ArticleVaccine2017
Challenges to estimating vaccine impact using hospitalization data.
Article in Vaccine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- Article
- Using LASSO Regression to Estimate the Population-Level Impact of Pneumococcal Conjugate Vaccines.American journal of epidemiology · 2023Article
- Article
- Spatio-temporal impact of self-financed rotavirus vaccination on rotavirus and acute gastroenteritis hospitalisations in the Valencia region, Spain.BMC infectious diseases · 2020Article
- Childhood pneumonia in low-and-middle-income countries: An update.Paediatric respiratory reviews · 2019Review
- Hospital and economic burden of influenza-like illness and lower respiratory tract infection in adults ≥50 years-old.BMC health services research · 2019Article
- Challenges in Estimating the Impact of Vaccination with Sparse Data.Epidemiology (Cambridge, Mass.) · 2019Article
- Elucidating the impact of the pneumococcal conjugate vaccine programme on pneumonia, sepsis and otitis media hospital admissions in England using a composite control.BMC medicine · 2018Article
- Rotavirus vaccination takes seasonal signature of childhood diarrhea back to pre-sanitation era in Brazil.The Journal of infection · 2018Article
- Impact of Pneumococcal Conjugate Vaccines on Pneumonia Hospitalizations in High- and Low-Income Subpopulations in Brazil.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2017Article
- Hospitalisation for lower respiratory tract infection in children in the province of Quebec, Canada, before and during the pneumococcal conjugate vaccine era.Epidemiology and infection · 2017Article
- Impact of PCV10 on pediatric pneumococcal disease burden in Brazil: time for new recommendations?Jornal de pediatriaReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Because the real-world impact of new vaccines cannot be known before they are implemented in national programs, post-implementation studies at the population level are critical. Studies based on analysis of hospitalization rates of vaccine-preventable outcomes are typically used for this purpose. However, estimates of vaccine impact based on hospitalization data are particularly prone to confounding, as hospitalization rates are tightly linked to changes in the quality, access and use of the healthcare system, which often occur simultaneously with introduction of new vaccines. Here we illustrate how changes in healthcare delivery coincident with vaccine introduction can influence estimates of vaccine impact, using as an example reductions in infant pneumonia hospitalizations after introduction of the 10-valent pneumococcal conjugate vaccine (PCV10) in Brazil. To this end, we explore the effect of changes in several metrics of quality and access to public healthcare on trends in hospitalization rates before (2008-09) and after (2011-12) PCV10 introduction in 2010. Changes in infant pneumonia hospitalization rates following vaccine introduction were significantly associated with concomitant changes in hospital capacity and the fraction of the population using public hospitals. Importantly, reduction of pneumonia hospitalization rates after PCV10 were also associated with the expansion of outpatient services in several Brazilian states, falling more sharply where primary care coverage and the number of health units offering basic and emergency care increased more. We show that adjustments for unrelated (non-vaccine) trends commonly employed by impact studies, such as use of single control outcomes, are not always sufficient for accurate impact assessment. We discuss several ways to identify and overcome such biases, including sensitivity analyses using different denominators to calculate hospitalizations rates and methods that track changes in the outpatient setting. Employing these practices can improve the accuracy of vaccine impact estimates, particularly in evolving healthcare settings typical of low- and middle-income countries.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.