PHLT 8450 Week 3 Community Profile Example

A Community Profile Where Every Figure Names Its Source: The Composite Border County Seat and Its Seven Colonias, Assembled From Census Estimates, School Records, County Rankings and Model-Based Tract Data

Student Name

Walden University

PHLT 8450: Community Health Assessment

Instructor Name

Month Day, Year

A Community Profile Where Every Figure Names Its Source: The Composite Border County Seat and Its Seven Colonias, Assembled From Census Estimates, School Records, County Rankings and Model-Based Tract Data

How the Profile Is Built

This profile describes the community defined in Week 1: the county seat and the seven colonias inside the same school district. No published dataset uses that boundary, so the profile assembles it from four sources, each with a different publisher, purpose, period and unit. Every figure carries its source, product and period, and every table is introduced by the question it answers. Where a source's unit does not match the community, the profile says so. All figures are composite, standing in for values a real profile would draw from each source.

Question 1: Who Lives Here?

The best source for population characteristics is the Census Bureau's American Community Survey, using five-year estimates for the four census tracts that together approximate the school district. Two tracts lie mostly within the city; two cover the colonias and surrounding ranchland.

The colonia tracts are younger, more Spanish-speaking, poorer and less often insured than the city tracts. The margins of error are wide, because survey samples in small areas are small; differences smaller than the margins should not be treated as real. Spielman et al. (2014) showed that tract-level estimates from this survey often carry margins of error large enough to change conclusions, which is why the profile prints them beside every colonia figure.

Table 1

Population Characteristics, American Community Survey 2019-2023 Five-Year Estimates (Composite Figures)

Note. Composite figures. The colonia tracts include about 1,400 residents outside the school district, so their figures slightly misstate the colonias themselves.

Question 2: What Do the Schools See?

The school district publishes enrollment and attendance by campus. Two elementary schools draw most of their students from the colonias.

School records cover the district boundary exactly, which no federal source does.

Chronic absence at the colonia-serving schools is markedly higher than in the district overall. Principals attribute part of it to flooded roads after storms, when buses cannot reach some colonia streets, a pattern a health assessment would not see in health data alone.

Table 2

School District Indicators, 2024-2025 School Year (Composite Figures)

Note. Composite figures from the district's public accountability reports.

Question 3: How Does the County Compare?

County Health Rankings combine many measures into summaries of health outcomes and health factors for every county, allowing comparison with other counties in the state (Remington et al., 2015). The county ranks in the bottom quarter of its state on both. These figures describe the whole county, including towns outside the district, so they provide context rather than a portrait of the community. Two component measures stand out for this assessment. The county's uninsured rate is higher than the state's, and it has fewer primary care physicians per resident, both consistent with what the clinic sees at its front desk. The rankings combine data from different years and sources by design (Remington et al., 2015), so they describe recent conditions in general rather than any single year.

Question 4: What Do Model-Based Estimates Suggest?

The PLACES estimates from CDC predict tract-level rates of chronic conditions and risk behaviors, pairing responses from a national telephone survey with each tract's demographic makeup (Greenlund et al., 2022). For the colonia tracts, PLACES estimates diagnosed diabetes at about 17% of adults and obesity at about 44%, both higher than in the city tracts. These are not measurements of the colonias; they are predictions based on who lives there. They are useful for showing where problems are likely to be larger and should be confirmed with local data before being treated as findings.

Question 5: What Do the Clinic's Own Records Add?

The federally qualified health center sees about 4,100 colonia residents a year, roughly three in five people living in the colonias, and its records are the closest thing to local measurement the profile has. Among adult patients with colonia addresses, 19% carry a diabetes diagnosis, close to the PLACES estimate of about 17% for the colonia tracts, which lends the model some support. Clinic records describe patients, though, and people who use care are not a random sample of residents: uninsured adults who never visit and families who cross into the next county for care are missing. The profile therefore prints the clinic figure beside the model estimate instead of replacing one with the other. Agreement between two imperfect sources is more persuasive than either alone, and a disagreement would have shown where the assessment's own data collection should concentrate.

What the Profile Cannot Show

None of the four sources measures what colonia residents identified in Week 2 as their first concerns: drinking water reliability, septic failures and road flooding. Those will need other sources, including county permitting records, the water utility's service maps and residents' own accounts gathered under the participation plan. A profile built only from standard health datasets would miss them entirely, which is itself a finding for the assessment, and one the steering group should see before it decides what the assessment will ask residents directly.

Summary

The community profile shows a colonia population that is younger, poorer, less often insured and more Spanish-speaking than the city it borders, with higher chronic absence at its schools and higher predicted chronic disease. Every figure comes with its source and limits, and the profile names the concerns no standard dataset captures.

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, E31. 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, 11. https://doi.org/10.1186/s12963-015-0044-2

Spielman, S. E., Folch, D., & Nagle, N. (2014). Patterns and causes of uncertainty in the American Community Survey. Applied Geography, 46, 147-157. https://doi.org/10.1016/j.apgeog.2013.11.002