Telehealth Uptake Among Hispanic People During COVID-19: Retrospective Observational Study
Bibliographic record
Abstract
Background: The Hispanic community represents a sizeable community that experiences inequities in the US health care system. As the system has moved toward digital health platforms, evaluating the potential impact on Hispanic communities is critical. Objective: The study aimed to investigate demographic, socioeconomic, and behavioral factors contributing to low telehealth use in Hispanic communities. Methods: We used a retrospective observation study design to examine the study objectives. The COVID-19 Research Database Consortium provided the Analytics IQ PeopleCore consumer data and Office Alley claims data. The study period was from March 2020 to April 2021. Multiple logistic regression was used to determine the odds of using telehealth services. Results: We examined 3,478,287 unique Hispanic patients, 16.6% (577,396) of whom used telehealth. Results suggested that patients aged between 18 and 44 years were more likely to use telehealth (odds ratio [OR] 1.07, 95% CI 1.05-1.1; P<.001) than patients aged older than 65 years. Across all age groups, patients with high incomes were at least 20% more likely to use telehealth than patients with lower incomes (P<.001); patients who had a primary care physician (P=.01), exhibited high medical usage (P<.001), or were interested in exercise (P=.03) were more likely to use telehealth; patients who had unhealthy behaviors such as smoking and alcohol consumption were less likely to use telehealth (P<.001). Male patients were less likely than female patients to use telehealth among patients aged 65 years and older (OR 0.94, 95% CI 0.93-0.95; P<.001), while male patients aged between 18 and 44 years were more likely to use telehealth (OR 1.05, 95% CI 1.03-1.07; P<.001). Among patients younger than 65 years, full-time employment was positively associated with telehealth use (P<.001). Patients aged between 18 and 44 years with high school or less education were 2% less likely to use telehealth (OR 0.98, 95% CI 0.97-0.99; P=.005). Results also revealed a positive association with using WebMD (WebMD LLC) among patients aged older than 44 years (P<.001), while there was a negative association with electronic prescriptions among those who were aged between 18 and 44 years (P=.009) and aged between 45 and 64 years (P=.004). Conclusions: This study demonstrates that telehealth use among Hispanic communities is dependent upon factors such as age, gender, education, socioeconomic status, current health care engagement, and health behaviors. To address these challenges, we advocate for interdisciplinary approaches that involve medical professionals, insurance providers, and community-based services actively engaging with Hispanic communities and promoting telehealth use. We propose the following recommendations: enhance access to health insurance, improve access to primary care providers, and allocate fiscal and educational resources to support telehealth use. As telehealth increasingly shapes health care delivery, it is vital for professionals to facilitate the use of all available avenues for accessing care.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".