Clostridium difficile infection among men: Implications for improved infection control policies
Methods: The Nationwide Inpatient Sample (2009-2011) was used with 279,072 men hospitalized for pneumonia and 75,600 men hospitalized for UTI. Survey-weighted multivariable regression analyses were conducted to assess the predictors and impact of CDI.
Results: Prevalence of CDI was noted to 10.7 per 1,000 pneumonia hospitalizations and 13.3 per 1,000 UTI hospitalizations. Patient and hospital characteristics associated with CDI included being 65 years or older, increasing co-morbidities, Medicare as the primary payer, and discharge from urban hospitals. CDI was also associated with higher in-hospital mortality among discharges for pneumonia (adjusted odds ratio [aOR] = 3.2) and UTI (aOR = 4.1). CDI was further associated with over double the length of hospital stay and also increased the total charges by approximately 80% and 60% among pneumonia and UTI discharges, respectively. Men who were aged 65 years and older, were over two times more likely to die from CDI upon being hospitalized for pneumonia or UTI. Similarly, increased length of stay and total charges were noted. The overall in-hospital mortality and healthcare burden (length of stay and charges) were higher among younger men, as compared to those aged 65 years and older.
Conclusion: CDI occurs frequently in hospitalizations for among men discharged from hospital for pneumonia and UTI, and is associated with increased in-hospital mortality and health resource utilization. Improve hospital policies to mitigate the burden of CDI in these high-risk populations are urgently needed, especially among younger men.
Public health or related research
Discuss burden of Clostridium difficile infection among men, by age group. Identify the impact of Clostridium difficile infection on mortality and hospital outcomes for men.
Keyword(s): Epidemiology, Health Care Costs
Qualified on the content I am responsible for because: I am a graduate student working on several projects related to big-data usage in addressing health disparities. I have worked on projects related to health service research, including impact of hospital characteristics on patient outcomes, length of stay, and disposition status. I am also a part-time instructor in statistics for various majors, giving me the competency to conduct big data analysis.
Any relevant financial relationships? No
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