The Medicare Part D by Drug dataset presents information on spending for drugs prescribed to Medicare beneficiaries enrolled in Part D by physicians and other healthcare providers. Drugs prescribed in the Medicare Part D program are drugs patients generally administer themselves.

The dataset focuses on average spending per dosage unit and change in average spending per dosage unit over time. It also includes spending information for manufacturer(s) of the drugs as well as consumer-friendly information of drug uses and clinical indications.

Drug spending metrics for Part D drugs are based on the gross drug cost, which represents total spending for the prescription claim, including Medicare, plan, and beneficiary payments. The Part D spending metrics do not reflect any manufacturers’ rebates or other price concessions as CMS is prohibited from publicly disclosing such information.

This dataset identifies health care spending at medical services such as hospitals, physicians, clinics, and nursing homes etc. as well as for medical products such as medicine, prescription glasses and hearing aids. This dataset pertains to personal health care spending in general. Other datasets in this series include Medicaid personal health care spending and Medicare personal health care spending.

Dataset

This dataset was created by koan

Released under U.S. Government Works

Contents

The Medicare Physician & Other Practitioners by Provider dataset provides information on use, payments, submitted charges and beneficiary demographic and health characteristics organized by National Provider Identifier (NPI). 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.

Summary 💯

The Disease Expenditure Project (DEX) at IHME produced estimates for US spending on health care according to 3 types of payers (public insurance [including Medicare, Medicaid, and other government programs], private insurance, or out-of-pocket payments) and by health condition, age group, sex, and type of care for 1996 through 2016. Types of care include ambulatory care, inpatient care, nursing care facility stay, emergency department care, dental care, prescribed pharmaceutical care, and government administration and net cost of insurance programs. Government budgets, insurance claims, facility records, household surveys, and official US records from 1996 through 2016 were used to produce the results. Spending estimates were produced for 154 conditons, which were aggregated into 14 health categories. This dataset contains estimates for the aggregate health categories.

Link to the Source:

Source

Medicare Geographic Variation - by National, State & County

  Description

The Medicare Geographic Variation by National, State & County dataset provides information on the geographic differences in the use and quality of health care services for the Original Medicare population. This dataset contains demographic, spending, use, and quality indicators at the state level (including the District of Columbia, Puerto Rico, and the Virgin Islands) and the county level. Spending… See the full description on the dataset page: https://huggingface.co/datasets/HHS-Official/medicare-geographic-variation-by-national-state-an.

analyze the basic stand alone medicare claims public use files (bsapufs) with r and monetdb the centers for medicare and medicaid services (cms) took the plunge. the famous medicare 5% sample has been released to the public, free of charge. jfyi - medicare is the u.s. government program that provides health insurance to 50 million elderly and disabled americans. the basic stand alone medicare claims public use files (bsapufs) contain either person- or event-level data on inpatient stays, durable medical equipment purchases, prescription drug fills, hospice users, doctor visits, home health provision , outpatient hospital procedures, skilled nursing facility short-term residents, as well as aggregated statistics for medicare beneficiaries with chronic conditions and medicare beneficiaries living in nursing homes. oh sorry, there's one catch: they only provide sas scripts to analyze everything. cue the villian music. that bored old game of monopoly ends today. the initial release of the 2008 bsapufs was accompanied by some major fanfare in the world of health policy , a big win for government transparency. unfortunately, the final files that cleared the confidentiality hurdles are heavily de-identified and obfuscated. prime examples: none of the files can be linked to any other file. not across years, not across expenditure categories costs are rounded to the nearest fifth or tenth dollar at lower values, nearest thousandth at higher values ages are categorized into five year bands so these files are baldly inferior to the unsquelched, linkable data only available through an expensive formal application process. any researcher with a budget flush enough to afford a sas license (the only statistical software mentioned in the cms official documentation) can probably also cough up the money to buy the identifiable data through resdac (resdac, btw, rocks). soapbox: cms released free public data sets that could only be analyzed with a software package costing thousands of dollars. so even though the actual data sets were free, researchers still needed deep pock ets to buy sas. meanwhile, the unsquelched and therefore superior data sets are also available for many thousands of dollars. researchers with funding would (reasonably) just buy the better data. researchers without any financial resources - the target audience of free, public data - were left out in the cold. no wonder these bsapufs haven't been used much. that ends now. using r, monetdb, and the personal computer you already own (mine cost $700 in 2009), researchers can, for the first time, seriously analyze these medicare public use files without spending another dime. woah. plus hey guess what all you researcher fat-cats with your federal grant streams and your proprietary software licenses: r + monetdb runs one heckuva lot faster than sas. woah^2. dump your sas license water wings and learn how to swim. the scripts below require monetdb . click here for step-by-step instructions of how to install it on windows and click here for speed tests. vroom. since the bsapufs comprise 5% of the medicare population, ya generally need to multiply any counts or sums by twenty. although the individuals represented in these claims are randomly sampled, this data should not be treated like a complex survey sample, meaning that the creation of a survey object is unnecessary. most bsapufs generalize to either the total or fee-for-service medicare population, but each file is different so give the documentation a hard stare before that eureka moment. this new github repository contains three scripts: 2008 - download all csv files.R loop through and download every zip file hosted by cms unzip the contents of each zipped file to the working directory 2008 - import all csv files into monetdb.R create the batch (.bat) file needed to initiate the monet database in the f uture loop through each csv file in the current working directory and import them into the monet database create a well-documented block of code to re-initiate the monetdb server in the future 2008 - replicate cms publications.R initiate the same monetdb server instance, unsing the same well-documented block of code as above replicate nine sets of statistics found in data tables provided by cms < a href="https://github.com/ajdamico/usgsd/tree/master/Basic%20Stand%20Alone%20Medicare%20Claims%20Public%20Use%20Files">click here to view these three scripts for more detail about the basic stand alone medicare claims public use files (bsapufs), visit: the centers for medicare and medicaid's bsapuf homepage a joint academyhealth webinar given by the organizations that partnered to create these files - cms, impaq, norc notes: the replication script has oodles of easily-modified syntax and should be viewed for analysis examples. if you know the name of the data table you want to examine, you can quickly modify these general monetdb analysis examples too. just run sql queries - sas users, that's "proc...

Personal healthcare spending in the United States. Data are from Health, United States. Source: Centers for Medicare & Medicaid Services, Office of the Actuary, National Health Statistics Group, National Health Expenditure Accounts, National health expenditures.
Search, visualize, and download these and other estimates from over 120 health topics with the NCHS Data Query System (DQS), available from: https://www.cdc.gov/nchs/dataquery/index.htm.

Final regression results, Centers for Medicare and Medicaid Services state resident healthcare expenditure, 1992–2009.

The dataset contains information about the prevalence of chronic conditions among Original Medicare beneficiaries as well as about the spending and co-occurring conditions for those with each condition. The data are available for California and for the rest of the United States, overall and by demographic and geographic groups. Additionally, the data are available for each of 19 California geographic regions overall and by demographic and geographic groups. The data represent Medicare beneficiaries who are in the Original Medicare program. Medicare offers health care coverage for older adults and certain individuals with disabilities. The Original Medicare program is Parts A and B of Medicare, administered by the U.S. Centers for Medicare & Medicaid Services. The analysis excludes enrollees of the Medicare Advantage program, administered by private insurers, because Medicare Advantage data are incomplete.

National health spending in the United States. Data are from Health, United States. Source: Centers for Medicare & Medicaid Services, Office of the Actuary, National Health Statistics Group, National Health Expenditure Accounts, National health expenditures. Search, visualize, and download these and other estimates from over 120 health topics with the NCHS Data Query System (DQS), available from: https://www.cdc.gov/nchs/dataquery/index.htm.

Contains full API download of all available datasets on data.medicaid.gov. Program overview Data.Medicaid.gov is a public platform offering open access to a diverse range of datasets related to Medicaid and the Children’s Health Insurance Program (CHIP). It is tailored to support policymakers, researchers, and the general public by providing critical data for research, reporting, and analysis. The platform covers various topics, including state Medicaid and CHIP programs, enrollment statistics, spending trends, and quality metrics. With data presented in multiple formats, it promotes transparency, allowing users to track program performance and make informed decisions based on reliable insights.

Prevalence and Medicare utilization and spending are presented for the 21 chronic conditions listed in the link. Information is presented for (1) U.S. counties, (2) U.S. states, including Washington, DC, Puerto Rico, and the U.S. Virgin Islands, and is available for the years 2007-2018.

Abstract (en): This data collection contains two data files derived from information gathered in the initial screening interview and Rounds 1-4 of the Household Survey component of the 1987 NATIONAL MEDICAL EXPENDITURE SURVEY (NMES). The Person File supplies data on each sampled person who reported coverage by Medicare at any time in 1987 and who responded to all rounds of the Household Survey for which he or she was eligible to respond. Data in this file include age, sex, race, marital status, education, employment status, personal and family income, coverage under private health insurance and public programs such as Medicaid and CAMPUS/CAMPVA, and the total number and cost of all prescriptions purchased in 1987 while under Medicare coverage. In addition, there are indicators of general health and specific medical conditions: stroke, cancer, heart disease, gallbladder disease, high blood pressure, hardening of the arteries, rheumatism, emphysema, arthritis and diabetes. The Prescribed Medicines Event File presents data pertaining to every instance a prescribed medicine was purchased or otherwise obtained by these Medicare beneficiaries during 1987. For respondents who were covered by Medicare for part of the year, only prescribed medicines acquired during the Medicare coverage period are included. This file gives the trade and generic name of each prescribed medication and reports the cost of the prescription and the medical condition for which it was prescribed. Civilian noninstitutionalized population of the United States living in housing units, group quarters, and other noninstitutional (nongroup) quarters. Stratified multistage area probability sample of dwelling units. Dwelling units including blacks, Hispanics, the elderly, the functionally impaired, and the poor were oversampled. 2006-03-30 File CB9340.SUPP.PDF was removed from any previous datasets and flagged as a study-level file, so that it will accompany all downloads.2006-03-30 All files were removed from dataset 3 and flagged as study-level files, so that they will accompany all downloads. (1) The principal investigator was formerly known as the National Center for Health Services Research and Health Care Technology Assessment. (2) The age distribution for Part 1: 17 and under (N=8), 18-63 (N=444), 64 (N=246), 65-74 (N=3,246), 75-84 (N=1,685), 85+ (N=409). (3) Parts 1 and 2 are linked by common identification variables. (4) Hard copy supplementary materials to the machine-readable documentation in Part 3 are supplied for this collection. (5) Part 2 contains alphabetic variables. (6) NMES consists of several surveys including two household panel surveys: the Household Survey and the Survey of American Indians and Alaska Natives (SAIAN). The Household Survey, from which this data collection is derived, surveyed the United States noninstitutionalized population and was fielded over four rounds of personal and telephone interviews at four-month intervals, with a short telephone interview constituting the fifth final round. SAIAN, which was conducted over three rounds of personal interviews, surveyed all persons who were eligible for care through the Indian Health Service and were living on or near reservations. These household surveys were supplemented by additional surveys, most important of which are the Health Insurance Plans Survey of employers and insurers of consenting household survey respondents, and the Medical Provider Survey of physicians, osteopaths, and inpatient and outpatient facilities, including home health care agencies reported as providing services to any member of the noninstitutionalized population sample. NMES also surveyed persons resident in or admitted to long-term care facilities (nursing homes and facilities for the mentally retarded) at any time in 1987. Information on these individuals was obtained from the Survey of Institutions, which collected data from facility administrators and designated staff, and the Survey of Next-of-Kin, which collected data from the respondent's next-of-kin or other knowledgeable persons. Together, the major components of NMES provide measures of health status and estimates of insurance coverage and the use of services, expenditures, and sources of payment for the period from January 1 to December 31, 1987 for the civilian population of the United States. NMES continues a series of national health care expenditure surveys carried out in the past, particularly the 1980 National Medical Care Utiliza...

Interest-Expense Time Series for CVS Health Corp. CVS Health Corporation provides health solutions in the United States. It operates through Health Care Benefits, Health Services, and Pharmacy & Consumer Wellness segments. The Health Care Benefits segment offers traditional, voluntary, and consumer-directed health insurance products and related services, including medical, pharmacy, dental and behavioral health plans, medical management capabilities, Medicare Advantage and Medicare Supplement plans, PDPs and Medicaid health care management services. It serves employer groups, individuals, college students, part-time and hourly workers, health plans, health care providers, governmental units, government-sponsored plans, labor groups, and expatriates. The Health Services segment offers pharmacy benefit management solutions, including plan design and administration, formulary management, retail pharmacy network management, specialty and mail order pharmacy, clinical, disease management, medical spend management services, and other administrative services. It serves employers, insurance companies, unions, government employee groups, health plans, prescription drug plans, Medicaid managed care plans, CMS, plans offered on public health insurance, and other sponsors of health benefit plans. The Pharmacy & Consumer Wellness segment sells prescription and over-the-counter drugs, consumer health and beauty products, and personal care products. This segment also distributes prescription drugs; and provides related pharmacy consulting and other ancillary services to care facilities and other care settings. It operates online retail pharmacy websites, LTC pharmacies and on-site pharmacies, retail specialty pharmacy stores, compounding pharmacies and branches for infusion and enteral nutrition services. The company was formerly known as CVS Caremark Corporation and changed its name to CVS Health Corporation in September 2014. CVS Health Corporation was incorporated in 1996 and is headquartered in Woonsocket, Rhode Island.

Retained-Earnings Time Series for CVS Health Corp. CVS Health Corporation provides health solutions in the United States. It operates through Health Care Benefits, Health Services, and Pharmacy & Consumer Wellness segments. The Health Care Benefits segment offers traditional, voluntary, and consumer-directed health insurance products and related services, including medical, pharmacy, dental and behavioral health plans, medical management capabilities, Medicare Advantage and Medicare Supplement plans, PDPs and Medicaid health care management services. It serves employer groups, individuals, college students, part-time and hourly workers, health plans, health care providers, governmental units, government-sponsored plans, labor groups, and expatriates. The Health Services segment offers pharmacy benefit management solutions, including plan design and administration, formulary management, retail pharmacy network management, specialty and mail order pharmacy, clinical, disease management, medical spend management services, and other administrative services. It serves employers, insurance companies, unions, government employee groups, health plans, prescription drug plans, Medicaid managed care plans, CMS, plans offered on public health insurance, and other sponsors of health benefit plans. The Pharmacy & Consumer Wellness segment sells prescription and over-the-counter drugs, consumer health and beauty products, and personal care products. This segment also distributes prescription drugs; and provides related pharmacy consulting and other ancillary services to care facilities and other care settings. It operates online retail pharmacy websites, LTC pharmacies and on-site pharmacies, retail specialty pharmacy stores, compounding pharmacies and branches for infusion and enteral nutrition services. The company was formerly known as CVS Caremark Corporation and changed its name to CVS Health Corporation in September 2014. CVS Health Corporation was incorporated in 1996 and is headquartered in Woonsocket, Rhode Island.

The Monthly Treasury Statement (MTS) dataset provides information on the flow of money into and out of the U.S. Department of the Treasury. It includes how deficits are funded, such as borrowing from the public or reducing operating cash, and how surpluses are distributed. Further tables categorize spending (outlays) by department and agency, revenue (receipts) by specific taxes or other government sources of income, and transactions with trust funds such as Social Security or Medicare. All values are reported in millions of U.S. dollars.

IntroductionCitalopram and escitalopram are among the most used medications and are key treatments for many psychiatric disorders. Previous findings suggest citalopram and escitalopram prescription rates are changing because of the patent for citalopram ending as opposed to evidence of a clear therapeutic advantage—so-called “evergreening”. This retrospective study focuses on characterizing the chronologic and geographic variation in the use of citalopram and escitalopram from 2015 to 2020 among US Medicaid and Medicare patients. We hypothesized that prescription rates of citalopram will decrease with a concurrent increase in escitalopram, consistent with “evergreening”.MethodsCitalopram and escitalopram prescription rates and costs per state were obtained from the Medicaid State Drug Utilization Database and Medicare Provider Utilization and Payment Data. States’ annual prescription rates outside a 95% confidence interval were considered significantly different from the average.ResultsOverall, a decreasing trend for citalopram and an increasing trend for escitalopram prescription rates were noted in both Medicare and Medicaid patients. The differences between generic and brand were noted for both drugs, with generic forms being less expensive than the brand-name version.DiscussionDespite limited evidence suggesting that citalopram and escitalopram have any meaningful differences in therapeutic or adverse effects, there exists a noticeable decline in the use of citalopram that cooccurred with an increase in escitalopram prescribing, consistent with our hypothesis. Moreover, among these general pharmacoepidemiologic trends exists significant geographic variability. There was disproportionate spending (relative to their use) on the brand versions of these medicines relative to their generic forms.

Objective: A one third reduction of premature deaths from non-communicable diseases by 2030 is a target of the United Nations Sustainable Development Goal for Health. Unlike in other developed nations, premature mortality in the United States (US) is increasing. The state of Oklahoma suffers some of the greatest rates in the US of both all-cause mortality and overdose deaths. Medicaid opioids are associated with overdose death at the patient level, but the impact of this exposure on population all-cause mortality is unknown. The objective of this study was to look for an association between Medicaid spending, as proxy measure for Medicaid opioid exposure, and all-cause mortality rates in the 45–54-year-old American Indian/Alaska Native (AI/AN45-54) and non-Hispanic white (NHW45-54) populations.Methods: All-cause mortality rates were collected from the US Centers for Disease Control & Prevention Wonder Detailed Mortality database. Annual per capita (APC) Medicaid spending, and APC Medicare opioid claims, smoking, obesity, and poverty data were also collected from existing databases. County-level multiple linear regression (MLR) analyses were performed. American Indian mortality misclassification at death is known to be common, and sparse populations are present in certain counties; therefore, the two populations were examined as a combined population (AI/NHW45-54), with results being compared to NHW45-54 alone.Results: State-level simple linear regressions of AI/NHW45-54 mortality and APC Medicaid spending show strong, linear correlations: females, coefficient 0.168, (R2 0.956; P < 0.0001; CI95 0.15, 0.19); and males, coefficient 0.139 (R2 0.746; P < 0.0001; CI95 0.10, 0.18). County-level regression models reveal that AI/NHW45-54 mortality is strongly associated with APC Medicaid spending, adjusting for Medicare opioid claims, smoking, obesity, and poverty. In females: [R2 0.545; (F)P < 0.0001; Medicaid spending coefficient 0.137; P < 0.004; 95% CI 0.05, 0.23]. In males: [R2 0.719; (F)P < 0.0001; Medicaid spending coefficient 0.330; P < 0.001; 95% CI 0.21, 0.45].Conclusions: In Oklahoma, per capita Medicaid spending is a very strong risk factor for all-cause mortality in the combined AI/NHW45-54 population, after controlling for Medicare opioid claims, smoking, obesity, and poverty.

Overview The Dartmouth Institute for Health Policy and Clinical Practice (TDI) has created a publicly available source of data that provides researchers, payers, regulators, and innovators with metrics that quantify temporal and regional patterns of health care spending and utilization in the United States. Using CMS Medicare claims data (mostly for age >64 enrollees), Atlas researchers built cohorts (“denominators”) and numerous measures or events (“numerators”) which were then used to calculate rates either by geography or for patients assigned to specific hospitals. These rates, which are calculated consistently across time and place, provide researchers with opportunities to evaluate spatial and temporal variation/trends. This entry contains Dartmouth Atlas rates for a variety of measures related to musculoskeletal care for fee-for-service (FFS) Medicare beneficiaries, age 65 to 99. Measures include physician supply by specialty, rates for surgical and diagnostic procedures related to the evaluation and treatment of spine problems, rates for surgical treatments for degenerative joint disease, and rates for treatment of fractures. The time period assessed varies across measures, but in general focuses on 1996 and 1997. Rates are provided at the hospital referral region (HRR) level and have been adjusted for age/sex or age/sex/race as appropriate (see report for details). Users downloading data should review the methods sections of the related publication for context. All reports in the Dartmouth Atlas of Health Care series are available from the National Library of Medicine https://www.ncbi.nlm.nih.gov/books/NBK584737/