Pfizer-BioNTech Coronavirus Disease 2019 Vaccine Effectiveness Against Severe Acute Respiratory Syndrome Coronavirus 2 Infection Among Long-term Care Facility Staff With and Without Prior Infection in New York City, January-June 2021.

BACKGROUND: Evidence is accumulating of coronavirus disease 2019 (COVID-19) vaccine effectiveness among persons with prior severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. METHODS: We evaluated the effect against incident SARS-CoV-2 infection of (1) prior infection without vaccination, (2) vaccination (2 doses of Pfizer-BioNTech COVID-19 vaccine) without prior infection, and (3) vaccination after prior infection, all compared with unvaccinated persons without prior infection. We included long-term care facility staff in New York City aged <65 years with weekly SARS-CoV-2 testing from 21 January to 5 June 2021. Test results were obtained from state-mandated laboratory reporting. Vaccination status was obtained from the Citywide Immunization Registry. Cox proportional hazards models adjusted for confounding with inverse probability of treatment weights. RESULTS: Compared with unvaccinated persons without prior infection, incident SARS-CoV-2 infection risk was lower in all groups: 54.6% (95% confidence interval, 38.0%-66.8%) lower among unvaccinated, previously infected persons; 80.0% (67.6%-87.7%) lower among fully vaccinated persons without prior infection; and 82.4% (70.8%-89.3%) lower among persons fully vaccinated after prior infection. CONCLUSIONS: Two doses of Pfizer-BioNTech COVID-19 vaccine reduced SARS-CoV-2 infection risk by ≥80% and, for those with prior infection, increased protection from prior infection alone. These findings support recommendations that all eligible persons, regardless of prior infection, be vaccinated against COVID-19.

Logically Inferred Tuberculosis Transmission (LITT): A Data Integration Algorithm to Rank Potential Source Cases.

Understanding tuberculosis (TB) transmission chains can help public health staff target their resources to prevent further transmission, but currently there are few tools to automate this process. We have developed the Logically Inferred Tuberculosis Transmission (LITT) algorithm to systematize the integration and analysis of whole-genome sequencing, clinical, and epidemiological data. Based on the work typically performed by hand during a cluster investigation, LITT identifies and ranks potential source cases for each case in a TB cluster. We evaluated LITT using a diverse dataset of 534 cases in 56 clusters (size range: 2-69 cases), which were investigated locally in three different U.S. jurisdictions. Investigators and LITT agreed on the most likely source case for 145 (80%) of 181 cases. By reviewing discrepancies, we found that many of the remaining differences resulted from errors in the dataset used for the LITT algorithm. In addition, we developed a graphical user interface, user's manual, and training resources to improve LITT accessibility for frontline staff. While LITT cannot replace thorough field investigation, the algorithm can help investigators systematically analyze and interpret complex data over the course of a TB cluster investigation. Code available at: https://github.com/CDCgov/TB_molecular_epidemiology/tree/1.0; https://zenodo.org/badge/latestdoi/166261171.

Delay in diagnosis leading to nosocomial transmission of tuberculosis at a New York City health care facility.

BACKGROUND: Demographic changes have increased the number of elderly individuals for whom age-related immunosenescence may increase latent tuberculosis (TB) infection (LTBI) activation risk. As TB rates decline, maintaining clinical suspicion for TB is challenging. Timely identification, isolation, and treatment of infectious patients are especially important in settings with vulnerable individuals. METHODS: An outbreak investigation was conducted at a long-term care facility/hospital complex after a prolonged TB exposure associated with delayed diagnosis in a tuberculin skin test (TST)-negative cancer patient resulted in a secondary TB case along with other evidence of transmission. RESULTS: Investigators identified 64 patient and 239 staff contacts. Among those tested with TST, 7 (23%) patients and 5 (8%) staff at the long-term care facility had conversions. Because of evidence of transmission, concerns about TST anergy, and the high number of patients with illnesses such as cancer and diabetes that increase TB risk, LTBI treatment was recommended for all exposed long-term care facility patients regardless of TST results once active TB was ruled out. After the investigation concluded, a former patient who tested TST-negative and did not receive LTBI treatment developed active TB. CONCLUSION: When evaluating symptomatic patients, especially elderly individuals, clinicians should "think TB" regardless of a negative test for TB infection. After known exposure and when transmission evidence exists, clinicians should consider providing LTBI treatment to elderly contacts with comorbidities regardless of LTBI test results.