| Course | HLTH 3115 Public and Global Health |
|---|---|
| Week | Week 5 |
| Paper type | Health systems comparison |
| Length | About 1,150 words, 7 pages |
| Format | APA 7 student paper |
| School | Walden University |
| Program | BS in Public Health |
| Updated | October 2026 |
Free sample paper for HLTH 3115 Week 5
Kenya and Rwanda Compared Only After the Definitions Are Matched: Insurance Coverage, Out-of-Pocket Spending and Hospital Beds, and Where the Matching Failed
Student Name
Walden University
HLTH 3115: Public and Global Health
Instructor Name
Month Day, Year
Kenya and Rwanda Compared Only After the Definitions Are Matched: Insurance Coverage, Out-of-Pocket Spending and Hospital Beds, and Where the Matching Failed
Why Definitions Come First
Kenya and Rwanda are neighbors in East Africa with very different approaches to paying for health care, which makes them a useful pair to compare. But the words used to describe health systems do not mean the same thing in every country. "Coverage" can mean that a person is enrolled in an insurance scheme, that a service is available or that a person is protected from large medical bills. A "hospital bed" may or may not include private, maternity or rehabilitation beds. This paper matches the definition of each measure across the two countries before reporting any number, and states plainly where the definitions could not be matched.
Measure 1: Health Insurance Coverage
Matching the Definition
Kutzin (2013) distinguishes three dimensions of coverage: how many people are covered, which services are covered and how much of the cost is covered. Most published coverage figures report only the first. For this comparison, coverage means population coverage as reported by individuals in a nationally representative household survey: the share of women and men aged 15 to 49 who say they are covered by any health insurance. Both countries' Demographic and Health Surveys ask this question in the same form, so the definition matches.
The Numbers
The figures come from Kenya's 2014 survey (Kenya National Bureau of Statistics et al., 2015) and Rwanda's 2019-20 survey (National Institute of Statistics of Rwanda et al., 2021). In Rwanda, 77.2% of women reported coverage through community-based health insurance; in Kenya, 14.3% reported coverage through the social security scheme.
Table 1
Self-Reported Health Insurance Coverage, Demographic and Health Surveys
| Country | Survey year | Women 15-49 with any insurance | Men 15-49 with any insurance | Main scheme reported | |
|---|---|---|---|---|
| Kenya | 2014 | 18.0% | 21.6% | Social security scheme, the national hospital insurance fund | |
| Rwanda | 2019-20 | 82.6% | 83.3% | Community-based health insurance, Mutuelles de Santé | |
Women's figures are calculated from the share reporting no insurance (82.0% in Kenya, 17.4% in Rwanda).
Where the Matching Failed
Two failures limit the comparison. First, the survey years differ by about five years. The question is the same, but Kenya's figure describes 2014 and Rwanda's describes 2019-20, and Kenya's national insurance arrangements have since been reorganized. A more recent Kenyan figure from the same survey question would be needed for a fair comparison. Second, the measure matches only the first of Kutzin's three dimensions. A Rwandan reporting Mutuelles membership and a Kenyan reporting national fund membership may be covered for different services and different shares of cost. Lu et al. (2012) found that Mutuelles improved use of care and protected households from catastrophic spending, but also that the poorest enrollees used less care and faced more catastrophic spending than other enrollees. Membership alone does not show depth.
Measure 2: Out-of-Pocket Spending
Matching the Definition
Out-of-pocket spending is the share of a country's current health expenditure paid directly by households at the point of care, as estimated in national health accounts and compiled by the World Health Organization. Both countries' figures come from the same international compilation using the same accounting framework, so the definition matches more closely than for insurance.
The Numbers
In 2023, households paid about one-quarter of Kenya's health spending out of pocket, compared with about 4% of Rwanda's.
Table 2
Out-of-Pocket Spending as a Share of Current Health Expenditure
| Country | 2019 | 2023 | |
|---|---|---|
| Kenya | 24.3% | 24.3% | |
| Rwanda | 10.1% | 4.1% | |
Source: World Bank (2026a), from WHO Global Health Expenditure Database.
Where the Matching Failed
The definition matches, but the figures depend on how completely each country's health accounts capture spending. Payments to unregistered providers and informal payments are hard to measure everywhere. Rwanda's sharp fall between 2019 and 2023 may reflect real change, changes in accounting or both; the series alone cannot say which.
Measure 3: Hospital Beds
Matching the Definition
The World Bank defines hospital beds as inpatient beds available in public, private, general and specialized hospitals and rehabilitation centers, usually including beds for both acute and chronic care (World Bank, 2026b). The definition is broad, which makes it easier to apply across countries but means that a bed in a district hospital and a bed in a national referral hospital count the same.
The Numbers and Where the Matching Failed
For 2019, the series reports 1.33 beds per 1,000 people in Kenya and 0.73 in Rwanda (World Bank, 2026b). Here the matching failed most clearly. Rwanda's figure appears every year from 2013 to 2023, suggesting a regular count. Kenya's appears only for 2010 and 2019, so it is not clear whether the two countries counted the same kinds of facilities in the same way. Whether private and faith-based hospital beds were fully counted in each country cannot be confirmed from the series. The difference between 1.33 and 0.73 may reflect real differences in capacity, differences in counting or both. This paper does not draw a conclusion from it.
What a Fuller Comparison Would Need
A comparison that could support firmer conclusions would need three things these series do not provide. It would need insurance figures from the same year, ideally from surveys using the same question, so that changes over time in either country do not masquerade as differences between them. It would need measures of the second and third dimensions of coverage, which services each scheme pays for and what share of the cost remains with the patient, so that enrollment in a thin scheme is not counted the same as enrollment in a comprehensive one. And it would need a facility census in each country using the same rules about which beds to count. Each of these exists in some form, but not in the international series used here, which is why this paper reports what the series can support rather than everything they contain.
Summary
Table 3
What the Comparison Can and Cannot Support
| Measure | Definition matched? | Comparison supported | |
|---|---|---|
| Insurance coverage | Same question, different years; population dimension only | Rwanda reported far higher enrollment, but not necessarily deeper coverage | |
| Out-of-pocket share | Same framework | Households in Kenya paid a much larger share of health spending directly | |
| Hospital beds | Same stated definition, uneven reporting | None with confidence | |
Only one of the three measures supports a direct comparison without major qualification.
Conclusion
After definitions are matched, the clearest finding is that households in Kenya paid a much larger share of health spending out of pocket than households in Rwanda in 2019 and 2023. Rwanda's far higher reported insurance enrollment fits that finding, but the insurance comparison mixes survey years and measures only one dimension of coverage. The hospital bed comparison fails, because the reporting behind the two countries' figures cannot be shown to match. A comparison that reported all three numbers side by side without this checking would have appeared more complete and been less true.
References
Kenya National Bureau of Statistics, Ministry of Health, National AIDS Control Council, Kenya Medical Research Institute, National Council for Population and Development, & ICF International. (2015). Kenya Demographic and Health Survey 2014. Kenya National Bureau of Statistics and ICF International. https://dhsprogram.com/
Kutzin, J. (2013). Health financing for universal coverage and health system performance: Concepts and implications for policy. Bulletin of the World Health Organization, 91(8), 602-611. https://doi.org/10.2471/BLT.12.113985
Lu, C., Chin, B., Lewandowski, J. L., Basinga, P., Hirschhorn, L. R., Hill, K., Murray, M., & Binagwaho, A. (2012). Towards universal health coverage: An evaluation of Rwanda Mutuelles in its first eight years. PLoS One, 7(6), Article e39282. https://doi.org/10.1371/journal.pone.0039282
National Institute of Statistics of Rwanda, Ministry of Health, & ICF. (2021). Rwanda Demographic and Health Survey 2019-20. National Institute of Statistics of Rwanda and ICF. https://dhsprogram.com/
World Bank. (2026a). Out-of-pocket expenditure (% of current health expenditure), Kenya and Rwanda (Indicator SH.XPD.OOPC.CH.ZS) [Data set]. World Development Indicators. https://data.worldbank.org/indicator/SH.XPD.OOPC.CH.ZS?locations=KE-RW
World Bank. (2026b). Hospital beds (per 1,000 people), Kenya and Rwanda (Indicator SH.MED.BEDS.ZS) [Data set]. World Development Indicators. https://data.worldbank.org/indicator/SH.MED.BEDS.ZS?locations=KE-RW
The Walden HLTH 3115 Week 5 instructions
Health systems comparisons ask you to compare two countries without letting different definitions distort the result. Choose two countries and a small set of measures. For each measure, state the definition you will use and check whether both countries' figures follow it before reporting any number. Present the numbers in tables with sources. Then say plainly where the definitions or data did not match, such as different years, different counting rules or measures that capture only part of a concept. Use a framework from the readings to show what each measure does and does not capture. Close with a summary of which comparisons hold and which do not, and explain what better data would allow, so the paper models the caution it asks of its readers.
How this HLTH 3115 Week 5 example is built
The paper opens by explaining why definitions come first. Three sections take one measure each. Insurance coverage is defined through the survey question and Kutzin's dimensions, reported in Table 1 from the two countries' surveys and qualified by the five-year gap and Lu and colleagues' findings on depth. Out-of-pocket spending is defined through national health accounts and reported in Table 2, with a caution about accounting completeness. Hospital beds are defined from the World Bank note, and the comparison is set aside because reporting differs. A section on what a fuller comparison would need precedes Table 3, a summary of what each comparison supports.
HLTH 3115 Week 5 rubric: what earns full marks
Faculty grade a systems comparison on whether definitions are matched before numbers are compared. Stating each definition and checking it against both countries' data earns credit. Honest reporting of where matching failed is often what distinguishes the best papers. Using a conceptual framework, such as the dimensions of coverage, shows depth. Tables should be accurate and sourced. A comparison that declines to draw a conclusion where data do not support one is valued over one that reports every number side by side. Faculty also check that sources are cited precisely. Papers that compare figures from different years or definitions without comment, or that rank countries on weak data, earn less.
HLTH 3115 Week 5 help: mistakes that cost marks
Pick measures that both countries report, then look for the definition behind each one. For survey measures, find the exact question; for administrative measures, find the counting rules. Write the definition down before you look at the numbers, so the numbers cannot shape your choice. When you find a mismatch, such as different years, report it rather than smoothing it over. Use a framework from your readings to explain what a measure leaves out. Keep a summary table that says, for each measure, whether the comparison holds. A sample comparison of two countries you choose is something our writers can draft. Read your conclusion and remove any claim that rests on a measure your own table says cannot be compared.
Get a HLTH 3115 Week 5 example written to your instructions
Two systems, named by you, plus the Week 5 prompt and the rubric your classroom posted: the desk returns a full comparison with definitions matched dimension by dimension, first sample free, inside 24-48h. Figures come from published national and cross-national series with publisher and year attached, and unmatched data are flagged instead of forced together.
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HLTH 3115 Week 5 questions, answered
Where can I find a free HLTH 3115 Week 5 sample paper?
The comparison of Kenya and Rwanda with matched definitions, summary tables and references, is free to read here.
Which measures does the HLTH 3115 Week 5 example compare?
Self-reported health insurance coverage, out-of-pocket spending as a share of health expenditure and hospital beds per 1,000 people.
Which comparison holds best?
Out-of-pocket spending, which uses the same accounting framework in both countries and shows Kenyan households paying a much larger share.
Where does the insurance comparison fall short?
The survey years differ by about five years, and enrollment says nothing about which services or costs are covered.
Why is the hospital bed comparison set aside?
The two countries' figures share a definition but are reported so differently that the counting cannot be shown to match.