| Course | NURS 8310 Epidemiology and Population Health |
|---|---|
| Week | Week 7 |
| Paper type | Social epidemiology analysis |
| Length | About 1,040 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 7
Where People Last Lived Before Homelessness: A Social Epidemiology Analysis of Prior Addresses, Eviction Filings and Rent Burden, Beginning With Which Data Can See the Population
Student Name
College of Nursing, Walden University
NURS 8310: Epidemiology and Population Health
Instructor Name
Month Day, Year
Where People Last Lived Before Homelessness: A Social Epidemiology Analysis of Prior Addresses, Eviction Filings and Rent Burden, Beginning With Which Data Can See the Population
The Question
Where do people entering homelessness in the composite county last live, and what surrounds those places? Social epidemiology asks how social conditions shape the distribution of health and its determinants in populations. Krieger (2001) describes an ecosocial perspective in which people literally embody the social and material conditions in which they live, and in which the distribution of outcomes reflects how power and resources are distributed across places and groups. Homelessness is itself a social condition with strong health consequences, and asking where it begins is a way of asking which conditions produce it.
This analysis uses surveillance and administrative data to map the last stable addresses of people entering homelessness against eviction filings and rent burden by census tract. Before any mapping, it audits which data systems can see this population at all, since the answer determines what the map can show. All figures are illustrative.
Step 1: A Data Audit
Table 1 lists the data systems considered and what each can and cannot see.
Table 1
Audit of Data Systems for Prior Address Among People Entering Homelessness
| Data system | Records prior address? | Who it sees | Who it misses |
|---|---|---|---|
| Homeless management information system (shelter and outreach intake) | Yes, self-reported last stable address; about 70% complete | People entering shelter or engaged by outreach | People staying in cars or with friends who never use services |
| County eviction court filings | Yes, the rental address named in the filing | Formal evictions filed in court | Informal evictions, lockouts and people who leave before a filing |
| Hospital discharge records | Yes, most recent home address | People hospitalized before becoming homeless | Anyone not hospitalized; addresses often outdated |
| American Community Survey tract estimates | Not individual; tract-level rent burden and poverty | The housed population's tract characteristics | Individuals; homeless population not surveyed as such |
No single system sees everyone who becomes homeless, and the most complete source of prior address depends on self-report.
What the Audit Changes
The audit changes the analysis in three ways. First, the numerator, people entering homelessness with a known prior address, comes from shelter and outreach intake and misses people who never use services, who may differ systematically, for example families doubled up with relatives. Second, about 30% of intake records lack a usable prior address, and those missing addresses are more common among people who have been homeless before, so the map will overrepresent first-time entrants. Third, eviction filings capture only formal court evictions, and research on eviction has shown that informal forced moves are common and fall more heavily on some groups than others (Desmond, 2012). The eviction map will therefore understate displacement in neighborhoods where informal evictions are the norm.
Step 2: Mapping Prior Addresses
Over two years, 3,100 people entered the shelter system with a usable prior address in the county. Geocoded to census tracts, their prior addresses were concentrated: one-fifth of the county's tracts accounted for about 55% of entries. The rate of entry per 10,000 renter households ranged from under 5 in the lowest tracts to over 60 in the highest.
Two tract-level measures were compared with entry rates. The first is the eviction filing rate, filings per 100 renter households per year from court records. The second is severe rent burden, the share of renter households paying at least half of income in rent, from survey estimates. Tracts in the highest fifth of eviction filing rates had entry rates about three times those in the lowest fifth. Tracts in the highest fifth of severe rent burden had entry rates about two and a half times those in the lowest. The two measures were correlated with each other, so their separate contributions cannot be cleanly distinguished.
Interpreting the Pattern
The pattern fits research on community-level determinants of homelessness, which has found rates associated with housing market conditions such as rent levels and with economic conditions across communities (Byrne et al., 2013). It suggests that entry into homelessness in the county is not randomly spread but arises disproportionately from neighborhoods where housing costs strain incomes and evictions are common.
Three cautions apply. The analysis is ecological: it describes tracts, not individuals, and inferring that the people who became homeless were themselves evicted or rent-burdened would risk the ecological fallacy (Piantadosi et al., 1988). The tracts with the highest entry rates may also have more outreach and shelter access, raising detection. And prior address is self-reported and may name the last place a person felt settled rather than the last place they slept.
What the Map Cannot Tell the County
The map cannot say how people move from a rent-burdened tract to a shelter bed. Some may be evicted formally, some may leave after a rent increase before any filing, some may lose housing after a job loss or a relationship ends and some may leave a doubled-up household when the host is threatened with eviction. Each route suggests a different point for prevention, and the tract-level data cannot distinguish them. Short interviews with a sample of recent entrants about the sequence of events before they lost housing would add what the map lacks.
The map also cannot say what happens in tracts with low entry rates. A low rate may mean residents are stably housed, or it may mean they move out of the county when they lose housing, or that they avoid shelters. Comparing entry rates with data on moves out of the county, where available, would help separate these possibilities. Until then, the county should treat low-entry tracts as places where the data are silent, not as places where the problem is absent.
Implications
Even with its limits, the analysis points prevention toward specific places. The county could concentrate eviction diversion, emergency rental assistance and legal representation in the tracts with the highest eviction filing rates, and it could link court filing data to shelter intake, with appropriate protections, to learn directly how many entrants had a recent filing. It should also improve the completeness of prior-address fields at intake, since the analysis depends on them. Social epidemiology here does what clinical data cannot: it shows that where a person becomes homeless is part of why.
References
Byrne, T., Munley, E. A., Fargo, J. D., Montgomery, A. E., & Culhane, D. P. (2013). New perspectives on community-level determinants of homelessness. Journal of Urban Affairs, 35(5), 607-625. https://doi.org/10.1111/j.1467-9906.2012.00643.x
Desmond, M. (2012). Eviction and the reproduction of urban poverty. American Journal of Sociology, 118(1), 88-133. https://doi.org/10.1086/666082
Krieger, N. (2001). Theories for social epidemiology in the 21st century: An ecosocial perspective. International Journal of Epidemiology, 30(4), 668-677. https://doi.org/10.1093/ije/30.4.668
Piantadosi, S., Byar, D. P., & Green, S. B. (1988). The ecological fallacy. American Journal of Epidemiology, 127(5), 893-904. https://doi.org/10.1093/oxfordjournals.aje.a114892
The Walden NURS 8310 Week 7 instructions
This week's assignment typically has you examine how social conditions shape the distribution of a health outcome or determinant using population data. Plan on several pages in APA style. Begin by auditing the data sources: what each can see, who it misses and how complete it is. Explain how the audit changes what the analysis can claim. Then describe the pattern you find, with rates and comparisons across areas or groups, labeling illustrative figures. Interpret it with reference to theory and published research, and name the ecological and measurement limits. Close with implications for prevention and for improving the data. Give each data system its own row in the audit table and keep the entries brief.
How the NURS 8310 Week 7 example is put together
The opening section frames the question with Krieger's ecosocial perspective. Table 1 audits four data systems. A section explains three ways the audit changes the analysis, citing Desmond on informal evictions. A mapping section reports illustrative concentration of entries and rate differences by eviction filing and rent burden quintiles. An interpretation section cites Byrne and colleagues and names three cautions, including the ecological fallacy from Piantadosi and colleagues. A section describes what the map cannot tell the county, including routes into homelessness and silent tracts. Implications include targeted prevention, data linkage and more complete intake fields. The paper keeps every figure labeled illustrative and names the ecological limit beside the strongest result.
Where the marks sit in the NURS 8310 Week 7 rubric
Social epidemiology analyses are generally assessed on attention to data sources, clarity of the pattern described, use of theory and research and awareness of limits. Data credit rewards an explicit audit of what each source sees and misses. Pattern credit goes to clear rates and comparisons. Theory credit depends on connecting the pattern to a social epidemiology framework and published findings. Limits earn credit when the ecological fallacy and measurement gaps are named specifically. Implications are credited when they follow from the pattern. Tables, references and credible sources earn what remains, and graders often credit analyses that start by asking who the data cannot see. Some rubrics also credit a plan for improving the data in future years.
Common NURS 8310 Week 7 mistakes, and how to avoid them
Social epidemiology papers lose marks when they map outcomes without asking who is missing from the data. Another common loss is inferring individual experiences from area-level associations. Some papers name a theory early and then leave it idle. Others offer implications unrelated to the pattern found. Audit the data first, describe the pattern with rates, interpret it with theory and research, name ecological and measurement limits and draw implications that follow. The desk can draft an analysis using data from your own county. Check the completeness of the address field in your main data source before mapping, since missing addresses rarely fall at random.
Get a NURS 8310 Week 7 example written to your instructions
Tell us the population and the determinant the Week 7 prompt focuses on, and include the rubric. The analysis audits which surveillance sources can see that population before using any of them, then argues the distribution of risk at the level the data support. The first one is free, delivered in 24-48 hours.
More NURS 8310 papers and DNP samples
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- NURS 8310 Week 2: Incidence and Prevalence Paper
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- NURS 8310 Week 4: Outbreak Design Memo
- NURS 8310 Week 5: Observational Study Critique
- NURS 8310 Week 6: Predictive Value Post
- NURS 8310 Week 8: Population Burden Estimate
- NURS 8310 Week 9: Causal Inference Post
- NURS 8310 Week 10: Population Health Analysis
- NURS 8310 Week 11: Epidemiologic Implications Report
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- NURS 8250 Week 7: Borrowed Theory Critique
- NURS 8211 Week 7: Level-Matched Data Displays
- NURS 8210 Week 7: Data Stewardship Analysis Paper
NURS 8310 Week 7 questions, answered
Why begin with an audit of data sources?
Because the population in this analysis falls largely outside the sampling frames of standard surveys, and an analysis that skips that point can mistake invisibility for good health. The audit tells the reader which numbers can describe this group and which cannot. If your population is well covered by household surveys, the custom analysis keeps the audit short and moves quickly to distribution.
What is the ecological fallacy?
The error of inferring individual relationships from group-level data. A tract with high eviction filings and many shelter entrants does not show that the individuals who entered shelter were the ones evicted. The example states its conclusions about places and treats individual pathways as a question for other data. Your analysis may use individual-level records, which permit different claims.
Where can I find a free NURS 8310 Week 7 sample paper?
The complete social epidemiology analysis, with its data audit, mapping and implications, is free to read here. An analysis for your own county can be requested.
Why does the NURS 8310 Week 7 paper audit data sources first?
Because no single system sees everyone who becomes homeless, and missing records are not random, so the audit determines what the map can and cannot show.
What does the map suggest for prevention?
Concentrating eviction diversion, emergency rental assistance and legal representation in the tracts with the highest eviction filing rates, and linking court filings to shelter intake.