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The detection of disease clusters in spatial data analysis plays a crucial role in public health, while the circular scan method is widely utilized for this purpose, accurately identifying non-circular (irregular) clusters remains challenging and reduces detection accuracy. To overcome this limitation, various extensions have been proposed to effectively detect arbitrarily shaped clusters. In this paper, we combine the strengths of two well-known methods, the flexible and elliptic scan methods, which are specifically designed for detecting irregularly shaped clusters. We leverage the unique characteristics of these methods to create candidate zones capable of accurately detecting irregularly shaped clusters, along with a modified likelihood ratio test statistic. By inheriting the advantages of the flexible and elliptic methods, our proposed approach represents a practical addition to the existing repertoire of spatial data analysis techniques.

 

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. 

Rationale: The prevalence of nontuberculous mycobacterial (NTM) pulmonary disease varies geographically in the United States (U.S.). Previous studies indicate that the presence of certain water-quality constituents in source water increase NTM infection risk. Objective: To identify water-quality constituents that influence the risk of NTM pulmonary infection in persons with cystic fibrosis (pwCF) in the U.S. Methods: We conducted a population-based case-control study using NTM incidence data collected from the Cystic Fibrosis Foundation Patient Registry (CFFPR) during 2010-2019. We linked patient zip code to county and associated patient county of residence with surface water data extracted from the Water Quality Portal. We used logistic regression models to estimate 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 CF 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 pwCF with MAC compared with CF-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. 

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. 

Abstract. The Arctic is warming 2 to 3 times faster than the global average, partly due to changes in short-lived climate forcers (SLCFs) including aerosols. In order to study the effects of atmospheric aerosols in this warming, recent past (1990–2014) and future (2015–2050) simulations have been carried out using the GISS-E2.1 Earth system model to study the aerosol burdens and their radiative and climate impacts over the Arctic (>60∘ N), using anthropogenic emissions from the Eclipse V6b and the Coupled Model Intercomparison Project Phase 6 (CMIP6) databases, while global annual mean greenhouse gas concentrations were prescribed and kept fixed in all simulations. Results showed that the simulations have underestimated observed surface aerosol levels, in particular black carbon (BC) and sulfate (SO42-), by more than 50 %, with the smallest biases calculated for the atmosphere-only simulations, where winds are nudged to reanalysis data. CMIP6 simulations performed slightly better in reproducing the observed surface aerosol concentrations and climate parameters, compared to the Eclipse simulations. In addition, simulations where atmosphere and ocean are fully coupled had slightly smaller biases in aerosol levels compared to atmosphere-only simulations without nudging. Arctic BC, organic aerosol (OA), and SO42- burdens decrease significantly in all simulations by 10 %–60 % following the reductions of 7 %–78 % in emission projections, with the Eclipse ensemble showing larger reductions in Arctic aerosol burdens compared to the CMIP6 ensemble. For the 2030–2050 period, the Eclipse ensemble simulated a radiative forcing due to aerosol–radiation interactions (RFARI) of -0.39±0.01 W m−2, which is −0.08 W m−2 larger than the 1990–2010 mean forcing (−0.32 W m−2), of which -0.24±0.01 W m−2 was attributed to the anthropogenic aerosols. The CMIP6 ensemble simulated a RFARI of −0.35 to −0.40 W m−2 for the same period, which is −0.01 to −0.06 W m−2 larger than the 1990–2010 mean forcing of −0.35 W m−2. The scenarios with little to no mitigation (worst-case scenarios) led to very small changes in the RFARI, while scenarios with medium to large emission mitigations led to increases in the negative RFARI, mainly due to the decrease in the positive BC forcing and the decrease in the negative SO42- forcing. The anthropogenic aerosols accounted for −0.24 to −0.26 W m−2 of the net RFARI in 2030–2050 period, in Eclipse and CMIP6 ensembles, respectively. Finally, all simulations showed an increase in the Arctic surface air temperatures throughout the simulation period. By 2050, surface air temperatures are projected to increase by 2.4 to 2.6 ∘C in the Eclipse ensemble and 1.9 to 2.6 ∘C in the CMIP6 ensemble, compared to the 1990–2010 mean. Overall, results show that even the scenarios with largest emission reductions leads to similar impact on the future Arctic surface air temperatures and sea-ice extent compared to scenarios with smaller emission reductions, implying reductions of greenhouse emissions are still necessary to mitigate climate change.