| Course | NU 655 Foundations of Public Health Nursing |
|---|---|
| Unit | Unit 2 |
| Paper type | Local health data audit |
| Length | About 560 words, 5 pages |
| Format | APA 7 student paper |
| School | Herzing University |
| Program | MSN Public Health Nursing |
| Updated | October 2026 |
Free sample paper for NU 655 Unit 2
Local Health Data Audit: Nine Indicators for Children Under Three in a Rural County, and Where Each Number Came From
Student Name
Department of Nursing, Herzing University
NU 655: Foundations of Public Health Nursing
Instructor Name
January 29, 2026
Local Health Data Audit: Nine Indicators for Children Under Three in a Rural County, and Where Each Number Came From
Purpose
Before planning any work for the composite rural county's roughly 1,350 children under three, I gathered the indicators available for this population and recorded, for each, who published it, what period it describes, what geography it covers, what its denominator is, and what limits its use. The values are those found for this composite county; the sources and their characteristics are real types of data a county public health nurse would use.
The Indicators
Each indicator is shown with its source details.
| Indicator | Value | Publisher | Period described | Geography | Denominator | Main limitation |
|---|---|---|---|---|---|---|
| Live births | 452 | State vital records | Calendar 2023 | County of mother's residence | Not applicable | Released 18 months after the year ends |
| Preterm births | 10.8% | State vital records | 2021 to 2023 combined | County | 1,362 live births | Three years combined because single-year numbers are small |
| Low birth weight | 8.1% | State vital records | 2021 to 2023 combined | County | 1,362 live births | Same as above |
| Infant deaths | 4 deaths, rate not published | State vital records | 2019 to 2023 | County | About 2,250 births | Too few deaths for a reliable rate |
| Combined 7-vaccine series complete by 24 months | 58% | State immunization registry | Children born in 2022, assessed in 2024 | Registry address in county | Children in registry with a county address | Denominator includes children who moved away |
| Six or more well-child visits by 15 months, Medicaid children | 51% | State Medicaid agency quality report | 2023 | County of enrollment | Continuously enrolled Medicaid children | Excludes children who lost coverage during the year |
| Children under 5 in poverty | 24%, margin of error 7 points | Census Bureau, American Community Survey 5-year estimates | 2019 to 2023 | County | Survey sample | Wide margin of error in a small county |
| Children under 6 without health insurance | 5%, margin of error 3 points | Census Bureau, American Community Survey 5-year estimates | 2019 to 2023 | County | Survey sample | Wide margin of error |
| Infants enrolled in WIC | About 60% | State WIC program | Fiscal year 2024 | County WIC office | Estimated county infants | Denominator estimated from births |
Period Described Versus Year Released
Every figure describes a period that ended before the data were released, sometimes long before. The birth data describe 2023; the census estimates describe five years of survey responses averaged together, from 2019 to 2023, so a change in child poverty after a factory closed in 2022 would appear only partly and gradually. When these figures are presented to the board of health, each will carry its period, not the year of the report it came from, so that no one mistakes 2019 to 2023 poverty for current poverty.
Denominators
The denominators differ in ways that change the meaning of the figures. The vaccination coverage figure uses every child in the registry with a county address, including children whose families moved away but whose records were never updated; those children appear unvaccinated in the county because their later doses were given elsewhere. The true coverage of children actually living in the county is therefore probably somewhat higher than 58%, though how much higher is unknown. The well-child visit measure counts only children continuously enrolled in Medicaid, excluding those who lost and regained coverage, who are likely to be the children with the most disrupted care. The two measures cannot be combined into a single picture of how well children are served.
Small Numbers and Uncertainty
With about 450 births a year, single-year rates for preterm birth and low birth weight would swing widely by chance; the state combines three years for that reason. Infant deaths are too few for any rate to be meaningful, and the state does not publish one; four deaths in five years should prompt individual case review, not rate comparison. The census estimates carry wide margins of error: child poverty of 24% plus or minus 7 points means the true value could plausibly lie anywhere from 17% to 31%, and the county should not be ranked against its neighbors on that basis (U.S. Census Bureau, 2020).
Comparisons and What Can Bear Weight
National vaccination coverage is estimated from a household survey with provider record checks, a different method from the state registry (Hill et al., 2023), so the county's registry figure should be compared with the state's registry figure for the same birth cohort, not with the national survey estimate. Of the nine indicators, the registry coverage figure, used with its denominator problem stated, and the three-year birth outcome figures are strong enough to guide planning. The poverty and insurance estimates describe context but are too uncertain for tracking change. The next step is a registry clean-up of children who have moved, which would make the coverage figure more accurate and is itself an assessment task (Centers for Disease Control and Prevention [CDC], 2012).
References
Centers for Disease Control and Prevention. (2012). Principles of epidemiology in public health practice: An introduction to applied epidemiology and biostatistics (3rd ed.). U.S. Department of Health and Human Services.
Hill, H. A., Yankey, D., Elam-Evans, L. D., Chen, M., & Singleton, J. A. (2023). Vaccination coverage by age 24 months among children born in 2019 and 2020: National Immunization Survey-Child, United States, 2020-2022. MMWR Morbidity and Mortality Weekly Report, 72(44), 1190-1196.
U.S. Census Bureau. (2020). Understanding and using American Community Survey data: What all data users need to know. U.S. Government Publishing Office.
Reading the NU 655 Unit 2 instructions
Unit 2 asks you to audit the local data available for a population before using it to plan. Expect to list indicators, record each one's source, period, release date and denominator, assess limitations such as small numbers and data lag, and judge which comparisons are valid. Many versions ask what to do when local data are sparse. Check whether your section expects a specific number of indicators. State and county health department reports, immunization registries, vital records and the American Community Survey are common sources. Treat every figure as a claim to check rather than a fact. Small populations make single-year rates unreliable, so look for pooled years or confidence intervals. Comparisons across sources with different methods need caution.
Inside the NU 655 Unit 2 example
The audit opens with a short purpose statement explaining why data must be checked before planning. A table lists nine indicators with their source details, which is the core of the document. A section on period versus release explains data lag with specific examples, such as birth data two years old at release. The denominators section shows how the same word, coverage, can mean different things depending on who is counted. The small numbers section uses the county's birth count to show why pooled rates are needed. The comparisons section judges which figures can be set beside state or national ones, and which cannot, because methods differ. Short explanations follow every judgment, which keeps the audit analytical.
Where the points sit in the NU 655 Unit 2 rubric
Herzing's rubric for a data audit typically covers the selection of indicators, documentation of sources, assessment of data quality, attention to denominators and small numbers, valid comparisons and writing. Documentation is read closely, and every indicator needs its source, period, release date and denominator for the top band. Selection points reward indicators relevant to the population. Quality criteria look for lag, coverage and method limits named specifically. Small numbers and denominators often earn their own credit. Comparison points go to judgments about which figures can be compared and why. Presentation covers a readable table. CDC's Principles of Epidemiology and the Hill vaccination coverage report appear in the APA 7 list.
NU 655 Unit 2 help: mistakes that cost points
Data audits in Unit 2 usually lose points by listing figures without sources, periods or denominators. Another frequent problem is treating the release year as the year the data describe. Students often compare county figures with national ones without noting different methods. Small numbers are ignored, so a single-year rate is taken at face value. Denominators are assumed to be the same across sources. The audit stops at listing, with no judgment about what the data can support. Finally, record where each number came from in enough detail for someone to find it again, because graders check whether a figure can be traced, and untraceable numbers weaken every later unit.
Get a NU 655 Unit 2 example written to your instructions
Send the Unit 2 instructions and the rubric from your NU 655 classroom, together with the area and the indicators to be audited. We write a custom example with the row structure built, provenance fields specified, comparability handled honestly and the missing indicators listed. Figures for your own area stay yours to enter. First custom sample free, returned in 24 to 48 hours.
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NU 655 Unit 2 questions, answered
Where do local figures usually come from?
State and county health department reports, federal survey and vital statistics releases, and the data pages many hospitals publish for their service areas. Whatever the source, record the publishing body, the collection period and the geography with the number itself. A figure carried into a paper without those three things is unusable to anyone who wants to build on it, including you next term.
What if the area publishes almost nothing?
That is a finding worth stating rather than a problem to hide. Say which indicators are unavailable at the level you need, use the smallest geography that does publish, and be explicit that the figure describes a wider area than the population you are addressing. Substituting a national estimate and letting it stand as a local figure is the error that costs most credit.
Can I use figures from my clinical site?
Only with permission and only where they are genuinely shareable, and even then consider whether they belong in a submitted file. Aggregate published data serves the same purpose without the exposure. Anything you collected yourself, including counts from a placement, is yours to handle under whatever agreement covers it, and it should never be reproduced in an example written by somebody else.
Where can I find a free NU 655 Unit 2 sample paper?
Read the complete NU 655 Unit 2 local data audit here at no charge, nine indicators with sources, lag, denominators and valid comparisons. A custom audit can examine the data for your own population and area.
Why do small populations need pooled health data?
With few events, a rate can swing widely from year to year by chance. This sample's county has about 450 births a year, so preterm birth and low birth weight rates are pooled across several years to give a more stable figure.