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Background. Although vaccination with trivalent inactivated influenza vaccine (TIV) is recommended for all pregnant women, no vaccine effectiveness (VE) studies of TIV in pregnant women have assessed laboratory-confirmed influenza outcomes. Methods. We conducted a case-control study over 2 influenza seasons (2010–2011 and 2011–2012) among Kaiser Permanente health plan members in 2 metropolitan areas in California and Oregon. We compared the proportion vaccinated among 100 influenza cases (confirmed by reverse transcription polymerase chain reaction) with the proportions vaccinated among 192 controls with acute respiratory illness (ARI) who tested negative for influenza and 200 controls without ARI (matched by season, site, and trimester). Results. Among influenza cases, 42% were vaccinated during the study season compared to 58% and 63% vaccinated among influenza-negative controls and matched ARI-negative controls, respectively. The adjusted VE of the current season vaccine against influenza A and B was 44% (95% confidence interval [CI], 5%–67%) using the influenza-negative controls and 53% (95% CI, 24%–72%) using the ARI-negative controls. Receipt of the prior season's vaccine, however, had an effect similar to receipt of the current season's vaccine. As such, vaccination in either or both seasons had statistically similar adjusted VE using influenza-negative controls (VE point estimates range = 51%–76%) and ARI-negative controls (48%–76%). Conclusions. Influenza vaccination reduced the risk of ARI associated with laboratory-confirmed influenza among pregnant women by about one-half, similar to VE observed among all adults during these seasons.

COVID-19 vaccines are recommended during pregnancy to prevent severe maternal morbidity and adverse birth outcomes; however, vaccination coverage among pregnant women has been low (1). Concerns among pregnant women regarding vaccine safety are a persistent barrier to vaccine acceptance during pregnancy. Previous studies of maternal COVID-19 vaccination and birth outcomes have been limited by small sample size (2) or lack of an unvaccinated comparison group (3). In this retrospective cohort study of live births from eight Vaccine Safety Datalink (VSD) health care organizations, risks for preterm birth (<37 weeks' gestation) and small-for-gestational-age (SGA) at birth (birthweight <10th percentile for gestational age) after COVID-19 vaccination (receipt of ≥1 COVID-19 vaccine doses) during pregnancy were evaluated. Risks for preterm and SGA at birth among vaccinated and unvaccinated pregnant women were compared, accounting for time-dependent vaccine exposures and propensity to be vaccinated. Single-gestation pregnancies with estimated start or last menstrual period during May 17-October 24, 2020, were eligible for inclusion. Among 46,079 pregnant women with live births and gestational age available, 10,064 (21.8%) received ≥1 COVID-19 vaccine doses during pregnancy and during December 15, 2020-July 22, 2021; nearly all (9,892; 98.3%) were vaccinated during the second or third trimester. COVID-19 vaccination during pregnancy was not associated with preterm birth (adjusted hazard ratio [aHR] = 0.91; 95% CI = 0.82-1.01). Among 40,627 live births with birthweight available, COVID-19 vaccination in pregnancy was not associated with SGA at birth (aHR = 0.95; 95% CI = 0.87-1.03). Results consistently showed no increased risk when stratified by mRNA COVID-19 vaccine dose, or by second or third trimester vaccination, compared with risk among unvaccinated pregnant women. Because of the small number of first-trimester exposures, aHRs for first-trimester vaccination could not be calculated. These data add to the evidence supporting the safety of COVID-19 vaccination during pregnancy. To reduce the risk for severe COVID-19-associated illness, CDC recommends COVID-19 vaccination for women who are pregnant, recently pregnant (including those who are lactating), who are trying to become pregnant now, or who might become pregnant in the future (4).

Children may receive measles-mumps-rubella (MMR) and varicella (VAR) vaccines separately or as measles-mumps-rubella-varicella (MMRV). We examined whether pediatric herpes zoster (HZ) incidence varied by pattern of varicella vaccine administration. In six integrated health systems, we examined HZ incidence among children turning 12 months old during 2003–2008. All received varicella and MMR vaccines on recommended schedules. Cases were identified through 2014 using ICD-9 codes. Incidence was examined by number of varicella vaccine doses and same-day MMR. Among 199,797 children, overall HZ incidence was 18.6/100,000 person-years in the first-dose MMR + VAR group, 17.9/100,000 person-years in the MMRV group, and 7.5/100,000 person-years in the VAR-alone group. HZ incidence was lower following the second dose than before the second dose in all first-dose groups. HZ incidence was not meaningfully different between the MMRV and MMR + VAR first-dose groups. Overall and within first-dose groups, HZ incidence was lower among children receiving two varicella vaccine doses.

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.

Studies of influenza vaccine effectiveness in schools have assessed all-cause absenteeism rather than laboratory-confirmed influenza. We conducted an observational pilot study to identify absences due to respiratory illness and laboratory-confirmed influenza in schools with and without school-based vaccination. A local public health agency initiated school-based influenza vaccination in two Wisconsin elementary schools during October 2010 (exposed schools); two nearby schools served as a comparison group (non-exposed schools). Absences due to fever or cough illness were monitored for 12 weeks. During the 4 weeks of peak influenza activity, parents of absent children with fever/cough illness were contacted and offered influenza testing. Parental consent for sharing absenteeism data was obtained for 937 (57%) of 1,640 students. Fifty-two percent and 28%, respectively, of all students in exposed and non-exposed schools were vaccinated. Absences due to fever or cough illness were significantly lower in the exposed schools during seven of 12 surveillance weeks. Twenty-seven percent of students at exposed schools and 39% at unexposed schools had one or more days of absence due to fever/cough illness (p<0.0001). There was no significant difference in the proportion of students absent for other reasons (p = 0.23). During the 4 week period of influenza testing, respiratory samples were obtained for 68 (42%) of 163 episodes of absence due to fever or cough illness. Influenza was detected in 6 students; 3 attended exposed schools. Detection of laboratory-confirmed influenza in schools was challenging due to multiple consent requirements, difficulty obtaining samples from absent children, and a mild influenza season. School-based influenza vaccination was associated with reduced absenteeism due to fever or cough illness, but not absenteeism for other reasons. Although nonspecific, absence due to fever or cough illness may be a useful surrogate endpoint in school-based studies if identification of laboratory confirmed influenza is not feasible.

Electronic health record (EHR) data provide a unique opportunity to study the epidemiology of COVID-19, clinical outcomes of the infection, comparative effectiveness of therapies, and vaccine effectiveness but require a well-defined computable phenotype of COVID-19-like illness (CLI). The objective of this study was to evaluate the performance of pathogen-specific and other acute respiratory illness (ARI) International Statistical Classification of Diseases-9 and -10 codes in identifying COVID-19 cases in emergency department (ED) or urgent care (UC) and inpatient settings. We conducted a retrospective observational cohort study using EHR, claims, and laboratory information system data of ED or UC and inpatient encounters from 4 health systems in the United States. Patients who were aged ≥18 years, had an ED or UC or inpatient encounter for an ARI, and underwent a SARS-CoV-2 polymerase chain reaction test between March 1, 2020, and March 31, 2021, were included. We evaluated various CLI definitions using combinations of International Statistical Classification of Diseases-10 codes as follows: COVID-19-specific codes; CLI definition used in VISION network studies; ARI signs, symptoms, and diagnosis codes only; signs and symptoms of ARI only; and random forest model definitions. We evaluated the sensitivity, specificity, positive predictive value, and negative predictive value of each CLI definition using a positive SARS-CoV-2 polymerase chain reaction test as the reference standard. We evaluated the performance of each CLI definition for distinct hospitalization and ED or UC cohorts. Among 90,952 hospitalizations and 137,067 ED or UC visits, 5627 (6.19%) and 9866 (7.20%) were positive for SARS-CoV-2, respectively. COVID-19-specific codes had high sensitivity (91.6%) and specificity (99.6%) in identifying patients with SARS-CoV-2 positivity among hospitalized patients. The VISION CLI definition maintained high sensitivity (95.8%) but lowered specificity (45.5%). By contrast, signs and symptoms of ARI had low sensitivity and positive predictive value (28.9% and 11.8%, respectively) but higher specificity and negative predictive value (85.3% and 94.7%, respectively). ARI diagnoses, signs, and symptoms alone had low predictive performance. All CLI definitions had lower sensitivity for ED or UC encounters. Random forest approaches identified distinct CLI definitions with high performance for hospital encounters and moderate performance for ED or UC encounters. COVID-19-specific codes have high sensitivity and specificity in identifying adults with positive SARS-CoV-2 test results. Separate combinations of COVID-19-specific codes and ARI codes enhance the utility of CLI definitions in studies using EHR data in hospital and ED or UC settings.

Before the development of COVID-19 vaccines, researchers in the UK developed QCOVID, a population-based prediction model that estimates the risk of hospitalisation with COVID-19.5 After the mass roll-out of COVID-19 vaccines, additional research determined that older age, immunosuppression, and specific underlying chronic conditions were associated with severe outcomes (ie, hospitalisation and death) resulting from COVID-19 breakthrough infections among those with the primary vaccine series.6,7 Investigations of risk factors for severe outcomes in boosted populations, however, are sparse. Characteristics identified as the greatest risk factors for severe COVID-19 outcomes included the presence of five or more comorbidities (≥5 comorbidities vs none; adjusted rate ratio 9·51 [95% CI 9·07–9·97]), immunosuppressed status (yes vs no; 5·80 [5·53–6·09]), history of neurological disorders (yes vs no; 5·30 [4·90–5·74]), presence of chronic kidney disease (chronic kidney disease stage 5 vs no; 3·71 [2·90–4·74]), and older age (aged ≥80 years vs 18–49 years; 3·60 [3·45–3·75]). The authorisation from the US Food and Drug Administration in August, 2022, and subsequent recommendation from the US Centers for Disease Control and Prevention for the use of bivalent COVID-19 vaccines is a laudable first step,9 as early research shows that these new COVID-19 vaccines induce relatively higher and broad-spectrum cross-neutralising activities compared with the original COVID-19 vaccines.10 Where supply of bivalent vaccine is limited, roll-out should be targeted to groups identified as at high risk of severe COVID-19 outcomes, especially older age groups and those with multimorbidity.

AbstractObjectiveTo estimate the effectiveness of mRNA vaccines against moderate and severe covid-19 in adults by time since second, third, or fourth doses, and by age and immunocompromised status.DesignTest negative case-control study.SettingHospitals, emergency departments, and urgent care clinics in 10 US states, 17 January 2021 to 12 July 2022.Participants893 461 adults (≥18 years) admitted to one of 261 hospitals or to one of 272 emergency department or 119 urgent care centers for covid-like illness tested for SARS-CoV-2.Main outcome measuresThe main outcome was waning of vaccine effectiveness with BNT162b2 (Pfizer-BioNTech) or mRNA-1273 (Moderna) vaccine during the omicron and delta periods, and the period before delta was dominant using logistic regression conditioned on calendar week and geographic area while adjusting for age, race, ethnicity, local virus circulation, immunocompromised status, and likelihood of being vaccinated.Results45 903 people admitted to hospital with covid-19 (cases) were compared with 213 103 people with covid-like illness who tested negative for SARS-CoV-2 (controls), and 103 287 people admitted to emergency department or urgent care with covid-19 (cases) were compared with 531 168 people with covid-like illness who tested negative for SARS-CoV-2. In the omicron period, vaccine effectiveness against covid-19 requiring admission to hospital was 89% (95% confidence interval 88% to 90%) within two months after dose 3 but waned to 66% (63% to 68%) by four to five months. Vaccine effectiveness of three doses against emergency department or urgent care visits was 83% (82% to 84%) initially but waned to 46% (44% to 49%) by four to five months. Waning was evident in all subgroups, including young adults and individuals who were not immunocompromised; although waning was morein people who were immunocompromised. Vaccine effectiveness increased among most groups after a fourth dose in whom this booster was recommended.ConclusionsEffectiveness of mRNA vaccines against moderate and severe covid-19 waned with time after vaccination. The findings support recommendations for a booster dose after a primary series and consideration of additional booster doses.