5 research outputs found

    Maternal and neonatal data collection systems in low- and middle-income countries: Scoping review protocol

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    Background: Pregnant women and neonates represent one of the most vulnerable groups, especially in low- and middle-income countries (LMICs). A recent analysis reported that most vaccine pharmacovigilance systems in LMICs consist of spontaneous (passive) adverse event reporting. Thus, LMICs need effective active surveillance approaches, such as pregnancy registries. We intend to identify currently active maternal and neonatal data collection systems in LMICs, with the potential to inform active safety electronic surveillance for novel vaccines using standardized definitions. Methods: A scoping review will be conducted based on established methodology. Multiple databases of indexed and grey literature will be searched with a specific focus on existing electronic and paper-electronic systems in LMICs that collect continuous, prospective, and individual-level data from antenatal care, delivery, neonatal care (up to 28 days), and postpartum (up to 42 days) at the facility and community level, at the national and district level, and at large hospitals. Also, experts will be contacted to identify unpublished information on relevant data collection systems. General and specific descriptions of Health Information Systems (HIS) extracted from the different sources will be combined and duplicated HIS will be removed, producing a list of unique statements. We will present a final list of Maternal, Newborn, and Child Health systems considered flexible enough to be updated with necessary improvements to detect, assess and respond to safety concerns during the introduction of vaccines and other maternal health interventions. Selected experts will participate in an in-person consultation meeting to select up to three systems to be further explored in situ. Results and knowledge gaps will be synthesized after expert consultation.Fil: Berrueta, Mabel. Instituto de Efectividad Clínica y Sanitaria; ArgentinaFil: Bardach, Ariel Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Investigaciones en Epidemiología y Salud Pública. Instituto de Efectividad Clínica y Sanitaria. Centro de Investigaciones en Epidemiología y Salud Pública; ArgentinaFil: Ciapponi, Agustín. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Investigaciones en Epidemiología y Salud Pública. Instituto de Efectividad Clínica y Sanitaria. Centro de Investigaciones en Epidemiología y Salud Pública; ArgentinaFil: Xiong, Xu. University of Tulane; Estados UnidosFil: Stergachis, Andy. University of Washington; Estados UnidosFil: Zaraa, Sabra. University of Washington; Estados UnidosFil: Buekens, Pierre. University of Tulane; Estados UnidosFil: Absalon, Judith. No especifíca;Fil: Anderson, Steve. No especifíca;Fil: Althabe, Fernando. Instituto de Efectividad Clínica y Sanitaria; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Madhi, Shabir A.. No especifíca;Fil: McClure, Elizabeth. No especifíca;Fil: Munoz, Flor M.. No especifíca;Fil: Mwamwitwa, Kissa W.. No especifíca;Fil: Nakimuli, Annettee. No especifíca;Fil: Clark Nelson, Jennifer. No especifíca;Fil: Noguchi, Lisa. No especifíca;Fil: Panagiotakopoulos, Lakshmi. No especifíca;Fil: Sevene, Esperanca. No especifíca;Fil: Zuber, Patrick. No especifíca;Fil: Belizan, Maria. No especifíca;Fil: Bergel, Eduardo. No especifíca;Fil: Rodriguez Cairoli, Federico. No especifíca;Fil: Castellanos, Fabricio. No especifíca;Fil: Ciganda, Alvaro. No especifíca;Fil: Comande, Daniel. No especifíca;Fil: Pingray, Veronica. No especifíca

    Monitoring the safety of COVID-19 vaccines in pregnancy in the US

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    Pregnant persons are at increased risk of severe illness from COVID-19. The first COVID-19 vaccines in the U.S. were authorized for emergency use in December 2020 and pregnant persons were eligible and could get vaccinated despite scarce safety data in this population. To monitor the safety of COVID-19 vaccination during pregnancy, four surveillance systems are used by the Centers for Disease Control and Prevention (CDC). The Vaccine Adverse Event Reporting System is a national, passive system that captures reports of potential adverse events. V-safe is a novel, active system that uses text messaging and web-based surveys to provide health check-ins after vaccination; and enrolls eligible v-safe participants in the v-safe pregnancy registry. The Vaccine Safety Datalink is a collaboration between the CDC and nine integrated health care organizations which performs near-real time surveillance and traditional epidemiologic studies on pregnant vaccine recipients. The CDC is committed to timely and comprehensive monitoring of COVID-19 vaccine safety in pregnancy

    Safety of measles, mumps, and rubella vaccine in adolescents and adults in the vaccine safety Datalink

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    Background: Measles, mumps, and rubella vaccine (MMR) is routinely administered to children; however, adolescents and adults may receive MMR for various reasons. Safety studies in adolescents and adults are limited. We report on safety of MMR in this age group in the Vaccine Safety Datalink. Methods: We included adolescents (aged 9–17 years) and adults (aged ≥ 18 years) who received ≥ 1 dose of MMR from January 1, 2010–December 31, 2018. Pre-specified outcomes were identified by diagnosis codes. Clinically serious outcomes included anaphylaxis, encephalitis/myelitis, Guillain-Barré syndrome, immune thrombocytopenia, meningitis, and seizure. Non-serious outcomes were allergic reaction, arthropathy, fever, injection site reaction, lymphadenopathy, non-specific reaction, parotitis, rash, and syncope. All serious outcomes underwent medical record review. Outcome-specific incidence was calculated in pre-defined post-vaccination windows. A self-controlled risk interval design was used to determine the relative risk of each outcome in a risk window after vaccination compared to a more distal control window. Results: During the study period, 276,327 MMR doses were administered to adolescents and adults. Mean age of vaccinees was 34.8 years; 65.8 % were female; 53.2 % of doses were administered simultaneously with ≥ 1 other vaccine. Serious outcomes were rare, with incidence ≤ 6 per 100,000 doses for each outcome assessed, and none had a significant elevation in incidence during the risk window compared to the control window. Incidence of non-serious outcomes per 100,000 doses ranged from 3.4 for parotitis to 263.0 for arthropathy. Other common outcomes included injection site reaction and rash (157.0 and 112.9 per 100,000 doses, respectively). Significantly more outcomes were observed during the risk window compared to the control window for all non-serious outcomes except parotitis. Some variability was observed by sex and age group. Conclusion: Serious outcomes after MMR are rare in adolescents and adults, but vaccinees should be counseled regarding anticipated local and systemic non-serious adverse events

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

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    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
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