States may rely on eligibility information from "Express Lane" agency programs to streamline and simplify enrollment and renewal in Medicaid and CHIP. Express Lane agencies may include Supplemental Nutrition Assistance Program (SNAP), School Lunch programs, Temporary Assistance for Needy Families, Head Start, and the Women, infant, and children's program (WIC) , among others. States can also use state income tax data to determine Medicaid and CHIP eligibility for children.

The Medicaid Managed Care Enrollment Report profiles enrollment statistics on Medicaid managed care programs on a plan-specific level. The managed care enrollment statistics include enrollees receiving comprehensive benefits and limited benefits and are point-in-time counts.

1. Total Medicaid Enrollees represents an unduplicated count of all beneficiaries in FFS and any type of managed care, including Medicaid-only and Medicare-Medicaid ("dual") enrollees.
2. Total Medicaid enrollment in Any Type of Managed Care represents an unduplicated count of beneficiaries enrolled in any Medicaid managed care program, including comprehensive MCOs, limited benefit MCOs, and PCCMs.
3. The “Medicaid Enrollment in Comprehensive Managed Care” column represents an unduplicated count of Medicaid beneficiaries enrolled in a managed care plan that provides comprehensive benefits (acute, primary care, specialty, and any other), or PACE program. It excludes beneficiaries who are enrolled in a Financial Alignment Demonstration Medicare-Medicaid Plan as their only form of managed care.
4. The “Medicaid Enrollment in Comprehensive MCOs Under ACA Section VIII Expansion” column is a subset of the total reported in column C and includes individuals who are enrolled in comprehensive MCOs and are low-income adults, with or without dependent children, eligible for Medicaid under ACA Section VIII.
5. n/a" indicates that a state or territory was either not able to report data or does not operate a managed care program.

Analysis of ‘Managed Care Enrollment Summary’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/730ae026-2760-41fb-b423-6a55a1eb54c3 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

The Medicaid Managed Care Enrollment Report profiles enrollment statistics on Medicaid managed care programs on a plan-specific level. The managed care enrollment statistics include enrollees receiving comprehensive benefits and limited benefits and are point-in-time counts.

1. Total Medicaid Enrollees represents an unduplicated count of all beneficiaries in FFS and any type of managed care, including Medicaid-only and Medicare-Medicaid ("dual") enrollees.
2. Total Medicaid enrollment in Any Type of Managed Care represents an unduplicated count of beneficiaries enrolled in any Medicaid managed care program, including comprehensive MCOs, limited benefit MCOs, and PCCMs.
3. Medicaid Enrollment in Comprehensive Managed Care represents an unduplicated count of Medicaid beneficiaries enrolled in a managed care plan that provides comprehensive benefits (acute, primary care, specialty, and any other), as well as PACE programs. It excludes beneficiaries who are enrolled in a Financial Alignment Initiative Medicare-Medicaid Plan as their only form of managed care.
4. Medicaid Newly Eligible Adults Enrolled in Comprehensive MCOs represents individuals who are enrolled in comprehensive MCOs and are low-income adults, with or without dependent children, newly eligible for Medicaid under ACA Section VIII, and not covered under a waiver or other authority prior to 2014.

--- Original source retains full ownership of the source dataset ---

Data Layer Name: Vermont Rational Service Areas (RSAs)Alternate Name: Vermont RSAsOverview:Rational Service Areas (RSAs), originally developed in 2001 and revised in 2011, are generalized catchment areas relating to the delivery of primary health care services. In Vermont, RSA area delineations rely primarily on utilization data. The methods used are similar to those used by David Goodman to define primary care service areas based on Medicare data, but include additional sources of utilization data. Using these methods, towns were assigned based on where residents are going for their primary care. The process used to delineate Vermont RSAs was iterative. It began by examining utilization patterns based on: (1) the primary care service areas that Goodman had defined for Vermont from Medicare data; (2) Vermont Medicaid assignments of clients to primary care providers; and, (3) responses to the “town of residence”/”town of primary care” questions in the Vermont Behavioral Risk Factor survey. Taking into account the limitations of each of these sources of data, VDH statisticians defined preliminary town centers and were able to assign approximately two/thirds of the towns to a town center. For towns with no clear utilization patterns, they examined mileage from these preliminary centers, and mileage from towns that had primary care physicians. Contiguity of areas was also examined. A few centers were added and others were deleted. After all towns were assigned to a center and mapped, outliers were identified and reviewed by referring to both mileage maps and utilization patterns. Drive time information was not available. In some cases where the mileage map seemed to indicate one center, but the utilization patterns were strongly supportive of another center, utilization was used as a proxy for drive time.Preliminary RSAs were presented to the Vermont Primary Care Collaborative, the Vermont Coalition of Clinics for the Uninsured and other community members for their feedback. Department of Health District Directors from the Division of Community Public Health were also consulted. These groups suggested modifications to the areas based on their experience working in the areas in question. As a result of this review a few centers were added, deleted and combined, and several towns were reassigned. The Vermont Primary Care Collaborative reviewed the final version of RSAs. The result of this process is 38 Rational Service Areas.Given the limitations of the information available for this purpose, the delineation approach was deemed reasonable and has resulted in a set of RSAs that have been widely reviewed and accepted. Because of the iterative process, it is recognized that this is not a "pure" methodology in the sense that someone else attempting to replicate this process would probably not produce exactly the same results. RSAs have been reviewed periodically to keep up with changes in demographics and provider practice locations. One revision occurred in 2011. This 2011 revision took towns that had originally been assigned as using out-of-state providers and reassigned them to Vermont RSAs. Technical Details:Vermont RSAs were defined using 3 sources of primary care utilization data and mileage maps. Each of the data sources had limitations, and these limitations had to be considered as towns were assigned to a RSA. A description of each of these data sources is provided. Medicare utilization data was obtained from the Primary Care Service Areas developed by David Goodman using 1996 and 1997 Medicare Part B and Outpatient files. Thirty-eight primary care service areas were defined for Vermont. The major limitation of these assignments was that they were based on zip codes rather than town boundaries. Many small towns do not have their own zip code, or the town may be divided into multiple zip codes shared with multiple other towns. As the utilization data was reviewed consideration was given to whether the zip code in question represented the town, or whether utilization from that town may have been masked by a larger town's utilization patterns. A second consideration was that the Medicare data used 1996 & 1997 utilization. In areas where there were new practices established after 1997, the Medicare data would not be able to reflect their utilization.Medicaid claims data only included children age 17 and under. The file contained Medicaid clients in 2000 with the town of residence of the client and the town of the primary care provider. The limitation in this file was that although the Medicaid database included a field for the geographic location of the provider separate from the mailing address, after examining the file it was determined that in many cases the mailing address was also being entered into the geographic location. In areas where practices were owned by a larger organization, the utilization patterns could not be determined. For example, in the St. Johnsbury RSA there were practices owned by an out-of-state medical center. Although it is known that there are medicaid providers in some of the towns in that area, all of the utilization was coded to out of state. Therefore the Medicaid data had to be disregarded in this area. The St. Johnsbury RSA was subsequently defined around three town centers (St. Johnsbury, Lyndon, and Danville) because more precise utilization patterns could not be distinguished.The BRFSS data was obtained from the 1998-2000 surveys. Respondents were asked for the town of their primary care provider. The town of residence of the respondent is also collected. These responses represented all Vermonters age 18-64 years old, regardless of type of insurance. The limitation of this data was small number of respondents in the smaller towns. Mileage information was obtained from the Vermont Medicaid program. This mileage information was derived using GIS mapping software to assess all statewide roads. However, drive-time data could not be determined at that time because there was no distinction between primary and secondary roads. The Medicaid program applied GIS mapping software to assign clients to primary care providers using 15 miles as a proxy for 30-minute drive time. This standard was also used in 2001 when the original RSAs were developed.The VDH Public Health Statistics program periodically updates RSA GIS data. (last updated in 2011)

This series of files links two large population-based sources providing detailed data about Medicare beneficiaries with cancer. The SEER (Surveillance, Epidemiology, and End Results) program consists of clinical, demographic, and cause of death information collected from tumor registries beginning in January 1, 1973. The Medicare contribution includes all claims for covered health care services from beneficiaries’ time of eligibility until death. Linkage is processed biennially by SEER and Centers for Medicare and Medicaid Services (CMS) staff. 95% of individuals age 65 and older are included in the SEER files. Due to privacy concerns, access to this database requires an application, SEER-Medicare Data Use Agreement (DUA), and documentation of institutional review board approval. Additionally, the National Cancer Institute’s information technology contractor assesses a processing fee the amount of which is dependent upon the type and number of files requested.

This directory contains analytic code used to build cohorts, dependent variables, and covariates, and run all statistical analyses for the study, "Changes in care associated with integrating Medicare and Medicaid for dual eligible individuals: Examination of a Fully Integrated Special Needs Plan."The code files enclosed in this directory are:SAS_Cohorts_Outcomes 23-9-30.sas. This SAS code file builds study cohorts, dependent variables, and covariates. This code produced a person-by-month level database of outcomes and covariates for individuals in the integration and comparison cohorts.STATA_Models_23-6-5_weight_jama.do. This Stata program reads in the person-by-month level database (output from SAS) and conducts all statistical analyses used to produce the main and supplementary analyses reported in the manuscript.We have provided this code and documentation to disclose our study methods. Our Data Use Agreements prohibit publishing of row-level data for this study. Therefore, researchers would need to obtain Data Use Agreements with data providers to implement these analyses. We also note that some measures reference macros with proprietary code (e.g., Medispan® files) which require a separate user license to run. Interested readers should contact the study PI, Eric T. Roberts (eric.roberts@pennmedicine.upenn.edu) for further information.

BackgroundInfluenza-related healthcare utilization among Medicaid patients and commercially insured patients is not well-understood. This study compared influenza-related healthcare utilization and assessed disease management among individuals diagnosed with influenza during the 2015–2019 influenza seasons.MethodsThis retrospective cohort study identified influenza cases among adults (18–64 years) using data from the Transformed Medicaid Statistical Information System (T-MSIS) Analytic Files (TAF) Research Identifiable Files (RIF) and Optum’s de-identified Clinformatics® Data Mart Database (CDM). Influenza-related healthcare utilization rates were calculated per 100,000 patients by setting (outpatient, emergency department (ED), inpatient hospitalizations, and intensive care unit (ICU) admissions) and demographics (sex, race, and region). Rate ratios were computed to compare results from both databases. Influenza episode management assessment included the distribution of the index point-of-care, antiviral prescriptions, and laboratory tests obtained.ResultsThe Medicaid population had a higher representation of racial/ethnic minorities than the CDM population. In the Medicaid population, influenza-related visits in outpatient and ED settings were the most frequent forms of healthcare utilization, with similar rates of 652 and 637 visits per 100,000, respectively. In contrast, the CDM population predominantly utilized outpatient settings. Non-Hispanic Blacks and Hispanics exhibited the highest rates of influenza-related ED visits in both cohorts. In the Medicaid population, Black (64.5%) and Hispanic (51.6%) patients predominantly used the ED as their index point-of-care for influenza. Overall, a greater proportion of Medicaid beneficiaries (49.8%) did not fill any influenza antiviral prescription compared to the CDM population (37.0%).ConclusionAddressing disparities in influenza-related healthcare utilization between Medicaid and CDM populations is crucial for equitable healthcare access. Targeted interventions are needed to improve primary care and antiviral access and reduce ED reliance, especially among racial/ethnic minorities and low-income populations.