Socioeconomic disparities in healthcare access and implications for all-cause mortality among US adults: a 2000-2019 record linkage study
Bibliographic record
Abstract
The United States (US) has witnessed a notable increase in socioeconomic disparities in all-cause mortality since 2000. While this period is marked by significant macroeconomic and health policy changes, the specific drivers of these mortality trends remain poorly understood. In this study, we assessed healthcare access variables and their association with socioeconomic status (SES)-related differences (exposure) in US all-cause mortality (outcome) since 2000. Our research drew upon cross-sectional data from the National Health Interview Survey (NHIS, 2000-2018), linked to death records from the National Death Index (NDI, 2000-2019; n = 486 257). The findings reveal that the odds of a lack of health insurance and unaffordability of needed medical care were over 2-fold higher among individuals with lower education compared to those with high education, following differential time trends. Moreover, elevated mortality risk was associated with lower education (up to 77%), uninsurance (17%), unaffordability (43%), and delayed care (12%). Uninsurance and unaffordability accounted for 4%-6% of the disparities in time to mortality between low- and high-education groups. These findings were corroborated by income-based sensitivity analyses, emphasizing that inadequate healthcare access partially contributed to socioeconomic disparities in mortality. Effective policies promoting equitable healthcare access are imperative to mitigate socioeconomic disparities in mortality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".