Trends in Health Care Spending for Children in Medicaid With High Resource Use
Bibliographic record
Abstract
OBJECTIVES: To assess characteristics associated with health care spending trends among child high resource users in Medicaid. METHODS: This retrospective analysis included 48 743 children ages 1 to 18 years continuously enrolled from 2009-2013 in 10 state Medicaid programs (Truven MarketScan Medicaid Database) also in the top 5% of all health care spending in 2010. Using multivariable regression, associations were assessed between baseline demographic, clinical, and health services characteristics (using 2009-2010 data) with subsequent health care spending (ie, transiently, intermittently, persistently high) from 2011-2013. RESULTS: High spending from 2011-2013 was transient for 54.2%, persistent for 32.9%, and intermittent for 12.9%. Regarding demographic characteristics, the highest likelihood of persistent versus transient spending occurred in children aged 13 to 18 years versus 1 to 2 years in 2010 (odds ratio [OR], 3.0 [95% confidence interval (CI), 2.7-3.4]). Regarding clinical characteristics, the highest likelihoods were in children with ≥6 chronic conditions (OR, 4.8 [95% CI, 3.5-6.6]), a respiratory complex chronic condition (OR, 2.5 [95% CI, 2.2-2.8]), or a neuromuscular complex chronic condition (OR, 2.3 [95% CI, 2.2-2.5]). Hospitalization and emergency department (ED) use in 2010 were associated with a decreased likelihood of persistent spending in 2011-2013 (hospitalization OR, 0.7 [95% CI, 0.7-0.7]); ED OR, 0.8 [95% CI, 0.8-0.8]). CONCLUSIONS: Most children with high spending in Medicaid are without persistently high spending in subsequent years. Adolescent age, multiple chronic conditions, and certain complex chronic conditions increased the likelihood of persistently high spending; hospital and ED use decreased it. These data may help inform the development of new models of care and financing to optimize health and save resources in children with high resource use.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".