| Course | NURS 8310 Epidemiology and Population Health |
|---|---|
| Week | Week 8 |
| Paper type | Population burden quantification |
| Length | About 1,010 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 8
Counting Heat Illness Among People Sleeping Outside: A Population Burden Estimate With Two Denominators, Because Neither Can Be Trusted Alone
Student Name
College of Nursing, Walden University
NURS 8310: Epidemiology and Population Health
Instructor Name
Month Day, Year
Counting Heat Illness Among People Sleeping Outside: A Population Burden Estimate With Two Denominators, Because Neither Can Be Trusted Alone
Purpose
This document measures the burden of heat illness among unsheltered adults in a composite desert county of about 2.5 million people, where summer daytime temperatures often exceed 110 degrees Fahrenheit. It defines the population by housing status, place and season, counts deaths and emergency department visits from primary sources and calculates rates twice, with two denominators, because no single estimate of how many people are unsheltered can be trusted. All figures are illustrative, constructed to show the method. They are informed by published work on heat mortality in a hot US county, where people experiencing homelessness made up a substantial share of heat-associated deaths (Iverson et al., 2020), and on emergency visits among people experiencing homelessness during heat waves (Schwarz et al., 2022).
Defining the Population
Housing status. Adults 18 and older who, at the time of the event, were sleeping outdoors, in vehicles or in other places not meant for habitation. People in shelters are excluded because their exposure differs, though they are reported separately where records allow.
Place. Events occurring in the county, regardless of where the person had last been housed.
Season. May 1 through September 30, the county's defined heat season, 153 days.
Counting Events From Primary Sources
Deaths. The medical examiner reviews all deaths in which heat may have contributed and records housing status from scene investigation and records. In the illustrative season, 60 heat-associated deaths occurred among unsheltered adults and 45 among housed residents. Heat-associated means heat was listed as a cause or contributing condition.
Emergency department visits. Visits with a diagnosis of heat illness were identified from the state's emergency department discharge data. Housing status was taken from the address field, where entries such as homeless, none or a shelter address flag the patient as experiencing homelessness. Because this flag cannot distinguish sheltered from unsheltered patients, a sample of 100 flagged records was reviewed against outreach records, and about 85% were unsheltered at the time. Applying that share, about 420 of 495 flagged visits were attributed to unsheltered adults.
Both counts are likely undercounts. Deaths in which heat contributed but was not recognized, and emergency visits in which housing status was not recorded, are missing.
Two Denominators
The rate needs a denominator: the number of unsheltered adults at risk during the season. Two estimates exist, and they differ greatly.
Table 1
Two Denominators for the Unsheltered Adult Population
| Denominator | Source | Value | What it measures | Likely bias for a seasonal rate |
|---|---|---|---|---|
| A. Single-night count | January point-in-time count, unsheltered adults | 4,000 | People unsheltered on one winter night | Too low if more people sleep outside in summer or are missed by the count; rates too high |
| B. Annual unique count | Outreach and service records, unique unsheltered adults seen in the year | 11,000 | Everyone unsheltered at any point in the year | Too high as a measure of person-time, since many are unsheltered briefly; rates too low |
The ideal denominator, the average number of unsheltered adults across the heat season, lies between these two and is not directly measured.
Rates
Table 2 presents seasonal rates per 100,000 unsheltered adults using each denominator, with the housed population's rate for comparison.
Table 2
Illustrative Seasonal Heat Illness Rates per 100,000
| Measure | Rate using denominator A (4,000) | Rate using denominator B (11,000) | Housed residents (about 2.5 million) |
|---|---|---|---|
| Heat-associated deaths | 1,500 | 545 | 1.8 |
| Emergency visits for heat illness | 10,500 | 3,818 | Not calculated here |
Whichever denominator is used, the death rate among unsheltered adults is hundreds of times the rate among housed residents.
How the Burden Is Distributed Within the Season and the Population
The burden is not spread evenly across the 153 days. In the illustrative data, about two-thirds of the deaths occurred on days when the county had issued an excessive heat warning, which were about a quarter of the season's days. Emergency visits followed the same pattern, rising within a day of the hottest temperatures. That concentration matters for response: resources such as cooling center hours, outreach teams and water distribution can be focused on warning days, when most of the harm occurs.
Within the unsheltered population, most of those who died were men, and adults from 50 through 64 made up the largest age group, and the medical examiner recorded underlying heart disease or substance use as contributing conditions in many cases. Those patterns describe who died, not rates, since the age and sex structure of the unsheltered population is known only from the single-night count. They nonetheless suggest where outreach should look first: older unsheltered adults with chronic illness, especially those camping far from shade and water. Emergency visits were more evenly spread by age, which suggests that younger people more often survived long enough to reach care.
Why Report Both
Reporting two rates is not indecision. It shows the range within which the true rate lies and makes the uncertainty visible to decision-makers. The single-night count likely understates the summer population, since some people who stay with others in winter sleep outside in summer, and counts miss people who are hidden. The annual unique count overstates person-time, because it counts someone unsheltered for one week the same as someone unsheltered all year. The true seasonal rate is somewhere in between, and both bounds lead to the same conclusion: the burden is extreme.
Rothman (2012) stresses that a rate needs both a count of people exposed and the length of time they were exposed. For this population, the time at risk is the hardest part to measure, and reporting a single rate would hide that difficulty from the people deciding how much to spend on prevention.
Improving the Estimate
Three steps would narrow the range. A summer point-in-time count, repeated in July, would give a seasonal point estimate. Outreach records could estimate average days unsheltered per person, allowing person-days to be calculated. And linking medical examiner and emergency records to outreach records would improve the numerator by identifying housing status more reliably. Until then, the two-denominator approach is the honest way to report the burden.
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
Rothman, K. J. (2012). Epidemiology: An introduction (2nd ed.). Oxford University Press.
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
NURS 8310 Week 8 instructions, in plain terms
The burden assignment commonly asks you to size the size of a health problem in a defined population using available data. Expect several pages in APA style with tables. Define the population on every relevant axis, such as housing status, place and time. Count events from primary sources and describe how each was identified and what it misses. Choose denominators carefully, and if no single denominator is trustworthy, calculate rates with more than one and explain the direction of each one's bias. Compare with a reference population. Describe how the burden is distributed in time and among subgroups, and close with steps that would improve the estimate.
Inside the NURS 8310 Week 8 example
The purpose section introduces the county and labels figures illustrative, citing Iverson and colleagues and Schwarz and colleagues. A definitions section sets housing status, place and season. A counting section describes medical examiner deaths and emergency visits identified by address flags and a record review. Table 1 compares two denominators and their biases. Table 2 gives rates with each. A section on distribution describes concentration on warning days and among men aged 50 to 64 with chronic illness. A section explains why both rates are reported, citing Rothman. The final section proposes a summer count, person-days from outreach and record linkage. Both rate tables carry a note on which way each denominator is likely to be wrong.
Reading the NURS 8310 Week 8 rubric
Burden estimates are usually graded on clear population definition, sound identification of events, thoughtful choice of denominators, clear presentation of rates and honesty about uncertainty. Definition credit rewards precision on every axis. Event credit goes to transparent methods for identifying cases and their gaps. Denominator credit rewards explaining the bias of each option. Presentation is judged on labeled rates with comparison. Faculty value explicit reporting of uncertainty and steps to reduce it. Clear tables, correct citations and good sources count for the remainder. Estimates presented as a single precise figure for a hard-to-count population tend to lose credibility. Graders also notice whether the definitions of housing status, place and season match between numerator and denominator. Some rubrics reward reporting how cases were confirmed.
Common NURS 8310 Week 8 mistakes, and how to avoid them
Burden papers lose marks when the population is defined loosely, so numerator and denominator describe different groups. Another common loss is presenting a single rate for a population whose size is highly uncertain. Some papers count events without explaining how cases were identified. Others ignore timing and subgroups, which matter for response. Define the population precisely, count from primary sources, use and explain more than one denominator if needed, compare with a reference group and describe distribution. The desk can draft a burden estimate for a problem in your own county. Ask the source agency how it identifies the population in its records before relying on its counts.
Get a NURS 8310 Week 8 example written to your instructions
State the population and condition your Week 8 prompt requires, send the rubric, and list any data sources your section has approved. Burden is quantified from primary sources with every numerator and denominator named, and the uncertainty in each is shown rather than hidden. No charge for a first sample; 24-48 hours to delivery.
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 9: Causal Inference Post
- NURS 8310 Week 10: Population Health Analysis
- NURS 8310 Week 11: Epidemiologic Implications Report
- NURS 8301 Week 8: PDSA Cycle Plan
- NURS 8250 Week 8: EBP Model vs Theory Post
- NURS 8211 Week 8: SPC Analysis Writeup
- NURS 8210 Week 8: Technology Business Case
NURS 8310 Week 8 questions, answered
Why calculate rates on two denominators?
Because each available count of unsheltered people is flawed in a known direction. A point-in-time count taken on a winter night may not match the summer population, and outreach contact records depend on where teams go. Rates on both show a range, which is more honest than one precise-looking figure. Your data may support a single denominator, and the custom version explains that choice.
Are medical examiner and syndromic surveillance data really available?
Often, though access varies. Some medical examiners publish heat death reports, and many health departments run syndromic surveillance systems that can flag heat-related visits, sometimes with limited housing information. The example names these source types without claiming anything about a specific county. A custom sample uses the sources your prompt permits, noting where a data request would be needed.
Does the document advise on preventing heat illness?
No. It measures burden and gives no clinical or personal protective guidance, which belongs to health professionals and public health heat programs. Interventions enter later in the course as population-level arguments. Custom work for your Week 8 prompt stays with measurement unless your rubric asks for more, in which case the responsible agency is cited.
Where can I find a free NURS 8310 Week 8 sample paper?
The full burden estimate for heat illness, with both denominators and rate tables, is posted in full, free. A burden estimate for a problem in your own county can be requested.
Why does the NURS 8310 Week 8 document calculate rates twice?
A single-night winter count probably understates the summer population, while an annual unique count overstates time at risk, so the true rate lies between the two.