{'fraction_non_alphanumeric': 0.009990917347865577, 'fraction_numerical': 0.007266121707538601, 'mean_word_length': 5.333333333333333, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Spatio-Time Interaction with with Disease

Markov chain Monte Carlo methods are used to estimate mortality rates under a Bayesian hierarchical model. Spatial correlations are introduced to examine spatial eeects relative to both regional and regional changes over time by groups. A special feature of the models is the inclusion of longitudinal variables which will describe temporal trends in mortality or incidences for diierent population groups. Disease maps are used to illustrate the role of diierent parameters in the model and pinpointing areas of interesting patterns. The methods are demonstrated by male cancer mortality data from the state of Missouri during 1973{1992. Of special interest will be the geographic variations in the trend of lung cancer mortality over the recent past. Marginal posterior distributions and Bayesian model selection methods are used to examine eeects due to spatial correlations and age diierence in temporal trends. Numerical results from the Missouri data show that though spatial correlations exist, they have little eeect on the estimated mortality rates.