| Course | HLTH 6205 Assessing Health and Wellness Needs for Communities |
|---|---|
| Week | Week 4 |
| Paper type | Secondary data analysis |
| Length | About 1,210 words, 7 pages |
| Format | APA 7 student paper |
| School | Walden University |
| Program | MS in Health Education and Promotion |
| Updated | October 2026 |
Free sample paper for HLTH 6205 Week 4
Is Binge Drinking More Common in Calumet County Than in Wisconsin? A Secondary Data Analysis Setting One County Estimate Beside Three Reference Figures and Reading the Differences Without Explaining Them
Student Name
Walden University
HLTH 6205: Assessing Health and Wellness Needs for Communities
Instructor Name
Month Day, Year
Is Binge Drinking More Common in Calumet County Than in Wisconsin? A Secondary Data Analysis Setting One County Estimate Beside Three Reference Figures and Reading the Differences Without Explaining Them
The Measure and Why This One
This analysis examines one measure: the share of adults who report binge drinking at least once in the past 30 days, counted as at least four drinks on one occasion for a woman or at least five for a man. I chose it over two neighbors. Heavy drinking, measured by weekly volume, is reported for the state but is less clearly tied to the injuries and acute harms that make up much of the burden of excessive alcohol use. Alcohol-attributable deaths are closer to harm but, for a county of about 53,000 people, too few in any year to compare reliably. Binge drinking is the measure for which a county estimate, a state estimate and a national figure all exist for the same year.
Nationally, binge drinking is not a minor behavior. In 2015, adults who binge drank averaged 53 episodes a year at about seven drinks per episode, roughly 467 binge drinks per binge drinker, and half of all binge drinks were consumed by adults aged 35 and older (Kanny et al., 2018). Excessive alcohol use accounted for an estimated 12.9% of deaths among U.S. adults aged 20 to 64 between 2015 and 2019 (Esser et al., 2022). Those figures explain why the measure matters; they say nothing yet about Calumet County.
The Figures
The county figure comes from CDC's PLACES 2025 release, which models 2023 survey data down to counties (Centers for Disease Control and Prevention [CDC], 2025). The state figure is Wisconsin's direct 2023 survey estimate, and the national figure is the median of state estimates for the same year, both from CDC's BRFSS prevalence data (CDC, 2026). A fourth figure, the median of all 72 Wisconsin county estimates in the same PLACES release, was computed from the county file.
Table 1
Adult Binge Drinking Prevalence, Calumet County and Reference Groups, 2023
| Population | Estimate type | Prevalence, % | 95% CI | Difference from county crude, points | |
|---|---|---|---|---|
| Calumet County | PLACES, crude | 20.4 | 17.1-24.1 | Reference | |
| Calumet County | PLACES, age-adjusted | 22.5 | 19.0-26.5 | Not comparable | |
| Wisconsin | BRFSS direct | 18.4 | 17.4-19.5 | -2.0 | |
| Wisconsin counties, median of 72 | PLACES, crude | 18.3 | Not applicable | -2.1 | |
| United States, median of states | BRFSS direct | 15.1 | Not applicable | -5.3 | |
County and county-median values from PLACES 2025 release; state and national values from BRFSS 2023. Calumet ranks eighth highest of 72 Wisconsin counties on the crude estimate.
Were These Figures Entitled to Sit Together?
Before reading any difference, each pair has to pass three checks: the same definition, the same reference period and a compatible method of estimation.
All five rows use the same binge drinking definition and the same 2023 survey year, so the first two checks pass. Newer state results exist, 19.1% for 2024 and 17.6% for 2025 (CDC, 2026), but the county model rests on 2023, so setting a 2025 state figure against a 2023 county figure would mix two years and was avoided.
The third check is where the pairs differ. The county estimate is not a count of county respondents. PLACES fits a multilevel model to the statewide survey and predicts each county's prevalence from its age, sex and race composition plus county-level information, then applies the predictions to census population counts (Zhang et al., 2014). The state figure, by contrast, is a direct weighted estimate from roughly 11,600 Wisconsin respondents. Setting a modeled figure against a direct one is defensible, because the model is anchored to the same survey, and a validation study found model-based estimates for most measures fell close to locally collected direct estimates (Wang et al., 2017). It is still a comparison between two kinds of number, and the table says so.
The county median is the cleanest referent in the table. It comes from the same model, the same release and the same year as the Calumet figure, so any modeling tendency affects both sides equally. The national median is the weakest. It is a median of state prevalence values, not the prevalence among all U.S. adults, and it gives Wyoming the same weight as California.
The age-adjusted county estimate cannot be set against any other row, because the state and national figures in the table are crude. It is reported to show what age adjustment does: the adjusted figure is higher because the 2000 U.S. standard population is younger than Calumet County's adults, and younger adults binge drink more often. In Wisconsin in 2023, prevalence was 28.3% among adults aged 25 to 34 and 6.5% among those 65 and older (CDC, 2026).
Reading the Differences
The county crude estimate is two points above the state and 2.1 points above the median Wisconsin county. Neither difference survives the uncertainty around it. The county's 95% interval runs from 17.1% to 24.1%, which comfortably contains the state's 18.4%. A reader cannot conclude from these figures that Calumet adults binge drink more often than Wisconsin adults generally; the honest statement is that the county estimate is at the high end of a state that is itself high, and the data do not distinguish the county from the state.
The ranking says the same thing more carefully. Eighth of 72 sounds decisive, but Calumet's neighbors Outagamie (21.6%) and Winnebago (21.4%) rank above it with overlapping intervals, and the whole range across Wisconsin counties runs only from 15.4% to 21.6%. In a distribution that narrow, with intervals around seven points wide, small differences in rank carry little information.
The national comparison is the largest difference, 5.3 points, and here the interval excludes the national median. Much of that gap, however, is a Wisconsin gap rather than a Calumet gap: the state itself sits 3.3 points above the median of states. The county difference from the nation is mostly the state's difference, carried down.
What the Analysis Does and Does Not Establish
The analysis establishes that binge drinking among Calumet County adults is common in absolute terms, about one adult in five, and that the county belongs to a state where the behavior is more common than in most of the country. It does not establish that the county differs from the rest of Wisconsin. It also says nothing about why. The county's higher median household income and the national pattern of more frequent binge drinking at higher incomes are tempting to connect, but this analysis has no way to test that link, and the model that produced the county estimate already uses demographic composition in ways that would make such a reading circular.
For the assessment, that means the need cannot be framed as "Calumet is worse than Wisconsin." It can be framed as a high and common behavior in a high-prevalence state, measured by an estimate whose interval is wide enough that local data on frequency and intensity would add real information.
Limits
Every figure is self-reported, and surveys of drinking undercount consumption. The county estimate depends on the PLACES model's assumptions, which a reader cannot check from the published file. The analysis used crude estimates for the main comparison because the reference figures were crude; an age-adjusted state figure would allow a second comparison.
References
Centers for Disease Control and Prevention. (2025). PLACES: Local data for better health, county data, 2025 release [Data set]. https://data.cdc.gov/d/swc5-untb
Centers for Disease Control and Prevention. (2026). Behavioral Risk Factor Surveillance System (BRFSS) prevalence data (2011 to present) [Data set]. https://data.cdc.gov/d/dttw-5yxu
Esser, M. B., Leung, G., Sherk, A., Bohm, M. K., Liu, Y., Lu, H., & Naimi, T. S. (2022). Estimated deaths attributable to excessive alcohol use among US adults aged 20 to 64 years, 2015 to 2019. JAMA Network Open, 5(11), Article e2239485. https://doi.org/10.1001/jamanetworkopen.2022.39485
Kanny, D., Naimi, T. S., Liu, Y., Lu, H., & Brewer, R. D. (2018). Annual total binge drinks consumed by U.S. adults, 2015. American Journal of Preventive Medicine, 54(4), 486-496. https://doi.org/10.1016/j.amepre.2017.12.021
Wang, Y., Holt, J. B., Zhang, X., Lu, H., Shah, S. N., Dooley, D. P., Matthews, K. A., & Croft, J. B. (2017). Comparison of methods for estimating prevalence of chronic diseases and health behaviors for small geographic areas: Boston validation study, 2013. Preventing Chronic Disease, 14, Article E99. https://doi.org/10.5888/pcd14.170281
Zhang, X., Holt, J. B., Lu, H., Wheaton, A. G., Ford, E. S., Greenlund, K. J., & Croft, J. B. (2014). Multilevel regression and poststratification for small-area estimation of population health outcomes: A case study of chronic obstructive pulmonary disease prevalence using the Behavioral Risk Factor Surveillance System. American Journal of Epidemiology, 179(8), 1025-1033. https://doi.org/10.1093/aje/kwu018
HLTH 6205 Week 4 instructions, in plain terms
Week 4 asks students to compare one measure for their population with the same measure for a larger reference group and to interpret the difference carefully. The paper first justifies the measure against close alternatives. The comparison then appears in a table or simple chart holding the local figure, the reference figure and the difference, drawn from the same source family and year wherever possible. Any mismatch in definition, adjustment or period must be stated in the table itself. The discussion's most important part concerns uncertainty: whether the local estimate rests on a small or modeled base, whether it was flagged and how much of the difference survives. Students should justify the choice of referent and avoid explaining causes at this stage. A short limits section is expected, with APA references for each data source.
How this HLTH 6205 Week 4 example is built
The paper opens by choosing binge drinking over heavy drinking and alcohol-attributable deaths, explaining that only binge drinking has county, state and national figures for one year. National context from Kanny and colleagues and Esser and colleagues explains why the measure matters. Table 1 lists five rows with estimate type, prevalence, interval and difference from the county figure. A section titled for the entitlement check tests definition, year and method, noting newer state figures for 2024 and 2025 that were deliberately not used. It explains how PLACES builds its county estimates and why the county median is the cleanest referent and the national median the weakest. The reading section shows overlapping intervals and a narrow county distribution. The conclusion frames the need as a common behavior in a high-prevalence state.
Where the marks sit in the HLTH 6205 Week 4 rubric
The comparison carries the grade, and in particular whether the figures being compared were entitled to sit together. Papers that subtract numbers without checking definition, adjustment and period lose credit even when the arithmetic is correct. Faculty give heavy weight to the uncertainty discussion and notice immediately when a wide or flagged estimate is treated as solid. A justified choice of referent earns further marks, especially when the writer explains why one referent is cleaner than another. Restraint about causes is credited, since the course reserves explanation for later. A clear table that states estimate types prevents misreading. A limits section that names self-report and modeling assumptions shows maturity. APA references for each data set and study, with matching in-text citations, complete the requirements.
HLTH 6205 Week 4 help from the desk
Secondary analyses are often returned because the writer compared a modeled county figure with a direct national figure and called the difference a disparity, never checking whether the methods match. Others mix years, setting a recent state figure against an older local one. Another frequent slip is reporting a difference without its interval, which hides that the gap may be noise. Before drafting, list every figure you plan to use with its definition, year and estimate type. Remove any pair that fails one of those checks or state the mismatch clearly. Write the uncertainty paragraph before the interpretation. Choose the referent most like your population and explain why. Keep causes out of the paper. For an analysis of your own county's data on a different measure, our writers can build one with the same checks.
Get a HLTH 6205 Week 4 example written to your instructions
The prompt and rubric are enough for the desk to return a sample analysis within 24-48h, free the first time, matched to the comparison structure your section is grading. No rate for any real place is produced here; the sample uses plainly labeled stand-in values so the alignment and uncertainty reasoning stays visible without a fabricated statistic anywhere in it.
More HLTH 6205 papers and MS in Health Education and Promotion samples
- HLTH 6205 Week 1: Population Boundary Discussion Post
- HLTH 6205 Week 2: Calumet County Community Profile
- HLTH 6205 Week 3: Binge Drinking Data Source Review
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HLTH 6205 Week 4 questions, answered
Where can I find a free HLTH 6205 Week 4 sample paper?
The whole Week 4 secondary data analysis comparing Calumet County's binge drinking estimate with four referents, with a table and six references, appears on this page.
Is Calumet County's binge drinking higher than Wisconsin's?
The estimate is two points higher, 20.4% against 18.4%, but the county's 95% interval of 17.1% to 24.1% contains the state figure, so the data cannot separate them.
Why is the county median the cleanest referent?
It comes from the same PLACES model, release and year as the county figure, so any modeling tendency affects both sides equally.
Why is the age-adjusted county figure higher than the crude one?
The 2000 U.S. standard population is younger than Calumet County's adults, and younger adults binge drink more often, so adjusting to it raises the estimate to 22.5%.
Does the Week 4 paper explain why Calumet residents binge drink?
No. It notes that income patterns are tempting to connect but untested, and that the model already uses demographic composition, so such a reading would be circular.