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BACKGROUND

Between December 2015 and July 2016, a yellow fever (YF) outbreak affected urban areas of Angola and the Democratic Republic of the Congo (DRC). We described the outbreak in DRC and assessed the accuracy of the YF case definition, to facilitate early diagnosis of cases in future urban outbreaks.

METHODOLOGY/PRINCIPAL FINDINGS

In DRC, suspected YF infection was defined as jaundice within 2 weeks after acute fever onset and was confirmed by either IgM serology or PCR for YF viral RNA. We used case investigation and hospital admission forms. Comparing clinical signs between confirmed and discarded suspected YF cases, we calculated the predictive values of each sign for confirmed YF and the diagnostic accuracy of several suspected YF case definitions. Fifty seven of 78 (73%) confirmed cases had travelled from Angola: 88% (50/57) men; median age 31 years (IQR 25–37). 15 (19%) confirmed cases were infected locally in urban settings in DRC. Median time from symptom onset to healthcare consultation was 7 days (IQR 6–9), to appearance of jaundice 8 days (IQR 7–11), to sample collection 9 days (IQR 7–14), and to hospitalization 17 days (IQR 11–26). A case definition including fever or jaundice, combined with myalgia or a negative malaria test, yielded an improved sensitivity (100%) and specificity (57%).

CONCLUSIONS/SIGNIFICANCE

As jaundice appeared late, the majority of cases were diagnosed too late for supportive care and prompt vector control. In areas with known local YF transmission, a suspected case definition without jaundice as essential criterion could facilitate earlier YF diagnosis, care and control.

","PublicationOrPresentationDate":"2018-12-07T00:00:00Z","LastModifiedDate":"2024-08-29T23:04:21.507Z","IsDeleted":false,"DigitalObjectIdentifier":"10.1371/journal.pntd.0007029","ContentTypeName":"Research","ParentContentTypeName":"Journal Article","JournalName":"PLOS Neglected Tropical Diseases","ContentType":"Journal Article > Research","AvailabilityTypeId":1,"AvailabilityType":"Full Text","JournalIssue":"12","JournalVolume":"12","JournalPages":"e0007029","JournalAbbreviation":"PLoS Negl Trop Dis","Conference":null,"ConferenceStartDate":null,"ConferenceEndDate":null,"content":"\nRESEARCH ARTICLE\n\nUrban yellow fever outbreak—Democratic\n\nRepublic of the Congo, 2016: Towards more\n\nrapid case detection\n\nBrecht IngelbeenID\n1,2¤*, Nadine A. Weregemere1,3, Harold NoelID\n\n4, Gaston P. Tshapenda5,\n\nMathias Mossoko5, Justus Nsio5, Axelle Ronsse1, Steve Ahuka-Mundeke6,\n\nSandra Cohuet7, Benoı̂t I. Kebela5\n\n1 Médecins sans Frontières, Kinshasa, Democratic Republic of the Congo, 2 European Programme for\n\nIntervention Epidemiology Training (EPIET), European Centre for Disease Prevention and Control (ECDC),\n\nStockholm, Sweden, 3 Ministry of Health, Kinshasa, Democratic Republic of the Congo, 4 Direction des\n\nMaladies Infectieuses, Santé publique France, Saint-Maurice, France, 5 Direction de Lutte contre la Maladie,\n\nMinistry of Health, Kinshasa, Democratic Republic of the Congo, 6 Laboratoire de virologie, Institut National\n\nde Recherche Biologique, Kinshasa, Democratic Republic of the Congo, 7 Epicentre, Paris, France\n\n¤ Current address: Department of Public Health, Institute of Tropical Medicine, Antwerp, Belgium\n\n* brechtingelbeen@gmail.com\n\nAbstract\n\nBackground\n\nBetween December 2015 and July 2016, a yellow fever (YF) outbreak affected urban areas\n\nof Angola and the Democratic Republic of the Congo (DRC). We described the outbreak in\n\nDRC and assessed the accuracy of the YF case definition, to facilitate early diagnosis of\n\ncases in future urban outbreaks.\n\nMethodology/Principal findings\n\nIn DRC, suspected YF infection was defined as jaundice within 2 weeks after acute fever\n\nonset and was confirmed by either IgM serology or PCR for YF viral RNA. We used case\n\ninvestigation and hospital admission forms. Comparing clinical signs between confirmed\n\nand discarded suspected YF cases, we calculated the predictive values of each sign for con-\n\nfirmed YF and the diagnostic accuracy of several suspected YF case definitions. Fifty seven\n\nof 78 (73%) confirmed cases had travelled from Angola: 88% (50/57) men; median age 31\n\nyears (IQR 25–37). 15 (19%) confirmed cases were infected locally in urban settings in\n\nDRC. Median time from symptom onset to healthcare consultation was 7 days (IQR 6–9), to\n\nappearance of jaundice 8 days (IQR 7–11), to sample collection 9 days (IQR 7–14), and to\n\nhospitalization 17 days (IQR 11–26). A case definition including fever or jaundice, combined\n\nwith myalgia or a negative malaria test, yielded an improved sensitivity (100%) and specific-\n\nity (57%).\n\nConclusions/Significance\n\nAs jaundice appeared late, the majority of cases were diagnosed too late for supportive care\n\nand prompt vector control. In areas with known local YF transmission, a suspected case\n\nPLOS Neglected Tropical Diseases | https://doi.org/10.1371/journal.pntd.0007029 December 7, 2018 1 / 11\n\na1111111111\n\na1111111111\n\na1111111111\n\na1111111111\n\na1111111111\n\nOPEN ACCESS\n\nCitation: Ingelbeen B, Weregemere NA, Noel H,\n\nTshapenda GP, Mossoko M, Nsio J, et al. (2018)\n\nUrban yellow fever outbreak—Democratic\n\nRepublic of the Congo, 2016: Towards more rapid\n\ncase detection. PLoS Negl Trop Dis 12(12):\n\ne0007029. https://doi.org/10.1371/journal.\n\npntd.0007029\n\nEditor: David W.C. Beasley, University of Texas\n\nMedical Branch, UNITED STATES\n\nReceived: September 27, 2018\n\nAccepted: November 27, 2018\n\nPublished: December 7, 2018\n\nCopyright: © 2018 Ingelbeen et al. This is an open\n\naccess article distributed under the terms of the\n\nCreative Commons Attribution License, which\n\npermits unrestricted use, distribution, and\n\nreproduction in any medium, provided the original\n\nauthor and source are credited.\n\nData Availability Statement: Anonymized data of\n\nsuspected and confirmed yellow fever cases\n\nreported during the outbreak and for which\n\nsymptoms were recorded (as of 11/08/2016) is\n\navailable from the Open Science Framework\n\ndatabase (url: osf.io/xkafm). Diagnostic and\n\noutcome data may have been updated after 11/08/\n\n2016.\n\nFunding: The authors received no specific funding\n\nfor this work.\n\nhttp://orcid.org/0000-0002-2214-6858\nhttp://orcid.org/0000-0003-3011-9267\nhttps://doi.org/10.1371/journal.pntd.0007029\nhttp://crossmark.crossref.org/dialog/?doi=10.1371/journal.pntd.0007029&domain=pdf&date_stamp=2018-12-19\nhttp://crossmark.crossref.org/dialog/?doi=10.1371/journal.pntd.0007029&domain=pdf&date_stamp=2018-12-19\nhttp://crossmark.crossref.org/dialog/?doi=10.1371/journal.pntd.0007029&domain=pdf&date_stamp=2018-12-19\nhttp://crossmark.crossref.org/dialog/?doi=10.1371/journal.pntd.0007029&domain=pdf&date_stamp=2018-12-19\nhttp://crossmark.crossref.org/dialog/?doi=10.1371/journal.pntd.0007029&domain=pdf&date_stamp=2018-12-19\nhttp://crossmark.crossref.org/dialog/?doi=10.1371/journal.pntd.0007029&domain=pdf&date_stamp=2018-12-19\nhttps://doi.org/10.1371/journal.pntd.0007029\nhttps://doi.org/10.1371/journal.pntd.0007029\nhttp://creativecommons.org/licenses/by/4.0/\n\n\ndefinition without jaundice as essential criterion could facilitate earlier YF diagnosis, care\n\nand control.\n\nAuthor summary\n\nYellow fever is a mosquito-borne viral infection characterized by fever, followed after sev-\n\neral days by jaundice, liver or kidney failure, shock or bleeding in up to 25% of cases.\n\nAlthough the virus primarily circulates in forests among primates, it can also be transmit-\n\nted from human to human by mosquitoes in urban areas. If infected patients are detected\n\nearly, they could benefit from timely supportive treatment, and control measures such as\n\nmosquito bite prevention, mosquito control, and mass vaccination campaigns, could pre-\n\nvent further spread of the disease. During 2015–16 a yellow fever outbreak spread in\n\nurban areas of Angola and DRC. The present study showed that most yellow fever patients\n\nthat were diagnosed in DRC had travelled from Angola where they have been infected,\n\nand that most were adult men. Nevertheless, several patients have been infected locally, in\n\nurban settings in three provinces of DRC. Patients were diagnosed only when jaundice\n\nappeared, more than a week after their illness started, too late to fully benefit from sup-\n\nportive treatment. During urban outbreaks, improving early access to healthcare and ear-\n\nlier detection of patients by recognizing acute fever when malaria infection is excluded,\n\ncould improve yellow fever care and control.\n\nIntroduction\n\nYellow fever (YF) is a mosquito-borne viral infection characterized by an initial non-specific\n\nflu-like phase that lasts for 3 to 6 days and includes fever, headaches and myalgia. In 15%–25%\n\nof cases, a toxic phase follows with mild or severe jaundice, liver and kidney failure, which\n\nmight lead to shock or bleeding [1,2]. No specific treatment exists. Approximately half of the\n\nsevere cases lead to death within 7 to 10 days [2,3]. YF virus circulates primarily among forest-\n\nbound primates in a sylvatic cycle. Like other flaviviruses, YF can spread widely in urban areas,\n\nwhen transmitted from human to human by mosquito vector Aedes aegypti or potentially\n\nAedes albopictus [4]. Female mosquitos become infected from a blood meal of an infected\n\nhuman. The incubation period in humans is 3–6 days [5]. Outbreak control relies on mosquito\n\nbite prevention, vector control, and mass vaccination campaigns. Early case detection by using\n\nan adapted case definition could allow earlier implementation of control measures for out-\n\nbreak containment.\n\nIn the Democratic Republic of the Congo (DRC) each year a large number of sporadic YF\n\ninfections occur following sylvatic transmission, when YF virus is transmitted by mosquitoes\n\nfrom non-human primates to persons living or working in forest areas [6]. Because of limited\n\nmobility of patients infected in forest areas, YF transmission rarely reaches urban\n\nenvironments.\n\nIn December 2015 YF cases were detected in the Angolan capital Luanda. In March 2016,\n\nthe outbreak in Angola intensified, resulting in cases spreading to bordering provinces of DRC\n\nand its capital Kinshasa [7]. We carried out an investigation of the urban DRC outbreak to\n\nidentify cases and describe the outbreak. Furthermore, we compared the performance of the\n\ncase definition applied during the outbreak to alternative case definitions, aiming at an\n\nimproved, timelier detection of cases in future urban outbreaks.\n\nUrban yellow fever outbreak—Democratic Republic of the Congo, 2016\n\nPLOS Neglected Tropical Diseases | https://doi.org/10.1371/journal.pntd.0007029 December 7, 2018 2 / 11\n\nCompeting interests: The authors have declared\n\nthat no competing interests exist.\n\nhttps://doi.org/10.1371/journal.pntd.0007029\n\n\nMethods\n\nStudy design and population\n\nWe present a detailed description of the 2016 YF outbreak in DRC and an analysis of the diag-\n\nnostic accuracy of the case definition used, compared to alternatives. The YF cases related to\n\nthis outbreak were reported to the national surveillance system between January and August\n\n2016. In the analysis of the case definitions, we excluded vaccinated patients (at least ten days\n\nbefore symptom onset) and patients infected through sylvatic YF transmission (staying in a\n\nforest area in the two weeks to three days before symptom onset).\n\nCase definitions and YF confirmation\n\nDuring the outbreak in DRC, the suspected case definition for routine surveillance in DRC\n\nwas used, as also proposed in WHO guidelines [8]: an acute onset of fever followed by jaundice\n\nwithin 14 days after first symptoms onset. Any patient presenting at a healthcare facility meet-\n\ning with this suspected case definition was notified to the Ministry of Health. Blood samples\n\ncollected for every suspected case were tested for laboratory confirmation of yellow fever at the\n\nInstitut National de Recherche Biomédicale (INRB), Kinshasa. A suspected case became con-\n\nfirmed when anti-YF IgM antibodies or YF viral RNA was detected in serum, if the patient was\n\nnot immunized against YF. YF IgM detection consisted of an initial enzyme-linked immuno-\n\nsorbent assay (ELISA) to detect anti-flavivirus IgM antibodies, followed by a series of consecu-\n\ntive virus-specific ELISA tests to exclude other flavivirus infections such as Zika, dengue, and\n\nWest Nile viruses. The ELISA results needed to be further confirmed by demonstrating a four-\n\nfold increase in YF neutralizing antibodies or by a Plaque Reduction Neutralization Test.\n\nSimultaneously a RT-PCR assay tested the presence of YF viral RNA in the blood sample. A\n\nsuspected case was discarded when neither YF specific IgM antibodies nor YF viral RNA were\n\ndetected. Confirmed cases were further classified as imported or autochthonous relying on\n\ntravel history to Angola within two weeks to three days before symptom onset. Current or\n\nrecent malaria (co-)infection was tested during July-August 2016 among patients admitted to\n\na YF management facility, through detection of P. falciparum HRP-2 antigen (SD BIOLINE\n\nMalaria Ag P.f, Standard Diagnostics Inc.).\n\nData sources\n\nPatient demographics, symptoms, malaria co-infections, laboratory YF confirmation results,\n\ntravel and vaccination history were extracted from case investigation forms (with suggested\n\nsymptoms), patient medical files, and daily reports of notified suspected cases. Symptoms and\n\nmalaria co-infections were systematically recorded in Kinshasa between 28 May and 02 August\n\n2016, and thus only available for 14 confirmed cases and 97 discarded cases. GPS coordinates\n\nof places visited by patients while infectious, during the first 6 days after symptom onset, were\n\nused to map areas with possible ongoing transmission of YF.\n\nData analysis\n\nWe described recorded characteristics and deaths as frequencies, percentages or medians with\n\ninterquartile range. We compared differences in frequencies between cases by using Pearson’s\n\nChi-squared test (or Fisher’s exact test, as appropriate) and differences in median age using the\n\nWilcoxon rank-sum test (when not normally distributed). We used QGIS 2.18 with Open-\n\nStreetMap shapefiles to generate a geographical dot distribution map of cases in Kinshasa. To\n\navoid revealing the exact locations of the cases, we rounded longitude and latitude coordinates\n\nUrban yellow fever outbreak—Democratic Republic of the Congo, 2016\n\nPLOS Neglected Tropical Diseases | https://doi.org/10.1371/journal.pntd.0007029 December 7, 2018 3 / 11\n\nhttps://doi.org/10.1371/journal.pntd.0007029\n\n\nto 10−3 degrees, to assign a random point location within a 110m radius of the patients’\n\nrecorded residences.\n\nWe identified potential predictors of YF by calculating positive and negative likelihood\n\nratios (LR+ and LR-) for the presence or absence of every recorded symptom, severe anemia\n\nand a positive malaria test among confirmed and discarded cases for which those symptoms\n\nwere recorded. LR+ is the increase in the probability of YF infection when the symptom is\n\npresent, in other words sensitivity/(1-specificity) of that symptom to detect infection. LR- is\n\nthe decrease in the probability of YF infection when the symptom is absent. We used combina-\n\ntions of predictive signs (LR+ or LR- larger than 2.5) to create new case definitions (four\n\noptions). We drew receiver operating characteristic (ROC) curves to compare the diagnostic\n\nperformance (sensitivity, specificity and Area Under the Curve (AUC)) of the DRC outbreak\n\ncase definitions with our optional case definitions and those used in previous urban YF out-\n\nbreaks in Uganda (2010/11), Brasil (2009) and Bolivia (1997/98) [9–11]. We performed analy-\n\nses in R 3.4.1 and STATA 12.\n\nEthics statement\n\nThe Ethical Review Committee at the University of Kinshasa approved the study (reference\n\nESP/CE/049/2017). Only anonymized routine surveillance data, collected for the outbreak\n\ninvestigation was retrospectively analyzed. Therefore, no individual patient informed consent\n\nwas asked.\n\nResults\n\nThe yellow fever outbreak in DRC\n\nBetween 1 January and 11 August 2016, 2,269 suspected cases were reported in DRC. Of the\n\n2,025 cases that underwent confirmatory testing, 78 (4%) were confirmed. Cases were con-\n\nfirmed in Kinshasa and two provinces neighbouring the Angolan border, Kongo-Central and\n\nKwango. The first confirmed case had onset of symptoms on 22 February, the final case on 12\n\nJuly (Fig 1). Of the 78 confirmed cases, 57 (73%) were imported from Angola. Imported cases\n\noccurred mostly among adult male patients (Table 1): 88% (50/57) men; median age 31 years\n\n(interquartile rate (IQR) 25–37). Fifteen (19%) YF confirmed case patients had not travelled to\n\nAngola, and acquired YF in urban settings in Kinshasa (n = 8), in the Angola-bordering\n\nKwango (n = 4), and Kongo-Central (n = 3) provinces. Of these autochthonous cases 67% (10/\n\n15) were male; median age was 20 years (IQR 12–29; p<0.01). For six cases, no travel history\n\ncould be retrieved (not classified). Six sylvatic cases, not related to this outbreak, were con-\n\nfirmed during the same period.\n\nMapping the imported and autochthonous confirmed cases led to the identification of one\n\ngeographical cluster of three autochthonous confirmed cases occurring between 30 May and\n\n10 June 2016 in the same neighborhood of Kinshasa, where case investigations revealed\n\nanother 3 deaths of suspected cases with symptom onset in the same period (https://osf.io/\n\ntk3gn/). This cluster was linked to a previously unidentified case who had returned from\n\nAngola with a fever 22 days before (8 May). All other cases were widespread, and could not be\n\nlinked to one another.\n\nThe median time from symptom onset to a first healthcare consultation in any healthcare\n\nfacility was 7 days (IQR 6–9), to appearance of jaundice 8 days (IQR 7–11), to sample collec-\n\ntion 9 days (IQR 7–14), and to hospitalization 17 days (IQR 11–26) (Table 2). The delay to\n\nsample collection was not significantly different (p = 0.88) among imported and autochtho-\n\nnous confirmed cases.\n\nUrban yellow fever outbreak—Democratic Republic of the Congo, 2016\n\nPLOS Neglected Tropical Diseases | https://doi.org/10.1371/journal.pntd.0007029 December 7, 2018 4 / 11\n\nhttps://osf.io/tk3gn/\nhttps://osf.io/tk3gn/\nhttps://doi.org/10.1371/journal.pntd.0007029\n\n\nAmong the 74 confirmed cases tested by RT-PCR, 9 (12%) had detectable YF viral RNA.\n\nThe blood samples of the 9 PCR-positive cases were collected at a median of 7 (range 1–14)\n\ndays after onset of symptoms.\n\nWe recorded 18 deaths among confirmed cases, resulting in a case fatality of 23%. Con-\n\nfirmed cases died after a median of 15 days following the onset of symptoms.\n\nFig 1. Weekly distribution of confirmed imported, autochtonous and non classified yellow fever cases, DRC, 2016.\n\nhttps://doi.org/10.1371/journal.pntd.0007029.g001\n\nTable 1. Patient characteristics and outcome among imported and autochthonous confirmed yellow fever cases, DRC, 2016.\n\nCharacteristics Imported (N = 57) Autochthonous (N = 15) p value\n\nn % n %\n\nAged�5 years 0 0.0 1 6.7 0.20\n\nMale sex 50 87.7 10 66.7 0.05\n\nHospitalized 25 (N = 28) 89.3 7 (N = 8) 87.5 0.89\n\nDied 17 29.9 1 7.1 0.08\n\nLiving in district where autochthonous cases have earlier been diagnosed 6 10.5 4 26.7 0.11\n\nhttps://doi.org/10.1371/journal.pntd.0007029.t001\n\nUrban yellow fever outbreak—Democratic Republic of the Congo, 2016\n\nPLOS Neglected Tropical Diseases | https://doi.org/10.1371/journal.pntd.0007029 December 7, 2018 5 / 11\n\nhttps://doi.org/10.1371/journal.pntd.0007029.g001\nhttps://doi.org/10.1371/journal.pntd.0007029.t001\nhttps://doi.org/10.1371/journal.pntd.0007029\n\n\nSymptoms\n\nSymptoms and malaria (co-)infection were recorded for 14 confirmed and 97 discarded cases\n\nfrom Kinshasa. The median age of those confirmed cases was 24 years compared with 31 years\n\namong confirmed cases without recorded symptoms (p = 0.01); 64% and 86% (p = 0.06) were\n\nmale, respectively. The 97 discarded cases had a median age of 15 years, compared with 16\n\nyears among discarded cases without recorded symptoms (p = 0.09); 53% and 56% (p = 0.36)\n\nwere male, respectively.\n\nThirteen (92.9%) confirmed cases had fever and 10 (71.4%) had jaundice (Table 3). Of\n\nsymptoms not included in the routine suspected case definition, myalgia, vomiting and head-\n\naches were most frequently reported, respectively among 88.9%, 77.8% and 66.7% of con-\n\nfirmed cases. One had hemorrhagic signs. We identified no confirmed cases with severe\n\nanemia at admission. Of 9 tested confirmed cases, 3 (33.3%) were malaria co-infected. None\n\n(0/3) showed rapid clinical improvement after starting antimalarial treatment.\n\nDiagnostic performance of the suspected case definition\n\nAlso 88 (91%) discarded cases had both fever and jaundice, i.e. the DRC suspected case defini-\n\ntion, resulting in a 9% specificity of the case definition. Considering that 1,947 of 2,025 tested\n\nTable 2. Median delays between symptom onset and seeking healthcare, yellow fever diagnosis, hospitalization\n\nand death among confirmed yellow fever cases, DRC, 2016.\n\nDelay from symptom onset to. . . Median (days) Interquartile range (days)\n\nHealthcare consultation (N = 13) 7 6–9\n\nJaundice (N = 5) 8 7–11\n\nSuspected case notification and sample collection (N = 77) 9 7–14\n\nHospitalisation (N = 4) 17 11–26\n\nDeath (N = 7) 15 10–16\n\nhttps://doi.org/10.1371/journal.pntd.0007029.t002\n\nTable 3. Percentage, frequency and predictive value of signs/symptoms reported among confirmed and discarded yellow fever cases, DRC, 2016.\n\nClinical sign Confirmed\n\ncases\n\nDiscarded\n\ncases\n\np-value Positive likelihood\n\nratio (95%CI)\n\nNegative likelihood\n\nratio (95%CI)\n\nn/N % n/N %\n\nFever 13/14 92.9 93/97 95.9 0.61 1.0 (0.8–1.1) 1.7 (0.2–14.4)\n\nJaundice 10/14 71.4 93/97 95.9 <0.01 0.7 (0.5–1.0) 6.9 (2.0–24.6)\n\nBleeding signs 1/10 10.0 3/84 3.6 0.34 2.8 (0.3–24.4) 0.9 (0.8–1.2)\n\nDiuresis decrease 4/10 40.0 7/80 8.8 <0.01 4.6 (1.6–12.9) 0.7 (0.4–1.1)\n\nMyalgia 8/9 88.9 25/81 30.9 <0.01 2.9 (1.9–4.3) 0.2 (0.0–1.0)\n\nHeadache 6/9 66.7 46/81 56.8 0.57 1.2 (0.7–1.9) 0.8 (0.3–2.0)\n\nNausea 4/9 44.4 33/81 40.7 0.61 1.1 (0.5–2.2) 0.9 (0.5–2.0)\n\nVomiting 7/9 77.8 66/85 77.6 0.99 1.0 (0.7–1.5) 1.0 (0.3–3.6)\n\nEpigastric tenderness 3/8 37.5 22/81 27.2 <0.01 1.4 (0.5–3.6) 0.9 (0.5–1.5)\n\nSevere anaemia� 0/3 0.0 12/20 60.0 0.09 0.0\n\nMalaria HRP-2 positive�� 3/9 33.3 73/90 81.1 <0.01 0.4 (0.2–1.0) 3.5 (1.9–6.6)\n\nMalaria HRP-2 positive with good response on treatment 0/3 0.0 11/13 84.6 0.02 0.0\n\n� Severe anemia as defined by WHO: a hemoglobin level of below 80 g/l, or below 70 g/l for pregnant women and children between 6 and 59 months old.\n\n�� HRP-2 = histidine-rich protein II, an antigen expressed by P. falciparum trophozoites.\n\nhttps://doi.org/10.1371/journal.pntd.0007029.t003\n\nUrban yellow fever outbreak—Democratic Republic of the Congo, 2016\n\nPLOS Neglected Tropical Diseases | https://doi.org/10.1371/journal.pntd.0007029 December 7, 2018 6 / 11\n\nhttps://doi.org/10.1371/journal.pntd.0007029.t002\nhttps://doi.org/10.1371/journal.pntd.0007029.t003\nhttps://doi.org/10.1371/journal.pntd.0007029\n\n\nsuspects were discarded, it had a positive predictive value of 3.2%. Of 90 tested discarded cases,\n\n73 (81.1%) were malaria positive. Malaria positive discarded cases had a median age of 12\n\n(IQR 5–20) years, with 67% being under 18 years old. Malaria negative discarded cases were\n\nolder (p = 0.03), median age 22 years (IQR 12–36), with 35% being under 18 years old. Of 13\n\nmalaria infected discarded cases, two (15.4%) did not improve after starting malaria treatment.\n\nDecreased diuresis, myalgia and bleeding signs had the highest positive likelihood ratios,\n\nrespectively 4.6, 2.9 and 2.8. Absence of malaria had the highest negative likelihood ratio, of 3.5.\n\nWhen comparing the DRC suspected case definition with four case definitions based on the\n\nmost predictive and frequent signs (Options A, B, C and D), and case definitions used during\n\nurban outbreaks, we found that a combination of fever or jaundice and myalgia or a negative\n\nmalaria test (Option C), yields the best combination of sensitivity (100%) and specificity (57%)\n\nresulting in an AUC of 0.78 (Fig 2 and Table 4). Other combinations with early symptoms\n\nresult in lower sensitivity, but improved specificity. The two 2010/11 Ugandan outbreak case\n\ndefinitions had better specificity than the DRC case definition, but at the cost of lower sensitiv-\n\nity (AUC 0.58 in 2010 and 0.69 in 2011). The 1997/98 Bolivia case definition improved sensi-\n\ntivity (79%), but did not improve the specificity (7%; AUC 0.43). The 2009 Brazil case\n\ndefinition was not substantially different from that in DRC to allow comparison.\n\nDiscussion\n\nAlthough >2000 suspected YF cases were reported and tested in DRC, only 78 cases were con-\n\nfirmed, with symptom onset between February and July 2016. The peak of the YF outbreak in\n\nDRC followed and mirrored the ongoing outbreak in Angola, and started to wane as vaccina-\n\ntion went on in Angola. The majority of confirmed YF affected young men from DRC working\n\nin Angola, who returned to DRC to seek healthcare following a YF infection contracted in\n\nAngola. Despite evidence of 15 locally transmitted cases in three different provinces, we\n\nobserved no widespread urban YF transmission, as in Angola. Possible reasons for this might\n\nbe (i) the timing of the first local transmission when the dry season took off, not allowing the\n\nvector carrying YF virus to replicate, (ii) the implementation of vector control measures\n\naround confirmed cases’ homes, or (iii) the mass YF vaccination campaigns before the end of\n\nthe dry season in the affected health zones.\n\nPatients were diagnosed too late for effective supportive care and to guide potential vector\n\ncontrol measures. By the time infected patients with severe symptoms received appropriate\n\nhealthcare (median time to hospitalization 17 days after symptom onset), the 12 to 15 critical\n\ndays to prevent death through supportive care had already passed [2,3]. Several elements con-\n\ntributed to this delay: First, the majority of patients did not seek healthcare when going\n\nthrough the febrile phase of the disease within 5 days after symptom onset. YF testing is free in\n\nDRC but patients waited until symptoms worsened, afraid they might bear the cost of tests and\n\ntreatments of other diagnosed conditions. Second, the suspected case definition applied during\n\nthis outbreak encouraged notifying and testing only once jaundice appeared, 9 days after\n\nsymptom onset. Finally, test results could take days to weeks because YF confirmatory testing\n\nwas carried out in only one laboratory in the capital.\n\nLow specificity of the DRC suspected case definition could only partially be explained by\n\nviral hepatitis. A 44% seroprevalence of viral hepatitis was found among suspected YF cases\n\ndiscarded during 2003–2012 in DRC [12]. Dengue virus RNA and chikungunya virus RNA\n\nwere found in respectively 3.5% and in 0.4% of those discarded YF cases during the same\n\nperiod [13]. In our study, 81% of discarded cases were found to be malaria infected. Of those\n\ntwo thirds were children. This suggests that malaria may have been a leading cause of fever\n\nand jaundice among discarded cases in children.\n\nUrban yellow fever outbreak—Democratic Republic of the Congo, 2016\n\nPLOS Neglected Tropical Diseases | https://doi.org/10.1371/journal.pntd.0007029 December 7, 2018 7 / 11\n\nhttps://doi.org/10.1371/journal.pntd.0007029\n\n\nA case definition in which jaundice would no longer be the main clinical criterion would\n\nallow more rapid detection of cases in districts where local transmission of YF is established.\n\nNevertheless, considering that for each confirmed case another twenty suspected cases were\n\nnotified and that no options for decentralized YF testing exist, other signs than fever are\n\nneeded in the case definition to improve its specificity. Suspected case definitions used in pre-\n\nvious urban YF outbreaks have relied on at least one severe sign occurring during the toxic\n\nFig 2. Receiver operating characteristic (ROC) curves of yellow fever suspected case definitions, applied on confirmed and discarded yellow fever cases with\n\nrecorded clinical signs, DRC, 2016.\n\nhttps://doi.org/10.1371/journal.pntd.0007029.g002\n\nUrban yellow fever outbreak—Democratic Republic of the Congo, 2016\n\nPLOS Neglected Tropical Diseases | https://doi.org/10.1371/journal.pntd.0007029 December 7, 2018 8 / 11\n\nhttps://doi.org/10.1371/journal.pntd.0007029.g002\nhttps://doi.org/10.1371/journal.pntd.0007029\n\n\nphase of infection, and are therefore not more appropriate for timely diagnosis [9–11]. When\n\ncomparing the performance of different case definitions applied on the confirmed and dis-\n\ncarded cases in our study, “fever or jaundice, and myalgia or a negative malaria rapid diagnos-\n\ntic test (or blood slide)” (Option C) provided the most robust combination of sensitivity and\n\nspecificity. Once index cases and clusters of local transmission are identified in an area using\n\nthe DRC/WHO case definition, a switch to case definition option C in the area with estab-\n\nlished YF transmission could speed up the identification of YF cases. Ideally, the case defini-\n\ntion we propose should be externally validated against clinical data from ongoing or future\n\noutbreaks in a similar urban context.\n\nA limitation to the comparison of case definitions is that our reference group is composed\n\nof discarded cases, which met the suspected case definition. Those were likely not representa-\n\ntive of the source population (any patient presenting at a healthcare facility), and therefore, the\n\ncalculated specificities are probably underestimated, limiting the external validity of our esti-\n\nmates. Second, our study evaluated the diagnostic performance of the suspected case definition\n\nusing symptoms of only 14 (out of 78) confirmed and 97 (of 1947) discarded cases with symp-\n\ntoms systematically recorded. Although age and sex distributions were slightly different of\n\nthose of confirmed cases without recorded symptoms, we think this may be due to chance. We\n\ndid not expect any differences in clinical presentation to occur among slightly older adult\n\ncases, or among cases reported earlier during the outbreak. Therefore, we assumed the fre-\n\nquencies of symptoms we reported, were representative of all cases. Our sample of cases was\n\nhowever too small for a precise estimate of the proportion of cases failing to respond to malaria\n\ntreatment when malaria and YF co-infected. Finally, we were not able to quantitatively estab-\n\nlish the improved timing of early diagnosis in our comparison of case definitions’ diagnostic\n\nperformance, because only for jaundice the timing of onset was recorded. Recording the time\n\nof onset of each symptom could have allowed to compare the case definitions’ performance\n\nearlier through the course of the disease. Nevertheless, the case definition we proposed would\n\nprobably have performed just as well when applied during the first days of illness, since fever\n\nand malaria infection would have been present already.\n\nDue to the low accuracy of the case definition used during the 2016 YF outbreak in DRC\n\nand delays in accessing healthcare, most patients were diagnosed too late to receive beneficial\n\nsupportive treatment and mitigate the complications of severe YF. Timely diagnosis of YF\n\nwould also allow implementing vector control measures around confirmed cases’ homes to\n\nprevent further transmission. Improving early access to healthcare and developing case\n\nTable 4. Case definitions with sensitivity and specificity derived from the clinical signs of confirmed and discarded yellow fever cases, DRC, 2016.\n\nCase\n\ndefinition\n\nDescription Sensitivity\n\n(%)\n\nSpecificity\n\n(%)\n\nArea under the\n\ncurve (95%CI)\n\nDRC 2016 Fever followed by jaundice (case definition applied during the DRC outbreak) 71 8 0.40 (0.27–0.52)\n\nOption A (Fever or jaundice) AND (malaria negative or not responding to malaria treatment) 67 82 0.74 (0.57–0.91)\n\nOption B (Fever or jaundice) AND myalgia 78 70 0.74 (0.59–0.89)\n\nOption C (Fever or jaundice) AND (malaria negative or myalgia) 100 57 0.78 (0.73–0.84)\n\nOption D (Fever or jaundice) AND (malaria negative or not responding to malaria treatment) AND\n\n(hemorrhage, decreased diuresis or myalgia)\n\n50 89 0.69 (0.51–0.88)\n\nUganda 2010\n\n[10]\n\n1st stage (Nov-Dec) of the Uganda 2010/11 outbreak. Severe headache AND at least three of\n\nfollowing: GI, dizziness, weakness, convulsions, unexplained bleeding\n\n38 78 0.58 (0.39–0.77)\n\nUganda 2011\n\n[10]\n\n2nd stage (Jan-Feb) of the Uganda 2010/11 outbreak. Acute onset of fever AND (malaria\n\nnegative or not responding to malaria treatment) AND (jaundice or unexplained bleeding)\n\n56 82 0.69 (0.51–0.86)\n\nBolivia 1997/\n\n98 [9]\n\nFever AND (jaundice or haemorrhagic symptoms or oliguria or death) 79 7 0.43 (0.32–0.54)\n\nhttps://doi.org/10.1371/journal.pntd.0007029.t004\n\nUrban yellow fever outbreak—Democratic Republic of the Congo, 2016\n\nPLOS Neglected Tropical Diseases | https://doi.org/10.1371/journal.pntd.0007029 December 7, 2018 9 / 11\n\nhttps://doi.org/10.1371/journal.pntd.0007029.t004\nhttps://doi.org/10.1371/journal.pntd.0007029\n\n\ndefinitions that do not include jaundice as essential criterion, in areas where urban YF trans-\n\nmission is established, will facilitate early case detection and management.\n\nAcknowledgments\n\nWe acknowledge the invaluable role of chief medical officers and epidemiologists of the health\n\nzones of Kinshasa, Kongo Central and Kwango provinces, who reported suspected YF cases,\n\nspent long days investigating cases, and mobilized their communities for vector control activi-\n\nties. We thank Dr. Kostas Danis for final edits to the manuscript.\n\nAuthor Contributions\n\nConceptualization: Brecht Ingelbeen, Nadine A. Weregemere, Sandra Cohuet.\n\nData curation: Brecht Ingelbeen, Mathias Mossoko.\n\nFormal analysis: Brecht Ingelbeen.\n\nInvestigation: Brecht Ingelbeen, Nadine A. Weregemere, Gaston P. Tshapenda, Justus Nsio,\n\nAxelle Ronsse, Steve Ahuka-Mundeke, Sandra Cohuet, Benoı̂t I. Kebela.\n\nMethodology: Brecht Ingelbeen, Harold Noel.\n\nSupervision: Sandra Cohuet, Benoı̂t I. Kebela.\n\nValidation: Gaston P. Tshapenda, Steve Ahuka-Mundeke.\n\nVisualization: Brecht Ingelbeen.\n\nWriting – original draft: Brecht Ingelbeen.\n\nWriting – review & editing: Brecht Ingelbeen, Nadine A. Weregemere, Harold Noel, Axelle\n\nRonsse, Steve Ahuka-Mundeke, Sandra Cohuet, Benoı̂t I. Kebela.\n\nReferences\n\n1. Monath TP. Yellow fever: An update. Lancet Infect Dis. 2001; 1: 11–20. https://doi.org/10.1016/S1473-\n\n3099(01)00016-0 PMID: 11871403\n\n2. 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Makiala-mandanda S, Ahuka-mundeke S, Abbate JL, Pukuta-simbu E, Nsio-mbeta J, Berthet N, et al.\n\nIdentification of Dengue and Chikungunya Cases Among Suspected Cases of Yellow Fever. Vector-\n\nborne Zoonotic Dis. 2018; XX: 1–7. https://doi.org/10.1089/vbz.2017.2176 PMID: 29768102\n\nUrban yellow fever outbreak—Democratic Republic of the Congo, 2016\n\nPLOS Neglected Tropical Diseases | https://doi.org/10.1371/journal.pntd.0007029 December 7, 2018 11 / 11\n\nhttps://doi.org/10.1016/j.ijid.2012.03.004\nhttps://doi.org/10.1016/j.ijid.2012.03.004\nhttp://www.ncbi.nlm.nih.gov/pubmed/22575876\nhttp://www.ncbi.nlm.nih.gov/pubmed/24626492\nhttps://doi.org/10.1128/JCM.01847-16\nhttps://doi.org/10.1128/JCM.01847-16\nhttp://www.ncbi.nlm.nih.gov/pubmed/28202798\nhttps://doi.org/10.1089/vbz.2017.2176\nhttp://www.ncbi.nlm.nih.gov/pubmed/29768102\nhttps://doi.org/10.1371/journal.pntd.0007029\n\n","UrlSlug":"urban-yellow-fever-outbreak-democratic-republic-congo-2016-towards-more-rapid-case-detection","collections":[],"countries":[{"Name":"Democratic Republic of Congo","Id":52}],"asset_languages":[{"AlternateTitle":null,"language":[{"Id":1,"Name":"English"}],"contentTranslation":[],"contentLocation":[]}],"creators":[{"Name":"Weregemere NA","Id":16045,"ac":[{"Order":1}]},{"Name":"Noel H","Id":16046,"ac":[{"Order":2}]},{"Name":"Ahuka-Mundeke S","Id":16048,"ac":[{"Order":7}]},{"Name":"Cohuet S","Id":17084,"ac":[{"Order":8}]},{"Name":"Ingelbeen B","Id":19340,"ac":[{"Order":0}]},{"Name":"Ronsse A","Id":20470,"ac":[{"Order":6}]},{"Name":"Kebela B","Id":21158,"ac":[{"Order":9}]},{"Name":"Mossoko M","Id":23289,"ac":[{"Order":4}]},{"Name":"Tshapenda G","Id":24647,"ac":[{"Order":3}]},{"Name":"Nsio JM","Id":32720,"ac":[{"Order":5}]}],"keywords":[{"Name":"outbreaks","Id":31},{"Name":"yellow fever","Id":77}]}}

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Urban yellow fever outbreak—Democratic Republic of the Congo, 2016: Towards more rapid case detection