Relative Vaccine Effectiveness of Cell- vs Egg-Based Quadrivalent Influenza Vaccine Against Test-Confirmed Influenza Over 3 Seasons Between 2017 and 2020 in the United States.

Background: Influenza vaccine viruses grown in eggs may acquire egg-adaptive mutations that may reduce antigenic similarity between vaccine and circulating influenza viruses and decrease vaccine effectiveness. We compared cell- and egg-based quadrivalent influenza vaccines (QIVc and QIVe, respectively) for preventing test-confirmed influenza over 3 US influenza seasons (2017-2020). Methods: Using a retrospective test-negative design, we estimated the relative vaccine effectiveness (rVE) of QIVc vs QIVe among individuals aged 4 to 64 years who had an acute respiratory or febrile illness and were tested for influenza in routine outpatient care. Exposure, outcome, and covariate data were obtained from electronic health records linked to pharmacy and medical claims. Season-specific rVE was estimated by comparing the odds of testing positive for influenza among QIVc vs QIVe recipients. Models were adjusted for age, sex, geographic region, influenza test date, and additional unbalanced covariates. A doubly robust approach was used combining inverse probability of treatment weights with multivariable regression. Results: The study included 31 824, 33 388, and 34 398 patients in the 2017-2018, 2018-2019, and 2019-2020 seasons, respectively; ∼10% received QIVc and ∼90% received QIVe. QIVc demonstrated superior effectiveness vs QIVe in prevention of test-confirmed influenza: rVEs were 14.8% (95% CI, 7.0%-22.0%) in 2017-2018, 12.5% (95% CI, 4.7%-19.6%) in 2018-2019, and 10.0% (95% CI, 2.7%-16.7%) in 2019-2020. Conclusions: This study demonstrated consistently superior effectiveness of QIVc vs QIVe in preventing test-confirmed influenza over 3 seasons characterized by different circulating viruses and degrees of egg adaptation.

Incidence of Influenza-Related Medical Encounters and the Associated Healthcare Resource Use and Complications Across Adult Age Groups in The United States During the 2015-2020 Influenza Seasons.

BACKGROUND: Research on influenza burden in adults has focused on crude subgroups with cut-points at 65-years, limiting insight into how burden varies with increasing age. This study describes the incidence of influenza-related outpatient visits, emergency room (ER) visits, and hospitalizations, along with healthcare resource use and complications in the aging adult population. METHODS: Individuals ≥18 years of age in the United States were evaluated retrospectively in five seasonal cohorts (2015-2020 seasons) in strata of age with 5-year increments. Person-level electronic medical records linked to pharmacy and medical claims were used to ascertain patient characteristics and outcomes. Influenza-related medical encounters were identified based on diagnostic codes (ICD-10 codes J09*-J11*). RESULTS: Incidence of influenza-related outpatient visits was highest among people aged 18-34 years and declined with increasing age. For ER visits, incidence tended to be elevated for people aged 18-34 years, relatively stable from 35 through 60, and increased rapidly after 60. Hospitalization incidence remained relatively stable until about 50 years of age and then increased with age. One in three patients was diagnosed with pneumonia after hospitalization, regardless of age. Across seasons, age groups, and clinical settings, on average, 40.8% of individuals were prescribed antivirals and 17.2% antibiotics. CONCLUSIONS: Incidence of influenza-related hospitalizations begins to increase around age 50 rather than the more common cut-point of 65, whereas incidence of outpatient visits was highest among younger adults. Influenza infections frequently led to antiviral and antibiotic prescriptions, underscoring the role influenza vaccination can play in combating antimicrobial resistance.

A Qualitative Analysis of Provider Notes of Atopic Dermatitis-Related Visits Using Natural Language Processing Methods.

INTRODUCTION: Real-world disease management of atopic dermatitis (AD) is hampered by a lack of consistency between providers that treat AD regarding assessment of severity, disease activity, and quality of life. Variability and inconsistency in documentation makes it difficult to understand the impact of AD. This study summarizes AD-related symptoms and concerns captured in unstructured qualitative provider notes by healthcare providers during visits with patients with AD. METHODS: Provider notes were obtained for patients with AD (n = 133,025) from a USA-based ambulatory electronic health records system. The sample included both children (n = 69,551) and adults at least 18 years of age (n = 63,474) receiving treatment from a variety of specialties including primary care, dermatology, and allergy/immunology. Key skin-related words were identified from a review of a sample of notes and natural language processing (NLP) was applied to determine the frequency of the keywords and bigram patterns. RESULTS: Provider notes largely focused on symptoms (primarily itch) and symptom relief rather than the impact of AD on work or lifestyle. Despite the known relationship between itch and skin pain, neuralgia was not widely documented. Compared to primary care providers, dermatologists' and allergist/immunologists' notes had more documentation of symptom-related issues. Personal and work/life burden issues were not widely documented regardless of specialty. CONCLUSION: The topics documented in case notes by healthcare providers about their patients with AD focus largely on symptoms and, to a lesser extent, treatment, but do not reflect the burden of AD on patients' lives. This finding highlights a potential care gap that warrants further investigation.

How well can familial hypercholesterolemia be identified in an electronic health record database?

BACKGROUND: Familial hypercholesterolemia (FH) is a condition characterized by high cholesterol levels and increased risk for coronary heart disease (CHD) that often goes undiagnosed. The Dutch Lipid Network Criteria (DLNC) are used to identify FH in clinical settings via physical examination, personal and family history of CHD, in addition to the presence of deleterious mutations of the LDLR, ApoB, and PCSK9 genes. Agreement between clinical and genetic diagnosis of FH varies. While an ICD diagnosis code was not available for coding FH until 2016, Systematized Nomenclature of Medicine (SNOMED) clinical concept codes, including genetic diagnoses, for FH have been utilized in electronic health records (EHRs). OBJECTIVE: To evaluate the concordance of identifying FH via SNOMED and ICD-10 CM codes vs the DLNC in an EHR database. METHODS: Using the Practice Fusion EHR database, the sensitivity, specificity, positive predictive value (PPV), and negative predictive value were calculated comparing an FH cohort identified via SNOMED and ICD-10 CM codes to one identified via the DLNC. RESULTS: Among 907,616 patients with hypercholesterolemia, 2,180 were identified as FH via SNOMED code (zero were identified via ICD-10 CM), 259 had a DLNC score 6-8 (probable FH), and 45 had a DLNC score >8 (definite FH). Compared to DLNC score >8, the sensitivity, specificity, and PPV of the FH SNOMED code were 84.4%, 99.4%, and 6.4%, respectively. Compared to DLNC score ≥6, the sensitivity was 36.8% and the specificity was 99.5% with a PPV of 18.7%. CONCLUSION: Compared to the clinical criteria for FH, identification of FH patients via SNOMED diagnosis codes had high sensitivity and specificity, but low PPV. The discordance of these two techniques in identifying FH patients speaks to the challenges in identifying FH patients in large electronic databases such as administrative claims and EHR.

Effectiveness of a Cloud-Based EHR Clinical Decision Support Program for Body Mass Index (BMI) Screening and Follow-up.

The effectiveness of a clinical decision support (CDS) program encouraging clinicians to record patient's Body Mass Index (BMI) and document appropriate follow-up plans is evaluated. Test (4,987 practices, 33,445 clinicians) and control groups (881 practices, 6,316 clinicians) were selected using stratified random sampling. Three CDS alerts for BMI screening and follow-up based on evidence based clinical quality guidelines were displayed at the point of care in a cloud-based EHR. The effectiveness of the CDS program was measured over 4 months by tracking recorded BMI and documented follow-up plans. Over the program, BMI recording increased minimally and documentation of follow-up plans increased 5-fold (p=0.05) compared to the control group. The overweight test group patients (18- 64yo) gained less weight (p=0.06) than the control group and underweight patients gained more weight (p<0.01) during the program period. Outcome studies with longer follow-up periods are needed to further confirm positive outcomes.