Medicaid Expansion and Restriction Policies for Hepatitis C Treatment.

Importance: Hepatitis C can be cured with direct-acting antivirals (DAAs), but Medicaid programs have implemented fibrosis, sobriety, and prescriber restrictions to control costs. Although restrictions are easing, understanding their association with hepatitis C treatment rates is crucial to inform policies that increase access to lifesaving treatment. Objective: To estimate the association of jurisdictional (50 states and Washington, DC) DAA restrictions and Medicaid expansion with the number of Medicaid recipients with filled prescriptions for DAAs. Design, Setting, and Participants: This cross-sectional study used publicly available Medicaid documents and claims data from January 1, 2014, to December 31, 2021, to compare the number of unique Medicaid recipients treated with DAAs in each jurisdiction year with Medicaid expansion status and categories of fibrosis, sobriety, and prescriber restrictions. Medicaid recipients from all 50 states and Washington, DC, during the study period were included. Multilevel Poisson regression was used to estimate the association between Medicaid expansion and DAA restrictive policies on jurisdictional Medicaid DAA prescription fills. Data were analyzed initially from August 15 to November 15, 2023, and subsequently from April 15 to May 9, 2024. Exposures: Jurisdictional Medicaid expansion status and fibrosis, sobriety, and prescriber DAA restrictions. Main Outcomes and Measures: Number of people treated with DAAs per 100 000 Medicaid recipients per year. Results: A total of 381 373 Medicaid recipients filled DAA prescriptions during the study period (57.3% aged 45-64 years; 58.7% men; 15.2% non-Hispanic Black and 52.2% non-Hispanic White). Medicaid nonexpansion jurisdictions had fewer filled DAA prescriptions per 100 000 Medicaid recipients per year than expansion jurisdictions (38.6 vs 86.6; adjusted relative risk [ARR], 0.56 [95% CI, 0.52-0.61]). Jurisdictions with F3 to F4 (34.0 per 100 000 Medicaid recipients per year; ARR, 0.39 [95% CI, 0.37-0.66]) or F1 to F2 fibrosis restrictions (61.9 per 100 000 Medicaid recipients per year; ARR, 0.62 [95% CI, 0.59-0.66]) had lower treatment rates than jurisdictions without fibrosis restrictions (94.8 per 100 000 Medicaid recipients per year). Compared with no sobriety restrictions (113.5 per 100 000 Medicaid recipients per year), 6 to 12 months of sobriety (38.3 per 100 000 Medicaid recipients per year; ARR, 0.65 [95% CI, 0.61-0.71]) and screening and counseling requirements (84.7 per 100 000 Medicaid recipients per year; ARR, 0.87 [95% CI, 0.83-0.92]) were associated with reduced treatment rates, while 1 to 5 months of sobriety was not statistically significantly different. Compared with no prescriber restrictions (97.8 per 100 000 Medicaid recipients per year), specialist consult restrictions was associated with increased treatment (66.2 per 100 000 Medicaid recipients per year; ARR, 1.05 [95% CI, 1.00-1.10]), while specialist required restrictions were not statistically significant. Conclusions and Relevance: In this cross-sectional study, Medicaid nonexpansion status, fibrosis, and sobriety restrictions were associated with a reduction in the number of people with Medicaid who were treated for hepatitis C. Removing DAA restrictions might facilitate treatment of more people diagnosed with hepatitis C.

Hepatitis C Virus Clearance Cascade - United States, 2013-2022.

Approximately 2.4 million adults were estimated to have hepatitis C virus (HCV) infection in the United States during 2013-2016 (1). Untreated, hepatitis C can lead to advanced liver disease, liver cancer, and death (2). The Viral Hepatitis National Strategic Plan for the United States calls for ≥80% of persons with hepatitis C to achieve viral clearance by 2030 (3). Characterizing the steps that follow a person's progression from testing to viral clearance and subsequent infection (clearance cascade) is critical for monitoring progress toward national elimination goals. Following CDC guidance (4), a simplified national laboratory results-based HCV five-step clearance cascade was developed using longitudinal data from a large national commercial laboratory throughout the decade since highly effective hepatitis C treatments became available. During January 1, 2013-December 31, 2021, a total of 1,719,493 persons were identified as ever having been infected with HCV. During January 1, 2013-December 31, 2022, 88% of those ever infected were classified as having received viral testing; among those who received viral testing, 69% were classified as having initial infection; among those with initial infection, 34% were classified as cured or cleared (treatment-induced or spontaneous); and among those persons, 7% were categorized as having persistent infection or reinfection. Among the 1.0 million persons with evidence of initial infection, approximately one third had evidence of viral clearance (cured or cleared). This simplified national HCV clearance cascade identifies substantial gaps in cure nearly a decade since highly effective direct-acting antiviral (DAA) agents became available and will facilitate the process of monitoring progress toward national elimination goals. It is essential that increased access to diagnosis, treatment, and prevention services for persons with hepatitis C be addressed to prevent progression of disease and ongoing transmission and achieve national hepatitis C elimination goals.

A Multistate Decomposition Analysis of Cesarean Rate Variations, Associated Health Outcomes, and Financial Implications in the United States.

OBJECTIVES: Cesarean rates vary widely across the U.S. states; however, little is known about the causes and implications associated with these variations. The objectives of this study were to quantify the contribution of the clinical and nonclinical factors in explaining the difference in cesarean rates across states and to investigate the associated health outcome of cesarean variations. STUDY DESIGN: Using the Hospital Cost and Utilization Project State Inpatient Databases, this retrospective study included all nonfederal hospital births from Wisconsin, Florida, and New York. A nonlinear extension of the Oaxaca-Blinder method was used to decompose the contributions of differences in characteristics to cesarean variations between these states. The risk factors for cesarean delivery were identified using separate multivariable logistic regression analysis for each State. RESULTS: The difference in clinical and nonclinical factors explained a substantial (~46.57-65.45%) proportion of cesarean variations between U.S. states. The major contributors of variation were patient demographics, previous cesareans, hospital markup ratios, and social determinants of health. Cesarean delivery was significantly associated with higher postpartum readmissions and unplanned emergency department visits, greater lengths of stay, and hospital costs across all states. CONCLUSION: Although a proportion of variations in cesarean rates can be explained by the differences in risk factors, the remaining unexplained variations suggest differences in practice patterns and imply potential quality concerns. Since nonclinical factors are likely to play an important role in cesarean variation, we recommend targeted initiatives increasing access to maternal care and improving maternal health literacy. KEY POINTS: · Cesarean rates vary widely almost two folds within U.S. states.. · The difference in risk factors explained substantial (~46.57-65.45%) of the cesarean variations.. · Mother race, hospital factors, and social determinants comprised major proportion of explained variation.. · Adverse outcomes and increased expenditures were associated with cesarean than vaginal delivery.. · Significant potential cost savings for Medicaid if the unnecessary cesarean deliveries are reduced..

Multi-Objective Reinforcement Learning Based Healthcare Expansion Planning Considering Pandemic Events.

Hospital capacity expansion planning is critical for a healthcare authority, especially in regions with a growing diverse population. Policymaking to this end often requires satisfying two conflicting objectives, minimizing capacity expansion cost and minimizing the number of denial of service (DoS) for patients seeking hospital admission. The uncertainty in hospital demand, especially considering a pandemic event, makes expansion planning even more challenging. This work presents a multi-objective reinforcement learning (MORL) based solution for healthcare expansion planning to optimize expansion cost and DoS simultaneously for pandemic and non-pandemic scenarios. Importantly, our model provides a simple and intuitive way to set the balance between these two objectives by only determining their priority percentages, making it suitable across policymakers with different capabilities, preferences, and needs. Specifically, we propose a multi-objective adaptation of the popular Advantage Actor-Critic (A2C) algorithm to avoid forced conversion of DoS discomfort cost to a monetary cost. Our case study for the state of Florida illustrates the success of our MORL based approach compared to the existing benchmark policies, including a state-of-the-art deep RL policy that converts DoS to economic cost to optimize a single objective.

Vital Signs: Hepatitis C Treatment Among Insured Adults - United States, 2019-2020.

INTRODUCTION: Over 2 million adults in the United States have hepatitis C virus (HCV) infection, and it contributes to approximately 14,000 deaths a year. Eight to 12 weeks of highly effective direct-acting antiviral (DAA) treatment, which can cure ≥95% of cases, is recommended for persons with hepatitis C. METHODS: Data from HealthVerity, an administrative claims and encounters database, were used to construct a cohort of adults aged 18-69 years with HCV infection diagnosed during January 30, 2019-October 31, 2020, who were continuously enrolled in insurance for ≥60 days before and ≥360 days after diagnosis (47,687). Multivariable logistic regression was used to assess the association between initiation of DAA treatment and sex, age, race, payor, and Medicaid restriction status; adjusted odds ratios (aORs) and 95% CIs were calculated. RESULTS: The prevalence of DAA treatment initiation within 360 days of the first positive HCV RNA test result among Medicaid, Medicare, and private insurance recipients was 23%, 28%, and 35%, respectively; among those treated, 75%, 77%, and 84%, respectively, initiated treatment within 180 days of diagnosis. Adjusted odds of treatment initiation were lower among those with Medicaid (aOR = 0.54; 95% CI = 0.51-0.57) and Medicare (aOR = 0.62; 95% CI = 0.56-0.68) than among those with private insurance. After adjusting for insurance type, treatment initiation was lowest among adults aged 18-29 and 30-39 years with Medicaid or private insurance, compared with those aged 50-59 years. Among Medicaid recipients, lower odds of treatment initiation were found among persons in states with Medicaid treatment restrictions (aOR = 0.77; 95% CI = 0.74-0.81) than among those in states without restrictions, and among persons whose race was coded as Black or African American (Black) (aOR = 0.93; 95% CI = 0.88-0.99) or other race (aOR = 0.73; 95% CI = 0.62-0.88) than those whose race was coded as White. CONCLUSIONS AND IMPLICATIONS FOR PUBLIC HEALTH PRACTICE: Few insured persons with diagnosed hepatitis C receive timely DAA treatment, and disparities in treatment exist. Unrestricted access to timely DAA treatment is critical to reducing viral hepatitis-related mortality, disparities, and transmission. Treatment saves lives, prevents transmission, and is cost saving.

Impact of Statewide Mandatory Medicaid Managed Care (SMMC) Programs on Hospital Obstetric Outcomes.

The state of Florida implemented mandatory managed care for Medicaid enrollees via the Statewide Medicaid Managed Care (SMMC) program in April of 2014. The objective of this study was to examine the impact of the implementation of the SMMC program on the access to care and quality of maternal care for Medicaid enrollees, as measured by several hospital obstetric outcomes. The primary data source for this retrospective observational study was the Hospital Cost and Utilization Project (HCUP) all-payer State ED (SED) visit and State Inpatient Databases (SIDs) from 2010 to 2017. The primary health outcomes for obstetric care were primary cesarean, preterm birth, postpartum preventable ED visits, postpartum preventable readmissions, and vaginal delivery after cesarean (VBAC) rates. Using difference-in-differences (DID) estimation, selected health outcomes were examined for Florida residents with Medicaid beneficiaries (treatment) and the commercially insured population (comparison), before and after the implementation of SMMC. Improvement in disparities for racial/ethnic minority Medicaid enrollees was estimated relative to whites, compared to the relative change among commercially insured patients. From the DID estimation, the findings showed that SMMC is statistically significantly associated with a higher reduction in primary cesarean rates, preterm births, preventable postpartum ED visits, and readmissions among Medicaid beneficiaries relative to their commercially insured counterparts. However, this study did not find any significant reduction in racial/ethnic disparities in obstetric outcomes. In general, this study highlights the impact of SMMC implementation on obstetric outcomes in Florida and provides important insights and potential scope for improvement in obstetric care quality and associated racial/ethnic disparities.

Impact of the COVID-19 Pandemic on the Pediatric Hospital Visits: Evidence from the State of Florida.

Although early evidence reported a substantial decline in pediatric hospital visits during COVID-19, it is unclear whether the decline varied across different counties, particularly in designated Medically Underserved Areas (MUA). The objective of this study is to explore the state-wide impact of COVID-19 on pediatric hospital visit patterns, including the economic burden and MUA communities. We conducted a retrospective observational study of pediatric hospital visits using the Florida State all-payer Emergency Department (ED) and Inpatient dataset during the pandemic (April-September 2020) and the same period in 2019. Pediatric Treat-and-Release ED and inpatient visit rates were compared by patient demographics, socioeconomic, diagnosis, MUA status, and hospital characteristics. Pediatric hospital visits in Florida decreased by 53.7% (62.3% in April-June, 44.2% in July-September) during the pandemic. The Treat-and-Release ED and inpatient visits varied up to 5- and 3-fold, respectively, across counties. However, changes in hospital visits across MUA counties were similar compared with non-MUA counties except for lower Treat-and-Release ED volume in April-May. The disproportional decrease in visits was notable for the underserved population, including Hispanic and African American children; Medicaid coverages; non-children's hospitals; and diagnosed with respiratory diseases, appendicitis, and sickle-cell. Florida Hospitals experienced a USD 1.37 billion (average USD 8.3 million) decline in charges across the study period in 2020. Disproportionate decrease in hospital visits, particularly in the underserved population, suggest a combined effect of the persistent challenge of care access and changes in healthcare-seeking behavior during the pandemic. These findings suggest that providers and policymakers should emphasize alternative interventions/programs ensuring adequate care during the pandemic, particularly for high-risk children.

Characteristics and Outcomes of Pediatric Nonindex Readmission: Evidence From Florida Hospitals.

OBJECTIVES: Increasing pediatric care regionalization may inadvertently fragment care if children are readmitted to a different (nonindex) hospital rather than the discharge (index) hospital. Therefore, this study aimed to assess trends in pediatric nonindex readmission rates, examine the risk factors, and determine if this destination difference affects readmission outcomes. METHODS: In this retrospective cohort study, we use the Healthcare Cost and Utilization Project State Inpatient Database to include pediatric (0 to 18 years) admissions from 2010 to 2017 across Florida hospitals. Risk factors of nonindex readmissions were identified by using logistic regression analyses. The differences in outcomes between index versus nonindex readmissions were compared for in-hospital mortality, morbidity, hospital cost, length of stay, against medical advice discharges, and subsequent hospital visits by using generalized linear regression models. RESULTS: Among 41 107 total identified readmissions, 5585 (13.6%) were readmitted to nonindex hospitals. Adjusted nonindex readmission rate increased from 13.3% in 2010% to 15.4% in 2017. Patients in the nonindex readmissions group were more likely to be adolescents, live in poor neighborhoods, have higher comorbidity scores, travel longer distances, and be discharged at the postacute facility. After risk adjusting, no difference in in-hospital mortality was found, but morbidity was 13% higher, and following unplanned emergency department visits were 28% higher among patients with nonindex readmissions. Length of stay, hospital costs, and against medical advice discharges were also significantly higher for nonindex readmissions. CONCLUSIONS: A substantial proportion of children experienced nonindex readmissions and relatively poorer health outcomes compared with index readmission. Targeted strategies for improving continuity of care are necessary to improve readmission outcomes.

Identifying Children at Readmission Risk: At-Admission versus Traditional At-Discharge Readmission Prediction Model.

The timing of 30-day pediatric readmissions is skewed with approximately 40% of the incidents occurring within the first week of hospital discharges. The skewed readmission time distribution coupled with delay in health information exchange among healthcare providers might offer a limited time to devise a comprehensive intervention plan. However, pediatric readmission studies are thus far limited to the development of the prediction model after hospital discharges. In this study, we proposed a novel pediatric readmission prediction model at the time of hospital admission which can improve the high-risk patient selection process. We also compared proposed models with the standard at-discharge readmission prediction model. Using the Hospital Cost and Utilization Project database, this prognostic study included pediatric hospital discharges in Florida from January 2016 through September 2017. Four machine learning algorithms-logistic regression with backward stepwise selection, decision tree, Support Vector machines (SVM) with the polynomial kernel, and Gradient Boosting-were developed for at-admission and at-discharge models using a recursive feature elimination technique with a repeated cross-validation process. The performance of the at-admission and at-discharge model was measured by the area under the curve. The performance of the at-admission model was comparable with the at-discharge model for all four algorithms. SVM with Polynomial Kernel algorithms outperformed all other algorithms for at-admission and at-discharge models. Important features associated with increased readmission risk varied widely across the type of prediction model and were mostly related to patients' demographics, social determinates, clinical factors, and hospital characteristics. Proposed at-admission readmission risk decision support model could help hospitals and providers with additional time for intervention planning, particularly for those targeting social determinants of children's overall health.

Prediction of Chronic Disease-Related Inpatient Prolonged Length of Stay Using Machine Learning Algorithms.

OBJECTIVES: The study aimed to develop and compare predictive models based on supervised machine learning algorithms for predicting the prolonged length of stay (LOS) of hospitalized patients diagnosed with five different chronic conditions. METHODS: An administrative claim dataset (2008-2012) of a regional network of nine hospitals in the Tampa Bay area, Florida, USA, was used to develop the prediction models. Features were extracted from the dataset using the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) codes. Five learning algorithms, namely, decision tree C5.0, linear support vector machine (LSVM), k-nearest neighbors, random forest, and multi-layered artificial neural networks, were used to build the model with semi-supervised anomaly detection and two feature selection methods. Issues with the unbalanced nature of the dataset were resolved using the Synthetic Minority Over-sampling Technique (SMOTE). RESULTS: LSVM with wrapper feature selection performed moderately well for all patient cohorts. Using SMOTE to counter data imbalances triggered a tradeoff between the model's sensitivity and specificity, which can be masked under a similar area under the curve. The proposed aggregate rank selection approach resulted in a balanced performing model compared to other criteria. Finally, factors such as comorbidity conditions, source of admission, and payer types were associated with the increased risk of a prolonged LOS. CONCLUSIONS: Prolonged LOS is mostly associated with pre-intraoperative clinical and patient socioeconomic factors. Accurate patient identification with the risk of prolonged LOS using the selected model can provide hospitals a better tool for planning early discharge and resource allocation, thus reducing avoidable hospitalization costs.