Using community health centers to mitigate the impact of snakebite envenoming in low resource areas

摘要 在全球范围内，超过90%的毒蛇咬伤(SBE)发生在资源匮乏的环境中。 这种被忽视的热带疾病影响着270万人，有8.1万至13.8万人死亡，以及 每年约有400,000名永久性残疾人。抗蛇毒血清是一种安全有效的时间- 依赖和循证治疗，但可获得性和及时获得这种治疗 护理标准是不够的。让当地社区参与进来以及时改进是至关重要的 在SBE发病率最高的偏远、低资源地区获得服务。这项提议将 开发和评估创新的多模式干预措施，以改善SBE护理，包括 分散分布在现有社区保健中心(CHC)的抗蛇毒血清 在巴西亚马逊的网络。这一新的干预将整合(A)高级地理空间 使用现有CHC创建分散式抗蛇毒血清分布模型的人工智能 这优化了人口覆盖率和获得护理的时间，(B)成本效益评估 与主要利益攸关方共同制定的模式，以及(C)与文化相关的SBE护理包 包括教育、治疗指南、关于抗蛇毒血清输送和设施间护理的培训 协调。利用实施研究综合框架，我们将进行 对这种多模式干预进行形成性评估，并为 巴西亚马逊，以及准备扩大到其他低资源地区。因此，我们 将采用标准化方法使用高级分析来优化低成本的医疗保健服务 可以在其他国家使用的资源设置，以及其他医疗干预措施。

Abstract Globally, more than 90% of all snakebite envenomation (SBE) occur in low resource settings. This neglected tropical disease, affects 2.7 million people, with 81,000-138,000 deaths, and approximately 400,000 permanent disabilities annually. Antivenom is a safe, efficacious, time- dependent, and evidence-based treatment, but the availability and timely access to this standard of care is inadequate. It is critical to engage local communities to improve timely access in remote, low resource areas where most SBE morbidity occurs. This proposal will develop and evaluate an innovative multi-modal intervention to improve SBE care, including decentralized antivenom distribution among the existing community healthcare center (CHC) network in the Brazilian Amazon. This novel intervention will integrate (a) advanced geospatial artificial intelligence to create decentralized antivenom distribution models using existing CHCs that optimizes population coverage and time to reach care, (b) a cost-effectiveness evaluation model developed with key stakeholders, and (c) a culturally relevant SBE care package containing education, treatment guidelines, training on antivenom delivery and interfacility care coordination. Using the Consolidated Framework for Implementation Research, we will conduct a formative evaluation of this multi-modal intervention and prepare an implementation strategy for the Brazilian Amazon, as well as prepare to scale up to other low resource locations. Consequently, we will have a standardized approach to use advanced analytics to optimize healthcare delivery in low resource settings that can be used in other countries and with other healthcare interventions.