The purpose of the project is to detect unreported Supplemental Security Income (SSI) recipient admissions to Title XIX institutions. A file containing SSN's of SSI recipients (all eligible individuals and members of eligible couples in current pay) will be matched against the Health Care Financing Administration's (HCFA) Minimum Data Set (MDS) database which contains admission, discharge, re-entry and assessment information about persons in Title XIX facilities for all 50 States and Washington, D.C. This database is updated monthly. The match will produce an output file containing MDS data pertinent to SSI eligibility on matched records. This data will be compared back to the SSR data to generate alerts to the Field Offices for their actions.

The table Lab Results Claims and Encounters is part of the dataset MarketScan Medicare Supplemental, available at https://redivis.com/datasets/jv2x-25dm36err. It contains 122345530 rows across 44 variables.

This is a peer-reviewed supplementary table for the article 'Healthcare resource utilization, costs and treatment associated with myasthenia gravis exacerbations among patients with myasthenia gravis in the USA: a retrospective analysis of claims data' published in the Journal of Comparative Effectiveness Research.Supplementary Table 1: MG treatment definitionsAim: There are limited data on the clinical and economic burden of exacerbations in patients with myasthenia gravis (MG). We assessed patient clinical characteristics, treatments and healthcare resource utilization (HCRU) associated with MG exacerbation. Patients & methods: This was a retrospective analysis of adult patients with MG identified by commercial, Medicare or Medicaid insurance claims from the IBM MarketScan database. Eligible patients had two or more MG diagnosis codes, without evidence of exacerbation or crisis in the baseline period (12 months prior to index [first eligible MG diagnosis]). Clinical characteristics were evaluated at baseline and 12 weeks before each exacerbation. Number of exacerbations, MG treatments and HCRU costs associated with exacerbation were described during a 2-year follow-up period. Results: Among 9352 prevalent MG patients, 34.4% (n = 3218) experienced ≥1 exacerbation after index: commercial, 53.0% (n = 1706); Medicare, 39.4% (n = 1269); and Medicaid, 7.6% (n = 243). During follow-up, the mean (standard deviation) number of exacerbations per commercial and Medicare patient was 3.7 (7.0) and 2.7 (4.1), respectively. At least two exacerbations were experienced by approximately half of commercial and Medicare patients with ≥1 exacerbation. Mean total MGrelated healthcare costs per exacerbation ranged from $26,078 to $51,120, and from $19,903 to $49,967 for commercial and Medicare patients, respectively. AChEI use decreased in patients with multiple exacerbations, while intravenous immunoglobulin use increased with multiple exacerbations. Conclusion: Despite utilization of current treatments for MG,MG exacerbations are associated with a high clinical and economic burden in both commercial and Medicare patients. Additional treatment options and improved disease management may help to reduce exacerbations and disease burden.

Summary of model objectives.

The Agency for Healthcare Research and Quality (AHRQ) created SyH-DR from eligibility and claims files for Medicare, Medicaid, and commercial insurance plans in calendar year 2016. SyH-DR contains data from a nationally representative sample of insured individuals for the 2016 calendar year. SyH-DR uses synthetic data elements at the claim level to resemble the marginal distribution of the original data elements. SyH-DR person-level data elements are not synthetic, but identifying information is aggregated or masked.

The table Annual Enrollment Summary is part of the dataset V3 2021 Files: MarketScan Medicare Supplemental Database, available at https://redivis.com/datasets/3bqx-13fserq78. It contains 1133959 rows across 83 variables.

Individual outpatient claim records.

The table Outpatient Services Claims is part of the dataset MarketScan Medicare Supplemental, available at https://redivis.com/datasets/jv2x-25dm36err. It contains 2146376466 rows across 67 variables.

In this dataset the place of service codes and their descriptions have been cited. These codes should be used on professional claims to specify the entity where service(s) were rendered. These Place of Service Codes database have been updated from November 17, 2016.

The Revalidation Due Date List dataset contains revalidation due dates for Medicare providers who are due to revalidate in the following six months. If a provider's due date does not fall within the ensuing six months, the due date is marked 'TBD'. In addition the dataset also includes subfiles with reassignment information for a given provider as well as due date listings for clinics and group practices and their providers. Note: This full dataset contains more records than most spreadsheet programs can handle, which will result in an incomplete load of data. Use of a database or statistical software is required.

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Baseline characteristics of HF patients stratified by ejection fraction class (HFrEF, < 0.45; or HFpEF, ≥ 0.45).

The Healthcare Cost and Utilization Project (HCUP) State Emergency Department Databases (SEDD) contain the universe of emergency department visits in participating States. The data are translated into a uniform format to facilitate multi-State comparisons and analyses. The SEDD consist of data from hospital-based emergency department visits that do not result in an admission. The SEDD include all patients, regardless of the expected payer including but not limited to Medicare, Medicaid, private insurance, self-pay, or those billed as ‘no charge’. Developed through a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality (AHRQ), HCUP data inform decision making at the national, State, and community levels. The SEDD contain clinical and resource use information included in a typical discharge abstract, with safeguards to protect the privacy of individual patients, physicians, and facilities (as required by data sources). Data elements include but are not limited to: diagnoses, procedures, admission and discharge status, patient demographics (e.g., sex, age, race), total charges, length of stay, and expected payment source, including but not limited to Medicare, Medicaid, private insurance, self-pay, or those billed as ‘no charge’. In addition to the core set of uniform data elements common to all SEDD, some include State-specific data elements. The SEDD exclude data elements that could directly or indirectly identify individuals. For some States, hospital and county identifiers are included that permit linkage to the American Hospital Association Annual Survey File and the Bureau of Health Professions' Area Resource File except in States that do not allow the release of hospital identifiers. Restricted access data files are available with a data use agreement and brief online security training.

The Healthcare Cost and Utilization Project (HCUP) Nationwide Emergency Department Sample (NEDS) is the largest all-payer emergency department (ED) database in the United States. yielding national estimates of hospital-owned ED visits. Unweighted, it contains data from over 30 million ED visits each year. Weighted, it estimates roughly 145 million ED visits nationally. Developed through a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality, HCUP data inform decision making at the national, State, and community levels. Sampled from the HCUP State Inpatient Databases (SID) and State Emergency Department Databases (SEDD), the HCUP NEDS can be used to create national and regional estimates of ED care. The SID contain information on patients initially seen in the ED and subsequently admitted to the same hospital. The SEDD capture information on ED visits that do not result in an admission (i.e., treat-and-release visits and transfers to another hospital). Developed through a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality, HCUP data inform decision making at the national, State, and community levels. The NEDS contain information about geographic characteristics, hospital characteristics, patient characteristics, and the nature of visits (e.g., common reasons for ED visits, including injuries). The NEDS contains clinical and resource use information included in a typical discharge abstract, with safeguards to protect the privacy of individual patients, physicians, and hospitals (as required by data sources). It includes ED charge information for over 85% of patients, regardless of expected payer, including but not limited to Medicare, Medicaid, private insurance, self-pay, or those billed as ‘no charge’. The NEDS excludes data elements that could directly or indirectly identify individuals, hospitals, or states.Restricted access data files are available with a data use agreement and brief online security training.

An exact copy for MI purposes of PT18 database which stores Medicare Part C & Part D data for premiums and Medicare Part D Prescription Drug Subsidy processing.

The Nuclear Medicine National HQ System database is a series of MS Excel spreadsheets and Access Database Tables by fiscal year. They consist of information from all Veterans Affairs Medical Centers (VAMCs) performing or contracting nuclear medicine services in Veterans Affairs medical facilities. The medical centers are required to complete questionnaires annually (RCS 10-0010-Nuclear Medicine Service Annual Report). The information is then manually entered into the Access Tables, which includes: * Distribution and cost of in-house VA - Contract Physician Services, whether contracted services are made via sharing agreement (with another VA medical facility or other government medical providers) or with private providers. * Workload data for the performance and/or purchase of PET/CT studies. * Organizational structure of services. * Updated changes in key imaging service personnel (chiefs, chief technicians, radiation safety officers). * Workload data on the number and type of studies (scans) performed, including Medicare Relative Value Units (RVUs), also referred to as Weighted Work Units (WWUs). WWUs are a workload measure calculated as the product of a study's Current Procedural Terminology (CPT) code, which consists of total work costs (the cost of physician medical expertise and time), and total practice costs (the costs of running a practice, such as equipment, supplies, salaries, utilities etc). Medicare combines WWUs together with one other parameter to derive RVUs, a workload measure widely used in the health care industry. WWUs allow Nuclear Medicine to account for the complexity of each study in assessing workload, that some studies are more time consuming and require higher levels of expertise. This gives a more accurate picture of workload; productivity etc than using just 'total studies' would yield. * A detailed Full-Time Equivalent Employee (FTEE) grid, and staffing distributions of FTEEs across nuclear medicine services. * Information on Radiation Safety Committees and Radiation Safety Officers (RSOs). Beginning in 2011 this will include data collection on part-time and non VA (contract) RSOs; other affiliations they may have and if so to whom they report (supervision) at their VA medical center.Collection of data on nuclear medicine services' progress in meeting the special needs of our female veterans. Revolving documentation of all major VA-owned gamma cameras (by type) and computer systems, their specifications and ages. * Revolving data collection for PET/CT cameras owned or leased by VA; and the numbers and types of PET/CT studies performed on VA patients whether produced on-site, via mobile PET/CT contract or from non-VA providers in the community.* Types of educational training/certification programs available at VA sites * Ongoing funded research projects by Nuclear Medicine (NM) staff, identified by source of funding and research purpose. * Data on physician-specific quality indicators at each nuclear medicine service.* Academic achievements by NM staff, including published books/chapters, journals and abstracts. * Information from polling field sites re: relevant issues and programs Headquarters needs to address. * Results of a Congressionally mandated contracted quality assessment exercise, also known as a Proficiency study. Study results are analyzed for comparison within VA facilities (for example by mission or size), and against participating private sector health care groups. * Information collected on current issues in nuclear medicine as they arise. Radiation Safety Committee structures and membership, Radiation Safety Officer information and information on how nuclear medicine services provided for female Veterans are examples of current issues.The database is now stored completely within MS Access Database Tables with output still presented in the form of Excel graphs and tables.

The State Emergency Department Databases (SEDD) contain the universe of emergency department visits in participating States. The data are translated into a uniform format to facilitate multi-State comparisons and analyses. The SEDD consist of data from hospital-based emergency departments and include all patients, regardless of payer, e.g., persons covered by Medicare, Medicaid, private insurance, and the uninsured. The SEDD contain clinical and resource use information included in a typical discharge abstract, with safeguards to protect the privacy of individual patients, physicians, and facilities (as required by data sources). Data elements include but are not limited to: diagnoses, procedures, admission and discharge status, patient demographics (e.g., gender, age), total charges, length of stay, and expected payment source (e.g., Medicare, Medicaid, private insurance, self-pay; for some States, additional discrete payer categories, such as managed care). In addition to the core set of uniform data elements common to all SEDD, some include State-specific data elements, such as the patient's race. The SEDD exclude data elements that could directly or indirectly identify individuals. For some States, hospital and county identifiers are included that permit linkage to the American Hospital Association Annual Survey File and the Area Resource File.

The Healthcare Cost and Utilization Project (HCUP) State Inpatient Databases (SID) are a set of hospital databases that contain the universe of hospital inpatient discharge abstracts from data organizations in participating States. The data are translated into a uniform format to facilitate multi-State comparisons and analyses. The SID are based on data from short term, acute care, nonfederal hospitals. Some States include discharges from specialty facilities, such as acute psychiatric hospitals. The SID include all patients, regardless of payer and contain clinical and resource use information included in a typical discharge abstract, with safeguards to protect the privacy of individual patients, physicians, and hospitals (as required by data sources). Developed through a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality (AHRQ), HCUP data inform decision making at the national, State, and community levels. The SID contain clinical and resource-use information that is included in a typical discharge abstract, with safeguards to protect the privacy of individual patients, physicians, and hospitals (as required by data sources). Data elements include but are not limited to: diagnoses, procedures, admission and discharge status, patient demographics (e.g., sex, age), total charges, length of stay, and expected payment source, including but not limited to Medicare, Medicaid, private insurance, self-pay, or those billed as ‘no charge’. In addition to the core set of uniform data elements common to all SID, some include State-specific data elements. The SID exclude data elements that could directly or indirectly identify individuals. For some States, hospital and county identifiers are included that permit linkage to the American Hospital Association Annual Survey File and county-level data from the Bureau of Health Professions' Area Resource File except in States that do not allow the release of hospital identifiers. Restricted access data files are available with a data use agreement and brief online security training.

Background: Trajectories of comorbidities among individuals at risk of Alzheimer’s disease (AD) may differ from those aging without AD clinical syndrome. Therefore, characterizing the comorbidity burden and pattern associated with AD risk may facilitate earlier detection, enable timely intervention, and help slow the rate of cognitive and functional decline in AD. This case-control study was performed to compare the prevalence of comorbidities between AD cases and controls during the 5 years prior to diagnosis (or index date for controls); and to identify comorbidities with a differential time-dependent prevalence trajectory during the 5 years prior to AD diagnosis.Methods: Incident AD cases and individually matched controls were identified in a United States claims database between January 1, 2000 and December 31, 2016. AD status and comorbidities were defined based on the presence of diagnosis codes in administrative claims records. Generalized estimating equations were used to assess evidence of changes over time and between AD and controls. A principal component analysis and hierarchical clustering was performed to identify groups of AD-related comorbidities with respect to prevalence changes over time (or trajectory), and differences between AD and controls.Results: Data from 186,064 individuals in the IBM MarketScan Commercial Claims and Medicare Supplementary databases were analyzed (93,032 AD cases and 93,032 non-AD controls). In total, there were 177 comorbidities with a ≥ 5% prevalence. Five main clusters of comorbidities were identified. Clusters differed between AD cases and controls in the overall magnitude of association with AD, in their diverging time trajectories, and in comorbidity prevalence. Three clusters contained comorbidities that notably increased in frequency over time in AD cases but not in controls during the 5-year period before AD diagnosis. Comorbidities in these clusters were related to the early signs and/or symptoms of AD, psychiatric and mood disorders, cerebrovascular disease, history of hazard and injuries, and metabolic, cardiovascular, and respiratory complaints.Conclusion: We demonstrated a greater comorbidity burden among those who later developed AD vs. controls, and identified comorbidity clusters that could distinguish these two groups. Further investigation of comorbidity burden is warranted to facilitate early detection of individuals at risk of developing AD.

The State Ambulatory Surgery Databases (SASD) contain the universe of hospital-based ambulatory surgery encounters in participating States. Some States include ambulatory surgery encounters from free-standing facilities as well. Restricted access data files are available with a data use agreement and brief online security training.

The data are translated into a uniform format to facilitate multi-State comparisons and analyses. The SASD include all patients in participating settings, regardless of payer, e.g., persons covered by Medicare, Medicaid, private insurance, and the uninsured.

The SASD contain clinical and resource use information included in a typical discharge abstract, with safeguards to protect the privacy of individual patients, physicians, and facilities (as required by data sources).

Data elements include but are not limited to: diagnoses, procedures, admission and discharge status, patient demographics (e.g., gender, age), total charges, and expected payment source (e.g., Medicare, Medicaid, private insurance, self-pay; for some States, additional discrete payer categories, such as managed care). In addition to the core set of uniform data elements common to all SASD, some include State-specific data elements, such as the patient's race. The SASD exclude data elements that could directly or indirectly identify individuals.

For some States, hospital and county identifiers are included that permit linkage to the American Hospital Association Annual Survey File and the Area Resource File.

Individual outpatient prescription drug claim records.