医疗保健相关感染(HAIs)是全球主要的健康负担。这需要有效的框架来识别潜在的风险因素并估计相应的直接经济疾病负担。在这篇文章中,我们通过在湖北省结核病(TB)医院进行的案例研究,提出了一个旨在满足这些需求的框架,中国,使用2018年至2019年的数据。开发了一个全面的多步骤程序,包括道德申请,参与者包容,危险因素识别,和直接的经济疾病负担估计。在案例研究中,获得了伦理批准,患者数据被匿名化,以确保隐私。在筛选纳入和排除标准后,纳入研究期间所有TB住院患者,并将其分为有和无HAIs组。关键风险因素,包括性别,年龄,通过单变量和多变量分析确定侵入性程序。然后,倾向评分匹配用于选择具有相似特征的平衡组.医疗支出比较(医疗支出总额,医药支出,和抗生素支出)和平衡组之间的住院天数被计算为由HAIs引起的额外直接经济疾病负担指标。这个框架不仅可以作为医院管理和决策的工具,还要实施有针对性的感染防控措施。此外,它有可能应用于当地的各种医疗保健环境,区域,国家,和国际层面来确定高风险地区,优化资源配置,改善医院管理和治理,以及组织间学习。还提出了执行该框架的挑战,例如数据质量,法规遵从性,考虑传染病和其他疾病的独特性质,以及对专业人员的培训需求。
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.