Model estimates of the burden of outpatient visits attributable to influenza in the United States

Abstract Background Although many studies have modelled the national burdens of hospitalizations and deaths due to influenza, few studies have considered the outpatient burden. To fill this gap for the United States (US), we applied traditional statistical modelling approaches to time series derived from large medical claims databases held in the private sector. Methods We accessed ICD-9-coded office visit data extracted from Truven Health Analytics’ MarketScan Commercial database covering about one third of the US population <65 years during 2001–2009, and Medicare Supplemental data covering about one fifth of US seniors 65+ during 2006–2009. We extracted weekly time series of visits due to respiratory diagnoses, otitis media (OM), and urinary tract infections (UTI), a “negative control”. We used multiple linear regression modelling to estimate age-specific influenza-related excess in office visits. Results In the <65 year age group, in the 8 pre-pandemic seasons studied and for the broadest defined respiratory outcome, the model attributed an average of ~14.5 M (Standard deviation [SD] across seasons 3.9 million) office visits to influenza (rate of 5,581/100,000 population). Of these, ~80 % of visits occurred in the 5–17 and 18–49 age group. In school children aged 5–17 year olds and adult 18–64 year age groups the majority of visits were due to influenza B, while A/H3N2 explained most visits in children <5 year olds. The model further attributed ~2.2 M OM visits (SD across seasons 790,000) annually to influenza, of which 86 % of these occurred in children <18 years; this indicates that 6.4 % of all infants <2 years and 4.9 % of all toddlers aged 2–4 years in the US have an influenza-attributable outpatient visit with an OM diagnosis. In seniors 65 years and older, our model attributed ~0.7 M (SD across seasons 351,000) respiratory visits to influenza (rate of 1,887/100,000 population). The model identified no significant excess UTI (negative control) visits in most seasons. Conclusions This is to our knowledge a first study of the outpatient burden of influenza in the US in a large database. The model estimated that 10 % of all children <18 years and 4 % of the entire population <65 years seek outpatient care for respiratory illness attributable to influenza annually. Trial registration ClinicalTrial.gov, NCT02019732

Optimizing Signal Management in a Vaccine Adverse Event Reporting System : A Proof-of-Concept with COVID-19 Vaccines Using Signs, Symptoms, and Natural Language Processing

Introduction: The Vaccine Adverse Event Reporting System (VAERS) has already been challenged by an extreme increase in the number of individual case safety reports (ICSRs) after the market introduction of coronavirus disease 2019 (COVID-19) vaccines. Evidence from scientific literature suggests that when there is an extreme increase in the number of ICSRs recorded in spontaneous reporting databases (such as the VAERS), an accompanying increase in the number of disproportionality signals (sometimes referred to as ‘statistical alerts’) generated is expected. Objectives: The objective of this study was to develop a natural language processing (NLP)-based approach to optimize signal management by excluding disproportionality signals related to listed adverse events following immunization (AEFIs). COVID-19 vaccines were used as a proof-of-concept. Methods: The VAERS was used as a data source, and the Finding Associated Concepts with Text Analysis (FACTA+) was used to extract signs and symptoms of listed AEFIs from MEDLINE for COVID-19 vaccines. Disproportionality analyses were conducted according to guidelines and recommendations provided by the US Centers for Disease Control and Prevention. By using signs and symptoms of listed AEFIs, we computed the proportion of disproportionality signals dismissed for COVID-19 vaccines using this approach. Nine NLP techniques, including Generative Pre-Trained Transformer 3.5 (GPT-3.5), were used to automatically retrieve Medical Dictionary for Regulatory Activities Preferred Terms (MedDRA PTs) from signs and symptoms extracted from FACTA+. Results: Overall, 17% of disproportionality signals for COVID-19 vaccines were dismissed as they reported signs and symptoms of listed AEFIs. Eight of nine NLP techniques used to automatically retrieve MedDRA PTs from signs and symptoms extracted from FACTA+ showed suboptimal performance. GPT-3.5 achieved an accuracy of 78% in correctly assigning MedDRA PTs. Conclusion: Our approach reduced the need for manual exclusion of disproportionality signals related to listed AEFIs and may lead to better optimization of time and resources in signal management. © 2023, The Author(s)

Additional file 1: Table S1. of Estimates of hospitalization attributable to influenza and RSV in the US during 1997–2009, by age and risk status

Seasonal burden of hospitalization attributable to influenza and RSV by season in the US, 1997–2009 (respiratory broad outcome, any mention). 1Annual mean rate per 100,000 population; *Data included up to 31st March 2009; CI: confidential interval. Table S2. Number of hospitalizations attributable to influenza and RSV according to risk status and age in the US, 1997–2009 (respiratory broad outcome, any mention). SD: standard deviation; RSV: respiratory syncytial virus. (DOCX 42 kb

Risk of solid organ transplant rejection following vaccination with seasonal trivalent inactivated influenza vaccines in England: A self-controlled case-series

AbstractBackgroundAnnual seasonal influenza vaccination is recommended for transplant recipients. No formal pharmacoepidemiology study has been published on the association between solid organ transplant (SOT) rejection and vaccination with seasonal trivalent inactivated influenza vaccines (TIIVs).MethodsThe risk of SOT (liver, kidney, lung, heart or pancreas) rejection after TIIV vaccination was assessed using a self-controlled case-series method (NCT01715792). SOT recipients in England with transplant rejection were selected from the Clinical Practice Research Datalink and linked Hospital Episode Statistics inpatient data. The study period (September 2006 to August 2009) encompassed three consecutive influenza seasons. We calculated the relative incidence (RI) of SOT rejection between the 30- and 60-day post-vaccination risk periods and the control periods (any follow-up period excluding risk periods), using a Poisson regression model.ResultsIn seasons 2006/07, 2007/08, 2008/09 and pooled seasons, 132, 136, 168 and 375 subjects, respectively, experienced at least one transplant rejection; approximately half (45%–51%) of these subjects had received a TIIV. For season 2006/07, the RI of rejection of any organ, adjusted for time since transplantation, was 0.74 (95% CI: 0.24–2.28) and 0.58 (95% CI: 0.24–1.38) during the 30-day and 60-day risk periods, respectively. Corresponding RIs for season 2007/08 were 1.21 (95% CI: 0.55–2.64) and 1.31 (95% CI: 0.69–2.48); for season 2008/09, 0.99 (95% CI: 0.43–2.28) and 0.64 (95% CI: 0.31–1.33); and for pooled seasons 1.01 (95% CI: 0.58–1.76) and 0.88 (95% CI: 0.56–1.38). The results of a separate analysis of kidney rejections and analyses that took into account additional potential confounders were consistent with those of the main analyses, with 95% CIs including 1 and upper limits below 3.ConclusionThis study provides reassuring evidence of the safety profile of TIIVs in SOT recipients, thus supporting current recommendations to vaccinate this risk group annually

Additional file 1: Table S1. of Modelling estimates of age-specific influenza-related hospitalisation and mortality in the United Kingdom

Outcomes: hospitalisations (HES) and deaths (ONS). Table S2. Definition of risk factors; any mention of any of these codes placed the patient in the “high-risk” category. Table S3. Mean number of other hospitalisations due to non-respiratory diagnoses attributable to influenza in the United Kingdom. Table S4. Mean number of deaths due to respiratory diagnoses attributable to influenza in the United Kingdom. (DOCX 85 kb