| Course | HLTH 4343 Health and Wellness Across Populations |
|---|---|
| Module | Module 1 |
| Paper type | Population health profile |
| Length | 1,240 words, about 5 pages plus title and reference pages |
| Format | APA 7 student paper |
| School | American College of Education |
| Program | B.S. in Healthcare Administration |
| Updated | September 2026 |
Free sample paper for HLTH 4343 Module 1
One Adult in Seven: A Population Health Profile of Adults in Imperial County, California, Built From CDC PLACES and American Community Survey Data
Student Name
American College of Education
HLTH4343: Health and Wellness Across Populations
Module 1 Assignment
Instructor Name
October 5, 2026
Why This Population
Imperial County sits in the southeastern corner of California, bordering Mexico and Arizona. Its economy rests on irrigated agriculture in the desert, cross-border trade and public employment, and summer temperatures regularly exceed 110 degrees Fahrenheit. I chose its adult population for this profile because I am preparing to join a composite network of four community health centers there as an operations analyst, and the network's leaders have asked what the county's data say about the people it serves.
A population health profile describes a defined group's health status, risks and the conditions that shape them, using data rather than impression. This one uses two public sources. The first is PLACES, a CDC project that models county-level figures for dozens of health measures using the Behavioral Risk Factor Surveillance System; the figures below are age-adjusted estimates for 2023 from the 2025 release (Centers for Disease Control and Prevention [CDC], 2025). The second is the Census Bureau's American Community Survey. To give the numbers meaning, each is compared with neighboring San Diego County or with California as a whole.
Who Lives in the County
Imperial County is home to about 179,000 people. According to the American Community Survey's 2020-2024 five-year estimates, 86.0% of residents are Hispanic or Latino, compared with 40.2% statewide, and 74.0% of residents aged five and older speak Spanish at home, compared with 28.3% in California (U.S. Census Bureau, 2025b). About a third of residents, 33.9%, speak Spanish and speak English less than very well, three times the state figure of 11.1%. The county is also poorer than the state. The 2024 one-year estimates put the share of residents below the federal poverty line at 19.6%, compared with 11.8% statewide, and the median household income at $60,749, about 61% of California's $100,149 (U.S. Census Bureau, 2025a).
These figures matter for health services before any disease rates are considered. A clinic serving this county must work in Spanish as a matter of course, not as an accommodation, and must expect that cost, rather than preference, will shape many patients' choices about care.
How the Profile Was Built
Before reporting any numbers, I set three rules for choosing them. First, every measure had to come from a source that publishes its methods, so that the network's leaders or a grader could check it. Second, every measure had to be paired with a comparison, because a prevalence of 14.6% means little until the reader knows what it is in a similar place. San Diego County was chosen as the comparison because it borders Imperial County, shares the same state policies and Medi-Cal program, and draws on the same regional hospitals for some specialty care, so differences between the two are less likely to reflect state-level factors. Third, I used age-adjusted estimates for health measures. Imperial County's age structure differs from San Diego's, and age adjustment removes the part of a difference that is due only to one population being older or younger, which makes comparisons fairer.
I grouped the measures into four areas, who lives in the county, health status, access and social conditions, and the limits of the data, so that the profile moves from people to problems to the conditions behind them. That order also matches how the network plans services: first understanding whom it serves, then what they need, then what stands between them and care.
Health Status and Chronic Disease
The PLACES estimates show a heavy burden of chronic disease. Diagnosed diabetes, age adjusted, stands at 14.6% of adults, about one adult in seven, compared with 9.4% in San Diego County (CDC, 2025). Obesity affects 35.2% of adults against 25.4% in San Diego, and high blood pressure 32.5% against 27.4%. Physical inactivity, defined as no leisure-time physical activity in the past month, is reported by 35.1% of adults in Imperial County and 20.4% in San Diego. About a third of adults, 32.4%, rate their general health as fair or poor.
Mental health measures are closer to the neighboring county. Depression is estimated at 20.0% of adults in Imperial County and 21.2% in San Diego, and frequent mental distress at 18.2%. These similarities are a useful caution: the county's disadvantage is not uniform across every measure, and a profile that presented it that way would mislead planners.
The diabetes gap is the clearest signal in the data: five points higher than the neighboring county, in a population where the risk factors that drive diabetes are all higher too. Aguayo-Mazzucato et al. (2019), reviewing type 2 diabetes among Hispanic people in the United States, describe higher prevalence than the national average, driven partly by lower income and reduced access to education and health care and partly by greater susceptibility to obesity and insulin resistance, and they emphasize culturally adapted self-management education as a critical element of care.
Access and Social Conditions
The PLACES data include measures of access and social need that help explain the disease figures. Among adults aged 18 to 64, 22.2% lack health insurance, compared with 8.7% in San Diego County (CDC, 2025). Food insecurity is estimated at 31.3% of adults, more than double San Diego's 15.2%, and 15.5% report that a lack of reliable transportation kept them from medical appointments, meetings, work or getting things needed for daily living, against 8.3% in San Diego. Slightly fewer adults, 68.4% against 71.9%, had seen a clinician for a checkup within the last twelve months.
Taken together, the access figures describe a population that faces obstacles at every stage of care: paying for it, reaching it and, for many, communicating in the language in which care is offered. They also point to conditions outside the clinic, such as food supply and transportation, that a health center cannot fix alone but must plan around.
Limits of the Data
The profile has limits that the network's leaders should understand before using it. PLACES estimates are modeled from state survey data and census characteristics, not counted directly in the county, so they carry uncertainty; the 95% confidence interval for the county's diabetes estimate runs from 12.7% to 16.6%. The survey behind them is conducted by telephone, and it counts only diagnosed diabetes, so the true burden is likely higher among people without regular care. Survey-based figures may also undercount farmworkers, seasonal residents and people living on both sides of the border. Finally, county averages hide differences between the county's cities and its rural communities. The next step for the network should be to compare these estimates with its own patient data, which will show whether the people it already serves resemble the county as a whole.
What the Profile Suggests
Three priorities follow from the data. First, diabetes and its risk factors should be the network's leading chronic disease focus, since the county's excess is largest there and the condition is highly responsive to consistent primary care and self-management support. Second, access barriers deserve as much attention as clinical programs: coverage enrollment help, transportation and Spanish-language services are part of care, not extras. Third, the network should look beyond its walls, because food insecurity and heat shape how patients can follow advice on diet and activity. Later modules will examine these determinants and access barriers in more depth and propose a service aimed at adults with diabetes, but the profile gives those proposals a factual base that anyone can check.
References
Aguayo-Mazzucato, C., Diaque, P., Hernandez, S., Rosas, S., Kostic, A., & Caballero, A. E. (2019). Understanding the growing epidemic of type 2 diabetes in the Hispanic population living in the United States. Diabetes/Metabolism Research and Reviews, 35(2), Article e3097. https://doi.org/10.1002/dmrr.3097
Centers for Disease Control and Prevention. (2025). PLACES: Local data for better health, county data 2025 release [Data set]. https://data.cdc.gov/
U.S. Census Bureau. (2025a). American Community Survey 1-year estimates, 2024: Tables B17001 and B19013 [Data set].
U.S. Census Bureau. (2025b). American Community Survey 5-year estimates, 2020-2024: Tables B03003 and C16001 [Data set].
Reading the HLTH 4343 Module 1 instructions
HLTH 4343 Module 1 typically asks you to describe the health of one population with data. Expect to define the population, often by place and sometimes by age, income or another shared feature, then report demographics, health status, major risk factors and access to care, with comparison figures and sources. Prompts often point you to federal data such as the CDC's PLACES or BRFSS, the American Community Survey and state health department reports. Most submissions fill three or four pages in APA 7, and a short table often helps the grader. Check the Canvas instructions for whether you must choose a local population or can profile one elsewhere, and for how many data sources are required.
How this HLTH 4343 Module 1 example is built
The sample is organized the way a planner would read data. It starts by defining the population and naming each data source with its method and year, so every figure can be traced. Demographics and income come next, with state comparisons and a sentence on what they mean for a clinic. The health status section reports chronic disease estimates against a neighboring county and deliberately includes mental health measures where the county is not worse. Access and social need measures follow and help explain the disease pattern. A limitations section explains model-based estimates, confidence intervals and likely undercounts. The profile ends with three priorities that the course's later modules develop further.
HLTH 4343 Module 1 rubric: what full marks look like
On the rubric for this profile, the heaviest criterion is usually data: current, relevant figures from credible sources, cited with years, and compared with a reference population. The analysis criterion rewards interpretation, explaining what the numbers mean and how they connect, instead of a list of statistics. Graders increasingly reward honesty about data quality, so a limitations section earns points. A cultural sensitivity or respect criterion often appears in population courses; describe groups with data rather than assumptions. Organization matters in a data-heavy paper, and a clear table helps. The last share of points covers APA 7, including correct citation of data sets.
HLTH 4343 Module 1 help: mistakes that cost points
Profiles lose points most often through old data, figures without sources or years, and national statistics used to describe a local population. Another frequent problem is a list of numbers with no comparison, which leaves the reader unable to tell whether a figure is high. Students also present estimates as exact counts, or mix crude and age-adjusted rates without saying so. Avoid describing a population in stereotypes; the data should do the describing. Finally, say what the profile means for the services an organization should offer, since that is the reason for building it. If you are profiling a different county or group and want a custom Module 1 built from its current data, the desk can prepare one to your rubric.
Write yours, or have the desk draft it
This paper is an original model document written by our desk, not a submitted student paper and not an official American College of Education document. Read it for the moves, then write your own to the instructions in your classroom. If you want one built to your exact prompt and rubric, the first custom sample is free and arrives in 24 to 48 hours.
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HLTH 4343 Module 1 questions, answered
What does HLTH4343 Module 1 usually ask for?
HLTH4343 Module 1 typically asks you to build a health profile of one population using current data: demographics, health status, risks and access, with comparisons and sources. Your classroom's instructions decide the population.
What is CDC PLACES?
A CDC project that publishes model-based estimates of health measures, such as diabetes, obesity and lack of insurance, for every U.S. county and many smaller areas, drawn from the Behavioral Risk Factor Surveillance System.
Why compare a county with a neighbor or the state?
A figure alone does not show whether it is high or low. Comparing with a neighboring county or the state shows where the population differs and where it does not.
Where can I find a free HLTH 4343 Module 1 sample paper?
Here: this page holds the whole Module 1 population health profile of adults in Imperial County, California, open to read from the title page to the reference list, with a margin note beside every section.
Should a population profile mention data limitations?
Yes. Explain how the data were produced, their uncertainty and whom they may undercount, so readers do not treat estimates as exact counts.