Trends in Healthcare Expenditures among Individuals with Arthritis in the United States from 2008 to 2014
Bibliographic record
Abstract
OBJECTIVE: With the expected rise in the arthritis population, information is required regarding trends of healthcare expenditures among individuals with arthritis in the United States. We examined temporal trends in direct and out-of-pocket (OOP) healthcare expenditures among individuals with arthritis using a nationally representative database, the Medical Expenditures Panel Survey. METHODS: The study population was composed of cross-sectional cohorts of individuals aged ≥ 18 years from 2008 to 2014. Two-part models were used to estimate the incremental total and types of annual direct and OOP healthcare expenditures (adjusted to 2014 US dollars) for arthritis, after controlling for predisposing, enabling, need, personal health practice, and external environmental factors, as per the Anderson Healthcare Behavioral Model. RESULTS: An annual weighted arthritis population rose from 56.1 million in 2008 to 65.1 million in 2014. Among individuals with arthritis, the annual average direct and OOP expenditure was $10,424 [standard error (SE) = $345, aggregate = $584.8 billion] and $1493 (SE = $50, aggregate = $83.8 billion) in 2008, respectively, and $910 (SE = $279, total = $645.1 billion) and $1099 (SE = $36, aggregate = $71.5 billion) in 2014, respectively. In the fully adjusted model, individuals with arthritis had significantly greater total and OOP expenditures from 2008 to 2014; however, the magnitude of incremental OOP expenditure declined from 2008 to 2014. CONCLUSION: Although the annual direct healthcare expenditures per person remained stable over the years, the rise in proportion of the arthritis population led to a huge increase in aggregate economic burden to the US healthcare system.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".