In 2021, California reported some 6.49 million Medicare beneficiaries and therefore was the U.S. state with the highest number of beneficiaries. Medicare is a U.S. publicly funded health insurance program that covers those that are aged 65 years and older and those that have certain disabilities. This statistic depicts the leading 10 U.S. states based on their number of Medicare beneficiaries in 2021.

With 26 percent, Maine had the highest percentage of Medicare beneficiaries among its total population in 2021. This statistic depicts the top 10 U.S. states based on Medicare beneficiaries as a percentage of the total population in the calendar year 2021.

In 2023, Alabama and Michigan had the highest rate of Medicare Advantage (MA) penetration, meaning that ** percent of Medicare beneficiaries in these three states were enrolled in MA plans rather than traditional Medicare plans. The national average was ** percent that year. This statistic depicts the leading 10 U.S. states by percentage of Medicare beneficiaries enrolled in a Medicare Advantage plan in 2024.

The Medicare Current Beneficiary Survey (MCBS) - Survey File Microdata Public Use File (PUF) dataset provides information on topics such as Medicare beneficiaries' access to care, health status, other information regarding beneficiaries’ knowledge of, attitudes toward, and satisfaction with their health care, as well as demographic data and information on all types of health insurance coverage.Resources for Using and Understanding the DataThis dataset is based on information from the MCBS and administrative data. The MCBS is a continuous, multi-purpose longitudinal survey covering a representative national sample of the Medicare population, including the population of beneficiaries aged 65 and over and beneficiaries aged 64 and below with certain disabling conditions. The MCBS collects this information in three data collection periods, or rounds, per year. Disclosure protections have been applied to the file, including de-identification and other methods. As a result, the MCBS Survey File Microdata file does not require a Data Use Agreement (DUA). In contrast, the MCBS Limited Data Set (LDS) releases contain beneficiary-level protected health information (PHI) and therefore require a DUA. The MCBS - Survey File Microdata file is not intended to replace the more detailed LDS files but, rather, it makes available a general-use publicly-available alternative that provides the highest degree of protection to the Medicare beneficiaries’ PHI. The main benefits of using the MCBS - Survey File Microdata file are:Increased data access for researchers of the MCBS through a free file download that is consistent with other U.S. Department of Health and Human Services (HHS) public-use survey files.Enhanced potential for policy-relevant analyses, by attracting new researchers and policymakers. Accessing the MCBS LDS can be a significant deterrent due to the associated costs and time but the MCBS - Survey File Microdata file mitigates these barriers to encourage broader utilization. A link to the more detailed MCBS LDS files is provided in the Resources section on this page. MCBS LDS data are also presented in the MCBS Chartbook linked in the Visualization section on this page.

Medicare FFS Beneficiary Utilization and Costs

2007-2014 Washington State and Counties

By Health [source]

About this dataset

This dataset contains info on the number of Medicare Fee-for-Service Beneficiaries (FFS) receiving healthcare services from hospitals, physicians, and other providers, as well as their associated charges and payments. It provides in-depth, detailed demographics like age group, gender, all kinds of race/ethinicity data and geographical regions. This information can be used to better understand existing health disparities among Medicare FFS beneficiaries across the U.S., examine trends in utilization over time to identify areas where changes are needed within the system or research a wide range of policy issues in healthcare

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How to use the dataset

This dataset provides a look into Medicare Fee-for-Service beneficiaries in health services being utilized by those enrolled in the Medicare program. The information included can help to paint a picture of how Medicare recipients are using health services, such as hospital and physician visits, laboratory tests and procedures, prescription drugs and imaging services.

In order to make the most use of this data set for research or analysis purposes, there are several key pieces of information that should be taken into account. This includes examining both utilization data (such as the numbers of recommended specific procedures) as well as cost components (such as fee schedules). Specifics within this data set include the average estimated Submitted Charges for each procedure code from nationwide claims from 2011 to 2018.

When looking at utilization portion of this dataset it is important to consider: • Total number of services provided for each condition identified by ICD-9 or ICD10 code • Average total service minutes per beneficiary / patient with national average levels listed across five years throughout the period previously mentioned • Percentage change across accessed service types over time period wherein 2011 have been viewed versus more recent statistics • Top five provider specialty types who render service • Number of facilities providing care on annual basis along with percentages utilizing Rural Health Centers grouped together categories including but not exclusive not limited to metropolitan areas; counties; Congressional Districts ; Regions; states plus other geographic entities • Age groups who have used these facilities based on gender plus new acute admissions reported same time frame

A secondary component yet equally important component regarding fees associated with different medical therapies should be considered additionally when uses dataset  which includes:

• Amounts charged by certain facilities based upon current expenses related dates whether patient purchased generic version or brand-name medication due its additional costs relates most significantly towards said medication choices National level along with regional percentage splits relating drug alternatives utilized per given month Actual recharge associated calculated mechanism/formulae , sometimes may refer UPFS methodology Those charges represent sum total averages against whom paid expense examples include: Part B drugs recipients outpatient surgeries & facility visits Note future amounts collected depend upon patients Choice whether require certain distinct E&M codes sometimes need submit ancillary components( diagnoses codes ) separate selections meant cater both facility site & practitioner’s overall needs Sometimes technology assigns relative value unit ( RVU ) defining severity factors linked coding differing specialties so their respective fields well documented Finally analyzing any detail reporting requirements varying specialties

Research Ideas

• Analyze various patterns in health services utilization by Medicare beneficiaries to provide insight into the most commonly used services and ways to improve care.
• Track the number of Medicare beneficiaries using each type of health service in order to identify potential underserved populations or areas with high usage levels that necessitate additional coverage or resources.
• Identify regional differences in provider use rates and payment amounts for specific types of health services, which can help inform efforts to improve equity and access across different geographical regions

Acknowledgements

If you use this dataset in your research, please credit the original authors. [Data Sou...

Data consist of CMS Medicare data files which are restricted access and cannot be released publicly. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. EPA cannot release CBI, or data protected by copyright, patent, or otherwise subject to trade secret restrictions. Request for access to CBI data may be directed to the dataset owner by an authorized person by contacting the party listed. It can be accessed through the following means: CMS Medicare data are available from: https://www.cms.gov/data-research/files-for-order/data-disclosures-and-data-use-agreements-duas/limited-data-set-lds with the requirement of a signed Data Use Agreement. . Weather data are available at https://prism.oregonstate.edu/. Format: The data that support the findings of this study are available from the Centers for Medicare and Medicaid Services (CMS). Restrictions apply to the availability of these data, which were provided under a Data Use Agreement specific to this study. Data are available from: https://www.cms.gov/data-research/files-for-order/data-disclosures-and-data-use-agreements-duas/limited-data-set-lds with the requirement of a signed Data Use Agreement. Data do not contain personally identifiable information but contain are classified as Limited Data Set files and their distribution require an agreement and between CMS and the requester and approval by CMS. Weather data are available at https://prism.oregonstate.edu/. Because the data do not contain identifiable private information and were not obtained through interaction or intervention with individuals, the Institutional Review Board for the University of North Carolina and the US Environmental Protection Agency Human Research Protocol Officer determined that use of this data does not constitute human subjects research. This dataset is associated with the following publication: Wade, T., and C. Herbert. Weather conditions and legionellosis: a nationwide case-crossover study among Medicare recipients. EPIDEMIOLOGY AND INFECTION. Cambridge University Press, Cambridge, UK, 152: E125, (2024).

Context

There are all sorts of reasons why you'd want to know a hospital's quality rating.

• Your mom is having her second hip replacement. Her first one went terribly and you're nervous about how she'll do. Which hospital would you suggest she have her surgery?
• You're selecting a health plan on your state's Exchange, but your top two choices partner with different hospitals. How will you decide which plan to pick?
• Your brother has Cystic Fibrosis and has to go to the ER frequently. He hates waiting. Which hospitals/states provide care in the timeliest manner?
• Your in-laws moved to Florida recently to retire, and have been trying to convince you to move there too. You're looking for any way possible to show them that your state is better. Does your state have better hospitals?

Every hospital in the United States of America that accepts publicly insured patients (Medicaid or MediCare) is required to submit quality data, quarterly, to the Centers for Medicare & Medicaid Services (CMS). There are very few hospitals that do not accept publicly insured patients, so this is quite a comprehensive list.

Content

This file contains general information about all hospitals that have been registered with Medicare, including their addresses, type of hospital, and ownership structure. It also contains information about the quality of each hospital, in the form of an overall rating (1-5, where 5 is the best possible rating & 1 is the worst), and whether the hospital scored above, same as, or below the national average for a variety of measures.

This data was updated by CMS on July 25, 2017. CMS' overall rating includes 60 of the 100 measures for which data is collected & reported on Hospital Compare website (https://www.medicare.gov/hospitalcompare/search.html). Each of the measures have different collection/reporting dates, so it is impossible to specify exactly which time period this dataset covers. For more information about the timeframes for each measure, see: https://www.medicare.gov/hospitalcompare/Data/Data-Updated.html# For more information about the data itself, APIs and a variety of formats, see: https://data.medicare.gov/Hospital-Compare

Acknowledgements

Attention: Works of the U.S. Government are in the public domain and permission is not required to reuse them. An attribution to the agency as the source is appreciated. Your materials, however, should not give the false impression of government endorsement of your commercial products or services. See 42 U.S.C. 1320b-10.

Inspiration

Which hospital types & hospital ownerships are most common?

Which hospital types & ownerships are associated with better than average ratings/mortality/readmission/etc?

What is the average hospital rating, by state?

Which hospital types & hospital ownerships are more likely to have not submitted proper data ("Not Available" & "Results are not available for this reporting period")?

Which parts of the country have the highest & lowest density of religious hospitals?

Medicare outlays in the United States amounted to 1.01 trillion U.S. dollars in 2023. The forecast predicts an increase in Medicare outlays up to one trillion U.S. dollars in 2034.

a Cases in which the beneficiary was continuously enrolled in Medicare Part D (prescription drug coverage) during the 7 days following diagnosis.b Cases with at least one NDC recorded during the 7-day observation window.c Cases with at least one NDC recorded but who were not treated with any of the medications listed above.Medication Use in the 7 Days following Babesiosis Diagnosis, Overall and in the Four Babesiosis Groups.

The interactive map shows the data differences between 2012-2018, and does not include Veterans’ personal information. The posted information shows opioid-dispensing rates for each VA facility and how much those rates have decreased over time. Prescribing Rates:Opioid prescribing rates are calculated by dividing the number of Veterans who received any opioid prescription by the total number of Veterans who received a prescription from that pharmacy within the specified time period.Percent ChangeThe percent change represents the relative decrease (or increase) in opioid prescribing rates between 2012 and 2018. Overall, 99% of VA facilities have decreased prescribing rates since 2012.Regional Non-VA Prescribing Rate:It is well-established that opioid prescribing rates and abuse vary across different parts of the country. Regional comparison categories were generated using publicly available data from the Centers for Medicare & Medicaid Services (CMS). CMS reports opioid prescribing rates by state, which are calculated by dividing the number of Medicare Part D claims for opioid medications by the total number of prescription claims.Data for states are aggregated in to 5 groups by CMS. The "Low" Regional Non-VA comparison category represents the 40% of states with the lowest prescribing rates for Medicare beneficiaries. The "High" Regional Non-VA comparison category represents the 40% of states with the highest prescribing rates. The "Average" Regional Non-VA comparison category represents the 20% of states in the middle. The most current available comparison data is from 2015."***Microdata: Yes Level of Analysis: Group - Facilities Variables Present: Yes File Layout: .csv Codebook: Yes Methods: Yes Weights (with appropriate documentation): No Publications: Yes Aggregate Data: Yes

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. 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. Comprehensive Managed Care Including LTSS does not include PACE programs. 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. 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. 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. The “Comprehensive Managed Care Including LTSS” column does not include PACE programs. Columns indicating the "Number of enrollees using LTSS" reflect what states reported. In addition to the three states that reported LTSS users (Arizona, New York and Wisconsin), California and Delaware also offer LTSS services in a stand-alone program. Note: "n/a" indicates that a state or territory was not able to report data or does not have a managed care program. "--" indicates that a state or territory does not operate programs of the type listed in the column heading. Enrollment and user counts include both Medicaid-only 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. Columns indicating the "Number of enrollees using LTSS" reflect what states reported. In addition to the three states that reported LTSS users (Arizona, New York and Wisconsin), California and Delaware also offer LTSS services in a stand-alone program.

Context

Every year, all U.S. hospitals that accept payments from Medicare and Medicaid must submit quality data to The Centers for Medicare and Medicaid Services (CMS). CMS' Hospital Compare program is a consumer-oriented website that provides information on "the quality of care hospitals are providing to their patients." CMS releases this quality data publicly in order to encourage hospitals to improve their quality and to help consumer make better decisions about which providers they visit.

"Hospital Compare provides data on over 4,000 Medicare-certified hospitals, including acute care hospitals, critical access hospitals (CAHs), children’s hospitals, Veterans Health Administration (VHA) Medical Centers, and hospital outpatient departments"

The Centers for Medicare & Medicaid Services (CMS) uses a five-star quality rating system to measure the experiences Medicare beneficiaries have with their health plan and health care system — the Star Rating Program. Health plans are rated on a scale of 1 to 5 stars, with 5 being the highest.

Content

• Includes the most recent Hospital General Information.csv data for each archive year found on CMS' archive site. Years: 2016-2020

| Column Name | Data Type | Description | | --- | --- | -- | | Facility ID | Char(6) | Facility Medicare ID | | Facility Name | Char(72) | Name of the facility | | Address | Char(51) | Facility street address | | City | Char(20) | Facility City | | State | Char(2) | Facility State | | ZIP Code | Num(8) | Facility ZIP Code | | County Name | Char(25) | Facility County | | Phone Number | Char(14) | Facility Phone Number | | Hospital Type | Char(34) | What type of facility is it? | | Hospital Ownership | Char(43) | What type of ownership does the facility have? | | Emergency Services | Char(3)) | Does the facility have emergency services Yes/No? | | Meets criteria for promoting interoperability of EHRs | Char(1) | Does facility meet government EHR standard Yes/No? | | Hospital overall rating | Char(13) | Hospital Overall Star Rating 1=Worst; 5=Best. Aggregate measure of all other measures | | Hospital overall rating footnote | Num(8) | | | Mortality national comparison | Char(28) | Facility overall performance on mortality measures compared to other facilities | | Mortality national comparison footnote | Num(8) | | | Safety of care national comparison | Char(28) | Facility overall performance on safety measures compared to other facilities | | Safety of care national comparison footnote | Num(8) | | | Readmission national comparison | Char(28) | Facility overall performance on readmission measures compared to other facilities | | Readmission national comparison footnote | Num(8) | | | Patient experience national comparison | Char(28) | Facility overall performance on pat. exp. measures compared to other facilities | | Patient experience national comparison footnote | Char(8) | | | Effectiveness of care national comparison | Char(28) | Facility overall performance on effect. of care measures compared to other facilities | | Effectiveness of care national comparison footnote | Char(8) | | | Timeliness of care national comparison | Char(28) | Facility overall performance on timeliness of care measures compared to other facilities | | Timeliness of care national comparison footnote| Char(8) | | | Efficient use of medical imaging national comparison | Char(28) | Facility overall performance on efficient use measures compared to other facilities | | Efficient use of medical imaging national comparison footnote | Char(8) | | | Year | Char(4) | cms data release year |

Acknowledgements

A similar dataset called Hospital General Information was previously uploaded to Kaggle. However, that dataset only includes data from one year (2017). I was inspired by this dataset to go a little further and try to add a time dimension. This dataset includes a union of Hospital General Information for the years 2016-2020. The python script used to collect and union all the datasets can be found on my [github[(https://github.com/abrambeyer/cms_hospital_general_info_file_downloader). Thanks to this dataset owner for the inspiration.

Thanks to CMS for releasing this dataset publicly to help consumers find better hospitals and make better-informed decisions.

***All Hospital Compare websites are publically accessible. As works of the U.S. government, Hospital Compare data are in the public domain and permission is not required to reuse them. An attribution to the agency as the source is appreciated. Your ...

RationaleThe extent to which geographic variability in ICU admission across the United States is driven by patients with lower risk of death is unknown.ObjectivesTo determine whether patients at low to moderate risk of death contribute to geographic variation in ICU admission.MethodsRetrospective cohort of hospitalizations among Medicare beneficiaries (age > 64 years) admitted for ten common medical and surgical diagnoses (2004 to 2009). We examined population-adjusted rates of ICU admission per 100 hospitalizations in 304 health referral regions (HRR), and estimated the relative risk of ICU admission across strata of regional ICU and risk of death, adjusted for patient and regional characteristics.Measurement and Main ResultsICU admission rates varied nearly two-fold across HRR quartiles (quartile 1 to 4: 13.6, 17.3, 20.0, and 25.2 per 100 hospitalizations, respectively). Observed mortality for patients in regions (quartile 4) with the greatest ICU use was 17% compared to 21% in regions with lowest ICU use (quartile 1) (p

PurposeTo describe epidemiologic features of patients with presumed ocular histoplasmosis syndrome (POHS) in the United States using insurance claims data and compare POHS patients with and without choroidal neovascularization (CNV).DesignRetrospective cohort study.MethodsPatients with International Classification of Diseases, Ninth Revision, Clinical Modification diagnosis codes for histoplasmosis retinitis on an outpatient claim in the 2014 IBM® MarketScan® Commercial Database and the Medicare Supplemental Database who were enrolled for at least 2 years after the POHS code.Main outcome measuresData related to testing, treatment, and direct medical costs.ResultsAmong >50 million total MarketScan enrollees, 6,678 (13 per 100,000) had a POHS diagnosis code. Of those, 2,718 were enrolled for 2 years; 698 (25%) of whom had a CNV code. Eleven of the 13 states with the highest POHS rates bordered the Mississippi and Ohio rivers. CNV patients had significantly more eye care provider visits (mean 8.8 vs. 3.2, p

In Tennessee, some 13.4 percent of elderly patients received high-risk drugs in 2016. That percentage was one of the highest in the United States where the average stood at 9.6 percent. This statistic depicts the percentage of elderly patienst that received high-risk drugs in 2016, by state.

Cohort demographics by compensated and decompensated cirrhosis state.

Pre-emptive pharmacogenomic (PGx) testing of a panel of genes may be easier to implement and more cost-effective than reactive pharmacogenomic testing if a sufficient number of medications are covered by a single test and future medication exposure can be anticipated. We analysed the incidence of exposure of individual patients in the United States to multiple drugs for which pharmacogenomic guidelines are available (PGx drugs) within a selected four-year period (2009–2012) in order to identify and quantify the incidence of pharmacotherapy in a nation-wide patient population that could be impacted by pre-emptive PGx testing based on currently available clinical guidelines. In total, 73 024 095 patient records from private insurance, Medicare Supplemental and Medicaid were included. Patients enrolled in Medicare Supplemental age > = 65 or Medicaid age 40–64 had the highest incidence of PGx drug use, with approximately half of the patients receiving at least one PGx drug during the 4 year period and one fourth to one third of patients receiving two or more PGx drugs. These data suggest that exposure to multiple PGx drugs is common and that it may be beneficial to implement wide-scale pre-emptive genomic testing. Future work should therefore concentrate on investigating the cost-effectiveness of multiplexed pre-emptive testing strategies.

The Medicaid by Drug dataset presents information on spending for covered outpatient drugs prescribed to beneficiaries enrolled in Medicaid by physicians and other healthcare professionals. The dataset focuses on average spending per dosage unit and change in average spending per dosage unit over time. Units refer to the drug unit in the lowest dispensable amount. 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 Medicaid represent the total amount reimbursed by both Medicaid and non-Medicaid entities to pharmacies for the drug. Medicaid drug spending contains both the Federal and State reimbursement and is inclusive of any applicable dispensing fees. In addition, this total is not reduced or affected by Medicaid rebates paid to the states.

To evaluate the impact of a comprehensive set of social determinants of health (SDOH) on treatments, timing, and key biomarker testing for early-stage non-small cell lung cancer (NSCLC). Patients with the first diagnosis of stage I–III NSCLC from 1 January 2015 to 15 October 2023 and treated at community health systems in the United States were eligible for this retrospective database study. We summarized initial primary treatment and time-to-treatment initiation (TTI) by Social Vulnerability Index (SVI), primary care provider (PCP) shortage areas, household income, and insurance type. Data cutoff was 15 October 2024. Of 8501 patients with stage I–III NSCLC, 32% underwent surgery-only and 14% also received neoadjuvant and/or adjuvant therapy. Greater percentages underwent surgery (with/without neoadjuvant/adjuvant therapy) in counties with lowest SVI/vulnerability, highest median income, and no PCP shortage, and among those with private healthcare insurance (vs. Medicare/Medicaid). Median (range) TTI for any NSCLC-related treatment after diagnosis was 41 days (0–1846); TTI increased across treatment strategies by increasing SVI/vulnerability and decreasing household income. Annual rates of programmed death-ligand 1/EGFR mutation testing rose from 60%/51% in 2020 to 84%/82% in 2023, with greatest rates in counties with no PCP shortage. Disparities in early-stage NSCLC treatment by SDOH factors call for efforts to improve access to timely care for NSCLC. The survival rate for non-small cell lung cancer (NSCLC), the most common lung cancer type, has been improving in the United States over the past decade. This is because better treatments are available and also because of earlier diagnoses, when the disease is easier to treat. However, prior studies have reported that patients who have poor so-called ‘social determinants of health’ (SDOH)—such as food insecurity, living in socioeconomically disadvantaged areas, or belonging to certain racial or ethnic minority groups—are more likely to have later NSCLC diagnosis, delays in starting treatment, and worse survival. The goal of our study was to update these earlier findings with more recent patient data (2015–2024) to evaluate SDOH factors for their impact on treatments, timing, and key tests for early-stage NSCLC. We found that patients with early-stage NSCLC who lived in counties or areas with lowest social vulnerability, highest median income, and no primary care provider shortage, and those who had private healthcare insurance (instead of Medicare or Medicaid) more commonly had surgery and started treatment sooner for their NSCLC. Instead, the opposite was true for patients who lived in areas with highest vulnerability, lowest income, and shortages of primary care doctors, and who had Medicare or Medicaid insurance: they more commonly had no initial primary treatment. Moreover, the average time until starting treatment increased with greater social vulnerability and less household income. These findings call for improving patient access to timely care for NSCLC.

In 2018, the prevalence of Crohn's disease was highest among white, non-Hispanic Medicare beneficiaries and lowest among Asian/Pacific Islander, non-Hispanic beneficiaries. A similar trend could be found for ulcerative colitis. These diseases are the principle types of inflammatory bowel disease (IBD).

This statistic shows the share of Medicare fee-for-service beneficiaries in the United States with Crohn's disease and ulcerative colitis in 2018, by ethnicity.