NR 550 Week 2 Local Disparity Profile Example

Local Disparity Profile: Childhood Asthma Emergency Visits in One County by Census Tract Group, With Counts Printed Beside Every Rate and Small Cells Withheld Rather Than Dropped

Student Name

College of Nursing, Chamberlain University

NR 550: Population Health Disparities: Local, National, and Global Perspectives

Instructor Name

Month Day, Year

Local Disparity Profile: Childhood Asthma Emergency Visits in One County by Census Tract Group, With Counts Printed Beside Every Rate and Small Cells Withheld Rather Than Dropped

The Measure

This profile describes asthma emergency visits made by county children up to age 17, a mixed urban and rural county of about 210,000 residents and 45,000 children. The source is the state's emergency department discharge dataset, which records the patient's residential address and principal diagnosis. Visits with a principal diagnosis of asthma were counted for 2021 to 2023 and divided by the number of resident children from census population estimates, then expressed per 10,000 children per year. The measure counts visits, not children, so repeat visits by one child are included.

Asthma was identified by the principal diagnosis code on the visit record, so a child seen for pneumonia who also had wheezing would not be counted, and a visit coded as asthma by a busy emergency physician would be counted even if the diagnosis was uncertain. These choices match the state's published definition, which keeps the county's figures comparable with the state's. Population denominators are three-year averages of the census estimates for children in each tract, which smooths small year-to-year shifts in population.

The Depth I Can Defend

The profile uses groups of census tracts rather than single tracts or ZIP codes. The county has 52 tracts. At the single-tract level, many tracts recorded fewer than 11 visits over three years, and the state's small-cell rule requires counts below 11 to be withheld. Rates built on counts that small also move sharply if a single visit is recorded differently. Grouping tracts into five areas, each with at least 5,000 children, gives counts large enough to publish and to trust. ZIP codes were considered and rejected because two of the county's ZIP codes cross into neighboring counties and do not match census population estimates well.

The choice of areas is itself an analytic decision. Krieger and colleagues showed that the choice of geographic level and area-based measure changes the size of socioeconomic gradients that can be detected (Krieger et al., 2003). Tract groups are fine enough to show the east side's concentration and coarse enough to avoid suppression.

The Profile

Table 1 gives the counts, populations and rates for the five tract groups and the county.

Asthma emergency department visits, children 0 to 17, 2021 to 2023 pooled

Table 1

Individual Tracts on the East Side

Within the east side, the six tracts differ. Table 2 shows them, with small cells withheld rather than removed.

East side tracts, visits and rates, 2021 to 2023 pooled

Table 2

Withheld, Not Deleted

Tract E6 is shown with its count withheld because the three-year total is below the state's threshold of 11. The five published tracts total 602 visits, and the east side total of 610 appears in Table 1, so a reader could recover the withheld count by subtraction. Before release, the state's rules would therefore require a second step, either rounding the east side total or withholding a second cell, and this internal profile flags that for the data steward. Removing the row altogether would make the east side look like five tracts with an average rate above 290, and would hide the fact that one east side tract has a much lower rate. Keeping the row, marked, tells a reader that the east side is not uniform.

What the Profile Shows

The east side rate of about 261 per 10,000 is roughly 6.5 times the rate in the rest of the county combined, which is about 40 per 10,000, and 13 times the rate in the rural townships. The central city has a moderately elevated rate. The suburbs and rural areas are low and similar. The gradient is steep and concentrated: about 17% of the county's children live on the east side, and they account for about 58% of the county's childhood asthma emergency visits.

Within the east side, tracts E1 and E2, along the rail yard and the truck route, have the highest rates, while E6, at the edge of the east side next to a park, is far lower. Housing age and condition, which are closely tied to childhood asthma (Krieger & Higgins, 2002), differ across these tracts and will be examined in a later brief. That within-area variation will matter when the profile is used to plan interventions.

How the Profile Should and Should Not Be Used

The profile is suitable for three uses. It can show the county board and the hospital where childhood asthma emergency use is concentrated, it can set a baseline against which future years are compared, and it can guide where to place resources such as home visits or an evening clinic. It is not suitable for judging individual families, for ranking single tracts in public, where small counts make rankings unstable, or for claims about how many east side children have asthma.

Because the profile will be read by people outside public health, every rate is printed with its count, and every table notes that the figures are visits, not children. A reader who sees 345 per 10,000 in tract E1 should also see that it rests on 171 visits over three years, which is solid, while a reader of the rural townships' figure should see that it rests on 45, which is solid for the area but would not be for any single rural tract.

Limits

The measure counts emergency visits, which reflect both how common and severe asthma is and how families use care. An area with poor access to primary care may have high emergency visit rates even if asthma is no more common. Three years of data smooth year-to-year variation but cannot show recent change. Visits by children living in the county but treated in another state are missing. And the address on the record is the address at the time of the visit, so families who moved are counted where they lived then. Braveman (2006) notes that disparity measures depend heavily on how groups and outcomes are defined, and this profile defines both narrowly on purpose.

References

Braveman, P. (2006). Health disparities and health equity: Concepts and measurement. Annual Review of Public Health, 27, 167-194. https://doi.org/10.1146/annurev.publhealth.27.021405.102103

Krieger, J., & Higgins, D. L. (2002). Housing and health: Time again for public health action. American Journal of Public Health, 92(5), 758-768. https://doi.org/10.2105/AJPH.92.5.758

Krieger, N., Chen, J. T., Waterman, P. D., Soobader, M.-J., Subramanian, S. V., & Carson, R. (2003). Choosing area based socioeconomic measures to monitor social inequalities in low birth weight and childhood lead poisoning: The Public Health Disparities Geocoding Project (US). Journal of Epidemiology and Community Health, 57(3), 186-199. https://doi.org/10.1136/jech.57.3.186