Costs Associated with Health Care Services Accessed through <scp>VA</scp> and in the Community through Medicare for Veterans Experiencing Homelessness
Bibliographic record
Abstract
OBJECTIVE: To estimate health care utilization and costs incurred by homeless Veterans relative to nonhomeless Veterans and to examine the impact of a VA homelessness program on these outcomes. DATA SOURCES/STUDY SETTING: Combined Department of Veterans Affairs (VA) administrative and Medicare claims data. STUDY DESIGN: Observational study using longitudinal data from Veterans engaged with the VA system and enrolled in Medicare. Veterans with administrative evidence of homelessness at any point during 2006-2010 were matched on period of military service to Veterans with no evidence of homelessness. PRINCIPAL FINDINGS: Experience of homelessness was associated with 1.37 (95 percent CI = 1.34-1.40) and 0.16 (95 percent CI = 0.14-0.17) more outpatient encounters per quarter in VA and non-VA settings, respectively, and 1.31 (95 percent CI = 1.30-1.32) and 0.49 (95 percent CI = 0.48-0.49) more inpatient days per quarter in VA and non-VA hospitals, respectively. These were associated with higher costs. Relative to stably housed Veterans less than 65 years of age, those enrolled in a VA homelessness program had 94.4 percent (95 percent CI = 90.7 percent-98.1 percent) more VA outpatient visits but 5.5 percent (95 percent CI = 3.0 percent-7.9 percent) fewer Medicare outpatient visits. CONCLUSIONS: Homelessness was associated with an increase in VA and Medicare utilization and cost. A VA homelessness program decreased use of Medicare outpatient services.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".