Peer-Reviewed Journal Tracking and Analyzing Disease Trends

pages 557–740

EDITOR-IN-CHIEF D. Peter Drotman
Associate Editors Paul Arguin, Atlanta, Georgia, USA Charles Ben Beard, Ft. Collins, Colorado, USA Ermias Belay, Atlanta, Georgia, USA David Bell, Atlanta, Georgia, USA Sharon Bloom, Atlanta, GA, USA Mary Brandt, Atlanta, Georgia, USA Corrie Brown, Athens, Georgia, USA Charles H. Calisher, Ft. Collins, Colorado, USA Michel Drancourt, Marseille, France Paul V. Effler, Perth, Australia David Freedman, Birmingham, Alabama, USA Peter Gerner-Smidt, Atlanta, Georgia, USA Stephen Hadler, Atlanta, Georgia, USA Nina Marano, Nairobi, Kenya Martin I. Meltzer, Atlanta, Georgia, USA David Morens, Bethesda, Maryland, USA J. Glenn Morris, Gainesville, Florida, USA Patrice Nordmann, Fribourg, Switzerland Didier Raoult, Marseille, France Pierre Rollin, Atlanta, Georgia, USA Ronald M. Rosenberg, Fort Collins, Colorado, USA Frank Sorvillo, Los Angeles, California, USA David Walker, Galveston, Texas, USA Senior Associate Editor, Emeritus Brian W.J. Mahy, Bury St. Edmunds, Suffolk, UK Managing Editor Byron Breedlove, Atlanta, Georgia, USA Copy Editors Claudia Chesley, Laurie Dietrich, Karen Foster, Thomas Gryczan, Jean Michaels Jones, Shannon O’Connor, P. Lynne Stockton Production William Hale, Barbara Segal, Reginald Tucker Editorial Assistant Jared Friedberg Communications/Social Media Sarah Logan Gregory Founding Editor Joseph E. McDade, Rome, Georgia, USA
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EDITORIAL BOARD Dennis Alexander, Addlestone, Surrey, UK Timothy Barrett, Atlanta, Georgia, USA Barry J. Beaty, Ft. Collins, Colorado, USA Martin J. Blaser, New York, New York, USA Christopher Braden, Atlanta, Georgia, USA Arturo Casadevall, New York, New York, USA Kenneth C. Castro, Atlanta, Georgia, USA Louisa Chapman, Atlanta, Georgia, USA Thomas Cleary, Houston, Texas, USA Vincent Deubel, Shanghai, China Ed Eitzen, Washington, DC, USA Daniel Feikin, Baltimore, Maryland, USA Anthony Fiore, Atlanta, Georgia, USA Kathleen Gensheimer, College Park, MD, USA Duane J. Gubler, Singapore Richard L. Guerrant, Charlottesville, Virginia, USA Scott Halstead, Arlington, Virginia, USA Katrina Hedberg, Portland, Oregon, USA David L. Heymann, London, UK Charles King, Cleveland, Ohio, USA Keith Klugman, Seattle, Washington, USA Takeshi Kurata, Tokyo, Japan S.K. Lam, Kuala Lumpur, Malaysia Stuart Levy, Boston, Massachusetts, USA John S. MacKenzie, Perth, Australia Marian McDonald, Atlanta, Georgia, USA John E. McGowan, Jr., Atlanta, Georgia, USA Jennifer H. McQuiston, Atlanta, Georgia, USA Tom Marrie, Halifax, Nova Scotia, Canada Philip P. Mortimer, London, UK Fred A. Murphy, Galveston, Texas, USA Barbara E. Murray, Houston, Texas, USA P. Keith Murray, Geelong, Australia Stephen M. Ostroff, Silver Spring, MD, USA Gabriel Rabinovich, Buenos Aires, Argentina Mario Raviglione, Geneva, Switzerland David Relman, Palo Alto, California, USA Connie Schmaljohn, Frederick, Maryland, USA Tom Schwan, Hamilton, Montana, USA Ira Schwartz, Valhalla, New York, USA Tom Shinnick, Atlanta, Georgia, USA Bonnie Smoak, Bethesda, Maryland, USA Rosemary Soave, New York, New York, USA P. Frederick Sparling, Chapel Hill, North Carolina, USA Robert Swanepoel, Pretoria, South Africa Phillip Tarr, St. Louis, Missouri, USA Timothy Tucker, Cape Town, South Africa Elaine Tuomanen, Memphis, Tennessee, USA John Ward, Atlanta, Georgia, USA J. Todd Weber, Atlanta, Georgia, USA Mary E. Wilson, Cambridge, Massachusetts, USA
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April 2015
On the Cover
Pam Longobardi (1958–)
“Ghosts of Consumption/Archaeology of Culture (for Piet M.)” 2011 Found ocean plastic, steel pins, silicone. 110 x 75 x 5 in. / 279 x 191 x 13 cm. ©The Artist / Image Courtesy of Crystal Bridges Museum of American Art. Bentonville, Arkansas. Photography by Edward C. Robison III.

Research
Population Structure and Antimicrobial Resistance of Invasive Serotype IV Group B Streptococcus, Toronto, Ontario, Canada ............ 585

S. Teatero et al.

Conjugate vaccines should include polysaccharide or virulence proteins of this serotype to provide complete protection. Norovirus Genotype Profiles Associated with Foodborne Transmission, 1999–2012 ........... 592

Perspective
Reappearance of Chikungunya, Formerly Called Dengue, in the Americas ........................... 557

L. Verhoef et al.

S.B. Halstead

This zoonosis causes global pandemics among humans every 40–50 years.

Foodborne transmission accounts for 10% of outbreaks caused by GII.4, 27% by all other single genotypes, and 37% by mixtures of GII.4 and other genotypes.

Synopses
Hantavirus Pulmonary Syndrome, Southern Chile, 1995–2012 ......... 562

R. Riquelme et al.

Early suspicion should prompt urgent transfer of patients to a hospital with intensive care facilities.

p. 588

Deaths Associated with Respiratory Syncytial and Influenza Viruses among Persons >5 Years of Age in HIV-Prevalent Area, South Africa, 1998–2009 ............ 600

S. Tempia et al.

Animal-Associated Exposure to Rabies Virus among Travelers, 1997–2012 .... 569

Mortality rates were higher among HIV-infected and older persons who had influenza than in the rest of the study population. Sequence Variability and Geographic Distribution of Lassa Virus, Sierra Leone ................................ 609

P. Gautret et al.

p. 620

Most exposures occurred among short-term tourists to Asia; no demographic characteristics identified who might benefit from pretravel counseling. Evolution of Ebola Virus Disease from Exotic Infection to Global Health Priority, Liberia, Mid-2014 ........... 578

T.A. Leski et al.

Circulating strains cluster geographically and belong to at least 5 distinct clades. Influenza A(H7N9) Virus Transmission between Finches and Poultry.................................. 619

M.A. Arwady et al.

J.C. Jones et al.

As the disease spread, the scale of the epidemic required a multi-faceted public health response.

Transmission via shared water implicates passerine birds as possible vectors for dissemination of this virus.

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Highly Pathogenic Avian Influenza A(H5N1) Virus Infection among Workers at Live Bird Markets, Bangladesh, 2009–2010 .............. 629

April 2015
677 Peste des Petits Ruminants Virus in Heilongjiang Province, China, 2014

S. Nasreen et al.

Evidence of infection was low despite frequent exposure to infected poultry and low use of personal protective equipment. Increased Risk for Group B Streptococcus Sepsis in Young Infants Exposed to HIV, Soweto, South Africa, 2004–2008 ............. 638 681

J. Wang et al.

C.L. Cutland et al.

Vaccination of pregnant women could prevent 2,105 invasive GBS cases and 278 deaths among infants annually.

D.T. Cervantes et al.
684 p. 652 688

West Nile Virus Infection Incidence Based on Donated Blood Samples and Neuroinvasive Disease Reports, Northern Texas, 2012

Dispatches
646 La Crosse Virus in Aedes japonicus japonicus Mosquitoes in the Appalachian Region, United States M.C. Harris et al. Pathogenicity of 2 Porcine Deltacoronavirus Strains in Gnotobiotic Pigs K. Jung et al. Multidrug-Resistant Salmonella enterica Serotype Typhi, Gulf of Guinea Region, Africa

J.S. Krog et al.

Influenza A(H10N7) Virus in Dead Harbor Seals, Denmark

650

J.W. Thiga et al.
692

High Seroprevalence of Antibodies against Spotted Fever and Scrub Typhus Bacteria in Patients with Febrile Illness, Kenya

655

O. Mor et al.
p. 661 695

Prevalence of Hepatitis E Virus Antibodies, Israel, 2009–2010

M. Baltazar et al.
660

E. Mihalov-Kovács et al.
664

Candidate New Rotavirus Species in Sheltered Dogs, Hungary

J.S. Abrahão et al.
699

Outbreak of Severe Zoonotic Vaccinia Virus Infection, Southeastern Brazil

N.M. Iovine et al.
668

Severity of Influenza A(H1N1) Illness and Emergence of D225G Variant, 2013–14 Influenza Season, Florida, USA

M.G. Hemida et al.
702

Lack of Middle East Respiratory Syndrome Coronavirus Transmission from Infected Camels

A. Postel et al.
673

Close Relationship of Ruminant Pestiviruses and Classical Swine Fever Virus

E. de Wit et al.
705

Safety of Recombinant VSV–Ebola Virus Vaccine Vector in Pigs

Another Dimension
V.M. Dato
Lives of a Cell: 40 Years Later, A Third Interpretation

M. Chaudhry et al.

Reassortant Avian Influenza A(H9N2) Viruses in Chickens in Retail Poultry Shops, Pakistan, 2009–2010

Letters
707 Enterovirus 71 Subgenotype B5, France, 2013

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April 2015
709 Avian Influenza A(H7N9) Virus Antibodies in Close Contacts of Infected Persons, China, 2013–2014 Hepatitis E Epidemic, Biratnagar, Nepal, 2014 Human Parvovirus 4 Infection among Mothers and Children in South Africa Co-infection with Avian (H7N9) and Pandemic (H1N1) 2009 Influenza Viruses, China Nairobi Sheep Disease Virus RNA in Ixodid Ticks, China Avian Influenza A(H10N7) Virus–Associated Mass Deaths among Harbor Seals Zika Virus Infection, Philippines, 2012 Chikungunya Outbreak, French Polynesia, 2014 p. 721 731 p. 671 732 728

Influenza A and B Viruses but Not MERS-CoV in Hajj Pilgrims, Austria, 2014 Enterovirus D68 Infection, Chile, Spring 2014 Bat Coronavirus in Brazil Related to Appalachian Ridge and Porcine Epidemic Diarrhea Viruses Tandem Repeat Insertion in African Swine Fever Virus, Russia, 2012 Norovirus GII.21 in Children with Diarrhea, Bhutan

729

711

713

715

718

Book Review
735 Australia’s War against Rabbits: The Story of Rabbit Haemorrhagic Disease

720

About the Cover
736 “Welcome to the World of the Plastic Beach” Etymologia Varicella Zoster Virus

722

724

698

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Presenting the ongoing challenges that emerging microbial threats pose to global health

PERSPECTIVE

Reappearance of Chikungunya, Formerly Called Dengue, in the Americas
Scott B. Halstead
After an absence of ≈200 years, chikungunya returned to the American tropics in 2013. The virus is maintained in a complex African zoonotic cycle but escapes into an urban cycle at 40- to 50-year intervals, causing global pandemics. In 1823, classical chikungunya, a viral exanthem in humans, occurred on Zanzibar, and in 1827, it arrived in the Caribbean and spread to North and South America. In Zanzibar, the disease was known as kidenga pepo, Swahili for a sudden cramp-like seizure caused by an evil spirit; in Cuba, it was known as dengue, a Spanish homonym of denga. During the eighteenth century, dengue (present-day chikungunya) was distinguished from breakbone fever (present-day dengue), another febrile exanthem. In the twentieth century, experiments resulted in the recovery and naming of present-day dengue viruses. In 1952, chikungunya virus was recovered during an outbreak in Tanzania, but by then, the virus had lost its original name to present-day dengue viruses.

subsequently applied to what we now call dengue should be well known by persons who deal with these 2 similar, but crucially different, global diseases. For more details on the name switch, the reader should consult the historical account by Carey (1). The Chikungunya Epidemic of 1827–1828 According to a contemporary medical observer of the chikungunya epidemic of 1827–1828, S. Henry Dickson, Professor of Medicine, Medical College of South Carolina, “[A]n arthritic fever with cutaneous exanthema [(2)]… appeared first in the island of St. Thomas, the chief town of which it invaded in September, 1827, attacking in rapid succession almost every individual in a population of about 12,000. Towards the end of October, it passed over to the neighbouring island of St. Croix. We hear of it, in November, in St. Bartholomew’s, and in Antigua in January, 1828. It prevailed at Havanna [sic] in the succeeding April, at New Orleans in May and June; and in July and August affected very generally the inhabitants of Charleston, South Carolina, and reached Savannah (Georgia) in September and October.” (3) On clinical evidence, this outbreak was caused by chikungunya virus. However, that clinical evidence is supplemented by the eyewitness report and the epidemiologic detective work of James Christie, physician to His Excellency Syud Bargash, Sultan of Zanzibar, 1865–1873. In his report, published in the British Medical Journal in 1872, Christie described the onset in July 1870 on Zanzibar of an acute febrile exanthem that rapidly achieved epidemic proportions (4). He himself was sick and in early convalescence experienced “pain on rising from my chair [that] was very severe after a short interval of rest....I suffered severely [from joint pain] for more than two months afterward” (4). From older patients in his practice, Christie learned that there had been a similar epidemic on Zanzibar 48 years earlier that was known by the Swahili term kidinga pepo (also called kidenga or kidyenga pepo). In this phrase, “ki… simply means ‘a kind of,’” the word
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hikungunya has returned to the Americas after an absence of ≈200 years. The return of this viral exanthem was first recognized on St. Martin, in the Caribbean, in December 2013, and as of January 9, 2015, the US Centers for Disease Control and Prevention reported that the disease had been identified in 42 countries or territories in the Caribbean, Central America, South America, and North America. A total of 1,094,661 suspected and 26,606 laboratory-confirmed chikungunya cases have been reported (http://www.cdc.gov/chikungunya/geo/americas.html). The return of chikungunya virus to the Americas provides an opportunity to revisit the epidemiology of this zoonotic togavirus from Africa and to contrast it with that of dengue viruses, flaviviruses that are maintained as zoonoses in Southeast Asia. All of these viruses can be transmitted by Aedes aegypti and Ae. albopictus mosquitoes in an urban cycle. In the course of history, a remarkable name change has taken place because of the similarities between the clinical syndromes caused by dengue and chikungunya virus infections. The story of how the term dengue was originally applied to what we now call chikungunya and then
Author affiliation: Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA DOI: http://dx.doi.org/10.3201/eid2104.141723

C

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“dinga or dyenga… means sudden cramp-like seizure,” and “pepo, means wind and also a spirit… so that the full designation of the term signifies a disease characterized by a sudden cramp-like seizure, caused by an evil spirit” (5). Origin of the Term Dengue Christie explicitly linked the 1827–1828 epidemic of kidinga pepo in the Americas to the 1823 epidemic on Zanzibar. He noted that published reports indicated that the 1823 epidemic soon spread from Zanzibar to Gujarat, India, and then to Calcutta, India, and by 1824 it had spread to Rangoon in present-day Myanmar. In 1827, there were reports of a similar disease in St. Thomas in the West Indies. Christie stated, “I am of the opinion that both the disease and its designation were imported in the West Indian Islands direct from the East Coast of Africa” (5). It was in the West Indies, as Christie observed, that the medical term dengue was introduced. Dumaresq, an observer of the dengue epidemic in New Orleans, Louisiana, USA, in 1828 commented, “The disease alluded to is supposed to have been brought from Africa, with some slaves imported into the Havana. In that place it obtained the name of Dingee, Dengue, Danga, etc. It was there very prevalent, and also in Barbadoes [sic], where it received the name of Dandy fever, from the stiffened form and dread of motion in patients” (6). In New Orleans, the disease “spread was so rapid among the inhabitants that in eight or ten days at least one third of the population was laboring under its influence, including persons of all ages and different sexes” (6). Dumaresq goes on to say, “A person on the disappearance of this fever would attempt to rise from bed, feeling not much loss of strength, and a consciousness of being able to move about and attend to a little to business; but how egregiously would he be mistaken when he assumed the upright posture! The joints felt as if fettered or anchylosed, and the advance of one foot or leg beyond the other, would cost more pain and effort than the purpose for which it may have been advanced was worth, —aye,—a thousand times told!” (6) The arthritic component of this febrile exanthem is unique to epidemic human chikungunya infections. It has been variously called scarlatina rheumatica, exanthesis arthrosia, and an eruptive articular or rheumatic fever (7). An interesting further insight into the colloquial Spanish meaning of dengue may contribute to an understanding why this term prevailed so quickly. In 1952, when Sabin inquired into the etymology of the term dengue, the standard Spanish dictionary meaning was affectation (8).
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Dengue researchers at that time were unable to make a connection between this term and characteristic signs and symptoms of dengue. However, an interesting connection does exist, but it is to the disease caused by what we now call chikungunya, not dengue. In 1828, contemporary observers were struck by the post-illness arthralgia and disability caused by dengue (i.e., present-day chikungunya), including the post-illness symptoms cited above in comments by Dumaresq (6). Stedman noted an even more extreme manifestation of dengue, reporting, “It is even said that when the disease first appeared in St. Thomas, several negroes, who, being all at once attacked with pain in the knees, had fallen down, were actually apprehended by the police for drunkenness” (9). Lehman, the lazaretto (i.e., quarantine station) physician for the port of Philadelphia, Pennsylvania, USA, interviewed a ship captain from Cuba who declared that, “It [dengue] is a vulgar phrase, and implies a ‘staggering weakness,’ and is somewhat similar in its import to our term of ‘corned’ [drunk] as applied to a man reeling about from intoxication” (10). The original meaning of kidinga pepo has been consistently maintained from Swahili to the colloquial eighteenth century Spanish term dengue as an apt name for a disease that produces a post-illness stagger. Discovery of Chikungunya Pandemics When Dr. Christie left Africa in 1876 to assume a post as a lecturer on public health at Anderson’s College, Glasgow, he discovered reports in the medical literature of 3 pandemics of kidinga pepo. The epidemic of 1870–1880 had begun in Zanzibar and then spread to India and Southeast Asia. That epidemic had been preceded by one in 1823– 1828 that originated in Africa and then spread to India, Southeast Asia, and the Americas, and that epidemic had been preceded by an even earlier one in 1779–1785 that was reported in Egypt, Africa, Arabia, India, and Southeast Asia (5). Of interest to contemporary observers, in 1872, an epidemic of kidinga pepo affected most inhabitants of lowlying areas of Réunion Island, the site where the chikungunya pandemic of 2005–2006 was first recognized (11). Dr. Christie suspected that the illness in all 3 pandemics was kidinga pepo because he had personally observed the 1870 epidemic spread from Zanzibar to the entire Indian subcontinent and progress on to Southeast Asia. Then, from a published report, he learned of an epidemic of kidinga pepo in Cairo in 1779. This report was followed by others that reported outbreaks in Arabia, India, and Southeast Asia. This epidemic reached Indonesia in 1779, where another astute physician, David Bylon, municipal surgeon for the city of Batavia (now Jakarta, Indonesia), acquired the disease. Dr. Bylon described the epidemic in a classic account, which has been widely cited as the initial clinical description of dengue fever:

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Reappearance of Chikungunya in the Americas

“It was last May 25, in the afternoon at 5:00 when I noted while talking with two good friends of mine, a growing pain in my right hand, and the joints of the lower arm, which step by step proceeded upward to the shoulder and then continued onto all my limbs; so much so that at 9:00 that same evening I was already in my bed with a high fever.… It has now been three weeks since I… was stricken by the illness, and because of that had to stay home for 5 days; but even until today I have continuously pain and stiffness in the joints of both feet, with swelling of both ankles; so much so, that when I get up in the morning, or have sat up for a while and start to move again, I cannot do so very well and going up and down stairs is very painful.” (12, as translated by K. DeHeer) Carey, who rediscovered Christie’s work, noted that chikungunya pandemics originating in eastern Africa had crossed the Indian Ocean at roughly 40- to 50-year intervals: the 1770s, 1824, 1871, 1902, 1923, and 1963–1964 (1). To those cycles we now can add 2005–2014. The last 2 trans–Indian Ocean pandemics occurred in the modern virologic era and have been documented by the isolation of virus. In 1963–1964, a chikungunya epidemic swept down the eastern coast of India from Calcutta to Sri Lanka (13–15). It was this epidemic that resulted in the recognition of the pronounced clinical differences between syndromes caused by dengue viruses and chikungunya virus. During the 1964 epidemic in Vellore, in southern India, most of the 275 patients with virologically or serologically confirmed chikungunya were adults (16). The patients had “sudden onset… of fever, headache and severe pains in the joints, these last being the dominant complaint. The pains mainly affected the small joints of the hands, wrist and feet, but frequently occurred in the knees as well” (16). After 1964, chikungunya virus gradually disappeared from India, with the last isolates recorded in 1972 (17,18). During 2005–2006, a chikungunya epidemic that originated in eastern Africa was observed on Réunion Island and then in Mauritius, Madagascar, Mayotte, and Seychelles (19); the epidemic soon spread to India and Southeast Asia (20,21). The Réunion Island outbreak was noteworthy because Ae. albopictus mosquitoes were efficient vectors that were aided by a genetic mutation in the virus (22). This virus was subsequently introduced into Europe by tourists returning from visits to Réunion Island or India, resulting in modest outbreaks of autochthonous Ae. albopictus mosquitoborne chikungunya in southeastern France and northeastern Italy (23,24). History of Disease Caused by Dengue Viruses The first clinical description of a syndrome likely to have been caused by a dengue virus was one by Benjamin Rush,

who in 1789 described an epidemic of a disease he called bilious remitting fever (25). The epidemic occurred from mid-August through September 1780 in Philadelphia, principally among residents living along the Delaware River waterfront. According to Rush (25): “The fever generally came on with rigor… In some persons it was introduced by a slight sore throat…. The pains which accompanied this fever were exquisitely severe in the head, back and limbs. The pains in the head were sometimes in the back parts of it and sometime occupied only the eyeballs…. A few complained of their flesh being sore to touch… the disease was sometimes believed to be a rheumatism. But, its more general name among all classes of people was breakbone fever…. A nausea universally, and in some instances, vomiting, accompanied by a disagreeable taste in the mouth, accompanied this fever…. A rash often appeared on the third and fourth days.” Rush’s description of bilious remitting fever was well known to physicians who attended to patients during the 1828 outbreak in the Caribbean. However, at the same time, George Stedman, a former president of the Royal Medical Society of Scotland, who practiced medicine on St. Croix, felt that the 1828 dengue was quite different from bilious remitting fever. He observed, “I think that it will be evident to everyone who pays the least attention to the symptoms, that the diseases, though somewhat alike in a few symptoms, are essentially different” (9). The principal distinctions made by Stedman were in the suddenness of the onset and the nature and duration of the after-pains of dengue (present-day chikungunya) (9). Christie also recognized the existence of 2 distinct febrile exanthems, 1 with and 1 without post-illness arthritis. He cited a description of dengue “with an almost entire absence of the articular pains”; this description of illness during an 1853–1854 epidemic in Calcutta was from The Science and Practice of Medicine (26), an authoritative text authored by William Aitken (4,27). History of Chikungunya Name Change Why then did chikungunya lose and dengue gain a name? Throughout the nineteenth century, astute observers of outbreaks in the Americas and India recognized the clinical differences between dengue and breakbone fever, principally the duration of fever and the occurrence of post-illness arthritis (27–29). The term dengue was in use to describe an epidemic that reached India in 1871 from Zanzibar and eastern Africa (30–32). However, once Reed and coworkers identified Ae. aegypti mosquitoes as the vector of
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yellow fever, the epidemiologic similarities between dengue and yellow fever led researchers in Lebanon, Australia, and the Philippines to investigate the etiology of dengue and the mode of transmission of dengue virus (33–36). At that time, by coincidence, dengue but not chikungunya viruses were endemic at these 3 sites. Two groups, one in Australia and the other in the Philippines, apparently successfully transmitted virus from sick humans to healthy volunteers through the bite of infected Ae. aegypti mosquitoes (35) and Culex fatigans (now called C. quinquefasciatus) mosquitoes (36). Ashburn and Craig successfully infected human volunteers by inoculating them with diatomaceous earth–filtered blood from patients with dengue, thereby proving a viral etiology for the disease (36). It remained for Siler and Simmons and co-workers in the Philippines in 1923 and 1929 to definitively demonstrate that Ae. aegypti mosquitoes, but not C. quinquefasciatus mosquitoes, are a biological vector of dengue virus (37–39). During the first half of the twentieth century, many experimental infections with dengue viruses were studied in human volunteers, and the clinical features of the infections were recorded in detail; all authors referred to the disease under study as dengue. In 1952, decades after these experiments were begun, a virus was recovered from an outbreak of an exanthematous febrile disease in Southern Province, Tanganyika Territory (now in Tanzania). The virus was called chikungunya, which in the Makonde language (spoken by an ethnic group in southeast Tanzania and northern Mozambique) means that which bends up (40). The name change was complete.
Acknowledgment I thank Koon DeHeer for his translation of David Bylon’s paper (12) from original archaic Dutch. Dr. Halstead is an adjunct professor in the Department of Preventive Medicine and Biometrics, Uniformed Services University of the Health Sciences; a consultant to the Rockefeller Foundation; and Research Director of and Senior Advisor to the Pediatric Dengue Vaccine Initiative (PDVI). His career interests have included arbovirology, epidemiology, and international health, and these interests culminated in establishment of PDVI in 2003. References
1. 2. 3. 4. 5.

6.

7. 8. 9.

10. 11. 12.

13. 14. 15. 16.

17. 18.

19. 20. 21.

Carey DE. Chikungunya and dengue: a case of mistaken identity? J Hist Med Allied Sci. 1971;26:243–62. http://dx.doi.org/10.1093/ jhmas/XXVI.3.243 Dickson SH. On dengue: its history, pathology and treatment. Philadelphia: Haswell, Barrington and Haswell; 1839. p. 5–23. Dickson SH. Dengue. Elements of medicine: a compendious view of pathology and therapeutics; or the history and treatment of diseases. Philadelphia: Blanchard and Lea; 1859. p. 744–49. Christie J. Remarks on “kidinga pepo”’ a peculiar form of exanthematous disease. BMJ. 1872;1:577–9. http://dx.doi. org/10.1136/bmj.1.596.577 Christie J. On epidemics of dengue fever: their diffusion and etiology. Glasgow Medical Journal. 1881;3:161–76.

22.

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Dumaresq PJ. An account of dengue, danga or dandy fever, as it occurred in New- Orleans, and in the person of the writer, communicated in a letter to one of the editors. Boston Med Surg J. 1828;1:497–502. http://dx.doi.org/10.1056/ NEJM182809230013201 Bernal Munoz J. Memoria sobre la epidemic que ha sufrido esta ciudad nombrada vulgalmente el dengue. Havana (Cuba): Oficina del Gobierno y Capitania General H, Cuba; 1828. p. 5–26. Sabin AB. Dengue. In: Rivers TM, editor. Viral and rickettsial infections of man, 2nd edition. Philadelphia: J.B. Lippincott Company; 1952. p. 556–68. Stedman GW. Some account of an anomalous disease that raged in the islands of St. Thomas and Santa Cruz in the West Indies during the months of September, October, November, December and January, 1827–8. Edinburgh Medical and Surgical Journal. 1828;30:227–48. Lehman GF. An account of the disease called dengue which has prevailed so extensively in Havanna. American Journal of Medical Sciences. 1828;2:477–80. Matas R. Dengue. In: Keating JM, editor. Cyclopedia of the diseases of children. Philadelphia: J.B. Lippincott Company; 1889. p. 878–99. Bylon D. Korte aatekening, wegens eene algemeene ziekte, doorgans genaamd de knokkel-koorts. Verhandelungen van het Bataviaasch Genootschop der Konsten in Wetenschappen. 1780;2:17–30. Sarkar JK, Chatterjee SN, Chakravarty SK, Mitra AC. The causative agent of Calcutta haemorrhagic fever: chikungunya or dengue. Bull Calcutta Sch Trop Med. 1965;13:53–4. Rao R. Recent epidemics caused by chikungunya virus in India, 1963–65. Science and Culture. 1966;32:215–20. Rao TR, Carey DE, Pavri KM. Preliminary isolation and identification of chikungunya virus from cases of dengue-like illness in Madras city. Indian J Med Res. 1965;53:689–93. Carey DE, Myers RM, DeRanitz CM, Jadhav M. The 1964 chikungunya epidemic at Vellore, south India, including observations on concurrent dengue. Trans R Soc Trop Med Hyg. 1969;63:434–45. http://dx.doi.org/10.1016/0035-9203(69)90030-3 Pavri K. Disappearance of chikungunya virus from India and South East Asia. Trans R Soc Trop Med Hyg. 1986;80:491. http://dx.doi.org/10.1016/0035-9203(86)90358-5 Arankalle VA, Shrivastava S, Cherian S, Gunjikar RS, Walimbe AM, Jadhav SM, et al. Genetic divergence of chikungunya viruses in India (1963–2006) with special reference to the 2005–2006 explosive epidemic. J Gen Virol. 2007;88:1967–76. http://dx.doi.org/10.1099/vir.0.82714-0 Renault P, Solet JL, Sissoko D, Balleydier E, Larrieu S, Filleul L, et al. A major epidemic of chikungunya virus infection on Réunion Island, France, 2005–2006. Am J Trop Med Hyg. 2007;77:727–31. Lahariya C, Pradhan SK. Emergence of chikungunya virus in Indian subcontinent after 32 years: a review. J Vector Borne Dis. 2006;43:151–60. Pulmanausahakul R, Roytrakul S, Auewarakul P, Smith DR. Chikungunya in Southeast Asia: understanding the emergence and finding solutions. Int J Infect Dis. 2011;15:e671–6. http://dx.doi. org/10.1016/j.ijid.2011.06.002 Tsetsarkin KA, Vanlandingham DL, McGee CE, Higgs S. A single mutation in chikungunya virus affects vector specificity and epidemic potential. PLoS Pathog. 2007;3:e201. http://dx.doi. org/10.1371/journal.ppat.0030201 Grandadam M, Caro V, Plumet S, Thiberge JM, Souares Y, Failloux AB, et al. Chikungunya virus, southeastern France. Emerg Infect Dis. 2011;17:910–3. http://dx.doi.org/10.3201/ eid1705.101873 Watson R. Europe witnesses first local transmission of chikungunya fever in Italy. BMJ. 2007;335:532–3. http://dx.doi.org/10.1136/bmj.39332.708738.DB

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25. Rush B. An account of the bilious remitting fever, as it appeared in Philadelphia, in the summer and autumn of the year 1780. Medical inquiries and observations, 1st ed. Philadelphia: Prichard and Hall; 1789. p. 89–100. 26. Aitken W. Dengue. The science and practice of medicine. Philadelphia: Lindsay and Blakeston; 1868. p. 323–5. 27. MacKinnon K. On the epidemics of the Bangal and North-West Presidencies. Indian Annals of the Medical Sciences. 1854;3:147–81. 28. Wragg WT. History of the break-bone fever; an epidemic that prevailed in Charleston in the summer of 1851. Charleston Medical Journal and Review. 1851;6:153–82. 29. Goodeve E. Observations on the epidemic fever with scarlet eruption, prevalent in Calcutta in the hot and rainy season of 1853. Indian Annals of the Medical Sciences. 1853;1:248–68. 30. Shircore SM. Notes on the eruptive fever prevailing in and around Calcutta. Indian Medical Gazette. 1872;7:33–4. 31. Verchere AM, Raye DO. Remarks on some of the symptoms of dengue. Indian Medical Gazette. 1872;7:132–3. 32. Sheriff M. History of the epidemic of dengue in Madras in 1872. Medical Times and Gazette. 1873;2:543–6. 33. Reed W, Carroll J, Agramonte A. The etiology of yellow fever: an additional note. JAMA. 1901;XXXVI:431–40. Republished with abstract in: JAMA. 1983:250:649–58. http://dx.doi.org/10.1001/ jama.1901.52470070017001f 34. 35. 36. Graham H. Dengue: a study of its mode of propagation and pathology. Medical Record New York. 1902;64:204–7. Bancroft TL. On the etiology of dengue fever. Australasian Medical Gazette. 1906;25:17–8. Ashburn PM, Craig CF. Experimental investigations regarding the etiology of dengue fever. J Infect Dis. 1907;4:440–75. Republished in: J Infect Dis. 2004;189:1747–83; discussion 1744–6. http://dx.doi.org/10.1093/infdis/4.3.440 Siler JF, Hall MW, Hitchens AP. Dengue: its history, epidemiology, mechanism of transmission, etiology, clinical manifestations, immunity, and prevention. Philippine Journal of Science. 1926;29:1–304. Siler JF, Hall MW, Hitchen AP. Transmission of dengue fever by mosquitoes. Proc Soc Exp Biol Med. 1925;23:197–201. http://dx.doi.org/10.3181/00379727-23-2890 Simmons JS, St John JH, Reynolds FHK. Experimental studies of dengue. Philippine Journal of Science.1931;44:1–252. Robinson MC. An epidemic of virus disease in Southern Province, Tanganyika Territory, in 1952–53. I. Clinical features. Trans R Soc Trop Med Hyg. 1955;49:28–32. http://dx.doi.org/10.1016/00359203(55)90080-8

37. 38. 39. 40.

Address for correspondence: Scott B. Halstead, 5824 Edson Ln, North Bethesda, MD 20852, USA; email: halsteads@erols.com

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SYNOPSIS

Hantavirus Pulmonary Syndrome, Southern Chile, 1995–2012
Raúl Riquelme, María Luisa Rioseco, Lorena Bastidas, Daniela Trincado, Mauricio Riquelme, Hugo Loyola, Francisca Valdivieso

Hantavirus is endemic to the Region de Los Lagos in southern Chile; its incidence is 8.5 times higher in the communes of the Andean area than in the rest of the region. We analyzed the epidemiologic aspects of the 103 cases diagnosed by serology and the clinical aspects of 80 hospitalized patients during 1995–2012. Cases in this region clearly predominated during winter, whereas in the rest of the country, they occur mostly during summer. Mild, moderate, and severe disease was observed, and the case-fatality rate was 32%. Shock caused death in 75% of those cases; high respiratory frequency and elevated creatinine plasma level were independent factors associated with death. Early clinical suspicion, especially in rural areas, should prompt urgent transfer to a hospital with an intensive care unit and might help decrease the high case-fatality rate.

We examined the clinical and epidemiologic features of HPS during 17 years in the provinces of Llanquihue and Palena, which had the highest incidences of this disease in Chile. This geographic area is served by the Health Service of Reloncaví (HSR) in Puerto Montt city, which has its 420-bed reference center at the Hospital of Puerto Montt in Puerto Montt. Material and Methods The provinces of Llanquihue and Palena are located in southern Chile, on the western edge of South America. Together they comprise 30,178 km2 and 340,464 inhabitants. These 2 provinces are subdivided into 13 communes (Figure 1). Our study comprised all HPS cases reported to HSR during 1995–2012. All were confirmed by serologic tests performed at the National Reference Centers at the Public Health Institute (Santiago) or Universidad Austral (Valdivia). These tests are ELISAs for IgM and IgG that use hantavirus Sin Nombre antigen provided by the US Centers for Disease Control and Prevention (Atlanta, GA, USA).
Data Collection Study Site and Population

S

ince the first cases described in United States in 1993, hantavirus pulmonary syndrome (HPS) has been reported in the United States, Argentina, Bolivia, Brazil, Chile, Ecuador, Paraguay, Panama, Uruguay, and Venezuela (1). Several types of New World hantaviruses (family Bunyaviridae) have been recognized. Their distribution is determined by the density of rodent populations serving as specific reservoirs of each virus type. In Chile, Andes virus is the only identified hantavirus (2). It was first reported in 1995 during an outbreak in Argentina and is carried by the murid rodent Oligoryzomyslongicaudatus (i.e., long-tailed mouse or “colilargo”) in southern Argentina and central and southern Chile (3). In Chile, where HPS is subject to immediate mandatory reporting to health authorities, a total of 786 cases occurred during 1995–2012. Regional and seasonal incidences varied from 0.17 to 0.53 cases per 100,000 inhabitants (4). Despite such low incidence, HPS is of public health concern because of its severity and its high case-fatality rate (CFR) (20%–60%).

Author affiliations: Universidad San Sebastian, Hospital de Puerto Montt, Puerto Montt, Chile (R. Riquelme, M.L. Rioseco, M Riquelme); Hospital de Puerto Montt, Puerto Montt (L. Bastidas, D. Trincado); Clínica Alemana Universidad del Desarrollo, Santiago, Chile (H. Loyola, F. Valdivieso) DOI: http://dx.doi.org/10.3201/eid2104.141437 562

We obtained data from 3 sources. First, we used epidemiologic records from all cases reported during 1995–2012. Data included patient age, sex, occupation, residence, site of probable infection, contact with other HPS patients, dates of hospitalization, and outcome. Second, we reviewed clinical records of all patients admitted to Hospital of Puerto Monttwith confirmed HPS during the same period. Data recorded were age, sex, probable mechanism of infection, incubation period (only for patients for whom precise information about the time of rodent exposure and onset of symptoms was available), and medical history. On admission, presence of dyspnea, fever, asthenia, headache, myalgias, chills, cough, abdominal pain, and cyanosis and blood pressure, pulse, temperature, and respiratory frequency were recorded. During hospital stay, the following data were collected: presence of bleeding, alterations in renal and hepatic functions, admissions to intensive care unit (ICU), oxygen support,

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Hantavirus Pulmonary Syndrome, Chile

Figure 1. The 13 communes in the provinces of Llanquihue and Palena, southern Chile. (Two communes share the name of the province to which they belong.) Asterisk indicates Andean communes. Inset: South America, with study area in box.

arterial oxygen tension/inspiratory oxygen fraction (PAFI) index, steroid administration, mechanical ventilation (MV) (specifying timing of connection), and circulatory shock. Shock was defined as systolic blood pressure 50% in 48 hours. Furthermore, we found no relation to CFR for patients with hematocrits >45% or >50%; platelet counts 5 years of age in 2009; persons 5–64 years of age accounted for 85% of this population. During the study period, a mean of 463,594 deaths occurred annually among persons from South Africa >5 years of age, of which 101,450 (22%) were attributable to respiratory and 112,716 (24%) to circulatory causes of death (online Technical Appendix Table 1). The mean annual mortality rate for all-cause death increased from 112 for persons 5–19 years of age to 9,732 for persons >75 years of age. Similar patterns were observed regarding the other underlying causes of death evaluated in this study. Among persons 20–44 years of age, for which the HIV burden is greatest (24% HIV prevalence in 2009 [12]), the annual mortality rate for all respiratory deaths increased from 78 in 1998 to 310 in 2004, subsequently declining to 233 in 2009 (monthly trends provided in the Figure, panel A). In contrast, no evident secular trend for all respiratory mortality rates was observed among persons >75 years of age, for whom HIV incidence is lowest (45 years. We hypothesized that differences in the timing of the RSV and influenza seasons may explain these discrepancies because RSV and influenza rarely co-circulate in South Africa, but they do in other temperate settings (3,4). This difference could potentially confound burden of illness estimates for these pathogens. We implemented a sensitivity analysis to explore this hypothesis by artificially shifting the RSV surveillance time series so that it overlapped with the influenza season and repeating model 1 calculations (online Technical Appendix).
Ethics

A mean of 3,403 (range 227–15,321) and 1,810 (range 578– 5,247) respiratory specimens were tested annually for influenza virus and RSV, respectively. The mean annual number of specimens that tested positive was 937 (27%) for influenza virus and 356 (20%) for RSV. During the study period, the influenza season peaked between May and August (winter in South Africa); 10 of the 12 years showed peak activity during June–July (Figure, panel C). In 2009, an epidemic of influenza A(H3N2) peaked in June, and influenza A(H1N1) pdm09 activity peaked in August. RSV peak activity was observed during March and April (autumn in South Africa) in 8 of the 12 years. Early or late peaks were observed in February or May in the remaining years.
Influenza- and RSV-Associated Deaths

This analysis used only publicly available mortality data and deidentified and aggregated laboratory data. Therefore, the study was considered to be exempt from human subjects ethics review.

During 1998–2009, the estimated annual number of allcause seasonal influenza-associated deaths in persons >5 years of age (model 1) ranged from 6,450 to 11,012 (rate 16.7–24.5) (online Technical Appendix Table 2). In the same population, estimated annual all-cause RSV-associated deaths ranged from 292 to 626 (rate 0.7–1.4) (online Technical Appendix Table 2). The estimated mean seasonal influenza–associated mortality rate for all-cause deaths increased progressively from 0.8 for the 5–19-year age group to 379.2 for the >75year age group. Similar trends were observed for the other causes of death evaluated in this study (Table 1). Overall, the estimated mean seasonal influenza–associated mortality

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rate for all-cause deaths was higher for HIV-positive persons than for those who were HIV-negative (age-adjusted relative risk [aRR] 7.9, 95% CI 7.1–8.9). Overall, 28% (2,564/9,093) of estimated all-cause seasonal influenza–associated deaths occurred among HIV-positive persons.

In 2009, we estimated 4,113 (rate 9.2) all-cause influenza A(H1N1)pdm09–associated deaths among persons >5 years of age (Table 2). The mortality rate associated with influenza A(H1N1)pdm09 in 2009 in persons 5–19 years of age was 5.4 times higher than the mean for prepandemic years. In contrast, persons >75 years of age experienced ≈100 times lower influenza A(H1N1) pdm09 rates than expected in typical nonpandemic years. A similar trend was observed for other causes of death evaluated in this study. The estimated RSV-associated mortality rate for allcause deaths was 0.4 for the 5–19-year age group and 2.4 for the 20–44-year age group (Table 3). However, no RSVassociated deaths were estimated among persons >45 years of age. Among persons 5–44 years of age, the RSV-associated mortality rate for all causes of death was considerably higher for HIV-positive than HIV-negative persons (aRR 66.1, 95% CI 26.0–167.8). Similar trends were observed for the RSV-associated mortality rate among all respiratory and P&I deaths. A nonsignificant RSV-associated mortality rate (mean annual deaths: 8) was identified among circulatory deaths only in the 5–19-year age group. Overall, 89% (455/511) of all-cause RSV-associated death occurred among HIV-positive persons.
Sensitivity Analysis of Influenza- and RSV-Associated Deaths among Persons >45 Years of Age

Figure. Monthly mortality and detection rates for influenza and respiratory syncytial virus in South Africa, 1998–2009. A) Observed respiratory deaths, predicted deaths, and predicted baseline by month (model 1) of persons 20–44 years of age. B) Observed respiratory deaths, predicted deaths, and predicted baseline by month (model 1) in persons ≥75 years of age. C) Detection rate (i.e., monthly number of positive specimens divided by annual number of specimens tested) of influenza and respiratory syncytial virus (all ages). A color version of this figure is available online (wwwnc.cdc.gov/eid/article/21/4/14-1033-f.htm)

On sensitivity analysis, we applied an artificial incremental shift of the RSV laboratory surveillance time series to make the influenza and RSV seasons more synchronous, resulting in a progressive increase in estimated RSV-associated deaths (online Technical Appendix Table 3). We found that annual all-cause RSV-associated deaths peaked at 3,661 among persons >45 years of age (compared with 0 in the main analysis) when the RSV and influenza seasons coincided in most years (2 months incremental shift of the RSV season). Thereafter, the estimated mean annual all-cause RSV-associated deaths decreased again to 0 when the peak circulation of the 2 pathogens were farther apart (5-month incremental shift of the RSV season). This trend was observed for all causes of death evaluated in this study. Conversely, the estimated influenza-associated deaths remained stable throughout sensitivity analyses, without regard to the shift in RSV season. Specifically, the estimated all-cause influenza-associated deaths among persons >45 years of age remained within 10% of its original value (online Technical Appendix Table 3). Discussion We reported estimates of influenza- and RSV-associated deaths in persons >5 years of age in a high HIV prevalence setting in Africa. The number of seasonal influenza–associated deaths was substantial and observed across age groups and underlying causes of death evaluated
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RESEARCH

*Estimated from model 1 (excess deaths irrespective of HIV status) and model 2 (excess deaths by HIV status). HIV, human immunodeficiency virus; NED, no estimated deaths; NA, not applicable. An expanded version of this table that includes 95% CIs is available online (http://wwwnc.cdc.gov/EID/article/21/4/14-1033-T1.htm). †Mortality rates per 100,000 person-years. ‡Age-adjusted relative risk.

Table 1. Seasonal influenza virus mean annual excess deaths and relative risk for death related to HIV infection among persons ≥5 y of age, South Africa, 1998–2009* Mean annual excess deaths Total Relative risk, HIV+ HIV– HIV+ vs Cause of death by % Death over age, y No. Rate† model baseline No. Rate† No. Rate† HIV– All causes 5–19 127 0.8 2.4 52 23.6 75 0.5 46.6 20–44 1,966 10.7 3.0 1,851 56.3 114 0.7 72.8 45–64 2,447 37.3 5.7 661 163.9 1,785 29.3 5.6 65–74 1,664 115.4 8.1 NED NED 1,664 115.4 NA ≥75 2,888 379.2 10.7 NED NED 2,888 379.2 NA ≥5 9,093 21.6 5.7 2,564 64.7 7,189 18.9 7.9‡ All respiratory 5–19 96 0.6 9.8 55 24.6 41 0.3 87.7 20–44 778 4.2 5.0 722 21.5 56 0.4 57.1 45–64 1,106 16.8 11.4 380 93.9 725 11.9 7.9 65–74 626 43.4 14.3 NED NED 626 43.4 NA ≥75 1,005 132.3 17.3 NED NED 1,005 132.3 NA ≥5 3,613 8.5 10.0 1,157 28.7 2,455 6.4 11.1‡ All circulatory 5–19 28 0.2 6.2 NED NED 28 0.2 NA 20–44 252 1.4 4.0 226 7.2 26 0.2 41.2 45–64 854 13.2 6.9 258 66.3 596 9.9 6.6 65–74 749 52.1 8.3 NED NED 749 52.1 NA ≥75 1,270 167.4 10.1 NED NED 1,270 167.4 NA ≥5 3,153 7.5 7.8. 484 12.0 2,669 6.9 6.8‡ Pneumonia and influenza 5–19 86 0.6 13.7 50 22.4 36 0.2 91.1 20–44 569 3.1 5.4 522 15.5 47 0.3 48.5 45–64 612 9.3 12.5 279 67.1 336 5.5 12.2 65–74 299 20.8 16.1 NED NED 299 20.8 NA ≥75 620 83.0 21.2 NED NED 620 83.0 NA ≥5 2,186 5.2 10.8 848 20.9 1,341 3.5 17.3‡

in this study, irrespective of the person’s HIV status. However, the seasonal influenza-associated mortality rates were highest among persons >65 years of age and HIV-positive adults 5–64 years of age. The seasonal influenza–associated deaths in these groups accounted for 50% and 28%, respectively, of the total influenza-associated deaths among persons >5 years of age. Conversely, a moderate number of deaths associated with RSV infection was found mainly among HIV-positive persons 5–44 years of age; the model did not estimate RSV-associated deaths for persons >45 years of age. Previous studies have reported elevated influenzaassociated mortality rates among the elderly (3,4,19–21) and HIV-positive persons (6,8,9,11,14). We did not find an excess risk for seasonal influenza–associated death due to HIV infection among persons >65 years of age across the underlying causes of deaths evaluated in this study. This finding may reflect the low HIV prevalence among elderly persons that may have hindered our ability to estimate the extent of disease in this group using our described method. Among persons >5 years of age years in South Africa, the number of deaths associated with pandemic influenza
604

A(H1N1)pdm09 during 2009 was approximately half that of an average influenza season in prepandemic years. However, pandemic-related mortality rates were higher in the 5–19-year age group and lower in the >45-year age group compared with typical seasons. Other studies have reported overall lower mortality rates associated with the first year of circulation of the 2009 pandemic virus, compared with that of seasonal influenza, but have found a higher disease burden for children and young adults (4,21–25). Our estimates are similar to the lower-bound estimates for South Africa from a global influenza A(H1N1)pdm09 mortality model (26). For persons >5 years of age in South Africa, ≈90% of RSV-associated deaths were estimated to have occurred among HIV-positive persons 5–44 years of age, although our model did not estimate RSV-associated deaths among persons >45 years of age, an age group in which the HIV infection rate is low (1.3% in 2009) (12). This finding suggests that HIV infection may be a major risk factor for RSV-associated death for persons >5 years of age, consistent with our high estimate of HIV as a risk factor for RSVassociated death (aRR 66.1, 95% CI 26.0–167.8). Other

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Deaths Associated with Respiratory Viruses in HIV-Prevalent Area

Table 2. Influenza A(H1N1)pdm09 excess deaths among persons ≥5 y of age, South Africa, July–September 2009* Influenza A(H1N1)pdm09 excess deaths in 2009 Cause of death by age, y No. Rate† % Death over model baseline Mortality rate ratio‡ All causes 5–19 682 4.4 12.7 5.4 20–44 1,820 9.3 2.6 0.9 45–64 1,301 17.2 2.5 0.5 65–74 279 17.6 1.2 0.2 ≥75 31 3.6 0.1 0.01 ≥5 4,113 9.2 2.6 0.4§ All respiratory 5–19 626 4.1 61.6 6.9 20–44 936 4.8 6.0 1.2 45–64 729 9.6 6.3 0.6 65–74 159 10.1 3.3 0.2 ≥75 16 1.8 0.2 0.01 ≥5 2,466 5.5 7.1 0.7§ All circulatory 5–19 7 0.05 1.7 0.2 20–44 252 1.3 4.1 1.0 45–64 404 5.3 3.0 0.4 65–74 75 4.7 0.8 0.1 ≥75 13 1.5 0.09 0.01 ≥5 751 1.7 1.7 0.2§ Pneumonia and influenza 5–19 449 2.9 73.1 5.5 20–44 548 2.8 5.7 0.9 45–64 421 5.6 7.3 0.6 65–74 90 5.7 4.3 0.3 ≥75 3 0.4 0.08 0.005 ≥5 1,511 3.4 3.7 0.7§
*Estimated from model 1 (excess deaths irrespective of HIV status). An expanded version of this table that includes 95% CIs is available online (http://wwwnc.cdc.gov/EID/article/21/4/14-1033-T2.htm). †Mortality rates per 100,000 person-years. ‡Mortality rate ratio: 2009 influenza A(H1N1)pdm09 vs. 1998–2009 mean annual seasonal influenza. §Age-adjusted rate ratio.

studies have reported an increased risk for RSV-associated death among HIV-positive persons (5,7). Our findings differ from those of similar studies from the United States and England, where RSV-associated deaths have been reported across age groups (including persons referred to as elderly in the respective studies) and where the influenza and RSV seasons are, in most cases, synchronous (3,4). However, there are notable geographic variations in the timing of RSV circulation across the United States (27). In southern Florida, where the RSV season precedes the influenza season by several weeks as in South Africa, 1 peak of pneumonia hospitalizations among persons >65 years of age could be detected concurrently with the influenza season. In contrast, among children 65 years of age, compared with RSV detection rates of >20% in infants and young children (31,33–35). Reinfection with RSV during life has been reported (36), but titers of serum-neutralizing antibodies >6 (log2 scale) have been associated with 3 times lower risk for RSVassociated hospitalizations (37). Adults reinfected with RSV may have high levels of serum-neutralizing antibodies that have potential to lower the prevalence and severity of RSV-associated hospitalizations in this age group. In South Africa during 2009–2010, the RSV detection rate among patients hospitalized with SARI decreased
606

from 26.8% among infants 65 years of age. In the same study population, the influenza detection rate across age groups was 8%–12% (38). The low RSV detection rate among adults and elderly persons with SARI suggests a lower rate of RSV-associated hospitalization than that for influenza for this group (and as a result, a potentially low number of RSV-associated deaths). Although RSV-associated deaths among persons >45 years of age are expected to occur in South Africa, our modeling approach may fail to statistically estimate a small number of cases. Ecologic studies conducted in settings similar to ours, where influenza and RSV peak activities are not synchronous, may assist in better differentiating the relative impact of these pathogens, especially in adults. In addition, results obtained from ecologic models should be interpreted along with findings from case-based studies and the strengths and weaknesses of both approaches should be evaluated. Our study has limitations that warrant discussion. First, the lack of weekly mortality statistics and the paucity of virologic data before 2002 may have hindered the ability

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to accurately estimate the relative contribution of RSV and influenza virus on number of associated deaths. Second, the lack of influenza incidence data (such as influenza-like illness indicators) hampered our ability to consider more refined indicators of respiratory virus activity in our time series models as reported by Goldstein et al. in 2012 (39). Third, because of poor records of HIV infection in the death register documenting the early years of our study, we used indirect methods to estimate the number of deaths associated with respiratory viruses among HIV-positive and HIV-negative persons. Although the HIV epidemic in South Africa is considered to be a major factor responsible for the increased mortality rates observed over the years (40), the lack of time series data for other potential co-occurring conditions and risk factors may have resulted in overestimation of the increased risk for death associated with HIV infection. Last, we could not estimate the influenza A(H1N1)pdm09–associated mortality by HIV status because our method requires availability of HIV prevalence data over several years of A(H1N1)pdm09 circulation. In conclusion, we report a substantial risk for death associated with seasonal influenza virus infection, especially for persons >65 years of age and HIV-positive adults 20–64 years of age. The risk for death associated with RSV was mainly found among HIV-positive persons 5–44 years of age; our model did not identify excess RSV-associated deaths in persons >45 years of age. We also report low to moderate numbers of RSV-associated deaths among persons >5 years of age; however, clinical diagnosis and surveillance for RSV should be continued and strengthened to better describe the consequences and severity associated with RSV infection in this age group.
Dr. Tempia is a veterinary epidemiologist working for the Influenza Division, US Centers for Disease Control and Prevention, and based at the National Institute for Communicable Diseases of the National Health Laboratory Services, Johannesburg, South Africa. His research interests are focused on infectious diseases. References

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Moyes J, Cohen C, Pretorius M, Groom M, von Gottberg A, Wolter N, et al. Epidemiology of respiratory syncytial virus– associated acute lower respiratory tract infection hospitalizations among HIV-infected and HIV-uninfected South African children, 2010–2011. J Infect Dis. 2013;208(Suppl 3):S217–26. http://dx.doi.org/10.1093/infdis/jit479 Sheth AN, Althoff KN, Brooks JT. Influenza susceptibility, severity, and shedding in HIV-infected adults: a review of the literature. Clin Infect Dis. 2011;52:219–27. http://dx.doi. org/10.1093/cid/ciq110 Madhi SA, Schoub B, Simmank K, Blackburn N, Klugman KP. Increased burden of respiratory viral associated severe lower respiratory tract infections in children infected with human immunodeficiency virus type-1. J Pediatr. 2000;137:78–84. http://dx.doi.org/10.1067/mpd.2000.105350 Madhi SA, Ramasamy N, Bessellar TG, Saloojee H, Klugman KP. Lower respiratory tract infections associated with influenza A and B viruses in an area with a high prevalence of pediatric human immunodeficiency type 1 infection. Pediatr Infect Dis J. 2002;21:291–7. http://dx.doi.org/10.1097/00006454-200204000-00007 Ope MO, Katz MA, Aura B, Gikunju S, Njenga MK, Ng’ang’a Z, et al. Risk factors for hospitalized seasonal influenza in rural Western Kenya. PLoS ONE. 2011;6:e20111. http://dx.doi. org/10.1371/journal.pone.0020111 Madhi SA, Kuwanda L, Cutland C, Klugman KP. Five-year Cohort study of hospitalisation for respiratory syncytial virus associated lower respiratory tract infection in African children. J Clin Virol. 2006;36:215–21. http://dx.doi.org/10.1016/ j.jcv.2006.03.010 Cohen C, Moyes J, Tempia S, Groom M, Wakaza S, Pretorius M, et al. Severe influenza-associated lower respiratory tract infection in a high HIV prevalence setting, South Africa, 2009–2011. Emerg Infect Dis. 2013;19:1766–74. Actuarial Society of South Africa (ASSA). AIDS and demographic model [cited 2012 Sep 20]. http://aids.actuarialsociety.org.za/ ASSA2008-Model-3480.htm Statistics South Africa. Mortality and cause of death in South Africa 2009: Findings from death notification. Pretoria, Statistics South Africa, 2009 [cited 2013 June 12]. http://www.statssa.gov.za/ publications/P03093/P030932009.pdf. Tempia S, Walaza S, Viboud C, Cohen AL, Madhi SA, Venter M, et al. Mortality associated with seasonal and pandemic influenza and respiratory syncytial virus among children 30 years ago; only 2 more recent sequences (GPC and L gene fragments) from strain SL062057 were isolated in 2006 (17,19). To fill this gap, we investigated the sequence diversity of strains circulating among small rodents captured in peridomestic settings in Sierra Leone. In 2014, we screened 214 samples collected during 2009 from several species of rodents trapped in villages where LF was reported in humans. We used diagnostic reverse transcription PCR (RTPCR) and high-density resequencing microarrays to detect LASV and amplify fragments of NP, GPC, and L genes. The obtained amplicons were sequenced and compared with previously published sequences from Sierra Leone to obtain a more complete and updated picture of the strains circulating in this country. Methods
Rodent Sample Collection

the villages according to approved guidelines (26). The animals were anesthetized with isoflurane, and their morphometrics recorded. Animals were euthanized by exsanguination using cardiac puncture or cervical dislocation, and necropsies were performed. Spleen sections were stored in RNALater or TRIzol for RNA extraction (Life Technologies, Grand Island, NY, USA). Rodents were identified to the genus level in the field. Animals identified as Mastomys sp. were further identified down to species level by using molecular methods as described previously (27).
Nucleic Acid Extraction

RNA from 10 mg of spleen of each rodent was extracted with TRIzol following the manufacturer’s recommendations. The samples were stored at –80°C.
RT-PCR and Sequencing

The rodent samples collected were part of a separate project (L.M. Moses, unpub. data). Thirteen locations were selected for study in the LF-endemic region of eastern Sierra Leone. The geographic coordinates of the sampling locations and details of rodent trapping methods are available in the online Technical Appendix (Technical Appendix Table 1, Rodent Trapping Procedures, http://wwwnc.cdc.gov/EID/ article/21/4/14-1469-Techapp1.pdf). Traps with captured small animals were processed in remote areas outside of

RNA were reverse-transcribed by using the SuperScript III Reverse Transcriptase kit (Life Technologies) according to the manufacturer’s instructions, and RT products were stored at –20°C. Specific oligonucleotide primer pairs were used for the PCR targets of interest (Table 1) at final concentrations of 0.25 μM each. For PCR, 2 μL of RT reaction was used as template in 25 μL reactions containing 1.25 mM dNTPs, 1× Taq buffer, 0.2 μM each of primers, and 1.25 U FastStart Taq enzyme (Roche Diagnostics, Indianapolis, IN, USA). NP targets were amplified by using an initial 2-min denaturation at 95°C, followed by 40 cycles of 95°C for 30 sec, 55°C for 30 sec, and 72°C for 1 min. Some specimens produced poor PCR products, with low yields or multiple bands when we used published primer pair 1010C/ OW1696R (17); 1 μL of PCR product from those specimens was amplified in nested PCR by using the primer pair LAS_NP_F_1/LAS_NP_R_1 and the same thermal cycling program to generate DNA fragments suitable for sequencing (Table 1). GPC targets were amplified by using 36E2 and LVS339-rev primers (24) and a PCR profile consisting of 2-min denaturation at 95°C, followed by 45 cycles of 95°C for 30 sec, 58°C for 30 sec, and 72°C for 1 min. L gene targets were amplified by using modified primers, LVL3359-F and LVL3754-R, based on published sequences (28) and a PCR program consisting of 2-min denaturation at 95°C followed by 45 cycles of 95°C for 30 sec,

Table 1. PCR and sequencing primers used in study of Lassa virus, Sierra Leone* Primer name Sequence, 5′  3′ Target gene 1010C TCIGGIGAIGGITGGCC NP OW1696R AIATGAIGCAGTCCAIIAGTGCACAGTG LAS_NP_F_1 GGGTGGCCATAYATTGCATC LAS_NP_R_1 GTCCAGGAGTGCACAGTGAG 36E2 ACCGGGGATCCTAGGCATTT GPC LVS339-rev GTTCTTTGTGCAGGAMAGGGGCATKGTCAT LVL3359-F AGAATYAGTGAAAGGGARAGCAATTC L LVL3754-R CACATCATTGGTCCCCATTTACTRTGATC
*GPC, glycoprotein precursor; L, polymerase; NP, nucleoprotein.

Amplicon size 670 650 317 394

Reference (17) (17) This study This study (24) (24) (28) (28)

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53°C for 30 sec, and 72°C for 1 min. PCR amplicons were size-confirmed by electrophoresis by using 1.2% FlashGel DNA cassettes (Lonza, Walkersville, MD, USA) and purified on Zymo DNA Clean & Concentrator columns (Zymo Research, Irvine, CA, USA). All DNA sequencing was performed by Eurofins MWG Operon (Huntsville, AL, USA). The sequences were deposited into GenBank under the following accession numbers: NP sequences, KM406518– KM406556; GPC sequences, KM406590–KM406623; and L sequences, KM406557–KM406589.
RPM-TEI Microarray Analysis

The resequencing pathogen microarray (RPM) analysis was conducted by using Tropical and Emerging Infections microarrays (RPM-TEI v. 1.0; TessArae, Potomac Falls, VA, USA). The RPM-TEI microarray enables detection of 84 biothreat agents, including all lineages of LASV (29). Sample preparation was conducted as previously described (29). Pathogen identification was performed using the “C3 Score” identification algorithm (30).
Phylogenetic Analysis

We conducted the sequence alignment using the MUSCLE algorithm implemented in the MEGA 6.0 software package (31). In addition to partial NP, GPC, and L sequences obtained in this study, we included in the alignments all homologous sequences from these genes in samples collected in Sierra Leone (or clustering with Sierra Leone sequences) available in GenBank. Twenty-seven NP, 10 GPC, and 8 L sequences were available that meet these criteria. To root the trees, sequences from more distantly related, lineage IV isolate Z-158, which originated from Macenta district in Guinea, were used as an outgroup on the basis of the previous phylogenetic analyses (17). We also used MEGA 6.0 to perform statistical selection of the nucleotide substitution model for each sequence collection. We selected the Tamura 3-parameter model with discrete γ-distributed rate variation as the best-fitting model for NP and L sequence sets and the Kimura 2-parameter model with a fraction of evolutionary invariant sites for GPC sequences. The phylogenies were inferred by using the Bayesian, Markov Chain Monte Carlo method, as implemented in MrBayes v3.2.2 (32). The analysis was run without an assumption of a molecular clock. The resulting phylogenies were presented as 50% majority rule consensus trees in which the branches with posterior probability 1 positive test result, Sierra Leone* Collection site PCR† Sample Date collected Village/town District NP GPC LM0034 2009 Jan 27 Bumpeh Kenema + + LM0036 2009 Jan 27 Bumpeh Kenema + + LM0047 2009 Jan 28 Bumpeh Kenema + + LM0054 2009 Jan 28 Bumpeh Kenema + + LM0058 2009 Jan 29 Bumpeh Kenema + + LM0064 2009 Jan 30 Bumpeh Kenema + + LM0068 2009 Jan 30 Bumpeh Kenema + + LM0087 2009 Feb 3 Largo Kenema + Neg LM0091 2009 Feb 3 Largo Kenema + + LM0092 2009 Feb 3 Largo Kenema + + LM0093 2009 Feb 3 Largo Kenema + + LM0111 2009 Feb 4 Largo Kenema + + LM0122 2009 Feb 5 Largo Kenema + + LM0123 2009 Feb 5 Largo Kenema + + LM0124 2009 Feb 5 Largo Kenema + + LM0224 2009 Feb 18 Koi Kenema + + LM0250 2009 Feb 19 Koi Kenema + + LM0273 2009 Feb 20 Koi Kenema + + LM0395 2009 Jul 22 Ngiehun Kenema + + LM0396 2009 Jul 22 Ngiehun Kenema + Neg LM0434 2009 Jul 23 Ngiehun Kenema + + LM0473 2009 Jul 24 Ngiehun Kenema + + LM0513 2009 Aug 1 Saama Kenema + Neg LM0582 2009 Aug 8 Barlie Bo + + LM0591 2009 Aug 8 Barlie Bo Neg Neg LM0610 2009 Aug 8 Barlie Bo + + LM0619 2009 Aug 8 Barlie Bo + Neg LM0645 2009 Aug 8 Barlie Bo + + LM0649 2009 Aug 8 Barlie Bo Neg Neg LM0657 2009 Aug 8 Barlie Bo + + LM0660 2009 Aug 8 Barlie Bo + + LM0661 2009 Aug 8 Barlie Bo + + LM0676 2009 Aug 8 Barlie Bo + + LM0677 2009 Aug 8 Barlie Bo + + LM0678 2009 Aug 8 Barlie Bo + + LM0680 2009 Aug 8 Barlie Bo + + LM0714 2009 Aug 14 Yawei Kenema + + LM0716 2009 Aug 14 Yawei Kenema + + LM0729 2009 Aug 15 Yawei Kenema + Neg Z0005 2009 Dec 17 Taiama Kenema + + Z0007 2009 Dec 17 Taiama Kenema + +

*GPC, glycoprotein precursor; L, polymerase; Neg, negative; NP, nucleoprotein; NT, sample not tested; RPM, resequencing pathogen microarray; +, positive. †Results of PCR detection using appropriate diagnostic primers. ‡Results of RPM-TEI detection of LASV.

L + + Neg + + + Neg Neg + + + + + + + + + + + + + + Neg + Neg + + + Neg + + + + + + + Neg + Neg + +

RPM‡ Neg NT NT NT Neg NT Neg NT + NT NT NT NT NT NT NT NT NT + + + NT NT NT + + NT NT + NT NT NT NT NT NT NT NT + + NT NT

Kenema, Panguma, and Segbwema, areas well known to have regular LASV transmission, might be due to the small number of traps used. The town of Joru was extensively trapped, and no LASV was found. This finding is not surprising because Joru is south of the area where LASV is usually found. All positive samples came from multimammate rats, which is considered the sole vector species for LASV (13). The results of LASV detection using several different RTPCR strategies and a broad-range resequencing microarray (RPM-TEI v. 1.0) showed that none of the techniques applied alone detected viral RNA in all positive samples. This result underscores the difficulty of developing a truly universal diagnostic assay for this highly variable virus, even in the case of closely related strains belonging to lineage IV.
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The analysis of the new sequences of LASV strains circulating in rodents in Sierra Leone indicated that the viral genome diversity is higher than previously estimated (17). For all available Sierra Leone sequences (including this study) the mean difference calculated for partial NP, GPC, and L sequences was 7.01% nt, 8.92% nt, and 9.83% nt, respectively, and 2.82% aa, 4.06% aa, and 0.71% aa, respectively (Table 3). These differences are higher than the reported 4.6% nt and 1.7% aa differences based on partial NP sequences in a study with fewer isolates (17). The L gene fragment seemed to vary the most at the nucleotide level, followed by GPC and NP, which is consistent with previous observations (33). However, at the amino acid level, the GPC gene varied most, followed by the NP and L genes. The high conservation of the protein sequence of L

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Table 3. Estimates of average evolutionary divergence of NP, GPC, and L gene fragments for Lassa virus strains, Sierra Leone* Difference† Gene, grouping Nucleotide Amino acid NP Overall 7.01 2.82 Clade A 5.03 2.06 Clade B 0.62 0.77 Clade C 6.44 2.42 GPC Overall 8.92 4.06 Clade A 6.26 2.60 Clade B 0.68 0.49 Clade D 7.49 3.29 L Overall 9.83 0.71 Clade A 6.59 0.58 Clade B 0.89 0.00 Clade D 0.88 0.00
*GPC, glycoprotein precursor; L, polymerase; NP, nucleoprotein. †The numbers of nucleotide and amino acid differences per site from averaging over all sequence pairs or all sequence pairs within a clade multiplied by 100 are shown. All positions containing gaps and missing data were eliminated. Values for clade E defined for GPC and L sequences were not calculated because clade E contained only 1 sequence.

Figure 1. A) Locations of origin for Lassa virus (LASV) nucleic acid sequences, Sierra Leone. B) Enlarged view of region from which rodent specimens were collected. Major roads (red) and waterways (blue) are indicated. Symbols indicate major cities and towns (stars); sites in this study with rodent samples that were PCR positive for LASV (circles); sites in this study from which all samples from mulitmammate rats were PCR negative for LASV (squares); and sites from which published LASV sequences originated (diamonds). The color of the symbols in panel B indicates the clade for nucleoprotein sequence: black, clade A; green, clade B; blue, clade C. Fractions indicate, for each site included in this study, number of PCR-positive samples and total number of samples. Other designations for published sequence sites indicate type of isolate (H, human; R, rodent) and year(s) of isolation. No published information about geographic origin was available for the following strains: 807875, 331, 523, IJ531, Josiah, NL, SL06–2057, SL15, SL20, SL21, SL25, SL26, SL620.

gene fragment analyzed in this study (0.71% mean difference) seemed have resulted from selection of a highly conserved part of L gene (located in RNA polymerase domain) when the diagnostic assay was designed (28). The analysis of phylogenetic trees constructed by using all available partial sequences of NP, GPC, and L genes from Sierra Leone confirmed previous findings that the strains circulating in this country belong to lineage IV and are closely related to each other (17,19). The topology of the largest NP-based tree (Figure 2) strongly supports the hypothesis that the isolates from Sierra Leone belong to at

least 3 distinct major clades (posterior probability 1.00 in all cases): the first clade (A), including a large cluster of strains originating from a group of villages to the north and east of Kenema in the Eastern Province (Bumpeh, Gondama, Koi, Konia, Largo, Ngiehun, Panguma, Segbwema, Taiama, Tongo, and Yawei; Figure 1); the second clade (B), including several strains isolated from rodents captured in Barlie (located a few kilometers southeast of Bo) and 1 isolate from Saama (located northeast of Kenema); and the third clade (C) represented by just 2 older human isolates from Mano and Mobai. Phylogenetic trees based on GPC and L sequences (Figures 3, 4) had similar topology and supported existence of clades A (with posterior probabilities 0.74 and 1.00, respectively) and B (with posterior probabilities 1.00 for both trees). However, the clade C was not present because the sequences for GPC and L gene fragments were not available for the strains forming this cluster in the NP-based tree. In addition to clades A and B, GPC- and L-based trees suggested existence of 2 additional and distinct clades. Clade D was represented by 2 sequences from human isolates SL25 and SL26, which formed a separate cluster (posterior probability 1.00 for both trees), and clade E represented by sequences obtained from a single strain isolated in 2006 (SL06–2057). These clades are defined by a very small number of sequences, and the GPC- and L-based trees disagree on the order of their separation from other clades. In addition, no data have been published on geographic origin of clade D and E samples. More data are needed (including corresponding NP sequences) to establish the existence and position of clades D and E with more certainty.
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Figure 2. Phylogenetic analysis of Lassa virus isolates from Sierra Leone based on partial nucleoprotein (NP) gene sequences. The homologous NP fragments of 621 nt were aligned. The isolate Z-158, which originated from Macenta district in Guinea, was used as outgroup based on the previous phylogenetic analyses to root the tree. The 50% majority rule consensus tree was estimated by using Bayesian Inference method implemented in MrBayes software (32) using the Tamura 3-parameter substitution model with discrete γ-distributed rate variation. The strain labels contain information on the country of origin (SL, Sierra Leone; GUI, Guinea), strain designation, village or town of origin, type of isolate (H, human; R, rodent), and year of isolation. The numbers next to the branches indicate the posterior probability of particular clades. The clades as defined in this study (clades A, B, and C) are also indicated next to the appropriate branches. Scale bar indicates substitutions per site.

All of the trees indicate a high degree of geographic clustering of the strains. This kind of clustering has been reported previously over large geographic distances and is believed to have resulted from limited dispersal and migration of the host species (17,19). Results of this study show that this phenomenon also can be observed over relatively short distances. Isolates originating from multimammate rat specimens obtained in a particular location tended to cluster, and conversely sequences present in specific branches of the trees in many cases originated from a single location or few locations not far from each other. This kind of clustering could be observed especially well in samples from Barlie, Largo, Bumpeh, Konia, and Yawei (Figures 2–4). In addition to the general pattern of geographic clustering, in several cases single isolates clustered with strains from different locations. For example, the sequence from a single sample from Saama (LM513) was closely related to that of strains from Barlie (based on NP sequence analysis). In another example, 1 GPC sequence originating from
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Liberia (523) clustered with Sierra Lone clade A sequences. In some cases (e.g., Saama sample LM513), such unusual clustering patterns may be explained by cross contamination or mislabeling of the samples. They also might result from relative proximity of all sampling sites and inadvertent anthropogenic transfer of rodents. Massive population movements that occurred in Sierra Leone during the 1991–2002 civil war could contribute to the process of mixing multimammate rat subpopulations carrying different LASV strains (34). The geographic location of human cases at such a fine spatial scale can be problematic because humans can move large distances after exposure before disease is detected. For the human isolates, the clustering inconsistent with geographic location might have resulted from recording of the hospital location or patient’s current location as strain’s origin instead of the actual location of rodent–human transmission. For example, the NP-based phylogenetic tree indicates that human isolates from Segbwema and Gondama

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Figure 3. Phylogenetic analysis of Lassa virus (LASV) isolates from Sierra Leone based on partial glycoprotein precursor (GPC) gene sequences. The homologous GPC fragments of 284 nt were aligned. The isolate Z-158, which originated from Macenta district in Guinea were used as outgroup based on the previous phylogenetic analyses to root the tree. The 50% majority rule consensus tree was estimated by using Bayesian Inference method implemented in MrBayes software (32) using the Kimura 2-parameter substitution model with a fraction of evolutionary invariant sites. The strain labels contain information on the country of origin (SL, Sierra Leone; GUI, Guinea; LIB, Liberia), strain designation, village or town of origin, type of isolate (H, human; R, rodent), and year of isolation. The numbers next to the branches indicate the posterior probability of particular clades. The clades as defined in this study (clades A, B, D, and E) are also indicated next to the appropriate branches. Scale bar indicates substitutions per site.

(obtained in 1996 and 1977, respectively) most likely originated from the Yawei village area because they cluster closely. A few other human isolates (SL15, SL20, and SL21) for which no location information is available also clustered with Yawei isolates on the basis of GPC and L sequences, suggesting their origin in the same area. These sequences were obtained in 2002 from United Nations peacekeepers stationed in this part of Sierra Leone (28,35). Recent epidemiologic data show that LF was detected in 10 of 13 districts in Sierra Leone, which suggests that the infection is much more common that previously recognized (36). Phylogenetic analysis of the sequences revealed that strains circulating in districts to the west of the traditional hyperendemic area from which most sequence information is available differ significantly (clade B),

which suggests that these could be distinct LASV strains that circulated in local multimammate rat populations for a long time since diverging from a common ancestor and are unlikely to have resulted from recent expansion of this rodent to new areas, as was recently suggested to explain emergence of cases from districts in which LF was not previously reported (36). Furthermore, the presence of LASV in Barlie with such high prevalence was surprising because this area historically has had few reports of LASV until 2 human LF cases reported in 2009 (L.M. Moses, unpub. data). The lack of reported LF cases from this area leads to speculation that clade B may be a less pathogenic form of LASV, and transmission to humans might have occurred previously but went unrecognized because of milder, nonhemorrhagic symptoms. In fact, the
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Figure 4. Phylogenetic analysis of Lassa virus (LASV) isolates from Sierra Leone based on partial polymerase (L) gene sequences. The homologous L fragments of 373 nt were aligned. The isolate Z-158, which originated from Macenta district in Guinea, was used as outgroup based on the previous phylogenetic analyses to root the tree. The 50% majority rule consensus tree was estimated by using Bayesian Inference method implemented in MrBayes software (32) using the Tamura 3-parameter substitution model with and a fraction of evolutionary invariant sites. The strain labels contain information on the country of origin (SL, Sierra Leone; GUI, Guinea), strain designation, village or town of origin, type of isolate (H, human; R, rodent), and year of isolation. The numbers next to the branches indicate the posterior probability of particular clades. The clades as defined in this study (clades A, B, D, and E) are also indicated next to the appropriate branches. Scale bar indicates substitutions per site.

idea of broader area of LASV endemicity in Sierra Leone is consistent with results of serosurveys conducted during the 1980s by McCormick, who found seroprevalence levels ranging from 8% in southern coastal areas to 15% in villages in Northern Province (6). Molecular characterization of isolates from a wider geographic area of the country is needed to fully understand the diversity of the LASV strains in Sierra Leone and its impact on disease distribution and risk. Such information would be useful for developing efficient viral detection technologies, for example, enabling design of PCR primers and antibodies specific for a broad range of LASV types.
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These diagnostic tests are extremely relevant to disease surveillance and monitoring and evaluation of interventions to prevent primary LASV infection in humans. More extensive information about sequence diversity affecting the antigenicity of the virus or the function of its RNAdependent RNA polymerase may help in the development of vaccines and antiviral drugs. It will also lead to deeper understanding of the biology and pathogenesis of LASV.
Acknowledgments We thank Benjamin Kirkup and Zheng Wang for their critical evaluation of this manuscript.

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Lassa Virus, Sierra Leone Funding for this project was provided by the Office of Naval Research. M.P. was a Science and Engineering Apprenticeship Program (SEAP) summer intern supported by the American Society for Engineering Education as part of the Office of Naval Research, SEAP, at the Naval Research Laboratory. Dr. Leski is a research biologist at the Center for Bio/Molecular Science and Engineering at the Naval Research Laboratory. His research interests include the development and application of molecular diagnostics for pathogen detection and tracking the spread of antimicrobial resistance determinants in bacterial pathogens. References consumption of their meat as possible risk factors for rodent-tohuman transmission of Lassa virus in the Republic of Guinea. Am J Trop Med Hyg. 1996;55:661–6. Lukashevich I, Salvato MS. Lassa virus genome. Curr Genomics. 2006;7:351–79. http://dx.doi.org/10.2174/138920206778948673 Bowen MD, Rollin PE, Ksiazek TG, Hustad HL, Bausch DG, Demby AH, et al. Genetic diversity among Lassa virus strains. J Virol. 2000;74:6992–7004. http://dx.doi.org/10.1128/ JVI.74.15.6992-7004.2000 Safronetz D, Sogoba N, Lopez JE, Maiga O, Dahlstrom E, Zivcec M, et al. Geographic distribution and genetic characterization of Lassa virus in sub-Saharan Mali. PLoS Negl Trop Dis. 2013;7:e2582. http://dx.doi.org/10.1371/journal.pntd.0002582 Ehichioya DU, Hass M, Becker-Ziaja B, Ehimuan J, Asogun DA, Fichet-Calvet E, et al. Current molecular epidemiology of Lassa virus in Nigeria. J Clin Microbiol. 2011;49:1157–61. http://dx.doi. org/10.1128/JCM.01891-10 Lecompte E, Fichet-Calvet E, Daffis S, Koulemou K, Sylla O, Kourouma F, et al. Mastomys natalensis and Lassa fever, West Africa. Emerg Infect Dis. 2006;12:1971–4. http://dx.doi. org/10.3201/eid1212.060812 Günther S, Emmerich P, Laue T, Kuhle O, Asper M, Jung A, et al. Imported Lassa fever in Germany: molecular characterization of a new Lassa virus strain. Emerg Infect Dis. 2000;6:466–76. http://dx.doi.org/10.3201/eid0605.000504 Günther S, Weisner B, Roth A, Grewing T, Asper M, Drosten C, et al. Lassa fever encephalopathy: Lassa virus in cerebrospinal fluid but not in serum. J Infect Dis. 2001;184:345–9. http://dx.doi. org/10.1086/322033 Trappier SG, Conaty AL, Farrar BB, Auperin DD, McCormick JB, Fisher-Hoch SP. Evaluation of the polymerase chain reaction for diagnosis of Lassa virus infection. Am J Trop Med Hyg. 1993;49:214–21. Olschläger S, Lelke M, Emmerich P, Panning M, Drosten C, Hass M, et al. Improved detection of Lassa virus by reverse transcription–PCR targeting the 5′ region of S RNA. J Clin Microbiol. 2010;48:2009–13. http://dx.doi.org/10.1128/JCM.02351-09 Demby AH, Chamberlain J, Brown DW, Clegg CS. Early diagnosis of Lassa fever by reverse transcription–PCR. J Clin Microbiol. 1994;32:2898–903. Mills JN, Childs JE, Ksiazek TG, Peters CJ, Wallis MV. Methods for trapping and sampling small mammals for virologic testing. Atlanta: US Department of Health and Human Services; 1995. Lecompte E, Brouat C, Duplantier J-M, Galan M, Granjon L, Loiseau A, et al. Molecular identification of four cryptic species of Mastomys (Rodentia, Murinae). Biochem Syst Ecol. 2005;33: 681–9. http://dx.doi.org/10.1016/j.bse.2004.12.015 Vieth S, Drosten C, Lenz O, Vincent M, Omilabu S, Hass M, et al. RT-PCR assay for detection of Lassa virus and related Old World arenaviruses targeting the L gene. Trans R Soc Trop Med Hyg. 2007;101:1253–64. http://dx.doi.org/10.1016/j.trstmh.2005.03.018 Leski TA, Lin B, Malanoski AP, Wang Z, Long NC, Meador CE, et al. Testing and validation of high density resequencing microarray for broad range biothreat agents detection. PLoS ONE. 2009;4:e6569. http://dx.doi.org/10.1371/journal.pone.0006569 Metzgar D, Myers CA, Russell KL, Faix D, Blair PJ, Brown J, et al. Single assay for simultaneous detection and differential identification of human and avian influenza virus types, subtypes, and emergent variants. PLoS ONE. 2010;5:e8995. http://dx.doi. org/10.1371/journal.pone.0008995 Tamura K, Stecher G, Peterson D, Filipski A, Kumar S. MEGA6: molecular evolutionary genetics analysis version 6.0. Mol Biol Evol. 2013;30:2725–9. http://dx.doi.org/10.1093/molbev/mst197 Huelsenbeck JP, Ronquist F. MRBAYES: Bayesian inference of phylogenetic trees. Bioinformatics. 2001;17:754–5. http://dx.doi. org/10.1093/bioinformatics/17.8.754 617

16. 17.

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33. 34. Vieth S, Torda AE, Asper M, Schmitz H, Gunther S. Sequence analysis of L RNA of Lassa virus. Virology. 2004;318:153–68. http://dx.doi.org/10.1016/j.virol.2003.09.009 Lalis A, Leblois R, Lecompte E, Denys C, Ter Meulen J, Wirth T. The impact of human conflict on the genetics of Mastomys natalensis and Lassa virus in West Africa. PLoS ONE. 2012;7:e37068. http://dx.doi.org/10.1371/journal.pone.0037068 ter Meulen J, Lenz O, Koivogui L, Magassouba N, Kaushik SK, Lewis R, et al. Short communication: Lassa fever in Sierra Leone: UN peacekeepers are at risk. Trop Med Int Health. 2001;6:83–4. http://dx.doi.org/10.1046/j.1365-3156.2001.00676.x Shaffer JG, Grant DS, Schieffelin JS, Boisen ML, Goba A, Hartnett JN, et al. Lassa fever in post-conflict Sierra Leone. PLoS Negl Trop Dis. 2014;8:e2748. http://dx.doi.org/10.1371/journal.pntd.0002748

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Address for correspondence: Tomasz A. Leski, Naval Research Laboratory, 4555 Overlook Ave SW, Code 6910, Washington, DC 20375, USA; email: tomasz.leski@nrl.navy.mil

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Influenza A(H7N9) Virus Transmission between Finches and Poultry
Jeremy C. Jones, Stephanie Sonnberg, Richard J. Webby, Robert G. Webster
Low pathogenicity avian influenza A(H7N9) virus has been detected in poultry since 2013, and the virus has caused >450 infections in humans. The mode of subtype H7N9 virus transmission between avian species remains largely unknown, but various wild birds have been implicated as a source of transmission. H7N9 virus was recently detected in a wild sparrow in Shanghai, China, and passerine birds, such as finches, which share space and resources with wild migratory birds, poultry, and humans, can be productively infected with the virus. We demonstrate that interspecies transmission of H7N9 virus occurs readily between society finches and bobwhite quail but only sporadically between finches and chickens. Inoculated finches are better able to infect naive poultry than the reverse. Transmission occurs through shared water but not through the airborne route. It is therefore conceivable that passerine birds may serve as vectors for dissemination of H7N9 virus to domestic poultry.

n spring 2013, novel avian influenza A(H7N9) viruses emerged in eastern China (1). These viruses are reassortants of subtype H7 and H9N2 viruses from wild birds and poultry (2,3) and were detected in humans and subsequently in chickens, ducks, pigeons, water, and soil at bird markets (4,5). H7N9 virus does not induce clinical signs in poultry (6), and genetic analyses show a monobasic cleavage site in the hemagglutinin (HA) protein (1); H7N9 virus is therefore classified as a low pathogenicity avian influenza virus (LPAIV). However, the virus can infect humans and cause severe disease (7). Human infection with H7N9 virus was first reported in China in March 2013 (8). By October 2, 2014, a total of 453 confirmed cases and 175 associated deaths had been reported (http://www.who.int/influenza/human_animal_interface/ influenza_h7n9/riskassessment_h7n9_2Oct14.pdf?ua=1). Despite their avian genetic background, some H7N9 viruses have HA and polymerase protein mutations that confer a replication advantage in mammals (1). Human infection has been associated with exposure to poultry or live poultry markets (7,9); market closings likely
Author affiliation: St. Jude Children’s Research Hospital, Memphis, Tennessee, USA DOI: http://dx.doi.org/10.3201/ eid2104.141703

I

contributed to infection declines in mid-2013 (10). Nevertheless, H7N9 virus persists in poultry, and human infections surged in the late 2013, demonstrating that this virus is an ongoing public health threat (11). The polymerase acidic (PA) and polymerase basic 2 genes derived from A/Anhui/1/2013 (H7N9)–like virus are homologous to those from A/brambling/Beijing/16/2012 (H9N2) (1,8), a strain isolated from a brambling (Fringilla montifringilla, a small passerine bird). In addition, during surveillance in 2013, the influenza strain A/tree sparrow/ Shanghai/01/2013 (H7N9) was identified in a tree sparrow (Passer montanus, a passerine bird) found at a site where migratory and local birds congregate (12). We previously showed that society finches (Lonchura striata domestica), zebra finches (Taeniopygia guttata), sparrows (P. domesticus), and parakeets (Melopsittacus undulates) are susceptible to H7N9 virus and shed virus into water (13). The birds used in those experiments are examples of passerine and psittacine birds, which include individual species that are migratory, peridomestic, and domesticated. The interaction of wild birds, humans, and domesticated animals may contribute to the maintenance and spread of H7N9 virus. To further address the contribution of passerines to the ecology of H7N9 virus, we modeled potential interspecies virus transmission by using society finches (a passerine bird) and poultry (bobwhite quail and chickens) and determined the route of virus transmission. Methods
Viruses and Facilities

For the experiments, we used A/Anhui/1/2013 (H7N9) (hereafter referred to as Anhui/1) from an index human patient (14–18) and a poultry isolate, A/chicken/ Rizhao/867/2013 (H7N9) (hereafter referred to as Ck/ Rizhao), from an original swab sample. Anhui/1 and Ck/ Rizhao (provided by Huachen Zhu [Shantou University, Shantou, China] and Yi Guan [University of Hong Kong, Hong Kong, China]) were propagated and titrated in embryonated chicken eggs (13). Pooled allantoic fluid was used as virus stock, and the viruses were passaged 3 times in eggs. The genomic sequence of the Anhui/1 sample corresponded to those of an isolate from GISAID
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(Global Initiative on Sharing Avian Influenza Data; accession no. EPI_ISL_138739), and genomic sequences of the Ck/Rizhao sample corresponded to those of an isolate from GenBank (accession nos. KF260954, KF259043, and KF259731). Experiments were performed under Animal Biosafety Level 3+ conditions as defined in US Department of Agriculture regulatory documents 9 CFR part 121 and 7 CFR part 331 (http://www.aphis.usda.gov/programs/ ag_selectagent/downloads/FinalRule3-18-05.pdf).
Animals

Study birds were of both sexes and included 3- to 6-monthold society finches (L. striata domestica) (Birds Express, South El Monte, CA, USA); 5-week-old white leghorn hens (Gallus gallus domesticus) (McMurray Hatchery, Webster City, IA, USA); and 16-week-old bobwhite quail (Colinus virginianus) (B&D Game Farm, Harrah, OK, USA). The birds were quarantined for 1 week, and prechallenge swab samples were confirmed influenza virus–negative by egg isolation. Food and water were provided ad libitum. Animal experiments were approved by the St. Jude Children’s Research Hospital Animal Care and Use Committee and complied with all applicable US regulations. Birds were inoculated intranasally, intraocularly, or orally with 105 log10 50% egg infectious doses (EID50) of virus in 100 mL (finches) or 500 mL (poultry) of phosphate buffered saline. Oropharyngeal and cloacal swab samples were collected on days postinoculation (dpi) 2, 4, 6, 8, 10, and 13. Water samples (500 mL) were obtained 1–4 and 8 dpi. Samples were titrated in eggs (13).
Interspecies Transmission Study Design Inoculation and Sampling

Figure 1. Design model for an interspecies study of influenza A(H7N9) virus transmission. Birds were housed in a cage-withina-cage setup with a 30 cm × 41 cm × 41 cm finch cage placed within a 97 cm × 58 cm × 53 cm poultry cage. A) Waterborne transmission was examined by sliding a 15 cm × 25 cm pan containing ≈1 L water halfway into a notched hole in the finch cage. All birds had shared access to the water, but poultry and finches were excluded from physical contact with each other. B) Airborne transmission was examined by inserting an airpermeable cage barrier (represented by double-dashed line) between the poultry and the finch cage and providing separate water supplies so that poultry and finches had no direct physical contact and did not share food or water resources.

saline–antimicrobial drugs. Virus was isolated and titrated in eggs (13).
Serologic Testing

Birds were cohoused in a cage-within-a-cage setup. Poultry (n = 3) were housed in a 97 cm × 58 cm × 53 cm cage that contained a 30 cm × 41 cm × 41 cm cage housing finches (n = 4 or 5). This setup was used in duplicate for each experiment, and the data obtained from each set of cages were combined. Waterborne transmission was examined by sliding a water pan (15 cm × 25 cm) halfway into a notched hole in the finch cage (Figure 1, panel A); birds shared water but did not have physical contact. For airborne transmission experiments, an air-permeable barrier separated poultry from the finch cage, and water sources were separate (Figure 1, panel B). Each day, 1 L of filtered, nonchlorinated water was provided by topping off the supply remaining in the water pans; every 96 h, the full water supply in the pans was replaced.
Necropsy

Before beginning the experiments, we tested >5 birds from each species for influenza A virus antibodies; all results were negative. On dpi 16, we collected blood samples from the surviving birds and tested them for H7N9 virus seroconversion by using the IDEXX AI MultiS-Screen Ab Test (IDEXX Laboratories, Westbrook, ME, USA) according to the manufacturer’s instructions.
Statistical Analyses

Mean infectious titers were compared by using the 1-tailed Student t-test in Excel (Microsoft, Redmond, WA, USA) or GraphPad Prism v5 (La Jolla, CA, USA). Area under the curve (AUC) analysis for cumulative shedding was performed by using GraphPad Prism v5. Results
Waterborne Transmission between Society Finches and Chickens

Necropsies were performed on birds that died during the study. Trachea and/or lung and intestine samples were harvested (Table 1) and homogenized in 1 mL of
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Waterborne virus transmission between finches and chickens was investigated by inoculating 1 species (donors) with 105 log10 EID50 units of virus and pairing the donor birds

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Table 1. Virus isolation from organs of dead birds in an interspecies study of influenza A(H7N9) virus transmission* Transmission Virus titer, log10 EID50/mL† Time of Bird species death, dpi Influenza virus exposure Route Direction Trachea and/or lung Intestine Naive contact Finch 4 A/Anhui/1/2013 (H7N9) Airborne 0 0 Chicken Finch Finch 5 A/Anhui/1/2013 (H7N9) Waterborne Chicken Finch 4.3 (combined) 0 Finch 5 A/chicken/Rizhao/867/2013 (H7N9) Waterborne Chicken Finch 6.5 (combined) 0 Quail 15 A/Anhui/1/2013 (H7N9) Waterborne 4.7 (trachea); 5.5 (lung) ND Finch Quail Quail 15 A/Anhui/1/2013 (H7N9) Waterborne 7.3 (trachea); 8.3 (lung) ND Finch Quail Quail 10 A/chicken/Rizhao/867/2013 (H7N9) Waterborne 6.5 (trachea); 7.5 (lung) ND Finch Quail Inoculated Finch 6 A/Anhui/1/2013 (H7N9) Waterborne Finch Chicken 4.5 (combined) 0 Finch 2 A/Anhui/1/2013 (H7N9) Waterborne 2.5 (trachea); 2.3 (lung) ND Finch Quail Quail 15 A/Anhui/1/2013 (H7N9) Airborne 3.3 (trachea); 4.5 (lung) ND Quail Finch
*dpi, days postinfection; EID50, 50% egg infectious dose; ND, not determined. †Assessed in embryonated chicken eggs.

with the naive bird species (water contacts) (Figure 1, panel A). We previously observed little to no shedding in society finches via the cloaca (13); thus, in this study, we collected swab samples at a single time point (4 dpi). We obtained oropharyngeal and cloacal swab samples from poultry at each time point. All donor finches were productively infected with Anhui/1 or Ck/Rizhao and shed virus by the oropharyngeal route for 10–13 dpi (Figure 2, panels A, C; Table 2). No virus was detected in cloacal swab samples. Using AUC analysis, including all animals, we found no statistical difference between cumulative shedding of the 2 viruses from donor finches.

During oropharyngeal sampling of the naive water contacts, we considered the possibility that we were obtaining transient virus that the birds acquired during recent drinking. To differentiate transiently acquired virus from replicated/shed virus, we defined a transmission event as an instance when samples from a naive water contact contained >2.5 log10 EID50/mL of virus and/or when the bird shed during >2 consecutive time points. Under such criteria, waterborne transmission from finches to chickens was limited. Of 6 water-contact chickens paired with Anhui/1-donor finches, 2 shed 1 of the following: hunched posture, ruffled feathers, and lethargy.

chickens paired with Ck/Rizhao-donor finches shed virus over multiple time points (Figure 2, panel C; Table 2). Water-contact chickens shed virus by the oropharyngeal route; virus was not detected in cloacal swab samples. Cumulative shedding was not significantly different for the 2 viruses in the water contacts. In the converse experiment, all donor chickens were productively infected with both viruses and shed virus an average of 10 days (Figure 2, panels B, D; Table 2). Chickens shed virus by the oropharyngeal route only, and cumulative shedding was statistically higher in birds inoculated with Ck/Rizhao than with Anhui/1 (AUC analysis, p40 (equivalent to World Health Organization criteria >80) and confirmation by an H5-specific Western blot (28–30). Seroconversion against H5N1 virus was defined as detection of a >4fold rise in microneutralization antibody titer between the initial serum sample and a paired second serum sample, with the second sample achieving a titer >40. Serum samples were tested >2 times by using the microneutralization assay. Microneutralization titers were expressed as the geometric mean of replicate titers.
Estimating Seroprevalence and Seroconversion

We calculated the proportion of poultry workers and nonpoultry workers that were seropositive at baseline, the proportion of poultry workers that seroconverted against H5N1 virus, and 95% CIs of the proportions, assuming binomial distribution. We calculated the incidence of seroconversion against H5N1 virus among workers with
Figure. Enrollment and data for participants in a study of influenza A(H5N1) virus infection among workers at live bird markets (LBMs), Bangladesh, 2009–2010. ILI, influenza-like illness.

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paired serum samples who were from LBMs where H5N1 virus was detected through poultry surveillance; workers who were seropositive at baseline were excluded. We calculated the incidence by dividing the number of seroconversions by the person-time each participant contributed to the study between baseline and follow-up data collection and calculated 95% CIs, assuming a Poisson distribution. To be conservative, we assumed that workers were at risk of acquiring H5N1 virus between baseline and follow-up serum collection even though the LBM may have been free of H5N1 virus during some of that period. We extrapolated our calculated incidence of seroconversion among the participating poultry workers to estimate the annual number of poultry workers infected with H5N1 virus among the 721 eligible workers. To compare characteristics between poultry workers and nonpoultry workers, exposure to poultry, and use of PPE between workers who were followed versus those who were lost to followup, we performed the 2-sample Wilcoxon rank-sum test and 2-sample test of proportions.
Statistical Analysis of Potential Risk Factors for H5N1 Virus Infection

robust sandwich SE estimation strategy to account for the correlation (33).
Protection of Human Subjects

We obtained written informed consent from all participants before enrollment. Institutional review boards at icddr,b and CDC approved the study protocol. Results We enrolled 404 LBM poultry workers in the study: 332 from Dhaka and 72 from rural subdistricts. The percentage of refusals was 18% (71/403) in LBMs in Dhaka and 17% (15/89) in those outside Dhaka. Most refusals were due to an unwillingness to provide a serum sample. We collected data from 101 nonpoultry workers, all of whom were from Dhaka. Overall, compared with nonpoultry workers, poultry workers were younger (median age 28.0 years [interquartile range (IQR) 22.5–38.0 y] vs. 36.0 years [IQR 32–40 y]) and more likely to be male (100% vs. 78%) and to smoke (58% vs. 34%) (p90% of variance among the candidate variables. Using the contribution of individual behavior (factor loading) as the basis, we grouped the behaviors into 3 sets and estimated the factor score for each set. Poultry workers with scores above median and those with scores below median were classified, respectively, as frequently and infrequently engaging in these sets of behaviors. We used a log-linear model, adjusted for clustering at the market level, to calculate risk ratio of serologic evidence of H5N1 virus infection for each set of behaviors between workers who were seropositive or seroconverted and those who were not seropositive and did not seroconvert against H5N1 virus (32). We applied

Of 404 poultry workers, 9 (2%) were seropositive for H5N1 virus antibodies at baseline (95% CI 1%–4%). During November 2009–March 2010, routine icddr,b poultry surveillance identified H5N1 virus at 11 (92%) of the 12 LBMs and in 25 (93%) of 27 monthly samples. We obtained a second blood specimen from 278 (72%) of 387 participating poultry workers from the 11 LBMs (online Technical Appendix 2 Table 1, http://wwwnc.cdc.gov/ EID/article/21/4/14-1281-Techapp2.pdf). Because of a delay in the availability of laboratory results for poultry and environmental surveillance samples, the median interval between detection of H5N1 virus at LBMs and collection of a second blood sample from poultry workers at the corresponding LBM was 56 days (IQR 49–61 days). Of 9 seropositive poultry workers at baseline, 5 remained seropositive and 1 was seronegative for H5N1 virus antibodies at follow-up (online Technical Appendix 2 Figure); the remaining 3 workers were lost to follow-up. Six (2%) of 284 poultry workers seroconverted during the study period (95% CI 1%–5%) (Table 2). Six other workers p value 1 activity. None of the workers who were seropositive or who seroconverted reported exposure to poultry at home, at their farm, or at another place. Three sets of behaviors explained 95% of the variability among risk behaviors at baseline and follow-up. However, the risk for H5N1 virus infection (risk ratio) was not equal for each set of behaviors (online Technical Appendix 2 Table 2). The set of behaviors with the highest risk ratio consisted of feeding poultry, cleaning feeding trays and water containers, not washing hands after working with sick poultry, and cleaning feces from pens; this set of behaviors was classified as high exposure. The set of behaviors with the second highest risk ratio consisted of slaughtering, defeathering, eviscerating, collecting or transporting feces, and stuffing poultry into bags; this set
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Table 3. Risks for testing seropositive or seroconverting against avian influenza A(H5N1) virus among live bird market workers, Bangladesh 2009–2010* Poultry workers Regression model Seropositive or Seronegative, seroconverted, Simple RR Multiple RR Characteristic/behavior n = 272 n = 18 (95% CI) (95% CI) p value† Median age, y (IQR) 27 (23–38) 27 (20–30) 0.9 (0.9–1.0) 0.9 (0.9–1.1) 0.8 Risk behavior High exposure 4.8 (0.8–28.2) 7.6 (2.8–20.9)