The aim of the current study was to investigate the prevalence and clinical associations of nontuberculous mycobacteria (NTM) in a well-characterised cohort of patients with adult-onset bronchiectasis.The sputum of all patients attending a tertiary referral bronchiectasis clinic between April 2002 and August 2003 was examined for mycobacteria as part of an extensive diagnostic work-up. NTM-positive patients subsequently had further sputa examined. A modified bronchiectasis scoring system was applied to all high-resolution computed tomography (HRCT) scans from NTM-positive patients, and a matched cohort without NTM.Out of 98 patients attending the clinic, 10 had NTM in their sputum on first culture; of those, eight provided multiple positive cultures. Three patients were treated for NTM infection. A higher proportion of NTM-positive than -negative patients were subsequently diagnosed with cystic fibrosis (two out of nine versus two out of 75). On HRCT scoring, more patients in the NTMpositive group had peripheral mucus plugging than in the NTM-negative group.In the current prospective study of a large cohort of patients with bronchiectasis, 10% cultured positive for nontuberculous mycobacteria in a random clinic sputum sample. Few clinical parameters were helpful in discriminating between groups, except for a higher prevalence of previously undiagnosed cystic fibrosis and of peripheral mucus plugging on high-resolution computed tomography in the nontuberculous mycobacteria group.

An exceedance region is the set of locations in a spatial domain where a process exceeds some threshold. Examples of exceedance regions include areas where ozone concentrations exceed safety standards, there is high risk for tornadoes or floods, or heavy-metal levels are dangerously high. Identifying these regions in a spatial or spatio-temporal setting is an important responsibility in environmental monitoring. Exceedance regions are often estimated by finding the areas where predictions from a statistical model exceed some threshold. Even when estimation error is quantifiable at individual locations, the overall estimation error of the estimated exceedance region is still unknown. A method is presented for constructing a confidence region containing the true exceedance region of a spatio-temporal process at a certain time. The underlying latent process and any measurement error are assumed to be Gaussian. Conventional techniques are used to model the spatio-temporal data, and then conditional simulation is combined with hypothesis testing to create the desired confidence region. A simulation study is used to validate the approach for several levels of spatial and temporal dependence. The methodology is used to identify regions of Oregon having high precipitation levels and also used in comparing climate models and assessing climate change using climate models from the North American Regional Climate Change Assessment Program.Comment: Published in at http://dx.doi.org/10.1214/13-AOAS631 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Rationale: Nontuberculous mycobacteria (NTM) are ubiquitous environmental microorganisms. Infection is thought to result primarily from exposure to soil and/or water sources. NTM disease prevalence varies greatly by geographic region, but the geospatial factors influencing this variation remain unclear.Objectives: To identify sociodemographic and environmental ecological risk factors associated with NTM infection and disease in Colorado. Methods:We conducted an ecological study, combining data from patients with a diagnosis of NTM disease from National Jewish Health's electronic medical record database and ZIP code-level sociodemographic and environmental exposure data obtained from the U.S. Geological Survey, the U.S. Department of Agriculture, and the U.S. Census Bureau. We used spatial scan methods to identify high-risk clusters of NTM disease in Colorado. Ecological risk factors for disease were assessed using Bayesian generalized linear models assuming Poisson-distributed discrete responses (case counts by ZIP code) with the log link function.Results: We identified two statistically significant high-risk clusters of disease. The primary cluster included ZIP codes in urban regions of Denver and Aurora, as well as regions south of Denver, on the east side of the Continental Divide. The secondary cluster was located on the west side of the Continental Divide in rural and mountainous regions. After adjustment for sociodemographic, drive time, and soil variables, we identified three watershed areas with relative risks of 12.2, 4.6, and 4.2 for slowly growing NTM infections compared with the mean disease risk for all watersheds in Colorado. This study population carries with it inherent limitations that may introduce bias. The lack of complete capture of NTM cases in Colorado may be related to factors such as disease severity, education and income levels, and insurance status.Conclusions: Our findings provide evidence that water derived from particular watersheds may be an important source of NTM exposure in Colorado. The watershed with the greatest risk of NTM disease contains the Dillon Reservoir. This reservoir is also the main water supply for major cities located in the two watersheds with the second and third highest disease risk in the state, suggesting an important possible source of infection.

Tree recruitment is a spatially structured process that may undergo change over time because of variation in postdispersal processes. We examined seed pilferage, seed germination, and seedling survival in whitebark pine to determine whether 1) microsite type alters the initial spatial pattern of seed caches, 2) higher abiotic stress (i.e. higher elevations) exacerbates spatial distribution changes, and 3) these postdispersal processes are spatially clustered. At two study areas, we created a seed distribution pattern by burying seed caches in microsite types frequently used by whitebark pine's avian seed disperser (Clark's nutcracker) in upper subalpine forest and at treeline, the latter characterized by high abiotic environmental stress. We monitored caches for two years for pilferage, germination, and seedling survival. Odds of pilferage (both study areas), germination (northern study area), and survival (southern study area) were higher at treeline relative to subalpine forest. At the southern study area, we found higher odds of 1) pilferage near rocks and trees relative to no object in subalpine forest, 2) germination near rocks relative to trees within both elevation zones, and 3) seedling survival near rocks and trees relative to no object at treeline. No microsite effects were detected at the northern study area. Findings indicated that the microsite distribution of seed caches changes with seed/seedling stage. Higher odds of seedling survival near rocks and trees were observed at treeline, suggesting abiotic stress may limit safe site availability, thereby shifting the spatial distribution toward protective microsites. Higher odds of pilferage at treeline, however, suggest rodents may limit treeline recruitment. Further, odds of pilferage were higher near rocks and trees relative to no object in subalpine forest but did not differ among microsites at treeline, suggesting pilferage can modulate the spatial structure of regeneration, a finding supported by limited clustering of postdispersal processes.

Contour plots are often used by environmental scientists to display the distribution of a variable over a two-dimensional region of interest, but the uncertainty associated with the estimated level curves seldom receives attention. Few methods are available for identifying the true level curve with quantifiable confidence, and typically, these methods only deal with the uncertainty at individual locations. Methodology is introduced for constructing a confidence region for the entire level curve of a Gaussian random field at a specified level. The methodology utilizes common geostatistical methods to produce confidence regions containing the entire level curve with high confidence. The validity of this approach is investigated using simulation studies involving several covariance functions, dependence levels, and response quantiles. This methodology is then applied in constructing a confidence region for a level curve of the total monthly precipitation for the state of Colorado in May 1997. Simulation results indicate that this approach works well for the level curves of Gaussian random fields under a variety of conditions. Additionally, this methodology does not depend on the location of estimated level curves, so confidence regions may appear in areas where an estimated level curve is not present.