| Course | HLTH 3110 Current Issues in Healthcare Policy and Practice |
|---|---|
| Week | Week 5 |
| Paper type | Policy cost analysis |
| Length | About 1,120 words, 7 pages |
| Format | APA 7 student paper |
| School | Walden University |
| Program | BS in Healthcare Administration |
| Updated | October 2026 |
Free sample paper for HLTH 3110 Week 5
Counting the Cost of Ten More Months of Postpartum Coverage Without Double-Counting: A Cost Analysis That Keeps Federal, State and Offset Dollars in Separate Columns
Student Name
Walden University
HLTH 3110: Current Issues in Healthcare Policy and Practice
Instructor Name
Month Day, Year
Counting the Cost of Ten More Months of Postpartum Coverage Without Double-Counting: A Cost Analysis That Keeps Federal, State and Offset Dollars in Separate Columns
Purpose and Ground Rules
Cost analyses of health policies often go wrong not in the arithmetic but in the bookkeeping: total costs are confused with state costs, savings are counted before they are shown to exist and one-time costs are mixed with recurring ones. Since federal law made the longer postpartum period a state option (Pub. L. No. 117-2), each state has had to price it. This analysis estimates the cost to a composite state of providing ten additional months of postpartum Medicaid coverage, from month 3 to month 12 after birth, using deliberately simple illustrative inputs. It follows five ground rules. Every input is labeled as an assumption and explained. Federal and state shares are kept in separate columns. Recurring costs are separated from one-time costs. Possible savings, called offsets, are listed separately and not subtracted unless evidence supports them. And the analysis states what it leaves out. The figures are illustrative and do not describe any real state.
Inputs and Assumptions
Table 1 lists the inputs.
Table 1
Illustrative Inputs for the Cost of Extending Postpartum Coverage From 2 to 12 Months
| Input | Value used | Basis for the assumption |
|---|---|---|
| Medicaid-covered births per year | 30,000 | Illustrative mid-sized state |
| Share who would otherwise lose Medicaid at 2 months | 40% | Assumes 60% would qualify in another category or keep coverage another way |
| People newly covered for months 3 to 12 | 12,000 | 30,000 x 40% |
| Average months of added coverage per person | 8 | Assumes some leave for other coverage, move or are lost to follow-up before month 12 |
| Average monthly Medicaid cost per postpartum adult | $450 | Illustrative capitation-like amount |
| Federal share of costs | 60% | Illustrative matching rate for this state |
| One-time systems and outreach cost | $1,200,000 | Illustrative: eligibility system change plus first-year notices |
Note. All values are illustrative assumptions for teaching. A real analysis would use the state's own enrollment, claims and matching rate.
The Calculation
Recurring annual cost. People newly covered (12,000) multiplied by average added months (8) multiplied by monthly cost ($450) gives $43.2 million a year in total Medicaid spending.
Split by payer. Medicaid's federal share differs from state to state because it is tied to state income (Shi & Singh, 2022). At an illustrative 60% federal share, the federal government pays about $25.9 million and the state pays about $17.3 million each year.
One-time cost. The $1.2 million for systems and outreach is administrative, and administrative costs are often matched at different rates from benefit costs, typically 50% for general administration and higher for certain systems work. To keep the bookkeeping clean, this analysis reports the one-time cost separately and assumes, for simplicity, a 50% federal share: $600,000 federal and $600,000 state in the first year only.
First-year state cost. $17.3 million recurring plus $0.6 million one-time, about $17.9 million. Later years: about $17.3 million, before any offsets and before any change in enrollment.
Offsets: Listed, Not Subtracted
Advocates often argue that extended coverage reduces other spending, for example by preventing emergency visits, hospital readmissions or uncompensated care. These offsets may be real, but my evidence review found no source that measured them for the 12-month extension. They are therefore listed here, not subtracted.
Possible offset 1: Fewer uncompensated hospital and clinic visits. This would reduce costs for providers, not for the state Medicaid budget, so it would not appear in the state's columns at all.
Possible offset 2: Fewer costly complications later in the year. If coverage leads to earlier treatment of conditions like high blood pressure or depression, some later hospital costs could fall. Studies of coverage show more outpatient care with broader eligibility (Gordon et al., 2020), but more care can raise spending as well as lower it.
Possible offset 3: Lower administrative churn. Fewer people leaving and reapplying could reduce eligibility processing costs. This is plausible and could be measured from the state's own records.
Subtracting any of these without evidence would make the policy look cheaper than the analysis can support. If the state later measures them, they can be added in their own column.
What the Analysis Leaves Out
The analysis does not include the cost of care that newly covered people would have received anyway under another coverage category, which would reduce the true added cost if some were double-counted in the 40% assumption. It does not include changes in births or in Medicaid enrollment over time. It does not value health benefits in dollars. And it does not include costs to families of losing coverage, such as medical debt, which are real but do not appear in any government budget. Each omission could change the answer, and a real analysis would test them with the state's own data.
How a Decision-Maker Should Read These Numbers
A legislator or budget analyst reading this analysis should focus on three numbers rather than the total. The first is the state's recurring share, about $17.3 million a year in this illustration, because that is what the state must budget every year. The second is the one-time cost, because it falls only in the first year and can often be absorbed in an administrative budget. The third is the size of the uncertainty: a plausible change in one assumption moves the total by a quarter in either direction. A decision-maker who knows these three things can judge whether the policy fits the budget and what further data are worth collecting.
The analysis should not be read as saying the extension costs a particular amount in any real state. Its value is in showing how the estimate should be built, so that a real one can be produced quickly from a state's own data and audited later against what was actually spent.
Sensitivity: Which Assumption Matters Most
The estimate is most sensitive to two inputs: the share of people who would otherwise lose coverage and the average months of added coverage. If the share were 30% instead of 40%, the recurring total would fall by a quarter, to about $32.4 million. If people kept added coverage for an average of 10 months instead of 8, it would rise by a quarter, to $54 million. The monthly cost and the matching rate matter too, but they are easier to obtain from existing data. A state planning its budget should spend its analytical effort on the first two inputs.
The point of a cost analysis like this is not the bottom line but the structure: knowing which numbers drive the result and which columns they belong in lets decision-makers ask the right questions, and lets a later evaluation compare actual costs with the estimate column by column.
References
American Rescue Plan Act of 2021, Pub. L. No. 117-2, 135 Stat. 4 (2021).
Gordon, S. H., Sommers, B. D., Wilson, I. B., & Trivedi, A. N. (2020). Effects of Medicaid expansion on postpartum coverage and outpatient utilization. Health Affairs, 39(1), 77-84. https://doi.org/10.1377/hlthaff.2019.00547
Shi, L., & Singh, D. A. (2022). Delivering health care in America: A systems approach (8th ed.). Jones & Bartlett Learning.
Reading the HLTH 3110 Week 5 instructions
Cost analyses in this course typically ask you to estimate what a policy costs while handling the figures carefully. State your ground rules before any numbers. List every input in a table with its value and the basis for the assumption, and label illustrative values clearly. Show the arithmetic step by step. Keep costs for different payers in separate columns, and separate one-time from recurring costs. List possible savings separately, and subtract them only if evidence supports them. State what the analysis leaves out. Run a simple sensitivity check on the assumptions that matter most, and explain how a decision-maker should read the result rather than focusing only on the total.
How the HLTH 3110 Week 5 example is put together
The ground rules section sets five rules and notes the figures are illustrative, citing the 2021 law. Table 1 lists seven inputs with their bases. The calculation section multiplies 12,000 people by eight months by $450, splits the $43.2 million by payer with Shi and Singh on matching, and reports a $1.2 million one-time cost at a separate administrative match. The offsets section lists fewer uncompensated visits, fewer later complications and lower churn costs, citing Gordon and colleagues, and subtracts none. The omissions section names five gaps. A section explains which three numbers a legislator should read. The sensitivity section shows the total moving by a quarter with plausible changes.
Where the marks sit in the HLTH 3110 Week 5 rubric
Faculty generally reward cost analyses that are transparent about inputs and disciplined about categories. Mixing federal and state money, or counting savings without evidence, loses more marks than any arithmetic slip. Credit increases when every assumption is labeled and explained, the arithmetic can be checked and one-time costs are kept apart from recurring ones. Faculty value a sensitivity check that identifies the inputs driving the result, and guidance on how a decision-maker should use the numbers. Stating what the analysis leaves out shows honesty. Tables a reader can follow and sums that add up are part of the grade too. Instructors also check that the narrative and the table use the same numbers, since a mismatch suggests the figures were not checked.
HLTH 3110 Week 5 help: mistakes that cost marks
Cost papers often produce a single total with no table of assumptions, which no one can check. Others subtract hoped-for savings and conclude a policy pays for itself. Some confuse what the state pays with what the program costs in total. Build your input table first and label each value as data or assumption. Do the arithmetic in separate columns for each payer. Put possible savings in a list, not a subtraction. Change your two most uncertain inputs and see how much the total moves. Our writers can prepare a cost analysis for this course with labeled assumptions, separate payer columns and a sensitivity check.
Get a HLTH 3110 Week 5 example written to your instructions
The desk builds a cost analysis around the policy in your prompt, table included, sourced to public series and matched to the rubric your classroom posted. First sample free, delivered in 24-48h. If the assignment asks for figures held by the organization that employs you, those remain yours; the example is written on published data and marked where a local number would slot in.
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HLTH 3110 Week 5 questions, answered
Where can I find a free HLTH 3110 Week 5 sample paper?
The cost analysis, with its input table, arithmetic and sensitivity check, is set out above in full at no cost.
What does the HLTH 3110 Week 5 analysis estimate?
An illustrative recurring cost of about $43.2 million a year for ten extra months of postpartum coverage, with about $17.3 million paid by the state.
Why are federal and state costs kept separate?
Because Medicaid is jointly funded, and a state budget decision depends on the state share, not the program total.
Why are savings not subtracted?
Because no reviewed source measured offsets for the extension, so subtracting them would make the policy look cheaper than the evidence supports.
Which assumptions matter most?
The share of people who would otherwise lose coverage and the average months of added coverage; plausible changes in either move the total by about a quarter.