The Impact of Health Care Reform On Hospital and Preventive Care: Evidence from Massachusetts
Bibliographic record
Abstract
In April 2006, the state of Massachusetts passed legislation aimed at achieving near universal health insurance coverage.A key provision of this legislation, and of the national legislation passed in March 2010, is an individual mandate to obtain health insurance.Although previous researchers have studied the impact of expansions in health insurance coverage among the indigent, children, and the elderly, the Massachusetts reform gives us a novel opportunity to examine the impact of expansion to near-universal health insurance coverage among the entire state population.In this paper, we are the first to use hospital data to examine the impact of this legislation on insurance coverage, utilization patterns, and patient outcomes in Massachusetts.We use a difference-in-difference strategy that compares outcomes in Massachusetts after the reform to outcomes in Massachusetts before the reform and to outcomes in other states.We embed this strategy in an instrumental variable framework to examine the effect of insurance coverage on utilization patterns.Using the Current Population Survey, we find that the reform increased insurance coverage among the general Massachusetts population.Our main source of data is a nationally-representative sample of approximately 20% of hospitals in the United States.Among the population of hospital discharges in Massachusetts, the reform decreased uninsurance by 36% relative to its initial level.We also find that the reform affected utilization patterns by decreasing length of stay and the number of inpatient admissions originating from the emergency room.Using new measures of preventive care, we find some evidence that hospitalizations for preventable conditions were reduced.The reform affected nearly all age, gender, income, and race categories.We also examine costs on the hospital level and find that hospital cost growth did not increase after the reform in Massachusetts relative to other states.
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".