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RESEARCH ARTICLE
Characterization of circulating RSV strains
among subjects in the OUTSMART-RSV
surveillance program during the 2016-17
winter viral season in the United States
Alexey Ruzin
1
*, Susan T. Pastula
2
, Elizabeth Levin-Sparenberg
2
, Xiaohui Jiang
2
,
Jon Fryzek
2
, Andrey Tovchigrechko
1
, Bin Lu
3
, Yanping Qi
3
, Hui Liu
3
, Hong Jin
3
, Li Yu
1
,
Judith Hackett
1
, Tonya Villafana
1
, Mark T. Esser
1
1AstraZeneca/MedImmune, Gaithersburg, Maryland, United States of America, 2Epidstat Institute, Ann
Arbor, Michigan, United States of America, 3AstraZeneca/MedImmune, Mountain View, California, United
States of America
*ruzina@medimmune.com
Abstract
Background
Respiratory syncytial virus (RSV) is an established cause of serious lower respiratory dis-
ease in infants, elderly and high-risk populations. The OUTSMART surveillance program
aims to characterize patient populations and currently circulating RSV strains, and monitor
temporal and geographic evolution of RSV F and G proteins in the U.S.
Methods
The OUTSMART 2016–17 study collected RSV-positive samples from 25 RSVAlert
®
labo-
ratories from 4 U.S. regions and Puerto Rico during November 2016 through March 2017.
Frequencies of A and B subtypes and genotypes were determined for several demographic
and geographic variables. To gauge the representativeness of the OUTSMART patients,
results were compared to discharge data from the NEDS and NIS databases.
Results
A total of 1,041 RSV-positive samples with associated demographic data were obtained
and the RSV F gene and second variable region of the G gene were sequenced. The major-
ity of samples (76.0%) came from children under 2 years old: <1 year (48.4%), 1–2 years
(27.6%). The OUTSMART patient sample was similar to NEDS and NIS for age, gender,
and geographic location. Both OUTSMART and national RSV cases peaked in January. Of
OUTSMART samples, 45.3% were subtype A, 53.7% were subtype B and 1.0% were mixed
A and B. The percentage of RSV B cases increased with increasing age. Hospitalization
(length of hospital stay, LOS, >24 hrs) occurred in 29.0% of patients of which 52.0% had
RSV B. Outpatients (LOS <24 hrs) were 64.4% of total of which 73.3% were diagnosed in
the ER and discharged, while only 6% were diagnosed in other outpatient settings.
PLOS ONE | https://doi.org/10.1371/journal.pone.0200319 July 24, 2018 1 / 16
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OPEN ACCESS
Citation: Ruzin A, Pastula ST, Levin-Sparenberg E,
Jiang X, Fryzek J, Tovchigrechko A, et al. (2018)
Characterization of circulating RSV strains among
subjects in the OUTSMART-RSV surveillance
program during the 2016-17 winter viral season in
the United States. PLoS ONE 13(7): e0200319.
https://doi.org/10.1371/journal.pone.0200319
Editor: Stephania A Cormier, Louisiana State
University System, UNITED STATES
Received: April 7, 2018
Accepted: June 22, 2018
Published: July 24, 2018
Copyright: ©2018 Ruzin et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: All relevant
surveillance data are within the paper and its
Supporting Information files. The analysis code
and reference data specific to the bioinformatics
portion of the project are available at (https://
github.com/andreyto/rsv_epi_2017_suppl).
Funding: This work was supported by
AstraZeneca/MedImmune. The funder had no role
in study design, data collection and analysis,
Conclusions
The OUTSMART 2016–17 study was representative of the U.S. RSV experience. Geo-
graphic and temporal information from the RSV surveillance program will be used to estab-
lish a molecular baseline of RSV F and G sequence variability and to help inform
development of novel agents for RSV prophylaxis and treatment.
Introduction
Respiratory syncytial virus (RSV) is an established cause of serious lower respiratory disease,
particularly among children [1]. RSV typically exhibits distinct seasonality in temperate
regions, with onset beginning in late fall or early winter, and ending in late spring [1].
In 2015 there were an estimated 33 million RSV infections globally in children under 5
years old, resulting in about 3 million hospitalizations and 60,000 deaths [2]. In the U.S.,
approximately 2.1 million children under age 5 require medical care for RSV each year, with
3% hospitalized, 25% treated in emergency departments (ED), and 73% seen in pediatric prac-
tices [3]. Reinfection is common throughout life, although symptoms in adults and older chil-
dren are often milder or absent [4].
Recent studies reported that among viral respiratory admissions of young children, RSV
hospitalizations are 6–14 times higher than for influenza [5,6]. In the U.S., annual costs for
RSV in children under 5 are estimated to be $400 million for RSV hospitalizations, $258 mil-
lion for ambulatory medical care and more than $300 million for direct hospital charges [7,8].
RSV is a non-segmented, single strand negative virus comprised of 11 proteins including 3
surface proteins (F, G, SH), of which F and G are the most important as they elicit both neu-
tralizing and non-neutralizing antibodies. RSV has two major subtypes, A and B, based on
antigenic and genetic variation in the G attachment protein [9]. The F fusion protein is highly
conserved with 90% sequence identity between the subgroups [10], elicits broadly neutralizing
antibodies, and is the target of the licensed mAb, palivizumab [11]. The F protein is also the
target of a more potent neutralizing mAb, MEDI8897, with half-life extension technology that
is currently being evaluated in pre-term infants [12,13]. In contrast, the heavily glycosylated G
attachment protein is highly variable, differing by 53% at the amino acid level between A and
B subtypes [10].
A number of studies show RSV A and B can cocirculate during a single epidemic and tem-
poral and geographic clustering of RSV genotypes can occur [14]. The evolution of RSV geno-
types through accumulated changes in amino acids of the G protein are likely due to immune
pressure from neutralizing antibodies elicited following infection [15,16]. RSV is sub-classi-
fied into 13 RSV A genotypes and 20 RSV B genotypes based on the second hyper-variable
region of the G gene [17,18]. Currently, predominate RSV B genotypes are derived from the
Buenos Aires strain, first identified in 1999, which has a 60 base pair duplication in the second
hyper-variable region of the G gene [19]. The predominant RSV A genotypes are derived from
Ontario 1 (ON1), first described in 2006, which has a 72 base pair duplication in the G protein
[20]. Numerous studies compared the severity of RSV A and B infections in hospitalized chil-
dren with inconsistent results as to which subtype is more likely to cause severe infections
[14]. These conflicting reports suggest temporal and geographic differences may be important
in understanding the association of RSV genotype and disease and that monitoring the molec-
ular evolution of RSV would be useful in assisting the development of anti-RSV drugs and pro-
phylactic approaches.
U.S. RSV surveillance winter 2016-17
PLOS ONE | https://doi.org/10.1371/journal.pone.0200319 July 24, 2018 2 / 16
decision to publish, or preparation of the
manuscript.
Competing interests: This work was sponsored by
AstraZeneca/MedImmune. AR, AT, BL, YQ, HL, HJ,
LY, JH, TV, and ME are employees and
stockholders of AstraZeneca/MedImmune. SP, EL-
S, XJ, and JF are employees of EpidStat Institute,
which is a research institute that provides expert
assistance on the evaluation of complex health
issues and on the conduct and interpretation of
epidemiological studies to pharmaceutical and
medical device companies, and are paid
consultants to AstraZeneca. ME, AR, HL, and LY
(AstraZeneca/MedImmune) conceptualized the
study design and set up data collection. XJ, SP, EL-
S, JF, ME, AR, BL, YQ, HL, AT, HJ, JH, and TV
made contributions to the analysis and
interpretation of the data (AstraZeneca/
MedImmune and EpidStat). SP, EL-S, ME, AR, and
TV (AstraZeneca/MedImmune and EpidStat)
drafted the manuscript and, along with BL, HJ, YQ,
JH, (AstraZeneca/MedImmune) critically revised
the manuscript. ME, AR, LY, TV, AT, SP, EL-S, and
JF (AstraZeneca/MedImmune and EpidStat) were
active participants in discussions regarding the
development of the manuscript and decision to
publish. This does not alter our adherence to PLOS
ONE policies on sharing data and materials.
AstraZeneca/Medimmune and Epidstat had no
direct roles in study design, data collection,
analysis, manuscript development or revision.
Abbreviations: ED, Emergency Department; ER,
Emergency Room; HCUP, Healthcare Cost and
Utilization Project; ICD9-CM, International
Classification of Diseases, Ninth Revision, Clinical
Modification; LOS, length of stay; mAb,
monoclonal antibody; NA, Not Available; NEDS,
National Emergency Department Sample; NIS,
National Inpatient Sample; OUTSMART,
Observational United States Targeted Surveillance
of Monoclonal Antibody Resistance and Testing;
PCR, Polymerase Chain Reaction; QNS, Quality/
Quantity Not Sufficient; RNA, Ribonucleic Acid;
RSV, Respiratory Syncytial Virus; UTM, Universal
Transport Medium; VTM, Viral Transport Medium.
The first experimental RSV vaccine was tested in the 1960s and not only failed to protect
against RSV in clinical trials, but led to enhanced disease following subsequent RSV infection
such that 80% of infants who received the vaccine were hospitalized and two died [21,22]. A
successful passive immunization approach with immunoglobulin was developed over 25 years
later with the approval of Respigam™in 1996 [23] followed by the approval of a monoclonal
antibody (mAb), palivizumab (Synagis™) in 1998 [24]. Currently, Palivizumab is the only pro-
phylactic agent approved by the FDA for prevention of RSV in high-risk infants and children
[25]. Although rare, Palivizumab resistant viruses have been identified in the clinical setting
[26]. Several novel vaccines and mAbs are in development to prevent RSV disease in infants
and the elderly [27–29]. To assist with medical decision making regarding current RSV pro-
phylaxis and to help inform the development of new agents, the RSVAlert system was devel-
oped [30]. RSVAlert currently tracks RSV testing and results from approximately 480 hospital
laboratories across the U.S. (https://rsvalert.com). The Observational United States Targeted
Surveillance of Monoclonal Antibody Resistance and Testing of RSV (OUTSMART-RSV) pro-
gram was developed to collect samples and associated case information and to provide F and
G sequence data from a subset of laboratories participating in RSVAlert. OUTSMART was
piloted in 2015–2016 and allows more complete characterization of currently circulating
strains, including their temporal and geographic evolution in the U.S., and further characteri-
zation of the RSV patient population.
Materials and methods
Study design
The OUTSMART 2016–17 study collected and analyzed a series of RSV-positive samples and
associated anonymized, demographic data from a subset of hospital-based laboratories partici-
pating in RSVAlert and included 25 laboratories from 4 U.S. regions and Puerto Rico during
November 2016 to March 2017 (Fig 1). Participating laboratories were selected and recruited
based on their geographic location to represent all US regions including: West (including
Alaska and Hawaii), Midwest, South, Northeast and Puerto Rico. The number of sites per each
region were selected to provide approximately equal representation by region. Historical
reporting of >50 RSV-positive samples per season to RSVAlert system was also taken into
consideration during the site selection. Additionally, RSVAlert provided numbers of RSV-
positive tests and total RSV tests conducted per month for each of the participating laborato-
ries (S1 Data).
Sample collection and analysis
Participating laboratories were instructed to provide a single RSV-positive respiratory sample
(in UTM or VTM) per patient for a maximum of ten samples each month during the five
months of the study period, resulting in a maximum total of 50 samples from each laboratory
throughout the study period. The sites were instructed to provide the first 10 RSV-positive
samples collected from the beginning of each month. The variability in the number of samples
received from each site is primarily attributed to RSV-positive sample availability at that site
for each month (some sites received less than 10 samples in a given month) and also by the
compliance of each site to the study protocol. One laboratory, provided forty purified RSV
RNA samples. In addition to samples, information collected included lab location (U.S. region
and division, state, county, city, zip), date of sample collection, sample number, de-identified
patient information such as age, gender, and length of hospital stay (LOS)(S2 Data). In this
study, inpatients and outpatients were defined as those with LOS of either >24 hours or <24
hours in the hospital, respectively.
U.S. RSV surveillance winter 2016-17
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Sequencing and bioinformatic analyses
Next generation sequencing (NGS) using the MiSeq (Illumina) was conducted on the PCR-
amplified second hypervariable region of the G gene and the full-length F gene. Samples that
did not generate at least 1,000 mapped reads with at least 4-fold depth of coverage of both F
and G genes were marked as QNS and were excluded from the analysis. Contigs were con-
structed from the de-multiplexed MiSeq reads using Geneious software (Version 10.0.9, Bio-
matters Inc. Newark, NJ). A multiple sequence alignment (MSA) was built from the translated
G protein sequences using MAFFT [31], and pairwise dissimilarity matrix was computed
using Bishop–Friday substitution model [32]. To reduce the effects of PCR and sequencing
artifacts, sequences were clustered at 97% similarity cutoff. A single representative sequence
was picked within each cluster to build a neighbor-joining phylogenetic tree [33]. Detailed
sequencing and bioinformatic methods are available in S1 Text.
Comparison to national databases
To assess the representativeness of the OUTSMART patient sample with that of the U.S.,
results were compared to discharge data from the November 2013-March 2014 Nationwide
Fig 1. Map of participating OUTSMART laboratories during the 2016–2017 season. Pie-charts represent proportions of RSV A (blue), RSV B (orange),RSV A+B
(red) and QNS (yellow) samples per lab. Numbers within the pie charts represent the total number of samples per lab.
https://doi.org/10.1371/journal.pone.0200319.g001
U.S. RSV surveillance winter 2016-17
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Emergency Department Sample (NEDS) [34] and the National Inpatient Sample (NIS) [35].
The NIS is a nationally representative sample of hospital inpatient stays and the NEDS is a
nationally representative sample of hospital-based ED visits. Both were developed by the
Healthcare Cost and Utilization Project (HCUP) and sponsored by the Agency for Healthcare
Research and Quality (AHRQ). The NEDS contains data from approximately 30 million all-
payer ED visits annually, and when weighted, represents approximately 135 million ED visits.
The NIS contains records from more than 7 million all-payer hospital stays annually and rep-
resents more than 35 million hospitalizations when weighted. Both databases contain multiple
diagnostic codes for each hospitalization or ED visit, based on the International Classification
of Diseases,Ninth Revision,Clinical Modification (ICD-9-CM; hereafter, described as ICD-9).
During the study period, there were 3 RSV-specific ICD-9 codes: 480.1: Pneumonia due to
RSV; 466.11: Bronchiolitis due to RSV; and 079.6: RSV. Our analysis included all hospitaliza-
tions and ED visits with at least one of the three RSV-specific ICD-9 codes listed in any diag-
nostic position in the patient record. Frequencies of ED visits and hospitalizations were
calculated by age group, gender, U.S. region, and month based on the weighted estimate of
total number of hospitalizations or ED visits due to RSV during the study period.
Statistical methods
Frequencies of A and B subtypes were calculated by age group, gender, LOS, and U.S. region.
The frequency of samples per month for each lab was also determined as was the percent posi-
tive among all tests conducted for each month, and for each month by RSV subtype. The per-
cent of RSV B between age groups was compared using logistic regression with a Bonferroni
correction to adjust for multiple comparisons. Chi-square tests were used to compare the age
distributions in OUTSMART with the national samples. All data management and statistical
analyses for this study were carried out using SAS version 9.4 (SAS Institute Inc., Cary, NC,
USA), with procedures that incorporated NIS- and NEDS-provided weights to account for the
structure of the sample survey data.
Results
The twenty-five laboratories that participated in OUTSMART throughout the U.S. West
(including Alaska and Hawaii), Midwest, South, Northeast regions and Puerto Rico (Fig 1)
reported a total of 9,758 RSV-positive tests (10.7%) out of 90,840 tests conducted during
November 2016—March 2017. Of the 25 participating laboratories, 16 submitted less than 50
samples (range: 23–48), 6 submitted 50 samples, and 1 laboratory submitted more than 50
samples (n = 60), resulting in a subset of 1,041 RSV positive samples with associated demo-
graphic data that were submitted to the OUTSMART surveillance program for F and G
sequencing analyses to characterize variability of the F and G antigens and to determine the
temporal and geographic distributions of RSV A and B genotypes. Of the 1,041 RSV positive
samples, 836 samples (80.3%) yielded specific PCR amplified fragment of sufficient quantity
and quality suitable for sequencing analysis. The remaining samples (205 samples; 19.7%)
were marked as QNS (quantity/quality non-sufficient) and were not used in sequencing analy-
sis as they failed to produce enough material suitable for sequencing. Thus, all samples with
sufficient quantity and quality of DNA were sequenced and analyzed.
The monthly positive samples were reported by RSV subtype and the temporal distribution
of both A and B subtypes was generally similar (Fig 2A). The number of positive samples for
subtype B and subtype A peaked in December 2016 and January 2017, respectively. To deter-
mine if the seasonality pattern identified in the OUTSMART study was generalizable to the
U.S. RSV experience, OUTSMART data was compared with NEDS and NIS databases. The
U.S. RSV surveillance winter 2016-17
PLOS ONE | https://doi.org/10.1371/journal.pone.0200319 July 24, 2018 5 / 16
proportion of positive RSV tests out of all RSV tests conducted by OUTSMART participating
laboratories had an approximately normal distribution which peaked in January and was simi-
lar to that of the NEDS and NIS databases (Fig 2B).
OUTSMART subject demographics were also similar to NEDS and NIS. The largest disease
burden was in those <1 year (OUTSMART: 48.4%, NEDS 59.7%, NIS 57.8%), followed by the
1–2 year age group (OUTSMART: 27.6%, NEDS 28.2%, NIS 22.3%) (Table 1). The databases
were also similar by gender (Percent male—OUTSMART: 53.1%, NEDS 53.8%, NIS 54.6%)
(Table 1), and region. The largest proportions of cases occurred in the South (OUTSMART:
27.2%, NEDS: 34.4%, NIS: 37.8%), though the national samples had larger proportions of sam-
ples from the South than OUTSMART (Table 1). Subtype B was more frequent in the Midwest
and South regions. The Northeast and West had almost equal distributions of A and B sub-
types (Fig 1 and S1 Table).
There were 387 subtype A and 457 subtype B viral sequences determined from the five dif-
ferent geographic regions. These sequences were assigned to genotypes based on the sequence
of the second hyper-variable region of G gene. All RSV A samples belonged to the Ontario 1
(ON1) genotype [20] and all RSV B samples belonged to the Buenos Aires 9 (BA9) genotype
[19], except one which belonged to the Buenos Aires 10 (BA10) genotype. In addition, we
combined RSV A and RSV B sequences into distinct sub-genotypes or clusters based upon a
97% identity in the G second hyper-variable region (61 clusters for RSV A and 73 clusters for
RSV B) and mapped them to different geographic regions (Fig 3). This analysis revealed that
the 5 most frequent RSV A clusters comprised 48% of the 387 RSV A samples and the 5 most
frequent RSV B clusters comprised 39% of the 457 RSV B samples. There were no obvious
Fig 2. Temporal distributions of RSV positive tests. (A) OUTSMART 2016–17 RSV positive tests by RSV subtype. (B) All RSV positive tests in OUTSMART—
participating laboratories, and RSV in NEDS and NIS.
https://doi.org/10.1371/journal.pone.0200319.g002
U.S. RSV surveillance winter 2016-17
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differences in geographic distribution of these strains in the West, Midwest, South and North-
east suggesting they were broadly distributed across the different U.S. regions.
The age distributions of OUTSMART samples were compared separately for inpatient and
ER admissions with the two different national databases. OUTSMART inpatients included
fewer infants <1 year old than NIS (46.7% vs. 57.8%) and more older patients over age 60
(13.9% vs. 8.2%) (Table 2). The distribution of ER patients by age was similar in the OUT-
SMART and NEDS databases (Table 2).
Of the OUTSMART samples with determined RSV subtype (n = 836; 80.3%), 45.3% were
subtype A, 53.7% were subtype B, and 1% had both A and B subtypes (Fig 4 and S2 Table).
Most samples (76.0%) came from children 2 years of age: <1 year (48.4%) and 1–2 years
(27.6%) (Table 1). RSV B was more frequent in all ages with the exception of <1 month and
1–2 year old children, in which RSV A was more common (Fig 3). The highest proportion of
RSV B cases (73.4%) was observed in subjects ages 60+ followed by the 6-59-year-old group
(71.9%) (Fig 4 and S2 Table).
Severity of illness associated with RSV subtype, gender, or age was explored by categoriz-
ing RSV hospitalizations into LOS <24 hours and >24 hours. Hospitalizations >24 hours
occurred among 29.0% of patients. Young children, 2 years of age, were the most fre-
quently seen with RSV as both inpatients (214/302, 70.9%) and outpatients (546/670, 81.5%).
(Table 3). LOS was stratified by referring department (Table 4) and 73.3% (n = 491) of RSV
subjects with <24 hour LOS had samples collected from the ED and were discharged whereas
only 6% of the samples came from an outpatient setting such as a doctor’s office. In contrast,
only 19.2% of inpatient RSV cases were diagnosed in the ER with a significant number of
samples coming from the pediatric intensive care unit and ICU. However, no information
was provided for 55.3% of inpatient RSV cases (Table 4). These data suggest that a significant
Table 1. Comparison of OUTSMART November 2016—March 2017 RSV positive tests with RSV in NEDS and NIS November 2013-March 2014 by age, gender and
region.
OUTSMART NEDS
a,c
NIS
b,c
N % N % N %
Age <1 year 504 48.4% 66,982 59.7% 39,130 57.8%
1–2 year 287 27.6% 31,628 28.2% 15,070 22.3%
3–5 year 74 7.1% 6,293 5.6% 3,750 5.5%
6–59 year 90 8.6% 2,958 2.6% 4,195 6.2%
60+ year 86 8.3% 4,352 3.9% 5,545 8.2%
All 1,041 100.0%112,213 100.0%67,690 100.0%
Gender Male 553 53.1% 60,449 53.8% 37,030 54.6%
Female 488 46.9% 51,841 46.2% 30,830 45.4%
All 1,041 100.0%112,290 100.0%67,860 100.0%
Region Mid-West 241 23.2% 34,258 30.5% 15,800 23.3%
North East 219 21.0% 19,509 17.4% 13,005 19.2%
South 283 27.2% 38,611 34.4% 25,640 37.8%
West 263 25.3% 19,912 17.7% 13,415 19.8%
PR 35 3.4% - - - -
Total 1,041 100.0%112,290 100.0%67,860 100.0%
a
NEDS: National Emergency Room Sample—Approx. 20% stratified sample of U.S. emergency room visits
b
NIS: National Inpatient Sample—Approx. 20% stratified sample of U.S. hospital discharges
c
RSV identified by ICD-9 codes 480.1, 466.11, 079.6 in NEDS and NIS
https://doi.org/10.1371/journal.pone.0200319.t001
U.S. RSV surveillance winter 2016-17
PLOS ONE | https://doi.org/10.1371/journal.pone.0200319 July 24, 2018 7 / 16
proportion of RSV disease in both the young and the old is managed in the emergency room
without admitting the subject into the hospital.
Discussion
The OUTSMART RSV surveillance program characterizes circulating RSV strains and moni-
tors their temporal and geographic evolution in the U.S. to help inform the development of
Fig 3. RSV A and B genotypes of 2016–17 OUTSMART samples by geographic region. The phylogenetic tree in the
left panel was built using the representative G protein sequences from 97%-identity clusters, with the horizontal scale
under the tree showing branch lengths derived from the dissimilarity metric. The corresponding bars in the right panel
represent the number of samples in each cluster, with horizontal scale under the bar plot showing sample counts.
https://doi.org/10.1371/journal.pone.0200319.g003
U.S. RSV surveillance winter 2016-17
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anti-RSV mAbs and vaccines. RSV surveillance is also important in providing timely informa-
tion to physicians for the administration of Palivizumab to eligible high-risk infants [36,37].
The OUTSMART program was generally representative of the U.S. RSV infection experience
in terms of age, gender distribution and seasonality compared to national data (Fig 2B). The
OUTSMART RSV program is designed to run for several years to monitor temporal and
regional differences in predominant subtype [38,39], specifically in the southeastern U.S.
where the RSV season typically begins earlier and lasts longer [40,41] than in other areas of
the country. RSV surveillance is additionally conducted by the Centers for Disease Control
and Prevention (NREVSS) [42], the international Respiratory Syncytial Virus Network
(ReSVinet) [43] and the European Influenza Surveillance Network (EISN) [44]. While all show
differences in onset and length of RSV seasons depending on regional setting, none provide
molecular typing of strains or characterize them temporally or geographically. This study,
which characterized both G and F genotypes, along with surveillance data from these other
networks can help inform timing of administration of a mAb or vaccine in clinical trials and
provide a baseline for molecular heterogeneity of viruses currently in circulation prior to test-
ing and licensure of an RSV mAb or vaccine [36,37].
Compared to the national databases for inpatient and emergency admissions, OUT-
SMART-participating laboratories differed in their age distributions of annual positive RSV
cases (Chi-square p-value <0.001 and 0.010, respectively) (Table 2). This may have been due
to patient sampling or a shift in age-specific infection rates since the national data were from a
different season compared to OUTSMART. As the OUTSMART program progresses, accu-
mulation of results from additional seasons will allow for a more accurate comparison to
national trends.
Table 2. OUTSMART November 2016—March 2017 RSV positive inpatient and Emergency Room cases com-
pared with NIS and NEDS RSV positive cases during November 2013-March 2014 by age group.
Age OUTSMART Inpatient
a
NIS
b,d
N % N %
<1 year 141 46.7% 39,130 57.8%
1–2 year 73 24.2% 15,070 22.3%
3–5 year 20 6.6% 3,750 5.5%
6–59 year 26 8.6% 4,195 6.2%
60+ year 42 13.9% 5,545 8.2%
Total 302 100.0%67,690 100.0%
Age OUTSMART Emergency Room/
department
NEDS
c,d
N % N %
<1 year 305 55.6% 66,982 59.7%
1–2 year 176 32.1% 31,628 28.2%
3–5 year 32 5.8% 6,293 5.6%
6–59 year 24 4.4% 2,958 2.6%
60+ year 12 2.2% 4,352 3.9%
Total 549 100.0%112,213 100.0%
a.
OUTSMART Inpatient: Length of stay>24 hr
b.
NIS: National Inpatient Sample—Approx. 20% stratified sample of U.S. hospital discharges
c.
NEDS: National Emergency Room Sample—Approx. 20% stratified sample of U.S. ER visits
d.
RSV identified by ICD-9 codes 480.1, 466.11, 079.6 in NIS
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U.S. RSV surveillance winter 2016-17
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Fig 4. OUTSMART 2016–17 percent RSV-positive tests by age and subtype. Error bars represent 95% confidence intervals.
https://doi.org/10.1371/journal.pone.0200319.g004
Table 3. OUTSMART 2016–17 RSV-positive tests by LOS, age and subtype.
LOS Age RSV subtype All
A B A+B QNS
N % N % N % N N %
<24 hr
a
2 year 227 47.3% 248 51.7% 5 1.0% 66 546 52.4%
3–59 year 24 33.8% 46 64.8% 1 1.4% 31 102 9.8%
60 year 3 20.0% 12 80.0% 0 0% 7 22 2.1%
Total 254 44.9%306 54.1%6 1.1%104 670 64.4%
>24 hr
a
2 year 86 52.8% 76 46.6% 1 0.6% 51 214 20.6%
3–59 year 10 40.0% 15 60.0% 0 0% 21 46 4.4%
60 year 9 27.3% 24 72.7% 0 0% 9 42 4.0%
Total 105 47.5%115 52.0%1 0.5%81 302 29.0%
NA 2 year 12 52.2% 11 47.8% 0 0% 8 31 3.0%
3–59 year 3 30.0% 6 60.0% 1 10.0% 6 16 1.5%
60 year 5 31.3% 11 68.8% 0 0% 6 22 2.1%
Total 20 40.8%28 57.1%1 2.0%20 69 6.6%
Total 379 45.3%449 53.7%8 1.0%205 1,041
b
100.0%
a.
<24 hours defined as outpatient, >24 hours defined as inpatient
b.
836 total samples with known subtype, 205 QNS
https://doi.org/10.1371/journal.pone.0200319.t003
U.S. RSV surveillance winter 2016-17
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There were limitations to this study design, which was intended to cover all 4 regions and 9
divisions of the country defined by the U.S. census. Some areas were not as well represented as
others such as the upper Midwest and West. When comparing data to national trends, NIS
and NEDS estimates are based on ICD9 codes and not laboratory-confirmed diagnoses, so
comparability to the OUTSMART data is limited. OUTSMART could not be compared to the
national sample from the same season because the 2016–17 national data was not yet available.
Lastly, the use of hospital-based laboratory data on RSV infections markedly underestimates
the full burden of RSV disease in the U.S. However, because these cases are laboratory-verified,
the data are useful in providing additional information on hospital and ER burden of RSV
disease.
Hospitalizations are often used as the key measure of severity and to estimate the economic
impact of RSV infection. This significantly underestimates the true burden of RSV disease by
not evaluating cases, which may include severe cases, which are medically managed in the ER
without hospital admission or in physician offices as outpatients. In 2003, Leader and Kohlhase
examined several national databases and found that between 1997–2000, there were 718,000
ER visits for lower respiratory infections in infants <1 year, with a cost of $202 million. Only
29% of these patients were admitted [45]. An additional challenge to accurate estimation of
total RSV burden is that routine RSV testing is rarely performed in outpatient settings and is
not recommended by the American Academy of Pediatrics (AAP) [46].
One of the strengths of the OUTSMART study is that it was designed to be an ongoing sur-
veillance program with widespread participation and laboratory-confirmed diagnoses of RSV.
A database of trends in infection rates will be built to inform drug and vaccine development
programs. Information from OUTSMART will also be used to establish a baseline of RSV F
and G sequences as a reference for future epidemiology studies and clinical trials. A separate
report will describe the conservation of the F protein and the susceptibility of different RSV
isolates to neutralization by a novel mAb MEDI8897, currently being developed to prevent
medically attended lower respiratory tract infections due to RT-PCR confirmed RSV in all
infants [13,47]. In addition, an ex-U.S. RSV surveillance program entitled INFORM-RSV has
Table 4. OUTSMART 2016–17 RSV-positive tests by LOS, referring department and subtype.
RSV Subtype (Sequencing Results)
A B AB QNS All
LOS Referring department N % N % N % N N % of LOS category
<24 hr
a
Emergency room/department (ER/ED) 207 47.9% 220 50.9% 5 1.2% 59 491 73.3%
Pediatric Intensive Care Unit (PICU) 0 0% 1 100.0% 0 0% 0 1 0.1%
Outpatient Facility 13 37.1% 21 60.0% 1 2.9% 5 40 6.0%
Other 34 34.7% 64 65.3% 0 0% 40 138 20.6%
Total <24 hr 670 64.4%
>24 hr
a
Emergency room/department (ER/ED 26 54.2% 22 45.8% 0 0% 10 58 19.2%
Pediatric Intensive Care Unit (PICU) 21 67.7% 10 32.3% 0 0% 16 47 15.6%
ICU (Other than PICU) 1 12.5% 7 87.5% 0 0% 6 14 4.6%
Pediatric Ward 4 25.0% 12 75.0% 0 0% 0 16 5.3%
Other 53 44.9% 64 54.2% 1 0.8% 49 167 55.3%
Total >24 hr 302 29.0%
Other 20 40.8% 28 57.1% 1 2.0% 20 69 100.0%
Total Other 69 6.6%
a.
<24 hours defined as outpatient, >24 hours defined as inpatient
https://doi.org/10.1371/journal.pone.0200319.t004
U.S. RSV surveillance winter 2016-17
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been launched in collaboration with ReSViNET (www.resvinet.org) to collect RSV samples
from Europe, South America, South Africa, Australia and Japan.
An interesting observation in this study was that there was a significantly larger proportion
of RSV B detected in the 6–59 (p = 0.001) and 60+ (p<0.001) age groups as compared to the
1–2 year old age group (Fig 4). The difference in RSV A and B prevalence in the elderly versus
the very young may be the result of pre-existing immunity to RSV A gained from previous
infections. It will be interesting to see if the prevalence of A and B in different age groups
changes over time and whether that correlates with changes in the F and G genes.
Nearly one-third of RSV-positive cases identified in the OUTSMART program were hospi-
talized for greater than 24 hours. Hospitalization rates for RSV positive patients published by
Radin et al. [48] were similar to those estimated using data from OUTSMART. Radin et al.
reported that 28% of all RSV cases were hospitalized in their study of three separate U.S. popu-
lations. They also found that 71% of identified RSV cases were under age 4 [48], similar to the
infection rate of 76% found amongst OUTSMART patients of 2 years of age.
Most RSV cases in OUTSMART were diagnosed in the ER and did not result in the subject
being admitted to the hospital for more than 24 hours. Over 70% of RSV cases with <24 hours
LOS were diagnosed in the ED, and only 6% in doctor’s offices or clinics likely due to primarily
hospital-based case collection. Most ER diagnoses were in children less than 2 years old.
Parents may be choosing the costlier treatment setting of the ER for their children over waiting
for a pediatrician appointment due to perceived urgency of symptoms, or lack of private health
insurance or primary care provider. In total, 64.4% of RSV cases that spent less than 24 hours
in the hospital were seen in the ER. This is an important finding from the OUTSMART study
in that much of the burden of RSV disease does not appear to lie in hospitalizations, but in the
ER. OUTSMART has identified a signal for future research to gain more clarity of the full bur-
den of RSV disease in all healthcare settings.
An additional explanation for the large proportion of cases diagnosed in the ED compared
with other outpatient settings such as a physician’s office, is that very little testing for RSV is
conducted in these settings as it does not alter treatment decisions [3,7,49]. A better under-
standing of the burden of disease and related costs in the outpatient setting is necessary to bet-
ter inform the design of clinical studies and the future impact of novel interventions.
Despite the inability to completely capture all circulating RSV cases due to lack of uniform
diagnostic testing in all healthcare settings, OUTSMART provides a reasonable description of
verified RSV diagnoses based on current medical practice. Future RSV surveillance and epide-
miology studies will need to address the burden of disease in all settings, including outpatient
clinics and the ER.
Supporting information
S1 Data. Total number of tests conducted and total number of positive tests at OUT-
SMART participating labs.
(XLSX)
S2 Data. 2016–2017 OUTSMART surveillance data.
(XLSX)
S1 Table. OUTSMART 2016–17 RSV-positive tests by region and age and subtype.
(DOCX)
S2 Table. OUTSMART 2016–17 RSV-positive tests by gender, age and subtype.
(DOCX)
U.S. RSV surveillance winter 2016-17
PLOS ONE | https://doi.org/10.1371/journal.pone.0200319 July 24, 2018 12 / 16
S1 Text. Supplementary methods.
(DOCX)
Acknowledgments
The authors would like to thank all the members of the RSV Alert™and OUTSMART laborato-
ries that contributed samples and data to the study.
Author Contributions
Conceptualization: Alexey Ruzin, Hui Liu, Li Yu, Mark T. Esser.
Data curation: Xiaohui Jiang.
Formal analysis: Alexey Ruzin, Susan T. Pastula, Elizabeth Levin-Sparenberg, Xiaohui Jiang,
Jon Fryzek, Andrey Tovchigrechko, Bin Lu, Yanping Qi, Hui Liu, Hong Jin, Judith Hackett,
Tonya Villafana, Mark T. Esser.
Methodology: Alexey Ruzin, Jon Fryzek, Andrey Tovchigrechko, Mark T. Esser.
Project administration: Alexey Ruzin, Bin Lu, Mark T. Esser.
Supervision: Alexey Ruzin, Jon Fryzek, Mark T. Esser.
Writing – original draft: Alexey Ruzin, Susan T. Pastula, Elizabeth Levin-Sparenberg, Tonya
Villafana, Mark T. Esser.
Writing – review & editing: Alexey Ruzin, Susan T. Pastula, Elizabeth Levin-Sparenberg,
Andrey Tovchigrechko, Bin Lu, Yanping Qi, Hui Liu, Hong Jin, Li Yu, Judith Hackett,
Tonya Villafana, Mark T. Esser.
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