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评估基于症状的算法在塞内加尔用于识别发热患者以进行疟疾诊断检测的效用。

Assessment of the utility of a symptom-based algorithm for identifying febrile patients for malaria diagnostic testing in Senegal.

作者信息

Thwing Julie, Ba Fatou, Diaby Alou, Diedhiou Younouss, Sylla Assane, Sall Guelaye, Diouf Mame Birame, Gueye Alioune Badara, Gaye Seynabou, Ndiop Medoune, Cisse Moustapha, Ndiaye Daouda, Ba Mady

机构信息

U.S. Centers for Disease Control and Prevention and President's Malaria Initiative, Atlanta, USA.

Senegal National Malaria Control Programme, Dakar, Senegal.

出版信息

Malar J. 2017 Mar 1;16(1):95. doi: 10.1186/s12936-017-1750-y.

DOI:10.1186/s12936-017-1750-y
PMID:28249580
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5333468/
Abstract

BACKGROUND

Malaria rapid diagnostic tests (RDTs) enable point-of-care testing to be nearly as sensitive and specific as reference microscopy. The Senegal National Malaria Control Programme introduced RDTs in 2007, along with a case management algorithm for uncomplicated febrile illness, in which the first step stipulates that if a febrile patient of any age has symptoms indicative of febrile illness other than malaria (e.g., cough or rash), they would not be tested for malaria, but treated for the apparent illness and receive an RDT for malaria only if they returned in 48 h without improvement.

METHODS

A year-long study in 16 health posts was conducted to determine the algorithm's capacity to identify patients with Plasmodium falciparum infection identifiable by RDT. Health post personnel enrolled patients of all ages with fever (≥37.5 °C) or history of fever in the previous 2 days. After clinical assessment, a nurse staffing the health post determined whether a patient should receive an RDT according to the diagnostic algorithm, but performed an RDT for all enrolled patients.

RESULTS

Over 1 year, 6039 patients were enrolled and 58% (3483) were determined to require an RDT according to the algorithm. Overall, 23% (1373/6039) had a positive RDT, 34% (1130/3376) during rainy season and 9% (243/2661) during dry season. The first step of the algorithm identified only 78% of patients with a positive RDT, varying by transmission season (rainy 80%, dry 70%), malaria transmission zone (high 75%, low 95%), and age group (under 5 years 68%, 5 years and older 84%).

CONCLUSIONS

In all but the lowest malaria transmission zone, use of the algorithm excludes an unacceptably large proportion of patients with malaria from receiving an RDT at their first visit, denying them timely diagnosis and treatment. While the algorithm was adopted within a context of malaria control and scarce resources, with the goal of treating patients with symptomatic malaria, Senegal has now adopted a policy of universal diagnosis of patients with fever or history of fever. In addition, in the current context of malaria elimination, the paradigm of case management needs to shift towards the identification and treatment of all patients with malaria infection.

摘要

背景

疟疾快速诊断检测(RDTs)使即时检测的敏感性和特异性几乎与参考显微镜检测相当。塞内加尔国家疟疾控制项目于2007年引入了RDTs,以及针对非复杂性发热疾病的病例管理算法,其中第一步规定,如果任何年龄的发热患者有除疟疾之外的发热疾病症状(如咳嗽或皮疹),则不进行疟疾检测,而是针对明显疾病进行治疗,只有在48小时后未见好转而返回时才接受疟疾RDT检测。

方法

在16个卫生站进行了为期一年的研究,以确定该算法识别RDT可检测出的恶性疟原虫感染患者的能力。卫生站工作人员纳入了所有年龄在发热(≥37.5°C)或过去2天有发热史的患者。经过临床评估,卫生站的护士根据诊断算法确定患者是否应接受RDT检测,但对所有纳入的患者都进行了RDT检测。

结果

在1年多的时间里,共纳入6039例患者,根据算法确定58%(3483例)需要进行RDT检测。总体而言,23%(1373/6039)RDT检测呈阳性,雨季为34%(1130/3376),旱季为9%(243/2661),该算法的第一步仅识别出78%的RDT检测呈阳性的患者,因传播季节(雨季80%、旱季70%)、疟疾传播区(高传播区75%、低传播区95%)和年龄组(5岁以下68%、5岁及以上84%)而异。

结论

除了疟疾传播率最低的地区外,在其他所有地区,使用该算法都会使很大一部分疟疾患者在首次就诊时被排除在接受RDT检测之外,从而无法得到及时诊断和治疗。虽然该算法是在疟疾控制和资源稀缺背景下采用的,目的是治疗有症状的疟疾患者,但塞内加尔现在已采取了对发热或有发热史患者进行普遍诊断的政策。此外,在当前疟疾消除的背景下,病例管理模式需要转向识别和治疗所有疟疾感染患者。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/940b/5333468/88c2c4f371f7/12936_2017_1750_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/940b/5333468/aefa758bc1bb/12936_2017_1750_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/940b/5333468/88c2c4f371f7/12936_2017_1750_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/940b/5333468/aefa758bc1bb/12936_2017_1750_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/940b/5333468/88c2c4f371f7/12936_2017_1750_Fig2_HTML.jpg

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