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 Medicare personal health care spending. Other datasets in this series include Medicaid personal health care spending and personal health care spending in general.

This database includes the averages (2006, 2010, 2014, 2016 and 2017) of state financial condition in the United States, Medicaid spending per enrollee and a number of control variables in an analysis of the effects of Medicaid spending on state financial condition. The SPSS database is included here. The hypothesis was that the effects were significant and possibly positive. I found an offsetting effect on revenues and expenditures which nulled the overall effect on state financial condition.

This dataset contains aggregate Medicaid payments, and counts for eligible recipients and recipients served by month and county in Iowa, starting with month ending 1/31/2011.

Eligibility groups are a category of people who meet certain common eligibility requirements. Some Medicaid eligibility groups cover additional services, such as nursing facility care and care received in the home. Others have higher income and resource limits, charge a premium, only pay the Medicare premium or cover only expenses also paid by Medicare, or require the recipient to pay a specific dollar amount of their medical expenses. Eligible Medicaid recipients may be considered medically needy if their medical costs are so high that they use up most of their income. Those considered medically needy are responsible for paying some of their medical expenses. This is called meeting a spend down. Then Medicaid would start to pay for the rest. Think of the spend down like a deductible that people pay as part of a private insurance plan.

This dataset reports summary state-by-state total expenditures by program for the Medicaid Program, Medicaid Administration and CHIP programs. These state expenditures are tracked through the automated Medicaid Budget and Expenditure System/State Children's Health Insurance Program Budget and Expenditure System (MBES/CBES).

For more information, visit https://medicaid.gov/medicaid/finance/state-expenditure-reporting/expenditure-reports/index.html.

This dataset reports summary level expenditure data associated with the new adult group established under the Affordable Care Act. These state expenditures are reported through the federal Medicaid Budget and Expenditure System (MBES). Notes: 1. “VIII GROUP” is also known as the “New Adult Group.” 2. The VIII Group is only applicable for states that have expanded their Medicaid programs by adopting the VIII Group. VIII Group expenditure information for the states that have not expanded their Medicaid program is noted as “N/A.” 3. States that have reported “0” either have no expenditures for that reporting category or have not yet reported expenditures for that category. 4. MCHIP expenditures are not included in the All Medical Assistance Expenditures.

In 2008, a group of uninsured low-income adults in Oregon was selected by lottery to be given the chance to apply for Medicaid. This lottery provides an opportunity to gauge the effects of expanding access to public health insurance on the health care use, financial strain, and health of low-income adults using a randomized controlled design. The Oregon Health Insurance Experiment follows and compares those selected in the lottery (treatment group) with those not selected (control group). The data collected and provided here include data from in-person interviews, three mail surveys, emergency department records, and administrative records on Medicaid enrollment, the initial lottery sign-up list, welfare benefits, and mortality. This data collection has seven data files: Dataset 1 contains administrative data on the lottery from the state of Oregon. These data include demographic characteristics that were recorded when individuals signed up for the lottery, date of lottery draw, and information on who was selected for the lottery, applied for the lotteried Medicaid plan if selected, and whose application for the lotteried plan was approved. Also included are Oregon mortality data for 2008 and 2009. Dataset 2 contains information from the state of Oregon on the individuals' participation in Medicaid, Supplemental Nutrition Assistance Program (SNAP), and Temporary Assistance to Needy Families (TANF). Datasets 3-5 contain the data from the initial, six month, and 12 month mail surveys, respectively. Topics covered by the surveys include demographic characteristics; health insurance, access to health care and health care utilization; health care needs, experiences, and costs; overall health status and changes in health; and depression and medical conditions and use of medications to treat them. Dataset 6 contains an analysis subset of the variables from the in-person interviews. Topics covered by the survey questionnaire include overall health, health insurance coverage, health care access, health care utilization, conditions and treatments, health behaviors, medical and dental costs, and demographic characteristics. The interviewers also obtained blood pressure and anthropometric measurements and collected dried blood spots to measure levels of cholesterol, glycated hemoglobin and C-reactive protein. Dataset 7 contains an analysis subset of the variables the study obtained for all emergency department (ED) visits to twelve hospitals in the Portland area during 2007-2009. These variables capture total hospital costs, ED costs, and the number of ED visits categorized by time of the visit (daytime weekday or nighttime and weekends), necessity of the visit (emergent, ED care needed, non-preventable; emergent, ED care needed, preventable; emergent, primary care treatable), ambulatory case sensitive status, whether or not the patient was hospitalized, and the reason for the visit (e.g., injury, abdominal pain, chest pain, headache, and mental disorders). The collection also includes a ZIP archive (Dataset 8) with Stata programs that replicate analyses reported in three articles by the principal investigators and others: Finkelstein, Amy et al "The Oregon Health Insurance Experiment: Evidence from the First Year". The Quarterly Journal of Economics. August 2012. Vol 127(3). Baicker, Katherine et al "The Oregon Experiment - Effects of Medicaid on Clinical Outcomes". New England Journal of Medicine. 2 May 2013. Vol 368(18). Taubman, Sarah et al "Medicaid Increases Emergency Department Use: Evidence from Oregon's Health Insurance Experiment". Science. 2 Jan 2014.

Medicaid CMS-64 FFCRA Increased FMAP Expenditure

  Description

During a public health emergency in the Families First Coronavirus Response Act (FFCRA), a new optional Medicaid eligibility group was added called COVID-19 testing eligibility group. States reported these expenditures under sections 6004 and 6008 through the Medicaid Budget and Expenditure System (MBES) on the Form CMS-64. The data in these reports constitute summary level preliminary expenditure information… See the full description on the dataset page: https://huggingface.co/datasets/HHS-Official/medicaid-cms-64-ffcra-increased-fmap-expenditure.

The CMS Program Statistics - Medicare Part D tables provide use and Part D drug costs by type of Part D plan (stand-alone prescription drug plan and Medicare Advantage prescription drug plan). For additional information on enrollment, providers, and Medicare use and payment, visit the CMS Program Statistics page. These data do not exist in a machine-readable format, so the view data and API options are not available. Please use the download function to access the data. Below is the list of tables: MDCR UTLZN D 1. Medicare Part D Utilization: Average Annual Prescription Drug Fills by Type of Plan, Low Income Subsidy (LIS) Eligibility, and Generic Dispensing Rate, Yearly Trend MDCR UTLZN D 2. Medicare Part D Utilization: Average Annual Gross Drug Costs Per Part D Enrollee, by Type of Plan, Low Income Subsidy (LIS) Eligibility, and Brand/Generic Drug Classification, Yearly Trend MDCR UTLZN D 3. Medicare Part D Utilization: Average Annual Gross Drug Costs Per Part D Enrollee, by Type of Plan, Low Income Subsidy (LIS) Eligibility, and Brand/Generic Drug Classification, Yearly Trend MDCR UTLZN D 4. Medicare Part D Utilization: Average Annual Prescription Drug Fills and Average Annual Gross Drug Cost Per Part D Enrollee, by Type of Plan and Demographic Characteristics MDCR UTLZN D 5. Medicare Part D Utilization: Average Annual Prescription Drug Fills and Average Annual Gross Drug Cost Per Part D Utilizer, by Type of Plan and Demographic Characteristics MDCR UTLZN D 6. Medicare Part D Utilization: Average Annual Prescription Drug Fills and Average Annual Gross Drug Cost Per Part D Enrollee, by Type of Plan, by Area of Residence MDCR UTLZN D 7. Medicare Part D Utilization: Average Annual Prescription Drug Fills and Average Annual Gross Drug Cost Per Part D Utilizer, by Type of Plan, by Area of Residence MDCR UTLZN D 8. Medicare Part D Utilization: Number of Part D Utilizers and Average Annual Prescription Drug Fills by Type of Part D Plan, Low Income Subsidy (LIS) Eligibility, and Part D Coverage Phase, Yearly Trend MDCR UTLZN D 9. Medicare Part D Utilization: Number of Part D Utilizers and Drug Costs by Type of Part D Plan, Low Income Subsidy (LIS) Eligibility, and Part D Coverage Phase, Yearly Trend MDCR UTLZN D 10. Medicare Part D Utilization: Number of Part D Utilizers, Average Annual Prescription Drug Events (Fills) and Average Annual Gross Drug Cost Per Part D Utilizer, by Part D Coverage Phase and Demographic Characteristics MDCR UTLZN D 11. Medicare Part D Utilization: Number of Part D Utilizers, Average Annual Prescription Drug Fills and Average Annual Gross Drug Cost Per Part D Utilizer, by Part D Coverage Phase and Area of Residence

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 is standardized to remove geographic differences in payment rates for individual services as a source of variation. In general, total standardized per capita costs are less than actual per capita costs because the extra payments Medicare made to hospitals were removed, such as payments for medical education (both direct and indirect) and payments to hospitals that serve a disproportionate share of low-income patients. Standardization does not adjust for differences in beneficiaries’ health status.

NADAC (National Average Drug Acquisition Cost) 2024

  Description

National Average Drug Acquisition Cost (NADAC) weekly reference data for the calendar year.

  Dataset Details

Publisher: Centers for Medicare & Medicaid Services Last Modified: 2024-12-23 Contact: Medicaid.gov (Medicaid.gov@cms.hhs.gov)

  Source

Original data can be found at: https://healthdata.gov/d/3tha-57c6

  Usage

You can load this dataset using: from datasets import… See the full description on the dataset page: https://huggingface.co/datasets/HHS-Official/nadac-national-average-drug-acquisition-cost-2024.

NCHS has linked data from various surveys with Medicare program enrollment and health care utilization and expenditure data from the Centers for Medicare & Medicaid Services (CMS). Linkage of the NCHS survey participants with the CMS Medicare data provides the opportunity to study changes in health status, health care utilization and costs, and prescription drug use among Medicare enrollees. Medicare is the federal health insurance program for people who are 65 or older, certain younger people with disabilities, and people with End-Stage Renal Disease.

1. Enrollment includes both Medicaid-only and Medicare-Medicaid (“dual”) enrollees. For both types of enrollees, Medicaid covers LTSS. For dual enrollees, Medicaid may also cover Medicare cost-sharing for acute, primary care, and specialty services covered by Medicare, and other non-LTSS services that are not covered by Medicare.
2. Some comprehensive managed care programs enroll beneficiaries who may be at risk of needing LTSS but do not receive any LTSS. These counts only include individuals that receive LTSS. Moreover, states differ in their ability to report individuals who use MLTSS versus those who are enrolled (and may or may not be using LTSS). This table reports MLTSS users unless otherwise noted.
3. Comprehensive Managed Care Including LTSS does not include PACE programs.
4. MLTSS Only programs cover LTSS under capitation; acute, primary, and specialty care services for these enrollees may be covered by another Medicaid MCO, Medicaid FFS, or by Medicare for dual enrollees. These data include states that provide MLTSS plus other benefits in a package that does not include inpatient medical care.
5. The indicated territory was not able to supply data for this report. The Northern Mariana Islands reported that they have no Medicaid managed care enrollment, but they did not report total Medicaid enrollees.
6. Enrollment and user counts include both Medicaid-only enrollees and dually eligible individuals. For both types of enrollees, Medicaid covers LTSS. For dually eligible individuals, Medicaid may also cover Medicare cost-sharing for acute, primary care, and specialty services covered by Medicare, and other non-LTSS services that are not covered by Medicare.

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.

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.

Abstract (en): This study comprises enrollment, utilization, and cost data for a number of state-sponsored high-risk health insurance plans. These plans, known as state risk pools, were primarily established for persons who wanted to buy health insurance but either were medically uninsurable or unable to find a policy at a reasonable cost. Enrollment variables in the data collection include reason for eligibility, preexisting conditions, Medicaid status, and month and year of enrollment and disenrollment. Utilization and cost variables include person's age and gender, coinsurance and deductible payments, and allowed charges by type of disease and type of service (outpatient, inpatient, pharmacy, or physician). The utilization and cost data are aggregated by person and month, with each observation representing a single month of enrollment for an individual. All persons enrolled during 1988-1991 in state-sponsored high-risk comprehensive health insurance risk pools in Connecticut, Florida, Minnesota, Nebraska, Washington, and Wisconsin. 2008-07-24 The codebook was revised to reflect the changes in the numbering of the datasets. Funding insitution(s): Robert Wood Johnson Foundation (19190). (1) The utilization and cost data files (Datasets 1-6) can be linked to the enrollment data (Dataset 7) by matching on variables FAMID and FAMMEM. (2) Some variables are not available for all states.

Estimated California smoking prevalence, cigarettes per capita, and per capita healthcare expenditure.

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

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...

Content

Kaiser Health News evaluated the capacity of intensive care unit (ICU) beds around the nation by first identifying the number of ICU beds each hospital reported in its most recent financial cost report, filed annually to the Centers for Medicare & Medicaid Services. KHN included beds reported in the categories of intensive care unit, surgical intensive care unit, coronary care unit and burn intensive care unit.

KHN then totaled the ICU beds per county and matched the data with county population figures from the Census Bureau’s American Community Survey. KHN focused on the number of people 60 and older in each county because older people are considered the most likely group to require hospitalization, given their increased frailty and existing health conditions compared with younger people. For each county, KHN calculated the number of people 60 and older for each ICU bed. KHN also calculated the percentage of county population who were 60 or older.

KHN’s ICU bed tally does not include Veterans Affairs hospitals, which are sure to play a role in treating coronavirus victims, because VA hospitals do not file cost reports. The total number of the nation’s ICU beds in the cost reports is less than the number identified by the American Hospital Association’s annual survey of hospital beds, which is the other authoritative resource on hospital characteristics. Experts attributed the discrepancies to different definitions of what qualifies as an ICU bed and other factors, and told KHN both sources were equally credible.

Acknowledgements

Kaiser Health News

https://khn.org/news/as-coronavirus-spreads-widely-millions-of-older-americans-live-in-counties-with-no-icu-beds/ https://khn.org/news/as-coronavirus-spreads-widely-millions-of-older-americans-live-in-counties-with-no-icu-beds/

Fred Schulte: fschulte@kff.org, @fredschulte

Elizabeth Lucas: elucas@kff.org, @eklucas

Jordan Rau: jrau@kff.org, @JordanRau

Liz Szabo: lszabo@kff.org, @LizSzabo

Jay Hancock: jhancock@kff.org, @JayHancock1

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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.