| Course | HLTH 6493 Community Health Analysis |
|---|---|
| Module | Module 1 |
| Paper type | Community profile |
| Length | 1,280 words, about 5 pages plus title and reference pages |
| Format | APA 7 student paper |
| School | American College of Education |
| Program | Ed.S. in Public Health Education |
| Updated | October 2026 |
Free sample paper for HLTH 6493 Module 1
Robeson County in Numbers: A Community Health Profile Built From Census and CDC PLACES Data
Student Name
American College of Education
HLTH6493: Community Health Analysis
Module 1 Assignment
Instructor Name
November 2, 2026
Introduction
Robeson County lies in southeastern North Carolina along the Lumber River, with Lumberton as its county seat and the University of North Carolina at Pembroke on its western side. It is home to much of the Lumbee Tribe and is one of the most racially diverse rural counties in the United States. It is also a county where many health indicators are among the worst in the state. This paper builds a community health profile of Robeson County from public data, the first step in a community health analysis that later modules extend through asset mapping, priority setting, community engagement, review of an existing assessment and an improvement plan. The profile covers who lives in the county, the social and economic conditions that shape health, chronic disease and risk behaviors, prevention and social needs, and it ends with what the data do not show.
Data Sources
Demographic, economic, education and insurance figures come from the 2020 to 2024 pooled release of the American Community Survey (U.S. Census Bureau, 2025). Health figures come from PLACES, a CDC collaboration that provides model-based estimates for chronic disease measures, risk behaviors, prevention and social needs for every county, drawing mainly on the Behavioral Risk Factor Surveillance System (Greenlund et al., 2022). The 2025 PLACES release reports county estimates for 2023, or 2022 for some measures, and age-adjusted values are used here so that Robeson County can be compared fairly with places that have older or younger populations (Centers for Disease Control and Prevention, 2025). For comparison, the profile uses the median of North Carolina's 100 counties and of all U.S. counties in the same release, calculated for this paper.
Who Lives in Robeson County
The county had an estimated 116,902 residents in 2020 through 2024. American Indian residents who are not Hispanic made up 38.4% of the population, non-Hispanic White residents 23.3%, non-Hispanic Black residents 23.2%, Hispanic residents of any race 10.9% and residents of two or more races 3.2% (U.S. Census Bureau, 2025). No group forms a majority. This diversity is a strength and a planning challenge: communities within the county have distinct histories, institutions and relationships with health care, and a single approach will not reach everyone.
Social and Economic Conditions
Economic hardship is widespread. Median household income was $41,978, compared with $72,388 in North Carolina and $80,734 nationally. The share of residents living below the federal poverty level was 27.2%, more than double the state's 13.0% and the nation's 12.5%. Among adults 25 and older, 14.7% held a bachelor's degree or higher, compared with 35.7% both statewide and nationally, while 20.0% had not completed high school, about twice the state and national shares. An estimated 14.2% of residents had no health insurance, compared with 9.9% statewide and 8.4% nationally (U.S. Census Bureau, 2025). Income, education and insurance all point the same way: residents of Robeson County carry a heavy load of the conditions that shape health long before anyone sees a clinician.
Chronic Disease
PLACES estimates show a heavy chronic disease burden among adults. Age-adjusted, 16.4% of Robeson County adults had diagnosed diabetes, compared with a North Carolina county median of 10.8% and a U.S. county median of 10.7%. High blood pressure was reported by 42.9%, against medians of 34.2% statewide and 32.8% nationally. Obesity affected 48.1% of adults, compared with 37.2% and 38.2%. Coronary heart disease prevalence was 8.1%, against 6.1% and 5.9%, and chronic obstructive pulmonary disease 10.3%, against 7.3% and 6.8% (Centers for Disease Control and Prevention, 2025). Reported depression, at 23.4%, was slightly below the North Carolina median of 25.8%, a notable exception that may reflect underdiagnosis as much as better mental health, since frequent mental distress was reported by 20.9% of adults.
Risk Behaviors and General Health
Risk behaviors follow the same pattern. Current cigarette smoking was 23.1%, compared with 16.2% for the state median and 16.1% nationally. Physical inactivity outside work was 34.9%, against 24.9% and 26.5%. Nearly a third of adults, 30.6%, rated their health as fair or poor, compared with 19.6% and 19.9%. Oral health is a particular concern: 31.1% of adults aged 65 and older had lost all their natural teeth, compared with 17.6% for the state median and 15.4% nationally (Centers for Disease Control and Prevention, 2025).
Prevention and Health Care Use
Prevention measures present a mixed picture. Annual checkups were reported by 80.1% of adults, slightly above the North Carolina county median of 78.0%, and 63.3% of adults with high blood pressure reported taking medication for it, above the state median of 58.8%. Those who reach care, in other words, often receive it. Screening and dental care lag, however. Up-to-date colorectal screening reached 55.4% of residents in the 45 to 75 age band against a state median of 59.7%, mammography in the 50 to 74 band reached 74.9% against 77.4%, and a past-year dental visit was reported by just 48.8% of adults against 60.3% across North Carolina counties (Centers for Disease Control and Prevention, 2025). The dental gap fits the high rate of complete tooth loss among older adults and points to access problems that routine medical visits do not solve.
Social Needs
The newest PLACES measures capture social needs that link economic conditions to health. In Robeson County, 31.2% of adults reported food insecurity, compared with a state county median of 17.1% and a national median of 15.8%. Transportation barriers to daily activities were reported by 17.6%, roughly double the state and national medians of 9.3% and 8.7%. Housing insecurity was reported by 23.0%, and 17.4% reported a threat of having utilities shut off in the past year. The PLACES model put the uninsured share of working-age adults, 18 through 64, at 16.7%, consistent with the Census estimate for the whole population.
What the Profile Suggests
Taken together, the data describe a county where poverty, low educational attainment, lack of insurance, food insecurity and transportation barriers coincide with high rates of diabetes, hypertension, obesity, smoking and poor self-rated health. These conditions are linked: the County Health Rankings model, which many community assessments use, treats social and economic factors, health behaviors, clinical care and the physical environment as the drivers of health outcomes, with social and economic factors weighted most heavily (Remington et al., 2015). The profile suggests that improvements in Robeson County will depend at least as much on food access, transportation and economic security as on clinical services.
What the Data Cannot Show
The profile has important limits. PLACES figures are model-based estimates, not direct measurements for the county; they combine national survey data with local population characteristics and may miss local variation, and they cannot be broken down by race or ethnicity at the county level. Survey-based estimates such as diabetes rely on residents having been diagnosed, so underdiagnosis in a county with low insurance coverage could lead to underestimates. The Census figures are five-year averages that smooth over recent changes, including recovery from the hurricanes that flooded the county in 2016 and 2018. The profile also lacks mortality, maternal and infant health, injury and environmental data, which the full assessment will add, and it contains nothing yet from residents themselves about what they see as their community's strengths and problems.
Conclusion
Robeson County's public data show a diverse community facing some of the heaviest social and health burdens in North Carolina. The numbers point to chronic disease, tobacco use, oral health and food and transportation insecurity as likely priorities, but numbers alone cannot set priorities or reveal the community's strengths. Module 2 turns to the county's assets, the organizations, institutions and people on which improvement can be built.
References
Centers for Disease Control and Prevention. (2025). PLACES: Local data for better health, county data 2025 release [Data set].
Greenlund, K. J., Lu, H., Wang, Y., Matthews, K. A., LeClercq, J. M., Lee, B., & Carlson, S. A. (2022). PLACES: Local data for better health. Preventing Chronic Disease, 19, Article 210459. https://doi.org/10.5888/pcd19.210459
Remington, P. L., Catlin, B. B., & Gennuso, K. P. (2015). The County Health Rankings: Rationale and methods. Population Health Metrics, 13, Article 11. https://doi.org/10.1186/s12963-015-0044-2
U.S. Census Bureau. (2025). American Community Survey 5-year estimates, 2020-2024 [Data set].
What the HLTH 6493 Module 1 instructions ask for
Expect Module 1 of HLTH 6493 usually to ask for a profile of a community using health data. Prompts generally want demographics, social and economic conditions, health status and behaviors, compared with state or national benchmarks, and an interpretation of what stands out. Some sections ask for specific sources, such as census and county health data; others let you choose, as long as the sources are current and credible. Real public data make the strongest profile, and each figure should name its source, year and whether it is age-adjusted. Choose a community you can follow through asset mapping, priority setting, engagement and planning in later modules, and note where the data fall silent.
Inside the HLTH 6493 Module 1 example
An introduction places the county on the map, names what makes it distinctive and explains the profile's purpose within the course. A data section names the census and PLACES sources, the years covered and how the comparison medians were calculated. Sections then describe the population, social and economic conditions, chronic disease, risk behaviors and general health, prevention and health care use, and social needs, each with county, state and national figures. A section interprets the patterns through the County Health Rankings model, which weights social and economic factors most heavily, and a limits section explains what model-based estimates and five-year averages cannot show before the conclusion looks ahead.
HLTH 6493 Module 1 rubric: what full marks look like
Community profiles are graded mostly on the quality and use of data. Rubrics tend to reward current, credible sources named with their years and release versions, appropriate comparisons with state and national benchmarks, organization by domain and interpretation that identifies patterns rather than listing numbers. Correct handling of age-adjusted and model-based estimates shows data literacy that many profiles lack. Honest discussion of data limits and gaps, including the absence of community voice, prepares the ground for later modules on assets and engagement. APA 7 citations for every data source complete the profile. Noticing where a county does better than its peers, not only worse, shows balanced reading of the data.
HLTH 6493 Module 1 help: mistakes that cost points
Profiles often paste in a table of statistics with no benchmark, no year and no interpretation. If you want help finding census and county health data, choosing comparisons or explaining what the numbers mean, a writer can take this on. Name your community and paste in the prompt, and the Module 1 profile a writer prepares will pull current public data for that place, compare it fairly and point to likely priorities. If your instructor requires particular sources, such as County Health Rankings or state vital statistics, the profile will use them and cite each one correctly. We can also help you compute age-adjusted comparisons and state medians for your benchmarks.
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.
More HLTH 6493 and Ed.S. in Public Health Education sample papers
- HLTH 6493 Module 2: Community Asset Map
- HLTH 6493 Module 3: Needs Prioritization Paper
- HLTH 6493 Module 4: Community Engagement Methods
- HLTH 6493 Module 5: Existing Assessment Analysis
- HLTH 6493 Module 6: Community Health Improvement Plan
- HLTH 6483 Module 5: Community Program Evaluation
- HLTH 6433 Module 3: Relational Skills and Trust
- HLTH 6473 Module 5: Program Cost Analysis
- HLTH 6443 Module 6: Technology Leadership Plan
HLTH 6493 Module 1 questions, answered
What does HLTH6493 Module 1 usually ask for?
The opening HLTH6493 module usually centers on a community profile: population, social and economic conditions and health status, built from public data and compared with benchmarks.
What is CDC PLACES?
A CDC collaboration that provides model-based estimates of chronic disease, risk behaviors, prevention and social needs down to the county, census tract and ZIP code level across the whole country.
Why use age-adjusted estimates in a community profile?
Age adjustment removes differences caused by one community being older or younger than another, so comparisons reflect differences in health rather than age structure.
Where can I find a free HLTH 6493 Module 1 sample paper?
This page has the complete Module 1 profile of Robeson County, North Carolina, built from American Community Survey and CDC PLACES data with state and national comparisons.
What are the limits of model-based county estimates?
They are statistical estimates rather than direct measurements, may miss local variation and usually cannot be broken down by race or ethnicity at the county level.