Using Asthma-Related Housing Complaints to Target Residents With Uncontrolled Asthma in Salt Lake County, Utah

Using Asthma-Related Housing Complaints to Target Residents With Uncontrolled Asthma in Salt Lake County, Utah

GIS SNAPSHOT — Volume 16 — May 23, 2019

This article is part of the Population Health, Place, and Space: Spatial Perspectives in Chronic Disease Research and Practice collection.

Jiyoung Byun, MPH1; Sophie McDonnell, BS2; Jenny Robertson, MSPH1 (View author affiliations)

Suggested citation for this article: Byun J, McDonnell S, Robertson J. Using Asthma-Related Housing Complaints to Target Residents With Uncontrolled Asthma in Salt Lake County, Utah. Prev Chronic Dis 2019;16:180463. DOI: http://dx.doi.org/10.5888/pcd16.180463external icon.

PEER REVIEWED

On This Page

• Background
• Methods
• Findings
• Action
• Acknowledgments
• Author Information
• References

High-resolution JPG for printimage icon

Maps A and B compare rates of asthma-related housing complaints and rates of asthma-related emergency department encounters, by small-area boundaries, Salt Lake County, Utah, 2012–2014. Map C depicts hot spots of asthma-related housing complaints that were identified in north-central Salt Lake County, Utah, January 1, 2012, through April 30, 2017. [A text description of this figure is available.]

Top

Background

The prevalence of asthma is high in Salt Lake County, Utah; 9.5% of adults aged 18 years or older (1) and 6.7% of children and adolescents aged 0 to 17 years have asthma (2). On average, 1,800 adults and 1,200 children visit an emergency department (ED) (3) and 400 adults and 400 children are hospitalized (4) with a primary diagnosis of asthma each year.

In 2015, the Utah Asthma Program and partners from the Utah Asthma Task Force developed the Utah Asthma Home Visiting Program (UAHVP). This program serves families with uncontrolled asthma and is only available in Salt Lake and Utah counties (5). The Salt Lake County Health Department (SLCoHD) collaborated with the Utah Asthma Program to explore using recent and up-to-date housing complaint data to more efficiently target the UAHVP. Currently, the UAHVP is targeted in areas by using ED data, which have a reporting lag time of several years. In comparison, housing complaint data are collected in real time and readily accessible from the SLCoHD Environmental Health Division.

The goals of this project were to retrospectively identify asthma-related housing complaints, geocode these complaints, assess their relationship to the rate of asthma ED encounters, and analyze emerging hot spots to determine whether these data and methods could identify communities that may benefit from the UAHVP.

Top

Methods

Housing complaints reported to the SLCoHD Environmental Health Division from January 1, 2012, to April 30, 2017, were manually reviewed and categorized as asthma-related if the complaint described an asthma trigger defined by the Centers for Disease Control and Prevention (CDC) (eg, smoke, mites, mold, pets, cockroaches, rodents, strong odors, cigarettes, birds, pollution) (6). The final data set included 1,959 asthma-related housing complaints. Geocoding and spatial analyses were performed by using ArcGIS Pro 2.0 (Esri). Ninety-nine percent of complaints were geocoded with a match score of 90 or higher, aggregated to a small area as defined by the Utah Department of Health (7), and used to calculate and map crude incidence rates.

Crude rates of asthma ED encounters from 2012 through 2014 were mapped by Utah small area and compared visually with crude rates of asthma-related housing complaints from 2012 through 2014 (8). The Pearson correlation coefficient between rates was calculated by using Microsoft Excel (Microsoft Corp) to determine the strength of the relationship.

We analyzed emerging hot spots (9) of asthma-related housing complaints by aggregating cases into space-time cubes of 6 months and 5,500 feet and evaluating trends over time by using a neighborhood distance of 11,000 feet and a time-step interval of 2. The appropriate distance band for the space-time cube was determined by plotting global Moran’s I z-scores from spatial autocorrelation analysis using 1,000-foot intervals from 1,000 to 20,000 feet and identifying the distance with the highest z-score peak (5,500 feet).

Top

Findings

Visual comparison suggested that the rate of asthma-related housing complaints was positively correlated with the rate of asthma ED encounters by small area. Correlation analysis supported this finding and indicated a strong positive relationship (r = 0.77). Analysis of emerging hot spots of asthma-related housing complaints identified consecutive, intensifying, and persistent hot spots in communities of north central Salt Lake County. These hot spots may reflect communities with older housing that may benefit from the resources provided by the UAHVP.

Top

Action

Our findings demonstrate the potential of using asthma-related housing complaints as a current, proxy data source for measuring asthma burden and of analyzing emerging hot spots to target or expand the UAHVP. Next steps include investigating factors that explain the spatial pattern of asthma-related housing complaints. If the pattern can be explained by factors addressed in the UAHVP or by participating partners, such as Green and Healthy Homes, a national initiative to create safe and healthy homes for low-income families, the findings would provide additional support for the use of these data and methods to guide program decisions. Further exploratory work could investigate the types, number, and causes of asthma triggers occurring in hot spots, which could be useful for measuring severity and customizing asthma control strategies in neighborhoods.

This project had several limitations. First, we could not confirm that the positive relationship of asthma-related housing complaints with asthma ED encounters existed in recent years because we lacked recent data on ED encounters. Second, housing complaints are reported predominantly by renters, so asthma-related housing issues that may exist for homeowners were not captured. Third, the high rates of asthma ED encounters were likely influenced by the underlying spatial distribution of social determinants that contribute to asthma burden, such as low household income and barriers to health care access.

Top

Acknowledgments

Maps were created by the SLCoHD Epidemiology Bureau in conjunction with the offices of Salt Lake county assessor, clerk, surveyor, recorder and mayor. The work of Jiyoung Byun and Jenny Robertson was financially supported by Salt Lake County Health Fund tax revenue; the work of Sophie McDonnell was funded by the CDC Comprehensive Asthma Control Through Evidence-Based Strategies and Public Health, Health Care Collaboration grant, CFDA number 93.070, via monies passed through the Utah Department of Health. We thank Holly Uphold, PhD, in the Utah Asthma Program for idea conception and guidance. Sophie McDonnell is now with the Bureau of Health Promotion, Utah Department of Health, Salt Lake City, Utah.

Top

Author Information

Corresponding Author: Jenny Robertson, MSPH, Epidemiology Bureau, Salt Lake County Health Department, 610 S 200 E, Salt Lake City, UT 84111. Telephone: 385-468-4203. Email: JRobertson@slco.org.

Author Affiliations: 1Epidemiology Bureau, Salt Lake County Health Department, Salt Lake City, Utah. 2Health Promotion Bureau, Salt Lake County Health Department, West Jordan, Utah.

Top

References

1. Public Health Indicator Based Information System (IBIS). 2017 Health indicator report of asthma: adult prevalence. https://ibis.health.utah.gov/indicator/view/AsthAdltPrev.LHD.html. Accessed April 11, 2019.
2. Public Health Indicator Based Information System (IBIS). 2016–2017 Health indicator report of asthma: child prevalence. https://ibis.health.utah.gov/indicator/view/AsthChiPrev.LHD.html. Accessed April 11, 2019.
3. Public Health Indicator Based Information System (IBIS). 2014 Query results for emergency department encounter query module for Utah counties and local health districts-count-ED encounters. https://ibis.health.utah.gov/query/result/ed/EDCntyHospED/Count.html. Accessed August 13, 2018.
4. Public Health Indicator Based Information System (IBIS). 2014 Query results for inpatient hospital discharge query module for Utah counties and local health districts-count. https://ibis.health.utah.gov/query/result/hddb/HDDBCntyHosp/CountHosp.html. Accessed August 13, 2018.
5. Utah Department of Health. Utah asthma home visiting program. http://health.utah.gov/asthma/homevisit/index.html. Accessed August 6, 2018.
6. Centers for Disease Control and Prevention. Common asthma triggers. https://www.cdc.gov/asthma/triggers.html. Accessed August 6, 2018.
7. Utah Department of Health. Small areas. https://ibis.health.utah.gov/pdf/resource/SmallAreas.pdf. Accessed August 6, 2018.
8. Public Health Indicator Based Information System (IBIS). 2014 Query results for emergency department encounter query module for Utah small areas-crude rates-ED encounters. https://ibis.health.utah.gov/query/result/ed/EDSareaHospED/CrudeRate.html. Accessed August 6, 2018.
9. Environmental Systems Research Institute. Emerging hot spot analysis. http://pro.arcgis.com/en/pro-app/tool-reference/space-time-pattern-mining/emerginghotspots.htm. Accessed January 3, 2019.