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By using commercial insurance claims data, we estimated that Lyme disease was diagnosed and treated in ≈476,000 patients in the United States annually during 2010-2018. Our results underscore the need for accurate diagnosis and improved prevention.

We evaluated MarketScan, a large commercial insurance claims database, for its potential use as a stable and consistent source of information on Lyme disease diagnoses in the United States. The age, sex, and geographic composition of the enrolled population during 2010-2018 remained proportionally stable, despite fluctuations in the number of enrollees. Annual incidence of Lyme disease diagnoses per 100,000 enrollees ranged from 49 to 88, ≈6-8 times higher than that observed for cases reported through notifiable disease surveillance. Age and sex distributions among Lyme disease diagnoses in MarketScan were similar to those of cases reported through surveillance, but proportionally more diagnoses occurred outside of peak summer months, among female enrollees, and outside high-incidence states. Misdiagnoses, particularly in low-incidence states, may account for some of the observed epidemiologic differences. Commercial claims provide a stable data source to monitor trends in Lyme disease diagnoses, but certain important characteristics warrant further investigation.

Surveys indicate US residents spent more time outdoors in 2020 than in 2019, but fewer tick bite–related emergency department visits and Lyme disease laboratory tests were reported. Despite ongoing exposure, Lyme disease case reporting for 2020 might be artificially reduced due to coronavirus disease–associated changes in healthcare-seeking behavior.

Tick-transmitted Borrelia fall into 2 heterogeneous bacterial complexes comprised of multiple species, the relapsing fever (RF) group and the Borrelia burgdorferi sensu lato group, which are the causative agents of Lyme borreliosis (LB), the most common tickborne disease in the Northern Hemisphere. Geographic expansion of LB in the United States and discovery of emerging Borrelia pathogens underscores the importance of surveillance for disease-causing Borrelia. De-identified clinical specimens, submitted by providers throughout the United States, for patients suspected of LB, anaplasmosis, ehrlichiosis, or babesiosis were screened using a Borrelia genus-level TaqMan polymerase chain reaction (PCR). Borrelia species and sequence types (STs) were characterized by multilocus sequence typing (MLST) utilizing next-generation sequencing. Among 7292 specimens tested, 5 Borrelia species were identified: 2 causing LB, B. burgdorferi (n = 25) and B. mayonii (n = 9), and 3 RF borreliae, B. hermsii (n = 1), B. miyamotoi (n = 8), and Candidatus B. johnsonii (n = 1), a species previously detected only in the bat tick, Carios kelleyi. ST diversity was greatest for B. burgdorferi-positive specimens, with new STs identified primarily among synovial fluids. These results demonstrate that broad PCR screening followed by MLST is a powerful surveillance tool for uncovering the spectrum of disease-causing Borrelia species, understanding their geographic distribution, and investigating the correlation between B. burgdorferi STs and joint involvement. Detection of Candidatus B. johnsonii in a patient with suspected tickborne disease suggests this species may be a previously undetected cause of illness in humans exposed to bat ticks.

Positive laboratory results are increasingly used for Lyme disease surveillance in the United States. We found 6%-15% of persons with a positive test each year tested positive in a prior year; repeat testing frequency increased with patient age. Repeat testing of persons with previous seropositivity could affect surveillance data interpretation.

Borrelia miyamotoi, transmitted by Ixodes spp. ticks, was recognized as an agent of hard tick relapsing fever in the United States in 2013. Nine state health departments in the Northeast and Midwest have conducted public health surveillance for this emerging condition by using a shared, working surveillance case definition. During 2013–2019, a total of 300 cases were identified through surveillance; 166 (55%) were classified as confirmed and 134 (45%) as possible. Median age of case-patients was 52 years (range 1–86 years); 52% were male. Most cases (70%) occurred during June–September, with a peak in August. Fever and headache were common symptoms; 28% of case-patients reported recurring fevers, 55% had arthralgia, and 16% had a rash. Thirteen percent of patients were hospitalized, and no deaths were reported. Ongoing surveillance will improve understanding of the incidence and clinical severity of this emerging disease.

Background Commercial insurance claims data are a stable and consistent source of information on Lyme disease diagnoses in the United States and can contribute to our understanding of overall disease burden and the tracking of epidemiological trends. Algorithms consisting of diagnosis codes and antimicrobial treatment information have been used to identify Lyme disease diagnoses in claims data, but there might be opportunity to improve their accuracy. Methods We developed three modified versions of our existing claims-based Lyme disease algorithm; each reflected refined criteria regarding antimicrobials prescribed and/or maximum days between diagnosis code and qualifying prescription claim. We applied each to a large national commercial claims database to identify Lyme disease diagnoses during 2016–2019. We then compared characteristics of Lyme disease diagnoses identified by each of the modified algorithms to those identified by our original algorithm to assess differences from expected trends in demographics, seasonality, and geography. Results Observed differences in characteristics of patients with diagnoses identified by the three modified algorithms and our original algorithm were minimal, and differences in age and sex, in particular, were small enough that they could have been due to chance. However, one modified algorithm resulted in proportionally more diagnoses in men, during peak summer months, and in high-incidence jurisdictions, more closely reflecting epidemiological trends documented through public health surveillance. This algorithm limited treatment to only first-line recommended antimicrobials and shortened the timeframe between a Lyme disease diagnosis code and qualifying prescription claim. Conclusions As compared to our original algorithm, a modified algorithm that limits the antimicrobials prescribed and shortens the timeframe between a diagnosis code and a qualifying prescription claim might more accurately identify Lyme disease diagnoses when utilizing insurance claims data for supplementary, routine identification and monitoring of Lyme disease diagnoses.

As of March 2021, three COVID-19 vaccines had been authorized by the U.S. Food and Drug Administration (FDA) for use in the United States. Each has substantial efficacy in preventing COVID-19. However, as efficacy from trials was <100% for all three vaccines, disease in vaccinated people is expected to occur. We created a spreadsheet-based tool to estimate the number of symptomatic COVID-19 cases among vaccinated people (vaccine breakthrough infections) based on published vaccine efficacy (VE) data, percent of the population that has been fully vaccinated, and average number of COVID-19 cases reported per day. We estimate that approximately 199,000 symptomatic vaccine breakthrough infections (95% CI: ~183,000–214,000 cases) occurred in the United States during January–July 2021 among >156 million fully vaccinated people. With high SARS-CoV-2 transmission and increasing numbers of people vaccinated in the United States, vaccine breakthrough infections will continue to accumulate. Understanding expectations regarding number of vaccine breakthrough infections enables accurate public health messaging to help ensure that the occurrence of such cases does not negatively affect vaccine perceptions, confidence, and uptake.

Background. In the United States, tularemia is caused by Francisella tularensis subsps. tularensis (type A) and holarctica (type B). Molecular subtyping has further divided type A into 2 subpopulations, A1 and A2. Significant mortality differences were previously identified between human infections caused by A1 (14%), A2 (0%) and type B (7%). To verify these findings and to further define differences among genotypes, we performed a large-scale molecular epidemiologic analysis of F. tularensis isolates from humans and animals. Methods. Pulsed-field gel electrophoresis with PmeI was performed on 302 type A and 61 type B isolates. Pulsed-field gel electrophoresis pattern and epidemiologic analyses were performed. Logistic regression was used to assess factors associated with human mortality. Results. Pulsed-field gel electrophoresis typing identified 4 distinct type A genotypes, A1a, A1b, A2a, and A2b, as well as type B. Genotypic and geographic divisions observed among isolates from humans were mirrored among isolates from animals, specifically among animal species that are linked to human infection and to enzootic maintenance of tularemia. Significant differences between human infections caused by different genotypes were identified with respect to patient age, site of organism recovery, and mortality. Human infections due to A1b resulted in significantly higher mortality (24%) than those caused by A1a (4%), A2 (0%), and type B (7%). Conclusions. Three type A genotypes, A1a, A1b, and A2, were shown to be epidemiologically important. Our analysis suggests that A1b strains may be significantly more virulent in humans than A1a, A2, or type B strains. These findings have important implications for disease progression, disease prevention, and basic research programs.

Background COVID-19 vaccines are an effective tool to prevent illness due to SARS-CoV-2 infection. However, infection after vaccination still occurs. We evaluated all infections identified among recipients of either the Pfizer-BioNTech or Moderna COVID-19 vaccine in five U.S. states during January–March 2021. Methods Using observational data reported to CDC, we compared the incidence of SARS-CoV-2 infection among vaccinated and unvaccinated persons, and the sex, age, and vaccine product received for individuals with vaccine breakthrough infections to those of the vaccinated population using Poisson regression models. We also compared the proportion of vaccine breakthrough cases due to a SARS-CoV-2 variant of concern to data reported to CDC’s national genomic surveillance program. Results The age-adjusted incidence of reported SARS-CoV-2 infection was 97% lower among vaccinated as compared to unvaccinated persons aged ≥ 16 years (68 vs 2252 cases per 100,000 people). Vaccinated adults aged ≥ 85 years were 1.6 times (95% CI 1.3–1.9) as likely to become infected with SARS-CoV-2 than vaccinated adults aged < 65 years. Pfizer-BioNTech COVID-19 vaccine recipients were 1.4 times (95% CI 1.3–1.6) as likely to experience infection compared to Moderna COVID-19 recipients. The proportion of infections among vaccinated persons caused by SARS-CoV-2 variants of concern was similar to the proportion of circulating viruses identified as variants of concern in the five states during the same time. Conclusions Vaccinated persons had a substantially lower incidence of SARS-CoV-2 infection compared to unvaccinated persons. Adults aged ≥ 85 years and Pfizer-BioNTech vaccine recipients had a higher risk of infection following vaccination. We provide an analytic framework for ongoing evaluation of patterns associated with SARS-CoV-2 infection among vaccinated persons using observational surveillance and immunization data. Our findings reinforce the effectiveness of COVID-19 vaccines in preventing infection in real-world settings.