Comparison of social determinants of health in Medicaid vs commercial health plans
Bibliographic record
Abstract
Incorporating the measurement of social determinants of health (SDOH) into health care practice and US health policy reforms is a promising approach to improving population health nationwide. One way health care practitioners have started to incorporate consideration of SDOH in clinical care is by using International Classification of Diseases, Tenth Revision (ICD-10), Z-codes, a set of diagnosis codes spanning a range of social and economic circumstances. Our study summarizes Z-codes used by code type, setting, and patient demographics between Medicaid and commercial insurance to help identify strategies to optimize their use within each program and understand their differences. Overall, Z-code use was highly limited nationwide in Medicaid and commercial insurance between 2020 and 2021. Still, we found notable differences in the use of Z-codes between the programs; Medicaid beneficiaries were more likely to receive Z-codes related to financial and economic issues, while commercially insured beneficiaries were more likely to receive Z-codes indicating problems with social and familial relationships. Policy efforts focused on increasing the rate and ease of patient SDOH screening will potentially expand SDOH measurement and facilitate actions to address patient social needs.
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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 | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".