Shaping Health Policy for Low-Income Populations: An Assessment of Public Comments in a New Medicaid Waiver Process

Since the Supreme Court decided that the Affordable Care Act\u27s (ACA) Medicaid expansion is optional for the states, several have obtained federal approval to use Section 1115 waivers to expand Medicaid while changing its coverage and benefits design. There has long been concern that policy making for Medicaid populations may lack meaningful engagement with low-income constituents, and therefore the ACA established a new process under which the public can submit comments on pending Medicaid waiver applications. We analyzed 291 comment letters submitted to federal regulators pertaining to Medicaid Section 1115 waiver applications in the first five states to seek such waivers: Arkansas, Indiana, Iowa, Michigan, and Pennsylvania. We found that individual citizens, including those who identified as Medicaid-eligible, submitted a sizable majority of the comment letters. Comment letters tended to mention controversial provisions of the waivers and reflected the competing political rhetoric of “personal responsibility” versus “vulnerable populations.” Despite the fact that the federal government seemed likely to approve the waiver applications, we found robust public engagement, reflecting the salience of the issue of Medicaid expansion under the ACA. Our findings are consistent with the argument that Medicaid is a program of growing centrality in US health politics

Diabetes Self-Management Education Effects on Hemoglobin A1c

abstract: Diabetes, a common chronic condition, effects many individuals causing poor quality of life, expensive medical bills, and devasting medical complications. While health care providers try to manage diabetes during short office visits, many patients still struggle to control their diabetes at home. Lack of diabetes self-management (DSM) is a potential barrier for people with diabetes having to maintain healthy hemoglobin A1cs (HgA1c). In hopes of addressing this concern, an evidenced-based intervention; diabetic education and phone calls, using the chronic care model as its framework was implemented. The intervention targeted people with type II diabetes at a transitional care setting. Measured variables included HgA1c and DSM. Statistically significant improvements were seen in reported physical activity. Average improvements were seen in HgA1c and DSM after three months of diabetes self-management education (DSME). Attrition, cultural sensitivity, and increasing DSME hours should be further evaluated for future projects

Proving Foundation Impact on Public Policy Empirically: The Case of the Robert Wood Johnson Foundation and Consumer Choice for Adults With Developmental Disabilities

· Foundations that work on national public policy issues face challenges in demonstrating impact. · This case study of how the Robert Wood Johnson Foundation’s initiative to support choice of program provider for developmentally disabled adults uses some advanced statistical techniques to demonstrate the impact of the foundation’s funding. · This study suggests that to get the greatest impact on policy change, foundations should consider offering modest competitive grants to governmental departments; spending the funds in regional groupings; and focus on jurisdictions that have demonstrated interest in the policy area by spending their own funds

Penalties for Emergency Medical Treatment and Labor Act Violations Involving Obstetrical Emergencies

Introduction: The Emergency Medical Treatment and Labor Act (EMTALA) was intended to prevent inadequate, delayed, or denied treatment of emergent conditions by emergency departments (ED). While controversies exist regarding the scope of the law, there is no question that EMTALA applies to active labor, a key tenet of the statute and the only medical condition – labor – specifically included in the title of the law. In light of rising maternal mortality rates in the United States, further exploration into the state of emergency obstetrical (OB) care is warranted. Understanding civil monetary penalty settlements levied by the Office of the Inspector General (OIG) related to EMTALA violations involving labor and other OB emergencies will help to inform the current state of access to and quality of OB emergency care.Methods: We reviewed descriptions of all EMTALA-related OIG civil monetary penalty settlements from 2002-2018. OB-related cases were identified using keywords in settlement descriptions. We described characteristics of settlements including the nature of the allegation and compared them with non-OB settlements.Results: Of 232 EMTALA-related OIG settlements during the study period, 39 (17%) involved active labor and other OB emergencies. Between 2002 and 2018 the proportion of settlements involving OB emergencies increased from 17% to 40%. Seven (18%) of these settlements involved a pregnant minor. Most OB cases involved failure to provide screening exam (82%) and/or stabilizing treatment (51%). Failure to arrange appropriate transfer was more common for OB  (36%) compared with non-OB settlements (21%) (p = 0.041). Fifteen (38%) involved a provider specifically directing a pregnant woman to proceed to another hospital, typically by private vehicle.Conclusion: Despite inclusion of the term “labor” in the law’s title, one in six settlements related to EMTALA violations involved OB emergencies. One in five settlements involved a pregnant minor, indicating that providers may benefit from education regarding obligations to evaluate and stabilize minors absent parental consent. Failure to arrange appropriate transfer was more common among OB settlements. Findings suggesting need for providers to understand EMTALA-specific requirements for appropriate transfer and for EDs at hospitals without dedicated OB services to implement policies for evaluation of active labor and protocols for transfer when indicated

Outlining the State Budget: “Taxpayers Expect Us to Live within Our Means, part 1”

Revised budget fully funds pre–K-12 education growth, cuts $468 million from budget proposed in January with no new taxes.state, Tennessee, budget

Big Data Analytics of Medical Data

Data has become a huge part of modern decision making. With the improvements in computing performance and storage in the past two decades, storing large amounts of data has become much easier. Analyzing large amounts of data and creating data models with them can help organizations obtain insights and information which helps their decision making. Big data analytics has become an integral part of many fields such as retail, real estate, education, and medicine. In the project, the goal is to understand the working of Apache Spark and its different storage methods and create a data warehouse to analyze data. The data used is obtained from the CMS (Centers for Medicare & Medicaid Services) website. The data consists of information such as prescriber name, drug generic name, drug brand name, drug price. Using Apache Spark, a data warehouse, the data can be queried and transformed to obtain valuable insights. Using Power BI, the data can be visualized which makes the data easier to understand and highlights the trends in the data

Resurrecting immortal‐time bias in the study of readmissions

ObjectiveTo compare readmission rates as measured by the Centers for Medicare and Medicaid Services and the American College of Surgeons National Surgical Quality Improvement Program (NSQIP) methods.Data Sources20 percent sample of national Medicare data for patients undergoing cystectomy, colectomy, abdominal aortic aneurysm (AAA) repair, and total knee arthroplasty (TKA) between 2010 and 2014.Study DesignRetrospective cohort study comparing 30‐day readmission rates.Data Collection/Extraction MethodsPatients undergoing cystectomy, colectomy, abdominal aortic aneurysm repair, and total knee arthroplasty between 2010 and 2014 were identified.Principal FindingsCystectomy had the highest and total knee arthroplasty had the lowest readmission rate. The NSQIP measure reported significantly lower rates for all procedures compared to the CMS measure, which reflects an immortal‐time bias.ConclusionsWe found significantly different readmission rates across all surgical procedures when comparing CMS and NSQIP measures. Longer length of stay exacerbated these differences. Uniform outcome measures are needed to eliminate ambiguity and synergize research and policy efforts.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154628/1/hesr13252-sup-0001-Authormatrix.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154628/2/hesr13252.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154628/3/hesr13252_am.pd

Prediction models for hospital readmissions in patients with heart disease: a systematic review and meta-analysis

Objective: To describe the discrimination and calibration of clinical prediction models, identify characteristics that contribute to better predictions and investigate predictors that are associated with unplanned hospital readmissions. Design: Systematic review and meta-analysis. Data source: Medline, EMBASE, ICTPR (for study protocols) and Web of Science (for conference proceedings) were searched up to 25 August 2020. Eligibility criteria for selecting studies: Studies were eligible if they reported on (1) hospitalised adult patients with acute heart disease; (2) a clinical presentation of prediction models with c-statistic; (3) unplanned hospital readmission within 6 months. Primary and secondary outcome measures: Model discrimination for unplanned hospital readmission within 6 months measured using concordance (c) statistics and model calibration. Meta-regression and subgroup analyses were performed to investigate predefined sources of heterogeneity. Outcome measures from models reported in multiple independent cohorts and similarly defined risk predictors were pooled. Results: Sixty studies describing 81 models were included: 43 models were newly developed, and 38 were externally validated. Included populations were mainly patients with heart failure (HF) (n=29). The average age ranged between 56.5 and 84 years. The incidence of readmission ranged from 3% to 43%. Risk of bias (RoB) was high in almost all studies. The c-statistic was 0.8 in 5 models. The study population, data source and number of predictors were significant moderators for the discrimination. Calibration was reported for 27 models. Only the GRACE (Global Registration of Acute Coronary Events) score had adequate discrimination in independent cohorts (0.78, 95% CI 0.63 to 0.86). Eighteen predictors were pooled. Conclusion: Some promising models require updating and validation before use in clinical practice. The lack of independent validation studies, high RoB and low consistency in measured predictors limit their applicability. Prospero registration number: CRD42020159839. Keywords: adverse events; cardiology; risk management