Employing Markov Chain Monte Carlo (MCMC) Bayesian Poissonian and a Second-Order Eigenfunction Eigendecomposition Algorithm to Geostatistically Target Landscape Covariates Associated with Leukemia in Hillsborough County, Florida
DOI:
https://doi.org/10.14738/bjhmr.114.17413Keywords:
leukemia, zip-code, Spatial autocorrelation, Bayesian analyses, Hillsborough County, FloridaAbstract
Leukemia is a cancer of the blood and bone marrow, that hinders the normal production of healthy blood cells. In exploring mathematical hypotheses for leukemia, three distinct approaches are proposed. First is an over-dispersed Poisson leukemia regression model, with the consideration of outliers being addressed by applying 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, there was no need to utilize the negative binomial regression to treat the outliers. A second-order eigenfunction eigendecomposition revealed multiple non-zero autocorrelated clusters throughout various zip codes in Hillsborough County. The hot spots were 33647, 33578, and 33511 and the cold spots were 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. By leveraging social media and fostering collaboration between public health and technology sectors, we can work towards achievable leukemia prevention for at-risk populations. Future research should explore the method's applicability at the state level and promote routine blood tests among people.
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