Risk factors for recognized and unrecognized SARS-CoV-2 infection: a seroepidemiologic analysis of the Prospective Urban Rural Epidemiology (PURE) study
Bibliographic record
Abstract
ABSTRACT There are limited data on individual risk factors for SARS-CoV-2 infection (including unrecognized infection). In this seroepidemiologic substudy of an ongoing prospective cohort study of community-dwelling adults, participants were thoroughly characterized pre-pandemic. The SARS-CoV-2 infection was ascertained by serology. Among 8,719 participants from 11 high-, middle-, and low-income countries, 3,009 (35%) were seropositive for SARS-CoV-2. Characteristics independently associated with seropositivity were younger age (odds ratio, OR; 95% confidence interval, CI, per five-year increase: 0.95; 0.91–0.98) and body mass index >25 kg/m 2 (OR, 95% CI: 1.16, 1.01–1.34). Smoking (as compared with never smoking, OR, 95% CI: 0.83, 0.70–0.97) and COVID-19 vaccination (OR, 95% CI: 0.70, 0.60–0.82) were associated with a reduced risk of seropositivity. Among seropositive participants, 83% were unaware of having been infected with SARS-CoV-2. Seropositivity and a lack of awareness of infection were more common in lower-income countries. The COVID-19 vaccination reduces the risk of SARS-CoV-2 infection (including recognized and unrecognized infections). Overweight or obesity is an independent risk factor for SARS-CoV-2 infection. Infection and lack of infection awareness are more common in lower-income countries. IMPORTANCE In this large, international study, evidence of SARS-CoV-2 infection was obtained by testing blood specimens from 8,719 community-dwelling adults from 11 countries. The key findings are that (i) the large majority (83%) of community-dwelling adults from several high-, middle-, and low-income countries with blood test evidence of SARS-CoV-2 infection were unaware of this infection—especially in lower-income countries; and (ii) overweight/obesity predisposes to SARS-CoV-2 infection, while COVID-19 vaccination is associated with a reduced risk of SARS-CoV-2 infection. These observations are not attributable to other individual characteristics, highlighting the importance of the COVID-19 vaccination to prevent not only severe infection but possibly any infection. Further research is needed to understand the mechanisms by which overweight/obesity might increase the risk of SARS-CoV-2 infection.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".