Journal of Clinical Virology - Volume 82 - Supplement 1 - September 2016

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S116 Abstracts / Journal of Clinical Virology 82S (2016) S1–S142
Methods: In this retrospective study we examined a total of
2637 samples (nasopharyngeal, nose and throat swabs). They were
taken from patients with symptoms of respiratory infection admit-
ted to University Medical Centre Maribor during the years of 2014
and 2015. HPIV RNAs were detected with a commercial automated
multiplex PCR system (FilmArray, Biofire).
Results: Out of 2637 samples, 173 (6.56%) tested positive for
HPIVs. Nearly half of the HPIV-positive patients were infected
with HPIV-3 (49.71%, 86), followed by HPIV-4 (21.39%, 37), HPIV-1
(16.76%, 29) and HPIV-2 (15.03%, 26), respectively. Most frequently
identified type was HPIV-3, with regular activity throughout 2014
and 2015, including a substantial increase in both autumn-winter
seasons with peaks in November of 2014 and 2015. It was also the
predominant HPIV type represented in summer months of both
years alongside minute occurrences of type 2 and 4. An apparent
outbreak of HPIV-4 infections starting in summer, and progressing
in autumn of 2015 with a peak in September, was observed. At
the same time, HPIV-3 and HPIV-2 were in decline. Also, type 1
and 3 started to increase as HPIV-4 decreased. Type 2 was com-
pletely absent in spring 2014 but had a slight peak in October 2014
and was subsequently present in smaller numbers for the rest of
2015. The median age of HPIV-tested patients was 5, and ranged
from less than a year to 96 years old. The majority (82.08%, 142)
of infected patients were children under the age of 5. Among the
elderly (>65 years old) 12.75% (13/102) tested positive for one of
HPIVs, the oldest being 87 years old. The male to female ratio of
patients infected with HPIV was 1:1. HPIV was detected as the
only cause of infection in 60.11% (107) of cases and 5 of them
tested positive for two types of HPIV. In forty-eight (26.97%) HPIV-
positive samples one co-infection with other respiratory pathogen
was detected, 18 (10.11%) had two co-infections and 5 (2.81%)
had three or more co-infections. The prevalent (50.00%) pathogen
of co-infection was rhinovirus, followed by adenovirus in 18.00%,
enterovirus in 12.00% and respiratory syncytial virus in 10.00% of
samples. Coronavirus (HKU1 and OC43), Mycoplasma pneumoniae ,
humanmetapneumovirus and Bordetella pertussis accounted for the
remaining 10.00%.
Conclusions: Overall, the analysed data suggests HPIV-3 as the
most prevalent type of HPIV infections in NE Slovenia. Both HPIV-2
andHPIV-3 showed continual presence in the studied 2-year period
with the latter greatly outnumbered the former. A similar bien-
nial distribution pattern for HPIV-1 and HPIV-4 was noted, which
couldmean that they tend to occur in odd-numbered years. We also
observed an epidemic of HPIV-4 which is rarely reported in liter-
ature. From previously published reports it appears that seasonal
trends vary in different parts of the world and that the distribu-
tion of HPIV types is also affected by environmental conditions.
Additional data from following years is needed for a more clear
understanding of HPIV seasonal trends and interactions between
all four types in NE Slovenia.
Abstract no: 252
Presentation at ESCV 2016: Poster 194
Seasonality of respiratory syncytial virus
infection in the EU/EEA, 2010–2016
Eeva Broberg ∗, Kari Johansen ,
Cornelia Adlhoch , René Snacken ,
Pasi Penttinen , on behalf of the European
Influenza Surveillance Networ
1 European Centre for Disease Prevention and
Control (ECDC), Sweden
2 The European Influenza Surveillance Network,
Sweden
Background: Respiratory syncytial virus (RSV) is considered
the most common pathogen causing severe lower respiratory tract
infections among infants and children. RSV vaccine candidates are
in development and the World Health Organization is prepar-
ing global RSV surveillance to estimate the impact of future RSV
vaccines. One of the surveillance objectives is to monitor RSV
seasonality and intensity. A subset of EU/EEA Member States (MS)
are already testing clinical specimens for influenza and RSV as part
of their routine influenza surveillance. In this study, we are describ-
ing the seasonality of RSV infection in these countries.
Methods: We performed a retrospective descriptive study of
laboratory-confirmed RSV detections reported weekly through the
European Influenza Surveillance Network based on influenza-like
illness (ILI) or acute respiratory infection case definitions between
weeks 40/2010 and 14/2016. We compared findings between sys-
tematically sampled primary-care-based sentinel specimens tested
according to a standard protocol and convenience sampled primary
and hospital-care-based non-sentinel specimens. We also studied
the correlation between the median week of peak RSV detections
and the latitude of each reporting country’s capital by Pearson’s cor-
relation. RSV seasons were defined as the number of weeks when
detections exceeded 5% of total detections per season per country.
Results: SeventeenMS reportedRSVdetections during the study
period: seven MS reported 4399 sentinel detections and fifteen
MS reported 156,698 non-sentinel detections. Two MS contributed
60% of sentinel and 61% of non-sentinel detections. Seasonality was
observed within both surveillance systems. The median length of
RSV season estimated based on sentinel and non-sentinel surveil-
lance was 11 (with country range 6–28) and 10 (range 6–18)
weeks, respectively. The median peak week for sentinel detec-
tions was week 6 (range 48–18), and for non-sentinel detections
week 5 (range 49–17). RSV was detected by non-sentinel surveil-
lance throughout the year but in sentinel systemonly during weeks
45–13 with consistent reporting. RSV detections peaked later with
increasing latitude (r = 0.41 for sentinel and 0.46 for non-sentinel).
Conclusions: RSV detections in 17EU/EEA MS followed a sea-
sonal pattern, peaking regularly early February and lasting around
10 weeks. Our data confirm the moderate correlation between
the timing of the epidemic peak and increasing latitude that has
been shown earlier. Our study suggests that RSV seasonality can be
assessed through both sentinel and non-sentinel influenza surveil-
lance systems but more sensitively in the latter one. Overall, the
number of sentinel RSV detections were vastly lower compared to
non-sentinel specimens which is a reflection of different surveil-
lance systems and number of participating countries. We do not
have RSV-specific denominator data and can therefore not calculate
proportions. Further limitations of the data include that large detec-
tion volumes originate from only two MS. Despite the limitations,
this study supports the use of influenza surveillance systems for
monitoring RSV seasonalitywith consideration to adjust the ILI case
definition to establish an RSV-specific surveillance system. Further-