Up-to-Date on Preventive Care Services Under Affordable Care Act
Bibliographic record
Abstract
BACKGROUND: The utilization of preventive care services has been less than optimal. As part of an effort to address this, the Affordable Care Act (ACA) mandated that private health insurance plans cover evidence-based preventive services. OBJECTIVES: To evaluate whether the provisions of ACA have increased being up-to-date on recommended preventive care services among privately insured individuals aged 18-64. RESEARCH DESIGN: Multivariate linear regression models were used to examine trends in prevalence of being up-to-date on selected preventive services, diagnosis of health conditions, and health expenditures between pre-ACA (2007-2010) and post-ACA (2011-2014). Adjusted difference-in-difference analyses were used to estimate changes in those outcomes in the privately insured that differed from changes in the uninsured (control group). RESULTS: After the passage of ACA, up-to-date rates of routine checkup (2.7%; 95% confidence interval, 0.8%-4.7%; P=0.007) and flu vaccination (5.9%; 95% confidence interval, 4.2%-7.6%; P<0.001) increased among those with private insurance, as compared with the control group. Changes in blood pressure check, cholesterol check and cancer screening (pap smear test, mammography, and colorectal cancer screening) were not associated with the ACA. Prevalence in diagnosis of health conditions remained constant. Slower uptrends in adjusted total health care expenditures and downtrends in adjusted out-of-pocket costs were observed during the study period. CONCLUSIONS: The provisions of the ACA have resulted in trivial increases in being up-to-date on selected preventive care services. Additional efforts may be required to take full advantage of the elimination of cost-sharing under the ACA.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.004 | 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".