Effect of Pediatric Influenza Vaccination on Antibiotic Resistance, England and Wales

Vaccines against viral infections have been proposed to reduce prescribing of antibiotics and thereby help control resistant bacterial infections. However, by combining published data sources, we predict that pediatric live attenuated influenza vaccination in England and Wales will not substantially reduce antibiotic consumption or adverse health outcomes associated with antibiotic resistance.

to account for skew). Then, the probability of a GP visit given PCR-confirmed illness is taken the results of Fleming et al. (1), Meier et al. (5), and Pitman et al. (6). That is, we draw a value d from a normal distribution with mean 0.2 and standard deviation 0.05, and assume that the relative prescribing rate for under-5s is (1d) times the rate for 5-44-year-olds, while the relative prescribing rate for over-45s is (1 + d) times the rate for 5-44-year-olds.

Impact of LAIV on rates of GP consultation
We use fitted models from Baguelin et al. (7) projecting the impact of LAIV on influenza cases in different age groups, assuming either a 50% vaccine uptake (base-case estimate), 30% uptake (low estimate), or 70% uptake (high estimate).

Age-stratified rates for uncertainty analysis
We summarize the base-case and uncertainty-analysis estimates of age-stratified consultation rates, prescription rates, and overall LAIV effectiveness in the Appendix Table. Prediction of prescription rate impact on resistance-associated health burdens

Defined daily doses per prescribed antibiotic course
We assume that each prescription comprises 7 defined daily doses (DDD), as 7 days is the typical duration of antibiotic treatment for upper respiratory tract infections (8).

Main scenario
We use total primary care antibiotic consumption (ATC code J01C) for European countries for 2015 from the ECDC (9) as the predictor variable, and per-country median health burden (DALYs, cases, or deaths) attributed to each of 16 resistant strains analyzed by Cassini et al. (10) as the outcome variable, in a series of country-level linear regressions from which we separately predict the impact of reducing overall prescribing by a defined amount. For each country, we normalize each resistant-strain-specific health burden to the total number of bloodstream infections caused by the species in question before performing the regression to control for differences in the population and the per-capita incidence of infection between countries.
To estimate the total number of bloodstream infections caused by a given species in a given country, we begin by taking the maximum of the number of total tested isolates recorded by the ECDC for that country and species. Then we correct that figure according to the estimated population coverage for that country and species to the ECDC (i.e., an estimate of what fraction of the population is covered by the hospitals submitting resistance testing data to national surveillance programs which then report their data to the ECDC). For example, for S. pneumoniae infections in the United Kingdom in 2015, 1095 isolates were tested for penicillin non-susceptibility, 1077 isolates were tested for macrolide non-susceptibility, and 1060 isolates were tested for combined non-susceptibility to both penicillins and macrolides. Additionally, these isolates were reported as covering an estimated 21% of the entire population of the UK.

Alternative scenario 1
Rather than using the overall antibiotic consumption for each country as the sole predictor in the regression model, we also built a separate series of models where we used as predictors each country's consumption of tetracyclines (J01AA), extended spectrum penicillins (J01CA), β-lactamase sensitive penicillins (J01CE), and macrolides (J01FA), as these four classes comprise the majority of antibiotics prescribed for sore throat and cough (11). We assume that for a given reduction in the overall prescription rate x, there is a reduction 0.0620x in tetracycline prescribing, 0.4752x in extended spectrum penicillin prescribing, 0.2793x in βlactamase sensitive penicillin prescribing, and 0.1835x in macrolide prescribing. This predicted a smaller impact upon resistance than the main scenario (4.1) and comprises the "low-effect" statistical model for the uncertainty analysis ( Figure 2B, main text).

Alternative scenario 2
We follow the same procedure as in 4.2, but if any predictor variable is negatively correlated with a resistance related health burden (i.e., the best fitting linear model suggests that decreasing use of that antibiotic would increase resistance), we remove it from the linear regression and rerun the model, continuing this process until all predictors are positively associated with the outcome variable. If more than one variable has a negative association in a given round, all negative-association variables are removed for the next round. This predicted a larger impact upon resistance than the main scenario (4.1) and comprises the "high-effect" statistical model for the uncertainty analysis ( Figure 2B, main text).

Economic calculations
To convert between U.S. and UK healthcare expenditures, we use hospital-service price level indices for health care purchasing power parity published by the OECD (12) (see their Figure 1).

Secular trends in antibiotic prescribing rates
Data from NHS Digital show that community antibiotic use in England has decreased by 2.5% per year from 2012 to 2018 (Appendix Figure).

The impact of influenza vaccination on antibiotic use in different settings
A systematic review (13) found that in randomized trials, the direct effect of influenza vaccines on vaccinated children has ranged from a 44% reduction in antibiotic prescriptions in Italy (14) to a 6% increase in the United States (15), both over the 4-month period following vaccination. Published estimates of the impact over entire populations (all ages, vaccinated and unvaccinated, i.e., incorporating both direct and indirect protection) range from 11.3 fewer prescriptions per 1000 person-years in Ontario, Canada (16) to 3.9 fewer in South Africa and Senegal (17).