Whitney E Zahnd1, Georgia S Mueller-Luckey, Kendra Ratnapradipa, Tracey Smith. 1. Office of Population Science and Policy (Ms Zahnd), Department of Family and Community Medicine (Dr Smith), and Center for Clinical Research (Ms Mueller-Luckey), Southern Illinois University School of Medicine, Springfield, Illinois; and Department of Epidemiology, Saint Louis University, St Louis, Missouri (Ms Ratnapradipa).
Abstract
CONTEXT: Radon is the second leading cause of lung cancer, but exposure can be reduced through testing one's home and mitigating if levels are high. OBJECTIVE: To determine what factors predict radon testing and to identify, through spatial analysis, areas in Illinois with lower or higher than expected testing rates. DESIGN, SETTING, PARTICIPANTS, AND MAIN OUTCOMES: An ecological study design was used to evaluate data on radon tests performed in Illinois by a licensed professional or a home radon test kit analyzed by a state-approved laboratory between 2005 and 2012. Zip code-level rates of testing per 1000 occupied residences were calculated for all testing methods combined and for licensed professional testing and home kit testing separately. The following zip code-level factors associated with radon testing were considered: Environmental Protection Agency (EPA) radon zones (ie, categorization of areas by predicted radon risk), socioeconomic characteristics, homeowner occupancy, and rurality. Univariate and multivariable incidence rate ratios were calculated to examine what factors were associated with each testing type. Hotspot analysis was performed to identify zip codes with lower than expected and higher than expected testing rates (ie, "coldspots" and "hotspots," respectively). RESULTS: Radon testing rates varied across EPA zone, socioeconomic characteristics, and level of rurality. In multivariable analysis, EPA zone, education, and median household income positively predicted all testing types combined. Median home value was associated with licensed testing, whereas rurality was negatively associated with licensed testing. Owner occupancy positively predicted home kit testing. Between 19.6% and 31.1% of zip codes were coldspots for radon testing rates, dependent upon testing type. Coldspots of all testing method rates were concentrated in the southern part of the state. CONCLUSION: Public health professionals can benefit from understanding what area-level factors predict radon testing and what geographic areas may under-utilize testing. Such information can aid the development of geographically targeted, cost-effective interventions that increase radon testing and subsequently reduce lung cancer risk.
CONTEXT: Radon is the second leading cause of lung cancer, but exposure can be reduced through testing one's home and mitigating if levels are high. OBJECTIVE: To determine what factors predict radon testing and to identify, through spatial analysis, areas in Illinois with lower or higher than expected testing rates. DESIGN, SETTING, PARTICIPANTS, AND MAIN OUTCOMES: An ecological study design was used to evaluate data on radon tests performed in Illinois by a licensed professional or a home radon test kit analyzed by a state-approved laboratory between 2005 and 2012. Zip code-level rates of testing per 1000 occupied residences were calculated for all testing methods combined and for licensed professional testing and home kit testing separately. The following zip code-level factors associated with radon testing were considered: Environmental Protection Agency (EPA) radon zones (ie, categorization of areas by predicted radon risk), socioeconomic characteristics, homeowner occupancy, and rurality. Univariate and multivariable incidence rate ratios were calculated to examine what factors were associated with each testing type. Hotspot analysis was performed to identify zip codes with lower than expected and higher than expected testing rates (ie, "coldspots" and "hotspots," respectively). RESULTS: Radon testing rates varied across EPA zone, socioeconomic characteristics, and level of rurality. In multivariable analysis, EPA zone, education, and median household income positively predicted all testing types combined. Median home value was associated with licensed testing, whereas rurality was negatively associated with licensed testing. Owner occupancy positively predicted home kit testing. Between 19.6% and 31.1% of zip codes were coldspots for radon testing rates, dependent upon testing type. Coldspots of all testing method rates were concentrated in the southern part of the state. CONCLUSION: Public health professionals can benefit from understanding what area-level factors predict radon testing and what geographic areas may under-utilize testing. Such information can aid the development of geographically targeted, cost-effective interventions that increase radon testing and subsequently reduce lung cancer risk.
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