![]() Moreover, the estimation of vaccine impact on less specific endpoints, such as all-cause pneumonia, is more challenging because when multiple pathogens can contribute to the outcome, the reduction in outcome due to the change in one pathogen (the one that is targeted by the vaccine) would be much smaller 13, 23, 24 and therefore can be easily missed even if there is a true effect. Since statistical models rely on observational data such as the incidence rates of pneumococcal diseases before and after the introduction of PCVs, they are prone to sources of confounding (such as changes in surveillance system, reporting behavior and the demographics of the population), as acknowledged by previous studies 16, 18, 19, 22. Hence, statistical models are routinely employed to emulate the counterfactual disease burden in a hypothetical unvaccinated version of the population 16– 22. RCTs may substantially underestimate vaccine impact on the population level, because vaccinating infants with PCV protects not only the vaccinated but also unvaccinated children and adults against invasive pneumococcal diseases (IPD) and pneumonia 14– 16.Īddressing the limitation of RCTs in vaccine impact estimation means finding a suitable unvaccinated comparison population, which is difficult, if not impossible, to identify. The efficacy measured in RCTs is different from the actual vaccine impact on a population level – that is, the reduction of disease burden in a population consisting of vaccinated and unvaccinated individuals in comparison with an otherwise similar but universally unvaccinated population 13 – after PCV introduction. invasive diseases and, to a lesser extent, pneumonia 12) by comparing vaccinated groups to unvaccinated groups. Randomized controlled trials (RCTs) demonstrated the efficacy of PCVs against diseases (e.g. Unlike previous anti-pneumococcal vaccines that merely reduced the risk of disease 9, PCVs also protect against carriage of vaccine serotypes and can therefore contribute to herd immunity 10, 11. Following the widespread adoption of a 7-valent PCV (PCV7) into national childhood immunization programs, PCVs of higher valency – PCV10 and PCV13 – have been introduced 5, 6 while the third generation PCVs with even higher valency – PCV15 and PCV20 – are recently licenced 7, 8. In 2019, the GBD study also identified lower respiratory infections including pneumonia to be the leading contributor to disability-adjusted life years (DALY) among children and the elderly globally 3.Īnti-pneumococcal vaccines were developed to combat pneumococcal infections and the most widely used ones are pneumococcal conjugate vaccines (PCVs), in which several types of the pneumococcus’ capsular polysaccharide are conjugated to carrier proteins to elicit immunity against a subset among around 100 serotypes of pneumococcus 4. The Global Burden of Disease (GBD) study found that pneumococcal pneumonia was the most common cause of lower respiratory infection morbidity and mortality worldwide, causing 1 200 000 deaths in 2016 2. Although it typically colonizes the human nasopharynx asymptomatically, it can disseminate to cause a diverse array of diseases that ranges from mild (such as sinusitis and otitis media) to more severe infections (such as pneumonia) and invasive diseases (such as meningitis and septicemia) 1. The bacterium Streptococcus pneumoniae (the pneumococcus) poses a substantial health burden globally. The LASSO method is accurate, easily implementable, and can be applied to study the impact of PCVs and of other vaccines. ![]() We then applied LASSO to real-world data and found that it yielded estimates of vaccine impact similar to SC. We found that LASSO achieved accurate and precise estimation, even in complex simulation scenarios where the association between outcome and all control variables was non-causal. We first used a simulation study to test the performance of LASSO regression and established methods including the synthetic control (SC) approach. Here we present a new approach to estimate PCVs’ impact – using LASSO regression to predict the counterfactual outcome for vaccine impact inference. It is challenging to estimate their population-level impact due to the lack of a perfect control population and the subtleness of signals when the endpoint – like all-cause pneumonia – is non-specific. The pneumococcal conjugate vaccines (PCVs) protect against diseases caused by Streptococcus pneumoniae, such as meningitis, bacteremia, and pneumonia.
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