Background: Population attributable risk percent (PAR%) is an epidemiological tool that provides an estimate of the percent reduction in total disease burden if that disease could be entirely eliminated among a subpopulation. As such, PAR% is used to efficiently target prevention interventions. Due to significant limitations in current Clostridium difficile Infection (CDI) prevention practices and the development of new approaches to prevent CDI, such as vaccina- tion, we determined the PAR% for CDI in various subpopulations in the Medicare 5% random sample. Methods: This was a retrospective cohort study using the 2009 Medicare 5% random sample. Comorbidities, infections, and healthcare exposures during the 12 months prior to CDI were identified. CDI incidence and PAR% were calculated for each condition/exposure. Easy to identify subpopulations that could be targeted from prevention interventions were identified based on PAR%. Findings: There were 1,465,927 Medicare beneficiaries with 9,401 CDI cases for an incidence of 677/ 100,000 persons. Subpopulations representing less than 15% of the entire population and with a PAR% >= 30% were identified. These included deficiency anemia (PAR% = 37.9%), congestive heart failure (PAR% = 30.2%), fluid and electrolyte disorders (PAR% = 29.6%), urinary tract infections (PAR% = 40.5%), pneumonia (PAR% = 35.2%), emergent hospitalization (PAR% = 48.5%) and invasive procedures (PAR% = 38.9%). Stratification by age and hospital exposures indicates hospital exposures are more strongly associated with CDI than age. Significance: Small and identifiable subpopulations that account for relatively large proportions of CDI cases in the elderly were identified. These data can be used to target specific subpopulations for CDI prevention interventions.
Dartmouth Digital Commons Citation
Dubberke, Erik R.; Olsen, Margaret A.; Stwalley, Dustin; Kelly, Ciarán P.; Gerding, Dale N.; Young-Xu, Yinong; and Mahé, Cedric, "Identification of Medicare Recipients at Highest Risk for Clostridium difficile Infection in the US by Population Attributable Risk Analysis" (2016). Dartmouth Scholarship. 2762.