Temporal and spatial trends and determinants of aggressive prostate cancer among Black and White men with prostate cancer.

PURPOSE: Few studies have reported temporal and spatial trends of aggressive prostate cancer (PC) among black men who are known to have more aggressive disease. We examined these trends for highly aggressive PC at diagnosis among black and white men in Pennsylvania (PA). METHODS: Men, aged ≥ 40 years, with a primary, clinical PC diagnosis were identified from the Pennsylvania Cancer Registry, 2004-2014. Joinpoint analysis was used to evaluate the temporal trend of highly aggressive PC (clinical/pathologic Gleason score ≥ 7 [4 + 3], clinical/pathologic tumor stage ≥ T3, or distant metastasis) and identify change points by race in which annual percent change (APC) was calculated. Logistic regression analyses were used to examine the association between race and highly aggressive PC, after adjusting for covariates with and without spatial dependence. RESULTS: There were 89,133 PC cases, which included 88.7% white and 11.3% black men. The APC of highly aggressive PC was 8.7% from 2011 to 2014 among white men and 3.6% from 2007 to 2014 among black men (p values ≤ 0.01). The greatest odds of having highly aggressive PC among black compared to white men were found in counties where the black male population was ≤ 5.3%. CONCLUSIONS: Highly aggressive PC increased for both black and white men in PA between 2004 and 2014. Black men had more aggressive disease, with the greatest odds in counties where the black male population was small. The increase in highly aggressive PC may be due to less screening for PC, resulting in more advanced disease at diagnosis.

Bayesian Forecasting with a Regime-Switching Zero-Inflated Multilevel Poisson Regression Model: An Application to Adolescent Alcohol Use with Spatial Covariates.

In this paper, we present and evaluate a novel Bayesian regime-switching zero-inflated multilevel Poisson (RS-ZIMLP) regression model for forecasting alcohol use dynamics. The model partitions individuals' data into two phases, known as regimes, with: (1) a zero-inflation regime that is used to accommodate high instances of zeros (non-drinking) and (2) a multilevel Poisson regression regime in which variations in individuals' log-transformed average rates of alcohol use are captured by means of an autoregressive process with exogenous predictors and a person-specific intercept. The times at which individuals are in each regime are unknown, but may be estimated from the data. We assume that the regime indicator follows a first-order Markov process as related to exogenous predictors of interest. The forecast performance of the proposed model was evaluated using a Monte Carlo simulation study and further demonstrated using substance use and spatial covariate data from the Colorado Online Twin Study (CoTwins). Results showed that the proposed model yielded better forecast performance compared to a baseline model which predicted all cases as non-drinking and a reduced ZIMLP model without the RS structure, as indicated by higher AUC (the area under the receiver operating characteristic (ROC) curve) scores, and lower mean absolute errors (MAEs) and root-mean-square errors (RMSEs). The improvements in forecast performance were even more pronounced when we limited the comparisons to participants who showed at least one instance of transition to drinking.

GPS2space: An Open-source Python Library for Spatial Measure Extraction from GPS Data.

Global Positioning System (GPS) data have become one of the routine data streams collected by wearable devices, cell phones, and social media platforms in this digital age. Such data provide research opportunities in that they may provide contextual information to elucidate where, when, and why individuals engage in and sustain particular behavioral patterns. However, raw GPS data consisting of densely sampled time series of latitude and longitude coordinate pairs do not readily convey meaningful information concerning intra-individual dynamics and inter-individual differences; substantial data processing is required. Raw GPS data need to be integrated into a Geographic Information System (GIS) and analyzed, from which the mobility and activity patterns of individuals can be derived, a process that is unfamiliar to many behavioral scientists. In this tutorial article, we introduced GPS2space, a free and open-source Python library that we developed to facilitate the processing of GPS data, integration with GIS to derive distances from landmarks of interest, as well as extraction of two spatial features: activity space of individuals and shared space between individuals, such as members of the same family. We demonstrated functions available in the library using data from the Colorado Online Twin Study to explore seasonal and age-related changes in individuals' activity space and twin siblings' shared space, as well as gender, zygosity and baseline age-related differences in their initial levels and/or changes over time. We concluded with discussions of other potential usages, caveats, and future developments of GPS2space.