Mortality risk after COVID-19 vaccination: A self-controlled case series study

Background 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. Methods 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. Results 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. Conclusion 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

Predictors of short-term (seven-day) cardiac outcomes after emergency department visit for syncope.

Syncope is a common reason for emergency department (ED) visits, and patients are often admitted to exclude syncope of cardiovascular origin. Population-based data on patterns and predictors of cardiac outcomes may improve decision-making. Our objective was to identify patterns and predictors of short-term cardiac outcomes in ED patients with syncope. Administrative data from an integrated health system of 11 Southern California EDs were used to identify cardiac outcomes after ED presentation for syncope from January 1, 2002, to December 31, 2005. Syncope and cause of death were identified by codes from the International Classification of Disease, Ninth Revision. Cardiac outcomes included cardiac death and hospitalization or procedure consistent with ischemic heart disease, valvular disease, or arrhythmia. Predictors of cardiac outcomes were identified through multivariate logistic regression. There were 35,330 adult subjects who accounted for 39,943 ED visits for syncope. Risk of cardiac outcome sharply decreased following the 7 days after syncope. A 7-day cardiac outcome occurred in 893 cases (3%). Positive predictors of 7-day cardiac outcomes included age > or =60 years, male gender, congestive heart failure, ischemic heart disease, cardiac arrhythmia, and valvular heart disease. Negative predictors included dementia, pacemaker, coronary revascularization, and cerebrovascular disease. There was an age-dependent relation between 7-day cardiac outcomes and arrhythmia and valvular disease, with younger patients (<60 years of age) having greater risk of an event compared to their same-age counterparts. In conclusion, ED decision-making should focus on risk of cardiac event in the first 7 days after syncope and special attention should be given to younger patients with cardiac co-morbidities

Predictors of short-term (seven-day) cardiac outcomes after emergency department visit for syncope.

Syncope is a common reason for emergency department (ED) visits, and patients are often admitted to exclude syncope of cardiovascular origin. Population-based data on patterns and predictors of cardiac outcomes may improve decision-making. Our objective was to identify patterns and predictors of short-term cardiac outcomes in ED patients with syncope. Administrative data from an integrated health system of 11 Southern California EDs were used to identify cardiac outcomes after ED presentation for syncope from January 1, 2002, to December 31, 2005. Syncope and cause of death were identified by codes from the International Classification of Disease, Ninth Revision. Cardiac outcomes included cardiac death and hospitalization or procedure consistent with ischemic heart disease, valvular disease, or arrhythmia. Predictors of cardiac outcomes were identified through multivariate logistic regression. There were 35,330 adult subjects who accounted for 39,943 ED visits for syncope. Risk of cardiac outcome sharply decreased following the 7 days after syncope. A 7-day cardiac outcome occurred in 893 cases (3%). Positive predictors of 7-day cardiac outcomes included age > or =60 years, male gender, congestive heart failure, ischemic heart disease, cardiac arrhythmia, and valvular heart disease. Negative predictors included dementia, pacemaker, coronary revascularization, and cerebrovascular disease. There was an age-dependent relation between 7-day cardiac outcomes and arrhythmia and valvular disease, with younger patients (<60 years of age) having greater risk of an event compared to their same-age counterparts. In conclusion, ED decision-making should focus on risk of cardiac event in the first 7 days after syncope and special attention should be given to younger patients with cardiac co-morbidities

Impact of the COVID-19 Pandemic on Health Care Utilization in the Vaccine Safety Datalink: Retrospective Cohort Study

BackgroundUnderstanding the long-term impact of the COVID-19 pandemic on health care utilization is important to health care organizations and policy makers for strategic planning, as well as to researchers when designing studies that use observational electronic health record data during the pandemic period. ObjectiveThis study aimed to evaluate the changes in health care utilization across all care settings among a large, diverse, and insured population in the United States during the COVID-19 pandemic. MethodsWe conducted a retrospective cohort study within 8 health care organizations participating in the Vaccine Safety Datalink Project using electronic health record data from members of all ages from January 1, 2017, to December 31, 2021. The visit rates per person-year were calculated monthly during the study period for 4 health care settings combined as well as by inpatient, emergency department (ED), outpatient, and telehealth settings, both among all members and members without COVID-19. Difference-in-difference analysis and interrupted time series analysis were performed to assess the changes in visit rates from the prepandemic period (January 2017 to February 2020) to the early pandemic period (April-December 2020) and the later pandemic period (July-December 2021), respectively. An exploratory analysis was also conducted to assess trends through June 2023 at one of the largest sites, Kaiser Permanente Southern California. ResultsThe study included more than 11 million members from 2017 to 2021. Compared with the prepandemic period, we found reductions in visit rates during the early pandemic period for all in-person care settings. During the later pandemic period, overall use reached 8.36 visits per person-year, exceeding the prepandemic level of 7.49 visits per person-year in 2019 (adjusted percent change 5.1%, 95% CI 0.6%-9.9%); inpatient and ED visits returned to prepandemic levels among all members, although they remained low at 0.095 and 0.241 visits per person-year, indicating a 7.5% and 8% decrease compared to pre-pandemic levels among members without COVID-19, respectively. Telehealth visits, which were approximately 42% of the volume of outpatient visits during the later pandemic period, were increased by 97.5% (95% CI 86.0%-109.7%) from 0.865 visits per person-year in 2019 to 2.35 visits per person-year in the later pandemic period. The trends in Kaiser Permanente Southern California were similar to those of the entire study population. Visit rates from January 2022 to June 2023 were stable and appeared to be a continuation of the use levels observed at the end of 2021. ConclusionsTelehealth services became a mainstay of the health care system during the late COVID-19 pandemic period. Inpatient and ED visits returned to prepandemic levels, although they remained low among members without evidence of COVID-19. Our findings provide valuable information for strategic resource allocation for postpandemic patient care and for designing observational studies involving the pandemic period

Vaccine Safety Datalink infrastructure enhancements for evaluating the safety of maternal vaccination

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