Employing Bayesian Poissonian and a second-order eigenfunction eigendecomposition algorithm to geostatistically target landscape covariates associated with leukemia in Hillsborough County, Florida
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Mentor Information
Dr. Benjamin Jacob
Description
Leukemia, a form of cancer affecting the blood and bone marrow, hinders the normal production of healthy blood cells, leading to health complications. In exploring mathematical hypotheses for leukemia, three distinct approaches are proposed. Firstly, an over-dispersed Poisson leukemia regression model is suggested, with the consideration of outliers being addressed through the application of a negative binomial model featuring a non-homogenously distributed mean. Secondly, an eigenfunction, eigendecomposition spatial filter algorithm is introduced, aiming to identify potential leukemia clusters based on hyper/hypo-endemic aggregation/non-aggregation orientations. Lastly, a Bayesian hierarchical model is advocated for determining causation covariates within a non-frequentistic model. This research examined the spatial aggregation of leukemia cases by utilizing sociodemographic data at the zip code level in Hillsborough County, Florida. The investigation involved spatial autocorrelation and Bayesian analyses to pinpoint the covariates linked to the risk of leukemia. The Poissonian regression model revealed a nondispersed paradigm. Hence, we did not need to utilize the negative binomial regression to treat the outliers. We conducted a second-order eigenfunction eigendecomposition which revealed multiple non-zero autocorrelated clusters throughout various zip codes in Hillsborough County. The hot spots were in 33647, 33578, and 33511 and the cold spots were in 33621, 33503, and 33530. Our proposed approach identifies leukemia hotspots among whites and Asians aged 65+. Urban residential communities in 33647 were most vulnerable to leukemia. The most common landscape variable associated with leukemia was Urban Residential. Future research should explore the method’s applicability at the state level.
Employing Bayesian Poissonian and a second-order eigenfunction eigendecomposition algorithm to geostatistically target landscape covariates associated with leukemia in Hillsborough County, Florida
Leukemia, a form of cancer affecting the blood and bone marrow, hinders the normal production of healthy blood cells, leading to health complications. In exploring mathematical hypotheses for leukemia, three distinct approaches are proposed. Firstly, an over-dispersed Poisson leukemia regression model is suggested, with the consideration of outliers being addressed through the application of a negative binomial model featuring a non-homogenously distributed mean. Secondly, an eigenfunction, eigendecomposition spatial filter algorithm is introduced, aiming to identify potential leukemia clusters based on hyper/hypo-endemic aggregation/non-aggregation orientations. Lastly, a Bayesian hierarchical model is advocated for determining causation covariates within a non-frequentistic model. This research examined the spatial aggregation of leukemia cases by utilizing sociodemographic data at the zip code level in Hillsborough County, Florida. The investigation involved spatial autocorrelation and Bayesian analyses to pinpoint the covariates linked to the risk of leukemia. The Poissonian regression model revealed a nondispersed paradigm. Hence, we did not need to utilize the negative binomial regression to treat the outliers. We conducted a second-order eigenfunction eigendecomposition which revealed multiple non-zero autocorrelated clusters throughout various zip codes in Hillsborough County. The hot spots were in 33647, 33578, and 33511 and the cold spots were in 33621, 33503, and 33530. Our proposed approach identifies leukemia hotspots among whites and Asians aged 65+. Urban residential communities in 33647 were most vulnerable to leukemia. The most common landscape variable associated with leukemia was Urban Residential. Future research should explore the method’s applicability at the state level.
