2450. A data-driven model of the economic burden of healthcare-associated infections as impacted by use of comprehensive genomic analysis of bacteria

Abstract

BackgroundEach year, nearly 2 million patients contract and are affected by healthcare-associated infections (HAIs) in the United States alone, resulting in nearly 100K deaths. According to the Centers for Disease Control and Prevention (CDC), more patients die from HAIs in the United States per year than all breast and prostate cancer cases combined (National Vital Statistics Report, 2016). In addition to the mortality burden, the financial impact of HAIs within the hospital ecosystem is estimated to total between $28–45 billion. However, no economic model has demonstrated how early effective identification and mitigation of infection clusters can result in cost savings for hospitals until now.MethodsAs there is no publicly available data for infection cluster rates, we based our analysis on anonymized real-world retrospective data spanning 18 months (November 2016 to June 2018) from two US-based academic tertiary hospitals with a combined total of about 1,700 beds, then normalized to 800 beds. A cloud-computing platform (Philips IntelliSpace Epidemiology) was used for whole-genome sequence analysis and cluster identification. We determined that an average 800-bed facility would have an occurrence of 46 genetically related infectious clusters involving 2 or more patients (mean of 7.9, median of 3), affecting 180 patients in total.ResultsGiven the average HAI treatment cost of $24,512 (average costs rescaled from literature to 2019 USD using PPI data), this represents a total cost of $4,412,160. If these clusters could have been limited to 2 patients, an additional 96 infections might have been prevented, representing a potentially avoidable economic burden of $2,353,152 for this 800-bed institution. Our data show that a 20% reduction in transmissions would drive a 3% overall reduction in HAIs, but results in savings of over $450,000.ConclusionActive, genomic-based surveillance can inform timely and precise preventative steps to help lower the size of infectious clusters. This health economic modeling shows that such measures can result in significant cost savings. As such, it recommends that prompt, dynamic detection of infectious clusters via genomics and active surveillance offers a relevant and timely strategy for cost savings within the healthcare ecosystem. Disclosures All authors: No reported disclosures.