Incidence of COVID-19 reinfection among Midwestern healthcare employees
Bibliographic record
Abstract
Abstract Importance Given the overwhelming worldwide rate of infection and the disappointing pace of vaccination, addressing reinfection is critical. Understanding reinfection, including protection longevity after natural infection, will allow us to better know the prospect of herd immunity, which hinges on the assumption that natural infection generates sufficient, protective immunity. The primary aim of this paper is to provide data on SARS-CoV-2 reinfection over a 10-month period. Objective The primary objective of this study is to establish the incidence of reinfection of COVID-19 among healthcare employees who experienced a prior COVID-19 infection. Design This observational cohort study followed a convenience sample of 2,625 participants who experienced a COVID-19 infection for subsequent COVID-19 infection. Setting Healthcare employees were recruited across a large Midwestern healthcare system. Positive PCR test results were administered and recorded by the system-affiliated lab serving Illinois and Wisconsin. Participants Adult healthcare system employees who enrolled in a research study focused on SARS-CoV-2 antibodies (N = 16,357) and had at least one positive PCR test result between March 1, 2020 and January 10, 2021 were included (N = 2,625). Exposure Positive PCR test for SARS-CoV-2 Main Outcome(s) and Measure(s) The primary outcome is incidence of COVID-19 reinfection, defined by current CDC guidelines (i.e. subsequent COVID-19 infection ≥ 90 days from prior infection). COVID-19 recurrence, defined as subsequent COVID-19 infection after prior infection irrespective of time, is also described. Results Of 2,625 participants who experienced at least one COVID-19 infection during the 10-month study period, 156 (5.94%) experienced reinfection and 540 (20.57%) experienced recurrence after prior infection. Median days were 126.50 (105.50-171.00) to reinfection and 31.50 (10.00-72.00) to recurrence. Incidence rate of COVID-19 reinfection was 0.35 cases per 1,000 person-days, with participants working in COVID-clinical and clinical units experiencing 3.77 and 3.57 times, respectively, greater risk of reinfection relative to those working in non-clinical units. Incidence rate of COVID-19 recurrence was 1.47 cases per 1,000 person-days. Conclusions and Relevance This study supports the consensus that COVID-19 reinfection, defined as subsequent infection ≥ 90 days after prior infection, is rare, even among a sample of healthcare workers with frequent exposure.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".