Distribution of the Costs of Antimicrobial Resistant Infections

存在可以大幅减少抗生素耐药性负担的干预措施，但 这些措施的实施进展缓慢。采用的一个障碍是谁支付 减少感染和耐药性的成本-主要是卫生保健提供者-以及谁获得了 利益-提供者，付款人，感染的患者，以及其他当前和未来的患者， 可能会被感染在经济方面，围绕减少温室气体排放的行动具有很大的外部性。 抗生素耐药性，这减少了投资于减少耐药性措施的经济激励。 拟议的研究将对与以下方面相关的额外成本的分布进行估计： 抗生素耐药性，并评估改变激励措施的政策如何刺激有效的 干预措施。它将补充现有的两个研究领域。经济学家认为， 理论上的外部性，并产生了这些影响的潜在规模的全球水平的估计， 对特定情况或病原体的有限提及。卫生服务研究人员估计， 在特定病原体的情况下，耐药性，但没有提到这些成本的分配。我们将 在这些文献的基础上，估计医院环境中病原体耐药性的总成本。我们将 根据病原体的类型以及感染是否与社区或医院相关来比较这些成本。 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 将比较支付者和医院之间的费用分配， 不同的支付系统。第二，指标病人和其他病人之间的成本分配 这取决于耐药病例对其他患者的护理的影响。我们将研究成本（以及 支付），并估计这些增量成本。接下来我们就 评估可能改变付款政策的政策选择。我们将使用NICU的CIRAR研究数据 干预措施（萨尔曼），以评估支付政策的变化将如何影响采取特定措施的激励措施。 干预We will estimate估计how total总payments支付for resistant耐药and susceptible敏感infections感染in the hospital医院 根据不同的支付策略， 有耐药或易感感染的病人这些分析的数据将来自四个纽约市 医院站点是同一医院系统的一部分，但服务于不同人群， 付款人档案。所有医院都有完善的感染控制数据库系统。我们将把这些 感染控制数据到医院成本核算数据、患者位置记录和订单输入数据。 分析将匹配耐药感染患者（医院和社区获得性）和易感患者 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. _^