The Ecology of Medical Care Before and After the Affordable Care Act: Trends From 2002 to 2016
Bibliographic record
Abstract
BACKGROUND: The initial ecology of medical care study was published in 1961, offering a framework by which to investigate individuals' contact with the medical system. We studied changes in the framework around the implementation of the Patient Protection and Affordable Care Act (ACA) within longer-term trends. METHODS: The 2002-2016 Medical Expenditure Panel Survey was used to determine rates of visit/contact per 1,000 individuals per month for physicians, primary care physicians, specialty physicians, emergency departments, inpatient hospitalizations, dental visits, and home health visits for the overall population and by age group, poverty category, health status, and race/ethnicity. Adjusted Wald tests were used to investigate differences between the pre-ACA (2012-2013) and post-ACA (2014-2015) periods. Multivariable linear regression was used to determine trends over the study period (2002-2016). RESULTS: The survey included 525,804 person-years. The uninsured rate decreased from 12.8% (95% CI, 12.0%-13.7%) in 2013 to 7.6% (95% CI, 7.0%-8.3%) in 2016. From 2002 to 2016, the numbers of individuals in a month who had contact with primary care physicians, dental care, and inpatient hospitalizations decreased. Primary care physician contact decreased most among the elderly and those reporting fair/poor health. After ACA implementation, few significant changes were identified in the overall population or by age, poverty category, race/ethnicity, or health status. CONCLUSIONS: The medical ecology framework was not notably altered 2 years after implementation of the ACA. The long-term decrease in primary care contact does not appear to have been interrupted after implementation of the ACA, was observed across income and age categories, and was most evident among the elderly and individuals reporting fair/poor health.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".