Developing a framework for identifying risk factors and estimating direct economic disease burden attributable to healthcare-associated infections: a case study of a Chinese Tuberculosis hospital

Abstract

Healthcare-associated infections (HAIs) represent a major global health burden, which necessitate effective frameworks to identify potential risk factors and estimate the corresponding direct economic disease burden. In this article, we proposed a framework designed to address these needs through a case study conducted in a Tuberculosis (TB) hospital in Hubei Province, China, using data from 2018 to 2019. A comprehensive multistep procedure was developed, including ethical application, participant inclusion, risk factor identification, and direct economic disease burden estimation. In the case study, ethical approval was obtained, and patient data were anonymized to ensure privacy. All TB hospitalized patients over the study period were included and classified into groups with and without HAIs after screening the inclusion and exclusion criteria. Key risk factors, including gender, age, and invasive procedure were identified through univariate and multivariate analyses. Then, propensity score matching was employed to select the balanced groups with similar characteristics. Comparisons of medical expenditures (total medical expenditure, medicine expenditure, and antibiotics expenditure) and hospitalization days between the balanced groups were calculated as the additional direct economic disease burden measures caused by HAIs. This framework can serve as a tool for not only hospital management and policy-making, but also implementation of targeted infection prevention and control measures. Moreover, it has the potential to be applied in various healthcare settings at local, regional, national, and international levels to identify high-risk areas, optimize resource allocation, and improve hospital management and governance, as well as inter-organizational learning. Challenges to implement the framework are also raised, such as data quality, regulatory compliance, considerations on unique nature of communicable diseases and other diseases, and training need for professionals.