Determinants of high residual post-PCV13 pneumococcal vaccine-type carriage in Blantyre, Malawi: a modelling study

Abstract

BackgroundIn November 2011, Malawi introduced the 13-valent pneumococcal conjugate vaccine (PCV13) into the routine infant schedule. Four to 7 years after introduction (2015–2018), rolling prospective nasopharyngeal carriage surveys were performed in the city of Blantyre. Carriage of Streptococcus pneumoniae vaccine serotypes (VT) remained higher than reported in high-income countries, and impact was asymmetric across age groups.MethodsA dynamic transmission model was fit to survey data using a Bayesian Markov-chain Monte Carlo approach, to obtain insights into the determinants of post-PCV13 age-specific VT carriage.ResultsAccumulation of naturally acquired immunity with age and age-specific transmission potential were both key to reproducing the observed data. VT carriage reduction peaked sequentially over time, earlier in younger and later in older age groups. Estimated vaccine efficacy (protection against carriage) was 66.87% (95% CI 50.49–82.26%), similar to previous estimates. Ten-year projected vaccine impact (VT carriage reduction) among 0–9 years old was lower than observed in other settings, at 76.23% (CI 95% 68.02–81.96%), with sensitivity analyses demonstrating this to be mainly driven by a high local force of infection.ConclusionsThere are both vaccine-related and host-related determinants of post-PCV13 pneumococcal VT transmission in Blantyre with vaccine impact determined by an age-specific, local force of infection. These findings are likely to be generalisable to other Sub-Saharan African countries in which PCV impact on carriage (and therefore herd protection) has been lower than desired, and have implications for the interpretation of post-PCV carriage studies and future vaccination programs.

High residual carriage of vaccine-serotype Streptococcus pneumoniae after introduction of pneumococcal conjugate vaccine in Malawi

“…However, these countries have also not achieved the low carriage prevalence seen in Europe and North America 2-3 years post introduction 4,18 . As presented by Lourenço et al 53 , we propose that a high FOI in settings such as Malawi limits a 3 + 0 schedule to achieving only a short duration of VT carriage control in infants. While a 2 + 1 schedule, as deployed in South Africa, may improve colonisation control, this remains unproven in other African settings.…”

“…These indirect benefits augmented by naturally acquired immunity explains, in part, the more pronounced decline in VT carriage among PCV-unvaccinated children 6-8 years old (40.5%) and HIV-infected adults on ART (41.4%), compared with younger vaccinated children 3-5 years old (16.1%; Table 2). Using a dynamic transmission model fitted to data from this population in Blantyre, Malawi, we have shown that the force of infection (FOI; the rate by which a certain age group of susceptible individuals is infected) is characterised by different transmission potentials within and between age groups 53 . This analysis suggests that the time period of the fastest FOI reduction for the 0-5 years old was between vaccine introduction and 2015 (when no carriage data were collected), which contrasted with the older age groups, for which the period of the fastest FOI reduction was predicted to be just before or during the first three surveys.…”

Childhood vaccines and antibiotic use in low- and middle-income countries

“…1). In addition, the reported extent of vaccine-driven serotype replacement in pneumococcal carriage and disease has remained lower in LMICs 35,44,45 than in high-income settings, for reasons that remain incompletely understood 46 .…”

Fundamental principles of epidemic spread highlight the immediate need for large-scale serological surveys to assess the stage of the SARS-CoV-2 epidemic

“…Model output on cumulative death counts ( ) is fitted to the reported time series of deaths Λ (see Data) using a Bayesian MCMC approach previously implemented in other modelling studies [7][8][9][10] . Model variables are summarized in probability of dying with severe disease θ Gaussian distribution G(M=0.14, SD=0.007) [1,2,11,17] proportion of population at risk of severe disease ρ Gamma distribution G1(S=5, R=5/0.01), G2(S=5, R=5/0.001) --population size N UK 66.87M, Italy 60M ---…”