Modeling Bronchiolitis Incidence Proportions in the Presence of Spatio-Temporal Uncertainty

Matthew J. Heaton, Candace Berrett, Sierra Pugh, Amber Evans and Chantel Sloan

Journal of the American Statistical Association, 2020, vol. 115, issue 529, 66-78

Abstract: Bronchiolitis (inflammation of the lower respiratory tract) in infants is primarily due to viral infection and is the single most common cause of infant hospitalization in the United States. To increase epidemiological understanding of bronchiolitis (and, subsequently, develop better prevention strategies), this research analyzes data on infant bronchiolitis cases from the U.S. Military Health System between the years 2003–2013 in Norfolk, Virginia, USA. For privacy reasons, child home addresses, birth dates, and diagnosis dates were randomized (jittered) creating spatio-temporal uncertainty in the geographic location and timing of bronchiolitis incidents. Using spatio-temporal point patterns, we created a modeling strategy that accounts for the jittering to estimate and quantify the uncertainty for the incidence proportion (IP) of bronchiolitis. Additionally, we regress the IP onto key covariates including pollution where we adequately account for uncertainty in the pollution levels (i.e., covariate uncertainty) using a land use regression model. Our analysis results indicate that the IP is positively associated with sulfur dioxide and population density. Further, we demonstrate how scientific conclusions may change if various sources of uncertainty (either spatio-temporal or covariate uncertainty) are not accounted for. Code submitted with this article was checked by an Associate Editor for Reproducibility and is available as an online supplement.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:taf:jnlasa:v:115:y:2020:i:529:p:66-78

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DOI: 10.1080/01621459.2019.1609480

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