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 patients with asthma do not achieve sustained asthma control. The “preventable” burden associated with lack of proper control is likely taking a high toll at the personal and population level. Objectives We predicted the future excess health and economic burden associated with uncontrolled asthma among American adolescents and adults for the next 20 years. Methods We built a probabilistic model that linked state-specific estimates of population growth, aging, asthma prevalence, and 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 (due to productivity loss) costs (in 2018 dollars) and QALYs lost because of uncontrolled asthma from 2019 to 2038. Measurements and Main Results Total 20-year direct costs associated with uncontrolled asthma are estimated to be $300.6 billion (95% confidence interval [CI], $190.1 billion–411.1 billion). When indirect costs are added, total economic burden will be $963.5 billion (95% CI, $664.1 billion–1,262.9 billion). American adolescents and adults will lose an estimated 15.46 million (95% CI, 12.77 million–18.14 million) QALYs over this period because of uncontrolled asthma. Across states, the average 20-year per capita costs due to uncontrolled asthma ranged from $2,209 (Arkansas) to $6,132 (Connecticut). Conclusions The burden of uncontrolled asthma is substantial and will continue to grow. Given that a substantial fraction of this burden is preventable, better adherence to evidence-informed asthma management strategies by care providers and patients has the potential to substantially reduce costs and improve quality of life.
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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 | 0.001 | 0.000 |
| 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 | 0.001 |
| 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".