Distribution of the Costs of Antimicrobial Resistant Infections

可以大幅减轻抗菌素耐药性负担的干预措施已经存在，但 这些措施的实施一直很缓慢。采用的一个障碍是谁为 减少感染和耐药性的成本--主要是医疗保健提供者--以及谁受益 福利-提供者、付款人、受感染患者以及其他当前患者和未来患者 可能会被感染。在经济方面，围绕减少碳排放的行动存在巨大的外部性 抗菌素耐药性，这减少了投资于减少耐药性措施的财政激励。 拟议的研究将对与以下项目相关的额外费用的分布进行估计 抗菌素耐药性，并评估改变激励机制的政策如何能够刺激采用有效的 干预措施。它将补充现有的两个研究线索。经济学家描述了这种现象的存在 理论上的外部性，并对这些影响的潜在规模进行了全球水平的估计， 对特定情况或病原体的引用有限。卫生服务研究人员估计， 在特定病原体情况下的耐药性，但不涉及这些费用的分配。我们会 在这些文献的基础上，估计病原体在医院环境下耐药的总成本。我们会 根据病原体的类型以及感染是与社区有关还是与医院有关来比较这些成本。 然后，我们将研究成本和收益之间不匹配的两个来源。首先，使用支付数据，我们 是否会比较一系列保险公司的支付者和医院之间的费用分配，这些保险公司使用 不同的支付制度。第二，指标患者和其他患者之间的费用分摊 这取决于对其他患者的护理如何受到耐药病例的影响。我们将检查成本(和 对暴露在耐药病例中的其他患者)，并估计这些增量成本。接下来，我们将 评估可能会改变支付政策的政策选项。我们将使用来自NICU的CIRAR研究的数据 干预措施(Salman)，以评估支付政策的变化将如何影响采用特定政策的激励 干预。我们将估计医院内耐药和易感感染的总费用 在替代支付策略下会有所不同，并根据此模型估计相关的净收入 在每一家医院都有抵抗力或易感感染。这些分析的数据将来自纽约的四个城市 属于同一医院系统的医院站点，但服务于完全不同的人群 付款人配置文件。所有医院都有完善的感染控制数据库系统。我们将把这些链接起来 感染控制数据到医院成本核算数据、患者位置记录和订单录入数据。 分析将把耐药感染患者(医院和社区获得的)与易感患者进行配对 患者和未感染的患者。_^

Interventions that could substantially reduce the burden of antimicrobial resistance exist, but the pace of adoption of these measures has been slow. One barrier to adoption is the mismatch between who pays the costs of reducing infections and resistance - predominantly health care providers - and who gainsthe benefits -- the provider, payers, infected patients, as well as other current patients and future patients who could become infected. In economic terms, there are large externalities of action around the reduction of antimicrobial resistance, which reduce the financial incentives to invest in measures that reduce resistance. The proposed researchwill develop estimates of the distribution of the extra costs associated with antimicrobial resistance and assess how policies that change incentives could spur adoption ofeffective interventions. It will supplement two existing strands of research. Economists have described the existence of externalities in theory, and have produced global level estimates of the potential size of these effects,with limited reference to specific situations or pathogens. Health services researchers have estimated the costs of resistance in the case of particular pathogens, but without reference to the allocation of these costs. We will build on these literatures and estimate the total costs of resistance by pathogen in hospital settings. We will compare these costs by type of pathogen and by whether the infection is community- or hospital-associated. We will then examine two sources of mismatch between costs and benefits. First, using payment data,we will comparethe allocation of costs between payers and hospitals across a range of insurers, who use different payment systems.Second, the allocation of costs between the index patient and other patients depends on how care for other patients is affectedby resistant cases.We will examine the costs(and payments) for other patients exposed to a resistant case and estimate these incremental costs. Next, we will assess policy options that would change payment policies. We will use data from the CIRAR study of NICU interventions (Salman) to assess how changes in payment policy will affect incentives to adopt a particular intervention. We will estimate how total paymentsfor resistant and susceptible infections in the hospital would vary under alternative payment strategies and, basedon this model, estimate net revenuesassociated with resistant or susceptible infection in each hospital. Data for these analyses will be drawn from fourNYC hospital sites that are part of the same hospital system but serve very different populations with distinct payer profiles. All hospital sites have well-developed infection control database systems. We will link these infection control data to hospital cost accounting data, patient location records, and order entry data.The analyses will match patients with resistant infections (hospital- and community-acquired) to susceptible patients and to uninfected patients. _^