Medicare monthly enrollment
Centers for Medicare & Medicaid Services
In a RepVector build, this dataset sizes the addressable population behind a territory.
| Year | Total beneficiaries | Original Medicare | Medicare Advantage | Aged beneficiaries | Disabled beneficiaries |
|---|---|---|---|---|---|
| 2013 | 260,086 | 181,362 | 78,724 | 219,268 | 40,818 |
| 2014 | 270,682 | 185,711 | 84,970 | 228,899 | 41,783 |
| 2015 | 282,037 | 190,666 | 91,371 | 239,329 | 42,708 |
| 2016 | 293,978 | 200,016 | 93,962 | 250,754 | 43,224 |
| 2017 | 306,388 | 210,391 | 95,997 | 262,927 | 43,461 |
| 2018 | 320,217 | 216,836 | 103,381 | 276,642 | 43,575 |
| 2019 | 334,440 | 220,056 | 114,384 | 290,968 | 43,473 |
| 2020 | 349,164 | 219,565 | 129,599 | 305,985 | 43,179 |
What this dataset is
Monthly counts of Medicare beneficiaries by state, broken out by original Medicare vs. Medicare Advantage, age band, dual-eligibility status and demographics. The size and composition of the Medicare-covered population in a market. It is published by Centers for Medicare & Medicaid Services and can be queried at state level, refreshed monthly. Each row is an aggregate statistic for a geography, so results describe a market as a whole rather than a single account.
Why it is useful
- Turns a place name into a denominator, so counts from other datasets become rates you can compare across markets.
- Separates markets that look busy because they are large from markets that are genuinely underserved.
- Supports age, income and household splits that shape which offering fits a territory.
Questions it helps answer
- How many people actually live inside this territory?
- Is demand here driven by population size or by unusually high per-capita need?
- Which nearby counties are large enough to justify a second visit each month?
How different verticals use it
RepVector is vertical-agnostic: this dataset is selected by what your question asks for, not by industry. These are common ways teams put it to work.
Converts patient volumes into per-capita rates so a small county with high need outranks a large one with low need.
Sizes catchment areas and household mix before committing to a site or a route.
Segments a territory by income and household composition to match product to market.
Projects the school-age and working-age base a programme would serve.
How RepVector uses it
- You describe a market in plain language. The geography resolver turns the place you named into the exact identifiers this dataset needs — city to county to state, plus DMA where relevant — so a query at state runs without you knowing any codes.
- The planner selects Medicare monthly enrollment only when your question matches what it actually reports. It is never included to pad a result.
- Returned rows are normalised into the shared metric layer, so figures from Centers for Medicare & Medicaid Services line up with every other source in the same dashboard instead of sitting in an isolated table.
- Quality checks score coverage and recency. If Centers for Medicare & Medicaid Services suppresses or omits a value for your geography, the gap is reported as a gap — nothing is estimated or filled in.
- The result renders as charts, KPIs and, where the rows are entities, prioritised targets you can export to PDF, Excel, CSV or JSON.
What it does not tell you
- Describes people, not buyers — pair it with provider or facility data before building a call list.
| Field | Key | Type |
|---|---|---|
| Year | year | string |
| Total beneficiaries | totalBeneficiaries | number |
| Original Medicare | originalMedicare | number |
| Medicare Advantage | medicareAdvantage | number |
| Aged beneficiaries | agedBenes | number |
| Disabled beneficiaries | disabledBenes | number |
| Dual-eligible | dualEligible | number |
| Part D beneficiaries | prescriptionDrugBenes | number |
How it fits with the rest of the catalog
Population figures become decision-grade once divided into provider counts and facility capacity for the same geography. RepVector queries every selected dataset against the same resolved geography and time window, then normalises the results into one metric layer so they can be charted side by side. These datasets are the most common companions to Medicare monthly enrollment:
Other population datasets
Common questions
What does Medicare monthly enrollment contain?
Monthly counts of Medicare beneficiaries by state, broken out by original Medicare vs. Medicare Advantage, age band, dual-eligibility status and demographics. The size and composition of the Medicare-covered population in a market. It is published by Centers for Medicare & Medicaid Services at state grain.
How often is it refreshed?
Centers for Medicare & Medicaid Services publishes on a monthly cycle. RepVector re-reads the source on that cadence and keeps every prior capture, so you can see what changed.
Do I need an account to use it?
No. Browsing the catalog and the free lookup tools needs no account. Running a full market build across every county in a territory is the paid product.
Build the list for one county and see whether the names are right.
Ask in plain language. RepVector finds the universe, enriches each target from its own website, scores it 0–100, and hands you a ranked call list with phones, addresses and named contacts.
3 free runs — no card required.