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Consensus is growing on the need to investigate the joint impact of neighborhood-level social factors and environmental hazards on respiratory health. This study used latent profile analysis (LPA) to empirically identify distinct neighborhood subtypes according to a clustering of social factors and environmental hazards, and to examine whether those subtypes are associated with lung function. The study included 182 low-income participants who were enrolled in the Colorado Home Energy Efficiency and Respiratory Health (CHEER) study during the years 2015–2017. Distinct neighborhood typologies were identified based on analyses of 632 census tracts in the Denver-Metro and Front Range area of Colorado; neighborhood characteristics used to identify typologies included green space, traffic-related air pollution, violent and property crime, racial/ethnic composition, and socioeconomic status (SES). Generalized estimating equations were used to examine the association between neighborhood typology and lung function. We found four distinct neighborhood typologies and provide evidence that these social and environmental aspects of neighborhoods cluster along lines of advantage/disadvantage. We provide suggestive evidence of a double jeopardy situation where low-income populations living in disadvantaged neighborhoods may have decreased lung function. Using LPA with social and environmental characteristics may help to identify meaningful neighborhood subtypes and inform research on the mechanisms by which neighborhoods influence health.

Objective: To investigate hospital room and patient-level risk factors associated with increased risk of healthcare-facility–onset Clostridioides difficile infection (HO-CDI). Design: The study used a retrospective cohort design that included patient data from the institution’s electronic health record, existing surveillance data on HO-CDI, and a walk-through survey of hospital rooms to identify potential room-level risk factors. The primary outcome was HO-CDI diagnosis. Setting: A large academic medical center. Patients and participants: All adult patients admitted between January 1, 2015, and December 31, 2016 were eligible for inclusion. Prisoners were excluded. Patients who only stayed in rooms that were not surveyed were excluded. Results: The hospital room survey collected room-level data on 806 rooms. Included in the study were 17,034 patients without HO-CDI and 251 with HO-CDI nested within 535 unique rooms. In this exploratory study, room-level risk factors associated with the outcome in the multivariate model included wear on furniture and flooring and antibiotic use by the prior room occupant. Hand hygiene devices and fixed in-room computers were associated with reduced odds of a HO-CDI. Differences between hospital buildings were also detected. The only individual patient factors that were associated with increased odds of HO-CDI were antibiotic use and comorbidity score. Conclusion: Combining a hospital-room walk-through data collection survey, EHR data, and CDI surveillance data, we were able to develop a model to investigate room and patient-level risks for HO-CDI.

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