Recent trends in the probability of high out-of-pocket medical expenses in the United States
Bibliographic record
Abstract
OBJECTIVE: This article measures the probability that out-of-pocket expenses in the United States exceed a threshold share of income. It calculates this probability separately by individuals' health condition, income, and elderly status and estimates changes occurring in these probabilities between 2010 and 2013. DATA AND METHOD: This article uses nationally representative household survey data on 344,000 individuals. Logistic regressions estimate the probabilities that out-of-pocket expenses exceed 5% and alternatively 10% of income in the two study years. These probabilities are calculated for individuals based on their income, health status, and elderly status. RESULTS: Despite favorable changes in both health policy and the economy, large numbers of Americans continue to be exposed to high out-of-pocket expenditures. For instance, the results indicate that in 2013 over a quarter of nonelderly low-income citizens in poor health spent 10% or more of their income on out-of-pocket expenses, and over 40% of this group spent more than 5%. Moreover, for Americans as a whole, the probability of spending in excess of 5% of income on out-of-pocket costs increased by 1.4 percentage points between 2010 and 2013, with the largest increases occurring among low-income Americans; the probability of Americans spending more than 10% of income grew from 9.3% to 9.6%, with the largest increases also occurring among the poor. CONCLUSION: The magnitude of out-of-pocket's financial burden and the most recent upward trends in it underscore a need to develop good measures of the degree to which health care policy exposes individuals to financial risk, and to closely monitor the Affordable Care Act's success in reducing Americans' exposure to large medical bills.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".