmonary arterial hypertension: analysis of 2 cohorts.

Objective. To evaluate routinely collected non-invasive tests from 2 systemic sclerosis (SSc) cohorts to determine their predictive value alone and in combination versus right heart catheterization (RHC)-confirmed pulmonary arterial hypertension (PAH). Methods. We evaluated 2 cohorts of patients who were at risk or with incident PAH: (1) The Pulmonary Hypertension Assessment and Recognition Outcomes in Scleroderma (PHAROS) cohort and (2) an inception SSc cohort at Cochin Hospital, Paris, France. Estimated right ventricular systolic pressure (eRVSP) as determined by transthoracic echocardiogram (TTE) and pulmonary function test (PFT) measures was evaluated, and the predictive values determined. We then evaluated patients with PAH missed on TTE cutoffs that were subsequently identified by a PFT measure. Results. In the PHAROS cohort (n = 206), 59 (29%) had RHC-defined PAH. An eRVSP threshold of 35–50 mm Hg failed to diagnose PAH in 7% to 31% of patients, 50% to 70% of which (n = 2–13) were captured by PFT measures. In the Cochin cohort (n = 141), 10 (7%) patients had RHC confirmed PAH. An eRVSP threshold of 35–50 mm Hg missed 0% to 70% (n = 0–7) of patients, of which 0% to 68% (n = 0–6) were met by PFT measures. The combination of TTE and PFT improved the negative predictive value for diagnosing PAH. Conclusion. In 2 large SSc cohorts, screening with TTE and PFT captured a majority of patients with PAH. TTE and PFT complement each other for the diagnosis of PAH.

Correlates and Responsiveness to Change of Measures of Skin and Musculoskeletal Disease in Early Diffuse Systemic Sclerosis

Skin and musculoskeletal involvement are frequently present early in diffuse cutaneous systemic sclerosis (dcSSc). The current study examined the correlates for skin and musculoskeletal measures in a 1‐year longitudinal observational study.

Distributed Lag Models: Examining Associations Between the Built Environment and Health.

Built environment factors constrain individual level behaviors and choices, and thus are receiving increasing attention to assess their influence on health. Traditional regression methods have been widely used to examine associations between built environment measures and health outcomes, where a fixed, prespecified spatial scale (e.g., 1 mile buffer) is used to construct environment measures. However, the spatial scale for these associations remains largely unknown and misspecifying it introduces bias. We propose the use of distributed lag models (DLMs) to describe the association between built environment features and health as a function of distance from the locations of interest and circumvent a-priori selection of a spatial scale. Based on simulation studies, we demonstrate that traditional regression models produce associations biased away from the null when there is spatial correlation among the built environment features. Inference based on DLMs is robust under a range of scenarios of the built environment. We use this innovative application of DLMs to examine the association between the availability of convenience stores near California public schools, which may affect children’s dietary choices both through direct access to junk food and exposure to advertisement, and children’s body mass index z scores.