Identifying Plausible Ranges For Differential Vaccine Efficacy Across High- And Low-Income Settings: A Systematic Review, Descriptive Meta-Analysis, And Illustrative Evidence Analysis
Katama, E. N.; Gallagher, K. E.; Shah, A.; Nokes, J. D.; McAllister, D. A.
Show abstract
BackgroundRandomized clinical trials provide the highest standard of evidence about vaccine efficacy. Modelling exercises such as in evidence synthesis and health economic models where efficacy estimates are combined with other data to obtain effectiveness and cost-effectiveness estimates help inform policy decisions. The main challenge with such sensitivity analyses is in deciding on which assumptions to model. PurposeTo identify plausible ranges for differential vaccine efficacy across high- and low-income settings. Data Sources and Study SelectionMEDLINE, EMBASE, clinicaltrials.gov, and the World Health Organization International Clinical Trials Registry Platform (WHO-ICTRP) were searched for multi-site randomized clinical trials of bacterial and viral vaccines for the period of 01/01/1990 to 31/12/2020. Articles were restricted to those where at least one trial had included a low- or lower-middle-income setting, published in English, and conducted in humans. MethodsA Bayesian random-effects meta-analysis was used to estimate the difference in vaccine efficacy in high-(high or upper middle) and low-(low or lower middle) income settings. A single hierarchical model that included all trials was used so that the degree to which estimates of vaccine efficacy against different diseases influenced one another was estimated from the observed data. ResultsAcross 65 eligible trials (37 high-income, 21 low-income, and 7 both) covering 7 pathogens, only one trial reported efficacy estimates stratified by setting. Trials were similar in terms of design across settings. There was evidence of heterogeneity by vaccine target, typhoid vaccine demonstrated higher vaccine efficacy in low-income settings than in high-income settings but for all other vaccines, the point estimates indicated efficacy was lower in low-income settings; however, all credible intervals crossed the null. ConclusionsThe percentage of trials in low-income settings poorly reflects the burden of disease experienced in low-income settings. While there is evidence of lower vaccine efficacy in low-income settings relative to high-income settings, the credible intervals were very wide. Vaccine efficacy trials should report treatment effects stratified by settings.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- What is the quality of evidence informing vaccine clinical practice recommendations in Australia? 96%
- A Meta-Analysis of Influenza Vaccination Following Correspondence: Considerations for COVID-19 95%
- Comparative effectiveness of four COVID-19 vaccines, BNT162b2 mRNA, mRNA-1273, ChAdOx1 nCov-19 and NVX-CoV2373 against SARS-CoV-2 B.1.1.529 (Omicron) infection 95%
Similar papers in this journal
Similar papers in this journal
- Effectiveness of inactivated and Ad5-nCoV COVID-19 vaccines against SARS-CoV-2 Omicron BA. 2 variant infection, severe illness, and death 94%
- Potential health and economic impact of paediatric vaccination using next generation influenza vaccines in Kenya: a modelling study 94%
- Global diversity of policy, coverage, and demand of COVID-19 vaccines: a descriptive study 94%
Similar papers in this journal
- Effectiveness of Covid-19 vaccines against SARS-CoV-2 Omicron variant (B.1.1.529): A systematic review with meta-analysis and meta-regression 96%
- Effectiveness of the WHO-authorized Covid-19 Vaccines: a Rapid Review of Global Reports till June 30, 2021 95%
- COVID-19 vaccine uptake and effectiveness by time since vaccination in the Western Cape province, South Africa: An observational cohort study during 2020-2022 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.