Evidence map›Paper›PMID 29287070›Full record

ArticlePloS one2017

A diagnostic and epidemiologic investigation of acute febrile illness (AFI) in Kilombero, Tanzania.

Christine Hercik, Leonard Cosmas, Ondari D Mogeni, Newton Wamola, Wanze Kohi, Victor Omballa, Melvin Ochieng, Shirley Lidechi, Juma Bonventure, Caroline Ochieng and 4 more

Abstract read
In one paragraph

Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 6 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

24 citing papers in PubMed, 6 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Pooled it
  6. Pooled it
  7. Article
  8. Article
  9. Article
  10. Multi-Pathogen Surveillance of Acute Febrile Illness in Nigeria Using TaqMan Array Cards: Implementation Successes and Lessons Learned From the SAFIAN Study.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2025
    Article
  11. A Pathogen Selection Tool for Surveillance of Acute Febrile Illness in Nigeria.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2025
    Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Review
  17. Review
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Christine HercikGeorgetown University, Washington, DC, United States of America.ORCID 0000-0002-0858-9281
Leonard CosmasDisease Detection Branch, Division of Global Health Protection, US Centers for Disease Control and Prevention (CDC), CDC-Kenya, Nairobi, Kenya.
Ondari D MogeniKenya Medical Research Institute-Centre for Global Health Research (KEMRI-CGHR), Nairobi, Kenya.
Newton WamolaKenya Medical Research Institute-Centre for Global Health Research (KEMRI-CGHR), Nairobi, Kenya.
Wanze KohiNational Institute of Medical Research (NIMR) Muhimbili Research Centre, Dar es Salaam, Tanzania.
Victor OmballaKenya Medical Research Institute-Centre for Global Health Research (KEMRI-CGHR), Nairobi, Kenya.
Melvin OchiengKenya Medical Research Institute-Centre for Global Health Research (KEMRI-CGHR), Nairobi, Kenya.
Shirley LidechiKenya Medical Research Institute-Centre for Global Health Research (KEMRI-CGHR), Nairobi, Kenya.
Juma BonventureDisease Detection Branch, Division of Global Health Protection, US Centers for Disease Control and Prevention (CDC), CDC-Kenya, Nairobi, Kenya.
Caroline OchiengKenya Medical Research Institute-Centre for Global Health Research (KEMRI-CGHR), Nairobi, Kenya.
Clayton OnyangoDisease Detection Branch, Division of Global Health Protection, US Centers for Disease Control and Prevention (CDC), CDC-Kenya, Nairobi, Kenya.
Barry S FieldsDisease Detection Branch, Division of Global Health Protection, US Centers for Disease Control and Prevention (CDC), CDC-Kenya, Nairobi, Kenya.
Sayoki MfinangaNational Institute of Medical Research (NIMR) Muhimbili Research Centre, Dar es Salaam, Tanzania.
Joel M MontgomeryDisease Detection Branch, Division of Global Health Protection, US Centers for Disease Control and Prevention (CDC), CDC-Kenya, Nairobi, Kenya.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionIn low-resource settings, empiric case management of febrile illness is routine as a result of limited access to laboratory diagnostics. The use of comprehensive fever syndromic surveillance, with enhanced clinical microbiology, advanced diagnostics and more robust epidemiologic investigation, could enable healthcare providers to offer a differential diagnosis of fever syndrome and more appropriate care and treatment.

methodsWe conducted a year-long exploratory study of fever syndrome among patients ≥ 1 year if age, presenting to clinical settings with an axillary temperature of ≥37.5°C and symptomatic onset of ≤5 days. Blood and naso-pharyngeal/oral-pharyngeal (NP/OP) specimens were collected and analyzed, respectively, using AFI and respiratory TaqMan Array Cards (TAC) for multi-pathogen detection of 57 potential causative agents. Furthermore, we examined numerous epidemiologic correlates of febrile illness, and conducted demographic, clinical, and behavioral domain-specific multivariate regression to statistically establish associations with agent detection.

resultsFrom 15 September 2014-13 September 2015, 1007 febrile patients were enrolled, and 997 contributed an epidemiologic survey, including: 14% (n = 139) 1<5yrs, 19% (n = 186) 5-14yrs, and 67% (n = 672) ≥15yrs. AFI TAC and respiratory TAC were performed on 842 whole blood specimens and 385 NP/OP specimens, respectively. Of the 57 agents surveyed, Plasmodium was the most common agent detected. AFI TAC detected nucleic acid for one or more of seven microbial agents in 49% of AFI blood samples, including: Plasmodium (47%), Leptospira (3%), Bartonella (1%), Salmonella enterica (1%), Coxiella burnetii (1%), Rickettsia (1%), and West Nile virus (1%). Respiratory TAC detected nucleic acid for 24 different microbial agents, including 12 viruses and 12 bacteria. The most common agents detected among our surveyed population were: Haemophilus influenzae (67%), Streptococcus pneumoniae (55%), Moraxella catarrhalis (39%), Staphylococcus aureus (37%), Pseudomonas aeruginosa (36%), Human Rhinovirus (25%), influenza A (24%), Klebsiella pneumoniae (14%), Enterovirus (15%) and group A Streptococcus (12%). Our epidemiologic investigation demonstrated both age and symptomatic presentation to be associated with a number of detected agents, including, but not limited to, influenza A and Plasmodium. Linear regression of fully-adjusted mean cycle threshold (Ct) values for Plasmodium also identified statistically significant lower mean Ct values for older children (20.8), patients presenting with severe fever (21.1) and headache (21.5), as well as patients admitted for in-patient care and treatment (22.4).

conclusionsThis study is the first to employ two syndromic TaqMan Array Cards for the simultaneous survey of 57 different organisms to better characterize the type and prevalence of detected agents among febrile patients. Additionally, we provide an analysis of the association between adjusted mean Ct values for Plasmodium and key clinical and demographic variables, which may further inform clinical decision-making based upon intensity of infection, as observed across endemic settings of sub-Saharan Africa.

Indexed as

AdolescentAdultAgedChildChild, PreschoolCross-Sectional StudiesFemaleFeverHumansInfantMaleMiddle AgedPopulation SurveillanceTanzaniaYoung Adult

Identifiers

PMID29287070
PMCPMC5747442

What OpenQuestion holds

Textmetadata
LicenceCC0
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.