A101 TRENDS IN MEDICAID SPENDING FOR HEPATITIS C TREATMENT FROM 2012-2021
Bibliographic record
Abstract
Abstract Background Hepatitis C virus (HCV) infection is a pervasive disease that reduces quality and quantity of life. Medicaid is the largest health insurance program in the United States that provides coverage to individuals with low income including children, pregnant individuals, and people with disabilities. Due to the impact of HCV on individuals and the world goal for elimination of HCV by 2030, an analysis of healthcare costs attributed to hepatitis C medications is imperative to guide further policy and coverage changes. Aims To analyze Medicaid spending and utilization for hepatitis C treatment from 2012 to 2021 and provide a reflection on the impact it has on healthcare costs in the USA. Methods The Centers for Medicare & Medicaid Services public database was accessed to obtain Medicaid spending on Hepatitis C medications from 2012-2021. Data extracted included brand and generic drugs to treat HCV, total annual spending, total dosage units prescribed, total number of claims, and average spending per dosage unit and claim. Microsoft Excel was used for data analysis and creation of graphs. Results A total of 20 oral medications were included in this study. Total spending on all medications per year increased from $184 million in 2012 and peaked in 2016 at $3.3 billion. In 2021, the highest amount of Medicaid spending was on Mavyret ($658 million) followed by generic sofosbuvir-velpatasvir ($389 million) and Epclusa ($262 million). The total number of claims decreased from 135,542 in 2012 to 121,101 in 2021 with a peak in 2016 at 159,826. During the same time, the average spending per claim increased from 2012 ($6,518) to 2021 ($146,792). In 2021, the highest average spending per claim was seen with Harvoni ($30,941) followed by Sovaldi ($27,247), Vosevi ($23,877), and Epclusa ($22,917). In the same year, Mavyret had the greatest number of claims at 51,500 followed by generic sofosbuvir-velpatasvir (50,115), Epclusa (11,437), and Vosevi (2,177). Conclusions Despite the number of claims declining from 2012 to 2021, the average spending has increased due to the significant cost of HCV medications on Medicaid spending. The highest spending was noted to be with originator medications while generic formulations had the highest number of claims. Understanding the trends in spending on HCV medications can help guide insurance coverage changes for those depending on Medicaid. Lastly, shifting policies towards increased use of generic medications can help with increasing access and reducing costs to the population. Figure 1. Total annual spending per year in billions ($ USD, blue bars) and total number of claims (orange line) per year from 2012 to 2021. Funding Agencies None
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".