Patient-Physician Language Concordance and Cardiovascular Outcomes Among Patients With Hypertension
Bibliographic record
Abstract
Importance: Patients who live in minority language communities often receive health care services of lower quality and safety compared with patients who speak the majority language. Yet the outcomes associated with care provided by physicians who speak a patient's primary language remain unknown. Objective: To examine patient-physician language concordance and the risk of major adverse cardiovascular events (MACEs) among patients with hypertension. Design, Setting, and Participants: This retrospective cohort study identified adults with self-reported hypertension in the Canadian Community Health Survey, a national survey that collects data from a representative sample of Canadians, from January 1, 2003, to December 31, 2014. Respondents (excluding those living in Quebec) had their hospitalization and mortality records linked to their survey responses. Data were analyzed from October 2023 to May 2024. Exposures: Respondents' primary home language was defined using language spoken most often at home. Language spoken with a regular physician was used to measure patient-physician language concordance. Respondents who spoke to their regular physician in their primary home language were classified as having received language-concordant care, while all other respondents were classified as having received language-discordant care. Main Outcomes and Measures: MACEs within 5 years of survey completion. Results: Among the 124 583 patients included in this study, 114 239 (91.7%) spoke English, 4790 (3.8%) spoke French, 325 (0.3%) spoke an Indigenous language, and 5229 (4.2%) spoke an allophone (ie, other) language. The mean (SD) age of the cohort was 63.7 (14.8) years; 57.1% of the patients reported their sex as female. Very few respondents who spoke an Indigenous language at home (<4.6%) received language-concordant care. For French-speaking patients, there was no statistically significant difference in the risk of MACE between those who received language-concordant care and those who received language-discordant care (hazard ratio [HR], 1.09; 95% CI, 0.86-1.36). Allophone-speaking patients who received language-concordant care were 36% less likely to experience MACE (HR, 0.64; 95% CI, 0.51-0.80) compared with allophone-speaking patients who received language-discordant care. Conclusions and Relevance: This retrospective cohort study found large disparities in both access to language-concordant care and risk of MACEs. These findings suggest that language-concordant care could potentially improve the health of individuals in minority language communities.
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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 | 0.000 | 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) | 0.002 | 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".