| Course | PUBH 8033 Interpretation and Application of Public Health Data |
|---|---|
| Week | Week 11 |
| Paper type | Applied interpretation paper |
| Length | About 1,050 words, 6 pages |
| Format | APA 7 student paper |
| School | Walden University |
| Program | PhD in Public Health |
| Updated | October 2026 |
Free sample paper for PUBH 8033 Week 11
From the Fatal Crash Census to a State Licensing Rule: An Applied Interpretation Paper Taking National Data on Older People's Crash Deaths, 1975 to 2024, From Provenance to a Defended Recommendation on In-Person License Renewal for Drivers 85 and Older
Student Name
Walden University
PUBH 8033: Interpretation and Application of Public Health Data
Instructor Name
Month Day, Year
From the Fatal Crash Census to a State Licensing Rule: An Applied Interpretation Paper Taking National Data on Older People's Crash Deaths, 1975 to 2024, From Provenance to a Defended Recommendation on In-Person License Renewal for Drivers 85 and Older
The Dataset and Its Provenance
This paper works from one public dataset: the Insurance Institute for Highway Safety's tabulation of motor vehicle crash deaths among people aged 70 and older in the United States from 1975 to 2024, built from the federal Fatality Analysis Reporting System and Census population estimates (Insurance Institute for Highway Safety [IIHS], 2026). The underlying federal system is a census of fatal crashes on public roads, recording every death within 30 days of a crash, with detailed information on vehicles, people and circumstances. It is built to describe fatal crashes completely; it does not record nonfatal crashes or miles driven, which come from separate surveys.
The dataset reports deaths of people 70 and older in all roles, including drivers, passengers and pedestrians, and a rate per 100,000 population. It answers a question about population burden: how many older people die in crashes, relative to how many older people there are.
What the Data Show
In 2024, 5,679 people aged 70 and older died in motor vehicle crashes, 50% more than in 1975. Over the same period, the population of people 70 and older nearly tripled, from about 14.6 million to 41.7 million, so the death rate per 100,000 fell from 25.9 to 13.6, a decline of 47% (IIHS, 2026). The rate fell steadily from the mid-1990s through about 2014, then leveled off around 14 per 100,000, with a dip to 12.2 in 2020, when driving fell during the pandemic.
Reading the Numbers Carefully
Three cautions apply. First, the per capita rate does not adjust for driving. Older people drive fewer miles than younger people, and measured against miles driven, the fatal crash rate starts climbing in the early seventies and peaks among drivers past 85 (IIHS, 2026). Second, deaths reflect fragility as well as crash involvement. Decomposing national trends, Cicchino (2015) showed that in the late 1990s the excess per-mile death rate of older drivers owed more to their vulnerability in a crash than to crashing more often, while the later improvement among drivers 75 and up came chiefly from a drop in crashes. Third, the dataset combines all roles, so it cannot by itself tell us how many deaths involved an older driver at the wheel.
The Question for Action
A state legislature considering changes to driver licensing for older people asks whether any licensing rule is associated with fewer fatal crashes among the oldest drivers. The national death series shows the problem's size and trend but cannot answer that question; it is descriptive. A different analysis is needed, and one exists.
The Evidence on Licensing Rules
An analysis of fatal crash involvement among drivers 55 and older across 46 states from 1986 to 2011, using the same federal crash census, examined licensing policies while adjusting for time trends, other traffic laws and economic factors, and compared changes among older drivers with changes among drivers aged 40 to 54 (Tefft, 2014). Mandatory in-person license renewal was associated with a 31% reduction in fatal crash involvement among drivers 85 and older (ratio of relative risks 0.69, 95% CI 0.48 to 0.97). Where in-person renewal was not required, a vision test requirement was associated with a similar reduction (0.64, 0.49 to 0.85). Requiring knowledge or road tests, more frequent renewal or physician reporting showed no statistically significant additional effect.
The Defended Recommendation
A state that does not now require in-person renewal for drivers 85 and older should adopt it, with a vision test at the renewal visit. The recommendation rests on three points. The national data show that the oldest drivers have the highest fatal crash rates per mile, so a measure targeted at them addresses real risk. The multistate analysis found in-person renewal associated with lower fatal crash involvement in exactly this group, after adjustment and with a comparison to middle-aged drivers. And the policy is modest: it does not take licenses away by age, but brings the oldest drivers into a licensing office where vision can be checked and concerns noticed.
A Note on the Data's Currency
The death series runs through 2024, but the licensing analysis ends in 2011. Vehicle design, licensing practices and the age profile of drivers have all changed since, so a state adopting the rule should plan to measure its own results rather than assume the older estimate applies.
Limits of the Recommendation
The supporting evidence is observational. States that adopted in-person renewal may differ in other ways, such as road design, enforcement or the health of older residents, and adjustment cannot remove all such differences. The confidence interval for in-person renewal reaches 0.97, close to no effect. The analysis covered 1986 to 2011; vehicles have become more protective since, which may change the size of any benefit. And the policy has costs for older people without easy transport to licensing offices, which a state should reduce with mobile or extended-hours renewal.
Why Not a Broader Rule
The same analysis found no significant additional benefit from road tests, knowledge tests, more frequent renewal or physician reporting (Tefft, 2014). A state tempted to adopt all of them at once would add costs and burdens on older drivers without evidence of further benefit. The recommendation is deliberately narrow because the evidence is narrow.
What Would Change the Recommendation
New evidence showing no association after more recent years were added, or showing that in-person renewal led older people to give up licenses they could safely hold and suffer worse outcomes from isolation, would argue against it. A natural experiment in a state that adopts the rule, evaluated with a comparison state, would be the best next test.
Conclusion
The national death series, read with its provenance and denominators in view, shows a problem that has shrunk per capita but persists, and is concentrated, per mile, among the oldest drivers. It cannot by itself justify any policy. Paired with a multistate analysis of licensing rules, it supports a narrow recommendation: in-person renewal with a vision test for drivers 85 and older, adopted with attention to access and evaluated as it is implemented.
References
Cicchino, J. B. (2015). Why have fatality rates among older drivers declined? The relative contributions of changes in survivability and crash involvement. Accident Analysis & Prevention, 83, 67-73. https://doi.org/10.1016/j.aap.2015.06.012
Insurance Institute for Highway Safety. (2026). Fatality facts 2024: Older people. https://www.iihs.org/research-areas/fatality-statistics/detail/older-people
Tefft, B. C. (2014). Driver license renewal policies and fatal crash involvement rates of older drivers, United States, 1986-2011. Injury Epidemiology, 1, Article 25. https://doi.org/10.1186/s40621-014-0025-0
What the PUBH 8033 Week 11 instructions ask for
The final paper in a data interpretation course asks students to take one dataset through every step practiced during the term: provenance, description, careful interpretation, linkage to further evidence and a defended recommendation. Prompts usually require a real public dataset, a clear account of its source and limits, analysis or description appropriate to it, connection to evidence that addresses the decision, a recommendation, limitations and conditions for change. Instructors reward submissions that respect what a dataset can and cannot answer, bring in stronger evidence where the dataset falls short and confine the recommendation to what the evaluative evidence can bear. Many instructors ask students to choose a dataset they could download and recheck, so others can verify the figures.
How this PUBH 8033 Week 11 example is built
The paper begins with the dataset's provenance and what the underlying system records. It reports the main numbers and trend, then applies three cautions about denominators, fragility and roles in crashes. A section states the policy question and explains why the descriptive series cannot answer it. A multistate analysis of licensing rules supplies the missing evidence. The recommendation follows with three supporting points, then limits, a section on why broader rules are not recommended, conditions that would change the advice and a conclusion. The paper keeps descriptive and evaluative evidence clearly separate, so readers can see which supports which claim. A section explains why broader licensing rules are not recommended.
Where the marks sit in the PUBH 8033 Week 11 rubric
Graders evaluate accuracy in describing the dataset, care in interpretation, appropriate use of additional evidence and the soundness and narrowness of the recommendation. Explaining provenance and limits earns credit. Interpreting rates with attention to denominators and confounders shows the course's habits. Bringing in an analysis designed to evaluate policy, rather than stretching descriptive data, shows judgment. Limitations and conditions for change should be specific. Papers that state what their dataset cannot answer, and then find evidence that can, show the central skill of applied interpretation. Graders reward recommendations as narrow as the evidence and limitations that include practical concerns such as access. Explaining why tempting broader actions are not supported shows restraint. Graders reward papers that report figures exactly as published, with the year of the source table, so readers can check them.
PUBH 8033 Week 11 help from the desk
Students often draw policy conclusions directly from a descriptive trend. Use the dataset for what it describes, then find evidence designed to test the policy question. Keep the recommendation as narrow as that evidence. State limits plainly. Say what would change your mind. Check every number against its source. Ask for one written around your own question. Download and check the dataset yourself rather than relying on a summary. Write a provenance paragraph before any analysis. Ask what kind of study would test your recommendation, and look for it. Keep your recommendation to what that study supports.
Get a PUBH 8033 Week 11 example written to your instructions
Pass along the Week 11 instructions and rubric, naming the dataset and the decision your paper addresses. A paper taking one dataset from provenance through a transfer argument to a qualified recommendation returns in 24-48h, and a first-time request is free of charge. The MEPS figures in it are illustrative inventions, so the national-to-local argument in yours rests on your own comparison.
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PUBH 8033 Week 11 questions, answered
Where can I find a free PUBH 8033 Week 11 sample paper?
The full Week 11 paper is shown above, taking national older-adult crash death data to a licensing recommendation.
How many people 70 and older died in U.S. motor vehicle crashes in 2024?
5,679, a 50% increase since 1975, while the per capita rate fell 47%.
Why can't the death series justify a policy on its own?
It describes deaths per population over time but does not compare states with and without licensing rules.
What licensing rule was linked to fewer fatal crashes among the oldest drivers?
Mandatory in-person renewal, associated with 31% lower fatal crash involvement among drivers 85 and older.
What are the recommendation's main limits?
The evidence is observational, the interval nearly reaches no effect and the data end in 2011.