Estimating seasonal onsets and peaks of bronchiolitis with spatially and temporally uncertain data.
Sierra Pugh, Matthew J Heaton, Brian Hartman, Candace Berrett, Chantel Sloan, Amber M Evans, Tebeb Gebretsadik, Pingsheng Wu, Tina V Hartert, Rees L Lee
Author Information
Sierra Pugh: Department of Statistics, Brigham Young University, Provo, Utah. ORCID
Matthew J Heaton: Department of Statistics, Brigham Young University, Provo, Utah. ORCID
Brian Hartman: Department of Statistics, Brigham Young University, Provo, Utah.
Candace Berrett: Department of Statistics, Brigham Young University, Provo, Utah.
Chantel Sloan: Department of Health Science, Brigham Young University, Provo, Utah.
Amber M Evans: Health ResearchTx LLC, Trevose, Pennsylvania.
Tebeb Gebretsadik: Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee.
Pingsheng Wu: Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee.
Tina V Hartert: Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee.
Rees L Lee: Naval Medical Research Unit Dayton, Wright-Patterson Air Force Base, Dayton, Ohio.
RSV bronchiolitis (an acute lower respiratory tract viral infection in infants) is the most common cause of infant hospitalizations in the United States (US). The only preventive intervention currently available is monthly injections of immunoprophylaxis. However, this treatment is expensive and needs to be administered simultaneously with seasonal bronchiolitis cycles in order to be effective. To increase our understanding of bronchiolitis timing, this research focuses on identifying seasonal bronchiolitis cycles (start times, peaks, and declinations) throughout the continental US using data on infant bronchiolitis cases from the US Military Health System Data Repository. Because this data involved highly personal information, the bronchiolitis dates in the dataset were "jittered" in the sense that the recorded dates were randomized within a time window of the true date. Hence, we develop a statistical change point model that estimates spatially varying seasonal bronchiolitis cycles while accounting for the purposefully introduced jittering in the data. Additionally, by including temperature and humidity data as regressors, we identify a relationship between bronchiolitis seasonality and climate. We found that, in general, bronchiolitis seasons begin earlier and are longer in the southeastern states compared to the western states with peak times lasting approximately 1 month nationwide.
