Despite a large increase in Clostridium difficile infection (CDI) severity, morbidity and mortality in the U.S. since the early 2000s, CDI burden estimates have had limited generalizability and comparability due to widely varying clinical settings, populations, or study designs. Desai, et al. (2016) developed a decision-analytic model incorporating key input parameters important in CDI epidemiology to estimate the annual number of initial and recurrent CDI cases, attributable and all-cause deaths, economic burden in the general population, and specific number of high-risk patients in different healthcare settings and the community in the US. Economic burden was calculated adopting a societal perspective using a bottom-up approach that identified healthcare resources consumed in the management of CDI.
Annually, a total of 606,058 (439,237 initial and 166,821 recurrent) episodes of CDI were predicted in 2014: 34.3 percent arose from community exposure. More than 44,500 CDI-attributable deaths in 2014 were estimated to occur. High-risk susceptible individuals representing 5 percent of the total hospital population accounted for 23 percent of hospitalized CDI patients. The economic cost of CDI was $5.4 billion ($4.7 billion (86.7 percent) in healthcare settings; $725 million (13.3 percent) in the community), mostly due to hospitalization.
The researchers conclude that a modeling framework provides more comprehensive and detailed national-level estimates of CDI cases, recurrences, deaths and cost in different patient groups than currently available from separate individual studies. As new treatments for CDI are developed, this model can provide reliable estimates to better focus healthcare resources to those specific age-groups, risk-groups, and care settings in the U.S. where they are most needed. (Trial Identifier ClinicaTrials.gov: NCT01241552)
Reference: Desai K, Gupta SB, Dubberke ER, Prabhu VS, Browne C and Christopher Mast TC. Epidemiological and economic burden of Clostridium difficile in the United States: estimates from a modeling approach. BMC Infectious Diseases. 2016;16:303