Primary Care Attributes and Mortality: A National Person-Level Study
Bibliographic record
Abstract
PURPOSE: Research demonstrates an association between the geographic concentration of primary care clinicians and mortality in the area, but there is limited evidence of a mortality benefit of primary care at the individual patient level. We examined whether patient-reported access to selected primary care attributes, including some emphasized in the medical home literature, is associated with lower individual mortality risk. METHODS: We analyzed data from 2000-2005 Medical Expenditure Panel Survey respondents aged 18 to 90 years (N = 52,241), linked to the National Death Index through 2006. A score was constructed from 5 yes/no items assessing whether the respondent's usual source of care had 3 attributes: comprehensiveness, patient-centeredness, and enhanced access. Scores ranged from 0 to 1 (higher scores = more attributes). We examined the association between the primary care attributes score and mortality during up to 6 years of follow-up using Cox survival analysis, adjusted for social, demographic, and health-related characteristics. RESULTS: Racial/ethnic minorities, poorer and less educated persons, individuals without private insurance, healthier persons, and residents of regions other than the Northeast reported less access to primary care attributes than others. The primary care attributes score was inversely associated with mortality (adjusted hazard ratio = 0.79; 95% confidence interval, 0.64-0.98; P = .03); supplementary analyses showed mortality decreased linearly with increasing score. CONCLUSIONS: Greater reported patient access to selected primary care attributes was associated with lower mortality. The findings support the current interest in ensuring that patients have access to a medical home encompassing these attributes.
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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 | 0.003 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".