Data Scope and Structure

 

What dimensions of Medicare Advantage are captured by these variables?

The dataset tracks five primary dimensions of Medicare Advantage enrollment, contract structures, and performance at the regional or state level:

• Plan and Product Types: Proportions of beneficiaries enrolled in traditional HMOs (pct_bene_hmo), PPOs (pct_bene_ppo), Private Fee-for-Service (pct_bene_pffs), and specialized configurations like employer/union group health plans (pct_employer_union_grppct_).

• Financial and Premium Structures: Indicators tracking plan affordability, specifically the percentage of beneficiaries enrolled in plans requiring no additional monthly premium for prescription drugs (pct_bene_zero_cd_premium_plan) or medical care (pct_bene_zero_ab_premium_plan).

• Geographic Plan Reach: Differentiation between plans operating across multiple states (pct_bene_plan_multiple_states) versus those confined to a single state footprint (pct_bene_plan_single_state).

• Quality Ratings: A granular 1-to-5 star metric mapping exactly how plan enrollment is distributed across CMS Contract Star Ratings.

• Enrollment Stability and Churn: Tracking consumer movement at the year's end by capturing the percentage of beneficiaries who remained with the same MA contract (pct_bene_same_ma_contract), migrated to a competing MA contract (pct_bene_different_ma_contract), or opted out entirely to return to Traditional Medicare (pct_bene_switched_to_tm).
   

How do the measurement units differ between the MA-focused variables?

The variables utilize two mathematical expressions depending on whether they track a market share rate or raw volume:

• Percentage Variables (pct_...): Calculated as a rate out of the total enrolled population for that unit of observation. For example, pct_bene_tax_profit measures the percentage of dually eligible or standard beneficiaries enrolled in an MA plan operated by a for-profit entity.

• Count Variables (num_...): Provide the raw number of individuals captured in specific clinical or residential sub-categories, such as num_bene_adrd_20pct_sample (the physical count of individuals in the Alzheimer's/dementias 20% tracking pool).

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• What geographic regions and timeframes are covered in this dataset?

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• The dataset contains state-level aggregates for the years 2016-2023. It includes data for various U.S. states (e.g., Alabama, Alaska, Arizona) as well as Washington, D.C. , which is notable because D.C. was excluded from the core LTCFocus facility files.

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• How do I interpret the variable naming conventions?

• The variables utilize specific prefixes and structural formats to indicate their focus:

• pct_bene_...: Represents the percentage of beneficiaries falling into a specific category.

• num_bene_...: Represents the absolute number of beneficiaries counted in that category.

• _hk...: Indicates variables related to specific health, demographic, or plan sub-categories.

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Understanding Specific Variables

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• What do the race and ethnicity variables measure?

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• The variables beginning with pct_bene_race_ethncty_ break down the beneficiary population by racial and ethnic percentages. For example:

• _white: White beneficiaries.

• _black: Black/African American beneficiaries.

• _hispanic: Hispanic/Latino beneficiaries.

• _asian: Asian beneficiaries.

• _natve_amin: Native American/Alaskan Native beneficiaries.

• _unk_othr: Unknown or other racial categories.

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• How is dual-eligibility for Medicare and Medicaid captured?

• Dual eligibility is broken down into three distinct metrics:

• pct_bene_dual: Total percentage of dually eligible beneficiaries.

• pct_bene_partial_dual: Percentage of beneficiaries receiving partial Medicaid benefits.

• pct_bene_full_dual: Percentage of beneficiaries receiving full Medicaid benefits.

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• What do the esrd variables represent?

• esrd stands for End-Stage Renal Disease. The dataset tracks several metrics related to this condition:

• pct_bene_esrd: Overall percentage of beneficiaries with ESRD.

• stat_esrd, pct_bene_hkesrd, pct_bene_hkorgen_esrd, and pct_bene_hkcuren_esrd: Track specific clinical sub-categories, enrollment origins, or current statuses of ESRD patients within the health system.

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Special Missing Values and Formatting Rules
 

• What does MM mean in the dataset?

• MM represents Missing Data or a value withheld due to data constraints. For example, in this dataset, the variable pct_bene_adrd_20pct_sample shows MM across all listed states, indicating that this specific Alzheimer's Disease and Related Dementias (ADRD) 20% sample metric was not available or suppressed for this reporting period.

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• Why do some fields have 0.00%?

• A value of 0.00% indicates that the occurrence was either non-existent or statistically rounded down to zero within that state's sample. For example, pct_bene_pace (Program of All-Inclusive Care for the Elderly) shows 0.00% for Alabama, Arizona, Arkansas, Florida, and D.C., meaning there was no measurable PACE plan enrollment among the beneficiaries captured there.

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Enrollment and Quality Metrics
 

• What is the difference between the Plan Star Ratings?
  The dataset tracks the percentage of beneficiaries enrolled in health plans based on their CMS Quality Star Ratings, using variables from pct_bene_contract_star_rating_1 (lowest) to pct_bene_contract_star_rating_5 (highest).
  Example: In Alabama, 32.26% of beneficiaries are in 4-star plans, and 1.50% are in 5-star plans. In contrast, California has 44.70% of its beneficiaries enrolled in 4-star plans and 1.29% in 5-star plans.

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• How is plan switching and stability tracked?

• At the end of the data row, three variables track beneficiary movement between Medicare Advantage (MA) contracts or back to Traditional Medicare (TM):

• pct_bene_same_ma_contract: Beneficiaries who stayed with the same MA plan.

• pct_bene_different_ma_contract: Beneficiaries who changed to a different MA plan.

• pct_bene_switched_to_tm: Beneficiaries who dropped Medicare Advantage and switched back to Traditional Medicare.

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What are Special Needs Plans (SNPs) in the dataset?

Special Needs Plans (SNPs) are specialized Medicare Advantage plans designed for specific groups of beneficiaries with unique healthcare needs. The dataset breaks these down by the percentage of beneficiaries enrolled in three distinct types of SNPs:

• Chronic Condition SNPs (csnp): For individuals with severe, disabling chronic conditions such as heart failure, diabetes, or end-stage renal disease.

• Dual Eligible SNPs (dsnp): For individuals who qualify for both Medicare and Medicaid, helping to coordinate benefits between the two programs.

• Institutional SNPs (isnp): Restructured specifically for beneficiaries who require an institutional level of care, such as those residing in a nursing home for 90 days or longer.

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Why are facility dwelling measures tracked specifically in April?

Research indicates that nursing home and long-term care populations fluctuate significantly by season throughout the calendar year, peaking during the winter months due to seasonal illnesses and dropping during the summer months. To prevent these seasonal spikes and valleys from skewing the data, April was selected as a neutral, stable baseline to measure the standard "prevalence" of facility-dwelling beneficiaries.
 

Why use the first Thursday in April for these metrics?

In addition to seasonal variations, nursing home admissions and discharges experience predictable volatility based on the day of the week. Admissions consistently peak on Mondays as hospitals discharge patients from the weekend, while facility discharges peak on Fridays. Measuring the population on the first Thursday of April captures data at a stable, mid-week equilibrium point, ensuring that weekly administrative trends do not distort the tracking of long-term care utilization.