P6371Network characteristics of a hypertension referral system in western kenya
P6371Network characteristics of a hypertension referral system in western kenya
复制标题
P6371肯尼亚西部高血压转诊系统的网络特征
DOI：
10.1093/eurheartj/ehz746.0967
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发表时间：
2019
影响因子：
39.3
通讯作者：
C. Akwanalo
中科院分区：
文献类型：
--
作者：
A. Thakkar;T. Valente;J. Andesia;B. Njuguna;J. Miheso;Tim Mercer;E. Mwangi;S. Pastakia;M. Pillsbury;Shravani Pathak;J. Kamano;V. Naanyu;R. Vedanthan;G. Bloomfield;C. Akwanalo
加强整个卫生系统高血压管理转诊网络 (STRENGTHS) 试验正在制定和测试干预措施，以提高肯尼亚西部高血压患者转诊网络的有效性。
对基于设施的医疗保健提供者的网络分析用于了解现有的转诊网络。最终目标是确定结构性差距和实施计划干预措施的机会。
对为高血压患者提供护理的提供者进行了一项网络调查，要求个人提名 a) 将患者转诊至卫生系统上下的个人，以及 b) 将患者转诊至的机构。我们使用入度和出度（分别是每个提供商接收和发送的链接数量）的中心性度量以及拟合核心外围（CP）模型来分析调查数据。较高的 CP 表明推荐网络较强，而较低的 CP 表明相对较弱的网络。
数据收集自 7 个不同地理位置的网络集群内 39 个站点的 130 个提供商。每个集群由一级、二级和/或三级设施混合组成。与完美的 CP 推荐网络模型（相关性得分 [CP] = 1.00）和随机推荐网络模型（CP = 0.200）相比，每个集群内的提供商推荐网络表现出 CP 结构较弱的趋势。 CP 的范围很大，从 0.334 到 0.639。相比之下，集群级设施网络表现出强烈的CP结构趋势，CP范围为0.857至0.949。
核心外围相关性得分 [CP] 网络集群 1 集群 2 集群 3 集群 4 集群 5 集群 6 集群 7 提供商推荐 0.433 0.424 0.334 0.639 0.535 0.448 0.407 设施推荐 0.949 0.894 0.871 0.949 0.949 0.904 0.857 每个集群代表一个地理上独立的推荐网络。随机推荐网络的 CP 分数为 0.200；而一个完美的推荐网络会给 CP 1.00。
推荐网络模型
肯尼亚西部目前的卫生系统在高血压患者的提供者之间没有表现出强大的转诊网络。虽然设施到设施的推荐更符合完美的推荐模型，但特定提供者之间的沟通存在差距。这些结果凸显了需要设计和测试加强提供者转诊模式的干预措施，以改善血压控制并降低心血管风险。
美国国立卫生研究院：国家心肺和血液研究所、多丽丝杜克慈善基金会：国际临床研究奖学金
The Strengthening Referral Networks for Management of Hypertension Across the Health System (STRENGTHS) trial is creating and testing interventions to improve the effectiveness of referral networks for patients with hypertension in Western Kenya.
Network analysis of facility-based healthcare providers was used to understand the existing network of referrals. The ultimate goal was to identify both structural gaps and opportunities for implementation of the planned intervention.
A network survey was administered to providers who deliver care to patients with hypertension asking individuals to nominate a) individuals to whom, and b) facilities to which they refer patients, both up and down the health system. We analyzed survey data using centrality measures of in-degree and out-degree (number of links each provider received and sent, respectively), as well as fitting a core-periphery (CP) model. A higher CP indicates a strong referral network, while a lower CP indicates a relatively weaker network.
Data were collected from 130 providers across 39 sites within 7 geographically separate network clusters. Each cluster consists of a mix of primary, secondary, and/or tertiary facilities. Compared to a perfect CP referral network model (Correlation Score [CP] = 1.00) and a random referral network model (CP = 0.200), the provider referral networks within each cluster showed a weak tendency for CP structure. There was a large range in CP from 0.334 to 0.639. In contrast, cluster-level facility networks showed a strong tendency for CP structure, with a CP range of 0.857 to 0.949.
Core Periphery Correlation Scores [CP] Network Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6 Cluster 7 Provider Referrals 0.433 0.424 0.334 0.639 0.535 0.448 0.407 Facility Referrals 0.949 0.894 0.871 0.949 0.949 0.904 0.857 Each cluster represents a geographically separate referral network. A random referral network would reveal a CP score of 0.200; while a perfect referral network would give a CP of 1.00.
Referral Network Models
The current health system across Western Kenya does not demonstrate a strong network of referrals between providers for patients with hypertension. While facility-to-facility referrals are more in-line with a perfect referral model, there are gaps in communication between the specific providers. These results highlight the need for STRENGTHS to design and test interventions that strengthen provider referral patterns in order to improve blood pressure control and reduce cardiovascular risk.
National Institutes of Health: National Heart Lung and Blood Institute, Doris Duke Charitable Foundation:International Clinical Research Fellowship