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Record W4362522301 · doi:10.1001/jama.2023.1699

Differences in Treatment Patterns and Outcomes of Acute Myocardial Infarction for Low- and High-Income Patients in 6 Countries

2023· article· en· W4362522301 on OpenAlexafffundabout
Bruce E. Landon, Laura A. Hatfield, Pieter Bakx, Amitava Banerjee, Yu-Chin Chen, Christina Fu, Michal Gordon, Renaud Heine, Nicole Huang, Dennis T. Ko, Lisa M. Lix, Victor Novack, Laura Pasea, Feng Qiu, Thérèse A. Stukel, Carin A. Uyl‐de Groot, Lin Yan, Gabe Weinreb, Peter Cram

Bibliographic record

VenueJAMA · 2023
Typearticle
Languageen
FieldMedicine
TopicAcute Myocardial Infarction Research
Canadian institutionsUniversity of ManitobaUniversity of TorontoGeorge & Fay Yee Centre for Healthcare InnovationHealth Sciences CentreInstitute for Clinical Evaluative SciencesSunnybrook Health Science Centre
FundersNational Institute on AgingUniversity of TorontoOntario Ministry of Health and Long-Term Care
KeywordsMedicineMyocardial infarctionPercutaneous coronary interventionCardiac catheterizationMortality rateCohortRevascularizationInternal medicineDisadvantagedPopulationDemographyEmergency medicineCardiologyEnvironmental health

Abstract

fetched live from OpenAlex

Importance: Differences in the organization and financing of health systems may produce more or less equitable outcomes for advantaged vs disadvantaged populations. We compared treatments and outcomes of older high- and low-income patients across 6 countries. Objective: To determine whether treatment patterns and outcomes for patients presenting with acute myocardial infarction differ for low- vs high-income individuals across 6 countries. Design, Setting, and Participants: Serial cross-sectional cohort study of all adults aged 66 years or older hospitalized with acute myocardial infarction from 2013 through 2018 in the US, Canada, England, the Netherlands, Taiwan, and Israel using population-representative administrative data. Exposures: Being in the top and bottom quintile of income within and across countries. Main Outcomes and Measures: Thirty-day and 1-year mortality; secondary outcomes included rates of cardiac catheterization and revascularization, length of stay, and readmission rates. Results: We studied 289 376 patients hospitalized with ST-segment elevation myocardial infarction (STEMI) and 843 046 hospitalized with non-STEMI (NSTEMI). Adjusted 30-day mortality generally was 1 to 3 percentage points lower for high-income patients. For instance, 30-day mortality among patients admitted with STEMI in the Netherlands was 10.2% for those with high income vs 13.1% for those with low income (difference, -2.8 percentage points [95% CI, -4.1 to -1.5]). One-year mortality differences for STEMI were even larger than 30-day mortality, with the highest difference in Israel (16.2% vs 25.3%; difference, -9.1 percentage points [95% CI, -16.7 to -1.6]). In all countries, rates of cardiac catheterization and percutaneous coronary intervention were higher among high- vs low-income populations, with absolute differences ranging from 1 to 6 percentage points (eg, 73.6% vs 67.4%; difference, 6.1 percentage points [95% CI, 1.2 to 11.0] for percutaneous intervention in England for STEMI). Rates of coronary artery bypass graft surgery for patients with STEMI in low- vs high-income strata were similar but for NSTEMI were generally 1 to 2 percentage points higher among high-income patients (eg, 12.5% vs 11.0% in the US; difference, 1.5 percentage points [95% CI, 1.3 to 1.8 ]). Thirty-day readmission rates generally also were 1 to 3 percentage points lower and hospital length of stay generally was 0.2 to 0.5 days shorter for high-income patients. Conclusions and Relevance: High-income individuals had substantially better survival and were more likely to receive lifesaving revascularization and had shorter hospital lengths of stay and fewer readmissions across almost all countries. Our results suggest that income-based disparities were present even in countries with universal health insurance and robust social safety net systems.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.326

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.306
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations65
Published2023
Admission routes3
Has abstractyes

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