Centro Internacional de Entrenamiento e Investigaciones Médicas, CIDEIM, Cali, Colombia.
Universidad Icesi, Cali, Colombia.
PLoS Negl Trop Dis. 2021 Feb 11;15(2):e0008989. doi: 10.1371/journal.pntd.0008989. eCollection 2021 Feb.
Detection and management of neglected tropical diseases such as cutaneous leishmaniasis present unmet challenges stemming from their prevalence in remote, rural, resource constrained areas having limited access to health services. These challenges are frequently compounded by armed conflict or illicit extractive industries. The use of mobile health technologies has shown promise in such settings, yet data on outcomes in the field remain scarce.
We adapted a validated prediction rule for the presumptive diagnosis of CL to create a mobile application for use by community health volunteers. We used human-centered design practices and agile development for app iteration. We tested the application in three rural areas where cutaneous leishmaniasis is endemic and an urban setting where patients seek medical attention in the municipality of Tumaco, Colombia. The application was assessed for usability, sensitivity and inter-rater reliability (kappa) when used by community health volunteers (CHV), health workers and a general practitioner, study physician.
The application was readily used and understood. Among 122 screened cases with cutaneous ulcers, sensitivity to detect parasitologically proven CL was >95%. The proportion of participants with parasitologically confirmed CL was high (88%), precluding evaluation of specificity, and driving a high level of crude agreement between the app and parasitological diagnosis. The chance-adjusted agreement (kappa) varied across the components of the risk score. Time to diagnosis was reduced significantly, from 8 to 4 weeks on average when CHV conducted active case detection using the application, compared to passive case detection by health facility-based personnel.
Translating a validated prediction rule to a mHealth technology has shown the potential to improve the capacity of community health workers and healthcare personnel to provide opportune care, and access to health services for underserved populations. These findings support the use of mHealth tools for NTD research and healthcare.
由于皮肤利什曼病等被忽视的热带病在偏远、农村、资源有限且难以获得卫生服务的地区流行，因此在这些地区发现和管理这些疾病存在未满足的挑战。武装冲突或非法采掘业常常使这些挑战更加复杂。移动医疗技术在这些环境中显示出了希望，但有关实地结果的数据仍然很少。
我们改编了一个经过验证的用于疑似皮肤利什曼病诊断的预测规则，创建了一个适用于社区卫生志愿者使用的移动应用程序。我们使用以人为中心的设计实践和敏捷开发来进行应用程序迭代。我们在三个皮肤利什曼病流行的农村地区和哥伦比亚图马科市的一个城市地区进行了应用测试，在那里患者在该市寻求医疗服务。该应用程序由社区卫生志愿者（CHV）、卫生工作者和一名普通医生（研究医生）评估其可用性、敏感性和组内一致性（kappa）。
该应用程序易于使用和理解。在 122 例经筛查有皮肤溃疡的病例中，检测寄生虫学证实的皮肤利什曼病的敏感性>95%。有寄生虫学确诊皮肤利什曼病的参与者比例很高（88%），排除了特异性评估的可能性，并导致应用程序与寄生虫学诊断之间存在高度的粗一致性。风险评分各组成部分的机会调整一致性（kappa）各不相同。当 CHV 使用该应用程序进行主动病例发现时，与基于卫生机构人员的被动病例发现相比，诊断时间从平均 8 周减少到 4 周，显著缩短。
将经过验证的预测规则转化为移动医疗技术，显示出提高社区卫生工作者和医疗保健人员提供及时护理和为服务不足人群获得卫生服务的能力的潜力。这些发现支持将移动医疗工具用于 NTD 研究和医疗保健。
