Roles for Pathogen Interference in Influenza Vaccination, with Implications to Vaccine Effectiveness (VE) and Attribution of Influenza Deaths

Abstract

Pathogen interference is the ability of one pathogen to alter the course and clinical outcomes of infection by another. With up to 3000 species of human pathogens the potential combinations are vast. These combinations operate within further immune complexity induced by infection with multiple persistent pathogens, and by the role which the human microbiome plays in maintaining health, immune function, and resistance to infection. All the above are further complicated by malnutrition in children and the elderly. Influenza vaccination offers a measure of protection for elderly individuals subsequently infected with influenza. However, all vaccines induce both specific and non-specific effects. The specific effects involve stimulation of humoral and cellular immunity, while the nonspecific effects are far more nuanced including changes in gene expression patterns and production of small RNAs which contribute to pathogen interference. Little is known about the outcomes of vaccinated elderly not subsequently infected with influenza but infected with multiple other non-influenza winter pathogens. In this review we propose that in certain years the specific antigen mix in the seasonal influenza vaccine inadvertently increases the risk of infection from other non-influenza pathogens. The possibility that vaccination could upset the pathogen balance, and that the timing of vaccination relative to the pathogen balance was critical to success, was proposed in 2010 but was seemingly ignored. Persons vaccinated early in the winter are more likely to experience higher pathogen interference. Implications to the estimation of vaccine effectiveness and influenza deaths are discussed.

Keywords: age; influenza; influenza-like illness; pathogen burden; pathogen interference; persistent pathogens; spatiotemporal variability; vaccination; vaccination coverage; vaccine effectiveness; virus interference.

Figure A1

The age-specificity of population-adjusted male deaths in 2018 versus 2017 (high mortality) and 2004 versus 2003 (low mortality). Data sources as per Figure 6.

Figure A2

Cumulative proportion of influenza (ICD J10 and J11) admissions for different age groups to English NHS hospitals for various financial years, 1998/99 to 2019/20. Data are from NHS Digital, Hospital Admitted Patient Care Activity—NHS Digital (https://digital.nhs.uk/data-and-information/publications/statistical/hospital-admitted-patient-care-activity accessed on 1 September 2022).

Figure A3

Cumulative proportion of ‘caused by’ influenza deaths in England and Wales for different age groups over the period 2001 to 2016. Before 2009 coding of deaths to influenza is very low. Data are from the Office for National Statistics: Number of deaths where influenza was the underlying cause of death or was mentioned on the death certificate, by five-year age group, England and Wales, 2001 to 2016—Office for National Statistics (ons.gov.uk) (https://www.ons.gov.uk/peoplepopulationandcommunity/healthandsocialcare/causesofdeath/adhocs/007849numberofdeathswhereinfluenzawastheunderlyingcauseofdeathorwasmentionedonthedeathcertificatebyfiveyearagegroupenglandandwales2001to2016, accessed on 1 September 2022).

Figure A4

Influenza deaths in England and Wales for the years 2009 to 2016 where the death is directly due to (caused by) influenza or where influenza is mentioned in any place in the death certificate. Data are from Figure A3. Deaths have been corrected for underlying growth of +13.1 extra deaths per year for ‘caused by’ or +16.6 for influenza mentioned deaths. Underlying growth arises from increasing population and changing age profiles.

Figure 1

Percentage difference between the running/rolling 12-month total of monthly deaths and the long-term trend for three local authorities in the county of Essex, East of England, 2001 to 2021. The underlying/long-term trend was determined by a second order polynomial curve fit. The total number of deaths increases over time. Data are from the Office for National Statistics in [21,91].

Figure 2

Effect of influenza vaccination upon excess winter mortality (EWM) and age 65+ vaccinated over the period 1980/81 to 2019/20. Footnote: The slope is expressed as the percentage point change in EWM (USA equivalent) at 100% vaccination of the entire population aged 65+. Amount of available data increases over time from 30 in 1980/81 to 74 in 2013/14. From 2005/06 onward there are 69+ data points. The accuracy of the estimated slope increases with time due to higher number of data and a higher range in the proportion vaccinated, i.e., a maximum of only 12% vaccinated in 1988/89, a maximum of 22% vaccinated in 1996/97, rising to a maximum of 51% in 2013/14. The net effect shown in this figure is the average of up to four different methods. The net effect from 1980/81 to 1986/87 only uses one method. Adapted from [3].

Figure 3

Notifiable disease statutory notifications (NOIDS) in England and Wales as a percentage difference relative to the average in the pre-COVID era 2015 to 2019, calendar years 2015 to 2022 [162]. Footnote: 2022 has been estimated from 2022 up to week 31 relative to 2021 at week 31 multiplied by the 2021 annual total. Sept = septicemia.

Figure 4

No relationship between calculated VE (under the limiting assumption for no role of pathogen interference) and the effect of 100% influenza vaccination upon international all-cause excess winter mortality, from [3].

Figure 5

Longitudinal behavior of international vaccine effectiveness (VE) studies. Data come from a random search using Google Scholar covering different countries, and both interim and final year estimates [24,123,128,129,130,131,132,133,172,173,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219].

Figure 6

Effect of single-year-of-age on total deaths (population adjusted) in 2015 versus 2014 in England and Wales [216,218]. Footnote: Population data are only available for age 90+. Age 50 was chosen as the cut-off since there are more than 1 000 deaths per year beyond this point and Poisson variation is minimized. Above age 83 one standard deviation (STDEV) of Poisson variation is less than ±1%. In addition, there was a statistically significant (+7.2 STDEV) 18% increase in deaths of male infants (first year of life).

Figure 7

Role of the latitude of Brazilian states on the average amplitude of winter influenza (pneumonia + influenza) deaths, 1979–2001. Northern/Southern hemisphere latitudes all shown as a positive number. Adapted from Alonso et al. [241].