Medicare Chronic Conditions APIs

library(provider)

Information on prevalence, use and spending by count of select chronic conditions among Original Medicare (or fee-for-service) beneficiaries.

Medicare Multiple Chronic Conditions API

The Multiple Chronic Conditions dataset provides information on the number of chronic conditions among Original Medicare beneficiaries. The dataset contains prevalence, use and spending organized by geography and the count of chronic conditions from the set of select 21 chronic conditions. The count of conditions is grouped into four categories (0-1, 2-3, 4-5 and 6 or more).

Examples

# Multiple Chronic Conditions
cc_multiple(year = 2018, 
            level = "National", 
            age_group = "All", 
            demographic = "All")
#> # A tibble: 4 × 13
#>    year level    sublevel fips  age_group demographic subdemo mcc     prevalence
#>   <int> <chr>    <chr>    <chr> <chr>     <chr>       <chr>   <ord>        <dbl>
#> 1  2018 National National NA    All       All         All     [0,1]        0.311
#> 2  2018 National National NA    All       All         All     [2,3]        0.291
#> 3  2018 National National NA    All       All         All     [4,5]        0.221
#> 4  2018 National National NA    All       All         All     [6, In…      0.177
#> # ℹ 4 more variables: tot_std_pymt_percap <dbl>, tot_pymt_percap <dbl>,
#> #   hosp_readmsn_rate <dbl>, er_visits_per_1k <dbl>

cc_multiple(year = 2018, 
            level = "State", 
            sublevel = "Alabama", 
            age_group = "All", 
            demographic = "All")
#> # A tibble: 4 × 13
#>    year level sublevel fips  age_group demographic subdemo mcc      prevalence
#>   <int> <chr> <chr>    <chr> <chr>     <chr>       <chr>   <ord>         <dbl>
#> 1  2018 State Alabama  01    All       All         All     [0,1]         0.258
#> 2  2018 State Alabama  01    All       All         All     [2,3]         0.285
#> 3  2018 State Alabama  01    All       All         All     [4,5]         0.250
#> 4  2018 State Alabama  01    All       All         All     [6, Inf)      0.207
#> # ℹ 4 more variables: tot_std_pymt_percap <dbl>, tot_pymt_percap <dbl>,
#> #   hosp_readmsn_rate <dbl>, er_visits_per_1k <dbl>

cc_multiple(year = 2018, 
            level = "County", 
            sublevel = "Alabama : Autauga", 
            fips = "01001", 
            age_group = "All", 
            demographic = "All")
#> # A tibble: 4 × 13
#>    year level  sublevel     fips  age_group demographic subdemo mcc   prevalence
#>   <int> <chr>  <chr>        <chr> <chr>     <chr>       <chr>   <ord>      <dbl>
#> 1  2018 County Alabama : A… 01001 All       All         All     [0,1]      0.266
#> 2  2018 County Alabama : A… 01001 All       All         All     [2,3]      0.281
#> 3  2018 County Alabama : A… 01001 All       All         All     [4,5]      0.248
#> 4  2018 County Alabama : A… 01001 All       All         All     [6, …      0.205
#> # ℹ 4 more variables: tot_std_pymt_percap <dbl>, tot_pymt_percap <dbl>,
#> #   hosp_readmsn_rate <dbl>, er_visits_per_1k <dbl>

Medicare Specific Chronic Conditions API

The Select Chronic Conditions dataset provides information on 21 selected chronic conditions among Original Medicare beneficiaries. The dataset contains prevalence, use and spending organized by geography and distinct chronic conditions listed below.

Alcohol Abuse Drug Abuse / Substance Abuse
Alzheimer’s Disease and Related Dementia
Arthritis (Osteoarthritis and Rheumatoid)
Asthma
Atrial Fibrillation
Autism Spectrum Disorders
Cancer (Breast, Colorectal, Lung, and Prostate)
Chronic Kidney Disease
Chronic Obstructive Pulmonary Disease
Depression
Diabetes
Drug Abuse / Substance Abuse
Heart Failure
Hepatitis (Chronic Viral B & C)
HIV/AIDS
Hyperlipidemia (High cholesterol)
Hypertension (High blood pressure)
Ischemic Heart Disease
Osteoporosis
Schizophrenia and Other Psychotic Disorders
Stroke

Examples

# Specific Chronic Conditions
cc_specific(year = 2018, 
            level = "State", 
            sublevel = "California", 
            demographic = "All")
#> # A tibble: 63 × 13
#>     year level sublevel fips  age_group demographic subdemo condition prevalence
#>    <int> <chr> <chr>    <chr> <chr>     <chr>       <chr>   <chr>          <dbl>
#>  1  2018 State Califor… 06    All       All         All     Alcohol …     0.0215
#>  2  2018 State Califor… 06    All       All         All     Alzheime…     0.105 
#>  3  2018 State Califor… 06    All       All         All     Arthritis     0.312 
#>  4  2018 State Califor… 06    All       All         All     Asthma        0.053 
#>  5  2018 State Califor… 06    All       All         All     Atrial F…     0.0749
#>  6  2018 State Califor… 06    All       All         All     Autism S…     0.0022
#>  7  2018 State Califor… 06    All       All         All     COPD          0.0952
#>  8  2018 State Califor… 06    All       All         All     Cancer        0.0784
#>  9  2018 State Califor… 06    All       All         All     Chronic …     0.243 
#> 10  2018 State Califor… 06    All       All         All     Depressi…     0.162 
#> # ℹ 53 more rows
#> # ℹ 4 more variables: tot_std_pymt_percap <dbl>, tot_pymt_percap <dbl>,
#> #   hosp_readmsn_rate <dbl>, er_visits_per_1k <dbl>

cc_specific(year = 2011, 
            level = "County", 
            fips = "01001")
#> # A tibble: 63 × 13
#>     year level sublevel fips  age_group demographic subdemo condition prevalence
#>    <int> <chr> <chr>    <chr> <chr>     <chr>       <chr>   <chr>          <dbl>
#>  1  2011 Coun… Alabama… 01001 All       All         All     Alcohol …     0.0116
#>  2  2011 Coun… Alabama… 01001 All       All         All     Alzheime…     0.100 
#>  3  2011 Coun… Alabama… 01001 All       All         All     Arthritis     0.274 
#>  4  2011 Coun… Alabama… 01001 All       All         All     Asthma        0.037 
#>  5  2011 Coun… Alabama… 01001 All       All         All     Atrial F…     0.0748
#>  6  2011 Coun… Alabama… 01001 All       All         All     Autism S…    NA     
#>  7  2011 Coun… Alabama… 01001 All       All         All     COPD          0.122 
#>  8  2011 Coun… Alabama… 01001 All       All         All     Cancer        0.0792
#>  9  2011 Coun… Alabama… 01001 All       All         All     Chronic …     0.137 
#> 10  2011 Coun… Alabama… 01001 All       All         All     Depressi…     0.134 
#> # ℹ 53 more rows
#> # ℹ 4 more variables: tot_std_pymt_percap <dbl>, tot_pymt_percap <dbl>,
#> #   hosp_readmsn_rate <dbl>, er_visits_per_1k <dbl>

Data Dictionary

Variable	Description
`level`	Beneficiary Geographic Level Identifies the level of geography that the data in the row has been aggregated. A value of 'County' indicates the data in the row is aggregated to the county level and identifies a Medicare beneficiary’s geographic place of residence. A value of 'State' indicates the data in the row is aggregated to a single state identified as a Medicare beneficiary’s geographic place of residence. A value of 'National' indicates the data in the row is aggregated across all states, the District of Columbia, Puerto Rico and the U.S. Virgin Islands.
`sublevel`	Beneficiary Geographic Description The state and/or county where the Medicare beneficiary resides. The values include the 50 United States, District of Columbia, Puerto Rico or U.S. Virgin Islands. Data aggregated at the National level are identified by 'National'.
`fips`	Beneficiary Geographic Code FIPS state and/or county code where the Medicare beneficiary resides. The `Bene_Geo_Cd` will be blank for data aggregated at the National level or for Puerto Rico and Virgin Islands.
`age_group`	Beneficiary Age Level Identifies the age level of the population that the data has been aggregated. A value of 'All' indicates the data in the row represents all Fee-for-Service Medicare Beneficiaries. A value of '
`demographic`	Beneficiary Demographic Level Identifies the demographic level of the population that the data has been aggregated. A value of 'All' indicates the data in the row is represents all Fee-for-Service Medicare beneficiaries. A value of 'Sex' indicates that the data has been aggregated by the Medicare beneficiary's sex. A value of 'Race' indicates that the data has been aggregated by the Medicare beneficiary's race. A value of 'Dual Status' indicates that the data has been aggregated by the Medicare beneficiary's dual eligibility status.
`subdemo`	Beneficiary Demographic Description For Bene_Demo_Lvl = 'Sex', a beneficiary’s sex is classified as Male or Female and is identified using information from the CMS enrollment database. For Bene_Demo_Lvl = 'Race', the race/ethnicity classifications are: Non-Hispanic White, Black or African American, Asian/Pacific Islander, Hispanic, and American Indian/Alaska Native. All the chronic condition tables use the variable RTI_RACE_CD, which is available on the Master Beneficiary Files in the CCW. For Bene_Demo_Lvl = 'Dual Status', beneficiaries can be classified as 'Medicare & Medicaid' or 'Medicare Only'. Beneficiares enrolled in both Medicare and Medicaid are known as `dual eligibles`. Medicare beneficiaries are classified as dual eligibles if in any month in the given calendar year they were receiving full or partial Medicaid benefits.
`mcc`	Beneficiary Chronic Condition Identifies the chronic condition for which the prevalence and utilization is compiled. There are 21 chronic conditions identified using Medicare administrative claims. A Medicare beneficiary is considered to have a chronic condition if the CMS administrative data have a claim indicating that the beneficiary received a service or treatment for the specific condition. Beneficiaries may have more than one of the chronic conditions listed.
`condition`	Beneficiary Multiple Chronic Condition Group To classify MCC for each Medicare beneficiary, the 21 chronic conditions are counted and grouped into four categories (0-1, 2-3, 4-5 and 6 or more).
`prevalence`	Prevalence Prevalence estimates are calculated by taking the beneficiaries with a particular condition divided by the total number of beneficiaries in our fee-for-service population, expressed as a percentage.
`tot_std_pymt_percap`¹	Total Medicare Standardized Per Capita Spending Medicare standardized spending includes total Medicare payments for all covered services in Parts A and B and is presented per beneficiary (i.e. per capita). Standardized payments are presented to allow for comparisons across geographic areas in health care use among beneficiaries.
`tot_pymt_percap`¹	Total Medicare Per Capita Spending Medicare spending includes total Medicare payments for all covered services in Parts A and B and is presented per beneficiary (i.e. per capita).
`hosp_readmn_rate`¹	Hospital Readmission Rate Hospital readmissions are expressed as a percentage of all admissions. A 30-day readmission is defined as an admission to an acute care hospital for any cause within 30 days of discharge from an acute care hospital. Except when the patient died during the stay, each inpatient stay is classified as an index admission, a readmission, or both.
`er_vs_per_1k`¹	Emergency Room Visits per 1,000 Beneficiaries Emergency department visits are presented as the number of visits per 1,000 beneficiaries. ED visits include visits where the beneficiary was released from the outpatient setting and where the beneficiary was admitted to an inpatient setting.
¹ This information should not be used to attribute utilization or payments strictly to the specific condition selected, as beneficiaries with any of the specific conditions presented may have other health conditions that contribute to their Medicare utilization and spending amounts.

Medicare Multiple Chronic Conditions API

Examples

Medicare Specific Chronic Conditions API

Examples

Data Dictionary

Related Links