Wuhan virus 2.0 breeding ground in sinkieland yesterday.

[blackmondy]

 

[laksaboy]

The stupid barricades and entrance closures they set up in such events makes the congestion worse. Because to get from Point A to Point B, people have to make meandering detours and inevitably mix with other groups of people.

Akan datang: CNY eve at the Chinatown area.

 

[k1976]

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10071822/

Reduced effectiveness of repeat influenza vaccination: distinguishing among within-season waning, recent clinical infection, and subclinical infection​

Qifang Bi,1,⇞ Barbra A. Dickerman,2 Huong Q. McLean,3 Emily T. Martin,4 Manjusha Gaglani,5,6 Karen J. Wernli,7 G.K. Balasubramani,8 Brendan Flannery,9 Marc Lipsitch,*,2 Sarah Cobey,*,1 and US Flu Vaccine Effectiveness Network Investigators**
Author information Copyright and License information PMC Disclaimer



As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health.
Learn more: PMC Disclaimer | PMC Copyright Notice

Version 2. medRxiv. Preprint. 2023 Mar 17 [revised 2023 Sep 27].
doi: 10.1101/2023.03.12.23287173
PMCID: PMC10071822
Other versions
PMID: 37016669

This is a preprint.​

It has not yet been peer reviewed by a journal.
The National Library of Medicine is running a pilot to include preprints that result from research funded by NIH in PMC and PubMed.

Reduced effectiveness of repeat influenza vaccination: distinguishing among within-season waning, recent clinical infection, and subclinical infection​

Qifang Bi,1,⇞ Barbra A. Dickerman,2 Huong Q. McLean,3 Emily T. Martin,4 Manjusha Gaglani,5,6 Karen J. Wernli,7 G.K. Balasubramani,8 Brendan Flannery,9 Marc Lipsitch,*,2 Sarah Cobey,*,1 and US Flu Vaccine Effectiveness Network Investigators**
Author information Copyright and License information PMC Disclaimer

The complete version history of this preprint is available at medRxiv.

Associated Data​

Supplementary Materials
Go to:

Abstract​

Studies have reported that prior-season influenza vaccination is associated with higher risk of clinical influenza infection among vaccinees. This effect might arise from incomplete consideration of within-season waning and recent infection. Using data from the US Flu Vaccine Effectiveness (VE) Network (2011–2012 to 2018–2019 seasons), we found that repeat vaccinees were vaccinated earlier in a season by one week. After accounting for waning VE, repeat vaccinees were still more likely to test positive for A(H3N2) (OR=1.11, 95%CI:1.02–1.21) but not for influenza B or A(H1N1). We found that clinical infection influences individuals’ decision to vaccinate in the following season while protecting against clinical infection of the same (sub)type. However, adjusting for recent clinical infections did not strongly influence the estimated effect of prior-season vaccination. In contrast, we found that adjusting for subclinical infection could theoretically attenuate this effect. Additional investigation is needed to determine the impact of subclinical infections on VE.
Keywords: influenza, vaccine, waning vaccine protection, infection history, infection block hypothesis, immunogenicity, test negative design
Go to:

2. Introduction​

The World Health Organization recommends annual influenza vaccination of persons at high risk, with some countries recommending universal vaccination[1,2]. A controlled study in the 1970s first raised questions about repeated annual influenza vaccination, reporting that prior vaccination indirectly increased the risk of infection in the current season[3,4]. It was not until a test-negative study in Canada[5], a vaccine trial in Hong Kong[6] and a household-based study in the United States[7] found differences in vaccine effectiveness (VE) and immunogenicity among repeat vaccinees and non-repeat vaccinees in the 2009/10 and 2010/11 seasons that the phenomenon was investigated routinely[8–14]. Since then, increased infection risk against A(H3N2) in repeat vaccinees was observed in multiple seasons and countries[7,11,12,13,15,16]. Increased risk is less often reported for the less prevalent A(H1N1) and type B[8,9].
Test-negative studies conducted in healthcare settings have become the standard way to evaluate vaccine protection. A test-negative design estimates VE by comparing vaccination coverage in persons with a medically attended acute respiratory illness who test positive for influenza with those who test negative[17]. Several factors that may bias estimates of repeat vaccination effects in test-negative design have not been accounted for.
Vaccine-induced protection against influenza virus infection wanes within a season[18–27]. As a result, the vaccine protection estimated among otherwise similar vaccinees may differ if the timing of vaccination is not considered. If repeat vaccinees tend to vaccinate substantially earlier in a season, waning protection could make the risk of infection among repeat vaccinees appear higher than in non-repeat vaccinees.
Infection in past seasons may protect against infection in the current season. The infection block hypothesis[4,28–30] suggests that prior vaccinations block opportunities to experience immunogenic influenza virus infections, which can lead to more cross-reactive and durable immune responses than vaccination, especially when circulating viruses differ from vaccine strains[31,32]. If true, the infection-block hypothesis could explain increased risk of infection among repeat vaccinees compared to non-repeat vaccinees: Because repeat vaccinees are less likely to have been infected in the previous season than non-repeat vaccinees due to protection from prior vaccinations, repeat vaccinees may have less immune protection at the start of an influenza season and hence a higher incidence of clinical infection in that season than non-repeat vaccinees. The difference in risk between the two groups can be further amplified if recent infection improves vaccine immunogenicity and vaccine-induced protection[31], as has also been recently observed for SARS-CoV-2[33].
In epidemiologic terms[34], under the infection block hypothesis, infection in the previous season is a mediator between vaccination in the previous season and a clinical infection outcome in the current season. When we estimate the effect of repeated vaccination, as a mediator, infection in the previous season does not on its own introduce a source of bias. However, if infection in the previous season influences the decision to vaccinate in the current season as well as the probability of clinical infection in the current season, then it is also a confounder that can bias the estimated effect of repeated vaccination on clinical infection. Because infection in the previous season may be both a mediator and a confounder, appropriately adjusting for it requires an approach that can handle this treatment-confounder feedback, such as inverse-probability weighting[35].
In this study, we first assessed the effect of repeated vaccination after accounting for intra-season waning of vaccine protection (results in Section 4.1). We then assessed whether clinical infection in the prior season. a potential confounder, may have biased our estimate of the effect of repeated vaccination (Section 4.2). Finally, we theoretically assessed the plausibility of the infection block hypothesis and enhanced VE from recent subclinical infections as explanations for the repeat vaccination effect (Section 4.3).
Go to:

3. Methods​

3.1. Study setting and population​

During the study period, the US Flu VE Network consisted of five study sites in Wisconsin, Michigan, Washington, Pennsylvania, and Texas[7,10,11,15,19,27,36] (SSection 1). During each enrollment season, outpatients 6 months of age and older were eligible for recruitment if they presented with acute respiratory illness with symptom onset within the last 7 or 10 days depending on the Flu VE site. Each eligible patient completed an enrollment interview that included questions on specific time and location for plausibility and status of influenza vaccination in the study enrollment season (the current season), status of influenza vaccination in the season immediately preceding the study enrollment season (the previous season), demographic information, and underlying health conditions. Participants were tested for influenza by real-time reverse transcription polymerase chain reaction (rRT-PCR) assay. Influenza-positive samples were first typed and then A-sub- or B-lineage-typed. For simplicity throughout we refer to individuals with PCR-confirmed symptomatic influenza virus infection (including those medically attended and not attended) as having “clinical infection”. Influenza vaccination status was confirmed by reviewing immunization records and state registries.
We analyzed data collected over 8 seasons (from the 2011–2012 through the 2018–2019 seasons) from all five sites. We excluded individuals who were vaccinated within 14 days of illness onset, for consistency with prior analyses. We excluded individuals who received more than one dose each season before symptom onset and were under 1 year of age at enrollment.
To study the impact of clinical infection history, we additionally obtained enrollment history and rRT-PCR testing history from the Marshfield Clinic (MCHS), the US Flu VE Network site in Wisconsin. As the primary outpatient and inpatient care provider in its catchment area, MCHS is able to collect data on enrollment and testing history that are not available from other sites [37]. In particular, they are able to link participant data across seasons. We analyzed data from the MCHS over 12 seasons (from the 2007–2008 through the 2018–2019 seasons). The analyses using exclusively MCHS data are described in the subsection ‘Adjustment for clinical infection history’ of Section 3.2, and the results are shown in Section 4.2.

3.2. Statistical analyses​

Accounting for within-season waning of vaccine protection:​

Using data from the five sites in the US Flu VE Network, we first determined whether the timing of vaccination differed between repeat and non-repeat vaccinees by fitting a linear regression model.
Using logistic regression models, we then estimated the relative odds of clinical infection among repeat vaccinees with reference to non-repeat vaccinees after adjusting for time of vaccination in the current season (to account for the waning of vaccine protection; SSection 3).

Adjustment for clinical infection history​

Because MCHS was the only site that had linked participants’ previous study enrollment and infection history, only data from MCHS could be used to assess the impact of clinical infection history.
To determine how a clinical infection in the current season is associated with clinical infection with the same and other (sub)types in prior seasons, we assessed the odds ratio of clinical infection in the current season among individuals with no prior clinical infections or clinical infections 3–5 seasons or 6 seasons ago with reference to those whose last detected clinical infection was 1–2 seasons before the current season (SSection 4).
We then assessed whether clinical influenza virus infections in the previous season influenced the decision to vaccinate in the current season using logistic regression models. The dependent variable was vaccination in the current season, and the independent variable was infection status of any (sub)type in the previous season. The model was stratified by previous-season vaccination status (SSection 4).
Next, we estimated the effect of repeated vaccination after adjusting for the clinical infection status of any (sub)type in the previous season using inverse-probability weighting (SSection 4).

Impact of subclinical infection​

To understand how subclinical infection history, i.e., infections not detected by the US Flu VE Network, may impact the estimated effect of repeated vaccination, we evaluated the proportion of repeat and non-repeat vaccinees who would have had to have been subclinically infected in the previous season to reproduce the estimated effect of repeated vaccination. To achieve this objective, we built a theoretical model and created a pseudo-population of repeat and non-repeat vaccinees with various infection statuses in the previous seasons (SSection 5). We illustrated how the results are consistent with both the infection block hypothesis and the hypothesis of enhanced vaccine immunogenicity post-infection.
The study obtained the institutional review board approval at participating institutions and the Centers for Disease Control and Prevention.
Go to:

4. Results​

Between the 2011–2012 and 2018–2019 seasons, individuals enrolled in the US Flu VE Network contributed 61,943 visits, of which 55,728 (90.0%) met the inclusion criteria of our analyses. Of those, 50.2% (27,986/55,728) of visits were by individuals who had received one dose of the current seasonal influenza vaccine ≥14 days prior to illness onset date (SFig 1.1). Among those vaccinated ≥14 days prior to illness onset, 73.7% (20,630/27,986) of visits were by individuals who were vaccinated at least once in the previous season, and whom we refer to as repeat vaccinees (STable 1.1).

4.1. Impact of waning vaccine protection​

On average, repeat vaccinees of similar age, sex, and comorbidities were vaccinated 1.1 (95%CI:1.0–1.2) weeks earlier than non-repeat vaccinees (Figure 1A). Thus, we adjusted for the timing of vaccination in the current season to account for waning vaccine protection within a season when estimating the impact of repeat vs. non-repeat vaccination on odds of clinical infection in the current season. Adjusting for the timing of vaccination in the current season did not notably change the marked repeat vaccination effect for A/H3N2 and had little to no effect for A/H1N1pdm09 and type B (Figure 1B; SFig 3.3 shows variation in estimates by season and site).

[IMG alt="An external file that holds a picture, illustration, etc.
Object name is nihpp-2023.03.12.23287173v2-f0001.jpg"]https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10071822/bin/nihpp-2023.03.12.23287173v2-f0001.jpg[/IMG]
Figure 1:
That repeat vaccinees vaccinate earlier in a season, which increases their susceptibility to infection due to waning vaccine protection, does not explain their higher odds of infection compared to non-repeat vaccinees.
A) Average calendar week of vaccination among repeat and non-repeat vaccinees over the study enrollment seasons. Repeat vaccinees consistently get vaccinated earlier than non-repeat vaccinees. B) Adjusted odds ratio for clinical infection among individuals vaccinated this season stratified based on whether the individuals were also vaccinated in the prior season (repeat vaccinees) or not (non-repeat vaccinees) before (yellow) and after (red) adjusting for the timing of vaccination within a season. Site- and season- specific data are shown in SFig 3.3). C) Adjusted odds ratio of clinical infection comparing individuals vaccinated 2–9, 10–13, 14–17, 18–21, and 22+ weeks before testing positive with respect to those not vaccinated in the current season. Site- and season- specific data are shown in SFig 3.1. See SSection 3 for detailed definitions of the quantities reported here.
In models accounting for the timing of vaccination as well as for whether an individual was vaccinated in the previous season, we observed that odds of infection against all three (sub)types increased within a season (Figure 1C). Compared with individuals not vaccinated in the current season (who had the highest risk of testing positive), individuals vaccinated 2–9 weeks before testing had lower OR (0.29 [95%CI:0.23–0.35]) for A/H1N1pdm09-associated illness than those vaccinated 18–21 weeks before testing (OR=0.66; 95%CI:0.56–0.78). In the 2014–2015 season, when there was a mismatch between the A/H3N2 component and the circulating strains, the odds of infection decreased with time from vaccination in Wisconsin (SFig 3.1).

4.2. Impact of clinical infection history​

We used data from MCHS to assess the impact of clinical infection history since it was the only site in the US Flu VE Network that had linked previous enrollment and testing history on participants. We found that prior clinical infections of the homologous (sub)type protected against clinical infections of type B or A/H3N2, with more recent infections conferring stronger protection (Figure 2A); those infected with type B more than 6 seasons ago had 3.60 (95%CI:1.08–11.9) times the odds of testing positive for type B in the current season than those who were clinically infected in the previous 1–2 seasons (Figure 2A). A similar trend was observed for clinical infections against A/H3N2 (OR=32.4, 95%CI:4.4–242, Figure 2A). We did not find clinical infections of a heterologous (sub)type to be protective (SFig 4.1). Due to the limited number of A/H1N1pdm09 infections during our enrollment period, we were not able to assess the impact of homologous infection with A/H1N1pdm09.
[IMG alt="An external file that holds a picture, illustration, etc.
Object name is nihpp-2023.03.12.23287173v2-f0002.jpg"]https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10071822/bin/nihpp-2023.03.12.23287173v2-f0002.jpg[/IMG]
Figure 2:
Recent clinical infections, which induce non-vaccinees to vaccinate the next season and which can protect against clinical reinfection for years, cannot explain the effect of repeated vaccination.
A) Association between recent clinical infections and odds of current-influenza-season clinical infection. More distant clinical infections of the homologous subtype are associated with a higher odds of current-season clinical infection. B) Tendency to switch vaccination status in the current influenza season after clinical infection in the previous season. Compared with individuals without confirmed infections, unvaccinated individuals who were clinically infected in the previous season were more likely to vaccinate in the current season. C) Estimated effect of repeat vaccination after adjusting for recent clinical infections. Adjusted odds ratio for clinical infection comparing repeat vaccinees with non-repeat vaccinees before (light blue) and after adjusting for clinical infection status in the previous season (dark blue) using inverse-probability weighting. Adjustment did not significantly impact the estimates. In all panels, error bars indicate 95% confidence intervals.
We also found that having confirmed influenza virus infections in previous seasons can influence people’s decision to vaccinate in the current season. Individuals unvaccinated in the previous season were more likely to vaccinate in the current season (OR=1.30, 95%CI:1.18–1.44) if they were clinically infected in the previous season than if they were not infected. However, individuals who became infected after being vaccinated in a previous season were as likely to be unvaccinated in the current season as those not infected (OR=0.96, 95%CI:0.85–1.10), with the exception of the oldest age group, which tended to vaccinate again (Figure 2B).
Adjusting for clinical infection in the previous season had little influence on the estimated effect of repeated vaccination (Figure 2C). After adjustment, repeat vaccinees enrolled during the 2008–2009 season and between the 2010–2011 and the 2018–2019 seasons had 1.33 (95%CI:0.99–1.77) times the odds of testing positive for A/H1N1dpm09 than those who were only vaccinated in the current season. Accounting for clinical infection history did not significantly change the estimates for the effect of repeated vaccination against A/H3N2 (from 1.02, 95%CI:0.84–1.23 to 1.16, 95%CI:0.96–1.40 post adjustment) or against type B (from 1.18, 95%CI:0.91–1.53 to 1.10, 95%CI:0.85–1.41 post adjustment). Excluding the 194 individuals who presented with acute respiratory illness but refused enrollment in the previous season did not significantly change the results (SFig 4.2). Not adjusting for waning vaccine protection in the weighted outcome model yielded similar results (SFig 4.3, SSection 4).

4.3. Impact of clinical and subclinical infection history​

4.3.1. Infection block hypothesis​

In the previous section we estimated that repeat vaccinees had a 10% increase (OR~1.1) in the odds of current seasonal infection, an effect that could be partially mediated by clinical infection in the prior season, a version of the infection block hypothesis. In this section, we use a theoretical model to explore the degree to which subclinical infection – which would not be observed in any of the data sets we consider – could fully explain the observed repeat vaccination effect. To do so, we evaluated the proportion of repeat and non-repeat vaccinees who would have had to have been subclinically infected in previous seasons to reproduce the elevated odds of clinical infection among repeat vaccinees observed in the US Flu VE Network, assuming that the subclinical infection-block hypothesis was the only explanation for the observed elevated risk. These models varied rates of subclinical infections in repeat- and non-repeat vaccinees and how much subclinical infection protected against future clinical infection (30%, 50%, and 70% reduction in clinical infection risk). We initially assumed the clinical attack rate among vaccinees in a season is 1%, that the current-season clinical attack rate among current-season vaccinees who were not infected in the previous season is 1.5%, and that prior-season clinical infections protect perfectly against current-season infections.
To produce the estimated effect of repeated vaccination (i.e., OR for clinical infection comparing repeat with non-repeat vaccinees against A/H3N2 or type B of 1.1) in the US Flu VE Network, non-repeat vaccinees would have to be subclinically infected in the prior season at a substantially higher rate than repeat vaccinees (Figure 3; SFig 5.1). For example, if subclinical infection reduces the probability of next-season clinical infection by 70% (the dark green curve in Figure 3A), ~5% of repeat vaccinees and ~15% of non-repeat vaccinees would have to have been subclinically infected in the prior season to observe the estimated effect of repeated vaccination in the US Flu VE Network.

 

[k1976]

View attachment 193089

Wow see many successful talented people around....really place where satik people want to be de woh

 

[k1976]

The stupid barricades and entrance closures they set up in such events makes the congestion worse. Because to get from Point A to Point B, people have to make meandering detours and inevitably mix with other groups of people.

Akan datang: CNY eve at the Chinatown area.

Lai Pak Jiam, Gan Gan Pak, Gian Gian Pak

 

[Sustanon]

Lai Pak Jiam, Gan Gan Pak, Gian Gian Pak

Meh Meh Pak Ji Ki Lan Jiao....... lol....

 

[laksaboy]

Lai Pak Jiam, Gan Gan Pak, Gian Gian Pak

New Year celebration is for piak, not for pak. But if your lanjiao is small like needle then it feels about the same.