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Background. Recent studies suggest that influenza vaccination in the previous season may influence the effectiveness of current-season vaccination, but this has not been assessed in a single population over multiple years. Methods. Patients presenting with acute respiratory illness were prospectively enrolled during the 2004–2005 through 2012–2013 influenza seasons. Respiratory swabs were tested for influenza and vaccination dates obtained from a validated registry. Vaccination status was determined for the current, previous, and prior 5 seasons. Vaccine effectiveness (VE) was calculated for participants aged ≥9 years using logistic regression models with an interaction term for vaccination history. Results. There were 7315 enrollments during 8 seasons; 1056 (14%) and 650 (9%) were positive for influenza A (H3N2) and B, respectively. Vaccination during current only, previous only, or both seasons yielded similar protection against H3N2 (adjusted VE range, 31%–36%) and B (52%–66%). In the analysis using 5 years of historical vaccination data, current season VE against H3N2 was significantly higher among vaccinated individuals with no prior vaccination history (65%; 95% confidence interval [CI], 36%–80%) compared with vaccinated individuals with a frequent vaccination history (24%; 95% CI, 3%–41%; P = .01). VE against B was 75% (95% CI, 50%–87%) and 48% (95% CI, 29%–62%), respectively (P = .05). Similar findings were observed when analysis was restricted to adults 18–49 years. Conclusions. Current- and previous-season vaccination generated similar levels of protection, and vaccine-induced protection was greatest for individuals not vaccinated during the prior 5 years. Additional studies are needed to understand the long-term effects of annual vaccination.

•Waning protection against influenza A (H3N2) occurred in a 2007–2008 community study.•Patients vaccinated earlier were more likely to develop H3N2 illness.•Effects were seen in young children and older adults, but not working age adults. Recent studies have suggested that vaccine-induced protection against influenza may decline within one season. We reanalyzed data from a study of influenza vaccine effectiveness to determine if time since vaccination was an independent predictor of influenza A (H3N2). Patients with acute respiratory illness were actively recruited during the 2007–2008 season. Respiratory swabs were tested for influenza, and vaccination dates were determined by a validated immunization registry. The association between influenza RT-PCR result and vaccination interval (days) was examined using multivariable logistic regression, adjusting for calendar time, age and other confounders. There were 629 vaccinated participants, including 177 influenza A (H3N2) cases and 452 test negative controls. The mean (SD) interval from vaccination to illness onset was 101.7 (25.9) days for influenza cases and 93.0 (29.9) days for controls. There was a significant association between vaccination interval and influenza result in the main effects model. The adjusted odds ratio (aOR) for influenza was 1.12 (CI 1.01, 1.26) for every 14 day increase in the vaccination interval. Age modified the association between vaccination interval and influenza (p=0.005 for interaction). Influenza was associated with increasing vaccination interval in young children and older adults, but not in adolescents or non-elderly adults. Similar results were found when calendar week of vaccine receipt was assessed as the primary exposure variable. Identification of influenza A (H3N2) was associated with increasing time since vaccination among young children and older adults during a single influenza season.

COVID-19 vaccinations protect against severe illness and death, but associations with post-COVID conditions (PCC) are less clear. We aimed to evaluate the association between prior COVID-19 vaccination and new-onset PCC among individuals with SARS-CoV-2 infection across eight large healthcare systems in the United States. This retrospective matched cohort study used electronic health records (EHR) from patients with SARS-CoV-2 positive tests during March 2021-February 2022. Vaccinated and unvaccinated COVID-19 cases were matched on location, test date, severity of acute infection, age, and sex. Vaccination status was ascertained using EHR and integrated data on externally administered vaccines. Adjusted relative risks (RRs) were obtained from Poisson regression. PCC was defined as a new diagnosis in one of 13 PCC categories 30 days to 6 months following a positive SARS-CoV-2 test. The study included 161,531 vaccinated COVID-19 cases and 161,531 matched unvaccinated cases. Compared to unvaccinated cases, vaccinated cases had a similar or lower risk of all PCC categories except mental health disorders (RR: 1.06, 95% CI: 1.02–1.10). Vaccination was associated with ≥10% lower risk of sensory (RR: 0.90, 0.86–0.95), circulatory (RR: 0.88, 0.83–0.94), blood and hematologic (RR: 0.79, 0.71–0.89), skin and subcutaneous (RR: 0.69, 0.66–0.72), and non-specific COVID-19 related disorders (RR: 0.53, 0.51–0.56). In general, associations were stronger at younger ages but mostly persisted regardless of SARS-CoV-2 variant period, receipt of ≥3 vs. 1–2 vaccine doses, or time since vaccination. Pre-infection vaccination was associated with reduced risk of several PCC outcomes and hence may decrease the long-term consequences of COVID-19. The impact of COVID-19 vaccination on post-COVID conditions is not well understood. Here, the authors use electronic health record data from a network of eight integrated healthcare systems in the United States to compare rates of post-COVID conditions in those with and without vaccination.

In 2022, the Centers for Disease Control and Prevention updated opioid prescribing guideline to emphasize use of non-addictive pharmacotherapies or nonpharmacologic procedures in place of or as an aid in starting the lowest feasible opioid dosage. However, the impact of nonpharmacologic pain management utilization on concurrent or subsequent opioid therapy dosing remains unexplored. We described patterns of nonpharmacologic pain management utilization prior to initial dose level in patients starting long-term opioid therapy. Utilizing electronic health data, we created a nonpharmacologic pain management utilization code list and applied it in a pre-existing cohort of patients with chronic pain prescribed long-term opioid therapy (LTOT) from August 2016 through September 2021. Univariate descriptions and bivariate associations of covariates with the initial LTOT mean daily morphine milligram equivalents (MME) categories were described via counts, percentages, and chi-square or Kruskal Wallis tests as appropriate. We also conducted a secondary multivariate regression analysis among patients with at least 12 months of health plan enrollment. There were 7679 patients for analysis with initial mean daily LTOT levels spanning 1 to 90 + MME. Nonpharmacologic therapies for pain management were infrequently utilized among patients starting LTOT and were dose dependent. This novel approach to identifying and categorizing nonpharmacologic therapies may help assess their clinical effectiveness in future studies.

Background The burden of human metapneumovirus (hMPV) among community‐dwelling high‐risk adults is understudied. We calculate the cumulative incidence of outpatient hMPV in high‐risk adults, over five consecutive winter respiratory virus seasons (2015–2016 through 2019–2020), and describe clinical characteristics of their illnesses. Methods We conducted a retrospective analysis of data and respiratory specimens from adults ≥ 18 years old originally participating in a test‐negative study of influenza vaccine effectiveness. We included adults with ≥ 1 high‐risk condition in 2015–2016 through 2019–2020 seasons. Residual respiratory specimens were retested for hMPV using a multiplex viral panel. We calculated seasonal incidence using Poisson regression and population weighting, with the sum of observed and extrapolated hMPV cases in the study cohort divided by the number of adults with high‐risk conditions in the underlying source population. Results We tested 3601 respiratory samples; the mean (SD) age of individuals contributing samples was 53 (19) years. We identified 289 individuals (8.0%) with a respiratory sample positive for human metapneumovirus. The estimated seasonal incidence of outpatient hMPV‐associated acute respiratory illness was 95.6 (95% CI: 80.5–113.4) cases per 10,000 high‐risk adults. These values varied by season, with the highest incidence in 2015–2016 (276.8 cases per 10,000; 95% CI: 210.7–363.5) and lowest in 2016–17 (55.0 cases per 10,000; 95% CI: 31.2–97.0). Conclusions We identified substantial seasonal incidence of hMPV cases in community‐dwelling high‐risk adults in a Wisconsin population cohort.

Diagnostic testing for respiratory syncytial virus (RSV) is not routinely performed in adults. We estimated medically attended RSV seasonal incidence in a community cohort of adults ≥50 years old during four influenza seasons (2006-07 through 2009-10). Patients seeking care for acute respiratory illness (ARI) were prospectively enrolled and tested for RSV by multiplex RT-PCR. Results from enrolled patients were used to estimate projected cases among non-enrolled patients with ARI. The seasonal incidence of medically attended RSV was the sum of actual and projected cases divided by the community cohort denominator. Since each enrollment period did not include the entire RSV season, incidence estimates were adjusted to account for the statewide proportion of RSV occurring outside the study enrollment period. There were 16,088 to 17,694 adults in the cohort each season and 164 RSV cases in all 4 seasons. The overall seasonal incidence of medically attended RSV was 154 episodes (95% CI, 132-180) per 10,000 persons; the incidence was highest in 2007-08 (179) and lowest in 2006-07 (110). Among persons 50-59, 60-69, and ≥70 years old, RSV incidence was 124 (95% CI, 99-156), 147 (95% CI, 110-196), and 199 (95% CI, 153-258), respectively. The incidence of medically attended RSV increased with age and was similar during four seasons.

Background: Identifying pregnancy episodes and accurately estimating their beginning and end dates are imperative for observational maternal vaccine safety studies using electronic health record (EHR) data. Methods: We modified the Vaccine Safety Datalink (VSD) Pregnancy Episode Algorithm (PEA) to include both the International Classification of Disease, ninth revision (ICD-9 system) and ICD-10 diagnosis codes, incorporated additional gestational age data, and validated this enhanced algorithm with manual medical record review. We also developed the new Dynamic Pregnancy Algorithm (DPA) to identify pregnancy episodes in real time. Results: Around 75% of the pregnancy episodes identified by the enhanced VSD PEA were live births, 12% were spontaneous abortions (SABs), 10% were induced abortions (IABs), and 0.4% were stillbirths (SBs). Gestational age was identified for 99% of live births, 89% of SBs, 69% of SABs, and 42% of IABs. Agreement between the PEA-assigned and abstractor-identified pregnancy outcome and outcome date was 100% for live births, but was lower for pregnancy losses. When gestational age was available in the medical record, the agreement was higher for live births (97%), but lower for pregnancy losses (75%). The DPA demonstrated strong concordance with the PEA and identified pregnancy episodes ⩾6 months prior to the outcome date for 89% of live births. Conclusion: The enhanced VSD PEA is a useful tool for identifying pregnancy episodes in EHR databases. The DPA improves the timeliness of pregnancy identification and can be used for near real-time maternal vaccine safety studies. Plain Language Summary Improving identification of pregnancies in the Vaccine Safety Datalink electronic medical record databases to allow for better and faster monitoring of vaccination safety during pregnancy Introduction: It is important to monitor of the safety of vaccines after they have been approved and licensed by the Food and Drug Administration, especially among women vaccinated during pregnancy. The Vaccine Safety Datalink (VSD) monitors vaccine safety through observational studies within large databases of electronic medical records. Since 2012, VSD researchers have used an algorithm called the Pregnancy Episode Algorithm (PEA) to identify the medical records of women who have been pregnant. Researchers then use these medical records to study whether receiving a particular vaccine is linked to any negative outcomes for the woman or her child. Methods: The goal of this study was to update and enhance the PEA to include the full set of medical record diagnostic codes [both from the older International Classification of Disease, ninth revision (ICD-9 system) and the newer ICD-10 system] and to incorporate additional sources of data about gestational age. To ensure the validity of the PEA following these enhancements, we manually reviewed medical records and compared the results with the algorithm. We also developed a new algorithm, the Dynamic Pregnancy Algorithm (DPA), to identify women earlier in pregnancy, allowing us to conduct more timely vaccine safety assessments. Results: The new version of the PEA identified 2,485,410 pregnancies in the VSD database. The enhanced algorithm more precisely estimated the beginning of pregnancies, especially those that did not result in live births, due to the new sources of gestational age data. Conclusion: Our new algorithm, the DPA, was successful at identifying pregnancies earlier in gestation than the PEA. The enhanced PEA and the new DPA will allow us to better evaluate the safety of current and future vaccinations administered during or around the time of pregnancy.

•Spontaneous abortion was not associated with influenza vaccination.•No association seen regardless of whether women were vaccinated in previous season.•No association in any season or any risk window.•Findings lend support to recommendations for influenza vaccination during pregnancy. A recent study reported an association between inactivated influenza vaccine (IIV) and spontaneous abortion (SAB), but only among women who had also been vaccinated in the previous influenza season. We sought to estimate the association between IIV administered in three recent influenza seasons and SAB among women who were and were not vaccinated in the previous influenza season. We conducted a case-control study over three influenza seasons (2012–13, 2013–14, 2014–15) in the Vaccine Safety Datalink (VSD). Cases (women with SAB) and controls (women with live births) were matched on VSD site, date of last menstrual period, age group, and influenza vaccination status in the previous influenza season. Of 1908 presumptive cases identified from the electronic record, 1236 were included in the main analysis. Administration of IIV was documented in several risk windows, including 1–28, 29–56, and >56 days before the SAB date. Among 627 matched pairs vaccinated in the previous season, no association was found between vaccination in the 1–28 day risk window and SAB (adjusted odds ratio (aOR) 0.9; 95% confidence interval (CI) 0.6–1.5). The season-specific aOR ranged from 0.5 to 1.7 with all CIs including the null value of 1.0. Similarly, no association was found among women who were not vaccinated in the previous season; the season-specific aOR in the 1–28 day risk window ranged from 0.6 to 0.7 and the 95% CI included 1.0 in each season. There was no association found between SAB and influenza vaccination in the other risk windows, or when vaccine receipt was analyzed relative to date of conception. During these seasons we found no association between IIV and SAB, including among women vaccinated in the previous season. These findings lend support to current recommendations for influenza vaccination at any time during pregnancy, including the first trimester.

•We assessed mortality risk after COVID-19 vaccination using a self-controlled case series study.•Relative incidences of 6 death outcomes with risk intervals of 14 and 28 days were obtained.•Relative incidences of non-COVID-19 and all-cause deaths for vaccinated individuals were below 1.•Relative incidences of four cardiac-related death outcomes for vaccinated individuals were below 1. Although previous studies found no-increased mortality risk after COVID-19 vaccination, residual confounding bias might have impacted the findings. Using a modified self-controlled case series (SCCS) design, we assessed the risk of non-COVID-19 mortality, all-cause mortality, and four cardiac-related death outcomes after primary series COVID-19 vaccination. We analyzed all deaths between December 14, 2020, and August 11, 2021, among individuals from eight Vaccine Safety Datalink sites. Demographic characteristics of deaths in recipients of COVID-19 vaccines and unvaccinated individuals were reported. We conducted SCCS analyses by vaccine type and death outcomes and reported relative incidences (RI). The observation period for death spanned from the dates of emergency use authorization to the end of the study period (August 11, 2021) without censoring the observation period upon death. We pre-specified a primary risk interval of 28-day and a secondary risk interval of 14-day after each vaccination dose. Adjusting for seasonality in mortality analyses is crucial because death rates vary over time. Deaths among unvaccinated individuals were included in SCCS analyses to account for seasonality by incorporating calendar month in the models. For Pfizer-BioNTech (BNT162b2), RIs of non-COVID-19 mortality, all-cause mortality, and four cardiac-related death outcomes were below 1 and 95 % confidence intervals (CIs) excluded 1 across both doses and both risk intervals. For Moderna (mRNA-1273), RI point estimates of all outcomes were below 1, although the 95 % CIs of two RI estimates included 1: cardiac-related (RI = 0.78, 95 % CI, 0.58–1.04) and non-COVID-19 cardiac-related mortality (RI = 0.80, 95 % CI, 0.60–1.08) 14 days after the second dose in individuals without pre-existing cancer and heart disease. For Janssen (Ad26.COV2.S), RIs of four cardiac-related death outcomes ranged from 0.94 to 0.98 for the 14-day risk interval, and 0.68 to 0.72 for the 28-day risk interval and 95 % CIs included 1. Using a modified SCCS design and adjusting for temporal trends, no-increased risk was found for non-COVID-19 mortality, all-cause mortality, and four cardiac-related death outcomes among recipients of the three COVID-19 vaccines used in the US.

Traditional active vaccine safety monitoring involves pre-specifying health outcomes and biologically plausible outcome-specific time windows of concern, limiting the adverse events that can be evaluated. In this study, we used tree-based scan statistics to look broadly for >60,000 possible adverse events after bivalent COVID-19 vaccination. Vaccine Safety Datalink enrollees aged ≥5 years receiving Moderna or Pfizer-BioNTech bivalent COVID-19 vaccine through November 2022 were followed for 56 days post-vaccination. Incident diagnoses in inpatient or emergency department settings were analyzed for clustering within the hierarchical ICD-10-CM diagnosis code “tree” and temporally within post-vaccination follow-up. The conditional self-controlled tree-temporal scan statistic was used, conditioning on total number of cases of each diagnosis and total number of cases of any diagnosis occurring during the scanning risk window across the entire tree. P = 0.01 was the pre-specified cut-off for statistical significance. Analysis included 352,509 doses of Moderna and 979,189 doses of Pfizer-BioNTech bivalent vaccines. After Moderna vaccination, no statistically significant clusters were found. After Pfizer-BioNTech, there were clusters of unspecified adverse events (Days 1–3, p = 0.0001–0.0007), influenza (Days 35–56, p = 0.0001), cough (Days 44–55, p = 0.0002), and COVID-19 (Days 52–56, p = 0.0004). For Pfizer-BioNTech only, we detected clusters of: (1) unspecified adverse effects, as have been observed in other vaccine studies using this method, and (2) respiratory disease toward the end of follow-up. The respiratory clusters were likely due to overlap of follow-up with the spread of respiratory syncytial virus, influenza, and COVID-19, i.e., confounding by seasonality. The untargeted nature of the method and its inherent adjustment for the many diagnoses and risk intervals evaluated are unique advantages. Limitations include susceptibility to time-varying confounding, lower statistical power for assessing risks of specific outcomes than in traditional studies targeting fewer outcomes, and the possibility of missing adverse events not strongly clustered in time or within the “tree.”