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General Differences In Risk Of Bloodstream And Surgical Site Infections Essay

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Citation:
Cohen, B., Choi, Y.J., Hyman, S., Furuya, E.Y., Neidell, M., & Larson, E. (2013). General Differences in Risk of Bloodstream and Surgical Site Infections. Journal of General Internal Medicine, 28(10), 1318-25.
Purpose (or Aim) of the Study:
The aim of the study is to “investigate whether rates of community- and healthcare-associated bloodstream and surgical site infections varied by patient gender in a large cohort after controlling a wide variety of possible confounders” (Cohen et al., 2013).
Conceptual Framework:
Prevention of bacterial infections is an important goal since antibiotic-resistant organisms continue to grow. Understanding individual characteristics that put people at risk for community- and …show more content…

Major Variable Studied and Their Definitions:
Independent Variables:
V1= Male patients with bloodstream infections and surgical site infections
V2= Female patients with bloodstream infections and surgical site infections
Dependent Variables:
D= results from categories (labs, MARs, ICD-9 codes, Operative data, International Classification of Diseases, EMRs)
• Labs- blood cultures and/or bacterial cultures
• MARs- Medication Administration Records
• ICD-9 codes- Diagnosis codes
• Operative data- Incision and closure times, type of procedure, and anesthesia given.
• International Classification of Diseases- Classifies different types of diseases
• EMRs- Electronic Medical Records
Measurement of Major Variables:
“Infections were identified using previously validated computerized algorithms” (Cohen et al., 2013). Cases of bloodstream infections were patients who had a positive blood culture in the presence of a negative culture for the same organism in another body site within the previous 2 weeks (Cohen et al., 2013). Cases of surgical site infections were patients who had a surgical procedure as demonstrated by an ICD-9 code and a positive surgical wound culture within 30 days after surgery (Cohen et al., 2013). For community-associated infections, data including age, gender, diagnoses, health history, and hospital admissions were identified. For hospital-associated infection, data including hospital admissions, ICD-9 codes, EMRs, MARs, and pre- and

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