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%的蛇咬伤毒液中毒（SBE）发生在低资源环境中。 这种被忽视的热带疾病影响了270万人，81，000 - 138，000人死亡， 每年约有40万人终身残疾。抗蛇毒血清是一种安全，有效，时间- 依赖，循证治疗，但可用性和及时获得这一点， 护理水平不够。关键是要让当地社区参与， 在偏远、资源匮乏的地区，大多数SBE发病率发生。这项建议会 开发和评估创新的多模式干预措施，以改善SBE护理，包括 在现有的社区卫生保健中心（CHC）之间分散分配抗蛇毒血清 巴西亚马逊的网络。这一新的干预措施将整合（一）先进的地理空间 人工智能使用现有的CHC创建分散的抗蛇毒血清分配模型 优化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）具有文化相关性的小型企业一揽子护理计划 包括教育、治疗指南、抗蛇毒血清输送培训和机构间护理 协同利用实施研究综合框架，我们将开展一项 对这一多模式干预措施进行形成性评价，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 将采用标准化的方法，使用高级分析来优化医疗保健服务， 可用于其他国家和其他医疗保健干预措施的资源设置。

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.