Effect of Medicaid Policy Changes on Medication Adherence: Differences by Baseline Adherence
Bibliographic record
Abstract
BACKGROUND: In 2001, the North Carolina (NC) Medicaid program reduced the number of days prescription supply that enrollees could fill from 100 days to 34 days and increased copayments for brand-name medications. Previous work has shown that a change in these policies led to a decrease in medication adherence from 2.9 to 8.0 percentage points in specific populations with chronic conditions. Studies have also shown that days supply limits and copayment increases have heterogeneous effects based on enrollees' baseline characteristics, including baseline adherence. However, this phenomenon has not been studied in the Medicaid population. We undertook this study to assess the heterogeneous effect of the NC Medicaid policy changes in groups with varying levels of baseline adherence. OBJECTIVE: To examine whether restrictions on days supply had heterogeneous effects in subgroups defined by medication adherence before the policy changes. METHODS: A partial difference-in-difference-in-differences model with fixed effects was used to compare medication adherence before and after the NC Medicaid policy changes among Medicaid enrollees subject to the policy changes because of their use of long prescriptions (> 40 days) as compared with (a) NC Medicaid enrollees using short prescriptions (< 40 days) before policy adoption, as well as (b) Medicaid enrollees in Georgia restricted to a 31 days supply through the study period. Medicaid enrollees were included if they filled a prescription for 1 of the following medication classes: antihypertensives, lipid-lowering drugs, or antipsychotics. The effect of the policy changes on medication adherence, calculated using the proportion of days covered (PDC) each quarter by baseline adherence level and clinical condition group, was studied. Average adherence levels over the 18-month prechange period were used to stratify individuals into 3 baseline adherence groups: fully adherent (PDC ≥ 80%), partially adherent (50%-79%), and nonadherent (PDC ≤ 50%). RESULTS: Enrollees fully adherent at baseline observed a 2.0 (P = 0.001) and 1.2 (P < 0.001) percentage-point decline in adherence for the lipid-lowering drug and antihypertensive cohorts, respectively, in the period after the policy changes. The nonadherent and partially adherent cohorts in the statin group observed an increase in adherence by 1.7-2.6 (P < 0.05) percentage points in the post-index period. CONCLUSIONS: Adherence changes after cost containment policies have a heterogeneous effect on individuals with varying baseline adherence in the Medicaid population. Individuals fully adherent at baseline decreased adherence following policy changes, while individuals partially adherent and nonadherent at baseline either had no change or showed increases in adherence, possibly because of increased contact with pharmacists and clinicians required by shorter prescription lengths. Managed care strategies to control costs should take into consideration the heterogeneity of responses by the enrollees to these policies. Furthermore, policies that consider baseline characteristics of enrollees may be more effective in improving adherence. DISCLOSURES: This study was partly funded by a grant from the Robert Wood Johnson Foundation for use in data creation. Maciejewski was supported by a Research Career Scientist Award from the Department of Veterans Affairs (RCS 10-391) and owns stock in Amgen. Farley reports consultancy fees from Daiichi Sankyo outside of the conduct of this study. The other authors report no financial or other conflicts of interest related to the subject of this article. The views expressed in this article are those of the authors and do not reflect the position or policy of the Centers for Medicare & Medicaid Services, University of North Carolina at Chapel Hill, Department of Veteran Affairs, or Duke University. Study design and concept were contributed by Amin and Domino, along with Farley and Maciejewski. Domino collected the data, and data interpretation was performed primarily by Amin, along with Domino, with assistance from Farley and Maciejewski. The manuscript was primarily written by Amin, along with Domino, and revised by all the authors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 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".