Adults Living With Diabetes in One County: A Population Profile Built From PLACES, the American Community Survey, CDC WONDER and Hospital Discharge Data
Student Name
American College of Education
HLTH5623: Epidemiology and Public Health for Healthcare Administrators
Module 5 Assignment
Instructor Name
May 29, 2028
Defining the Population
The profile covers every adult resident, 18 or over, who has been diagnosed with diabetes in Brookfield County, a county of about 340,000 people invented for this assignment and served by the regional health system discussed in earlier modules. Diagnosed diabetes is used rather than all diabetes because the public data sources used here measure diagnosis, typically through survey respondents reporting that a health professional told them they have diabetes. An estimated additional share of adults have undiagnosed diabetes, which none of these sources captures, so the profile describes the known population, not the full burden.
The profile is built to answer the questions a health system would ask before designing a response: how many adults are affected, where they live, what their circumstances are, how many die of diabetes-related causes and how often they are hospitalized. Every figure is drawn from a named source and would be presented with that source in any report. A population profile is only as trustworthy as the reader's understanding of where each number came from.
Prevalence From Model-Based Local Estimates
The Centers for Disease Control and Prevention's PLACES project provides estimates of chronic disease prevalence, including diagnosed diabetes, for counties, places, census tracts and ZIP code areas across the United States. Greenlund et al. (2022) describe how the estimates are produced: by combining survey responses from the Behavioral Risk Factor Surveillance System with census population data in a multilevel statistical model, which allows prevalence to be estimated for small areas where too few people are surveyed to calculate a direct estimate. For Brookfield County, the composite PLACES estimate of diagnosed diabetes among adults is 11.8 percent, compared with 10.9 percent for the state. Applied to the county's roughly 262,000 adults, that is about 31,000 adults living with diagnosed diabetes.
At the census tract level, estimates range from 7.2 percent in the county's northern suburbs to 17.4 percent in three tracts on the east side of its central city. Because the estimates are model-based, they reflect the demographic and socioeconomic characteristics of each tract as well as survey data, and they should be used to identify patterns and priority areas rather than treated as precise counts for individual tracts.
Context From the American Community Survey
The American Community Survey, conducted continuously by the U.S. Census Bureau, provides the demographic and economic context that helps explain the tract-level pattern. Five-year estimates for the three east-side tracts with the highest diabetes prevalence show a median household income of about $36,000, compared with $71,000 for the county; 22 percent of adults aged 18 to 64 without health insurance, compared with 9 percent countywide; and 16 percent of households without a vehicle, compared with 6 percent. The population in those tracts is younger than the county average but has a higher share of residents who identify as Black or Hispanic, groups with higher national diabetes prevalence.
The survey's estimates for small areas carry margins of error that can be large, and the five-year estimates describe an average over the period rather than a single year. They are best used, as here, to characterize the circumstances of residents in high-burden areas rather than to make precise comparisons between neighboring tracts.
Mortality From National Vital Statistics
CDC WONDER, an online system that provides access to national vital statistics based on death certificates, allows mortality to be examined by county and by underlying cause of death. For Brookfield County, composite data for the most recent five-year period show an age-adjusted death rate with diabetes as the underlying cause of 27.4 per 100,000, compared with 23.1 for the state. Age adjustment matters here because the county's population is older than the state's, and the crude rate alone would overstate the difference.
Death certificate data have a known limitation for diabetes: diabetes is often a contributing rather than an underlying cause of death, particularly among people who die of heart disease or kidney failure, and it is inconsistently recorded. Analyses using multiple causes of death, which CDC WONDER also allows, produce substantially higher figures and give a fuller picture of diabetes-related mortality. Where diabetes kills, the death certificate often names something else.
Hospital Use From Discharge Data
The state's hospital discharge database, which records all inpatient stays at licensed hospitals, shows how often county residents with diabetes are hospitalized for complications that good outpatient care can often prevent. Composite figures show 1,940 admissions last year among county residents for short- or long-term complications of diabetes or for lower-extremity amputation, a rate of about 740 per 100,000 adults. Admissions were concentrated geographically: residents of the three east-side tracts, about 6 percent of the county's adults, accounted for 17 percent of these admissions. County Health Rankings, which assemble many of these public measures into a common framework for comparing counties (Remington et al., 2015), place Brookfield County in the bottom half of its state on preventable hospital stays.
Discharge data have their own limits. They count admissions rather than people, so a resident admitted three times appears three times, and they depend on how hospitals code diagnoses. They also miss care delivered in emergency departments without admission, which in some states is held in a separate outpatient database. Linking the admission figures to the health system's own records would show how many distinct patients account for the admissions and how many were already the system's primary care patients.
Where the Burden Concentrates
Read together, the sources describe a county whose overall diabetes burden is somewhat above the state's, with a sharp concentration in a few urban tracts. Those tracts combine the highest estimated prevalence, the lowest incomes, the highest uninsurance and the least access to a vehicle, and their residents account for a disproportionate share of preventable diabetes hospitalizations. The pattern suggests that the gap is less about the number of people with diabetes than about access to the ongoing care that prevents complications. The next module will convert this profile into actions a health system could take.
That interpretation is consistent with a broad body of research. A scientific review commissioned by the American Diabetes Association concluded that social determinants, including income, education, neighborhood conditions, food environment and access to health care, are strongly associated with diabetes risk and outcomes, and that addressing them is necessary to reduce disparities (Hill-Briggs et al., 2021). For a health system, the practical meaning is that clinical programs alone may not close the gap in the east-side tracts unless they also address transportation, cost of care and the availability of services close to where residents live.
Conclusion
A population profile built from named public sources shows that about 31,000 adults in Brookfield County live with diagnosed diabetes, that prevalence and preventable hospitalizations are concentrated in a few low-income urban tracts and that mortality from diabetes is higher than the state average even after age adjustment. Each source contributes something different and carries its own limits: model-based prevalence estimates, survey-based context with margins of error, death certificates that undercount diabetes and discharge data that capture only hospital care. Understanding those methods is what makes the profile usable for decisions.
References
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
Hill-Briggs, F., Adler, N. E., Berkowitz, S. A., Chin, M. H., Gary-Webb, T. L., Navas-Acien, A., Thornton, P. L., & Haire-Joshu, D. (2021). Social determinants of health and diabetes: A scientific review. Diabetes Care, 44(1), 258-279. https://doi.org/10.2337/dci20-0053
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
How this HLTH 5623 Module 5 example is structured
HLTH 5623 Module 5 typically profiles one defined population using named public data sources; your classroom's instructions decide the population and required sources. This example defines the population first, then builds the profile source by source: prevalence from model-based local estimates, demographic and economic context from the census survey, mortality from national vital statistics and hospital use from state discharge data. Each section states how the source is produced and what its limits are, because an administrator who does not understand a source's method may draw the wrong conclusion. A synthesis section identifies where the burden concentrates.
HLTH5623 Module 5 questions, answered
What does HLTH5623 Module 5 usually ask for?
HLTH5623 Module 5 typically asks students to build a health profile of a defined population using named public data sources, such as PLACES, the American Community Survey, CDC WONDER and state health data. Many sections expect the strengths and limits of each source to be discussed. Your classroom's instructions decide the population and which sources are required.
What is CDC PLACES and how should it be used?
PLACES provides model-based estimates of chronic disease measures, such as diagnosed diabetes, for counties, census tracts and other small areas by combining national survey data with census information. The estimates are useful for identifying patterns and priority areas but should not be treated as exact counts for small geographic units.
Why is diabetes undercounted in death statistics?
Diabetes is often a contributing rather than the underlying cause of death, especially when people die of heart disease or kidney failure, and it is inconsistently listed on death certificates. Analyzing multiple causes of death gives a more complete picture of diabetes-related mortality than the underlying cause alone.
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