BackgroundThe coronavirus disease 2019 (COVID-19) public health emergency has amplified the potential value of deploying telehealth solutions. Less is known about how trends in access to care through telehealth changed over time.ObjectivesTo investigate trends in forgone care and telehealth coverage among Medicare beneficiaries during the COVID-19 pandemic.MethodsA cross-sectional study design was used to analyze the outcomes of 31,907 Medicare beneficiaries using data from three waves of survey data from the Medicare Current Beneficiary Survey COVID-19 Supplement (Summer 2020, Fall 2020, and Winter 2021). We identified informative variables through a multivariate classification analysis utilizing Random Forest machine learning techniques.FindingsThe rate of reported forgone medical care because of COVID-19 decreased largely (22.89–3.31%) with a small increase in telehealth coverage (56.24–61.84%) from the week of June 7, 2020, to the week of April 4 to 25, 2021. Overall, there were 21.97% of respondents did not know whether their primary care providers offered telehealth services; the rates of forgone care and telehealth coverage were 11.68 and 59.52% (11.73 and 81.18% from yes and no responses). Our machine learning model predicted the outcomes accurately utilizing 43 variables. Informative factors included Medicare beneficiaries' age, Medicare-Medicaid dual eligibility, ability to access basic needs, certain mental and physical health conditions, and interview date.ConclusionsThis cross-sectional survey study found proliferation and utilization of telehealth services in certain subgroups during the COVID-19 pandemic, providing important access to care. There is a need to confront traditional barriers to the proliferation of telehealth. Policymakers must continue to identify effective means of maintaining continuity of care and growth of telehealth services.

This table presents the number of pregnant and postpartum Medicaid and CHIP beneficiaries, 2017-2021. It includes (1) the number and percentage of beneficiaries ever pregnant in the year; (2) the number and percentage of live births in the year; (3) the number and percentage of miscarriages, stillbirths, or terminations in the year; and (4) the number and percentage of births with an unknown delivery outcome in the year. These metrics are based on data in the T-MSIS Analytic Files (TAF). Some states have serious data quality issues, making the data unusable for identifying this population. Data for a state are considered unusable based on DQ Atlas thresholds for the following topics: Total Medicaid and CHIP Enrollment, Claims Volume - IP, Claims Volume - OT, Claims Volume - IP, Diagnosis Code - IP, Diagnosis Code - OT, Procedure Codes - OT Professional. Cells with a value of “DQ” indicate that data were suppressed due to unusable data. Data from Maryland, Tennessee, and Utah are omitted from the tables due to data quality concerns. Maryland was excluded in 2017 due to unusable diagnosis codes in the IP file and the OT file. Tennessee was excluded due to unusable diagnosis codes in the IP file in 2017 - 2019. Utah was excluded due to unusable procedure codes on OT professional claims in 2017 - 2020. In addition, states with a high data quality concern on one or more measures are noted in the table in the "Data Quality" column. Please refer to the DQ Atlas at http://medicaid.gov/dq-atlas for more information about data quality assessment methods. Some cells have a value of “DS”. This indicates that data were suppressed for confidentiality reasons because the group included fewer than 11 beneficiaries.

The Medicare COVID-19 Hospitalization Trends dataset contains aggregate information from Medicare Fee-for-Service claims, Medicare Advantage encounter, and Medicare enrollment data. It provides insight around the groups of beneficiaries that were hospitalized at different points during the pandemic. CMS publicly released the first Preliminary Medicare COVID-19 Snapshot in June 2020 during the early stages of the Public Health Emergency for COVID-19. That report focused on COVID-19 cases and hospitalizations data for Medicare beneficiaries with a COVID-19 diagnosis. Throughout 2020 and 2021, that report was subsequently updated with refreshed data 13 times. Beginning in October 2021, CMS shifted its public COVID-19 reporting away from cumulative case and hospitalization rates to hospitalization trends over time with the release of this report, the Medicare COVID-19 Hospitalization Trends Report. All prior releases of both the Preliminary Medicare COVID-19 Snapshot and the Medicare COVID-19 Hospitalization Trends Report are available for download in the Medicare COVID-19 Data - Prior Releases file.

ABSTRACT: We estimated excess mortality in Medicare recipients with probable and confirmed Covid-19 infections in the general community and amongst residents of long-term care (LTC) facilities. We considered 28,389,098 Medicare and dual-eligible recipients from one year before February 29, 2020 through September 30, 2020, with mortality followed through November 30th, 2020. Probable and confirmed Covid-19 diagnoses, presumably mostly symptomatic, were determined from ICD-10 codes. We developed a Risk Stratification Index (RSI) mortality model which was applied prospectively to establish baseline mortality risk. Excess deaths attributable to Covid-19 were estimated by comparing actual-to-expected deaths based on historical (2017-2019) comparisons and in closely matched concurrent (2020) cohorts with and without Covid-19. 677,100 (2.4%) beneficiaries had confirmed Covid-19 and 2,917,604 (10.3%) had probable Covid-19. 472,329 confirmed cases were community living and 204,771 were in LTC. Mortality following a probable or confirmed diagnosis in the community increased from an expected incidence of about 4.0% to actual incidence of 7.5%. In long-term care facilities, the corresponding increase was from 20.3% to 24.6%. The absolute increase was therefore similar at 3-4% in the community and in LTC residents. The percentage increase was far greater in the community (89.5%) than among patients in chronic care facilities (21.1%) who had higher baseline risk. The LTC population without probable or confirmed Covid-19 diagnoses experienced 38,932 excess deaths (34.8%) compared to historical estimates. Limitations in access to Covid-19 testing and disease under-reporting in LTC patients probably were important factors, although social isolation and disruption in usual care presumably also contributed. Remarkably, there were 31,360 (5.4%) fewer deaths than expected in community dwellers without probable or confirmed Covid-19 diagnoses. Disruptions to the healthcare system and avoided medical care were thus apparently offset by other factors, representing overall benefit. The Covid-19 pandemic had marked effects on mortality, but the effects were highly context-dependent.

The table Master Beneficiary Summary File (MBSF) - Base (A/B/C/D) is part of the dataset Medicare 20% [2019-2020] Enrollment/Summary, available at https://stanford.redivis.com/datasets/2ewt-f9320gt59. It contains 26718736 rows across 185 variables.

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.

This dataset contains summarized information of Hospital Service Area (HHA) file by provider number and ZIP code of the Medicare beneficiary.

Medicaid is an important public health insurance for individuals with a low income, those that are pregnant, disabled or are children. It was projected that by 2020 there would be approximately **** million Medicaid enrollees. By 2027 that number is expected to increase to ** million individuals covered.

Medicaid in the focus

Medicaid has recently been in the news for several reasons. A proposed Medicaid expansion was announced with the implementation of the Affordable Care Act in 2010. According to the expansion, all states were given the option to expand Medicaid programs to help provide insurance coverage to millions of U.S. Americans. As of 2019, ** states have accepted federal funding to expand their Medicaid programs. Medicaid, after Medicare and private insurance, provides a significant proportion of the total health expenditures in the United States. In general, Medicaid expenditure, like the number of enrollees, has been growing over time.

Medicaid demographics

A significant proportion of Medicaid enrollees in the U.S. are children and low-income adults. Despite children accounting for most of the enrollees in the Medicaid program, the largest percentage of expenditures for Medicaid is dedicated to those enrolled as a disabled individual. Expenditures for the program also vary regionally. The states with the highest Medicaid expenditures include California, New York and Texas, to name a few.

BackgroundThe objective of this study was to examine differences in availability and use of telehealth services among Medicare enrollees according to Alzheimer’s disease and related dementias (ADRD) status and enrollment in Medicare Advantage (MA) versus Traditional Medicare (TM) during the period surrounding the COVID-19 pandemic.MethodsThis was a retrospective cross-sectional analysis of data from community-dwelling MA and TM enrollees with and without ADRD from the Medicare Current Beneficiary Survey (MCBS) Fall 2020 and Winter 2021 COVID-19 Supplement Public Use Files. We examined self-reported availability of telehealth service before and during the COVID-19 pandemic and use of telehealth services during COVID-19. We analyzed marginal effects under multivariable logistic regression.ResultsThere were 13,700 beneficiaries with full-year enrollment in MA (6,046) or TM (7,724), 518 with ADRD and 13,252 without ADRD. Telehealth availability during COVID-19 was positively associated with having a higher income (2.81 pp. [percentage points]; 95% CI: 0.57, 5.06), having internet access (7.81 pp.; 95% CI: 4.96, 10.66), and owning telehealth-related technology (3.86; 95% CI: 1.36, 6.37); it was negatively associated with being of Black Non-Hispanic ethnicity (−8.51 pp.; 95% CI: −12.31, −4.71) and living in a non-metro area (−8.94 pp.; 95% CI: −13.29, −4.59). Telehealth availability before COVID-19 was positively associated with being of Black Non-Hispanic ethnicity (9.34 pp.; 95% CI: 3.74, 14.94) and with enrollment in MA (4.72 pp.; 95% CI: 1.63, 7.82); it was negatively associated having dual-eligibility (−5.59 pp.; 95% CI: −9.91, −1.26). Telehealth use was positively associated with being of Black Non-Hispanic ethnicity (6.47 pp.; 95% CI: 2.92, 10.01); it was negatively associated with falling into the age group of 75+ years (−4.98 pp.; 95% CI: −7.27, −2.69) and with being female (−4.98 pp.; 95% CI: −7.27, −2.69).ConclusionTelehealth services were available to and used by Medicare enrollees with ADRD to a similar extent compared to their non-ADRD counterparts. Telehealth services were available to MA enrollees to a greater extent before COVID-19 but not during COVID-19, and this group did not use telehealth services more than TM enrollees during COVID-19.

2018 - 2020, county-level U.S. heart disease hospitalization rates. Dataset developed by the Centers for Disease Control and Prevention, Division for Heart Disease and Stroke Prevention.Create maps of U.S. heart disease hospitalization rates among Medicare fee-for-service beneficiaries aged 65 and older, by county. Data can be stratified by race/ethnicity and sex.Visit the CDC/DHDSP Atlas of Heart Disease and Stroke for additional data and maps. Atlas of Heart Disease and StrokeData SourceHospitalization data were obtained from the Centers for Medicare and Medicaid Services Medicare Provider Analysis and Review (MEDPAR) file, Part A and the Master Beneficiary Summary File (MBSF). International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) codes: I00-I09, I11, I13, I20-I51; principle (i.e., first-listed) diagnosis. Medicare fee-for-service beneficiaries 65 and older were included. Visit the Atlas of Heart Disease and Stroke Statistical Methods pages for more detailed Medicare data inclusion criteriaData DictionaryData for counties with small populations are not displayed when a reliable rate could not be generated. These counties are represented in the data with values of '-1.' CDC/DHDSP excludes these values when classifying the data on a map, indicating those counties as 'Insufficient Data.'Data field names and descriptionsstcty_fips: state FIPS code + county FIPS codeOther fields use the following format: RRR_S_aaaa (e.g., API_M_35UP)  RRR: 3 digits represent race/ethnicity    All - Overall   BLK - Black, non-Hispanic    HIS - Hispanic    WHT - White, non-Hispanic  S: 1 digit represents sex    A - All    F - Female    M - Male  aaaa: 4 digits represent age. The first 2 digits are the lower bound for age and the last 2 digits are the upper bound for age. 'UP' indicates the data includes the maximum age available and 'LT' indicates ages less than the upper bound. Example: The column 'BLK_M_65UP' displays rates per 1,000 black Medicare beneficiaries aged 65 years and older.MethodologyRates are calculated using a 3-year average and are age-standardized in 10-year age groups using the 2000 U.S. Standard Population. Rates are calculated and displayed per 1,000 Medicare beneficiaries. Rates were spatially smoothed using a Local Empirical Bayes algorithm to stabilize risk by borrowing information from neighboring geographic areas, making estimates more statistically robust and stable for counties with small populations. Data for counties with small populations are coded as '-1' when a reliable rate could not be generated. County-level rates were generated when the following criteria were met over a 3-year time period within each of the filters (e.g., age, race, and sex).At least one of the following 3 criteria:At least 20 events occurred within the county and its adjacent neighbors.ORAt least 16 events occurred within the county.ORAt least 5,000 population years within the county.AND all 3 of the following criteria:At least 6 population years for each age group used for age adjustment if that age group had 1 or more event.The number of population years in an age group was greater than the number of events.At least 100 population years within the county.More Questions?Interactive Atlas of Heart Disease and StrokeData SourcesStatistical Methods

The table Master Beneficiary Summary File - Chronic Conditions (CC30) is part of the dataset Medicare 20% [2019-2020] Enrollment/Summary, available at https://stanford.redivis.com/datasets/2ewt-f9320gt59. It contains 52153096 rows across 62 variables.

This table presents the number of beneficiaries with a delivery, the number of beneficiaries with any SMM condition, and the rate of SMM conditions per 10,000 deliveries, 2017 - 2021. These metrics are based on data in the T-MSIS Analytic Files (TAF). Some states have serious data quality issues, making the data unusable for identifying this population. Data for a state are considered unusable based on DQ Atlas thresholds for the following topics: Total Medicaid and CHIP Enrollment, Claims Volume - IP, Claims Volume - OT, Claims Volume - IP, Diagnosis Code - IP, Diagnosis Code - OT, Procedure Codes - OT Professional. Cells with a value of “DQ” indicate that data were suppressed due to unusable data. Data from Maryland, Tennessee, and Utah are omitted from the tables due to data quality concerns. Maryland was excluded in 2017 due to unusable diagnosis codes in the IP file and the OT file. Tennessee was excluded due to unusable diagnosis codes in the IP file in 2017 - 2019. Utah was excluded due to unusable procedure codes on OT professional claims in 2017 - 2020. In addition, states with a high data quality concern on one or more measures are noted in the table in the "Data Quality" column. Please refer to the DQ Atlas at http://medicaid.gov/dq-atlas for more information about data quality assessment methods. Some cells have a value of “DS”. This indicates that data were suppressed for confidentiality reasons because the group included fewer than 11 beneficiaries.

Characteristics of Medicare fee-for-service beneficiaries aged 66 years or older with non-hospitalized COVID-19 and matched controls, Medicare 2020–2021 matched cohort.

These data track state Medicaid programs' policies for paying Medicare's cost sharing for low-income individuals who have Medicare and Medicaid ("dual eligibles"). Our database focuses on Medicaid policies for paying the cost sharing for outpatient and physician services covered by the Medicare Part B program. We track state policies longitudinally from 2004-2018 based on information abstracted from online Medicaid policy documents, legal databases, and policy data reported to us by 22 state Medicaid programs. We also developed a Medicaid payment index, which reflects the proportion of the Medicare Part B allowed amount (i.e., price) for physician office visits that providers would expect to be paid per service provided to a dual eligible patient, in aggregate from Medicare and Medicaid, given these state policies. One version of this index reflects payments to physicians who qualified for higher Medicaid fees under the Affordable Care Act's Medicaid Fee Bump (implemented nationally from 2013-14) and one version reflects payments to physicians who were ineligible for the fee bump.Download the attached Excel files to retrieve the database and additional documentation. The compressed folder 'final document library' contains the original source policy documents (in PDF format) that are catalogued in the database.More detail about this database and our findings can be found in the article:Roberts ET, Nimgaonkar A, Aarons J, Tomko H, Shartzer A, Zuckerman SB, and James AE. "New Evidence of State Variation in Medicaid Payment Policies for Low-Income Medicare Beneficiaries," Health Services Research 2020 (doi: 10.1111/1475-6773.13545).

This table presents three populations of beneficiaries who could benefit from different levels of integrated care, 2017-2021: (1) beneficiaries who received services for a behavioral health (BH) condition; (2) beneficiaries who received services for a behavioral health condition who also received services for at least one of a number of select physical health (PH) conditions (a subset of population 1); and (3) beneficiaries prescribed medications for substance use disorders who do not have a medical claim for a behavioral health condition (a subset of population 1). Some states have serious data quality issues, making the data unusable for identifying this population. To assess data quality, analysts used measures featured in the DQ Atlas. Data for a state are considered unusable based on DQ Atlas thresholds for the following topics: Total Medicaid and CHIP Enrollment, Claims Volume - IP, Claims Volume - OT, Claims Volume - IP, Diagnosis Code - IP, Diagnosis Code - OT, Procedure Codes - OT Professional, Gender, Age, Zip code, Race and ethnicity, Eligibility group code, Enrollment in CMC Plans. Data from Maryland, Tennessee, and Utah are omitted for the tables due to data quality concerns. Maryland was excluded in 2017 due to unusable diagnosis codes in the IP file and the OT file. Tennessee was excluded due to unusable diagnosis codes in the IP file in 2017 - 2019. Utah was excluded due to unusable procedure codes on OT professional claims in 2017 - 2020. In addition, states with a high data quality concern on one or more measures are noted in the table in the "Data Quality" column. Please refer to the DQ Atlas at http://medicaid.gov/dq-atlas for more information about data quality assessment methods.

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The U.S. Department of Health and Human Services (HHS) via the Health Resources and Services Administration (HRSA) is releasing American Rescue Plan payments to providers and suppliers who have served rural Medicaid, Children's Health Insurance Program (CHIP), and Medicare beneficiaries from January 1, 2019 through September 30, 2020. The dataset will be updated as additional payments are released. Data does not reflect recipients’ attestation status, returned payments, or unclaimed funds.

This dataset shows the Medicare Shared Savings Program Accountable Care Organizations (ACOs) for 2015-2020. The Shared Savings Program ACO participants are groups of doctors and other health care providers who voluntarily work together with Medicare to give high-quality service to Medicare Fee-for-Service beneficiaries.

2018 - 2020, county-level U.S. stroke hospitalization rates. Dataset developed by the Centers for Disease Control and Prevention, Division for Heart Disease and Stroke Prevention.Create maps of U.S. stroke hospitalization rates among Medicare fee-for-service beneficiaries aged 65 and older, by county. Data can be stratified by race/ethnicity and sex.Visit the CDC/DHDSP Atlas of Heart Disease and Stroke for additional data and maps. Atlas of Heart Disease and StrokeData SourceHospitalization data were obtained from the Centers for Medicare and Medicaid Services Medicare Provider Analysis and Review (MEDPAR) file, Part A and the Master Beneficiary Summary File (MBSF). International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) codes: I60-I69; principle (i.e., first-listed) diagnosis. Medicare fee-for-service beneficiaries 65 and older were included. Visit the Atlas of Heart Disease and Stroke Statistical Methods pages for more detailed Medicare data inclusion criteria.Data DictionaryData for counties with small populations are not displayed when a reliable rate could not be generated. These counties are represented in the data with values of '-1.' CDC/DHDSP excludes these values when classifying the data on a map, indicating those counties as 'Insufficient Data.'Data field names and descriptionsstcty_fips: state FIPS code + county FIPS codeOther fields use the following format: RRR_S_aaaa (e.g., API_M_35UP)  RRR: 3 digits represent race/ethnicity    All - Overall   BLK - Black, non-Hispanic    HIS - Hispanic    WHT - White, non-Hispanic  S: 1 digit represents sex    A - All    F - Female    M - Male  aaaa: 4 digits represent age. The first 2 digits are the lower bound for age and the last 2 digits are the upper bound for age. 'UP' indicates the data includes the maximum age available and 'LT' indicates ages less than the upper bound. Example: The column 'BLK_M_65UP' displays rates per 1,000 black Medicare beneficiaries aged 65 years and older.MethodologyRates are calculated using a 3-year average and are age-standardized in 10-year age groups using the 2000 U.S. Standard Population. Rates are calculated and displayed per 1,000 Medicare beneficiaries. Rates were spatially smoothed using a Local Empirical Bayes algorithm to stabilize risk by borrowing information from neighboring geographic areas, making estimates more statistically robust and stable for counties with small populations. Data for counties with small populations are coded as '-1' when a reliable rate could not be generated. County-level rates were generated when the following criteria were met over a 3-year time period within each of the filters (e.g., age, race, and sex).At least one of the following 3 criteria:At least 20 events occurred within the county and its adjacent neighbors.ORAt least 16 events occurred within the county.ORAt least 5,000 population years within the county.AND all 3 of the following criteria:At least 6 population years for each age group used for age adjustment if that age group had 1 or more event.The number of population years in an age group was greater than the number of events.At least 100 population years within the county.More Questions?Interactive Atlas of Heart Disease and StrokeData SourcesStatistical Methods

The table ACO Beneficiary is part of the dataset Medicare 20% [2019-2020] ACO, available at https://stanford.redivis.com/datasets/9btr-cz55fk1hw. It contains 5471797 rows across 44 variables.