Prefiltered component-based greedy (PreCoG) scan method.

The spatial distribution of disease cases can provide important insights into disease spread and its potential risk factors. Identifying disease clusters correctly can help us discover new risk factors and inform interventions to control and prevent the spread of disease as quickly as possible. In this study, we propose a novel scan method, the Prefiltered Component-based Greedy (PreCoG) scan method, which efficiently and accurately detects irregularly shaped clusters using a prefiltered component-based algorithm. The PreCoG scan method's flexibility allows it to perform well in detecting both regularly and irregularly-shaped clusters. Additionally, it is fast to apply while providing high power, sensitivity, and positive predictive value for the detected clusters compared to other scan methods. To confirm the effectiveness of the PreCoG method, we compare its performance to many other scan methods. Additionally, we have implemented this method in the smerc R package to make it publicly available to other researchers. Our proposed PreCoG scan method presents a unique and innovative process for detecting disease clusters and can improve the accuracy of disease surveillance systems.

The risk of pulmonary NTM infections and water-quality constituents among persons with cystic fibrosis in the United States, 2010-2019.

Rationale: The prevalence of nontuberculous mycobacterial (NTM) pulmonary disease varies geographically in the United States. Previous studies indicate that the presence of certain water-quality constituents in source water increases NTM infection risk. Objective: To identify water-quality constituents that influence the risk of NTM pulmonary infection in persons with cystic fibrosis in the United States. Methods: We conducted a population-based case-control study using NTM incidence data collected from the Cystic Fibrosis Foundation Patient Registry during 2010-2019. We linked patient zip code to the county and associated patient county of residence with surface water data extracted from the Water Quality Portal. We used logistic regression models to estimate the odds of NTM infection as a function of water-quality constituents. We modeled two outcomes: pulmonary infection due to Mycobacterium avium complex (MAC) and Mycobacterium abscessus species. Results: We identified 484 MAC cases, 222 M. abscessus cases and 2816 NTM-negative cystic fibrosis controls resident in 11 states. In multivariable models, we found that for every 1-standardized unit increase in the log concentration of sulfate and vanadium in surface water at the county level, the odds of infection increased by 39% and 21%, respectively, among persons with cystic fibrosis with MAC compared with cystic fibrosis-NTM-negative controls. When modeling M. abscessus as the dependent variable, every 1-standardized unit increase in the log concentration of molybdenum increased the odds of infection by 36%. Conclusions: These findings suggest that naturally occurring and anthropogenic water-quality constituents may influence the NTM abundance in water sources that supply municipal water systems, thereby increasing MAC and M. abscessus infection risk.

Environmental risk of nontuberculous mycobacterial infection: Strategies for advancing methodology.

The National Institute of Allergy and Infectious Diseases organized a symposium in June 2022, to facilitate discussion of the environmental risks for nontuberculous mycobacteria exposure and disease. The expert researchers presented recent studies and identified numerous research gaps. This report summarizes the discussion and identifies six major areas of future research related to culture-based and culture independent laboratory methods, alternate culture media and culturing conditions, frameworks for standardized laboratory methods, improved environmental sampling strategies, validation of exposure measures, and availability of high-quality spatiotemporal data.

Detecting clusters of high nontuberculous mycobacteria infection risk for persons with cystic fibrosis - An analysis of U.S. counties.

Nontuberculous mycobacteria are ubiquitous environmental bacteria that frequently cause disease in persons with cystic fibrosis (pwCF). The risks for NTM infection vary geographically. Detection of high-risk areas is important for focusing prevention efforts. In this study, we apply five cluster detection methods to identify counties with high NTM infection risk. Four clusters were detected by at least three of the five methods, including twenty-five counties in five states. The geographic area and number of counties in each cluster depended upon the detection method used. Identifying these clusters supports future studies of environmental predictors of infection and will inform control and prevention efforts.

Vanadium in groundwater aquifers increases the risk of MAC pulmonary infection in O'ahu, Hawai'i.

Hawai'i has the highest prevalence of nontuberculous mycobacterial (NTM) pulmonary disease in the United States. Previous studies indicate that certain trace metals in surface water increase the risk of NTM infection. Objective: To identify whether trace metals influence the risk of NTM infection in O'ahu, Hawai'i. Methods: A population-based ecologic cohort study was conducted using NTM infection incidence data from patients enrolled at Kaiser Permanente Hawai'i during 2005-2019. We obtained sociodemographic, microbiologic, and geocoded residential data for all Kaiser Permanente Hawai'i beneficiaries. To estimate the risk of NTM pulmonary infection from exposure to groundwater constituents, we obtained groundwater data from three data sources: (1) Water Quality Portal; (2) the Hawai'i Department of Health; and (3) Brigham Young University, Department of Geological Science faculty. Data were aggregated by an aquifer and were associated with the corresponding beneficiary aquifer of residence. We used Poisson regression models with backward elimination to generate models for NTM infection risk as a function of groundwater constituents. We modeled two outcomes: Mycobacterium avium complex (MAC) species and Mycobacterium abscessus group species. Results: For every 1-unit increase in the log concentration of vanadium in groundwater at the aquifer level, infection risk increased by 22% among MAC patients. We did not observe significant associations between water-quality constituents and infection risk among M. abscessus patients. Conclusions: Concentrations of vanadium in groundwater were associated with MAC pulmonary infection in O'ahu, Hawai'i. These findings provide evidence that naturally occurring trace metals influence the presence of NTM in water sources that supply municipal water systems.

Nontuberculous Mycobacteria Infection Risk and Trace Metals in Surface Water: A Population-based Ecologic Epidemiologic Study in Oregon.

Rationale: Nontuberculous mycobacteria (NTM) are ubiquitous environmental bacteria, and some pathogenic species cause lung disease. Environmental factors contribute to increased NTM abundance, with higher potential for exposure and infection. Objectives: To identify water-quality constituents that influence the risk of NTM infection in Oregon. Methods: We conducted a population-based cohort study using patient incidence data from the Oregon statewide NTM laboratory data collected as part of a public health surveillance project from 2007 through 2012. To estimate the risk of NTM pulmonary infection (PI) from exposure to water constituents, we extracted water-quality data from the Water Quality Portal and associated these data with corresponding patient county of residence. Using generalized linear models, we modeled two outcomes: Mycobacterium avium complex species PI and Mycobacterium abscessus group species PI. Results: For every 1-unit increase in the log concentration of vanadium in surface water, infection risk increased by 49% among persons with Mycobacterium avium complex PI. Among those with Mycobacterium abscessus PI, we observed that for every 1-unit increase in the log concentration of molybdenum in surface water, infection risk increased by 41%. The highest risk of infection due to Mycobacterium abscessus group infection was concentrated in counties within the Northwestern region of Oregon. High infection risk associated with Mycobacterium avium complex species did not show any geographic pattern. Conclusions: Concentrations of the trace metals molybdenum and vanadium in surface water sources were associated with NTM infection in Oregon. These findings may help identify regions at higher risk of NTM infection to guide risk reduction strategies.

Estimating the optimal population upper bound for scan methods in retrospective disease surveillance.

Correctly and quickly identifying disease patterns and clusters is a vital aspect of public health and epidemiology so that disease outbreaks can be mitigated as effectively as possible. The circular scan method is one of the most commonly used methods for detecting disease outbreaks and clusters in retrospective and prospective disease surveillance. The circular scan method requires a population upper bound in order to construct the set of candidate zones to be scanned, which is usually set to 50% of the total population. The performance of the circular scan method is affected by the choice of the population upper bound, and choosing an upper bound different from the default value can improve the method's performance. Recently, the Gini coefficient based on the Lorenz curve, which was originally used in economics, was proposed to determine a better population upper bound. We present the elbow method, a new method for choosing the population upper bound, which seeks to address some of the limitations of the Gini-based method while improving the performance of the circular scan method over the default value. To evaluate the performance of the proposed approach, we evaluate the sensitivity and positive predictive value of the circular scan method for publicly-available benchmark data for the default value, the Gini coefficient method, and the elbow method.

A modified CUSUM test to control postoutbreak false alarms.

The cumulative sum (CUSUM) control chart is a method for detecting whether the mean of a time series process has shifted beyond some tolerance (ie, is out of control). Originally developed in an industrial process control setting, the CUSUM statistic is typically reset to zero once a process is discovered to be out of control since the industrial process is then recalibrated to be in control. The CUSUM method is also used to detect disease outbreaks in prospective disease surveillance, with a disease outbreak coinciding with an out-of-control process. In a disease surveillance setting, resetting the CUSUM statistic is unrealistic, and a nonrestarting CUSUM chart is used instead. In practice, the nonrestarting CUSUM provides more information but suffers from a high false alarm rate following the end of an outbreak. In this paper, we propose a modified hypothesis test for use with the nonrestarting CUSUM when testing whether a process is out of control. By simulating statistics conditional on the presence of an out-of-control process in recent time periods, we are able to retain the CUSUM's power to detect an out-of-control process while controlling the post-out-of-control false alarm rate at the desired level. We demonstrate this method using data on a Salmonella Newport outbreak that occurred in Germany in 2011. We find that in 7 out of 8 states where the outbreak was detected, the outbreak was detected at the same speed as an unmodified nonrestarting CUSUM while controlling the postoutbreak rate of false alarms at the desired level.

Microsite and elevation zone effects on seed pilferage, germination, and seedling survival during early whitebark pine recruitment.

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.

autoimage: Multiple Heat Maps for Projected Coordinates.

Heat maps are commonly used to display the spatial distribution of a response observed on a two-dimensional grid. The autoimage package provides convenient functions for constructing multiple heat maps in unified, seamless way, particularly when working with projected coordinates. The autoimage package natively supports: 1. automatic inclusion of a color scale with the plotted image, 2. construction of heat maps for responses observed on regular or irregular grids, as well as non-gridded data, 3. construction of a matrix of heat maps with a common color scale, 4. construction of a matrix of heat maps with individual color scales, 5. projecting coordinates before plotting, 6. easily adding geographic borders, points, and other features to the heat maps. After comparing the autoimage package's capabilities for constructing heat maps to those of existing tools, a carefully selected set of examples is used to highlight the capabilities of the autoimage package.

Assessing NARCCAP climate model effects using spatial confidence regions.

We assess similarities and differences between model effects for the North American Regional Climate Change Assessment Program (NARCCAP) climate models using varying classes of linear regression models. Specifically, we consider how the average temperature effect differs for the various global and regional climate model combinations, including assessment of possible interaction between the effects of global and regional climate models. We use both pointwise and simultaneous inference procedures to identify regions where global and regional climate model effects differ. We also show conclusively that results from pointwise inference are misleading, and that accounting for multiple comparisons is important for making proper inference.

An improved cumulative sum-based procedure for prospective disease surveillance for count data in multiple regions.

We present an improved procedure for detecting outbreaks in multiple spatial regions using count data. We combine well-known methods for disease surveillance with recent developments from other areas to provide a more powerful procedure that is still relatively simple and fast to implement. Disease counts from neighboring regions are aggregated to compute a Poisson cumulative sum statistic for each region of interest. Instead of controlling the average run length criterion in the monitoring process, we instead utilize the FDR, which is more appropriate in a public health context. Additionally, p-values are used to make decisions instead of traditional critical values. The use of the FDR and p-values in testing allows us to utilize recently developed multiple testing methodologies, greatly increasing the power of this procedure. This is verified using a simulation experiment. The simplicity and rapid detection ability of this procedure make it useful in disease surveillance settings. The procedure is successfully applied in detecting the 2011 Salmonella Newport outbreak in 16 German federal states. Copyright © 2016 John Wiley & Sons, Ltd.