Disparities in Access to Virtual Care for Urinary Tract Infections During the COVID-19 Era
Bibliographic record
Abstract
Objective To characterize the difference in uptake of virtual care for urinary tract infections (UTIs) by demographic variables in the COVID-19 era. Methods We conducted a retrospective review of outpatient encounters for UTIs across a large health care system. The cohort was defined as patients with an encounter diagnosis of UTI via in-person or virtual care (telephone or technology-supported care), between March 1, 2020, and February 28, 2021. Analysis was limited to the first UTI encounter of the year for each patient. We compared the use of in-person and virtual visits by demographic variables using chi-square tests and multivariate logistic regression. Results A total of 6744 patients, with a mean age of 61 years, were seen for UTI during the study period. The majority of patients were White (85.5%) and female (83.7%), and were seen in person (55.9%). Of those seen virtually, 52.0% participated in telephone-only visits, and 47.9% were seen via technology-supported care, using video or chatbased platforms. On multivariate logistic regression, age under 30, lowest-quartile income, male sex, and a primary language other than English increased the odds that patients had been seen in person. Among those seen virtually, age over 50 significantly increased the odds of a telephone visit, as did being Black or Native American, having a lower-quartile income, and speaking a non-English primary language. Conclusions Although the expansion in virtual care has given some patients easier access to necessary care, the “digital divide” has worsened existing disparities for certain vulnerable populations. We demonstrate a difference in uptake of virtual health care by age, race, primary language, and income.
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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 | 0.006 |
| 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 | 0.001 |
| 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".