Non-pandemic Seasons Research Articles

How loneliness is talked about in social media during COVID-19 pandemic: Text mining of 4,492 Twitter feeds

BackgroundLoneliness is a public health problem that is expected to rise during the COVID-19 pandemic, given the widespread policy of quarantine. The literature is unclear whether loneliness during COVID-19 is similar to those of non-pandemic seasons. This study examined the expression of loneliness on Twitter during COVID-19 pandemic, and identified key areas of loneliness across diverse communities. MethodsTwitter was searched for feeds that were:(1) in English; (2) posted from May 1, 2020 to July 1, 2020; (3) posted by individual users (not organisations); and (4) contained the words ‘loneliness’ and ‘COVID-19’. A machine-learning approach (Topic Modeling) identified key topics from the Twitter feeds; Hierarchical Modeling identified overarching themes. Variations in the prevalence of the themes were examined over time and across the number of followers of the Twitter users. Results4492 Twitter feeds were included and classified into 3 themes: (1) Community impact of loneliness during COVID-19; (2) Social distancing during COVID-19 and its effects on loneliness; and (3) Mental health effects of loneliness during COVID-19. The 3 themes demonstrated temporal variations. Particularly in Europe, Theme 1 showed a drastic reduction over time, with a corresponding rise in Theme 3. The themes also varied across number of followers. Highly influential users were more likely to talk about Theme 3 and less about Theme 2. ConclusionsThe findings reflect close-to-real-time public sentiments on loneliness during the COVID-19 pandemic and demonstrated the potential usefulness of social media to keep tabs on evolving mental health issues. It also provides inspiration for potential interventions to address novel problems–such as loneliness–during COVID-19 pandemic.

Cost-Effectiveness of Routine Childhood Vaccination Against Seasonal Influenza in Germany

ObjectivesIn Germany, routine influenza vaccination with quadrivalent influenza vaccines (QIV) is recommended and reimbursed for individuals ≥60 years of age and individuals with underlying chronic conditions. The present study examines the cost-effectiveness of a possible extension of the recommendation to include strategies of childhood vaccination against seasonal influenza using QIV. MethodsA dynamic transmission model was used to examine the epidemiological impact of different childhood vaccination strategies. The outputs were used in a health economic decision tree to calculate the costs per quality-adjusted life year (QALY) gained from a societal and a third-party payer (TPP) perspective. Strain-specific epidemiology, vaccine uptake, and vaccine efficacy data from the 10 non-pandemic seasons from 2003/2004 to 2013/2014 were used, and cost data were drawn mainly from a health insurance claims data analysis and supplemented by estimates from literature. Uncertainty is explored via scenario, deterministic, and probabilistic sensitivity analyses. ResultsVaccinating 2- to 9-year-olds with QIV assuming a vaccine uptake of 40% is cost-saving with a benefit–cost ratio of 1.66 from a societal perspective and an incremental cost-effectiveness ratio of €998/QALY from a TPP perspective. Lower and higher vaccine uptakes show marginal effects, while extending the target group to 2- to 17-year-olds further increases the health benefits while still being below the willingness-to-pay (WTP) threshold. Assuming no vaccine-induced herd protection has a negative effect on the cost-effectiveness ratio, but childhood vaccination remains cost-effective. ConclusionRoutine childhood vaccination against seasonal influenza in Germany is most likely to be cost-saving from a societal perspective and highly cost-effective from a TPP perspective.

Temporal patterns of influenza A subtypes and B lineages across age in a subtropical city, during pre-pandemic, pandemic, and post-pandemic seasons

BackgroundSeasonal patterns of influenza A subtypes and B lineages in tropical/subtropical regions across age have remained to be explored. The impact of the 2009 H1N1 pandemic on seasonal influenza activity have not been well understood.MethodsBased on a national sentinel hospital-based influenza surveillance system, the epidemiology of influenza virus during 2006/07–2015/16 was characterized in the subtropical city, Chengdu. Chengdu is one of the most populous cities in southwestern China, where the first reported case of A/H1N1pdm09 in mainland China was identified. Wavelet analysis was applied to identify the periodicities of A/H3N2, seasonal A/H1N1, A/H1N1pdm09, Victoria, and Yamagata across age, respectively. The persistence and age distribution patterns were described during the pre-pandemic (2006/07–2008/09), pandemic (2009/10), and post-pandemic (2010/11–2015/16) seasons.ResultsA total of 10,981 respiratory specimens were collected, of which 2516 influenza cases were identified. Periodicity transition from semi-annual cycles to an annual cycle was observed for composite influenza virus as well as A/H3N2 along in Chengdu since the 2009 H1N1 pandemic. Semi-annual cycles of composite influenza virus and A/H3N2 along were observed again during 2014/15–2015/16, coinciding with the emergence and predominance of A/H3N2 significant antigenic drift groups. However, A/H1N1pdm09, Victoria, and Yamagata generally demonstrated an annual winter-spring peak in non-pandemic seasons. Along with periodicity transitions, age groups with higher positive rates shifted from school-aged children and adults to adults and the elderly for A/H1N1pdm09 during 2009/10–2010/11 and for A/H3N2 during 2014/15–2015/16.ConclusionsDifferences in periodicity and age distribution by subtype/lineage and by season highlight the importance of increasing year-round influenza surveillance and developing subtype/lineage- and age-specific prevention and control measures. Changes of periodicity and age shifts should be considered in public health response to influenza pandemics and epidemics. In addition, it is suggested to use quadrivalent influenza vaccines to provide protection against both influenza B lineages.

Burden of medically attended influenza in Norway 2008-2017.

BackgroundThe burden of influenza in Norway remains uncertain, and data on seasonal variations and differences by age groups are needed.ObjectiveTo describe number of patients diagnosed with influenza in Norway each season and the number treated in primary or specialist health care by age. Further, to compare the burden of seasonal influenza with the 2009‐2010 pandemic outbreak.MethodsWe used Norwegian national health registries and identified all patients diagnosed with influenza from 2008 to 2017. We calculated seasonal rates, compared hospitalized patients with patients in primary care and compared seasonal influenza with the 2009‐2010 pandemic outbreak.ResultsEach season, on average 1.7% of the population were diagnosed with influenza in primary care, the average rate of hospitalization was 48 per 100 000 population while the average number of hospitalized patients each season was nearly 2500. The number of hospitalized influenza patients ranged from 579 in 2008‐2009 to 4973 in 2016‐2017. Rates in primary care were highest among young adults while hospitalization rates were highest in patients 80 years and older and in children below 5 years. The majority of in‐hospital deaths were in patients 70 years and older. Fewer patients were hospitalized during the 2009‐2010 pandemic than in seasonal outbreaks, but during the pandemic, more people in the younger age groups were hospitalized and fatal cases were younger.ConclusionInfluenza causes a substantial burden in primary care and hospitals. In non‐pandemic seasons, people above 80 years have the highest risk of influenza hospitalization and death.

Pre-and post-pandemic trends in antiviral use in hospitalized patients with laboratory-confirmed influenza: 2004/05–2013/14, Toronto, Canada

BackgroundData on factors associated with the use of antiviral medication to treat influenza in both children and adults are limited. ObjectivesTo describe trends in antiviral use, analyze factors associated with antiviral treatment of hospitalized patients with influenza, and to compare use based on guidelines. Study designA retrospective observational cohort of hospitalized patients with laboratory confirmed influenza in southern Ontario hospitals for the 2004/05–2013/14 seasons. ResultsOf the 7967 patients, 18% of the 1779 children (<15 years) and 66% of the 6188 adults received antiviral therapy. The percentage treated increased from 29% pre-pandemic to 74% during the pandemic, decreased to 55% in 2011/12 and then increased to 65% in 2013/14. Factors significantly associated with antiviral prescription across all age groups during the non-pandemic seasons include influenza type, disease severity, interval between symptom onset and test sample submission, and clinician suspicion of influenza. Rate of treatment of patients meeting guideline criteria was low for children and moderate for adults. ConclusionsSince the pandemic, there has been a sustained increase in the use of antiviral medication for all age groups of hospitalized patients with influenza, but much higher for adults than children. The odds of treatment are higher for patients with more severe disease as well as for those tested within 48 h of symptom onset, both of which are part of the guidelines for treatment with anti-influenza medications.

Counting pandemic deaths: comparing reported numbers of deaths from influenza A(H1N1)pdm09 with estimated excess mortality

During the wave 1 of the influenza A(H1N1)pdm09 virus, Norway appeared to be suffering from high mortality rates. However, by the end of the pandemic, it was widely reported that the number of deaths were much lower than previous years. The mortality burden from influenza is often assessed by two different approaches: counting influenza-certified deaths and estimating the mortality burden using models. The purpose of this study is to compare the number of reported deaths with results from two different models for estimating excess mortality during the pandemic in Norway. Additionally, mortality estimates for the pandemic season are compared with non-pandemic influenza seasons. Numbers on reported influenza A(N1h1)pdm09 deaths are gived by the Cause of Death Registry at Statistics Norway and an ad hoc registry at the Norwegian Institute of Public Health. Overall and Pnemumonia and Influenza certified mortality is modeled using Poission regression, adjusting for levels of reported influenza-like illness and seasonal and year-to-year variation. Modelling results suggest that the excess mortality in older age groups is considerably lower during the pandemic than non-pandemic seasons, but there are indications of an excess beyond what was reported during the pandemic. This highlights the benefits of both methods and the importance of explaining where these numbers come from.

Coordination Costs for School-Located Influenza Vaccination Clinics, Maine, 2009 H1N1 Pandemic

School nurses played a key role in Maine's school-located influenza vaccination (SLV) clinics during the 2009-2010 pandemic season. The objective of this study was to determine, from the school district perspective, the labor hours and costs associated with outside-clinic coordination activities (OCA). The authors defined OCA as labor hours spent by staff outside of clinic operations. The authors surveyed a convenience sample of 10 school nurses from nine school districts. Eight nurses responded to the survey, representing seven districts, 45 schools and 84 SLV clinics that provided a total of 22,596 vaccine doses (H1N1 and seasonal combined) to children and adolescents. The mean total OCA time per clinic was 69 hours: out of total hours, 22 (36%) were spent outside regular clinic operation time. The authors estimated the mean cost of OCA to be $15.36 per dose. Survey respondents reported that costs would be lower during non-pandemic seasons and as schools become more proficient at planning clinics.

2009 Pandemic Influenza A Vaccination of Pregnant Women: King County, Washington State, 2009–2010

The objectives were to estimate 2009 pandemic influenza A (pH1N1) vaccination coverage among pregnant women and identify associated factors. A multimodal survey was distributed to 5341 women who gave birth between November 1, 2009, and January 31, 2010, identified by hospitals in King County, Washington State, with maternity services (n=11). Of 4205 respondents, 3233 (76.9%) reported that they had received pH1N1 vaccine during pregnancy or within 2 weeks after delivery. Women whose prenatal care provider recommended vaccine had a higher vaccination prevalence than women whose provider did not (81.5% vs 29.6%; adjusted prevalence ratio=2.1; 95% CI=1.72, 2.58). Vaccination prevalence was lower among women who had received prenatal care from a midwife only compared with women who had received care from other providers (62.9% vs 78.8%; adjusted prevalence ratio=0.89; 95% CI=0.83, 0.96). Among pregnant women in King County, pH1N1 vaccination coverage was high. To improve coverage during nonpandemic seasons, influenza vaccine should be recommended routinely by prenatal care providers and vaccination provided where prenatal care is received. Barriers to midwives providing vaccination recommendations to patients should be explored.

2009 pandemic influenza a vaccination of pregnant women--King County, Washington State, 2009-2010.

Our objectives were to estimate 2009 pandemic influenza A (pH1N1) vaccination coverage among pregnant women and identify associated factors. We distributed a multimodal survey to 5341 women who gave birth between November 1, 2009, and January 31, 2010, identified by hospitals in King County, Washington State, with maternity services (n = 11). Of 4205 respondents, 3233 (76.9%) reported that they had received pH1N1 vaccine during pregnancy or within 2 weeks after delivery. Women whose prenatal care provider recommended vaccine had a higher vaccination prevalence than women whose provider did not (81.5% vs 29.6%; adjusted prevalence ratio = 2.1; 95% confidence interval = 1.72, 2.58). Vaccination prevalence was lower among women who had received prenatal care from a midwife only compared with women who had received care from other providers (62.9% vs 78.8%; adjusted prevalence ratio = 0.89; 95% confidence interval = 0.83, 0.96). Among pregnant women in King County, pH1N1 vaccination coverage was high. To improve coverage during nonpandemic seasons, influenza vaccine should be recommended routinely by prenatal care providers and vaccination provided where prenatal care is received. Barriers to midwives providing vaccination recommendations to patients should be explored.

Modelling the Proportion of Influenza Infections within Households during Pandemic and Non-Pandemic Years

BackgroundThe key epidemiological difference between pandemic and seasonal influenza is that the population is largely susceptible during a pandemic, whereas, during non-pandemic seasons a level of immunity exists. The population-level efficacy of household-based mitigation strategies depends on the proportion of infections that occur within households. In general, mitigation measures such as isolation and quarantine are more effective at the population level if the proportion of household transmission is low.Methods/ResultsWe calculated the proportion of infections within households during pandemic years compared with non-pandemic years using a deterministic model of household transmission in which all combinations of household size and individual infection states were enumerated explicitly. We found that the proportion of infections that occur within households was only partially influenced by the hazard h of infection within household relative to the hazard of infection outside the household, especially for small basic reproductive numbers. During pandemics, the number of within-household infections was lower than one might expect for a given because many of the susceptible individuals were infected from the community and the number of susceptible individuals within household was thus depleted rapidly. In addition, we found that for the value of at which 30% of infections occur within households during non-pandemic years, a similar 31% of infections occur within households during pandemic years.InterpretationWe suggest that a trade off between the community force of infection and the number of susceptible individuals in a household explains an apparent invariance in the proportion of infections that occur in households in our model. During a pandemic, although there are more susceptible individuals in a household, the community force of infection is very high. However, during non-pandemic years, the force of infection is much lower but there are fewer susceptible individuals within the household.

Trends in recorded influenza mortality: United States, 1900-2004.

I sought to describe trends in historical influenza mortality data in the United States since 1900 and compare pandemic with nonpandemic influenza seasons. I compiled a database of monthly influenza-classed death rates from official US mortality tables for the years 1900 to 2004 (1905-1909 excluded), from which I calculated adjusted influenza season (July 1-June 30) mortality rates. An overall and substantial decline in influenza-classed mortality was observed during the 20th century, from an average seasonal rate of 10.2 deaths per 100 000 population in the 1940s to 0.44 per 100 000 by the 1990s [corrected] . The 1918-1919 pandemic stands out as an exceptional outlier. The 1957-1958 and 1968-1969 influenza pandemic seasons, by contrast, displayed substantial overlap in both degree of mortality and timing compared with nonpandemic seasons. The considerable similarity in mortality seen in pandemic and non-pandemic influenza seasons challenges common beliefs about the severity of pandemic influenza. The historical decline in influenza-classed mortality rates suggests that public health and ecological factors may play a role in influenza mortality risk. Nevertheless, the actual number of influenza-attributable deaths remains in doubt.