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Over the past decade, the reported incidence of Legionnaires' disease (LD) in the northeastern United States has increased, reaching 1-3 cases per 100,000 population. There is reason to suspect that this is an underestimate of the true burden, since LD cases may be underdiagnosed. In this analysis of pneumonia and influenza (P&I) hospitalizations, we estimated the percentages of cases due to Legionella, influenza, and respiratory syncytial virus (RSV) by age group. We fitted mixed-effects models to estimate attributable percents using weekly time series data on P&I hospitalizations in Connecticut from 2000 to 2014. Model-fitted values were used to calculate estimates of numbers of P&I hospitalizations attributable to Legionella (and influenza and RSV) by age group, season, and year. Our models estimated that 1.9%, 8.8%, and 5.1% of total (all-ages) inpatient P&I hospitalizations could be attributed to Legionella, influenza, and RSV, respectively. Only 10.6% of total predicted LD cases had been clinically diagnosed as LD during the study period. The observed incidence rate of 1.2 cases per 100,000 population was substantially lower than our estimated rate of 11.6 cases per 100,000 population. Our estimates of numbers of P&I hospitalizations attributable to Legionella are comparable to those provided by etiological studies of community-acquired pneumonia and emphasize the potential for underdiagnosis of LD in clinical settings., (© The Author(s) 2019. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: There has been a dramatic increase in the incidence of sporadic legionnaires' disease in Connecticut since 1999, but the exact reasons for this are unknown. Therefore, there is a growing need to understand the drivers of legionnaires' disease in the community. In this study, we explored the relationship between the natural environment and the spatial and temporal distribution of legionellosis cases in Connecticut., Methods: We used spatial models and time series methods to evaluate factors associated with the increase and clustering of legionellosis in Connecticut. Stream flow, proximity to rivers, and residence in regional watersheds were explored as novel predictors of disease, while controlling for testing intensity and correlates of urbanization., Results: In Connecticut, legionellosis incidence exhibited a strong pattern of spatial clustering. Proximity to several rivers and residence in the corresponding watersheds were associated with increased incidence of the disease. Elevated rainfall and stream flow rate were associated with increases in incidence 2 weeks later., Conclusions: We identified a novel relationship between the natural aquatic environment and the spatial distribution of sporadic cases of legionellosis. These results suggest that natural environmental reservoirs may have a greater influence on the spatial distribution of sporadic legionellosis cases than previously thought., (© The Author 2017. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com.)