Prescription Drug Utilization and Reimbursement Increased Following State Medicaid Expansion in 2014
Bibliographic record
Abstract
BACKGROUND: The Affordable Care Act (ACA) expanded health care and medication insurance coverage through Medicaid expansion in select states. Expansion has the potential to increase the availability of health services to patients, including prescription medications. However, limited studies have examined how expansion affected prescription drug utilization and reimbursement. OBJECTIVE: To compare prescription drug utilization (number of prescriptions filled) and reimbursement trends between states that did and did not expand Medicaid coverage in 2014, while accounting for known effects of expansion on Medicaid enrollment. METHODS: We conducted a comparative interrupted time series using retrospective Medicaid state drug utilization data from 2011 to 2014. After inclusion/exclusion criteria, 8 states that expanded Medicaid in 2014 and 10 states that did not expand Medicaid were studied. Primary outcomes were changes in quarterly prescription drug utilization and quarterly total prescription drug reimbursement before and after expansion. To account for increases in enrollment in expansion states, secondary outcomes were per-member-per-quarter (PMPQ) utilization and reimbursement before and after expansion. RESULTS: Expansion states experienced a 1.4 million prescriptions per quarter and $163 million per quarter increase in utilization and reimbursement above the change in rates observed in nonexpansion states after expansion (P < 0.001). Specifically, 1 year after ACA implementation, expansion states used 17.0% more prescriptions and spent 36.1% more in reimbursement than the quarter preceding expansion. Expansion and nonexpansion states experienced significant drops in PMPQ prescriptions immediately after expansion (P < 0.001), but PMPQ prescriptions and reimbursement trends increased by the end of the postexpansion period in expansion states (P < 0.029 and P < 0.001, respectively). CONCLUSIONS: Study results suggest that Medicaid expansion offers vulnerable patients who were previously uninsured increased access to health care resources, specifically prescription drugs. Although this hypothesis would benefit from further testing, it aligns with previous studies that have shown that Medicaid expansion has led to increased access to coverage and care. While enrollment contributes to the increase in prescription utilization and reimbursement, the drop in PMPQ utilization suggests that the patients entering the program are healthier than existing patients. This shows that risk pooling is working. However, the increase in PMPQ reimbursement suggests that new enrollment may not be the only factor driving reimbursement changes. Factors such as changes in product mix, risk pool composition, and drug pricing and their effects on total and per-member reimbursement should be evaluated in future studies. DISCLOSURES: No outside funding supported this study. Mahendraratnam is currently a Worldwide Health Economics and Outcomes Research Pre-doctoral Fellow at Bristol-Myers Squibb and previously provided advisory services to public and private sector clients while employed at Avalere Health, an Inovalon Company, as well as completed an internship at Genentech, a member of the Roche Group. Farley and Dusetzina have no conflicts of interest to report. Preliminary results of this study were presented at the 2016 International Society for Pharmacoeconomics and Outcomes Research (ISPOR) 21st Annual Meeting in Washington, DC, on May 21-25, 2016, and the 2016 AcademyHealth Annual Research Meeting (ARM) in Boston, Massachusetts, on June 26-28, 2016. Study concept and design were contributed by Farley, Mahendraratnam, and Dusetzina. Mahendraratnam, Farley, and Dusetzina collected the data, and data interpretation was performed by all the authors. The manuscript was written by Mahendraratnam, Farley, and Dusetzina and revised by Farley, Dusetzina, and Mahendraratnam.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".