Age-specific differences in influenza virus type and subtype distribution in the 2012/2013 season in 12 European countries

SUMMARYThe epidemiology of seasonal influenza is influenced by age. During the influenza season, the European Influenza Surveillance Network (EISN) reports weekly virological and syndromic surveillance data [mostly influenza-like illness (ILI)] based on national networks of sentinel primary-care providers. Aggregated numbers by age group are available for ILI, but not linked to the virological data. At the end of the influenza season 2012/2013, all EISN laboratories were invited to submit a subset of their virological data for this season, including information on age. The analysis by age group suggests that the overall distribution of circulating (sub)types may mask substantial differences between age groups. Thus, in cases aged 5–14 years, 75% tested positive for influenza B virus whereas all other age groups had an even distribution of influenza A and B viruses. This means that the intepretation of syndromic surveillance data without age group-specific virological data may be misleading. Surveillance at the European level would benefit from the reporting of age-specific influenza data.

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Is the onset of influenza in the community age-related?

Epidemiology and Infection ◽

10.1017/s0950268816000510 ◽

2016 ◽

Vol 144 (11) ◽

pp. 2295-2305 ◽

Cited By ~ 3

Author(s):

D. M. FLEMING ◽

H. DURNALL ◽

F. WARBURTON ◽

J. S. ELLIS ◽

M. C. ZAMBON

Keyword(s):

Symptom Onset ◽

Influenza A ◽

Age Groups ◽

Virus Detection ◽

Threshold Value ◽

Incidence Rates ◽

Influenza B Virus ◽

Influenza B ◽

Age Group ◽

Age Related

SUMMARYWe studied the spread of influenza in the community between 1993 and 2009 using primary-care surveillance data to investigate if the onset of influenza was age-related. Virus detections [A(H3N2), B, A(H1N1)] and clinical incidence of influenza-like illness (ILI) in 12·3 million person-years in the long-running Royal College of General Practitioners-linked clinical-virological surveillance programme in England & Wales were examined. The number of days between symptom onset and the all-age peak ILI incidence were compared by age group for each influenza type/subtype. We found that virus detection and ILI incidence increase, peak and decrease were in unison. The mean interval between symptom onset to peak ILI incidence in virus detections (all ages) was: A(H3N2) 20·5 [95% confidence interval (CI) 19·7–21·6] days; B, 18·8 (95% CI 15·8·0–21·7) days; and A(H1N1) 17·0 (95% CI 15·6–18·4) days. Differences by age group were examined using the Kruskal–Wallis test. For A(H3N2) and A(H1N1) viruses the interval was similar in each age group. For influenza B there were highly significant differences by age group (P= 0·0001). Clinical incidence rates of ILI reported in the 8 weeks preceding the period of influenza virus activity were used to estimate a baseline incidence and threshold value (upper 95% CI of estimate) which was used as a marker of epidemic progress. Differences between the age groups in the week in which the threshold was reached were small and not localized to any age group. In conclusion we found no evidence to suggest that influenza A(H3N2) and A(H1N1) occurs in the community in one age group before another. For influenza B, virus detection was earlier in children aged 5–14 years than in persons aged ⩾25 years.

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Population health challenges in primary care: What are the unfinished tasks and who should do them?

SAGE Open Medicine ◽

10.1177/2050312118800209 ◽

2018 ◽

Vol 6 ◽

pp. 205031211880020 ◽

Cited By ~ 1

Author(s):

Frederick North ◽

Sidna M Tulledge-Scheitel ◽

John C Matulis ◽

Jennifer L Pecina ◽

Andrew M Franqueira ◽

...

Keyword(s):

Primary Care ◽

Chronic Disease ◽

Disease Management ◽

Population Health ◽

Chronic Disease Management ◽

Age Groups ◽

Software Tool ◽

Primary Care Providers ◽

Age Group ◽

Care Providers

Background: There are numerous recommendations from expert sources that help guide primary care providers in cancer screening, infectious disease screening, metabolic screening, monitoring of drug levels, and chronic disease management. Little is known about the potential effort needed for a healthcare system to address these recommendations, or the patient effort needed to complete the recommendations. Methods: For 73 recommended population healthcare items, we examined each of 28,742 patients in a primary care internal medicine practice to determine whether they were up-to-date on recommended screening, immunizations, counseling, and chronic disease management goals. We used a rule-based software tool that queries the medical record for diagnoses, dates, laboratory values, pathology reports, and other information used in creating the individualized recommendations. We counted the number of uncompleted recommendations by age groups and examined the healthcare staff needed to address the recommendations and the potential patient effort needed to complete the recommendations. Results: For the 28,742 patients, there were 127,273 uncompleted recommendations identified for population health management (mean recommendations per patient 4.36, standard deviation of 2.65, range of 0–17 recommendations per patient). The age group with the most incomplete recommendations was age of 50–65 years with 5.5 recommendations per patient. The 18–35 years age group had the fewest incomplete recommendations with 2.6 per patient. Across all age groups, initiation of these recommendations required high-level input (physician, nurse practitioner, or physician’s assistant) in 28%. To completely adhere to recommended services, a 1000-patient cross-section cohort would require a total of 464 procedures and 1956 lab tests. Conclusion: Providers and patients face a daunting number of tasks necessary to meet guideline-generated recommendations. We will need new approaches to address the burgeoning numbers of uncompleted recommendations.

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Characteristics of Neonatal Abstinence Syndrome (NAS) in a rural clinic population: Tracking with Electronic Medical Health Records (EHR)

Online Journal of Rural Nursing and Health Care ◽

10.14574/ojrnhc.v20i2.625 ◽

2020 ◽

Vol 20 (2) ◽

pp. 194-212

Author(s):

Jill D Cochran ◽

Traci Jarrett ◽

Adam Baus

Keyword(s):

Health Care ◽

High Risk ◽

Health Care Providers ◽

Age Groups ◽

Primary Care Providers ◽

Age Group ◽

Care Providers ◽

Rural Clinic ◽

Age Related ◽

High Risk Populations

PurposeThe impact of intrauterine exposure of opioids and other addictive substances on pediatric patients is concerning for health care providers in rural WV. NAS patients must be identified, screened, and treated during the pediatric years to facilitate improved outcomes. The purpose of this research was to evaluate the ability of rural providers to use EHRs to identify, describe, and monitor aspects of NAS across the pediatric health span. MethodsThe research team used de-identified data of patients that had the NAS diagnosis from a rural clinic. One hundred fifty-five charts were evaluated. Demographics, clinical characteristics, and developmental milestone status were extracted from charts. ResultsThere were differences in characteristics across age groups. Reported secondhand smoke was higher among the 0-3 year olds. Normal BMI percentile was highest among 4-5 year olds. The Ages and Stages Developmental screening was abnormal more in those aged 6-19 years. Foster care was highest among the ages 0-3 years. The 4-12 age groups highest amount of no show visits. Respiratory illness was the most frequent diagnosis and was highest in the 4-5 age group. Eye and ear diagnosis were noted as a recurrent diagnosis in the 4-5 year old group. Diagnosis related to mental health were highest in the 6-18 age group. DiscussionThe EHR can be used to describe and track special populations such as NAS in rural areas. Tagging and tacking patients with NAS can help primary care providers manage care and anticipate age related health care needs. Tracking high risk populations assures that the patient care is maintained. Tracking no show rates assists providers in assuring that patient’s caregivers are compliant in necessary treatments and referrals. Child Protection can also be involved if medical neglect is noted. EHRs are useful in identifying high risk populations such as NAS to facilitate treatments and continuity of care. DOI: http://doi.org/10.14574/ojrnhc.v20i2.625

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Clinical Investigations. Comparison of Directigen Flu A+B with Real Time PCR in the Diagnosis of Influenza / Сравнение Иммунохроматографического Метода (Directigen Flu A+B) И Теста RT-PCR При Инфекциях Гриппа

Folia Medica ◽

10.1515/folmed-2015-0027 ◽

2015 ◽

Vol 57 (2) ◽

pp. 104-110 ◽

Cited By ~ 4

Author(s):

Golubinka Bosevska ◽

Nikola Panovski ◽

Elizabeta Janceska ◽

Vladimir Mikik ◽

Irena Kondova Topuzovska ◽

...

Keyword(s):

Real Time ◽

Influenza A Virus ◽

Real Time Pcr ◽

Influenza A ◽

Age Groups ◽

Influenza Viruses ◽

Influenza B Virus ◽

Influenza B ◽

B Virus ◽

Nose And Throat

AbstractEarly diagnosis and treatment of patients with influenza is the reason why physicians need rapid high-sensitivity influenza diagnostic tests that require no complex lab equipment and can be performed and interpreted within 15 min. The Aim of this study was to compare the rapid Directigen Flu A+B test with real time PCR for detection of influenza viruses in the Republic of Macedonia. MATERIALS AND METHODS: One-hundred-eight respiratory samples (combined nose and throat swabs) were routinely collected for detection of influenza virus during influenza seasons. Forty-one patients were pediatric cases and 59 were adult. Their mean age was 23 years. The patients were allocated into 6 age groups: 0 - 4 yrs, 5 - 9 yrs, 10 - 14 yrs, 15 - 19 yrs, 20-64 yrs and > 65 yrs. Each sample was tested with Directigen Flu A+B and CDC real time PCR kit for detection and typisation/subtypisation of influenza according to the lab diagnostic protocol. RESULTS: Directigen Flu A+B identified influenza A virus in 20 (18.5%) samples and influenza B virus in two 2 (1.9%) samples. The high specificity (100%) and PPV of Directigen Flu A+B we found in our study shows that the positive results do not need to be confirmed. The overall sensitivity of Directigen Flu A+B is 35.1% for influenza A virus and 33.0% for influenza B virus. The sensitivity for influenza A is higher among children hospitalized (45.0%) and outpatients (40.0%) versus adults. CONCLUSION: Directigen Flu A+B has relatively low sensitivity for detection of influenza viruses in combined nose and throat swabs. Negative results must be confirmed.

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Co-circulation of the two influenza B lineages during 13 consecutive influenza surveillance seasons in Italy, 2004–2017

BMC Infectious Diseases ◽

10.1186/s12879-019-4621-z ◽

2019 ◽

Vol 19 (1) ◽

Cited By ~ 6

Author(s):

Simona Puzelli ◽

◽

Angela Di Martino ◽

Marzia Facchini ◽

Concetta Fabiani ◽

...

Keyword(s):

Influenza A ◽

Respiratory Illness ◽

Influenza Surveillance ◽

Influenza B Virus ◽

Influenza B ◽

Virus Infections ◽

Ha Gene ◽

B Virus ◽

B Lineage ◽

Trivalent Vaccine

Abstract Background Since 1985, two antigenically distinct lineages of influenza B viruses (Victoria-like and Yamagata-like) have circulated globally. Trivalent seasonal influenza vaccines contain two circulating influenza A strains but a single B strain and thus provide limited immunity against circulating B strains of the lineage not included in the vaccine. In this study, we describe the characteristics of influenza B viruses that caused respiratory illness in the population in Italy over 13 consecutive seasons of virological surveillance, and the match between the predominant influenza B lineage and the vaccine B lineage, in each season. Methods From 2004 to 2017, 26,886 laboratory-confirmed influenza cases were registered in Italy, of which 18.7% were type B. Among them, the lineage of 2465 strains (49%) was retrieved or characterized in this study by a real-time RT-PCR assay and/or sequencing of the hemagglutinin (HA) gene. Results Co-circulation of both B lineages was observed each season, although in different proportions every year. Overall, viruses of B/Victoria and B/Yamagata lineages caused 53.3 and 46.7% of influenza B infections, respectively. A higher proportion of infections with both lineages was detected in children, and there was a declining frequency of B/Victoria detections with age. A mismatch between the vaccine and the predominant influenza B lineage occurred in eight out of thirteen influenza seasons under study. Considering the seasons when B accounted for > 20% of all laboratory-confirmed influenza cases, a mismatch was observed in four out of six seasons. Phylogenetic analysis of the HA1 domain confirmed the co-circulation of both lineages and revealed a mixed circulation of distinct evolutionary viral variants, with different levels of match to the vaccine strains. Conclusions This study contributes to the understanding of the circulation of influenza B viruses in Italy. We found a continuous co-circulation of both B lineages in the period 2004–2017, and determined that children were particularly vulnerable to Victoria-lineage influenza B virus infections. An influenza B lineage mismatch with the trivalent vaccine occurred in about two-thirds of cases.

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Incorporating calendar effects to predict influenza seasonality in Milwaukee, Wisconsin

Epidemiology and Infection ◽

10.1017/s0950268819001511 ◽

2019 ◽

Vol 147 ◽

Cited By ~ 3

Author(s):

Ryan B. Simpson ◽

Tania M. Alarcon Falconi ◽

Aishwarya Venkat ◽

Kenneth H. H. Chui ◽

Jose Navidad ◽

...

Keyword(s):

Public Schools ◽

Negative Binomial ◽

Age Groups ◽

Influenza Surveillance ◽

Influenza B ◽

Sporting Events ◽

Seasonal Incidence ◽

Calendar Effects ◽

Holiday Effects ◽

The Impact

Abstract Social outings can trigger influenza transmission, especially in children and elderly. In contrast, school closures are associated with reduced influenza incidence in school-aged children. While influenza surveillance modelling studies typically account for holidays and mass gatherings, age-specific effects of school breaks, sporting events and commonly celebrated observances are not fully explored. We examined the impact of school holidays, social events and religious observances for six age groups (all ages, ⩽4, 5–24, 25–44, 45–64, ⩾65 years) on four influenza outcomes (tests, positives, influenza A and influenza B) as reported by the City of Milwaukee Health Department Laboratory, Milwaukee, Wisconsin from 2004 to 2009. We characterised holiday effects by analysing average weekly counts in negative binomial regression models controlling for weather and seasonal incidence fluctuations. We estimated age-specific annual peak timing and compared influenza outcomes before, during and after school breaks. During the 118 university holiday weeks, average weekly tests were lower than in 140 school term weeks (5.93 vs. 11.99 cases/week, P < 0.005). The dampening of tests during Winter Break was evident in all ages and in those 5–24 years (RR = 0.31; 95% CI 0.22–0.41 vs. RR = 0.14; 95% CI 0.09–0.22, respectively). A significant increase in tests was observed during Spring Break in 45–64 years old adults (RR = 2.12; 95% CI 1.14–3.96). Milwaukee Public Schools holiday breaks showed similar amplification and dampening effects. Overall, calendar effects depend on the proximity and alignment of an individual holiday to age-specific and influenza outcome-specific peak timing. Better quantification of individual holiday effects, tailored to specific age groups, should improve influenza prevention measures.

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Dual and Triple Infections With Influenza A and B Viruses: A Case-Control Study in Southern Brazil

The Journal of Infectious Diseases ◽

10.1093/infdis/jiz221 ◽

2019 ◽

Vol 220 (6) ◽

pp. 961-968 ◽

Cited By ~ 6

Author(s):

Tatiana Schäffer Gregianini ◽

Ivana R Santos Varella ◽

Patricia Fisch ◽

Letícia Garay Martins ◽

Ana B G Veiga

Keyword(s):

Influenza Virus ◽

Influenza A ◽

Clinical Symptoms ◽

Antiviral Treatment ◽

Influenza Virus Infection ◽

H3n2 Virus ◽

Influenza Surveillance ◽

Influenza B Virus ◽

Influenza B ◽

Influenza A H1n1

Abstract Influenza surveillance is important for disease control and should consider possible coinfection with different viruses, which can be associated with disease severity. This study analyzed 34 459 patients with respiratory infection from 2009 to 2018, of whom 8011 were positive for influenza A virus (IAV) or influenza B virus (IBV). We found 18 cases of dual influenza virus infection, including coinfection with 2009 pandemic influenza A(H1N1) virus (A[H1N1]pdm09) and influenza A(H3N2) virus (1 case), A(H1N1)pdm09 and IBV (6 cases), A(H3N2) and IBV (8 cases), and nonsubtyped IAV and IBV (3 cases); and 1 case of triple infection with A(H3N2), A(H1N1)pdm09, and IBV. Compared with 76 monoinfected patients, coinfection was significantly associated with cardiopathy and death. Besides demographic characteristics and clinical symptoms, we assessed vaccination status, antiviral treatment, timeliness of antiviral use, hospitalization, and intensive care unit admission, but no significant differences were found between coinfected and monoinfected cases. Our findings indicate that influenza virus coinfection occurs more often than previously reported and that it can lead to a worse disease outcome.

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What will the next influenza season bring about: seasonal influenza or the new A(H1N1)v? An analysis of German influenza surveillance data

Eurosurveillance ◽

10.2807/ese.14.32.19303-en ◽

2009 ◽

Vol 14 (32) ◽

Author(s):

H Uphoff ◽

S Geis ◽

A Grüber ◽

A M Hauri

Keyword(s):

Influenza A ◽

Seasonal Influenza ◽

Influenza Season ◽

Influenza Viruses ◽

Moderate Intensity ◽

Influenza Surveillance ◽

Influenza B ◽

Low Intensity ◽

Influenza A H1n1 ◽

Using Data

For the next influenza season (winter 2009-10) the relative contributions to virus circulation and influenza-associated morbidity of the seasonal influenza viruses A(H3N2), A(H1N1) and B, and the new influenza A(H1N1)v are still unknown. We estimated the chances of seasonal influenza to circulate during the upcoming season using data of the German influenza sentinel scheme from 1992 to 2009. We calculated type and subtype-specific indices for past exposure and the corresponding morbidity indices for each season. For the upcoming season 2009-10 our model suggests that it is unlikely that influenza A(H3N2) will circulate with more than a low intensity, seasonal A(H1N1) with more than a low to moderate intensity, and influenza B with more than a low to median intensity. The probability of a competitive circulation of seasonal influenza A with the new A(H1N1)v is low, increasing the chance for the latter to dominate the next influenza season in Germany.

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Hemagglutination inhibition antibody titers as a correlate of protection against influenza disease in the 2018/2019 epidemic season in Poland

Acta Biochimica Polonica ◽

10.18388/abp.2020_5088 ◽

2020 ◽

Author(s):

Ewelina Hallmann-Szelińska ◽

Karol Szymański ◽

Katarzyna Łuniewska ◽

Katarzyna Kondratiuk ◽

Lidia Bernadeta Brydak

Keyword(s):

Hemagglutination Inhibition ◽

Influenza A ◽

Age Groups ◽

Influenza Viruses ◽

Clinical Samples ◽

Influenza B Virus ◽

Influenza B ◽

Epidemic Season ◽

Hemagglutination Inhibition Test ◽

Inhibition Antibody

The aim of this study was to determine the level of antibodies against hemagglutinin of influenza viruses in the sera of people in the seven age groups in the epidemic season 2018/2019 in Poland. The level of anti-hemagglutinin antibodies was determined by hemagglutination inhibition test (HAI). 1050 clinical samples from all over the country were tested. The level of antibodies against influenza viruses was highest in the 10–14 age group for A/Singapore/INFIMH-16-0019/2016 (H3N2) and B/Phuket/3073/2013 Yamagata lineage antigens. These results confirm the circulation of four antigenically different influenza virus strains, two subtypes of influenza A virus – A/Michigan/45/2015 (H1N1)pdm09 and A/Singapore/INFIMH-16-0019/2016 (H3N2) and two lineages of influenza B virus – B/Colorado/06/2017 – Victoria lineage and B/Phuket/3073/2013 Yamagata lineage.

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Transmissibility of influenza during the 21st-century epidemics, Spain, influenza seasons 2001/02 to 2017/18

Eurosurveillance ◽

10.2807/1560-7917.es.2020.25.21.1900364 ◽

2020 ◽

Vol 25 (21) ◽

Author(s):

Lidia Redondo-Bravo ◽

Concepción Delgado-Sanz ◽

Jesús Oliva ◽

Tomás Vega ◽

Jose Lozano ◽

...

Keyword(s):

Influenza A ◽

Influenza Pandemic ◽

Age Groups ◽

H3n2 Virus ◽

Influenza B Virus ◽

Influenza B ◽

Intensity Level ◽

Influenza A H1n1 ◽

Epidemic Period ◽

System Data

Background Understanding influenza seasonality is necessary for determining policies for influenza control. Aim We characterised transmissibility during seasonal influenza epidemics, including one influenza pandemic, in Spain during the 21th century by using the moving epidemic method (MEM) to calculate intensity levels and estimate differences across seasons and age groups. Methods We applied the MEM to Spanish Influenza Sentinel Surveillance System data from influenza seasons 2001/02 to 2017/18. A modified version of Goldstein’s proxy was used as an epidemiological-virological parameter. We calculated the average starting week and peak, the length of the epidemic period and the length from the starting week to the peak of the epidemic, by age group and according to seasonal virus circulation. Results Individuals under 15 years of age presented higher transmissibility, especially in the 2009 influenza A(H1N1) pandemic. Seasons with dominance/co-dominance of influenza A(H3N2) virus presented high intensities in older adults. The 2004/05 influenza season showed the highest influenza-intensity level for all age groups. In 12 seasons, the epidemic started between week 50 and week 3. Epidemics started earlier in individuals under 15 years of age (−1.8 weeks; 95% confidence interval (CI):−2.8 to −0.7) than in those over 64 years when influenza B virus circulated as dominant/co-dominant. The average time from start to peak was 4.3 weeks (95% CI: 3.6–5.0) and the average epidemic length was 8.7 weeks (95% CI: 7.9–9.6). Conclusions These findings provide evidence for intensity differences across seasons and age groups, and can be used guide public health actions to diminish influenza-related morbidity and mortality.

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