Patient-Physician Language Concordance, Antihypertensive Medications, and Cardiovascular Outcomes Among Allophone-Speaking Patients with Hypertension
Bibliographic record
Abstract
Background: Patient-physician language concordance is associated with better outcomes. However, the mechanism(s) explaining these associations are poorly understood. Our objective was to determine if antihypertensive medication use mediates the association between patient-physician language concordance and major adverse cardiovascular events (MACEs). Methods: Our population-based, retrospective cohort study used data from the Canadian Community Health Survey (CCHS) from January 1, 2003 to December 31, 2014. We identified Allophone-speaking respondents (ie, the language spoken most often at home is one other than English, French, or an Indigenous language) with self-reported hypertension. We defined patient-physician language concordance as agreement between language spoken most often at home and language spoken with one's regular medical doctor. Survey responses were linked to hospitalization and mortality records. We identified all MACEs within 5 years after survey completion. The associations between patient-physician language concordance, antihypertensive medication use, and MACEs were explored using multivariable logistic and Cox proportional hazards regression, respectively. The mediating effect of antihypertensive medication use was tested with natural effect models. Results: We studied 5013 Allophone-speaking patients, including 1708 (34.1%) who received language-concordant care and 3305 (65.9%) who received language-discordant care. Patients who received language-concordant care were 38% less likely to experience a MACE compared to patients who received language-discordant care (hazard ratio 0.62, 95% confidence interval 0.48-0.80). No evidence was found that this association was mediated by antihypertensive medication use. Conclusions: Patient-physician language concordance was associated with a lower risk of a MACE. However, this association was not mediated by antihypertensive medication use. Further research could explore potentially modifiable mediators of this association.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".