Estimating Local Attributable Cost of Clostridium difficile Infection Using Electronic Medical Record Data

Abstract

Background. Clostridium difficile infection (CDI) is an increasingly prevalent threat to public health in the United States, causing >$1 billion in excess medical costs per year. Part of the difficulty in designing infection control interventions for CDI, however-even with precise data on local incidence and transmission-is whether the expense of new interventions (e.g. unit cleaning, antimicrobial stewardship programs, or more frequent screening) can be outweighed by the potential reduction in healthcare costs. Estimates of cost attributable to CDI range from $2871 to $90,664 per patient and depend strongly on the patient population, suggesting that hospitals may need to analyze local data, such as electronic medical records (EMRs), to reasonably estimate local impact of CDI. Methods. De-identified data on patient visits between 2009 and 2014 were exported from the EMR data warehouse of an urban tertiary-care hospital. CDI presence was defined by a positive toxin assay or an assigned ICD9 diagnosis code. The MatchIt R package was used to perform nonparametric case-control matching (1:2 ratio) between CDI patients and a weighted cohort of comparable non-CDI patients via logistic regression of age, gender, diagnosis codes, and surgery procedures against CDI status; this simulates a data set from a randomized experiment suitable for parametric modeling. ACox proportional hazards model was then used to estimate the effect of CDI on length of stay, which is a proxy for attributable cost, since the marginal cost of one inpatient-day in a given hospital unit is relatively stable. Results. On a set of 30,000 matched EMRs, we found an attributable median increase of 10 inpatient-days per CDI patient, higher than the national average of 3.3. One limitation is that the model does not correct for reverse causation (longer stays increasing CDI risk). Conclusion. Despite the lack of randomization inherent to EMR data, matching strategies can help compare a CDI patient cohort against control patients with equal modeled propensity for CDI, allowing estimation of cost attributable to CDI within a single hospital. This estimate may justify the scope of spending for infection control measures specifically targeting CDI.