Local multiplicity adjustment for the spatial scan statistic using the Gumbel distribution

Mapping Spatial Variation and Impact of the National MDA Program on Lymphatic Filariasis Elimination in Ghana: An Initial Study

Lymphatic filariasis (LF) is a public health menace, especially in developing countries. A periodic review of mass drug administration (MDA) performance is critical to monitoring elimination progress. However, investigating the spatial pattern of LF with respect to MDA intervention is yet to be documented. This is essential to appreciating the transmission dynamics across LF-endemic communities and how it is spatially impacted by MDA programs. The aim of this study was to map and explore the spatial variation and hotspots of LF infection among endemic communities and evaluate the impact of the MDA intervention program on its spatial pattern in Ghana. Relative risks, clustering and clusters, prevalence odds ratios, and their confidence intervals were studied with community-level LF data prior to intervention and post intervention periods. The overall risk of LF infection was 0.12% and 0.02% before and after MDA, respectively, suggesting reduced transmission. Using empirical Bayesian smoothing to map the relative risk, a substantial variation in the spatial distribution of the relative risk of LF among endemic communities was observed. Most of the excess prevalence communities were unexpectedly visible even after years of MDA. The Empirical Bayesian Moran’s Index for global clustering showed a reduction in clustering of LF prevalence after MDA with IM = 0.455 and 0.119 for before and after MDA, respectively. Furthermore, examining risks associated with ecological zones, it was observed that the Guinea Savannah and the Transition Zone were the most vulnerable zones for LF infection with prevalence odds ratios 18.70- and 13.20-fold higher than in the reference Moist evergreen zone, respectively. We observed a drastic reduction in risk in the Wet evergreen zone after MDA, while the Guinea Savannah sustained high levels of risk even after MDA. These findings should prompt public health officials to adopt stratified cluster sampling in LF-endemic regions to monitor the rate and density of microfilaria.

Clustered spatio-temporal varying coefficient regression model

In regression analysis for spatio-temporal data, identifying clusters of spatial units over time in a regression coefficient could provide insight into the unique relationship between a response and covariates in certain subdomains of space and time windows relative to the background in other parts of the spatial domain and the time period of interest. In this article, we propose a varying coefficient regression method for spatial data repeatedly sampled over time, with heterogeneity in regression coefficients across both space and over time. In particular, we extend a varying coefficient regression model for spatial-only data to spatio-temporal data with flexible temporal patterns. We consider the detection of a potential cylindrical cluster of regression coefficients based on testing whether the regression coefficient is the same or not over the entire spatial domain for each time point. For multiple clusters, we develop a sequential identification approach. We assess the power and identification of known clusters via a simulation study. Our proposed methodology is illustrated by the analysis of a cancer mortality dataset in the Southeast of the U.S.

Cluster detection of spatial regression coefficients

Popular approaches to spatial cluster detection, such as the spatial scan statistic, are defined in terms of the responses. Here, we consider a varying-coefficient regression and spatial clusters in the regression coefficients. For varying-coefficient regression, such as the geographically weighted regression, different regression coefficients are obtained for different spatial units. It is often of interest to the practitioners to identify clusters of spatial units with distinct patterns in a regression coefficient, but there is no formal statistical methodology for that. Rather, cluster identification is often ad-hoc such as by eyeballing the map of fitted regression coefficients and discerning patterns. In this paper, we develop new methodology for spatial cluster detection in the regression setting based on hypotheses testing. We evaluate our methods in terms of power and coverages for true clusters via simulation studies. For illustration, our methodology is applied to a cancer mortality dataset. Copyright © 2016 John Wiley & Sons, Ltd.

Detection of spatial change points in the mean and covariances of multivariate simultaneous autoregressive models

In this paper, we propose a test procedure to detect change points of multidimensional autoregressive processes. The considered process differs from typical applied spatial autoregressive processes in that it is assumed to evolve from a predefined center into every dimension. Additionally, structural breaks in the process can occur at a certain distance from the predefined center. The main aim of this paper is to detect such spatial changes. In particular, we focus on shifts in the mean and the autoregressive parameter. The proposed test procedure is based on the likelihood-ratio approach. Eventually, the goodness-of-fit values of the estimators are compared for different shifts. Moreover, the empirical distribution of the test statistic of the likelihood-ratio test is obtained via Monte Carlo simulations. We show that the generalized Gumbel distribution seems to be a suitable limiting distribution of the proposed test statistic. Finally, we discuss the detection of lung cancer in computed tomography scans and illustrate the proposed test procedure.

Public domain small-area cancer incidence data for New York State, 2005-2009

There has long been a demand for cancer incidence data at a fine geographic resolution for use in etiologic hypothesis generation and testing, methodological evaluation and teaching. In this paper we describe a public domain dataset containing data for 23 anatomic sites of cancer diagnosed in New York State, USA between 2005 and 2009 at the census block group level. The dataset includes 524,503 tumours distributed across 13,823 block groups with an average population of about 1400. In addition, the data have been linked with race/ethnicity and with socioeconomic indicators such as income, educational attainment and language proficiency. We demonstrate the application of the dataset by confirming two well-established relationships: that between breast cancer and median household income and that between stomach cancer and Asian race. We foresee that this dataset will serve as the basis for a wide range of spatial analyses and as a benchmark for evaluating spatial methods in the future.