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Effectiveness of vaccination policies and current treatment stratification approaches for pneumonia: A regression discontinuity analysis
Safe People
University of Heidelberg
Academic Institute
Till Bärnighausen - Chief Investigator - University of HeidelbergJulia Lemp - Corresponding Applicant - University of HeidelbergBhautesh Jani - Collaborator - University of GlasgowJustine Davies - Collaborator - University of BirminghamMaike Hohberg - Collaborator - University Hospital HeidelbergManuel Hoffmann - Collaborator - University of HeidelbergMaximilian Schuessler - Collaborator - University of HeidelbergPascal Geldsetzer - Collaborator - University of HeidelbergSebastian Vollmer - Collaborator - Georg-August-Universität Göttingen
Safe Projects
CPRD15
Pneumonia is an infection that inflames the lungs and can pose mild to life-threatening symptoms. Lung infections are most serious for young children, people older than age 65, and people with comorbidities. Vaccinations for influenza and pneumococcus protect these risk groups from contracting viruses that may cause pneumonia and other life-threatening conditions. When patients contract pneumonia, doctors in the UK use the C(U)RB65 score based on a person’s level of confusion, urea levels, respiratory rate, blood pressure, and age to decide how a patient should be treated. Observational studies suggest that pneumococcal and influenza vaccination reduces hospitalisations and mortality among elderly persons, which is reflected in clinical guidelines. Nevertheless, questions remain about vaccine effectiveness given the potential for confounding in observational data. Similarly, it is debated whether the C(U)RB65 score is appropriate for guiding the treatment of pneumonia patients. Clinical trials can prove the efficacy of vaccinations and treatments, but may not fully capture effects in routine care. Moreover, the number of patients included in clinical studies is often too small to examine outcomes such as hospitalisation or mortality. Non-experimental studies can harness real-life data but cannot control for unknown factors that may influence the results. To overcome these problems, this study uses patient records and focuses on vaccination and treatment decisions based on guidelines. We focus on pneumonia, a leading cause of death in the UK. Thanks to the number of patients included in the Clinical Practice Research Datalink (CPRD) and its time horizon, we can analyse long-term outcomes.
This study uses a regression discontinuity (RD) approach to determine the effectiveness of current vaccine and treatment stratification approaches for people at risk for pneumonia. Current clinical guidelines recommend (i) both influenza and pneumococcal vaccination for patients above a certain age threshold and (ii) treatment options for patients with pneumonia above certain threshold values in the CRB65 score (calculated based on the following criteria: patients' level of confusion, blood urea nitrogen, respiratory rate, blood pressure, and age). The RD design takes advantage of this decision threshold to estimate the causal effects of (i) providing vaccination vs. not and of (ii) intensifying treatment for pneumonia vs not. Specifically, we aim to determine whether or not the primary and secondary outcomes differ among patients ages just below and just above 65 (the latter should be prioritized for influenza and pneumococcal vaccines) and among patients with pneumonia whose CRB65 scores are just below or above an arbitrary threshold (the latter should be hospitalized and should receive a more escalated antibiotic treatment regimen). Primary outcomes include hospitalizations, ICU admission, and all-cause mortality. Secondary outcomes comprise of pneumonia-related complications, serious adverse events (including, but not limited to, meningitis, sepsis, or organ failure), and pneumonia-related mortality. We will estimate “fuzzy” RD models using a local linear regression and triangular weights to avoid overfitting the data and to give more influence to observations close to the threshold. In addition, we will use a mean squared error optimal bandwidth that is empirically derived. We assess the sensitivity of the results using alternative bandwidths (e.g. bandwidths that are 50%, 75%, 125%, and 150% of the empirically derived bandwidth). The findings of this study are expected to provide novel insights into the long-term health effects and appropriateness of using age- and score-based thresholds in pneumonia prevention and treatment.
01/06/2021
Safe Data
2011 Rural-Urban Classification at LSOA level
HES Admitted Patient Care
ONS Death Registration Data
Patient Level Index of Multiple Deprivation
Safe Setting
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