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1. Nontuberculous mycobacterial infection and environmental molybdenum in persons with cystic fibrosis: a case–control study in Colorado

   https://doi.org/10.1038/s41370-021-00360-2  

   Lipner, Ettie M.; Crooks, James L.; French, Joshua; Strong, Michael; Nick, Jerry A.; Prevots, D. Rebecca (July 2021, Journal of Exposure Science & Environmental Epidemiology)

   null (Ed.)

   Rationale: Nontuberculous mycobacteria (NTM) are ubiquitous environmental bacteria that may cause chronic lung disease and are one of the most difficult-to-treat infections among persons with cystic fibrosis (pwCF). Environmental factors likely contribute to increased NTM densities, with higher potential for exposure and infection. Objective: To identify water-quality constituents that influence odds of NTM infection among pwCF in Colorado. Methods: We conducted a population-based nested case–control study using patient data from the Colorado CF Center NTM database. We associated data from pwCF and water-quality data extracted from the Water Quality Portal to estimate odds of NTM infection. Using Bayesian generalized linear models with binomial-distributed discrete responses, we modeled three separate outcomes; any NTM infection, infections due to Mycobacterium avium complex species, and infections due to M. abscessus group species. Results: We observed a consistent association with molybdenum in the source water and M. abscessus group species infection among pwCF in all models. For every 1-unit increase in the log concentration of molybdenum in surface water, the odds of infection for those with M. abscessus group species compared to those who were NTM culture-negative increased by 79%. The odds of M. abscessus group infection varied by county; the counties with the highest probability of infection are located along the major rivers. Conclusions: We have identified molybdenum in the source water as the most predictive factor of M. abscessus group infection among pwCF in Colorado. This finding will help inform patients at risk for NTM of their relative risks in residing within specific regions. 

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

   https://doi.org/10.1097/EE9.0000000000000266  

   Lipner, Ettie M.; French, Joshua P.; Mercaldo, Rachel A.; Nelson, Stephen; Zelazny, Adrian M.; Marshall, Julia E.; Strong, Michael; Falkinham, Joseph O.; Prevots, D. Rebecca (January 2023, Environmental Epidemiology)

   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. 

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3. Soil Properties and Moisture Synergistically Influence Nontuberculous Mycobacterial Prevalence in Natural Environments of Hawai’i

   https://doi.org/10.1128/aem.00018-22  

   Parsons, Arielle W.; Dawrs, Stephanie N.; Nelson, Stephen T.; Norton, Grant J.; Virdi, Ravleen; Hasan, Nabeeh A.; Epperson, L. Elaine; Holst, Brady; Chan, Edward D.; Leos-Barajas, Vianey; et al (May 2022, Applied and Environmental Microbiology)

   Semrau, Jeremy D. (Ed.)

   ABSTRACT Nontuberculous mycobacteria (NTM) are opportunistic pathogens that cause chronic pulmonary disease (PD). NTM infections are thought to be acquired from the environment; however, the basal environmental factors that drive and sustain NTM prevalence are not well understood. The highest prevalence of NTM PD cases in the United States is reported from Hawai’i, which is unique in its climate and soil composition, providing an opportunity to investigate the environmental drivers of NTM prevalence. We used microbiological sampling and spatial logistic regression complemented with fine-scale soil mineralogy to model the probability of NTM presence across the natural landscape of Hawai’i. Over 7 years, we collected and microbiologically cultured 771 samples from 422 geographic sites in natural areas across the Hawaiian Islands for the presence of NTM. NTM were detected in 210 of these samples (27%), with Mycobacterium abscessus being the most frequently isolated species. The probability of NTM presence was highest in expansive soils (those that swell with water) with a high water balance (>1-m difference between rainfall and evapotranspiration) and rich in Fe-oxides/hydroxides. We observed a positive association between NTM presence and iron in wet soils, supporting past studies, but no such association in dry soils. High soil-water balance may facilitate underground movement of NTM into the aquifer system, potentially compounded by expansive capabilities allowing crack formation under drought conditions, representing further possible avenues for aquifer infiltration. These results suggest both precipitation and soil properties are mechanisms by which surface NTM may reach the human water supply. IMPORTANCE Nontuberculous mycobacteria (NTM) are ubiquitous in the environment, being found commonly in soils and natural bodies of freshwater. However, little is known about the environmental niches of NTM and how they relate to NTM prevalence in homes and other human-dominated areas. To characterize NTM environmental associations, we collected and cultured 771 samples from 422 geographic sites in natural areas across Hawai’i, the U.S. state with the highest prevalence of NTM pulmonary disease. We show that the environmental niches of NTM are most associated with highly expansive, moist soils containing high levels of iron oxides/hydroxides. Understanding the factors associated with NTM presence in the natural environment will be crucial for identifying potential mechanisms and risk factors associated with NTM infiltration into water supplies, which are ultimately piped into homes where most exposure risk is thought to occur. 

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4. Nontuberculous Mycobacterial Disease and Molybdenum in Colorado Watersheds

   https://doi.org/10.3390/ijerph17113854  

   Lipner, Ettie M.; French, Joshua; Bern, Carleton R.; Walton-Day, Katherine; Knox, David; Strong, Michael; Prevots, D. Rebecca; Crooks, James L. (June 2020, International Journal of Environmental Research and Public Health)

   Nontuberculous mycobacteria (NTM) are environmental bacteria that may cause chronic lung disease. Environmental factors that favor NTM growth likely increase the risk of NTM exposure within specific environments. We aimed to identify water-quality constituents (Al, As, Cd, Ca, Cu, Fe, Pb, Mg, Mn, Mo, Ni, K, Se, Na, Zn, and pH) associated with NTM disease across Colorado watersheds. We conducted a geospatial, ecological study, associating data from patients with NTM disease treated at National Jewish Health and water-quality data from the Water Quality Portal. Water-quality constituents associated with disease risk were identified using generalized linear models with Poisson-distributed discrete responses. We observed a highly robust association between molybdenum (Mo) in the source water and disease risk. For every 1- unit increase in the log concentration of molybdenum in the source water, disease risk increased by 17.0%. We also observed a statistically significant association between calcium (Ca) in the source water and disease risk. The risk of NTM varied by watershed and was associated with watershed-specific water-quality constituents. These findings may inform mitigation strategies to decrease the overall risk of exposure. 

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5. A Spatio-temporal Cluster-aware Supervised Learning Framework for Predicting County-level Drug Overdose Deaths

   https://doi.org/10.1609/aaai.v39i27.35015  

   Feng, Zixuan; Ye, Qing; Xie, Weijun; Chen, Qiushi (April 2025, Proceedings of the AAAI Conference on Artificial Intelligence)

   The soaring drug overdose crisis in the United States has claimed more than half a million lives in the past decade and remains a major public health threat. The ability to predict drug overdose deaths at the county level can help local communities develop action plans in response to emerging changes. Applying off-the-shelf machine learning algorithms for prediction can be challenging due to the heterogeneous risk profiles of the counties and suppressed data in common publicly available data sources. To fill these gaps, we develop a cluster-aware supervised learning (CASL) framework to enhance the prediction of county-level drug overdose deaths. This CASL model simultaneously clusters counties into groups based on geographical and socioeconomic characteristics and minimizes the loss function that accounts for suppressed values and cluster-specific regularization. Our computational study uses real-world data from 2010 to 2021, focusing on the ten states most severely impacted by the drug overdose crisis. The results demonstrate that our proposed CASL framework significantly outperforms state-of-the-art methods by achieving a superior balance in prediction accuracy for both unsuppressed and suppressed observations. The proposed model also identifies different clusters of counties, capturing heterogeneous patterns of overdose mortality among counties of diverse characteristics. 

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