The projected economic and health burden of uncontrolled asthma in the United States
Bibliographic record
Abstract
Abstract Rationale Despite effective treatments, a large proportion of asthma patients do not achieve sustained asthma control. The ‘preventable’ burden associated with lack of proper control is likely taking a high toll at the population level. Objective We predicted the future health and economic burden of uncontrolled asthma among American adults for the next 20 years. Methods We built a probabilistic model that linked state-specific estimates of population growth, asthma prevalence rates, and distribution of asthma control levels. We conducted several meta-analyses to estimate the adjusted differences in healthcare resource use, quality-adjusted life years (QALYs), and productivity loss across control levels. We projected, nationally and at the state-level, total direct and indirect costs (in 2018 USD) and QALYs lost due to uncontrolled asthma from 2019 to 2038 in the United States. Measurements and Main Results Over the next 20 years, the total undiscounted direct costs associated with suboptimal asthma control will be $300.6 billion (95% confidence interval [CI] $190.1 – $411.1). When indirect costs are added, total economic burden will be $963.5 billion (95%CI $664.1 – $1,262.9). American adolescents and adults will lose 15.46 million (95%CI 12.77 million – 18.14 million) QALYs over this period due to suboptimal control of asthma. In state-level analysis, the average 20-year per-capita costs due to uncontrolled asthma ranged from $2,209 (Arkansas) to $6,132 (Connecticut). Conclusion The burden of uncontrolled asthma will continue to grow for the next twenty years. Strategies towards better management of asthma may be associated with substantial return on investment.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".