Estimating the impact of insurance expansion on colorectal cancer and related costs in North Carolina: A population-level simulation analysis.

Although screening is effective in reducing incidence, mortality, and costs of treating colorectal cancer (CRC), it remains underutilized, in part due to limited insurance access. We used microsimulation to estimate the health and financial effects of insurance expansion and reduction scenarios in North Carolina (NC). We simulated the full lifetime of a simulated population of 3,298,265 residents age-eligible for CRC screening (ages 50-75) during a 5-year period starting January 1, 2018, including polyp incidence and progression and CRC screening, diagnosis, treatment, and mortality. Insurance scenarios included: status quo, which in NC includes access to the Health Insurance Exchange (HIE) under the Affordable Care Act (ACA); no ACA; NC Medicaid expansion, and Medicare-for-all. The insurance expansion scenarios would increase percent up-to-date with screening by 0.3 and 7.1 percentage points for Medicaid expansion and Medicare-for-all, respectively, while insurance reduction would reduce percent up-to-date by 1.1 percentage points, compared to the status quo (51.7% up-to-date), at the end of the 5-year period. Throughout these individuals' lifetimes, this change in CRC screening/testing results in an estimated 498 CRC cases averted with Medicaid expansion and 6031 averted with Medicare-for-all, and an additional 1782 cases if health insurance gains associated with ACA are lost. Estimated cost savings - balancing increased CRC screening/testing costs against decreased cancer treatment costs - are approximately $30 M and $970 M for Medicaid expansion and Medicare-for-all scenarios, respectively, compared to status quo. Insurance expansion is likely to improve CRC screening both overall and in underserved populations while saving money, with the largest savings realized by Medicare.

Declining liver graft quality threatens the future of liver transplantation in the United States.

National liver transplantation (LT) volume has declined since 2006, in part because of worsening donor organ quality. Trends that degrade organ quality are expected to continue over the next 2 decades. We used the United Network for Organ Sharing (UNOS) database to inform a 20-year discrete event simulation estimating LT volume from 2010 to 2030. Data to inform the model were obtained from deceased organ donors between 2000 and 2009. If donor liver utilization practices remain constant, utilization will fall from 78% to 44% by 2030, resulting in 2230 fewer LTs. If transplant centers increase their risk tolerance for marginal grafts, utilization would decrease to 48%. The institution of "opt-out" organ donation policies to increase the donor pool would still result in 1380 to 1866 fewer transplants. Ex vivo perfusion techniques that increase the use of marginal donor livers may stabilize LT volume. Otherwise, the number of LTs in the United States will decrease substantially over the next 15 years. In conclusion, the transplant community will need to accept inferior grafts and potentially worse posttransplant outcomes and/or develop new strategies for increasing organ donation and utilization in order to maintain the number of LTs at the current level.

When History and Heterogeneity Matter: A Tutorial on the Impact of Markov Model Specifications in the Context of Colorectal Cancer Screening.

BACKGROUND: Markov models are used in health research to simulate health care utilization and disease states over time. Health phenomena, however, are complex, and the memoryless assumption of Markov models may not appropriately represent reality. This tutorial provides guidance on the use of Markov models of different orders and stratification levels in health decision-analytic modeling. Colorectal cancer (CRC) screening is used as a case example to examine the impact of using different Markov modeling approaches on CRC outcomes. METHODS: This study used insurance claims data from commercially insured individuals in Oregon to estimate transition probabilities between CRC screening states (no screen, colonoscopy, fecal immunochemical test or fecal occult blood test). First-order, first-order stratified by sex and geography, and third-order Markov models were compared. Screening trajectories produced from the different Markov models were incorporated into a microsimulation model that simulated the natural history of CRC disease progression. Simulation outcomes (e.g., future screening choices, CRC incidence, deaths due to CRC) were compared across models. RESULTS: Simulated CRC screening trajectories and resulting CRC outcomes varied depending on the Markov modeling approach used. For example, when using the first-order, first-order stratified, and third-order Markov models, 30%, 31%, and 44% of individuals used colonoscopy as their only screening modality, respectively. Screening trajectories based on the third-order Markov model predicted that a higher percentage of individuals were up-to-date with CRC screening as compared with the other Markov models. LIMITATIONS: The study was limited to insurance claims data spanning 5 y. It was not possible to validate which Markov model better predicts long-term screening behavior and outcomes. CONCLUSIONS: Findings demonstrate the impact that different order and stratification assumptions can have in decision-analytic models. HIGHLIGHTS: This tutorial uses colorectal cancer screening as a case example to provide guidance on the use of Markov models of different orders and stratification levels in health decision-analytic models.Colorectal cancer screening trajectories and projected health outcomes were sensitive to the use of alternate Markov model specifications.Although data limitations precluded the assessment of model accuracy beyond a 5-y period, within the 5-y period, the third-order Markov model was slightly more accurate in predicting the fifth colorectal cancer screening action than the first-order Markov model.Findings from this tutorial demonstrate the importance of examining the memoryless assumption of the first-order Markov model when simulating health care utilization over time.