Serotype-specific pneumococcal invasiveness: a global meta-analysis of paired estimates of disease incidence and carriage prevalence
Bibliographic record
Abstract
Abstract Background Serotype-specific estimates of pneumococcal invasiveness used in pneumococcal carriage transmission models to predict changes in disease incidence post-vaccination are largely derived from high-income settings. We conducted a systematic review of carriage prevalence and invasive pneumococcal disease (IPD) incidence to calculate case-carrier ratios (CCRs) in different income settings. Methods A systematic search of Medline, Embase, and Global Health databases in March 2022 identified publications on pneumococcal carriage prevalence or IPD incidence; we requested individual-level data from authors of relevant texts. Serotype-specific CCRs, calculated as IPD incidence divided by carriage prevalence, were pooled across settings using random effects meta-analyses, stratified by pre-/post-pneumococcal conjugate vaccine (PCV) introduction, country income group, age-group, sex and HIV status. Findings We identified 80 publications from 18 countries (13 upper-middle- or high-income countries (UM/HIC), 5 low/lower-middle income (L/LMIC)) reporting carriage prevalence or IPD incidence in overlapping geographical areas, time periods, and age-groups. We calculated CCRs for >70 serotypes, stratified by age group, income settings, and pre- and post-vaccine introduction. In children under five, pre-PCV CCRs for serotypes not included in the 20-valent PCV were higher in L/LMICs than UM/HICs, 152 (95% Confidence interval 103-226) versus 102 (50-209). Post-PCV CCRs for non-vaccine serotypes dropped in UM/HICs but not in L/LMICs, 19 (16-22) versus 154 (119-200) respectively. Pre-/post PCV changes varied by serotype and age-group. CCRs were lowest in 5–14-year-olds and were higher in HIV positive than HIV negative individuals. There were no differences in CCRs by sex. Interpretation Pneumococcal invasiveness varies by serotype, age-group, country income-group, HIV status and over time; however, substantial variation remained unexplained. Our CCRs represent the most representative estimates of invasiveness currently available for use in statistical or mathematical prediction models of disease incidence, where only carriage prevalence data are available. Funding The Wellcome Trust, Great Britain (098532) Panel: Research in context Evidence before this study There are three estimates of the absolute risk of invasive pneumococcal disease, given carriage, derived from data from high-income settings (two studies in the UK, and one in the USA). A fourth set of estimates have been derived from data collated by a recent review of studies that reported both carriage and IPD data in the same publication. This review and re-analysis combined data from 12 countries to report case-carrier ratios in children under-5, pre- and post-vaccine introduction. The review did not include data from IPD surveillance sites in low- and middle-income countries, nor carriage prevalence data in adults. Added value of this study We conducted an extensive systematic review to identify high quality IPD incidence estimates and a comprehensive database of carriage prevalence estimates that arise from the same country, age-group and time period as these IPD incidence estimates. We employed stringent matching criteria to only include the results of carriage surveys that were conducted in a random sample of the general population, and IPD surveillance activities that were conducted in a systematic way across a defined population. This enabled us to estimate serotype-specific pneumococcal case-carrier ratios, stratified by age group, country income group, and time period pre- or post-vaccine introduction. Implications of all the available evidence Invasive pneumococcal disease surveillance is resource intensive to establish and sustain and is therefore infeasible for most countries worldwide. Pneumococcal vaccine policy is often made on the basis of carriage data alone, or mathematical models which predict changes in disease incidence by combining changes in carriage prevalence with pre-specified case-carrier ratios. We have used all available data globally to estimate serotype-specific case-carrier ratios, which previously have been derived from data from high income settings. Both statistical and mathematical models predicting changes in disease incidence in low-income settings, can now utilise case-carrier ratios from more relevant population groups. This will be of increasing importance as policy makers attempt to make evidence-based decisions on whether to change pneumococcal vaccine product, schedule, or simply increase coverage of the existing programme.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.062 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.066 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".