Real‐world risk factors of confirmed or probable <scp>COVID</scp>‐19 in Americans with diabetes: A prospective, community‐based study (<scp>iNPHORM</scp>)
Bibliographic record
Abstract
Abstract Introduction Americans with diabetes are clinically vulnerable to worse COVID‐19 outcomes; thus, insight into how to prevent infection is imperative. Using longitudinal, prospective data from the real‐world iNPHORM study, we identify the intrinsic and extrinsic risk factors of confirmed or probable COVID‐19 in people with type 1 or 2 diabetes. Methods The iNPHORM study recruited 1206 Americans (18–90 years) with insulin‐ and/or secretagogue‐treated type 1 or 2 diabetes from a probability‐based internet panel. Online questionnaires (screener, baseline and 12 monthly follow‐ups) assessed COVID‐19 incidence and various plausible intrinsic and extrinsic factors. Multivariable Cox regression was used to model the rate of COVID‐19 (confirmed or probable). Risk factors were selected using a repeated backwards‐selection ‘voting’ procedure. Results A sub‐sample of 817 iNPHORM participants (type 1 diabetes: 16.9%; age: 52.1 [SD: 14.2] years; female: 50.2%) was analysed between May 2020 and March 2021. During this period, 13.7% reported confirmed or probable COVID‐19. Age, body mass index, number of chronic comorbidities, most recent A1C, past severe hypoglycaemia, and employment status were selected in our final model. Body mass index ≥30 kg/m2 versus <30 kg/m2 (HR 1.63 [1.05; 2.52]95% CI), and increased number of comorbidities (HR 1.16 [1.05; 1.27]95% CI) independently predicted COVID‐19 incidence. Marginally significant effects were observed for overall A1C (p = .06) and employment status (p = .07). Conclusions This is the first US‐based epidemiologic investigation to characterize community‐based COVID‐19 susceptibility in diabetes. Our results reveal specific and promising avenues to prevent COVID‐19 in this at‐risk population. ClinicalTrials.gov Identifier: NCT04219514.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".