| Course | NURS 8310 Epidemiology and Population Health |
|---|---|
| Week | Week 10 |
| Paper type | Population health analysis |
| Length | About 1,080 words, 6 pages |
| Format | APA 7 student paper |
| School | Walden University |
| Program | Doctor of Nursing Practice (DNP) |
| Updated | September 2026 |
Free sample paper for NURS 8310 Week 10
Burden, Causes, Responses: A Population Health Analysis of Summer Heat Harm to People Sleeping Outside in a Desert County, Written as One Argument
Student Name
College of Nursing, Walden University
NURS 8310: Epidemiology and Population Health
Instructor Name
Month Day, Year
Burden, Causes, Responses: A Population Health Analysis of Summer Heat Harm to People Sleeping Outside in a Desert County, Written as One Argument
The Burden in One Statement
In the composite desert county, unsheltered adults die of heat-associated causes at a seasonal rate somewhere between about 550 and 1,500 per 100,000, depending on the denominator used, compared with about 2 per 100,000 among housed residents; emergency visits for heat illness among unsheltered adults run between about 3,800 and 10,500 per 100,000 each season, and both deaths and visits cluster on excessive heat warning days. These figures are illustrative, carried forward from the earlier burden estimate. They describe a burden that is concentrated in a small population, is overwhelmingly preventable and falls most heavily on older unsheltered adults with chronic illness.
Published work supports the broad shape of this picture. A study of heat-associated mortality in a hot US county found that people experiencing homelessness accounted for a large share of heat deaths (Iverson et al., 2020), and a study in San Diego found that emergency visits among people experiencing homelessness rose during heat waves (Schwarz et al., 2022).
Causes, From Structural to Proximate
The causes of this burden can be arranged in layers, from the most distant to the most immediate.
Structural. The unsheltered population exists because housing costs exceed what many residents can pay and because shelter capacity is far below need. Eviction and rent burden, mapped earlier, concentrate the inflow in particular neighborhoods. Enforcement policies that clear encampments push people toward the edges of the city, away from shade, water and services.
Environmental. Temperatures in the county are rising, and urban heat islands make some areas hotter than others. Pavement, lack of tree cover and exposure to direct sun raise the heat load where encampments are forced to locate.
Access. Cooling centers are few, keep daytime hours and are often far from encampments. Water is not reliably available at night.
Individual susceptibility. Age, heart disease, some medications and substance use, particularly stimulants, reduce the body's ability to manage heat. Dehydration and lack of sleep add to risk.
Proximate. The immediate cause of a heat death is sustained exposure to high temperatures without enough cooling or fluids.
Arranging causes this way shows that most of the burden is set by structural and environmental layers, while clinical care and individual advice act mainly on the last two. Rose (1985) argued that the causes of a population's rate of disease often differ from the causes of individual cases, and that shifting the whole population's exposure can prevent more disease than targeting high-risk individuals. Here, everyone who is unsheltered is exposed; the population-level cause is being unsheltered in extreme heat.
Candidate Responses, Each Tested Against the Risk It Can Change
Table 1 tests each candidate response against the layer of risk it acts on and what share of the burden it could plausibly reach.
Table 1
Candidate Responses Matched to Layers of Risk
| Response | Layer it acts on | What it could change | What it cannot change |
|---|---|---|---|
| Permanent supportive and rapid rehousing | Structural | Removes exposure entirely for those housed | Inflow from eviction; limited by housing supply |
| Pause encampment sweeps during heat warnings | Structural and environmental | Lets people stay near shade, water and outreach | Heat itself; long-term exposure |
| Extended-hour and overnight cooling centers near encampments | Access | Reduces time exposed on the hottest days | People who will not use institutional spaces |
| Water and shade stations at encampments | Access and environmental | Reduces dehydration and direct sun | High ambient temperatures |
| Outreach checks on warning days for high-risk people | Individual and proximate | Finds people in early heat illness | Everyone else; depends on outreach capacity |
| Clinical advice on medications and hydration | Individual | Reduces susceptibility in those seen | Exposure; reaches few |
Responses that act on structural and environmental layers reach the whole exposed population; those that act on individuals reach only those seen.
What the Analysis Implies
The analysis implies a portfolio weighted toward the upper layers. Housing is the only response that removes exposure, and its reach is limited by supply, so it should be prioritized for the people at highest risk, older adults with chronic illness. Pausing sweeps during heat warnings is inexpensive and acts on a structural cause the county controls. Cooling centers and water stations reduce exposure on the days when most deaths occur, but only if they are placed near encampments and open when heat peaks. Outreach checks and clinical advice matter for the people they reach but cannot shift the population rate much alone.
The earlier causal analysis cautioned that distance to cooling centers may not be the main driver of risk. That caution applies here: cooling centers should be rolled out in a way that tests their effect, while the county invests in responses that act on exposure more directly.
How the County Would Know the Portfolio Is Working
Because the responses act on different layers, each needs its own measure, and the county should decide them before the next season begins. For housing, the measure is the number of unsheltered adults over 50 with chronic illness who were housed before summer. For the sweep pause, it is the number of encampment clearances conducted on heat warning days, which should fall to zero. For cooling centers and water stations, it is the number of visits from people walking in from nearby encampments, recorded without names, and the hours centers were open on the hottest evenings. For outreach, it is the share of people on a high-risk list checked on each warning day.
The outcome measures are the ones used in the burden estimate: heat-associated deaths from the medical examiner and emergency visits for heat illness, each reported with both denominators. Because the summer-to-summer count is small and weather varies, the county should compare rates per warning day rather than per season, so that a cooler summer does not look like a successful one. Reporting process and outcome measures together will show whether a disappointing result reflects a response that was not delivered or one that did not work.
Limits
The analysis rests on illustrative figures, a denominator known only within a wide range and case counts that are likely undercounts. It does not estimate how many deaths each response would prevent, because the evidence to do so is thin. What it offers is a structure: burden, then causes in layers, then responses tested against the layer they act on. That structure should guide both the county's choices and the evaluation of them.
References
Iverson, S. A., Gettel, A., Bezold, C. P., Goodin, K., McKinney, B., Sunenshine, R., & Berisha, V. (2020). Heat-associated mortality in a hot climate: Maricopa County, Arizona, 2006-2016. Public Health Reports, 135(5), 631-639. https://doi.org/10.1177/0033354920938006
Rose, G. (1985). Sick individuals and sick populations. International Journal of Epidemiology, 14(1), 32-38. https://doi.org/10.1093/ije/14.1.32
Schwarz, L., Castillo, E. M., Chan, T. C., Brennan, J. J., Sbiroli, E. S., Carrasco-Escobar, G., Nguyen, A., Clemesha, R. E. S., Gershunov, A., & Benmarhnia, T. (2022). Heat waves and emergency department visits among the homeless, San Diego, 2012-2019. American Journal of Public Health, 112(1), 98-106. https://doi.org/10.2105/AJPH.2021.306557
The Walden NURS 8310 Week 10 instructions
Population health analyses in this course usually ask you to bring together the burden, determinants and possible responses for a health problem in one argument. Expect several pages in APA style with a table. State the burden briefly, drawing on earlier measures. Arrange determinants in layers from structural to proximate, and explain which layers set most of the burden. Test each candidate response against the layer it acts on and what it cannot change. Recommend a combination, explaining the weight given to each. Say how the effect of each response would be measured, and close with limits, including where evidence is too thin to estimate effects.
How this NURS 8310 Week 10 example is built
The opening section restates the illustrative rates and anchors them in the Arizona mortality study and the San Diego emergency visit study. A causes section describes structural, environmental, access, individual and proximate layers and cites Rose. Table 1 matches six responses to layers, with what each could and could not change. A section explains the recommended portfolio, prioritizing housing for older adults with chronic illness and pausing sweeps, and recalls the causal caution about cooling centers. A measurement section lists process measures per response and outcome rates per warning day with both denominators. A limits section notes that preventable deaths per response cannot yet be estimated. The argument returns to the burden figures at the end, so readers see what the portfolio is meant to reduce.
NURS 8310 Week 10 rubric: what earns full marks
Population health analyses are usually graded on integration, clear arrangement of determinants, logical matching of responses to causes and attention to measurement and limits. Integration credit rewards a single argument rather than separate summaries. Determinant credit goes to layered causes with an explanation of which drive most of the burden. Response credit depends on testing each option against the causes it can change. Measurement credit rewards process and outcome measures tied to each response. Limits earn credit when specific. The response table, references and sources supply the balance, and instructors frequently credit analyses that explain why responses acting on individuals cannot move population rates alone. Some rubrics also reward a clear explanation of how each response would be measured.
NURS 8310 Week 10 help: mistakes that cost marks
Analyses lose marks when burden, causes and responses are written as three unconnected sections. Another common loss is listing determinants without saying which set most of the burden. Some papers recommend every response equally. Others offer no way to tell whether responses worked. Write one argument, arrange causes in layers, test responses against the layers they change, weight the recommendation and specify measures. The desk can draft a population health analysis for a problem in your own county. Draw the layers of causes on a single page before writing, since the drawing usually shows where responses are missing. A single-page sketch of causes and responses also helps a reader follow the argument.
Get a NURS 8310 Week 10 example written to your instructions
Supply your earlier sections, or summaries of their findings, with the Week 10 prompt and rubric. They are joined into one argument, determinants arranged by distance from the outcome and each response matched to the risk it could change, with every figure traceable to its source. The first sample carries no fee and arrives inside 24-48 hours.
More NURS 8310 papers and DNP samples
- NURS 8310 Week 1: Epidemiologic Measure Post
- NURS 8310 Week 2: Incidence and Prevalence Paper
- NURS 8310 Week 3: Rate Standardization Problem Set
- NURS 8310 Week 4: Outbreak Design Memo
- NURS 8310 Week 5: Observational Study Critique
- NURS 8310 Week 6: Predictive Value Post
- NURS 8310 Week 7: Social Epidemiology Analysis
- NURS 8310 Week 8: Population Burden Estimate
- NURS 8310 Week 9: Causal Inference Post
- NURS 8310 Week 11: Epidemiologic Implications Report
- NURS 8301 Week 10: Executive QI Proposal
- NURS 8250 Week 10: Theory Application Paper
- NURS 8211 Week 10: Unit Council Data Briefing
- NURS 8210 Week 10: Informatics Evaluation Plan
NURS 8310 Week 10 questions, answered
Why arrange determinants by distance from the outcome?
Because responses act at different points on the chain. Housing placement addresses a structural cause; water distribution addresses a proximate exposure. Arranging determinants in layers shows what each response changes and what it leaves untouched. The example uses three layers. A named model, such as a social-ecological framework, may be assigned in your course, and the custom analysis organizes determinants under it.
Where can I find a free NURS 8310 Week 10 sample paper?
The full population health analysis of heat illness, with its layered causes and response table, is published here free. A version built on your own county's problem can be requested.
Why does the NURS 8310 Week 10 paper cite Geoffrey Rose?
To distinguish the causes of individual cases from the causes of a population's rate: every unsheltered adult faces extreme heat, so responses that change exposure for all prevent more harm than those aimed at individuals.
Why measure outcomes per warning day?
Because harm concentrates on the hottest days and summers vary, so rates per warning day keep a cooler season from looking like a successful response.
Which responses does the analysis prioritize?
Housing for older unsheltered adults with chronic illness and pausing encampment sweeps during heat warnings, followed by cooling centers and water stations near encampments.