Background incidence rates of health outcomes of interest for COVID-19 vaccine safety monitoring in a US population: a claims database analysis
Bibliographic record
Abstract
OBJECTIVE: To evaluate background incidence rates of 59 health outcomes of interest (HOI) in a diverse population, including important subpopulations, during the pre-COVID-19 era (1 January 2017-31 December 2019) and the COVID-19 era (1 March 2020-31 December 2020), before the introduction of COVID-19 vaccines. DESIGN: Observational retrospective cohort study. Annual incidence rates and 95% confidence intervals (CIs) of HOIs were estimated for each population of interest, stratified by: age, sex, age and sex and seasonality. DATA SOURCE: Optum's de-identified Clinformatics Data Mart Database (CDM). PARTICIPANTS: Individuals from the US general population and four subgroups of interest: influenza-vaccinated, paediatric (<18 years of age), elderly (≥65 years of age) and pregnant women. RESULTS: During the COVID-19 era, the incidence of several cardiac conditions, coagulation disorders and acute liver injury increased across all populations assessed while the rates of some dermatological and neurological HOIs decreased relative to the pre-COVID-19 era. The incidence of acute respiratory distress syndrome (ARDS) varied considerably by subgroup: among the elderly, it decreased annually during the pre-COVID-19 era but peaked during the COVID-19 era; among pregnant women, it slightly increased annually during the pre-COVID-19 era and substantially increased during the COVID-19 era; among paediatrics, it decreased annually over the entire study. The incidence of the majority of HOIs increased with age, but were generally comparable between sexes with few exceptions. Cardiac, gastrointestinal, neurological and haematological HOIs, along with acute kidney injury and ARDS, were more common in males, whereas several immunological HOIs and chilblain-like lesions were more common in females. Pregnancy-related HOIs did not increase during the COVID-19 era, except for spontaneous abortions which increased annually over the entire study. CONCLUSION: These observations help contextualise fluctuations in background rates of adverse events noted during the COVID-19 era, and provide insight on how their use may impact safety surveillance for other vaccines.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".