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利用关联诊断来提高肯尼亚疟疾服务的准确诊断、治疗和有条件支付水平。

Connected diagnostics to improve accurate diagnosis, treatment, and conditional payment of malaria services in Kenya.

机构信息

PharmAccess Foundation, Amsterdam, The Netherlands.

PharmAccess Foundation, Nairobi, Kenya.

出版信息

BMC Med Inform Decis Mak. 2021 Aug 4;21(1):233. doi: 10.1186/s12911-021-01600-z.

DOI:10.1186/s12911-021-01600-z
PMID:34348696
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8335459/
Abstract

BACKGROUND

In sub-Saharan Africa, the material and human capacity to diagnose patients reporting with fever to healthcare providers is largely insufficient. Febrile patients are typically treated presumptively with antimalarials and/or antibiotics. Such over-prescription can lead to drug resistance and involves unnecessary costs to the health system. International funding for malaria is currently not sufficient to control malaria. Transition to domestic funding is challenged by UHC efforts and recent COVID-19 outbreak. Herewith we present a digital approach to improve efficiencies in diagnosis and treatment of malaria in endemic Kisumu, Kenya: Connected Diagnostics. The objective of this study is to evaluate the feasibility, user experience and clinical performance of this approach in Kisumu.

METHODS

Our intervention was performed Oct 2017-Dec 2018 across five private providers in Kisumu. Patients were enrolled on M-TIBA platform, diagnostic test results digitized, and only positive patients were digitally entitled to malaria treatment. Data on socio-demographics, healthcare transactions and medical outcomes were analysed using standard descriptive quantitative statistics. Provider perspectives were gathered by 19 semi-structured interviews.

RESULTS

In total 11,689 febrile patients were digitally tested through five private providers. Malaria positivity ranged from 7.4 to 30.2% between providers, significantly more amongst the poor (p < 0.05). Prescription of antimalarials was substantially aberrant from National Guidelines, with 28% over-prescription (4.6-63.3% per provider) and prescription of branded versus generic antimalarials differing amongst facilities and correlating with the socioeconomic status of clients. Challenges were encountered transitioning from microscopy to RDT.

CONCLUSION

We provide full proof-of-concept of innovative Connected Diagnostics to use digitized malaria diagnostics to earmark digital entitlements for correct malaria treatment of patients. This approach has large cost-saving and quality improvement potential.

摘要

背景

在撒哈拉以南非洲,医疗服务提供者诊断发热患者的物质和人力资源严重不足。发热患者通常被推定使用抗疟药和/或抗生素治疗。这种过度处方会导致耐药性,并给卫生系统带来不必要的成本。目前,用于疟疾的国际资金不足以控制疟疾。全民医保的努力和最近的 COVID-19 疫情对向国内资金过渡提出了挑战。在此,我们提出了一种数字方法来提高肯尼亚基苏木疟疾的诊断和治疗效率:连接诊断。本研究的目的是评估这种方法在基苏木的可行性、用户体验和临床性能。

方法

我们的干预措施于 2017 年 10 月至 2018 年 12 月在基苏木的五家私人提供商中进行。患者在 M-TIBA 平台上注册,诊断测试结果数字化,只有阳性患者才能获得数字化的疟疾治疗权利。使用标准描述性定量统计方法分析社会人口统计学、医疗交易和医疗结果数据。通过 19 次半结构化访谈收集提供者的观点。

结果

总共有 11689 名发热患者通过五家私人提供商进行了数字检测。各提供者的疟疾阳性率在 7.4%至 30.2%之间,贫困人口中的阳性率明显更高(p<0.05)。抗疟药物的处方与国家指南有很大出入,过度处方率为 28%(每个提供者的 4.6-63.3%),品牌与通用抗疟药物的处方在各医疗机构之间存在差异,并与客户的社会经济地位相关。从显微镜检查过渡到 RDT 时遇到了挑战。

结论

我们充分证明了创新的连接诊断的可行性,使用数字化疟疾诊断为患者正确的疟疾治疗提供数字权利。这种方法具有很大的成本节约和质量改进潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b35/8336342/71fc69976c7b/12911_2021_1600_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b35/8336342/9500cdbd5d28/12911_2021_1600_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b35/8336342/08b906afbd3d/12911_2021_1600_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b35/8336342/2d6961e19065/12911_2021_1600_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b35/8336342/186c67204073/12911_2021_1600_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b35/8336342/27738d4e3377/12911_2021_1600_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b35/8336342/71fc69976c7b/12911_2021_1600_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b35/8336342/9500cdbd5d28/12911_2021_1600_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b35/8336342/08b906afbd3d/12911_2021_1600_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b35/8336342/2d6961e19065/12911_2021_1600_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b35/8336342/186c67204073/12911_2021_1600_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b35/8336342/27738d4e3377/12911_2021_1600_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b35/8336342/71fc69976c7b/12911_2021_1600_Fig6_HTML.jpg

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