482. Association between Socioeconomic Status Factors and Incidence of Community-Associated Clostridium difficile Infection Utilizing Factor Analysis—United States, 2014–2015. (26th November 2018)

• Record Type: Journal Article
• Authors: Skrobarcek, Kimberly; Mu, Yi; Ahern, Jennifer; Beldavs, Zintars; Brousseau, Geoff; Dumyati, Ghinwa; Farley, Monica M; Holzbauer, Stacy; Kainer, Marion A; Meek, James I; Perlmutter, Rebecca; Phipps, Erin C; Winston, Lisa G; Guh, Alice Y
• Is Part Of: Open forum infectious diseases. Volume 5(2018)Supplement 1
• Page Start: S179
• Page End: S179
• Publication Date: 2018-11-26
• Subjects: Communicable diseases -- Periodicals; Medical microbiology -- Periodicals; Infection -- Periodicals; 616.9
• DOI: 10.1093/ofid/ofy210.491
• Languages: English
• ISSNs: 2328-8957

Abstract

Abstract: Background: Traditionally a healthcare-associated infection, Clostridium difficile infection (CDI) is increasingly emerging in communities. Health disparities in CDI exist, but the social determinants of health that influence community-associated (CA) CDI are unknown. We used factor analysis and disparate data sources to identify area-based socioeconomic status (SES) factors associated with CA-CDI incidence. Methods: CDC's Emerging Infections Program conducts population-based CDI surveillance in 35 US counties. A CA-CDI case is defined as a positive C. difficile specimen collected as an outpatient or within 3 days of hospitalization in a person aged ≥1 year without a positive test in the prior 8 weeks or an overnight stay in a healthcare facility in the prior 12 weeks. 2014–2015 CA-CDI case addresses were geocoded to a 2010 census tract (CT) and incidence rates were calculated. CT-level SES variables were obtained from the 2011–2015 American Community Survey. The Health Resources and Services Administration provided medically underserved area (MUA) designations. Exploratory factor analysis transformed 15 highly correlated SES variables into threefactors using scree plot and Kaiser criteria: "Low Income, " "Foreign-born, " and "High Income." To account for CT-level clustering, a negative binomial generalized linear mixed model was used to evaluate the associations of these factors and MUA with CA-CDI incidence, adjusting for age, sex, race and diagnostic test. Results: Of 13, 903 CA-CDI geocoded cases, 63% were female, 80% were white, and 36% were aged ≥65 years. Annual CA-CDI incidence was 63.4/100, 000 persons. In multivariable analysis, "Low Income" (rate ratio [RR]: 1.09; 95% confidence interval [CI]: 1.05–1.13) and "High Income" (RR: 0.90; CI: 0.87–0.93) were significantly associated with CA-CDI incidence. Conclusion: Factor analysis was instrumental in identifying key drivers of disparities among interrelated SES variables. Low-income areas were surprisingly associated with higher CA-CDI incidence, given that known CDI risk factors include increased access to healthcare. Understanding how SES factors might impact CA-CDI incidence can inform prevention strategies in low-income areas. Disclosures: G. Dumyati, Seres: Scientific Advisor, Consulting fee.