| Course | NU713 Epidemiology and Social Determinants of Population Health |
|---|---|
| Unit | Unit 8 |
| Paper type | Secondary data appraisal |
| Length | About 880 words, 6 pages double-spaced |
| Format | APA 7 student paper |
| School | Purdue University Global |
| Program | Doctor of Nursing Practice (DNP) |
| Updated | September 2026 |
Free sample paper for NU713 Unit 8
Fine for the County, Blind at the Tract: A Secondary Data Appraisal of CDC WONDER's Multiple Cause of Death Files for a Diabetes Project on the East Side of Composite Brennard County
[Student Name]
Purdue University Global
NU713: Epidemiology and Social Determinants of Population Health
Unit 8 Secondary Data Appraisal
[Instructor Name]
[Date]
Brennard County and the project are composite; the description of CDC WONDER follows its public documentation as of [month, year], and features should be rechecked before use.
Verdict
CDC WONDER's Multiple Cause of Death data are suitable for county-level trends and for comparing Brennard County with the state and nation. They are unsuitable for the project's central comparison, between the east side's fourteen tracts and the west suburbs, because the database does not release data below the county. For that comparison the project must request records from the state vital records office.
The Database
CDC WONDER is a public, web-based system through which the Centers for Disease Control and Prevention has long made its data available without programming or special software (Friede et al., 1993). Its Multiple Cause of Death files are built from death certificates filed in the United States and processed by the National Center for Health Statistics. Each record carries an underlying cause of death and up to twenty contributing causes, coded with the International Classification of Diseases, tenth revision, which is what allows a count of deaths with diabetes anywhere on the certificate (National Center for Health Statistics, 2024).
The Appraisal
Coverage: adequate. The files include essentially every death certificate filed for US residents, assigned by place of residence rather than place of death. A Brennard County resident who dies in a hospital in the next county is counted in Brennard. For deaths, this is as complete as population data get.
Timeliness: limited. Final annual files are released a year or more after the deaths occur, so the most recent final year available is always behind. Provisional data are released sooner but can change as certificates are completed and causes are coded. For a project comparing five pooled years, the delay is acceptable; for monitoring a new program, it is not.
Geography: inadequate for this purpose. The smallest unit released publicly is the county. The database cannot separate the east side from the west suburbs, which is the comparison the project needs. This single criterion decides the verdict.
Suppression: limited. To protect confidentiality, counts of zero to nine deaths are suppressed for geographic units below the nation, and rates based on fewer than twenty deaths are flagged as unreliable (National Center for Health Statistics, 2024). For Brennard County as a whole, most diabetes-related counts by age exceed these thresholds, but counts by race and age often do not, and several cells would be hidden.
Race coding: limited. Race and Hispanic origin on death certificates are usually reported by a funeral director, often from a family member, rather than by the person who died. A study comparing death certificates with survey self-reports found good agreement for white and Black decedents but misclassification of other groups, especially American Indian and Alaska Native decedents, whose deaths were substantially undercounted (Arias et al., 2016). The database also offers two race classifications, bridged-race categories for earlier years and single-race categories for recent years, and trends that cross the change must be handled carefully.
A database can be excellent for the question it was built to answer and still be the wrong source for yours.
What CDC WONDER Can Carry
The project will use the database for three tasks: the county's diabetes-related death rate over time, age-adjusted to the 2000 standard; comparisons between the county, the state and the nation; and county-level rates for Black and white residents in pooled years where counts exceed suppression thresholds. These figures set the context for the local findings.
What Must Come From Elsewhere
The tract-level comparison must come from the state vital records office, which holds geocoded death records. That request will require a data use agreement, a description of how small counts will be protected, and time, typically [two to four] months. The project will then apply the same definitions, diabetes anywhere on the certificate and the same age bands, so that local and county figures can be compared. Race will be handled with the same caution, and groups with fewer than twenty deaths will not be reported separately.
Alternatives Considered
Two other sources were weighed. The state health department's own online query system reports deaths by county and, for some years, by ZIP code, but ZIP codes do not match the fourteen tracts, and several east-side ZIP codes extend into the suburbs, which would blur the comparison the project depends on. The national restricted-use mortality files include more detail than the public database, but they still do not release tract identifiers, and access requires an application to the National Center for Health Statistics that would take longer than the state request. Neither alternative solves the geography problem, which is why the state vital records office remains the only workable source for the tract comparison.
Recommendation
Use CDC WONDER for the county's context and trend, request geocoded state vital records for the tract comparison, and state in every table which source each figure comes from, with its years and definitions.
References
Arias, E., Heron, M., & Hakes, J. K. (2016). The validity of race and Hispanic-origin reporting on death certificates in the United States: An update. Vital and Health Statistics, 2(172), 1-21.
Friede, A., Reid, J. A., & Ory, H. W. (1993). CDC WONDER: A comprehensive on-line public health information system of the Centers for Disease Control and Prevention. American Journal of Public Health, 83(9), 1289-1294. https://doi.org/10.2105/AJPH.83.9.1289
National Center for Health Statistics. (2024). Multiple cause of death data on CDC WONDER: Documentation.
NU713 Unit 8 instructions, in plain terms
For Unit 8 you appraise a secondary data source for a specific population health question. Most versions ask for a verdict, criteria such as coverage, timeliness, geography, suppression and variable quality, a rating and evidence for each, and a recommendation about what the source can and cannot support. Around five pages, often as a scored table, is usual. Faculty reward a verdict tied to the project's actual question, criteria rated with evidence from the source's documentation, and a recommendation that names an alternative when the source falls short. Check current documentation before writing, since databases change their suppression rules and race categories, and say which version of each rule you relied on and the date you checked it.
Inside the NU713 Unit 8 example
The appraisal opens with its verdict and then describes the database and how multiple cause coding allows a count of deaths with diabetes anywhere on the certificate. Five criteria follow, each with a rating and reason: coverage by residence, delays before final files, the county as the smallest unit, suppression of counts below ten and flags on rates below twenty, and race recorded by funeral directors, with evidence of misclassification for some groups. The highlighted sentence separates a good database from the right one. Sections set out what the database will carry and what must come from geocoded state records, alternatives such as a state query system and restricted national files are weighed, and a recommendation closes.
NU713 Unit 8 rubric: what earns full points
Data appraisals are typically assessed on the verdict, the criteria and evidence, accuracy about the source, the recommendation and presentation. The verdict earns credit when it answers the project's own question. Criteria are judged on relevance and on evidence drawn from documentation, not general impressions. Accuracy covers suppression thresholds, timeliness and geography as the source currently defines them. The recommendation scores well when it assigns the source to tasks it can do and names a workable alternative for the rest. Presentation rewards a table or list that pairs each rating with its reason, and APA citation of both documentation and published studies. Weighing alternatives often lifts the recommendation from adequate to strong.
NU713 Unit 8 help from the desk
A common weakness is describing a database's features without relating them to the project's question. Another is rating every criterion adequate, which suggests the source was not tested against the need. Some appraisals state suppression rules from memory and get them wrong. Others ignore race coding, which affects any disparity analysis using death certificates. Many conclude that a source is unsuitable without naming what would work instead. And the verdict is often buried at the end. State your question, put the verdict first, rate each criterion with evidence from current documentation, and finish with a clear division of labor between sources.
Get an NU713 Unit 8 example written to your instructions
Tell us the dataset your Unit 8 appraisal must judge and the question it is meant to answer, then pass along the prompt and rubric. Within 24-48h a free first custom sample follows your instructions, rating each criterion against the source's own documentation and closing on a plain verdict about use. The paper above is an original model document written by our desk, not a submitted student paper and not an official Purdue University Global document.
More NU713 papers and DNP samples
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- NU713 Unit 4: Fundamental Cause Paper, Colon Cancer
- NU713 Unit 5: Seminar Reflection, Reference Groups
- NU713 Unit 6: Disparities Analysis, Heart Failure
- NU713 Unit 7: Age-Adjusted Rate Comparison, Diabetes
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NU713 Unit 8 questions, answered
What does CDC WONDER suppress, and why?
For areas smaller than the nation, counts from zero to nine are hidden to protect confidentiality, and rates built on fewer than 20 events are marked unreliable because their relative error is large. In small counties or narrow age groups, most cells can disappear. Pooling years or combining categories often recovers usable figures, and the appraisal should say which choice was made.
Is multiple cause data better than underlying cause data?
It depends on the question. Underlying cause counts each death once and suits comparisons of leading causes. Multiple cause captures every condition listed on the certificate, which matters for diseases like diabetes that often contribute to death without being selected as its underlying cause. State which file you used and why, since the two produce very different rates.
What if the appraisal concludes the dataset is unsuitable?
Then it should name what would be suitable and how to reach it, such as a state vital records request, a hospital discharge file or a registry. A negative verdict with an alternative is a complete appraisal. Instructors usually value a clear no with reasons over a hedged yes that leaves the reader unsure whether the data can be used.
Where can I find a free NU713 Unit 8 sample paper?
This page presents the full NU713 Unit 8 appraisal, with a verdict, five rated criteria with evidence, what the database can carry, what must come from state records, alternatives weighed and a recommendation, plus three references. An appraisal of the source for your own project can be requested.
Why is geography rated inadequate in NU713 Unit 8?
The public database releases nothing below the county, so it cannot separate the east side's fourteen tracts from the west suburbs, the comparison the project depends on. That single criterion decides the verdict and sends the tract analysis to state vital records.