National Health Care Spending In 2018: Growth Driven By Accelerations In Medicare And Private Insurance Spending
Bibliographic record
Abstract
US health care spending increased 4.6 percent to reach $3.6 trillion in 2018, a faster growth rate than the rate of 4.2 percent in 2017 but the same rate as in 2016. The share of the economy devoted to health care spending declined to 17.7 percent in 2018, compared to 17.9 percent in 2017. The 0.4-percentage-point acceleration in overall growth in 2018 was driven by faster growth in both private health insurance and Medicare, which were influenced by the reinstatement of the health insurance tax. For personal health care spending (which accounted for 84 percent of national health care spending), growth in 2018 remained unchanged from 2017 at 4.1 percent. The total number of uninsured people increased by 1.0 million for the second year in a row, to reach 30.7 million in 2018.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".