Int J Med Inform

Objective:Local reactions are the most common vaccine-related adverse event. There is no specific diagnosis code for local reaction due to vaccination. Previous vaccine safety studies used non-specific diagnosis codes to identify potential local reaction cases and confirmed the cases through manual chart review. In this study, a natural language processing (NLP) algorithm was developed to identify local reaction associated with tetanus-diphtheria-acellular pertussis (Tdap) vaccine in the Vaccine Safety Datalink.Methods:Presumptive cases of local reactions were identified among members 65 11 years of age using ICD-9-CM codes in all care settings in the 1\u20136 days following a Tdap vaccination between 2012 and 2014. The clinical notes were searched for signs and symptoms consistent with local reaction. Information on the timing and the location of a sign or symptom was also extracted to help determine whether or not the sign or symptom was vaccine related. Reactions triggered by causes other than Tdap vaccination were excluded. The NLP algorithm was developed at the lead study site and validated on a stratified random sample of 500 patients from five institutions.Results:The NLP algorithm achieved an overall weighted sensitivity of 87.9%, specificity of 92.8%, positive predictive value of 82.7%, and negative predictive value of 95.1%. In addition, using data at one site, the NLP algorithm identified 3326 potential Tdap-related local reactions that were not identified through diagnosis codes.Conclusion:The NLP algorithm achieved high accuracy, and demonstrated the potential of NLP to reduce the efforts of manual chart review in vaccine safety studies.CC999999/Intramural CDC HHS/United States2020-07-01T00:00:00Z31128829PMC66456787915vault:3260

Vaccine safety publications

CDC\u2019s Immunization Safety Office monitors the safety of licensed and authorized vaccines and conducts high-quality vaccine safety research. This research is peer-reviewed and published in reputable scientific outlets.The vaccine safety articles and studies listed on this page include a full citation, a short summary, and a link to the free PMC article, when available.index.html#anchor_1639772389647CDC Publications by Vaccine Safety System -- Publications About Specific Vaccine Safety Topics -- COVID-19 Vaccine Safety Articles and Studies by Topic -- CDC Vaccine Safety Publications by Year.20221132

Pharmacoepidemiol Drug Saf

Purpose:The objective was to develop a natural language processing (NLP) algorithm to identify vaccine-related anaphylaxis from plain-text clinical notes, and to implement the algorithm at five health care systems in the Vaccine Safety Datalink.Methods:The NLP algorithm was developed using an internal NLP tool and training dataset of 311 potential anaphylaxis cases from Kaiser Permanente Southern California (KPSC). We applied the algorithm to the notes of another 731 potential cases (423 from KPSC; 308 from other sites) with relevant codes (ICD-9-CM diagnosis codes for anaphylaxis, vaccine adverse reactions, and allergic reactions; Healthcare Common Procedure Coding System codes for epinephrine administration). NLP results were compared against a reference standard of chart reviewed and adjudicated cases. The algorithm was then separately applied to the notes of 6 427 359 KPSC vaccination visits (9 402 194 vaccine doses) without relevant codes.Results:At KPSC, NLP identified 12 of 16 true vaccine-related cases and achieved a sensitivity of 75.0%, specificity of 98.5%, positive predictive value (PPV) of 66.7%, and negative predictive value of 99.0% when applied to notes of patients with relevant diagnosis codes. NLP did not identify the five true cases at other sites. When NLP was applied to the notes of KPSC patients without relevant codes, it captured eight additional true cases confirmed by chart review and adjudication.Conclusions:The current study demonstrated the potential to apply rule-based NLP algorithms to clinical notes to identify anaphylaxis cases. Increasing the size of training data, including clinical notes from all participating study sites in the training data, and preprocessing the clinical notes to handle special characters could improve the performance of the NLP algorithms. We recommend adding an NLP process followed by manual chart review in future vaccine safety studies to improve sensitivity and efficiency.CC999999/ImCDC/Intramural CDC HHS/United StatesCC/CDC HHS/United States2021-02-01T00:00:00Z31797475PMC75288878412vault:3605

Vaccine

Major vaccine safety controversies have arisen in several countries beginning in the last decades of 20th century. Such periodic vaccine safety controversies are unlikely to go away in the near future as more national immunization programs mature with near elimination of target vaccine-preventable diseases that result in relative greater prominence of adverse events following immunizations, both true reactions and temporally coincidental events. There are several ways in which vaccine safety capacity can be improved to potentially mitigate the impact of future vaccine safety controversies. This paper aims to take a "lifecycle" approach, examining some potential pre- and post-licensure opportunities to improve vaccine safety, in both developed (specifically U.S. and Europe) and low- and middle-income countries.20152015-11-29T00:00:00Z001/World Health Organization/InternationalCC999999/Intramural CDC HHS/United States26433922PMC466311

How Pregnant Women in the United States Perceive Vaccines for Themselves, their Close Contacts and their Children

Vaccine hesitancy has grown in recent decades [1-4], leading to the clustering of vaccine refusal and associated outbreaks of vaccine preventable diseases (VPDs) [5-11]. Vaccination rates of pregnant women in particular are suboptimal [12]. This dissertation contains three manuscripts discussing research performed as part of an NIH-funded large randomized controlled trial of a comprehensive prenatal intervention to increase uptake of maternal and infant vaccines (referred to as P3+) and its add-on study sponsored by Walgreen Co to increase knowledge and uptake of cocooning vaccines among close friends and family of participating P3+ pregnant women. As part of the P3+ provider-level intervention package, we performed a systematic review to update and succinctly summarize the scientific evidence assessing possible causal associations of adverse events following immunization (AEFI), with refined causality conclusions intended for health care providers. Although for 12 of the 47 AEFI studied a causal relationship was established with at least one vaccine currently routinely recommended to the general population in the United States, most of these were rare or mild, and no causal relationship was established for the other 35 AEFI studied. As part of the P3+ patient-level intervention package, we developed an application called MomsTalkShots for smartphones, tablets and computers that delivers patient-tailored education materials to pregnant women and collects survey data to monitor vaccine knowledge, attitudes and beliefs. As part of the add-on study, the MomsTalkShots app encouraged P3+ pregnant women to refer their close friends and family to the app. Baseline survey data showed suboptimal maternal vaccine knowledge and intentions among P3+ pregnant women, especially among first-time pregnant women. In addition, pregnant women who valued vaccination and perceived their social network to value vaccination were more likely to refer their close friends and family to the app. This research demonstrates the opportunity for individually-tailored vaccine education of pregnant women and their social networks to increase vaccine confidence and informed decision making at this stage of life

Advisory Committee on Immunization Practices (ACIP) summary report : October 22-23, 2008, Atlanta, Georgia

min-oct08-508.pdf2008607

Ann Intern Med

Background:Although shoulder conditions have been reported as an adverse event after intramuscular vaccination in the deltoid muscle, epidemiologic data on shoulder conditions after vaccination are limited.Objective:To estimate the risk for shoulder conditions after vaccination and assess possible risk factors.Design:Retrospective cohort study.Setting:Kaiser Permanente Southern California, a large integrated health care organization.Participants:Kaiser Permanente Southern California members aged 3 years or older who had an intramuscular vaccination administered in the deltoid muscle between 1 April 2016 and 31 December 2017.Measurements:A natural language processing (NLP) algorithm was used to identify potential shoulder conditions among vaccinated persons with shoulder disorder diagnosis codes. All NLP-identified cases were manually chart confirmed on the basis of our case definition. The characteristics of vaccinated persons with and without shoulder conditions were compared.Results:Among 3 758 764 administered vaccinations, 371 cases of shoulder condition were identified, with an estimated incidence of 0.99 (95% CI, 0.89 to 1.09) per 10 000 vaccinations. The incidence was 1.22 (CI, 1.10 to 1.35) for the adult (aged 6518 years) and 0.05 (CI, 0.02 to 0.14) for the pediatric (aged 3 to 17 years) vaccinated populations. In the adult vaccinated population, advanced age, female sex, an increased number of outpatient visits in the 6 months before vaccination, lower Charlson Comorbidity Index, and pneumococcal conjugate vaccine were associated with a higher risk for shoulder conditions. Among influenza vaccines, quadrivalent vaccines were associated with an increased risk for shoulder conditions. Simultaneous administration of vaccines was associated with a higher risk for shoulder conditions among elderly persons.Limitation:Generalizability to other health care settings, use of administrative data, and residual confounding.Conclusion:These population-based data suggest a small absolute risk for shoulder conditions after vaccination. Given the high burden of shoulder conditions, clinicians should pay attention to any factors that may further increase risks.Primary Funding Source:Centers for Disease Control and Prevention.CC999999/ImCDC/Intramural CDC HHSUnited States/2022-11-01T00:00:00Z35313110PMC91175071205

Advisory Committee on Immunization Practices (ACIP) summary report : June 25-26, 2008, Atlanta, Georgia

min-jun08-508.pdf2008607

Record of the meeting of the Advisory Committee on Immunization Practices : February 21-22, 2006

Publication date from document properties-created: 3/1/18; modified: 3/21/18.min-2006-02-508.pdf2006607

Law in the Service of Misinformation: How Anti-Vaccine Groups Use the Law to Help Spin a False Narrative

Social movements use legal tools to create narratives. Those narratives support social agendas which certain movements leverage to mislead their followers and potential followers. In this Article, we examine one influential anti-vaccine organization, the Informed Consent Action Network (ICAN), that uses its far-reaching platform to create false narratives around legal action. Again and again, this anti-vaccine group misrepresented both the legal and the factual meanings of court decisions, settlements, and other legal actions to create a narrative to galvanize its followers and influence newcomers. ICAN filed lawsuits that make anti-vaccine arguments—even when the legal framework did not fit doing so—and misrepresented the results. Most commonly in this category, while FOIA requests can only ask for documents and cannot ask queries, ICAN framed its frequent FOIA requests and subsequent lawsuits as if they were asking the agency to answer questions, rather than provide records. The group then presented the results to support one of its narratives—that vaccines cause autism—when the results did not, in fact, support such a narrative. This Article shows how legal tools advance disinformation and misinformation, creating a misleading, alternative reality