Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/36883
Title: Pneumococcal vaccination prevented severe LRTIs in adults: a causal inference framework applied in registry data
Authors: Mamouris, Pavlos
Henrard, Severine
MOLENBERGHS, Geert 
Verhaegen, Jan
Lin , Guohao
Vaes , Bert
Issue Date: 2022
Publisher: ELSEVIER SCIENCE INC
Source: JOURNAL OF CLINICAL EPIDEMIOLOGY, 143 , p. 118 -127
Abstract: Objectives: We estimated the effect of pneumococcal vaccination (PV) on acute lower respiratory tract infections (LRTIs) in various age and risk groups using different methods within a causal inference methodological framework. Study design and setting: We used data from a general practitioners' morbidity registry for the year 2019. Both traditional statistical methods (regression-based and propensity score methods) and machine learning techniques were deployed. Multiple imputation was used to account for missing data. Relative risks (RRs) with 95% confidence intervals were estimated. Sensitivity analyses were performed to account for the severity of LRTIs and differences in vaccination registration. Results: All methods showed a standardized mean difference below 0.1 for each covariate. No method was found to be superior to another. PV (combination of conjugate and polysaccharide vaccine) had an overall protective effect for severe LRTIs. PV was protective in different age and risk groups, especially in people aged 50-84 years with an intermediate risk group. Conclusion: Using several techniques, PV was found to prevent severe LRTIs and confirmed the recommendations of the Belgian Superior Health Council. (c) 2021 Elsevier Inc. All rights reserved.
Notes: Mamouris, P (corresponding author), Katholieke Univ Leuven, Dept Publ Hlth & Primary Care, Leuven, Belgium.
pavlos.mamouris@kuleuven.be
Keywords: Pneumococcal vaccine;Relative risk;Causal inference;Propensity score;Registry data;Machine learning
Document URI: http://hdl.handle.net/1942/36883
ISSN: 0895-4356
e-ISSN: 1878-5921
DOI: 10.1016/j.jclinepi.2021.12.008
ISI #: WOS:000740331000010
Rights: 2021 Elsevier Inc. All rights reserved.
Category: A1
Type: Journal Contribution
Validations: ecoom 2023
Appears in Collections:Research publications

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