viernes, 1 de julio de 2016

Ahead of Print -Unmet Needs for a Rapid Diagnosis of Chikungunya Virus Infection - Volume 22, Number 10—October 2016 - Emerging Infectious Disease journal - CDC

Ahead of Print -Unmet Needs for a Rapid Diagnosis of Chikungunya Virus Infection - Volume 22, Number 10—October 2016 - Emerging Infectious Disease journal - CDC

Volume 22, Number 10—October 2016

Letter

Unmet Needs for a Rapid Diagnosis of Chikungunya Virus Infection

Tables

Downloads

To the Editor: Chikungunya virus (CHIKV) has become a global health problem. Clinical manifestations are not specific and are difficult to differentiate from those of similar viral diseases (e.g., dengue and Zika virus disease). Diagnostic laboratories must be prepared to meet the changing epidemiology of viral diseases. CHIKV infection is currently identified by viral genome detection, using reverse transcription PCR (RT-PCR), viral culture, and serologic testing for IgG and IgM by indirect immunofluorescence (IFA) or ELISA. RT-PCR is most sensitive during the early phase of CHIKV infection (within 5–7 days of symptom onset), but its use is limited by the short viremic phase of the disease. After the acute phase, serologic testing for IgG and IgM is a more accurate indicator of disease. Molecular and serologic tests are complementary, reliable, and sensitive methods, but they require special equipment and a medium-to-high level of technical skill that may not be available in many laboratories, especially those in rural areas, where outbreaks usually occur.
Accurate and rapid detection of CHIKV infection by reliable point-of-care (POC) assays has been recommended to facilitate outbreak control. To meet this need, rapid CHIKV IgM POC tests are now available, but little information exists regarding their performance. The sensitivity of these tests evaluated in settings with a high prevalence of CHIKV infection is poor (range 1.9%–50.8%) compared with that for reference assays, especially in the acute phase of disease (15). In low-prevalence settings, CHIKV infection generally occurs as imported cases in travelers returning from disease-endemic countries. Diagnosis of such cases requires discrimination between CHIKV, dengue, Zika, and other febrile diseases in the differential diagnosis; this discrimination could be facilitated by the use of a reliable POC assay. The recent Zika virus disease outbreak in South America also highlights the worldwide need for rapid reliable POC tests.
From June 2014 through November 2015, eight patients who had returned to Italy from the Caribbean and Latin America were referred to the regional Center for Infectious Diseases, Amedeo di Savoia Hospital, in Turin for travel-associated CHIKV infection. These cases were the first in the region after 3 years without imported cases. We used IFA (Euroimmun AG, Lubek, Germany) and real-time RT-PCR (TIB MOLBIOL GmbH, Berlin, Germany) for CHIKV diagnosis. In addition, we evaluated the OnSite Chikungunya IgM Combo Rapid Test CE (CTK Biotech, San Diego, CA, USA) for CHIKV infection.
The rapid test identified IgM in only 3 of 8 patients (sensitivity 37.5%). All patients were negative for viral RNA, probably due to the time elapsed between symptom onset and serum sample collection, as confirmed by the presence of CHIKV IgG in most patients. No false-positive or invalid results were recorded with the rapid test on 30 CHIKV-negative serum samples (specificity 100%; positive and negative predictive value 37.5% and 100%, respectively).
Rapid and appropriate diagnostic tools are needed to slow or stop the worldwide spread of CHIKV. Rapid POC tests are highly cost-effective because they are easy to perform and can be disseminated to many laboratories for differentiating between diseases that are similar. Moreover, their results can easily be evaluated and shared within networks of reference laboratories.
However, our findings, in agreement with those of others, show that current rapid CHIKV tests perform poorly and need major improvement (Table) (15). This poor performance might have several explanations. For example, CHIKV patients do not often seek medical care in the early course of the disease. Most patients in our study were no longer in the acute phase of illness: the diagnosis was made a mean of 16.8 (range 7–30) days after fever onset, and when tested, all patients were viral RNA–negative by real-time RT-PCR. POC reactivity generally increases in patients with illness duration of >1 week (15), but this was not the case in our study. Genetic differences in circulating CHIKV lineages could also explain poor testing performance. Furthermore, the OnSite Chikungunya IgM Combo CE POC test uses a recombinant antigen covering the 226 residues of the E1 gene from CHIKV variant A226; recent studies on CHIKV protein characterization showed that more sensitive serologic assays can be obtained using specific early-phase E2 glycoprotein as antigens (3).
The successful use of rapid immunochromatography-based assays with monoclonal antibodies to detect viral diseases (e.g., dengue) has encouraged the development of rapid immunoassays for CHIKV antigens, and preliminary results for these assays seem promising (6). External quality assessment programs for POC tests and quality controls consisting of standardized positive serum could also be helpful for improving the performance of diagnostic tests.
In conclusion, returning travelers are sentinels of the rapidly changing epidemiology of CHIKV; thus, they require a prompt diagnosis and careful surveillance for their possible role in subsequent autochthonous disease transmission. Implementation of user-friendly, rapid, and easily deliverable POC tests for a prompt and accurate laboratory diagnosis is therefore needed to improve patient management and disease control measures.
Elisa BurdinoComments to Author , Guido Calleri, Pietro Caramello, and Valeria Ghisetti
Author affiliations: Amedeo di Savoia Hospital, Torino, Italy

Acknowledgment

We thank the Carlo De Negri Foundation for their support (N.650/007B/2011) and staff of the Unit of Serology and Molecular Biology, Laboratory of Microbiology and Virology, Amedeo di Savoia Hospital, for their excellent technical assistance.

References

  1. Kosasih HWidjaja SSurya EHadiwijaya SHButarbutar DPJaya UAEvaluation of two IgM rapid immunochromatographic tests during circulation of Asian lineage Chikungunya virus. Southeast Asian J Trop Med Public Health2012;43:5561.PubMed
  2. Rianthavorn PWuttirattanakowit NPrianantathavorn KLimpaphayom NTheamboonlers APoovorawan YEvaluation of a rapid assay for detection of IgM antibodies to chikungunya. Southeast Asian J Trop Med Public Health2010;41:926.PubMed
  3. Yap GPok KYLai YLHapuarachchi HCChow ALeo YSEvaluation of Chikungunya diagnostic assays: differences in sensitivity of serology assays in two independent outbreaks. PLoS Negl Trop Dis2010;4:e753DOIPubMed
  4. Blacksell SDTanganuchitcharnchai AJarman RGGibbons RVParis DHBailey MSPoor diagnostic accuracy of commercial antibody-based assays for the diagnosis of acute Chikungunya infection. Clin Vaccine Immunol2011;18:17735DOIPubMed
  5. Prat CMFlusin OPanella ATenebray BLanciotti RLeparc-Goffart IEvaluation of commercially available serologic diagnostic tests for chikungunya virus. Emerg Infect Dis2014;20:212932DOIPubMed
  6. Okabayashi TSasaki TMasrinoul PChantawat NYoksan SNitatpattana NDetection of chikungunya virus antigen by a novel rapid immunochromatographic test. J Clin Microbiol2015;53:3828DOIPubMed

Table

Suggested citation for this article: Burdino E, Calleri G, Caramello P, Ghisetti V. Unmet needs for a rapid diagnosis of Chikungunya virus infection [letter]. Emerg Infect Dis. 2016 Sep [date cited]. http://dx.doi.org/10.3201/eid2210.151784


DOI: 10.3201/eid2210.151784

Ahead of Print -Chikungunya Virus in Febrile Humans and Aedes aegypti Mosquitoes, Yucatan, Mexico - Volume 22, Number 10—October 2016 - Emerging Infectious Disease journal - CDC

Ahead of Print -Chikungunya Virus in Febrile Humans and Aedes aegypti Mosquitoes, Yucatan, Mexico - Volume 22, Number 10—October 2016 - Emerging Infectious Disease journal - CDC

Volume 22, Number 10—October 2016

Dispatch

Chikungunya Virus in Febrile Humans and Aedes aegypti Mosquitoes, Yucatan, Mexico

Nohemi Cigarroa-Toledo, Bradley J. Blitvich, Rosa C. Cetina-Trejo, Lourdes G. Talavera-Aguilar, Carlos M. Baak-Baak, Oswaldo M. Torres-Chablé, Md-Nafiz Hamid, Iddo Friedberg, Pedro González-Martinez, Gabriela Alonzo-Salomon, Elsy P. Rosado-Paredes, Nubia Rivero-Cárdenas, Guadalupe C. Reyes-Solis, Jose A. Farfan-Ale, Julian E. Garcia-Rejon, and Carlos Machain-WilliamsComments to Author 
Author affiliations: Universidad Autonoma de Yucatan, Merida, Mexico (N. Cigarroa-Toledo, R.C. Cetina-Trejo, L.G Talavera-Aguilar, C.M. Baak-Baak, O.M. Torres-Chablé, P. González-Martinez, G. Alonzo-Salomon, E.P. Rosado-Paredes, N. Rivero-Cárdenas, G.C. Reyes-Solis, J.A. Farfan-Ale, J.E. Garcia-Rejon, C. Machain-Williams);Iowa State University, Ames, Iowa, USA (B.J. Blitvich, M. Hamid, I. Friedberg)

Abstract

Chikungunya virus (CHIKV) was isolated from 12 febrile humans in Yucatan, Mexico, in 2015. One patient was co-infected with dengue virus type 1. Two additional CHIKV isolates were obtained from Aedes aegyptimosquitoes collected in the homes of patients. Phylogenetic analysis showed that the CHIKV isolates belong to the Asian lineage.
Chikungunya virus (CHIKV; family Togaviridae, genus Alphavirus) is transmitted to humans by Aedes spp. mosquitoes (1,2). The virus is the etiologic agent of chikungunya, an acute febrile illness that is often accompanied by debilitating arthralgia. Historically, CHIKV has been restricted to the Eastern Hemisphere, but in 2013, the virus was reported in the Western Hemisphere during a large outbreak in the Caribbean region. CHIKV spread rapidly to South America, Central America, Mexico, and the United States. The Pan American Health Organization estimated that >1.7 million suspected and laboratory-confirmed cases of chikungunya have occurred in the Western Hemisphere (http://www.paho. org/hq/index.php?option=com_topics&view=readall&cid=5927&Itemid=40931&lang=en).
CHIKV was isolated in Mexico from a patient from Jalisco in whom symptoms developed in May 2014 shortly after the patient returned from the Caribbean region (3). The first autochthonous case was reported in October 2014 after CHIKV was isolated from a patient in southeastern state of Chiapas (4). CHIKV-infected Aedes aegypti mosquitoes and additional chikungunya cases were identified in Chiapas later in 2014 (5,6). To our knowledge, no reports of CHIKV in any other states in Mexico have been published. In this study, we tested febrile patients in the state of Yucatan and mosquitoes temporally and spatially associated with these patients for CHIKV infection.

The Study

Written informed consent was obtained from all patients who participated in the study or their legal guardians. The study population was composed of patients who came to hospitals or clinics in Yucatan during August–October 2015 with chikungunya-like illness. These patients were referred to the hematology laboratory at the Hideyo Noguchi Research Center (Merida, Yucatan, Mexico). A patient was considered to have chikungunya-like illness if he or she had fever and arthralgia. Travel history of each study participant was recorded, and any patient who had traveled outside Yucatan in the past 30 days before disease onset was excluded from the study.
Blood was collected from the cephalic vein of each patient, dispensed into a vacutainer tube (BD Diagnostics, Franklin Lakes, NJ, USA), and centrifuged. Serum was collected and stored at −80°C. Resting mosquitoes were collected from the homes of each study participant by using Centers for Disease Control and Prevention (Atlanta, GA, USA) backpack-mounted aspirators. Each house was examined once, and collections were made between 9:00 amand noon. All rooms were inspected, particularly dark areas (i.e., underneath furniture, in closets, and in curtains). Backyards were also searched, particularly shaded areas (i.e., pet homes, tool sheds, and underneath vegetation).
Mosquitoes were transported alive to the laboratory and identified on chill tables by using morphologic characteristics (7). Female mosquitoes were sorted into pools of <10 and homogenized in phosphate-buffered saline (pH 7.2) by using a mortar and pestle. Male mosquitoes were discarded.
An aliquot of each serum sample and mosquito homogenate was filtered and inoculated onto subconfluent monolayers of Ae. albopictus (C6/36) cells in 25-cm2 flasks. Cells were incubated for 7 days at 28°C. Second and third blind passages were performed in C6/36 and African green monkey kidney (Vero) cells, respectively. Vero cells were incubated for 3–7 days at 37°C in an atmosphere of 5% CO2. Cells were scraped from flasks after each passage and centrifuged at 10,000 × g for 10 min at 4°C. Supernatants were collected and stored at −80°C. Cell pellets were resuspended in Trizol (Invitrogen, Carlsbad, CA, USA), and total RNA was extracted following the manufacturer’s instructions.
Total RNA was analyzed by using reverse transcription PCR (RT-PCR) and CHIKV-specific primers for a 107-nt region of the nonstructural protein 1 gene (primer sequences available upon request from the authors) and dengue virus (DENV)–specific primers for a 511-nt region of the capsid–membrane genes of all 4 serotypes (8). If DENV RNA was detected, a semi-nested RT-PCR was performed with serotype-specific primers. If CHIKV RNA was detected, a 3,744-nt region that spans the structural protein genes (capsid-E3-E2-6K-E1) (E, envelope; 6K, membrane-associated peptide) was amplified as 2 overlapping fragments (primer sequences available upon request from the authors).
Complementary DNAs were generated by using Superscript III reverse transcriptase (Invitrogen), and PCRs were performed by using Taq polymerase (Invitrogen). RT-PCR products were purified by using the Purelink Gel Extraction Kit (Invitrogen) and sequenced by using a 3730x1 DNA sequencer (Applied Biosystems, Foster City, CA, USA).
CHIKV was isolated from 12 (23.5%) of 51 study participants. DENV type 1 was also isolated from 1 CHIKV-positive patient. DENV was readily detected in cultured cells after the first blind passage, but its ability to replicate decreased after subsequent passages, presumably because CHIKV outcompeted this slower-replicating flavivirus.
The most common symptoms in patients infected with only CHIKV, in addition to fever, during the first 3 days of disease onset were arthralgia (100%), myalgia (100%), asthenia (90.9%), and rash (45.5%) (Table). Symptoms of the co-infected patient (a 31-year-old woman) included headache, myalgia, and rash. Age range of patients infected with only CHIKV was 9–59 years (mean age 31 years).
A total of 237 female mosquitoes were collected, and all were identified as Ae. aegypti mosquitoes. CHIKV was isolated from 2 pools. One pool contained mosquitoes collected in the living room of a 53-year-old patient who had a confirmed CHIKV infection. The other pool contained mosquitoes collected in bedroom of the co-infected patient. DENV was not isolated from any mosquitoes.
The capsid-E3-E2-6K-E1 region of each CHIKV isolate was sequenced and submitted to GenBank under accession nos. KU295117–KU295130. Pairwise alignments of the nucleotide and deduced amino acid sequences were performed by using Clustal Omega (http://www.ebi.ac.uk/Tools/ msa/clustalo/). Analysis showed that nucleotide sequences had 99.41%–99.97% identity and amino acid sequences 99.44%–100% identity with each other. The nucleotide sequence of 1 isolate (GenBank accession no. KU295121) was aligned with all other CHIKV sequences in GenBank and shown to have highest identity (99.52%) with the corresponding gene region of CHIKV isolates from Panama and El Salvador, followed by an identity of 99.49% with isolates from Chiapas, Mexico; Guatemala; Puerto Rico; Guyana; and elsewhere in the Western Hemisphere. Analysis of deduced amino acid sequences showed that mutations associated with increased infectivity of Ae. albopictus mosquitoes (E1-A226V and E2-L210Q) (9,10) were not present in genomes of any isolates.
Thumbnail of Phylogenetic analysis of chikungunya virus (CHIKV) isolates from Yucatan, Mexico. Analysis was based on a 3,744-nt structural gene region (capsid-E3-E2-6K-E1) of 63 CHIKV isolates, including the 14 isolates from Yucatan. Sequences were aligned by using MUSCLE (11), and the tree was constructed by using the neighbor-joining algorithm as implemented in PHYLIP (12) and using ETE3 (Environment for Tree Exploration 3) (13). Isolates are identified by GenBank accession number, country, an
Figure. Phylogenetic analysis of chikungunya virus (CHIKV) isolates from Yucatan, Mexico. Analysis was based on a 3,744-nt structural gene region (capsid-E3-E2-6K-E1) of 63 CHIKV isolates, including the 14 isolates from Yucatan. Sequences...
Complete structural gene sequences of 60 CHIKV isolates, including the 14 isolates from Yucatan, were aligned by using MUSCLE (11), and phylogenetic trees were constructed by using the neighbor-joining algorithm as implemented in PHYLIP (12) (Figure). We observed 4 lineages, Asian, East/Central/South African, Indian Ocean, and West African lineages, which was consistent with results of previous studies (1,5). CHIKV isolates from Yucatan belonged to the Asian lineage and shared a close phylogenetic relationship with other isolates from the Western Hemisphere (Figure). Our isolates formed a nested clade within the Asian lineage. However, bootstrap support (0.61) for this topologic arrangement was not strong.

Conclusions

We isolated CHIKV from febrile patients and Ae. aegypti mosquitoes in Yucatan, Mexico, which provided additional evidence that this virus is spreading throughout the Americas at an alarming rate. Concurrent isolation of CHIKV and DENV from a patient in this study and patients in previous studies (14,15) underscores the need for differential diagnosis in areas where these viruses co-circulate.
Ms. Cigarroa-Toledo is a graduate student in the Arbovirus Laboratory at the Universidad Autonoma de Yucatan, Merida, Mexico. Her research interests include arbovirus epidemiology, evolution, and surveillance.

Acknowledgments

We thank Irma Correa May for providing assistance during serum collection.
This study was supported by a grant (2014-247005) from Consejo Nacional de Ciencia y Tecnología de Mexico.

References

  1. Weaver SCForrester NLChikungunya: evolutionary history and recent epidemic spread. Antiviral Res2015;120:329DOIPubMed
  2. Powers AMRisks to the Americas associated with the continued expansion of chikungunya virus. J Gen Virol2015;96:15DOIPubMed
  3. Rivera-Ávila RCChikungunya fever in Mexico: confirmed case and notes on the epidemiologic response [in Spanish]Salud Publica Mex.2014;56:4024.PubMed
  4. Díaz-Quinonez JAOrtiz-Alcantara JFragoso-Fonseca DEGarces-Ayala FEscobar-Escamilla NVazquez-Pichardo MComplete genome sequences of chikungunya virus strains isolated in Mexico: first detection of imported and autochthonous cases. Genome Announc2015;3:e0030015DOIPubMed
  5. Kautz TFDiaz-Gonzalez EEErasmus JHMalo-Garcia IRLangsjoen RMPatterson EIChikungunya virus as cause of febrile illness outbreak, Chiapas, Mexico, 2014. Emerg Infect Dis2015;21:20703DOIPubMed
  6. Díaz-González EEKautz TFDorantes-Delgado AMalo-Garcia IRLaguna-Aguilar MLangsjoen RMFirst report of Aedes aegypti transmission of chikungunya virus in the Americas. Am J Trop Med Hyg2015;93:13259DOIPubMed
  7. Darsie RF JrA survey and bibliography of the mosquito fauna of Mexico (Diptera: Culicidae). J Am Mosq Control Assoc.1996;12:298306.PubMed
  8. Lanciotti RSCalisher CHGubler DJChang GJVorndam AVRapid detection and typing of dengue viruses from clinical samples by using reverse transcriptase-polymerase chain reaction. J Clin Microbiol1992;30:54551.PubMed
  9. Tsetsarkin KAWeaver SCSequential adaptive mutations enhance efficient vector switching by Chikungunya virus and its epidemic emergence.PLoS Pathog2011;7:e1002412DOIPubMed
  10. Tsetsarkin KAVanlandingham DLMcGee CEHiggs SA single mutation in chikungunya virus affects vector specificity and epidemic potential. PLoS Pathog2007;3:e201DOIPubMed
  11. Edgar RCMUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res2004;32:17927DOIPubMed
  12. Felsenstein JPHYLIP: Phylogeny Inference Package (version 3.2). Cladistics1989;5:1646.
  13. Huerta-Cepas JDopazo JGabaldon TETE: a python environment for tree exploration. BMC Bioinformatics2010;11:24DOIPubMed
  14. Chang S-FSu C-LShu P-YYang C-FLiao T-LCheng C-HConcurrent isolation of chikungunya virus and dengue virus from a patient with coinfection resulting from a trip to Singapore. J Clin Microbiol2010;48:45869DOIPubMed
  15. Parreira RCenteno-Lima SLopes APortugal-Calisto DConstantino ANina JDengue virus serotype 4 and chikungunya virus coinfection in a traveller returning from Luanda, Angola, January 2014. Euro Surveill2014;19:20730DOIPubMed

Figure

Table

Suggested citation for this article: Cigarroa-Toledo N, Blitvich BJ, Cetina-Trejo RC, Talavera-Aguilar LG, Baak-Baak CM, Torres-Chablé OM, et al. Chikungunya virus in febrile humans and Aedes aegypti mosquitoes, Yucatan, Mexico. Emerg Infect Dis. 2016 Oct [date cited].http://dx.doi.org/10.3201/eid2210.152087


DOI: 10.3201/eid2210.152087

Ahead of Print -Time Lags between Exanthematous Illness Attributed to Zika Virus, Guillain-Barré Syndrome, and Microcephaly, Salvador, Brazil - Volume 22, Number 8—August 2016 - Emerging Infectious Disease journal - CDC

Ahead of Print -Time Lags between Exanthematous Illness Attributed to Zika Virus, Guillain-Barré Syndrome, and Microcephaly, Salvador, Brazil - Volume 22, Number 8—August 2016 - Emerging Infectious Disease journal - CDC







Volume 22, Number 8—August 2016

Research

Time Lags between Exanthematous Illness Attributed to Zika Virus, Guillain-Barré Syndrome, and Microcephaly, Salvador, Brazil

Igor A.D. Paploski1, Ana Paula P.B. Prates1, Cristiane W. Cardoso, Mariana Kikuti, Monaise M. O. Silva, Lance A. Waller, Mitermayer G. Reis, Uriel Kitron1, and Guilherme S. Ribeiro1Comments to Author 
Author affiliations: Centro de Pesquisas Gonçalo Moniz, Salvador, Brazil (I.A.D. Paploski, M. Kikuti, M.M.O. Silva, M.G. Reis, U. Kitron, G.S. Ribeiro)Universidade Federal da Bahia, Salvador (I.A.D. Paploski, M. Kikuti, M.G. Reis, G.S. Ribeiro)Secretaria Municipal de Saúde de Salvador, Salvador (A.P.P.B. Prates, C.W. Cardoso)Emory University, Atlanta, Georgia, USA (L.A. Waller, U. Kitron)

Abstract

Zika virus infection emerged as a public health emergency after increasing evidence for its association with neurologic disorders and congenital malformations. In Salvador, Brazil, outbreaks of acute exanthematous illness (AEI) attributed to Zika virus, Guillain-Barré syndrome (GBS), and microcephaly occurred in 2015. We investigated temporal correlations and time lags between these outbreaks to identify a common link between them by using epidemic curves and time series cross-correlations. Number of GBS cases peaked after a lag of 5–9 weeks from the AEI peak. Number of suspected cases of microcephaly peaked after a lag of 30–33 weeks from the AEI peak, which corresponded to time of potential infections of pregnant mothers during the first trimester. These findings support the association of GBS and microcephaly with Zika virus infection and provide evidence for a temporal relationship between timing of arboviral infection of pregnant women during the first trimester and birth outcome.
In late 2014, cases of acute exanthematous illness (AEI), involving widespread rash of unclear etiology, were reported in several municipalities in northeastern Brazil. By April 2015, Zika virus was identified in patients from the states of Bahia (1) and Rio Grande do Norte, Brazil (2). In Salvador, the capital of Bahia, during February–June 2015, ≈15,000 cases of indeterminate AEI were reported (3). Reverse transcription PCR performed on 58 serum samples from AEI outbreak case-patients identified Zika virus in 3 (5.2%) of them. (3). Although chikungunya and dengue viruses were also detected at similar frequencies, the low frequency of fever (35.1%) and arthralgia (26.5%) among AEI patients suggested that Zika virus was the likeliest etiology for the outbreak (3).
The virus has continued to spread, and by the end of 2015, laboratory-confirmed autochthonous Zika virus cases have been identified in all 5 regions of Brazil; the Brazilian Ministry of Health estimated that 500,000−1.5 million persons were infected (4). Zika virus has since spread to other regions of the Americas and resulted in large epidemics (5).
Studies conducted during a Yap Island (Federated States of Micronesia) outbreak found that ≈20% of Zika virus infections showed clinical symptoms (6). For most patients in whom symptoms develop, the disease is self-limited and clinical manifestations (exanthema [rash], arthralgia, fever, and conjunctivitis) are mild (6). However, during the outbreak in French Polynesia, a 20-fold increase in the incidence of GBS was observed (7), and concerns about an association between Zika virus infection and GBS were first raised. A case–control study subsequently identified strong associations of GBS with positive Zika virus seroneutralization and Zika virus IgM or IgG (8). Since 2015, an increase in GBS rates has also been observed in Brazil, Colombia, El Salvador, Suriname, and Venezuela (9).
The increase in newborns with microcephaly in northeastern Brazil in late 2015 called global attention to Zika virus as a major public health threat to pregnant women and their newborns (10). Even without a conclusive association between a prenatal Zika virus infection and neurologic disorders in the offspring, the Brazilian Ministry of Health and World Health Organization declared a public health emergency (11). Since then, clinical evidence increasingly supports an association of prenatal Zika virus infection with birth of babies with microcephaly, and other neurologic and ophthalmologic complications, as well as miscarriages and stillbirths (1217).
Salvador, the largest city in northeastern Brazil (2015 population of 2.9 million persons) has been one of the main epicenters for epidemics of Zika virus infection, GBS, and microcephaly. Using raw and smoothed temporal data collected during these outbreaks, we investigated the temporal associations and determined the time lags between epidemiologic curves of the suspected Zika virus infection outbreak, reported cases of GBS, and reported suspected cases of microcephaly.

Methods

Data Collection and Case Definitions
In April 2015, the Centers for Information and Epidemiologic Surveillance of Salvador (CIES) established 10 public emergency health centers as sentinel units for systematic surveillance of patients with AEI of unknown cause in Salvador. A case-patient was defined as a resident of Salvador who had a rash, with or without fever, and whose clinical and epidemiologic characteristics did not satisfy the criteria for dengue, chikungunya, measles, or rubella (18). The public health units searched retrospectively for suspected cases by review of medical charts of patients treated starting on February 15, 2015; continued with prospective case detection; and submitted weekly reports of identified cases to CIES. On May 25, 2015, because of the sharp decrease in the number of outbreak cases, CIES reduced the number of sentinel health units to the 3 that reported the most cases, although several of the other units continued to report AEI cases voluntarily. For our analyses, we used the reported number of cases for February 15–December 31, 2015.
After neurologic syndrome cases in adults potentially associated with a previous Zika virus infection were first reported in Salvador in late May, CIES initiated surveillance for hospitalizations caused by neurologic manifestations that might be linked to Zika. Cases were identified retrospectively during April–May and followed by prospective case detection. CIES regularly contacted all city hospital epidemiologic services and investigated all suspected case-patients who resided in Salvador. Surveillance personnel, supported by infectious disease physicians and neurologists, ruled out cases for which clinical and laboratory manifestations indicated other diagnoses, and only included cases of GBS and its variants (e.g., Miller-Fisher syndrome). For our analyses, we used the number of hospitalized patients with GBS or GBS variants identified in Salvador during 2015.
After the increase in number of cases of microcephaly in newborns first noticed in Pernambuco State in September 2015, and the request from the Brazilian Ministry of Health that all suspected cases of microcephaly in newborns be reported, CIES established a reporting system in October 2015. Since then, CIES has requested and received reports of all newborns with suspected neurologic impairments and has been investigating all potential cases of microcephaly.
Suspected cases of microcephaly in newborns were reported on the basis of a reduced occiptofrontal perimeter at birth. The initial criteria for reporting was newborns delivered after >37 gestational weeks with an occiptofrontal perimeter <33 cm, or newborns delivered before 37 gestational weeks with a perimeter less than the third percentile of the Fenton curve (19). In December, 2015, the Brazilian Ministry of Health changed the first criterion to an occiptofrontal perimeter <32 cm (20).
For our analyses, we only included suspected microcephaly case-patients that fulfilled these latest criteria. The first such case-patient was born on July 11, 2015, and a search of the national information system on live births from Salvador for the AEI outbreak period produced no additional cases of congenital malformation fulfilling these criteria. We included all of suspected cases of microcephaly up to March 10, 2016 (the 10th epidemiologic week of 2016); and data for the last case-patient was updated on March 17, 2016.
We opted to analyze all reported suspected cases of microcephaly, instead of only those investigated and confirmed, because only 27.7% of the reported cases had been investigated. Limiting analysis to only confirmed cases could potentially introduce bias because cases that were reported earlier during the outbreak were more likely to have had the investigation concluded. In contrast, including all reported cases might introduce some false-positive diagnoses. Because both inclusion criteria are not free of a potential bias, we analyzed all reported suspected cases of microcephaly.
CIES served as the repository of all AEI, GBS, and suspected microcephaly data from all contributing sources. CIES evaluated and integrated data, including merging of different reporting spreadsheets, and removed duplicate information (on the basis of name, age, date of reporting, and sanitary districts of residence) and nonsense data (e.g., all missing information). Numbers of cases of AEI, GBS, and suspected microcephaly per epidemiologic week were then tabulated.
Data Analysis
We analyzed case-patients with AEI, GBS, and suspected microcephaly by date of medical care, date of hospitalization, and date of birth, respectively. We used the documented date of medical care or hospitalization, rather than the presumed day when symptoms began, to avoid recall error and reduce missing information.
We constructed epidemiologic curves by week and with 3-week and 5-week moving averages by using Stata software (21). We smoothed data by using 3-week and 5-week moving averages to reduce week-to-week variation, wherein the count of events for a given week was averaged with values of the previous and following weeks (3 weeks) or with the 2 previous and 2 following weeks (5 weeks). Because the weekly increase in cases during the outbreak was much larger than the observed weekly variation, there was little difference between crude and smoothed data.
We assessed temporal correlations between our time series by using standard estimation of lagged time-series cross-correlations (22) to identify lag times showing the highest correlations between weekly numbers of AEI and ensuing weekly numbers of 1) GBS cases and 2) suspected cases of microcephaly. Although one could evaluate statistical significance by comparing cross-correlations to those expected under a null hypothesis of no association (22), our primary focus was to estimate lags with the strongest correlation (i.e., at what lags do the strongest correlations occur?), not a strict evaluation of whether any correlations occurred. Because both time series showed single large increases, our goal was to identify time lags between these series. Specifically, we examined lag times of 0–40 weeks and compared the AEI time series to those for GBS and suspected microcephaly to cover the full pregnancy period. Because of observed timing of initial epidemic curves, we present only results for positive time lags (i.e., AEI preceding GBS or suspected microcephaly). We also assessed cross-correlations for raw and 3-week and 5-week smoothed data.

Results

Thumbnail of Epidemiologic curves of weekly cases and moving averages of 3 weeks and 5 weeks for A) acute exanthematous illness (AEI), B) Guillain-Barré syndrome, and C) microcephaly, Salvador, Brazil, 2015–2016. The specific starting date during week 7 was February 15, 2105.
Figure 1. Epidemiologic curves of weekly cases and moving averages of 3 weeks and 5 weeks for A) acute exanthematous illness (AEI), B) Guillain-Barré syndrome, and C) microcephaly, Salvador, Brazil, 2015–2016. The specific...
During the study, CIES recorded 17,503 reported cases of AEI (5.99 cases/1,000 persons during 2015), 51 hospitalizations of persons with of GBS (1.74 cases/100,000 persons during 2015), and 367 newborns with suspected microcephaly (15.6 cases/1,000 newborns during July 2015–February 2016, which peaked at 31.4 cases/1,000 newborns in December) (Table). Raw and smoothed data (3-week and 5-week moving averages) had a clear initiation, peak, and reduction of cases, and followed a classic epidemic time series of incidence for AEI, GBS, and suspected microcephaly (Figure 1).
Number of AEI cases with available data for date of medical care (16,986 [97.1%]) (Figure 1, panel A) peaked during week 18 (May 3–9, 2015), as reported (3). The peak during week 18 was confirmed by 3-week and 5-week moving averages. During weeks 16–20 (April 19–May 23, 2015), >1,000 AEI cases/week were reported.
Number of GBS cases with a known date of hospitalization (49 [96.1%]) (Figure 1, panel B) peaked during weeks 23–27 (June 7–July 11, 2015). Using the 5-week moving average, we found that >4 cases were reported during weeks 23–27. The 5-week and 3-week moving averages provided a clearer picture of the GBS epidemic curve, which was susceptible to higher variability, given the relatively low number of cases per week.
Suspected cases of microcephaly that satisfied our criteria and included a date of birth (357 [97.3%]) (Figure 1, panel C) peaked during weeks 47–49 (November 22–December 12, 2015), during which there were >20 cases/week. Moving averages helped smooth the epidemiologic curve, which is susceptible to uneven time lags between a potential prenatal infection and outcome (i.e., a mother could have been infected at any time during the first trimester or even later). The 18-week period of increase in the number of suspected cases of microcephaly (weeks 31–48) corresponds to a 12-week increase in number of AEI cases (weeks 7–18), and is probably longer because pregnant women throughout the first trimester might have been infected at the onset of the AEI outbreak. For 328 (91.9%) of 357 suspected cases of microcephaly for which data on gestational age at birth were available, the median gestational week was 39 weeks (range 34–41 weeks), which coincided with the first trimester of pregnancy when the AEI outbreak peaked.
Thumbnail of Cross-correlation of acute exanthematous illness with A) Guillain-Barré syndrome and B) microcephaly, Salvador, Brazil, 2015–2016, for a 5-week moving average. Dotted horizontal lines indicate 95% tolerance intervals for a null model of no association. Negative correlations observed at early lag periods are a function of large numbers of acute exanthematous illness cases that occurred early in the study period when there were no suspected cases of microcephaly.
Figure 2. Cross-correlation of acute exanthematous illness with A) Guillain-Barré syndrome and B) microcephaly, Salvador, Brazil, 2015–2016, for a 5-week moving average. Dotted horizontal lines indicate 95% tolerance intervals for a null model...
Cross-correlation analyses (Figure 2) confirmed the patterns shown in Figure 1 (i.e., a strong positive correlation between temporally lagged time series driven by observed time lags between peaks in case numbers). Findings were consistent for results based on the raw time series and either the 3-week or 5-week moving averages, and peak correlations differed by <1 week. Number of GBS cases peaked after a lag of 5–9 weeks from the peak in AEI cases (Figure 2, panel A), thus providing strong support for a direct association of the GBS outbreak with the AEI outbreak 1–2 months earlier.
The number of suspected cases of microcephaly peaked after a lag of 30–33 weeks from the peak in AEI cases (Figure 2, panel B), which corresponded to potential infections of mothers during the first trimester of gestation (7–8 months before giving birth). Negative correlations observed at early lag periods were a function of the fact that most AEI cases occurred early in the study period when there were no suspected cases of microcephaly.

Discussion

Our analyses showed clear and strong cross-correlations for GBS and suspected cases of microcephaly with the original AEI outbreak in Salvador during 2015. These correlations were particularly noteworthy, given delays in case reporting, challenges with diagnosis, and ongoing investigations. Correlations were particularly clear-cut for GBS when a lag of 5–9 weeks from AEI was considered. These results complement a recent case–control study (8), which reported an association of GBS with Zika virus in French Polynesia.
Of even more public health interest might be the strong association between outbreaks of AEI and children born with suspected microcephaly (30–33 weeks apart), which demonstrated a strong temporal association between potential exanthematous disease in the first trimester of pregnancy and birth outcome. These results also complement results of studies that linked febrile rash illness suggestive of Zika virus infection during the first trimester of pregnancy and an increased incidence of microcephaly in newborns (23,24). The ongoing decrease in number of suspected cases of microcephaly in 2016, which has occurred despite continuing and increasing public health and media attention to this serious pregnancy outcome, is particularly noteworthy and matches the reduction in number of cases of microcephaly predicted for Salvador in early 2016 (25,26).
Recent statements by researchers in Brazil and elsewhere and reports in the media have raised doubts about the actual baseline number of cases of microcephaly in Brazil and questioned the number of cases associated with exposure to Zika virus, given limited baseline data and greatly increased recognition and attention to this phenomenon (27). Our results support the link between births of children suspected of having microcephaly and exposure of a pregnant woman to an AEI putatively caused by Zika virus during the first trimester of pregnancy. This link was based on the time-lagged correlation between these 2 factors and the decrease in incidence of congenital manifestations since mid-December 2015.
Although such temporal associations do not prove causation, their strength and pattern makes a major contribution to the growing body of data supporting the association of GBS and congenital malformations with previous exposure to Zika virus (or, at least, an AEI). Furthermore, estimated time lags provide insight into the high-risk exposure period that might lead to these complications and, consequently, help public health and vector control authorities target control and protection efforts more effectively. Additional individual and population level investigations, both clinical and epidemiologic (case–­control and cohort studies) are needed, as are increased resources for surveillance, vector control, and diagnostic capabilities to make definitive connections. With emerging infectious diseases increasing worldwide (28), investing in public health surveillance on the city, state, national, and global levels is one of the most cost effective way to help address these ongoing and increasing challenges (29).
As an epidemiologic investigation relying on population-level analyses, this study had several limitations. Our data were collected by CIES from different sources, diagnoses were not always definitive, and case definition criteria and case ascertainment were prone to changes, as is common during initial outbreak investigations of novel events. This limitation is particularly true for the AEI outbreak, for which cases were not subjected to an extensive laboratory investigation. In a previous study, we showed that Zika virus, chikungunya virus (CHIKV), and dengue virus were circulating and associated with AEI cases during the outbreak (3). On the basis of clinical manifestations for reported AEI case-patients and epidemiologic evidence for the spread of Zika virus in Brazil and to the rest of the Americas, and given the challenges in identifying Zika virus in serum samples, this virus was probably the main arbovirus involved in the AEI outbreak in Salvador during our study. Furthermore, although dengue (30,31) and chikungunya (32,33) have been associated with GBS, dengue epidemics have occurred for decades without any associated outbreaks of microcephaly or other severe congenital malformations, and CHIKV infections that occur in pregnant women before the peripartum period do not appear to pose congenital risks (34,35).
In French Polynesia, during the chikungunya outbreak in 2014–2015, an increase in GBS cases was observed (33). Thus, Zika virus and CHIKV might have played a role in emergence of GBS cases in Salvador. Unfortunately, our study design (because of limited available diagnostic data) precluded determining the frequency of each circulating arbovirus during the AEI outbreak. These data are needed to determine whether different arboviral infections peaked at the same time or whether the AEI peak represented the junction of distinct epidemic curves for sequential arbovirus outbreaks.
The presence of 2 infectious triggers, whose temporal distribution might not have coincided at the AEI peak, might partly explain why we observed GBS cases peaking 5–9 weeks after the peak of AEI cases, while in French Polynesia, the lag between peaks of GBS and cases of Zika virus infection was only 3 weeks (8). Use of date of medical care for AEI and date of hospitalization for GBS, rather than the presumed day when symptoms began, also might have contributed to the difference in observed time lags. For case-patients for whom data were available, the median interval between AEI symptoms onset and medical care was 1 day, and the median interval between onset of GBS symptoms and hospitalization was 5 days. In addition, patients with AEI might have been less likely to seek medical care for their symptoms, once the community perceived Zika virus infection as benign, making the AEI epidemic curve shorter. Therefore, actual time lags might be shorter than what we observed.
Another limitation was the change in case ascertainment for AEI from retrospective to prospective, and then from using 10 health units to using the 3 units that reported most cases (although several of the other units continued to report AEI cases voluntarily). Retrospective data collection is the common method for detecting a baseline level and initiating an outbreak investigation, and reduction of the number of health units was made after the large decrease in AEI cases. Thus, the effect of these changes on the shape of the epidemic curve is small.
As another limitation, the epidemiologic curve for suspected cases of microcephaly potentially overestimated the actual number of cases. Ongoing investigation of the 5,909 reported suspected cases of microcephaly and other central nervous system impairments in newborns, stillbirths, and abortions in Brazil was completed for 1,687 cases by mid-February 2016. Of these cases, 641 (38.0%) were confirmed (36). In Salvador, CIES investigated 99 reported cases of Zika virus congenital syndrome, of which 43 (43.4%) were confirmed.
On the basis of the reported number of suspected cases of microcephaly and the number of births in Salvador during the study, 3.1% of newborns were reported as having suspected cases of microcephaly during the peak month of December 2015. However, if we consider that in December only 20 (58%) of the 34 investigated cases were confirmed, a more realistic estimation for the suspected microcephaly risk in that period is 1.8 cases/100 newborns. We believe that the temporal distribution of reported cases parallels that of actual cases. Also, by analyzing all reported cases, we reduced a major source of observation bias (i.e., investigations of cases reported earlier were more likely to have been completed). The consistent shape and mode of the epidemiologic curves, with or without smoothing, support the robustness of our data and findings.
Our case ascertainments of suspected cases of microcephaly were also potentially influenced by spontaneous and nonspontaneous abortions. Although spontaneous abortions could have occurred because of virus effects during embryogenesis, nonspontaneous abortions might have increased after intense media coverage of the microcephaly outbreak. Abortion is prohibited in Brazil (except for a few situations, such as rape, anencephaly, or risk for death of the mother), but it is commonly performed illegally, and 16.4% of women reported having had >1 abortion (37). Unfortunately, no official data are available to help understand the likely effect of abortions on the outbreak of congenital Zika virus syndrome. In addition, the database for suspected microcephaly is restricted to live births, and data on stillbirths and abortions are not available.
Finally, we focused on cross-correlation between the time series because we did not have individual links between GBS cases and earlier AEI in the same person or between suspected microcephaly and prior AEI of the mother. Retrospective studies indicate a recall of AEI by women who have given birth to microcephalic babies, but there are few direct demonstrations of virus transfer (17). Use of aggregate data enabled us to test for a temporal association between AEI, GBS, and suspected microcephaly, taking advantage of the establishment in Salvador of a surveillance system for detecting and recording AEI cases early during the outbreak. Consequently, Salvador recorded 17,503 of the 72,062 suspected cases of Zika virus infection in Brazil by February 25, 2016 (38).
After the AEI outbreak in Salvador, attention was given to the increased number of cases of microcephaly. However, it is becoming clear that newborns also manifest other congenital malformations (12,16), and that microcephaly might be the most extreme outcome of arboviral infection of the mother. The recently proposed term congenital Zika syndrome (39) might better capture the spectrum of possible clinical manifestation of newborns exposed to Zika virus during gestation. The Brazilian Ministry of Health is now conducting surveillance of microcephaly or changes in the central nervous system (36). As neonatal outcomes are likely to be observed in other countries, attention must be given to the full range of potential congenital malformations.
Dr. Paploski is a veterinarian and public health doctoral candidate at the Federal University of Bahia, Salvador, Brazil. His primary research interests are epidemiology of arboviruses and infectious diseases that disproportionally affect neglected populations.

Acknowledgments

We thank health professionals in Salvador, Brazil, especially those working in surveillance activities, and municipal laboratory staff for providing assistance; Gubio Soares Campos for detecting arboviruses involved in the AEI outbreak; and Isadora Cristina de Siqueira for assisting in identification of Guillain-Barré syndrome.
This study was supported by the National Council for Scientific and Technological Development (grant 400830/2013-2 and scholarships to I.A.D.P., U.K., M.G.R., and G.S.R); the Bahia Foundation for Research Support (scholarship to M.M.O.S.); and the Coordination for the Improvement of Higher Education Personnel–Brazilian Ministry of Education (scholarship to M.K.).

References

  1. Campos GSBandeira ACSardi SIZika virus outbreak, Bahia, Brazil. Emerg Infect Dis2015;21:18856DOIPubMed
  2. Zanluca Cde Melo VCMosimann ALdos Santos GIdos Santos CNLuz KFirst report of autochthonous transmission of Zika virus in Brazil. Mem Inst Oswaldo Cruz2015;110:56972DOIPubMed
  3. Cardoso CWPaploski IAKikuti MRodrigues MASilva MMCampos GSOutbreak of exanthematous illness associated with Zika, chikungunya, and dengue viruses, Salvador, Brazil. Emerg Infect Dis2015;21:22746DOIPubMed
  4. Ribeiro GSKitron UZika virus pandemic: a human and public health crisis [in Portuguese]Rev Soc Bras Med Trop2016;49:13DOI
  5. Weaver SCCosta FGarcia-Blanco MAKo AIRibeiro GSSaade GZika virus: history, emergence, biology, and prospects for control. Antiviral Res.2016;130:6980.PubMed
  6. Duffy MRChen T-HHancock WTPowers AMKool JLLanciotti RSZika virus outbreak on Yap Island, Federated States of Micronesia. N Engl J Med2009;360:253643DOIPubMed
  7. Oehler EWatrin LLarre PLeparc-Goffart ILastere SValour FZika virus infection complicated by Guillain-Barre syndrome–case report, French Polynesia, December 2013. Euro Surveill2014;19:20720DOIPubMed
  8. Cao-Lormeau V-MBlake AMons SLastère SRoche CVanhomwegen JGuillain-Barré syndrome outbreak associated with Zika virus infection in French Polynesia: a case–control study. Lancet2016;387:15319DOIPubMed
  9. World Health Organization. Zika situation report, February 26, 2016 [cited 2016 Mar 2]. http://who.int/emergencies/zika-virus/situation-report/26-february-2016/en/
  10. Zika virus—Brazil (16): (Pernambuco) Microcephaly cause undetermined. ProMED-mail [cited 2016 Apr 27]. http://www.promedmail.org/archive no./20151118.3799192
  11. World Health Organization. WHO Director-General summarizes the outcome of the emergency committee regarding clusters of microcephaly and Guillain-Barré syndrome, 2016 [cited 2016 Mar 2]. http://www.who.int/mediacentre/news/statements/2016/emergency-committee-zika-microcephaly/en/
  12. Sarno MSacramento GAKhouri Rdo Rosáario MSCosta FArchanio GZika virus infection and stillbirths: a case of hydrops fetalis, hydranencephaly and fetal demise. PLoS Negl Trop Dis2016;10:e0004517DOIPubMed
  13. Schuler-Faccini LRibeiro EMFeitosa IMHorovitz DDCavalcanti DPPessoa APossible association between Zika virus infection and microcephaly—Brazil, 2015. MMWR Morb Mortal Wkly Rep2016;65:5962DOIPubMed
  14. Mlakar JKorva MTul NPopović MPoljšak-Priiateli MMraz JZika virus associated with microcephaly. N Engl J Med2016;374:9518DOIPubMed
  15. Calvet GAguiar RSMelo ASSampaio SAde Filippis IFabri ADetection and sequencing of Zika virus from amniotic fluid of fetuses with microcephaly in Brazil: a case study. Lancet Infect Dis. 2016;Feb 17:pii: S1473-3099(16)00095-5.
  16. de Paula Freitas Bde Oliveira Dias JRPrazeres JSacramento GAKo AIMaia MOcular findings in infants with microcephaly associated with presumed Zika virus congenital infection in Salvador, Brazil. JAMA Ophthalmol. 2016 Feb 9.
  17. Brasil PPereira JP JrRaja Gabaglia CDamasceno LWakimoto MRibeiro Nogueira RMZika virus infection in pregnant women in Rio de Janeiro—preliminary report. [Epub ahead of print]. N Engl J Med. 2016.
  18. Government of the State of Bahia. Alert for indeterminate and exanthema disease transmission risk of epidemics of dengue and chikungunya fever in the State of Bahia, June 4, 2015. Technical note [in Portuguese] [cited 2016 Mar 2].http://www.suvisa.ba.gov.br/sites/default/files/doenca_transmissao_vetorial/arquivo/2015/05/14/NOTA T%C3%89CNICA 02–2015 Doen%C3%A7a Exantem%C3%A1tica Indeterminada.pdf
  19. Ministry of Health of Brazil. Information note N01/2015. COES microcephaly, 2015 [in Portuguese] [cited 2016 May 2].http://portalsaude.saude.gov.br/images/pdf/2015/novembro/18/microcefalia-nota-informativa-17nov2015-c.pdf
  20. Ministry of Health of Brazil. Surveillance and response protocol for occurrence of microcephaly, 2016 [in Portuguese] [cited 2016 May 2].http://portalsaude.saude.gov.br/images/pdf/2016/janeiro/22/microcefalia-protocolo-de-vigilancia-e-resposta-v1-3-22jan2016.pdf
  21. Stata statistical software: release 14. College Station (TX): StataCorp LP; 2015 [cited 2016 Apr 27]. http://www.stata.com/
  22. Diggle P. Time series: a biostatistical introduction. New York: Oxford University Press; 1990.
  23. Kleber de Oliveira WCortez-Escalante JDe Oliveira WTdo Carmo GMHenriques CMCoelho GEIncrease in reported prevalence of microcephaly in infants born to women living in areas with confirmed Zika virus transmission during the first trimester of pregnancy—Brazil, 2015.MMWR Morb Mortal Wkly Rep2016;65:2427DOIPubMed
  24. Cauchemez SBesnard MBompard PDub TGuillemette-Artur PEvrolle-Guignot DAssociation between Zika virus and microcephaly in French Polynesia, 2013–15: a retrospective study. Lancet. 2016;Mar 15:pii: S0104-6736(16)00651-6.
  25. Reefhuis JGilboa SMJohansson MAValencia DSimeone RMHills SLProjecting month of birth for at-risk infants after Zika virus disease outbreaks. Emerg Infect Dis2016;22:82832 . DOIPubMed
  26. Nah KMizumoto KMiyamatsu YYasuda YKinoshita RNishiura H. Estimating risks of importation and local transmission of Zika virus infection. PeerJ. 2016;4:e1904.
  27. Butler DZika virus: Brazil’s surge in small-headed babies questioned by report. Nature2016;530:134DOIPubMed
  28. Cutler SJFooks ARvan der Poel WHPublic health threat of new, reemerging, and neglected zoonoses in the industrialized world. Emerg Infect Dis.2010;16:17DOIPubMed
  29. Vazquez-Prokopec GMChaves LFRitchie SADavis JKitron UUnforeseen costs of cutting mosquito surveillance budgets. PLoS Negl Trop Dis.2010;4:e858DOIPubMed
  30. Esack ATeelucksingh SSingh NThe Guillain-Barré syndrome following dengue fever. West Indian Med J1999;48:367 .PubMed
  31. Santos NQAzoubel ACLopes AACosta GBacellar AGuillain-Barré syndrome in the course of dengue: case report. Arq Neuropsiquiatr.2004;62:1446DOIPubMed
  32. Lebrun GChadda KReboux A-HMartinet OGaüzère B-AGuillain-Barré syndrome after chikungunya infection. Emerg Infect Dis2009;15:4956.DOIPubMed
  33. Oehler EFournier ELeparc-Goffart ILarre PCubizolle SSookhareea CIncrease in cases of Guillain-Barré syndrome during a chikungunya outbreak, French Polynesia, 2014 to 2015. Euro Surveill2015;20:30079DOIPubMed
  34. Gérardin PSampériz SRamful DBoumahni BBintner MAlessandri JLNeurocognitive outcome of children exposed to perinatal mother-to-child chikungunya virus infection: the CHIMERE cohort study on Reunion Island. PLoS Negl Trop Dis2014;8:e2996DOIPubMed
  35. Fritel XRollot OGérardin PGauzere BABideault JLagarde LChikungunya virus infection during pregnancy, Réunion, France, 2006. Emerg Infect Dis2010;16:41825DOIPubMed
  36. Ministry of Health of Brazil. Epidemiological report N15. Epidemiological week (IF) 8/2016 (21 to 2/27/2016) monitoring cases of microcephaly in Brazil, 2016 [in Portuguese] [cited 2016 Mar 3]. http://portalsaude.saude.gov.br/images/pdf/2016/marco/01/coes-microcefalia-informe-epid15-se08-2016-01mar2016.pdf
  37. Cecatti JGGuerra GVQLSousa MHMenezes GMAbortion in Brazil: a demographic approach [in Portuguese]Rev Bras Ginecol Obstet.2010;32:10511DOIPubMed
  38. Pan American Health Organization/World Health Organization. Cumulative Zika suspected and confirmed cases reported by countries and territories in the Americas, 2015–2016, Updated as of February 25, 2016, with data received by February 24, 2016 [cited 2016 Mar 2].http://ais.paho.org/phip/viz/ed_zika_cases.asp
  39. Costa FSarno MKhouri Rde Paulo Freitas BSiqueira IRibeiro GSEmergence of congenital Zika syndrome: viewpoint from the front lines. Ann Intern Med. 2016 Feb 24;

Figures

Table

Suggested citation for this article: Paploski IAD, Prates APPB, Cardoso CW, Kikuti M, Silva MMO, Waller LA, et al. Time lags between exanthematous illness attributed to Zika virus, Guillain-Barré syndrome, and microcephaly, Salvador, Brazil. Emerg Infect Dis. 2016 Aug [date cited].http://dx.doi.org/10.3201/eid2208.160496
DOI: 10.3201/eid2208.160496


1These authors contributed equally to this article.